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240 results for “Antarctic Peninsula”
Dissolved inorganic carbon and alkalinity of discrete water column samples, collected aboard Palmer LTER annual cruises of the Western Antarctic Peninsula, 1993 - 2019.
Dissolved inorganic carbon (DIC or total CO2 – TCO2) and total alkalinity (TALK) are two of the four parameters defining the carbonate system in seawater. DIC is composed of dissolved CO2 gas, which dissociates into carbonate, 〖CO〗_3^(2-), and bicarbonate, 〖HCO〗_3^- in seawater. About 90% of the DIC is in the form of bicarbonate, ~10% is carbonate, and ~1% is CO2. The dissociation of CO2 dissolved in seawater into carbonate and bicarbonate gives seawater its great capacity to absorb CO2 from the atmosphere. Alkalinity (also known as “buffer capacity”) is a measure of the capacity of water to neutralize acids. Alkalinity is a complex product of the concentrations of (in decreasing order of importance) the DIC components, borate, hydroxide, phosphate, silicate and dissolved ammonium. Ocean biology regulates the alkalinity through the uptake and release of the DIC and the macronutrients N, P and Si. We measure surface DIC and ALK to understand the exchange of CO2 across the air-sea interface in our study area. With DIC and dissolved CO2, we can also derive estimates of ocean pH and thus monitor the extent and evolution of ocean acidification. Analytical methods and QC are presented under the Methods and Protocols tab.
A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula
<p>This data repository is a permanent archive of the results presented in the associated publication (Turner et al. 2024, Marine Ecology Progress Series, <a href="https://doi.org/10.3354/meps14567">https://doi.org/10.3354/meps14567</a>). The objective of this study was to investigate phytoplankton phenology patterns west of the Antarctic Peninsula using satellite ocean color remote sensing data. This dataset extends from 80<sup>o</sup>W to 55<sup>o</sup>W longitude and from 70<sup>o</sup>S to 60<sup>o</sup>S latitude. The data span the time period September 1997 through August 2022. This dataset includes the data and code used to create the figures in the publication. The data in this repository include chlorophyll-a concentration (Chl-a), dates of phytoplankton bloom start date and phytoplankton bloom peak date, photosynthetically active radiation (PAR), sea surface temperature (SST), wind speed, and dates of sea ice retreat and advance. Downloaded spatially-subsetted data files are included as netCDF files (extension .nc) compressed into .zip archives. Additional files used to perform the analyses and make the figures are included as MATLAB scripts and MATLAB data files (extensions .m and .mat, respectively). </p> <p>Recommended citation:</p> <p>Turner, Jessica S., (2024) A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula. Zenodo. https://doi.org/10.5281/zenodo.10790613</p>
Nonbreeding adult Adélie penguin data northern Antarctic Peninsula
<p>Raw, unprocessed satellite derived location data (Kiwisat PTT, Sirtrack New Zealand) Adélie penguins (adult, nonbreeding, N=30) tracked from two colonies at research stations in the South Shetland Islands, western Antarctic Peninsula. Fifteen individuals were tracked each at Carlini Station (ARG) and Henryk Arktowski Station (POL) during the austral summer of 2016-2017 (Nov to Feb). </p> <p>Data are provided in raw, csv format with lon, lat, individual ID, date/time, Argos-CLS location quality, and uw.prop (proportion of time spend underwater). Time is in UTC. </p>
Nitrate (15N) Uptake from samples collected aboard Palmer LTER annual cruises off the Western Antarctic Peninsula, 2012-2014
Nitrate uptake by the bulk phytoplankton community was determined using tracer (<10%) additions of labeled 15-NO3. Samples were collected by Go-Flo from 5 depths 0, 5, 10, 20, 65 m and incubated for 24 h at light levels of 100%, 50%, 25%, 10%, and 0% surface irradiance, respectively.
ASCAT-ERA5 and ASCAT-MERRA-2 Antarctic Peninsula Daily Surface Meltwater Production (2007-2022)
<p>Estimates of daily surface meltwater production across the Antarctic Peninsula over 2007-2022. These data were generated using a random forest model combining enhanced resolution ASCAT C-Band radar backscatter, ASCAT-detected surface melt presence (https://zenodo.org/record/7995998), and ERA5 or MERRA-2 reanalysis sensible and latent heat fluxes, downward shortwave and longwave radiation, and 2-m temperature. Initial model training was undertaken using meteorological observations and surface melt estimates from surface energy balance modeling at several Larsen ice shelf automated weather stations (Jakobs et al., 2020), before scaling up to the Antarctic Peninsula region using ERA5 or MERRA-2. <br><br>Note that the ASCAT-ERA5 melt estimates here are updated to v2 using a revised random forest melt prediction model. <br>The ASCAT-MERRA-2 melt estimates here are the initial release (v1). </p> <p>This record will be updated upon publication of a forthcoming manuscript. </p> <p>Please reach out to Luke Trusel with questions! </p>
GPS time series of solid Earth deformation for the southern Antarctic Peninsula
<p>This dataset contains vertical GPS time series observed from selected sites in the southern Antarctic Peninsula. The time series were processed using GAMIT-GLOBK software (Herring et al., 2016) in combination with a globally distributed network that includes all available data from the International GNSS Service (IGS), US POLENET-ANET and UKANET networks. The provided archive contains raw and corrected time series for the effect of elastic deformation-induced from the RACMO surface mass balance (SMB) model with a 5.5 km horizontal resolution (van Wessem et al. 2016). This data is used in the manuscript "GPS-observed elastic deformation due to surface mass balance variability in the Southern Antarctic Peninsula" to study how modelled elastic deformation due to SMB variation can explain vertical land motion observed derived from GPS signals.</p>
Investigation of New Particle Formation mechanisms and aerosol processes at the Marambio Station, Antarctic Peninsula
<p>This dataset belongs to : <strong>Investigation of New Particle Formation mechanisms and aerosol processes at the Marambio Station, Antarctic Peninsula</strong></p> <p><em>Quéléver, L. L. J., Dada, L., Asmi, E., Lampilahti, J., Chan, T., Ferrara, J. E., Copes, G. E., Pérez-Fogwill, G., Barreira, L., Aurela, M., Worsnop, D. R., Jokinen, T., and Sipila, M.: Investigation of New Particle Formation mechanisms and aerosol processes at the Marambio Station, Antarctic Peninsula, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-1063, in review, 2022</em></p> <p> </p> <p>The Mass spectrometry and particle data were the result of a measurement campaign performed at the Marambio Station, Antarctica during the austral summer 2018 </p> <p>The data set includes:</p> <p>- Time series for sulfuric acid, methane sulfonic acid and iodic acid concentration </p> <p>- Mass defect data for a new particle formation case study on 16-02-2018</p> <p>- Time series for nucleation rates </p> <p>- Time series for formation rates </p> <p>Please refer to the individual *README.txt files and the corresponding publication.</p> <p>For further information please contact the corresponding author: Lauriane L.J. Quéléver: Lauriane.quelever@helsinki.fi</p>
Figure 6. 24 h in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 6. 24 h rose plots of leopard seal dive activity by hour of day from the parametric data set. Red arrows represent the mean vector of dive activity. (A) all dives pooled from the 2010 season (n = 6,017) from three seals (4OR, 9OR, and 390G). (B) Activity for leopard seal 4OR (n = 2,292 dives) was significantly different from the 2010 mean and the other two seals; (Watson's two sample tests, P <0.05). (C) Activity for leopard seal 9OR (n = 2,283 dives) was significantly different from the 2010 mean and the other two seals (Watson's two sample tests, P <0.001). (D) Activity for leopard seal 390G (n = 1,442 dives) was significantly different from the 2010 mean and the other two seals (Watson's two sample tests, P <0.001).
Figure 5. 24 h in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 5. 24 h rose plots of dive activity by hour of day. The red arrows represents the mean vector (direction = time of day, length = mean number of dives) of dive activity (dives/h) for: (A) all dives (n = 40,308). Gray shaded areas represent the crepuscular periods (+1 h from sunset and sunrise) across the study; (B) all dives pooled from the 2010 season (n = 13,373); (C) all dives pooled from the 2011 season (n = 6,545); (D) all dives pooled from the 2014 season (n = 8,723). The null hypothesis that patterns of diel dive activity were equivalent between seasons could not be rejected (Watson's two-sample tests, P> 0.05).
Figure 1 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 1. Cape Shirreff, Livingston Island, Antarctica. The black star in the right pane indicates the location of Cape Shirreff in the western Antarctic Peninsula region.
Figure 4 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 4. Comparison by dive types between (A) behavior predicted from the k-means cluster analysis of time-depth dive records (n = 38,338) and (B) behavior manually scored from animal-borne video dive data (n = 309).
Figure 3 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 3. The mean proportion (with SD whiskers) of dives that were classified into each dive type (1–4) for all dives in the cluster data set (n = 38,338).
Figure 2 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 2. (A) Empirical haul-out probability distributions for leopard seals at Cape Shirreff based on 209 haul outs from 18 animals in January and February from 2008 to 2014. (B) A polynomial linear regression (solid line) which predicts haul-out probability based on time (h) from local apparent noon; 95% confidence intervals (dashed lines).
Linked collectors and determiners for: Sea spiders (Arthropoda, Pycnogonida) from ten recent research expeditions to the Antarctic Peninsula, Scotia Arc and Weddell Sea - data.
Natural history specimen data linked to collectors and determiners held within, "Sea spiders (Arthropoda, Pycnogonida) from ten recent research expeditions to the Antarctic Peninsula, Scotia Arc and Weddell Sea - data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1e7b6980-0842-4c4a-8b14-541b95d2ed3c">https://bionomia.net/dataset/1e7b6980-0842-4c4a-8b14-541b95d2ed3c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1e7b6980-0842-4c4a-8b14-541b95d2ed3c">https://gbif.org/dataset/1e7b6980-0842-4c4a-8b14-541b95d2ed3c</a>. Formatted as a Frictionless Data package.
West Antarctic Peninsula (WAP) MITgcm-REcoMv2 outputs for 1991 (T, S, DIC, TA, DIN, Chl, air-sea CO2 flux)
<p>This dataset contains the netCDF files for the year 1991 of dissolved inorganic nitrogen (DIN), dissolved inorganic carbo (DIC), total alkalinity (TA), diatom chlorophyll concentration (DiaPhyto), small phytoplankton chlorophyll (SmPhyto), air-sea CO<sub>2</sub> fluxes (CO2flx), ocean temperature (Temp) and salinity (Salt) for the MITgcm-REcoMv2 ocean circulation and biogeochemistry model implemented for the West Antarctic Peninsula. Latitude and longitude for all variables are contained in the grid.glob.nc file.</p> <p>Files names start with the variable name, followed by the month and year. They are saved either as monthly means, as 10 day means, or 5 day means.</p> <p>The units for each variable are as follows:</p> <p>DIN: mmol/m<sup>3</sup></p> <p>DIC: mmol/m<sup>3</sup></p> <p>TA: mmol/m<sup>3</sup></p> <p>DiaPhyto: mg/m<sup>3</sup></p> <p>SmPhyto: mg/m<sup>3</sup></p> <p>CO2flx: mmol/s</p> <p>Temp: ˚C</p> <p>Results from this simulation are published in:</p> <p><strong><span>SCHULTZ, C., </span></strong><span>DONEY, S.C., ZHANG, W.G., REGAN, H.C., HOLLAND, P., MEREDITH, M., STAMMERJOHN, S.</span><strong><span> </span></strong><span>Modeling of the Influence of Sea Ice Cycle and Langmuir Circulation on the Upper Ocean Mixed Layer Depth and Freshwater distribution at the West Antarctic Peninsula. Journal of Geophysics Research Oceans, 125, 8, 2020. doi:10.1029/2020JC016109</span></p> <p><strong><span>SCHULTZ, C., </span></strong><span>DONEY, S.C., HAUCK, J., KAVANAUGH, M.T., SCHOFIELD, O.</span><strong><span> </span></strong><span>Modeling Phytoplankton Blooms and Inorganic Carbon Responses to Sea-Ice Variability in the West Antarctic Peninsula. Journal of Geophysical Research Biogeosciences, 126, 4, 2021. doi: 10.1029/2020JG006227</span></p> <p> </p>
Supplementary material_Paleoenvironment of the Cerro Negro Formation (Aptian, Early Cretaceous) of Snow Island, Antarctic Peninsula
<p>Supplementary material of the article entitled "Paleoenvironment of the Cerro Negro Formation (Aptian, Early Cretaceous) of Snow Island, Antarctic Peninsula", submitted to Anais da Academia Brasileira de Ciências</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Landsat-8 Group 2
<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. 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>Alongside Landsat-8 Group 1, this dataset provides the 17,571 polygons from L8 imagery.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Sentinel-2 Group 2
<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. 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>Alongside Sentinel-2 Group 1, this dataset provides the 23,389 polygons from S2 imagery.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Landsat-8 Group 1
<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. 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>Alongside Landsat-8 Group 2, this dataset provides the 17,571 polygons from L8 imagery.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Sentinel-2 Group 1
<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. 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>Alongside Sentinel-2 Group 2, this dataset provides the 23,389 polygons from S2 imagery.</p>
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.