Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
240
datasets available to search
ShareScore release 0.7.1
Dataset results
240 results for “Antarctic Peninsula”
Large scale seabird community stucture along oceanographic gradients in the Scotia Sea and northern Antarctic Peninsula
<p>Dataset and associated code for publication submitted to Frontiers in Marine Science. The data represent strip transect data collection efforts of seabirds aboard 2 tourist ships during the 2019-2020 Antarctic summer season throughout the Scotia Sea and Antarctic Peninsula. Data were analysed using R.</p>
Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'
<p>Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article: </p> <p>Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405</p> <p>The .zip file has the following folders:</p> <p>1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents</p> <p>2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction</p> <p>3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil texture prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil texture prediction</p>
The FjordPhyto citizen science project in the Antarctic Peninsula
<p>FjordPhyto, funded by the United States National Science Foundation (NSF) in 2016-2019 and by the National Aeronautics and Space Agency (NASA) Citizen Science for Earth Systems Program since 2021, is a citizen science project that examines the impacts of increasing glacier meltwater on local ecosystems at the ice-ocean interface of the Antarctic Peninsula (AP), with an emphasis on the western coast (WAP). The citizen science module is based on a collaboration with the International Association of Antarctica Tour Operators (IAATO). Citizen scientists participate in a "validation safari" in which satellite data informs sampling to validate and refine a new ocean color algorithm to detect the glacial meltwater content of seawater from space. The in-situ measurements are combined with remote sensing data products to address scientific questions related to the impacts of glacial meltwater on phytoplankton community abundance and taxonomic composition. This project implements new field sampling techniques and conducts analyses of phytoplankton diversity through a microscopic and genomics approach.</p> <p>The scientific goals of this Citizen Science project are to determine the spatial extent of glacial meltwater through the seasons and identify concomitant shifts in phytoplankton abundance and community diversity in coastal Antarctic waters. Repeated sampling of this region from November to March along 3-6 degrees of latitude (62<sup>o</sup>S to 65<sup>o</sup>S and down to 68<sup>o</sup>S) is only feasible with tourist ships, or through remote sensing. The addition of a remote sensing component, validated by citizen scientists, is crucial for describing long-term synoptic trends and variability in the abundance and spatio-temporal extent of phytoplankton in this region, and for discerning how these patterns are likely to alter in response to changes in climate. This study provides a foundation to better understand phytoplankton diversity under current and potential future ocean conditions, and lead to more robust predictions on potential impacts to upper trophic levels and biogeochemical cycling within this rapidly changing ecosystem.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2, filename: TNNXXX_YYYYMMDD) and Landsat-8 (L8, filename: LC08_L1GT_NNNNNN_YYYYMMDD_YYYYMMDD_01_T2) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included in supplementary datasets. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p>
Antarctic Peninsula MAR 3-hourly data
<p>Modèle Atmosphérique Régionale (MAR), was utilized Wille et al. (2022, Commun. Earth Environ.) . The files contain surface melt, runoff, surface temperature at three-hour timesteps. A description of MAR from Wille et al. (2022) follows as...</p> <p>MAR is a regional climate model specifically designed for simulating polar climate. MAR atmospheric dynamics are based on the hydrostatic approximation of the primitive equations. The exchanges between the atmospheric part of MAR and the surface are handled by the complex energy-balance snow model SISVAT, based on CROCUS that explicitly simulates 30 layers resolving the 20 first meters of snow or ice. The surface module notably represents percolation of meltwater and its retention into the snowpack. Runoff occurs when the snowpack can no longer absorb additional liquid water (i.e., snowpack water content exceeding 5% or surface liquid water over bare ice or an ice-lense layer). MARv3.11 was run at a resolution of 7.5 km and was forced by 6-hourly outputs of the latest ERA5 reanalysis between 1979 and March 2020. The first year was discarded as spin-up.</p> <p> </p>
Skua and plant dispersal: Lessons from the Argentine Islands - Kyiv Peninsula region in the maritime Antarctic
<p class="MsoNormal"><span>Birds are one of the most likely dispersal vectors for plants in Antarctica. We studied the nesting behavior of south polar skua (<em>Catharacta maccormicki</em>) and brown skua (<em>Catharacta antarctica lonnbergi</em>) to assess their potential role in ornithochory in the Argentine Islands - Kyiv Peninsula</span><span> </span><span>region. Nest </span><span>samples</span><span> were collected during 2009-2020 years in the Argentine Islands - Kyiv Peninsula</span><span> </span><span>region including all islands and coasts of the Graham Land from the Lemaire Channel to the islands of Berthelot Islands from north to south and extending from west to east from the Roca Islands, Cruls Islands, Rasmussen Point to the coast. We found that skuas utilize different nest building materials, including bryophytes, vascular plants (hairgrass</span><span> </span><em><span>Deschampsia antarctica</span></em><span>)</span><span>, and lichens. In south polar skua nests, mosses and lichens dominate in the nest material; in brown skuas <em>Deschampsia antarctica</em> and mosses dominate. Both bird species likely collect nest components from nearby vegetation formations (<</span><span>1</span><span> m distant). We conclude that <em>C. maccormicki</em> and <em>C. antarctica</em> <em>lonnbergi</em> are not selective in their choice of plant species, simply using the materials that dominate near the nest. Therefore, both species carry these materials from nearby sites, and only occasionally bring them from distant places. In conclusion, for both species we did not find any evidence to support their involvement in long-distance ornithochory (stomatochory) in the region. </span></p>
WAIS model for "Antarctic Peninsula warming triggers enhanced basal melt rates throughout West Antarctica"
<p>This data set contains the code and input files for WAIS-1080 model, which is based on the Massachusetts Institute of Technology general circulation model (MITgcm), including dynamic/thermodynamic sea-ice model and sub-ice-shelf cavity model for freezing/melting processes.</p> <p>code_was.tar contains source files for compiling model;</p> <p>init.tar and input.tar contain run-time parameter files and binary files for running model</p> <p> </p> <p> </p>
VCF for neutral data set and potential connectivity matrices of Harpagifer antarcticus, along the Western Antarctic Peninsula
<p>Connectivity is a fundamental process of population dynamics in marine ecosystems. In the last decade, with the emergence of new methods, combining different approaches to understand the patterns of connectivity among populations and their regulation has become increasingly feasible. The Western Antarctic Peninsula (WAP) is characterized by complex oceanographic dynamics, where local conditions could act as barriers to population connectivity. Here, the notothenioid fish <em>Harpagifer antarcticus</em>, a demersal species with a complex life cycle (adults with poor swim capabilities and pelagic larvae), was used to assess connectivity along the WAP by combining biophysical modeling and population genomics methods. Both approaches showed congruent patterns. Areas of larvae retention and low potential connectivity, observed in the biophysical model output, coincide with four genetic groups within the WAP: (1) South Shetland Islands, (2) Bransfield Strait, (3) the central, and (4) the southern area of WAP (Marguerite Bay). These genetic groups exhibited limited gene flow between them, consistent with local oceanographic conditions, which would represent barriers to larval dispersal. The joint effect of geographic distance and larval dispersal by ocean currents, had a greater influence on the observed population structure, than each variable evaluated separately. The combined effect of geographic distance and a complex oceanographic dynamic would be generating limited levels of population connectivity in the fish <em>H. antarcticus</em>along the WAP. Based on this population connectivity estimations, priority areas for conservation were discussed, considering the Marine Protected Area proposed for this threatened region of the Southern Ocean.</p>
In-situ observations of surface Chlorophyll-a (HPLC) in the Western Antarctic Peninsula during 2008-2018 (GOAL-FURG)
<p>This dataset contains observations of in-situ chlorophyll-a (mg/m3) along the Western Antarctic Peninsula from 2008 to 2018. All data included in this dataset were collected by the Brazillian High Latitude Oceanography Group (GOAL), based at the Federal University of Rio Grande (FURG), aboard research vessels from the Brazillian Navy.</p> <p>All chlorophyll-a samples were collected at 5 metres depth. Chlorophyll-a concentration was determined through High Pressure Liquid Chromatography. This dataset has been collected thanks to a 10 year effort of sampling by GOAL in the Western Antarctic Peninsula. As such, many researchers, students, and crew members contributed to this valuable dataset. Therefore, we ask that users acknowledge the use of the dataset.</p> <p>Please consult Ferreira et al. 2024 for more information on how the data was collected and analysed.</p>
Antarctic Peninsula basins - Cook et al. (2014)
<p>Antarctic Peninsula drainage basins from Cook et al. (2014). </p> <p>Uploaded to Zenodo because the basins seem to be no longer available from https://add.scar.org/ </p> <p>cite: Cook, A. J., Vaughan, D. G., Luckman, A. J., and Murray, T. 2014. A new Antarctic Peninsula glacier basin inventory and observed area changes since the 1940s. <em>Antarctic Science. </em>26(6):614-624. doi: 10.1017/S0954102014000200.</p>
Data and code for "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. 2024
<p>This data and code is made available to support the article: "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. 2024".</p> <p>Includes: TMOC method output tide correlation, Antarctic Peninsula grounding line, DInSAR data, TMOC Code.</p> <p> </p> <p><strong>For the data:</strong></p> <p>These data are made available to acompany the article "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al. (2024)</p> <p>This datset contains:</p> <p>AP_TMOC_tide_correlation_2019_2020.tif - Significance adjusted tide correlation values for the TMOC method for 2019-2020 for the Antarctic Peninsula.</p> <p>AP_GL_TMOC_2019_2020.shp - A continuous grounding line made from TMOC data and British Antarctic Survey Coastline Data. Intended for use by others.</p> <p>AP_GL_TMOC_2019_2020_source.shp - A discontinuous grounding line made from TMOC data and British Antarctic Survey Coastline Data including the source of each line segment.</p> <p>The folder 'Interferograms' contains the DInSAR products used in the manuscript, sorted by Sentinel-1 frame</p> <p> </p> <p><strong>For the code:</strong></p> <p>This code is made available to support the article "Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method" by Wallis et al.</p> <p>The authors take no responsibility for the quality of results derived using this code.</p> <p>This code is licensed under a Creative Commons Attribution 4.0 International Licence: http://creativecommons.org/licenses/by/4.0/</p> <p>external functions required:<br>geoimread - https://uk.mathworks.com/matlabcentral/fileexchange/46904-geoimread<br>polarstreo_inv - https://uk.mathworks.com/matlabcentral/fileexchange/32907-polar-stereographic-coordinate-transformation-map-to-lat-lon<br>CATS208 tide model and TMD 2.5 matlab toolbox - https://www.esr.org/research/polar-tide-models/tmd-software/</p> <p>The function TMOC_GL_v8 implements the TMOC method descibed in Wallis et al. 2024. This is a 'bring your own data' version.</p> <p>The script pp_folder prost-processes the outputs using the functuon LPfilt_cc</p> <p> </p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Maximum Extent
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2) and Landsat-8 (L8) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included in supplementary datasets. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p> <p> </p>
Intra‐season variations in distribution and abundance of humpback whales in the West Antarctic Peninsula using cruise vessels as opportunistic platforms
<p class="MsoNormal"><span>Following the near collapse of several whale populations in the Southern Ocean, some baleen whale stocks are on the rise again. Combined with the recent increase in fishery of Antarctic Krill (Euphausiia superba) around the Western Antarctic Peninsula (WAP) there is a growing need to quantify several aspects of some of these whale species in this area. In this study we use data collected from tourist vessels performing several trips during the Austral summer to quantify the beginning of the foraging season for Antarctic Humpback whales, estimate abundance, as well as use predictive habitat model to identify potential areas for interaction between this species and fishing vessels. </span></p> <p class="MsoNormal"><span>The following dataset includes the GPS track of both vessels and all marine mammal and seabird observations collected on two ships between late November 2019 and mid-January 2020. These data were gathered following standard Distance Sampling protocols, recorded in Logger2010 software </span>(<a href="http://www.marineconservationresearch.co.uk/downloads/logger-2000-rainbowclick-software-downloads/">http://www.marineconservationresearch.co.uk/downloads/logger-2000-rainbowclick-software-downloads/</a>), stored in MS Access database files and subset in .RData files for analysis.</p>
Baseline concentrations, spatial distribution and origin of trace elements in marine surface sediments of the northern Antarctic Peninsula
<p>Supplementary data to article published in Marine Pollution Bulletin 187 (2023) 114501: https://doi.<br> org/10.1016/j.marpolbul.2022.114501</p>
Regional controls on the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)
<p>Data corresponding to "Regional controls of the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)" (Bischof et al., in prep.).</p> <p>model_setup contains the input and code directories needed to run our model using MITgcm. data contains all model output used to plot the figures contained in the paper.</p> <p> </p>
Data from: Early spring phytoplankton dynamics in the western Antarctic Peninsula
Open the record for dataset details and reuse information.
VCF for neutral data set and potential connectivity matrices of Harpagifer antarcticus, along the Western Antarctic Peninsula
Open the record for dataset details and reuse information.
Skua and plant dispersal: Lessons from the Argentine Islands - Kyiv Peninsula region in the maritime Antarctic
Open the record for dataset details and reuse information.
Intra‐season variations in distribution and abundance of humpback whales in the West Antarctic Peninsula using cruise vessels as opportunistic platforms
Open the record for dataset details and reuse information.
The FjordPhyto citizen science project in the Antarctic Peninsula
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.