This Project generated data in the proces of replication of Weber et al 2025, paper validated on UK Biobank subset of 15000people, by reuse of MIMIC-IV-ECG Physionet clinical dataset, on close to 300000 records, in creation of ECG-extracted enriched cluster biomarkers acting as autonomic profiles for estimation of depression severity and potential suicide risk. FAIR Mind micu@3ega.nl his paper describes the use of dynamic focus control in a coherent lidar and compares the theoretical and experimental results. The system uses a MEMS variable-focus mirror, which has an electrostatically driven deformable membrane with a diameter of 4 mm and a manufacturer’s published 10%-to-90% rise time of less than 200 µs. The device permits fast, intra-scan adjustment of the downrange beam focus from 2.2 m to infinity for a lidar with a 25 mm diameter exit pupil. Experimentally, the variable-focus mirror improved the signal strength by more than 20 dB for objects within the first Rayleigh distance, in good agreement with theory. The fast response time allows the system to dynamically adjust the focus to different distances within a single sweep of a raster scan, in order to match the system focus on-the-fly to targets at different distances. Three-dimensional point clouds show that this technique allows some targets that are below the noise level when using a fixed focus to become visible when the focus is changed dynamically. Dynamic focusing using a MEMS mirror in a coherent lidar drandrewoliver@ieee.org April 24 2025 2024 Computational prediction of AgMata aggregation propensity score (0.7004717948717948, unitless) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using the Bio2Byte AgMata predictor. Sequence-derived: one FDO per variant. Alpha AgMata = 0.7004717948717948 — Bio2Byte 0.7004717948717948 e.a.schultes@lacdr.leidenuniv.nl Deep mutational scanning measurements (Bloom Lab) for SARS-CoV-2 Spike RBD variant Alpha (N169Y). Six values: bind=9.81374, delta_bind=1.04213, expr=10.05266, delta_expr=-0.13322, confidence_bind=0.0732354593527125, confidence_expr=0.0713853600674372. All unitless. Alpha DMS observation — Bloom Lab (bind=9.81374, expr=10.05266) 9.81374 0.0732354593527125 0.0713853600674372 1.04213 -0.13322 10.05266 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of RMSD (13.378861773140596 Angstroms) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using ESM2. Alpha RMSD = 13.378861773140596 Å — ESM2 13.378861773140596 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of RMSD (2.277628683231952 Angstroms) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using AlphaFold 2. Alpha RMSD = 2.277628683231952 Å — AlphaFold 2 2.277628683231952 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of SASA (10141.236988525969 square Angstroms) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using AlphaFold 2. Alpha SASA = 10141.236988525969 Å² — AlphaFold 2 10141.236988525969 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of SASA (16494.878394358453 square Angstroms) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using ESM2. Alpha SASA = 16494.878394358453 Å² — ESM2 16494.878394358453 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of pLDDT (23.673561732385306 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using ESM2. Alpha pLDDT = 23.673561732385306 — ESM2 23.673561732385306 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of pLDDT (93.74392833444035 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Alpha (N169Y) using AlphaFold 2. Alpha pLDDT = 93.74392833444035 — AlphaFold 2 93.74392833444035 e.a.schultes@lacdr.leidenuniv.nl Real-world detection of SARS-CoV-2 Spike variant Alpha (B.1.1.7) in England during late 2020-2021, as reported in GISAID (accession EPI_ISL_601443). Alpha real-world occurrence — England (late 2020-2021) EPI_ISL_601443 late 2020-2021 e.a.schultes@lacdr.leidenuniv.nl WHO classification of SARS-CoV-2 Spike variant Alpha (B.1.1.7) as a Variant of Concern (VOC) on 2020-12-18. Alpha — WHO Variant of Concern (2020-12-18) 2020-12-18 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of AgMata aggregation propensity score (0.7194974358974362, unitless) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using the Bio2Byte AgMata predictor. Sequence-derived: one FDO per variant. Epsilon AgMata = 0.7194974358974362 — Bio2Byte 0.7194974358974362 e.a.schultes@lacdr.leidenuniv.nl Deep mutational scanning measurements (Bloom Lab) for SARS-CoV-2 Spike RBD variant Epsilon (L120R). Six values: bind=8.93862, delta_bind=0.16701, expr=10.25356, delta_expr=0.06768, confidence_bind=0.0729917177063099, confidence_expr=0.0705230077448188. All unitless. Epsilon DMS observation — Bloom Lab (bind=8.93862, expr=10.25356) 8.93862 0.0729917177063099 0.0705230077448188 0.16701 0.06768 10.25356 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of RMSD (12.801648143493145 Angstroms) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using ESM2. Epsilon RMSD = 12.801648143493145 Å — ESM2 12.801648143493145 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of RMSD (2.975776098053212 Angstroms) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using AlphaFold 2. Epsilon RMSD = 2.975776098053212 Å — AlphaFold 2 2.975776098053212 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of SASA (10181.968717666516 square Angstroms) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using AlphaFold 2. Epsilon SASA = 10181.968717666516 Å² — AlphaFold 2 10181.968717666516 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of SASA (17620.862527029858 square Angstroms) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using ESM2. Epsilon SASA = 17620.862527029858 Å² — ESM2 17620.862527029858 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of pLDDT (23.73609314359643 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using ESM2. Epsilon pLDDT = 23.73609314359643 — ESM2 23.73609314359643 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of pLDDT (94.19297808764844 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Epsilon (L120R) using AlphaFold 2. Epsilon pLDDT = 94.19297808764844 — AlphaFold 2 94.19297808764844 e.a.schultes@lacdr.leidenuniv.nl Real-world detection of SARS-CoV-2 Spike variant Epsilon (B.1.427 + B.1.429) in the United States on 2021-01-13, as reported in GISAID (accession EPI_ISL_2295356). Epsilon real-world occurrence — United States (2021-01-13) EPI_ISL_2295356 2021-01-13 e.a.schultes@lacdr.leidenuniv.nl WHO classification of SARS-CoV-2 Spike variant Epsilon (B.1.427 + B.1.429) as a Variant of Interest (VOI) on 2021-03-05. Epsilon — WHO Variant of Interest (2021-03-05) 2021-03-05 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of AgMata aggregation propensity score (0.680579487179487, unitless) for SARS-CoV-2 Spike RBD variant Eta (E152K) using the Bio2Byte AgMata predictor. Sequence-derived: one FDO per variant. Eta AgMata = 0.680579487179487 — Bio2Byte 0.680579487179487 e.a.schultes@lacdr.leidenuniv.nl Deep mutational scanning measurements (Bloom Lab) for SARS-CoV-2 Spike RBD variant Eta (E152K). Six values: bind=9.01256, delta_bind=0.24095, expr=10.21898, delta_expr=0.03311, confidence_bind=0.0722604927671021, confidence_expr=0.0697838486111459. All unitless. Eta DMS observation — Bloom Lab (bind=9.01256, expr=10.21898) 9.01256 0.0722604927671021 0.0697838486111459 0.24095 0.03311 10.21898 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of RMSD (2.476768477258109 Angstroms) for SARS-CoV-2 Spike RBD variant Eta (E152K) using ESM2. Eta RMSD = 2.476768477258109 Å — ESM2 2.476768477258109 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of RMSD (2.7195180252543003 Angstroms) for SARS-CoV-2 Spike RBD variant Eta (E152K) using AlphaFold 2. Eta RMSD = 2.7195180252543003 Å — AlphaFold 2 2.7195180252543003 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of SASA (10226.641288430395 square Angstroms) for SARS-CoV-2 Spike RBD variant Eta (E152K) using AlphaFold 2. Eta SASA = 10226.641288430395 Å² — AlphaFold 2 10226.641288430395 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of SASA (18026.596842240684 square Angstroms) for SARS-CoV-2 Spike RBD variant Eta (E152K) using ESM2. Eta SASA = 18026.596842240684 Å² — ESM2 18026.596842240684 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of pLDDT (23.15035644847704 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Eta (E152K) using ESM2. Eta pLDDT = 23.15035644847704 — ESM2 23.15035644847704 e.a.schultes@lacdr.leidenuniv.nl Computational prediction of pLDDT (94.05421698739244 unitless, 0-100) for SARS-CoV-2 Spike RBD variant Eta (E152K) using AlphaFold 2. Eta pLDDT = 94.05421698739244 — AlphaFold 2 94.05421698739244 e.a.schultes@lacdr.leidenuniv.nl Real-world detection of SARS-CoV-2 Spike variant Eta (B.1.525) in Nigeria on 2020-12-15, as reported in GISAID (accession EPI_ISL_941290). Eta real-world occurrence — Nigeria (2020-12-15) EPI_ISL_941290 2020-12-15 e.a.schultes@lacdr.leidenuniv.nl WHO classification of SARS-CoV-2 Spike variant Eta (B.1.525) as a Variant of Interest (VOI) on 2021-03-05. Eta — WHO Variant of Interest (2021-03-05) 2021-03-05 e.a.schultes@lacdr.leidenuniv.nl AlphaFold 2 (DeepMind) protein structure prediction system. AlphaFold 2 — DeepMind structure predictor e.a.schultes@lacdr.leidenuniv.nl AgMata aggregation propensity predictor, Bio2Byte toolkit. Bio2Byte AgMata — aggregation propensity predictor e.a.schultes@lacdr.leidenuniv.nl Deep mutational scanning of SARS-CoV-2 RBD binding and expression, Bloom Lab. Deep mutational scanning — Bloom Lab SARS-CoV-2 RBD e.a.schultes@lacdr.leidenuniv.nl ESM2 protein language model (Meta AI) for structure and property prediction from sequence. ESM2 — Meta AI protein language model e.a.schultes@lacdr.leidenuniv.nl GISAID SARS-CoV-2 genomic surveillance platform. GISAID — Global Initiative on Sharing All Influenza Data e.a.schultes@lacdr.leidenuniv.nl SARS-CoV-2 Spike RBD variant Alpha, carrying the N169Y substitution (corresponding to N501Y in the full Spike numbering). Pango lineage B.1.1.7. Designated a Variant of Concern (VOC) by the WHO. RBD anchor FDO for the MAC FAIR Digital Twin observation cluster — all Type 2-8 observation FDOs of this variant link back here via mac:isObservationOf. Spike RBD Variant Alpha (N169Y) EPI_ISL_601443 N501Y N169Y B.1.1.7 TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTYGVGYQPYRVVVLSFELLHAPATVCGP Alpha e.a.schultes@lacdr.leidenuniv.nl SARS-CoV-2 Spike RBD variant Epsilon, carrying the L120R substitution (corresponding to L452R in the full Spike numbering — Epsilon's canonical defining mutation). Pango lineage B.1.427 + B.1.429 (composite). RBD anchor FDO for the MAC FAIR Digital Twin observation cluster — all Type 2-8 observation FDOs of this variant link back here via mac:isObservationOf. Spike RBD Variant Epsilon (L120R) EPI_ISL_2295356 L452R L120R B.1.427 + B.1.429 TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYRYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP Epsilon e.a.schultes@lacdr.leidenuniv.nl SARS-CoV-2 Spike RBD variant Eta, carrying the E152K substitution (corresponding to E484K in the full Spike numbering). Pango lineage B.1.525. Designated a Variant of Interest (VOI) by the WHO. RBD anchor FDO for the MAC FAIR Digital Twin observation cluster — all Type 2-8 observation FDOs of this variant link back here via mac:isObservationOf. Spike RBD Variant Eta (E152K) EPI_ISL_941290 E484K E152K B.1.525 TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVKGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP Eta e.a.schultes@lacdr.leidenuniv.nl 2025-07-01 v1.0.0 SARS-COV-2 Spike protein RBD variant structural features calculated with AlphaFold2 AlphaFold2 Dataset STAYAHEAD e.a.schultes@lacdr.leidenuniv.nl 2025-07-01 v1.0.0 SARS-COV-2 Spike protein RBD variant structural features calculated with ESM ESM Dataset STAYAHEAD e.a.schultes@lacdr.leidenuniv.nl 2023-03-01 This project will develop rapid SARS-CoV-2 detection and variant characterization using mass spectrometry, FAIR Digital Twins of variants, and coupled to AI predictions of variants of high risk. STAYAHEAD

LSHM22038-H026

e.a.schultes@lacdr.leidenuniv.nl 2026-03-31 2023-03-01

Chemistry Małgorzata Wolniewicz 3749 https://api.rohub.org/api/ros/0c470650-84d9-40e1-bc80-4591a27f6c4d/crate/download/ 2022-01-12 16:34:39.917729+00:00 2026-05-19 11:46:00.057896+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/0c470650-84d9-40e1-bc80-4591a27f6c4d chemistry Aromatic compounds MANUAL

Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d.

False 2022-01-14 22:19:57.392548+00:00 False 2025-07-05 19:04:55.078129+00:00 False 2025-07-05 18:47:59.392957+00:00 False 2025-07-04 09:08:44.261623+00:00 none Geographical Scope arene 4.658385093167703 4.5 User Needs (RAST) Other Chemical Sciences none none none none Funding none aromatic compound benzene 13.168724279835391 6.4 chemical compound 12.525879917184268 12.1 Key Type Measures scent 5.279503105590063 5.1 aromatic compounds aromatic compounds 46.70781893004115 22.7 IPCC carbon atom 10.973084886128367 10.6 benzene 12.224108658743631 7.2 aliphatic compound 8.488964346349743 5.0 nitrogen 4.244306418219462 4.1 electron 4.244306418219462 4.1 Chemistry and materials (general) organic chemistry 65.86151368760065 40.9 compound 17.48726655348047 10.3 chemistry 34.13848631239936 21.2 aromatic hydrocarbon 7.979626485568759 4.7 Climate-ADAPT Adaptation Sectors ring 3.209109730848862 3.1 none Aromatic compounds Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. 47.727272727272734 27.3 Chemicals heterocyclic compound 6.521739130434784 6.3 carbon atom 15.789473684210526 9.3 Chemical Sciences Climate Hazard nitrogen atom 18.51851851851852 9.0 larger compound 10.493827160493826 5.1 Organic Chemistry none none aliphatic compound 5.900621118012424 5.7 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical 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. 19.230769230769234 11.0 Chemistry and materials aromatic hydrocarbon 5.693581780538303 5.5 Inorganic, organic and physical chemistry Methodology Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery oxygen atom 11.11111111111111 5.4 aromatic 19.772256728778473 19.1 benzene 9.316770186335406 9.0 heterocyclic compound 9.507640067911712 5.6 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. 33.04195804195804 18.9 benzene ring 3.4161490683229823 3.3 aromatic compound 28.522920203735136 16.8 oxygen atom 4.244306418219462 4.1 Knowledge Sector (EEA) Stakeholders none Policy Scale service-account-enrichment Earth sciences ISIDe Working Group data span Dipartimento della Protezione Civile monitoring network Dipartimento della Protezione Civile Creative Commons Attribution Mt. Etna website data Italy seismicity Etna Istituto Nazionale di Geofisica Ro monitoring INGV website Internet Italy POINT (14.9063 37.6002) POINT (15.1897 37.793) POINT (15.1075 37.711) POINT (14.9805 37.8083) 10f9db58-8991-4b1f-b552-35fbc6cb71df POINT (15.1897 37.793) 14.9063 37.6002 POINT (14.9063 37.6002) 53655fd9-abb3-4276-b2ff-d53bca6d3fad POINT (14.9805 37.8083) 15.1075 37.711 POINT (15.1075 37.711) 15.1897 37.793 POINT (15.1897 37.793) 14.9805 37.8083 POINT (14.9805 37.8083) c1fb3e30-00ac-40ca-82ed-285f663b7fdf POINT (14.9063 37.6002) service-account-enrichment fa2c8fea-8cc9-44b7-9adc-b18ebfdd1b43 POINT (15.1075 37.711) 54109 https://api.rohub.org/api/ros/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/crate/download/ 2022-02-02 13:22:22.374580+00:00 2025-03-05 01:04:55.096466+00:00 2022-02-02 13:22:22.374580+00:00 This RO contains the seismicity occurring at Mt. Etna (Italy) from local and national monitoring networks managed by INGV. Data span of 12 days, 2016-07-06 - 2016-07-17 and it is taken from the INGV website. Data and results published on this website by Istituto Nazionale di Geofisica e Vulcanologia are licensed under a Creative Commons Attribution 4.0 International License. ISIDe Working Group at National Earthquake Observatory benefited from funding provided by the Italian Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile. application/ld+json https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8 Mt. Etna (Italy) seismic activity from 2016-07-06 - 2016-07-17 AUTOMATIC https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/3d0e7c5a-bedd-4802-aa5d-ae94adb6e1da https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/86640593-6e89-4c59-9335-e875ce2e7e94 https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/c742e8e6-d19f-4d43-9ca8-db1807596aa7 https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8/cff0fc1f-eaae-4657-978b-cadf35dbcf76 Degassing Etna Seismic Actitvity Monitoring Networks Seismic Activity

service-account-generation-service. "Mt. Etna (Italy) seismic activity from 2016-07-06 - 2016-07-17." ROHub. Feb 02 ,2022. https://w3id.org/ro-id/7dbe7eb1-c0c9-4722-ad47-885dd01eaee8.

Data biblio 49499 https://api.rohub.org/api/resources/6fa21b20-d907-425d-9f50-88c998128b1e/download/ 2022-02-02 13:22:34.686024+00:00 2022-02-02 13:22:34.686925+00:00 image/png Sketch 2022-02-02 13:22:34.686024+00:00 14649 https://api.rohub.org/api/resources/711ccdaa-7da2-435c-94b0-f8ddf68d395a/download/ 2022-02-02 13:22:27.784863+00:00 2022-02-02 13:22:27.785737+00:00 application/vnd.google-earth.kml+xml Seismic locations - kml 2022-02-02 13:22:27.784863+00:00 606 https://api.rohub.org/api/resources/9b066a45-103f-46b9-bdd3-996acc663d93/download/ 2022-02-02 13:22:31.494399+00:00 2022-02-02 13:22:31.495365+00:00 text/plain Seismic locations - txt 2022-02-02 13:22:31.494399+00:00 service-account-generation-service Earth sciences ISIDe Working Group data span Dipartimento della Protezione Civile monitoring network Dipartimento della Protezione Civile Creative Commons Attribution https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5/Mt.%20Etna website data Italy seismicity Etna Istituto Nazionale di Geofisica Ro monitoring INGV website Internet Italy POINT (14.9557 37.6837) POINT (14.9435 37.6752) 14.9435 37.6752 POINT (14.9435 37.6752) 14.9557 37.6837 POINT (14.9557 37.6837) d35ebce5-c992-4bcb-9b59-9aceec571bd2 POINT (14.9557 37.6837) e2a11086-31fb-46e1-9d46-9b5dcd2368e9 POINT (14.9435 37.6752) service-account-enrichment 50261 https://api.rohub.org/api/ros/8f442e85-5fef-4ac9-ba51-510ecb12b6e5/crate/download/ 2022-02-02 13:23:00.765925+00:00 2025-03-05 01:04:55.534739+00:00 2022-02-02 13:23:00.765925+00:00 This RO contains the seismicity occurring at Mt. Etna (Italy) from local and national monitoring networks managed by INGV. Data span of 12 days, 2016-07-30 - 2016-08-10 and it is taken from the INGV website. Data and results published on this website by Istituto Nazionale di Geofisica e Vulcanologia are licensed under a Creative Commons Attribution 4.0 International License. ISIDe Working Group at National Earthquake Observatory benefited from funding provided by the Italian Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile. application/ld+json https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5 Mt. Etna (Italy) seismic activity from 2016-07-30 - 2016-08-10 AUTOMATIC https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5/6335b778-a46a-452d-8b06-280671309547 https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5/9dc5ab51-8961-4f72-b28b-96f00a7cbb29 Degassing Etna Seismic Actitvity Monitoring Networks Seismic Activity

service-account-generation-service. "Mt. Etna (Italy) seismic activity from 2016-07-30 - 2016-08-10." ROHub. Feb 02 ,2022. https://w3id.org/ro-id/8f442e85-5fef-4ac9-ba51-510ecb12b6e5.

Data biblio 13565 https://api.rohub.org/api/resources/268bca24-da87-4ee1-9914-ee87a39a5e0c/download/ 2022-02-02 13:23:04.933416+00:00 2022-02-02 13:23:04.934205+00:00 application/vnd.google-earth.kml+xml Seismic locations - kml 2022-02-02 13:23:04.933416+00:00 45584 https://api.rohub.org/api/resources/8d4174be-b324-4ee4-80fa-d46e6d8fbce3/download/ 2022-02-02 13:23:12.296870+00:00 2022-02-02 13:23:12.297677+00:00 image/png Sketch 2022-02-02 13:23:12.296870+00:00 367 https://api.rohub.org/api/resources/e4ffa5d4-891f-4864-81f0-61ab6ff7016a/download/ 2022-02-02 13:23:08.432203+00:00 2022-02-02 13:23:08.432976+00:00 text/plain Seismic locations - txt 2022-02-02 13:23:08.432203+00:00 service-account-generation-service Earth sciences ISIDe Working Group data span Dipartimento della Protezione Civile monitoring network Dipartimento della Protezione Civile Creative Commons Attribution Mt. Etna website data Italy seismicity Etna Istituto Nazionale di Geofisica Ro monitoring INGV website Internet Italy POINT (14.9513 37.8298) 14.9513 37.8298 POINT (14.9513 37.8298) 9303ad21-e095-4a8b-944e-76347e5c27c6 POINT (14.9513 37.8298) service-account-enrichment 46670 https://api.rohub.org/api/ros/618c682d-22bf-4f4d-9fdb-3ca9196897a2/crate/download/ 2022-02-02 13:23:19.199340+00:00 2025-03-05 01:04:55.749971+00:00 2022-02-02 13:23:19.199340+00:00 This RO contains the seismicity occurring at Mt. Etna (Italy) from local and national monitoring networks managed by INGV. Data span of 12 days, 2016-08-11 - 2016-08-22 and it is taken from the INGV website. Data and results published on this website by Istituto Nazionale di Geofisica e Vulcanologia are licensed under a Creative Commons Attribution 4.0 International License. ISIDe Working Group at National Earthquake Observatory benefited from funding provided by the Italian Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile. application/ld+json https://w3id.org/ro-id/618c682d-22bf-4f4d-9fdb-3ca9196897a2 Mt. Etna (Italy) seismic activity from 2016-08-11 - 2016-08-22 AUTOMATIC https://w3id.org/ro-id/618c682d-22bf-4f4d-9fdb-3ca9196897a2/0f6d8100-fa61-4557-99bb-82924ebc9a7d Degassing Etna Seismic Actitvity Monitoring Networks Seismic Activity

service-account-generation-service. "Mt. Etna (Italy) seismic activity from 2016-08-11 - 2016-08-22." ROHub. Feb 02 ,2022. https://w3id.org/ro-id/618c682d-22bf-4f4d-9fdb-3ca9196897a2.

Data biblio 13019 https://api.rohub.org/api/resources/529ba3b6-ab55-4a46-9d86-51a7e8ef8c88/download/ 2022-02-02 13:23:23.316237+00:00 2022-02-02 13:23:23.317209+00:00 application/vnd.google-earth.kml+xml Seismic locations - kml 2022-02-02 13:23:23.316237+00:00 254 https://api.rohub.org/api/resources/7298b9d7-0696-423f-a4b4-28bf98d2ce53/download/ 2022-02-02 13:23:26.542196+00:00 2022-02-02 13:23:26.543388+00:00 text/plain Seismic locations - txt 2022-02-02 13:23:26.542196+00:00 43187 https://api.rohub.org/api/resources/e70a2924-4465-403b-a450-51c0783fc5cd/download/ 2022-02-02 13:23:31.102326+00:00 2022-02-02 13:23:31.104047+00:00 image/png Sketch 2022-02-02 13:23:31.102326+00:00 service-account-generation-service Mathematics cohomology of the mod usage of the dataset mathematics minimal resolution algebra map mountain range document cohomology dataset range usage document CohomA2.pdf Steenrod algebra squaring resolution subject service-account-enrichment 7227 https://api.rohub.org/api/ros/ea70fc98-a80f-4758-8ae5-067ae64905ed/crate/download/ 2022-03-22 01:20:46.276139+00:00 2025-03-05 01:26:34.044209+00:00 2022-03-22 01:20:46.276139+00:00 The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail. application/ld+json https://w3id.org/ro-id/ea70fc98-a80f-4758-8ae5-067ae64905ed The cohomology of the mod 2 Steenrod algebra MANUAL

Robert Bruner, and John Rognes. "The cohomology of the mod 2 Steenrod algebra." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/ea70fc98-a80f-4758-8ae5-067ae64905ed.

biblio raw data data metadata https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00078 2022-03-22 01:21:00.644689+00:00 2022-03-22 01:21:05.591782+00:00 The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail. The cohomology of the mod 2 Steenrod algebra 2022-03-22 01:21:00.644689+00:00 Geo H. john.rognes@rohub.com John Rognes robert.bruner@rohub.com Robert Bruner Mathematics cohomology of the mod usage of the dataset mathematics minimal resolution algebra map mountain range document cohomology dataset range usage document CohomA2.pdf Steenrod algebra squaring resolution subject service-account-enrichment 8675 https://api.rohub.org/api/ros/32eba436-e9ef-4ed2-8911-fec14c5a3779/crate/download/ 2022-03-22 01:21:07.680973+00:00 2025-03-05 01:26:33.828703+00:00 2022-03-22 01:21:07.680973+00:00 The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail. application/ld+json https://w3id.org/ro-id/32eba436-e9ef-4ed2-8911-fec14c5a3779 The cohomology of the mod 2 Steenrod algebra MANUAL

Robert Bruner, and John Rognes. "The cohomology of the mod 2 Steenrod algebra." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/32eba436-e9ef-4ed2-8911-fec14c5a3779.

raw data biblio data metadata Bruner, R., Rognes, J. (2022).The cohomology of the mod 2 Steenrod algebra [Data set]. Norstore. https://doi.org/10.11582/2022.00015 Robert Ray Bruner Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00077 None 2022-03-22 01:21:29.913760+00:00 The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail. The cohomology of the mod 2 Steenrod algebra None Robert Ray Bruner Geo H. john.rognes@rohub.com John Rognes robert.bruner@rohub.com Robert Bruner Environmental research Life sciences Physical sciences Biology Svalbard time series station West Spitsbergen Current West Spitsbergen Current ecology Atlantic Ocean mouth of Adventfjorden Atlantic water hydrography ecosystem time series dataset stream mouth broadcasting station Spitsbergen inflow Atlantic Ocean ecosystem effects of climate change effects of climate change Spitsbergen station variability 15.52992 78.26105 POINT (15.52992 78.26105) c9a6edac-883c-4415-aad5-5270dff5e8fb POINT (15.52992 78.26105) service-account-enrichment 6795 https://api.rohub.org/api/ros/1b7874e4-6c2f-4b84-87cb-74db13d49196/crate/download/ 2022-03-22 01:21:31.715074+00:00 2025-03-05 01:01:11.704641+00:00 2022-03-22 01:21:31.715074+00:00 The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths. application/ld+json https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196 ISA_Svalbard_Chlorophyll_A_2011_2019 MANUAL https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196/870010a7-224a-4b80-8ee6-bcdb220e619e

University Centre in Svalbard (UNIS). "ISA_Svalbard_Chlorophyll_A_2011_2019." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196.

POINT (15.52992 78.26105) raw data data metadata biblio https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00069 2022-03-22 01:21:41.637352+00:00 2022-03-22 01:21:46.049432+00:00 The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths. ISA_Svalbard_Chlorophyll_A_2011_2019 2022-03-22 01:21:41.637352+00:00 UNIS@rohub.com University Centre in Svalbard (UNIS) Geo H. Environmental research Life sciences Physical sciences Earth sciences Svalbard temperature logger physics medicine temperature processing observatory fluorescence diagram dataset recovery moor Svalbard mooring diagram fluorescence data observatory layout marine biology logger layout information watercraft and nautical navigation UiT The Arctic University of Norway and The Scottish Association observatories consist occupational overuse syndrome 11.8239 78.9589 POINT (11.8239 78.9589) bece0544-4961-412d-91ad-ecf25c63d637 POINT (11.8239 78.9589) service-account-enrichment 11468 https://api.rohub.org/api/ros/b4960d2f-d2a6-462c-83b1-9b85f4c046ac/crate/download/ 2022-03-22 01:21:47.502650+00:00 2025-03-05 01:24:10.636796+00:00 2022-03-22 01:21:47.502650+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. application/ld+json https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 MANUAL https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac/1815b2b8-8a48-4223-a69b-52dcee7b0fca

Finlo Cottier, Jørgen Berge, Estelle Dumont, Tomasz Piotr Kopec, Emily Joanne Venables, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac.

POINT (11.8239 78.9589) data biblio metadata raw data Cottier, F., Berge, J., Dumont, E., Kopec, T. P., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 [Data set]. Norstore. https://doi.org/10.11582/2021.00065 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00065 2021-07-16 00:00:00 2022-03-22 01:22:40.368999+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 2021-07-16 00:00:00 Daniel Ludwig Vogedes SAMS@rohub.com Scottish Association for Marine Science (SAMS) UiT@rohub.com UiT The Arctic University of Norway (UiT) daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes emily.joanne.venables@rohub.com Emily Joanne Venables estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. jorgen.berge@rohub.com Jørgen Berge tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences Earth sciences 11.8237 78.9592 POINT (11.8237 78.9592) 6dbca1e0-2173-4f1d-b61e-f4e28bec383e POINT (11.8237 78.9592) service-account-enrichment 11480 https://api.rohub.org/api/ros/04c3f1b2-96ec-4065-98a1-9499762d2405/crate/download/ 2022-03-22 01:22:41.914748+00:00 2025-03-05 01:24:10.453697+00:00 2022-03-22 01:22:41.914748+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. At this deployment, two settlement plates were deployed (25m and 208m). application/ld+json https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405 Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017 MANUAL Svalbard dataset diagram fluorescence information layout mooring observatory occupational overuse syndrome processing recovery temperature earth sciences CTD Kongsfjorden Rijpfjorden Observatory Programme data diagram fluorescence mooring observatory space sciences fluorescence data mooring diagram observatories consist observatory layout recovery processing As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017. 2016-2017 Aug-2016-Aug-2017 https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405/43a67b20-7c3d-4cd5-b299-35283289cf7f armed forces medicine physics Svalbard

Finlo Cottier, Jørgen Berge, Estelle Dumont, Colin Griffith, John Beaton, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405.

POINT (11.8237 78.9592) metadata raw data biblio data Cottier, F., Berge, J., Dumont, E., Griffith, C., Beaton, J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017 [Data set]. Norstore. https://doi.org/10.11582/2021.00062 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00062 2021-07-12 00:00:00 2022-03-22 01:23:32.132406+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. At this deployment, two settlement plates were deployed (25m and 208m). Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017 2021-07-12 00:00:00 Daniel Ludwig Vogedes SAMS@rohub.com Scottish Association for Marine Science (SAMS) UiT@rohub.com UiT The Arctic University of Norway (UiT) colin.griffith@rohub.com Colin Griffith daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. john.beaton@rohub.com John Beaton jorgen.berge@rohub.com Jørgen Berge Environmental research Life sciences Physical sciences Earth sciences 40e24f2d-a1b1-42dd-ba2e-eedfdfeea4ec POINT (11.8238 78.9589) 11.8238 78.9589 POINT (11.8238 78.9589) service-account-enrichment 11633 https://api.rohub.org/api/ros/868972b2-e340-4007-8521-8f03b58cb7b9/crate/download/ 2022-03-22 01:23:33.372212+00:00 2025-03-05 01:24:10.793750+00:00 2022-03-22 01:23:33.372212+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. For this deployment a RAS500 water sampler and a SUNA nitrate sensor were deployed for a specific project, data are not part of the long-term monitoring efforts and are available upon request. application/ld+json https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9 Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016 MANUAL Svalbard data dataset diagram fluorescence information mooring nitrate observatory occupational overuse syndrome sampler sensor temperature earth sciences CTD Kongsfjorden Rijpfjorden Observatory Programme data diagram fluorescence mooring observatory space sciences SUNA nitrate sensor fluorescence data mooring diagram observatories consist observatory layout As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016. 2015-2016 Sep-2015-Aug-2016 https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9/f68b0cde-9eb9-4e5e-a13e-f980ee3ccbf5 armed forces marine biology medicine physics Svalbard

Finlo Cottier, Jørgen Berge, Colin Griffith, Estelle Dumont, John Beaton, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9.

POINT (11.8238 78.9589) metadata biblio raw data data Cottier, F., Berge, J., Griffith, C., Dumont, E., Beaton, J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016 [Data set]. Norstore. https://doi.org/10.11582/2021.00061 Daniel Ludwig Vogedes Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00061 2021-07-12 00:00:00 2022-03-22 01:24:23.830029+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. For this deployment a RAS500 water sampler and a SUNA nitrate sensor were deployed for a specific project, data are not part of the long-term monitoring efforts and are available upon request. Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016 2021-07-12 00:00:00 Daniel Ludwig Vogedes SAMS@rohub.com Scottish Association for Marine Science (SAMS) UiT@rohub.com UiT The Arctic University of Norway (UiT) colin.griffith@rohub.com Colin Griffith daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. john.beaton@rohub.com John Beaton jorgen.berge@rohub.com Jørgen Berge Environmental research Life sciences Physical sciences Svalbard National Institute of Standards and Technology Ny-Ålesund Svalbard Australia time-series measurement USSIMO sensor output Norway USSIMO spectroradiometer raw data time series physics United States of America light observatory data observatory Teflon sensor time series spectroradiometer dataset raw data Norway Arctic Zone UiT The Arctic University of Norway Norwegian University of Science and Technology Kings Bay N-NW NTNU UiT The Arctic University Perth UiT 56f9c025-4431-44aa-8c4a-c90ceb56df10 POINT (11.84213 78.94116) 11.84213 78.94116 POINT (11.84213 78.94116) service-account-enrichment 13192 https://api.rohub.org/api/ros/2eed7eb1-bb6d-4336-b4ec-b832d41270af/crate/download/ 2022-03-22 01:31:31.343064+00:00 2025-03-05 02:47:05.411745+00:00 2022-03-22 01:31:31.343064+00:00 UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2019 data. application/ld+json https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) MANUAL https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/50972f9a-041f-46bd-9d1e-f5c5a55a40db

Jørgen Berge, Stephen Grant, Rune Bjørgum, Jonathan H. Cohen, David McKee, Geir Johnsen, Artur Zolich, Tomasz Piotr Kopec, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af.

POINT (11.84213 78.94116) metadata biblio raw data data Berge, J., Grant, S., Bjørgum, R., Cohen, J. H., McKee, D., Johnsen, G., Zolich, A., Kopec, T. P., Vogedes, D. L., UiT The Arctic University of Norway (2021).USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) [Data set]. Norstore. https://doi.org/10.11582/2021.00045 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00045 2021-05-19 00:00:00 2022-03-22 01:32:34.318806+00:00 UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2019 data. USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) 2021-05-19 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) artur.zolich@rohub.com Artur Zolich daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes david.mckee@rohub.com David McKee geir.johnsen@rohub.com Geir Johnsen Geo H. jonathan.h.cohen@rohub.com Jonathan H. Cohen jorgen.berge@rohub.com Jørgen Berge rune.bjorgum@rohub.com Rune Bjørgum stephen.grant@rohub.com Stephen Grant tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences 11.84213 78.94116 POINT (11.84213 78.94116) 2b40daef-2152-44ad-9a28-a0d37f288bec POINT (11.84213 78.94116) service-account-enrichment 13206 https://api.rohub.org/api/ros/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f/crate/download/ 2022-03-22 01:32:35.810584+00:00 2025-03-05 02:47:05.631433+00:00 2022-03-22 01:32:35.810584+00:00 UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2020 data. application/ld+json https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) MANUAL Norway Teflon data dataset observatory raw data sensor spectroradiometer time series earth sciences IT-computer sciences Nanotechnology Software Synthetic and plastic chemicals University Norway data dataset observatory sensor spectroradiometer time series engineering USSIMO spectroradiometer raw data time series UiT The Arctic University light observatory sensor output time-series measurement The observatory consists of an array of light sensors including an all sky camera. This dataset contains the data of the hyperspectral radiometer USSIMO In-situ Marine Optics, Perth, WA, Australia) It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund Svalbard, Norway) in January 2017. 2020 30 seconds in Jan-2017 of 6 seconds https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f/83569c44-db68-4953-bb10-2a5bb71c2635 computer science database physics software National Institute of Standards and Technology Arctic Zone Australia Norway Perth Svalbard United States of America

Jørgen Berge, Stephen Grant, Rune Bjørgum, Jonathan H. Cohen, David McKee, Geir Johnsen, Artur Zolich, Tomasz Piotr Kopec, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f.

POINT (11.84213 78.94116) metadata raw data data biblio Berge, J., Grant, S., Bjørgum, R., Cohen, J. H., McKee, D., Johnsen, G., Zolich, A., Kopec, T. P., Vogedes, D. L., UiT The Arctic University of Tromsø (2021).USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) [Data set]. Norstore. https://doi.org/10.11582/2021.00046 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00046 2021-05-19 00:00:00 2022-03-22 01:33:42.099209+00:00 UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2020 data. USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) 2021-05-19 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) artur.zolich@rohub.com Artur Zolich daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes david.mckee@rohub.com David McKee geir.johnsen@rohub.com Geir Johnsen Geo H. jonathan.h.cohen@rohub.com Jonathan H. Cohen jorgen.berge@rohub.com Jørgen Berge rune.bjorgum@rohub.com Rune Bjørgum stephen.grant@rohub.com Stephen Grant tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences Earth sciences 22.2991 80.2951 POINT (22.2991 80.2951) 8a93a745-c942-415f-a9d8-def5bede99b1 POINT (22.2991 80.2951) service-account-enrichment 11405 https://api.rohub.org/api/ros/4ff3f101-109b-4e35-babc-46862cd4d330/crate/download/ 2022-03-22 01:39:08.957789+00:00 2025-03-05 01:24:11.330191+00:00 2022-03-22 01:39:08.957789+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. The mooring was deployed for 2 years due to heavy ice cover on Rijpfjorden in 2019 which made recovery impossible. It was equipped with 6 SBE37 to get a good picture of the water mass exchange throughout the water column. All sensor still logging after 2 years, the sediment trap only collected the 2018-19 samples. application/ld+json https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330 Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020 MANUAL Svalbard dataset diagram fluorescence information mooring observatory occupational overuse syndrome recovery sediment temperature trap earth sciences CTD Kongsfjorden Rijpfjorden Observatory Programme Rijpfjorden data fluorescence mooring observatory space sciences fluorescence data mooring diagram observatories consist observatory layout sediment trap As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020. 2018-2020 Aug-2018-Sep-2020 after 2 years for 2 years in 2019 which the 2018-2019 https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330/0bf6a835-85bd-4000-bb1d-2c6077e214bb armed forces medicine physics Svalbard

Jørgen Berge, Finlo Cottier, Tomasz Piotr Kopec, Estelle Dumont, Emily Joanne Venables, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330.

POINT (22.2991 80.2951) biblio data raw data metadata Berge, J., Cottier, F., Kopec, T. P., Dumont, E., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020 [Data set]. Norstore. https://doi.org/10.11582/2021.00031 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00031 2021-04-26 00:00:00 2022-03-22 01:40:00.650124+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. The mooring was deployed for 2 years due to heavy ice cover on Rijpfjorden in 2019 which made recovery impossible. It was equipped with 6 SBE37 to get a good picture of the water mass exchange throughout the water column. All sensor still logging after 2 years, the sediment trap only collected the 2018-19 samples. Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020 2021-04-26 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes emily.joanne.venables@rohub.com Emily Joanne Venables estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. jorgen.berge@rohub.com Jørgen Berge tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences Earth sciences 5fd6c729-070e-411d-b60c-08a8daa9cce2 POINT (22.3038 80.2943) 22.3038 80.2943 POINT (22.3038 80.2943) service-account-enrichment 11334 https://api.rohub.org/api/ros/65a52384-b1db-4b77-ab42-d61b495ba937/crate/download/ 2022-03-22 01:49:03.345354+00:00 2025-03-05 01:24:12.299726+00:00 2022-03-22 01:49:03.345354+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. Together with the top and bottom SBE37 two plastic settlement plates had been deployed for a settlement experiment for the recruitment of benthic invertebrates. The sediment trap was mounted at 58m instead the usual depth of 100 m because of specific requirements for an experiment. The observatory layout is available in the mooring diagram provided. application/ld+json https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937 Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015 MANUAL Svalbard dataset diagram fluorescence information layout mooring observatory occupational overuse syndrome sediment temperature trap earth sciences Synthetic and plastic chemicals CTD Kongsfjorden Rijpfjorden Observatory Programme Rijpfjorden data fluorescence mooring observatory space sciences fluorescence data mooring diagram observatories consist observatory layout sediment trap As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015. 2014-2015 Sep-2014-Sep-2015 https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937/1280bc3c-1c8e-41f1-be9b-3e605e49280a armed forces marine biology medicine physics Svalbard

Jørgen Berge, Finlo Cottier, Estelle Dumont, John Beaton, Colin Griffith, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937.

POINT (22.3038 80.2943) raw data biblio metadata data Berge, J., Cottier, F., Dumont, E., Beaton, J., Griffith, C., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015 [Data set]. Norstore. https://doi.org/10.11582/2021.00018 Daniel Ludwig Vogedes Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00018 2021-03-22 00:00:00 2022-03-22 01:49:53.563806+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. Together with the top and bottom SBE37 two plastic settlement plates had been deployed for a settlement experiment for the recruitment of benthic invertebrates. The sediment trap was mounted at 58m instead the usual depth of 100 m because of specific requirements for an experiment. The observatory layout is available in the mooring diagram provided. Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015 2021-03-22 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) colin.griffith@rohub.com Colin Griffith daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. john.beaton@rohub.com John Beaton jorgen.berge@rohub.com Jørgen Berge Environmental research Life sciences Physical sciences Earth sciences 22.29918 80.29443 POINT (22.29918 80.29443) d52a1692-016e-478f-95fc-cc06435a0ee2 POINT (22.29918 80.29443) service-account-enrichment 11088 https://api.rohub.org/api/ros/2939a6bb-c42b-4c16-af6a-a56cc065079b/crate/download/ 2022-03-22 01:51:43.001070+00:00 2025-03-05 01:24:11.147171+00:00 2022-03-22 01:51:43.001070+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. application/ld+json https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 MANUAL Svalbard dataset diagram fluorescence information layout mooring observatory occupational overuse syndrome processing recovery temperature earth sciences CTD Kongsfjorden Rijpfjorden Observatory Programme Rijpfjorden data fluorescence mooring observatory space sciences fluorescence data mooring diagram observatories consist observatory layout recovery processing As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018. 2017-2018 Aug-2017-Aug-2018 https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b/e1bb7d0c-c504-422b-a45e-3dbd5b72448d medicine physics Svalbard

Jørgen Berge, Finlo Cottier, Tomasz Piotr Kopec, Estelle Dumont, Emily Joanne Venables, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b.

POINT (22.29918 80.29443) biblio metadata raw data data Berge, J., Cottier, F., Kopec, T. P., Dumont, E., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 [Data set]. Norstore. https://doi.org/10.11582/2021.00017 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00017 2021-03-12 00:00:00 2022-03-22 01:52:37.735565+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 2021-03-12 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes emily.joanne.venables@rohub.com Emily Joanne Venables estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. jorgen.berge@rohub.com Jørgen Berge tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences Earth sciences data from Norwegian Meteorological Institute meteorology Norway trends in cold spell observational data frequency analysis cold weather dataset reanalysis data data from ERA5 Norway spell re-analysis trend information Norwegian Meteorological Institute POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0)) -20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0 b5d7b903-059c-4f00-9347-ffa7082d698d POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0)) service-account-enrichment 7693 https://api.rohub.org/api/ros/cb7986b3-a55f-4281-af69-7269917b8a02/crate/download/ 2022-03-22 01:52:57.468777+00:00 2025-03-05 00:50:49.038103+00:00 2022-03-22 01:52:57.468777+00:00 This data set contains an analysis of observational data from Norwegian Meteorological Institute and reanalysis data from ERA5. application/ld+json https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02 Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation MANUAL https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02/f7002b1d-65c6-4458-9cf7-c54a1f7585b9

Marek Ratajczak. "Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02.

POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0)) biblio metadata data raw data Ratajczak, M. (2021).Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation [Data set]. Norstore. https://doi.org/10.11582/2021.00016 Marek Grzegorz Ratajczak Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00016 2021-03-11 00:00:00 2022-03-22 01:53:15.007448+00:00 This data set contains an analysis of observational data from Norwegian Meteorological Institute and reanalysis data from ERA5. Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation 2021-03-11 00:00:00 Marek Grzegorz Ratajczak Geo H. marek.ratajczak@rohub.com Marek Ratajczak Environmental research Life sciences Physical sciences Biology service-account-enrichment 7126 https://api.rohub.org/api/ros/d6328caa-df3b-4b17-9595-d2e361dfccf1/crate/download/ 2022-03-22 01:55:05.007973+00:00 2025-03-05 01:19:15.480150+00:00 2022-03-22 01:55:05.007973+00:00 Custom sequence database from assembled NCBI SRA reads. Supplementary Data to Undheim and Jenner, Nat. Commun., 2021 application/ld+json https://w3id.org/ro-id/d6328caa-df3b-4b17-9595-d2e361dfccf1 SRA transcriptome assemblies MANUAL custom data database national sequence transcriptome earth sciences Genetics IT-computer sciences Newspaper Periodical Commun NCBI SRA Undheim custom data database transcriptome mathematical and computer sciences Nat. Commun SRA transcriptome assembly custom sequence database data to Undheim supplementary data Custom sequence database from assembled NCBI SRA reads. SRA transcriptome assemblies. Supplementary Data to Undheim and Jenner, Nat. Commun. database

Eivind Undheim. "SRA transcriptome assemblies." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/d6328caa-df3b-4b17-9595-d2e361dfccf1.

data metadata biblio raw data Undheim, E. (2020).SRA transcriptome assemblies [Data set]. Norstore. https://doi.org/10.11582/2020.00067 Eivind Andreas Baste Undheim Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00067 2020-12-29 00:00:00 2022-03-22 01:55:21.374031+00:00 Custom sequence database from assembled NCBI SRA reads. Supplementary Data to Undheim and Jenner, Nat. Commun., 2021 SRA transcriptome assemblies 2020-12-29 00:00:00 Eivind Andreas Baste Undheim eivind.undheim@rohub.com Eivind Undheim Geo H. Environmental research Life sciences Physical sciences Earth sciences ground station report on the gap fill process physics machine learning soil short wave dataset composition system mailto forest radiation stations from GEBA archive total information station aperture mailing machine learning technique UiO trude.storelvmo@geo.uio.no Trude Storelvmo 0000-0002-0068-2430 service-account-enrichment 7618 https://api.rohub.org/api/ros/46f90d7b-1e36-4701-9acd-2681dfe565a6/crate/download/ 2022-03-22 01:55:23.106126+00:00 2025-03-05 00:59:11.026646+00:00 2022-03-22 01:55:23.106126+00:00 Global (diffuse and direct) shortwave downwelling radiation at the surface between year 1961 and 2014. A total of 1847 ground stations from GEBA archive has been selected and been through the machine learning technique "random forests" (Breiman, 2001) to fill gaps in from original GEBA dataset. A report on the gap filling process can be attained by e-mailing Trude Storelvmo (LINK: mailto:truds@uio.no). application/ld+json https://w3id.org/ro-id/46f90d7b-1e36-4701-9acd-2681dfe565a6 Gap filled GEBA data MANUAL

Trude Storelvmo. "Gap filled GEBA data." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/46f90d7b-1e36-4701-9acd-2681dfe565a6.

biblio raw data metadata data Storelvmo, T. (2020).Gap filled GEBA data [Data set]. Norstore. https://doi.org/10.11582/2020.00066 Trude Storelvmo Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00066 2020-12-21 00:00:00 2022-03-22 01:55:40.621748+00:00 Global (diffuse and direct) shortwave downwelling radiation at the surface between year 1961 and 2014. A total of 1847 ground stations from GEBA archive has been selected and been through the machine learning technique "random forests" (Breiman, 2001) to fill gaps in from original GEBA dataset. A report on the gap filling process can be attained by e-mailing Trude Storelvmo (<a href="mailto:truds@uio.no" class="linkified" target="_blank">LINK</a>). Gap filled GEBA data 2020-12-21 00:00:00 Trude Storelvmo Geo H. Environmental research Life sciences Physical sciences Biology 15.52992 78.26105 POINT (15.52992 78.26105) dc5618a8-8b14-4ece-a082-c6a3b691c89b POINT (15.52992 78.26105) service-account-enrichment 8670 https://api.rohub.org/api/ros/271b95da-7537-43a1-b65b-a5d84f2227ab/crate/download/ 2022-03-22 01:55:42.873140+00:00 2025-03-05 01:01:11.472723+00:00 2022-03-22 01:55:42.873140+00:00 The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths. application/ld+json https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab ISA_Svalbard_Chlorophyll_A_2011_2019 MANUAL Atlantic Ocean Spitsbergen broadcasting station dataset ecosystem effects of climate change inflow station stream mouth time series variability earth sciences Climate change Ecosystem IT-computer sciences Weather Atlantic Ocean ISA_Svalbard_Chlorophyll_A_2011_2019 Isfjorden-Adventfjorden University Centre dataset inflow time series geosciences Atlantic water West Spitsbergen Current ecosystem effects of climate change mouth of Adventfjorden time series station IsA has been sampled on a monthly basis since December 2011. The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS) It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. since Dec-2011 https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab/388c2df4-da51-4c59-8696-918f504891a6 ecology hydrography Atlantic Ocean Spitsbergen Svalbard

University Centre in Svalbard (UNIS). "ISA_Svalbard_Chlorophyll_A_2011_2019." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab.

POINT (15.52992 78.26105) data raw data metadata biblio University Centre in Svalbard (2020).ISA_Svalbard_Chlorophyll_A_2011_2019 [Data set]. Norstore. https://doi.org/10.11582/2020.00063 University Centre in Svalbard (UNIS) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00063 2020-12-09 00:00:00 2022-03-22 01:56:01.214326+00:00 The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths. ISA_Svalbard_Chlorophyll_A_2011_2019 2020-12-09 00:00:00 Luke Marsden UNIS@rohub.com University Centre in Svalbard (UNIS) Geo H. Environmental research Life sciences Physical sciences Chemistry force field 23.076923076923077 9.9 biochemistry 58.82352941176471 6.0 file 13.51981351981352 5.8 results file 13.58428805237316 8.3 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical betterment 11.616766467065867 9.7 Physics Science and technology/Natural science/Physics raw data 7.18562874251497 6.0 chemistry and materials (general) 100.0 0.38146594166755676 validation 10.955710955710956 4.7 force field 16.047904191616766 13.4 Newspaper Arts, culture and entertainment/Mass media/Newspaper Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. 37.755102040816325 14.8 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. 28.061224489795915 11.0 improvement 16.083916083916083 6.9 earth sciences 100.0 0.620610237121582 result 6.107784431137724 5.1 manuscripts file 8.67430441898527 5.3 comparison 4.6706586826347305 3.9 quartermaster 8.622754491017965 7.2 chemistry and materials 100.0 0.38146594166755676 force field comparison 20.29459901800327 12.4 QM calculation 12.76595744680851 7.8 computer science 18.627450980392158 1.9 service-account-enrichment 9478 https://api.rohub.org/api/ros/b7df46c2-d54e-4cf0-8600-36516706cfbe/crate/download/ 2022-03-22 02:18:37.698329+00:00 2025-03-05 00:45:29.658387+00:00 2022-03-22 02:18:37.698329+00:00 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. If you have trouble navigating, please send email to LINK: mailto:reza.611@gmail.com. ### Please see the file "file_organisations" for the directories and subdirectories. # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test. The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case. For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference. # For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019. ### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories. *** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to LINK: mailto:reza.611@gmail.com. application/ld+json https://w3id.org/ro-id/b7df46c2-d54e-4cf0-8600-36516706cfbe 2016_Khan_etal_JCTC MANUAL https://w3id.org/ro-id/0d57f62d-6c70-40b8-9d8e-e9a566cad649 https://w3id.org/ro-id/ae6ff238-5828-468f-96fb-4ccf675ca305 https://w3id.org/ro-id/f026d752-ef4f-4eb3-bd58-204c343a38a3 https://w3id.org/ro-id/15b367a2-7f08-4111-9f25-e305fe38a0d4 https://w3id.org/ro-id/4a19e75c-d6a3-4d35-b5a2-983f9386e7df https://w3id.org/ro-id/6a5072ec-d759-43ed-bffe-54269bbaec0a https://w3id.org/ro-id/76b25a83-0ac2-47af-ba30-a57d580444ac https://w3id.org/ro-id/7be25221-aa5c-4111-baae-72c2b0a9ea3d https://w3id.org/ro-id/83227a6c-fc8b-4c33-8e0a-e22a7ecbd466 https://w3id.org/ro-id/c93a421e-e398-46dc-b619-e8c2c2d6b754 https://w3id.org/ro-id/cec39022-070a-4186-9a3b-f4afb95e4e04 https://w3id.org/ro-id/d00a2fea-2639-4214-8e89-e7e029ee4cc3 https://w3id.org/ro-id/dfa0f404-0ce4-42e0-9f20-f0a49017bab8 https://w3id.org/ro-id/e5db7894-f353-4242-bd8e-1b9193d7da4e https://w3id.org/ro-id/ef77f5d5-6dbb-49f0-a362-52fd884161bd https://w3id.org/ro-id/76682de5-ba8d-4121-a995-42edee55b9ea https://w3id.org/ro-id/c8330e75-3669-4710-8eae-233dff9ed7db https://w3id.org/ro-id/10c24aae-d46c-48e3-8a62-130cba301ea2 https://w3id.org/ro-id/3d2a408d-b8b1-49b5-9eee-2384a5b9c45c https://w3id.org/ro-id/6ada0939-f2a7-48e3-bf28-ded4054d05c5 https://w3id.org/ro-id/fe92320b-d092-4d17-9002-09f6a1cafac6 https://w3id.org/ro-id/fef725a8-2d68-4745-868b-5f8276d364b5 https://w3id.org/ro-id/0af7beec-53cf-4022-b048-2843ecf01aa4 https://w3id.org/ro-id/0e4e9ab8-d4e9-41ef-b6a1-655dd17d5415 https://w3id.org/ro-id/69eca4e5-31ca-47e2-b7ed-19e4a5d3f053 https://w3id.org/ro-id/75647177-6b16-4ebf-a946-48ff3b600413 https://w3id.org/ro-id/ee1bbe17-772c-46bb-b304-baade63555a8 https://w3id.org/ro-id/f534d7b0-e5e1-4f73-adeb-7478f274586a https://w3id.org/ro-id/fd42eafd-774b-4be5-81db-1fa4019a03e8 https://w3id.org/ro-id/677261f6-b685-4b94-99ef-4cd89dfa629b https://w3id.org/ro-id/83f81bde-e952-47fc-9794-d26d6d19d31f https://w3id.org/ro-id/10343264-da33-4b51-98dd-78bb264b85e4 https://w3id.org/ro-id/7a1ad00d-5694-444f-9d82-88ac60ea68f7 https://w3id.org/ro-id/8a35b2bf-6a3d-4f32-bded-47ebd8d39999 https://w3id.org/ro-id/9c4bb3c1-8f1a-4bd0-a8b7-474606e4312a https://w3id.org/ro-id/c61ae304-ecf9-4e49-b012-0db5e0453981 https://w3id.org/ro-id/fe12d1bc-0348-4616-b250-9382962a23bc https://w3id.org/ro-id/6c07622b-9d77-48b3-a892-f24e34ef7c57 https://w3id.org/ro-id/6f457be9-fb82-4597-80a3-bf0c1a8faa75 https://w3id.org/ro-id/f43a3122-c71d-487c-bf34-0aea1c6a60d1

Nathalie Reuter. "2016_Khan_etal_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/b7df46c2-d54e-4cf0-8600-36516706cfbe.

biblio data metadata raw data Reuter, N. (2021).2016_Khan_etal_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00103 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00103 2021-11-22 00:00:00 2022-03-22 02:18:56.113155+00:00 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. If you have trouble navigating, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>. ### Please see the file "file_organisations" for the directories and subdirectories. # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test. The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case. For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference. # For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019. ### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories. *** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>. 2016_Khan_etal_JCTC 2021-11-22 00:00:00 Nathalie Reuter https://doi.org/10.1021/acs.jctc.6b00654 2022-03-22 02:18:51.634853+00:00 2022-03-22 02:18:51.891040+00:00 https://doi.org/10.1021/acs.jctc.6b00654 2022-03-22 02:18:51.634853+00:00 force field parameter validation 14.238952536824875 8.7 geology 100.0 0.620610237121582 subdirectory 5.389221556886228 4.5 choline 8.502994011976048 7.1 file 9.461077844311378 7.9 directory 8.74251497005988 7.3 validation 7.664670658682635 6.4 directory 12.121212121212121 5.2 calculation 5.9880239520958085 5.0 armed forces 22.549019607843135 2.3 # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. 34.183673469387756 13.4 choline 12.121212121212121 5.2 QM 12.121212121212121 5.2 tryptophan-choline force field improvement 30.441898527004913 18.6 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences Diseases and conditions Health/Diseases and conditions Geo H. nathalie.reuter@rohub.com Nathalie Reuter Environmental research Life sciences Physical sciences Chemistry Physics Science and technology/Natural science/Physics tryptophan-choline force field improvement 30.441898527004913 18.6 manuscripts file 8.67430441898527 5.3 subdirectory 5.389221556886228 4.5 quartermaster 8.622754491017965 7.2 biochemistry 58.82352941176471 6.0 improvement 16.083916083916083 6.9 betterment 11.616766467065867 9.7 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical comparison 4.6706586826347305 3.9 earth sciences 100.0 0.620610237121582 force field 23.076923076923077 9.9 validation 7.664670658682635 6.4 Diseases and conditions Health/Diseases and conditions armed forces 22.549019607843135 2.3 force field 16.047904191616766 13.4 geology 100.0 0.620610237121582 force field parameter validation 14.238952536824875 8.7 calculation 5.9880239520958085 5.0 service-account-enrichment 9458 https://api.rohub.org/api/ros/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/crate/download/ 2022-03-22 02:18:57.862656+00:00 2025-03-05 00:45:31.255755+00:00 2022-03-22 02:18:57.862656+00:00 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. If you have trouble navigating, please send email to LINK: mailto:reza.611@gmail.com. ### Please see the file "file_organisations" for the directories and subdirectories. # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test. The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case. For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference. # For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019. ### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories. *** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to LINK: mailto:reza.611@gmail.com. application/ld+json https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561 2019_Khan_etal_JCTC MANUAL https://w3id.org/ro-id/1eb8b1da-9a9a-4f3d-85dd-f7806d7716ac https://w3id.org/ro-id/45c97f98-4cfc-4aec-a86d-972e5ee7c719 https://w3id.org/ro-id/f94049ca-f965-407c-bbd6-9dec6d0b4f36 https://w3id.org/ro-id/1b8dc5b9-3af9-4db4-91b7-47e4762481cf https://w3id.org/ro-id/1e7f2235-59f3-486d-9c05-8208bc9b6650 https://w3id.org/ro-id/2497f9a5-a789-4c12-87f7-36adaafe0b2f https://w3id.org/ro-id/38d6400b-ef9c-491a-87db-aa0e19241052 https://w3id.org/ro-id/42da6fae-3c5d-402f-a475-e3de433fb9c5 https://w3id.org/ro-id/51678519-ffd4-4990-a2ef-45e8cebea870 https://w3id.org/ro-id/6d49e53b-ee7d-487c-8005-f3d33075c010 https://w3id.org/ro-id/9d724b70-f51e-4801-a33c-0ddacb9f6380 https://w3id.org/ro-id/a373b5e2-9377-4ea5-ac77-3000d7e4ea1d https://w3id.org/ro-id/b4696554-3ad0-482c-a812-64ad881bf2df https://w3id.org/ro-id/bd4b37ee-9510-4d1b-a866-d5ac716d7d6d https://w3id.org/ro-id/f1de38ae-6531-4b97-b7cb-871e09afdb6c https://w3id.org/ro-id/3b59da1c-05ba-4240-9d9c-5fb7b28ad53c https://w3id.org/ro-id/5a3f3091-042f-4b19-9448-202c9bc06e4a https://w3id.org/ro-id/02142eed-e7ad-4ecf-b92c-17535047e45e https://w3id.org/ro-id/2a817a60-bda4-4de6-811b-8b464c6f4fbe https://w3id.org/ro-id/43eecec7-f276-4fa6-a009-b6d74bee5ac4 https://w3id.org/ro-id/8fe39cdd-2592-442a-ab2f-75bce594e8bc https://w3id.org/ro-id/ebb04bc3-1b64-4723-abb1-2bb1e9fb5c41 https://w3id.org/ro-id/1fd4a1b7-7f08-4932-81b9-e3e8219a45bd https://w3id.org/ro-id/42c9b12d-dec9-4e1d-bfa3-e6b20ca574ed https://w3id.org/ro-id/77b8f708-1da5-4937-88c1-bf913536e396 https://w3id.org/ro-id/780df816-5a29-4a1e-ada6-975c80157441 https://w3id.org/ro-id/91a19a15-66ea-4795-b53f-18d843c39303 https://w3id.org/ro-id/dfcc6de7-f307-4a3d-b80f-1ea59b685cea https://w3id.org/ro-id/e68dd0da-25b0-4f21-83a8-2090c91f2290 https://w3id.org/ro-id/931b0404-6d29-470f-bb13-ac082723d322 https://w3id.org/ro-id/dbfc01ff-6296-4efc-bf3a-072f7c2409b6 https://w3id.org/ro-id/08b34e3d-b475-4ece-a0e7-0a6e52e6453e https://w3id.org/ro-id/1a33ec44-1314-4e9b-b0f4-9476e11d0c7b https://w3id.org/ro-id/6510d15b-80ba-43f6-bd4e-bd8cb28ff442 https://w3id.org/ro-id/822a91de-8ad6-49aa-821a-fed4a5f225a0 https://w3id.org/ro-id/c9e71228-0405-4c6b-9183-c94103544c47 https://w3id.org/ro-id/e13cc288-ae77-4cc5-9a27-2223c47c4bd9 https://w3id.org/ro-id/b2bb9f47-e1bf-472d-b70d-b04bbbb8cdd1 https://w3id.org/ro-id/c15316b5-7fc7-4545-a8bb-f8d31e4060d6 https://w3id.org/ro-id/fe8868e4-2180-433d-b349-a8b9f0a2692c

Nathalie Reuter. "2019_Khan_etal_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561.

metadata raw data data biblio https://doi.org/10.1021/acs.jctc.8b00839 2022-03-22 02:19:11.787124+00:00 2022-03-22 02:19:12.026713+00:00 https://doi.org/10.1021/acs.jctc.8b00839 2022-03-22 02:19:11.787124+00:00 Reuter, N. (2021).2019_Khan_etal_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00104 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00104 2021-11-22 00:00:00 2022-03-22 02:19:16.071315+00:00 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. If you have trouble navigating, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>. ### Please see the file "file_organisations" for the directories and subdirectories. # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test. The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case. For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference. # For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019. ### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories. *** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>. 2019_Khan_etal_JCTC 2021-11-22 00:00:00 Nathalie Reuter directory 12.121212121212121 5.2 QM 12.121212121212121 5.2 force field comparison 20.29459901800327 12.4 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences choline 12.121212121212121 5.2 chemistry and materials (general) 100.0 0.38146594166755676 result 6.107784431137724 5.1 raw data 7.18562874251497 6.0 Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. 37.755102040816325 14.8 choline 8.502994011976048 7.1 directory 8.74251497005988 7.3 # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. 34.183673469387756 13.4 QM calculation 12.76595744680851 7.8 chemistry and materials 100.0 0.38146594166755676 validation 10.955710955710956 4.7 results file 13.58428805237316 8.3 file 13.51981351981352 5.8 Newspaper Arts, culture and entertainment/Mass media/Newspaper file 9.461077844311378 7.9 computer science 18.627450980392158 1.9 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. 28.061224489795915 11.0 Geo H. nathalie.reuter@rohub.com Nathalie Reuter Environmental research Life sciences Physical sciences service-account-enrichment 7833 https://api.rohub.org/api/ros/d5486b94-4c58-4cb4-8b1e-6945adf5eba9/crate/download/ 2022-03-22 02:19:17.388294+00:00 2025-03-05 01:19:14.776191+00:00 2022-03-22 02:19:17.388294+00:00 This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions. The enzymes simulated are Li_alphaIA1, St_beta1B1, Ll_alphaIII and R44Y/S60Y St_beta1B1. The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer. The trajectories are in DCD format and the topology file in PSF format.The following simulations are available: 1. Li_alphaIA1 on a pure POPC bilayer; 2. Li_alphaIA1 on a PC:SM:CHOL (70:20:10) bilayer; 3. Li_alphaIA1 on a POPC:POPE (50:50) bilayer; 4. Ll_alphaIII on a pure POPC bilayer; 5. Ll_alphaIII on a PC:SM:CHOL (70:20:10) bilayer; 6. R44Y/S60Y St_beta1B1 on a pure POPC bilayer; 7. St_beta1B1 on a pure POPC bilayer; 8. St_beta1B1 on a PC:SM:CHOL (70:20:10) bilayer; 9. St_beta1B1 on a POPC:POPE (50:50) bilayer; application/ld+json https://w3id.org/ro-id/d5486b94-4c58-4cb4-8b1e-6945adf5eba9 Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage MANUAL bilayer choline clade dataset enzyme information lipid phospholipase simulation earth sciences Hardware IT-computer sciences Pope Religious leader POPC S60Y St_beta1B1 bilayer clade enzyme lipid phospholipase chemistry and materials Loxosceles phospholipase d enzyme POPC bilayer bilayer composition choline-containing lipid pure POPC Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage. The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer. This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions. biochemistry

Emmanuel Moutoussamy. "Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/d5486b94-4c58-4cb4-8b1e-6945adf5eba9.

metadata biblio data raw data Moutoussamy, E. (2021).Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage [Data set]. Norstore. https://doi.org/10.11582/2021.00099 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00099 2021-11-19 00:00:00 2022-03-22 02:19:33.483910+00:00 This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions. The enzymes simulated are Li_alphaIA1, St_beta1B1, Ll_alphaIII and R44Y/S60Y St_beta1B1. The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer. The trajectories are in DCD format and the topology file in PSF format.The following simulations are available: 1. Li_alphaIA1 on a pure POPC bilayer; 2. Li_alphaIA1 on a PC:SM:CHOL (70:20:10) bilayer; 3. Li_alphaIA1 on a POPC:POPE (50:50) bilayer; 4. Ll_alphaIII on a pure POPC bilayer; 5. Ll_alphaIII on a PC:SM:CHOL (70:20:10) bilayer; 6. R44Y/S60Y St_beta1B1 on a pure POPC bilayer; 7. St_beta1B1 on a pure POPC bilayer; 8. St_beta1B1 on a PC:SM:CHOL (70:20:10) bilayer; 9. St_beta1B1 on a POPC:POPE (50:50) bilayer; Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage 2021-11-19 00:00:00 Emmanuel Edouard Moutoussamy emmanuel.moutoussamy@rohub.com Emmanuel Moutoussamy Geo H. Environmental research Life sciences Physical sciences Earth sciences Norway debris flow field observation calculations data geology West Norway RAMMS simulation slide deposit measure perimeter data mudslide sediment data type treatise angle of repose domain dataset GNSS measurement profile Department of Geosciences Oslo calculation University of Oslo service-account-enrichment 8150 https://api.rohub.org/api/ros/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707/crate/download/ 2022-03-22 02:21:05.280489+00:00 2025-03-05 00:50:09.588296+00:00 2022-03-22 02:21:05.280489+00:00 Data created for Marius Julian Grønli’s Master thesis at the Department of Geosciences at the University of Oslo fall 2021. Title: Quantitative back calculation of three debris flows in western Norway Dataset includes: GNSS measurements of three debris flows on the west coast of Norway. Logged perimeter with 15 m increments and several profiles across the flow paths. Grain size distribution, angle of repose for several soil samples at each study site. Including samples of slide deposits and Origin material. RAMMS simulations of the three events with varying input parameters. Each data type has a documentation file explaining the workflow of each data set. Event dates: Stamnes: 16th of February 2020 Jordalen: 5th of August 2019 Osdalsvatenet: 21st of January 2020 application/ld+json https://w3id.org/ro-id/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707 Debris flow field observations and RAMMS back calculations MANUAL

Marius Julian Grønli. "Debris flow field observations and RAMMS back calculations." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707.

raw data biblio metadata data Grønli, M. J. (2021).Debris flow field observations and RAMMS back calculations [Data set]. Norstore. https://doi.org/10.11582/2021.00092 Marius Julian Grønli Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00092 2021-10-14 00:00:00 2022-03-22 02:21:24.160454+00:00 Data created for Marius Julian Grønli’s Master thesis at the Department of Geosciences at the University of Oslo fall 2021. Title: Quantitative back calculation of three debris flows in western Norway Dataset includes: GNSS measurements of three debris flows on the west coast of Norway. Logged perimeter with 15 m increments and several profiles across the flow paths. Grain size distribution, angle of repose for several soil samples at each study site. Including samples of slide deposits and Origin material. RAMMS simulations of the three events with varying input parameters. Each data type has a documentation file explaining the workflow of each data set. Event dates: Stamnes: 16th of February 2020 Jordalen: 5th of August 2019 Osdalsvatenet: 21st of January 2020 Debris flow field observations and RAMMS back calculations 2021-10-14 00:00:00 Marius Julian Grønli Geo H. marius.julian.gronli@rohub.com Marius Julian Grønli Environmental research Life sciences Physical sciences publication 12.651821862348179 12.5 data for the publication 11.322645290581162 11.3 computer modelling 24.898785425101217 24.6 experiment 29.322548028311424 29.0 data 33.46814964610718 33.1 service-account-enrichment 7174 https://api.rohub.org/api/ros/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9/crate/download/ 2022-03-22 02:21:25.532255+00:00 2025-03-05 00:45:26.451604+00:00 2022-03-22 02:21:25.532255+00:00 raw experiment/simulation data for the publication application/ld+json https://w3id.org/ro-id/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9 2012_Grauffel_etal_PLoSONE MANUAL https://w3id.org/ro-id/df41cd03-d30a-42df-9c90-70a3a34bfe9f https://w3id.org/ro-id/118fb288-3b7c-4e01-8a2d-604ee92fdec6 https://w3id.org/ro-id/24d62ac8-ce28-468c-b126-d51d011d186f https://w3id.org/ro-id/bd9594b0-a34c-4a13-9e05-1f53a8a7cf77 https://w3id.org/ro-id/c3dbab5f-3057-475d-8d95-e81050e0af2c https://w3id.org/ro-id/8e20766c-e0f4-4c8d-9ed1-2be1852aaaa6 https://w3id.org/ro-id/a2bd4f65-14ed-4820-ad2d-3d7c5eb57515 https://w3id.org/ro-id/4179efb5-d3c5-4ec7-90c1-29d0a08fa7c3 https://w3id.org/ro-id/5285a352-03c0-4dac-97f9-7c8a1176b0af https://w3id.org/ro-id/6c5ad61d-9ed9-4975-96f0-72bf193f5d10 https://w3id.org/ro-id/9c06af6c-1ff6-4fd5-acc6-ec57b549da79 https://w3id.org/ro-id/d5d9a091-9e58-4a01-a8ed-728f8ec8dd8d https://w3id.org/ro-id/e6948f96-6520-4b3f-9646-ddb3925dde31 https://w3id.org/ro-id/12fdff6e-f2ec-490d-b64b-7fa11480934b https://w3id.org/ro-id/685b3d26-2957-47d4-bff7-4a355bebf2bb https://w3id.org/ro-id/7d1d290d-bc88-48b2-8895-9ba409d88240 https://w3id.org/ro-id/d8911953-eb97-44fe-8dcd-16b624f19d22

Nathalie Reuter. "2012_Grauffel_etal_PLoSONE." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9.

raw data metadata biblio data Reuter, N. (2021).2012_Grauffel_etal_PLoSONE [Data set]. Norstore. https://doi.org/10.11582/2021.00090 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00090 2021-10-13 00:00:00 2022-03-22 02:21:45.800191+00:00 raw experiment/simulation data for the publication 2012_Grauffel_etal_PLoSONE 2021-10-13 00:00:00 Nathalie Reuter https://doi.org/10.1371/journal.pone.0052642 2022-03-22 02:21:40.999932+00:00 2022-03-22 02:21:41.269967+00:00 https://doi.org/10.1371/journal.pone.0052642 2022-03-22 02:21:40.999932+00:00 simulation data 88.27655310621243 88.1 simulation 25.176946410515672 24.9 simulation data for the publication 0.40080160320641284 0.4 earth sciences 100.0 0.9631021022796631 publication 12.032355915065722 11.9 atmospheric sciences 100.0 0.9631021022796631 data 31.781376518218625 31.4 experiment 30.668016194331983 30.3 life sciences (general) 100.0 0.756223201751709 2012_Grauffel_etal_PLoSONE. raw experiment/simulation data for the publication 100.0 100.0 computer science 100.0 16.0 life sciences 100.0 0.756223201751709 Geo H. nathalie.reuter@rohub.com Nathalie Reuter Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 8544 https://api.rohub.org/api/ros/c35062bf-df50-4667-b876-e5c69570125c/crate/download/ 2022-03-22 02:21:47.331157+00:00 2025-03-05 00:56:57.081384+00:00 2022-03-22 02:21:47.331157+00:00 This dataset contains the model output (atmospheric component only) used in Blichner, S. M., Sporre, M. K., and Berntsen, T. K.: Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos. Chem. Phys., LINK: http://doi.org/10.5194/acp-2021-151, accepted, 2021. See LINK: http://github.com/sarambl/OAS-ERF for analysis code. application/ld+json https://w3id.org/ro-id/c35062bf-df50-4667-b876-e5c69570125c Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" MANUAL aerosol cloud interaction output radiative forcing therapy tumor earth sciences Therapy T. K. Reduced aerosol cloud growth interaction radiative forcing treatment mathematical and computer sciences Chem. Phys aerosol growth cloud-aerosol interaction improved treatment model output Blichner, S. M. Sporre, M. K. and Berntsen, T. K. Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos. Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" This dataset contains the model output (atmospheric component only) used in See LINK: http: github.com/sarambl/OAS-ERF for analysis code. medicine physics

Sara Blichner, and University of Oslo (UiO). "Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model"." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/c35062bf-df50-4667-b876-e5c69570125c.

raw data metadata data biblio https://acp.copernicus.org/preprints/acp-2021-151/ 2022-03-22 02:22:07.384799+00:00 2022-03-22 02:22:07.648299+00:00 https://acp.copernicus.org/preprints/acp-2021-151/ 2022-03-22 02:22:07.384799+00:00 Blichner, S., University of Oslo (2021).Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" [Data set]. Norstore. https://doi.org/10.11582/2021.00087 Sara Marie Blichner Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00087 2021-10-11 00:00:00 2022-03-22 02:22:11.450221+00:00 This dataset contains the model output (atmospheric component only) used in Blichner, S. M., Sporre, M. K., and Berntsen, T. K.: Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos. Chem. Phys., <a href="http://doi.org/10.5194/acp-2021-151" class="linkified" target="_blank">LINK</a>, accepted, 2021. See <a href="http://github.com/sarambl/OAS-ERF" class="linkified" target="_blank">LINK</a> for analysis code. Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" 2021-10-11 00:00:00 Sara Marie Blichner UiO@rohub.com University of Oslo (UiO) Geo H. sara.blichner@rohub.com Sara Blichner Environmental research Life sciences Physical sciences Chemistry computer modelling 16.75025075225677 16.7 computer science 29.648241206030153 11.8 mathematical and computer sciences 100.0 0.29949691891670227 manuscript 7.321965897693079 7.3 source code 38.581856100104275 37.0 atmospheric sciences 100.0 0.7998380064964294 analysis data 35.03503503503503 35.0 manuscript 7.7163712200208545 7.4 source code 36.91073219658976 36.8 computer programming and software 100.0 0.29949691891670227 source simulation 64.96496496496498 64.9 publication 6.673618352450469 6.4 simulation 18.039624608967674 17.3 data 28.988529718456725 27.8 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences service-account-enrichment 7302 https://api.rohub.org/api/ros/bc9a215f-a0b0-4a5b-85e9-668f59874669/crate/download/ 2022-03-22 02:22:13.239874+00:00 2025-03-05 00:45:27.011181+00:00 2022-03-22 02:22:13.239874+00:00 source simulation files, analysis data, source code, manuscript etc. for the publication application/ld+json https://w3id.org/ro-id/bc9a215f-a0b0-4a5b-85e9-668f59874669 2013_Fuglebakk_Reuter_Hinsen_JCTC MANUAL https://w3id.org/ro-id/21c7efbc-5b32-4252-80de-54332db3c22e https://w3id.org/ro-id/be1f0560-2efb-451a-a480-fbb59273aa12 https://w3id.org/ro-id/0e60f9a3-f509-473d-9a01-daa3b9af0c71 https://w3id.org/ro-id/2f981695-fcdb-4a4d-a62a-a416209dff5f https://w3id.org/ro-id/91ff8e38-d049-4480-8c58-a03a85c6d88d https://w3id.org/ro-id/d44514a5-4fa2-4f20-ba70-9f65dc27e197 https://w3id.org/ro-id/d6888ca3-5ceb-4084-b28b-78afa529f035 https://w3id.org/ro-id/d708cc8d-b7c1-4844-874e-386804236c5d https://w3id.org/ro-id/81b43073-1f12-4ec3-bda0-31871849c11b https://w3id.org/ro-id/e80c508a-eca3-422c-beda-5efab23b0e1f https://w3id.org/ro-id/b1205b18-910f-49c5-b884-3f07f974e3e2 https://w3id.org/ro-id/702af2a6-4a74-491c-98c7-6ff822826ed6 https://w3id.org/ro-id/8b4f92fc-157c-4f04-90b9-1374279a7827 https://w3id.org/ro-id/9ff7e001-1a91-49bc-a62a-64545b49363a https://w3id.org/ro-id/aca95257-9b1b-4273-b9e4-bc9e3cb28849 https://w3id.org/ro-id/b01a20c5-99b9-4550-8b4c-2eed1451cc07 https://w3id.org/ro-id/2a3e1c91-9dd8-4008-a413-921498379217 https://w3id.org/ro-id/9ab04305-2c0f-4079-87dd-2c08edbf499d https://w3id.org/ro-id/844ae3d8-ebe8-46c9-904d-06f0515adb07 https://w3id.org/ro-id/9da1ffd8-f477-445b-88b3-138e0db8ff94 https://w3id.org/ro-id/fb3dea67-85bc-40ab-b9b2-75efecaf7471

Nathalie Reuter. "2013_Fuglebakk_Reuter_Hinsen_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/bc9a215f-a0b0-4a5b-85e9-668f59874669.

data metadata biblio raw data Reuter, N. (2021).2013_Fuglebakk_Reuter_Hinsen_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00085 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00085 None 2022-03-22 02:22:36.027755+00:00 source simulation files, analysis data, source code, manuscript etc. for the publication 2013_Fuglebakk_Reuter_Hinsen_JCTC None Nathalie Reuter https://doi.org/10.1021/ct400399x 2022-03-22 02:22:31.146218+00:00 2022-03-22 02:22:31.420467+00:00 https://doi.org/10.1021/ct400399x 2022-03-22 02:22:31.146218+00:00 computer programming 70.35175879396985 28.0 publication 6.41925777331996 6.4 data 28.68605817452357 28.6 analysis 3.9117352056168504 3.9 earth sciences 100.0 0.7998380064964294 2013_Fuglebakk_Reuter_Hinsen_JCTC. source simulation files, analysis data, source code, manuscript etc. for the publication 100.0 100.0 Geo H. nathalie.reuter@rohub.com Nathalie Reuter mailto:nathalie.reuter@rohub.com 7203 https://api.rohub.org/api/ros/359fc143-420a-45a9-8cdb-fc03c7188df5/crate/download/ mailto:georgehadib@gmail.com 2022-03-22 02:22:37.897281+00:00 2025-03-05 00:45:25.879983+00:00 2022-03-22 02:22:37.897281+00:00 source simulation/analysis data for the publication application/ld+json https://w3id.org/ro-id/359fc143-420a-45a9-8cdb-fc03c7188df5 2009_Hajjar_Dejaegere_Reuter_JPCA http://eurovoc.europa.eu/2919 http://eurovoc.europa.eu/3941 http://eurovoc.europa.eu/3946 http://eurovoc.europa.eu/5966 MANUAL https://w3id.org/ro-id/b598ff14-9f40-4dd4-9a51-c6c876d6c4f9 https://w3id.org/ro-id/2d2279a8-e28b-41b5-8d9b-c84d8c56b8cd https://w3id.org/ro-id/8c471337-2d21-4603-af51-8c51424cbc15 https://w3id.org/ro-id/93d0b3c7-5dea-4858-9818-e71de222b644 https://w3id.org/ro-id/b71244db-b6a4-4983-ac77-8aff82f87827 https://w3id.org/ro-id/e87b3e9f-cd16-4868-8c58-cd48d7be649c https://w3id.org/ro-id/4e238fdb-4e45-4543-85f9-56abf3a1c08b https://w3id.org/ro-id/cc72f552-8791-47f5-b30a-26a4a0932ba5 https://w3id.org/ro-id/6829dc3e-b758-42a8-8f96-1accf4a2bca5 https://w3id.org/ro-id/87d5a9cb-bf7e-4817-a12c-95bc4f2a2e67 https://w3id.org/ro-id/8b5e5476-a79e-4512-9427-614a57ffc7e5 https://w3id.org/ro-id/dcb3f7ba-85af-42fb-9f23-bfbc19e7037c https://w3id.org/ro-id/f342e568-22c4-4f61-b78c-511f0e6655c4 https://w3id.org/ro-id/a01cccbe-ac89-4399-b258-c4a5822d7d46 https://w3id.org/ro-id/ebcad90a-8c01-4dee-9547-bb3e6c1f2dcb https://w3id.org/ro-id/d03cfd7d-7cad-411f-9af0-9621c4a2497f https://w3id.org/ro-id/f9191935-e027-403b-9935-032cc2a49be6 https://w3id.org/ro-id/36bd90da-c1d0-4adc-82c4-14adf1a98f01

Nathalie Reuter. "2009_Hajjar_Dejaegere_Reuter_JPCA." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/359fc143-420a-45a9-8cdb-fc03c7188df5.

raw data data metadata biblio Reuter, N. (2021).2009_Hajjar_Dejaegere_Reuter_JPCA [Data set]. Norstore. https://doi.org/10.11582/2021.00086 Published Nathalie Reuter Simulation mailto:nathalie.reuter@rohub.com https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00086 mailto:georgehadib@gmail.com None 2022-03-22 02:22:57.660517+00:00 source simulation/analysis data for the publication CC-BY-4.0 https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00086 None Nathalie Reuter Dandan Xue mailto:nathalie.reuter@rohub.com https://doi.org/10.1021/jp902930u mailto:georgehadib@gmail.com 2022-03-22 02:22:52.717818+00:00 2022-03-22 02:22:52.997316+00:00 https://doi.org/10.1021/jp902930u 2022-03-22 02:22:52.717818+00:00 Geo H. nathalie.reuter@rohub.com Nathalie Reuter mailto:service-account-enrichment Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 8000 https://api.rohub.org/api/ros/e7f5d844-fa99-4407-af00-82fc4ba618f1/crate/download/ 2022-03-22 02:22:59.259226+00:00 2025-03-05 01:23:33.595114+00:00 2022-03-22 02:22:59.259226+00:00 Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations. In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region. All simulations cover the time period 1982-2000. application/ld+json https://w3id.org/ro-id/e7f5d844-fa99-4407-af00-82fc4ba618f1 Subset of the AFES simulations used for doi 10.5194/wcd-2020-50 MANUAL Gulf stream latent heat precipitation pressure sea surface temperature simulation subset supersonic transport aircraft temperature earth sciences Weather AFES Gulf stream SST doi 10.5194 hPa simulation subset geosciences AFES simulation Kuroshio Extension region convective precipitation sensible heat fluxes smoothed SST In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region. Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations. Subset of the AFES simulations used for doi 10.5194/wcd-2020-50. the time period 1982-2000 meteorology physics

Akira Kuwano-Yoshida. "Subset of the AFES simulations used for doi 10.5194/wcd-2020-50." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/e7f5d844-fa99-4407-af00-82fc4ba618f1.

metadata biblio raw data data Kuwano-Yoshida, A. (2021).Subset of the AFES simulations used for doi 10.5194/wcd-2020-50 [Data set]. Norstore. https://doi.org/10.11582/2021.00075 Akira Kuwano-Yoshida Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00075 2021-09-22 00:00:00 2022-03-22 02:23:18.675518+00:00 Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations. In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region. All simulations cover the time period 1982-2000. Subset of the AFES simulations used for doi 10.5194/wcd-2020-50 2021-09-22 00:00:00 Clemens Spensberger https://wcd.copernicus.org/preprints/wcd-2020-50/ 2022-03-22 02:23:14.397390+00:00 2022-03-22 02:23:14.649349+00:00 https://wcd.copernicus.org/preprints/wcd-2020-50/ 2022-03-22 02:23:14.397390+00:00 akira.kuwano-yoshida@rohub.com Akira Kuwano-Yoshida Geo H. Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 8136 https://api.rohub.org/api/ros/c0f7a882-6123-477c-aac5-25e8f9e49dcd/crate/download/ 2022-03-22 02:23:20.356464+00:00 2025-03-05 12:49:07.078365+00:00 2022-03-22 02:23:20.356464+00:00 The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea. The user is encourage to read the README file contained within the data set. application/ld+json https://w3id.org/ro-id/c0f7a882-6123-477c-aac5-25e8f9e49dcd Norwegian hindcast archive's wind power data set (NORA3-WP) MANUAL Barents Sea North Sea Norwegian Sea archive file dataset hindcast user variable wind power earth sciences Alternative energy Hardware IT-computer sciences Renewable energy Barents Sea North Sea Norwegian Sea archive data set variable wind power mathematical and computer sciences archive's wind power data set contain wind power hindcast archive's wind power data set parts of the Barents Sea related variable Norwegian hindcast archive's wind power data set (NORA3-WP) The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea. The user is encourage to read the README file contained within the data set. database hydrography software Barents Sea North Sea Norwegian Sea

Ida Marie Solbrekke, Asgeir Sorteberg, and University of Bergen, Institute of biomedicine (UiB). "Norwegian hindcast archive's wind power data set (NORA3-WP)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/c0f7a882-6123-477c-aac5-25e8f9e49dcd.

raw data metadata biblio data Solbrekke, I. M., Sorteberg, A., University of Bergen (2021).Norwegian hindcast archive's wind power data set (NORA3-WP) [Data set]. Norstore. https://doi.org/10.11582/2021.00068 University of Bergen (UiB) Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00068 2021-08-25 00:00:00 2022-03-22 02:23:42.107526+00:00 The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea. The user is encourage to read the README file contained within the data set. Norwegian hindcast archive's wind power data set (NORA3-WP) 2021-08-25 00:00:00 Ida Marie Solbrekke UiB@rohub.com University of Bergen, Institute of biomedicine (UiB) asgeir.sorteberg@rohub.com Asgeir Sorteberg Geo H. ida.marie.solbrekke@rohub.com Ida Marie Solbrekke Environmental research Life sciences Physical sciences Earth sciences WRF model datum South America newspaper understanding fact Future Precipitation Projections for South America South America Understanding Model Diversity in Future Precipitation Projections further detail South America WRF model datum detail newspaper publisher service-account-enrichment 7320 https://api.rohub.org/api/ros/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9/crate/download/ 2022-03-22 02:23:45.142410+00:00 2025-03-05 02:47:01.794301+00:00 2022-03-22 02:23:45.142410+00:00 WRF model data used in the paper "Understanding Model Diversity in Future Precipitation Projections for South America", in review. Further details are given in the paper and in the README file. application/ld+json https://w3id.org/ro-id/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9 Understanding Model Diversity in Future Precipitation Projections for South America MANUAL

Center for International Climate Research (CICERO). "Understanding Model Diversity in Future Precipitation Projections for South America." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9.

metadata raw data biblio data Center for International Climate Research (2021).Understanding Model Diversity in Future Precipitation Projections for South America [Data set]. Norstore. https://doi.org/10.11582/2021.00067 Øivind Hodnebrog Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00067 2021-08-12 00:00:00 2022-03-22 02:24:01.479217+00:00 WRF model data used in the paper "Understanding Model Diversity in Future Precipitation Projections for South America", in review. Further details are given in the paper and in the README file. Understanding Model Diversity in Future Precipitation Projections for South America 2021-08-12 00:00:00 Øivind Hodnebrog CICERO@rohub.com Center for International Climate Research (CICERO) Geo H. Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 7111 https://api.rohub.org/api/ros/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c/crate/download/ 2022-03-22 02:24:02.954436+00:00 2025-03-05 01:19:16.225993+00:00 2022-03-22 02:24:02.954436+00:00 Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021. application/ld+json https://w3id.org/ro-id/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c Statistisk prediksjonsmodellering av steinbreer i Norge MANUAL Norway Oslo earth sciences Harald Wathne Hestad Norway Oslo geosciences Bakgrunnsdata for masteroppgaven Institutt for geofag ved Institutt for geofag ved Institutt Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021. Statistisk prediksjonsmodellering av steinbreer i Norge. Norway Oslo

Harald Wathne Hestad. "Statistisk prediksjonsmodellering av steinbreer i Norge." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c.

metadata biblio data raw data Hestad, H. W. (2021).Statistisk prediksjonsmodellering av steinbreer i Norge [Data set]. Norstore. https://doi.org/10.11582/2021.00066 Harald Wathne Hestad Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00066 2021-08-05 00:00:00 2022-03-22 02:24:19.675060+00:00 Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021. Statistisk prediksjonsmodellering av steinbreer i Norge 2021-08-05 00:00:00 Harald Wathne Hestad Geo H. harald.wathne.hestad@rohub.com Harald Wathne Hestad Environmental research Life sciences Physical sciences Biology in silico antibody-antigen binding database. 25.165562913907287 22.8 The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction 41.16997792494481 37.3 publication 7.323943661971831 5.2 antigen 27.605633802816904 19.6 life sciences 100.0 0.6574541926383972 antibody 23.253012048192772 19.3 geochemistry 100.0 0.840290367603302 software 9.014084507042254 6.4 51258: antigen 24.698795180722893 20.5 forecast 4.457831325301205 3.7 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences publication 6.506024096385542 5.4 service-account-enrichment 8927 https://api.rohub.org/api/ros/71c90c65-34e6-441e-bb44-d1b31dd1007b/crate/download/ 2022-03-22 02:24:21.217126+00:00 2025-03-05 00:45:34.790548+00:00 2022-03-22 02:24:21.217126+00:00 This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods. The content of the files is explained in Readme.pdf The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction The software used to generate the database is available at LINK: http://github.com/csi-greifflab/Absolut for all explanations. application/ld+json https://w3id.org/ro-id/71c90c65-34e6-441e-bb44-d1b31dd1007b Absolut! in silico antibody-antigen binding database MANUAL https://w3id.org/ro-id/b3fb3d46-5fd8-41e7-a090-58181fbb01f1 https://w3id.org/ro-id/e0ff8858-262a-485b-a8dd-728c838e8a36 https://w3id.org/ro-id/15a39d24-4daa-417e-99ed-4b635b56cae9 https://w3id.org/ro-id/4ad66687-f699-45ff-a561-8aea7c629d2a https://w3id.org/ro-id/5195bb19-e10e-4d1d-90e7-b7a591e2af67 https://w3id.org/ro-id/6aa13e0d-8332-4955-9f68-5e08e7b97b22 https://w3id.org/ro-id/7b70fcab-fd6b-461b-a79b-43cb859348f1 https://w3id.org/ro-id/85946983-f823-4c28-88ad-638fd7745f37 https://w3id.org/ro-id/89a060f4-22d1-43df-8cca-c29ee9213d21 https://w3id.org/ro-id/9d840ee9-e861-4815-ad57-62fc3d3d062b https://w3id.org/ro-id/c1a70a12-6e53-4c98-9b4d-481c69d5a481 https://w3id.org/ro-id/c4ebfa3e-5087-4695-abbd-28cb65a0d1e6 https://w3id.org/ro-id/49e70145-19cf-4ac3-9da9-fb2b49f5471c https://w3id.org/ro-id/286e5fd8-7d69-4aaf-854e-a0921db660a8 https://w3id.org/ro-id/8063038f-6dbd-4148-85a2-afb472b141cd https://w3id.org/ro-id/5782c378-2b05-41eb-b48b-de733932007a https://w3id.org/ro-id/c008e2b7-f43b-4e10-ae2b-e9b7c99d2c24 https://w3id.org/ro-id/0ce573ff-277b-4be8-9a5b-6a9c675e96f5 https://w3id.org/ro-id/0f6898fa-86e3-42c4-8cff-48968d07dbdf https://w3id.org/ro-id/45689fb7-0f9f-4c04-b03e-13e29ca1b3b7 https://w3id.org/ro-id/9021ea62-7039-4cc7-b810-4c89cf540ee5 https://w3id.org/ro-id/a164d3a9-ac2e-4c63-b573-2cc2e497ee5e https://w3id.org/ro-id/c6ab0333-3816-48d6-a0c5-f430a5569eff https://w3id.org/ro-id/e912021a-590a-404b-a2e0-3f2335f06f0a https://w3id.org/ro-id/14804972-fa34-4e28-b8d1-7c1a8c800f2e https://w3id.org/ro-id/fec8824a-34e3-4f6c-be08-b0863b0c242a https://w3id.org/ro-id/7247b893-bbcd-47aa-86dd-b27e9f592c25 https://w3id.org/ro-id/a1c41d03-c971-45df-bfc6-b023608097a5 https://w3id.org/ro-id/c2b8dfdb-c1fc-42e1-841c-7b0e25ca1af3 https://w3id.org/ro-id/d1dc98f7-1b59-477f-b20e-5ce10570a46b https://w3id.org/ro-id/fff4fa92-d823-45bd-8cbb-df9573cbb862 https://w3id.org/ro-id/011545de-72ea-4477-b87e-e4a0e9c8df4b https://w3id.org/ro-id/092bc252-092d-439d-8551-fada7f4d509e https://w3id.org/ro-id/89a51f68-a057-4f1a-9f39-051fe69eefb6

Philippe ROBERT, Victor Greiff, and Rahmad Akbar. "Absolut! in silico antibody-antigen binding database." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/71c90c65-34e6-441e-bb44-d1b31dd1007b.

data raw data metadata biblio https://www.biorxiv.org/content/10.1101/2021.07.06.451258v2 2022-03-22 02:24:46.046298+00:00 2022-03-22 02:24:46.241115+00:00 https://www.biorxiv.org/content/10.1101/2021.07.06.451258v2 2022-03-22 02:24:46.046298+00:00 ROBERT, P., Greiff, V., Akbar, R. (2021).Absolut! in silico antibody-antigen binding database [Data set]. Norstore. https://doi.org/10.11582/2021.00063 University of Oslo (UiO) Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00063 2021-07-12 00:00:00 2022-03-22 02:24:50.074536+00:00 This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods. The content of the files is explained in Readme.pdf The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction The software used to generate the database is available at <a href="http://github.com/csi-greifflab/Absolut" class="linkified" target="_blank">LINK</a> for all explanations. Absolut! in silico antibody-antigen binding database 2021-07-12 00:00:00 Philippe Paul Auguste Robert unconstrained machine-learning 7.515923566878981 5.9 database 6.144578313253011 5.1 earth sciences 100.0 0.840290367603302 binding 7.590361445783133 6.3 machine learning 12.891566265060241 10.7 This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods. 33.6644591611479 30.5 antibody 25.774647887323944 18.3 complex 5.783132530120482 4.8 machine learning 14.647887323943662 10.4 silico antibody-antigen 24.331210191082807 19.1 biochemistry 50.0 12.7 Software Economy, business and finance/Economic sector/Computing and information technology/Software specificity 3.6144578313253013 3.0 specificity prediction 22.802547770700635 17.9 investigation 5.0602409638554215 4.2 binding 8.591549295774648 6.1 antigens times 7.388535031847134 5.8 immunology 50.0 12.7 Robert 7.042253521126761 5.0 life sciences (general) 100.0 0.6574541926383972 antibody-antigen binding 37.961783439490446 29.8 Geo H. philippe.robert@rohub.com Philippe ROBERT rahmad.akbar@rohub.com Rahmad Akbar victor.greiff@rohub.com Victor Greiff Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 7096 https://api.rohub.org/api/ros/063f3c98-2306-4388-86b2-4b09976fc16d/crate/download/ 2022-03-22 02:24:51.576414+00:00 2025-03-05 00:52:20.015916+00:00 2022-03-22 02:24:51.576414+00:00 Accumulated preciptiation, 15 min temporal resolution, 3km spatial resolution. Year 1985 to 2005. Covering the inner most part of Oslofjorden in Norway. application/ld+json https://w3id.org/ro-id/063f3c98-2306-4388-86b2-4b09976fc16d HARMONIE-AROME precipitation MANUAL Norway precipitation earth sciences Executive (government) Medicine Ministers (government) Weather HARMONIE-AROME Norway Oslofjorden precipitation general HARMONIE-AROME precipitation part of Oslofjorden Covering the inner most part of Oslofjorden in Norway. HARMONIE-AROME precipitation. Year 1985 to 2005. 15 min Year 1985 to 2005 Norway

Eirik Nordgård. "HARMONIE-AROME precipitation." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/063f3c98-2306-4388-86b2-4b09976fc16d.

biblio metadata data raw data Nordgård, E., Nordgård, E. (2021).HARMONIE-AROME precipitation [Data set]. Norstore. https://doi.org/10.11582/2021.00058 Eirik Nordgård Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00058 2021-06-21 00:00:00 2022-03-22 02:25:09.852200+00:00 Accumulated preciptiation, 15 min temporal resolution, 3km spatial resolution. Year 1985 to 2005. Covering the inner most part of Oslofjorden in Norway. HARMONIE-AROME precipitation 2021-06-21 00:00:00 Eirik Nordgård eirik.nordgard@rohub.com Eirik Nordgård Geo H. Elisa Trasatti; Stefano Sal http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48/changes/07defd9e-e852-4b3b-bd53-d8804eabf75c http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48/changes/51cc4efc-08f0-4f07-9e5f-adf0d894c4af http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf deformation model United Kingdom University of Leeds Fortran code seismic hazard University of LeedsU.K. Fortran Mogi model Tim Wright computer programming computer code volcanology models volcano deformation model system Elisa Trasatti service-account-enrichment http://sandbox.rohub.org/rodl/ROs/Mogi_model/ 2021-05-11T11:11:04.115+02:00 https://plus.google.com/103457129851450819242 http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48 6343 https://api.rohub.org/api/ros/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76/crate/download/ 2021-05-11 09:11:04.115000+00:00 2025-03-05 00:56:57.842643+00:00 2021-05-11 09:11:04.115000+00:00 This is the Mogi (1958) model. Fortran code. application/ld+json https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76 Mogi model (1958) https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76

Elisa Trasatti; Stefano Sal. "Mogi model (1958)." ROHub. May 11 ,2021. https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76.

Biblio tool http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf 2022-03-24 13:14:05.984220+00:00 2022-03-24 13:14:09.157706+00:00 application/pdf presentation of the model 2022-03-24 13:14:05.984220+00:00 1104 https://api.rohub.org/api/resources/a6fe3471-3b69-4508-a607-0851897d612a/download/ 2021-05-11 09:05:12.945000+00:00 2022-03-24 13:14:08.904850+00:00 mogi.f 2021-05-11 09:05:12.945000+00:00 service-account-generation-service Elisa Trasatti; Stefano Sal https://w3id.org/ro-id/1a03b374-b9cd-467b-a8b8-39c5a31a2624 2021-05-11T11:11:04.115+02:00 https://plus.google.com/103457129851450819242 http://sandbox.rohub.org/rodl/ROs/Mogi_model-fork/ deformation model United Kingdom University of Leeds Fortran code seismic hazard University of LeedsU.K. Fortran Mogi model Tim Wright computer programming computer code volcanology models volcano deformation model system Elisa Trasatti Elisa Trasatti service-account-enrichment http://sandbox.rohub.org/rodl/ROs/Mogi_model-snapshot/ 202373 https://api.rohub.org/api/ros/1a03b374-b9cd-467b-a8b8-39c5a31a2624/crate/download/ https://orcid.org/0000-0002-2983-045X 2021-05-11 08:47:47.797000+00:00 2025-03-05 00:56:58.032094+00:00 2021-05-11 08:47:47.797000+00:00 This is the Mogi (1958) model. Fortran code. application/ld+json https://w3id.org/ro-id/1a03b374-b9cd-467b-a8b8-39c5a31a2624 volcanoes, model, isotropic source, mogi Mogi model (1958)

Trasatti, Elisa, and Elisa Trasatti. "Mogi model (1958)." ROHub. May 11 ,2021. https://w3id.org/ro-id/1a03b374-b9cd-467b-a8b8-39c5a31a2624.

Biblio tool 1104 https://api.rohub.org/api/resources/033b8c11-3a5f-4f84-8c67-1868dddd65ab/download/ 2021-05-11 09:05:12.945000+00:00 2022-03-24 13:14:30.663106+00:00 mogi.f 2021-05-11 09:05:12.945000+00:00 http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf 2021-05-11 08:47:47.797000+00:00 2022-03-24 13:14:29.572171+00:00 application/pdf presentation of the model 2021-05-11 08:47:47.797000+00:00 279102 https://api.rohub.org/api/resources/db406fa3-53da-40a8-8a8a-03158c27ff56/download/ 2021-05-11 09:13:42.221000+00:00 2022-03-24 13:14:31.624390+00:00 image/jpeg galland-epsl-2012-figura-2.jpg 2021-05-11 09:13:42.221000+00:00 service-account-generation-service material data benthic habitat map hydrography workflow material results Italy Mariacristina PRAMPOLINI Adriatic Sea 12.990527740189444 9.6 Italy 11.785714285714286 9.9 data 13.802435723951282 10.2 dataset 8.928571428571429 7.5 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences hydrography 35.820895522388064 2.4 habitat 13.69047619047619 11.5 Adriatic Sea https://www.wikidata.org/wiki/Q13924 earth sciences 100.0 0.9174467921257019 RSOBIA analysis 17.057569296375267 16.0 map 11.19047619047619 9.4 geosciences 100.0 0.9408681392669678 Southern Adriatic Sea CNR-ISMAR 14.073071718538564 10.4 Adriatic Sea 11.30952380952381 9.5 Substrate and benthic habitat map of the southern Adriatic Sea (Italy) using RSOBIA - Supplementary material. 48.748748748748746 48.7 job market 64.17910447761194 4.3 habitat map 45.3091684434968 42.5 information 12.5 10.5 material 7.142857142857143 6.0 workflow 15.714285714285714 13.2 supplementary material 7.249466950959489 6.8 service-account-enrichment Paper supplementary material 52301134 https://api.rohub.org/api/ros/a21484de-74d2-4fd0-9518-d3faa42130ad/crate/download/ 2021-04-29 14:17:24.578000+00:00 2025-03-05 01:23:33.856799+00:00 2021-04-29 14:17:24.578000+00:00 Data, workflow and results of the RSOBIA analysis carried out on the Southern Adriatic Sea CNR-ISMAR datasets application/ld+json https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad classification cnr-ismar rsobia seafloor map south adriatic sea Substrate and benthic habitat map of the southern Adriatic Sea (Italy) using RSOBIA - Supplementary material https://w3id.org/ro-id/0f56917f-89a6-49e0-b74e-4721b39ab6ab https://w3id.org/ro-id/57eaad62-13bf-42c3-98dc-89930f63b144 https://w3id.org/ro-id/167cd9a8-64c4-49ba-83f2-f8b4f37a4f5b https://w3id.org/ro-id/b0e9c5ef-aa89-4e22-9969-484f1d3935de https://w3id.org/ro-id/038c37fc-f633-43ab-b91e-814df615b163 https://w3id.org/ro-id/0717885d-1341-4ec5-b405-17568b0b8c09 https://w3id.org/ro-id/1131d28e-5777-4754-b8ff-fae6f2162044 https://w3id.org/ro-id/269c9763-3216-404c-b19f-28b5cdb3021d https://w3id.org/ro-id/480375e0-ce99-4a3c-9451-39dcf3fab588 https://w3id.org/ro-id/742934b0-41d9-43d4-bab4-812630f76885 https://w3id.org/ro-id/7cb26031-0ece-42c2-85bd-5efde7c66cbe https://w3id.org/ro-id/81d93820-e04f-431b-a241-89ce079eb767 https://w3id.org/ro-id/b3fc971b-70c3-44f8-85d0-57bd021abe6c https://w3id.org/ro-id/19235d32-e494-4dc2-bb59-4158bbfaa820 https://w3id.org/ro-id/f0cfb04e-b4fe-4b71-bb2c-2f4e732d0d9b https://w3id.org/ro-id/0a240d58-d17d-4ac0-8ad0-157f55775d5f https://w3id.org/ro-id/01313f54-7dcd-4e6a-b889-5540148df8b0 https://w3id.org/ro-id/048b7846-0bcd-449e-a128-4c7f37037ad0 https://w3id.org/ro-id/44f4ae2d-b271-49c5-bcf1-69ea2013e41f https://w3id.org/ro-id/ca7f3f46-1151-4085-b00b-926ab6c5757b https://w3id.org/ro-id/eabb6159-7b16-450d-a6f5-3805400d54af https://w3id.org/ro-id/f88b2cb6-1be8-40c3-a602-e8e3cd7d8b15 https://w3id.org/ro-id/fb207d76-e5a5-44db-b5c5-201481e471d7 https://w3id.org/ro-id/3048115c-48a6-4e1f-8717-0e4602e40366 https://w3id.org/ro-id/e8ca07f4-0e17-403c-9313-39b2a042b63d https://w3id.org/ro-id/20643c5d-d46c-4618-b2cb-7deb90d9fc72 https://w3id.org/ro-id/60ec083d-7aa7-400e-8f44-0612b10877de https://w3id.org/ro-id/8c078dad-54e7-450f-99af-4ad758980b42 https://w3id.org/ro-id/c9469245-f3db-4904-bd03-74b9ba6575ca https://w3id.org/ro-id/d0404f70-00a1-44e9-b589-ce4c615760e7 https://w3id.org/ro-id/4bd9a70d-6536-441a-a7c9-b4dce10aad43 https://w3id.org/ro-id/d6dd5213-b29f-4388-a307-d7db7620f6b7

Mariacristina PRAMPOLINI, Mariacristina PRAMPOLINI, and Valentina Grande. "Substrate and benthic habitat map of the southern Adriatic Sea (Italy) using RSOBIA - Supplementary material." ROHub. Apr 29 ,2021. https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad.

Backscatter Bathymetry Input_data Workflow Results http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/144be6fc-af99-4ec1-ae06-67e576928f8e 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:58.359642+00:00 Bathymetry MAGIC0409 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/76516bee-05e1-43b1-8f28-67d2aa26f9d0 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:56.812624+00:00 Backscatter ARCADIA 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/fa1e20e6-219a-4124-a5bc-2948a28cbb7c 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:56.370373+00:00 Backscatter MEMA12 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/4348d806-9658-40e7-b14e-94b90e0dd537 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:57.232467+00:00 Bathymetry MEMA12 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/dcab0978-d86c-46e0-8600-d6e3351583d9 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:58.513017+00:00 Bathymetry ALTRO 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/daa62bbb-7bbc-4fb7-9b6a-e09355292532 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:58.216770+00:00 Bathymetry MAGIC0910 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/9acb93d8-b520-42cc-8663-ea37390b7cb2 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:57.940864+00:00 Bathymetry ARCADIA 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/41073552-bec2-45d4-8735-ab066908fdc9 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:58.073162+00:00 Bathymetry MAGIC0709-ADRIA 2021-04-29 14:17:24.578000+00:00 1433 https://api.rohub.org/api/resources/49fedaac-e090-4cd1-8709-f0e7cc314b22/download/ 2021-05-04 13:01:48.522000+00:00 2022-03-24 13:17:09.986181+00:00 A huge amount of multibeam backscatter data has been acquired from the east to the west side of thein the southern Adriatic Sea in the last 15 years and coveringby CNR âISMAR. from the continental shelf down to the basin floor, from the west to east side of the Adriatic Basin. These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of all the acquired backscatter data has never been carried out. Here, we aimed at coherently interpreting the seafloor reflectivity datasets in order to produce aseabed and benthic habitat maps of the southern Adriatic Sea showing the spatial distribution of substrate and sediment type and grain size within the basin. The methodology here applied consists of a semi-automated classification of backscatter images through object-based image analysis (OBIA) performed through the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each backscatter dataset separately and then validated through comparison with bottom samples and images acquired during the different oceanographic cruises. The substrate was then classified following the a classification scheme proposed within the CoCoNet specifically elaborated in-house project. The results were described and discussed as well as the methodology applied and the significance of the backscatter data in general. text/plain Abstract of the scientific paper 2021-05-04 13:01:48.522000+00:00 57493288 https://api.rohub.org/api/resources/51d0e2aa-38ce-462d-ae38-148baa2ddd52/download/ 2021-05-04 12:58:45.722000+00:00 2022-03-24 13:17:01.157003+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/a99af2f9-a4d5-4c57-9477-b01935bed0e8 application/pdf RSOBIA_map_30.04.2021.pdf 2021-05-04 12:58:45.722000+00:00 https://www.mdpi.com/2072-4292/13/15/2913 201216 https://api.rohub.org/api/resources/588e8076-6ffb-40fb-ae05-7f43a8e8153a/download/ 2021-05-04 12:14:22.858000+00:00 2022-03-24 13:16:55.387554+00:00 The workflow consits of three steps: 1. RSOBIA segmentation Segmentation is a method to aggregate pixels together to create a thematic map. The segmentation process is a licensed tool taking a multi-layered image and creates a set of polygons defined by the statistics associated with the layered image. Clusters of the imagery pixels are created in n-dimensional space and created into classes. Aggregation into geographic regions (polygons) is done according to a minimum polygon size rule, and clustering rules. Input is a single multi-layered file â not a geodatabase. Output is a single polygon vector shapefile and its default filename is the same as the input basename but with a different filetype. 2.Add Segment Attributes Following segmentation, the attributes for each polygon only relate to the polygon shape and class. It is often required to view and use the values of initial raster layers. These can be aggregated for each polygon as an attribute value of the mean and standard deviation of the pixel values within the polygon for each layer. 3. GroundTruth Samples The attributes for each polygon can be extended if groundtruth point data is available. This is a very simple join of two datasets and effectively adds the attributes of a groundtruth data point to the relevant polygon. In this way the samples may be utilised to characterise the class type. Some polygons may not have groundtruth samples and will therefore be left blank. Hopefully enough of the polygons will have groundtruth points to inform the class descriptions. Arc Toolbox - Prampolini et al. 2021 2021-05-04 12:14:22.858000+00:00 ArcGIS 10.1-10.5 https://w3id.org/ro-id/a21484de-74d2-4fd0-9518-d3faa42130ad/RSOBIA_WF.JPG http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/b2e5125f-9737-4371-ac8c-852379dee2aa 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:57.658822+00:00 Bathymetry COCOMAP13 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/61b97f61-c059-42e0-b251-92f560a740b4 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:56.659787+00:00 Backscatter ALTRO 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/f36ab0c1-ce7c-480c-b677-8299cc3cb023 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:56.507037+00:00 Backscatter MAGIC0211 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/4ee520c1-e5ee-475f-acb9-29707e68561a 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:57.804850+00:00 Bathymetry COCOMAP14 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/da33524a-f985-4e4b-a287-030d5cc14968 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:57.087312+00:00 Bathymetry SAGA-03 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/0fa7c323-276e-461b-8269-bbd42fc98219 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:57.363622+00:00 Bathymetry OBAMA 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/43dacd1e-433a-4376-aad8-eccbcf9725a5 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:55.939095+00:00 Backscatter COCOMAP14 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/c6970d73-2192-48ce-b273-85f24cb859fb 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:52.030015+00:00 TOBI side scan sonar image Side scan sonar SAGA03 2021-04-29 14:17:24.578000+00:00 3186458 https://api.rohub.org/api/resources/ae5ce1a9-47b9-4d4d-967c-ca80f4274eb7/download/ 2021-05-31 13:49:48.700000+00:00 2022-03-24 13:17:03.336757+00:00 Result of the paper image/jpeg Substrate and benthic hanitat map of the sourthern Adriatic Sea 2021-05-31 13:49:48.700000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/37732044-52bf-49f7-bcdd-2cf70012372e 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:56.958674+00:00 Backscatter COCOMAP13 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/0571ed5c-eb2b-471f-b974-69b788a1aecc 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:55.616892+00:00 Backscatter MAGIC0409 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/31884c40-2f2b-4452-a060-59c3a2ab645f 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:56.080163+00:00 Backscatter OBAMA 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/3c202e6b-bbde-4761-8537-941fd0feb6fb 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:56.225023+00:00 Backscatter MAGIC0709 2021-04-29 14:17:24.578000+00:00 545334 https://api.rohub.org/api/resources/d6f030ec-71ee-424e-a7f9-f306b32945d0/download/ 2021-08-27 09:12:01.708000+00:00 2022-03-24 13:17:07.765085+00:00 Workflow applied in the present work visualized in Model Builder (ArcGIS 10.5) and showing the tools of RSOBIA toolset used in the present work (yellow), the input data - rasters to be seg-mented and ground-truthing data â (purple), the segmentation parameters to be chosen by the operator (light blue), the results (green) and the ArcGIS workspace (dark blue) image/jpeg Image of the workflow in model builder 2021-08-27 09:12:01.708000+00:00 1306366 https://api.rohub.org/api/resources/da5ca13c-7abe-4245-8bc4-d3215e52791a/download/ 2021-05-31 13:53:02.389000+00:00 2022-03-24 13:17:04.989241+00:00 image/jpeg Samples used to classify substrates and benthic habitats 2021-05-31 13:53:02.389000+00:00 https://www.mdpi.com/2072-4292/13/15/2913 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:17:07.985558+00:00 Prampolini, M.; Angeletti, L.; Castellan, G.; Grande, V.; Le Bas, T.; Taviani, M.; Foglini, F. Benthic Habitat Map of the Southern Adriatic Sea (Mediterranean Sea) from Object-Based Image Analysis of Multi-Source Acoustic Backscatter Data. Remote Sens. 2021, 13, 2913. https://doi.org/10.3390/rs13152913 Scientific paper 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/31aea371-7e29-42c2-883a-ab580fd0d3d9 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:55.801602+00:00 Backscatter MAGIC040910 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/bddc9e61-07f8-4c69-bc98-33d54ed3daf2 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:57.516122+00:00 Bathymetry MAGIC0211 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/d07a74a7-e953-457f-9e6c-0d0a27b8ab69 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:58.294604+00:00 Bathymetry MS15 2021-04-29 14:17:24.578000+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/metadata/54fa2a6d-25e8-4135-982c-1713674d7d70 2021-04-29 14:17:24.578000+00:00 2022-03-24 13:16:51.953806+00:00 Backscatter acquired with Reson Seabat 7160 Backscatter MSFD15 2021-04-29 14:17:24.578000+00:00 Italy https://www.wikidata.org/wiki/Q38 testing 7.738095238095238 6.5 map of the southern Adriatic Sea 11.087420042643924 10.4 Italy 12.990527740189444 9.6 Southern Adriatic Sea CNR-ISMAR dataset 19.296375266524525 18.1 Data, workflow and results of the RSOBIA analysis carried out on the Southern Adriatic Sea CNR-ISMAR datasets 51.25125125125125 51.2 geophysics 100.0 0.9408681392669678 habitat 15.155615696887685 11.2 oceanography 100.0 0.9174467921257019 map 12.719891745602164 9.4 workflow 18.267929634641405 13.5 service-account-generation-service Giorgio Castellan Kd Kw A nLw nLw B Estimation Mediterranean Sea University of Hawai reach the seabed satellite Kd physics attenuation coefficient KdPAR hydrography visible light seabed seabed in the Mediterranean Sea Mediterranean Sea m s estimation of light waters andthe Kd fluorescence relationships PAR sensors C. Samples andmodelled KdPAR species Satellite surface Ed E depth RLCs equivalent Estimation estimate irradiance photons Morel algae nm Fm morel s Kd chemistry attenuation coefficient Kd seabed Estimation of PAR seabed Estimation service-account-enrichment 3397641 https://api.rohub.org/api/ros/41a2f46e-9d0e-4bba-ac26-87c7a0ae3856/crate/download/ 2021-04-29 14:00:35.810000+00:00 2025-03-05 00:51:30.258243+00:00 2021-04-29 14:00:35.810000+00:00 Estimation of PAR light reaching the seabed in the Mediterranean Sea application/ld+json https://w3id.org/ro-id/41a2f46e-9d0e-4bba-ac26-87c7a0ae3856 Mesophotic Zone, Light at seabed, Mediterranean Sea Estimation of light at seabed

Giorgio Castellan. "Estimation of light at seabed." ROHub. Apr 29 ,2021. https://w3id.org/ro-id/41a2f46e-9d0e-4bba-ac26-87c7a0ae3856.

web services software components inputs datasets config results produced nested main scripts biblio workflows setup used 439711 https://api.rohub.org/api/resources/2274ebbc-d191-4931-b861-6e83165a2c32/download/ 2021-04-29 14:02:59.465000+00:00 2022-03-24 13:19:09.850550+00:00 application/pdf Runcieetal2008.pdf 2021-04-29 14:02:59.465000+00:00 1753877 https://api.rohub.org/api/resources/990815c4-3542-4b48-8290-056c055633fd/download/ 2021-04-29 14:03:11.390000+00:00 2022-03-24 13:19:10.921810+00:00 application/pdf Salquinetal2013.pdf 2021-04-29 14:03:11.390000+00:00 771593 https://api.rohub.org/api/resources/c5b90b04-7070-4215-b64c-4febbccc5d7f/download/ 2021-04-29 14:05:46.800000+00:00 2022-03-24 13:19:14.443309+00:00 image/tiff Bathymetry 2021-04-29 14:05:46.800000+00:00 1574331 https://api.rohub.org/api/resources/cf7e33ee-3826-4cfc-a798-eddc323eb72e/download/ 2021-04-29 14:04:28.758000+00:00 2022-03-24 13:19:15.456932+00:00 image/tiff Mean_kd490_2002-2018 2021-04-29 14:04:28.758000+00:00 1574325 https://api.rohub.org/api/resources/f0e4289d-f889-49a3-8c5b-fa79794d845c/download/ 2021-04-29 14:04:10.572000+00:00 2022-03-24 13:19:16.464477+00:00 image/tiff Mean_surfacePAR_2002-2018 2021-04-29 14:04:10.572000+00:00 service-account-generation-service Earth system model resolution mesh Norwegian Earth system model generate a Variable Resolution Mesh tool container system model mechanics industry create container Resolution Mesh create container Anne Fouilloux service-account-enrichment 632799 https://api.rohub.org/api/ros/170460ba-d68c-4090-96de-6ed98f6b453a/crate/download/ 2021-04-29 13:53:58.420000+00:00 2025-03-05 02:46:54.890197+00:00 2021-04-29 13:53:58.420000+00:00 Dockerfile to create container for generating a new Variable Resolution Mesh for running the Norwegian Earth System Model (NorESM) or Community Earth System Model (CESM) application/ld+json https://w3id.org/ro-id/170460ba-d68c-4090-96de-6ed98f6b453a climate docker Tool to generate a Variable Resolution Mesh for CESM/NorESM

Fouilloux, Anne. "Tool to generate a Variable Resolution Mesh for CESM/NorESM." ROHub. Apr 29 ,2021. https://w3id.org/ro-id/170460ba-d68c-4090-96de-6ed98f6b453a.

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fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (EKG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including: amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. application/ld+json https://w3id.org/ro-id/6a6127bb-03b0-4d0c-a5a9-52cfd0913fd4 Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants database dataset electrocardiography emotion experience individual information laboratory participant plethysmography response testing environmental sciences Biology Medical procedure-test Psychology Science and technology Psychophysiology of Positive and Negative Emotions data database dataset emotion individual participant life sciences available dataset dataset of psychophysiological response electrodermal activity models of emotion subjective experience To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. http://sandbox.rohub.org/rodl/ROs/POPANE-snapshot/ medicine psychology

Szymon KupiÅ?ski. "Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/6a6127bb-03b0-4d0c-a5a9-52cfd0913fd4.

Metadata Biblio Raw Data Dataset Data Used Produced Documentation https://data.psychosensing.psnc.pl/popane/ 2022-03-24 13:24:28.053728+00:00 2022-03-24 13:24:29.534204+00:00 https://data.psychosensing.psnc.pl/popane/ 2022-03-24 13:24:28.053728+00:00 service-account-generation-service https://data.psychosensing.psnc.pl/popane/ physiology recorded affect dataset impedance cardiography activity International Crisis Group electrocardiography emotions Szymon KupiÅ?ski service-account-enrichment false http://sandbox.rohub.org/rodl/ROs/POPANE/ 2021-03-11T14:49:03.095+01:00 https://orcid.org/0000-0002-2455-4556 8794 https://api.rohub.org/api/ros/fb39cfdd-d93a-4f89-af5f-2c944b85c05d/crate/download/ 2021-03-11 13:49:03.095000+00:00 2025-03-05 01:17:00.039657+00:00 2021-03-11 13:49:03.095000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (EKG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including: amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. application/ld+json https://w3id.org/ro-id/fb39cfdd-d93a-4f89-af5f-2c944b85c05d Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants database dataset electrocardiography emotion experience individual information laboratory participant plethysmography response testing environmental sciences Biology Medical procedure-test Psychology Science and technology Psychophysiology of Positive and Negative Emotions data database dataset emotion individual participant life sciences available dataset dataset of psychophysiological response electrodermal activity models of emotion subjective experience To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. medicine psychology

Szymon KupiÅ?ski. "Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/fb39cfdd-d93a-4f89-af5f-2c944b85c05d.

Biblio Produced Data Metadata Used Raw Data Dataset Documentation https://data.psychosensing.psnc.pl/popane/ 2022-03-24 13:24:35.249281+00:00 2022-03-24 13:24:36.181299+00:00 https://data.psychosensing.psnc.pl/popane/ 2022-03-24 13:24:35.249281+00:00 service-account-generation-service Neurobiology http://sandbox.rohub.org/rodl/ROs/POPANE-1-fork/ http://w3id.org/ro-id/rohub/model#change_specifications/2b6b4a8f-9933-497e-bf11-4137ffdaf516/changes/0c67378d-9501-4a8c-8477-33570a984349 http://w3id.org/ro-id/rohub/model#change_specifications/2b6b4a8f-9933-497e-bf11-4137ffdaf516/changes/2540f165-0e17-4152-944a-67c59a311d5e https://data.psychosensing.psnc.pl/popane/index.html https://data.psychosensing.psnc.pl/popane/index.html psychology physiology popane dataset psychophysiology Of Positive medicine database data electrocardiography dataset neuropsychology DATASET impedance cardiography Of Positive emotion POPANE dataset studies psychophysiology of positive activity plethysmography International Crisis Group experience individual impedance cardiography emotions information response individuals data Maciej Behnke service-account-enrichment http://sandbox.rohub.org/rodl/ROs/POPANE-1/ 2021-03-11T14:07:59.683+01:00 https://orcid.org/0000-0002-2455-4556 http://w3id.org/ro-id/rohub/model#change_specifications/2b6b4a8f-9933-497e-bf11-4137ffdaf516 9094 https://api.rohub.org/api/ros/a3a81739-a5b7-4897-b732-7aba23d6fa5a/crate/download/ 2021-03-11 13:07:59.683000+00:00 2025-03-05 01:14:11.927117+00:00 2021-03-11 13:07:59.683000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. application/ld+json https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a POPANE DATASET - Psychophysiology Of Positive And Negative Emotions http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot-1/ https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a

Maciej Behnke. "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a.

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2021-03-11 13:05:41.562000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. application/ld+json https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0 POPANE DATASET - Psychophysiology Of Positive And Negative Emotions http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot/

Maciej Behnke. "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0.

Metadata Dataset Produced Used Documentation Biblio Data Raw Data https://data.psychosensing.psnc.pl/popane/index.html 2022-03-24 13:24:58.732136+00:00 2022-03-24 13:25:00.483590+00:00 text/html https://data.psychosensing.psnc.pl/popane/index.html 2022-03-24 13:24:58.732136+00:00 service-account-generation-service Neurobiology psychology physiology popane dataset psychophysiology Of Positive medicine database data electrocardiography dataset neuropsychology DATASET impedance cardiography Of Positive emotion POPANE dataset studies psychophysiology of positive activity plethysmography International Crisis Group experience individual impedance cardiography emotions information response individuals data Maciej Behnke service-account-enrichment false http://sandbox.rohub.org/rodl/ROs/POPANE-1/ 2021-03-11T14:04:33.909+01:00 https://orcid.org/0000-0002-2455-4556 8553 https://api.rohub.org/api/ros/a415c54e-7d07-43c8-bcbe-1f76220f473f/crate/download/ 2021-03-11 13:04:33.909000+00:00 2025-03-05 01:14:12.144536+00:00 2021-03-11 13:04:33.909000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. application/ld+json https://w3id.org/ro-id/a415c54e-7d07-43c8-bcbe-1f76220f473f POPANE DATASET - Psychophysiology Of Positive And Negative Emotions

Maciej Behnke. "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/a415c54e-7d07-43c8-bcbe-1f76220f473f.

Biblio Used Metadata Raw Data Dataset Produced Data Documentation https://data.psychosensing.psnc.pl/popane/index.html 2022-03-24 13:25:06.838155+00:00 2022-03-24 13:25:07.593414+00:00 text/html https://data.psychosensing.psnc.pl/popane/index.html 2022-03-24 13:25:06.838155+00:00 service-account-generation-service Neurobiology https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db 2021-03-11T14:05:41.562+01:00 https://orcid.org/0000-0002-2455-4556 https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db 2021-03-11T14:07:59.683+01:00 https://orcid.org/0000-0002-2455-4556 https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db 2021-03-11T14:04:33.909+01:00 https://orcid.org/0000-0002-2455-4556 http://sandbox.rohub.org/rodl/ROs/POPANE-1-fork/ psychology physiology popane dataset psychophysiology Of Positive medicine database data electrocardiography dataset neuropsychology DATASET impedance cardiography Of Positive emotion POPANE dataset studies psychophysiology of positive activity plethysmography International Crisis Group experience individual impedance cardiography emotions information response individuals data Maciej Behnke service-account-enrichment http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot-1/ http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot-2/ http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot/ 8850 https://api.rohub.org/api/ros/9bf840e3-7a39-41fc-be39-7eed9dc294db/crate/download/ 2021-03-11 12:48:21.251000+00:00 2025-03-05 01:14:11.720011+00:00 2021-03-11 12:48:21.251000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. application/ld+json https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db POPANE DATASET - Psychophysiology Of Positive And Negative Emotions

Maciej Behnke. "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db.

Data Dataset Produced Metadata Used Raw Data Documentation Biblio https://data.psychosensing.psnc.pl/popane/index.html 2021-03-11 12:48:21.251000+00:00 2022-03-24 13:25:15.335097+00:00 text/html https://data.psychosensing.psnc.pl/popane/index.html 2021-03-11 12:48:21.251000+00:00 service-account-generation-service https://w3id.org/ro-id/acfec1a0-48af-48c3-9083-3c1969a31c21 2021-03-11T15:18:55.068+01:00 https://orcid.org/0000-0002-4704-6802 https://w3id.org/ro-id/acfec1a0-48af-48c3-9083-3c1969a31c21 2021-03-11T14:49:03.095+01:00 https://orcid.org/0000-0002-2455-4556 https://data.psychosensing.psnc.pl/popane/ physiology recorded affect dataset impedance cardiography activity International Crisis Group electrocardiography emotions Szymon KupiÅ?ski service-account-enrichment http://sandbox.rohub.org/rodl/ROs/POPANE-snapshot-1/ http://sandbox.rohub.org/rodl/ROs/POPANE-snapshot/ 8763 https://api.rohub.org/api/ros/acfec1a0-48af-48c3-9083-3c1969a31c21/crate/download/ 2021-03-02 08:34:45.016000+00:00 2025-03-05 01:16:59.821378+00:00 2021-03-02 08:34:45.016000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (EKG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including: amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. application/ld+json https://w3id.org/ro-id/acfec1a0-48af-48c3-9083-3c1969a31c21 Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants database dataset electrocardiography emotion experience individual information laboratory participant plethysmography response testing environmental sciences Biology Medical procedure-test Psychology Science and technology Psychophysiology of Positive and Negative Emotions data database dataset emotion individual participant life sciences available dataset dataset of psychophysiological response electrodermal activity models of emotion subjective experience To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. medicine psychology

Szymon KupiÅ?ski. "Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants." ROHub. Mar 02 ,2021. https://w3id.org/ro-id/acfec1a0-48af-48c3-9083-3c1969a31c21.

Data Documentation Metadata Produced Dataset Biblio Raw Data Used https://data.psychosensing.psnc.pl/popane/ 2021-03-02 08:34:45.016000+00:00 2022-03-24 13:26:12.310038+00:00 https://data.psychosensing.psnc.pl/popane/ 2021-03-02 08:34:45.016000+00:00 service-account-generation-service Ecology galaxy climate botany tree size distribution FATES restart years year Anne Fouilloux simulation 14.890016920473775 8.8 trees 8.383233532934131 7.0 climate 12.859560067681894 7.6 ecosystem 7.7844311377245505 6.5 astrophysics 100.0 0.3373451828956604 growth 3.712574850299401 3.1 size distribution 33.131067961165044 27.3 mannequin 10.419161676646706 8.7 Hybrid run (started from a 300 year restart) of 5 years with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a numerical terrestrial ecosystem model that simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions. 74.04809619238478 73.9 size 4.910179640718562 4.1 year restart 8.495145631067961 7.0 distribution 15.905245346869712 9.4 reboot 5.269461077844312 4.4 ecosystem model 31.31067961165048 25.8 earth sciences 100.0 0.9884166121482849 service-account-enrichment 56176 https://api.rohub.org/api/ros/4086e5b7-284e-4551-a156-e3453ddcee58/crate/download/ 2021-01-12 07:48:50.406000+00:00 2025-03-05 00:45:33.066263+00:00 2021-01-12 07:48:50.406000+00:00 Hybrid run (started from a 300 year restart) of 5 years with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a numerical terrestrial ecosystem model that simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions. The simulation has been done with Galaxy climate (https://climate.usegalaxy.eu/) application/ld+json https://w3id.org/ro-id/4086e5b7-284e-4551-a156-e3453ddcee58 CWL FATES galaxy 5 years CLM-FATES simulation for Nordic site ALP1 5 years CLM-FATES simulation for Nordic site ALP1 https://w3id.org/ro-id/629864fb-93b2-4be3-a890-e9ea28e26493 https://w3id.org/ro-id/8fc73c90-ac1d-40bc-bdf8-59ae8363b776 https://w3id.org/ro-id/11991ee0-53b7-4075-a67e-6ff6d61cfe5b https://w3id.org/ro-id/1a195447-3fd8-45b1-93e0-1339321e3e63 https://w3id.org/ro-id/208c7871-2339-4f65-9ddc-716c769a7ceb https://w3id.org/ro-id/2a0be9f1-e45f-4386-a7d5-643d65a09f5c https://w3id.org/ro-id/2db8a698-5133-4b98-a02b-56ea3e0531ad https://w3id.org/ro-id/328147af-93a1-4f4d-b9cc-b1fb0d2c956e https://w3id.org/ro-id/4876115a-30f5-4c0c-9ed0-e4b993bb5e77 https://w3id.org/ro-id/6ceb9abc-2027-4d4f-9d11-fd6eee62e7c3 https://w3id.org/ro-id/73b195e9-0df9-4eed-a2f4-c5312c5a0058 https://w3id.org/ro-id/ad5654a7-d821-469a-823f-01ae2ec8a40f https://w3id.org/ro-id/bc50622e-2ffd-46e1-a3f1-4006bda7dfda https://w3id.org/ro-id/d1bfec6d-2df9-4c37-8b1d-b1e55400d079 https://w3id.org/ro-id/403f8590-e78e-4479-85df-c7b3f53777ee https://w3id.org/ro-id/b10c39dd-beb0-4370-91a0-113e7e549eb5 https://w3id.org/ro-id/4f4d5e03-8d3d-49bd-87c4-c16d018644b8 https://w3id.org/ro-id/73be0529-7ad3-4385-bf07-8e6897aad6be https://w3id.org/ro-id/753d913e-550e-4c06-aefa-3a7f36d6c9e4 https://w3id.org/ro-id/d9f79a3c-9c3d-45b0-927e-264d19dc315b https://w3id.org/ro-id/fe8c1ed6-1543-439e-9bd5-c31bc961f150 https://w3id.org/ro-id/0f0963bd-c886-44e6-9ff2-0be64e5f65ee https://w3id.org/ro-id/1856bb10-e431-43a7-b980-1fd2ff4acd2d https://w3id.org/ro-id/326afebf-7a12-4b1b-8dd5-624e45fe4289 https://w3id.org/ro-id/7ded6348-4362-4b43-9f93-357c6cb38a04 https://w3id.org/ro-id/9554adec-5201-4db9-9d2a-b519cd9ee0cc https://w3id.org/ro-id/a6e3b2dd-1bcc-4025-b262-12d4156be3c3 https://w3id.org/ro-id/e87ad228-21df-4b77-acfe-c5ba67b102f1 https://w3id.org/ro-id/1c3ab0e6-20ee-4524-a616-d48e94cf7610 https://w3id.org/ro-id/8001189e-9a48-4e1c-8f57-a41d9e63e638 https://w3id.org/ro-id/29a12137-373b-4d46-a648-de00ea10519e https://w3id.org/ro-id/2e933347-2e99-4257-a22b-af679de384c5 https://w3id.org/ro-id/3431e1a2-094f-41d0-a8e8-a83b19726915 https://w3id.org/ro-id/5733ed6b-ac53-4e4f-ba7d-e8d2e78ebfe7 https://w3id.org/ro-id/da620f27-9164-4279-bd9e-814de56eb258 https://w3id.org/ro-id/2bfc63ba-becf-4e5e-9659-beb0b2d97440 https://w3id.org/ro-id/4d1be4c6-ae8e-4d49-9f90-592f277faa6e https://w3id.org/ro-id/b25440f9-f459-4080-acc4-7ee27ae80e0c https://w3id.org/ro-id/87095d85-791a-427d-854a-873bbebfdd35 https://w3id.org/ro-id/af5b88b8-b48a-47c2-8534-6eeedc5a276a https://w3id.org/ro-id/ee99313a-437c-4544-ad83-0be3ce0dbc72

Fouilloux, Anne. "5 years CLM-FATES simulation for Nordic site ALP1 ." ROHub. Jan 12 ,2021. https://w3id.org/ro-id/4086e5b7-284e-4551-a156-e3453ddcee58.

20149 https://api.rohub.org/api/resources/38f14396-c1fd-478e-b5b2-135655b83783/download/ 2021-01-12 07:48:52+00:00 2022-03-24 13:26:32.864730+00:00 Galaxy Workflow CLM-FATES_ALP1_simulation_5years.ga 2021-01-12 07:48:52+00:00 3407 https://api.rohub.org/api/resources/496190bd-ec8e-4d8f-a76b-77f61b82258d/download/ 2021-01-12 07:48:51.919000+00:00 2022-03-24 13:26:31.837948+00:00 Workflow in Common Workflow Language CLM-FATES_ALP1_simulation_5years.cwl 2021-01-12 07:48:51.919000+00:00 42158 https://api.rohub.org/api/resources/b7cf277d-f2e7-44e2-9e61-f6d67f8bb8aa/download/ 2021-04-29 14:26:54.977000+00:00 2022-03-24 13:26:29.832570+00:00 image/png Overview of FATES workflow for a single location in Norway 2021-04-29 14:26:54.977000+00:00 distribution 10.538922155688624 8.8 5 years CLM-FATES simulation for Nordic site ALP1 . 13.426853707414828 13.4 Weather Weather Galaxy climate 14.320388349514563 11.8 computer science 67.79661016949152 4.0 climate 8.982035928143713 7.5 fate 6.347305389221557 5.3 Wireless technology Economy, business and finance/Economic sector/Computing and information technology/Wireless technology Ecosystem Environment/Nature/Ecosystem ALP1 13.874788494077832 8.2 space sciences 100.0 0.3373451828956604 5 years botany 32.20338983050847 1.9 model 14.890016920473775 8.8 regeneration 14.213197969543147 8.4 simulation 19.161676646706585 16.0 from a 300 year atmospheric sciences 100.0 0.9884166121482849 The simulation has been done with Galaxy climate (https://climate.usegalaxy.eu/ 12.5250501002004 12.5 rebirth 9.580838323353293 8.0 http 4.910179640718562 4.1 Death and dying Society/Values/Death and dying regeneration of plants 12.742718446601941 10.5 Functionally Assembled Terrestrial Ecosystem Simulator 13.367174280879864 7.9 of 5 years Industrial accident and incident Disaster, accident and emergency incident/Accident and emergency incident/Explosion accident and incident/Industrial accident and incident service-account-generation-service agriculture home farming practices in the context use fertilizers Esteban Gonzï¿½ï¿½lez service-account-enrichment 67072 https://api.rohub.org/api/ros/a3f460ba-5e28-465f-9043-575ccc3692c1/crate/download/ 2021-01-11 02:30:21.779000+00:00 2025-03-05 00:55:14.657661+00:00 2021-01-11 02:30:21.779000+00:00 The project aims to understand and map the use of pesticides and fertilizers and sustainable alternative practices in the context of home farming and gardening. Simultaneously, it aims to disseminate information on the topic with the final aim of reducing the use of pesticides and fertilizers. application/ld+json https://w3id.org/ro-id/a3f460ba-5e28-465f-9043-575ccc3692c1 In My Backyard backyard context farming fertiliser horticulture information pesticide practice project purpose subject use earth sciences Agriculture Fertiliser farming fertilizer gardening pesticide practice project topic life sciences alternative practice final aim home farming information on the topic use of pesticide In My Backyard. Simultaneously, it aims to disseminate information on the topic with the final aim of reducing the use of pesticides and fertilizers. The project aims to understand and map the use of pesticides and fertilizers and sustainable alternative practices in the context of home farming and gardening. agriculture

Esteban Gonzï¿½ï¿½lez. "In My Backyard." ROHub. Jan 11 ,2021. https://w3id.org/ro-id/a3f460ba-5e28-465f-9043-575ccc3692c1.

Dataset Raw Data Documentation Biblio Used Produced Metadata Data https://doi.org/10.5281/zenodo.4081597 https://doi.org/10.5281/zenodo.4081585 2021-01-11 02:30:21.779000+00:00 2022-03-24 13:26:50.237880+00:00 In My Backyard: project reflections and take aways 2021-01-11 02:30:21.779000+00:00 https://doi.org/10.5281/zenodo.4081597 2021-01-11 02:30:21.779000+00:00 2022-03-24 13:26:50.367381+00:00 In My Backyard: project final report 2021-01-11 02:30:21.779000+00:00 61755 https://api.rohub.org/api/resources/66bbddcb-e258-434f-a684-5361e6688032/download/ 2021-01-11 02:30:51.468000+00:00 2022-03-24 13:26:49.761269+00:00 image/png diagram-InMyBackyard.png 2021-01-11 02:30:51.468000+00:00 https://doi.org/10.5281/zenodo.4081606 https://doi.org/10.5281/zenodo.4081778 2021-01-11 02:30:21.779000+00:00 2022-03-24 13:26:49.904734+00:00 In My Backyard: On-Site Survey Responses Raw Dataset 2021-01-11 02:30:21.779000+00:00 https://doi.org/10.5281/zenodo.4081597 https://doi.org/10.5281/zenodo.4081606 2021-01-11 02:30:21.779000+00:00 2022-03-24 13:26:50.093236+00:00 In My Backyard: key insights 2021-01-11 02:30:21.779000+00:00 service-account-generation-service gain real time feedback of the data multimedia time and date hydrophone speaker hardware recording cost electronics Esteban Gonzï¿½ï¿½lez service-account-enrichment 662879 https://api.rohub.org/api/ros/e3d872de-a4bd-4803-bf31-5395d4748905/crate/download/ 2021-01-11 02:09:08.766000+00:00 2025-03-05 01:19:10.897816+00:00 2021-01-11 02:09:08.766000+00:00 The Sonic Kayak system is a low cost open hardware for gathering and mapping fine-scale marine environmental data, which has not been previously possible to obtain. Data is sonified through an onboard speaker allowing paddlers to seek out areas of interest and gain real time feedback of the data. The system currently includes underwater temperature sensors and a hydrophone for measuring underwater sound, each recording data every second with GPS, time and date. application/ld+json https://w3id.org/ro-id/e3d872de-a4bd-4803-bf31-5395d4748905 Sonic Kayacs amplifier canoeist cost data equipment feedback global positioning system hydrophone information real time sensor temperature time and date earth sciences Canoeing Sonic Kayak data feedback hydrophone real time sensor speaker engineering Sonic Kayak system real time feedback recording data temperature sensor time and date Data is sonified through an onboard speaker allowing paddlers to seek out areas of interest and gain real time feedback of the data. The Sonic Kayak system is a low cost open hardware for gathering and mapping fine-scale marine environmental data, which has not been previously possible to obtain. The system currently includes underwater temperature sensors and a hydrophone for measuring underwater sound, each recording data every second with GPS, time and date. computer science electronics

Esteban Gonzï¿½ï¿½lez. "Sonic Kayacs." ROHub. Jan 11 ,2021. https://w3id.org/ro-id/e3d872de-a4bd-4803-bf31-5395d4748905.

Biblio Metadata Raw Data Used Device Data Dataset Produced Dissemination Documentation 594964 https://api.rohub.org/api/resources/1a799453-ca28-41fa-963f-eb323f6c722d/download/ 2021-01-11 02:14:55.977000+00:00 2022-03-24 13:27:07.750442+00:00 image/png full-wiring-action.png 2021-01-11 02:14:55.977000+00:00 https://doi.org/10.5281/zenodo.3923743 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:08.698797+00:00 Sonic Kayak survey 2021-01-11 02:09:08.766000+00:00 https://github.com/fo-am/sonic-kayaks/wiki https://doi.org/10.5281/zenodo.4041588 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:08.225461+00:00 Dataset generated for the device Measurements 2021-01-11 02:09:08.766000+00:00 95539 https://api.rohub.org/api/resources/424544a5-6157-4d09-ad73-5e3ac1f9d2cf/download/ 2021-01-11 02:09:44.849000+00:00 2022-03-24 13:27:06.391281+00:00 image/png diagram-sonickayacs.png 2021-01-11 02:09:44.849000+00:00 https://magpi.raspberrypi.org/issues/97/pdf 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:08.042715+00:00 Magazine with an article describing the device The MagPi â Issue 97 2021-01-11 02:09:08.766000+00:00 https://www.flickr.com/photos/foam/albums/72157715979200366 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:08.589186+00:00 Sonic Kayak data visualisations 2021-01-11 02:09:08.766000+00:00 https://doi.org/10.5281/zenodo.3923743 https://fo.am/blog/2020/08/17/sonic-kayak-update-new-sensors-sonifications-and-visualisations/ 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:08.480939+00:00 Sonic Kayak update - new sensors, sonifications, and visualisations 2021-01-11 02:09:08.766000+00:00 https://github.com/fo-am/sonic-kayaks/raw/master/hardware/full-wiring-action.png 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:06.600078+00:00 image/png https://github.com/fo-am/sonic-kayaks/raw/master/hardware/full-wiring-action.png 2021-01-11 02:09:08.766000+00:00 https://www.youtube.com/watch?v=puLXKj1AVAk 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:08.915023+00:00 Sonic Kayaks - citizen science in the marine environment for the ACTION project 2021-01-11 02:09:08.766000+00:00 https://fo.am/blog/2020/06/30/sonic-kayak-environmental-data-sonification/ 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:08.374657+00:00 Sonic Kayaks are rigged with sensors, both underwater (temperature, sound, and turbidity) and above water (air pollution). As the kayaker paddles around, the sensors pick up changes in the environment, and these are played to the kayaker in real time through an on-board speaker. Originally this approach came from a sound art project (Kaffe Matthewsâ Sonic Bikes), but we realised it could also be useful for researchers to be able to follow data or seek out particular environments through sound, and along the way we gathered interest from the visually impaired community for using the sounds to augment their kayaking experience or guide navigation on the water. Sonic Kayak environmental data sonification 2021-01-11 02:09:08.766000+00:00 https://doi.org/10.5281/zenodo.3923743 https://fo.am/blog/2020/05/05/sonic-kayak-progress-new-pollution-sensors-for-citizen-science/ 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:08.806456+00:00 Sonic Kayak progress â new pollution sensors for citizen science 2021-01-11 02:09:08.766000+00:00 https://github.com/fo-am/sonic-kayaks/wiki 2021-01-11 02:09:08.766000+00:00 2022-03-24 13:27:06.743586+00:00 This wiki documents the hardware and software used in the current Sonic Kayaks setup. Sonic Kayacs Wiring Schematic: Water and Air Pollution Edition 2021-01-11 02:09:08.766000+00:00 https://w3id.org/ro-id/e3d872de-a4bd-4803-bf31-5395d4748905/full-wiring-action.png service-account-generation-service maps noise map empower community Maps Noise heritage folklore Esteban Gonzï¿½ï¿½lez service-account-enrichment 135152 https://api.rohub.org/api/ros/54e02cd6-ee8c-4d6d-af46-c7421de99997/crate/download/ 2020-11-25 09:14:49.401000+00:00 2025-03-05 02:45:35.165781+00:00 2020-11-25 09:14:49.401000+00:00 NOISE MAPS allows citizens to generate and analyse urban sound data, empowering communities to take action to reduce unwanted noise and protect the local sonic heritage. The pilot builds on existing cultural practices of collective documentation of the sound heritage of neighbourhoods (Mapa Sonor). Thanks to project activities citizens will be able to filter unwanted noise out from authentic, locally unique sounds, thus allowing communities to take action to preserve their sonic heritage. NOISE MAPS deploys a combination of tested tech and methods with a novel approach, to empower communities to leverage the power of citizen science to tackle local challenges of global relevance. application/ld+json https://w3id.org/ro-id/54e02cd6-ee8c-4d6d-af46-c7421de99997 Noise Maps activity citizen combination community documentation engineering heritage information noise pilot practice quarter environmental sciences Customs and tradition MAPS citizen community heritage noise pilot practice life sciences combination of tested tech noise MAPS project activities citizen sound heritage unwanted noise NOISE MAPS allows citizens to generate and analyse urban sound data, empowering communities to take action to reduce unwanted noise and protect the local sonic heritage. NOISE MAPS deploys a combination of tested tech and methods with a novel approach, to empower communities to leverage the power of citizen science to tackle local challenges of global relevance. The pilot builds on existing cultural practices of collective documentation of the sound heritage of neighbourhoods (Mapa Sonor) Thanks to project activities citizens will be able to filter unwanted noise out from authentic, locally unique sounds, thus allowing communities to take action to preserve their sonic heritage. sociology

Esteban Gonzï¿½ï¿½lez. "Noise Maps." ROHub. Nov 25 ,2020. https://w3id.org/ro-id/54e02cd6-ee8c-4d6d-af46-c7421de99997.

Software Raw_Data Datasets Dissemination https://freesound.org/people/bitlab_coop/packs/ https://github.com/pzinemanas/AudioMoth-Firmware-SPL 2020-11-25 09:14:49.401000+00:00 2022-03-24 13:27:28.032478+00:00 Sounds recorded 2020-11-25 09:14:49.401000+00:00 https://ars.electronica.art/keplersgardens/en/sonic-heritage/ 2020-11-25 09:14:49.401000+00:00 2022-03-24 13:27:28.365817+00:00 Ars Electronica - Sonic Heritage 2020-11-25 09:14:49.401000+00:00 https://github.com/pzinemanas/AudioMoth-Firmware-SPL 2020-11-25 09:14:49.401000+00:00 2022-03-24 13:27:28.439176+00:00 https://github.com/pzinemanas/AudioMoth-Firmware-SPL 2020-11-25 09:14:49.401000+00:00 138785 https://api.rohub.org/api/resources/d4898e2f-704f-407d-b143-d8a16f981950/download/ 2020-11-25 09:15:52.369000+00:00 2022-03-24 13:27:27.751199+00:00 image/png diagram-noisemaps.png 2020-11-25 09:15:52.369000+00:00 https://ars.electronica.art/keplersgardens/en/what-is-noise/ 2020-11-25 09:14:49.401000+00:00 2022-03-24 13:27:28.214158+00:00 Ars Electronica - What is noise 2020-11-25 09:14:49.401000+00:00 https://doi.org/10.5281/zenodo.4059533 2020-11-25 09:14:49.401000+00:00 2022-03-24 13:27:27.896482+00:00 Noise Maps ACTION pilot data 2020 2020-11-25 09:14:49.401000+00:00 service-account-generation-service Nordic country ecology air pollution in schools Norway Oslo electric cars Oslo air quality pilot ACTION Esteban GonzÃ¡lez+Guardia High schools Education/School/High schools sensor platform 49.447077409162716 31.3 high school 6.68859649122807 6.1 student 8.333333333333334 7.6 Oslo 11.764705882352942 6.0 electric car 13.92156862745098 7.1 project 3.6184210526315788 3.3 Air pollution Environment/Environmental pollution/Air pollution Oslo https://www.wikidata.org/wiki/Q585 Oslo 8.552631578947368 7.8 atmospheric sciences 100.0 0.6394369006156921 earth sciences 100.0 0.6394369006156921 information 4.166666666666667 3.8 pilot of the action project 7.266982622432858 4.6 air quality project 24.170616113744074 15.3 PM10 7.2368421052631575 6.6 electric car 10.087719298245613 9.2 ecology 100.0 13.0 air pollution 14.117647058823529 7.2 We use the Nova SDS011 sensor for measuring PM2.5 and PM10 that is transmitting data to an Arduino board. 24.727272727272727 13.6 engineering 100.0 0.7565824389457703 PM10 10.392156862745098 5.3 air pollution 10.197368421052632 9.3 By actively promoting the purchase of electric cars, the Norwegian government is aiming at protecting the environment and not least improving air quality, especially in urban areas. 30.90909090909091 17.0 air quality 26.274509803921568 13.4 service-account-enrichment 202135 https://api.rohub.org/api/ros/b04c829e-997d-44e6-8ef2-5893622403d2/crate/download/ 2020-11-03 17:22:37.815000+00:00 2025-03-05 00:45:35.558116+00:00 2020-11-03 17:22:37.815000+00:00 Norway is the land of fjords, trolls and – electric cars. By actively promoting the purchase of electric cars, the Norwegian government is aiming at protecting the environment and not least improving air quality, especially in urban areas. Air quality is still a reason for concern in many European countries, including the Nordic countries. Not many people are aware of this fact, and this is where the Norwegian pilot of the ACTION project comes in.The pilot gives high school students in Oslo and the larger Oslo area the opportunity to design and carry out their own air quality projects, using an off-the-shelf air quality sensor platform. The aim is to create awareness about the sources of air pollution, make the students think of ways to reduce both emission and exposure and teach them scientific working methods. We use the Nova SDS011 sensor for measuring PM2.5 and PM10 that is transmitting data to an Arduino board. The data can be obtained through an SD card. application/ld+json https://w3id.org/ro-id/b04c829e-997d-44e6-8ef2-5893622403d2 ACTION - Air Pollution in Schools https://w3id.org/ro-id/628c2aeb-5036-4192-95c0-11fead761441 https://w3id.org/ro-id/37394f65-575e-41f1-9c3b-8886317b9379 https://w3id.org/ro-id/bdb8e64c-d4c8-41ba-95a3-d43005c062b7 https://w3id.org/ro-id/10de924a-94ca-4126-bf32-847de386f7a4 https://w3id.org/ro-id/128b085e-0ab8-404d-9a66-c90d6ae05e90 https://w3id.org/ro-id/2e2e97a5-493c-49cd-b0b0-26763a4dbd8c https://w3id.org/ro-id/3fc4bb19-f881-48b8-a8ff-c400e2f78171 https://w3id.org/ro-id/49e46db1-78f3-40bc-a454-8027f4e7168a https://w3id.org/ro-id/583ae044-ba1c-4302-b34d-8451107e6de4 https://w3id.org/ro-id/58c9fa1d-994b-42e5-a662-32d05cda9be1 https://w3id.org/ro-id/85077b7a-4107-406f-a23e-8b6775c00af5 https://w3id.org/ro-id/b2e63529-710c-47ff-8fd7-8ce2718e0088 https://w3id.org/ro-id/d133904e-b935-4a36-ad0f-a60d3baa1451 https://w3id.org/ro-id/ec4cd90d-be2b-457e-a49a-bc77d729aa5f https://w3id.org/ro-id/ee74f830-35df-41e4-b9ae-0f731ab41038 https://w3id.org/ro-id/ef8a303d-1d19-4b73-b0f6-66c1dcbb9977 https://w3id.org/ro-id/4128512f-df8d-465e-9eef-4f92af23a5a7 https://w3id.org/ro-id/4219f70b-3a6f-4cd1-b0db-7f9ddbec5f15 https://w3id.org/ro-id/07fe7d14-d4bf-4540-b8f9-c5691f488d62 https://w3id.org/ro-id/34bbfc83-4b45-4f51-8011-d46bcb83c955 https://w3id.org/ro-id/ef8b75da-19d0-43d7-a2ff-bb06c52d9cae https://w3id.org/ro-id/1b45fcec-bd5c-4d73-8f91-a5ff895651d5 https://w3id.org/ro-id/2ae05b14-ac5f-4e55-9446-0b3fcbec1912 https://w3id.org/ro-id/6564fc22-9a8b-4a3e-925c-bc4ab6c59c1d https://w3id.org/ro-id/827ca094-21bf-4063-93b7-b31122508431 https://w3id.org/ro-id/9d58dbb8-102c-4217-8d03-e34cba02385a https://w3id.org/ro-id/c48d970f-ec65-4bda-be91-cc2872842314 https://w3id.org/ro-id/e8eb655a-bc33-4705-9392-fb1a92c20095 https://w3id.org/ro-id/778c2726-c900-459c-a44b-2161059c95ae https://w3id.org/ro-id/e61b5bb9-7e0f-45f3-95e4-12a9a150b1de https://w3id.org/ro-id/0df21445-75d3-44dc-b745-8892f3b14578 https://w3id.org/ro-id/4f025fd9-55b7-4534-833f-7eb416e8c2ac https://w3id.org/ro-id/54bcf67c-d704-4844-93c8-445166d183af https://w3id.org/ro-id/e996a0c6-3624-4e6e-9214-be26a38e613b https://w3id.org/ro-id/ffe8406d-7011-4880-9a1f-d85467d6e39f https://w3id.org/ro-id/72402c2b-9c74-4ad5-8cde-067dd154cf3f https://w3id.org/ro-id/87648c4f-b7dc-4884-a6f2-93be7336f0a0 https://w3id.org/ro-id/ef99ba9f-8142-4cc1-a9af-ccf44ac6996e

Esteban GonzÃ¡lez+Guardia. "ACTION - Air Pollution in Schools." ROHub. Nov 03 ,2020. https://w3id.org/ro-id/b04c829e-997d-44e6-8ef2-5893622403d2.

Documentation Biblio Teaching_Material Produced Dataset Deliverables Metadata Raw Data Hardware Presentations Used Data https://zenodo.org/record/3737595#.X6GVelNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:31.216813+00:00 Systematiske mÃ¥linger: Bilfritt sentrum og virkningen av denne 2020-11-03 17:22:37.815000+00:00 195381 https://api.rohub.org/api/resources/2fdf70df-0dad-4b06-a941-710579f0bfd0/download/ 2020-11-03 17:23:51.299000+00:00 2022-03-24 13:28:29.878147+00:00 image/png research-object-nilu.png 2020-11-03 17:23:51.299000+00:00 https://zenodo.org/record/3737799#.X6GSnVNKi8o https://doi.org/10.5281/zenodo.3956481 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:30.158519+00:00 DATALOG_Lambertseter_VGS_20190329_Sensor_2 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3730478#.X6GUGFNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:30.725184+00:00 Forskningsprosjekt luftforurensning 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737595#.X6GVqlNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:31.359856+00:00 Systematiske mÃ¥linger: Bilfritt sentrum og virkningen av denne 2020-11-03 17:22:37.815000+00:00 NOVA SDS011 Sensor https://zenodo.org/record/3956481#.X6GWeVNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:32.083544+00:00 RDUINO_UNO_WITH_NOVASDS011_Firmware 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737565#.X6GWPVNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:31.940094+00:00 Er luftkvalitaten ved Ullern VGS skadelig for elevene? 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737759#.X6GSQFNKi8o https://zenodo.org/record/3956481#.X6GXUFNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:30.218641+00:00 DATALOG_Lambertseter_VGS_20190329 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737799 https://zenodo.org/record/3737759 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:32.467392+00:00 DATALOG 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3730465#.X6GTTFNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:30.595340+00:00 Air Pollution Perception in Public Participation 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737635#.X6GUj1NKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:30.880503+00:00 Trafikkforurensing: Trafikkerte omrÃ¥der er mer utsatt for forurensing 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737577#.X6GV-VNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:31.639184+00:00 Luftkvalitet: Andel svevestÃ¸v i lufta Ã¸ker ved rushtiden 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3730457#.X6GTGVNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:30.458800+00:00 Tutorial for air quality projects in high schools 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737569#.X6GWGVNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:31.787207+00:00 Hvordan utvikler luftkvaliteten i et klasserom seg? 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737608#.X6GVUFNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:31.020395+00:00 Systematiske mÃ¥linger: Vi Ã¸nsker Ã¥ finne ut om det akustiske miljÃ¸et har effekt pÃ¥ luftkvalitetens endringer 2020-11-03 17:22:37.815000+00:00 https://zenodo.org/record/3737589#.X6GV0FNKi8o 2020-11-03 17:22:37.815000+00:00 2022-03-24 13:28:31.504178+00:00 Optimalt arbeidsmiljÃ¸: systematiske mÃ¥linger av stÃ¸y og temperatur i klasserom, og sammenhengem mellom dem 2020-11-03 17:22:37.815000+00:00 air quality 18.750000000000004 17.1 Norway https://www.wikidata.org/wiki/Q20 sensor 12.156862745098039 6.2 data 4.934210526315789 4.5 instrumentation and photography 100.0 0.7565824389457703 student 11.372549019607844 5.8 in.the pilot 10.584518167456554 6.7 pilot 4.714912280701754 4.3 opportunity 3.837719298245614 3.5 sensor 8.881578947368421 8.1 Students Education/Teaching and learning/Students Not many people are aware of this fact, and this is where the Norwegian pilot of the ACTION project comes in.The pilot gives high school students in Oslo and the larger Oslo area the opportunity to design and carry out their own air quality projects, using an off-the-shelf air quality sensor platform. 44.36363636363637 24.4 air pollution in school 8.530805687203792 5.4 service-account-generation-service Antonio Petrizzo http://sandbox.rohub.org/rodl/ROs/sinktrack_FORK/ Matlab, Taverna, Linux environment http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/23e62dba-df50-4485-a4f5-2e508a712418 http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/332cb24b-df2a-4235-b600-51b78ea934f5 http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/37692d3f-0fea-4dd1-ad7b-ec8da1aca48a http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/3a10914c-23cb-4be9-b7cc-880d8c97dd25 http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/408afda8-85ab-4b04-9705-0fcaa16cb98d http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be/changes/ca895d77-7f18-46a5-aac8-0b0580021094 https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ standard deviation of backscatter transports elaborate ASCII file produce a graph SinkTrack It statistics angle standard deviation graph ASCII file net depth angle of net ping net graph with the track statistics backscattering angle of net FM Midwater service-account-enrichment http://ever-est.eu/value#/sinktrack http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431 http://ever-est.eu/value#Work 10.24424/ro-id.EHMJMDN68Q 2019-12-09T08:16:15.705+01:00 http://everest.psnc.pl/users/antonio.petrizzo http://sandbox.rohub.org/rodl/ROs/sinktrack/ http://w3id.org/ro-id/rohub/model#change_specifications/5ef61e03-1c33-483b-9abc-e527bc6ab6be An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack"). 18252 https://api.rohub.org/api/ros/7bc38514-796e-47e6-81cb-b8f91247a854/crate/download/ 2019-12-09 07:16:15.705000+00:00 2025-03-05 01:19:05.791030+00:00 2019-12-09 07:16:15.705000+00:00 It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net application/ld+json EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 https://w3id.org/ro-id/7bc38514-796e-47e6-81cb-b8f91247a854 MarGnet, net, sinking velocity, water column, backscatter SinkTrack Sea Monitoring http://sandbox.rohub.org/rodl/ROs/sinktrack-release/ https://w3id.org/ro-id/7bc38514-796e-47e6-81cb-b8f91247a854

Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), and Fantina Madricardo (ISMAR-CNR Venice). "SinkTrack." ROHub. Dec 09 ,2019. https://doi.org/10.24424/ro-id.EHMJMDN68Q.

produced web services main components software datasets inputs config setup workflows scripts biblio results nested used 105094 KB https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ 2022-03-24 13:29:20.753711+00:00 2022-03-24 13:29:22.977211+00:00 text/plain sinkTrackGraph1.t2flow 2022-03-24 13:29:20.753711+00:00 105100 KB https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ 2022-03-24 13:29:20.753264+00:00 2022-03-24 13:29:22.867495+00:00 text/plain sinkTrackRead.t2flow 2022-03-24 13:29:20.753264+00:00 111274 KB https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ 2022-03-24 13:29:20.754047+00:00 2022-03-24 13:29:22.725044+00:00 text/plain sinkTrack.t2flow 2022-03-24 13:29:20.754047+00:00 service-account-generation-service Antonio Petrizzo http://sandbox.rohub.org/rodl/ROs/sinktrack_FORK/ Matlab, Taverna, Linux environment http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/0153bea4-5f13-4ee7-b702-3df1e9e6af6c http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/056b9f5e-da5c-4242-b388-84a45f9d08f3 http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/414dbcbc-a673-41eb-882b-c9f804c97458 http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/8ef9d8c4-ff4b-44ea-8270-d4d3534f3331 http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/9e6e3487-2529-449d-a7f4-3865e8f95705 http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70/changes/f19400a3-8818-4a77-9170-9ddda31bee7a https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ standard deviation of backscatter transports elaborate ASCII file produce a graph SinkTrack It statistics angle standard deviation graph ASCII file net depth angle of net ping net graph with the track statistics backscattering angle of net FM Midwater service-account-enrichment http://ever-est.eu/value#/sinktrack http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431 http://ever-est.eu/value#Work 10.24424/ro-id.CGRSLQGW8A 2019-12-09T08:14:43.199+01:00 http://everest.psnc.pl/users/antonio.petrizzo http://sandbox.rohub.org/rodl/ROs/sinktrack/ http://w3id.org/ro-id/rohub/model#change_specifications/bb9cdc2c-73eb-4d80-800e-664c1275dc70 An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack"). 18326 https://api.rohub.org/api/ros/84eea92b-94f8-43ea-a9ee-ad57b832298f/crate/download/ 2019-12-09 07:14:43.199000+00:00 2025-03-05 01:19:06.321946+00:00 2019-12-09 07:14:43.199000+00:00 It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net application/ld+json EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 https://w3id.org/ro-id/84eea92b-94f8-43ea-a9ee-ad57b832298f MarGnet, net, sinking velocity, water column, backscatter SinkTrack Sea Monitoring http://sandbox.rohub.org/rodl/ROs/sinktrack_ARCHIVE/ https://w3id.org/ro-id/84eea92b-94f8-43ea-a9ee-ad57b832298f

Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), and Fantina Madricardo (ISMAR-CNR Venice). "SinkTrack." ROHub. Dec 09 ,2019. https://doi.org/10.24424/ro-id.CGRSLQGW8A.

components results biblio main scripts setup software inputs nested produced web services workflows config datasets used 105100 KB https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ 2022-03-24 13:29:33.236065+00:00 2022-03-24 13:29:36.949488+00:00 text/plain sinkTrackRead.t2flow 2022-03-24 13:29:33.236065+00:00 111274 KB https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ 2022-03-24 13:29:33.236597+00:00 2022-03-24 13:29:36.849067+00:00 text/plain sinkTrack.t2flow 2022-03-24 13:29:33.236597+00:00 105094 KB https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ 2022-03-24 13:29:33.237122+00:00 2022-03-24 13:29:37.076804+00:00 text/plain sinkTrackGraph1.t2flow 2022-03-24 13:29:33.237122+00:00 service-account-generation-service Antonio Petrizzo Antonio Petrizzo (ISMAR-CNR) Matlab, Taverna, Linux environment http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/119ac5c2-463b-4ede-be70-cc579609a602 http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/6c63d6b6-05a9-4384-b76f-bbf4317339d8 http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/74ce7a8a-fc42-4ccf-b6f9-5a8475997af9 http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/7b28d672-9760-4db0-bff1-acff17898381 http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/815809d7-4c1a-4970-a4d9-6c6c0f056f9c http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/a8984f97-b3e3-45fb-9868-ec2aade61d47 http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/a98bc604-d7ed-4d40-bb4e-6f75f9c604fc http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb/changes/e340790a-bc79-422b-a5f5-a86a43f9b1c1 https://box.everest.psnc.pl/f/da8ec060b9d84acbb927/ https://box.everest.psnc.pl/f/26ebe24c79a341dab257/ https://box.everest.psnc.pl/f/5c10551086ec469fab48/ https://box.everest.psnc.pl/f/ffc720db2af44b84bbe3/ https://box.everest.psnc.pl/f/5c10551086ec469fab48/ https://box.everest.psnc.pl/f/ffc720db2af44b84bbe3/ https://box.everest.psnc.pl/f/26ebe24c79a341dab257/ https://box.everest.psnc.pl/f/da8ec060b9d84acbb927/ start from water column data sink velocity of a net thermohydraulics velocity net service-account-enrichment http://ever-est.eu/value#/sinkvel http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431 http://ever-est.eu/value#Work 10.24424/ro-id.IKMY8URJ9Q 2019-12-06T12:11:06.653+01:00 http://everest.psnc.pl/users/antonio.petrizzo http://sandbox.rohub.org/rodl/ROs/sinkvel/ http://w3id.org/ro-id/rohub/model#change_specifications/bded69c5-cf1d-4133-881c-9a6b0910abeb An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The signal of the seafloor is identified and removed and the noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the values of the water column backscatter intensity for each beam over time. Observing the point with higher intensity it is possible to reconstruct the path of the ML type within the specified beam. To estimate the sinking velocity a portion of the stacked pings is selected on the basis of the minimum and maximum number of pings and depths extracted with FM Midwater when the ASCII files where created. The selected window corresponds to the time interval when the ML is freely falling in the water column. The first input asks for a link to a .zip file containing the ASCII .txt data of the water column backscatter intensity values extracted with FM Midwater and a .inp file containing the values of the window used to calculate the sinking velocity. The second input (“working_dir”) is need only to specify the work directory when workin in a local machine. 20786 https://api.rohub.org/api/ros/f8a252e5-47da-410b-9096-526bc50d19a3/crate/download/ 2019-12-06 11:11:06.653000+00:00 2025-03-05 01:19:06.823521+00:00 2019-12-06 11:11:06.653000+00:00 It calculates the sink velocity of a net floating in water starting from water column data. application/ld+json EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 https://w3id.org/ro-id/f8a252e5-47da-410b-9096-526bc50d19a3 MarGnet, net, sink velocity, water column, backscatter SinkVel information net sink velocity water column water earth sciences data net sink velocity water column water engineering float in water sink velocity of a net sink velocity velocity of a net water column data It calculates the sink velocity of a net floating in water starting from water column data. SinkVel. Sea Monitoring https://w3id.org/ro-id/f8a252e5-47da-410b-9096-526bc50d19a3 thermohydraulics

Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), Fantina Madricardo (ISMAR-CNR Venice), and Lukasz Janowski (University of Gdansk). "SinkVel." ROHub. Dec 06 ,2019. https://doi.org/10.24424/ro-id.IKMY8URJ9Q.

main config results web services workflows datasets software used inputs nested biblio scripts components produced setup 45619 B https://box.everest.psnc.pl/f/ffc720db2af44b84bbe3/ 2022-03-24 13:29:52.552114+00:00 2022-03-24 13:29:55.959020+00:00 image/png sinkVel_a.png 2022-03-24 13:29:52.552114+00:00 48521 KB https://box.everest.psnc.pl/f/5c10551086ec469fab48/ 2022-03-24 13:29:52.551666+00:00 2022-03-24 13:29:56.249419+00:00 Exemple of output application/zip results.zip 2022-03-24 13:29:52.551666+00:00 102628 KB https://box.everest.psnc.pl/f/26ebe24c79a341dab257/ 2022-03-24 13:29:52.551183+00:00 2022-03-24 13:29:56.066513+00:00 text/plain sinkVel.t2flow 2022-03-24 13:29:52.551183+00:00 7338044 KB https://box.everest.psnc.pl/f/da8ec060b9d84acbb927/ 2022-03-24 13:29:52.552700+00:00 2022-03-24 13:29:56.799060+00:00 Input file for the workflow application/zip myInput.zip 2022-03-24 13:29:52.552700+00:00 service-account-generation-service Antonio Petrizzo Matlab, Taverna, Linux environment http://sandbox.rohub.org/rodl/ROs/sinktrack/ standard deviation of backscatter transports elaborate ASCII file produce a graph SinkTrack It statistics angle standard deviation graph ASCII file net depth angle of net ping net graph with the track statistics backscattering angle of net FM Midwater service-account-enrichment http://ever-est.eu/value#/sinktrack http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431 http://ever-est.eu/value#Work An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack"). 16462 https://api.rohub.org/api/ros/ac013b9e-5f40-4fd1-98b8-b6159150c2bd/crate/download/ 2019-12-05 15:43:07.194000+00:00 2025-03-05 01:19:06.548263+00:00 2019-12-05 15:43:07.194000+00:00 It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net application/ld+json EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 https://w3id.org/ro-id/ac013b9e-5f40-4fd1-98b8-b6159150c2bd MarGnet, net, sink velocity, water column, backscatter SinkTrack Sea Monitoring http://sandbox.rohub.org/rodl/ROs/sinktrack/

Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), and Fantina Madricardo (ISMAR-CNR Venice). "SinkTrack." ROHub. Dec 05 ,2019. https://w3id.org/ro-id/ac013b9e-5f40-4fd1-98b8-b6159150c2bd.

biblio nested inputs web services scripts setup config datasets results components software used produced workflows main 111274 KB https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ 2022-03-24 13:30:11.012688+00:00 2022-03-24 13:30:12.439683+00:00 text/plain sinkTrack.t2flow 2022-03-24 13:30:11.012688+00:00 105100 KB https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ 2022-03-24 13:30:11.013158+00:00 2022-03-24 13:30:12.285662+00:00 text/plain sinkTrackRead.t2flow 2022-03-24 13:30:11.013158+00:00 105094 KB https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ 2022-03-24 13:30:11.013503+00:00 2022-03-24 13:30:12.231428+00:00 text/plain sinkTrackGraph1.t2flow 2022-03-24 13:30:11.013503+00:00 service-account-generation-service Antonio Petrizzo Matlab, Taverna, Linux environment http://w3id.org/ro-id/rohub/model#change_specifications/c0da4da3-f459-40d3-a1a3-08bffff46f50/changes/3b8beca2-f937-4ba5-bbeb-c1af8b52c742 http://w3id.org/ro-id/rohub/model#change_specifications/c0da4da3-f459-40d3-a1a3-08bffff46f50/changes/3e24c63b-0751-4125-9783-7e0fc229f80b http://w3id.org/ro-id/rohub/model#change_specifications/c0da4da3-f459-40d3-a1a3-08bffff46f50/changes/4b64aa38-11ca-41af-8f41-4f2541c0888a http://w3id.org/ro-id/rohub/model#change_specifications/c0da4da3-f459-40d3-a1a3-08bffff46f50/changes/574a825a-8df7-4356-b65c-c9d88f2664ce http://w3id.org/ro-id/rohub/model#change_specifications/c0da4da3-f459-40d3-a1a3-08bffff46f50/changes/ca338ac4-92a9-41a2-b2b2-81e201a81a37 http://w3id.org/ro-id/rohub/model#change_specifications/c0da4da3-f459-40d3-a1a3-08bffff46f50/changes/e4d136ed-22a9-4c31-95f0-ab50666954a7 https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ standard deviation of backscatter transports elaborate ASCII file produce a graph SinkTrack It statistics angle standard deviation graph ASCII file net depth angle of net ping net graph with the track statistics backscattering angle of net FM Midwater service-account-enrichment http://ever-est.eu/value#/sinktrack http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431 http://ever-est.eu/value#Work 2019-12-05T16:31:13.771+01:00 http://everest.psnc.pl/users/antonio.petrizzo http://sandbox.rohub.org/rodl/ROs/sinktrack/ http://w3id.org/ro-id/rohub/model#change_specifications/c0da4da3-f459-40d3-a1a3-08bffff46f50 An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack"). 16352 https://api.rohub.org/api/ros/aaed8d9a-b995-4de7-a84a-35534103581b/crate/download/ 2019-12-05 15:31:13.771000+00:00 2025-03-05 01:19:05.544145+00:00 2019-12-05 15:31:13.771000+00:00 It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net application/ld+json EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b SinkTrack Sea Monitoring http://sandbox.rohub.org/rodl/ROs/sinktrack_SNAPSHOT/ https://w3id.org/ro-id/aaed8d9a-b995-4de7-a84a-35534103581b

web services workflows results main config used inputs produced biblio nested datasets software components scripts setup 105100 KB https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ 2022-03-24 13:30:24.222675+00:00 2022-03-24 13:30:26.622565+00:00 text/plain sinkTrackRead.t2flow 2022-03-24 13:30:24.222675+00:00 111274 KB https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ 2022-03-24 13:30:24.222095+00:00 2022-03-24 13:30:27.147195+00:00 text/plain sinkTrack.t2flow 2022-03-24 13:30:24.222095+00:00 105094 KB https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ 2022-03-24 13:30:24.223337+00:00 2022-03-24 13:30:26.880661+00:00 text/plain sinkTrackGraph1.t2flow 2022-03-24 13:30:24.223337+00:00 service-account-generation-service Antonio Petrizzo Matlab, Taverna, Linux environment http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/20d53a40-6910-4c02-95c1-d23bcd344d32 http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/3134a63c-b0c9-4a2e-8e70-cd527ea27a48 http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/3b51b0a3-9887-4723-8ef4-1081f75c0cb0 http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/8e107b34-13bd-4272-b06c-5ae3f58a7f4e http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/b736aa68-a1d9-4d14-81b4-2cc0ed614040 http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/f6008087-6209-4593-9324-e6611dc34424 https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ standard deviation of backscatter transports elaborate ASCII file produce a graph SinkTrack It statistics angle standard deviation graph ASCII file net depth angle of net ping net graph with the track statistics backscattering angle of net FM Midwater service-account-enrichment http://ever-est.eu/value#/sinktrack http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431 http://ever-est.eu/value#Work http://sandbox.rohub.org/rodl/ROs/sinktrack/ 2019-12-05T16:29:35.863+01:00 http://everest.psnc.pl/users/antonio.petrizzo http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34 An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack"). 16324 https://api.rohub.org/api/ros/ed9a1b7c-fc01-4b4a-b274-0708f43b24e2/crate/download/ 2019-12-05 15:29:35.863000+00:00 2025-03-05 01:19:06.048079+00:00 2019-12-05 15:29:35.863000+00:00 It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net application/ld+json EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 https://w3id.org/ro-id/ed9a1b7c-fc01-4b4a-b274-0708f43b24e2 SinkTrack Sea Monitoring http://sandbox.rohub.org/rodl/ROs/sinktrack_ARCHIVE/

produced components datasets used results setup workflows scripts nested biblio config inputs main web services software 105100 KB https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ 2022-03-24 13:30:37.089841+00:00 2022-03-24 13:30:38.641614+00:00 text/plain sinkTrackRead.t2flow 2022-03-24 13:30:37.089841+00:00 111274 KB https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ 2022-03-24 13:30:37.090235+00:00 2022-03-24 13:30:38.484129+00:00 text/plain sinkTrack.t2flow 2022-03-24 13:30:37.090235+00:00 105094 KB https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ 2022-03-24 13:30:37.089386+00:00 2022-03-24 13:30:38.327875+00:00 text/plain sinkTrackGraph1.t2flow 2022-03-24 13:30:37.089386+00:00 service-account-generation-service Antonio Petrizzo 2019-12-06T12:11:06.653+01:00 http://everest.psnc.pl/users/antonio.petrizzo https://w3id.org/ro-id/c4be06dc-6af4-4bc1-8843-035e943a54ad Antonio Petrizzo (ISMAR-CNR) Matlab, Taverna, Linux environment start from water column data sink velocity of a net water column data sink velocity of a net data water supply water column net thermohydraulics velocity net velocity of a net sink float in water sink velocity 7338044 KB Input file for the workflow application/zip myInput.zip service-account-enrichment http://ever-est.eu/value#/sinkvel http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431 http://ever-est.eu/value#Work http://sandbox.rohub.org/rodl/ROs/sinkvel-release/ An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The signal of the seafloor is identified and removed and the noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the values of the water column backscatter intensity for each beam over time. Observing the point with higher intensity it is possible to reconstruct the path of the ML type within the specified beam. To estimate the sinking velocity a portion of the stacked pings is selected on the basis of the minimum and maximum number of pings and depths extracted with FM Midwater when the ASCII files where created. The selected window corresponds to the time interval when the ML is freely falling in the water column. The first input asks for a link to a .zip file containing the ASCII .txt data of the water column backscatter intensity values extracted with FM Midwater and a .inp file containing the values of the window used to calculate the sinking velocity. The second input (“working_dir”) is need only to specify the work directory when workin in a local machine. 23184 https://api.rohub.org/api/ros/c4be06dc-6af4-4bc1-8843-035e943a54ad/crate/download/ 2019-12-05 10:40:09.490000+00:00 2025-03-05 01:19:07.077298+00:00 2019-12-05 10:40:09.490000+00:00 It calculates the sink velocity of a net floating in water starting from water column data. application/ld+json EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 https://w3id.org/ro-id/c4be06dc-6af4-4bc1-8843-035e943a54ad MarGnet, net, sink velocity, water column, backscatter SinkVel Sea Monitoring

Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), Fantina Madricardo (ISMAR-CNR Venice), Lukasz Janowski (University of Gdansk), and Antonio Petrizzo. "SinkVel." ROHub. Dec 05 ,2019. https://w3id.org/ro-id/c4be06dc-6af4-4bc1-8843-035e943a54ad.

setup results datasets web services used software nested produced components main inputs config workflows biblio scripts 106716 KB https://box.everest.psnc.pl/f/26ebe24c79a341dab257/ 2019-12-05 10:40:09.490000+00:00 2022-03-24 13:31:06.395050+00:00 text/plain sinkVel.t2flow 2019-12-05 10:40:09.490000+00:00 45619 B https://box.everest.psnc.pl/f/ffc720db2af44b84bbe3/ 2019-12-05 10:40:09.490000+00:00 2022-03-24 13:31:06.465131+00:00 image/png sinkVel_a.png 2019-12-05 10:40:09.490000+00:00 7338044 KB https://box.everest.psnc.pl/f/83f6f22ec3d14233bc42/ 2019-12-05 10:40:09.490000+00:00 2022-03-24 13:31:05.479704+00:00 application/zip myInput.zip 2019-12-05 10:40:09.490000+00:00 48521 KB https://box.everest.psnc.pl/f/5c10551086ec469fab48/ 2019-12-05 10:40:09.490000+00:00 2022-03-24 13:31:06.579204+00:00 Exemple of output application/zip results.zip 2019-12-05 10:40:09.490000+00:00 service-account-generation-service