Chemistry
10.24424/jxpj-vv36
False
2025-07-04 09:08:44.261623+00:00
0
https://api.rohub.org/api/ros/de0b3951-0fa7-4b03-a1fa-d5c4da93a476/crate/download/
2022-01-12 16:34:39.917729+00:00
2025-10-16 11:15:06.613810+00:00
2022-01-12 16:34:39.917729+00:00
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom.
application/ld+json
https://w3id.org/ro-id/de0b3951-0fa7-4b03-a1fa-d5c4da93a476
Aromatic compounds - snapshot
Aromatic compounds
MANUAL
Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/jxpj-vv36.
arene
7.304347826086956
4.2
aliphatic compound
4.737903225806451
4.7
The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds.
21.401515151515152
11.3
Jewellery
Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery
oxygen atom
4.032258064516129
4.0
nitrogen
3.9314516129032255
3.9
organic chemistry
65.91639871382637
41.0
oxygen atom
17.794486215538846
7.1
benzene
9.274193548387096
9.2
geochemistry
100.0
0.4569866955280304
heterocyclic compound
9.73913043478261
5.6
chemistry and materials
100.0
0.8506659269332886
aromatic
19.657258064516128
19.5
benzene
12.695652173913043
7.3
monocyclic ring
14.285714285714286
5.7
chemistry and materials (general)
100.0
0.8506659269332886
arene
4.939516129032259
4.9
electron
4.435483870967742
4.4
chemistry
34.08360128617363
21.2
scent
4.536290322580645
4.5
aromatic hydrocarbon
5.94758064516129
5.9
chemical compound
15.826086956521738
9.1
nitrogen atom
29.573934837092732
11.8
aromatic hydrocarbon
8.695652173913043
5.0
ring
3.125
3.1
The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
39.015151515151516
20.6
organic compound
3.8306451612903225
3.8
chemical compound
10.786290322580644
10.7
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them.
39.583333333333336
20.9
carbon atom
15.999999999999998
9.2
aromatic compound benzene
24.81203007518797
9.9
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
heterocyclic compound
6.451612903225806
6.4
aromatic compound
29.739130434782613
17.1
larger compound
13.533834586466165
5.4
earth sciences
100.0
0.4569866955280304
carbon atom
10.786290322580644
10.7
benzene ring
3.528225806451613
3.5
Chemistry
10.24424/070n-rr14
False
2025-07-05 18:47:59.392957+00:00
0
https://api.rohub.org/api/ros/ba53e480-17bb-466f-b789-3533246d7b43/crate/download/
2022-01-12 16:34:39.917729+00:00
2025-10-16 11:14:31.884055+00:00
2022-01-12 16:34:39.917729+00:00
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom.
application/ld+json
https://w3id.org/ro-id/ba53e480-17bb-466f-b789-3533246d7b43
Aromatic compounds - snapshot
Aromatic compounds
MANUAL
Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/070n-rr14.
chemistry
34.08360128617363
21.2
scent
4.536290322580645
4.5
aromatic
19.657258064516128
19.5
The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds.
21.401515151515152
11.3
benzene
9.274193548387096
9.2
carbon atom
10.786290322580644
10.7
geochemistry
100.0
0.4569866955280304
aromatic compound benzene
24.81203007518797
9.9
aromatic compound
29.739130434782613
17.1
larger compound
13.533834586466165
5.4
arene
4.939516129032259
4.9
Jewellery
Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery
The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
39.015151515151516
20.6
arene
7.304347826086956
4.2
oxygen atom
17.794486215538846
7.1
carbon atom
15.999999999999998
9.2
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
electron
4.435483870967742
4.4
aromatic hydrocarbon
8.695652173913043
5.0
chemical compound
15.826086956521738
9.1
benzene ring
3.528225806451613
3.5
heterocyclic compound
6.451612903225806
6.4
aromatic hydrocarbon
5.94758064516129
5.9
nitrogen
3.9314516129032255
3.9
organic compound
3.8306451612903225
3.8
chemistry and materials (general)
100.0
0.8506659269332886
earth sciences
100.0
0.4569866955280304
monocyclic ring
14.285714285714286
5.7
aliphatic compound
4.737903225806451
4.7
benzene
12.695652173913043
7.3
chemical compound
10.786290322580644
10.7
organic chemistry
65.91639871382637
41.0
chemistry and materials
100.0
0.8506659269332886
heterocyclic compound
9.73913043478261
5.6
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them.
39.583333333333336
20.9
oxygen atom
4.032258064516129
4.0
nitrogen atom
29.573934837092732
11.8
ring
3.125
3.1
Chemistry
https://doi.org/10.24424/x0cn-va37
False
2025-07-05 19:04:55.078129+00:00
0
https://api.rohub.org/api/ros/54c22dc5-ace3-4aaa-be62-b5b4dab97be6/crate/download/
2022-01-12 16:34:39.917729+00:00
2025-10-16 11:14:13.082777+00:00
2022-01-12 16:34:39.917729+00:00
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom.
application/ld+json
https://w3id.org/ro-id/54c22dc5-ace3-4aaa-be62-b5b4dab97be6
Aromatic compounds - snapshot
Aromatic compounds
MANUAL
Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/x0cn-va37.
chemical compound
15.826086956521738
9.1
The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
39.015151515151516
20.6
aromatic hydrocarbon
5.94758064516129
5.9
benzene
9.274193548387096
9.2
carbon atom
15.999999999999998
9.2
chemical compound
10.786290322580644
10.7
electron
4.435483870967742
4.4
oxygen atom
4.032258064516129
4.0
arene
4.939516129032259
4.9
chemistry
34.08360128617363
21.2
organic chemistry
65.91639871382637
41.0
chemistry and materials
100.0
0.8506659269332886
scent
4.536290322580645
4.5
heterocyclic compound
9.73913043478261
5.6
benzene
12.695652173913043
7.3
earth sciences
100.0
0.4569866955280304
geochemistry
100.0
0.4569866955280304
The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds.
21.401515151515152
11.3
nitrogen atom
29.573934837092732
11.8
aromatic compound
29.739130434782613
17.1
arene
7.304347826086956
4.2
Jewellery
Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery
aliphatic compound
4.737903225806451
4.7
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
benzene ring
3.528225806451613
3.5
larger compound
13.533834586466165
5.4
nitrogen
3.9314516129032255
3.9
heterocyclic compound
6.451612903225806
6.4
aromatic hydrocarbon
8.695652173913043
5.0
aromatic
19.657258064516128
19.5
organic compound
3.8306451612903225
3.8
carbon atom
10.786290322580644
10.7
monocyclic ring
14.285714285714286
5.7
chemistry and materials (general)
100.0
0.8506659269332886
aromatic compound benzene
24.81203007518797
9.9
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them.
39.583333333333336
20.9
ring
3.125
3.1
oxygen atom
17.794486215538846
7.1
Biology
10.24424/20ms-v465
False
2025-08-12 08:02:25.321821+00:00
0
https://api.rohub.org/api/ros/07b99b7b-a209-44cc-86fd-327339b2599c/crate/download/
2022-01-19 13:47:59.181939+00:00
2025-10-16 11:12:08.755267+00:00
2022-01-19 13:47:59.181939+00:00
Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate.Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function. For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance. Additionally, it is associated with other mental disorders and substance use disorders. Although it causes impairment, particularly in modern society, many people with ADHD have sustained attention for tasks they find interesting or rewarding, known as hyperfocus.
application/ld+json
https://w3id.org/ro-id/07b99b7b-a209-44cc-86fd-327339b2599c
Attention deficit hyperactivity disorder - snapshot
Attention deficit hyperactivity disorder
MANUAL
Wolniewicz, Małgorzata. "Attention deficit hyperactivity disorder." ROHub. Jan 19 ,2022. https://doi.org/10.24424/20ms-v465.
life sciences
100.0
0.989045262336731
distraction
5.919003115264798
5.7
neurodevelopmental disorder
62.65984654731457
49.0
environmental science and management
100.0
0.6445436477661133
behavioural disorder
7.4766355140186915
7.2
environmental sciences
100.0
0.6445436477661133
inattention
9.515260323159785
5.3
substance use disorder
19.565217391304348
15.3
life sciences (general)
100.0
0.989045262336731
diagnosis
6.645898234683282
6.4
medicine
100.0
12.8
individual
4.7767393561786085
4.6
behavioral disorder
12.208258527827647
6.8
individuals with ADHD
7.416879795396419
5.8
mental disorder
3.426791277258567
3.3
problem
9.345794392523365
9.0
attention
5.815160955347872
5.6
impulsiveness
5.815160955347872
5.6
diagnosis
10.23339317773788
5.7
disorder
10.951526032315979
6.1
symptom
4.569055036344757
4.4
attention deficit hyperactivity disorder
21.599169262720665
20.8
Mental and behavioural disorder
Health/Diseases and conditions/Mental and behavioural disorder
mental disorders
4.731457800511508
3.7
Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate.
59.31758530183727
45.2
difficulty
10.412926391382404
5.8
disorder
12.772585669781932
12.3
For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance.
18.11023622047244
13.8
emotions
4.984423676012462
4.8
School
Education/School
Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function.
22.572178477690287
17.2
difficulty
6.853582554517134
6.6
attention deficit hyperactivity disorder
32.85457809694793
18.3
school performance
5.626598465473147
4.4
problem
13.824057450628365
7.7
Environmental research
Applied sciences
Ecology
biology
conservation strategy
ecology
Mediterranean Sea
endangered species
endangered species
ecosystem
habitat
strategy
connectivity
protected area
conservation
management
result
Mediterranean Sea
expert evaluation
shelf-slope connectivity
framework
want
Integrated Approach Benthic
results of a multi-criteria decision analysis
efficient set
Mediterranean Basin
priority
POLYGON ((-8.789062500000002 28.459033019728068, -8.789062500000002 48.69096039092552, 38.14453125000001 48.69096039092552, 38.14453125000001 28.459033019728068, -8.789062500000002 28.459033019728068))
-8.789062500000002 28.459033019728068, -8.789062500000002 48.69096039092552, 38.14453125000001 48.69096039092552, 38.14453125000001 28.459033019728068, -8.789062500000002 28.459033019728068
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service-account-enrichment
False
https://w3id.org/ro-id/6556cdf7-bcef-43d3-a3ce-3d45d14e4a24
2022-03-24 18:37:26.444410+00:00
https://orcid.org/0000-0002-2736-0052
228075
https://api.rohub.org/api/ros/4fd0f1c2-d58f-4b20-9f12-503c31c607d9/crate/download/
2022-03-24 17:00:15.895312+00:00
2024-03-05 12:18:48.335914+00:00
2022-03-24 17:00:15.895312+00:00
Benthic habitats of the deep Mediterranean Sea and the biodiversity they host are increasingly jeopardized by increasing human pressures, both direct and indirect, which encompass fisheries, chemical and acoustic pollution, littering, oil and gas exploration and production and marine infrastructures (i.e., cable and pipeline laying), and bioprospecting. To this, is added the pervasive and growing effects of human-induced perturbations of the climate system. International frameworks provide foundations for the protection of deep-sea ecosystems, but the lack of standardized criteria for the identification of areas deserving protection, insufficient legislative instruments and poor implementation hinder an efficient set up in practical terms. Here, we discuss the international legal frameworks and management measures in relation to the status of habitats and key species in the deep Mediterranean Basin. By comparing the results of a multi-criteria decision analysis (MCDA) and of expert evaluation (EE), we identify priority deep-sea areas for conservation and select five criteria for the designation of future protected areas in the deep Mediterranean Sea. Our results indicate that areas (1) with high ecological relevance (e.g., hosting endemic and locally endangered species and rare habitats),(2) ensuring shelf-slope connectivity (e.g., submarine canyons), and (3) subject to current and foreseeable intense anthropogenic impacts, should be prioritized for conservation. The results presented here provide an ecosystem-based conservation strategy for designating priority areas for protection in the deep Mediterranean Sea.
application/ld+json
https://w3id.org/ro-id/4fd0f1c2-d58f-4b20-9f12-503c31c607d9
Identifying Priorities for the Protection of Deep Mediterranean Sea Ecosystems Through an Integrated Approach - snapshot
Identifying Priorities for the Protection of Deep Mediterranean Sea Ecosystems Through an Integrated Approach
MANUAL
https://w3id.org/ro-id/4fd0f1c2-d58f-4b20-9f12-503c31c607d9/9233b6cc-5495-423f-9af0-b60a83db22c0
Castellan, Giorgio. "Identifying Priorities for the Protection of Deep Mediterranean Sea Ecosystems Through an Integrated Approach." ROHub. Mar 24 ,2022. https://w3id.org/ro-id/4fd0f1c2-d58f-4b20-9f12-503c31c607d9.
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265816
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2022-03-24 17:02:17.046117+00:00
2022-03-24 18:37:25.690510+00:00
image/jpeg
fmars-08-698890-t004.jpg
2022-03-24 17:02:17.046117+00:00
https://zenodo.org/record/6382778#.YjykQlXMKHs
2022-03-24 17:04:39.468927+00:00
2022-03-24 18:37:26.331838+00:00
Priority deep-sea areas for conservation in the deep Mediterranean Sea
Resources stored in Zenodo
2022-03-24 17:04:39.468927+00:00
Earth sciences
geology
100.0
0.8256934881210327
water clarity
30.407523510971785
29.1
earth sciences
100.0
0.8256934881210327
collection
13.091922005571032
9.4
Adriatic Sea
https://www.wikidata.org/wiki/Q13924
space
5.153203342618385
3.7
service-account-enrichment
False
https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140
2022-03-24 18:42:55.013290+00:00
https://orcid.org/0000-0002-2736-0052
533934
https://api.rohub.org/api/ros/28ff4f3e-c3f8-4bf0-8591-8fa36c378faa/crate/download/
2021-12-14 10:41:17.716553+00:00
2024-03-05 12:16:56.660585+00:00
2021-12-14 10:41:17.716553+00:00
Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea
application/ld+json
https://w3id.org/ro-id/28ff4f3e-c3f8-4bf0-8591-8fa36c378faa
Analysis from satellite data – Environmental monitoring from space - snapshot
Analysis from satellite data – Environmental monitoring from space
MANUAL
https://w3id.org/ro-id/19dabc1d-b24e-4e7b-bbc2-81f95da2f1ad
https://w3id.org/ro-id/14888c37-4830-4fd4-9b58-b7364d3b437e
https://w3id.org/ro-id/1eddab1d-4fc9-422e-92aa-d523326aa498
https://w3id.org/ro-id/3eaf4867-0bb2-4018-9eac-85eec6ee1309
https://w3id.org/ro-id/44252c81-5879-4430-9255-66dd383ed651
https://w3id.org/ro-id/5d7bef7a-401b-4fa7-a0c0-0960ac13899d
https://w3id.org/ro-id/94781348-96cf-459a-85de-405cc09226a3
https://w3id.org/ro-id/a3749fdc-4e70-4f7f-979f-8783827dd636
https://w3id.org/ro-id/af9e7155-28ad-4842-a304-dddf811c4d74
https://w3id.org/ro-id/f100fb36-2a32-44ca-82c2-7076c5835fa5
https://w3id.org/ro-id/0781334a-7a44-4a75-bae6-9b830ce25370
https://w3id.org/ro-id/136ded1a-6eba-470a-bd33-d741aee77ada
https://w3id.org/ro-id/6b83b885-0507-4e13-b35c-7aa124395fc2
https://w3id.org/ro-id/3866bcdc-c291-40bf-b3cc-63b22d75e3d5
https://w3id.org/ro-id/386c8b2a-a98e-45da-b12a-8aa8d05d4e3b
https://w3id.org/ro-id/6102d84d-2021-4d87-8ef7-17da4930646b
https://w3id.org/ro-id/6a7f5310-1a78-453d-ab8b-a5c9b320a4f6
https://w3id.org/ro-id/84271559-7e77-418f-a01c-b38b283b6183
https://w3id.org/ro-id/9cb825ce-e452-4549-abe2-2fdb59ae48b6
https://w3id.org/ro-id/ca4eaea0-0558-4095-b77f-cd7fc8bd1bfa
https://w3id.org/ro-id/2c11942c-9936-43aa-a307-fed88ea783c4
https://w3id.org/ro-id/456025f5-a651-498b-9f0a-15e56cd985bc
https://w3id.org/ro-id/0980e44a-005d-4804-8a6f-cb82bd6123e9
https://w3id.org/ro-id/75705433-1cc5-403c-baec-1bcd78a366a7
https://w3id.org/ro-id/91973553-ac02-4802-b419-47e6a54e2c06
https://w3id.org/ro-id/9760a79e-83ec-4e0a-9ee6-eb5ed95998c7
https://w3id.org/ro-id/f139b59a-eda3-4aab-ace7-68ad65bc1443
https://w3id.org/ro-id/c7fe0e0f-b774-44ab-9d23-4ef5608890c4
https://w3id.org/ro-id/f8b11e82-83e5-4022-ad0e-7f1229396a5c
https://w3id.org/ro-id/fcc1bbf5-28df-4f6e-b562-bc9fa809d641
Castellan, Giorgio. "Analysis from satellite data – Environmental monitoring from space." ROHub. Dec 14 ,2021. https://doi.org/10.5281/zenodo.6383036.
Discover, subset, download and visualize satellite data stored in a Data Cube from the ADAM Platform
Method
Results
Results
Satellite data on Chl-a and Kd490
Satellite_data
68452
https://api.rohub.org/api/resources/38441693-b827-4108-b4d5-b0d854030c88/download/
2021-12-14 14:32:01.240770+00:00
2022-03-24 18:42:53.767383+00:00
image/png
Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea
2021-12-14 14:32:01.240770+00:00
449579
https://api.rohub.org/api/resources/67b0bcab-22ec-4e02-8f28-37670338943c/download/
2021-12-14 14:38:21.837867+00:00
2022-03-24 18:42:50.464712+00:00
image/jpeg
Analysis from satellite data – Environmental monitoring from space during COVID-19 lockdown
2021-12-14 14:38:21.837867+00:00
https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7
2021-12-14 10:44:17.433059+00:00
2022-03-24 18:42:54.929213+00:00
Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown
Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown
2021-12-14 10:44:17.433059+00:00
70005
https://api.rohub.org/api/resources/85c7ebc3-6c32-4573-a828-96044eb3f9a2/download/
2021-12-14 14:33:06.849172+00:00
2022-03-24 18:42:52.754796+00:00
image/png
Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea
2021-12-14 14:33:06.849172+00:00
https://w3id.org/ro-id/34d648b3-0014-4a19-8469-40b9380ca4c3
2021-12-14 10:44:48.894463+00:00
2022-03-24 18:42:51.867845+00:00
Discover and subset satellite data from the ADAM Platform
Discover and subset satellite data from the ADAM Platform
2021-12-14 10:44:48.894463+00:00
geosciences
100.0
0.4130299687385559
environmental monitoring
11.413748378728926
8.8
analysis
13.618677042801558
10.5
result
4.735376044568246
3.4
water
11.142061281337048
8.0
geophysics
100.0
0.4130299687385559
lockdown
15.459610027855154
11.1
clarity
12.5810635538262
9.7
collection
11.932555123216602
9.2
Satellite technology
Economy, business and finance/Economic sector/Computing and information technology/Satellite technology
environmental monitoring from space
11.598746081504702
11.1
satellite data
23.47600518806745
18.1
effects of COVID-19 lockdown
15.256008359456635
14.6
Adriatic Sea
11.142061281337048
8.0
analysis from satellite data
17.03239289446186
16.3
lockdown
14.785992217898833
11.4
analysis
14.902506963788301
10.7
environmental monitoring
11.420612813370472
8.2
Analysis from satellite data –
22.02202202202202
22.0
covid 19
12.191958495460442
9.4
clarity
12.95264623955432
9.3
analysis of satellite data
25.705329153605014
24.6
Environmental monitoring from space.
8.708708708708707
8.7
Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea
69.26926926926926
69.2
WebProcessingServiceExecution
2
http://box.everest.psnc.pl:8000/f/0c4347ad9d/
2022-03-24 19:49:35.107140+00:00
2022-03-24 19:49:46.065773+00:00
.png
cd.png
2022-03-24 19:49:35.107140+00:00
WebProcessingServiceExecution
2018-05-08T16:22:04.503000+00:00
WebProcessingServiceExecution
2
http://box.everest.psnc.pl:8000/f/6157842c73/
2022-03-24 19:49:35.109031+00:00
2022-03-24 19:49:46.577143+00:00
.tgz
cd.tgz
2022-03-24 19:49:35.109031+00:00
WebProcessingServiceExecution
2018-05-08T16:22:04.503000+00:00
985000
http://box.everest.psnc.pl:8000/f/6a67815420/
2022-03-24 19:49:35.107688+00:00
2022-03-24 19:49:44.444293+00:00
.zip
S1A_IW_GRDH_1SDV_20170820T061754_20170820T061819_018003_01E376_9EC3.zip
2022-03-24 19:49:35.107688+00:00
WebProcessingServiceExecution
2
http://box.everest.psnc.pl:8000/f/a25b04c564/
2022-03-24 19:49:35.108676+00:00
2022-03-24 19:49:49.583999+00:00
.pngw
cd.pngw
2022-03-24 19:49:35.108676+00:00
WebProcessingServiceExecution
2018-05-08T16:22:04.503000+00:00
985000
http://box.everest.psnc.pl:8000/f/aa333acec2/
2022-03-24 19:49:35.108194+00:00
2022-03-24 19:49:48.154486+00:00
.zip
S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip
2022-03-24 19:49:35.108194+00:00
Cartography
anca popescu
EU SatCen
EU SatCen
POLYGON((-3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252))
WebProcessingService
AreaofInterest
MasterSentinel-1product
Polarization
SlaveSentinel-1product
SatCen Change Detection Workflow execution
Result Files Distribution Package
com.terradue.wps_oozie.process.OozieAbstractAlgorithm
SatCen Change Detection Workflow
POLYGON( ( -3.611394871468983 40.53713623661004, -3.5125914250302666 40.53713623661004, -3.4137879785915506 40.53713623661004, -3.4137879785915506 40.40969285491932, -3.5125914250302666 40.40969285491932, -3.611394871468983 40.40969285491932, -3.611394871468983 40.53713623661004 ) )
SlaveSentinel-1product
AreaofInterest
Polarization
MasterSentinel-1product
detection over Madrid
Madrid
Satcen 2018
Detection
earth sciences
17.014721850579107
0.6360710263252258
earth sciences
11.563625766334056
0.43228960037231445
space sciences
3.3287645856718493
0.05067460238933563
Change Detection Data Centric.
14.711033274956218
58.8
Master Image:
5.679259444583438
22.7
information
15.014299332697806
31.5
earth sciences
26.632741500408624
0.9956269264221191
earth resources and remote sensing
22.1282807437931
0.33686426281929016
POLYGON( ( -3.611394871468983 40.53713623661004, -3.5125914250302666 40.53713623661004, -3.4137879785915506 40.53713623661004, -3.4137879785915506 40.40969285491932, -3.5125914250302666 40.40969285491932, -3.611394871468983 40.40969285491932, -3.611394871468983 40.53713623661004 ) )
-3.611394871468983 40.53713623661004, -3.5125914250302666 40.53713623661004, -3.4137879785915506 40.53713623661004, -3.4137879785915506 40.40969285491932, -3.5125914250302666 40.40969285491932, -3.611394871468983 40.40969285491932, -3.611394871468983 40.53713623661004 )
0c9d4e35-d0e9-42de-9ec0-14fbd576f2a8
POLYGON((-3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252))
POLYGON((-3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252))
3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252
e3a64dbe-2fde-4df7-b99f-a434917ece9c
POLYGON( ( -3.611394871468983 40.53713623661004, -3.5125914250302666 40.53713623661004, -3.4137879785915506 40.53713623661004, -3.4137879785915506 40.40969285491932, -3.5125914250302666 40.40969285491932, -3.611394871468983 40.40969285491932, -3.611394871468983 40.53713623661004 ) )
http://ever-est.eu/value#My Library
10.5072/ro-id.BPIH2F2WOA
2018-06-15T10:32:34.526+02:00
34409
https://api.rohub.org/api/ros/246cce20-2f36-4bfb-8de4-256d0dcbe60c/crate/download/
2018-06-15 08:32:34.526000+00:00
2026-05-08 02:02:01.632870+00:00
2018-06-15 08:32:34.526000+00:00
Change Detection over Madrid
application/ld+json
https://w3id.org/ro-id/246cce20-2f36-4bfb-8de4-256d0dcbe60c
Change Detection Data Centric
Land Monitoring Community
Anca Popescu
Land Monitoring
S1A_IW_GRDH_1SDV_20170621T061751_20170621T061816_017128_01C8E4_C27D
https://w3id.org/ro-id/e7747b1e-fcb2-4d35-98e0-8570f0bc962d
https://w3id.org/ro-id/1920e4c9-9bef-4296-ae8d-cf1973241f34
https://w3id.org/ro-id/74bc7d96-0e18-4404-9f2b-43b5c632232f
https://w3id.org/ro-id/80cf4633-2db5-4b6c-80b9-bd4a101d2a32
https://w3id.org/ro-id/97c92958-6dba-40d1-95bc-e531d4b75117
https://w3id.org/ro-id/a151d7df-470a-47cd-ba52-2cdfc6b86d47
https://w3id.org/ro-id/a6953234-2102-4f9d-8f86-f683e132b758
https://w3id.org/ro-id/d9c4f010-4f47-4696-94e9-fecc207e7566
https://w3id.org/ro-id/e1cc4cd7-2b83-4889-9d6c-acceeae692c9
https://w3id.org/ro-id/0c66ac74-904f-4620-b3d5-07e713305635
https://w3id.org/ro-id/128bd7b9-e0ac-462f-bc13-309d32fd1449
https://w3id.org/ro-id/1bb2fbf0-fffd-445a-85b9-b98007104b0c
https://w3id.org/ro-id/26e9f570-a91d-418c-94c3-62574bc3bcd8
https://w3id.org/ro-id/3d276bcc-6201-4a36-9250-1c29f2fd729e
https://w3id.org/ro-id/478a560a-dc51-4622-b51d-18ee60cd9841
https://w3id.org/ro-id/49c0f08e-3193-4249-8cea-95e74617b660
https://w3id.org/ro-id/5134e6c4-2bcc-41cc-9199-2d86b3e1acb5
https://w3id.org/ro-id/855771fd-0c13-4a73-9e23-f1cc7496d8f2
https://w3id.org/ro-id/ac2dea90-5785-49da-9dc6-a15bcc832d4b
https://w3id.org/ro-id/61d7adda-bf15-48c1-b039-9ebdff3885b0
https://w3id.org/ro-id/79c87a38-5f2c-4446-9946-1cce96589177
https://w3id.org/ro-id/b4af4170-5ebe-471e-ba2f-4ab5172bc5bf
https://w3id.org/ro-id/bafb0fc0-dc53-4501-a909-78e10b2e5ed4
https://w3id.org/ro-id/ecfe8d56-b5c9-4e07-98f0-716fc6a1d842
https://w3id.org/ro-id/ed5f3686-0536-460b-9937-1afe43a872f7
https://w3id.org/ro-id/eeb64cd7-fbdf-48d9-9f85-81aa49c6946a
https://w3id.org/ro-id/14b43032-2626-4db6-897d-f8d39bbde913
https://w3id.org/ro-id/208f78cc-22e3-44f4-9edd-de8399042b6d
https://w3id.org/ro-id/629354fa-33da-435d-a18f-ce745fa08cd3
https://w3id.org/ro-id/7309b85a-fc84-4a66-9b1f-a02c913983a8
https://w3id.org/ro-id/821a65bb-90c6-4d53-a39e-acc4471fa99e
https://w3id.org/ro-id/85d83329-1740-4133-97fa-e87c66e94264
https://w3id.org/ro-id/bed6deff-2f38-418e-9821-d97016717681
https://w3id.org/ro-id/bf199369-43bd-4cb3-a599-fdefb0f7ba05
https://w3id.org/ro-id/d50aff65-bfd1-4791-9a72-207674eff947
https://w3id.org/ro-id/ff449878-5b19-412f-842b-d704ba70490b
https://w3id.org/ro-id/51d972c2-dc40-445a-9d01-8af28d329b82
https://w3id.org/ro-id/6c2a2e24-7836-4ee9-93df-16cd0db81f76
https://w3id.org/ro-id/93e53235-e3aa-4f74-b0fd-eb0ec21e8dea
https://w3id.org/ro-id/c5763f77-c624-454f-9fd9-071be4491d59
https://w3id.org/ro-id/1579b8fc-b386-408d-98ac-6ad6974ad8e7
https://w3id.org/ro-id/190ebab2-4735-45ef-a077-6379d24a4b0b
https://w3id.org/ro-id/2663c09b-e252-47d6-9397-4a7c0248c0e3
https://w3id.org/ro-id/682cd808-5849-41ec-9364-0b3d69d58870
https://w3id.org/ro-id/9f43d10b-addc-4182-8272-8211edeb4cea
https://w3id.org/ro-id/eed1c3e0-182a-4536-8814-9b677c4a53eb
https://w3id.org/ro-id/f6b7b7b5-bcb3-4e61-b7fe-e4c3b03278a7
EU SatCen. "Change Detection Data Centric." ROHub. Jun 15 ,2018. https://doi.org/10.5072/ro-id.BPIH2F2WOA.
datasets
produced
software
web services
inputs
main
nested
results
config
used
biblio
setup
workflows
components
scripts
ggg
143
https://api.rohub.org/api/resources/906a758b-2781-462c-8c3b-41cd882f632d/download/
2018-05-10 08:09:44.546000+00:00
2022-03-24 19:49:43.581239+00:00
.txt
Input-Master.txt
2018-05-10 08:09:44.546000+00:00
11
https://api.rohub.org/api/resources/9e39ca91-b2a0-4177-96d4-90d5a32b549a/download/
2018-05-10 10:50:29.452000+00:00
2022-03-24 19:49:45.325235+00:00
.txt
Copyright.txt
2018-05-10 10:50:29.452000+00:00
4
https://api.rohub.org/api/resources/d547a8e8-0d4f-4a73-9a3c-3be2ff00ce67/download/
2018-05-10 08:19:07.994000+00:00
2022-03-24 19:49:49.392487+00:00
.txt
workflow.txt
2018-05-10 08:19:07.994000+00:00
0
https://api.rohub.org/api/resources/f948aa0c-f8c4-45e3-b9a8-456307449a48/download/
2018-05-10 08:21:25.852000+00:00
2022-03-24 19:49:47.742295+00:00
.txt
definition.txt
2018-05-10 08:21:25.852000+00:00
test
25.018764073054793
100.0
geology
26.632741500408624
0.9956269264221191
earth sciences
26.214273292711976
0.9799830913543701
geology
17.014721850579107
0.6360710263252258
earth sciences
18.57463758996624
0.6943862438201904
oceanography
11.563625766334056
0.43228960037231445
master image
50.02501250625313
100.0
image
4.736419587904736
17.7
geosciences
22.1282807437931
0.33686426281929016
Satcen 2018
25.018764073054793
100.0
change Detection over Madrid
0.7003501750875437
1.4
astronautics
15.408499143145692
0.23456737399101257
uniform resource identifier
4.194470924690181
8.8
Madrid
6.208188386406208
23.2
Madrid
11.1534795042898
23.4
geophysics
8.407055595076324
0.12798267602920532
atmospheric sciences
18.57463758996624
0.6943862438201904
spacecraft design, testing and performance
15.408499143145692
0.23456737399101257
Detection over Madrid
31.715857928964482
63.4
image
13.203050524308864
27.7
URI: http://box.everest.psnc.pl:8000/f/aa333acec2/
4.928696522391794
19.7
expert
2.573879885605338
5.4
test
47.66444232602478
100.0
atmospheric sciences
26.214273292711976
0.9799830913543701
test
26.75943270002676
100.0
data
8.482740165908483
31.7
space sciences (general)
3.3287645856718493
0.05067460238933563
geosciences
8.407055595076324
0.12798267602920532
change Detection
17.55877938969485
35.1
earth resources and remote sensing
50.727399932313034
0.7722356915473938
centric
1.9542421353670159
4.1
http
4.242135367016206
8.9
Madrid
S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip
16.135937918116138
60.3
Satcen
26.75943270002676
100.0
Detection
10.917848541610917
40.8
S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip
14.360770577933451
57.4
Change Detection over Madrid
10.28271203402552
41.1
geosciences
50.727399932313034
0.7722356915473938
service-account-enrichment
service-account-generation-service
Music Classification Study musical genre classification
music classification study musical genre classification
television
musical genre classification
warning messsage
education
Linux
http
musical genre
feature
classification
Java
lib 3
audio
install
classification by ensemble
Music
libraries in the lib
Taverna Workbench 2.3.0 from http
user
taverna installation
Java
Classification
version
directory
release
ensemble
musical genre classification by ensemble
lyrics feature
classification by ensembles of audio and lyrics feature
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/musicStudy-5/
2014-07-29T15:05:31.747+02:00
https://www.google.com/accounts/o8/id?id=AItOawl6miGQ2NYnbP2-gtGZcqRkDRukz5GNfGc
4451939
https://api.rohub.org/api/ros/0741085b-b411-4b53-b00e-1311fecc8410/crate/download/
2014-07-29 12:47:19.237000+00:00
2025-03-05 01:04:17.160177+00:00
2014-07-29 12:47:19.237000+00:00
Musical genre classification by ensembles of audio and lyrics features
application/ld+json
https://w3id.org/ro-id/0741085b-b411-4b53-b00e-1311fecc8410
Music Classification Study
Raul Palma. "Music Classification Study." ROHub. Jul 29 ,2014. https://w3id.org/ro-id/0741085b-b411-4b53-b00e-1311fecc8410.
used
workflows
setup
produced
main
config
scripts
components
nested
lib
results
web services
datasets
inputs
software
biblio
160008
https://api.rohub.org/api/resources/02e2583c-cdb9-4948-b919-be41adaab300/download/
2014-07-29 13:04:04.741000+00:00
2022-03-25 08:54:50.057415+00:00
MusicClassification_WSDL-final.t2flow
2014-07-29 13:04:04.741000+00:00
Raul Palma
service-account-generation-service
Biosemantics
memory
epigenetic role
deregulate in HD
chromatin analysis
deregulate in HD
gene deregulation
gene
http
research
chromatin
web service
interpretation
analysis
workflow
deregulation
system
HD
participate in epigenetic process
HD
epigenetic process
Genoa
participate in epigenetic processes
HD gene deregulation
chromatin data interpretation
epigenetic
genetics
anni web services
research
have an epigenetic role
aim
role
information
HD chromatin analysis
Genoa
have an epigenetic role
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/data_interpretation-2/
2014-02-26T14:16:20.355+01:00
https://www.google.com/accounts/o8/id?id=AItOawlLcpRhy-5MtgIVFxuwLWcFFys5ZTC7w2c
81314
https://api.rohub.org/api/ros/d1b1d427-2332-428e-a205-726ac7a0e951/crate/download/
2014-02-21 13:36:32.163000+00:00
2025-03-05 00:48:41.666693+00:00
2014-02-21 13:36:32.163000+00:00
<p>This research object, was created in order to further analyse and interpret the results from the research object http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/ (HD chromatin analysis). The workflows in this research object are using the anni web services, implemented by the Biosemantics group.</p>
application/ld+json
https://w3id.org/ro-id/d1b1d427-2332-428e-a205-726ac7a0e951
HD data interpretation
chromatin data interpretation
Eleni Mina. "chromatin data interpretation." ROHub. Feb 21 ,2014. https://w3id.org/ro-id/d1b1d427-2332-428e-a205-726ac7a0e951.
data_interpretation
30787
https://api.rohub.org/api/resources/0390ff51-55a1-4af3-b99c-60ef059d0afc/download/
2014-02-25 16:10:46.463000+00:00
2022-03-25 09:07:52.840714+00:00
This workflow lists all IDs and descriptions of the predefined concept set
List Predefined Concept Sets
2014-02-25 16:10:46.463000+00:00
188023
https://api.rohub.org/api/resources/45b81758-2d25-4c33-87c8-cb2968fbd849/download/
2014-02-25 16:12:14.228000+00:00
2022-03-25 09:07:51.492625+00:00
This workflow can prioritize genes that are related to a specific concept, e.g. HTT. In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against
Prioritize gene list related to a concept /list of concepts
2014-02-25 16:12:14.228000+00:00
63
https://api.rohub.org/api/resources/7a3f5ba3-b292-4c80-b0a1-76a15b20e50c/download/
2014-02-25 16:03:44.993000+00:00
2022-03-25 09:07:53.677489+00:00
text/plain
hypothesis.txt
2014-02-25 16:03:44.993000+00:00
203555
https://api.rohub.org/api/resources/7aa50a5a-bc9a-4639-9e04-1fecc9bb5002/download/
2014-02-25 16:06:54.756000+00:00
2022-03-25 09:07:55.632943+00:00
This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. "5" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services
Annotate a gene list with Biological processes
2014-02-25 16:06:54.756000+00:00
69369
https://api.rohub.org/api/resources/99ba79b8-8172-4026-93b2-affd7320122f/download/
2014-02-26 13:14:06.663000+00:00
2022-03-25 09:07:56.525975+00:00
This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group).
This workflow can be used together with other workflows in this pack: http://www.myexperiment.org/packs/282 for functional gene and SNP annotation and knowledge discovery.
Explain concept scores
2014-02-26 13:14:06.663000+00:00
67
https://api.rohub.org/api/resources/da150b5d-9fd0-46fc-9745-06460dbed48e/download/
2014-02-25 16:03:12.283000+00:00
2022-03-25 09:07:49.577479+00:00
text/plain
conclusions.txt
2014-02-25 16:03:12.283000+00:00
41692
https://api.rohub.org/api/resources/dcaecdee-b61a-4d22-9610-be3770345a8e/download/
2014-02-25 16:09:23.429000+00:00
2022-03-25 09:07:54.760474+00:00
This workflow suggests concept ids that match the query term. The user can run this workflow with any term of interest as for example "human", "htt", "Transcription" etc, and will get suggestions for concept ids together with descriptions. Then can choose the concept id that matches the best to her/his needs and use it to the rest of the CPA workflows
Get concept suggestions from term
2014-02-25 16:09:23.429000+00:00
39476
https://api.rohub.org/api/resources/f34bc6b5-f6ab-41e7-b2fe-bd83df7041b6/download/
2014-02-25 16:04:13.823000+00:00
2022-03-25 09:07:50.657598+00:00
Sketch of the workflows and their explanation, for data interpretation, plus the connection to the output from the RO for chromatin analysis
image/png
workflow sketch data interpretation
2014-02-25 16:04:13.823000+00:00
Eleni Mina
Eleni Mina
service-account-generation-service
D3.1: Workflow Evolution, Sharing and Collaboration Initial Requirements
Taverna 2.4
HYPERLEDA. I. Identification and designation of galaxies
D4.1: Workflow Integrity and Authenticity Maintenance Initial Requirements
D2.1 Workflow Lifecycle Management Initial Requirements
Virtual Observatory activities in the AMIGA group
D1.2: Wf4Ever Sandbox v1
Python
Topcat
Capabilities of the HYPERLEDA database
from. to. forSb galaxy
logr25
tool session
Oxford University
Lawrence
property error value
Amiga data revision
mathematics
United Kingdom
astronomy
Verley
AMIGAsample
Poznan
Poland
Oxford
appendix B research object structure
Madrid
NamesLEDA.txt
Leiden
Hereafter
Edinburgh
axis ratio
dust extinction coefficient
deployment of a Research Object
cig SDSS sample
Colorado
physics
lumi nous galaxy
Karachentseva
HyperLEDA database
Netherlands
Calzetti
statistics
galaxy
database
research
data
sample
red shift
text file
AMIGA
FP ICT
workflow
value
correction
opticalAGN
ratio
information and communication technologies
value
optical luminosity in B-band
luminosity
e mail address
CIG
output
Enrique Ruiz Minor
Red Cross
interactions galaxy
University of Manchester
Naciones
evolution galaxy
property
mag
research object management
university
inAMIGA
Granada
Alaska
Manchester
property value
group database
Vulkan Shmidta
León
colors
SDSS
workflow building
sample
type
coordinate
propagation
Montenegro
appendices
database
property
value error
calculation
galaxy inclination
galaxies
galaxy
galaxy
sample
sc galaxy
type galaxies inAMIGA
Boadilla del Monte
propagation of property
assigned galaxy type
British Telecom
evolution galaxy
error of the coordinate
AKwas
Ai AK
AGN
IAA
Pozna
Mpc
Sect.
Spain
Golden Exemplar
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/Pack585/
2014-01-27T17:31:15.131+01:00
https://www.google.com/accounts/o8/id?id=AItOawnZHKwZJglv16YBiUjsWEnx39mHA0RB250
http://w3id.org/ro-id/rohub/model#change_specifications/d3cea99d-3ccd-489d-88d2-97c0392888ff
2908927
https://api.rohub.org/api/ros/9faa4bd7-7c10-4a60-a418-b9dc338d966d/crate/download/
2014-01-24 15:04:31.757000+00:00
2025-03-05 01:16:58.682001+00:00
2014-01-24 15:04:31.757000+00:00
The scientific experiment represented by this research object pertains to the multi-wavelength study for a sample of the most isolated galaxies in the local universe. This study characterizes each galaxy of this sample through both the measurement of basic astrophysical properties:
- The equatorial coordinates in J2000 epoch
- The velocities in km/s (v)
- The dust extinction coefficient (ag)
- The axis ratio of the isophote 25 mag/arcsec2 (logr25)
- The apparent total B magnitude (BT)
- The morphological type (t)
and the calculation of the more complex properties:
- The distance in Mega parsecs (D)
- The corrected apparent B magnitude (btc)
- The optical luminosity in B-band (LB)
Specifically, this research object is focused on the calculation of the intrinsic luminosity in the Johnson B-band, in order to achieve it the measurement or calculation of all those astrophysical properties is needed. All the data involved in the characterization of the sample are stored in a local relational MySQL database. To maintain up to date this database and to register all the updates properly is part of this scientific experiment.
application/ld+json
https://w3id.org/ro-id/9faa4bd7-7c10-4a60-a418-b9dc338d966d
Propagation of properties extracted from the HyperLEDA catalog in the calculation of luminosities of galaxies
Design Sketch and Experiment hypothesis will improve readability. Consider adding these elements.
Please, take into account Hubble constant value in the determination of distances
The Hubble constant value has been considered in the script calculateDistance.py
http://sandbox.rohub.org/rodl/ROs/Pack585-snapshot/
Jose Enrique Ruiz. "Propagation of properties extracted from the HyperLEDA catalog in the calculation of luminosities of galaxies." ROHub. Jan 24 ,2014. https://w3id.org/ro-id/9faa4bd7-7c10-4a60-a418-b9dc338d966d.
used
nested
config
produced
scripts
software
datasets
main
root
results
workflow_runs
components
web_services
inputs
setup
biblio
workflows
NamesLEDA.txt
733453
https://api.rohub.org/api/resources/04ff3d95-a0d3-4f66-80ad-637d0ded4591/download/
2014-01-24 15:05:08.250000+00:00
2022-03-25 09:12:12.261743+00:00
Gathering galaxy properties using Hyperleda
2014-01-24 15:05:08.250000+00:00
132151
https://api.rohub.org/api/resources/146edf59-bb21-4430-9917-97cbae988a60/download/
2014-01-24 15:06:27.574000+00:00
2022-03-25 09:12:00.536760+00:00
application/x-sql
bt.sql
2014-01-24 15:06:27.574000+00:00
212329
https://api.rohub.org/api/resources/1d94ef57-ed0c-40b8-9dc9-7fbbc554024b/download/
2014-01-24 15:06:24.130000+00:00
2022-03-25 09:11:51.326045+00:00
Propagation of physical quantities in the calculation of luminosities of galaxies
2014-01-24 15:06:24.130000+00:00
39043
https://api.rohub.org/api/resources/20ae1815-26a8-4f8c-8ed3-7fba7878bacc/download/
2014-01-24 15:05:33.094000+00:00
2022-03-25 09:11:47.329135+00:00
text/plain
LB3d.txt
2014-01-24 15:05:33.094000+00:00
2948908
https://api.rohub.org/api/resources/21bec57a-00f8-42c6-9d97-da774d800870/download/
2014-01-24 15:05:31.608000+00:00
2022-03-25 09:11:59.657024+00:00
session.vot
2014-01-24 15:05:31.608000+00:00
morphoNew.txt
887528
https://api.rohub.org/api/resources/2c82e292-ddf9-4def-be1e-f24354a9baef/download/
2014-01-24 15:05:59.044000+00:00
2022-03-25 09:12:08.867311+00:00
Calculation of distances, magnitutes, and luminosities using Hyperleda
2014-01-24 15:05:59.044000+00:00
1383
https://api.rohub.org/api/resources/37eb901f-e7d0-4edf-8177-5d7f896f9fc1/download/
2014-01-24 15:05:30.052000+00:00
2022-03-25 09:11:38.384866+00:00
Not working properly with break lines in linux formatted files
text/x-python
comparing.py
2014-01-24 15:05:30.052000+00:00
142994
https://api.rohub.org/api/resources/5356d55f-09e2-4a0d-af72-07db00734fb8/download/
2014-01-24 15:04:55.083000+00:00
2022-03-25 09:11:58.122018+00:00
application/x-sql
velocity.sql
2014-01-24 15:04:55.083000+00:00
112823
https://api.rohub.org/api/resources/56c5ce79-aa3a-4556-a999-7bc22c03cd0d/download/
2014-01-27 16:30:26.554000+00:00
2022-03-25 09:11:31.641822+00:00
image/png
Schema.png
2014-01-27 16:30:26.554000+00:00
1914881
https://api.rohub.org/api/resources/579dd361-ddf5-45e4-b658-83f1498f4d5a/download/
2014-01-24 15:05:54.932000+00:00
2022-03-25 09:12:06.825330+00:00
application/pdf
D5.3v1: Propagation of interdependent quantities in the calculation of luminosities of galaxies
2014-01-24 15:05:54.932000+00:00
9865
https://api.rohub.org/api/resources/63fd9bb1-4138-4c6b-9629-b5c8fadd142c/download/
2014-01-24 15:07:10.437000+00:00
2022-03-25 09:11:54.525445+00:00
text/plain
RECIPES
2014-01-24 15:07:10.437000+00:00
143737
https://api.rohub.org/api/resources/64994d23-fa5b-484e-b25d-408fb4b21dfa/download/
2014-01-24 15:05:13.745000+00:00
2022-03-25 09:11:57.237250+00:00
application/x-sql
lb.sql
2014-01-24 15:05:13.745000+00:00
8407
https://api.rohub.org/api/resources/7b6f3e2d-34d7-4f12-aed7-e6f522be5a39/download/
2014-01-24 15:05:15.222000+00:00
2022-03-25 09:12:05.958985+00:00
text/plain
NamesLEDA.txt
2014-01-24 15:05:15.222000+00:00
25
https://api.rohub.org/api/resources/84c8166d-8155-41cd-9797-c764ab6e14a9/download/
2014-01-24 15:04:57.259000+00:00
2022-03-25 09:11:52.208650+00:00
text/plain
local.txt
2014-01-24 15:04:57.259000+00:00
145314
https://api.rohub.org/api/resources/99b25137-d453-482b-9531-b478bf886467/download/
2014-01-24 15:07:01.927000+00:00
2022-03-25 09:11:53.051062+00:00
application/x-sql
btc.sql
2014-01-24 15:07:01.927000+00:00
157378
https://api.rohub.org/api/resources/a1df3dff-67e1-406d-a539-8de4ad182b12/download/
2014-01-24 15:06:00.661000+00:00
2022-03-25 09:12:02.410522+00:00
application/x-sql
distances.sql
2014-01-24 15:06:00.661000+00:00
15994
https://api.rohub.org/api/resources/a5f8f3fa-66bd-41a3-ad9f-b4e430159ce8/download/
2014-01-24 15:06:45.058000+00:00
2022-03-25 09:12:10.525314+00:00
text/plain
morphoNew.txt
2014-01-24 15:06:45.058000+00:00
138238
https://api.rohub.org/api/resources/df7721a9-ff51-4ace-9b13-8d922181fc43/download/
2014-01-24 15:04:59.351000+00:00
2022-03-25 09:12:11.380781+00:00
application/x-sql
logr25.sql
2014-01-24 15:04:59.351000+00:00
remote.txt
42662
https://api.rohub.org/api/resources/df86ccfe-ff14-48b8-9034-8776c3367931/download/
2014-01-24 15:06:03.290000+00:00
2022-03-25 09:12:07.963354+00:00
Comparison and update of values
2014-01-24 15:06:03.290000+00:00
25
https://api.rohub.org/api/resources/e17d348f-a004-4d58-831c-a565ff489bc6/download/
2014-01-24 15:07:15.314000+00:00
2022-03-25 09:12:03.503387+00:00
text/plain
remote.txt
2014-01-24 15:07:15.314000+00:00
501548
https://api.rohub.org/api/resources/e88d881d-7085-49ee-8163-278925cbaff4/download/
2014-01-24 15:06:10.435000+00:00
2022-03-25 09:11:49.550169+00:00
application/pdf
The AMIGA sample of isolated galaxies X. A first look at isolated galaxy colors
2014-01-24 15:06:10.435000+00:00
Jose Enrique Ruiz
service-account-generation-service
D3.1: Workflow Evolution, Sharing and Collaboration Initial Requirements
Taverna 2.4
HYPERLEDA. I. Identification and designation of galaxies
D4.1: Workflow Integrity and Authenticity Maintenance Initial Requirements
D2.1 Workflow Lifecycle Management Initial Requirements
Virtual Observatory activities in the AMIGA group
D1.2: Wf4Ever Sandbox v1
Python
Topcat
Capabilities of the HYPERLEDA database
from. to. forSb galaxy
logr25
tool session
Oxford University
Lawrence
property error value
Amiga data revision
mathematics
United Kingdom
astronomy
Verley
AMIGAsample
Poznan
Poland
Oxford
appendix B research object structure
Madrid
NamesLEDA.txt
Leiden
Hereafter
Edinburgh
axis ratio
dust extinction coefficient
deployment of a Research Object
cig SDSS sample
Colorado
physics
lumi nous galaxy
Karachentseva
HyperLEDA database
Netherlands
Calzetti
statistics
galaxy
database
research
data
sample
red shift
text file
AMIGA
FP ICT
workflow
value
correction
opticalAGN
ratio
information and communication technologies
value
optical luminosity in B-band
luminosity
e mail address
CIG
output
Enrique Ruiz Minor
Red Cross
interactions galaxy
University of Manchester
Naciones
evolution galaxy
property
mag
research object management
university
inAMIGA
Granada
Alaska
Manchester
property value
group database
Vulkan Shmidta
León
colors
SDSS
workflow building
sample
type
coordinate
propagation
Montenegro
appendices
database
property
value error
calculation
galaxy inclination
galaxies
galaxy
galaxy
sample
sc galaxy
type galaxies inAMIGA
Boadilla del Monte
propagation of property
assigned galaxy type
British Telecom
evolution galaxy
error of the coordinate
AKwas
Ai AK
AGN
IAA
Pozna
Mpc
Sect.
Spain
Golden Exemplar
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/Pack585/
2014-01-27T17:30:51.771+01:00
https://www.google.com/accounts/o8/id?id=AItOawnZHKwZJglv16YBiUjsWEnx39mHA0RB250
2908816
https://api.rohub.org/api/ros/12f54724-cd7f-4410-b788-3c062fc644f7/crate/download/
2014-01-24 15:04:31.757000+00:00
2025-03-05 01:16:58.921026+00:00
2014-01-24 15:04:31.757000+00:00
The scientific experiment represented by this research object pertains to the multi-wavelength study for a sample of the most isolated galaxies in the local universe. This study characterizes each galaxy of this sample through both the measurement of basic astrophysical properties:
- The equatorial coordinates in J2000 epoch
- The velocities in km/s (v)
- The dust extinction coefficient (ag)
- The axis ratio of the isophote 25 mag/arcsec2 (logr25)
- The apparent total B magnitude (BT)
- The morphological type (t)
and the calculation of the more complex properties:
- The distance in Mega parsecs (D)
- The corrected apparent B magnitude (btc)
- The optical luminosity in B-band (LB)
Specifically, this research object is focused on the calculation of the intrinsic luminosity in the Johnson B-band, in order to achieve it the measurement or calculation of all those astrophysical properties is needed. All the data involved in the characterization of the sample are stored in a local relational MySQL database. To maintain up to date this database and to register all the updates properly is part of this scientific experiment.
application/ld+json
https://w3id.org/ro-id/12f54724-cd7f-4410-b788-3c062fc644f7
Propagation of properties extracted from the HyperLEDA catalog in the calculation of luminosities of galaxies
Design Sketch and Experiment hypothesis will improve readability. Consider adding these elements.
Please, take into account Hubble constant value in the determination of distances
The Hubble constant value has been considered in the script calculateDistance.py
Jose Enrique Ruiz. "Propagation of properties extracted from the HyperLEDA catalog in the calculation of luminosities of galaxies." ROHub. Jan 24 ,2014. https://w3id.org/ro-id/12f54724-cd7f-4410-b788-3c062fc644f7.
workflows
datasets
config
used
workflow_runs
scripts
components
produced
web_services
main
biblio
nested
software
inputs
results
root
setup
local.txt
42662
https://api.rohub.org/api/resources/0f6a16b7-6aeb-4cca-ad41-92803445170f/download/
2014-01-24 15:06:03.290000+00:00
2022-03-25 09:14:48.242239+00:00
Comparison and update of values
2014-01-24 15:06:03.290000+00:00
112823
https://api.rohub.org/api/resources/11edc8d2-b1df-4726-abe1-95fa73972bed/download/
2014-01-27 16:30:26.554000+00:00
2022-03-25 09:14:14.355939+00:00
image/png
Schema.png
2014-01-27 16:30:26.554000+00:00
212329
https://api.rohub.org/api/resources/1b88d3c0-92d4-4dfb-9ac5-c62b91bf69c3/download/
2014-01-24 15:06:24.130000+00:00
2022-03-25 09:14:33.481796+00:00
Propagation of physical quantities in the calculation of luminosities of galaxies
2014-01-24 15:06:24.130000+00:00
9865
https://api.rohub.org/api/resources/23f6cea1-0f79-494b-8e6a-37247622766c/download/
2014-01-24 15:07:10.437000+00:00
2022-03-25 09:14:36.396498+00:00
text/plain
RECIPES
2014-01-24 15:07:10.437000+00:00
2948908
https://api.rohub.org/api/resources/4395a71c-5cfb-4502-bd08-18edc3a8993a/download/
2014-01-24 15:05:31.608000+00:00
2022-03-25 09:14:41.038608+00:00
session.vot
2014-01-24 15:05:31.608000+00:00
1914881
https://api.rohub.org/api/resources/487428fb-373f-4e85-a451-ce191af27c39/download/
2014-01-24 15:05:54.932000+00:00
2022-03-25 09:14:47.437302+00:00
application/pdf
D5.3v1: Propagation of interdependent quantities in the calculation of luminosities of galaxies
2014-01-24 15:05:54.932000+00:00
157378
https://api.rohub.org/api/resources/4b95d337-bb57-4946-bb59-bf4fd6f3e44c/download/
2014-01-24 15:06:00.661000+00:00
2022-03-25 09:14:43.657464+00:00
application/x-sql
distances.sql
2014-01-24 15:06:00.661000+00:00
145314
https://api.rohub.org/api/resources/6897e959-a5eb-4192-9cca-fd51e08831c4/download/
2014-01-24 15:07:01.927000+00:00
2022-03-25 09:14:35.138531+00:00
application/x-sql
btc.sql
2014-01-24 15:07:01.927000+00:00
25
https://api.rohub.org/api/resources/8218a726-7aad-4a00-aa0c-45bf0248d19a/download/
2014-01-24 15:04:57.259000+00:00
2022-03-25 09:14:34.317084+00:00
text/plain
local.txt
2014-01-24 15:04:57.259000+00:00
142994
https://api.rohub.org/api/resources/87c7d0be-53dc-47f9-943d-873775aa84fd/download/
2014-01-24 15:04:55.083000+00:00
2022-03-25 09:14:39.972234+00:00
application/x-sql
velocity.sql
2014-01-24 15:04:55.083000+00:00
25
https://api.rohub.org/api/resources/a002b7dc-4a7c-4e1b-865c-ef8e1fc079b1/download/
2014-01-24 15:07:15.314000+00:00
2022-03-25 09:14:44.487863+00:00
text/plain
remote.txt
2014-01-24 15:07:15.314000+00:00
501548
https://api.rohub.org/api/resources/bce8f604-1df5-42b1-b34e-f26ccfa740a0/download/
2014-01-24 15:06:10.435000+00:00
2022-03-25 09:14:31.790148+00:00
application/pdf
The AMIGA sample of isolated galaxies X. A first look at isolated galaxy colors
2014-01-24 15:06:10.435000+00:00
NamesLEDA.txt
733453
https://api.rohub.org/api/resources/bf716724-d9fc-4310-996c-71b389a00aca/download/
2014-01-24 15:05:08.250000+00:00
2022-03-25 09:14:52.421044+00:00
Gathering galaxy properties using Hyperleda
2014-01-24 15:05:08.250000+00:00
morphoNew.txt
887528
https://api.rohub.org/api/resources/c585ea92-5c82-4966-9df2-dc8c13b4645f/download/
2014-01-24 15:05:59.044000+00:00
2022-03-25 09:14:49.313000+00:00
Calculation of distances, magnitutes, and luminosities using Hyperleda
2014-01-24 15:05:59.044000+00:00
138238
https://api.rohub.org/api/resources/d293675e-ef7e-4981-bfeb-a08c1c28eaf7/download/
2014-01-24 15:04:59.351000+00:00
2022-03-25 09:14:51.438894+00:00
application/x-sql
logr25.sql
2014-01-24 15:04:59.351000+00:00
15994
https://api.rohub.org/api/resources/d4938b1d-d4f5-4335-bf90-22cea3aecdaa/download/
2014-01-24 15:06:45.058000+00:00
2022-03-25 09:14:50.521218+00:00
text/plain
morphoNew.txt
2014-01-24 15:06:45.058000+00:00
132151
https://api.rohub.org/api/resources/d9f83555-5069-4e1a-94e1-4f391556a395/download/
2014-01-24 15:06:27.574000+00:00
2022-03-25 09:14:41.873446+00:00
application/x-sql
bt.sql
2014-01-24 15:06:27.574000+00:00
1383
https://api.rohub.org/api/resources/e4e89aac-4342-41c6-8b0a-ae7606dcfbd6/download/
2014-01-24 15:05:30.052000+00:00
2022-03-25 09:14:21.123593+00:00
Not working properly with break lines in linux formatted files
text/x-python
comparing.py
2014-01-24 15:05:30.052000+00:00
39043
https://api.rohub.org/api/resources/eecc81f8-f194-448e-af6e-8705c3aff9c6/download/
2014-01-24 15:05:33.094000+00:00
2022-03-25 09:14:29.690600+00:00
text/plain
LB3d.txt
2014-01-24 15:05:33.094000+00:00
8407
https://api.rohub.org/api/resources/f00cdaba-9649-4117-8de6-3b48cb134bd9/download/
2014-01-24 15:05:15.222000+00:00
2022-03-25 09:14:46.533968+00:00
text/plain
NamesLEDA.txt
2014-01-24 15:05:15.222000+00:00
143737
https://api.rohub.org/api/resources/ffae12c4-dbfd-4f42-9708-164fe79505cc/download/
2014-01-24 15:05:13.745000+00:00
2022-03-25 09:14:39.125914+00:00
application/x-sql
lb.sql
2014-01-24 15:05:13.745000+00:00
Jose Enrique Ruiz
service-account-generation-service
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/04caa16f-2cf8-4116-88c7-ea2f7e2c06f3
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/147acc3e-9a37-4d2f-b3ce-995d3317d9a2
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/22a7fda1-aeec-4720-9fec-47ff97b2eefe
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/279a00e5-280b-46b2-9bf9-5f33e60c389e
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/38b038fa-2e13-4bb6-8627-440aee9d53c8
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/4cebce3b-4fab-4020-a0e4-a5220b72a1cd
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/4e7d9e49-18c1-429c-a208-e00e41b4ce07
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/52b202e6-206d-4dcb-9b2f-80ddf6b783c0
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/5e7173c2-f3e8-4bc9-98b9-c2cb627f66f1
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/6487d154-1479-4721-bae3-28d63eb37fba
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/698f6b28-40cb-44de-a0b5-87d054681719
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/70ef65f1-fbf8-495c-9ff1-0144b0a49786
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/7555f2f8-6ba2-4993-b90a-1f89ab53c4f6
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/a19ca7be-e526-4ab0-bbd6-12e7313ca8ed
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/a62982a0-158f-4679-851d-1c0c989f9436
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/bc474904-2d2d-410d-9aad-a0e3129f0679
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/d6750f82-981b-411b-a1d6-1b2ecc5e4c49
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/e0457c72-b3ff-429c-a6e3-65ed6f5a9283
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/f129e0fc-161e-4752-8876-da38688712dc
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/f3d060e3-a438-4e42-b8d4-bfb8275f6cdb
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/ff7a5d20-fad8-45c8-b3cf-c78bc889b1c3
f5b795c2-763e-4102-9df9-193691cd7bbf.rdf
78aecb3e-37ef-4f00-a649-668d0f418db4.rdf
97793295-96bf-4f5a-b640-25773d659e95.rdf
http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/.ro/annotations/b7a5a570-222d-4592-b292-06037a1d42e4
http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/4077d50e-efb4-4061-95d0-6c4cc1198bfa.rdf
1f063ea3-83ac-4dd2-9b3c-a493bd3f842c.rdf
http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/5d40cea9-e4f9-49bd-b5e4-ede288278a73.rdf
annotations/9f4a5dbd-bce9-4216-a3ed-51a7aeb0ce8d
annotations/fc6e78fc-77e5-40c3-9865-2df94702f59f
http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/annotate_genes_biological_processes.t2flow
annotations/b4acc451-7422-457a-b837-2826910122f9
annotations/f1c7adec-54d4-4e36-a186-56beb260b197
20d14edf-376d-4373-b7bd-5627ecf8e1a5.rdf
annotations/ba8a48e9-5156-48c5-a639-e1e7e0a117ec
http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/.ro/annotations/3b2e927c-c5d7-4b3e-b2b3-49ec67b2be00
annotate_genes_biological_processes_xpath_cpids.t2flow
http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/.ro/annotations/093fc059-3d5a-436e-a0f0-2bf07ce8a30b
annotations/6f077a8f-4bf0-4a0d-ac30-5d4dc48f970c
http://sandbox.rohub.org/workflows/2725.html
http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/c8d59b14-c3be-46d2-88ef-425e608a9d3d.rdf
c254eb0d-bebf-4f31-8e76-09afa077dd31.rdf
deregulate in HD
HD
participate in epigenetic processes
genetics
genes
have an epigenetic role
Purpose of workflow: This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group).
Explain Scores
missing link
5.304445274561076
14.2
life sciences (general)
35.87466373187602
0.9233048558235168
have an epigenetic role
0.06863417982155112
0.2
life sciences (general)
35.76177208527657
0.9203993678092957
chromatin
4.039375424304141
11.9
epigenetic process
17.84488675360329
52.0
role
5.491221516623086
14.7
epigenetic role
6.554564172958133
19.1
testing
2.545824847250509
7.5
participate in epigenetic process
0.20590253946465337
0.6
Genoa
20.570264765784113
60.6
epigenetic
1.5274949083503053
4.5
geology
47.691405491207696
0.9814894795417786
HD
3.9596563317146063
10.6
life sciences
28.363564182847412
0.7299919724464417
HD
8.146639511201629
24.0
chromatin
4.59469555472544
12.3
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/data_interpretation/
2014-02-11T16:28:33.504+01:00
https://www.google.com/accounts/o8/id?id=AItOawnOAeiyuU0cZ91YBD1EW7d43AlWw8xdALU
http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5
90137
https://api.rohub.org/api/ros/3a129ea9-7d50-4d4a-bcce-499729c5f3e0/crate/download/
2013-08-30 16:15:52.229000+00:00
2025-03-05 00:55:18.105872+00:00
2013-08-30 16:15:52.229000+00:00
This RO is an extension of the HD chromatin analysis to interpret the results and reveal missing links that can according to literature explain HD gene deregulation
application/ld+json
https://w3id.org/ro-id/3a129ea9-7d50-4d4a-bcce-499729c5f3e0
Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/
http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/
https://w3id.org/ro-id/9472831c-c0f7-4736-abb7-1130b6266567
https://w3id.org/ro-id/0ccc06e8-fa83-4607-a51a-d3960f236f2a
https://w3id.org/ro-id/1e86dc68-d7a4-4d76-b687-015183bfbbfc
https://w3id.org/ro-id/2adb1139-d0a2-4e40-a706-d4193311c6df
https://w3id.org/ro-id/2b8b4a6b-4fc5-4f60-8a3b-5798bd7aea12
https://w3id.org/ro-id/36273c7c-826e-426b-97e2-071775328169
https://w3id.org/ro-id/3d7af67d-d31a-4a39-85cf-da169fe3b217
https://w3id.org/ro-id/43eb0d5d-21e6-4061-ad5a-202d747f5c3d
https://w3id.org/ro-id/4d53d73d-0372-466d-bccd-001707a529af
https://w3id.org/ro-id/59f0f23a-584f-4b2d-87d3-197920b08374
https://w3id.org/ro-id/5a528e2e-c11d-4a5d-a145-b0dce36ee557
https://w3id.org/ro-id/60f9bf78-b591-4188-9863-6c3d8d9cba80
https://w3id.org/ro-id/61868dd9-a2b9-431b-b338-54b4dfff4c6d
https://w3id.org/ro-id/6912d56b-a19d-4241-a6fa-7a5591bc976d
https://w3id.org/ro-id/7ea94e74-e41d-4867-ab9a-ad911e8f46eb
https://w3id.org/ro-id/abde2abd-9c38-421d-a6ea-c99d13195606
https://w3id.org/ro-id/bd06484b-72bb-415a-a5fb-070daf77db0e
https://w3id.org/ro-id/d3ab53c4-e61b-4db2-ac6f-1c9027d8b582
https://w3id.org/ro-id/e949bf8b-77ef-4da1-aeac-724b4448ce37
https://w3id.org/ro-id/ef101db2-8b39-4c03-8d81-d8bdc6886b26
https://w3id.org/ro-id/334ba840-d8d4-4c0d-b231-8408fd684615
https://w3id.org/ro-id/3ab8fcb4-b92d-4aad-9efb-048e2597b3c4
https://w3id.org/ro-id/61b3eebb-f4b4-478f-9bb1-be6ac3ff25e2
https://w3id.org/ro-id/6afdce1a-0994-4502-8cc1-ac663cdaa173
https://w3id.org/ro-id/c77eeaa0-6cbb-4eae-9730-17d13d8b508e
https://w3id.org/ro-id/cc6d2248-5ba5-481b-bd6b-7be7eaa28110
https://w3id.org/ro-id/55c5275e-f220-4dd2-8e7f-ac8ddd54b9e4
https://w3id.org/ro-id/5d1d6480-6991-4fff-abc6-6c6d8fae6a6c
https://w3id.org/ro-id/96636e8f-065e-48eb-bb8d-2eb72469e890
https://w3id.org/ro-id/d47a097c-6764-44a4-abcd-83c5d6b54302
https://w3id.org/ro-id/003d5516-496b-448e-be40-1a9fee828b33
https://w3id.org/ro-id/163a1d35-bb78-4c0d-8f84-825b71393fe0
https://w3id.org/ro-id/3381c808-cd31-49f7-ba8f-63e427655bdd
https://w3id.org/ro-id/378be13f-1c57-4e19-bada-f51379af77eb
https://w3id.org/ro-id/3a36972b-e7c4-421b-b5eb-7c59f5ac0d98
https://w3id.org/ro-id/52b5ecb5-1fd2-49f6-ae9d-73cf988a26f2
https://w3id.org/ro-id/81827075-1bdc-4069-b65f-c2ba1a3ae182
https://w3id.org/ro-id/86ba70a2-46d2-4500-8717-772f2011064e
https://w3id.org/ro-id/8ef34ded-7d1a-4f69-a141-04d88f715dea
https://w3id.org/ro-id/9a2f156f-bba1-44b1-85f9-e0e12bcac90b
https://w3id.org/ro-id/abced32c-8c02-41af-8736-eef654f6d172
https://w3id.org/ro-id/aecb7a3f-c8fb-4b54-96d0-9d9846e82af8
https://w3id.org/ro-id/ce570653-b842-4017-823e-7b87339a3b69
https://w3id.org/ro-id/0182d2b6-0eb0-4178-b722-fa3d0ab62e43
https://w3id.org/ro-id/0c10fb4c-bf0b-42df-86e1-b01aac9925ca
https://w3id.org/ro-id/35b95332-6876-4c12-9cff-2e01851a7d0c
https://w3id.org/ro-id/4db8dcbb-0fec-42dc-9e76-ff2b75074831
https://w3id.org/ro-id/77956b14-fcc7-41f1-b9e0-15d0a5aadb21
https://w3id.org/ro-id/e7d9cd96-6b3f-4a49-bf12-3e1c238f2242
https://w3id.org/ro-id/054e977e-3849-4eb0-a305-72e7519b498c
https://w3id.org/ro-id/0ffc2e27-e1fa-445c-8be6-d18de1e83a10
https://w3id.org/ro-id/1c15e09e-1005-4e9f-875d-88fc9d7ca868
https://w3id.org/ro-id/25ab1a9f-2b58-46ee-82b6-881a2151fb25
https://w3id.org/ro-id/5466d907-5a67-4665-bd54-b3d55a1275a2
https://w3id.org/ro-id/7ff67d11-e3fe-4e18-8a3e-3b8736918030
https://w3id.org/ro-id/b3c9e0a2-9e14-416e-b2dd-ef8440bac92f
https://w3id.org/ro-id/c00f5093-2ca8-42b4-9786-0b35a4ef4a0e
https://w3id.org/ro-id/c6355f07-2f22-4e41-8388-a2ff4f90a02f
https://w3id.org/ro-id/e771ec0e-560b-4c5b-87b0-9436451f689b
https://w3id.org/ro-id/ed3cfcbc-9ead-43b9-98c5-8267c1ca4aff
https://w3id.org/ro-id/ee53e862-3f8f-498f-97ee-b20ad91894be
https://w3id.org/ro-id/5c589b0d-0fd4-4e10-a949-254f733bad51
https://w3id.org/ro-id/a8bea726-82f1-4049-a93d-facd6d258724
https://w3id.org/ro-id/d80cc364-e913-48af-842d-45af832e8775
Eleni Mina. "Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/." ROHub. Aug 30 ,2013. https://w3id.org/ro-id/3a129ea9-7d50-4d4a-bcce-499729c5f3e0.
data_interpretation
41573
https://api.rohub.org/api/resources/4264530f-42e9-45f0-9255-d232a3a558ac/download/
2013-11-05 13:09:46.561000+00:00
2022-03-25 09:24:37.622734+00:00
Purpose of workflow: This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group).
Explain Scores
2013-11-05 13:09:46.561000+00:00
67
https://api.rohub.org/api/resources/4e604e6a-6c92-414d-a49e-4e67a8b39116/download/
2013-08-31 15:11:30.970000+00:00
2022-03-25 09:24:40.710743+00:00
text/plain
conclusion
2013-08-31 15:11:30.970000+00:00
188023
https://api.rohub.org/api/resources/55c4a47f-eb4d-4461-aaf3-f718a38bc803/download/
2013-11-05 13:07:43.493000+00:00
2022-03-25 09:24:47.224634+00:00
This workflow can prioritize genes that are related to a specific concept, e.g. HTT. In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against
Prioritize gene list related to a concept /list of concepts
2013-11-05 13:07:43.493000+00:00
214639
https://api.rohub.org/api/resources/5872d7d2-57d6-4b92-854a-815c2dc665b4/download/
2014-02-11 15:25:01.110000+00:00
2022-03-25 09:24:49.007856+00:00
This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. "5" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services
Annotate a gene list with Biological processes
2014-02-11 15:25:01.110000+00:00
39476
https://api.rohub.org/api/resources/7a77bf73-c2d0-41ca-8a13-b91c31de14d8/download/
2013-08-31 15:37:06.058000+00:00
2022-03-25 09:24:39.842471+00:00
Sketch of the workflows and their explanation, for data interpretation, plus the connection to the output from the RO for chromatin analysis
image/png
workflow sketch data interpretation
2013-08-31 15:37:06.058000+00:00
63
https://api.rohub.org/api/resources/97a1ffa9-8d56-4499-827b-8d4e2d313fbc/download/
2013-08-31 15:04:48.724000+00:00
2022-03-25 09:24:28.805556+00:00
text/plain
hypothesis.txt
2013-08-31 15:04:48.724000+00:00
30787
https://api.rohub.org/api/resources/ab98dfb6-be33-4c6d-bb69-1ecccdac03b8/download/
2013-11-05 13:09:01.493000+00:00
2022-03-25 09:24:44.673277+00:00
This workflow lists all IDs and descriptions of the predefined concept set
ListPredefinedConceptSets
2013-11-05 13:09:01.493000+00:00
41692
https://api.rohub.org/api/resources/f0c4d8f6-4ef6-43bd-806f-f6ee04fe6cbf/download/
2013-08-31 15:02:18.108000+00:00
2022-03-25 09:24:48.176013+00:00
This workflow suggests concept ids that match the query term. The user can run this workflow with any term of interest as for example "human", "htt", "Transcription" etc, and will get suggestions for concept ids together with descriptions. Then can choose the concept id that matches the best to her/his needs and use it to the rest of the CPA workflows
Get concept suggestions from term
2013-08-31 15:02:18.108000+00:00
gene
3.399327605528577
9.1
earth sciences
47.691405491207696
0.9814894795417786
role
5.363204344874406
15.8
missing link
4.514596062457569
13.3
analyzation
1.4596062457569585
4.3
life sciences
35.76177208527657
0.9203993678092957
HD
4.632050803137841
12.4
analysis of Ro
3.843514070006863
11.2
Animal
Human interest/Animal
system
3.6320434487440596
10.7
deregulation
9.911744738628649
29.2
Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/. This RO is an extension of the HD chromatin analysis to interpret the results and reveal missing links that can according to literature explain HD gene deregulation
33.333333333333336
100.0
Economic policy
Economy, business and finance/Economy/Economic policy
HD
3.8696537678207736
11.4
deregulation
3.598099117447386
10.6
geochemistry
27.692441186701984
0.5699106454849243
hard drive
4.107264086897488
12.1
earth sciences
27.692441186701984
0.5699106454849243
life sciences
35.87466373187602
0.9233048558235168
epigenetic
1.357773251866938
4.0
HD gene deregulation
11.358956760466711
33.1
deregulation
11.31864026895779
30.3
analysis
3.0257751214045574
8.1
Genoa
25.476279417258127
68.2
linguistics
100.0
7.7
Economic policy
Economy, business and finance/Economy/Economic policy
HD
8.06873365707882
21.6
genes involved in HD gene deregulation have an epigenetic role
33.333333333333336
100.0
deregulation
4.333208815838626
11.6
http
2.0706042090970804
6.1
gene
14.60590212924916
39.1
gene deregulation
1.8188057652711047
5.3
Ro
4.989816700610998
14.7
chromatin analysis
0.857927247769389
2.5
HD gene deregulation
25.806451612903224
75.2
geology
24.616153322090323
0.5066006183624268
earth sciences
24.616153322090323
0.5066006183624268
Ro
5.790063503922301
15.5
outcome
1.2219959266802443
3.6
Language
Arts, culture and entertainment/Culture/Language
Genes deregulated in HD, are participating in epigenetic processes
33.333333333333336
100.0
HD chromatin analysis
14.13864104323953
41.2
life sciences (general)
28.363564182847412
0.7299919724464417
gene
13.06856754921928
38.5
results from the analysis
1.3040494166094714
3.8
deregulate in HD
16.197666437886067
47.2
gene
2.919212491513917
8.6
Eleni Mina
service-account-generation-service
involve in HD gene deregulation
HD
participate in epigenetic processes
genetics
genes
have an epigenetic role
Purpose of workflow: This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group).
Explain Scores
testing
2.545824847250509
7.5
HD
8.06873365707882
21.6
life sciences (general)
35.87466373187602
0.9233048558235168
hard drive
4.107264086897488
12.1
http
2.0706042090970804
6.1
analysis of Ro
3.843514070006863
11.2
Ro
5.790063503922301
15.5
Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/. This RO is an extension of the HD chromatin analysis to interpret the results and reveal missing links that can according to literature explain HD gene deregulation
33.333333333333336
100.0
genes involved in HD gene deregulation have an epigenetic role
33.333333333333336
100.0
deregulation
4.333208815838626
11.6
gene deregulation
1.8188057652711047
5.3
gene
2.919212491513917
8.6
Ro
4.989816700610998
14.7
system
3.6320434487440596
10.7
earth sciences
24.616153322090323
0.5066006183624268
earth sciences
27.692441186701984
0.5699106454849243
HD
4.632050803137841
12.4
epigenetic
1.357773251866938
4.0
geology
47.691405491207696
0.9814894795417786
role
5.491221516623086
14.7
results from the analysis
1.3040494166094714
3.8
Economic policy
Economy, business and finance/Economy/Economic policy
deregulate in HD
16.197666437886067
47.2
life sciences (general)
28.363564182847412
0.7299919724464417
gene
14.60590212924916
39.1
deregulation
11.31864026895779
30.3
life sciences (general)
35.76177208527657
0.9203993678092957
epigenetic
1.5274949083503053
4.5
geology
24.616153322090323
0.5066006183624268
epigenetic process
17.84488675360329
52.0
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/data_interpretation/
2014-02-11T16:00:29.893+01:00
https://www.google.com/accounts/o8/id?id=AItOawnOAeiyuU0cZ91YBD1EW7d43AlWw8xdALU
81525
https://api.rohub.org/api/ros/95a9036b-bee9-48fb-81eb-7e9a90014293/crate/download/
2013-08-30 16:15:52.229000+00:00
2025-03-05 00:55:19.037479+00:00
2013-08-30 16:15:52.229000+00:00
This RO is an extension of the HD chromatin analysis to interpret the results and reveal missing links that can according to literature explain HD gene deregulation
application/ld+json
https://w3id.org/ro-id/95a9036b-bee9-48fb-81eb-7e9a90014293
Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/
https://w3id.org/ro-id/fa027cf1-adc0-4728-b7ff-e32e144b7729
https://w3id.org/ro-id/049c3132-1ee1-408a-a753-c66351b9e823
https://w3id.org/ro-id/10d69f16-88d4-4fef-b498-9ca9f03584d7
https://w3id.org/ro-id/12dafbe1-09f7-4107-8496-c513f6b97367
https://w3id.org/ro-id/2d7423e4-ac2b-4eb0-81d1-20c2dc99d330
https://w3id.org/ro-id/31088bb6-aac7-4307-9471-48da9891b2dc
https://w3id.org/ro-id/433dc648-a386-4922-917e-6ed05d3a97ff
https://w3id.org/ro-id/64a17901-a541-4910-bc00-f7bb220ea386
https://w3id.org/ro-id/8c0cc9ba-6303-4cc6-a2ce-78e26ae86db7
https://w3id.org/ro-id/b55d00c3-b2c0-438e-a616-8b64a1273ff7
https://w3id.org/ro-id/bcb1bb76-fd8e-48f2-b107-553a5dcf0c5f
https://w3id.org/ro-id/cb1fc5e8-971c-4f0e-bfb7-bc0095ca4e98
https://w3id.org/ro-id/cb39a646-e1d1-4303-a4b4-717b7c663702
https://w3id.org/ro-id/cfb98605-5a52-4da9-b744-e9a354a8fe8c
https://w3id.org/ro-id/d30fb3af-cba6-402e-9916-fab7a97104a8
https://w3id.org/ro-id/d91ab93e-d975-4f2c-ae27-06033ff01bf8
https://w3id.org/ro-id/e74a5f9a-7b3b-4a0a-9c2d-e3f7aac43fba
https://w3id.org/ro-id/eac3c9d6-6030-40e3-84f0-f55ff8e1c18c
https://w3id.org/ro-id/ef870edf-e68d-4428-8a47-640c9c1cb3c4
https://w3id.org/ro-id/f59dceab-2927-465f-979e-2d937576206c
https://w3id.org/ro-id/50ef50fa-5449-4de2-b41e-983587738d78
https://w3id.org/ro-id/5b1503b4-4a1f-4abc-a2e5-e7bc4401cd07
https://w3id.org/ro-id/722d8898-aedd-4f21-bdb8-db907db59e5b
https://w3id.org/ro-id/9317e0fa-ec42-4a64-81be-7968b0420d7d
https://w3id.org/ro-id/afb76586-4c0a-40e6-99e6-c99890200df0
https://w3id.org/ro-id/d182acc9-493a-4d75-9bd4-871e2555265e
https://w3id.org/ro-id/7ad072ed-d161-4575-99a5-664cb4eb1b27
https://w3id.org/ro-id/97e1df9f-7ef9-4834-bfbb-dc8843faedab
https://w3id.org/ro-id/99fe1429-cbac-4a80-ba71-23c0ce6664ee
https://w3id.org/ro-id/dd80c5f0-30e7-46fa-8ec1-57c9c4ab0a29
https://w3id.org/ro-id/0dd15a1b-c0aa-455b-aa18-53b45dc979b4
https://w3id.org/ro-id/19e9bb94-844f-4a46-b361-5cf4e39ff804
https://w3id.org/ro-id/2651bc82-85b6-4992-b676-891270ca6fe1
https://w3id.org/ro-id/5dba2b2f-00f4-4162-9e62-2aa459ba88ea
https://w3id.org/ro-id/74c8f2b9-8571-4629-8e0e-82a4b59670be
https://w3id.org/ro-id/8611953b-d788-4e73-a8fb-bf132828997e
https://w3id.org/ro-id/8941cdba-2596-4fcc-b71a-514afaf40ebc
https://w3id.org/ro-id/9ba625ba-e500-4195-815b-54af2656d720
https://w3id.org/ro-id/abfdf7b4-b6dc-42ed-a544-c148ea552e3e
https://w3id.org/ro-id/b7572fc6-6c64-4e00-a8d4-0f522fc90b1e
https://w3id.org/ro-id/c283c1ef-c3f8-4703-a661-695109f19f88
https://w3id.org/ro-id/c2c10aca-8a73-442c-af9b-441ba68c1098
https://w3id.org/ro-id/dc837b23-a71d-4bd2-b2b4-080528e49fa9
https://w3id.org/ro-id/0ef41513-eaa9-4e76-994a-e90587f4069e
https://w3id.org/ro-id/80f188fc-a15a-40e1-9843-303b0be67656
https://w3id.org/ro-id/8978ed0f-74e3-4ca4-bea3-c366b5ac4a27
https://w3id.org/ro-id/b8bcb4cf-ba2b-438b-a8e3-d9e460152a09
https://w3id.org/ro-id/cb11d942-fce9-459d-bfe2-4a4ed9d1fe1d
https://w3id.org/ro-id/e24af000-2fbd-4820-bf60-bd01890f4b89
https://w3id.org/ro-id/15ba1d7a-c420-4e56-870a-99df018d8907
https://w3id.org/ro-id/2b8c0d56-d5b4-4689-a663-1752b449dc0a
https://w3id.org/ro-id/78cd33c6-3425-4ecf-a833-00cf96855d21
https://w3id.org/ro-id/7f465c71-cf0c-4f7d-8406-3eaef5ad7c0b
https://w3id.org/ro-id/93f1ab58-022a-4598-97f7-7807f93f2aa1
https://w3id.org/ro-id/9d438ec7-40ed-4892-a79f-86270050c4f8
https://w3id.org/ro-id/a852c4bf-efeb-436b-951e-b4dc59e1e4b7
https://w3id.org/ro-id/aa30e810-a7cf-4844-9c02-c5f2cb97b671
https://w3id.org/ro-id/af8a543b-f09a-4484-9a01-66fdcadbd631
https://w3id.org/ro-id/d6a3e45a-7fea-4bce-89ce-e0c2574d972f
https://w3id.org/ro-id/e907a28b-718c-45b7-9531-0e556e167c0c
https://w3id.org/ro-id/f8fb6331-272a-4818-ad9a-1fa334ce7606
https://w3id.org/ro-id/1b53e2a7-75e0-4b9a-97e0-c61af6f89d96
https://w3id.org/ro-id/1da386ca-5f4b-4458-9db0-2dfb98944d22
https://w3id.org/ro-id/fa01854e-8511-436f-9f73-5e8db98dec87
Eleni Mina. "Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/." ROHub. Aug 30 ,2013. https://w3id.org/ro-id/95a9036b-bee9-48fb-81eb-7e9a90014293.
data_interpretation
67
https://api.rohub.org/api/resources/207ea21e-4180-4f98-a6e9-9906672c0f49/download/
2013-08-31 15:11:30.970000+00:00
2022-03-25 09:26:52.370638+00:00
text/plain
conclusions.txt
2013-08-31 15:11:30.970000+00:00
30787
https://api.rohub.org/api/resources/2328b548-0032-4dfb-97dd-2892668282c5/download/
2013-11-05 13:09:01.493000+00:00
2022-03-25 09:27:02.866468+00:00
This workflow lists all IDs and descriptions of the predefined concept set
ListPredefinedConceptSets
2013-11-05 13:09:01.493000+00:00
41692
https://api.rohub.org/api/resources/574c5f85-a2ea-484f-92e6-f11fe184661d/download/
2013-08-31 15:02:18.108000+00:00
2022-03-25 09:27:05.517990+00:00
This workflow suggests concept ids that match the query term. The user can run this workflow with any term of interest as for example "human", "htt", "Transcription" etc, and will get suggestions for concept ids together with descriptions. Then can choose the concept id that matches the best to her/his needs and use it to the rest of the CPA workflows
Get concept suggestions from term
2013-08-31 15:02:18.108000+00:00
203555
https://api.rohub.org/api/resources/71f79523-cf5b-48fb-b7b9-92ec04636326/download/
2013-11-05 13:07:17.887000+00:00
2022-03-25 09:27:03.799736+00:00
This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. "5" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services
Annotate a gene list with Biological processes
2013-11-05 13:07:17.887000+00:00
39476
https://api.rohub.org/api/resources/a220e0d8-7cdd-40ba-a8a5-6b264086dde3/download/
2013-08-31 15:37:06.058000+00:00
2022-03-25 09:26:58.184590+00:00
Sketch of the workflows and their explanation, for data interpretation, plus the connection to the output from the RO for chromatin analysis
image/png
workflow sketch data interpretation
2013-08-31 15:37:06.058000+00:00
188023
https://api.rohub.org/api/resources/a8461986-76a4-49b2-8153-4c899df13854/download/
2013-11-05 13:07:43.493000+00:00
2022-03-25 09:27:04.663380+00:00
This workflow can prioritize genes that are related to a specific concept, e.g. HTT. In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against
Prioritize gene list related to a concept /list of concepts
2013-11-05 13:07:43.493000+00:00
63
https://api.rohub.org/api/resources/e60c67e6-6657-4ddf-99ed-b47687ce684c/download/
2013-08-31 15:04:48.724000+00:00
2022-03-25 09:26:50.571966+00:00
text/plain
hypothesis.txt
2013-08-31 15:04:48.724000+00:00
Language
Arts, culture and entertainment/Culture/Language
Animal
Human interest/Animal
analysis
3.0257751214045574
8.1
have an epigenetic role
0.06863417982155112
0.2
participate in epigenetic process
0.20590253946465337
0.6
chromatin analysis
0.857927247769389
2.5
HD
3.9596563317146063
10.6
HD gene deregulation
11.358956760466711
33.1
geochemistry
27.692441186701984
0.5699106454849243
HD
3.8696537678207736
11.4
chromatin
4.59469555472544
12.3
life sciences
35.87466373187602
0.9233048558235168
missing link
4.514596062457569
13.3
Genoa
25.476279417258127
68.2
missing link
5.304445274561076
14.2
life sciences
35.76177208527657
0.9203993678092957
Genoa
20.570264765784113
60.6
gene
13.06856754921928
38.5
analyzation
1.4596062457569585
4.3
earth sciences
47.691405491207696
0.9814894795417786
chromatin
4.039375424304141
11.9
epigenetic role
6.554564172958133
19.1
deregulation
3.598099117447386
10.6
gene
3.399327605528577
9.1
Economic policy
Economy, business and finance/Economy/Economic policy
life sciences
28.363564182847412
0.7299919724464417
role
5.363204344874406
15.8
HD gene deregulation
25.806451612903224
75.2
outcome
1.2219959266802443
3.6
deregulation
9.911744738628649
29.2
HD
8.146639511201629
24.0
HD chromatin analysis
14.13864104323953
41.2
Genes deregulated in HD, are participating in epigenetic processes
33.333333333333336
100.0
linguistics
100.0
7.7
Eleni Mina
service-account-generation-service
gene deregulation
Huntington's disease gene deregulation
HepG Permuted HNF
channel subunit
cell change
mRNAs encod ing proton channel subunit
frontal cortex
HD brain
Johann Sebastian Bach
aberrantprotein protein interaction
United States of America
New Hampshire
unfolded protein response protein
anatomy
enrichment
HD
enhancers
activity
genetics
caudate nucleus
a number of mRNA
mRNA change
HepG HUVECHSMMNHLFNHEKHMEC
chromatin
genes
motif
cell type
BA
changes
cell
clusters
state
islands
kB
s disease brain
disease
mRNA
disease
2.4802890932982917
15.1
cortices
1.7575558475689883
10.7
epigenetic phenomena
2.56078634247284
9.9
epigenetic dataset
1.0346611484738748
4.0
Genetics
Science and technology/Natural science/Biology/Genetics
genetics
19.45945945945946
43.2
caudate nucleus
1.3961892247043366
8.5
Economic policy
Economy, business and finance/Economy/Economic policy
Overall, the regional changes in gene expression areconsistent with the neuropathology in early grade HD, withcaudate being the most affected area, the cerebellum andBA cortex being relatively spared and the BA cortexshowing an intermediate pathology.
2.9443254817987152
11.0
Nova Scotia
https://www.wikidata.org/wiki/Q1952
Ro
1.9077901430842608
7.2
cerebellum
1.5768725361366625
9.6
sample
1.806833114323259
11.0
experiment document
2.56078634247284
9.9
workflow
2.3582405935347115
8.9
recentneuroimaging data
1.5002586652871184
5.8
Mental and behavioural disorder
Health/Diseases and conditions/Mental and behavioural disorder
biology
6.126126126126126
13.6
chemistry
5.585585585585585
12.4
Diseases and conditions
Health/Diseases and conditions
gene
11.990801576872537
73.0
University of California, Berkeley
https://www.wikidata.org/wiki/Q168756
biochemistry
17.34234234234234
38.5
Huntington's disease gene deregulation
44.43869632695292
171.8
Auckland City
https://www.wikidata.org/wiki/Q758634
deregulation
9.937582128777924
60.5
Vinh Yên
https://www.wikidata.org/wiki/Q36088
geophysics
5.460552638139745
0.20815198123455048
disease
2.888182299947006
10.9
Genetics
Science and technology/Natural science/Biology/Genetics
change
2.266754270696452
13.8
Mental and behavioural disorder
Health/Diseases and conditions/Mental and behavioural disorder
Hutchinson
https://www.wikidata.org/wiki/Q958555
brain
1.192368839427663
4.5
Wales
https://www.wikidata.org/wiki/Q25
geology
72.8531489829241
2.6746557652950287
epigenetic mechanism
1.810657009829281
7.0
Columbia University
https://www.wikidata.org/wiki/Q49088
environmental sciences
27.1468510170759
0.9966416358947754
gene deregulation
2.8453181583031557
11.0
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
Huntington's disease
17.6205617382088
66.5
l Globin
1.5002586652871184
5.8
In addition we include all related to our experiment documents, papers and datasets
2.43576017130621
9.1
data comefrom
1.577858251422659
6.1
phenomenon
2.266754270696452
13.8
chicken chicken e Globin c Globin
0.6725297465080186
2.6
Huntington s disease (HD) pathology is well understood at a histological level but a comprehensivemolecular analysis of the effect of the disease in the human brain has not previously been available.
3.747323340471092
14.0
life sciences
94.53944736186025
3.603769540786743
Lausanne
https://www.wikidata.org/wiki/Q807
medicine
23.73873873873874
52.7
earth sciences
72.8531489829241
2.6746557652950287
epigenetic information
2.1986549405069837
8.5
Animal
Human interest/Animal
Massachusetts
https://www.wikidata.org/wiki/Q771
Cardiff
https://www.wikidata.org/wiki/Q3398450
cerebellum
1.2718600953895072
4.8
mRNA
1.8018018018018018
6.8
Regional and cellular gene expression changes inhuman Huntington s disease brain
6.5310492505353315
24.4
Recently, regions with the sequence characteristics of CpG islands have been found associated with a number of genes, most of which are housekeeping genes (for reviews, see Cooper & Gerber Huber, ; Bird, ). Almost all CpG islands identified to date are associated with the ends of genes.
1.0438972162740898
3.9
linguistics
4.774774774774775
10.6
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
exon intron boundary
0.6466632177961718
2.5
mechanism
1.8812930577636462
7.1
protein
1.3513513513513513
5.1
Genetics
Science and technology/Natural science/Biology/Genetics
growth hormone growth hormone releasing factor
1.1639937920331092
4.5
workflow
1.3469119579500657
8.2
Switzerland
https://www.wikidata.org/wiki/Q39
geosciences
5.460552638139745
0.20815198123455048
Armed forces
Politics/Government/Defence/Armed forces
New York
https://www.wikidata.org/wiki/Q60
deregulation
17.56756756756757
66.3
service-account-enrichment
http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/
2013-09-03T13:21:39.640+02:00
https://www.google.com/accounts/o8/id?id=AItOawmTeIQ2KATi2wWQYhEpoOQ5e06_WUKcbO4
5135459
https://api.rohub.org/api/ros/9c56e407-c5bc-45bd-8636-2f1066e10e75/crate/download/
2013-08-30 16:02:59.633000+00:00
2025-03-05 00:46:19.861058+00:00
2013-08-30 16:02:59.633000+00:00
This RO is comprised by all workflows used for the integration and the analysis of Huntington's Disease (HD) gene expression data and epigenetic datasets in order to establish links between HD and epigenetic regulation in disease. In addition we include all related to our experiment documents, papers and datasets
application/ld+json
https://w3id.org/ro-id/9c56e407-c5bc-45bd-8636-2f1066e10e75
Analyzing gene derulation in Huntington's disease with respect to epigenetic information
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https://w3id.org/ro-id/956d5144-5ca7-44ed-bcd2-b8e32fe8c775
https://w3id.org/ro-id/9a676099-9a0c-4a38-9212-5d2d84d8e16a
https://w3id.org/ro-id/a66a62f4-54a5-4378-b82f-6398dc08a410
https://w3id.org/ro-id/ac1b03dc-3c7b-4cb3-b586-35d71fd33c9a
https://w3id.org/ro-id/b6233f43-573c-4c7a-a6fd-02e9af1f8239
https://w3id.org/ro-id/c948f933-78e6-4df7-b42b-baa91d57e4da
https://w3id.org/ro-id/c9acdd9f-5d2b-4e65-a72c-48ac32ef80a1
https://w3id.org/ro-id/d57a8b2a-e295-49fd-bf13-1168c3577963
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https://w3id.org/ro-id/f1c65e35-5a3c-4f6f-a4dc-fbf496c1ed39
https://w3id.org/ro-id/f611a2d5-b28e-4032-8bf0-9579e9130d8a
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https://w3id.org/ro-id/6545c265-a3d9-40e9-b231-c33e46a41c7b
https://w3id.org/ro-id/91f97abc-75f6-4e31-89ee-9a3c4bbd90a7
https://w3id.org/ro-id/af6e55e0-9d47-42e9-905c-4f1ef17bbe31
https://w3id.org/ro-id/b2306ff6-56f6-4590-80bf-fd46092b357c
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https://w3id.org/ro-id/d9eada64-848c-48bb-b3f1-fdf1734c6848
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https://w3id.org/ro-id/093902c9-ec8a-4cf4-8df0-3c779f0dd0d4
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https://w3id.org/ro-id/1e84a068-197b-425e-959a-918cd8ed9109
https://w3id.org/ro-id/208acacb-45a2-42db-869d-c32cdde94e7c
https://w3id.org/ro-id/3cb1ad14-3fe2-40ae-87c8-ec533e89c1bc
https://w3id.org/ro-id/4007d877-51e7-41fb-b013-f5eb4d43c815
https://w3id.org/ro-id/597c1a27-9ba7-412f-bfac-5c2ba1df456e
https://w3id.org/ro-id/756a4c94-6d70-4420-b616-97b3d05d0690
https://w3id.org/ro-id/89bbff4e-0c3f-439e-ac6d-3282b880ba41
https://w3id.org/ro-id/8fe72821-d808-487d-ac47-b03ef9dcf7ee
https://w3id.org/ro-id/96553723-3687-47f4-991e-bde2de0390ed
https://w3id.org/ro-id/ab032864-ce09-4d5d-859c-ecbb7838f984
https://w3id.org/ro-id/b904ba2d-dd06-48ab-9753-33c07b842fe5
https://w3id.org/ro-id/c1728c04-36dc-4c63-8496-e988b984143b
https://w3id.org/ro-id/d47154ce-f33a-4e1e-b8d5-4c652ca62c2f
https://w3id.org/ro-id/d8ff8c56-2019-4f09-a4ba-fe7ec7a63cbe
https://w3id.org/ro-id/deaafa3e-b71c-46ae-a0fc-7715af16506e
https://w3id.org/ro-id/126939fe-fe94-48fa-8906-398b50ebb4da
https://w3id.org/ro-id/1d3a1447-80ff-4d85-bafa-738ecd0e8a32
https://w3id.org/ro-id/39733903-7ef0-4c97-999a-b9df621101a9
https://w3id.org/ro-id/475f05b3-99ca-4917-a33b-ba81915f670a
https://w3id.org/ro-id/5da4428e-8c59-4a04-879f-0688c10ebe9c
https://w3id.org/ro-id/78f04ec2-e504-4b54-b2d9-baecc21ef7a5
https://w3id.org/ro-id/81727921-5926-4d5d-87ef-006b5dfda561
https://w3id.org/ro-id/8e73a440-44aa-4e93-862c-3fd1899f9ede
https://w3id.org/ro-id/8f07c998-0628-4786-8395-470058dbb309
https://w3id.org/ro-id/9c171c58-909e-4fc2-8411-b0a1cc1b77d1
https://w3id.org/ro-id/ad860dab-6cf5-481b-a838-8a8c7b6dfc5c
https://w3id.org/ro-id/b9b7dc10-092f-4486-b6da-99ba75a25b25
https://w3id.org/ro-id/ba2a3912-c79a-40b0-b97f-17e8c6f3e7b1
https://w3id.org/ro-id/c87410be-b822-4117-af83-1007c4edb2b4
https://w3id.org/ro-id/e16f0de6-ec11-4ea3-8435-b96a12fd9b6a
https://w3id.org/ro-id/e20a55fd-cba2-4943-ae44-f3b038ca33e0
https://w3id.org/ro-id/f1c90372-be12-4563-8b68-049113851bee
https://w3id.org/ro-id/f80b203b-2f2c-4115-9650-615dceb59fe2
https://w3id.org/ro-id/fd31190a-d55b-46d1-97cc-46fdc8f66eba
https://w3id.org/ro-id/377ca60c-eaae-4ddd-9f3f-0ea0ad3bd022
https://w3id.org/ro-id/6ac4513d-58df-4246-90dd-a77a2e6aef2b
https://w3id.org/ro-id/95e85503-d878-48a7-b1c2-b134697b026f
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https://w3id.org/ro-id/081c9d9a-1da6-4b05-8835-bf9fc945e53e
https://w3id.org/ro-id/0890c076-0594-4072-8012-d51be7cc0f02
https://w3id.org/ro-id/1a824b04-1ec3-413c-a139-48b6306b83e9
https://w3id.org/ro-id/1da5eac9-4c23-46f2-9a76-b4d797137564
https://w3id.org/ro-id/2c623ac8-541e-4d98-80f7-5b730e80dbc7
https://w3id.org/ro-id/4b69998b-cbda-4d1c-a128-52b8a2f17dfe
https://w3id.org/ro-id/551aa6d5-72e0-464e-83ea-2d21d11c3900
https://w3id.org/ro-id/5e4964ad-57d0-43fe-b9c4-a592ddc34e90
https://w3id.org/ro-id/63ff39fd-d94f-4513-8b01-e2ef47e004d9
https://w3id.org/ro-id/66f547ca-00fc-4a64-9cf1-c38c04474199
https://w3id.org/ro-id/72282f86-f3d2-41d2-915c-14635f2138bf
https://w3id.org/ro-id/8aa8c9b5-7144-411b-9943-cbe0c644cc4c
https://w3id.org/ro-id/90a6aca3-e591-4255-9454-ca320d3ab062
https://w3id.org/ro-id/ac989a20-5da1-407a-b622-0ce8655dfbbb
https://w3id.org/ro-id/c1e017ba-9bdf-4196-b9ef-2fcdceba9da2
https://w3id.org/ro-id/c53a5ce4-137d-473f-afdf-28fab6b87051
https://w3id.org/ro-id/de57df3a-087c-424c-b185-d8842c6981b9
https://w3id.org/ro-id/fa85e5ba-0644-4cb0-ad6c-ec187f84db38
https://w3id.org/ro-id/fe9bede8-ea76-4beb-b54d-df4de02b9a1f
https://w3id.org/ro-id/10b2bd0b-97eb-4577-934f-fccadaee0286
https://w3id.org/ro-id/6117cf1c-083e-4c9d-aad8-48890e465e84
https://w3id.org/ro-id/69cdf0d0-9633-40f2-ae93-43728bad2bea
https://w3id.org/ro-id/82244e9c-88e2-4fd7-b0a3-4bd30b611da9
https://w3id.org/ro-id/86620110-80c7-4fa1-ba82-d2b80920b27a
https://w3id.org/ro-id/a426e0b3-b213-47df-b5e2-077d47c898d7
https://w3id.org/ro-id/a97905b9-352b-40fe-b830-9dcb4df19666
https://w3id.org/ro-id/b2317ef5-8d59-4cc9-b759-7049d78f005c
https://w3id.org/ro-id/deaa4d84-c24c-45bd-bace-9cef3a3b5982
https://w3id.org/ro-id/ed7e4d46-6339-450c-8f6d-67151a0f134f
https://w3id.org/ro-id/fbbf7ae1-0b78-42c0-b76a-4ceea3eb583b
https://w3id.org/ro-id/fce32c15-bb79-4be6-8d76-c2e06e7390a8
Eleni Mina. "Analyzing gene derulation in Huntington's disease with respect to epigenetic information." ROHub. Aug 30 ,2013. https://w3id.org/ro-id/9c56e407-c5bc-45bd-8636-2f1066e10e75.
workflows
91
https://api.rohub.org/api/resources/30874a46-cbfd-424f-aef2-1a674f1d09f9/download/
2013-08-30 16:20:26.198000+00:00
2022-03-25 09:30:52.544644+00:00
text/plain
conclusions.txt
2013-08-30 16:20:26.198000+00:00
2454579
https://api.rohub.org/api/resources/330e91b4-3166-4525-af4f-565788e021f9/download/
2013-08-30 16:07:34.922000+00:00
2022-03-25 09:30:50.687347+00:00
application/pdf
CpG_islandsINVertebrateGenomes.pdf
2013-08-30 16:07:34.922000+00:00
78
https://api.rohub.org/api/resources/394a58eb-e038-47cd-b533-5f4aa6c611a6/download/
2013-08-30 16:18:12.111000+00:00
2022-03-25 09:30:57.894038+00:00
text/plain
hypothesis_data_analysis.txt
2013-08-30 16:18:12.111000+00:00
1162315
https://api.rohub.org/api/resources/4b6d3538-a47a-40d8-8354-fa2c15b3db90/download/
2013-08-30 16:08:44.993000+00:00
2022-03-25 09:30:56.214880+00:00
text/plain
broad_hmm_1_Active_Promoter.txt
2013-08-30 16:08:44.993000+00:00
27769
https://api.rohub.org/api/resources/51464740-4f5e-4580-81b4-1129b2328650/download/
2013-08-30 16:06:19.842000+00:00
2022-03-25 09:30:58.831975+00:00
image/png
workflow_sketch_hd_chromatin_analysis.png
2013-08-30 16:06:19.842000+00:00
2189245
https://api.rohub.org/api/resources/5163b815-f8ed-4104-9129-eb6f35f3b06b/download/
2013-08-30 16:11:15.136000+00:00
2022-03-25 09:30:53.428293+00:00
text/plain
broad_hmm_2_Weak_Promoter.txt
2013-08-30 16:11:15.136000+00:00
1757813
https://api.rohub.org/api/resources/5fbd6444-8406-4049-a538-771661a2813c/download/
2013-08-30 16:08:28.464000+00:00
2022-03-25 09:30:54.273817+00:00
text/plain
cpg_islands_info.txt
2013-08-30 16:08:28.464000+00:00
7531408
https://api.rohub.org/api/resources/603a6e96-5e2e-4717-8e74-1d07bd0ebd8f/download/
2013-08-30 16:11:42.316000+00:00
2022-03-25 09:30:55.352980+00:00
text/plain
broad_hmm_13_Heterochrom_lo.txt
2013-08-30 16:11:42.316000+00:00
359786
https://api.rohub.org/api/resources/7c5c11ad-cb92-4ee7-9eca-a847c171851f/download/
2013-08-30 16:07:50.243000+00:00
2022-03-25 09:30:51.608799+00:00
application/pdf
Hodges06_human_brain_Affy.pdf
2013-08-30 16:07:50.243000+00:00
704821
https://api.rohub.org/api/resources/7ffe1b97-93ae-49b2-b896-0d0a1017aa44/download/
2013-08-30 16:07:18.371000+00:00
2022-03-25 09:30:57.066677+00:00
application/pdf
chr_state_dynamics_in_nine_human_cell_types_nature.pdf
2013-08-30 16:07:18.371000+00:00
346969
https://api.rohub.org/api/resources/bf20597c-67c7-4ced-a5dd-7e4dfddcfaf2/download/
2013-08-30 16:11:28.501000+00:00
2022-03-25 09:31:00.106383+00:00
text/plain
broad_hmm_3_Poised_Promoter.txt
2013-08-30 16:11:28.501000+00:00
558
https://api.rohub.org/api/resources/c9055895-3de8-4f49-baaf-dc0a223f19fe/download/
2013-09-02 13:04:40.908000+00:00
2022-03-25 09:30:49.103437+00:00
text/plain
dummy_results.txt
2013-09-02 13:04:40.908000+00:00
environmental science and management
27.1468510170759
0.9966416358947754
Huntington's disease gene deregulation is mediated by alterations in epigenetic mechanisms
26.766595289079227
100.0
London
https://www.wikidata.org/wiki/Q84
Chicken Chicken
e Globin c Globin Ed Globin Globin globin cluster E Globin (:, Globin A, globin cluster a: Globin BhO Globin bhl Globin /l Globin, minor /I Globin, type allele b Globin fil Globin Growth Hormone Growth hormone releasing factor
3.185224839400428
11.9
Diseases and conditions
Health/Diseases and conditions
California
https://www.wikidata.org/wiki/Q99
gene expression data
12.31246766683911
47.6
intron
1.192368839427663
4.5
HD
1.4290407358738502
8.7
mRNA
2.3488830486202366
14.3
G/C boxes were found to be rare in CpG depleted DNA and plentiful in CpG islands, where they occurred in CpG islands, as well as in CpG islands associated with tissue specific and housekeeping genes.
1.2580299785867237
4.7
Huntington
https://www.wikidata.org/wiki/Q241808
Genetics
Science and technology/Natural science/Biology/Genetics
exon
1.1658717541070485
4.4
gene expression
3.6565977742448332
13.8
dataset
1.3469119579500657
8.2
anatomy
22.972972972972972
51.0
information
3.3672798948751645
20.5
Economic policy
Economy, business and finance/Economy/Economic policy
HD Ba withGrades pathology
0.6983962752198655
2.7
gene expression profile
3.4919813760993272
13.5
Ed Globin Globin
1.192368839427663
4.5
Bird Island
1.494743758212878
9.1
Bird Island
https://www.wikidata.org/wiki/Q28689
Seattle
https://www.wikidata.org/wiki/Q5083
number
1.5111695137976346
9.2
Philosophy
Science and technology/Social sciences/Philosophy
Australia
https://www.wikidata.org/wiki/Q408
Huntington's disease
10.446780551905388
63.6
Mental and behavioural disorder
Health/Diseases and conditions/Mental and behavioural disorder
gene expression
2.7266754270696456
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alteration
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life sciences (general)
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CpG
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Eleni Mina
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anca popescu
EU SatCen
EU SatCen
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WebProcessingService
AreaofInterest
MasterSentinel-1product
Polarization
SlaveSentinel-1product
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Result Files Distribution Package
com.terradue.wps_oozie.process.OozieAbstractAlgorithm
SatCen Change Detection Workflow
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S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip
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uniform resource identifier
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earth sciences
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earth sciences
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earth resources and remote sensing
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earth sciences
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atmospheric sciences
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http://ever-est.eu/value#My Library
2018-06-15T10:34:14.883+02:00
34078
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2018-06-15 08:34:14.883000+00:00
2026-03-13 11:12:45.114837+00:00
2018-06-15 08:34:14.883000+00:00
Change Detection over Madrid
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Change Detection Data Centric
Land Monitoring Community
Anca Popescu
Land Monitoring
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setup
components
datasets
produced
web services
inputs
results
biblio
main
scripts
workflows
nested
used
software
config
0
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ggg
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service-account-enrichment
service-account-generation-service
Sergio Ferraresi
television
EVER-EST
Everest
EVER-EST Images
100.0
100.0
all
3.027245206861756
3.0
image
40.46417759838547
40.1
geosciences
100.0
0.4913051426410675
meteorology and climatology
100.0
0.4913051426410675
Language
Arts, culture and entertainment/Culture/Language
earth sciences
100.0
0.9967114925384521
image
39.5010395010395
38.0
Everest
https://www.wikidata.org/wiki/Q513
service-account-enrichment
http://ever-est.eu/value#/everestimages
http://ever-est.eu/value#63286408-d88a-4f29-a88b-aa0a7b344b2d
http://ever-est.eu/value#ros
false
http://sandbox.rohub.org/rodl/ROs/everestimages/
2017-10-05T09:46:36.551+02:00
http://everest.psnc.pl/users/ferraresi_cnr
10433
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2017-10-05 07:46:36.551000+00:00
2025-03-05 00:51:38.564083+00:00
2017-10-05 07:46:36.551000+00:00
This RO contains all the images that can be inspected on EVER-EST.
application/ld+json
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EVER-EST Images
CNR
https://w3id.org/ro-id/6ab6d94a-4dfd-4b76-a00d-9434d691d2c1
https://w3id.org/ro-id/11fa1efd-da1c-4b16-b8ec-0bc70af224e9
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https://w3id.org/ro-id/c646080e-56aa-430b-97f4-20a41c8e205f
http://w3id.org/ro/earth-science#DataResearchObject
Sergio Ferraresi. "EVER-EST Images." ROHub. Oct 05 ,2017. https://w3id.org/ro-id/7497ed54-bbce-4835-9585-f0cdccb484fa.
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2022-03-25 15:18:19.913078+00:00
test_NHP_4326.png.aux (1).xml
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2022-03-25 15:18:19.628061+00:00
PosidoniaDistribution_1986.sbx
2022-03-25 15:18:15.607098+00:00
http://box.everest.psnc.pl/f/a79f0b4a90/
2022-03-25 15:18:15.609618+00:00
2022-03-25 15:18:17.842227+00:00
test_NHP_4326 (1).pngw
2022-03-25 15:18:15.609618+00:00
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geology
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Everest
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This RO contains all the images that can be inspected on EVER-EST.
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earth sciences
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computer operations and hardware
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2017-09-05 12:00:30.090000+00:00
2025-03-05 00:50:41.693011+00:00
2017-09-05 12:00:30.090000+00:00
example description
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CWU GKNA
PBO velocity field file format
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Velocity
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Central Washington University
North America
Tom Herring
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estimates
reference frame
files
site
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yr
T. A.
velocity solution
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Politics/International relations/Diplomacy/Treaty
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time series
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computer science
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database
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3.2
aircraft communications and navigation
15.692410630511509
0.23452769219875336
Massachusetts Institute of Technology
https://www.wikidata.org/wiki/Q49108
of the Aug-23-2011
Cwu rms calculation CRMS root mean square
2.664576802507837
3.4
Chur
https://www.wikidata.org/wiki/Q69007
National Research Council Canada
https://www.wikidata.org/wiki/Q1437507
time series
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geophysics
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gage GPS Data analysis plan
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geology
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0.6552424430847168
statistics
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Non-Aligned Movement
https://www.wikidata.org/wiki/Q83201
University
Education/School/Higher education/University
gage
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7.0
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3.8
data
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8.7
noise
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9.7
statistics
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engineering
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global positioning system
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earth sciences
31.456986173340194
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Jet Propulsion Laboratory
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aeronautics
15.692410630511509
0.23452769219875336
PBO velocity field file format
5.407523510971786
6.9
The GAGE GPS Analysis Centers process data from more than 2,000 GPS stations.
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37.8
information
2.5324675324675323
7.8
edits.eq list
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gage reference frame
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3.0
NASA Global Geodetic Network
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9.8
file format
2.5
7.7
earth sciences
41.24948833068723
0.9902867078781128
Also processed are stations from the Southern California Integrated GPS Network (SCIGN), the NASA Global Geodetic Network (GGN), the International GNSS Service (IGS) network, the Rio Grande Rift network, the GPS Array for Mid America (GAMA), the Basin and Range Geodetic Network (BARGEN), the Idaho National Laboratory (INL) network, the Pacific Northwest Geodetic Array (PANGA), the Western Canada Deformation Array, SuomiNet, GulfNet, and stations near the epicenter of the 23 August 2011 M5.8 Mineral, VA earthquake.
15.412511332728922
17.0
time series
4.765013054830288
7.3
National Aeronautics and Space Administration
4.308093994778068
6.6
New Mexico
https://www.wikidata.org/wiki/Q1522
Virginia
https://www.wikidata.org/wiki/Q1370
The AC solutions are delivered in SINEX format for the geodetic parameter estimates and tabular files containing information about the root mean square (RMS) scatter and number of phase residuals by site for each day.
4.0797824116047146
4.5
Data from GPS stations not archived by UNAVCO are obtained from the NOAA National Geodetic Survey (NGS) Continuously Operating Reference Station (CORS) data center, the NASA Crustal Dynamics Data Information System (CDDIS), the U.S. Geological Survey, the Scripps Orbit and Permanent Array Center (SOPAC), and the International GNSS Service (IGS)
22.665457842248415
25.0
velocity
4.503916449086162
6.9
field
2.6298701298701297
8.1
Ulm
https://www.wikidata.org/wiki/Q3012
chemistry
16.371681415929203
22.2
The final entries PBO and PBO are the earlier and PBO full solution generated in November and . is the number of common sites in the solutions.
2.2665457842248413
2.5
University
Education/School/Higher education/University
netsel.use list
2.664576802507837
3.4
Idaho National Laboratory
8.072100313479623
10.3
National Aeronautics and Space Administration
https://www.wikidata.org/wiki/Q23548
list
2.272727272727273
7.0
lead oxide
4.415584415584416
13.6
Television
Arts, culture and entertainment/Mass media/Television
atmospheric sciences
31.456986173340194
0.755195677280426
service-account-enrichment
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2017-09-04T13:00:43.328+02:00
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http://connect.unavco.org/display/org253530
2017-09-04 11:00:43.328000+00:00
2025-03-05 02:47:00.400590+00:00
2017-09-04 11:00:43.328000+00:00
The GAGE GPS Analysis Centers process data from more than 2,000 GPS stations. Most of these stations are operated by UNAVCO as part of the PBO, COCONet, TLALOCnet and smaller regional networks. Also processed are stations from the Southern California Integrated GPS Network (SCIGN), the NASA Global Geodetic Network (GGN), the International GNSS Service (IGS) network, the Rio Grande Rift network, the GPS Array for Mid America (GAMA), the Basin and Range Geodetic Network (BARGEN), the Idaho National Laboratory (INL) network, the Pacific Northwest Geodetic Array (PANGA), the Western Canada Deformation Array, SuomiNet, GulfNet, and stations near the epicenter of the 23 August 2011 M5.8 Mineral, VA earthquake. Data from GPS stations not archived by UNAVCO are obtained from the NOAA National Geodetic Survey (NGS) Continuously Operating Reference Station (CORS) data center, the NASA Crustal Dynamics Data Information System (CDDIS), the U.S. Geological Survey, the Scripps Orbit and Permanent Array Center (SOPAC), and the International GNSS Service (IGS).
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GAGE, GPS stations, PBO, COCONet, TLALOCnet, time series data, web services
UNAVCO GPS Timeseries
GPS time series data web service
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Scripps Orbit and Permanent Array Center
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coordinate file
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Global Geodetic Network
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Software
Economy, business and finance/Economic sector/Computing and information technology/Software
PBO GKNA CWU TSKF
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PBO GKNA PbO
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Civilian Conservation Corps
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geology
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data
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reference frame
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National Aeronautics and Space Administration
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coordinate system
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file
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linguistics
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International GNSS Service
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GLOBK SINEX combinations, GK ( ) time series analyses using weighted least squares
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Musical instrument
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Southern California Integrated GPS Network
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Space programme
Science and technology/Research/Scientific exploration/Space programme
GPS
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standard deviation
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file format
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velocity
site coordinate information
PBO
Central Washington University
North America
file naming
Tom Herring
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Analysis
estimates
geodetic products
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files
sites
mm
year
GPS data analysis method
T. A.
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Jet Propulsion Laboratory
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Treaty
Politics/International relations/Diplomacy/Treaty
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GLOBK SINEX combinations, GK ( ) time series analyses using weighted least squares
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University
Education/School/Higher education/University
Global Geodetic Network
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NASA Global Geodetic Network
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velocity
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Chur
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National Aeronautics and Space Administration
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Science and technology/Research/Scientific exploration/Space programme
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National Aeronautics and Space Administration
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2025-03-05 02:47:01.120606+00:00
2017-09-04 10:48:59.459000+00:00
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https://w3id.org/ro-id/6bf763e1-d9ad-482c-8281-16016e69fb25
https://w3id.org/ro-id/7e962a00-4076-4bd8-8438-ee991cbe65df
https://w3id.org/ro-id/85a3ddfd-d9ef-4111-8fca-78ae9db85b4a
https://w3id.org/ro-id/927bb0b6-7e98-4c15-b773-f6d327264426
https://w3id.org/ro-id/faf43667-64bf-4708-b5f5-5cd35c375903
https://w3id.org/ro-id/04cf24b0-094f-4cb6-b47e-e590fa4f74d1
https://w3id.org/ro-id/0f113774-4c3e-472a-9c5e-a0bf1e5185dc
https://w3id.org/ro-id/12f44f52-ccaf-48fb-a7cb-cf3d7e42eac4
https://w3id.org/ro-id/17ec6de5-e7ab-4ca3-8e17-e0648b962f5e
https://w3id.org/ro-id/3d550c45-b03f-4237-aa1b-2096d7f345e3
https://w3id.org/ro-id/496d865a-c05d-43f2-8870-7bc6480b643b
https://w3id.org/ro-id/49f63cc0-88e2-4ce6-b73a-5be7884577df
https://w3id.org/ro-id/6aa29260-17a8-4f35-8014-5f9d505f903f
https://w3id.org/ro-id/752bb183-310c-46d6-9a46-ee67f4b6265b
https://w3id.org/ro-id/781f1ccb-8540-4b04-9dc3-83b190036785
https://w3id.org/ro-id/8048e747-fc19-46d8-b350-8c86c92cf5af
https://w3id.org/ro-id/9dbdbb51-2b84-4fd1-9e85-ac954402b1f5
https://w3id.org/ro-id/a1fd0480-0ced-46de-a48e-ca33adc89bf3
https://w3id.org/ro-id/a8c94a19-5981-47b3-8e2d-007ff5ee3245
https://w3id.org/ro-id/b42c7a8d-5236-4909-a2d0-3786439821c1
https://w3id.org/ro-id/b5b5d15a-3e2f-4cd7-827a-59f96f96a793
https://w3id.org/ro-id/b7512215-a78f-42f5-9c30-3d732a80a211
https://w3id.org/ro-id/bc7843e4-5d36-4969-91f3-1166798f8bb3
https://w3id.org/ro-id/dad6872b-eca8-41a9-9bd9-da3963b9e527
https://w3id.org/ro-id/1de9d863-1909-413e-93dc-052ab58aa39d
https://w3id.org/ro-id/1ff83729-2311-485b-a9e5-413beaaf66e0
https://w3id.org/ro-id/3d0d15d6-0256-4b4d-898d-c7460ddf6227
https://w3id.org/ro-id/617b98f5-7455-4cd9-beb4-2b3f35b5c06d
https://w3id.org/ro-id/69571445-971c-4605-b7ca-cb7454615956
https://w3id.org/ro-id/8c35bba8-c182-4e3b-9018-2fcd158b3f3d
https://w3id.org/ro-id/9c0a0609-b55d-4c9f-b016-f301472c39b8
https://w3id.org/ro-id/b0bf7bab-71d8-4011-87b8-104dce0b688f
https://w3id.org/ro-id/d7660396-0834-46d6-86ff-e218052a2254
https://w3id.org/ro-id/f6084908-18fd-4f0c-9ea2-c43f4271e219
https://w3id.org/ro-id/0d0b7a54-e7e4-4e41-a616-ae303fd0d684
Jose Manuel Gomez Perez. "UNAVCO GPS Timeseries." ROHub. Sep 04 ,2017. https://doi.org/10.5072/ro-id.3RRRUMSLRG.
produced
workflows
main
nested
scripts
config
biblio
setup
used
components
44925
https://api.rohub.org/api/resources/0343ece9-ca85-4a00-8385-38f0861fb64c/download/
2017-05-23 16:40:01.455000+00:00
2022-03-25 15:25:33.898979+00:00
PNG
sequenceplotter3.png
2017-05-23 16:40:01.455000+00:00
286993
https://api.rohub.org/api/resources/0f448e7a-e009-47f9-8dae-f006cfe30cb1/download/
2017-05-23 17:16:54.207000+00:00
2022-03-25 15:25:27.280603+00:00
PDF
GAGE_GPS_Analysis_Plan_20170315.pdf
2017-05-23 17:16:54.207000+00:00
http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short
sequenceplotter.png
14182
https://api.rohub.org/api/resources/47c20188-060b-4393-b3a3-c8d88912f605/download/
2017-05-01 19:55:49.788000+00:00
2022-03-25 15:25:30.993161+00:00
audio/midi
UNAVCO workflow built around time series data web service
2017-05-01 19:55:49.788000+00:00
http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short
2022-03-25 15:25:13.146253+00:00
2022-03-25 15:25:24.869032+00:00
http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short
2022-03-25 15:25:13.146253+00:00
603474
https://api.rohub.org/api/resources/afa5e9de-7ae0-4915-ad66-70d4db5e0f87/download/
2017-05-23 17:18:37.911000+00:00
2022-03-25 15:25:31.889430+00:00
PDF
GAGE_Velocity_Release_Notes_20161230.pdf
2017-05-23 17:18:37.911000+00:00
42242
https://api.rohub.org/api/resources/d380f679-0171-46d2-a663-83c052b2fce2/download/
2017-05-23 16:39:24.912000+00:00
2022-03-25 15:25:30.136677+00:00
PNG
sequenceplotter2.png
2017-05-23 16:39:24.912000+00:00
42055
https://api.rohub.org/api/resources/d96bf278-8e0d-400d-ba69-24d48e266433/download/
2017-05-23 16:38:24.493000+00:00
2022-03-25 15:25:32.860377+00:00
PNG
sequenceplotter.png
2017-05-23 16:38:24.493000+00:00
258680
https://api.rohub.org/api/resources/ea62a7c7-2326-484a-8946-bb8c9e95cd01/download/
2017-05-23 14:58:35.829000+00:00
2022-03-25 15:25:29.083241+00:00
image/jpeg
perm_mtolympus1.jpg
2017-05-23 14:58:35.829000+00:00
aircraft communications and navigation
15.692410630511509
0.23452769219875336
Ulm
https://www.wikidata.org/wiki/Q3012
chemistry
16.371681415929203
22.2
The AC solutions are delivered in SINEX format for the geodetic parameter estimates and tabular files containing information about the root mean square (RMS) scatter and number of phase residuals by site for each day.
4.0797824116047146
4.5
edits.eq list
2.115987460815047
2.7
computer science
15.634218289085547
21.2
lead oxide
4.415584415584416
13.6
North America
https://www.wikidata.org/wiki/Q49
earthquake file
2.3510971786833856
3.0
Alberta
https://www.wikidata.org/wiki/Q1951
gage
4.569190600522194
7.0
coordinate system
3.7337662337662336
11.5
global positioning system
3.0194805194805197
9.3
National Aeronautics and Space Administration
https://www.wikidata.org/wiki/Q23548
Science and technology
Science and technology
PBO velocity field file format
5.407523510971786
6.9
information
2.5324675324675323
7.8
PBO GKNA PBO TSKF . . . . . .
1.4505893019038985
1.6
file format
3.133159268929504
4.8
rms calculation NRMS root mean square
1.9592476489028212
2.5
PBO GKNA PbO
2.037617554858934
2.6
Idaho National Laboratory
8.072100313479623
10.3
computer science
8.185840707964601
11.1
PBO GKNA PBO TSLS
3.2915360501567394
4.2
North America
https://www.wikidata.org/wiki/Q49
Southern California Integrated GPS Network
3.9164490861618804
6.0
file format
2.5
7.7
time series
3.4740259740259742
10.7
earth sciences
41.24948833068723
0.9902867078781128
data
5.678851174934725
8.7
Massachusetts Institute of Technology
https://www.wikidata.org/wiki/Q49108
U.S. Geological Survey
https://www.wikidata.org/wiki/Q193755
statistics
2.272727272727273
7.0
National Oceanic and Atmospheric Administration
https://www.wikidata.org/wiki/Q214700
Also processed are stations from the Southern California Integrated GPS Network (SCIGN), the NASA Global Geodetic Network (GGN), the International GNSS Service (IGS) network, the Rio Grande Rift network, the GPS Array for Mid America (GAMA), the Basin and Range Geodetic Network (BARGEN), the Idaho National Laboratory (INL) network, the Pacific Northwest Geodetic Array (PANGA), the Western Canada Deformation Array, SuomiNet, GulfNet, and stations near the epicenter of the 23 August 2011 M5.8 Mineral, VA earthquake.
15.412511332728922
17.0
International GNSS Service
4.960835509138382
7.6
atmospheric sciences
31.456986173340194
0.755195677280426
Civilian Conservation Corps
https://www.wikidata.org/wiki/Q1094508
UNAVCO GPS Timeseries
10.815047021943572
13.8
earth sciences
31.456986173340194
0.755195677280426
Non-Aligned Movement
https://www.wikidata.org/wiki/Q83201
statistics
6.342182890855457
8.6
time series
2.207792207792208
6.8
New Mexico
https://www.wikidata.org/wiki/Q1522
standard deviation
2.9870129870129865
9.2
field
2.6298701298701297
8.1
National Research Council Canada
https://www.wikidata.org/wiki/Q1437507
CWU snx/cwu . .a.rms ../NMT snx/nmt . .a.rms Format Version : . . Release Date : Start Field Description Dot character identifier for a given station GAGE Number sec phase epochs in hours for combined RMS calculation PRMS Root mean square (RMS) scatter of combined phase residuals, mm CWU Number sec phase epochs in hours for CWU RMS calculation CRMS Root mean square (RMS) scatter of CWU phase residuals, mm NMT Number sec phase epochs in hours for NMT or BSL (prior to Feb ) RMS calculation NRMS Root mean square (RMS) scatter of NMT or BSL phase residuals, mm A Coefficient from model fit RMS (elev) A B /sin(elev) where elev is elevation angle, mm B Coefficient from model fit, mm GPSW GPS Week for hour processing day D GPS Day of week for hour processing day YYYYMMDD Year, month, day of month for hour processing day End Field Description Dot GAGE PRMS CWU CRMS NMT NRMS A B GPSW D YYYYMMDD NSU . . . . . ULM . . . . . ODM . . . . . AB . . . . . AB . . . . . ZME . . . . . ZMP . . . . . ZNY . . . . . ZSE . . . . . ZTL . . . . .
3.0825022665457844
3.4
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
standard deviation
2.93733681462141
4.5
Television
Arts, culture and entertainment/Mass media/Television
communications and radar
41.26573884859473
0.6167286038398743
linguistics
9.660766961651918
13.1
service-account-generation-service
annotations/57a2d21c-4b1e-466b-b856-d40bb974eae3
annotations/d19e7805-0a25-45b8-a1b5-97492f01eea7
annotations/b6d3bd91-3e00-45ad-bdfc-0fba2568f613
703da877-9ab9-44c0-841d-321130fd79c4.rdf
38c363c1-140c-4d41-a170-7771700ec2bd.rdf
0b8d96d0-707d-4162-b533-7fb2a66751e6.rdf
token verifier
verifier
https://me.yahoo.com/a/cPNpaOo40eTDI9vLPxnedsPtiA--#26c9b
10.5072/ro-id.BOVDKRA9GA
2017-06-19T11:22:55.126+02:00
35303
https://api.rohub.org/api/ros/cbd101f5-2776-47c7-a819-2cff9538cdf5/crate/download/
2017-03-15 10:44:50.038000+00:00
2025-10-20 10:45:40.905985+00:00
2017-03-15 10:44:50.038000+00:00
the description
application/ld+json
https://w3id.org/ro-id/cbd101f5-2776-47c7-a819-2cff9538cdf5
token verifier
https://me.yahoo.com/a/cPNpaOo40eTDI9vLPxnedsPtiA--#26c9b. "token verifier." ROHub. Mar 15 ,2017. https://doi.org/10.5072/ro-id.BOVDKRA9GA.
be in RDF content
0.10924981791697012
0.3
verifier
16.3379355687048
49.7
environmental sciences
61.07196536602992
0.9750803112983704
computer programming
13.319672131147541
6.5
mathematical and computer sciences
71.34105769063186
0.5221161842346191
INFO 2017-03-15 11:37:03,026 (net.sf.taverna.t2.security.credentialmanager.CredentialManager:2207) - Credential Manager: inside TavernaTrustManager.init() - Reinitialising the TrustManager.
0.9633911368015414
1.0
verifier
17.22887196227784
47.5
network service
1.088139281828074
3.0
processing
7.181719260065289
19.8
TavernaTrustManager.init
4.0105193951347795
12.2
Mar-15-2017 11:37:07
Credential
7.856673241288626
23.9
JNDI name
1.6751638747268751
4.6
column
4.30637738330046
13.1
Disabled
Society/Mankind/Disabled
ConfirmTrustedCertificateSPI
1.051939513477975
3.2
Mar-15-2017 11:37:50
Mar-15-2017 11:37:08
Mar-15-2017 11:37:00
processing instruction
0.7647487254187909
2.1
Mar-15-2017 11:37:06
Mar-15-2017 11:37:33
geophysics
28.65894230936815
0.20974314212799072
reference
1.9586507072905333
5.4
column 36
0.6190823015294974
1.7
Mar-15-2017 11:37:05
reading mappings from resource
1.3474144209759649
3.7
Truststore
3.5174227481919793
10.7
manager
7.181719260065289
19.8
Mar-15-2017 11:38:06
vl
3.9119000657462197
11.9
environmental science and management
61.07196536602992
0.9750803112983704
Jena reader
0.7647487254187909
2.1
geosciences
28.65894230936815
0.20974314212799072
token
2.865433442147262
7.9
Mar-15-2017 11:37:55
load the Truststore
0.873998543335761
2.4
token verifier
36.416605972323374
100.0
Mar-15-2017 11:37:24
JDBC driver
3.1318281136198105
8.6
column
4.60645629307218
12.7
INFO 2017-03-15 11:37:00,424 (net.sf.taverna.t2.security.credentialmanager.CredentialManager:429) - Credential Manager: Loaded the Truststore.
0.9633911368015414
1.0
Phrae
geology
38.92803463397008
0.6215283870697021
variant learning
3.989844033369605
11.0
Mar-15-2017 11:37:04
Mar-15-2017 11:37:09
Mar-15-2017 11:37:44
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
loading XML bean definitions from class path resource
3.1318281136198105
8.6
case
1.4145810663764962
3.9
RDF content
8.193736343772759
22.5
context hierarchy
1.1653313911143481
3.2
Mar-15-2017 11:37:03 CET 2017
Bar
Lifestyle and leisure/Leisure/Leisure venue/Bar
description
14.990138067061144
45.6
token verifier.
53.371868978805395
55.4
database
45.49180327868853
22.2
Language
Arts, culture and entertainment/Culture/Language
Keystore
3.3859303090072324
10.3
default batch
0.8011653313911143
2.2
resource
2.10373594486761
5.8
manager
6.344510190664037
19.3
column 56
0.4369992716678805
1.2
instruction
2.728468113083498
8.3
resource
1.051939513477975
3.2
computer science
13.934426229508198
6.8
Mar-15-2017 11:37:40
processing
6.969099276791585
21.2
loading XML bean definition
0.7283321194464675
2.0
Wielkopolska
computer operations and hardware
71.34105769063186
0.5221161842346191
tabular array
4.425099746100835
12.2
Credential manager
35.506190823015295
97.5
the description
42.870905587668595
44.5
Mar-15-2017 11:37:03
definition
1.9949220166848025
5.5
http
10.979618671926364
33.4
network services
1.0924981791697013
3.0
ConfirmTrustedCertificateSPI instance
3.2410779315367804
8.9
description
16.140732680449766
44.5
software
27.25409836065574
13.3
earth sciences
38.92803463397008
0.6215283870697021
data
8.269858541893363
22.8
INFO 2017-03-15 11:37:00,396 (net.sf.taverna.t2.security.credentialmanager.CredentialManager:420) - Credential Manager: Loaded the Keystore.
0.8670520231213873
0.9
http
10.808850199492202
29.8
Mar-15-2017 11:38:05
Mar-15-2017 11:37:49
content
2.8928336620644317
8.8
subject
5.948494740660137
16.4
table
2.498356344510191
7.6
info
7.166337935568705
21.8
INFO 2017-03-15 11:37:06,687 (org.hibernate.cfg.Configuration:585) - Reading mappings from resource: net/sf/taverna/t2/reference/impl/external/object/VMObjectReference.hbm.xml
0.9633911368015414
1.0
instruction
2.7928908233587233
7.7
service-account-enrichment
service-account-generation-service
http://emanuele79.livejournal.com/
military
sentinel mission data acquisition
sentinel satellite
title sentinel user handbook
European Space Agency
Pierre Potin
Sentinel
Products
French Guiana
MISSION
Date
Reason
Segment
satellite description
2016-07-04T17:37:02.818+02:00
2966843
https://api.rohub.org/api/ros/cb7d509a-5048-4007-952b-6f4b2589583c/crate/download/
2016-07-04 15:20:38.038000+00:00
2025-10-20 10:41:59.207145+00:00
2016-07-04 15:20:38.038000+00:00
This RO describes the coregistering step
application/ld+json
https://w3id.org/ro-id/cb7d509a-5048-4007-952b-6f4b2589583c
Land Monitoring Coregistering Step
http://emanuele79.livejournal.com/. "Land Monitoring Coregistering Step." ROHub. Jul 04 ,2016. https://w3id.org/ro-id/cb7d509a-5048-4007-952b-6f4b2589583c.
Documents
Wokflow
152
https://api.rohub.org/api/resources/2d2e2ac2-325b-465d-892f-696cdd6ffd2e/download/
2016-07-04 15:25:13.656000+00:00
2022-03-25 16:06:15.581873+00:00
text/plain
output_prodID.txt
2016-07-04 15:25:13.656000+00:00
6227
https://api.rohub.org/api/resources/37cbbda7-30a6-45ae-af64-14f1701ed849/download/
2016-07-04 15:22:50.594000+00:00
2022-03-25 16:06:18.356879+00:00
workflow.t2flow
2016-07-04 15:22:50.594000+00:00
160
https://api.rohub.org/api/resources/3bcdeef3-54e8-4f93-bf82-3f0f16d6d1a7/download/
2016-07-04 15:24:48.072000+00:00
2022-03-25 16:06:14.747546+00:00
text/plain
input_prodID.txt
2016-07-04 15:24:48.072000+00:00
60
https://api.rohub.org/api/resources/6efdf8c1-107f-4c74-b2ed-72f61419002d/download/
2016-07-04 15:24:18.971000+00:00
2022-03-25 16:06:13.877443+00:00
text/plain
input_AoI.txt
2016-07-04 15:24:18.971000+00:00
3150939
https://api.rohub.org/api/resources/837b574e-8369-4e34-9423-0d57be9c5ff9/download/
2016-07-04 15:23:34.489000+00:00
2022-03-25 16:06:17.227724+00:00
application/pdf
Sentinel-1_User_Handbook.pdf
2016-07-04 15:23:34.489000+00:00
5913
https://api.rohub.org/api/resources/bf97a947-e3f3-41d1-acc8-de0c2b212588/download/
2016-07-04 15:21:50.980000+00:00
2022-03-25 16:06:16.403918+00:00
image/png
sketch.png
2016-07-04 15:21:50.980000+00:00
Monitoring
15.720978656949505
30.2
earth sciences
21.354949506400125
0.43228960037231445
space sciences (general)
8.025722697128423
0.05067460238933563
geosciences
70.04925223661134
0.44229263067245483
step
24.343675417661096
10.2
This RO describes the coregistering step
24.624624624624623
49.2
coregistering step
9.71943887775551
9.7
earth sciences
78.64505049359987
1.5920167565345764
astronautics
21.92502506626024
0.13843512535095215
S1A_IW_GRDH_1SDV_20160128T181049_20160128T181114_009698_00E26E_461A_calibration
26.028110359187924
50.0
S1A_IW_GRDH_1SDV_20151024T181049_20151024T181114_008298_00BB2A_4A13_calibration
26.028110359187924
50.0
land Monitoring Coregistering step
83.46693386773548
83.3
Language
Arts, culture and entertainment/Culture/Language
Monitoring Coregistering step
5.210420841683367
5.2
Ro
17.959396147839666
34.5
geology
78.64505049359987
1.5920167565345764
describe the coregistering step
0.20040080160320642
0.2
spacecraft propulsion and power
21.92502506626024
0.13843512535095215
Coregistering step
1.402805611222445
1.4
Ro
75.65632458233891
31.7
space sciences
8.025722697128423
0.05067460238933563
Coregistering
14.263404476834982
27.4
S1A_IW_GRDH_1SDV_20151024T181049_20151024T181114_008298_00BB2A_4A13_calibration
25.125125125125123
50.2
earth resources and remote sensing
70.04925223661134
0.44229263067245483
S1A_IW_GRDH_1SDV_20160128T181049_20160128T181114_009698_00E26E_461A_calibration
24.874874874874873
49.7
oceanography
21.354949506400125
0.43228960037231445
Land Monitoring Coregistering Step.
25.375375375375373
50.7
service-account-enrichment
service-account-generation-service
http://ffoglini.livejournal.com/
elaborate data
descriptors
marine biology
trend
trend in the evolution
Doctors Without Borders
jellyfish
2016-07-04T16:26:20.716+02:00
19908
https://api.rohub.org/api/ros/9c95ec04-1c8b-412b-9d8a-dc23d1103415/crate/download/
2016-07-04 14:17:38.590000+00:00
2025-10-20 10:41:16.726394+00:00
2016-07-04 14:17:38.590000+00:00
Starting from Jellyfish sightings, we elaborate data to produce explicit geographical information concerning trend about the evolution and distribution of alien species according with MSF directive descriptors.
application/ld+json
https://w3id.org/ro-id/9c95ec04-1c8b-412b-9d8a-dc23d1103415
Trend in the evolution of invasive jellyfish distribution
http://ffoglini.livejournal.com/. "Trend in the evolution of invasive jellyfish distribution." ROHub. Jul 04 ,2016. https://w3id.org/ro-id/9c95ec04-1c8b-412b-9d8a-dc23d1103415.
workflows
data
software
documents
10271
https://api.rohub.org/api/resources/2451971d-00d5-4b86-8d91-08bd3b42b1ac/download/
2016-07-04 14:20:08.546000+00:00
2022-03-25 16:10:44.570490+00:00
trend_invasive.t2flow
2016-07-04 14:20:08.546000+00:00
3057
https://api.rohub.org/api/resources/6808e606-a130-4e0d-b89d-c5372c8b1b59/download/
2016-07-04 14:25:37.053000+00:00
2022-03-25 16:10:45.551682+00:00
image/png
trend_invasive.png
2016-07-04 14:25:37.053000+00:00
118
https://api.rohub.org/api/resources/cddb09db-7a3a-4eb4-b9f0-aea7f3c7831c/download/
2016-07-04 14:21:14.727000+00:00
2022-03-25 16:10:47.815189+00:00
application/xml
wf_trend.xml
2016-07-04 14:21:14.727000+00:00
9994
https://api.rohub.org/api/resources/e6e85a89-bb59-4d12-aded-9a34328db616/download/
2016-07-04 14:20:43.843000+00:00
2022-03-25 16:10:46.695182+00:00
Workflow1.wfbundle
2016-07-04 14:20:43.843000+00:00
trend
12.533692722371969
9.3
distribution
12.707182320441989
11.5
sighting
7.624309392265193
6.9
geographical
6.077348066298343
5.5
data
21.293800539083556
15.8
geology
100.0
0.6029276847839355
life sciences (general)
100.0
0.9660021662712097
trend
10.497237569060774
9.5
Starting from Jellyfish sightings, we elaborate data to produce explicit geographical information concerning trend about the evolution and distribution of alien species according with MSF directive descriptors.
81.48148148148148
81.4
Doctors Without Borders
Animal
Human interest/Animal
jellyfish sighting
25.675675675675674
20.9
biology
100.0
5.6
distribution of alien species
8.476658476658477
6.9
Trend in the evolution of invasive jellyfish distribution.
18.51851851851852
18.5
MSF directive descriptor
34.15233415233415
27.8
earth sciences
100.0
0.6029276847839355
evolution
10.646900269541778
7.9
directive
7.292817679558011
6.6
jellyfish
17.250673854447438
12.8
evolution
8.839779005524862
8.0
life sciences
100.0
0.9660021662712097
Non-governmental organisation
Politics/Non-governmental organisation
distribution
15.49865229110512
11.5
Geography
Science and technology/Social sciences/Geography
jellyfish
14.033149171270718
12.7
trend in the evolution
11.425061425061426
9.3
alien species
10.242587601078167
7.6
information
17.016574585635357
15.4
descriptor
12.533692722371969
9.3
Foreign aid
Politics/International relations/Foreign aid
subject heading
9.613259668508286
8.7
jellyfish distribution
20.27027027027027
16.5
Health organisations
Health/Health organisations
Doctors Without Borders
6.298342541436464
5.7
service-account-enrichment
service-account-generation-service
http://ffoglini.livejournal.com/
data of the hydrodynamic model
bathymetric variable
hydrodynamic data
variable importance
Maxent software
presence data
hydrodynamic variable
Mount Wilson
Shetlands
bathymetric data
British Petroleum
Maximilian
hydrography
Area
water
geography
Shetland Islands
value range
Water
Wilson
trawl
dredges
species
Study
software
resolution
variables
model
north east
Habitat
seabed
mounds
variables
distribution
habitat suitability model
Elsevier Science Ltd.
UV mean
corals
David
variable influence
2016-04-05T13:41:34.053+02:00
46868119
https://api.rohub.org/api/ros/eb7b6b9e-070d-45cc-a3e7-b71c7fe4cb08/crate/download/
2016-04-05 11:16:51.848000+00:00
2025-10-20 10:38:37.858001+00:00
2016-04-05 11:16:51.848000+00:00
In this RO we derive the MSFD indicator 1.5 (Habitat area) to assess the biological diversity descriptor. To do this in deep sea environment, the scientist (user) needs to implement a habitat suitability model.
application/ld+json
https://w3id.org/ro-id/eb7b6b9e-070d-45cc-a3e7-b71c7fe4cb08
Deep Sea Habitat Suitabilty Model
http://ffoglini.livejournal.com/. "Deep Sea Habitat Suitabilty Model." ROHub. Apr 05 ,2016. https://w3id.org/ro-id/eb7b6b9e-070d-45cc-a3e7-b71c7fe4cb08.
Coral Occurences
data
documents
Env Variables
software
workflows
Maxent
3799421
https://api.rohub.org/api/resources/1df7ce2d-3749-47dc-8420-434a655ea9b3/download/
2016-04-05 11:24:57.936000+00:00
2022-03-25 16:30:53.503314+00:00
application/vnd.openxmlformats-officedocument.presentationml.presentation
Use of Maxent for predictive habitat mapping of.pptx
2016-04-05 11:24:57.936000+00:00
39410367
https://api.rohub.org/api/resources/363e66d9-0bc3-4eb8-a662-6e444681c70b/download/
2016-04-05 11:34:54.908000+00:00
2022-03-25 16:30:54.486785+00:00
application/x-7z-compressed
EnvVariable.7z
2016-04-05 11:34:54.908000+00:00
836294
https://api.rohub.org/api/resources/520e2bef-bd4d-4792-b735-c1fdd49242d5/download/
2016-04-05 11:27:35.281000+00:00
2022-03-25 16:30:55.327005+00:00
application/pdf
The-cold-water-coral-Lophelia-pertusa-Scleractinia-and-enigmatic-seabed-mounds-along-the-north-east-Atlantic-margin-are-they-related-_2003_Marine-Poll.pdf
2016-04-05 11:27:35.281000+00:00
2479847
https://api.rohub.org/api/resources/6e2613b6-b2c1-423a-aa43-3ab544abee94/download/
2016-04-05 11:24:20.214000+00:00
2022-03-25 16:30:52.493837+00:00
application/vnd.openxmlformats-officedocument.presentationml.presentation
Habitat suitability models for BARI canyon_2.pptx
2016-04-05 11:24:20.214000+00:00
13787
https://api.rohub.org/api/resources/736bd22b-80ad-4649-97a5-218ec4343dcb/download/
2016-04-05 11:26:11.633000+00:00
2022-03-25 16:30:49.505037+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
How_to_use.docx
2016-04-05 11:26:11.633000+00:00
5683
https://api.rohub.org/api/resources/c6b8c081-fc28-4067-84cd-f9118c4468e8/download/
2016-04-05 11:31:07.091000+00:00
2022-03-25 16:30:51.327456+00:00
text/csv
CWC_Bari_20m.csv
2016-04-05 11:31:07.091000+00:00
638331
https://api.rohub.org/api/resources/de10459e-db27-4394-8f0a-09a1c5f0fb75/download/
2016-04-05 11:37:23.476000+00:00
2022-03-25 16:30:50.325625+00:00
application/zip
maxent.zip
2016-04-05 11:37:23.476000+00:00
oceanography
22.0931087561896
0.728894829750061
In this RO we derive the MSFD indicator 1.5 (Habitat area) to assess the biological diversity descriptor.
44.617299315494705
71.7
Princeton University
Bari
3.627942879197221
9.4
information
9.648784253184099
25.0
earth sciences
22.0931087561896
0.728894829750061
raw data
2.7788498649170204
7.2
sea environment
6.5888240200166805
15.8
16-Nov-17
variable
5.725376031052887
11.8
descriptor
5.403319181783095
14.0
data
12.17855409995148
25.1
bathymetry
3.3478893740902476
6.9
hydrodynamic data
2.9190992493744785
7.0
From Nov-1-2011 to Jun-28-2012
statistics
17.980295566502463
7.3
row data
5.67139282735613
13.6
Geography
Science and technology/Social sciences/Geography
Tmean
2.37748665696264
4.9
Bari Bari Canyon
2.793994995829858
6.7
trade
1.7241379310344827
0.7
research and support facilities (air)
25.66363931506019
0.5196491479873657
Bari Canyon system
2.2518765638031693
5.4
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
mathematics
10.591133004926109
4.3
indicator
4.747201852566576
12.3
Language
Arts, culture and entertainment/Culture/Language
MSFD
6.404657933042213
13.2
Science and technology
Science and technology
Raw data : is the bathymetry used for the work, and the adelie observations of the CWC i used.
8.400746733042936
13.5
Textile and clothing
Economy, business and finance/Economic sector/Process industry/Textile and clothing
descriptor
6.986899563318778
14.4
MAxent
3.9786511402231923
8.2
Annaëlle
4.2212518195050945
8.7
Exports
Economy, business and finance/Economy/Macro economics/Exports
name
3.31918178309533
8.6
Here are all the data of the BARI Canyon used.
7.467330429371499
12.0
earth sciences
59.16887537665321
1.9520968198776245
file
2.161327672713238
5.6
sea habitat Suitabilty model
5.045871559633027
12.1
Habitat
2.8141678796700633
5.8
ecology
12.31527093596059
5.0
distribution
2.4745269286754
5.1
work
2.35430335777692
6.1
variable
8.220764183712852
21.3
AT&T
bargain Annaelle
6.630525437864887
15.9
linguistics
2.216748768472906
0.9
earth sciences
18.73801586715719
0.6182037591934204
To Feb-14
Biology
Science and technology/Natural science/Biology
aeronautics
25.66363931506019
0.5196491479873657
frequency distribution
3.3577769201080656
8.7
environment
6.11353711790393
12.6
scientist
2.1227325357005014
5.5
database
7.8817733990147785
3.2
Hydrodynamic data (Davide)
implementation of ROMS for ocean currents, coupled with SWAN within the COAWST modelling system
2.7380211574362168
4.4
computer science
28.32512315270936
11.5
bathymetric variable
2.1267723102585485
5.1
ENFA
6.307617661329452
13.0
CWC occurences
2.335279399499583
5.6
2008
Bologna
physics
9.35960591133005
3.8
Values
Society/Values
pdf name
5.713094245204337
13.7
UVmax
2.4745269286754
5.1
8 months
diversity
4.129679660362794
10.7
bargain
2.7171276079573023
5.6
raw data
2.8141678796700633
5.8
The pdf name "methodoly2" is in french, and not finished yet, but explian step by step how to use the ENFA, MAxent and R, and where to find the programs.
7.965152457996266
12.8
life sciences (general)
74.3363606849398
1.505196750164032
species occurrence data
2.6271893244370306
6.3
diversity descriptor
12.677231025854878
30.4
To do this in deep sea environment, the scientist (user) needs to implement a habitat suitability model.
13.378967019290602
21.5
atmospheric sciences
18.73801586715719
0.6182037591934204
Habitat area
3.9616346955796495
9.5
Weather
Weather
from Jan-25
ENFA model
7.756463719766472
18.6
presence data
4.08673894912427
9.8
environment
4.785796989579312
12.4
Bari
geology
59.16887537665321
1.9520968198776245
biology
7.389162561576354
3.0
Nov-25-2015
Bargain Annaelle –
4.107031736154324
6.6000000000000005
Geography
Science and technology/Social sciences/Geography
data of the BARI Canyon
4.5037531276063385
10.8
bathymetry
2.6244693168660747
6.8
Habitat Suitability model for Bari Canyon with Hydrodynamic variables
2.5513378967019285
4.1
software
2.216748768472906
0.9
diversity
5.240174672489083
10.8
Relate species occurrence data (distribution = biological data)
with environmental predictor variables (EGVs = Ecogeographic variables)
1.9912881144990664
3.2
habitat suitability
3.056768558951965
6.3
name
3.5904900533721493
7.4
CWC distribution
1.9182652210175142
4.6
life sciences
74.3363606849398
1.505196750164032
Wireless technology
Economy, business and finance/Economic sector/Computing and information technology/Wireless technology
Toulon
For ENFA, UVmax was not used (eigenvalues too high)
marginality (niche position in the ecological space)
specificity (niche size) = 1/tolerance
2.6135656502800244
4.2
data of the hydrodynamic model
1.7514595496246872
4.2
data
11.925897336935545
30.900000000000002
winter
Deep Sea Habitat Suitabilty Model.
4.16925948973242
6.7
indicator
6.11353711790393
12.6
of november to Jun-28
software
2.199922809725974
5.7
event
3.7051331532226937
9.6
Economic indicator
Economy, business and finance/Economy/Macro economics/Economic indicator
buy
3.7437282902354294
9.7
habitat suitability model
6.8807339449541285
16.5
specificity
2.392898494789656
6.2
service-account-enrichment
service-account-generation-service
http://emanuele79.livejournal.com/
107f503d-cf15-4c10-898f-74967aeed82c.rdf
change detection
military
European Space Agency
image processing algorithm
Pierre Potin
Synthetic aperture radar
SAR-based technique
Sentinel
Products
French Guiana
Level
MISSION
Date
Reason
Segment
results
image archive
small sample size
2016-06-28T11:09:08.412+02:00
3040650
https://api.rohub.org/api/ros/d18e59ae-ff07-4d59-a113-0202d37202aa/crate/download/
2016-03-11 08:32:14.144000+00:00
2025-10-20 10:37:57.843580+00:00
2016-03-11 08:32:14.144000+00:00
The Land Monitoring RO allows to monitor urban, built-up and natural environments in order to identify certain features and anomalies or changes over Areas of Interest.
application/ld+json
https://w3id.org/ro-id/d18e59ae-ff07-4d59-a113-0202d37202aa
Land Monitoring Workflow
http://emanuele79.livejournal.com/. "Land Monitoring Workflow." ROHub. Mar 11 ,2016. https://w3id.org/ro-id/d18e59ae-ff07-4d59-a113-0202d37202aa.
Documents
Wokflow
CalibrationWorkflow.t2flow
7159
https://api.rohub.org/api/resources/2d7a787e-aa3b-4f2d-b83a-f7f403927b77/download/
2016-03-11 08:35:13.404000+00:00
2022-03-25 16:34:25.931149+00:00
ChangeDetectionWorflow.t2flow
2016-03-11 08:35:13.404000+00:00
6670
https://api.rohub.org/api/resources/40a547f1-04e8-449a-b8c7-990efe8979d9/download/
2016-03-11 08:34:48.903000+00:00
2022-03-25 16:34:26.928335+00:00
CalibrationWorkflow.t2flow
2016-03-11 08:34:48.903000+00:00
ChangeDetectionWorflow.t2flow
25727
https://api.rohub.org/api/resources/8104d943-ea58-49c7-a701-d30d068523e6/download/
2016-03-11 08:34:26.608000+00:00
2022-03-25 16:34:23.719861+00:00
LandMonitoringWorkflow.t2flow
2016-03-11 08:34:26.608000+00:00
11466
https://api.rohub.org/api/resources/8594202e-9f60-4e54-ad9f-d2359d60e835/download/
2016-03-11 11:51:20.645000+00:00
2022-03-25 16:34:24.687253+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
Land_Monitoring_Workflow_conclusions.docx
2016-03-11 11:51:20.645000+00:00
3150939
https://api.rohub.org/api/resources/b116a635-2cd2-4c3d-babc-a92e87753a67/download/
2016-03-11 08:40:42.045000+00:00
2022-03-25 16:34:27.793051+00:00
application/pdf
Sentinel-1_User_Handbook.pdf
2016-03-11 08:40:42.045000+00:00
LandMonitoringWorkflow.t2flow
54130
https://api.rohub.org/api/resources/d4a24cd7-4e65-4aeb-b751-51c23837b968/download/
2016-03-11 08:33:08.200000+00:00
2022-03-25 16:34:29.627661+00:00
image/png
ChangeDetectionWorkflowChain.png
2016-03-11 08:33:08.200000+00:00
12037
https://api.rohub.org/api/resources/ec23ccfe-ceea-4bc0-b129-75c3238d3d63/download/
2016-03-11 11:48:32.006000+00:00
2022-03-25 16:34:28.620739+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
Land_Monitoring_Workflow_hypothesis.docx
2016-03-11 11:48:32.006000+00:00
heritage mission
2.585961921000284
9.1
opposite
1.834862385321101
6.2
geosciences
13.850684028604018
0.31724029779434204
atmospheric sciences
78.1177901124589
2.0325206220149994
sentinel mission data acquisition
2.131287297527707
7.5
European Space Agency
2.2129570237331624
6.9
payload
2.1604024859425865
7.3
size
2.693104468777745
9.1
life sciences (general)
19.373018192810438
0.4437255263328552
impact
5.420141116100063
16.9
Ecosystem
Environment/Nature/Ecosystem
anomaly
3.91276459268762
12.2
mapping capability
4.2057402671213415
14.8
anomaly
3.1962118970109503
10.8
. . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities .................
4.057553956834532
14.1
astronautics
40.98360655737705
22.5
instrument payload
2.4154589371980677
8.5
sample size
16.1409491332765
56.8
The Land Monitoring RO allows to monitor urban, built-up and natural environments in order to identify certain features and anomalies or changes over Areas of Interest.
21.8705035971223
76.0
mission
3.3441846700207165
11.3
Synthetic aperture radar (SAR) sensors represent an alternative to optical ones for monitoring environments, especially considering change detection analysis.
13.640287769784173
47.4
communications and radar
66.77629777858554
1.5294647216796875
earth resources and remote sensing
13.850684028604018
0.31724029779434204
sentinel family
3.752405388069275
11.7
geology
21.882209887541087
0.5693458914756775
detection
2.0525978191148173
6.4
Europe
Even though the proposed fully automated SAR-based technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based change detection to match the mapping capability of high-quality optical data.
5.72661870503597
19.9
synthetic aperture radar
5.163566388710713
16.1
Electrical appliance
Economy, business and finance/Economic sector/Manufacturing and engineering/Electrical appliance
physics
4.553734061930784
2.5
tweak the classifier
7.956805910770106
28.0
change detection analysis
4.688832054560955
16.5
land Monitoring workflow
23.245240125035522
81.8
sample
3.9656703166617344
13.4
SENTINEL USER GUIDE .............................................................................
4.690647482014389
16.3
sample
4.457985888389993
13.9
workflow
4.842847979474022
15.1
Prepared by Sentinel Team
4.575539568345324
15.9
- Tweaking the classifier has potentially more impact than tweaking the features.
11.39568345323741
39.6
feature
7.783367860313701
26.299999999999997
French Guiana
1.8940514945250075
6.4
Monitoring
5.772931366260424
18.0
conclusion
2.5336754329698525
7.9
decision
2.693104468777745
9.1
tweak the feature
3.296391020176187
11.6
aerospace engineering
31.876138433515482
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Software
Economy, business and finance/Economic sector/Computing and information technology/Software
system
10.59485054749926
35.8
classifier
5.4749926013613495
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sentinel B
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9.6
Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring change dynamics in specific areas.
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27.1
sentinel user guide
1.534526854219949
5.4
sensor
2.020525978191148
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orbit characteristic
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natural environment
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European Space Agency
3.9360757620597813
13.3
earth sciences
21.882209887541087
0.5693458914756775
workflow
3.9952648712636876
13.5
Land Monitoring Workflow.
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description
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This is in part related to the small sample size.
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25.1
sentinel data product dissemination
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European Space Agency
Monitoring workflow
1.3356067064506962
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French Guiana
baseline
4.746632456703015
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feature
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Land Monitoring RO
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geophysics
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sentinel mission guide
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16.0
microwave remote-sensing data
2.585961921000284
9.1
Armed forces
Politics/Government/Defence/Armed forces
life sciences
19.373018192810438
0.4437255263328552
computer science
12.932604735883425
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sentinel satellite
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classifier
6.350224502886466
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engineering
66.77629777858554
1.5294647216796875
impact
5.001479727730097
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Book industry
Economy, business and finance/Economic sector/Media/Book industry
country
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7.4
technique
3.4316869788325848
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sar-based technique
2.4154589371980677
8.5
Conclusions
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10.129496402877699
35.2
certain feature
2.813299232736573
9.9
natural environment
5.676715843489416
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IT-computer sciences
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size
2.9185375240538804
9.1
revision
1.9236460491269607
6.5
earth sciences
78.1177901124589
2.0325206220149994
Science and technology
Science and technology
The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C
band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
2.014388489208633
7.0
image archive
2.188121625461779
7.7
description
2.3675643681562595
8.0
service-account-enrichment
service-account-generation-service
http://emanuele79.livejournal.com/
change detection
military
European Space Agency
image processing algorithm
Pierre Potin
Synthetic aperture radar
SAR-based technique
Sentinel
Products
French Guiana
Level
MISSION
Date
Reason
Segment
results
image archive
small sample size
2016-03-11T15:06:25.156+01:00
3040404
https://api.rohub.org/api/ros/45a828a4-2697-44b1-aca4-c3a1a168cbeb/crate/download/
2016-03-11 08:32:14.144000+00:00
2025-10-20 10:37:26.631769+00:00
2016-03-11 08:32:14.144000+00:00
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Wokflow
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12037
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3150939
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application/pdf
Sentinel-1_User_Handbook.pdf
2016-03-11 08:40:42.045000+00:00
LandMonitoringWorkflow.t2flow
54130
https://api.rohub.org/api/resources/3ba6a55b-31b4-460c-969d-f6967dc2d87e/download/
2016-03-11 08:33:08.200000+00:00
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image/png
ChangeDetectionWorkflowChain.png
2016-03-11 08:33:08.200000+00:00
6670
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CalibrationWorkflow.t2flow
2016-03-11 08:34:48.903000+00:00
ChangeDetectionWorflow.t2flow
25727
https://api.rohub.org/api/resources/b3cb42e0-6429-43f5-9591-318d9833e424/download/
2016-03-11 08:34:26.608000+00:00
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2016-03-11 08:34:26.608000+00:00
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2016-03-11 08:35:13.404000+00:00
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ChangeDetectionWorflow.t2flow
2016-03-11 08:35:13.404000+00:00
11466
https://api.rohub.org/api/resources/e4451f7f-50fd-4585-96b5-4e01bd8319cc/download/
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application/vnd.openxmlformats-officedocument.wordprocessingml.document
Land_Monitoring_Workflow_conclusions.docx
2016-03-11 11:51:20.645000+00:00
Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring change dynamics in specific areas.
7.798561151079137
27.1
workflow
3.6689169357205755
12.5
technique
3.4250960307298337
10.7
Conclusions
- The results obtained are better than the baseline, but not statistically significant.
10.129496402877699
35.2
sample size
16.1409491332765
56.8
synthetic aperture radar
5.1536491677336755
16.1
baseline
4.737516005121639
14.8
natural environment
3.713188220230474
11.6
Land Monitoring Workflow.
6.474820143884892
22.5
anomaly
4.0653008962868125
12.7
microwave remote-sensing data
2.585961921000284
9.1
land Monitoring workflow
22.165387894288152
78.0
European Space Agency
2.2087067861715752
6.9
sentinel satellite
1.9371881420604637
6.6
certain feature
3.097470872406934
10.9
size
2.670971529204579
9.1
sentinel B
2.7280477408354646
9.6
French Guiana
Land Monitoring RO
5.56978233034571
17.4
. . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities .................
4.057553956834532
14.1
sentinel data product dissemination
1.9039499857914182
6.7
natural environment
3.0818902260052834
10.5
sentinel user guide
1.534526854219949
5.4
country
1.995890813031993
6.8
atmospheric sciences
78.1177901124589
2.0325206220149994
European Space Agency
physics
4.553734061930784
2.5
classifier
6.338028169014085
19.8
mapping capability
4.2057402671213415
14.8
Ecosystem
Environment/Nature/Ecosystem
aerospace engineering
31.876138433515482
17.5
tweak the feature
3.296391020176187
11.6
earth resources and remote sensing
13.850684028604018
0.31724029779434204
mission
3.3167009098914
11.3
Book industry
Economy, business and finance/Economic sector/Media/Book industry
revision
1.9078368065746991
6.5
sentinel family
3.745198463508323
11.7
Even though the proposed fully automated SAR-based technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based change detection to match the mapping capability of high-quality optical data.
5.72661870503597
19.9
instrument payload
2.4154589371980677
8.5
Monitoring workflow
1.278772378516624
4.5
heritage mission
2.585961921000284
9.1
feature
7.866157910184913
26.799999999999997
sample
4.449423815620999
13.9
European Space Agency
3.903727619606692
13.3
sar-based technique
2.4154589371980677
8.5
feature
6.850192061459667
21.4
geosciences
13.850684028604018
0.31724029779434204
anomaly
3.3167009098914
11.3
astronautics
40.98360655737705
22.5
change detection analysis
4.688832054560955
16.5
earth sciences
21.882209887541087
0.5693458914756775
monitoring
3.1690140845070425
9.9
Prepared by Sentinel Team
4.575539568345324
15.9
Armed forces
Politics/Government/Defence/Armed forces
image archive
2.188121625461779
7.7
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
description
2.348106838861168
8.0
monitoring
2.6122688582330498
8.9
description
2.176696542893726
6.8
Synthetic aperture radar (SAR) sensors represent an alternative to optical ones for monitoring environments, especially considering change detection analysis.
13.640287769784173
47.4
- Tweaking the classifier has potentially more impact than tweaking the features.
11.39568345323741
39.6
impact
5.409731113956465
16.9
tweak the classifier
7.956805910770106
28.0
size
2.9129321382842512
9.1
life sciences (general)
19.373018192810438
0.4437255263328552
workflow
4.449423815620999
13.9
communications and radar
66.77629777858554
1.5294647216796875
sample
3.9330789550924568
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engineering
66.77629777858554
1.5294647216796875
sentinel mission data acquisition
2.131287297527707
7.5
geology
21.882209887541087
0.5693458914756775
sentinel mission guide
4.5467462347257745
16.0
The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C
band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
2.014388489208633
7.0
The Land Monitoring RO refers to the monitoring of urban, built-up and natural environments to identify certain features and anomalies or changes over areas of interest.
22.273381294964032
77.4
earth sciences
78.1177901124589
2.0325206220149994
life sciences
19.373018192810438
0.4437255263328552
orbit characteristic
3.353225348110259
11.8
computer science
12.932604735883425
7.1
impact
4.960375697094217
16.9
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
detection
2.0486555697823303
6.4
geophysics
9.65391621129326
5.3
decision
2.670971529204579
9.1
This is in part related to the small sample size.
7.223021582733813
25.1
SENTINEL USER GUIDE .............................................................................
4.690647482014389
16.3
conclusion
2.528809218950064
7.9
system
10.507778103903727
35.8
Europe
classifier
5.429997064866452
18.5
Electrical appliance
Economy, business and finance/Economic sector/Manufacturing and engineering/Electrical appliance
payload
2.142647490460816
7.3
Monitoring
5.217669654289373
16.3
Science and technology
Science and technology
French Guiana
1.8784854710889347
6.4
service-account-enrichment
service-account-generation-service
http://emanuele79.livejournal.com/
sentinel user guide
military
sentinel mission data acquisition
sentinel satellite
title sentinel user handbook
European Space Agency
Pierre Potin
Sentinel
Products
payload data ground segment
French Guiana
Level
MISSION
Date
Reason
Segment
Document
satellite description
2016-03-11T15:04:44.660+01:00
3040289
https://api.rohub.org/api/ros/4d228be1-e395-4a7c-a8e4-a704a3556090/crate/download/
2016-03-11 08:32:14.144000+00:00
2025-10-20 10:37:11.409929+00:00
2016-03-11 08:32:14.144000+00:00
The Land Monitoring RO refers to the monitoring of urban, built-up and natural environments to identify certain features and anomalies or changes over areas of interest.
application/ld+json
https://w3id.org/ro-id/4d228be1-e395-4a7c-a8e4-a704a3556090
Land Monitoring Workflow
http://emanuele79.livejournal.com/. "Land Monitoring Workflow." ROHub. Mar 11 ,2016. https://w3id.org/ro-id/4d228be1-e395-4a7c-a8e4-a704a3556090.
Documents
Wokflow
11466
https://api.rohub.org/api/resources/2e9a5183-8f61-4912-95c0-26abd64ede42/download/
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2022-03-25 16:36:11.744201+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
Land_Monitoring_Workflow_conclusions.docx
2016-03-11 11:51:20.645000+00:00
3150939
https://api.rohub.org/api/resources/45fad927-133c-4479-91e6-c40125a6efbc/download/
2016-03-11 08:40:42.045000+00:00
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application/pdf
Sentinel-1_User_Handbook.pdf
2016-03-11 08:40:42.045000+00:00
6670
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2016-03-11 08:34:48.903000+00:00
2022-03-25 16:36:13.489008+00:00
CalibrationWorkflow.t2flow
2016-03-11 08:34:48.903000+00:00
ChangeDetectionWorflow.t2flow
25727
https://api.rohub.org/api/resources/b53c9c8e-4083-47e2-ab85-594d539e2b0c/download/
2016-03-11 08:34:26.608000+00:00
2022-03-25 16:36:10.820683+00:00
LandMonitoringWorkflow.t2flow
2016-03-11 08:34:26.608000+00:00
CalibrationWorkflow.t2flow
7159
https://api.rohub.org/api/resources/c864feb8-469d-4c9b-8692-1b17fb9aba05/download/
2016-03-11 08:35:13.404000+00:00
2022-03-25 16:36:12.582734+00:00
ChangeDetectionWorflow.t2flow
2016-03-11 08:35:13.404000+00:00
LandMonitoringWorkflow.t2flow
54130
https://api.rohub.org/api/resources/c9eb5034-4ce2-478e-959c-5cdaf2d900bd/download/
2016-03-11 08:33:08.200000+00:00
2022-03-25 16:36:16.617160+00:00
image/png
ChangeDetectionWorkflowChain.png
2016-03-11 08:33:08.200000+00:00
12037
https://api.rohub.org/api/resources/e8f36e30-98e2-43d9-a79e-941831722ac9/download/
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application/vnd.openxmlformats-officedocument.wordprocessingml.document
Land_Monitoring_Workflow_hypothesis.docx
2016-03-11 11:48:32.006000+00:00
aerospace engineering
31.876138433515482
17.5
size
2.9129321382842512
9.1
tweak the feature
3.296391020176187
11.6
sar-based technique
2.4154589371980677
8.5
geosciences
13.850684028604018
0.31724029779434204
computer science
12.932604735883425
7.1
system
10.507778103903727
35.8
European Space Agency
description
2.348106838861168
8.0
sentinel user guide
1.534526854219949
5.4
country
1.995890813031993
6.8
sample
4.449423815620999
13.9
sentinel B
2.7280477408354646
9.6
- Tweaking the classifier has potentially more impact than tweaking the features.
11.39568345323741
39.6
sentinel mission data acquisition
2.131287297527707
7.5
Armed forces
Politics/Government/Defence/Armed forces
classifier
5.429997064866452
18.5
conclusion
2.528809218950064
7.9
heritage mission
2.585961921000284
9.1
sample size
16.1409491332765
56.8
microwave remote-sensing data
2.585961921000284
9.1
earth sciences
21.882209887541087
0.5693458914756775
sentinel family
3.745198463508323
11.7
change detection analysis
4.688832054560955
16.5
French Guiana
1.8784854710889347
6.4
life sciences (general)
19.373018192810438
0.4437255263328552
physics
4.553734061930784
2.5
description
2.176696542893726
6.8
The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C
band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
2.014388489208633
7.0
classifier
6.338028169014085
19.8
Land Monitoring RO
5.56978233034571
17.4
land Monitoring workflow
22.165387894288152
78.0
sample
3.9330789550924568
13.4
orbit characteristic
3.353225348110259
11.8
engineering
66.77629777858554
1.5294647216796875
technique
3.4250960307298337
10.7
tweak the classifier
7.956805910770106
28.0
Conclusions
- The results obtained are better than the baseline, but not statistically significant.
10.129496402877699
35.2
geology
21.882209887541087
0.5693458914756775
Book industry
Economy, business and finance/Economic sector/Media/Book industry
baseline
4.737516005121639
14.8
The Land Monitoring RO refers to the monitoring of urban, built-up and natural environments to identify certain features and anomalies or changes over areas of interest.
22.273381294964032
77.4
SENTINEL USER GUIDE .............................................................................
4.690647482014389
16.3
Monitoring
5.217669654289373
16.3
This is in part related to the small sample size.
7.223021582733813
25.1
impact
4.960375697094217
16.9
revision
1.9078368065746991
6.5
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
communications and radar
66.77629777858554
1.5294647216796875
certain feature
3.097470872406934
10.9
Even though the proposed fully automated SAR-based technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based change detection to match the mapping capability of high-quality optical data.
5.72661870503597
19.9
Ecosystem
Environment/Nature/Ecosystem
Electrical appliance
Economy, business and finance/Economic sector/Manufacturing and engineering/Electrical appliance
French Guiana
Monitoring workflow
1.278772378516624
4.5
workflow
3.6689169357205755
12.5
European Space Agency
2.2087067861715752
6.9
Science and technology
Science and technology
anomaly
3.3167009098914
11.3
Prepared by Sentinel Team
4.575539568345324
15.9
earth sciences
78.1177901124589
2.0325206220149994
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
earth resources and remote sensing
13.850684028604018
0.31724029779434204
European Space Agency
3.903727619606692
13.3
feature
6.850192061459667
21.4
natural environment
3.713188220230474
11.6
life sciences
19.373018192810438
0.4437255263328552
atmospheric sciences
78.1177901124589
2.0325206220149994
. . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities .................
4.057553956834532
14.1
instrument payload
2.4154589371980677
8.5
sentinel satellite
1.9371881420604637
6.6
sentinel mission guide
4.5467462347257745
16.0
mission
3.3167009098914
11.3
Europe
Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring change dynamics in specific areas.
7.798561151079137
27.1
feature
7.866157910184913
26.799999999999997
Land Monitoring Workflow.
6.474820143884892
22.5
decision
2.670971529204579
9.1
astronautics
40.98360655737705
22.5
mapping capability
4.2057402671213415
14.8
synthetic aperture radar
5.1536491677336755
16.1
geophysics
9.65391621129326
5.3
natural environment
3.0818902260052834
10.5
sentinel data product dissemination
1.9039499857914182
6.7
detection
2.0486555697823303
6.4
monitoring
3.1690140845070425
9.9
impact
5.409731113956465
16.9
anomaly
4.0653008962868125
12.7
payload
2.142647490460816
7.3
workflow
4.449423815620999
13.9
Synthetic aperture radar (SAR) sensors represent an alternative to optical ones for monitoring environments, especially considering change detection analysis.
13.640287769784173
47.4
size
2.670971529204579
9.1
monitoring
2.6122688582330498
8.9
image archive
2.188121625461779
7.7
service-account-enrichment
service-account-generation-service
http://emanuele79.livejournal.com/
65e769be-d0b2-401e-996b-c5a4afb49cb6.rdf
annotations/572dfc14-5d3d-4245-bc9b-87e2209cf271
RO samples
military
data selection
SatCen
service information
social media information
archived data
programming
building industry
Sentinel
data provenance
Elizabeth
use
user
user
sensing
analysis
Everest
Monitoring
communities
LAND
land monitoring data
case
changes
data privacy
information
techniques
2016-02-24T17:27:21.745+01:00
3410577
https://api.rohub.org/api/ros/81063ed1-cac7-4ef6-9e3b-21a2e134ebd2/crate/download/
2016-02-24 16:08:48.221000+00:00
2025-10-18 11:56:40.477819+00:00
2016-02-24 16:08:48.221000+00:00
This is a RO created for Land Monitoring Activities
application/ld+json
https://w3id.org/ro-id/81063ed1-cac7-4ef6-9e3b-21a2e134ebd2
Land Monitoring RO
http://emanuele79.livejournal.com/. "Land Monitoring RO." ROHub. Feb 24 ,2016. https://w3id.org/ro-id/81063ed1-cac7-4ef6-9e3b-21a2e134ebd2.
manuals
datasets
biblio
workflows
RO samples
3150939
https://api.rohub.org/api/resources/48b045b1-6e8b-40d6-906a-eae9b700f7ca/download/
2016-02-24 16:13:07.590000+00:00
2022-03-25 16:36:47.758876+00:00
application/pdf
Sentinel 1 Handbook
2016-02-24 16:13:07.590000+00:00
11466
https://api.rohub.org/api/resources/885aee33-f35b-4958-a83c-1a3895f951d6/download/
2016-02-24 16:23:47.807000+00:00
2022-03-25 16:36:44.706586+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
Land Monitoring - conclusions.docx
2016-02-24 16:23:47.807000+00:00
450219
https://api.rohub.org/api/resources/c9c7726b-6070-44c4-a40b-402912c646fe/download/
2016-02-24 16:21:45.123000+00:00
2022-03-25 16:36:45.642194+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
LAND MONITORING-hypotesis.docx
2016-02-24 16:21:45.123000+00:00
24968
https://api.rohub.org/api/resources/ddc168b8-216d-4eb4-b997-111b8f998f5f/download/
2016-02-24 16:09:17.223000+00:00
2022-03-25 16:36:43.842776+00:00
image/png
imgTest.png
2016-02-24 16:09:17.223000+00:00
2020
computer science
41.54929577464788
47.2
description
1.6202049082678105
6.8
land monitoring use case
1.0309278350515463
3.4
heritage mission
2.7592480291085506
9.1
baseline
3.526328329759352
14.8
European Union
engineering
44.81040662436906
1.1378629207611084
data
2.7710257656781723
11.4
Sentinel user handbook
1.3341419041843543
4.4
activity
3.0384054448225575
12.5
environmental science and management
57.03765932858587
1.9248507618904114
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
classifier
4.496840058337384
18.5
SENTINEL USER GUIDE .............................................................................
4.520244037714919
16.3
Monitoring activity
2.031534263189812
6.7
French Guiana
Monitoring RO.
1.0006064281382656
3.3
Science and technology
Science and technology
Monitoring
11.26995472956874
47.3
earth resources and remote sensing
37.71514992396501
0.9576942920684814
impact
4.1079241614000965
16.9
sentinel mission data acquisition
2.274105518496058
7.5
Land Monitoring RO. This is a RO created for Land Monitoring Activities
27.73155851358846
100.0
operational scenario
8.02954491303312
33.7
in 2006 and 2010
politics
2.288732394366197
2.6
feature
3.9863879436071947
16.4
methodology
6.123421491541578
25.7
Ro
5.493437044239184
22.6
land Monitoring activity
10.491206791995149
34.6
in Feb-2016
country
6.0281964025279535
24.8
small sample size
1.0309278350515463
3.4
communications and radar
44.81040662436906
1.1378629207611084
activity
3.454848701453419
14.5
sentinel family
3.8598999285203717
16.2
land Monitoring RO.
16.73741661613099
55.2
aerospace engineering
15.404929577464788
17.5
Prepared by Sentinel Team
4.409317803660565
15.9
atmospheric sciences
42.96234067141413
1.4498507678508759
to create and access data/text mining tools as well as of automatic tools for processing high volumes of data;
1.0537992235163616
3.8
sentinel data product dissemination
2.031534263189812
6.7
instrument payload
2.5773195876288657
8.5
orbit characteristic
3.5779260157671313
11.8
output
1.701507049100632
7.0
mission
1.4772456516559447
6.2
software
8.098591549295772
9.2
Language
Arts, culture and entertainment/Culture/Language
mission
2.746718522119592
11.3
feature
2.382654276864427
10.0
sentinel mission guide
4.851425106124924
16.0
Ro
6.766738146294973
28.4
life sciences (general)
17.474443451665934
0.4437255263328552
Europe
decision
2.2119591638308216
9.1
classifier
4.717655468191565
19.8
size
2.2119591638308216
9.1
workflow
9.64974982130093
40.5
geosciences
37.71514992396501
0.9576942920684814
sentinel B
2.9108550636749544
9.6
tweak the feature
3.51728320194057
11.6
sample
3.3118894448415537
13.9
information
2.527953330092368
10.4
Conclusions
- The results obtained are better than the baseline, but not statistically significant.
9.76150859678314
35.2
system
3.2085561497326207
13.2
data
1.5963783654991661
6.7
Armed forces
Politics/Government/Defence/Armed forces
earth sciences
42.96234067141413
1.4498507678508759
European Space Agency
tweak the classifier
8.489993935718617
28.0
land
2.12056230640934
8.9
size
2.1682153919466285
9.1
workflow
12.129314535731648
49.9
sample
3.2571706368497813
13.4
Everest
payload
1.7744287797763734
7.3
. . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities .................
3.910149750415973
14.1
conclusion
1.8822968787228973
7.9
Armed forces
Politics/Government/Defence/Armed forces
law
12.852112676056336
14.6
description
1.9445794846864366
8.0
European Space Agency
3.232863393291201
13.3
With respect to the use of open data, dedicated satellites under the Copernicus[footnoteRef:1] programme can guarantee open and up-to-date information through a set of services dedicated to environmental and security issues.
0.9983361064891846
3.6
sample size
17.22255912674348
56.8
This is in part related to the small sample size.
6.9606211869107035
25.1
astronautics
19.806338028169012
22.5
The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C
band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
1.9412090959511923
7.0
life sciences
17.474443451665934
0.4437255263328552
- Tweaking the classifier has potentially more impact than tweaking the features.
10.98169717138103
39.6
sentinel mode
0.9702850212249848
3.2
impact
4.026685727900881
16.9
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
sentinel user guide
1.6373559733171619
5.4
Book industry
Economy, business and finance/Economic sector/Media/Book industry
Land monitoring is a issue common to a number of user communities; the main aim is to provide useful information to those entities that have to make informed decisions to address environmental, scientific, humanitarian, health, political and security issues as well as to adopt sustainable management practices.
27.73155851358846
100.0
environmental sciences
57.03765932858587
1.9248507618904114
European Space Agency
1.6440314510364546
6.9
community
1.8473505104521148
7.6
service-account-enrichment
service-account-generation-service
http://emanuele79.livejournal.com/
65e769be-d0b2-401e-996b-c5a4afb49cb6.rdf
annotations/572dfc14-5d3d-4245-bc9b-87e2209cf271
RO samples
military
data selection
SatCen
service information
social media information
archived data
programming
building industry
Sentinel
data provenance
Elizabeth
use
user
user
sensing
analysis
Everest
Monitoring
communities
LAND
land monitoring data
case
changes
data privacy
information
techniques
2016-02-24T17:27:21.745+01:00
3410623
https://api.rohub.org/api/ros/2d50b9a1-5eb8-46b2-a135-04c0db5da7e6/crate/download/
2016-02-24 16:08:48.221000+00:00
2025-10-18 11:56:27.862244+00:00
2016-02-24 16:08:48.221000+00:00
This is a RO created for Land Monitoring Activities
application/ld+json
https://w3id.org/ro-id/2d50b9a1-5eb8-46b2-a135-04c0db5da7e6
Land Monitoring RO
http://emanuele79.livejournal.com/. "Land Monitoring RO." ROHub. Feb 24 ,2016. https://w3id.org/ro-id/2d50b9a1-5eb8-46b2-a135-04c0db5da7e6.
datasets
RO samples
manuals
workflows
biblio
450219
https://api.rohub.org/api/resources/0182cb87-8d24-4b3a-afcc-2299d138cd6f/download/
2016-02-24 16:21:45.123000+00:00
2022-03-25 16:37:16.304854+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
LAND MONITORING-hypotesis.docx
2016-02-24 16:21:45.123000+00:00
24968
https://api.rohub.org/api/resources/370f3a5d-aa2a-4a02-af8e-87ac453eee55/download/
2016-02-24 16:09:17.223000+00:00
2022-03-25 16:37:14.517159+00:00
image/png
imgTest.png
2016-02-24 16:09:17.223000+00:00
3150939
https://api.rohub.org/api/resources/b68deac2-deac-47ac-8af0-0c9bd19d3f58/download/
2016-02-24 16:13:07.590000+00:00
2022-03-25 16:37:18.681313+00:00
application/pdf
Sentinel 1 Handbook
2016-02-24 16:13:07.590000+00:00
11466
https://api.rohub.org/api/resources/d5501788-252c-4c8d-b4d0-070356a92b91/download/
2016-02-24 16:23:47.807000+00:00
2022-03-25 16:37:15.403789+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
Land Monitoring - conclusions.docx
2016-02-24 16:23:47.807000+00:00
size
2.2119591638308216
9.1
environmental sciences
57.03765932858587
1.9248507618904114
European Space Agency
3.232863393291201
13.3
description
1.9445794846864366
8.0
tweak the feature
3.51728320194057
11.6
Europe
operational scenario
8.02954491303312
33.7
Monitoring RO.
1.0006064281382656
3.3
sentinel data product dissemination
2.031534263189812
6.7
impact
4.1079241614000965
16.9
small sample size
1.0309278350515463
3.4
software
8.098591549295772
9.2
land Monitoring RO.
16.73741661613099
55.2
European Union
size
2.1682153919466285
9.1
sentinel user guide
1.6373559733171619
5.4
Conclusions
- The results obtained are better than the baseline, but not statistically significant.
9.76150859678314
35.2
classifier
4.496840058337384
18.5
environmental science and management
57.03765932858587
1.9248507618904114
in Feb-2016
Everest
engineering
44.81040662436906
1.1378629207611084
geosciences
37.71514992396501
0.9576942920684814
life sciences (general)
17.474443451665934
0.4437255263328552
classifier
4.717655468191565
19.8
orbit characteristic
3.5779260157671313
11.8
Ro
5.493437044239184
22.6
system
3.2085561497326207
13.2
The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C
band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
1.9412090959511923
7.0
conclusion
1.8822968787228973
7.9
sentinel family
3.8598999285203717
16.2
sentinel mode
0.9702850212249848
3.2
SENTINEL USER GUIDE .............................................................................
4.520244037714919
16.3
Prepared by Sentinel Team
4.409317803660565
15.9
land
2.12056230640934
8.9
Land Monitoring RO. This is a RO created for Land Monitoring Activities
27.73155851358846
100.0
Sentinel user handbook
1.3341419041843543
4.4
Ro
6.766738146294973
28.4
decision
2.2119591638308216
9.1
Science and technology
Science and technology
to create and access data/text mining tools as well as of automatic tools for processing high volumes of data;
1.0537992235163616
3.8
impact
4.026685727900881
16.9
feature
2.382654276864427
10.0
tweak the classifier
8.489993935718617
28.0
land monitoring use case
1.0309278350515463
3.4
Armed forces
Politics/Government/Defence/Armed forces
French Guiana
mission
1.4772456516559447
6.2
output
1.701507049100632
7.0
Language
Arts, culture and entertainment/Culture/Language
2020
sample size
17.22255912674348
56.8
data
1.5963783654991661
6.7
sentinel mission data acquisition
2.274105518496058
7.5
life sciences
17.474443451665934
0.4437255263328552
sample
3.3118894448415537
13.9
activity
3.454848701453419
14.5
methodology
6.123421491541578
25.7
in 2006 and 2010
feature
3.9863879436071947
16.4
Armed forces
Politics/Government/Defence/Armed forces
With respect to the use of open data, dedicated satellites under the Copernicus[footnoteRef:1] programme can guarantee open and up-to-date information through a set of services dedicated to environmental and security issues.
0.9983361064891846
3.6
- Tweaking the classifier has potentially more impact than tweaking the features.
10.98169717138103
39.6
country
6.0281964025279535
24.8
Book industry
Economy, business and finance/Economic sector/Media/Book industry
payload
1.7744287797763734
7.3
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
This is in part related to the small sample size.
6.9606211869107035
25.1
earth resources and remote sensing
37.71514992396501
0.9576942920684814
instrument payload
2.5773195876288657
8.5
atmospheric sciences
42.96234067141413
1.4498507678508759
Land monitoring is a issue common to a number of user communities; the main aim is to provide useful information to those entities that have to make informed decisions to address environmental, scientific, humanitarian, health, political and security issues as well as to adopt sustainable management practices.
27.73155851358846
100.0
aerospace engineering
15.404929577464788
17.5
politics
2.288732394366197
2.6
sentinel B
2.9108550636749544
9.6
workflow
12.129314535731648
49.9
sentinel mission guide
4.851425106124924
16.0
European Space Agency
1.6440314510364546
6.9
workflow
9.64974982130093
40.5
community
1.8473505104521148
7.6
law
12.852112676056336
14.6
Monitoring
11.26995472956874
47.3
description
1.6202049082678105
6.8
. . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities .................
3.910149750415973
14.1
information
2.527953330092368
10.4
data
2.7710257656781723
11.4
baseline
3.526328329759352
14.8
computer science
41.54929577464788
47.2
activity
3.0384054448225575
12.5
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
land Monitoring activity
10.491206791995149
34.6
mission
2.746718522119592
11.3
Monitoring activity
2.031534263189812
6.7
earth sciences
42.96234067141413
1.4498507678508759
sample
3.2571706368497813
13.4
astronautics
19.806338028169012
22.5
heritage mission
2.7592480291085506
9.1
European Space Agency
communications and radar
44.81040662436906
1.1378629207611084
service-account-enrichment
service-account-generation-service
http://emanuele79.livejournal.com/
military
data selection
SatCen
service information
social media information
archived data
programming
building industry
Sentinel
data provenance
Elizabeth
use
user
user
sensing
analysis
Everest
Monitoring
communities
LAND
land monitoring data
case
changes
data privacy
information
techniques
2016-02-24T17:25:00.127+01:00
3409372
https://api.rohub.org/api/ros/94f3ec01-2fc1-4d36-9d33-df988345c96b/crate/download/
2016-02-24 16:08:48.221000+00:00
2025-10-18 11:56:15.168592+00:00
2016-02-24 16:08:48.221000+00:00
This is a RO created for Land Monitoring Activities
application/ld+json
https://w3id.org/ro-id/94f3ec01-2fc1-4d36-9d33-df988345c96b
Land Monitoring RO
http://emanuele79.livejournal.com/. "Land Monitoring RO." ROHub. Feb 24 ,2016. https://w3id.org/ro-id/94f3ec01-2fc1-4d36-9d33-df988345c96b.
workflows
datasets
manuals
biblio
24968
https://api.rohub.org/api/resources/7064747c-169d-40fb-9756-15d56b9e574b/download/
2016-02-24 16:09:17.223000+00:00
2022-03-25 16:37:45.006847+00:00
image/png
imgTest.png
2016-02-24 16:09:17.223000+00:00
450219
https://api.rohub.org/api/resources/770a0193-5dc6-4eec-b278-e392994716e0/download/
2016-02-24 16:21:45.123000+00:00
2022-03-25 16:37:46.854978+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
LAND MONITORING-hypotesis.docx
2016-02-24 16:21:45.123000+00:00
11466
https://api.rohub.org/api/resources/87037202-5ae1-4bfb-8303-a24c0d520357/download/
2016-02-24 16:23:47.807000+00:00
2022-03-25 16:37:45.836419+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
Land Monitoring - conclusions.docx
2016-02-24 16:23:47.807000+00:00
3150939
https://api.rohub.org/api/resources/9afe342c-ac1d-4c8c-89b0-0e9492b685be/download/
2016-02-24 16:13:07.590000+00:00
2022-03-25 16:37:48.918998+00:00
application/pdf
Sentinel 1 Handbook
2016-02-24 16:13:07.590000+00:00
sentinel user guide
1.6373559733171619
5.4
mission
2.746718522119592
11.3
in 2006 and 2010
impact
4.026685727900881
16.9
software
8.098591549295772
9.2
Conclusions
- The results obtained are better than the baseline, but not statistically significant.
9.76150859678314
35.2
European Space Agency
3.232863393291201
13.3
Europe
environmental science and management
57.03765932858587
1.9248507618904114
- Tweaking the classifier has potentially more impact than tweaking the features.
10.98169717138103
39.6
sentinel family
3.8598999285203717
16.2
Monitoring RO.
1.0006064281382656
3.3
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
2020
conclusion
1.8822968787228973
7.9
law
12.852112676056336
14.6
environmental sciences
57.03765932858587
1.9248507618904114
activity
3.454848701453419
14.5
European Space Agency
land Monitoring activity
10.491206791995149
34.6
system
3.2085561497326207
13.2
SENTINEL USER GUIDE .............................................................................
4.520244037714919
16.3
sample size
17.22255912674348
56.8
sentinel mode
0.9702850212249848
3.2
earth resources and remote sensing
37.71514992396501
0.9576942920684814
Book industry
Economy, business and finance/Economic sector/Media/Book industry
Sentinel user handbook
1.3341419041843543
4.4
Armed forces
Politics/Government/Defence/Armed forces
instrument payload
2.5773195876288657
8.5
information
2.527953330092368
10.4
operational scenario
8.02954491303312
33.7
The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C
band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
1.9412090959511923
7.0
Land monitoring is a issue common to a number of user communities; the main aim is to provide useful information to those entities that have to make informed decisions to address environmental, scientific, humanitarian, health, political and security issues as well as to adopt sustainable management practices.
27.73155851358846
100.0
computer science
41.54929577464788
47.2
sample
3.3118894448415537
13.9
land Monitoring RO.
16.73741661613099
55.2
size
2.2119591638308216
9.1
Monitoring
11.26995472956874
47.3
astronautics
19.806338028169012
22.5
small sample size
1.0309278350515463
3.4
tweak the classifier
8.489993935718617
28.0
to create and access data/text mining tools as well as of automatic tools for processing high volumes of data;
1.0537992235163616
3.8
data
1.5963783654991661
6.7
aerospace engineering
15.404929577464788
17.5
community
1.8473505104521148
7.6
Ro
5.493437044239184
22.6
classifier
4.496840058337384
18.5
geosciences
37.71514992396501
0.9576942920684814
data
2.7710257656781723
11.4
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
This is in part related to the small sample size.
6.9606211869107035
25.1
Land Monitoring RO. This is a RO created for Land Monitoring Activities
27.73155851358846
100.0
land monitoring use case
1.0309278350515463
3.4
earth sciences
42.96234067141413
1.4498507678508759
French Guiana
methodology
6.123421491541578
25.7
Language
Arts, culture and entertainment/Culture/Language
country
6.0281964025279535
24.8
life sciences
17.474443451665934
0.4437255263328552
. . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities .................
3.910149750415973
14.1
impact
4.1079241614000965
16.9
land
2.12056230640934
8.9
decision
2.2119591638308216
9.1
Science and technology
Science and technology
in Feb-2016
output
1.701507049100632
7.0
Ro
6.766738146294973
28.4
size
2.1682153919466285
9.1
sentinel B
2.9108550636749544
9.6
workflow
12.129314535731648
49.9
communications and radar
44.81040662436906
1.1378629207611084
baseline
3.526328329759352
14.8
politics
2.288732394366197
2.6
workflow
9.64974982130093
40.5
Monitoring activity
2.031534263189812
6.7
sentinel data product dissemination
2.031534263189812
6.7
description
1.9445794846864366
8.0
life sciences (general)
17.474443451665934
0.4437255263328552
sample
3.2571706368497813
13.4
mission
1.4772456516559447
6.2
description
1.6202049082678105
6.8
European Union
activity
3.0384054448225575
12.5
With respect to the use of open data, dedicated satellites under the Copernicus[footnoteRef:1] programme can guarantee open and up-to-date information through a set of services dedicated to environmental and security issues.
0.9983361064891846
3.6
Prepared by Sentinel Team
4.409317803660565
15.9
engineering
44.81040662436906
1.1378629207611084
sentinel mission data acquisition
2.274105518496058
7.5
European Space Agency
1.6440314510364546
6.9
sentinel mission guide
4.851425106124924
16.0
orbit characteristic
3.5779260157671313
11.8
payload
1.7744287797763734
7.3
heritage mission
2.7592480291085506
9.1
feature
2.382654276864427
10.0
feature
3.9863879436071947
16.4
Armed forces
Politics/Government/Defence/Armed forces
Everest
atmospheric sciences
42.96234067141413
1.4498507678508759
classifier
4.717655468191565
19.8
tweak the feature
3.51728320194057
11.6
service-account-enrichment
service-account-generation-service
http://tahsl.livejournal.com/
United Kingdom
model element
GIS raster data
workflow description
datasets
workflow
nets
impact
hazard
forecast
2.6887871853546907
4.7
ensemble rainfall
4.628890662410216
5.8
kind
3.4897025171624714
6.1
footprint
5.118961788031724
7.1
environmental sciences
61.02558789258154
0.9478866457939148
List of software used to generate elements in the workflow e.g. G2G modelling of SWF footprint
5.162827640984909
6.5
description
3.032036613272311
5.3
Language
Arts, culture and entertainment/Culture/Language
hazard impact modelling context
4.628890662410216
5.8
computer science
17.801047120418847
3.4
data
3.6041189931350113
6.3
guidance
3.432494279176201
6.0
Hazard Impact Model Development
15.35688536409517
21.3
ensemble surface runoff forecast
2.5538707102952913
3.2
early warning system
10.011441647597254
17.5
GIS database
2.633679169992019
3.3
software
25.13089005235602
4.8
workflow
4.3258832011535695
6.0
development of early warning systems
13.248204309656826
16.6
Datasets specific to hazard footprints e.g. MetOffice observed rainfall data and ensemble forecast rainfall (GIS gridded raster data)
6.910246227164415
8.7
ensemble
6.350114416475973
11.100000000000001
hazard
13.482335976928624
18.7
dataset
9.153318077803203
16.0
impact
13.501144164759724
23.6
Hardware
Economy, business and finance/Economic sector/Computing and information technology/Hardware
impact
13.554434030281184
18.8
geosciences
100.0
0.8209701478481293
earth sciences
38.97441210741846
0.6053743362426758
chance
10.583524027459953
18.5
workflow
4.519450800915331
7.9
geographic information system
3.832951945080091
6.7
early warning system
13.266041816870944
18.4
Weather
Weather
RO. Ro
3.5913806863527533
4.5
United Kingdom
15.42898341744773
21.4
system
5.892448512585812
10.3
United Kingdom
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
footprint
5.3775743707093815
9.4
element
3.4607065609228553
4.8
Description of datasets and primary purpose in hazard impact modelling context e.g. ensemble rainfall forecast input to G2G to yield ensemble surface runoff forecasts
8.498808578236696
10.7
workflow description
3.750997605746209
4.7
hazard impact modelling workflow
2.3942537909018355
3.0
Hazard Impact Model Development RO. Ro
47.88507581803671
60.0
earth resources and remote sensing
100.0
0.8209701478481293
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
GIS
3.6770007209805335
5.1
database
57.06806282722513
10.9
database
2.6315789473684204
4.6
dataset
4.974765681326605
6.9
impacts within the UK
5.107741420590583
6.4
method
3.893294881038212
5.4
Hazard Impact Model Development RO. RO to facilitate development of early warning systems for natural hazards and their impacts within the UK.
79.42811755361397
100.0
natural hazard
9.577015163607342
12.0
geophysics
38.97441210741846
0.6053743362426758
description
3.4607065609228553
4.8
United Kingdom
11.899313501144164
20.8
environmental science and management
61.02558789258154
0.9478866457939148
2016-02-02T13:43:52.345+01:00
38304
https://api.rohub.org/api/ros/fb83ce4a-bcbd-4708-984a-c67bdbc10bdd/crate/download/
2016-01-25 15:51:30.922000+00:00
2025-10-18 11:56:03.731636+00:00
2016-01-25 15:51:30.922000+00:00
RO to facilitate development of early warning systems for natural hazards and their impacts within the UK.
application/ld+json
https://w3id.org/ro-id/fb83ce4a-bcbd-4708-984a-c67bdbc10bdd
Hazard Impact Model Development RO
http://tahsl.livejournal.com/. "Hazard Impact Model Development RO." ROHub. Jan 25 ,2016. https://w3id.org/ro-id/fb83ce4a-bcbd-4708-984a-c67bdbc10bdd.
input data
method notes
definitions
framework
workflow
publications
case study
validation
risk
rainfall observed
software
generic datasets
standards
rainfall forecast
hazards
processing
intermediary
input
used
documentation
impacts
data
produced
methodology guidance
user guides
impact library
output
20825
https://api.rohub.org/api/resources/d23349d6-dd05-4568-9b79-f78022de0693/download/
2016-01-25 17:35:00.034000+00:00
2022-03-25 16:40:24.570337+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
NHP Research Object Hackathon v2.docx
2016-01-25 17:35:00.034000+00:00
service-account-enrichment
service-account-generation-service
http://rapw3k.livejournal.com/
input data Multitemporal InSAR image data
SBAS InSAR data processing
processing method
SBAS method
programming
velocities
GPS
telecommunications
file
residuals
ground
2016-01-22T14:47:35.818+01:00
749686
https://api.rohub.org/api/ros/85dd9dc0-7231-454e-b3c6-ca619c8d4df2/crate/download/
2016-01-22 11:43:56.224000+00:00
2025-10-18 11:55:41.598421+00:00
2016-01-22 11:43:56.224000+00:00
Ground deformation mapping is a typical use case for this VRC. It may be carried out by different researchers on different volcanoes or even on the same volcano
application/ld+json
https://w3id.org/ro-id/85dd9dc0-7231-454e-b3c6-ca619c8d4df2
Volcano deformation mapping
Please make sure that this workflow is executable
http://rapw3k.livejournal.com/. "Volcano deformation mapping." ROHub. Jan 22 ,2016. https://w3id.org/ro-id/85dd9dc0-7231-454e-b3c6-ca619c8d4df2.
output
data
figures
used
connected
figures
produced
web services
compilers
software
manuals
publications
documentation
configuration files
main
processing
workflows
annotations
scripts
input
consumption
13779
https://api.rohub.org/api/resources/0640ba54-ab25-47fb-a73a-2bf3762217f6/download/
2016-01-22 12:00:03.236000+00:00
2022-03-25 16:41:49.808434+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
SBAS InSAR data processing using SarScape.docx
2016-01-22 12:00:03.236000+00:00
13665
https://api.rohub.org/api/resources/38cea6c2-1621-41d2-8eb5-5c028f5dddf4/download/
2016-01-22 12:17:20.473000+00:00
2022-03-25 16:41:47.859397+00:00
image/jpeg
volano-def.jpg
2016-01-22 12:17:20.473000+00:00
12804
https://api.rohub.org/api/resources/52c75270-985b-4705-80ee-053bb3eff72e/download/
2016-01-22 12:02:23.324000+00:00
2022-03-25 16:41:48.903620+00:00
application/vnd.openxmlformats-officedocument.wordprocessingml.document
CC-BY-NC4
Validation of ground velocities using InSAR ground deformation and GPS.docx
2016-01-22 12:02:23.324000+00:00
702905
https://api.rohub.org/api/resources/61d312ce-035d-4006-9ce0-98afffda9b5d/download/
2016-01-22 12:23:54.578000+00:00
2022-03-25 16:41:45.477209+00:00
application/zip
vel_masked2.zip
2016-01-22 12:23:54.578000+00:00
This is a compressed file of all the output results
script
3.0653266331658293
6.1
ground velocity file
7.529055078322385
14.9
SarScape software interface
8.388074785245074
16.6
It may be carried out by different researchers on different volcanoes or even on the same volcano
7.288765088207985
15.7
geosciences
100.0
0.8578767776489258
soil
3.558576569372251
8.9
Volcanic eruption
Disaster, accident and emergency incident/Disaster/Natural disasters/Volcanic eruption
geology
100.0
0.9990125894546509
use case
3.6683417085427137
7.3
SBAS method
5.305709954522486
10.5
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
software interface
3.0653266331658293
6.1
mapping
12.11515393842463
30.3
same volcano
1.5159171298635674
3.0
Ground deformation mapping is a typical use case for this VRC.
25.580315691736303
55.1
global positioning system
4.998000799680128
12.5
processing SW SarScape
6.2152602324406265
12.3
SarScape
5.8291457286432165
11.6
Natural disasters
Disaster, accident and emergency incident/Disaster/Natural disasters
data
4.278288684526189
10.7
volcano
4.398240703718512
11.0
processing
2.3990403838464616
6.0
telecommunications
6.578947368421052
2.5
Activities Opens ArcMap interface Load ground velocity file to validate and test files (they should have the same Line of Sight Run scripts to compare two raster ground velocity files and calculate the statistics of residuals.
10.53853296193129
22.7
result
2.4390243902439024
6.1
input data
4.321608040201005
8.6
different researcher
0.8590197069226883
1.7
ground deformation mapping
31.985851440121273
63.3
earth sciences
100.0
0.9990125894546509
ground
8.190954773869347
16.3
line of sight run script
2.2233451237998993
4.4
velocity
5.728643216080402
11.4
ground
4.358256697321072
10.9
WF steps User selects the appropriate InSAR and DEM data for the processing.
4.8282265552460535
10.4
volcano
5.678391959798995
11.3
mapping
15.477386934673367
30.8
volcano deformation mapping
13.996968165740272
27.7
input data Multitemporal InSAR image data
6.06366851945427
12.0
vertical redundancy check
2.5589764094362257
6.4
validation
2.3115577889447234
4.6
Science and technology
Science and technology
data
4.623115577889447
9.2
law
8.157894736842104
3.1
computer programming
9.210526315789474
3.5
processing
2.613065326633166
5.2
VRC
3.3165829145728645
6.6
The user runs the SarScape software interface and displays one or more menus and graphic windows to select the input data, the processing method (SBAS) and parameters.
9.006499535747444
19.4
velocity
5.557776889244303
13.9
programming interface
2.7988804478208715
7.0
raster
2.7189124350259894
6.8
Discrimination
Society/Discrimination
Computer crime
Crime, law and justice/Crime/Computer crime
researcher
4.42211055276382
8.8
computer science
40.26315789473684
15.3
GPS
5.226130653266332
10.4
GPS site velocity
5.811015664477009
11.5
Workflow Title: SBAS InSAR data processing using SarScape
7.98514391829155
17.2
Workflow Title: Validation of ground velocities using InSAR ground deformation and GPS
12.070566388115134
26.0
input file
3.9184326269492207
9.8
raster
2.814070351758794
5.6
deformation
11.959798994974875
23.8
Volcano deformation mapping.
13.50974930362117
29.1
statistics
2.279088364654138
5.7
InSAR ground deformation
4.143506821627084
8.2
user
2.9588164734106357
7.4
deformation
9.636145541783288
24.1
SBAS InSAR
2.71356783919598
5.4
different volcano
1.010611419909045
2.0
rule
2.279088364654138
5.7
Input data Ground velocity file in raster format Ground velocity file(s) resulting from different analysis methods (e.g. PS, SBAS, mixed), different time periods, or different datasets (e.g. Sentinel-1, ALOS 2, GPS, optical levelling)
9.192200557103064
19.8
processing method
3.1328954017180393
6.2
geophysics
100.0
0.8578767776489258
Capital punishment
Crime, law and justice/Law enforcement/Punishment (criminal)/Capital punishment
script
2.838864454218313
7.1
digital elevation model
2.663316582914573
5.3
velocity file
1.819100555836281
3.6
researcher
3.358656537385046
8.4
software
35.78947368421053
13.6
service-account-enrichment
service-account-generation-service
memory
deregulate in HD
HD
participate in epigenetic processes
genetics
research
have an epigenetic role
aim
3.7729318103149883
10.9
gene deregulation
1.8138261464750172
5.3
genes involved in HD gene deregulation have an epigenetic role
33.34444814938313
100.0
participate in epigenetic process
0.20533880903490762
0.6
web service
4.084458290065767
11.8
have an epigenetic role
0.06844626967830253
0.2
life sciences
100.0
2.662090003490448
chromatin
5.430210325047801
14.2
HD chromatin analysis
10.095824777549623
29.5
http
3.530633437175493
10.2
deregulation
11.586998087954111
30.3
analysis
2.076843198338526
6.0
http
4.053537284894838
10.6
chromatin data interpretation
9.68514715947981
28.3
HD gene deregulation
25.735797399041754
75.2
deregulate in HD
16.1533196440794
47.2
earth sciences
67.21141059758304
1.1682264804840088
epigenetic role
6.536618754277893
19.1
research object
5.6125941136208075
16.4
Animal
Human interest/Animal
geochemistry
32.78858940241696
0.5699106454849243
Genoa
20.976116303219108
60.6
epigenetic
2.942194530979578
8.5
chromatin
4.7767393561786085
13.8
workflow
3.6711281070745696
9.6
chromatin data interpretation.
4.268089363121041
12.8
geology
67.21141059758304
1.1682264804840088
role
5.469020422291451
15.8
information
2.872966424368294
8.3
system
3.703703703703704
10.7
web service
4.7036328871892925
12.3
earth sciences
32.78858940241696
0.5699106454849243
research
4.091778202676864
10.7
research
4.534440983039114
13.1
HD
12.313575525812622
32.2
role
5.621414913957935
14.7
chromatin analysis
1.0609171800136894
3.1
object
4.130019120458891
10.8
workflow
3.0806507442021465
8.9
interpretation
2.492211838006231
7.2
Science and technology
Science and technology
life sciences (general)
100.0
2.662090003490448
epigenetic process
17.796030116358658
52.0
anni web services
5.236139630390144
15.3
HD
12.253374870197302
35.4
Genes deregulated in HD, are participating in epigenetic processes
33.34444814938313
100.0
Genoa
26.080305927342256
68.2
gene
14.952198852772467
39.1
deregulation
10.107303565247491
29.2
<p>This research object, was created in order to further analyse and interpret the results from the research object http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/ (HD chromatin analysis). The workflows in this research object are using the anni web services, implemented by the Biosemantics group.</p>
29.043014338112705
87.1
data
3.365200764818356
8.8
Economic policy
Economy, business and finance/Economy/Economic policy
gene
13.326410522672205
38.5
2014-02-26T14:16:20.355+01:00
81201
https://api.rohub.org/api/ros/f84d00ef-47b2-40b3-af26-099ed7e82f5b/crate/download/
2014-02-21 13:36:32.163000+00:00
2025-10-18 11:54:49.104646+00:00
2014-02-21 13:36:32.163000+00:00
<p>This research object, was created in order to further analyse and interpret the results from the research object http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/ (HD chromatin analysis). The workflows in this research object are using the anni web services, implemented by the Biosemantics group.</p>
application/ld+json
https://w3id.org/ro-id/f84d00ef-47b2-40b3-af26-099ed7e82f5b
chromatin data interpretation
Eleni Mina. "chromatin data interpretation." ROHub. Feb 21 ,2014. https://w3id.org/ro-id/f84d00ef-47b2-40b3-af26-099ed7e82f5b.
data_interpretation
30787
https://api.rohub.org/api/resources/2ea2f7b6-1c15-454f-b62a-e7656bb81db4/download/
2014-02-25 16:10:46.463000+00:00
2022-03-25 16:50:57.659485+00:00
This workflow lists all IDs and descriptions of the predefined concept set
List Predefined Concept Sets
2014-02-25 16:10:46.463000+00:00
69369
https://api.rohub.org/api/resources/473e57ab-4056-427a-a8d9-b2c62e9b34d4/download/
2014-02-26 13:14:06.663000+00:00
2022-03-25 16:51:01.271983+00:00
This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group).
This workflow can be used together with other workflows in this pack: http://www.myexperiment.org/packs/282 for functional gene and SNP annotation and knowledge discovery.
Explain concept scores
2014-02-26 13:14:06.663000+00:00
63
https://api.rohub.org/api/resources/5b168f58-186e-4335-8079-2c917c3173e3/download/
2014-02-25 16:03:44.993000+00:00
2022-03-25 16:50:58.525827+00:00
text/plain
hypothesis.txt
2014-02-25 16:03:44.993000+00:00
203555
https://api.rohub.org/api/resources/7f5b48a1-083d-4891-b311-b4627ceaa7ab/download/
2014-02-25 16:06:54.756000+00:00
2022-03-25 16:51:00.400167+00:00
This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. "5" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services
Annotate a gene list with Biological processes
2014-02-25 16:06:54.756000+00:00
188023
https://api.rohub.org/api/resources/840546e9-eecd-455e-b3f7-b1893b29e083/download/
2014-02-25 16:12:14.228000+00:00
2022-03-25 16:50:56.779303+00:00
This workflow can prioritize genes that are related to a specific concept, e.g. HTT. In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against
Prioritize gene list related to a concept /list of concepts
2014-02-25 16:12:14.228000+00:00
39476
https://api.rohub.org/api/resources/cb796e52-689f-4e9e-8842-09612588af02/download/
2014-02-25 16:04:13.823000+00:00
2022-03-25 16:50:55.921582+00:00
Sketch of the workflows and their explanation, for data interpretation, plus the connection to the output from the RO for chromatin analysis
image/png
workflow sketch data interpretation
2014-02-25 16:04:13.823000+00:00
41692
https://api.rohub.org/api/resources/ccd9b022-3e2d-422b-a5c5-1e12af73b5c7/download/
2014-02-25 16:09:23.429000+00:00
2022-03-25 16:50:59.349734+00:00
This workflow suggests concept ids that match the query term. The user can run this workflow with any term of interest as for example "human", "htt", "Transcription" etc, and will get suggestions for concept ids together with descriptions. Then can choose the concept id that matches the best to her/his needs and use it to the rest of the CPA workflows
Get concept suggestions from term
2014-02-25 16:09:23.429000+00:00
67
https://api.rohub.org/api/resources/f97e8e56-b0de-4fda-9e37-352412aa2d3c/download/
2014-02-25 16:03:12.283000+00:00
2022-03-25 16:50:54.643219+00:00
text/plain
conclusions.txt
2014-02-25 16:03:12.283000+00:00
Eleni Mina
Eleni Mina
service-account-enrichment
service-account-generation-service
Earth sciences
environmental monitoring
11.420612813370472
8.2
environmental monitoring from space
11.598746081504702
11.1
service-account-enrichment
False
https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140
2022-03-29 07:03:12.340483+00:00
https://orcid.org/0000-0002-2736-0052
533964
https://api.rohub.org/api/ros/15e9432f-53ee-4ea8-b1a3-6fdcaca7cf9e/crate/download/
2021-12-14 10:41:17.716553+00:00
2024-03-05 12:16:56.530781+00:00
2021-12-14 10:41:17.716553+00:00
Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea
application/ld+json
https://w3id.org/ro-id/15e9432f-53ee-4ea8-b1a3-6fdcaca7cf9e
Analysis from satellite data – Environmental monitoring from space - snapshot
Analysis from satellite data – Environmental monitoring from space
MANUAL
https://w3id.org/ro-id/81696625-5dc7-413b-a2c5-0815dee8dc2a
https://w3id.org/ro-id/08e10088-5d06-4d9a-a7a0-7a6bf4a0d199
https://w3id.org/ro-id/4266cc91-2256-44f6-b790-90445838a46e
https://w3id.org/ro-id/5549dbaf-583c-4c8c-8dfa-a39ad54b848e
https://w3id.org/ro-id/6c915ada-20b3-43cf-a69c-8773c78a0eaa
https://w3id.org/ro-id/6f7e5fac-e969-4048-9f37-c6b31a3c612e
https://w3id.org/ro-id/70d4f966-c4d0-48f5-9fa5-811f884e7b1e
https://w3id.org/ro-id/97bb139d-ef31-4724-96c5-ea9425fc7c37
https://w3id.org/ro-id/d2a7c5cc-1102-43cd-8f62-288373b07b06
https://w3id.org/ro-id/f3594549-487e-496f-9517-0860ce40f088
https://w3id.org/ro-id/74dd5244-227a-4a04-b482-735a591fd8c1
https://w3id.org/ro-id/b72312be-71d7-47a7-b84c-4bfe2e2e23dd
https://w3id.org/ro-id/7b96142d-d1dc-4dd2-9f96-5dd9fb851416
https://w3id.org/ro-id/32c4e511-07a6-4aed-8e1e-484785703c49
https://w3id.org/ro-id/3ee3f89c-1f32-41b6-a676-baa8abd9d56f
https://w3id.org/ro-id/63bbaf66-12ba-4747-88a4-b84d914c3b7e
https://w3id.org/ro-id/ac96b0ba-720f-47cb-a2fe-a19e3e7e7713
https://w3id.org/ro-id/c922ffbe-2d7f-4489-aaa7-b1aae735efc5
https://w3id.org/ro-id/cf67ee27-302a-48bc-a5ee-904c4e50fa3e
https://w3id.org/ro-id/d6fe7e21-0924-4201-b407-8df494f726ed
https://w3id.org/ro-id/5b45c2e1-bd6d-494f-8f0b-71842e9ff0c6
https://w3id.org/ro-id/aa125d6d-2a3d-4e86-a386-fca35f89cfff
https://w3id.org/ro-id/14db60f4-ba75-4054-8b25-9588535e5441
https://w3id.org/ro-id/3a19afd2-96fe-46f3-a8ff-a00267cec7a9
https://w3id.org/ro-id/3bea0d90-d8aa-4e43-9a10-49f192c86cf9
https://w3id.org/ro-id/99a7f0a1-d957-45a2-b2db-21a5a4d52bee
https://w3id.org/ro-id/ecb76892-a19f-4944-bdbc-7859c36c5de9
https://w3id.org/ro-id/6b907ad3-41f5-4c92-a60a-fa30c01895d6
https://w3id.org/ro-id/d95b93a0-106a-483a-af39-2d4e03335ad7
https://w3id.org/ro-id/f5db11a5-bf5a-4f0e-8924-4cef9408845f
Castellan, Giorgio. "Analysis from satellite data – Environmental monitoring from space." ROHub. Dec 14 ,2021. https://doi.org/10.5281/zenodo.6392772.
Discover, subset, download and visualize satellite data stored in a Data Cube from the ADAM Platform
Method
Results
Results
Satellite data on Chl-a and Kd490
Satellite_data
70005
https://api.rohub.org/api/resources/190680da-e8f0-45d9-91e0-504523674f2a/download/
2021-12-14 14:33:06.849172+00:00
2022-03-29 07:03:09.363944+00:00
image/png
Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea
2021-12-14 14:33:06.849172+00:00
449579
https://api.rohub.org/api/resources/34811ef9-67ba-4917-a0f5-a601d5d4f582/download/
2021-12-14 14:38:21.837867+00:00
2022-03-29 07:03:07.276437+00:00
image/jpeg
Analysis from satellite data – Environmental monitoring from space during COVID-19 lockdown
2021-12-14 14:38:21.837867+00:00
https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7
2021-12-14 10:44:17.433059+00:00
2022-03-29 07:03:12.216426+00:00
Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown
Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown
2021-12-14 10:44:17.433059+00:00
https://w3id.org/ro-id/34d648b3-0014-4a19-8469-40b9380ca4c3
2021-12-14 10:44:48.894463+00:00
2022-03-29 07:03:08.528464+00:00
Discover and subset satellite data from the ADAM Platform
Discover and subset satellite data from the ADAM Platform
2021-12-14 10:44:48.894463+00:00
68452
https://api.rohub.org/api/resources/d1404599-300c-4572-971f-90a3a6a5e72a/download/
2021-12-14 14:32:01.240770+00:00
2022-03-29 07:03:10.497073+00:00
image/png
Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea
2021-12-14 14:32:01.240770+00:00
environmental monitoring
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analysis
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analysis
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Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea
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space
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earth sciences
100.0
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Economy, business and finance/Economic sector/Computing and information technology/Satellite technology
Adriatic Sea
https://www.wikidata.org/wiki/Q13924
clarity
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geology
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clarity
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lockdown
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result
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covid 19
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Analysis from satellite data –
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Environmental monitoring from space.
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Climatology
10.13039/501100000781
European Commission
https://doi.org/10.5281/zenodo.4543739
2022-03-28 14:18:39.324751+00:00
2022-03-29 18:08:07.800368+00:00
By deploying JupyterLab with PANGEO, CESM and ESMValTool conda environments as a new Climate Galaxy interactive tool, we are aiming at bridging the gap between climate scientists and non-climate specialists.
Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational research. One of the strength of Galaxy is that it does not require programming experience and allow researchers to easily upload data, run complex tools and workflows in a reproducible manner, and visualize results. Galaxy Climate is quite new and aims at offering tools to everyone interested in Climate Science so that they can analyse and visualize climate data produced by climate scientists. However, climate scientists and in particular climate modellers have very different working practices: they often like to use command lines for running climate models and thanks to the PANGEO community (a community platform for Big Data geoscience) the Jupyter ecosystem has become very popular with several deployments of JupyterHubs dedicated to climate data analysis. By deploying JupyterLab with PANGEO, CESM and ESMValTool conda environments as a new Climate Galaxy interactive tool (https://live.usegalaxy.eu/?tool_id=interactive_tool_climate_notebook), we are aiming at bridging the gap between climate scientists and non-climate specialists. On this poster, we will show typical use cases both for research (https://nordicesmhub.github.io/eosc-nordic-climate-demonstrator/02-use-cases/) and for teaching (https://nordicesmhub.github.io/NEGI-Abisko-2019/intro).
Climate JupyterLab as an interactive tool in Galaxy
2022-03-28 14:18:39.324751+00:00
https://doi.org/10.5281/zenodo.6394185
2022-03-29 17:55:05.034625+00:00
2022-03-29 18:08:05.535928+00:00
This is a tarball for the Docker climate-JupyterLab image - Version 2021-03-18.
To use it:
download the image file docker-climate-notebook-2021-03-18.tar
load it with docker with the command: docker load --input docker-climate-notebook-2021-03-18.tar
launch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook:2021-03-18
start your favorite web browser and go to: http://localhost:7777/ipython/
See https://github.com/NordicESMhub/docker-climate-notebook for more details
Docker climate-JupyterLab image Version 2021-03-18
2022-03-29 17:55:05.034625+00:00
https://github.com/NordicESMhub/docker-climate-notebook
2022-03-29 12:01:31.834492+00:00
2022-03-29 18:08:06.488065+00:00
This github repository contains all the sources required for building the docker containers that are made available in Quay Container Registry.
Source code for building the docker container (github repository)
2022-03-29 12:01:31.834492+00:00
https://jupyterlab.readthedocs.io/en/stable/
2022-03-28 14:14:45.648769+00:00
2022-03-29 18:08:02.576584+00:00
Link to the online JupyterLab documentation.
JupyterLab Documentation
2022-03-28 14:14:45.648769+00:00
University of Freiburg, Freiburg (Germany)
bjoern.gruening@gmail.com
Björn Grüning
0000-0002-3079-6586
https://quay.io/repository/nordicesmhub/docker-climate-notebook
2022-03-29 11:58:28.213223+00:00
2022-03-29 18:08:06.281346+00:00
These docker images (different tags) correspond to the docker images built for Galaxy Climate JupyterLab.
The docker images can be used within Galaxy and as standalone docker images.
You can use the same images we use in Galaxy on your local computer or any other platform:
1. Pull an existing image locally
docker pull quay.io/nordicesmhub/docker-climate-notebook
2. Run a pre-build image from docker registry
3. To start your JupyterLab:
docker run -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook
and you will top open a new terminal and start your favorite web browser.
your running Jupyter Notebook instance on http://localhost:7777/ipython/.
Remark: for reproducibility purpose, we suggest you use a specific tag e.g.
docker pull quay.io/nordicesmhub/docker-climate-notebook:2021-03-18
Then use the same tag when starting your JupyterLab application:
docker run -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook:2021-03-18
Docker images for Galaxy Climate JupyterLab (Quay Container Registry)
2022-03-29 11:58:28.213223+00:00
https://raw.githubusercontent.com/NordicESMhub/docker-climate-notebook/ie2/climate-jupyter-galaxy_web.gif
2022-03-29 11:41:30.552728+00:00
2022-03-29 18:08:02.662870+00:00
This is a gif animated image showing how to start the Galaxy Climate JupyterLab in Galaxy Europe
image/gif
How to start Galaxy Climate JupyterLab (gif animated)
2022-03-29 11:41:30.552728+00:00
https://raw.githubusercontent.com/NordicESMhub/docker-climate-notebook/ie2/map_vis_Galaxy.gif
2022-03-29 11:43:11.075459+00:00
2022-03-29 18:08:03.597880+00:00
This is a gif animated image showing some of the functionalities of the Galaxy Climate JupyterLab
image/gif
Demo of some of the functionalities of the Galaxy Climate JupyterLab (gif animated)
2022-03-29 11:43:11.075459+00:00
01xtthb56
University of Oslo
04jcwf484
Nordic e-Infrastructure Collaboration
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2022-03-29 18:08:11.857053+00:00
30283
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2022-03-26 09:45:54.364171+00:00
2025-10-18 11:54:14.993216+00:00
2022-03-26 09:45:54.364171+00:00
🐳 🔬 📚 Jupyter running in a docker container. This image can be used to integrate Jupyter into Galaxy. This Jupyter Docker container is used by the Galaxy Project and can be installed from the quay.io index (https://quay.io/repository/nordicesmhub/docker-climate-notebook).
application/ld+json
https://w3id.org/ro-id/cb869c7a-7a89-49dd-9038-b8a05a91dc6e
cesm
climate
docker
esmvaltool
jupyterlab
pangeo
Docker Climate Analysis Jupyter Container - snapshot
Docker Climate Analysis Jupyter Container Version 2021-03-18
MANUAL
Anne Foilloux, and Björn Grüning. "Docker Climate Analysis Jupyter Container Version 2021-03-18." ROHub. Mar 26 ,2022. https://doi.org/10.24424/6mwg-cq92.
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input
tool
biblio
output
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2022-03-29 12:08:38.781608+00:00
2022-03-29 18:08:11.623739+00:00
Default Jupyter Notebook used when starting Galaxy Climate JupyterLab if no other Jupyter Notebook is passed by the user.
Default Jupyter Notebook for Galaxy Climate JupyterLab
2022-03-29 12:08:38.781608+00:00
6716
https://api.rohub.org/api/resources/c8a9d642-c401-43c4-9436-58f866edb277/download/
2022-03-29 12:06:48.073820+00:00
2022-03-29 18:08:09.556008+00:00
This is the Galaxy Climate JupyterLab tool wrapper used by Galaxy to start the Galaxy Climate JupyterLab on a Galaxy instance.
application/xml
Galaxy Climate JupyterLab Tool wrapper (xml)
2022-03-29 12:06:48.073820+00:00
29705
https://api.rohub.org/api/resources/f974c6a2-5fb5-45ae-b19f-03968d55060f/download/
2022-03-29 12:16:49.457762+00:00
2022-03-29 18:08:08.669861+00:00
Most of the resources and information of this Research Object were created from this Jupyter Notebook.
Jupyter Notebook used to create/update this Research Object
2022-03-29 12:16:49.457762+00:00
y. This Jupyter Docker container is used by the Galaxy Project and can be installed from the quay.io index (https://quay.io/repository/nordicesmhub/docker-climate-notebo
29.59697732997481
23.5
computer programming and software
100.0
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Samsung Galaxy
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image
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integrate Jupyter
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Docker Climate Analysis Jupyter Container Version 2021-03-18.
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http
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r. This image can be used to integrate Jupyter into Gala
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docker Climate analysis Jupyter container version
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Jupyter Docker container
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loader
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Waterway and maritime transport
Economy, business and finance/Economic sector/Transport/Waterway and maritime transport
shipping container
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docker container
17.672413793103445
16.4
mathematical and computer sciences
100.0
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model
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Jupyter
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Occupations
Labour/Employment/Occupations
analysis Jupyter container version
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http
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version
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100.0
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container
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Wireless technology
Economy, business and finance/Economic sector/Computing and information technology/Wireless technology
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
0000-0002-1784-2920
service-account-enrichment
Information science
Applied sciences
Climatology
The Nordic e-Infrastructure Collaboration (NeIC)
Finnish Meteorological Institute (Finland)
antti-ilari.partanen@fmi.fi
Antti-Ilari Partanen
0000-0002-0883-8161
NSC (Sweden)
struthers@nsc.liu.se
Hamish Struthers
0000-0002-4214-2213
NERSC (Norway)
yanchun.he@nersc.no
Yanchun He
0000-0002-5932-3627
Finnish Meteorological Institute (Finland)
tommi.bergman@fmi.fi
Tommi Bergman
0000-0002-6133-2231
Norwegian Meteorological Institute (Norway)
oskaral@met.no
Oskar Landgren
0000-0002-6264-8502
UiO
jeani@uio.no
Jean Iaquinta
0000-0002-8763-1643
Finnish Meteorological Institute (Finland)
risto.makkonen@fmi.fi
Risto Makkonen
0000-0002-8961-3393
04jcwf484
Nordic e-Infrastructure Collaboration
200505
NeIC-NICEST2
NICEST2
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2022-04-01 15:21:09.952162+00:00
2627956
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2022-04-01 14:09:22.878784+00:00
2025-10-18 11:50:35.391990+00:00
2022-04-01 14:09:22.878784+00:00
NICEST-2 is the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools and it focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives. It builds on previous efforts within NICEST (a 3-year NeIC project as of 2017-01) and NordicESM (3-year NordForsk funded project from 2014-12).
NICEST2 activities include: 1) Enhance the performance and optimize and homogenize workflows used, so climate models (like EC-EARTH and NorESM) can be run in an efficient way on future computing resources (like EuroHPC); 2) Widen the usage and expertise on evaluating Earth System Models and develop new diagnostic modules for the Nordic region within the ESMValTool; 3)Create a roadmap for FAIRification of Nordic climate model data.
application/ld+json
https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034
EC-EARTH
HPC
NorESM
Nordic
climate
earth system modelling
esm
NeIC NICEST2 Project - snapshot
NeIC NICEST2 Project
MANUAL
Anne Foilloux, Hamish Struthers, Risto Makkonen, Oskar Landgren, Antti-Ilari Partanen, Elina Miinalainen, Jean Iaquinta, et al. "NeIC NICEST2 Project." ROHub. Apr 01 ,2022. https://doi.org/10.24424/chnf-4g76.
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Initial Collaboration agreement for the NICEST2 project
application/pdf
NICEST2 Collaboration Agreement
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Achievements of the NeIC NICEST2 project at the beginning of January 2022. This slide is part of a presentation that has been shown during the NeIC AHM22.
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NICEST2 project outcomes (24th January 2022)
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Business Case for the NICEST2 project.
application/pdf
NICEST2 Business Case
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The submitted project proposal for the NICEST2 project.
application/pdf
NICEST2 Project proposal (submitted)
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NICEST2 project plan. Please note that some changes may have been agreed during the course of the project.
application/pdf
Project Plan (as agreed initially)
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Earth System Modeling Tools
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Greenland
Budgets and budgeting
Economy, business and finance/Economy/Macro economics/Budgets and budgeting
project manager
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Weather
Weather
obligation
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data
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23.4
European Community
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Weather
Weather
Earth system model
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Project outcome
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Rivers
Environment/Natural resources/Water/Rivers
physical geography and environmental geoscience
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0.9382504224777222
diagnostic
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15.2
NICEST-2
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general
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NICESTThe objective of the NICEST project was to strengthen the Nordic ESM community by supporting the efficientuse of various e infrastructures through competence building, sharing and exchanging knowledge.
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3.4
Nordic collaboration
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8.3
Intergovernmental Panel on Climate Change
ESMValTool
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The Nordics and NeICWithin the Nordic ESM modeling community there is significant and sustained support for the concept of aNordic collaboration.
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Human resources
Economy, business and finance/Business information/Human resources
Oslo
Weather
Weather
Europe
NICEST-2 is the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools and it focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives.
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IS ENES network
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Science and technology
Science and technology
atmospheric sciences
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Environmental politics
Environment/Environmental politics
climate modelling community
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general (general)
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business and commercial law
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meteorology and climatology
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roadmap
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earth resources and remote sensing
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plan
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party s liability
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Strengthen the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoinginitiatives to enable a future joint Nordic Climate Model Intercomparison Project and Nordic Climate services toassist decision making.
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Non-fiction
Arts, culture and entertainment/Arts and entertainment/Literature/Non-fiction
Coupled Model Intercomparison Project
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data
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12.2
The Climate Community needs to learn and understand FAIR principles to be able to
create a roadmap for FAIRification of Nordic climate model data.
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trade
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In addition to the comprehensive experiments made
available to the community through CMIP, NorESM is also used in Norway to study present
and past climate states and variability ranging from seasonal to multi centennial time scales.
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environmental science and management
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work
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College
Education/School/Higher education/College
Finland
job market
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from 2014
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With
regard to one another, each partner bears responsibility for implementation of the duties and obligations
specified in this collaboration agreement and the project description specified for the partner.
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North of Sixty
politics
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Science and technology
Science and technology
software
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finance
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collaboration agreement
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NICEST2
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work
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It builds on previous efforts within NICEST (a 3-year NeIC project as of 2017-01) and NordicESM (3-year NordForsk funded project from 2014-12)
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partner
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geosciences
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Norway
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European Open Science Cloud (EOSC) Nordic aims at bridging e services in the Nordic region with EuropeanOpen Science Cloud (EOSC). The Nordic Climate Community is represented in EOSC Nordic by a few partners ofthe NICEST project.
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Norway
project manager
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climate
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Cicero
workflow
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computer science
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geosciences
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climate modeling data
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Environmental politics
Environment/Environmental politics
title project management
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Students
Education/Teaching and learning/Students
Sweden
The Climate Modelling community is an essential component of joint European efforts to
build a European framework of earth system modelling as part of the ENES/IS ENES
network (European Network for Earth System Science), through Horizon projects (e.g.
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Iceland
law
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climate Community
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The partners will sign necessary agreements with owners, employees (including individuals with dual
employment), partners, sub contractors, and others that are required to fulfil the relevant partner s
obligations under this agreement, including measures to ensure the necessary transfer of intellectual
property rights.
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community
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EC earth
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NeIC project
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climate
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earth sciences
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community
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The Climate Community needs to work on improving the performance and
optimizing/homogenizing workflows used, so that climate models (like EC EARTH and
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environmental sciences
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Earth System Grid Federation
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When appropriate, the
project owner enters into a separate agreement with the employer of the project manager in a way that
does not violate the terms of this agreement.
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project owner
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United States of America
Metropolitan Police
European Community
Danish, Finnish, Norwegian and Swedish modeling groups have
committed to participate in phase of CMIP (CMIP )
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Trondheim
the economy
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30.0
project data reference syntax
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roadmap for FAIRification
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climate model
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in december
National Security Council
steering group
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Environment
Environment
EC EARTH and NorESM model throughput on LUMI EC EARTH and NorESM are portable e.g. they can run on different HPC national providers and on Virtual machines (cloud computing). EC EARTH and NorESM can scale on new architectures (performance analysis numbers compared to initially on HPC national providers and LUMI when available). Number of users running EC EARTH and NorESM on their own national facilities.
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Norway
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Po River
Nordic
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Coupled Model Intercomparison Project
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Nordic
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earth sciences
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NeIC NICEST2 Project
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climate model
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Esgf system
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climate model data
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agreement
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If not defined otherwise in the project description the project
owner does not require the right to conclude a contract on behalf of the other partners through this
collaboration agreement.
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Commercial contract
Economy, business and finance/Business information/Strategy and marketing/Commercial contract
European Community
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NICEST2 activities include: 1) Enhance the performance and optimize and homogenize workflows used, so climate models (like EC-EARTH and NorESM) can be run in an efficient way on future computing resources (like EuroHPC); 2) Widen the usage and expertise on evaluating Earth System Models and develop new diagnostic modules for the Nordic region within the ESMValTool; 3)Create a roadmap for FAIRification of Nordic climate model data.
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NORCE (Norway)
algu@norceresearch.no
Alok Kumar Gupta
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
0000-0002-1784-2920
CSC (Finland)
elina.miinalainen@csc.fi
Elina Miinalainen
USIT, University of Oslo (Norway)
j.h.nordmoen@usit.uio.no
Jørgen Halvorsen Nordmoen
CSC (Finland)
kimmo.ervasti@csc.fi
Kimmo Ervasti
USIT, University of Oslo (Norway)
maikenp@usit.uio.no
Maiken Pedersen
Norwegian Meteorological Institute (Norway)
oyvind.seland@met.no
Øyvind Seland
NSC (Sweden)
pchengi@nsc.liu.se
Prashanth Dwarakanath
service-account-enrichment
NORCE (Norway)
tylo@norceresearch.no
Tyge Løvseth
Applied sciences
Earth sciences
Earth observation
10.13039/501100000781
European Commission
10.24424/DXFH-X940
https://doi.org/10.24424/dxfh-x940
2022-04-07 19:22:56.289678+00:00
2022-04-10 17:18:00.927096+00:00
Application of VSM to the M7.1 Van Earthquake (Turkey) of 2011
M 7.1 Van Earthquake (Turkey) 2011
2022-04-07 19:22:56.289678+00:00
10.24424/WESR-P505
https://doi.org/10.24424/wesr-p505
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Data modelling related to the 2021 eruption at Nyiragongo volcano (DR Congo) using VSM
Nyiragongo volcano (DR Congo) 22 May 2021 eruption
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https://github.com/EliTras/VSM
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Link to the GitHub repository with the VSM code
VSM code in GitHub
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https://github.com/EliTras/VSM_test
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Tests of VSM in GitHub using InSAR and GNSS data at Campi Flegrei caldera (Italy)
VSM tests in GitHub using InSAR and GNSS data at Campi Flegrei caldera (Italy)
2022-04-07 13:59:08.278250+00:00
101017502
Reliance
RESEARCH LIFECYCLE MANAGEMENT FOR EARTH SCIENCE COMMUNITIES AND COPERNICUS USERS
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Modelling of the 2011-2012 unrest at Santorini (Greece).
Santorini (Greece) 2011-2012 unrest
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False
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2025-10-18 11:49:47.795813+00:00
2022-04-07 13:12:37.521036+00:00
Volcanic and Seismic source Modelling (VSM) is an open source Python tool to model ground deformation detected by satellite and terrestrial geodetic techniques. The VSM tool allows the user to choose one or more geometrical sources as forward model among sphere, spheroid, ellipsoid, fault, and sill. It supports multiple datasets from most satellite and terrestrial geodetic techniques: interferometric SAR, GNSS, levelling, Electro-optical Distance Measuring, tiltmeters and strainmeters. Two sampling algorithms are available, one is a global optimization algorithm based on the Voronoi cells and the second follows a probabilistic approach to parameters estimation based on the Bayes theorem. VSM can be executed as Python script, in Jupyter Notebook environments or by its Graphical User Interface.
Version 1.0 April 2022.
For any inquires, please write to elisa.trasatti@ingv.it
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https://w3id.org/ro-id/e97e6ada-276b-4406-b2ee-d3ec36e096c3
SAR data
deformation modelling
geodetic data inversion
open science
seismic cycle
volcanic activity
Volcanic and Seismic source Modelling (VSM). The Python toolkit for modelling geodetic data - snapshot
Volcanic and Seismic source Modelling (VSM). The Python toolkit for modelling geodetic data
MANUAL
Trasatti, Elisa. "Volcanic and Seismic source Modelling (VSM). The Python toolkit for modelling geodetic data." ROHub. Apr 07 ,2022. https://doi.org/10.24424/t83f-5t97.
Figures
Examples
VSM_src
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image/gif
VSM logo
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2022-04-10 17:18:33.705787+00:00
License of use of VSM
License of use of VSM
2022-04-07 14:03:09.752317+00:00
492577
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image/png
figure2.png
2022-04-07 13:15:18.286359+00:00
sampling
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computer operations and hardware
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Software
Economy, business and finance/Economic sector/Computing and information technology/Software
Language
Arts, culture and entertainment/Culture/Language
algorithm
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VSM tool
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deformation
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earth sciences
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software
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computer programming
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algorithm
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Volcanic and Seismic source Modelling (VSM) is an open source Python tool to model ground deformation detected by satellite and terrestrial geodetic techniques.
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Python toolkit
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environment
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VSM
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geophysics
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geology
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source Modelling
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open source Python tool
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Volcanic and Seismic source Modelling (VSM). The Python toolkit for modelling geodetic data.
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toolkit
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optimisation
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computer science
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1.0 Apr-2022
soil
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Modelling
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The VSM tool allows the user to choose one or more geometrical sources as forward model among sphere, spheroid, ellipsoid, fault, and sill.
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satellite
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toolchain
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dataset
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mathematical and computer sciences
100.0
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ellipsoid
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optimization algorithm
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satellite
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deformation
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2022-04-07 13:25:22.921145+00:00
2022-04-10 17:17:58.948454+00:00
Modelling of InSAR and GNSS data at Campi Flegrei by VSM
Campi Flegrei Caldera (Italy) 2011-2013 unrest
2022-04-07 13:25:22.921145+00:00
service-account-enrichment
Earth sciences
10.13039/501100000781
European Commission
https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007EDEE6736861726547756964236335616136613735373432353332343532623632653533653738663732373439636834366632233732356634616233366362323664306662666330633132346337373565666565636865653439233435386236633362393566303966653363323935373631346461373539666330636839376163/content
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2023-05-16 18:07:35.516891+00:00
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupyter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - Applied over Spain and variable Nitrogen Dioxide
2022-05-01 20:18:43.754840+00:00
https://datahub.egi.eu/share/00d23664c695cb6ce4c3f0438b1778f5ch5ad3
2022-05-01 20:18:45.772814+00:00
2022-05-01 20:23:03.244378+00:00
Monthly average maps of CAMS Nitrogen Dioxide [µg m-3] over Spain in 2019, 2020 and 2021
Nitrogen Dioxide [µg m-3] over Spain for March 2019, 2020 and 2021
2022-05-01 20:18:45.772814+00:00
https://datahub.egi.eu/share/2a9a7f334fe6e73f55bf83595d9aef84ch59d8
2022-05-01 20:18:36.927892+00:00
2022-05-01 20:22:56.674576+00:00
This dataset is a data-Cube retrieved from the ADAM platform over Spain in March 2019
Data-Cube from ADAM platform over Spain in March 2019
2022-05-01 20:18:36.927892+00:00
https://datahub.egi.eu/share/35c15202651e9a56b5791ddd9897fb33chd796
2022-05-01 20:18:50.139193+00:00
2022-05-01 20:23:06.486372+00:00
netCDF data corresponding to daily average of CAMS Nitrogen Dioxide [µg m-3] over Spain for March 2019, March 2020 and March 2021
netCDF data for daily NO2over Spain in March 2019, 2020 and 2021
2022-05-01 20:18:50.139193+00:00
https://datahub.egi.eu/share/50b107f60f369ff2414e679f6e411575ch4e1a
2022-05-01 20:18:47.735278+00:00
2022-05-01 20:23:06.803802+00:00
Daily average maps of CAMS Nitrogen Dioxideµg m-3] over Spain on March 15, 2021
Nitrogen Dioxide [µg m-3] over Spain on March 15, 2021
2022-05-01 20:18:47.735278+00:00
https://datahub.egi.eu/share/d7fb646b024a1d1f8b285f4ad9f313f6che876
2022-05-01 20:18:39.077503+00:00
2022-05-01 20:22:59.301409+00:00
This dataset is a data-Cube retrieved from the ADAM platform over Spain in March 2020
Data-Cube from ADAM platform over Spain in March 2020
2022-05-01 20:18:39.077503+00:00
https://datahub.egi.eu/share/f79280441b0944a05a60c79f4cc3ef22che4a8
2022-05-01 20:18:41.097182+00:00
2022-05-01 20:22:59.545655+00:00
This dataset is a data-Cube retrieved from the ADAM platform over Spain in March 2021
Data-Cube from ADAM platform over Spain in March 2021
2022-05-01 20:18:41.097182+00:00
https://datahub.egi.eu/share/fa58b7afada92ccf75ff97d1db4c1febch82eb
2022-05-01 20:18:34.542298+00:00
2022-05-01 20:23:06.971009+00:00
Geojson file used for retrieving data from the ADAM platform over Spain
Geojson for Spain
2022-05-01 20:18:34.542298+00:00
UiO
jeani@uio.no
Jean Iaquinta
0000-0002-8763-1643
01xtthb56
University of Oslo
04jcwf484
Nordic e-Infrastructure Collaboration
101017502
RELIANCE
Research Lifecycle Management for Earth Science Communities and Copernicus Users
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False
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2022-05-01 20:15:42.899723+00:00
2025-10-18 11:43:25.244419+00:00
2022-05-01 20:15:42.899723+00:00
This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of NO2 over a given geographical area, here Spain
application/ld+json
https://w3id.org/ro-id/a369aaf0-06f7-441a-9a18-3b79b9d45f8e
CAMS
NO2
Spain
air quality
copernicus
jupyter-notebook
Jupyter Notebook Analysing the Air quality during Covid-19 pandemic using Copernicus Atmosphere Monitoring Service - Applied over Spain (March 2019, 2020, 2021) with Nitrogen Dioxide
NO2 (March 2019, 2020, 2021) in Spain Jupyter notebook demonstrating the usage of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - snapshot
MANUAL
Anne Foilloux, Jean Iaquinta, and Simone Mantovani. "Jupyter Notebook Analysing the Air quality during Covid-19 pandemic using Copernicus Atmosphere Monitoring Service - Applied over Spain (March 2019, 2020, 2021) with Nitrogen Dioxide." ROHub. May 01 ,2022. https://doi.org/10.24424/y6d7-b622.
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tool
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biblio
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Conda environment used on EGI notebook on 01/05/2022
Conda environment
2022-05-01 20:17:35.890038+00:00
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2022-05-01 20:23:02.036373+00:00
Monthly average maps of CAMS Nitrogen Dioxide [µg m-3] over Spain in 2019, 2020 and 2021
image/png
Nitrogen Dioxide [µg m-3] over Spain for March 2019, 2020 and 2021
2022-05-01 20:15:59.357772+00:00
103103
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2022-05-01 20:23:05.357691+00:00
Conda environment generated with conda-lock for osx-64
Conda environment osx-64
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Conda environment generated with conda-lock for linux-64
Conda environment linux-64
2022-05-01 20:17:42.168217+00:00
map
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Research Object
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This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of NO2 over a given geographical area, here Spain
71.87187187187187
71.8
air quality analysis
31.85595567867036
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Jupyter Notebook Analysing the Air quality during Covid-19 pandemic using Copernicus Atmosphere Monitoring Service - Applied over Spain (March 2019, 2020, 2021) with Nitrogen Dioxide.
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Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
0000-0002-1784-2920
mantovani@meeo.it
Simone Mantovani
Raul Palma
service-account-enrichment
Earth sciences
https://doi.org/10.5281/zenodo.6653258
2022-07-05 12:52:22.146283+00:00
2022-07-06 15:27:08.164714+00:00
Full SPL dataset is located in Zenodo
Full SPL dataset
2022-07-05 12:52:22.146283+00:00
https://notebooks.egi.eu/user/da47d3640f619a02cb075c15d288fc09e053bf46b90d26ec335392acdfae866b@egi.eu/doc/tree/datahub/Reliance/Soundscape/SPL_PostProcessing_HDF5.ipynb
2022-07-06 15:19:37.054357+00:00
2022-07-06 15:27:07.980852+00:00
Link to EGI Jupyer HUB: It allows to post process spl data and to create graphs/tables
Jupyter notebook for SPLs processing
2022-07-06 15:19:37.054357+00:00
https://underwaternoise.ices.dk/continuous
2022-07-05 12:42:32.932255+00:00
2022-07-06 15:27:05.398965+00:00
Continuous Noise Database (https://underwaternoise.ices.dk/continuous), 2022. ICES, Copenhagen
Format of input file
2022-07-05 12:42:32.932255+00:00
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False
2022-07-06 15:27:15.164812+00:00
14297708
https://api.rohub.org/api/ros/7b86ece5-b588-416b-9c98-30bb63a5b9bc/crate/download/
2021-12-13 16:00:05.573722+00:00
2025-10-18 11:31:55.908591+00:00
2021-12-13 16:00:05.573722+00:00
This RO provides the Jupyter notebook used to process the Sound Pressure Levels, SPL, data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded. SPL data were calculated from wav data recorded by Develogic SonoVault Hydrophones (https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501).
application/ld+json
https://w3id.org/ro-id/7b86ece5-b588-416b-9c98-30bb63a5b9bc
Underwater Noise, SPLs, Soundscape
Sound Pressure Levels Post Processing within the Soundscape project - snapshot
Sound Pressure Levels Post Processing within the Soundscape project
MANUAL
Petrizzo, Antonio, Fantina Madricardo, Marta Picciulin, and Michol Ghezzo. "Sound Pressure Levels Post Processing within the Soundscape project." ROHub. Dec 13 ,2021. https://doi.org/10.24424/tkqc-zr42.
data input
input
Jupyter notebooks here
notebook
Here some information
metadata
Here some results
results
5833087
https://api.rohub.org/api/resources/63076219-d95a-4ffa-be04-e8c9ead051b7/download/
2022-07-06 10:25:59.163869+00:00
2022-07-06 15:27:12.295310+00:00
Jupyter notebook for processing SPL data.
application/zip
Jupyter notebook for processing SPL data.
2022-07-06 10:25:59.163869+00:00
1095417
https://api.rohub.org/api/resources/7cdcd447-39c2-4d6c-8982-4e3b30a4e216/download/
2022-07-06 15:07:05.061088+00:00
2022-07-06 15:27:00.351667+00:00
image/png
workflowPostProcessing.png
2022-07-06 15:07:05.061088+00:00
4863608
https://api.rohub.org/api/resources/912d02f1-4c08-46ce-8d75-ea2f16520385/download/
2022-07-05 12:05:48.435779+00:00
2022-07-06 15:27:14.989305+00:00
Example of SPL input file. HDF5 format, according to to ICES (International Council for the Exploration of the Sea) continuous noise data portal specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx).
Example of SPL input file
2022-07-05 12:05:48.435779+00:00
1628249
https://api.rohub.org/api/resources/a124318c-b696-4515-a1a1-6b37818b6db0/download/
2022-07-05 12:44:30.318843+00:00
2022-07-06 15:27:04.720891+00:00
Map of stations with their coordinates
image/png
Stations map
2022-07-05 12:44:30.318843+00:00
3034992
https://api.rohub.org/api/resources/b0fadc73-e542-4e0e-be88-473cd883bbf9/download/
2022-07-05 12:33:28.328984+00:00
2022-07-06 15:27:09.185481+00:00
Some examples of output files
application/zip
Some examples of output files
2022-07-05 12:33:28.328984+00:00
http
8.760330578512397
5.3
data
20.729684908789388
12.5
information
11.074380165289256
6.7
Develogic SonoVault hydrophone
18.2548794489093
15.9
Ro
8.099173553719009
4.9
soundscapes in the North Adriatic sea
7.921928817451206
6.9
Newspaper
Arts, culture and entertainment/Mass media/Newspaper
Language
Arts, culture and entertainment/Culture/Language
Mar-2020 - Jun-2021
sound pressure level
10.447761194029852
6.3
http
8.955223880597016
5.4
Soundscape Project
12.603648424543948
7.6
SPL data
46.84270952927669
40.8
Biology
Science and technology/Natural science/Biology
sound pressure
10.24793388429752
6.2
This RO provides the Jupyter notebook used to process the Sound Pressure Levels, SPL, data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded.
42.08416833667335
42.0
Sound Pressure Levels Post Processing within the Soundscape project.
13.42685370741483
13.4
hydrophone
5.785123966942149
3.5
Jupyter notebook
12.769485903814262
7.7
Hardware
Economy, business and finance/Economic sector/Computing and information technology/Hardware
acoustics
16.379310344827587
3.8
soundscape
10.743801652892563
6.5
computer science
44.827586206896555
10.4
sound pressure level
10.082644628099173
6.1
earth sciences
100.0
0.6904757022857666
atmospheric sciences
100.0
0.6904757022857666
noise data
15.040183696900115
13.1
Adriatic Sea
8.264462809917354
5.0
AND
6.446280991735537
3.9
soundscape
11.111111111111112
6.7
database
21.982758620689655
5.1
data
20.49586776859504
12.4
SPL data were calculated from wav data recorded by Develogic SonoVault Hydrophones (https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501)
44.48897795591183
44.4
SPL
23.383084577114428
14.1
life sciences
100.0
0.35060247778892517
physics
16.810344827586206
3.9
sound pressure Levels Post
11.940298507462687
10.4
Adriatic Sea
life sciences (general)
100.0
0.35060247778892517
of 1 year
https://www.italy-croatia.eu/web/soundscape
2022-07-06 10:06:13.532986+00:00
2022-07-06 15:27:15.088234+00:00
EU-Interreg Italy-Croatia 2014/2020 – CBC Program (Contract number 10043643)
Soundscape Project
2022-07-06 10:06:13.532986+00:00
CNR ISMAR Venice
antonio.petrizzo@ve.ismar.cnr.it
Antonio Petrizzo
CNR ISMAR
fantina.madricardo@ve.ismar.cnr.it
Fantina Madricardo
CNR ISMAR
marta.picciulin@ve.ismar.cnr.it
Marta Picciulin
CNR ISMAR
michol.ghezzo@ve.ismar.cnr.it
Michol Ghezzo
service-account-enrichment
Earth sciences
CNR-ISMAR
valentina.grande@bo.ismar.cnr.it
Valentina Grande
0000-0002-3489-268X
linguistics
5.949367088607595
4.7
Everest
https://www.wikidata.org/wiki/Q513
experiment result
2.640845070422535
4.5
engineering
12.302526414166074
0.28509142994880676
system
7.56167894905479
23.6
seabed
3.172060237103492
9.9
bathymetry
11.476355247981544
19.9
Software
Economy, business and finance/Economic sector/Computing and information technology/Software
input file
2.5632809996795896
8.0
workflow
3.8128804870233894
11.9
hydrography
11.89873417721519
9.4
geophysics
16.368849689426614
0.4702737629413605
National Educational Television
https://www.wikidata.org/wiki/Q3873154
engineering
2.755527074655559
8.6
Maritime accident and incident
Disaster, accident and emergency incident/Accident and emergency incident/Transport accident and incident/Maritime accident and incident
OBIA template matching was applied to the seafloor backscatter mosaic in this area.
2.3778071334214004
3.6
software
3.4177215189873418
2.7
atmospheric sciences
23.085019608000973
0.6632279753684998
seafloor
2.7681660899653977
4.8
data
4.325536686959308
13.5
atmospheric sciences
26.820732092685684
0.77055424451828
Automatic detection of MLs targets from the bathymetry.
26.55217965653897
40.2
European Commission
https://www.wikidata.org/wiki/Q8880
Hardware
Economy, business and finance/Economic sector/Computing and information technology/Hardware
workflow
7.324106113033449
12.7
ASCII.txt data
2.640845070422535
4.5
early spring
survey
3.1141868512110724
5.4
geophysics
39.18410222507813
0.9080290794372559
study
3.5565523870554308
11.1
Hardware
Economy, business and finance/Economic sector/Computing and information technology/Hardware
technology
5.017301038062283
8.7
Aquaculture
Economy, business and finance/Economic sector/Agriculture/Aquaculture
target
6.286043829296425
10.9
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
seafloor survey method
2.640845070422535
4.5
earth sciences
23.085019608000973
0.6632279753684998
Venice
https://www.wikidata.org/wiki/Q641
Oceans
Environment/Natural resources/Water/Oceans
experiment
3.7487984620314
11.7
filed experiment
2.171361502347418
3.7
dedicated workflow
6.748826291079812
11.5
late winter
earth sciences
26.820732092685684
0.77055424451828
Geography
Science and technology/Social sciences/Geography
Mapping and recycling of marine litter and Ghost nets on the sea floor marGnet
5.416116248348744
8.2
earth sciences
33.72539860988673
0.9689239263534546
linguistics
3.670886075949367
2.9
OBIA template matching
2.347417840375587
4.0
ML type
2.464788732394366
4.2
targets from the bathymetry
5.1056338028169
8.7
ml detection
2.992957746478873
5.1
Medieval Latin
2.5953220121755844
8.1
metadata
2.242870874719641
7.0
Microsoft Corporation
https://www.wikidata.org/wiki/Q2283
automatic detection
12.38262910798122
21.1
methodology
2.306805074971165
4.0
mathematical and computer sciences
20.683121381477395
0.47929835319519043
seafloor backscatter mosaic
1.9953051643192488
3.4
Natural science
Science and technology/Natural science
data
2.7875680871515534
8.7
ml type
2.171361502347418
3.7
bathymetry
6.247997436718999
19.5
Report on the survey and elaborted data in the Italian survey area
2.3117569352708056
3.5
A dedicated workflow in ArcGIS was developed to identify targets from the bathymetry within the MAELSTROM Project - Smart technology for MArinE Litter SusTainable RemOval and Management
39.43196829590489
59.7
Science and technology
Science and technology
removal
4.901960784313725
8.5
hydrography
20.37974683544304
16.1
output file
2.4798154555940024
4.3
target
3.3963473245754563
10.6
The experiment data metadata are saved on an open repository (the data are available on request) and the workflow is executable so that the analysis is completely reproducible.
3.6327608982826947
5.5
marGnet field experiments
2.9342723004694835
5.0
earth resources and remote sensing
27.830249979278406
0.6449216604232788
Geography
Science and technology/Social sciences/Geography
ML
2.3644752018454436
4.1
instrumentation and photography
12.302526414166074
0.28509142994880676
bathymetry
3.1079782121115023
9.7
geosciences
39.18410222507813
0.9080290794372559
data
2.537485582468282
4.4
Interior
https://www.wikidata.org/wiki/Q608427
hydrography
6.8354430379746836
5.4
computer programming and software
20.683121381477395
0.47929835319519043
MAELSTROM Project
6.113033448673587
10.6
removal
2.5953220121755844
8.1
computer science
27.72151898734177
21.9
Venetia
https://www.wikidata.org/wiki/Q1243
Environmental pollution
Environment/Environmental pollution
marGnet
2.5951557093425603
4.5
During the experiment, it was possible to recognise a unique track for as many categories as possible of benthic Marine Litter (ML) and outline their sinking velocity.
4.359313077939234
6.6
Mountains
Environment/Natural resources/Land resources/Mountains
detection
3.973085549503364
12.4
POLYGON ((12.308206558227539 45.42613630538257, 12.307777404785156 45.42538332041329, 12.315845489501953 45.42345563312358, 12.32764720916748 45.42216043125598, 12.32764720916748 45.42327490906539, 12.323012351989746 45.423425512487384, 12.316746711730955 45.425835112600126, 12.31241226196289 45.424901404761854, 12.308206558227539 45.42613630538257))
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c949f07d-ca16-4ba8-a9dc-6107b6c4a10b
POLYGON ((12.308206558227539 45.42613630538257, 12.307777404785156 45.42538332041329, 12.315845489501953 45.42345563312358, 12.32764720916748 45.42216043125598, 12.32764720916748 45.42327490906539, 12.323012351989746 45.423425512487384, 12.316746711730955 45.425835112600126, 12.31241226196289 45.424901404761854, 12.308206558227539 45.42613630538257))
service-account-enrichment
False
https://w3id.org/ro-id/9e5d1919-6462-41e1-a4ad-b9e1140925ca
2022-08-30 14:30:08.228321+00:00
mailto:antonio.petrizzo@ve.ismar.cnr.it
31188694
https://api.rohub.org/api/ros/b7f139b2-b89b-494a-8687-8f3fc4aaae83/crate/download/
2022-05-09 10:55:28.053444+00:00
2024-03-05 12:17:05.924803+00:00
2022-05-09 10:55:28.053444+00:00
A dedicated workflow in ArcGIS was developed to identify targets from the bathymetry within the MAELSTROM Project - Smart technology for MArinE Litter SusTainable RemOval and Management
application/ld+json
https://w3id.org/ro-id/b7f139b2-b89b-494a-8687-8f3fc4aaae83
Bathymetry
Detectoin
Marine Litter
Automatic detection of MLs targets from the bathymetry - snapshot
Automatic detection of MLs targets from the bathymetry
MANUAL
https://w3id.org/ro-id/b7f139b2-b89b-494a-8687-8f3fc4aaae83/88844951-1ba7-4328-b24f-0aa64694e9f2
https://w3id.org/ro-id/00c84d9b-b697-4030-a341-fb88dab7ccca
https://w3id.org/ro-id/0c13c89c-69b7-492c-89eb-cd34fa9efdc0
https://w3id.org/ro-id/1ded7eae-cd36-4e9f-8096-fcf9495c4b8a
https://w3id.org/ro-id/6eaeecde-23be-423c-86a3-1edd1d139e31
https://w3id.org/ro-id/83668f79-3066-40ee-93ad-438a11f94261
https://w3id.org/ro-id/9a39c8f9-2b72-4d38-89af-76fe0fa4ea02
https://w3id.org/ro-id/a4da0792-fb1b-4e10-b566-c6f1d47e26d3
https://w3id.org/ro-id/c44c8239-1126-413d-878b-5ab40731da3b
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https://w3id.org/ro-id/12646096-25d4-40ab-8b2f-d9b29c4c8e57
https://w3id.org/ro-id/33e1d667-1fa8-46e2-b711-3182e2c79b37
https://w3id.org/ro-id/7460d904-05f9-4710-9d01-64358107cc50
https://w3id.org/ro-id/9374b0bc-740f-4abe-bb6f-86c042b206c0
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https://w3id.org/ro-id/a1ee4c7e-dee0-4422-9176-70c289b8ddeb
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https://w3id.org/ro-id/b05ec41f-e5e5-45d5-8957-17891e714f81
https://w3id.org/ro-id/bc1a37b6-03ee-4000-9f8a-8966cf1f5924
https://w3id.org/ro-id/c050e1ee-d288-4cea-bab6-26c978ade3a3
https://w3id.org/ro-id/d7842191-ae53-44bd-996c-6a56e6dc663c
https://w3id.org/ro-id/d9f3778f-9f3d-43e6-9e5a-36b75a4b96ac
https://w3id.org/ro-id/45b83962-460b-40a8-8bcb-de6e2ff1294a
https://w3id.org/ro-id/64ca15de-e2eb-465e-8469-0c28a6df89ef
Petrizzo, Antonio, Valentina Grande, Vanessa Moschino, and Fantina Madricardo. "Automatic detection of MLs targets from the bathymetry." ROHub. May 09 ,2022. https://doi.org/10.24424/w5qx-b223.
POLYGON ((12.308206558227539 45.42613630538257, 12.307777404785156 45.42538332041329, 12.315845489501953 45.42345563312358, 12.32764720916748 45.42216043125598, 12.32764720916748 45.42327490906539, 12.323012351989746 45.423425512487384, 12.316746711730955 45.425835112600126, 12.31241226196289 45.424901404761854, 12.308206558227539 45.42613630538257))
Here some results
Results
Related documents and resources
Document
Data input
Input
It contains Jupyter notebook
Notebooks
1747374
https://api.rohub.org/api/resources/09e78055-8aad-4a02-8c5d-2574c53c6011/download/
2022-05-09 10:57:07.579893+00:00
2022-08-30 14:29:55.484341+00:00
application/pdf
EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet official document with description of the application of the tool.
2022-05-09 10:57:07.579893+00:00
4845062
https://api.rohub.org/api/resources/13faac2d-213a-45de-8936-09c728df7396/download/
2022-05-09 10:55:40.290485+00:00
2022-08-30 14:30:06.537698+00:00
image/png
Marine Litter Identification from Bathymetry
2022-05-09 10:55:40.290485+00:00
726695
https://api.rohub.org/api/resources/20119b4f-da6f-4723-8c5c-0f3367af1ec6/download/
2022-05-09 10:57:14.645946+00:00
2022-08-30 14:29:53.745052+00:00
application/pdf
EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet official document with description of ROs developed within MarGnet Project.
2022-05-09 10:57:14.645946+00:00
http://gismarcloud.myqnapcloud.com:8080/share.cgi?ssid=d0c16040872e4a47aee5b6664873057f
2022-08-29 09:49:06.931474+00:00
2022-08-30 14:29:48.715389+00:00
Shape file with targets detected from bathymetry
Arcgis workflow output
2022-08-29 09:49:06.931474+00:00
3737442
https://api.rohub.org/api/resources/2fcce231-9d47-4ec7-98d0-f11ce56703b4/download/
2022-05-09 10:57:00.318622+00:00
2022-08-30 14:29:57.372213+00:00
application/pdf
EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet official document with description of the tool.
2022-05-09 10:57:00.318622+00:00
1361042
https://api.rohub.org/api/resources/410ea324-cdcb-42ba-b086-b7ccf0b585a1/download/
2022-05-09 10:55:56.304257+00:00
2022-08-30 14:30:08.149000+00:00
image/png
ArcGis workflow scheme
2022-05-09 10:55:56.304257+00:00
9034979
https://api.rohub.org/api/resources/7c020821-7dd4-443a-abb7-e4d578bf91a7/download/
2022-05-09 10:56:09.722854+00:00
2022-08-30 14:30:03.652469+00:00
image/png
Example of targets detection from bathymetry
2022-05-09 10:56:09.722854+00:00
10597825
https://api.rohub.org/api/resources/aa1513e8-fb5d-4e6d-a1a3-2d3eed682878/download/
2022-05-09 10:56:21.011021+00:00
2022-08-30 14:30:00.441795+00:00
image/png
Example of targets detection from bathymetry (zoom)
2022-05-09 10:56:21.011021+00:00
https://doi.org/10.3997/1873-0604.2012018
2022-05-09 10:57:24.554269+00:00
2022-08-30 14:29:49.853481+00:00
This paper presents a semi-automated method to recognize, spatially delineate and characterise morphometrically pockmarks at the seabed
Semi-automated characterisation of seabed pockmarks in the central North Sea
2022-05-09 10:57:24.554269+00:00
https://notebooks.egi.eu/user/da47d3640f619a02cb075c15d288fc09e053bf46b90d26ec335392acdfae866b@egi.eu/doc/tree/datahub/Reliance/MarGnet_ML/arcWorkflow.ipynb
2022-05-09 10:55:53.416434+00:00
2022-08-30 14:29:46.615503+00:00
This Notebook provides a workflow of ArcGis toolboxes to identify ML targets from bathynetry.
Marine Litter Targets Identification
2022-05-09 10:55:53.416434+00:00
http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/dd465b46-0217-426a-ba81-4acadf0d12b9
2022-05-31 11:56:50.978305+00:00
2022-08-30 14:29:46.697417+00:00
Bathymetry metadata description
Sacca Fisola, Venice, 2021 metadata description
2022-05-31 11:56:50.978305+00:00
https://reliance.rohub.org/overview?7bc38514-796e-47e6-81cb-b8f91247a854&activetab=overview
2022-05-09 10:57:20.433002+00:00
2022-08-30 14:29:49.671093+00:00
Track of a net from water coloumn data
RO created wihin EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet
2022-05-09 10:57:20.433002+00:00
http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/b9f63328-264e-4d28-94b7-397e50cf2dad
2022-05-31 16:19:48.804055+00:00
2022-08-30 14:29:41.142106+00:00
ArcGis workflow output metadata
ArcGis workflow output description
2022-05-31 16:19:48.804055+00:00
http://gismarcloud.myqnapcloud.com:8080/share.cgi?ssid=ccf2ae1e2ec14a848b8607fc268d8bea
2022-08-29 09:41:27.976951+00:00
2022-08-30 14:29:47.792135+00:00
Bathymetric data from Sacca Fisola 2021 survey
Sacca Fisola, Venice, 2021 data
2022-08-29 09:41:27.976951+00:00
https://reliance.rohub.org/overview?f8a252e5-47da-410b-9096-526bc50d19a3&activetab=overview
2022-05-09 10:57:22.558491+00:00
2022-08-30 14:29:41.054228+00:00
calculates the sink velocity of a net floating in water starting from water column data
RO created wihin EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet
2022-05-09 10:57:22.558491+00:00
workflow in ArcGIS
11.502347417840376
19.6
The methodology proposed by the marGnet project is to use acoustic and video remote sensing on a large scale to map ML on the seafloor and to model the ML hotspot through modelling.
5.3500660501981505
8.1
bathymetry data
2.288732394366197
3.9
Moreover, the deliverable provides a comparison with the data and efficiency of other seafloor survey methods.
2.708058124174372
4.1
output file
2.7875680871515534
8.7
technical terminology
3.7974683544303796
3.0
experiment
4.2099192618223755
7.3
Fishing
Lifestyle and leisure/Leisure/Recreational activities/Fishing
experiment data
2.112676056338028
3.6
information
2.1147068247356615
6.6
geosciences
27.830249979278406
0.6449216604232788
Synthetic and plastic chemicals
Economy, business and finance/Economic sector/Chemicals/Synthetic and plastic chemicals
Library and museum
Arts, culture and entertainment/Culture/Library and museum
marine litter
5.4209919261822375
9.4
MBES
2.306805074971165
4.0
The first operation block (Execute code) creates the work directory, downloads and extracts the input file, downloads and executes the Matlab code (vel) and finally compresses the output file in only one.zip file.
4.425363276089828
6.7
The so created Research Object is composed by two inputs (Workflow input ports in Fig.) two operation blocks (in light blue in Fig.) and one output (Workflow output ports in Fig.)
3.4346103038309113
5.2
software
4.177215189873418
3.3
Matlab code
2.0539906103286385
3.5
data
3.9215686274509802
6.8
experiment location
1.9953051643192488
3.4
detection of ml
10.856807511737088
18.5
study
2.3389939122076258
7.3
Venice
https://www.wikidata.org/wiki/Q641
detection
7.439446366782007
12.9
earth sciences
16.368849689426614
0.4702737629413605
computer science
12.151898734177216
9.6
oceanography
33.72539860988673
0.9689239263534546
bathymetry
2.7681660899653977
4.8
CNR ISMAR Venice
antonio.petrizzo@ve.ismar.cnr.it
Antonio Petrizzo
direttore@ismar.cnr.it
CNR-ISMAR
CNR ISMAR
fantina.madricardo@ve.ismar.cnr.it
Fantina Madricardo
CNR ISMAR Venice
vanessa.moschino@ve.ismar.cnr.it
Vanessa Moschino
Chemistry
service-account-enrichment
10.24424/mgch-7b29
False
https://w3id.org/ro-id/72dc783e-9ce2-474c-99c0-3969c014c523
2022-09-20 12:43:22.456882+00:00
https://orcid.org/0000-0003-2388-0744
5871
https://api.rohub.org/api/ros/b90bc0b8-2d26-4e0c-b255-c2399b52d45d/crate/download/
2022-01-19 19:48:03.265688+00:00
2024-03-05 12:17:12.449514+00:00
2022-01-19 19:48:03.265688+00:00
A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion.
application/ld+json
https://w3id.org/ro-id/b90bc0b8-2d26-4e0c-b255-c2399b52d45d
Carboxylic acids - snapshot
Carboxylic acids
MANUAL
alkyl
amino acid
anion
carboxyl
carboxylate
carboxylic acid
chemical formula
fatty acid
free radical
organic acid
protonation
earth sciences
Chemistry
Organic chemical
amino acid
anion
carboxyl group
carboxylic acid
fatty acid
group
organic acid
chemistry and materials
carboxylate anion
contain a carboxyl group
formula of a carboxylic acid
include the amino acids
protonation of a carboxylic acid
A carboxylic acid is an organic acid that contains a carboxyl group (C(O)OH) attached to an R-group.
Deprotonation of a carboxylic acid gives a carboxylate anion.
The general formula of a carboxylic acid is R?
chemistry
organic chemistry
Wolniewicz, Małgorzata. "Carboxylic acids." ROHub. Jan 19 ,2022. https://doi.org/10.24424/mgch-7b29.
Pictures
https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Carboxyl-3D-space-filling-labelled.png/300px-Carboxyl-3D-space-filling-labelled.png
2022-01-19 19:49:57.141598+00:00
2022-09-20 12:43:21.522882+00:00
image/png
300px-Carboxyl-3D-space-filling-labelled.png
2022-01-19 19:49:57.141598+00:00
https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Carboxylic-acid.svg/300px-Carboxylic-acid.svg.png
2022-01-19 19:49:17.752475+00:00
2022-09-20 12:43:20.971265+00:00
image/png
300px-Carboxylic-acid.svg.png
2022-01-19 19:49:17.752475+00:00
https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Carboxylate-resonance-hybrid.png/300px-Carboxylate-resonance-hybrid.png
2022-01-19 19:49:42.590787+00:00
2022-09-20 12:43:21.378826+00:00
image/png
300px-Carboxylate-resonance-hybrid.png
2022-01-19 19:49:42.590787+00:00
Chemistry
https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Carboxyl-3D-space-filling-labelled.png/300px-Carboxyl-3D-space-filling-labelled.png
2022-01-19 19:49:57.141598+00:00
2022-09-21 19:34:48.406794+00:00
image/png
300px-Carboxyl-3D-space-filling-labelled.png
2022-01-19 19:49:57.141598+00:00
https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Carboxylate-resonance-hybrid.png/300px-Carboxylate-resonance-hybrid.png
2022-01-19 19:49:42.590787+00:00
2022-09-21 19:34:47.123191+00:00
image/png
300px-Carboxylate-resonance-hybrid.png
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image/png
300px-Carboxylic-acid.svg.png
2022-01-19 19:49:17.752475+00:00
2022-10-29 10:59:49.415347+00:00
https://doi.org/10.24424/sc7x-ha64
False
2022-09-21 19:35:01.092825+00:00
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2022-01-19 19:48:03.265688+00:00
A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion.
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https://w3id.org/ro-id/6aa4b4a0-c7dc-4762-aee1-e8dc94a1705c
Carboxylic acids
MANUAL
Wolniewicz, Małgorzata, and Paweł Babalski. "Carboxylic acids." ROHub. Jan 19 ,2022. https://doi.org/10.24424/sc7x-ha64.
Pictures
organic acid
16.073781291172594
12.2
chemistry and materials
100.0
0.7690860033035278
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
carboxyl group
9.617918313570486
7.3
geochemistry
100.0
0.9274783134460449
protonation of a carboxylic acid
30.099228224917308
27.3
chemistry and materials (general)
100.0
0.7690860033035278
carboxylate anion
44.65270121278942
40.5
fatty acid
12.012644889357217
11.4
Chemistry
Science and technology/Natural science/Chemistry
carboxylate
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chemistry
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include the amino acids
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fatty acid
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free radical
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The general formula of a carboxylic acid is R−
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Deprotonation of a carboxylic acid gives a carboxylate anion.
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organic chemistry
33.18250377073907
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alkyl
3.898840885142255
3.7
amino acid
11.462450592885373
8.7
A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group.
54.858934169278996
35.0
earth sciences
100.0
0.9274783134460449
contain a carboxyl group
2.976846747519294
2.7
carboxyl
7.270811380400421
6.9
anion
7.246376811594202
5.5
protonation
5.163329820864067
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organic acid
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12.1
chemical formula
5.5848261327713375
5.3
formula of a carboxylic acid
19.294377067254686
17.5
carboxylic acid
26.027397260273972
24.7
carboxylic acid
32.01581027667984
24.3
Paweł Babalski
service-account-enrichment
Chemistry
chemistry and materials
100.0
0.7690860033035278
fatty acid
12.012644889357217
11.4
service-account-enrichment
https://doi.org/10.24424/k5t9-z972
False
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2022-01-19 19:48:03.265688+00:00
2024-03-05 12:17:12.569780+00:00
2022-01-19 19:48:03.265688+00:00
A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion.
application/ld+json
https://w3id.org/ro-id/0566d7df-d790-44bd-bcd1-fe89c0582a29
Carboxylic acids - snapshot
Carboxylic acids
MANUAL
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Wolniewicz, Małgorzata. "Carboxylic acids." ROHub. Jan 19 ,2022. https://doi.org/10.24424/k5t9-z972.
Pictures
https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Carboxylic-acid.svg/300px-Carboxylic-acid.svg.png
2022-01-19 19:49:17.752475+00:00
2022-09-21 19:38:00.155957+00:00
image/png
300px-Carboxylic-acid.svg.png
2022-01-19 19:49:17.752475+00:00
https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Carboxyl-3D-space-filling-labelled.png/300px-Carboxyl-3D-space-filling-labelled.png
2022-01-19 19:49:57.141598+00:00
2022-09-21 19:38:03.215000+00:00
image/png
300px-Carboxyl-3D-space-filling-labelled.png
2022-01-19 19:49:57.141598+00:00
https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Carboxylate-resonance-hybrid.png/300px-Carboxylate-resonance-hybrid.png
2022-01-19 19:49:42.590787+00:00
2022-09-21 19:38:02.473015+00:00
image/png
300px-Carboxylate-resonance-hybrid.png
2022-01-19 19:49:42.590787+00:00
include the amino acids
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Science and technology/Natural science/Chemistry
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carboxyl group
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chemistry
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carboxylic acid
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carboxylate
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formula of a carboxylic acid
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organic acid
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protonation of a carboxylic acid
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organic chemistry
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anion
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contain a carboxyl group
2.976846747519294
2.7
A carboxylic acid is an organic acid that contains a carboxyl group (C(O)OH) attached to an R-group.
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earth sciences
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chemical formula
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The general formula of a carboxylic acid is R?
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alkyl
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fatty acid
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Applied sciences
Earth sciences
https://discourse.pangeo.io/t/september-1-2022-handling-large-geo-data-with-julia/2656
2022-09-02 19:15:52.939627+00:00
2022-10-05 11:05:10.738946+00:00
You will find here all the information published to advertise the Pangeo Show & Tell Talk frm Felix Cremer on "Handling large geo data with Julia ".
Pangeo discourse post announcing 1st September Show & Tell by Felix Cremer.
2022-09-02 19:15:52.939627+00:00
https://github.com/JuliaDataCubes/ESDLTutorials
2022-09-02 19:36:28.455672+00:00
2022-10-05 11:05:08.571565+00:00
This will become a selection of tutorials on the use of ESDL.jl and YAXArrays.jl julia packages for the handling of large scale out-of-core geospatial datasets.
github
ESDLtutorial Github repository.
2022-09-02 19:36:28.455672+00:00
https://github.com/JuliaDataCubes/ESDLTutorials/raw/main/data/ne_50m_admin_0_countries.dbf
2022-09-02 19:27:25.914754+00:00
2022-10-05 11:04:59.380562+00:00
Part of ne_50m_admin_0_countries shapefile.
ne_50m_admin_0_countries.dbf
2022-09-02 19:27:25.914754+00:00
https://github.com/JuliaDataCubes/ESDLTutorials/raw/main/data/ne_50m_admin_0_countries.shp
2022-09-02 19:28:35.477795+00:00
2022-10-05 11:05:01.072396+00:00
Part of ne_50m_admin_0_countries shapefile.
application/x-qgis
ne_50m_admin_0_countries.shp
2022-09-02 19:28:35.477795+00:00
https://github.com/JuliaDataCubes/ESDLTutorials/raw/main/data/ne_50m_admin_0_countries.shx
2022-09-02 19:29:06.833916+00:00
2022-10-05 11:05:08.283815+00:00
Part of ne_50m_admin_0_countries shapefile.
application/x-qgis
ne_50m_admin_0_countries.shx
2022-09-02 19:29:06.833916+00:00
https://hackmd.io/@pangeo/showandtell
2022-09-20 12:05:09.775445+00:00
2022-10-05 11:05:12.569218+00:00
This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback.
Each Show and Tell has its own sub-section.
HackMD Pangeo Show and Tell
2022-09-20 12:05:09.775445+00:00
https://juliadatacubes.github.io/YAXArrays.jl/dev/
2022-09-02 19:18:10.607898+00:00
2022-10-05 11:05:10.000002+00:00
YAXArrays.jl is another xarray-like Julia package.
A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.
Package Features:
- open datasets from a variety of sources (NetCDF, Zarr, ArchGDAL)
- interoperability with other named axis packages through YAXArrayBase
- efficient mapslices(x) operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets)
YAXArrays.jl Documentation
2022-09-02 19:18:10.607898+00:00
https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/data/ne_50m_admin_0_countries.README.html
2022-09-02 19:23:40.734491+00:00
2022-10-05 11:05:10.091697+00:00
Admin 0 & Countries | Natural Earth
text/html
ne_50m_admin_0_countries.README.html
2022-09-02 19:23:40.734491+00:00
https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/data/ne_50m_admin_0_countries.VERSION.txt
2022-09-02 19:24:56.813174+00:00
2022-10-05 11:05:08.830771+00:00
Version
text/plain
ne_50m_admin_0_countries.VERSION.txt
2022-09-02 19:24:56.813174+00:00
https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/data/ne_50m_admin_0_countries.cpg
2022-09-02 19:26:00.758390+00:00
2022-10-05 11:05:10.411799+00:00
cpg file from shapefile dataset.
ne_50m_admin_0_countries.cpg
2022-09-02 19:26:00.758390+00:00
https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/data/ne_50m_admin_0_countries.prj
2022-09-02 19:27:59.472971+00:00
2022-10-05 11:05:12.806251+00:00
Part of ne_50m_admin_0_countries shapefile (projection information).
ne_50m_admin_0_countries.prj
2022-09-02 19:27:59.472971+00:00
https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/overallintro.ipynb
2022-09-02 19:19:48.682613+00:00
2022-10-05 11:05:09.458760+00:00
Jupyter Notebook used by Felix during the Pangeo Show & Tell to demonstrate how to use EarthDataLab.jl to do large scale computations.
To execute this Jupyter Notebook, data contained in the "input folder" is needed (please create a folder called "data" in the folder where you have stored the notebook).
How to use EarthDataLab.jl to do large scale computations (Jupyter Notebook)
2022-09-02 19:19:48.682613+00:00
04jcwf484
Nordic e-Infrastructure Collaboration
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2022-10-05 11:05:15.777066+00:00
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2022-09-02 19:02:01.731061+00:00
2024-03-05 12:18:33.627372+00:00
2022-09-02 19:02:01.731061+00:00
This talk is part of the Pangeo Show & Tell series and was given on September 1st 2022 by Felix Cremer.
Bio
Felix Cremer received his diploma in mathematics from the University of Leipzig in 2014. In 2016 he started his PhD study on time series analysis of hypertemporal Sentinel-1 radar data. He currently works at the Max-Planck-Institute for Biogeochemistry on the development of the JuliaDataCubes ecosystem in the scope of the NFDI4Earth 5 project.
Abstract
The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data. It is based on the YAXArrays.jl package. YAXArrays.jl provides functionality to deal with labelled arrays, similar to the xarray python package and it also provides efficient and easy multithreading and distributed computation of user defined functions along arbitrary slices of the data.
EarthDataLab.jl uses DiskArrays.jl in the backend to deal with out of memory datasets. In this Show-and-Tell Felix is going to give a short introduction into the EarthDataLab.jl package for raster data handling in Julia.
application/ld+json
https://w3id.org/ro-id/77a61d94-3318-4d33-a3c0-4730e7026fdb
geodata
julia
Video
Handling large geo data with Julia - snapshot
Handling large geo data with Julia
MANUAL
Felix Cremer, and Pangeo Europe. "Handling large geo data with Julia." ROHub. Sep 02 ,2022. https://doi.org/10.24424/2byf-7r07.
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output
tool
biblio
input
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Plot from the Julia Jupyter notebook.
image/png
plot_italy_julia_pangeo_ST.png
2022-09-02 19:30:37.195378+00:00
A community platform for Big Data geoscience
pangeo-europe@gmail.com
Pangeo
https://pangeo.io/
raster data
13.14031180400891
5.9
memory dataset
14.823008849557521
6.7
computer operations and hardware
100.0
0.9168391823768616
on Sep-1-2022
diploma
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4.1
In this Show-and-Tell Felix is going to give a short introduction into the EarthDataLab.jl package for raster data handling in Julia.
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time series
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YAXArrays.jl package
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other earth sciences
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The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data.
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dataset
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This talk is part of the Pangeo Show & Tell series and was given on September 1st 2022 by Felix Cremer.
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multithreading
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geo data
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YAXArrays.jl
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In 2016
Felix Cremer
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series analysis
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https://youtu.be/18_e8wmI9Os
2022-09-02 19:13:04.311770+00:00
2022-10-05 11:05:08.693363+00:00
This is the recorded talk from Felix Cremer during the Pangeo Show & Tell in September 1st, 2022. Felix is going through his Julia Notebook and explain us about handling large geo data with Julia.
Youtube video "Handling large geo data with julia by Felix Cremer."
2022-09-02 19:13:04.311770+00:00
Max-Planck-Institute (Germany)
fcremer@bgc-jena.mpg.de
Felix Cremer
pangeo.europe@gmail.com
Pangeo Europe
Applied sciences
Earth sciences
Earth observation
https://discourse.pangeo.io/t/discrete-global-grid-systems-dggs-use-with-pangeo/2274
2022-10-07 12:57:56.628114+00:00
2022-10-25 15:48:28.199436+00:00
Discussion from Pangeo Discourse on DGGS use with Pangeo.
discussion
Pangeo discourse on "Discrete Global Grid Systems (DGGS) use with Pangeo"
2022-10-07 12:57:56.628114+00:00
https://discourse.pangeo.io/t/october-6-2022-dggs-and-their-potential-impact-in-geoscience-and-geospatial-communities/2759
2022-10-25 15:45:23.831383+00:00
2022-10-25 15:48:40.680580+00:00
Pangeo discourse announcement.
discourse
Pangeo discourse announcement Show & Tell on
"October 6, 2022: DGGS and their potential impact in Geoscience and Geospatial communities"
2022-10-25 15:45:23.831383+00:00
https://github.com/allixender/pangeo_dggs_2022
2022-10-07 12:51:00.692996+00:00
2022-10-25 15:48:26.907094+00:00
Github repository with examples used during the Pangeo Show and Tell - 06. Oct., 2022 on "DGGS and their potential impact in Geoscience and Geospatial" by Alexander Kmoch (Landscape Geoinformatics Lab, University of Tartu, Estonia).
Twitter:
@Lgeoinformatics │ @allixender
jupyter notebook
Pangeo Show and Tell : DGGS play ground
2022-10-07 12:51:00.692996+00:00
https://hackmd.io/@pangeo/showandtell
2022-10-25 07:27:19.067533+00:00
2022-10-25 15:48:28.394740+00:00
This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback.
Each Show and Tell has its own sub-section.
hackmd
HackMD Pangeo Show and Tell
2022-10-25 07:27:19.067533+00:00
University of Tartu, Estonia
alexander.kmoch@ut.ee
Alexander Kmoch
0000-0003-4386-4450
https://raw.githubusercontent.com/allixender/pangeo_dggs_2022/main/environment.yml
2022-10-17 14:04:26.842481+00:00
2022-10-25 15:48:24.056839+00:00
Conda environment for running DGGS notebook examples.
environment
environment.yml
2022-10-17 14:04:26.842481+00:00
https://raw.githubusercontent.com/allixender/pangeo_dggs_2022/main/h3_intro.ipynb
2022-10-17 14:07:52.027331+00:00
2022-10-25 15:48:40.921243+00:00
Jupyter Notebook demonstrating how to perform Spatial Data Analysis with H3.
H3
h3_intro.ipynb
2022-10-17 14:07:52.027331+00:00
post@simula.no
00vn06n10
Simula Research Laboratory
A community platform for Big Data geoscience
pangeo-europe@gmail.com
Pangeo
https://pangeo.io/
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A Discrete Global Grid Systems (DGGS) is a unique type of spatial
reference system comprising of a hierarchy of uniquely identifiable
discrete grid cells that span the globe at multiple resolutions. A DGGS
can support efficient management, storage, integration, exploration,
mining, and visualisation of large geospatial datasets, and several
systems of tesselation and indexing schemes exist.
The main topic of this session is to introduce the audience to the
theoretical background of Discrete Global Grid Systems (DGGS), current
real-world implementations and exemplary use cases. This includes grid
generation, data indexing and sampling with DGGRID, and some spatial
analysis with with H3 and rHealPix.
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DGGS and their potential impact in Geoscience and Geospatial communities
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Kmoch, Alexander, and Pangeo Europe. "DGGS and their potential impact in Geoscience and Geospatial communities." ROHub. Oct 04 ,2022. https://doi.org/10.24424/tg01-kv33.
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Slides for the presentation on DGGS given during Pangeo Show and Tell October 6, 2022 by Alex Kmoch.
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DGGS and their potential impact in Geoscience and Geospatial (pdf presentation)
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A Discrete Global Grid System is a spatial reference system that uses a hierarchical tessellation of cells to partition and address the globe.
OGC Abstract Specification, 2017
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https://youtu.be/kkLRtyZtxs0
2022-10-25 07:25:21.367265+00:00
2022-10-25 15:48:20.916495+00:00
This YouTube video is part of the Pangeo Show & Tell series and was given on October 6 2022 by Alexander Kmoch, Department of Geography of the University of Tartu, (Estonia).
show&tell
youtube
YouTube video "DGGS and their potential impact in Geoscience and Geospatial communities"
2022-10-25 07:25:21.367265+00:00
pangeo.europe@gmail.com
Pangeo Europe
service-account-enrichment
Oceanography
Environmental research
Earth observation
https://210507-004.oceansvirtual.com/view/content/skdwP611e3583eba2b/ecf65c2aaf278557ad05c213247d67a54196c9376a0aed8f1875681f182daeed
2022-01-28 16:07:40.875698+00:00
2022-10-27 21:00:18.383109+00:00
Related publication of the modelling published in OCEANS 2021
Detecting macro floating objects on coastal water bodies using sentinel-2 data
2022-01-28 16:07:40.875698+00:00
https://doi.org/10.5194/isprs-annals-V-3-2021-285-2021
2022-01-28 16:07:43.339740+00:00
2022-10-27 21:00:10.761926+00:00
Publication with further details of the modelling published in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Towards detecting floating objects on a global scale with learned spatial features using sentinel 2
2022-01-28 16:07:43.339740+00:00
https://doi.org/10.5281/zenodo.5827376
2022-01-28 16:07:34.662177+00:00
2022-10-27 21:00:06.699231+00:00
Contains input analysis-ready input images used in the Jupyter notebook of Detecting floating objects using deep learning and Sentinel-2 imagery
Input images
2022-01-28 16:07:34.662177+00:00
https://doi.org/10.5281/zenodo.5911143
2022-01-28 16:07:38.160206+00:00
2022-10-27 21:00:18.581833+00:00
Contains outputs, (predictions and interactive figure), generated in the Jupyter notebook of Detecting floating objects using deep learning and Sentinel-2 imagery
Outputs
2022-01-28 16:07:38.160206+00:00
https://github.com/Environmental-DS-Book/ocean-modelling-litter-philab/blob/main/.binder/environment.yml
2022-01-31 11:32:03.379546+00:00
2022-10-27 21:00:11.773076+00:00
Conda environment when user want to have the same libraries installed without concerns of package versions
Conda environment
2022-01-31 11:32:03.379546+00:00
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2022-01-31 11:16:54.901085+00:00
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Lock conda file for linux-64 OS of the Jupyter Book hosted by the Environmental Data Science Book
Lock conda file for linux-64
2022-01-31 11:16:54.901085+00:00
https://github.com/Environmental-DS-Book/ocean-modelling-litter-philab/blob/main/.lock/conda-osx-64.lock
2022-01-31 11:16:56.332731+00:00
2022-10-27 21:00:06.870068+00:00
Lock conda file for osx-64 OS of the Jupyter Book hosted by the Environmental Data Science Book
Lock conda file for osx-64
2022-01-31 11:16:56.332731+00:00
https://github.com/Environmental-DS-Book/ocean-modelling-litter-philab/blob/main/.lock/requirements.txt
2022-01-31 11:27:45.283002+00:00
2022-10-27 21:00:07.441883+00:00
Pip requirements file containing libraries to install after conda lock
text/plain
Pip requirements for lock conda environments
2022-01-31 11:27:45.283002+00:00
https://github.com/Environmental-DS-Book/ocean-modelling-litter-philab/blob/main/ocean-modelling-litter-philab.ipynb
2022-01-28 16:07:32.857476+00:00
2022-10-27 21:00:10.913044+00:00
Jupyter Notebook hosted by the Environmental Data Science Book
Jupyter notebook
2022-01-28 16:07:32.857476+00:00
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2022-01-31 11:16:52.095424+00:00
2022-10-27 21:00:18.188755+00:00
Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book
text/html
Online rendered version of the Jupyter notebook
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The research object refers to the Detecting floating objects using deep learning and Sentinel-2 imagery notebook published in the Environmental Data Science book.
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Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book - snapshot
Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book
MANUAL
Raquel Carmo, Jamila Mifdal, and Alejandro Coca-Castro. "Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Jan 28 ,2022. https://doi.org/10.24424/xe24-7z73.
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Image showing the prediction of marine litter, sargassum, in Cancun, Mexico
image/png
Prediction of marine litter, sargassum in Cancun, Mexico
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Computational notebooks community focused on Environmental Data Science
environmental.ds.book@gmail.com
Environmental Data Science Book Community
https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose
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The Alan Turing Institute
Alejandro Coca-Castro
European Space Agency Φ-lab
Jamila Mifdal
European Space Agency Φ-lab
Raquel Carmo
service-account-enrichment
http://doi.org/10.1109/IGARSS47720.2021.9553499
2022-09-21 22:55:46.631043+00:00
2022-10-31 19:41:21.071847+00:00
Related publication of the exploration presented in the Jupyter notebook
Global land use / land cover with Sentinel 2 and deep learning
2022-09-21 22:55:46.631043+00:00
Geography
Environmental research
https://doi.org/10.5281/zenodo.7101976
2022-09-21 22:55:41.737294+00:00
2022-10-31 19:41:23.625491+00:00
Contains outputs, (figures and tables), generated in the Jupyter notebook of Exploring Land Cover Data (Impact Observatory)
Outputs
2022-09-21 22:55:41.737294+00:00
https://github.com/Environmental-DS-Book/general-exploration-landcover_io/tree/master/.binder/environment.yml
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Conda environment when user want to have the same libraries installed without concerns of package versions
Conda environment
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Contains input of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory) used in the Jupyter notebook of Exploring Land Cover Data (Impact Observatory)
Input of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory)
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Jupyter Notebook hosted by the Environmental Data Science Book
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Related publication of the exploration presented in the Jupyter notebook
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Impact Observatory - Methodology & Accuracy Summary
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University of Cambridge
aed58@cam.ac.uk
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environmental.ds.book@gmail.com
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Research Lifecycle Management for Earth Science Communities and Copernicus Users
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The H2020 Reliance project delivers a suite of innovative and interconnected services that extend European Open Science Cloud (EOSC)’s capabilities to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The project has delivered 3 complementary technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining.
RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work.
RoHub implements the full RO model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable.
In our presentation at the 1st international FAIR Digital Object Conference, we will showcase different types of ROs for the 3 Earth Science communities represented in Reliance to highlight how the scientists in our respective disciplines changed their working methodology towards Open Science.
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Anne Foilloux, Federica Foglini, and Elisa Trasatti. "FDO Conference 2022: FAIR Research Objects for realizing Open Science with RELIANCE EOSC project." ROHub. Oct 23 ,2022. https://doi.org/10.24424/nz65-v565.
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Presentation given by Anne Fouilloux during the FDO 2022 Conference at Leiden.
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Youtube Video: FAIR Research Objects for realising Open Science with RELIANCE EOSC Project
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Slides used by Anne Fouilloux for the presentation of FAIR Research Objects for realising Open Science with RELIANCE EOSC Project.
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Presentation given by Anne Fouilloux at FDO 2022 Conference (slides)
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https://doi.org/10.3897/rio.8.e93940
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Conference Paper for the 1st International Conference on FAIR Digital Objects, 26-28 October 2022, Leiden (Nederlands).
Open Science
Conference Abstract: FAIR Research Objects for realizing Open Science with RELIANCE EOSC project
2022-10-23 19:38:58.377108+00:00
https://www.fdo2022.org
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Website for the 1st International Conference on FAIR Digital Objects.
WebSite
1st International Conference on FAIR Digital Objects
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https://www.fdo2022.org/programme/leiden-declaration
2022-10-29 17:11:06.766028+00:00
2022-11-05 10:31:36.622775+00:00
FDO2022 will conclude with the formal signing and publication of the ‘Leiden Declaration on FAIR Digital Objects’, which text you can see below. While the signing will take place at the end of the conference, in Leiden, we would like to invite you to add your name to the movement initiated by FDO2022 by signing the declaration online. It is an opportunity for all of us working in research, technology, policy and beyond to support an unprecedented effort to further develop FAIR digital objects, open standards and protocols, and increased reliability and trustworthiness of data. In short, a new environment that works as a truly meaningful data space.
You can sign the declaration using the button at the end of the page. Join us!
Leiden Declaration on FAIR Digital Objects
2022-10-29 17:11:06.766028+00:00
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Picture taken during the closing ceremony of the FDO2022 Conference at Leiden.
image/jpeg
FDO2022 sketch
2022-10-29 17:18:54.123218+00:00
Picture taken during the final ceremony of the FDO 2022 Conference in Leiden.
FDO2022-sketch.jpg
https://youtu.be/w39xvNrqTR8
2022-10-23 20:53:49.501941+00:00
2022-11-05 10:31:32.348496+00:00
A short video to introduce the 3 RELIANCE services. RoHUB is a Research Object web portal to create and manage Research Objects. Text mining service aims at enriching Research Objects (AI service). And the ADAM platform is a datacube service that enables efficient access to large amount of Earth Observation data such as Copernicus Satellite observations.
Introduction to the 3 RELIANCE services
2022-10-23 20:53:49.501941+00:00
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Presentation given at ESA-NASA Open Innovation for EO Programmes 2022. This presentation gives an overview of the PAngeo Community and perspectives on creating Open Source user workflows.
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Perspective from the Pangeo Community on creating Open Source user workflows - ESA-NASA Open Innovation for EO Programmes 2022 - snapshot
Perspective from the Pangeo Community on creating Open Source user workflows - ESA-NASA Open Innovation for EO Programmes 2022
MANUAL
Anne Foilloux, and Pangeo Europe. "Perspective from the Pangeo Community on creating Open Source user workflows - ESA-NASA Open Innovation for EO Programmes 2022." ROHub. Nov 05 ,2022. https://doi.org/10.24424/pe96-gn27.
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2022-11-05 10:42:27.761506+00:00
2022-11-05 10:54:33.520392+00:00
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ThePangeoCommunity.pptx.png
2022-11-05 10:42:27.761506+00:00
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2022-11-05 10:54:34.343706+00:00
image/png
ThePangeoCommunity.pptx.png
2022-11-05 10:42:51.431306+00:00
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2022-11-05 10:54:34.934021+00:00
pdf presentation. Slides used to present the perspectives from the Pangeo Community on creating Open Source User Workflows. This presentation has been given by Anne Fouilloux at the ESA-NASA Open Innovation for EO Programmes 2022 (November 2-4 2022).
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2022-11-05 10:47:51.193900+00:00
NICEST-2 - the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives.
Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools
https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034
A community platform for Big Data geoscience
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https://pangeo.io/
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Presentation (slides) and demo (video) by Anne Fouilloux for the RELIANCE use case on Climate Change. The presentation and demo were given during the pan-europeans digital assets supporting research communities.
Agenda of the event:
On 5-6 December 2022, EOSC Future and the INFRAEOSC-07 projects (C-SCALE, DICE, EGI-ACE, OpenAIRE Nexus, Reliance) are hosting an online use case showcase. Check out the agenda and register for this online interactive event by 4 December, 23.59 CET.
Over 2 half-day webinars, actual EOSC users will present how their research communities are using EOSC digital assets to address scientific and societal challenges related to 3 UN Sustainable Development Goals (SDGs):
• Climate action (SDG 13)
• Industry, Innovation & infrastructure (SDG 9)
• Good health & well-being (SDG 3)
There will also be a session with use cases related to Open Science more broadly.
Why ‘use cases’?
The demonstrative, first-hand format of the event will enable real research communities to show how
their work can be leveraged by EOSC.
Researchers, disciplinary groups and anyone interested to learn about both EOSC-related tools and
services for data sharing and discoverability as well as discipline-related solutions are invited to the
webinar. Attendees will also hear first-hand accounts from early-adopter communities that have
integrated some of these core EOSC services.
1 programme, 2 days
Check out the agenda to get a glimpse of the cases in the programme, in addition to a at the users,
EU and UN officials who will be weighing in on discussions.
Day 1 – 5 December
• 09.30-11.00: Digital assets supporting SDG 13: Climate action
• 11.15-12.00: Digital assets supporting SDG 3: Good health and well-being
• 12:15-13:00: Discovering services for open science
Day 2 – 6 December
• 09.00-09.45: Digital assets supporting SDG 9 Industry, innovation and infrastructure
• 10.00-10.45: Experiences from Early Adopters approaching EOSC: the RELIANCE Open
challenge
• 11.00-12.00: Lessons Learnt from use cases and Looking forward
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deep learning
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Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC - snapshot
Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC
MANUAL
https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/1c144684-1ea3-4cb5-93e6-8cffecf3b849
Anne Foilloux, Jean Iaquinta, and Alejandro Coca-Castro. "Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC." ROHub. Dec 05 ,2022. https://doi.org/10.24424/xnz3-m908.
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2022-12-10 21:40:04.312065+00:00
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EOSC-Webinar.mp4
2022-12-10 21:40:02.993477+00:00
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2022-12-05 13:24:37.948951+00:00
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reproducible.png
2022-12-05 12:22:12.769408+00:00
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EOSC-Webinar.mp4
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2022-12-10 21:40:02.721239+00:00
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ClimateChange-EOSC-RELIANCE.pptx.pdf
2022-12-10 21:40:01.253499+00:00
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Demonstration given during the webinar. This demo goes with the presentation (slides) and show how the original work was published s a paper in nature communications. The code and data were available and Alejandro Coca-Castro re-used it to create an executable Research Object with a Jupyter Notebook. This Jupyter Notebook examplifies the use of IceNet (probabilistic deep learning to forecast sea-ice). This executable Research Object was forked and deviated work was created e.g. the Jupyter notebook was updated to make it more accessible to people that are not from the Climate community. We use B2DROP to store the new Jupyter notebook and the results to share while doing. Whenever we update the notebook or add figures, the corresponding Research Object is updated live on RoHub. We are now getting close to Open Science e.g. sharing while doing.
video/mp4
mp4
Demo showing the usage of RELIANCE service for seasonal sea-ice forecasting.
2022-12-05 12:33:43.590961+00:00
https://docs.google.com/presentation/d/1QPrWh-PuW514mGsVrc9GEIrKYGY7k0L_/edit?usp=sharing&ouid=117642930190987755261&rtpof=true&sd=true
2022-12-05 12:24:19.690502+00:00
2022-12-05 13:24:34.792481+00:00
Climate change: Collaborative, reproducible and transparent science for seasonal sea-ice forecasting.
Digital assets supporting SDG 13: Climate action
Climate change presentation (slides) from Google doc
2022-12-05 12:24:19.690502+00:00
378820
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2022-12-10 21:40:01.285478+00:00
2022-12-10 21:40:02.732497+00:00
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reproducible.png
2022-12-10 21:40:01.285478+00:00
10.24424/9y70-kv25
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2022-12-05 13:24:39.265968+00:00
Presentation (same as the google doc) but in pdf format.
application/pdf
Climate change presentation (slides) showing the usage of RELIANCE services.
2022-12-05 12:27:21.702898+00:00
The Alan Turing Institute
acoca@turing.ac.uk
Alejandro Coca-Castro
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
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This presentation has been given at the Data Managers Network meeting on Tuesday 22 November 2022. The topic of the meeting was "EOSC in practise" where different speakers gave their perspectives and experience with involvement in EOSC.
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Experiences with involvement in EOSC - snapshot
Experiences with involvement in EOSC
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Anne Foilloux, and admin NordicESMHub. "Experiences with involvement in EOSC." ROHub. Nov 21 ,2022. https://doi.org/10.24424/m77z-a405.
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1st slide of Anne Fouilloux's presentation.
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Slides for Anne Fouilloux's presentation at UiO Data Manager Meeting on EOSC in practise.
application/pdf
My experience with EOSC (Slides, pdf)
2022-11-21 14:44:35.309807+00:00
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Timeline with Anne Fouilloux's EOSC journey.
image/png
EOSC-journey.png
2022-11-21 14:42:56.411912+00:00
https://youtu.be/tz0OqxHvnbw
2022-11-21 14:52:01.754759+00:00
2022-12-11 18:54:52.704491+00:00
Demo for EOSC-Future: Turning FAIR and Open Science into Reality. The example shown is about the "impact of the Covid-19 Lockdown on Air quality over Europe using Copernicus and EOSC project services".
youtube
Turning FAIR and Open Science into Reality (demo, video)
2022-11-21 14:52:01.754759+00:00
https://eoscfuture.eu/newsfuture/answering-research-questions-with-eosc/
2022-11-21 15:36:19.837234+00:00
2022-12-11 18:54:52.850465+00:00
Answering research questions with EOSC,
March 10, 2022.
Climate Data Scientist Anne Fouilloux and her team were faced with a research question: In France, have there been changes in air quality over the course of the COVID-19 pandemic?
With the help of compute services available through EOSC, Anne was able to search for European air quality data analysis.
NAVIGATING EOSC
Check our infographic and follow Anne as she:
- searches for European air quality data via OpenAIRE|Explore;
- selects a software (an EOSC Jupyter notebook);
- orders the notebook on the EOSC marketplace;
- accesses and aggregates research from the RELIANCE project;
- performs data analysis with air quality data in France;
- shares a new research object (via a B2Drop folder).
Anne Fouilloux is answering research questions with EOSC.
2022-11-21 15:36:19.837234+00:00
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https://w3id.org/ro/terms/earth-science#WorkflowCentricResearchObjectTemplate
PETRIZZO, ANTONIO, Fantina Madricardo, Marta Picciulin, and Michol Ghezzo. "Soundscape project: Sound Pressure Levels Post Processing." ROHub. Dec 22 ,2022. https://doi.org/10.24424/hrhm-8849.
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workflows
biblio
datasets
results
inputs
https://notebooks.egi.eu/user/da47d3640f619a02cb075c15d288fc09e053bf46b90d26ec335392acdfae866b@egi.eu/doc/tree/datahub/Reliance/Soundscape/SPL_PostProcessing_HDF5.ipynb
2022-12-22 10:33:02.338126+00:00
2022-12-22 10:56:26.537631+00:00
Link to open the file in Notebooks, an environment based on Jupyter and the EGI cloud service: It allows to post process SPLs data and to create graphs/tables. A valid EGI account is required.
Jupyter notebook for SPLs processing
2022-12-22 10:33:02.338126+00:00
1111721
https://api.rohub.org/api/resources/240112e9-d3e7-4221-8ea9-fd98ea4a092a/download/
2022-12-22 09:46:43.417490+00:00
2022-12-22 10:56:32.759217+00:00
image/png
sketches.png
2022-12-22 09:46:43.417490+00:00
9146360
https://api.rohub.org/api/resources/2b2bbff5-3a54-42f5-9889-123bacb66828/download/
2022-12-22 10:00:20.583758+00:00
2022-12-22 10:56:34.505368+00:00
Example of SPLs input file. HDF5 format, according to to ICES (International Council for the Exploration of the Sea) continuous noise data portal specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx).
Example of SPLs input file
2022-12-22 10:00:20.583758+00:00
3034992
https://api.rohub.org/api/resources/551e4e13-b14a-42fe-b654-788dd2fa2220/download/
2022-12-22 10:04:21.100557+00:00
2022-12-22 10:56:35.519720+00:00
Some examples of output files
application/zip
Some examples of output files
2022-12-22 10:04:21.100557+00:00
912578
https://api.rohub.org/api/resources/9424b92f-ac08-4c1d-b702-49f33508a9ca/download/
2022-12-22 10:07:51.947557+00:00
2022-12-22 10:56:36.131713+00:00
Map of stations with their coordinates
image/png
Stations map
2022-12-22 10:07:51.947557+00:00
5832938
https://api.rohub.org/api/resources/94c1d183-c6a0-44db-8ec7-262cb699e822/download/
2022-12-22 10:24:50.681134+00:00
2022-12-22 10:56:37.830882+00:00
The Jupyter notebook used to post process SPLs data and to create graphs/tables.
application/zip
Jupyter notebook for processing SPLs data.
2022-12-22 10:24:50.681134+00:00
https://doi.org/10.5281/zenodo.7472152
2022-12-22 09:55:32.356553+00:00
2022-12-22 10:56:21.474116+00:00
20 and 60 seconds SPLs dataset
Full SPLs dataset
2022-12-22 09:55:32.356553+00:00
https://underwaternoise.ices.dk/continuous
2022-12-22 10:06:10.843847+00:00
2022-12-22 10:56:25.348517+00:00
Continuous Noise Database (https://underwaternoise.ices.dk/continuous), 2022. ICES, Copenhagen
Format of input file
2022-12-22 10:06:10.843847+00:00
https://www.italy-croatia.eu/web/soundscape
2022-12-22 10:07:12.320583+00:00
2022-12-22 10:56:21.257593+00:00
EU-Interreg Italy-Croatia 2014/2020 – CBC Program (Contract number 10043643)
Soundscape Project
2022-12-22 10:07:12.320583+00:00
of 1 year
impact
5.103448275862069
3.7
computer science
13.559322033898306
2.4
soundscapes in the North Adriatic sea
29.855715871254162
26.9
noise data
41.620421753607104
37.5
http
6.620689655172414
4.8
continuo
7.310344827586207
5.3
resource
4.827586206896552
3.5
Language
Arts, culture and entertainment/Culture/Language
soundscape
16.413793103448278
11.9
year of continuo
8.213096559378469
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acoustics
46.89265536723165
8.3
physics (general)
100.0
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earth sciences
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0.41819459199905396
Mar-2020 - Jun-2021
Biology
Science and technology/Natural science/Biology
Jupyter notebook
16.104294478527606
10.5
atmospheric sciences
100.0
0.41819459199905396
Ro
9.517241379310345
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Adriatic Sea
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SPL
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sound pressure levels
17.53607103218646
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Hardware
Economy, business and finance/Economic sector/Computing and information technology/Hardware
soundscape
19.32515337423313
12.6
data
13.957055214723926
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information
11.448275862068968
8.3
Newspaper
Arts, culture and entertainment/Mass media/Newspaper
antonio.petrizzo@cnr.it
ANTONIO PETRIZZO
direttore@ismar.cnr.it
CNR-ISMAR
CNR ISMAR
fantina.madricardo@ve.ismar.cnr.it
Fantina Madricardo
CNR ISMAR
marta.picciulin@ve.ismar.cnr.it
Marta Picciulin
CNR ISMAR
michol.ghezzo@ve.ismar.cnr.it
Michol Ghezzo
Environmental research
Applied sciences
https://agu.confex.com/agu/fm22/meetingapp.cgi/Session/165616
2022-12-09 18:50:29.467565+00:00
2022-12-23 17:57:00.777346+00:00
Open science communities are pushing the boundaries of how we approach scientific research. With advancements in computing, software, and data management, the tools are available to transform science into a truly open, collaborative, and inclusive space. By following open science practices, we can increase accessibility of scientific research and findings, improve collaboration, and facilitate high quality, reproducible science.
This session will showcase success stories in the Earth and space sciences and highlight a range of open science platforms, datasets, and computational tools. Join this session for real-world examples of how open science practices have empowered and enabled scientists across disciplines to carry out successful research projects.
session
ED16B - Open Science Practices and Success Stories Across the Earth, Space, and Environmental Sciences IV Oral
2022-12-09 18:50:29.467565+00:00
jeani@uio.no
Jean Iaquinta
0000-0002-8763-1643
post@simula.no
00vn06n10
Simula Research Laboratory
https://w3id.org/ro-id/18269477-c1b8-4aa8-9b0e-372c7bb6b65c
2022-12-08 08:54:10.991302+00:00
2022-12-23 17:56:59.163701+00:00
This research object is a fork from RO examplifying Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. Its main purpose is to show how to make derivative work and keep all the history of contributions and contributors.
Sea ice forecasting using IceNet (Jupyter Notebook) forked from the Environmental Data Science book
2022-12-08 08:54:10.991302+00:00
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POINT (-87.62586593977177 41.875270331922245)
https://doi.org/10.24424/qkna-rz18
False
2022-12-23 17:57:11.722765+00:00
28405593
https://api.rohub.org/api/ros/871a1786-bc6a-4e60-a160-3f57e3869d35/crate/download/
2022-12-08 08:34:52.580430+00:00
2024-03-05 12:16:52.989585+00:00
2022-12-08 08:34:52.580430+00:00
This Research Object aggregates all the different Research Objects and resources used for presenting the Environmental Data Science Book at AGU 2022.
The Environmental Data Science book is a living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science.
The Environmental Data Science is:
a book
a community
a global collaboration
We target to make sense of:
environmental systems
environmental data and sensors
innovative research in Environmental Data Science
open-source tools for Environmental Data Science
We hope you find the content in the resource helpful.
The resource and executable notebooks are free under a CC-BY licence and OSI-approved MIT license, respectively.
application/ld+json
https://w3id.org/ro-id/871a1786-bc6a-4e60-a160-3f57e3869d35
open science
reproducible
sea-ice
Video
AGU 2022 - Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science - snapshot
AGU 2022 - Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science
MANUAL
Anne Foilloux, Alejandro Coca-Castro, Environmental Data Science Book Community, Jean Iaquinta, Tom Andersson, Nick Barlow, and . Scott Hosking. "AGU 2022 - Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science." ROHub. Dec 08 ,2022. https://doi.org/10.24424/qkna-rz18.
POINT (-87.62586593977177 41.875270331922245)
150689
https://api.rohub.org/api/resources/71b9b61c-81d7-4da7-bb03-72fbec48e993/download/
2023-01-17 14:37:57.030511+00:00
2023-01-17 14:37:58.189501+00:00
image/png
agu22-presentation_EnvDSBook-2.png
2023-01-17 14:37:57.030511+00:00
150689
https://api.rohub.org/api/resources/7a88f5ae-4de7-438f-8f10-25ba9f3736a2/download/
2022-12-22 16:28:38.705015+00:00
2022-12-23 17:57:10.622032+00:00
image/png
agu22-presentation_EnvDSBook-2.png
2022-12-22 16:28:38.705015+00:00
29440246
https://api.rohub.org/api/resources/89f226c9-2b9b-4921-a3a5-e9f24bf82b64/download/
2022-12-22 16:33:09.605035+00:00
2022-12-23 17:57:11.424898+00:00
Recording of the presentation given at AGU2022.
video/mp4
mp4
AGU presentation (recorded video)
2022-12-22 16:33:09.605035+00:00
29440246
https://api.rohub.org/api/resources/bd41569e-528c-4c05-b870-b405f2f30f9b/download/
2023-01-17 14:37:58.375885+00:00
2023-01-17 14:37:59.850179+00:00
video/mp4
DSEnvBook-AGU2022.mp4
2023-01-17 14:37:58.375885+00:00
https://w3id.org/ro-id/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef
2022-12-08 08:42:58.948034+00:00
2022-12-23 17:57:01.031243+00:00
Research Object demonstrating sea ice forecasting using IceNet. The corresponding Jupyter Notebook has been published in the Environmental Data Science book.
jupyter book
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book
2022-12-08 08:42:58.948034+00:00
Computational notebooks community focused on Environmental Data Science
environmental.ds.book@gmail.com
Environmental Data Science Book Community
https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose
software
12.612612612612613
1.4
research in Environmental Data Science
15.866084425036389
10.9
MIT license
17.903930131004365
12.3
community-driven resource
11.353711790393012
7.8
research
15.39855072463768
8.5
environmental science and management
100.0
0.9688456058502197
resource
15.191740412979351
10.3
executable notebook
31.732168850072778
21.8
computer science
87.38738738738738
9.7
resource
10.507246376811594
5.8
notebook
7.669616519174042
5.2
tool
10.914454277286136
7.4
publication of data
23.14410480349345
15.9
documentation and information science
100.0
0.30886638164520264
surface
6.48967551622419
4.4
book
10.471976401179942
7.1
Biology
Science and technology/Natural science/Biology
data
9.734513274336283
6.6
notebook
9.420289855072463
5.2
This Research Object aggregates all the different Research Objects and resources used for presenting the Environmental Data Science Book at AGU 2022.
41.69230769230769
27.1
aim
7.079646017699115
4.8
Research Object
13.22463768115942
7.3
license
7.374631268436579
5.0
Book industry
Economy, business and finance/Economic sector/Media/Book industry
tool
13.22463768115942
7.3
research
17.84660766961652
12.1
data
12.13768115942029
6.7
publication
7.227138643067848
4.9
Environmental Data Science
26.08695652173913
14.4
The Environmental Data Science book is a living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science.
23.692307692307693
15.4
We target to make sense of:
environmental systems
environmental data and sensors
innovative research in Environmental Data Science
open-source tools for Environmental Data Science
34.61538461538461
22.5
social and information sciences
100.0
0.30886638164520264
environmental sciences
100.0
0.9688456058502197
Environment
Environment
The Alan Turing Institute
acoca@turing.ac.uk
Alejandro Coca-Castro
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
0000-0002-1784-2920
Global
environmental.ds.book@gmail.com
Environmental Data Science Book Community
The Alan Turing Institute
Nick Barlow
he British Antarctic Survey
Tom Andersson
jask@bas.ac.uk
. Scott Hosking
service-account-enrichment
Applied sciences
https://docs.google.com/presentation/d/1jxxwEGLiyuqYRJlH5VswekPJVoe5oxjlxVQrEiOojfI/edit?usp=sharing
2023-01-08 20:47:05.281382+00:00
2023-02-19 13:17:27.238629+00:00
Poster on experiences from the NICEST2 project for NeIC All hands meeting 2023.
google-doc
Poster on our experiences from the NICEST2 project
2023-01-08 20:47:05.281382+00:00
https://doi.org/10.5281/zenodo.4749515
2023-01-08 21:06:32.125399+00:00
2023-02-19 13:17:19.315916+00:00
Report on the bottlenecks that would hinder the efficient usage of Nordic ESMs on EuroHPC and possible remediation actions (I/Os, adding GPU support, etc.) with clear information on costs in terms of manpower.
ESMs used in the Nordic countries are clearly not ready for EuroHPC and very little dedicated funding from the scientific community is used for porting existing codes to future architectures. Providers have hired several specialists to support the scientific community but the commitment from the scientific community is not there.
Exchange of knowledge of involved staff (scientists, RSEs, technical support) would be very helpful. For instance, being able to organize meetings/hackathons (online or face to face) with both experts from NorESM and EC-EARTH has been highlighted as an important requirements by those involved in the GPU hackathon.
Code refactoring and best software practices are the most important component for efficient usage of new architecture, including EuroHPC.
NICEST2 - D4.5: First report on the identified bottlenecks for an efficient usage of Nordic ESMs on EuroHPC
2023-01-08 21:06:32.125399+00:00
https://doi.org/10.5281/zenodo.4944686
2023-01-08 21:03:28.636733+00:00
2023-02-19 13:17:22.369015+00:00
This report summarizes the first NICEST2 hackathon with FAIR experts and Earth System Model specialists to understand what needs to be done to make climate data FAIR. It will help us to define our roadmap for FAIR Climate in the Nordics.
NICEST2 - D3.3: Report on NICEST2 FAIR climate data hackathon
2023-01-08 21:03:28.636733+00:00
https://doi.org/10.5281/zenodo.5571344
2023-01-08 21:08:01.968412+00:00
2023-02-19 13:17:27.394767+00:00
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system Models (ESMs) that is not widely used in the Nordics yet. A hackathon/workshop was held on March 12, 2021 as a joint event between the INES, NICEST2 and IS-ENES3 projects. During this hackathon, we identified the needs for specific diagnostics for the Nordics that could help researchers to diagnose strengths and deficiencies of current ESMs. We also discussed how to better organize access to data and share resources within the Nordics.
NICEST2 - D2.1: Short report from the Nordic ESM diagnostics hackathon
2023-01-08 21:08:01.968412+00:00
https://doi.org/10.5281/zenodo.5571416
2023-01-08 21:10:14.737755+00:00
2023-02-19 13:17:27.065885+00:00
The Nordic climate modeling community consists of research groups at universities, national meteorological institutes and research institutes, and holds demonstrable world class excellence in the field. Several of these groups contribute to the development of both global and regional climate models (GCMs and RCMs,respectively) in international projects with collaborations within Europe and the US. However, many users, including PhDs and postdocs, are developing and/or running Earth System Models (ESMs) for more fundamental scientific research and sensitivity studies, and/or cross-disciplinary research (economy & climate, biodiversity, etc.) and they do not always benefit from the advances, technologies or resources leveraged by these large projects/consortiums. One concrete example is IS-ENES project (https://is.enes.org/) where only one university, namely Linköpings Universitet (not part of the NICEST2 consortium) from the Nordics is involved; Nordic contributions are mostly from Meteorological services and Research Institutes. From a practical point of view this translates in a lot of time/energy wasted “reinventing the wheel”, repeated simulations, lack of transparency, suboptimal use of the infrastructures, etc. In this context, supporting these researchers and realizing the benefits of Open Science and EOSC are our priorities within NICEST2 and WP4.
NICEST2 - D4.1: Identification of the Nordic ESM community needs for ESM workflows
2023-01-08 21:10:14.737755+00:00
https://nordicesmhub.github.io/NorESM_user_workshop_2021/intro.html
2023-01-08 20:50:23.537300+00:00
2023-02-19 13:17:27.609612+00:00
Training material on containers for ESM. This training material uses the Norwegian Earth System Model (NorESM) and has been delivered in 2021 as part of the NorESM user meeting.
text/html
training
Running NorESM in a container
2023-01-08 20:50:23.537300+00:00
https://nordicesmhub.github.io/nicest2-fair-hackathon/
2023-01-08 20:52:04.176203+00:00
2023-02-19 13:17:18.829914+00:00
First NICEST2 hackathon to understand the FAIR concept and how they can apply to the Nordic Earth System Modelling Community.
NICEST2 hackathon on FAIR climate data
2023-01-08 20:52:04.176203+00:00
https://nordicesmhub.github.io/nicest2/2020/05/04/plan.html
2023-01-09 08:28:36.495799+00:00
2023-02-19 13:17:20.174631+00:00
Link to the NICEST2 project plan.
text/html
Project plan
2023-01-09 08:28:36.495799+00:00
Finnish Meteorological Institute (Finland)
antti-ilari.partanen@fmi.fi
Antti-Ilari Partanen
0000-0002-0883-8161
Simula Research Laboratory
annef@simula.no
Anne Fouilloux
0000-0002-1784-2920
NSC (Sweden)
struthers@nsc.liu.se
Hamish Struthers
0000-0002-4214-2213
NERSC (Norway)
yanchun.he@nersc.no
Yanchun He
0000-0002-5932-3627
Finnish Meteorological Institute (Finland)
tommi.bergman@fmi.fi
Tommi Bergman
0000-0002-6133-2231
Norwegian Meteorological Institute (Norway)
oskaral@met.no
Oskar Landgren
0000-0002-6264-8502
jeani@uio.no
Jean Iaquinta
0000-0002-8763-1643
Finnish Meteorological Institute (Finland)
risto.makkonen@fmi.fi
Risto Makkonen
0000-0002-8961-3393
post@simula.no
00vn06n10
Simula Research Laboratory
04jcwf484
Nordic e-Infrastructure Collaboration
8fc9a20e-fa82-43a8-93f4-cd4cc4a45ac8
POINT (10.138547627793743 61.47037998202813)
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POINT (10.138547627793743 61.47037998202813)
https://doi.org/10.24424/9y3x-hg89
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2023-02-19 13:17:31.060596+00:00
763258
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2023-01-08 20:41:20.579173+00:00
2024-03-05 12:18:19.992972+00:00
2023-01-08 20:41:20.579173+00:00
Overview of the NICEST2 project and reflection on the successes, failures and possible improvements for follow-up projects.
application/ld+json
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climate
e-infrastructure
Poster
Experience from the NeIC NICEST2 project - NeIC All-Hands meeting (23–26 Jan 2023)
Experience from the NeIC NICEST2 project - snapshot
MANUAL
Iaquinta, Jean, Oskar Landgren, Alok Kumar Gupta, Prashanth Dwarakanath, Anne Fouilloux, Tommi Bergman, Tyge Løvseth, et al. "Experience from the NeIC NICEST2 project - NeIC All-Hands meeting (23–26 Jan 2023)." ROHub. Jan 08 ,2023. https://doi.org/10.24424/9y3x-hg89.
POINT (10.138547627793743 61.47037998202813)
This folder contains NICEST2 deliverables.
deliverables
Folder containing training material developed and delivered within the NICEST2 projects (either as training or hackathons).
training
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2023-01-08 20:45:29.648781+00:00
2023-02-19 13:17:29.630057+00:00
Overall view of the NICEST2 poster for the NeIC all-hands meeting. It is mostly used for the sketch.
image/png
NICEST2 poster for NeIC AHM2023.png
2023-01-08 20:45:29.648781+00:00
771062
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2023-02-23 21:50:58.608624+00:00
image/png
NICEST2 poster for NeIC AHM2023.png
2023-02-23 21:50:55.971537+00:00
771062
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2023-02-19 13:17:30.657154+00:00
image/png
NICEST2 poster for NeIC AHM2023.png
2023-01-17 14:38:23.066910+00:00
NICEST-2 - the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives.
Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools
https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034
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26 Jan 2023). Overview of the NICEST2 project and reflection on the successes, failures and possible improvements for follow-up projects.
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Experience from the NeIC NICEST2 project - NeIC All-Hands meeting (23–
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overview of the NICEST2 project
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NORCE (Norway)
algu@norceresearch.no
Alok Kumar Gupta
CSC (Finland)
elina.miinalainen@csc.fi
Elina Miinalainen
CSC (Finland)
kimmo.ervasti@csc.fi
Kimmo Ervasti
USIT, University of Oslo (Norway)
maikenp@usit.uio.no
Maiken Pedersen
Norwegian Meteorological Institute (Norway)
oyvind.seland@met.no
Øyvind Seland
NSC (Sweden)
pchengi@nsc.liu.se
Prashanth Dwarakanath
service-account-enrichment
NORCE (Norway)
tylo@norceresearch.no
Tyge Løvseth
Environmental research
Applied sciences
Earth sciences
https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007E46B7736861726547756964236161643239616133666234633734356464393231356539663536613733616366636836643138233732356634616233366362323664306662666330633132346337373565666565636865653439236361386634383464346533366532646439643230336131383431616362656563636834393661/content
2023-01-08 19:37:15.986538+00:00
2023-02-19 13:22:45.129102+00:00
Data at the Acqua Alta oceanographic tower is a collection of physical and biogeochemical observation in the northern Adriatic Sea https://www.comune.venezia.it/it/content/3-piattaforma-ismar-cnr http://www.ismar.cnr.it/infrastrutture/piattaforma-acqua-alta
PTF dataset(2009-2020) Piattaforma acqua allta
2023-01-08 19:37:15.986538+00:00
https://doi.org/10.1016%2Fj.marpolbul.2021.112124
2023-01-08 19:24:00.526730+00:00
2023-02-19 13:22:53.090742+00:00
Reduction in the impact of human-induced factors is capable of enhancing the environmental health. In view of COVID-19 pandemic, lockdowns were imposed in India. Travel, fishing, tourism and religious activities were halted, while domestic and industrial activities were restricted. Comparison of the pre- and post-lockdown data shows that water parameters such as turbidity, nutrient concentration and microbial levels have come down from pre- to post-lockdown period, and parameters such as dissolved oxygen levels, phytoplankton and fish densities have improved. The concentration of macroplastics has also dropped from the range of 138 ± 4.12 and 616 ± 12.48 items/100 m2 to 63 ± 3.92 and 347 ± 8.06 items/100 m2. Fish density in the reef areas has increased from 406 no. 250 m−2 to 510 no. 250 m−2. The study allows an insight into the benefits of effective enforcement of various eco-protection regulations and proper management of the marine ecosystems to revive their health for biodiversity conservation and sustainable utilization.
Reef fish
covid-19
environmental health
plastic pollution
COVID-19 lockdown improved the health of coastal environment and enhanced the population of reef-fish
2023-01-08 19:24:00.526730+00:00
https://earthobservatory.nasa.gov/images/83394/parting-the-sea-to-save-venice
2023-01-08 19:58:47.516622+00:00
2023-02-19 13:22:55.402245+00:00
The natural-color Landsat images above show some of the MOSE engineering efforts that are visible above the water line near the Lido Inlet. The top image was acquired on June 20, 2000, by the Enhanced Thematic Mapper+ on Landsat 7. The second image, from the Operational Land Imager on Landsat 8, was collected on September 4, 2013. Turn on the image comparison tool to make the changes easier to see. (Note that Landsat 8 has a greater dynamic range than Landsat 7, so the Landsat 8 image is crisper the Landsat 7 image.)
Parting the Sea to Save Venice
2023-01-08 19:58:47.516622+00:00
giorgio.castellan@bo.ismar.cnr.it
Giorgio Castellan
0000-0001-6084-1504
Simula Research Laboratory
annef@simula.no
Anne Fouilloux
0000-0002-1784-2920
federica.foglini@ismar.cnr.it
Federica Foglini
0000-0002-2736-0052
jeani@uio.no
Jean Iaquinta
0000-0002-8763-1643
CNR-ISMAR
malek.belgacem@ve.ismar.cnr.it
Malek Belgacem
0000-0003-0745-4155
Małgorzata Wolniewicz
https://reliance.adamplatform.eu/?dataset=69623:EU_CAMS_SURFACE_NO2_G
2023-01-08 19:40:14.176174+00:00
2023-02-19 13:22:52.387115+00:00
CAMS NITROGEN DIOXIDE
2022-12-27T23:00:00Z
NO2
CAMS European air quality forecasts: NO2
2023-01-08 19:40:14.176174+00:00
2018-07-12T00:00:00Z
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mailto:govoni@meeo.it
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2023-01-08 19:41:17.789149+00:00
2023-02-19 13:22:49.529744+00:00
CAMS OZONE
2022-12-27T23:00:00Z
O3
CAMS European air quality forecasts: O3
2023-01-08 19:41:17.789149+00:00
2018-07-12T00:00:00Z
Float32
mailto:govoni@meeo.it
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https://reliance.adamplatform.eu/?dataset=69627:EU_CAMS_SURFACE_PM25_G
2023-01-08 19:42:25.690080+00:00
2023-02-19 13:22:51.053690+00:00
CAMS SURFACE PARTICULATE METTER D<2.5
2022-12-27T23:00:00Z
PM2.5
CAMS European air quality forecasts: PM25
2023-01-08 19:42:25.690080+00:00
2018-07-12T00:00:00Z
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mailto:govoni@meeo.it
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post@simula.no
00vn06n10
Simula Research Laboratory
https://w3id.org/ro-id/0869e396-3733-4aff-8fb2-94c8937b28aa
2023-01-08 19:15:20.212877+00:00
2023-02-19 13:22:55.556680+00:00
This is a case study of snapshot project http://snapshot.cnr.it/ to investigate the lockdown impact on the water quality at a selected site in the northern Adriatic Sea, precisely in Northern Adriatic Sea, the case of the Gulf of Venice using Machine Learning model.
Snapshot 2021 study case: Lockdown impacts on the Northern Adriatic Sea at selected site: AcquaAlta Platform Water quality
2023-01-08 19:15:20.212877+00:00
https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1
2023-01-08 19:14:03.311972+00:00
2023-02-19 13:22:50.947789+00:00
The COVID-19 pandemic has led to significant reductions in economic activity, especially during lockdowns. Several studies has shown that the concentration of nitrogen dioxyde and particulate matter levels have reduced during lockdown events. Reductions in transportation sector emissions are most likely largely responsible for the NO2 anomalies. In this study, we analyze the impact of lockdown events on the air quality using data from Copernicus Atmosphere Monitoring Service over Europe and at selected locations.
Impact of the Covid-19 Lockdown on Air quality over Europe
2023-01-08 19:14:03.311972+00:00
https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1/resources/2a2b6f01-be2e-414e-af08-d882aa995a71
2023-01-08 19:21:48.221333+00:00
2023-02-19 13:22:50.794426+00:00
In order to fight against the spread of COVID-19, the most hard-hit countries in the spring of 2020 implemented different lockdown strategies. To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2, SO2, CO, PM2.5 and PM10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown. This work shows the reduction of primary pollutants, especially NO2, is mainly due to lockdown policies. However, preexisting local environmental policy regulations also contributed to declining NO2, SO2 and PM2.5 emissions, especially in Asian countries. In addition, higher rainfall during the lockdown period could cause decline of PM2.5, especially in Johannesburg. By contrast, the changes of AQI in ground-level O3 were not significant in most of cities, as meteorological variability and ratio of VOC/NOx are key factors in ground-level O3 formation.
Impact of the COVID-19 Pandemic Lockdown on Air Quality Pollution in 20 Major cities around the World
2023-01-08 19:21:48.221333+00:00
https://w3id.org/ro-id/c2c64bf9-7625-4442-9ca9-dcd978b1d38b
2023-01-08 19:19:35.675216+00:00
2023-02-19 13:22:48.215320+00:00
Integration of data on Air and Water quality in the Venice Lagoon to assess the impact of the Covid-19 Lockdown
Impact of the Covid-19 Lockdown on Air and Water quality in the Venice Lagoon
2023-01-08 19:19:35.675216+00:00
The main interest is upon marine litter pollution and in particular ranging from marco to mirco and nano size. In encompasses data from citizen science monitoring and sampling activities in cooperation with research-educational institute and centers.
segreteria@plasticfreevenice.org
Marine Litter and plastics pollution
NICEST-2 - the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives.
Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools
https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034
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2023-01-08 18:47:51.996769+00:00
In this study, we focusing on understanding changes in air and water quality during the Covid-19 lockdown in the Venice Lagoon. We are re-using existing Research Objects, and in particular Jupyter Notebooks that were created in previous studies.
application/ld+json
https://w3id.org/ro-id/eec6faaa-e133-47d4-b377-44f7d06a9654
air
water
Jupyter Notebook
Changes in air and water quality during the Covid-19 Lockdown in the Venice Lagoon - snapshot
Changes in air and water quality during the Covid-19 Lockdown in the Venice Lagoon
MANUAL
https://w3id.org/ro/terms/earth-science#ExecutableResearchObjectTemplate
Fouilloux, Anne, Federica Foglini, Giorgio Castellan, Malek Belgacem, Jean Iaquinta, and Simone Mantovani. "Changes in air and water quality during the Covid-19 Lockdown in the Venice Lagoon." ROHub. Jan 08 ,2023. https://doi.org/10.24424/656f-rf51.
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The goal is to compare values of NO2 water quality before and during the covid-19 lockdown.
water
NO2 water quality in the Venice lagoon between March-June 2019 and 2020.
2023-01-08 20:25:23.565475+00:00
63516
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2023-02-19 13:23:06.267913+00:00
Bar plot showing NO2 averaged between March and June for 2019 and 2020. The goal is to compare values before and during the covid-19 lockdown.
NO2
NO2 Copernicus Air Quality forecasts for March-June 2019-2020
2023-01-08 20:22:56.960407+00:00
46709
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2023-01-08 19:35:05.569853+00:00
2023-02-19 13:23:04.921504+00:00
Dataset shows monthly values and error bars.
image/png
Water quality in the Venice Lagoon between 2010 and 2020.
2023-01-08 19:35:05.569853+00:00
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Changes in air and water quality during the Covid-19 Lockdown in the Venice Lagoon.
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Keywords: COVID ; AQI; lockdown policy; major cities; NO ; PM . ; ozone
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Brazil
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pollutant
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Tokyo
https://zenodo.org/record/7513765/files/NO2_EUROPE_ADAMAPI2019-03-01_2021-06-30.nc
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2023-02-19 13:22:54.308417+00:00
NO2 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube.
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NO2 CAMS over Europe March-June 2019, 2020 and 2021
2023-01-08 19:38:36.937507+00:00
mantovani@meeo.it
Simone Mantovani
Raul Palma
service-account-enrichment
Applied sciences
Climatology
https://doi.org/10.1525/collabra.35903
2022-10-14 12:43:21.299002+00:00
2023-02-19 13:45:14.242980+00:00
This paper is from Gisela H. Govaart, Simon M. Hofmann, Evelyn Medawar.
Ever-increasing anthropogenic greenhouse gas emissions narrow the timeframe for humanity to mitigate the climate crisis. Scientific research activities are resource demanding and, consequently, contribute to climate change; at the same time, scientists have a central role in advancing knowledge, also on climate-related topics. In this opinion piece, we discuss (1) how open science – adopted on an individual as well as on a systemic level – can contribute to making research more environmentally friendly, and (2) how open science practices can make research activities more efficient and thereby foster scientific progress and solutions to the climate crisis. While many building blocks are already at hand, systemic changes are necessary in order to create academic environments that support open science practices and encourage scientists from all fields to become more carbon-conscious, ultimately contributing to a sustainable future.
climate crisis
open science
sustainability
The Sustainability Argument for Open Science
2022-10-14 12:43:21.299002+00:00
https://doi.org/10.5281/zenodo.6589624
2022-10-14 13:44:35.094465+00:00
2023-02-19 13:45:14.339103+00:00
Sharan, Malvika
In this talk, I discuss open science as a framework to ensure that all our research components can be easily accessed, openly examined and built upon by others. I will introduce The Turing Way - an open source, open collaboration and community-driven guide to reproducible, ethical and inclusive data science and research. Drawing insights from the project, I will share best practices that researchers should integrate to ensure the highest reproducible and ethical standards from the start of their projects so that their research work is easy to reuse and reproduce at all stages of the development. All attendees will leave the talk understanding the many dimensions of openness and how they can participate in an inclusive, kind and inspiring open source ecosystem as they collaboratively seek to improve research culture. All questions and contributions are welcome at the GitHub repository: https://github.com/alan-turing-institute/the-turing-way.
Home page: https://malvikasharan.github.io/
This was a closing keynote at Concordia University in Montreal on 27 May 2022.
Open science for enabling reproducible, ethical and collaborative research: Insights from The Turing Way
2022-10-14 13:44:35.094465+00:00
https://en.wikipedia.org/wiki/Climate_justice
2022-10-15 08:03:53.413292+00:00
2023-02-19 13:45:22.058293+00:00
Definition of Climate Justice from Wikipedia.
wikipedia
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2022-10-15 08:03:53.413292+00:00
https://en.wikipedia.org/wiki/Environmental_justice
2022-10-15 08:02:56.218937+00:00
2023-02-19 13:45:11.910714+00:00
Definition of Environmental Justice from Wikipedia
wikipedia
Environmental Justice (Wikipedia)
2022-10-15 08:02:56.218937+00:00
Simula Research Laboratory
annef@simula.no
Anne Fouilloux
0000-0002-1784-2920
jeani@uio.no
Jean Iaquinta
0000-0002-8763-1643
post@simula.no
00vn06n10
Simula Research Laboratory
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OpenAIRE OAWeek - Research communities & climate action; being open to drive change
Description
This session will invite expert researchers to discuss their scientific impact in climate related topics. Why are they embracing open science practices in their current research workflows? How are they using the resources provided in the European Open Science Cloud (EOSC)? Is open access enabling researchers to contribute better to Climate Change solutions? If you’re curious about these innovative initiatives by the research communities, make sure you attend!
Speakers:
Anne Fouilloux (RELIANCE)
Anabela de Oliveira (EGI-ACE)
Bjorn Backeberg (C-SCALE)
Prof Spyridon Rapsomanikis, Athena RC, NEANIAS
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climate justice
open science
Presentation
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OpenAIRE OAWeek: Open for Climate Justice
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Fouilloux, Anne, Jean Iaquinta, and Pangeo Europe. "OpenAIRE OAWeek: Open for Climate Justice." ROHub. Oct 14 ,2022. https://doi.org/10.24424/pm9w-vq46.
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2023-02-23 21:51:16.814390+00:00
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2023-02-23 21:51:15.334939+00:00
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Photo by Markus Spiske on Unsplash.
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unsplash license
one_world_markus_spiske.jpg
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Slides used by Anne Fouilloux to present her work on Open Science and the link to Climate Justice.
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Open Science & Climate Justice: every little helps
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environmental.ds.book@gmail.com
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https://www.earthdata.nasa.gov/learn/backgrounders/environmental-justice
2022-10-25 15:36:55.221574+00:00
2023-02-19 13:45:21.937342+00:00
NASA data are being used to support environmental and climate justice efforts as highlighted in several use cases showing how scientists and decision-makers are applying a wide combination of datasets to assess the vulnerability and exposure of communities to environmental challenges.
climate change
climate justice
Environmental Justice at NASA
2022-10-25 15:36:55.221574+00:00
https://www.openaire.eu/oaweek2022
2022-10-25 19:28:42.371901+00:00
2023-02-19 13:45:22.163052+00:00
OpenAIRE participates in the International Open Access Week - Open for Climate Justice
24 - 30 October 2022
announcement
programme
OpenAIRE participates in the International Open Access Week - Open for Climate Justice
2022-10-25 19:28:42.371901+00:00
https://youtu.be/oHE0aD2JQ-k
2022-10-14 12:35:41.182975+00:00
2023-02-19 13:45:14.008945+00:00
This session on "Justice and Climate Change" has been held online during the CESM Workshop 2022.
Agenda:
- Jola Ajibade: "Understanding the complexity of Climate justice and Climate Change";
- Laura Landrum: "SEARCH - Study of Environmental Arctic Change Program";
- Yifan Cheng: "Informing Climate and Land Surface Model Decisions with Indigenous Guidance";
- Panel discussion with speakers.
discussion
Justice and Climate Change Cross Working Group - 2022 CESM Workshop Day 2
2022-10-14 12:35:41.182975+00:00
pangeo.europe@gmail.com
Pangeo Europe
service-account-enrichment
Applied sciences
Climatology
Anne Fouilloux
University of Freiburg, Freiburg (Germany)
bjoern.gruening@gmail.com
Björn Grüning
0000-0002-3079-6586
01xtthb56
University of Oslo
04jcwf484
Nordic e-Infrastructure Collaboration
Docker for Galaxy Pangeo notebook from official Pangeo image.
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This Jupyter Docker container is used by the Galaxy Project. It is based on Pangeo notebook docker image (https://github.com/pangeo-data/pangeo-docker-images) and contained a few additional packages required for Galaxy (to exchange data, etc.).
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pangeo
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Docker for Galaxy Pangeo notebook from official Pangeo image
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Copernicus Atmosphere Monitoring Service PM2.5, 2 day forecasts, 24th December 2021 at 12:00 UTC
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CAMS PM2.5, 2 day forecasts, 24th December 2021 at 12:00 UTC
2022-03-30 16:49:24.424570+00:00
https://training.galaxyproject.org/training-material/topics/climate/tutorials/pangeo-notebook/tutorial.html
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2023-02-19 13:49:56.427998+00:00
Training material (hands-on) where Pangeo Notebook is used to learn Xarray. This training is part of the Galaxy Training Network (GTN).
In this tutorial, we will learn about Xarray, one of the most used Python library from the Pangeo ecosystem.
We will be using data from Copernicus Atmosphere Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. Parallel data analysis with Pangeo is not covered in this tutorial.
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Pangeo Notebook in Galaxy - Introduction to Xarray (GTN)
2022-03-30 15:59:56.246391+00:00
https://quay.io/repository/nordicesmhub/docker-pangeo-notebook
2022-03-29 11:58:28.213223+00:00
2023-02-19 13:49:55.588074+00:00
These docker images (different tags) correspond to the docker images built for Galaxy Pangeo JupyterLab.
The docker images can be used within Galaxy and as standalone docker images.
You can use the same images we use in Galaxy on your local computer or any other platform:
1. Pull an existing image locally
docker pull quay.io/nordicesmhub/docker-pangeo-notebook
2. Run a pre-build image from docker registry
3. To start your JupyterLab:
docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook
and you will top open a new terminal and start your favorite web browser.
your running Jupyter Notebook instance on http://localhost:7777/ipython/.
Remark: for reproducibility purpose, we suggest you use a specific tag e.g.
docker pull quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b
Then use the same tag when starting your JupyterLab application:
docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b
Docker images for Galaxy Pangeo JupyterLab (Quay Container Registry)
2022-03-29 11:58:28.213223+00:00
https://doi.org/10.5281/zenodo.5805953
2022-03-30 16:52:19.796786+00:00
2023-02-19 13:49:55.345103+00:00
Dataset used in the Galaxy Pangeo tutorials on Xarray.
Data is in netCDF format and is from Copernicus Air Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset.
netCDF input file PM2.5 4 days forecast from December, 22 2020
2022-03-30 16:52:19.796786+00:00
10.5281/zenodo.6394185
https://doi.org/10.5281/zenodo.6399102
2022-03-29 17:55:05.034625+00:00
2023-02-19 13:49:56.124648+00:00
This is a tarball for the Docker Galaxy pangeo-JupyterLab image - Version 1c0f66b.
To use it:
download the image file docker-pangeo-notebook-1c0f66b.tar
load it with docker with the command: docker load --input docker-pangeo-notebook-1c0f66b.tar
launch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b
start your favorite web browser and go to: http://localhost:7777/ipython/
See https://github.com/NordicESMhub/docker-pangeo-notebook for more details
Docker Galaxy pangeo-JupyterLab image Version 1c0f66b
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Default Jupyter Notebook used when starting Galaxy Climate JupyterLab if no other Jupyter Notebook is passed by the user.
Default Jupyter Notebook for Galaxy Climate JupyterLab
2022-03-29 12:08:38.781608+00:00
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This is a gif animated image showing how to start the Galaxy Pangeo JupyterLab in Galaxy Europe. In this video, we pass an input file (this file will be imported in the Jupyter Notebook /import folder).
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How to start Galaxy Pangeo JupyterLab (gif animated)
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This is the Galaxy Pangeo JupyterLab tool wrapper used by Galaxy to start the Galaxy Pangeo JupyterLab on a Galaxy instance.
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Galaxy Pangeo JupyterLab Tool wrapper (xml)
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https://github.com/NordicESMhub/docker-pangeo-notebook
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This github repository contains all the sources required for building the docker containers that are made available in Quay Container Registry.
Source code for building the docker container (github repository)
2022-03-29 12:01:31.834492+00:00
https://jupyterlab.readthedocs.io/en/stable/
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Link to the online JupyterLab documentation.
JupyterLab Documentation
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2022-02-06 17:02:59.243925+00:00
2023-03-15 15:29:32.025227+00:00
Contains outputs, (regridded data and figures), generated in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data
Outputs
2022-02-06 17:02:59.243925+00:00
https://edsbook.org/gallery/exploration/urban-exploration-climate_ukv/urban-exploration-climate_ukv.html
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Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book
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Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book
Lock conda file for osx-64
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https://github.com/eds-book-gallery/urban-exploration-climate_ukv/blob/main/.lock/conda-win-64.lock
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Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book
Lock conda file for win-64
2023-03-11 23:00:41.147309+00:00
https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f
2022-02-06 17:03:00.565055+00:00
2023-03-15 15:29:15.911084+00:00
Related publication of the sensors presented in the Jupyter notebook
Met office and partners offer data and compute platform for covid-19 researchers
2022-02-06 17:03:00.565055+00:00
https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/metoffice_ukv_daily/t1o5m_mean/ukv_daily_t1o5m_mean_20150801.nc
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Contains input gridded data used in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data
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2022-02-06 17:02:56.603984+00:00
https://raw.githubusercontent.com/eds-book-gallery/urban-exploration-climate_ukv/main/.binder/environment.yml
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Conda environment when user want to have the same libraries installed without concerns of package versions
Conda environment
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Jupyter notebook
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The research object refers to the Met Office UKV high-resolution atmosphere model data notebook published in the Environmental Data Science book.
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Met Office UKV high-resolution atmosphere model data (Jupyter Notebook) published in the Environmental Data Science book
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Samantha Adams, and Alejandro Coca-Castro. "Met Office UKV high-resolution atmosphere model data (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Feb 06 ,2022. https://doi.org/10.5281/zenodo.7737881.
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Contains outputs, (regridded data and figures), generated in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data
Outputs
2022-02-06 17:02:59.243925+00:00
https://edsbook.org/gallery/exploration/urban-exploration-climate_ukv/urban-exploration-climate_ukv.html
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Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book
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Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book
Lock conda file for win-64
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Related publication of the sensors presented in the Jupyter notebook
Met office and partners offer data and compute platform for covid-19 researchers
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https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/metoffice_ukv_daily/t1o5m_mean/ukv_daily_t1o5m_mean_20150801.nc
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https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f
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Related publication of the sensors presented in the Jupyter notebook
Met office and partners offer data and compute platform for covid-19 researchers
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Met Office UKV high-resolution atmosphere model data (Jupyter Notebook) published in the Environmental Data Science book
MANUAL
Samantha Adams, and Alejandro Coca-Castro. "Met Office UKV high-resolution atmosphere model data (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Feb 06 ,2022. https://doi.org/10.24424/26sq-rt94.
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Related publication of the modelling published in OCEANS 2021
Detecting macro floating objects on coastal water bodies using sentinel-2 data
2022-01-28 16:07:40.875698+00:00
https://doi.org/10.5194/isprs-annals-V-3-2021-285-2021
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Publication with further details of the modelling published in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Towards detecting floating objects on a global scale with learned spatial features using sentinel 2
2022-01-28 16:07:43.339740+00:00
https://doi.org/10.5281/zenodo.5827376
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Related publication(s) of the modelling presented in the Jupyter notebook
Cross-site learning in deep learning rgb tree crown detection
2022-02-20 20:24:10.407957+00:00
https://doi.org/10.1111/2041-210X.13472
2022-02-20 20:24:04.687051+00:00
2023-03-20 17:41:48.100537+00:00
Related publication(s) of the modelling presented in the Jupyter notebook
Deepforest: a python package for rgb deep learning tree crown delineation
2022-02-20 20:24:04.687051+00:00
https://doi.org/10.3390/rs11111309
2022-02-20 20:24:06.974255+00:00
2023-03-20 17:41:46.667052+00:00
Related publication(s) of the modelling presented in the Jupyter notebook
Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks
2022-02-20 20:24:06.974255+00:00
https://doi.org/10.5281/zenodo.3459802
2022-02-20 20:23:52.295018+00:00
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https://doi.org/10.5194/acp-15-13217-2015
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Related publication of the sensors presented in the Jupyter notebook
Lsa saf meteosat frp products–part 1: algorithms, product contents, and analysis
2022-02-26 15:11:10.618950+00:00
https://doi.org/10.5281/zenodo.5717106
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2023-03-20 17:49:30.788991+00:00
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https://doi.org/10.5281/zenodo.6299135
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Related publication of the sensors presented in the Jupyter notebook
Frp - product user manual
2022-02-26 15:11:08.835451+00:00
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SEVIRI Level 1.5 (Jupyter Notebook) published in the Environmental Data Science book - snapshot
SEVIRI Level 1.5 (Jupyter Notebook) published in the Environmental Data Science book
MANUAL
Samuel Jackson, and Alejandro Coca-Castro. "SEVIRI Level 1.5 (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Feb 26 ,2022. https://doi.org/10.24424/w9n8-r354.
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Image showing interactive plot of EUMESAT LSA SAF fire pixels ontop of a SEVIRI Level 1.5 scene configured in thermal bands in Southern Africa
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https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose
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https://doi.org/10.5281/zenodo.5494629
2022-03-27 19:36:02.837817+00:00
2023-03-20 17:57:08.960207+00:00
Contains input Datasets of detectreeRGB AI4ER MRes Project used in the Jupyter notebook of Tree crown delineation using detectreeRGB
Input Datasets of detectreeRGB AI4ER MRes Project
2022-03-27 19:36:02.837817+00:00
https://doi.org/10.5281/zenodo.6387953
2022-03-27 19:36:05.938260+00:00
2023-03-20 17:57:09.323456+00:00
Contains outputs, (vector, raster and figures), generated in the Jupyter notebook of Tree crown delineation using detectreeRGB
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https://github.com/shmh40/detectreeRGB
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2023-03-20 17:57:13.751044+00:00
Related publication of the modelling presented in the Jupyter notebook
detectreeRGB source code
2022-03-27 19:36:09.352889+00:00
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Jupyter Notebook hosted by the Environmental Data Science Book
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The research object refers to the Tree crown delineation using detectreeRGB notebook published in the Environmental Data Science book.
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Environmental Science
Jupyter Notebook
Tree crown delineation using detectreeRGB (Jupyter Notebook) published in the Environmental Data Science book - snapshot
Tree crown delineation using detectreeRGB (Jupyter Notebook) published in the Environmental Data Science book
MANUAL
Sebastian H. M. Hickman, and Alejandro Coca-Castro. "Tree crown delineation using detectreeRGB (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Mar 27 ,2022. https://doi.org/10.24424/2h9y-jn41.
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Image showing interactive plot of detectreeRGB model predictions of tree crown over a sample drone image in Sepilok, Sabah, Malaysia
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https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose
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2022-04-03 22:38:18.897063+00:00
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Related publication of the modelling presented in the Jupyter notebook
Seasonal Arctic sea ice forecasting with probabilistic deep learning
2022-04-03 22:38:18.897063+00:00
https://doi.org/10.5281/zenodo.5516869
2022-04-03 22:38:16.031702+00:00
2023-03-20 18:04:53.431880+00:00
Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet
Input Dataset for IceNet's demo notebook
2022-04-03 22:38:16.031702+00:00
https://doi.org/10.5281/zenodo.6410246
2022-04-03 22:38:17.386248+00:00
2023-03-20 18:04:55.846637+00:00
Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet
Outputs
2022-04-03 22:38:17.386248+00:00
https://doi.org/10.5285/71820e7d-c628-4e32-969f-464b7efb187c
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2023-03-20 18:04:49.819080+00:00
Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet
Input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning'
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https://edsbook.org/notebooks/gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/notebook.html
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The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book.
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Environmental Science
Jupyter Notebook
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book - snapshot
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book
MANUAL
Alejandro Coca-Castro, Tom Andersson, and Nick Barlow. "Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Apr 03 ,2022. https://doi.org/10.24424/m8ew-pg51.
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Image showing interactive plot of IceNet seasonal forecasts of Artic sea ice according to four lead times and months in 2020
2022-04-03 22:38:08.092594+00:00
Computational notebooks community focused on Environmental Data Science
environmental.ds.book@gmail.com
Environmental Data Science Book Community
https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose
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Environmental Data Science Book Community
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service-account-enrichment
http://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2
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Related publication of the exploration presented in the Jupyter notebook
The NMC/NCAR 40-year reanalysis project
2022-07-24 18:44:23.809948+00:00
Environmental research
Climatology
https://doi.org/10.1175/BAMS-D-20-0117.1
2022-07-24 18:44:26.453549+00:00
2023-03-20 18:21:11.415216+00:00
Related publication of the exploration presented in the Jupyter notebook
Quantifying Causal Pathways of Teleconnections
2022-07-24 18:44:26.453549+00:00
https://doi.org/10.5281/zenodo.6824189
2022-07-24 18:44:21.623972+00:00
2023-03-20 18:21:06.510660+00:00
Contains outputs, (figures), generated in the Jupyter notebook of Concatenating a gridded rainfall reanalysis dataset into a time series
Outputs
2022-07-24 18:44:21.623972+00:00
https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis.derived/surface_gauss/prate.sfc.mon.mean.nc
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2023-03-20 18:21:11.297473+00:00
Contains input of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series used in the Jupyter notebook of Concatenating a gridded rainfall reanalysis dataset into a time series
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Input of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series
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Jupyter Notebook hosted by the Environmental Data Science Book
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2022-05-20 22:39:28.244049+00:00
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Related publication of the exploration presented in the Jupyter notebook
Soil water content in southern england derived from a cosmic-ray soil moisture observing system – cosmos-uk
2022-05-20 22:39:28.244049+00:00
https://doi.org/10.5194/hess-16-4079-2012
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Related publication of the exploration presented in the Jupyter notebook
Cosmos: the cosmic-ray soil moisture observing system
2022-05-20 22:39:29.949068+00:00
https://doi.org/10.5281/zenodo.6566942
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Daily and sub-daily hydrometeorological and soil data (2013-2019) [cosmos-uk]
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2022-09-21 22:55:46.631043+00:00
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Related publication of the exploration presented in the Jupyter notebook
Global land use / land cover with Sentinel 2 and deep learning
2022-09-21 22:55:46.631043+00:00
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Environmental research
https://doi.org/10.5281/zenodo.7101976
2022-09-21 22:55:41.737294+00:00
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2023-07-27 11:55:15.799692+00:00
This Research Object has as a main artefact a presentation (slides) on The Carpentries approach to training. It gives an overview of The Carpentries initiatives, how they operate, how they collaboratively develop and maintain training materials, and how they train their instructors. The Research Object also contains additional links to other presentations and material of interest for learning more about The Carpentries or other similar initiatives.
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backward design
community
training
Presentation
Tips and Approaches for collaboratively developing, maintaining and delivering training: example with The Carpentries approach - snapshot
Tips and Approaches for collaboratively developing, maintaining and delivering training: example with The Carpentries approach
MANUAL
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https://w3id.org/ro/terms/earth-science#BibliographyCentricResearchObjectTemplate
Fouilloux, Anne. "Tips and Approaches for collaboratively developing, maintaining and delivering training: example with The Carpentries approach." ROHub. Jul 27 ,2023. https://doi.org/10.24424/7gt2-h852.
biblio
https://docs.google.com/presentation/d/1Zf94N8sm-oVo5ypOXiuIQp9dMGOV_MvOkLKdbyX89o0/edit#slide=id.gcd7e7a0697_0_0
2023-07-27 12:09:06.279463+00:00
2023-07-27 12:22:17.741960+00:00
Presentation given by Toby Hodges on 29 April 2021 to reflect on the First round of Lesson Development Study Groups. Toby explains what the training material on "Lesson Development Study Group" is about and how it helps The Carpentries community to co-develop training material.
Lesson Development Study Groups: Reflecting on Round 1 & Planning for the Future
2023-07-27 12:09:06.279463+00:00
https://coderefinery.org
2023-07-27 12:13:39.430228+00:00
2023-07-27 12:22:10.948474+00:00
CodeRefinery is a community project where you can find Training and e-Infrastructure for Research Software Development.
The CodeRefinery website
2023-07-27 12:13:39.430228+00:00
https://galaxyproject.org
2023-07-27 12:18:17.787123+00:00
2023-07-27 12:22:13.425735+00:00
Galaxy is an open-source platform for data analysis that enables users to:
1) Use tools from various domains (that can be plugged into workflows) through its graphical web interface.
Run code in interactive environments (RStudio, Jupyter...) along with other tools or workflows;
2) Manage data by sharing and publishing results, workflows, and visualizations;
3) Ensure reproducibility by capturing the necessary information to repeat and understand data analyses;
4) The Galaxy Community is actively involved in helping the ecosystem improve and sharing scientific discoveries.
Project
The Galaxy Project website
2023-07-27 12:18:17.787123+00:00
https://doi.org/10.5281/zenodo.8189268
2023-07-27 12:15:32.339298+00:00
2023-07-27 12:22:12.823369+00:00
A short overview of The Carpentries initiative, how they operate and collaboratively develop, maintain and deliver training on foundational coding and data science skills to researchers worldwide for researchers.
Informal presentation given for the GO FAIR Foundation Fellow on July 27th 2023.
Galaxy Project
The Carpentries approach to training
2023-07-27 12:15:32.339298+00:00
https://carpentries.org
2023-07-27 12:12:23.447445+00:00
2023-07-27 12:22:13.228406+00:00
The Carpentries website is the main page where one can find about The Carpentries initiative. You can find many other links from there, including the Carpentries training material.
The Carpentries website
2023-07-27 12:12:23.447445+00:00
https://training.galaxyproject.org
2023-07-27 12:16:39.376538+00:00
2023-07-27 12:22:13.640548+00:00
Website where you can find all the training material for The Galaxy Project with many different topics.
Galaxy project
Galaxy Training website
2023-07-27 12:16:39.376538+00:00
https://docs.google.com/presentation/d/1MlZ5FWXc6pAOhioBlIOKj3RdAzQyk-1U1uTg6G9q1vM/edit#slide=id.g3b8317a2f2_1_29
2023-07-27 12:00:19.351898+00:00
2023-07-27 12:22:12.201561+00:00
Presentation from The Carpentries Community on "The Carpentries Instructor Training" and on how to build skills in a community of practice.
Carpentries Instructor Training: Building skills in a community of practice
2023-07-27 12:00:19.351898+00:00
example with The Carpentries
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jeani@uio.no
Jean Iaquinta
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University of Oslo
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2024-01-05 15:11:39.987851+00:00
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2024-01-05 14:14:55.022211+00:00
The Ohio State University (OSU) Micro Benchmarks (OMB) are a widely used suite of benchmarks for measuring and evaluating the performance of MPI operations for point-to-point, multi-pair, and collective communications. These benchmarks are often used for comparing different Message Passing Inerface (MPI) implementations and the underlying network interconnect.
Here we use the OSU micro-benchmark (version 7.2) to assess the performance in terms of bandwidth achieved with an Apptainer container between 2 processors on different nodes with OpenMPI (version 4.1.6) on the Norwegian academic High Performance Computers (HPC) located in Tromsø (Fram) and Trondheim (Betzy).
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Apptainer
HPC
MPI
OSU
Performance
bandwidth
container
interconnect
Dataset
OSU MPI Get Bandwidth Test v7.2 with OpenMPI 4.1.6 on Fram & Betzy
MANUAL
https://w3id.org/ro/terms/earth-science#DataCentricResearchObjectTemplate
Iaquinta, Jean. "OSU MPI Get Bandwidth Test v7.2 with OpenMPI 4.1.6 on Fram & Betzy." ROHub. Jan 05 ,2024. https://doi.org/10.24424/zcq6-9r81.
data
raw data
biblio
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10.24424/7nkm-2072
36301
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2024-01-05 15:11:39.077450+00:00
Plot showing the bandwidth as a function of the message size on Fram and Betzy
image/png
OSU-2023Dec.png
2024-01-05 14:26:46.457298+00:00
10.24424/pv5n-vq62
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2024-01-05 15:11:39.583041+00:00
Output of the OSU MPI Get Bandwidth Test with openMPI 4.1.6 on Fram and Betzy
text/csv
Apptainer
OpenMPI
OSU7.2-Fram-Betzy
2024-01-05 14:33:02.319503+00:00
NICEST-2 - the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives.
Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools
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Here we use the OSU micro-benchmark (version 7.2) to assess the performance in terms of bandwidth achieved with an Apptainer container between 2 processors on different nodes with OpenMPI (version 4.1.6) on the Norwegian academic High Performance Computers (HPC) located in Tromsø (Fram) and Trondheim (Betzy)
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https://www.osti.gov/servlets/purl/1997634
2024-01-17 10:54:09.114061+00:00
2024-01-17 10:54:10.405249+00:00
Abstract—Open MPI is an open-source implementation of the
MPI-3 standard that is developed and maintained by collaborators from academia, industry, and national laboratories.
Oak Ridge National Laboratory (ORNL) and Los Alamos
National Laboratory (LANL) are collaborating on porting and
optimizing Open MPI and related components for use on HPE
Cray EX systems, with a focus on the DOE Frontier and Aurora
exa-scale systems.
A key component of this effort involves development of a new
LinkX Open Fabrics Interface (OFI) provider. In this paper,
we describe enhancements to Open MPI, OpenPMIx runtime
components, and the LinkX OFI provider. Performance results
are presented for point to point and collective communication
operations using both the vendor CXI provider and the LinkX
provider, including results obtained using GPU accelerators. Recommended deployment options for EX systems will be discussed,
along with future work.
Slingshot 11
libfabric
Open MPI for HPE Cray EX Systems
2024-01-17 10:54:09.114061+00:00
Applied sciences
Simula Research Laboratory
annef@simula.no
Anne Fouilloux
0000-0002-1784-2920
jeani@uio.no
Jean Iaquinta
0000-0002-8763-1643
forecasting sea ice
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35.7
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Environment/Climate change
Arctic Zone
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Research Object
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Motivation impacts exceed local environments, populations and economies
need for climate change
research
accurate seasonal Arctic
sea ice forecasts with IceNet:
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http
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https://orcid.org/0000-0002-1784-2920
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2024-04-19 13:35:30.077318+00:00
2024-04-09 18:50:15.660054+00:00
This Research Object corresponds to the work done by Vanessa Stoeckl, and presented as a poster at EGU 2024, ESSI 2.9 "Seamless transitioning between HPC and cloud in support of Earth Observation, Earth Modeling and community-driven Geoscience approach PANGEO".
- Abstract submitted and accepted at EGU: [https://doi.org/10.5194/egusphere-egu24-8343](https://doi.org/10.5194/egusphere-egu24-8343)
- [Rendered Jupyter notebook](https://edsbook.org/notebooks/gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/notebook)
- [Galaxy workflow showcasing the pipeline for forecasting sea ice](https://usegalaxy.eu/u/vstoeckl/w/icenet)
application/ld+json
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Implementation of a reproducible pipeline for forecasting sea ice - snapshot
Implementation of a reproducible pipeline for forecasting sea ice
MANUAL
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https://w3id.org/ro-id/3f54a781-eb8a-46fb-96a1-a16ea9625173
https://w3id.org/ro-id/6e4e00c6-a12f-4edb-9fbc-335f75191294
https://w3id.org/ro-id/76f3e8ed-3db0-4133-afc2-1090cc82b84a
https://w3id.org/ro-id/8262af5b-77dd-4d59-9ba4-d741b4d10d69
https://w3id.org/ro-id/919b07eb-1ee3-43a3-9f08-b9f7bcb1392f
https://w3id.org/ro-id/c1edeba3-3148-4e17-91a8-9de20ad30a89
https://w3id.org/ro-id/df8ba451-5528-40e3-a780-c6de579a0de3
https://w3id.org/ro-id/74691455-a770-4cec-b980-c4c82a4f8ee1
https://w3id.org/ro-id/8532d638-bd04-4074-9569-4da28ee4f26e
https://w3id.org/ro-id/d712f5e0-72db-4188-abdd-93e225d20eee
https://w3id.org/ro-id/e1da4802-0052-457a-8eba-c9407c31c37b
https://w3id.org/ro-id/ebc9dc8b-2cab-49c1-84ad-7db71b8e5250
https://w3id.org/ro/terms/earth-science#ExecutableResearchObjectTemplate
Stoeckl, Vanessa, Alejandro Coca-Castro, Anne Fouilloux, Björn Grüning, and Jean Iaquinta. "Implementation of a reproducible pipeline for forecasting sea ice." ROHub. Apr 09 ,2024. https://doi.org/10.24424/vpkn-k902.
tool
biblio
output
input
772547
https://api.rohub.org/api/resources/0ad18b1b-594f-4b8a-937e-eff3460be9dd/download/
2024-04-09 19:02:15.319953+00:00
2024-04-19 13:34:19.301131+00:00
Poster EGU 2024 (pdf) Implementation of a reproducible pipeline for producing seasonal Arctic sea ice forecasts
application/pdf
Poster
Poster EGU 2024 (pdf)
2024-04-09 19:02:15.319953+00:00
https://usegalaxy.eu/u/vstoeckl/w/icenet
2024-04-09 18:52:16.320626+00:00
2024-04-19 13:34:22.939052+00:00
Galaxy workflow on the Galaxy Europe instance. To execute it, you would need first to get an account on Galaxy Europe (free of charge) and prepare the input dataset.
galaxy
Galaxy Workflow IceNet sea-ice forecasting
2024-04-09 18:52:16.320626+00:00
https://doi.org/10.1093/nar/gkac247
2024-04-09 18:59:25.010332+00:00
2024-04-19 13:34:23.594734+00:00
Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.
galaxy-platform
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update
2024-04-09 18:59:25.010332+00:00
10.24424/tckn-et23
323417298
https://api.rohub.org/api/resources/79406cf5-4e66-44dd-97ae-b996a17f2ec6/download/
2024-04-12 19:22:32.134693+00:00
2024-04-19 13:34:23.287670+00:00
video/mp4
Presentation
2024-04-12 19:22:32.134693+00:00
1260982
https://api.rohub.org/api/resources/8105ba74-e8df-435b-ac88-b2fb762365a5/download/
2024-04-11 11:31:09.064376+00:00
2024-04-19 13:34:22.725438+00:00
Sketch used in RoHub to illustrate the Research Object created for the poster at EGU 2024.
image/png
sketch (based on the poster)
2024-04-11 11:31:09.064376+00:00
1473306
https://api.rohub.org/api/resources/bccd43db-8caf-4734-93d7-c864fb8139c3/download/
2024-04-12 19:24:37.138056+00:00
2024-04-19 13:34:20.251203+00:00
application/pdf
Presentation slides
2024-04-12 19:24:37.138056+00:00
https://doi.org/10.5194/egusphere-egu24-8343
2024-04-09 18:57:09.049029+00:00
2024-04-19 13:34:20.491838+00:00
EGU abstract submitted.
Abstract EGU24-8343 (poster)
2024-04-09 18:57:09.049029+00:00
https://w3id.org/ro-id/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef
2024-04-09 18:54:45.057065+00:00
2024-04-19 13:34:22.131841+00:00
Research Object with the Jupyter Notebook showcasing Sea ice forecasting in the Environmental Data Science book [https://edsbook.org/welcome.html](https://edsbook.org/welcome.html)
Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book
2024-04-09 18:54:45.057065+00:00
Oil and gas - upstream activities
Economy, business and finance/Economic sector/Energy and resource/Oil and gas - upstream activities
http
10.720887245841034
11.6
implementation
5.082319255547603
7.1
reproducible pipeline
10.050251256281406
14.0
Hardware
Economy, business and finance/Economic sector/Computing and information technology/Hardware
computer science
21.50537634408602
2.0
Weather
Weather
Implementation of a reproducible pipeline for forecasting sea ice.
19.20485175202156
28.5
earth sciences
100.0
1.9435470700263977
geosciences
36.61731769463545
0.4983392357826233
Galaxy workflow
7.2505384063173
10.1
Implementation of a reproducible pipeline for forecasting sea ice
6.738544474393531
10.0
workflow
2.43378668575519
3.4
This Research Object corresponds to the work done by Vanessa Stoeckl, and presented as a poster at EGU 2024, ESSI 2.9 "Seamless transitioning between HPC and cloud in support of Earth Observation, Earth Modeling and community-driven Geoscience approach PANGEO".
- Abstract submitted and accepted at EGU: [https://doi.org/10.5194/egusphere-egu24-8343](https://doi.org/10.5194/egusphere-egu24-8343)
- [Rendered Jupyter notebook](https://edsbook.org/notebooks/gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/notebook)
- [Galaxy workflow showcasing the pipeline for forecasting sea ice](https://usegalaxy.eu/u/vstoeckl/w/icenet)
48.113207547169814
71.4
poster
2.1474588403722263
3.0
geosciences
63.38268230536455
0.8625994324684143
National Oceanic and Atmospheric Administration
https://www.wikidata.org/wiki/Q214700
environment
5.6377079482439925
6.1
The Alan Turing Institute
acoca@turing.ac.uk
Alejandro Coca-Castro
bjoern.gruening@gmail.com
Björn Grüning
vanessa-tamara@web.de
Vanessa Stoeckl
Oceanography
Environmental research
Applied sciences
https://destination-earth.eu/use-cases/global-fish-tracking-system-gfts
2024-03-13 08:41:04.316157+00:00
2024-08-12 19:50:27.276584+00:00
Link to the official GFTS DESP use case.
WebSite
DestinE Use Case official website: Global Fish Tracking System (GFTS) DESP Use Case
2024-03-13 08:41:04.316157+00:00
https://destination-earth.github.io/DestinE_ESA_GFTS
2024-03-13 08:45:35.825465+00:00
2024-08-12 19:50:16.380874+00:00
These webpages are rendered from GitHub repository and contain all the information about the GFTS project. This includes internal description of the use case, technical documentation, progress, presentations, etc.
WebSite
GFTS Use case project website
2024-03-13 08:45:35.825465+00:00
https://doi.org/10.5281/zenodo.10372387
2023-12-13 20:50:53.907411+00:00
2024-08-12 19:50:17.291963+00:00
Slides presented by Mathieu Woillez at the Roadshow Webinar: DestinE in action – meet the first DESP use cases (13 December 2023)
Global Fish Tracking System - DESP Use Case
2023-12-13 20:50:53.907411+00:00
https://doi.org/10.5281/zenodo.10809819
2024-03-12 15:34:59.645366+00:00
2024-08-12 19:50:18.034736+00:00
Poster presented at the 8th InternationalBio-logging Science Symposium by Tina Odaka, March 2024.
BSL8
Leveraging Pangeo to Geolocate Fish Using Biologging Data: The Pangeo-Fish Initiative
2024-03-12 15:34:59.645366+00:00
https://doi.org/10.5281/zenodo.11185948
2024-05-13 14:46:37.545543+00:00
2024-08-12 19:50:27.882827+00:00
Project Management Plan for the Global fish Tracking System Use Case on the DestinE Platform.
Deliverable 5.1 - Project Management Plan for GFTS Use Case Application
2024-05-13 14:46:37.545543+00:00
https://doi.org/10.5281/zenodo.11186084
2024-05-13 14:52:05.526290+00:00
2024-08-12 19:50:21.743081+00:00
Deliverable 5.2 - Use Case Descriptor for the Global fish Tracking System Use Case on the DestinE Platform.
Deliverable 5.2 - Use Case Descriptor for GFTS Use Case Application
2024-05-13 14:52:05.526290+00:00
https://doi.org/10.5281/zenodo.11186123
2024-05-13 14:53:36.574750+00:00
2024-08-12 19:50:20.705135+00:00
The Gobal Fish track system Use case Application on the DestinE Platform
Deliverable 5.3 - GFTS Use case Application
2024-05-13 14:53:36.574750+00:00
https://doi.org/10.5281/zenodo.11186179
2024-05-13 14:54:51.494324+00:00
2024-08-12 19:50:17.564291+00:00
Deliverable 5.5 corresponding to the Global Fish tracking System Use Case Promotion Package
Deliverable 5.5 - GFTS Use Case Promotion Package
2024-05-13 14:54:51.494324+00:00
https://doi.org/10.5281/zenodo.11186191
2024-05-13 14:56:18.168828+00:00
2024-08-12 19:50:26.999845+00:00
This report corresponds to the Software Reuse File for the GFTS DestinE Platform Use Case. New version will be uploaded regularly.
Software Reuse File for the GFTS DestinE Platform Use Case
2024-05-13 14:56:18.168828+00:00
https://doi.org/10.5281/zenodo.11186227
2024-05-13 14:57:17.755650+00:00
2024-08-12 19:50:15.236431+00:00
The Software Release Plan for the Global Fish Tracking System DestinE Use Case.
GFTS Software Release Plan
2024-05-13 14:57:17.755650+00:00
https://doi.org/10.5281/zenodo.11186257
2024-05-13 14:58:42.259068+00:00
2024-08-12 19:50:18.592163+00:00
The Software Requirement Specifications for the Global fish Tracking System DestinE Use Case.
GFTS Software Requirement Specifications
2024-05-13 14:58:42.259068+00:00
https://doi.org/10.5281/zenodo.11186288
2024-05-13 15:02:41.488702+00:00
2024-08-12 19:50:12.780989+00:00
The Software Verification and Validation Plan for the Global fish Tracking System DestinE Use Case.
GFTS Software Verification and Validation Plan
2024-05-13 15:02:41.488702+00:00
https://doi.org/10.5281/zenodo.11186318
2024-05-13 15:04:39.257557+00:00
2024-08-12 19:50:19.449409+00:00
The Software Verification and Validation Report from the Global Fish Tracking System DestinE Use Case.
GFTS Software Verification and Validation Report
2024-05-13 15:04:39.257557+00:00
https://gfts.minrk.net/
2024-04-03 08:56:53.930425+00:00
2024-08-12 19:50:18.317893+00:00
Link to the Pangeo JupyterHub we are using for developing Pangeo Fish. Only users from GFTS can register and authenticate to this JupyterHub
jupyterhub
Pangeo JupyterHub (OVH)
2024-04-03 08:56:53.930425+00:00
https://jupyter.central.data.destination-earth.eu/
2024-04-03 08:59:43.770409+00:00
2024-08-12 19:50:19.196703+00:00
JupyterHub on Destination Earth Data Lake
jupyterhub
JupyterHub on Destination Earth Data Lake
2024-04-03 08:59:43.770409+00:00
IFREMER
Emmanuelle Autret
0000-0002-0979-9192
IFREMER
Mathieu Woillez
0000-0002-1032-2105
Ifremer
tina.odaka@ifremer.fr
Tina Odaka
0000-0002-1500-0156
Simula Research Laboratory
annef@simula.no
Anne Fouilloux
0000-0002-1784-2920
Development Seed
danielwiesmann@developmentseed.org
Daniel Wiesmann
0000-0002-3190-4278
Development Seed
olaf@developmentseed.org
Olaf Veerman
0000-0002-5408-9923
Development Seed
Daniel da Silva
0009-0002-4476-7927
Development Seed
Ricardo Mestre
0009-0008-7946-8568
post@simula.no
00vn06n10
Simula Research Laboratory
dpo@ifremer.fr
044jxhp58
IFREMER
https://w3id.org/np/RAsFv4Wt5R_8zdUBoBBHAfqyDYNbfnrMEoJ4t6iDfBUUY
2024-03-13 08:29:17.573606+00:00
2024-08-12 19:50:27.586876+00:00
FAIR Implementation Profile (FIP) for the GFTS project.
FIP
FIP for GFTS project
2024-03-13 08:29:17.573606+00:00
-4.483552708405557
48.396968528918855
POINT (-4.483552708405557 48.396968528918855)
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38.705400547590436
POINT (-9.156135762570598 38.705400547590436)
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POINT (-4.483552708405557 48.396968528918855)
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POINT (-9.156135762570598 38.705400547590436)
7d448ca4-aaea-4492-a11c-b75fe0bf7b77
POINT (10.748991231319016 59.91003939873761)
10.748991231319016
59.91003939873761
POINT (10.748991231319016 59.91003939873761)
10.24424/sjfs-sn41
False
2024-08-12 19:50:28.397218+00:00
0
https://api.rohub.org/api/ros/9b361a58-e5ba-4683-a004-08a489be9df4/crate/download/
2023-11-28 14:53:38.668993+00:00
2024-08-12 19:50:47.316403+00:00
2023-11-28 14:53:38.668993+00:00
**Use Case topic**: The goal of this use case is the development and implementation of the Global Fish Tracking System (GFTS) to enhance understanding and management of wild fish stocks
**Scale of the Use Case (Global/Regional/National)**: Local to Global (various locations worldwide)
**Policy addressed**: Fisheries Management Policy
**Data Sources used**: Climate Change Adaptation (Climate DT: Routine and On-Demand for some higher resolution tracking), Sea Temperature observation (Satelite, in-situ) Copernicus Marine services (Sea temperature and associated value), Bathymetry (Gebco), biologging fish data
**Github Repository**: [https://github.com/destination-earth/DestinE_ESA_GFTS.git](https://github.com/destination-earth/DestinE_ESA_GFTS.git)
application/ld+json
https://w3id.org/ro-id/9b361a58-e5ba-4683-a004-08a489be9df4
fish
fish-tracking
Global Fish Tracking System (GFTS): a Destination Earth Service Platform Use Case - snapshot
Global Fish Tracking System (GFTS): a Destination Earth Service Platform Use Case
MANUAL
https://w3id.org/ro/terms/earth-science#ExecutableResearchObjectTemplate
Fouilloux, Anne, Benjamin Ragan-Kelley, Mathieu Woillez, Tina Odaka, Daniel Wiesmann, Emmanuelle Autret, Olaf Veerman, Daniel da Silva, and Ricardo Mestre. "Global Fish Tracking System (GFTS): a Destination Earth Service Platform Use Case." ROHub. Nov 28 ,2023. https://doi.org/10.24424/sjfs-sn41.
POINT (10.748991231319016 59.91003939873761)
POINT (-9.156135762570598 38.705400547590436)
POINT (-4.483552708405557 48.396968528918855)
This folder contains presentations or other kind of materials (such as training material) developed and presented during events.
events
This folder contains project documents such as DMP, link to website and github repository, etc.
documents
tool
output
input
reports_and_deliverables
2081827
https://api.rohub.org/api/resources/1997c5cc-9799-4c11-a43b-08ea1440d62f/download/
2024-03-13 07:40:26.715784+00:00
2024-08-12 19:50:21.505925+00:00
This pitcure shows Tina Odaka presenting the Global Fish Tracking System (GFTS) DestinE DESP Use Case at the 8th International Bio-logging Science Symposium, Tokyo, Japan (4-8 March 2024).
image/png
Photo of Tina Odaka at BSL8
2024-03-13 07:40:26.715784+00:00
258569
https://api.rohub.org/api/resources/91115265-0c34-4681-bdbf-d3d2683b1ed6/download/
2023-11-28 14:55:18.760223+00:00
2024-08-12 19:50:21.125141+00:00
image/png
GFTS.png
2023-11-28 14:55:18.760223+00:00
A community platform for Big Data geoscience
pangeo-europe@gmail.com
Pangeo
https://pangeo.io/
**Use Case topic**: The goal of this use case is the development and implementation of the Global Fish Tracking System (GFTS) to enhance understanding and management of wild fish stocks
**Scale of the Use Case (Global/Regional/National)**: Local to Global (various locations worldwide)
**Policy addressed**: Fisheries Management Policy
**Data Sources used**: Climate Change Adaptation (Climate DT: Routine and On-Demand for some higher resolution tracking), Sea Temperature observation (Satelite, in-situ) Copernicus Marine services (Sea temperature and associated value), Bathymetry (Gebco), biologging fish data
**Github Repository**: [https://github.com/destination-earth/DestinE_ESA_GFTS.git](https://github.com/destination-earth/DestinE_ESA_GFTS.git)
66.36636636636636
66.3
fish
12.665406427221173
6.7
earth sciences
100.0
0.8193894028663635
implementation of the Global Fish Tracking System
10.472972972972972
6.2
temperature
17.391304347826086
9.2
data source
7.503828483920369
4.9
http
11.1531190926276
5.9
value
8.728943338437979
5.7
Weather
Weather
sea
14.93383742911153
7.9
value
8.695652173913043
4.6
destination Earth service platform use case
28.885135135135137
17.1
http
10.719754977029098
7.0
temperature
17.151607963246555
11.2
Global Fish Tracking System (GFTS): a Destination Earth Service Platform Use Case.
33.63363363363363
33.6
oceanography
100.0
0.8193894028663635
subject
5.819295558958653
3.8
Hardware
Economy, business and finance/Economic sector/Computing and information technology/Hardware
tracking
4.900459418070445
3.2
Global Fish Tracking System
12.098298676748582
6.4
earth resources and remote sensing
100.0
0.43638113141059875
fish data
18.75
11.1
sea
15.007656967840738
9.8
geosciences
100.0
0.43638113141059875
fish
12.404287901990813
8.1
use case
23.062381852551987
12.2
meteorology
25.35211267605634
1.8
sea temperature observation
27.702702702702698
16.4
data
12.404287901990813
8.1
use case topic
14.189189189189188
8.4
Climate change
Environment/Climate change
logging
5.359877488514549
3.5
information technology
74.64788732394366
5.3
Simula, Department of Numerical Analysis and Scientific Computing (Norway)
benjaminrk@simula.no
Benjamin Ragan-Kelley
info@developmentseed.org
Development Seed
File
ts_cities.csv
2025-05-23T17:37:48.953747
Climate Stripes
datasets/stripes.png_31e7840b5aedca43c0a4f330c3d24460.png
2025-05-23T17:37:48.953745
workflows/f1ada1d68e850ab0.gxwf.yml
Galaxy workflow engine
File
stripes.png
2025-05-24T11:15:49.407239
Run of Galaxy workflow engine
2025-05-24T11:15:41.585048
#df271e0b-a648-4bff-95d4-739be6c1c6b4
Galaxy
CWL
Common Workflow Language
10.24424/9cee-cz89
False
2025-05-24 13:39:23.468184+00:00
0
https://api.rohub.org/api/ros/d5430aa5-7a8b-44fe-8d21-6a7c80ac36d4/crate/download/
2025-05-24 11:31:11+00:00
2025-10-16 11:38:20.704323+00:00
2025-05-24 11:31:11+00:00
# Galaxy Workflow Rerun Information
**Workflow:** Climate Stripes
**Execution Status:** scheduled
**Executed:** 2025-05-24 11:15:41.585048
## Workflow Inputs
### Formal Input Definitions
- **ts_cities.csv** (File)
### Actual Input Files Used
- **ts_cities.csv**
- Format: `text/plain`
- Path: `datasets/ts_cities.csv_31e7840b5aedca433fb349714141a239.tabular`
## Workflow Parameters
- **input:**
- __class__: `NoReplacement`
- **adv:**
- colormap: `RdBu_r`
- format_date: ``
- format_plot: ``
- nxsplit: `None`
- xname: ``
- **ifilename:**
- __class__: `ConnectedValue`
- **title:** `My ScienceLive Stripes`
- **variable:** `tg_anomalies_freiburg`
## Workflow Outputs
### Formal Output Definitions
- **stripes.png** (File)
### Actual Output Files Generated
- **stripes.png**
- Format: `application/octet-stream`
- Path: `datasets/stripes.png_31e7840b5aedca43c0a4f330c3d24460.png`
## Rerun Template
To rerun this workflow:
1. **Workflow:** Climate Stripes
2. **Required inputs:**
- ts_cities.csv (type: `File`)
3. **Parameters to set:**
- input:
- __class__: `NoReplacement`
- adv:
- colormap: `RdBu_r`
- format_date: ``
- format_plot: ``
- nxsplit: `None`
- xname: ``
- ifilename:
- __class__: `ConnectedValue`
- title: `My ScienceLive Stripes`
- variable: `tg_anomalies_freiburg`
4. **Expected outputs:**
- stripes.png (type: `File`)
application/ld+json
https://w3id.org/ro-id/d5430aa5-7a8b-44fe-8d21-6a7c80ac36d4
workflows/f1ada1d68e850ab0.gxwf.yml
#d31ef107-881d-4cf7-8f96-c91af5a2a368
74b68f2f-6fec-4a9e-85fd-c83574046358__climate.rocrate.zip
Fouilloux, Anne. "74b68f2f-6fec-4a9e-85fd-c83574046358__climate.rocrate.zip." ROHub. May 24 ,2025. https://doi.org/10.24424/9cee-cz89.
datasets
workflows
tmp3jnevr5o
tmp
4528
https://api.rohub.org/api/resources/3a3aae77-4357-49e4-82ac-b3bc8a081158/download/
2025-05-24 11:40:44.800306+00:00
2025-05-24 13:39:06.901275+00:00
text/html
workflows/f1ada1d68e850ab0
2025-05-24 11:40:44.800306+00:00
1460
https://api.rohub.org/api/resources/3d1c14ef-a393-4f71-99ab-9f065c9e07a4/download/
2025-05-24 11:40:44.798821+00:00
2025-05-24 13:39:05.161767+00:00
Climate Stripes
2025-05-24 11:40:44.798821+00:00
workflows/f1ada1d68e850ab0.abstract.cwl
#b3cb61d2-2f69-478a-8d99-b015043391d4
2
https://api.rohub.org/api/resources/3f513099-1a08-46a1-877e-3686194e9a25/download/
2025-05-24 11:40:44.790250+00:00
2025-05-24 13:39:10.463351+00:00
library folders properties
application/json
library_folders_attrs.txt
2025-05-24 11:40:44.790250+00:00
2.0
30
https://api.rohub.org/api/resources/7086ff93-15c5-4c42-9189-7d84bdcc8518/download/
2025-05-24 11:40:44.795199+00:00
2025-05-24 13:39:17.701983+00:00
export properties
application/json
export_attrs.txt
2025-05-24 11:40:44.795199+00:00
2.0
2945
https://api.rohub.org/api/resources/71ee97dc-10b8-4fd6-8187-ff6c9da64f90/download/
2025-05-24 11:40:44.794466+00:00
2025-05-24 13:39:16.881976+00:00
invocation properties
application/json
invocation_attrs.txt
2025-05-24 11:40:44.794466+00:00
2.0
45985
https://api.rohub.org/api/resources/8d4cb501-4697-45ed-be43-65e001e07e8f/download/
2025-05-24 11:40:44.796433+00:00
2025-05-24 13:39:12.105178+00:00
text/plain
#b3cb61d2-2f69-478a-8d99-b015043391d4
ts_cities.csv
2025-05-24 11:40:44.796433+00:00
12736
https://api.rohub.org/api/resources/940c8545-32e6-4043-bbfa-1b14170ab168/download/
2025-05-24 11:40:44.797284+00:00
2025-05-24 13:39:18.918458+00:00
application/octet-stream
#f358985c-9db4-42f0-b063-48d0d154953a
stripes.png_31e7840b5aedca43c0a4f330c3d24460.png
2025-05-24 11:40:44.797284+00:00
2
https://api.rohub.org/api/resources/a868f3bc-ec7a-4f58-9856-9c9afe7bc70e/download/
2025-05-24 11:40:44.788812+00:00
2025-05-24 13:39:09.617299+00:00
datasets provenance properties
application/json
datasets_attrs.txt.provenance
2025-05-24 11:40:44.788812+00:00
2.0
2134
https://api.rohub.org/api/resources/aac2fe5c-5d6d-4ec5-8675-f864c5a4f36e/download/
2025-05-24 11:40:44.787974+00:00
2025-05-24 13:39:11.683497+00:00
datasets properties
application/json
datasets_attrs.txt
2025-05-24 11:40:44.787974+00:00
2.0
764
https://api.rohub.org/api/resources/b5f6c475-b588-4581-8ec9-ff5d36a90f09/download/
2025-05-24 11:40:44.799549+00:00
2025-05-24 13:39:08.432963+00:00
workflows/f1ada1d68e850ab0.abstract
2025-05-24 11:40:44.799549+00:00
2
https://api.rohub.org/api/resources/c7f1c1fa-caa1-4d3f-8b81-9adf18090c74/download/
2025-05-24 11:40:44.793765+00:00
2025-05-24 13:39:22.602451+00:00
implicit collection jobs properties
application/json
implicit_collection_jobs_attrs.txt
2025-05-24 11:40:44.793765+00:00
2.0
2
https://api.rohub.org/api/resources/c891c902-f3d5-4ea8-b682-8553ea7ab434/download/
2025-05-24 11:40:44.789539+00:00
2025-05-24 13:39:14.036041+00:00
libraries properties
application/json
libraries_attrs.txt
2025-05-24 11:40:44.789539+00:00
2.0
2
https://api.rohub.org/api/resources/de997230-e253-4ea1-8ef3-367cad87f52e/download/
2025-05-24 11:40:44.791155+00:00
2025-05-24 13:39:23.412146+00:00
collections properties
application/json
collections_attrs.txt
2025-05-24 11:40:44.791155+00:00
2.0
2
https://api.rohub.org/api/resources/ee371b29-df2f-4f66-b301-1a25f5937d96/download/
2025-05-24 11:40:44.791921+00:00
2025-05-24 13:39:20.927491+00:00
text/plain
implicit_dataset_conversions.txt
2025-05-24 11:40:44.791921+00:00
3647
https://api.rohub.org/api/resources/ef444f9a-3d41-4f8a-8a62-b792e981a17a/download/
2025-05-24 11:40:44.798061+00:00
2025-05-24 13:39:20.496823+00:00
workflows/f1ada1d68e850ab0
2025-05-24 11:40:44.798061+00:00
job
3.710862002319289
9.6
other earth sciences
28.140164323698208
0.8604831099510193
earth sciences
15.5005337397516
0.473982572555542
galaxy
6.500646830530402
20.1
info
4.020100502512563
10.4
server
2.6843467011642956
8.3
rerun
6.841901816776189
17.7
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
oceanography
14.137058879162895
0.43228960037231445
designation ofilename
3.7470725995316156
12.8
metadata
6.015523932729626
18.6
ScienceLive
2.8604561267877853
7.4
geology
42.222243057387296
1.291091501712799
May-24-2025 11:18:13
life sciences (general)
48.48964280977599
0.691840410232544
atmospheric sciences
15.5005337397516
0.473982572555542
template
3.285659064553537
8.5
Hardware
Economy, business and finance/Economic sector/Computing and information technology/Hardware
param
3.33117723156533
10.3
database
39.473684210526315
19.5
computer programming
5.870445344129555
2.9
To rerun this workflow:
1.
2.6981450252951094
9.6
earth sciences
42.222243057387296
1.291091501712799
space sciences
3.5516765615937134
0.05067460238933563
life sciences
48.48964280977599
0.691840410232544
Capital punishment
Crime, law and justice/Law enforcement/Punishment (criminal)/Capital punishment
My ScienceLive Stripes
28.015222482435597
95.7
May-24-2025 11:15:49
name
4.915912031047865
15.2
dataset
8.796895213454075
27.2
[{"command_line": "python '/opt/galaxy/server/lib/galaxy/tools/data_fetch.py' --galaxy-root '/opt/galaxy/server' --datatypes-registry '/data/jwd05e/main/083/891/83891872/registry.xml' --request-version '1' --request '/data/jwd05e/main/083/891/83891872/configs/tmp9ijk_gw5'", "create_time": "2025-05-24T11:15:37.910670", "encoded_id": "9fa5573dd746a59a5204daf0450df025", "exit_code": 0, "galaxy_version": "24.2", "implicit_output_dataset_collection_mapping": {}, "info": null, "input_dataset_collection_element_mapping": {}, "input_dataset_collection_mapping": {}, "input_dataset_mapping": {}, "job_messages": [], "job_stderr": "", "job_stdout": "", "model_class": "Job", "output_dataset_collection_mapping": {}, "output_dataset_mapping": {"output0": ["31e7840b5aedca433fb349714141a239"]}, "params": {"file_count": "1", "files": [{"__index__": 0, "file_data": "/data/misc07/tus_upload/main/4c0201ed-0ac2-4bc9-bb7b-ee629ef6738b"}], "paramfile": null, "request_json": "{\"targets\": [{\"destination\": {\"type\": \"hdas\"}, \"elements\": [{\"name\": \"ts_cities.csv\", \"dbkey\": \"?\", \"ext\": \"auto\", \"space_to_tab\": false, \"to_posix_lines\": true, \"src\": \"path\", \"hashes\": [], \"in_place\": false, \"purge_source\": true, \"path\": \"/data/misc07/tus_upload/main/4c0201ed-0ac2-4bc9-bb7b-ee629ef6738b\", \"object_id\": 198375399}]}], \"auto_decompress\": false, \"check_content\": true}", "request_version": "1"}, "state": "ok", "tool_id": "__DATA_FETCH__", "tool_stderr": "", "tool_stdout": "", "tool_version": "0.1.0", "traceback": null, "update_time": "2025-05-24T11:16:35.950780"}, {"command_line": "python3 '/opt/galaxy/shed_tools/toolshed.g2.bx.psu.edu/repos/climate/climate_stripes/abdc27e01dca/climate_stripes/climate_stripes.py' '/data/dnb11/galaxy_db/files/b/3/c/dataset_b3cb61d2-2f69-478a-8d99-b015043391d4.dat' 'tg_anomalies_freiburg' --cmap 'RdBu_r' --title 'My ScienceLive Stripes' --output image.png", "create_time": "2025-05-24T11:15:49.359197", "encoded_id": "9fa5573dd746a59acc6f0e1eb8574ba7", "exit_code": 0, "galaxy_version": "24.2", "implicit_output_dataset_collection_mapping": {}, "info": null, "input_dataset_collection_element_mapping": {}, "input_dataset_collection_mapping": {}, "input_dataset_mapping": {"ifilename": ["31e7840b5aedca433fb349714141a239"]}, "job_messages": [], "job_stderr": "", "job_stdout": "", "model_class": "Job", "output_dataset_collection_mapping": {}, "output_dataset_mapping": {"ofilename": ["31e7840b5aedca43c0a4f330c3d24460"]}, "params": {"__input_ext": "auto", "__workflow_invocation_uuid__": "6db993da389011f08d47001e67d2ec02", "adv": {"colormap": "RdBu_r", "format_date": "", "format_plot": "", "nxsplit": null, "xname": ""}, "chromInfo": "/opt/galaxy/tool-data/shared/ucsc/chrom/?.len", "dbkey": "?", "ifilename": {"values": [{"id": "31e7840b5aedca433fb349714141a239", "src": "hda"}]}, "title": "My ScienceLive Stripes", "variable": "tg_anomalies_freiburg"}, "state": "ok", "tool_id": "toolshed.g2.bx.psu.edu/repos/climate/climate_stripes/climate_stripes/1.0.2", "tool_stderr": "", "tool_stdout": "", "tool_version": "1.0.2", "traceback": null, "update_time": "2025-05-24T11:18:13.429890"
28.10567734682406
100.0
b3cb61d2-2f69-478a-8d99-b015043391d4
2.8604561267877853
7.4
software
7.08502024291498
3.5
climate
3.4016235021260153
8.8
datum
3.0150753768844223
7.8
jwd05e
2.7831465017394668
7.2
name stripes.png
4.947306791569086
16.9
workflow input
2.781030444964871
9.5
data
9.184993531694696
28.4
metadata
3.8268264398917666
9.9
workflow output
2.839578454332552
9.7
WorkflowRequestInputParameter
4.290684190181678
11.1
earth sciences
14.137058879162895
0.43228960037231445
May-24-2025 11:15:41
output
5.450328565906456
14.1
earth sciences
28.140164323698208
0.8604831099510193
adverb
2.9754204398447603
9.2
May-24-2025 11:16:35
climate
2.652005174644243
8.2
IT-computer sciences
Science and technology/Technology and engineering/IT-computer sciences
galaxy
5.914186316196367
15.3
ScienceLive stripe
4.156908665105386
14.2
input
3.0150753768844223
7.8
information
4.406648627754156
11.4
# Galaxy Workflow Rerun Information
**Workflow:** Climate Stripes
**Execution Status:** scheduled
**Executed:** 2025-05-24 11:15:41.585048
10.286677908937605
36.6
dataset
9.277155005798223
24.0
computer programming and software
47.9586806286303
0.684264749288559
WorkflowInvocationStep
3.092385001932741
8.0
galaxy workflow rerun information
8.899297423887587
30.4
WorkflowInvocationOutputDatasetAssociation dataset
4.976580796252927
17.0
rerun template
3.0444964871194378
10.4
input
3.719275549805951
11.5
hda title
1.2295081967213113
4.2
delimiter t
4.332552693208431
14.8
workflow
6.841901816776189
17.7
fact
2.8137128072445017
8.7
space sciences (general)
3.5516765615937134
0.05067460238933563
[{"model_class": "WorkflowInvocation", "state": "scheduled", "create_time": "2025-05-24 11:15:41.585048", "update_time": "2025-05-24 11:15:49.407239", "steps": [{"model_class": "WorkflowInvocationStep", "state": "scheduled", "create_time": "2025-05-24 11:15:49.372141", "update_time": "2025-05-24 11:15:49.372142", "order_index": 0, "action": null, "outputs": [{"output_name": "output", "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca433fb349714141a239"}}], "output_collections": []}, {"model_class": "WorkflowInvocationStep", "state": "scheduled", "create_time": "2025-05-24 11:15:49.372143", "update_time": "2025-05-24 11:18:13.433603", "order_index": 1, "action": null, "job": {"model_class": "Job", "encoded_id": "9fa5573dd746a59acc6f0e1eb8574ba7"}, "outputs": [{"output_name": "ofilename", "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca43c0a4f330c3d24460"}}, {"output_name": "ofilename", "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca43c0a4f330c3d24460"}}], "output_collections": []}], "input_parameters": [{"model_class": "WorkflowRequestInputParameter", "name": "5453637", "value": "{\"title\": \"My ScienceLive Stripes\", \"variable\": \"tg_anomalies_freiburg\", \"adv|colormap\": \"RdBu_r\"}", "type": "step"}, {"model_class": "WorkflowRequestInputParameter", "name": "copy_inputs_to_history", "value": "false", "type": "meta"}, {"model_class": "WorkflowRequestInputParameter", "name": "use_cached_job", "value": "false", "type": "meta"}], "step_states": [{"model_class": "WorkflowRequestStepState", "value": {"__page__": 0, "__rerun_remap_job_id__": null, "input": "{\"__class__\": \"NoReplacement\"}"}, "order_index": 0}, {"model_class": "WorkflowRequestStepState", "value": {"__STEP_META_STATE__": "{\"__POST_JOB_ACTIONS__\": {}}", "__page__": 0, "__rerun_remap_job_id__": null, "adv": "{\"colormap\": \"RdBu_r\", \"format_date\": \"\", \"format_plot\": \"\", \"nxsplit\": null, \"xname\": \"\"}", "ifilename": "{\"__class__\": \"ConnectedValue\"}", "title": "\"My ScienceLive Stripes\"", "variable": "\"tg_anomalies_freiburg\""}, "order_index": 1}], "input_step_parameters": [], "input_datasets": [{"model_class": "WorkflowRequestToInputDatasetAssociation", "name": null, "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca433fb349714141a239"}, "order_index": 0}], "input_dataset_collections": [], "output_dataset_collections": [], "output_datasets": [{"model_class": "WorkflowInvocationOutputDatasetAssociation", "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca43c0a4f330c3d24460"}, "order_index": 1, "workflow_output": {"model_class": "WorkflowOutput", "output_name": "ofilename", "label": "stripes.png", "uuid": "8413b0d2-5eb1-419f-a1ed-a329fcf7366b"}}], "output_values": [], "encoded_id": "37738047cb7b8ee8", "workflow": "f1ada1d68e850ab0"
28.10567734682406
100.0
tool
4.172056921086676
12.9
rerun
5.271668822768435
16.3
output dataset
3.366510538641686
11.5
end product
2.5226390685640365
7.8
HistoryDatasetAssociation
3.0150753768844223
7.8
file
2.1345407503234153
6.6
tabular file
8.19672131147541
28.0
workflow
5.401034928848642
16.7
title
2.296248382923674
7.1
[{"annotation": "", "blurb": "836 lines 7 columns", "copied_from_history_dataset_association_id_chain": [], "create_time": "2025-05-24 11:15:37.899869", "dataset_uuid": "b3cb61d2-2f69-478a-8d99-b015043391d4", "deleted": false, "designation": null, "encoded_id": "31e7840b5aedca433fb349714141a239", "extension": "tabular", "file_metadata": {}, "file_name": "datasets/ts_cities.csv_31e7840b5aedca433fb349714141a239.tabular", "hid": 1, "history_encoded_id": "081c8f9306e90852", "info": "uploaded tabular file", "metadata": {"column_names": [], "column_types": ["str", "float", "float", "float", "float", "float", "float"], "columns": 7, "comment_lines": 0, "data_lines": 836, "dbkey": "?", "delimiter": "\t"}, "model_class": "HistoryDatasetAssociation", "name": "ts_cities.csv", "peek": "Year\ttg_avg_paris\ttg_anomalies_paris\ttg_avg_freiburg\ttg_anomalies_freiburg\ttg_avg_oslo\ttg_anomalies_oslo\n1950-01-16\t2.85\t-1.62\t-0.65999997\t-1.11\t-5.5299997\t-1.61\n1950-02-14\t7.5899997\t2.32\t3.77\t2.44\t-2.3999999\t1.24\n1950-03-16\t8.63\t0.35999998\t5.02\t0.28\t1.3199999\t1.3199999\n1950-04-15\t9.679999\t-1.37\t6.17\t-2.06\t5.5099998\t0.66999996\n", "state": "ok", "tags": [], "tool_version": null, "update_time": "2025-05-24 11:16:35.918742", "validated_state": "unknown", "validated_state_message": null, "visible": true}, {"annotation": "", "blurb": "12.4 KB", "copied_from_history_dataset_association_id_chain": [], "create_time": "2025-05-24 11:15:49.377150", "dataset_uuid": "f358985c-9db4-42f0-b063-48d0d154953a", "deleted": false, "designation": "ofilename", "encoded_id": "31e7840b5aedca43c0a4f330c3d24460", "extension": "png", "file_metadata": {"created_from_basename": "image.png"}, "file_name": "datasets/stripes.png_31e7840b5aedca43c0a4f330c3d24460.png", "hid": 2, "history_encoded_id": "081c8f9306e90852", "info": "", "metadata": {"dbkey": "?"}, "model_class": "HistoryDatasetAssociation", "name": "stripes.png", "peek": "Image in png format", "state": "ok", "tags": [], "tool_version": null, "update_time": "2025-05-24 11:18:13.393458", "validated_state": "unknown", "validated_state_message": null, "visible": true}
28.10567734682406
100.0
peek image
6.26463700234192
21.4
WorkflowInvocationStep state
4.566744730679156
15.6
computer science
47.57085020242915
23.5
mathematical and computer sciences
47.9586806286303
0.684264749288559
template
2.4902975420439843
7.7
WorkflowRequestInputParameter name
6.411007025761124
21.9
work
3.007761966364813
9.3
## Workflow Parameters
- **input:**
- __class__: `NoReplacement`
- **adv:**
- colormap: `RdBu_r`
- format_date: ``
- format_plot: ``
- nxsplit: `None`
- xname: ``
- **ifilename:**
- __class__: `ConnectedValue`
- **title:** `My ScienceLive Stripes`
- **variable:** `tg_anomalies_freiburg`
2.6981450252951094
9.6
May-24-2025 11:15:37
climate stripe
2.2248243559718968
7.6
Process Run Crate
0.1
Workflow Run Crate
0.1
Workflow RO-Crate
1.0
Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64
2025-05-27T10:25:53+00:00
COMPSs matmul_files.py execution at MacBook-Pro-Raul-2025.local
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.0
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.1
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.0
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.1
2025-05-27T10:25:48+00:00
application_sources/matmul_files.py
#compss_home
#compss_python_version
#localhost.matmul_tasks.multiply.avgTime
#localhost.matmul_tasks.multiply.executions
#localhost.matmul_tasks.multiply.maxTime
#localhost.matmul_tasks.multiply.minTime
#overall.matmul_files.py.executionTime
#overall.matmul_tasks.multiply.avgTime
#overall.matmul_tasks.multiply.executions
#overall.matmul_tasks.multiply.maxTime
#overall.matmul_tasks.multiply.minTime
COMPSs
COMPSs Programming Model
3.3.3
COMPSS_HOME
/Users/rsirvent/opt/COMPSs/
COMPSS_PYTHON_VERSION
3.10.16
avgTime
68
executions
8
maxTime
106
minTime
34
executionTime
5781
avgTime
68
executions
8
maxTime
106
minTime
34
Lezzi
Daniele
Daniele Lezzi
Vázquez Novoa
Fernando
Fernando Vázquez Novoa
Amela Milian
Ramon
Ramon Amela Milian
Conejero
Javier
Javier Conejero
Iraola de Acevedo
Eduardo
Eduardo Iraola de Acevedo
Vergés
Pere
Pere Vergés
Puigdemunt-Schmolling
Gabriel
Gabriel Puigdemunt-Schmolling
Bertran
Marta
Marta Bertran
Álvarez Vecino
Pol
Pol Álvarez Vecino
francesc.lordan@bsc.es
Lordan
Francesc
Francesc Lordan
Foyer
Clément
Clément Foyer
Sirvent
Raül
Raül Sirvent
Mammadli
Nihad
Nihad Mammadli
Badia
Rosa M
Rosa M Badia
Ramon-Cortes Vilarrodona
Cristian
Cristian Ramon-Cortes Vilarrodona
Ejarque
Jorge
Jorge Ejarque
Tatu
Cristian Cătălin
Cristian Cătălin Tatu
Giacomini
Nicolò
Nicolò Giacomini
Dabral
Archit
Archit Dabral
Indian Institute of Technology BHU
Universitat Politècnica de Catalunya
Association for Computing Machinery
Baku State University
Barcelona Supercomputing Center
Author
francesc.lordan@bsc.es
francesc.lordan@bsc.es
size
10.867052023121387
9.4
out-of-core using file
30.78470824949698
30.6
using
19.64085297418631
17.5
size 2x2
0.5030181086519114
0.5
other earth sciences
61.94550662720011
0.7036855816841125
using file
1.6096579476861166
1.6
computer operations and hardware
91.3961987340549
0.5383046269416809
block size 2x2 element
45.774647887323944
45.5
COMPSs Matrix Multiplication, out-of-core using files.
16.216216216216214
16.2
hyper
11.791907514450866
10.2
mathematical and computer sciences
91.3961987340549
0.5383046269416809
hyper
13.131313131313131
11.7
earth sciences
61.94550662720011
0.7036855816841125
element
45.79124579124579
40.8
earth sciences
38.05449337279989
0.43228960037231445
block size
21.436588103254774
19.1
element
40.80924855491329
35.3
Disabled
Society/Mankind/Disabled
using
17.22543352601156
14.9
oceanography
38.05449337279989
0.43228960037231445
block size
19.30635838150289
16.7
space sciences (general)
8.603801265945089
0.05067460238933563
Hypermatrix size 2x2 blocks, block size 2x2 elements
83.78378378378378
83.7
space sciences
8.603801265945089
0.05067460238933563
matrix size 2x2
21.327967806841045
21.2
10.24424/rwf8-yj04
False
2025-05-27 10:46:53.984042+00:00
0
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2025-05-27 10:25:54+00:00
2025-10-16 11:37:30.481824+00:00
2025-05-27 10:25:54+00:00
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Hypermatrix size 2x2 blocks, block size 2x2 elements
application/ld+json
https://w3id.org/ro-id/f8958193-a08c-4f9f-a26c-7bf3f640d76e
application_sources/matmul_files.py
#COMPSs_Workflow_Run_Crate_MacBook-Pro-Raul-2025.local_7defb487-c7d1-4c81-b77e-e886b9c7cbdf
COMPSs Matrix Multiplication, out-of-core using files - snapshot
COMPSs Matrix Multiplication, out-of-core using files
Cristian Ramon-Cortes Vilarrodona, https://orcid.org/0009-0003-8848-9436, Nihad Mammadli, Jorge Ejarque, Pol Álvarez Vecino, Gabriel Puigdemunt-Schmolling, Pere Vergés, et al. "COMPSs Matrix Multiplication, out-of-core using files." ROHub. May 27 ,2025. https://doi.org/10.24424/rwf8-yj04.
16
A.0.0
16
A.0.1
16
A.1.0
16
A.1.1
16
B.0.0
16
B.0.1
16
B.1.0
16
B.1.1
20
C.0.0
20
C.0.1
20
C.1.0
20
C.1.1
application_sources
6313
https://api.rohub.org/api/resources/5b331be1-dea5-4582-9bab-897dbe4cbd93/download/
2025-05-27 10:27:54.083294+00:00
2025-05-27 10:46:53.888452+00:00
The graph diagram of the workflow, automatically generated by COMPSs runtime
https://www.nationalarchives.gov.uk/PRONOM/fmt/92
complete_graph.svg
2025-05-27 10:27:54.083294+00:00
03fc6c911f447c2465e0d418fce444fdb574a6534fb66e086ff131ea23df414e
242
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2025-05-27 10:27:54.086528+00:00
2025-05-27 10:46:48.145820+00:00
COMPSs application Tasks profile
https://www.nationalarchives.gov.uk/PRONOM/fmt/817
App_Profile.json
2025-05-27 10:27:54.086528+00:00
6fdc527f609cad0e6b5ff9dd7f7a7bdfc05fac884d54747fc0d5f5da627e52c7
1549
https://api.rohub.org/api/resources/b49bdb09-698f-4bf6-93ef-44f232720598/download/
2025-05-27 10:27:54.085110+00:00
2025-05-27 10:46:49.576367+00:00
Auxiliary File
text/plain
matmul_tasks.py
2025-05-27 10:27:54.085110+00:00
154
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2025-05-27 10:27:54.087244+00:00
2025-05-27 10:46:50.643764+00:00
COMPSs submission command line (runcompss / enqueue_compss), including flags and parameters passed to the application
text/plain
compss_submission_command_line.txt
2025-05-27 10:27:54.087244+00:00
26cfe40aee0664efe823e349544073530f24b8e63852167d255fcc5082dd93ba
4076
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2025-05-27 10:27:54.082178+00:00
2025-05-27 10:46:51.473973+00:00
COMPSs Workflow Provenance YAML configuration file
AUTHORS_COMPSS_COMPLETE.yaml
2025-05-27 10:27:54.082178+00:00
46ec0e3f267505f663c4b36d8ef4a0f123bccac284ba75941640dbd27456e66c
2212
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2025-05-27 10:27:54.085813+00:00
2025-05-27 10:46:47.327353+00:00
Main file of the COMPSs workflow source files
text/plain
complete_graph.svg
matmul_files.py
#compss
2025-05-27 10:27:54.085813+00:00
Process Run Crate
0.5
Provenance Run Crate
0.5
Workflow Run Crate
0.5
Workflow RO-Crate
1.0
JSON Data Interchange Format
YAML
Scalable Vector Graphics
Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64
2025-05-27T10:25:53+00:00
COMPSs matmul_files.py execution at MacBook-Pro-Raul-2025.local
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.0
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.1
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.0
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.1
2025-05-27T10:25:48+00:00
application_sources/matmul_files.py
#compss_home
#compss_python_version
#localhost.matmul_tasks.multiply.avgTime
#localhost.matmul_tasks.multiply.executions
#localhost.matmul_tasks.multiply.maxTime
#localhost.matmul_tasks.multiply.minTime
#overall.matmul_files.py.executionTime
#overall.matmul_tasks.multiply.avgTime
#overall.matmul_tasks.multiply.executions
#overall.matmul_tasks.multiply.maxTime
#overall.matmul_tasks.multiply.minTime
COMPSs
COMPSs Programming Model
3.3.3
COMPSS_HOME
/Users/rsirvent/opt/COMPSs/
COMPSS_PYTHON_VERSION
3.10.16
avgTime
68
executions
8
maxTime
106
minTime
34
executionTime
5781
avgTime
68
executions
8
maxTime
106
minTime
34
Lezzi
Daniele
Daniele Lezzi
Vázquez Novoa
Fernando
Fernando Vázquez Novoa
Amela Milian
Ramon
Ramon Amela Milian
Conejero
Javier
Javier Conejero
Iraola de Acevedo
Eduardo
Eduardo Iraola de Acevedo
Vergés
Pere
Pere Vergés
Puigdemunt-Schmolling
Gabriel
Gabriel Puigdemunt-Schmolling
Bertran
Marta
Marta Bertran
Álvarez Vecino
Pol
Pol Álvarez Vecino
francesc.lordan@bsc.es
Lordan
Francesc
Francesc Lordan
Foyer
Clément
Clément Foyer
Sirvent
Raül
Raül Sirvent
Mammadli
Nihad
Nihad Mammadli
Badia
Rosa M
Rosa M Badia
Ramon-Cortes Vilarrodona
Cristian
Cristian Ramon-Cortes Vilarrodona
Ejarque
Jorge
Jorge Ejarque
Tatu
Cristian Cătălin
Cristian Cătălin Tatu
Giacomini
Nicolò
Nicolò Giacomini
Dabral
Archit
Archit Dabral
Indian Institute of Technology BHU
Universitat Politècnica de Catalunya
Association for Computing Machinery
Baku State University
Barcelona Supercomputing Center
Author
francesc.lordan@bsc.es
francesc.lordan@bsc.es
10.24424/28a3-r044
False
2025-05-27 17:56:14.628047+00:00
0
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2025-05-27 10:25:54+00:00
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2025-05-27 10:25:54+00:00
Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64
Hypermatrix size 2x2 blocks, block size 2x2 elements
application/ld+json
https://w3id.org/ro-id/9bfe8543-c088-4745-95c9-1f582c516dc6
application_sources/matmul_files.py
#COMPSs_Workflow_Run_Crate_MacBook-Pro-Raul-2025.local_7defb487-c7d1-4c81-b77e-e886b9c7cbdf
COMPSs Matrix Multiplication, out-of-core using files - snapshot
COMPSs Matrix Multiplication, out-of-core using files
Cristian Ramon-Cortes Vilarrodona, https://orcid.org/0009-0003-8848-9436, Nihad Mammadli, Jorge Ejarque, Pol Álvarez Vecino, Gabriel Puigdemunt-Schmolling, Pere Vergés, et al. "COMPSs Matrix Multiplication, out-of-core using files." ROHub. May 27 ,2025. https://doi.org/10.24424/28a3-r044.
16
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A.0.1
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A.1.0
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A.1.1
16
B.0.0
16
B.0.1
16
B.1.0
16
B.1.1
20
C.0.0
20
C.0.1
20
C.1.0
20
C.1.1
application_sources
4076
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2025-05-27 10:27:54.082178+00:00
2025-05-27 17:56:05.768985+00:00
COMPSs Workflow Provenance YAML configuration file
https://www.nationalarchives.gov.uk/PRONOM/fmt/818
AUTHORS_COMPSS_COMPLETE.yaml
2025-05-27 10:27:54.082178+00:00
46ec0e3f267505f663c4b36d8ef4a0f123bccac284ba75941640dbd27456e66c
1549
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Auxiliary File
text/plain
matmul_tasks.py
2025-05-27 10:27:54.085110+00:00
242
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2025-05-27 10:27:54.086528+00:00
2025-05-27 17:56:14.539842+00:00
COMPSs application Tasks profile
https://www.nationalarchives.gov.uk/PRONOM/fmt/817
App_Profile.json
2025-05-27 10:27:54.086528+00:00
6fdc527f609cad0e6b5ff9dd7f7a7bdfc05fac884d54747fc0d5f5da627e52c7
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2025-05-27 10:27:54.087244+00:00
2025-05-27 17:56:06.824982+00:00
COMPSs submission command line (runcompss / enqueue_compss), including flags and parameters passed to the application
text/plain
compss_submission_command_line.txt
2025-05-27 10:27:54.087244+00:00
26cfe40aee0664efe823e349544073530f24b8e63852167d255fcc5082dd93ba
2212
https://api.rohub.org/api/resources/c483c9d5-fe79-46d5-83ae-1f0396f27469/download/
2025-05-27 10:27:54.085813+00:00
2025-05-27 17:56:04.607316+00:00
Main file of the COMPSs workflow source files
text/plain
complete_graph.svg
matmul_files.py
#compss
2025-05-27 10:27:54.085813+00:00
6313
https://api.rohub.org/api/resources/d4088ff7-ced1-4596-ba85-a02a0aca4eb1/download/
2025-05-27 10:27:54.083294+00:00
2025-05-27 17:56:01.479452+00:00
The graph diagram of the workflow, automatically generated by COMPSs runtime
https://www.nationalarchives.gov.uk/PRONOM/fmt/92
complete_graph.svg
2025-05-27 10:27:54.083294+00:00
03fc6c911f447c2465e0d418fce444fdb574a6534fb66e086ff131ea23df414e
oceanography
38.05449337279989
0.43228960037231445
size
10.867052023121387
9.4
block size 2x2 element
45.774647887323944
45.5
element
40.80924855491329
35.3
element
45.79124579124579
40.8
using
17.22543352601156
14.9
matrix size 2x2
21.327967806841045
21.2
COMPSs Matrix Multiplication, out-of-core using files.
16.216216216216214
16.2
earth sciences
38.05449337279989
0.43228960037231445
block size
21.436588103254774
19.1
hyper
11.791907514450866
10.2
Disabled
Society/Mankind/Disabled
using file
1.6096579476861166
1.6
size 2x2
0.5030181086519114
0.5
out-of-core using file
30.78470824949698
30.6
space sciences (general)
8.603801265945089
0.05067460238933563
space sciences
8.603801265945089
0.05067460238933563
block size
19.30635838150289
16.7
earth sciences
61.94550662720011
0.7036855816841125
computer operations and hardware
91.3961987340549
0.5383046269416809
other earth sciences
61.94550662720011
0.7036855816841125
mathematical and computer sciences
91.3961987340549
0.5383046269416809
Hypermatrix size 2x2 blocks, block size 2x2 elements
83.78378378378378
83.7
hyper
13.131313131313131
11.7
using
19.64085297418631
17.5
Process Run Crate
0.5
Provenance Run Crate
0.5
Workflow Run Crate
0.5
Workflow RO-Crate
1.0
JSON Data Interchange Format
YAML
Scalable Vector Graphics
Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64
2025-05-27T10:25:53+00:00
COMPSs matmul_files.py execution at MacBook-Pro-Raul-2025.local
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.0
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.1
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.0
file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.1
2025-05-27T10:25:48+00:00
application_sources/matmul_files.py
#compss_home
#compss_python_version
#localhost.matmul_tasks.multiply.avgTime
#localhost.matmul_tasks.multiply.executions
#localhost.matmul_tasks.multiply.maxTime
#localhost.matmul_tasks.multiply.minTime
#overall.matmul_files.py.executionTime
#overall.matmul_tasks.multiply.avgTime
#overall.matmul_tasks.multiply.executions
#overall.matmul_tasks.multiply.maxTime
#overall.matmul_tasks.multiply.minTime
COMPSs
COMPSs Programming Model
3.3.3
COMPSS_HOME
/Users/rsirvent/opt/COMPSs/
COMPSS_PYTHON_VERSION
3.10.16
avgTime
68
executions
8
maxTime
106
minTime
34
executionTime
5781
avgTime
68
executions
8
maxTime
106
minTime
34
Lezzi
Daniele
Daniele Lezzi
Vázquez Novoa
Fernando
Fernando Vázquez Novoa
Amela Milian
Ramon
Ramon Amela Milian
Conejero
Javier
Javier Conejero
Iraola de Acevedo
Eduardo
Eduardo Iraola de Acevedo
Vergés
Pere
Pere Vergés
Puigdemunt-Schmolling
Gabriel
Gabriel Puigdemunt-Schmolling
Bertran
Marta
Marta Bertran
Álvarez Vecino
Pol
Pol Álvarez Vecino
francesc.lordan@bsc.es
Lordan
Francesc
Francesc Lordan
Foyer
Clément
Clément Foyer
Sirvent
Raül
Raül Sirvent
Mammadli
Nihad
Nihad Mammadli
Badia
Rosa M
Rosa M Badia
Ramon-Cortes Vilarrodona
Cristian
Cristian Ramon-Cortes Vilarrodona
Ejarque
Jorge
Jorge Ejarque
Tatu
Cristian Cătălin
Cristian Cătălin Tatu
Giacomini
Nicolò
Nicolò Giacomini
Dabral
Archit
Archit Dabral
Indian Institute of Technology BHU
Universitat Politècnica de Catalunya
Association for Computing Machinery
Baku State University
Barcelona Supercomputing Center
Author
francesc.lordan@bsc.es
francesc.lordan@bsc.es
10.24424/m037-s338
False
2025-05-29 12:43:56.884943+00:00
0
https://api.rohub.org/api/ros/d2838de8-72fc-4d17-83c3-fed943ac78f0/crate/download/
2025-05-27 10:25:54+00:00
2025-10-16 11:36:45.604258+00:00
2025-05-27 10:25:54+00:00
Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64
Hypermatrix size 2x2 blocks, block size 2x2 elements
application/ld+json
https://w3id.org/ro-id/d2838de8-72fc-4d17-83c3-fed943ac78f0
application_sources/matmul_files.py
#COMPSs_Workflow_Run_Crate_MacBook-Pro-Raul-2025.local_7defb487-c7d1-4c81-b77e-e886b9c7cbdf
COMPSs Matrix Multiplication, out-of-core using files - snapshot
COMPSs Matrix Multiplication, out-of-core using files
Cristian Ramon-Cortes Vilarrodona, https://orcid.org/0009-0003-8848-9436, Nihad Mammadli, Jorge Ejarque, Pol Álvarez Vecino, Gabriel Puigdemunt-Schmolling, Pere Vergés, et al. "COMPSs Matrix Multiplication, out-of-core using files." ROHub. May 27 ,2025. https://doi.org/10.24424/m037-s338.
16
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B.1.0
16
B.1.1
20
C.0.0
20
C.0.1
20
C.1.0
20
C.1.1
application_sources
1549
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Auxiliary File
text/plain
matmul_tasks.py
2025-05-27 10:27:54.085110+00:00
242
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2025-05-27 10:27:54.086528+00:00
2025-05-29 12:43:49.830103+00:00
COMPSs application Tasks profile
https://www.nationalarchives.gov.uk/PRONOM/fmt/817
App_Profile.json
2025-05-27 10:27:54.086528+00:00
6fdc527f609cad0e6b5ff9dd7f7a7bdfc05fac884d54747fc0d5f5da627e52c7
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2025-05-27 10:27:54.082178+00:00
2025-05-29 12:43:52.839172+00:00
COMPSs Workflow Provenance YAML configuration file
https://www.nationalarchives.gov.uk/PRONOM/fmt/818
AUTHORS_COMPSS_COMPLETE.yaml
2025-05-27 10:27:54.082178+00:00
46ec0e3f267505f663c4b36d8ef4a0f123bccac284ba75941640dbd27456e66c
154
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2025-05-27 10:27:54.087244+00:00
2025-05-29 12:43:51.868361+00:00
COMPSs submission command line (runcompss / enqueue_compss), including flags and parameters passed to the application
text/plain
compss_submission_command_line.txt
2025-05-27 10:27:54.087244+00:00
26cfe40aee0664efe823e349544073530f24b8e63852167d255fcc5082dd93ba
2212
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2025-05-27 10:27:54.085813+00:00
2025-05-29 12:43:48.857963+00:00
Main file of the COMPSs workflow source files
text/plain
complete_graph.svg
matmul_files.py
#compss
2025-05-27 10:27:54.085813+00:00
6313
https://api.rohub.org/api/resources/f5cf6566-7ce3-40a3-a8ec-b1d96689b850/download/
2025-05-27 10:27:54.083294+00:00
2025-05-29 12:43:54.435390+00:00
The graph diagram of the workflow, automatically generated by COMPSs runtime
https://www.nationalarchives.gov.uk/PRONOM/fmt/92
complete_graph.svg
2025-05-27 10:27:54.083294+00:00
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earth sciences
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element
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computer operations and hardware
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size
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Hypermatrix size 2x2 blocks, block size 2x2 elements
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space sciences
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block size 2x2 element
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using
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using
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space sciences (general)
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element
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out-of-core using file
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oceanography
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using file
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matrix size 2x2
21.327967806841045
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block size
19.30635838150289
16.7
mathematical and computer sciences
91.3961987340549
0.5383046269416809
hyper
13.131313131313131
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COMPSs Matrix Multiplication, out-of-core using files.
16.216216216216214
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block size
21.436588103254774
19.1
size 2x2
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Disabled
Society/Mankind/Disabled
other earth sciences
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earth sciences
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hyper
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Process Run Crate
0.5
Provenance Run Crate
0.5
Workflow Run Crate
0.5
Workflow RO-Crate
1.0
JSON Data Interchange Format
YAML
Scalable Vector Graphics
Chemistry
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False
2025-07-04 09:08:44.261623+00:00
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2022-01-12 16:34:39.917729+00:00
2025-10-16 11:15:06.613810+00:00
2022-01-12 16:34:39.917729+00:00
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom.
application/ld+json
https://w3id.org/ro-id/de0b3951-0fa7-4b03-a1fa-d5c4da93a476
Aromatic compounds - snapshot
Aromatic compounds
MANUAL
Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/jxpj-vv36.
arene
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4.2
aliphatic compound
4.737903225806451
4.7
The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds.
21.401515151515152
11.3
Jewellery
Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery
oxygen atom
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4.0
nitrogen
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3.9
organic chemistry
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41.0
oxygen atom
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7.1
benzene
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9.2
geochemistry
100.0
0.4569866955280304
heterocyclic compound
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5.6
chemistry and materials
100.0
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aromatic
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19.5
benzene
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7.3
monocyclic ring
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chemistry and materials (general)
100.0
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arene
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4.9
electron
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4.4
chemistry
34.08360128617363
21.2
scent
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4.5
aromatic hydrocarbon
5.94758064516129
5.9
chemical compound
15.826086956521738
9.1
nitrogen atom
29.573934837092732
11.8
aromatic hydrocarbon
8.695652173913043
5.0
ring
3.125
3.1
The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
39.015151515151516
20.6
organic compound
3.8306451612903225
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chemical compound
10.786290322580644
10.7
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them.
39.583333333333336
20.9
carbon atom
15.999999999999998
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aromatic compound benzene
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9.9
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
heterocyclic compound
6.451612903225806
6.4
aromatic compound
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17.1
larger compound
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earth sciences
100.0
0.4569866955280304
carbon atom
10.786290322580644
10.7
benzene ring
3.528225806451613
3.5
Chemistry
10.24424/070n-rr14
False
2025-07-05 18:47:59.392957+00:00
0
https://api.rohub.org/api/ros/ba53e480-17bb-466f-b789-3533246d7b43/crate/download/
2022-01-12 16:34:39.917729+00:00
2025-10-16 11:14:31.884055+00:00
2022-01-12 16:34:39.917729+00:00
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom.
application/ld+json
https://w3id.org/ro-id/ba53e480-17bb-466f-b789-3533246d7b43
Aromatic compounds - snapshot
Aromatic compounds
MANUAL
Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/070n-rr14.
chemistry
34.08360128617363
21.2
scent
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4.5
aromatic
19.657258064516128
19.5
The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds.
21.401515151515152
11.3
benzene
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9.2
carbon atom
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10.7
geochemistry
100.0
0.4569866955280304
aromatic compound benzene
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9.9
aromatic compound
29.739130434782613
17.1
larger compound
13.533834586466165
5.4
arene
4.939516129032259
4.9
Jewellery
Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery
The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
39.015151515151516
20.6
arene
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4.2
oxygen atom
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7.1
carbon atom
15.999999999999998
9.2
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
electron
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4.4
aromatic hydrocarbon
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chemical compound
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9.1
benzene ring
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3.5
heterocyclic compound
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aromatic hydrocarbon
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5.9
nitrogen
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3.9
organic compound
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3.8
chemistry and materials (general)
100.0
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earth sciences
100.0
0.4569866955280304
monocyclic ring
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5.7
aliphatic compound
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benzene
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chemical compound
10.786290322580644
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organic chemistry
65.91639871382637
41.0
chemistry and materials
100.0
0.8506659269332886
heterocyclic compound
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5.6
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them.
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20.9
oxygen atom
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4.0
nitrogen atom
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ring
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Chemistry
https://doi.org/10.24424/x0cn-va37
False
2025-07-05 19:04:55.078129+00:00
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2022-01-12 16:34:39.917729+00:00
2025-10-16 11:14:13.082777+00:00
2022-01-12 16:34:39.917729+00:00
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom.
application/ld+json
https://w3id.org/ro-id/54c22dc5-ace3-4aaa-be62-b5b4dab97be6
Aromatic compounds - snapshot
Aromatic compounds
MANUAL
Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/x0cn-va37.
chemical compound
15.826086956521738
9.1
The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
39.015151515151516
20.6
aromatic hydrocarbon
5.94758064516129
5.9
benzene
9.274193548387096
9.2
carbon atom
15.999999999999998
9.2
chemical compound
10.786290322580644
10.7
electron
4.435483870967742
4.4
oxygen atom
4.032258064516129
4.0
arene
4.939516129032259
4.9
chemistry
34.08360128617363
21.2
organic chemistry
65.91639871382637
41.0
chemistry and materials
100.0
0.8506659269332886
scent
4.536290322580645
4.5
heterocyclic compound
9.73913043478261
5.6
benzene
12.695652173913043
7.3
earth sciences
100.0
0.4569866955280304
geochemistry
100.0
0.4569866955280304
The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds.
21.401515151515152
11.3
nitrogen atom
29.573934837092732
11.8
aromatic compound
29.739130434782613
17.1
arene
7.304347826086956
4.2
Jewellery
Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery
aliphatic compound
4.737903225806451
4.7
Organic chemical
Economy, business and finance/Economic sector/Chemicals/Organic chemical
benzene ring
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3.5
larger compound
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5.4
nitrogen
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3.9
heterocyclic compound
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6.4
aromatic hydrocarbon
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5.0
aromatic
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19.5
organic compound
3.8306451612903225
3.8
carbon atom
10.786290322580644
10.7
monocyclic ring
14.285714285714286
5.7
chemistry and materials (general)
100.0
0.8506659269332886
aromatic compound benzene
24.81203007518797
9.9
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them.
39.583333333333336
20.9
ring
3.125
3.1
oxygen atom
17.794486215538846
7.1
Biology
10.24424/20ms-v465
False
2025-08-12 08:02:25.321821+00:00
0
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2025-10-16 11:12:08.755267+00:00
2022-01-19 13:47:59.181939+00:00
Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate.Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function. For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance. Additionally, it is associated with other mental disorders and substance use disorders. Although it causes impairment, particularly in modern society, many people with ADHD have sustained attention for tasks they find interesting or rewarding, known as hyperfocus.
application/ld+json
https://w3id.org/ro-id/07b99b7b-a209-44cc-86fd-327339b2599c
Attention deficit hyperactivity disorder - snapshot
Attention deficit hyperactivity disorder
MANUAL
Wolniewicz, Małgorzata. "Attention deficit hyperactivity disorder." ROHub. Jan 19 ,2022. https://doi.org/10.24424/20ms-v465.
life sciences
100.0
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distraction
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neurodevelopmental disorder
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49.0
environmental science and management
100.0
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behavioural disorder
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environmental sciences
100.0
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inattention
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substance use disorder
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life sciences (general)
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diagnosis
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medicine
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individual
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behavioral disorder
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individuals with ADHD
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mental disorder
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problem
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attention
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diagnosis
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disorder
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symptom
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attention deficit hyperactivity disorder
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Mental and behavioural disorder
Health/Diseases and conditions/Mental and behavioural disorder
mental disorders
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3.7
Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate.
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difficulty
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disorder
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For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance.
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emotions
4.984423676012462
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School
Education/School
Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function.
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difficulty
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attention deficit hyperactivity disorder
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school performance
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4.4
problem
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7.7
Earth sciences
10.13039/501100000780
European Commission
10.13039/501100000781
European Commission
Elisa Trasatti
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-11-08 16:30:52.813503+00:00
2021-11-08 17:06:22.193615+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-11-08 16:30:52.813503+00:00
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-11-08 16:31:25.130170+00:00
2021-11-08 17:06:22.296703+00:00
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-11-08 16:31:25.130170+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-11-08 16:31:09.076275+00:00
2021-11-08 17:06:22.491861+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-11-08 16:31:09.076275+00:00
101017501
RELIANCE
Research Lifecycle Management for Earth Science Communities and Copernicus Users
101017502
RELIANCE
Research Lifecycle Management for Earth Science Communities and Copernicus Users
POINT (38.0 38.0)
5926d4c9-986f-42f2-a840-79ae265f653f
POINT (38.0 38.0)
38.0
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POINT (38.0 38.0)
False
2021-11-08 17:06:28.738078+00:00
79418
https://api.rohub.org/api/ros/bcb5cdba-0605-4602-bd60-b59f2701e05b/crate/download/
2021-11-08 15:12:22.689370+00:00
2025-10-16 10:35:19.041970+00:00
2021-11-08 15:12:22.689370+00:00
This Research Object demonstrate how to compute monthly map of PM10 over your country - modified
application/ld+json
https://w3id.org/ro-id/bcb5cdba-0605-4602-bd60-b59f2701e05b
8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot
MANUAL
Jose Perez, and Elisa Trasatti. "8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 08 ,2021. https://doi.org/10.24424/1k12-x394.
ICHB-PAS
Jose Perez
PSNC
73394
https://api.rohub.org/api/resources/1f611f7e-a4b7-45de-be8e-d6f0e39d2fde/download/
2021-11-08 16:30:06.553639+00:00
2021-11-08 17:06:22.592157+00:00
image/png
flow-dcro.png
2021-11-08 16:30:06.553639+00:00
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
EU_CAMS_SURFACE_PM10_G
Flow to compute monthly map
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
research object
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map
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Copernicus Atmosphere Monitoring Service
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object
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research
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data cube research object
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8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot.
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research
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Palma, Raul. "9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 09 ,2021. https://doi.org/10.24424/j2gh-5322.
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
https://zenodo.org/record/5554786#.YYlWo9nMI-Q
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This dataset provides daily air quality analyses and forecasts for Europe.
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2021-11-09 15:51:51.850517+00:00
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Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
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Palma, Raul. "9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 09 ,2021. https://doi.org/10.24424/yw22-x266.
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
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EU_CAMS_SURFACE_PM10_G
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
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Flow to compute monthly map
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
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This Research Object demonstrate how to compute monthly map of PM10 over your country - modified
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https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1
9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot
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https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1/67781900-3d58-4580-83ff-ffe019453c87
https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1/bc445fcb-5960-4feb-a1ae-5ca50453ad6e
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https://w3id.org/ro-id/b7592ce2-424e-435f-b9e7-036738c1f17e
Palma, Raul. "9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 09 ,2021. https://doi.org/10.24424/zt8j-c157.
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
https://zenodo.org/record/5554786#.YYlWo9nMI-Q
2021-11-09 15:52:03.894247+00:00
2021-11-10 19:38:07.510465+00:00
https://zenodo.org/record/5554786#.YYlWo9nMI-Q
2021-11-09 15:52:03.894247+00:00
This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
EU_CAMS_SURFACE_PM10_G
73394
https://api.rohub.org/api/resources/7f087685-b1b1-42dc-90b0-ee6b56b2ab75/download/
2021-11-09 15:51:45.742090+00:00
2021-11-10 19:38:07.580119+00:00
image/png
flow-dcro.png
2021-11-09 15:51:45.742090+00:00
Flow to compute monthly map
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-11-09 15:51:51.850517+00:00
2021-11-10 19:38:07.439709+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-11-09 15:51:51.850517+00:00
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-11-09 15:51:59.534956+00:00
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2021-11-09 15:51:59.534956+00:00
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2021-11-09 15:51:56.143768+00:00
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
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PCSS
example3@hotmail.com
Pepito Bato
0000-0002-8316-3192
UNO-Recoletos
npepito@hotmail.com
Nieves Pepito
0000-0003-3784-6651
office@man.poznan.pl
025cj6e44
Poznan Supercomputing and Networking Center
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86656
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This Research Object demonstrate how to compute monthly map of PM10 over your country - modified
application/ld+json
https://w3id.org/ro-id/abebc0e7-87b6-4ed5-8a0e-9b71dc30e333
8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot
Copernicus Atmosphere Monitoring Service Data Cube RO December 8th - published v1
MANUAL
https://w3id.org/ro-id/abebc0e7-87b6-4ed5-8a0e-9b71dc30e333/df4db37c-7304-430d-b08e-ba41cdc33e9e
Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 8th - published v1." ROHub. Dec 08 ,2021. https://doi.org/10.24424/fehe-jb26.
metadata
data
biblio
raw data
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
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image/png
flow-dcro.png
2021-12-08 21:44:36.949407+00:00
This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
EU_CAMS_SURFACE_PM10_G
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-08 21:44:42.801819+00:00
2021-12-08 22:01:19.788776+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
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https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-08 21:44:46.533341+00:00
2021-12-08 22:01:20.217111+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-08 21:44:46.533341+00:00
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
Catch data records sample from 2019
Catch data from Norway
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-08 21:44:52.711669+00:00
2023-05-16 16:52:12.400121+00:00
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-08 21:44:52.711669+00:00
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-08 21:44:55.989277+00:00
2021-12-08 22:01:19.992473+00:00
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-08 21:44:55.989277+00:00
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
neworg1@example.org
abcd123
Example Org 1
Earth sciences
published v2
monthly map of PM10
Copernicus Atmosphere Monitoring Service Data Cube Ro
country
map
Ro
monthly map
map of PM10
PCSS
example3@hotmail.com
Pepito Bato
0000-0002-8316-3192
UNO-Recoletos
npepito@hotmail.com
Nieves Pepito
0000-0003-3784-6651
office@man.poznan.pl
025cj6e44
Poznan Supercomputing and Networking Center
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This Research Object demonstrate how to compute monthly map of PM10 over your country - modified
application/ld+json
https://w3id.org/ro-id/c737f695-6715-4916-8bef-8fc0ce879760
8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot
Copernicus Atmosphere Monitoring Service Data Cube RO December 8th - published v2
MANUAL
https://w3id.org/ro-id/c737f695-6715-4916-8bef-8fc0ce879760/df4db37c-7304-430d-b08e-ba41cdc33e9e
Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 8th - published v2." ROHub. Dec 08 ,2021. http://doi.org/10.23728/b2share.3c82435c669b49fcaa5541b465e055fa.
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https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-08 21:44:55.989277+00:00
Flow to compute monthly map
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-08 21:44:42.801819+00:00
2021-12-08 22:04:44.543287+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-08 21:44:42.801819+00:00
73394
https://api.rohub.org/api/resources/287efd15-0bd1-474d-88c2-4542e1393d8d/download/
2021-12-08 21:44:36.949407+00:00
2021-12-08 22:04:44.160524+00:00
image/png
flow-dcro.png
2021-12-08 21:44:36.949407+00:00
This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
EU_CAMS_SURFACE_PM10_G
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-08 21:44:52.711669+00:00
2023-05-16 16:53:21.645987+00:00
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-08 21:44:52.711669+00:00
Catch data records sample from 2019
Catch data from Norway
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-08 21:44:46.533341+00:00
2021-12-08 22:04:44.869071+00:00
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2021-12-08 21:44:46.533341+00:00
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2021-12-08 21:44:49.477592+00:00
2021-12-08 22:04:44.654574+00:00
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-12-08 21:44:49.477592+00:00
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
neworg1@example.org
abcd123
Example Org 1
Earth sciences
10.13039/501100000781
European Commission
published v1
monthly map of PM10
Copernicus Atmosphere Monitoring Service Data Cube Ro
country
map
Ro
monthly map
map of PM10
PCSS
example4@hotmail.com
Pepito Bato
0000-0002-8316-3192
UNO-Recoletos
npepito@hotmail.com
Nieves Pepito
0000-0003-3784-6651
office@man.poznan.pl
025cj6e44
Poznan Supercomputing and Networking Center
101017502
RELIANCE
Research Lifecycle Management for Earth Science Communities and Copernicus Users
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This Research Object demonstrate how to compute monthly map of PM10 over your country - modified
application/ld+json
https://w3id.org/ro-id/a08ddcb2-ae5f-40ba-b1b6-c64dd2e4d68c
8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot
Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v1
MANUAL
https://w3id.org/ro-id/a08ddcb2-ae5f-40ba-b1b6-c64dd2e4d68c/ea782618-0dff-4cfa-8604-e121ce29d3cf
Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v1." ROHub. Dec 09 ,2021. https://doi.org/10.24424/w44h-8089.
metadata
data
biblio
raw data
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-12-09 15:07:51.036076+00:00
2021-12-09 15:19:08.564064+00:00
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-12-09 15:07:51.036076+00:00
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-09 15:07:43.448712+00:00
2021-12-09 15:19:08.515865+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-09 15:07:43.448712+00:00
Flow to compute monthly map
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-09 15:07:55.588569+00:00
2023-05-16 16:54:04.603729+00:00
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-09 15:07:55.588569+00:00
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
73394
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2021-12-09 15:07:22.892363+00:00
2021-12-09 15:19:08.338406+00:00
image/png
flow-dcro.png
2021-12-09 15:07:22.892363+00:00
Catch data records sample from 2019
Catch data from Norway
This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
EU_CAMS_SURFACE_PM10_G
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-09 15:07:47.272247+00:00
2021-12-09 15:19:08.713366+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-09 15:07:47.272247+00:00
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-09 15:07:59.055468+00:00
2021-12-09 15:19:08.607528+00:00
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-09 15:07:59.055468+00:00
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
neworg2@example.org
abcd123
Example Org 2
Earth sciences
10.13039/501100000781
European Commission
published v2
monthly map of PM10
Copernicus Atmosphere Monitoring Service Data Cube Ro
country
map
Ro
monthly map
map of PM10
PCSS
example4@hotmail.com
Pepito Bato
0000-0002-8316-3192
UNO-Recoletos
npepito@hotmail.com
Nieves Pepito
0000-0003-3784-6651
office@man.poznan.pl
025cj6e44
Poznan Supercomputing and Networking Center
101017502
RELIANCE
Research Lifecycle Management for Earth Science Communities and Copernicus Users
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38.0
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service-account-enrichment
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2021-12-09 15:20:23.441762+00:00
mailto:rpalma@man.poznan.pl
87383
https://api.rohub.org/api/ros/57cf76e1-2179-4650-b48b-b5990dca86c1/crate/download/
2021-12-09 15:05:57.255344+00:00
2024-03-05 12:17:26.248043+00:00
2021-12-09 15:05:57.255344+00:00
This Research Object demonstrate how to compute monthly map of PM10 over your country - modified
application/ld+json
https://w3id.org/ro-id/57cf76e1-2179-4650-b48b-b5990dca86c1
8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot
Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v2
MANUAL
https://w3id.org/ro-id/57cf76e1-2179-4650-b48b-b5990dca86c1/ea782618-0dff-4cfa-8604-e121ce29d3cf
Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v2." ROHub. Dec 09 ,2021. https://doi.org/10.24424/yptf-km76.
biblio
metadata
raw data
data
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-12-09 15:07:51.036076+00:00
2021-12-09 15:20:20.634446+00:00
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-12-09 15:07:51.036076+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-09 15:07:47.272247+00:00
2021-12-09 15:20:20.738000+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-09 15:07:47.272247+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-09 15:07:43.448712+00:00
2021-12-09 15:20:20.597858+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-09 15:07:43.448712+00:00
Flow to compute monthly map
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-09 15:07:59.055468+00:00
2021-12-09 15:20:20.669306+00:00
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-09 15:07:59.055468+00:00
73394
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2021-12-09 15:07:22.892363+00:00
2021-12-09 15:20:20.444066+00:00
image/png
flow-dcro.png
2021-12-09 15:07:22.892363+00:00
Catch data records sample from 2019
Catch data from Norway
This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
EU_CAMS_SURFACE_PM10_G
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-09 15:07:55.588569+00:00
2023-05-16 16:54:33.185954+00:00
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-09 15:07:55.588569+00:00
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
POINT (38.0 38.0)
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
neworg2@example.org
abcd123
Example Org 2
Earth sciences
10.13039/501100000781
European Commission
published v2
monthly map of PM10
Copernicus Atmosphere Monitoring Service Data Cube Ro
country
map
Ro
monthly map
map of PM10
PCSS
example4@hotmail.com
Pepito Bato
0000-0002-8316-3192
UNO-Recoletos
npepito@hotmail.com
Nieves Pepito
0000-0003-3784-6651
office@man.poznan.pl
025cj6e44
Poznan Supercomputing and Networking Center
101017502
RELIANCE
Research Lifecycle Management for Earth Science Communities and Copernicus Users
56289eeb-73b2-4076-852c-6bf6fee8f381
POINT (38.0 38.0)
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service-account-enrichment
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2021-12-09 15:24:39.649872+00:00
mailto:rpalma@man.poznan.pl
87396
https://api.rohub.org/api/ros/6440c36b-44c8-48c5-9a2a-a3c47de70c8a/crate/download/
2021-12-09 15:05:57.255344+00:00
2024-03-05 12:17:26.121572+00:00
2021-12-09 15:05:57.255344+00:00
This Research Object demonstrate how to compute monthly map of PM10 over your country - modified
application/ld+json
https://w3id.org/ro-id/6440c36b-44c8-48c5-9a2a-a3c47de70c8a
8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot
Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v2
MANUAL
https://w3id.org/ro-id/6440c36b-44c8-48c5-9a2a-a3c47de70c8a/ea782618-0dff-4cfa-8604-e121ce29d3cf
Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v2." ROHub. Dec 09 ,2021. https://doi.org/10.24424/80ze-vx74.
biblio
data
raw data
metadata
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-09 15:07:55.588569+00:00
2023-05-16 16:55:20.098335+00:00
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2021-12-09 15:07:55.588569+00:00
Flow to compute monthly map
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-09 15:07:59.055468+00:00
2021-12-09 15:24:36.452503+00:00
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2021-12-09 15:07:59.055468+00:00
Catch data records sample from 2019
Catch data from Norway
This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
EU_CAMS_SURFACE_PM10_G
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-12-09 15:07:51.036076+00:00
2021-12-09 15:24:36.409139+00:00
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2021-12-09 15:07:51.036076+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-09 15:07:47.272247+00:00
2021-12-09 15:24:36.536458+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2021-12-09 15:07:47.272247+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-09 15:07:43.448712+00:00
2021-12-09 15:24:36.359834+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2021-12-09 15:07:43.448712+00:00
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
73394
https://api.rohub.org/api/resources/fe10d6ac-bc5f-4f26-a4ff-2b617fd1b443/download/
2021-12-09 15:07:22.892363+00:00
2021-12-09 15:24:36.183105+00:00
image/png
flow-dcro.png
2021-12-09 15:07:22.892363+00:00
POINT (38.0 38.0)
Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
neworg2@example.org
abcd123
Example Org 2
Earth sciences
Fundamental Research Funds for Central Universities
European Space Agency (ESA) and Ministry of Science and Technology (MOST), China
Natural Science Foundation of China
Italian Ministry of University
aerospace engineering
data at Changbaishan
Changbaishan Volcano
property of JAXA
raw data property
soil
China
North Korea
velocity
ground velocity
file
raster file
raster
Changbaishan
JAXA
Magma Migration
North Korea
Interior
China
Japan
INGV
cristiano.tolomei@ingv.it
Tolomei, Cristiano
0000-0001-7378-0712
-
Pianeta Dinamico
Working Earth
42071453
-
-
58029
Dragon 5
Cooperation project
N2001027
-
-
POLYGON ((127.82938662 41.702706825, 128.35894877 41.702706825, 128.35894877 42.1858125, 127.82938662 42.1858125, 127.82938662 41.702706825))
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POLYGON ((127.82938662 41.702706825, 128.35894877 41.702706825, 128.35894877 42.1858125, 127.82938662 42.1858125, 127.82938662 41.702706825))
service-account-enrichment
False
https://w3id.org/ro-id/677cf91e-880d-485a-b027-30ba523dac73
2021-12-13 17:51:45.412526+00:00
https://orcid.org/0000-0002-2983-045X
5016613
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2021-12-13 17:49:07.069454+00:00
2024-03-05 12:19:21.893221+00:00
2021-12-13 17:49:07.069454+00:00
This Research Object contains the raster file of the mean ground velocity at the Changbaishan Volcano (China/North Korea) from ALOS-2 satellite data during 2018-2020. Find more on processing and results in the related paper: 'Upward Magma Migration within the Multi-level Plumbing System of the Changbaishan Volcano (China/North Korea) Revealed by the Modeling of 2018-2020 SAR Data' by E. Trasatti, C. Tolomei, L. Wei, G. Ventura. DOI: 10.3389/feart.2021.741287 . Raw data property of JAXA (Japan).
application/ld+json
https://w3id.org/ro-id/61bceafe-5b48-4548-8caf-4142153b1b1b
Ground Velocities from ALOS-2 Data of the Changbaishan Volcanic Area (China/North Korea) - snapshot
Mean ground velocities from ALOS-2 data at Changbaishan volcano (China/North Korea) during 2018-2020
MANUAL
https://w3id.org/ro-id/61bceafe-5b48-4548-8caf-4142153b1b1b/3a69827c-fd1c-4765-a147-5d25c8b8cd38
Trasatti, Elisa, and Tolomei, Cristiano. "Mean ground velocities from ALOS-2 data at Changbaishan volcano (China/North Korea) during 2018-2020." ROHub. Dec 13 ,2021. https://doi.org/10.24424/vfp6-r230.
metadata
raw data
biblio
data
978596
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2021-12-13 17:49:37.806878+00:00
2021-12-13 17:51:43.786630+00:00
image/png
sketch.png
2021-12-13 17:49:37.806878+00:00
Mean ground velocities data
10222
https://api.rohub.org/api/resources/2ca3451c-643c-40de-b793-0280cd331831/download/
2021-12-13 17:49:41.744694+00:00
2021-12-13 17:51:41.042882+00:00
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
List_of_images.xlsx
2021-12-13 17:49:41.744694+00:00
460884
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2021-12-13 17:49:49.252182+00:00
2021-12-13 17:51:42.921270+00:00
image/png
connection_graph.png
2021-12-13 17:49:49.252182+00:00
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2021-12-13 17:49:28.816730+00:00
2021-12-13 17:51:40.107321+00:00
image/tiff
Changbaishan_ALOS2_asc_poly1.tif
2021-12-13 17:49:28.816730+00:00
https://www.frontiersin.org/articles/10.3389/feart.2021.741287/abstract
2021-12-13 17:49:53.455227+00:00
2021-12-13 17:51:39.306605+00:00
https://www.frontiersin.org/articles/10.3389/feart.2021.741287/abstract
2021-12-13 17:49:53.455227+00:00
List of the ALOS-2 images used in the processing.
Paper published in Frontiers Earth Science with data and modelling
link to paper
4891
https://api.rohub.org/api/resources/cfa05a53-9836-4c05-8bd5-b05a3a1ffe03/download/
2021-12-13 17:49:45.522927+00:00
2021-12-13 17:51:41.997249+00:00
application/rtf
readme.rtf
2021-12-13 17:49:45.522927+00:00
Details on the data
Details on the data
Map of the mean ground velocities
POLYGON ((127.82938662 41.702706825, 128.35894877 41.702706825, 128.35894877 42.1858125, 127.82938662 42.1858125, 127.82938662 41.702706825))
Chemistry
service-account-enrichment
False
https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d
2022-01-14 22:19:57.396191+00:00
https://orcid.org/0000-0003-2388-0744
3481
https://api.rohub.org/api/ros/41f2cfe8-4c5b-4d35-849a-5e4aa7be5b42/crate/download/
2022-01-12 16:34:39.917729+00:00
2024-03-05 12:17:02.627855+00:00
2022-01-12 16:34:39.917729+00:00
Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound.
Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom.
application/ld+json
https://w3id.org/ro-id/41f2cfe8-4c5b-4d35-849a-5e4aa7be5b42
Aromatic compounds - snapshot
Aromatic compounds
MANUAL
Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/41f2cfe8-4c5b-4d35-849a-5e4aa7be5b42.
Earth sciences
usage of cam
air quality analysis
analysis from Copernicus Atmosphere Monitoring
area
analysis
usage
map
air quality
reliance service
service
reliance
map of PM10
UiO
jeani@uio.no
Jean Iaquinta
0000-0002-8763-1643
01xtthb56
University of Oslo
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service-account-enrichment
ffb438ba-7570-455e-b28e-e63fa570f3bd
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False
https://w3id.org/ro-id/b13d7b6a-66bf-40df-84c8-f9c88775b6c1
2022-01-18 18:30:58.768020+00:00
mailto:annefou@geo.uio.no
180573
https://api.rohub.org/api/ros/0d5a0619-14d5-4b45-b925-a9432684f76a/crate/download/
2022-01-18 18:28:05.432674+00:00
2024-03-05 12:19:08.696869+00:00
2022-01-18 18:28:05.432674+00:00
This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of PM10 over a given geographical area.
application/ld+json
https://w3id.org/ro-id/0d5a0619-14d5-4b45-b925-a9432684f76a
Jupyter notebook demonstrating the usage of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - snapshot
MANUAL
https://w3id.org/ro-id/0d5a0619-14d5-4b45-b925-a9432684f76a/4b10ec21-8232-4aee-b23d-ee9f37dce383
Anne Foilloux, and Jean Iaquinta. "Jupyter notebook demonstrating the usage of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - snapshot." ROHub. Jan 18 ,2022. https://w3id.org/ro-id/0d5a0619-14d5-4b45-b925-a9432684f76a.
input
output
tool
biblio
Daily average of CAMS Particule matter < 10 μm [μg/m3] over Paris in September 2021
Timeseries of particule matter < 10 μm [μg/m3] over Paris in september 2021
This dataset is a data-Cube retrieved from the ADAM platform over France in September 2019
Data-Cube from ADAM platform over France in September 2019
https://datahub.egi.eu/share/117f0e2a8b5d6615974c6a941b093804ch8b23
2022-01-18 18:30:06.117346+00:00
2022-01-18 18:30:54.343006+00:00
https://datahub.egi.eu/share/117f0e2a8b5d6615974c6a941b093804ch8b23
2022-01-18 18:30:06.117346+00:00
This dataset is a data-Cube retrieved from the ADAM platform over France in September 2020
Data-Cube from ADAM platform over France in September 2020
Geojson file used for retrieving data from the ADAM platform over France
Geojson for France
https://datahub.egi.eu/share/0e87b0cdd21a4c147952d99ed302a957ch3802
2022-01-18 18:30:02.737890+00:00
2022-01-18 18:30:54.402658+00:00
https://datahub.egi.eu/share/0e87b0cdd21a4c147952d99ed302a957ch3802
2022-01-18 18:30:02.737890+00:00
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupyter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
https://datahub.egi.eu/share/f35b2956fc60a70f493e91278e08e3abchf56d
2022-01-18 18:30:26.410290+00:00
2022-01-18 18:30:54.498308+00:00
https://datahub.egi.eu/share/f35b2956fc60a70f493e91278e08e3abchf56d
2022-01-18 18:30:26.410290+00:00
https://datahub.egi.eu/share/e0e426bfbea07306695b82068898bdcachaa15
2022-01-18 18:30:31.670405+00:00
2022-01-18 18:30:54.569917+00:00
https://datahub.egi.eu/share/e0e426bfbea07306695b82068898bdcachaa15
2022-01-18 18:30:31.670405+00:00
Monthly average maps of CAMS Particule matter < 10 μm [μg/m3] over France in 2019, 2020 and 2021
Particule matter < 10 μm [μg/m3] over France for September 2019, 2020 and 2021
Monthly average maps of CAMS Particule matter < 10 μm [μg/m3] over France in 2019, 2020 and 2021
Particule matter < 10 μm [μg/m3] over France for September 2019, 2020 and 2021
https://datahub.egi.eu/share/a078eb09f1e3822c806bdc9cc530288bchf4c2
2022-01-18 18:30:29.465828+00:00
2022-01-18 18:30:54.623804+00:00
https://datahub.egi.eu/share/a078eb09f1e3822c806bdc9cc530288bchf4c2
2022-01-18 18:30:29.465828+00:00
netCDF data corresponding to daily average of CAMS Particule matter < 10 μm [μg/m3] over France for September 2019, September 2020 and September 2021
netCDF data for daily PM10 concentration over France in September 2019, 2020 and 2021
https://datahub.egi.eu/share/e5580c9233f571eacf7eb8ef71c0d7dcch294a
2022-01-18 18:30:23.256523+00:00
2022-01-18 18:30:54.532963+00:00
https://datahub.egi.eu/share/e5580c9233f571eacf7eb8ef71c0d7dcch294a
2022-01-18 18:30:23.256523+00:00
https://datahub.egi.eu/share/0ed7237e5dc09ba8ff353697fef6fc96ch04c1
2022-01-18 18:30:04.435676+00:00
2022-01-18 18:30:54.374174+00:00
https://datahub.egi.eu/share/0ed7237e5dc09ba8ff353697fef6fc96ch04c1
2022-01-18 18:30:04.435676+00:00
154837
https://api.rohub.org/api/resources/c13c349b-c738-4e88-82fa-c67b56f8f08d/download/
2022-01-18 18:28:39.211870+00:00
2022-01-18 18:30:55.438746+00:00
image/png
PM10_september_FR_2019-2021.png
2022-01-18 18:28:39.211870+00:00
Daily average maps of CAMS Particule matter < 10 μm [μg/m3] over France on September 15, 2021
Particule matter < 10 μm [μg/m3] over France on September 15, 2021
https://datahub.egi.eu/share/617299120e542500102b06860f1e6e15ch2f25
2022-01-18 18:30:19.396255+00:00
2022-01-18 18:30:55.512286+00:00
https://datahub.egi.eu/share/617299120e542500102b06860f1e6e15ch2f25
2022-01-18 18:30:19.396255+00:00
This dataset is a data-Cube retrieved from the ADAM platform over France in September 2021
Data-Cube from ADAM platform over France in September 2021
https://datahub.egi.eu/share/8368086b44835af9620d9f0eccf18d7bch39cc
2022-01-18 18:29:59.231939+00:00
2022-01-18 18:30:54.431313+00:00
https://datahub.egi.eu/share/8368086b44835af9620d9f0eccf18d7bch39cc
2022-01-18 18:29:59.231939+00:00
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Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
0000-0002-1784-2920
Earth sciences
published v1
monthly map of PM10
Copernicus Atmosphere Monitoring Service Data Cube Ro
country
map
Ro
monthly map
map of PM10
federica.foglini@ismar.cnr.it
Federica Foglini
PCSS
example5@hotmail.com
Pepito Baston
0000-0002-8316-3195
UNO-Recoletos
npepito@hotmail.com
Nieves Pepito
0000-0003-3784-6651
office@man.poznan.pl
025cj6e44
Poznan Supercomputing and Networking Center
neworg3@example.org
abcd56789
Example Org 3
17ac6881-a240-4b44-974c-a83463aca07a
MULTIPOLYGON (((9.4858320000001 42.615273, 9.49472 42.603607, 9.4827770000001 42.613052, 9.47778 42.617218, 9.465277 42.630829, 9.457777 42.643326, 9.4858320000001 42.615273)), ((9.446665 42.67889, 9.4480550000001 42.64944, 9.452221 42.630272, 9.473888 42.582222, 9.47805 42.576111, 9.50555 42.563889, 9.509998 42.563606, 9.51139 42.56721, 9.511665 42.571663, 9.509443 42.578049, 9.503054 42.59166, 9.497221 42.60083, 9.50028 42.59861, 9.5202770000001 42.572495, 9.531666 42.54916, 9.5338880000001 42.541939, 9.562222 42.272774, 9.5599990000001 42.19221, 9.5555550000001 42.127777, 9.5533330000001 42.115555, 9.54583 42.102219, 9.4480550000001 41.999443, 9.42555 41.975, 9.41111 41.954163, 9.405554 41.934998, 9.397192 41.875931, 9.396666 41.862778, 9.398611 41.85083, 9.402498 41.840271, 9.404444 41.828331, 9.404165 41.81916, 9.398888 41.698883, 9.382776 41.658882, 9.379444 41.651939, 9.366665 41.638611, 9.32055 41.60249, 9.27167 41.520554, 9.241943 41.451942, 9.247499 41.425827, 9.25194 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38.0
38.0
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a384dfb8-5a8b-4ded-86fd-3ac17ecab1d6
POINT (38.0 38.0)
service-account-enrichment
False
https://w3id.org/ro-id/88374f34-bdb3-4449-97b3-8e0a8483aa66
2022-02-17 21:13:07.580080+00:00
mailto:rpalma@man.poznan.pl
103398
https://api.rohub.org/api/ros/4e7a0712-e600-4645-a375-5a9b0d4ef122/crate/download/
2022-02-17 14:58:39.070868+00:00
2024-03-05 12:17:26.373675+00:00
2022-02-17 14:58:39.070868+00:00
This Research Object demonstrate how to compute monthly map of PM10 over your country - modified
application/ld+json
https://w3id.org/ro-id/4e7a0712-e600-4645-a375-5a9b0d4ef122
17th Feb - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot
Copernicus Atmosphere Monitoring Service Data Cube RO Feb 17th - published v1
MANUAL
https://w3id.org/ro-id/4e7a0712-e600-4645-a375-5a9b0d4ef122/0675dd94-c052-4526-9e7f-3274e2a20d63
https://w3id.org/ro-id/4e7a0712-e600-4645-a375-5a9b0d4ef122/a14e28c2-9990-45f1-a7f1-194103406268
https://w3id.org/ro-id/4e7a0712-e600-4645-a375-5a9b0d4ef122/f3ebe1ed-9c2d-4da4-8e05-55df51e8bcbb
Foglini, Federica, Nieves Pepito, and Pepito Baston. "Copernicus Atmosphere Monitoring Service Data Cube RO Feb 17th - published v1." ROHub. Feb 17 ,2022. https://doi.org/10.24424/kh1w-th55.
biblio
data
myfolder
mysubfolder
raw data
metadata
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2022-02-17 15:22:30.059240+00:00
2023-05-16 17:11:30.443960+00:00
https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb
2022-02-17 15:22:30.059240+00:00
This dataset provides daily air quality analyses and forecasts for Europe.
CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.
Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.
EU_CAMS_SURFACE_PM10_G
Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo
Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2022-02-17 15:22:03.118296+00:00
2022-02-17 21:13:02.711907+00:00
https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff
2022-02-17 15:22:03.118296+00:00
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2022-02-17 15:22:15.915421+00:00
2022-02-17 21:13:02.585683+00:00
https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01
2022-02-17 15:22:15.915421+00:00
Daily PM10 concentration for 1st September 2018 over Europe
Daily PM10 concentration
This resource has this description
Flow to compute monthly map - updated
List of hourly PM10 concentration data for September 1st 2018 over Europe
Index of daily PM10 concentration for September 1st 2018
73394
https://api.rohub.org/api/resources/a4a570d8-f83c-47e8-bf0f-50c2a66810b1/download/
2022-02-17 15:19:04.098429+00:00
2022-02-17 21:13:02.115316+00:00
image/png
flow-dcro.png
2022-02-17 15:19:04.098429+00:00
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2022-02-17 15:22:47.629469+00:00
2022-02-17 21:13:02.616681+00:00
https://box.psnc.pl/f/d90a0e1e0d/?raw=1
2022-02-17 15:22:47.629469+00:00
Catch data records sample from 2019
Catch data from Norway
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2022-02-17 15:21:26.221676+00:00
2022-02-17 21:13:02.517346+00:00
https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G
2022-02-17 15:21:26.221676+00:00
POINT (38.0 38.0)
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Applied sciences
Adriatic Sea
fair perspective
supplementary materials of the paper
material
press
perspective
Adriatic Sea
fragmented geodata
supplementary material
multi-disciplinary
experience
Adriatic Sea experience
inhomogeneous
federica.foglini@ismar.cnr.it
Federica Foglini
service-account-enrichment
False
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Supplementary materials of the paper "A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective - The Adriatic Sea experience" Oceanologia 2022 (in press)
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A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective - The Adriatic Sea experience - snapshot
A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective - The Adriatic Sea experience
MANUAL
Foglini, Federica. "A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective - The Adriatic Sea experience." ROHub. Jan 11 ,2022. https://doi.org/10.24424/zp5v-s174.
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UML structure of the theme Water Column shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system
Water Column UML structure
UML structure built in Enterprise Architect (© Sparx System) representing the theme “Water Column”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes.
Water Column Data Model
UML structure built in Enterprise Architect (© Sparx System) representing the theme “Geology”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes.
Geology Data Model
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GeologyDataModel.png
2022-01-11 10:19:47.491579+00:00
UML structure built in Enterprise Architect (© Sparx System) representing the theme “Geophysics”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes.
Geophysics Data Model
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2022-02-21 15:48:43.114756+00:00
application/xml
GeophysicsUML.XML
2022-01-11 10:36:29.647328+00:00
UML structure of the theme Geology shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system
Geology UML structure
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application/xml
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SeafloorMappingDataModel.png
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image/png
WaterColumnDataModel.png
2022-01-11 10:21:14.170980+00:00
UML structure built in Enterprise Architect (© Sparx System) representing the theme “Seafloor Mapping”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes.
Seafloor Mapping Data Model
Earth sciences
10.13039/501100000781
European Commission
INGV
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Reliance-Jupyter of the Adam platform
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It contains preliminary results from the run of the VSM code, related to the modelling of the dike feeding the eruption of 22 May 2021 at Nyiragongo Volcano (Dem. Rep. Congo) based on remote sensing data (Sentinel-1). Ascending orbit 13-05-2021/25-05-2021, descending orbit 21-05-2021/02-06-2021.
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Modelling of the syn-eruptive phase of the Nyiragongo volcano (D.R. Congo) from Copernicus Sentinel-1 data.
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VSM code
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service-account-enrichment
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https://orcid.org/0000-0002-2983-045X
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2022-03-09 21:47:32.448597+00:00
This Research Object has been created by the Reliance-Jupyter of the ADAM platform. It contains preliminary results from the run of the VSM code, related to the modelling of the dike feeding the eruption of 22 May 2021 at Nyiragongo Volcano (Dem. Rep. Congo) based on remote sensing data (Sentinel-1). Ascending orbit 13-05-2021/25-05-2021, descending orbit 21-05-2021/02-06-2021.
application/ld+json
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Modelling of the syn-eruptive phase of the Nyiragongo volcano (D.R. Congo) from Copernicus Sentinel-1 data - snapshot
Modelling of the syn-eruptive phase of the Nyiragongo volcano (D.R. Congo) from Copernicus Sentinel-1 data
MANUAL
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Trasatti, Elisa, and Tolomei, Cristiano. "Modelling of the syn-eruptive phase of the Nyiragongo volcano (D.R. Congo) from Copernicus Sentinel-1 data." ROHub. Mar 09 ,2022. https://doi.org/10.24424/wesr-p505.
input
tool
biblio
output
2D statistics
https://datahub.egi.eu/share/cbacf022b4c56b9ee8d47ebeacfb0b92ch45e5
2022-03-09 22:21:07.051627+00:00
2022-03-09 23:05:23.861578+00:00
https://datahub.egi.eu/share/cbacf022b4c56b9ee8d47ebeacfb0b92ch45e5
2022-03-09 22:21:07.051627+00:00
Best-fit values of the source
https://datahub.egi.eu/share/bd1540fb42e6bba2cee5bfc316b553dbch1c6f
2022-03-09 22:47:31.031460+00:00
2022-03-09 23:05:26.618588+00:00
https://datahub.egi.eu/share/bd1540fb42e6bba2cee5bfc316b553dbch1c6f
2022-03-09 22:47:31.031460+00:00
Data - Model - Residuals with InSAR descending data
Log of the run
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2022-03-09 22:50:53.290173+00:00
2022-03-09 23:05:25.629046+00:00
image/png
VSM_res_sar2.png
2022-03-09 22:50:53.290173+00:00
Data - Model - Residuals with InSAR ascending data
Data - Model - Residuals with InSAR ascending data
Synthetic SAR data descending orbit
VSM input file
VSM input file
Report of the Virunga Supersite 2020-2021
https://datahub.egi.eu/share/d982c24850fc6c129fe77c78b4063b7ech1fc0
2022-03-09 22:44:57.297598+00:00
2022-03-09 23:05:26.743368+00:00
https://datahub.egi.eu/share/d982c24850fc6c129fe77c78b4063b7ech1fc0
2022-03-09 22:44:57.297598+00:00
https://datahub.egi.eu/share/a17e21880aff718de28be63a6b77112cchf108
2022-03-09 22:43:13.021049+00:00
2022-03-09 23:05:25.668930+00:00
https://datahub.egi.eu/share/a17e21880aff718de28be63a6b77112cchf108
2022-03-09 22:43:13.021049+00:00
https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007E30ED736861726547756964233237386133666334663430663539366136626337383333363531393364323834636838373437233732356634616233366362323664306662666330633132346337373565666565636865653439236133646637653332633665343364373639316362653134363036383430626134636862656535/content
2022-03-11 17:48:08.391867+00:00
2023-05-16 17:24:50.152099+00:00
Jupyter Notebook for running the VSM code with geodetic data related to Nyiragongo syn-eruptive phase
Notebook with the modelling by VSM
2022-03-11 17:48:08.391867+00:00
https://datahub.egi.eu/share/be683e62ba0406d94847306489dcf7dfch8897
2022-03-09 22:18:19.933050+00:00
2022-03-09 23:05:23.827362+00:00
https://datahub.egi.eu/share/be683e62ba0406d94847306489dcf7dfch8897
2022-03-09 22:18:19.933050+00:00
139
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2022-03-09 22:49:02.403337+00:00
2022-03-09 23:05:26.522756+00:00
text/csv
VSM_best.csv
2022-03-09 22:49:02.403337+00:00
16805
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2022-03-09 23:05:29.689817+00:00
image/gif
VSM_logo.gif
2022-03-09 22:33:06.532144+00:00
Subsampled ascending and descending Sentinel-1 data
Ascending & descending data
https://zenodo.org/record/6338730#.YikSlxDMLAz
2022-03-09 22:55:04.975698+00:00
2022-03-09 23:05:23.738943+00:00
https://zenodo.org/record/6338730#.YikSlxDMLAz
2022-03-09 22:55:04.975698+00:00
Parameters vs sampling plot
https://datahub.egi.eu/share/d1b24099113be5c6855c7ac8233bf4c0ch1b03
2022-03-09 22:47:44.095060+00:00
2022-03-09 23:05:26.562006+00:00
https://datahub.egi.eu/share/d1b24099113be5c6855c7ac8233bf4c0ch1b03
2022-03-09 22:47:44.095060+00:00
4032
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2022-03-09 22:36:17.087313+00:00
2022-03-09 23:05:27.784565+00:00
VSM.log
2022-03-09 22:36:17.087313+00:00
Models generated by VSM during the search
1D2D statistics plot
Logo of VSM in Reliance
https://datahub.egi.eu/share/d2e5c634baee583520cbb9e1ef654a1ech5cca
2022-03-09 22:44:23.954293+00:00
2022-03-09 23:05:26.781538+00:00
https://datahub.egi.eu/share/d2e5c634baee583520cbb9e1ef654a1ech5cca
2022-03-09 22:44:23.954293+00:00
1D statistics
https://datahub.egi.eu/share/ae77baf09b5c9ca87048c8013015c6d0che5df
2022-03-09 22:46:24.342781+00:00
2022-03-09 23:05:26.654044+00:00
https://datahub.egi.eu/share/ae77baf09b5c9ca87048c8013015c6d0che5df
2022-03-09 22:46:24.342781+00:00
Synthetic SAR data ascending orbit
https://datahub.egi.eu/share/8fb75a57ee83025a6adc087a4f5a5c3bcha888
2022-03-09 22:45:50.514051+00:00
2022-03-09 23:05:26.690962+00:00
https://datahub.egi.eu/share/8fb75a57ee83025a6adc087a4f5a5c3bcha888
2022-03-09 22:45:50.514051+00:00
117617
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2022-03-09 21:52:49.360459+00:00
2022-03-09 23:05:28.580400+00:00
image/png
VSM_res_sar1.png
2022-03-09 21:52:49.360459+00:00
Raul Palma
Applied sciences
Earth observation
10.13039/501100000781
European Commission
CSC - IT Center for Science (Finland)
samantha.wittke@aalto.fi
Samantha Wittke
0000-0002-9625-7235
01xtthb56
University of Oslo
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Finnish Geospatial Research Institute
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earth sciences
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social and information sciences
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resource
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POLYGON ((24.18964452411491 61.191826100745374, 24.196058374024226 61.192475014763325, 24.196205529001745 61.19179482361619, 24.19026064542983 61.19107198695369, 24.18964452411491 61.191826100745374))
POLYGON ((24.192215188374867 61.18501915071051, 24.19578893147163 61.186083237631095, 24.19715287272243 61.184061770037154, 24.19399282834134 61.18289474417407, 24.192215188374867 61.18501915071051))
24.192215188374867 61.18501915071051, 24.19578893147163 61.186083237631095, 24.19715287272243 61.184061770037154, 24.19399282834134 61.18289474417407, 24.192215188374867 61.18501915071051
service-account-enrichment
False
https://w3id.org/ro-id/1f27c890-61d1-447d-a9a0-4b55d2c2282b
2022-03-12 15:16:04.842684+00:00
mailto:annefou@geo.uio.no
242685
https://api.rohub.org/api/ros/7f907b0e-d08b-4d55-a272-7561564d8272/crate/download/
2022-03-11 10:09:34.280134+00:00
2024-03-05 12:18:11.336331+00:00
2022-03-11 10:09:34.280134+00:00
This Research Object aggregates all the resources needed for running Galaxy EODIE: i) Examples of input datasets needed for running EODIE on Galaxy; ii) Galaxy Workflow (.ga) and corresponding CWL abstract and diagram; iii) Link to a published Galaxy history.
application/ld+json
https://w3id.org/ro-id/7f907b0e-d08b-4d55-a272-7561564d8272
copernicus
earth observation
galaxy
sentinel-2
EODIE Galaxy Tool Research Object showing its usage in Galaxy Europe
Galaxy EODIE Tool Example - snapshot
MANUAL
False
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https://w3id.org/ro-id/7f907b0e-d08b-4d55-a272-7561564d8272/38f18eb0-32db-4096-ab87-65d06506b13e
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https://w3id.org/ro-id/dcc8604a-ba39-41b7-a080-4970badab8f8
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https://w3id.org/ro-id/c151baa6-b189-434b-9e26-3db2f5538962
https://w3id.org/ro-id/c20bba1a-a448-4442-be4e-3373289185da
https://w3id.org/ro-id/d31e0aa9-5e27-4944-89ee-54130c313490
https://w3id.org/ro-id/da9c4719-1476-42dc-83ec-ea35affb89b7
https://w3id.org/ro-id/38cf8ce7-a2ea-44a6-8ffa-238cff1c1ea9
https://w3id.org/ro-id/dba6079c-5cec-4ed0-8162-a4f52f138c1a
https://w3id.org/ro-id/12f37bbc-7b8a-4c59-a26d-d8df66d4893d
https://w3id.org/ro-id/a4010ef5-5f76-4194-9cc0-55719801234d
https://w3id.org/ro-id/40f2c316-faf6-462c-b4b6-cdb16a0be1db
https://w3id.org/ro-id/55ca4e82-8338-4bc2-b9c5-6c9c2552ff8c
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https://w3id.org/ro-id/8463f241-14ba-4d69-bd60-a570b6fe0487
https://w3id.org/ro-id/9fd85dfb-81c5-4a4a-bdb2-b3fde0a8b657
https://w3id.org/ro-id/ea5c0f09-c581-4105-a279-ddce665d1c51
https://w3id.org/ro-id/3e04294e-c7f9-4a83-b199-7ab3281cbd82
https://w3id.org/ro-id/89bf1a14-9db7-43a7-ac0e-cdb9cd285d2e
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https://w3id.org/ro-id/e2075c38-7a18-4b42-845d-00e4ead8c77d
Anne Foilloux, and Samantha Wittke. "EODIE Galaxy Tool Research Object showing its usage in Galaxy Europe." ROHub. Mar 11 ,2022. https://doi.org/10.24424/4fpx-ks69.
POLYGON ((24.18406744995054 61.18989752053165, 24.18674075775491 61.19037141598483, 24.18716156079448 61.18986424310414, 24.185956130552082 61.18912805997288, 24.18430110034565 61.188898431974906, 24.18406744995054 61.18989752053165))
POLYGON ((22.811822629936163 60.294519011902665, 24.91411237826359 60.294519011902665, 24.91411237826359 61.32117938698947, 22.811822629936163 61.32117938698947, 22.811822629936163 60.294519011902665))
POLYGON ((24.192215188374867 61.18501915071051, 24.19578893147163 61.186083237631095, 24.19715287272243 61.184061770037154, 24.19399282834134 61.18289474417407, 24.192215188374867 61.18501915071051))
POLYGON ((24.18964452411491 61.191826100745374, 24.196058374024226 61.192475014763325, 24.196205529001745 61.19179482361619, 24.19026064542983 61.19107198695369, 24.18964452411491 61.191826100745374))
biblio
input
tool
output
This Python Jupyter Notebook load this Research Object, update it e.g. add additional metadata information and fill its content. It also show the area of interest by visualizing the Sentinel-2 tile and the parccel shapefile where NDVI is calculated with EODIE Galaxy Tool.
Jupyter Notebook to update Research Object and visualize area of interest
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a7f87b4af1c86f136/display?to_ext=shp
2022-03-11 12:49:24.260353+00:00
2022-03-12 15:15:47.216730+00:00
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a7f87b4af1c86f136/display?to_ext=shp
2022-03-11 12:49:24.260353+00:00
https://eodie.readthedocs.io/en/latest/
2022-03-11 10:13:54.654033+00:00
2022-03-12 15:15:46.885102+00:00
https://eodie.readthedocs.io/en/latest/
2022-03-11 10:13:54.654033+00:00
https://doi.org/10.5281/zenodo.4762323
2022-03-11 12:22:30.810417+00:00
2022-03-12 15:15:50.426715+00:00
https://doi.org/10.5281/zenodo.4762323
2022-03-11 12:22:30.810417+00:00
This input dataset is a shapefile corresponding the the area of interest e.g. on which statistics such as NDVI will be computed.
test_parcels_32635
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a35a2897d442f793e/display?to_ext=shp
2022-03-11 12:48:40.259848+00:00
2022-03-12 15:15:47.399170+00:00
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a35a2897d442f793e/display?to_ext=shp
2022-03-11 12:48:40.259848+00:00
This is a link to Workflow Research Object stored in workflowhub.eu. It contains a Galaxy workflow (.ga), abstract CWL (.cwl) and diagram (.png).
Galaxy Workflow for EODIE Galaxy Tool
https://toolshed.g2.bx.psu.edu/view/climate/eodie/81b0ca76435d
2022-03-12 14:23:32.995366+00:00
2022-03-12 15:15:50.335647+00:00
https://toolshed.g2.bx.psu.edu/view/climate/eodie/81b0ca76435d
2022-03-12 14:23:32.995366+00:00
https://workflowhub.eu/workflows/274
2022-03-11 12:38:11.563886+00:00
2022-03-12 15:15:50.537808+00:00
https://workflowhub.eu/workflows/274
2022-03-11 12:38:11.563886+00:00
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a1d47bf13200d6d20/display?to_ext=txt
2022-03-11 12:56:45.981431+00:00
2022-03-12 15:15:48.030119+00:00
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a1d47bf13200d6d20/display?to_ext=txt
2022-03-11 12:56:45.981431+00:00
Abstract CWL Automatically generated from the Galaxy workflow file: Workflow constructed from history 'EODIE Sentinel'
Abstract CWL figure for EODIE Galaxy Tool
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a325d171940e10e66/display?to_ext=tar
2022-03-11 12:47:07.047116+00:00
2022-03-12 15:15:47.705381+00:00
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a325d171940e10e66/display?to_ext=tar
2022-03-11 12:47:07.047116+00:00
https://workflowhub.eu/workflows/274/diagram?version=1
2022-03-11 12:44:21.317999+00:00
2022-03-12 15:15:48.334662+00:00
https://workflowhub.eu/workflows/274/diagram?version=1
2022-03-11 12:44:21.317999+00:00
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a15a5227662283bd6/display?to_ext=data&hdca_id=f0508a112d4c9309&element_identifier=ndvi_20200626_34VFN_statistics.csv
2022-03-11 12:54:38.554141+00:00
2022-03-12 15:15:48.196664+00:00
https://climate.usegalaxy.eu/datasets/11ac94870d0bb33a15a5227662283bd6/display?to_ext=data&hdca_id=f0508a112d4c9309&element_identifier=ndvi_20200626_34VFN_statistics.csv
2022-03-11 12:54:38.554141+00:00
This output is the logfile generated when running EODIE Galaxy Tool over the specified geographical area (test_parcels_32635) and with the input Sentinel-2 dataset.
logfile
This output dataset is a csv file containing NDVI values computed over the specific geographical area e.g. given by the input shapefile called 'test_parcels_32635'.
ndvi_20200626_34VFN_statistics.csv
10.5281/zenodo.4762323
Tarball containing the EODIE source code release 1.0.2 and its associated documentation.
Source code of EODIE version 1.0.2 (zenodo)
This input dataset corresponds to the Sentinel-2 tile shapefile, originally provided by https://fromgistors.blogspot.com/2016/10/how-to-identify-sentinel-2-granule.html,
sentinel2_tiles_world
https://usegalaxy.eu/u/annefou/h/eodie-sentinel-1
2022-03-12 14:27:30.928413+00:00
2022-03-12 15:15:48.697531+00:00
https://usegalaxy.eu/u/annefou/h/eodie-sentinel-1
2022-03-12 14:27:30.928413+00:00
Link to the Galaxy Tool shed for EODIE Galaxy Tool repository. This repository is useful whenever you want to install EODIE Galaxy Tool in your own Galaxy instance. The version used in this example is revision: 0:81b0ca76435d
Galaxy Toolshed for EODIE Galaxy Tool repository
Sentinel2 input data. This input dataset corresponds to the data itself while sentinel2_tiles_world would be the corresponding shapefile for the tile.
S2B_MSIL2A_20200626T095029_N0214_R079_T34VFN_20200626T123234.tar
Online documentation of EODIE Toolkit.
EODIE documentation
This Galaxy history contains all the inputs and generated outputs for this EODIE example. If you have an account on Galaxy Europe (if not you can open one), you can import this history and reuse it.
Galaxy history EODIE Sentinel
394610
https://api.rohub.org/api/resources/fa0f339c-0511-40b8-9465-3f0e74764a63/download/
2022-03-12 15:14:03.275057+00:00
2022-03-12 15:15:50.211920+00:00
RO-EODIE-Galaxy-history.ipynb
2022-03-12 15:14:03.275057+00:00
Einet Galaxy
25.394736842105264
19.3
documentation and information science
100.0
0.30704838037490845
This Research Object aggregates all the resources needed for running Galaxy EODIE: i) Examples of input datasets needed for running EODIE on Galaxy; ii) Galaxy Workflow (ga) and corresponding CWL abstract and diagram; iii) Link to a published Galaxy history.
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Galaxy Europe
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dataset
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tool
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abstraction
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input
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resource
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database
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EODIE Galaxy Tool Research Object showing its usage in Galaxy Europe.
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EODIE on Galaxy
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input dataset
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Galaxy EODIE
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Nordic e-Infrastructure Collaboration (NeIC)
annefou@geo.uio.no
Anne Fouilloux
0000-0002-1784-2920