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- Machine-actionable: `schema:ViewAction` links the dataset to the [FAIR2Adapt dashboard](https://fair2adapt.github.io/riomar-dashboard/) for interactive visualization at any zoom level
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### Context

Part of the [FAIR2Adapt](https://fair2adapt.eu) project, demonstrating that the HEALPix DGGS approach works for both ocean model outputs and Earth observation data. The multiscale pyramid enables efficient visualization from global overview to full 10m resolution.""";
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Part of the [FAIR2Adapt](https://fair2adapt.eu) project. Data generated by Yanchun He (NERSC) and formatted by NERSC under the FAIR2Adapt project (EU grant 101188256). Licensed under CC-BY 4.0.""";
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  <http://schema.org/name> "Engineering" .

<https://w3id.org/ro-id/enrichment/708d81d0-8ecf-479c-8d1e-895a491f1b52> a <https://w3id.org/ro/terms/earth-science#Concept>;
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<https://w3id.org/ro-id/enrichment/7550af98-9320-4c73-b8a9-e84c0aa5e598> a <https://w3id.org/ro/terms/earth-science#NASA>;
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- NetCDF model outputs converted to Zarr with 2D coordinates from the BLOM grid file
- Served through an authenticated HTTPS proxy for access-controlled sharing
- Machine-actionable: `schema:ViewAction` links the dataset to the [FAIR2Adapt dashboard](https://fair2adapt.github.io/riomar-dashboard/) for interactive visualization
- Metadata enriched with [I-ADOPT](https://i-adopt.github.io/) variable decomposition""";
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- **Variables**: sea surface temperature (SST), ocean temperature on 53 sigma density levels
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- **Spatial coverage**: Near-global ocean (-80°S to 90°N), BLOM tripolar curvilinear grid (385×360)
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<https://w3id.org/ro-id/enrichment/beeff710-2ad0-4878-9e6e-9fecd50ede7b> a <https://w3id.org/ro/terms/earth-science#FCID>;
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<https://w3id.org/ro-id/enrichment/ceb7cb06-7342-4f91-933c-fca77e59c868> a <https://w3id.org/ro/terms/earth-science#Concept>,
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  <http://schema.org/name> "dataset", "dataset";
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  <https://w3id.org/ro/terms/earth-science#score> "9.1", "9.1" .

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  <http://schema.org/name> "http", "http";
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<https://w3id.org/ro-id/enrichment/d95cb3a8-842a-4e47-b798-c76eb96d1778> a <https://w3id.org/ro/terms/earth-science#Concept>,
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<https://w3id.org/ro-id/enrichment/dff45906-4b7e-49ab-88e3-f08151d6c75a> a <https://w3id.org/ro/terms/earth-science#Concept>,
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  <https://w3id.org/ro/terms/earth-science#score> "6.7", "6.7" .

<https://w3id.org/ro-id/enrichment/e6f16f81-53c3-470b-a2e7-2fa7bcf93bbc> a <https://w3id.org/ro/terms/earth-science#NASA>,
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  <http://schema.org/name> "Physics", "Physics" .

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  <http://schema.org/name> "BLOM tripolar curvilinear grid", "BLOM tripolar curvilinear grid";
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<https://w3id.org/ro-id/enrichment/e8f23c03-a956-4534-a7b7-06647779c83f> a <https://w3id.org/ro/terms/earth-science#FCID>,
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  <http://schema.org/description> """Ocean reanalysis data from the **NorESM2/BLOM** model (JRA-OC20 forcing), providing monthly average sea surface temperature and 3D ocean temperature fields for 2010–2018.

### Dataset

- **Variables**: sea surface temperature (SST), ocean temperature on 53 sigma density levels
- **Temporal coverage**: January 2010 – December 2018, monthly averages (108 timesteps)
- **Spatial coverage**: Near-global ocean (-80°S to 90°N), BLOM tripolar curvilinear grid (385×360)
- **Grid**: Original BLOM tripolar curvilinear grid with 2D latitude/longitude coordinates
- **Format**: Cloud-optimized Zarr (Zstd compressed)

### FAIRification

- NetCDF model outputs converted to Zarr with 2D coordinates from the BLOM grid file
- Served through an authenticated HTTPS proxy for access-controlled sharing
- Machine-actionable: `schema:ViewAction` links the dataset to the [FAIR2Adapt dashboard](https://fair2adapt.github.io/riomar-dashboard/) for interactive visualization
- Metadata enriched with [I-ADOPT](https://i-adopt.github.io/) variable decomposition

### Context

Part of the [FAIR2Adapt](https://fair2adapt.eu) project. Data generated by Yanchun He (NERSC) and formatted by NERSC under the FAIR2Adapt project (EU grant 101188256). Licensed under CC-BY 4.0.""";
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  <http://schema.org/description> """By deploying JupyterLab with PANGEO, CESM and ESMValTool conda environments as a new Climate Galaxy interactive tool, we are aiming at bridging the gap between climate scientists and non-climate specialists.

Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational research. One of the strength of Galaxy is that it does not require programming experience and allow researchers to easily upload data, run complex tools and workflows in a reproducible manner, and visualize results. Galaxy Climate is quite new and aims at offering tools to everyone interested in Climate Science so that they can analyse and visualize climate data produced by climate scientists. However, climate scientists and in particular climate modellers have very different working practices: they often like to use command lines for running climate models and thanks to the PANGEO community (a community platform for Big Data geoscience) the Jupyter ecosystem has become very popular with several deployments of JupyterHubs dedicated to climate data analysis. By deploying JupyterLab with PANGEO, CESM and ESMValTool  conda environments as a new Climate Galaxy interactive tool (https://live.usegalaxy.eu/?tool_id=interactive_tool_climate_notebook), we are aiming at bridging the gap between climate scientists and non-climate specialists. On this poster, we will show typical use cases both for research (https://nordicesmhub.github.io/eosc-nordic-climate-demonstrator/02-use-cases/) and for teaching (https://nordicesmhub.github.io/NEGI-Abisko-2019/intro).""";
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  <http://schema.org/description> """This is a tarball for the Docker climate-JupyterLab image - Version 2021-03-18.

To use it:

download the image file docker-climate-notebook-2021-03-18.tar
load it with docker with the command: docker load --input docker-climate-notebook-2021-03-18.tar
launch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook:2021-03-18
start your favorite web browser and go to: http://localhost:7777/ipython/
See https://github.com/NordicESMhub/docker-climate-notebook for more details""";
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  <http://schema.org/email> "bjoern.gruening@gmail.com";
  <http://schema.org/name> "Björn Grüning";
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  <http://schema.org/creator> <https://orcid.org/0000-0002-1784-2920>;
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  <http://schema.org/description> """These docker images (different tags) correspond to the docker images built for Galaxy Climate JupyterLab.
The docker images can be used within Galaxy and as standalone docker images.
You can use the same images we use in Galaxy on your local computer or any other platform:

1. Pull an existing image locally

docker pull quay.io/nordicesmhub/docker-climate-notebook

2. Run a pre-build image from docker registry

3. To start your JupyterLab:

docker run -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook

and you will top open a new terminal and start your favorite web browser.

your running Jupyter Notebook instance on http://localhost:7777/ipython/.

Remark: for reproducibility purpose, we suggest you use a specific tag e.g.

docker pull quay.io/nordicesmhub/docker-climate-notebook:2021-03-18

Then use the same tag when starting your JupyterLab application:

docker run -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook:2021-03-18""";
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Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational research. One of the strength of Galaxy is that it does not require programming experience and allow researchers to easily upload data, run complex tools and workflows in a reproducible manner, and visualize results. Galaxy Climate is quite new and aims at offering tools to everyone interested in Climate Science so that they can analyse and visualize climate data produced by climate scientists. However, climate scientists and in particular climate modellers have very different working practices: they often like to use command lines for running climate models and thanks to the PANGEO community (a community platform for Big Data geoscience) the Jupyter ecosystem has become very popular with several deployments of JupyterHubs dedicated to climate data analysis. By deploying JupyterLab with PANGEO, CESM and ESMValTool  conda environments as a new Climate Galaxy interactive tool (https://live.usegalaxy.eu/?tool_id=interactive_tool_climate_notebook), we are aiming at bridging the gap between climate scientists and non-climate specialists. On this poster, we will show typical use cases both for research (https://nordicesmhub.github.io/eosc-nordic-climate-demonstrator/02-use-cases/) and for teaching (https://nordicesmhub.github.io/NEGI-Abisko-2019/intro).""";
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To use it:

download the image file docker-climate-notebook-2021-03-18.tar
load it with docker with the command: docker load --input docker-climate-notebook-2021-03-18.tar
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The docker images can be used within Galaxy and as standalone docker images.
You can use the same images we use in Galaxy on your local computer or any other platform:

1. Pull an existing image locally

docker pull quay.io/nordicesmhub/docker-climate-notebook

2. Run a pre-build image from docker registry

3. To start your JupyterLab:

docker run -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook

and you will top open a new terminal and start your favorite web browser.

your running Jupyter Notebook instance on http://localhost:7777/ipython/.

Remark: for reproducibility purpose, we suggest you use a specific tag e.g.

docker pull quay.io/nordicesmhub/docker-climate-notebook:2021-03-18

Then use the same tag when starting your JupyterLab application:

docker run -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook:2021-03-18""";
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To use it:

download the image file docker-pangeo-notebook-1c0f66b.tar
load it with docker with the command: docker load --input docker-pangeo-notebook-1c0f66b.tar
launch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b
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The docker images can be used within Galaxy and as standalone docker images.
You can use the same images we use in Galaxy on your local computer or any other platform:

1. Pull an existing image locally

docker pull quay.io/nordicesmhub/docker-pangeo-notebook

2. Run a pre-build image from docker registry

3. To start your JupyterLab:

docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook

and you will top open a new terminal and start your favorite web browser.

your running Jupyter Notebook instance on http://localhost:7777/ipython/.

Remark: for reproducibility purpose, we suggest you use a specific tag e.g.

docker pull quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b

Then use the same tag when starting your JupyterLab application:

docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b""";
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  <http://schema.org/about> <http://eurovoc.europa.eu/3952>;
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  <http://schema.org/dateCreated> "2022-04-29 14:55:19.274708+00:00";
  <http://schema.org/dateModified> "2025-10-18 11:43:45.996498+00:00";
  <http://schema.org/datePublished> "2022-04-29 14:55:19.274708+00:00";
  <http://schema.org/description> "This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of PM10 over a given geographical area, here Germany";
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  <http://schema.org/identifier> "https://w3id.org/ro-id/0583f2dd-d42a-4f20-9104-519d587cb30e";
  <http://schema.org/keywords> "CAMS", "Germany", "PM10", "air quality", "copernicus",
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  <http://schema.org/dateCreated> "2022-04-29 18:30:11.293583+00:00";
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  <http://schema.org/datePublished> "2022-04-29 18:30:11.293583+00:00";
  <http://schema.org/description> "This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of NO2 over a given geographical area, here Germany";
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Bio
Felix Cremer received his diploma in mathematics from the University of Leipzig in 2014. In 2016 he started his PhD study on time series analysis of hypertemporal Sentinel-1 radar data. He currently works at the Max-Planck-Institute for Biogeochemistry on the development of the JuliaDataCubes ecosystem in the scope of the NFDI4Earth 5 project.

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The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data. It is based on the YAXArrays.jl package. YAXArrays.jl provides functionality to deal with labelled arrays, similar to the xarray python package and it also provides efficient and easy multithreading and distributed computation of user defined functions along arbitrary slices of the data.
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  <http://schema.org/description> "The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations.";
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  <http://schema.org/description> """In maritime traffic surveillance, detecting illegal
activities, such as illegal fishing or transshipment of illicit
products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception
due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models.
Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process
AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations.""";
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Bio
Felix Cremer received his diploma in mathematics from the University of Leipzig in 2014. In 2016 he started his PhD study on time series analysis of hypertemporal Sentinel-1 radar data. He currently works at the Max-Planck-Institute for Biogeochemistry on the development of the JuliaDataCubes ecosystem in the scope of the NFDI4Earth 5 project.

Abstract
The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data. It is based on the YAXArrays.jl package. YAXArrays.jl provides functionality to deal with labelled arrays, similar to the xarray python package and it also provides efficient and easy multithreading and distributed computation of user defined functions along arbitrary slices of the data.
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  a <http://purl.org/wf4ever/wf4ever#Resource>, <http://schema.org/Article>, <http://schema.org/MediaObject>;
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  <http://schema.org/description> """Summary:

Climate change in northern high latitudes is predicted to be greater in winter rather than summer, yet little is known about the effects of winter climate change on northern ecosystems. Among the unknowns are the effects of an increasing frequency of acute, short-lasting winter warming events. Such events can damage higher plants exposed to warm, then returning cold, temperatures after snow melt, and it is not known how bryophytes and lichens, which are of considerable ecological importance in high-latitude ecosystems, are affected by such warming events. However, even physiological adaptations of these cryptogams to winter environments in general are poorly understood.

Here we describe findings from a novel field experiment that uses heating from infrared lamps and soil warming cables to simulate acute mid-winter warming events in a sub-Arctic heath. In particular, we report the growing season responses of the dominant lichen, Peltigera aphthosa, and bryophyte, Hylocomium splendens, to warming events in three consecutive winters.

While summertime photosynthetic performance of P. aphthosa was unaffected by the winter warming treatments, H. splendens showed significant reductions in net photosynthetic rates and growth rates (of up to 48% and 52%, respectively). Negative effects were evident already during the summer following the first winter warming event.

While the lichen develops without going through critical phenological stages during which vulnerable organs are produced, the moss has a seasonal rhythm, which includes initiation of growth of young, freeze-susceptible shoot apices in the early growing season; these might be damaged by breaking of dormancy during warm winter events.

Synthesis. Different sensitivities of the bryophyte and lichen species were unexpected, and illustrate that very little is known about the winter ecology of bryophytes and lichens from cold biomes in general. In sharp contrast to summer warming experiments that show increased vascular plant biomass and reduced lichen biomass, these results demonstrate that acute climate events in mid-winter may be readily tolerated by lichens, in contrast to previously observed sensitivity of co-occurring dwarf shrubs, suggesting winter climate change may compensate for (or even reverse) predicted lichen declines resulting from summer warming.""";
  <http://schema.org/keywords> "Winter warming";
  <http://schema.org/license> <https://choosealicense.com/no-permission/>;
  <http://schema.org/name> "Contrasting sensitivity to extreme winter warming events of dominant sub‐Arctic heathland bryophyte and lichen species";
  <http://schema.org/sdDatePublished> "2024-01-09 13:23:52.056842+00:00" .

<https://doi.org/10.1016/j.jhydrol.2022.128593> a <http://purl.org/wf4ever/roterms#Paper>,
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  <http://schema.org/author> <https://orcid.org/0000-0002-8763-1643>;
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  <http://schema.org/creator> <https://orcid.org/0000-0002-8763-1643>;
  <http://schema.org/dateCreated> "2022-11-17 12:42:58.014321+00:00";
  <http://schema.org/dateModified> "2022-11-17 12:42:59.601045+00:00";
  <http://schema.org/description> "Rain-on-snow (ROS) events can greatly affect the snow process and cause severe snowmelt-related hazards. It is important to monitor the spatiotemporal distribution of ROS events over the ungauged High Mountain Asia (HMA). This study investigated the spatiotemporal variability of ROS events over the HMA and its potential influencing factors from 1981 to 2020 based on stand-alone Noah-MP land surface model simulations forced by hourly HARv2 reanalysis dataset. The results demonstrated that ROS activity occurred more frequently in the higher-elevation (2500–4000 m and 5500–6000 m a.s.l) regions of the Tianshan Mountains, Pamir, eastern Hindu Kush, Himalayas, and the western Hengduan Shan, with an annual maximum ROS frequency exceeding 15 days and a maximum intensity reaching 40 mm concentrated in spring and summer. ROS frequency experienced a significant decrease in the high-elevation (3000–4500 m a.s.l) regions of the eastern Hindu Kush, West Himalaya, and western Hengduan Shan with a rate exceeding −1.5 days/decade. The decrease in ROS frequency could be explained by a shifting of precipitation type from snowfall to rain driven by dramatic warming and resulting in a decline in snowfall and shortened snow cover persistence, particularly in spring and summer. On the contrary, significantly increasing trend mainly prevailed in the high-elevation (5000–6000 m a.s.l) regions of Transhimalaya and East Himalaya, exceeding 0.9 days/decade.";
  <http://schema.org/license> <https://choosealicense.com/no-permission/>;
  <http://schema.org/name> "Trends and spatial variations of rain-on-snow events over the high Mountain Asia";
  <http://schema.org/sdDatePublished> "2022-11-17 12:42:58.014321+00:00" .

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  <http://schema.org/dateModified> "2023-04-05 15:02:46.070674+00:00";
  <http://schema.org/description> """ABSTRACT
 The origin of lichen-free areas in the High Arctic has been attributed to lichen-kill under permanent snowfields developed 300 yr ago during the Little Ice Age. There are inconsistencies in this hypothesis, particularly in regard to the manner of lichen-kill, the mechanism of dead lichen removal once the previously ice-covered ground is exposed again, the period when the lichen-kill occured, and the form of lichen trimlines. An alternative hypothesis is suggested whereby lichen-free areas occur where seasonal snowfields persist for a much greater part of the summer than elsewhere. As a result the lichen growth season there is very short.""";
  <http://schema.org/keywords> "lichen-kill";
  <http://schema.org/license> <https://choosealicense.com/no-permission/>;
  <http://schema.org/name> "The Problem of Lichen-Free Zones in Arctic Canada";
  <http://schema.org/sdDatePublished> "2023-04-05 15:02:43.479985+00:00" .

<https://doi.org/10.5194/egusphere-egu23-2579> a <http://purl.org/wf4ever/wf4ever#Resource>,
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  <http://schema.org/creator> <https://orcid.org/0000-0002-1784-2920>;
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  <http://schema.org/dateModified> "2023-05-06 08:25:47.523870+00:00";
  <http://schema.org/description> "Summary submitted at EGU 2023.";
  <http://schema.org/license> <https://choosealicense.com/no-permission/>;
  <http://schema.org/name> "Using FAIR and Open Science practices to better understand vegetation browning in Troms and Finnmark (Norway)";
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  <http://schema.org/dateModified> "2023-04-05 12:52:36.003321+00:00";
  <http://schema.org/description> """Abstract. Rain-on-snow (ROS) events in mountainous
catchments can cause enhanced snowmelt, leading to an increased risk of destructive winter floods. However, due to
differences in topography and forest cover, the generation of
snowpack outflow volumes and their contribution to streamflow are spatially and temporally variable during ROS events.
In order to adequately predict such flood events with hydrological models, an enhanced process understanding of the
contribution of rainwater and snowmelt to stream water is
needed.""";
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  <http://schema.org/keywords> "rain-on-snow";
  <http://schema.org/license> <https://choosealicense.com/no-permission/>;
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isotopic composition to better understand streamflow
generation during rain-on-snow events""";
  <http://schema.org/sdDatePublished> "2023-04-05 12:52:29.688139+00:00" .

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  <http://schema.org/description> """Abstract
Arctic ecosystems are increasingly exposed to extreme climatic events throughout the year, which can affect species performance. Cryptogams (bryophytes and lichens) provide important ecosystem services in polar ecosystems but may be physiologically affected or killed by extreme events. Through field and laboratory manipulations, we compared physiological responses of seven dominant sub-Arctic cryptogams (three bryophytes, four lichens) to single events and factorial combinations of mid-winter heatwave (6
C for 7 days), re-freezing, snow removal and summer nitrogen addition. We aimed to identify which mosses and lichens are vulnerable to these abiotic extremes and if combinations would exacerbate physiological responses. Combinations of extremes resulted in stronger species responses but included idiosyncratic species-specific responses. Species that remained dormant during winter (March), irrespective of extremes, showed little physiological response during summer (August). However, winter physiological activity, and response to winter extremes, was not consistently associated with summer physiological impacts. Winter extremes affect cryptogam physiology, but summer responses appear mild, and lichens affect the photobiont more than the mycobiont. Accounting for Arctic cryptogam response
to multiple climatic extremes in ecosystem functioning and modelling will require a better understanding of their winter eco-physiology and repair capabilities.""";
  <http://schema.org/encodingFormat> "application/pdf";
  <http://schema.org/keywords> "winter heatwaves";
  <http://schema.org/license> <https://choosealicense.com/no-permission/>;
  <http://schema.org/name> "Sub-arctic mosses and lichens show idiosyncratic responses to combinations of winter heatwaves, freezing and nitrogen deposition";
  <http://schema.org/sdDatePublished> "2023-04-05 15:35:11.553400+00:00" .

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Extreme climatic events are among the drivers of recent declines in plant biomass and productivity observed across Arctic ecosystems, known as “Arctic browning.” These events can cause landscape-scale vegetation damage and so are likely to have major impacts on ecosystem CO2 balance. However, there is little understanding of the impacts on CO2 fluxes, especially across the growing season. Furthermore, while widespread shoot mortality is commonly observed with browning events, recent observations show that shoot stress responses are also common, and manifest as high levels of persistent anthocyanin pigmentation. Whether or how this response impacts ecosystem CO2 fluxes is not known. To address these research needs, a growing season assessment of browning impacts following frost drought and extreme winter warming (both extreme climatic events) on the key ecosystem CO2 fluxes Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), ecosystem respiration (Reco) and soil respiration (Rsoil) was carried out in widespread sub-Arctic dwarf shrub heathland, incorporating both mortality and stress responses. Browning (mortality and stress responses combined) caused considerable site-level reductions in GPP and NEE (of up to 44%), with greatest impacts occurring at early and late season. Furthermore, impacts on CO2 fluxes associated with stress often equalled or exceeded those resulting from vegetation mortality. This demonstrates that extreme events can have major impac""";
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Droppings of Svalbard reindeer (Rangifer tarandus platyrhynchus) could affect the carbon and nitrogen cycles in tundra ecosystems. The aim of this study was to evaluate the potential of reindeer droppings originating from the winter diet for emission and/or absorption of methane (CH4) and nitrous oxide (N2O) in summer. An incubation experiment was conducted over 14 days using reindeer droppings and mineral subsoil collected from a mound near Ny-Ålesund, Svalbard, to determine the potential exchanges of CH4 and N2O for combinations of two factors, reindeer droppings (presence or absence) and soil moisture (dry, moderate, or wet). A line transect survey was conducted to determine the distribution density of winter droppings at the study site. The incubation experiment showed a weak absorption of CH4 and a weak emission of N2O. Reindeer droppings originating from the winter diet had a negligible effect on the exchange fluxes of both CH4 and N2O. Although the presence of droppings resulted in a short-lasting increase in N2O emissions on day 1 (24 h from the start) for moderate and wet conditions, the emission rates were still very small, up to 3 μg N2O m−2 h−1.""";
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Examples:

```
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!AIVDM,1,1,,A,133sVfPP00PD>hRMDH@jNOvN20S8,0*7F
!AIVDM,1,1,,B,100h00PP0@PHFV`Mg5gTH?vNPUIp,0*3B
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!AIVDM,1,1,,A,14eGrSPP00ncMJTO5C6aBwvP2D0?,0*7A
!AIVDM,1,1,,A,15MrVH0000KH<:V:NtBLoqFP2H9:,0*2F
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We will be using data from Copernicus Atmosphere Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. Parallel data analysis with Pangeo is not covered in this tutorial.""";
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The docker images can be used within Galaxy and as standalone docker images.
You can use the same images we use in Galaxy on your local computer or any other platform:

1. Pull an existing image locally

docker pull quay.io/nordicesmhub/docker-pangeo-notebook

2. Run a pre-build image from docker registry

3. To start your JupyterLab:

docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook

and you will top open a new terminal and start your favorite web browser.

your running Jupyter Notebook instance on http://localhost:7777/ipython/.

Remark: for reproducibility purpose, we suggest you use a specific tag e.g.

docker pull quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b

Then use the same tag when starting your JupyterLab application:

docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b""";
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load it with docker with the command: docker load --input docker-pangeo-notebook-1c0f66b.tar
launch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b
start your favorite web browser and go to: http://localhost:7777/ipython/
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- Abstract submitted and accepted at EGU: [https://doi.org/10.5194/egusphere-egu24-8343](https://doi.org/10.5194/egusphere-egu24-8343)
- [Rendered Jupyter notebook](https://edsbook.org/notebooks/gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/notebook)
- [Galaxy workflow showcasing the pipeline for forecasting sea ice](https://usegalaxy.eu/u/vstoeckl/w/icenet)""";
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