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3,592 results for “Grid”
Aboveground net primary productivity data for Saddle grid, 1992 - ongoing.
Total aboveground live vascular biomass was clipped from 50x20 cm plots in areas near the saddle grid permanent plots on Niwot Ridge to measure net primary productivity. NDVI measurements were also included in some years.
WSC - Gridded sample points at Wibu field site including yield, soil texture, water table depth, and estimated soil water retention parameters
A variety of data from gridded sampling points at the Wibu field site. The gridded sampling scheme is described in the Point Locations dataset. This dataset includes 2012 and 2013 absolute and normalized yield, soil textural characteristics (organic content, porosity, bulk density, particle size metrics, % sand/silt/clay), a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages), and soil water retention parameters estimated using the Rosetta pedotransfer function. It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
Snow depth data for Saddle grid, 1992 - ongoing.
The depth of snow was measured at 88 points on the saddle grid. The 500 x 350 m study area (17.5 ha) consisted of a grid of 8 rows of stakes in an east/west direction and 11 rows of stakes in a north/south direction (for a total of 88 stakes). The stakes were located 50 m apart. Each stake was given a point identification number starting with 1 in the southwest corner and progressing in an easterly direction for each of the east/west rows so that if head of this file represented the north compass point and the tail represented the south compass point, then the grid would look like this: 71 72 73 74 75 76 77 78 79 80 801(=80A) 61 62 63 64 65 66 67 68 69 70 701(=70A) 51 52 53 54 55 56 57 58 59 60 601(=60A) 41 42 43 44 45 46 47 48 49 50 501(=50A) 31 32 33 34 35 36 37 38 39 40 401(=40A) 21 22 23 24 25 26 27 28 29 30 301(=30A) 11 12 13 14 15 16 17 18 19 20 201(=20A) 1 2 3 4 5 6 7 8 9 10 101(=10A) Note that stakes along the east boundary of the grid, i.e. those ending with the "A", were given new designations to facilitate incorporation of the data into the Saddle GIS. Snow depths at each of the stakes were recorded on a weekly to biweekly basis throughout the period during which snow accumulation existed on the Saddle.
Plant species composition data for Saddle grid, 1989 - ongoing.
Permanent 1 m^2 vegetation plots were established near each of the 88 Saddle grid stakes in 1989 by Marilyn Walker, who led the sampling effort until 1997. To estimate plant canopy cover, point quadrat measurements have been made at irregular intervals from 1989 to the present (1989, 1990, 1995, 1997, 2006, 2008 and yearly from 2010 onward). The point-quadrat technique used for sampling was described in Spasojevic et al. (2013) and Auerbach (1992). Auerbach, N. 1992. Effects of road and dust disturbance in minerotrophic and acidic tundra ecosystems, northern Alaska. University of Colorado, Boulder, Colorado, USA. Spasojevic, Marko J, William D Bowman, Hope C Humphries, Timothy R Seastedt, and Katharine N Suding. Changes in alpine vegetation over 21 years: Are patterns across a heterogeneous landscape consistent with predictions?” Ecosphere 4, no. 9 (2013): 1–18. https://doi.org/10.1890/es13-00133.1.
Global monthly catches from tuna surface fisheries by 1° grid (1958-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries that use fishing gears set at the water's surface. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1958-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.</p> <p>Geo-referenced catch data from tuna surface fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 1° grid area of longitude and latitude, and taxon.</p> <p>The dataset encompasses 42 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 14 species of tunas, 9 species of billfish, 4 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 12 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.</p> <p>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries using surrounding nets, gillnets, entangling nets, and pole-and-lines from over 70 fishing fleets across 69 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than six decades.</p>
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains capacity factors timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000--2016. It has been generated using <a href="https://www.renewables.ninja">Renewables.ninja</a> and is based on MERRA-2 reanalysis data. For each of the ~2700 onshore location, it contains one time series for onshore wind turbines and five time series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops. For each of the ~2800 offshore location there is one timeseries for offshore wind turbines.</p> <p>Two GeoTIFF files contain spatial information of onshore and offshore locations. For each of the three technologies -- onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDF files are linked through unique IDs for all locations.</p> <p>This data serves as input data to euro-calliope, a model of the European electricity system.</p> <p>The following parameters have been used to generate the timeseries:</p> <pre><code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code></pre> <p>CHANGELOG:</p> <p>Version 3 (2022-05-18)</p> <p>* Update spatial scope to include Iceland and its offshore EEZ.<br> * Update temporal scope to include 2017 and 2018.</p> <p>Effect of increasing spatial scope is a slight change in the spatial position of the data points.</p> <p>Version 2 (2020-06-18)</p> <p>* Add time series for rooftop PV with different orientations.</p>
German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid
<h1>Overview</h1> <p>These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.</p> <p>The base rasters from DWD are:</p> <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> <p>To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.</p> <p>Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.</p> <p>The following image illustrates the generation of the refined rasters on a small example window:</p> <p></p>
Dataset of "Smart Grids Transmission Network Testbed: Design, Deployment, and Beyond"
<p>Our test environment incorporates a unique blend of physical, emulated, and virtualized<br>components, spanning from electrical substations to SCADA systems,<br>thereby offering a versatile platform for testing against cyber threats, facilitating<br>educational programs, and supporting advanced traffic simulation. Key findings<br>from our deployment highlight the testbed’s effectiveness in identifying vulnerabilities,<br>enhancing cybersecurity measures, and providing valuable hands-on<br>learning experiences. The integration of such diverse components not only exemplifies<br>a significant step forward in testbed design but also showcases its potential<br>in fostering innovation and security in the power sector. Through detailed comparisons<br>with existing testbeds, we underscore our testbed’s distinct features<br>and its contribution to bridging the gap in current methodologies, setting a new<br>benchmark for future developments in smart grid testing and education.</p>
Early EASE-GRID Sea Ice Age, 1978-1983
<p>Early spin-up period Arctic sea ice age data for 1978 through 1983. This product augments the NSIDC sea ice age product: "EASE-Grid Sea Ice Age, Version 4.1" (Tschudi et al., 2019a), which begins in January 1984. See the main product website for complete documentation. The age is estimated via Lagrangrian tracking based on the NSIDC sea ice motion product (Tschudi et al., 2019b), whose source data is primarily passive microwave brightness temperatures and drifting buoys. Age is estimated weekly as annual age categories. Values are: 1 for "first-year ice", ice that is 0-1 years old, and so on for older ice. The ice is "aged" once each year during the week of the annual sea ice minimum extent, generally sometime in September. </p> <p>In this product, the initialization of the field begins with the first available data in late-October 1978. For the existing ice at that time, age is initialized at the start of the product with age=1. The first week of the data, because it is after the minimum, the age of existing ice is augmented to age=2 and new ice is given age=1. So, the first field in 1978 has only two age categories of 1 (0-1 years old) or 2 (1-2 years old) and this continues through 1978. This means that the age of the ice that formed between the minimum in September and the beginning of the data in late-October 1978 is overestimated by one year. In subsequent years, the oldest ice category will continue to overestimate some of the ice pack until that initial ice either: (1) melts, (2) is transported out of the Arctic, or (3) reaches the maximum age in the product (16 years).<br> <br> Much of the the existing ice in 1978 may be older than 1-2 years old as ice may stay in the Arctic for 5 or more years, but the data availability and the Lagrangian methodology cannot give a specific until the product is fully "spun up". For each subsequent year, a one-year older age category is added in the week of each year's extent minimum. Note that due to the assumption made at the beginning of the product in 1978, the oldest ice category may overestimate the true age of some parcels by one year. </p> <p>Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik. (2019a). EASE-Grid Sea Ice Age, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/UTAV7490FEPB. Date Accessed 02-20-2023.</p> <p>Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik. (2019b). Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/INAWUWO7QH7B.</p>
MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0, Version 1.1.1 (JJA 1980-2022)
<p>This data set offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS).</p> <p>Included in this archive are files for the June-July-August (JJA) summer months during 1980-2022, formatted as a separate file for each year.</p> <p>Version 1.1 includes data for 2021-2022 to supplement the original 1980-2020 dataset from version 1.</p> <p>Version 1.1.1 corrects the 2022 file to include data for 2022-08-24 that was missing in version 1.1.</p>
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories
<p>CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories of anthropogenic CO2 emissions developed in the framework of the CoCO2 project (<a href="https://coco2-project.eu/">https://coco2-project.eu/</a>). CoCO2-MOSAIC 1.0 provides gridded (0.1˚×0.1˚) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors: energy_s (super-emitting sources above 7.9e-6 kg/m2/s), energy_a (average emitters), manufacturing, settlements, transport, aviation land/take-off (LTO) and other. The regional inventories included are CAMS-GHG-REG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (South-East Asia) and VULCAN 3.0 (USA). EDGAR 6.0 and CAMS-GLOB-SHIP 3.1 are used for gap-filling missing sectors and regions. CAMS-GLOB-TEMPO 3.1 is used for temporal disaggregation of inventories providing annual emissions. Aviation emissions from climb, descent, and cruise are not covered by regional inventories and are provided as a separate file. Note that 2015 is the only year when all regional inventories are simultaneously available. </p> <p>Compared to global inventories, CoCO2-MOSAIC 1.0 includes all the regional information available without the limitation of providing spatially consistent emissions. Therefore, CoCO2-MOSAIC 1.0 can be used as a global baseline inventory due to the higher level of detail, higher spatial resolution, and country-specific information included by regional inventories. </p> <p>For further details see Urraca et al. 2023 (ESSD submitted). The paper (i) describes the CoCO2-MOSAIC methodology and (ii) uses the mosaic to inter-compare the most widely used global inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0/7.0, ODIAC v2020b, and CEDS v2020_04_24.</p>
25-meter elevation lattice grid, Niwot Ridge LTER Project Area, Colorado
25-meter lattice made from the Niwot Ridge LTER TIN model (ltertin). This dataset was made to support hierarchical GIS databases at the Niwot Ridge LTER. Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
3.23-meter elevation lattice grid, Martinelli Snowfield, Niwot Ridge LTER, Colorado
Martinelli snow field lattice. This dataset is part of the Martinelli grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
1-meter elevation lattice grid, Martinelli Snowfield, Niwot Ridge LTER, Colorado
Resampled version of Martinelli snow field lattice grid (martlat) with finer resolution. This dataset is part of the Martinelli grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
Hybrid gridded demographic data for the world, 1950-2020
<p>This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.</p> <p>This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.</p> <p>Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)</p> <p><strong>Method - demographic fractions</strong></p> <p>Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.</p> <p>To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:</p> <p><span class="math-tex">\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)</span></p> <p> </p> <p>Where:</p> <p>- <span class="math-tex">\(\delta_{year,\ country,age}^{\text{wpp}}\)</span> is the ratio of change in demographic for a given age and and country from the UN WPP dataset.</p> <p>- <span class="math-tex">\(f_{year,\ country,age}^{\text{wpp}}\)</span> is the fraction of population in the UN WPP dataset for a given age band, country, and year.</p> <p>- <span class="math-tex">\(f_{2010,country,age}^{\text{wpp}}\)</span> is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.</p> <p> </p> <p>The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.</p> <p>For each subset of cells corresponding to a given country <em>c</em>, the fraction of population in a given age band is calculated as:</p> <p><span class="math-tex">\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)</span></p> <p>Where:</p> <p>- <span class="math-tex">\(f_{year,c,age}^{\text{gpw}}\)</span> is the fraction of the population in a given age band for given year, for the grid cell <em>c</em>.</p> <p>- <span class="math-tex">\(f_{2010,c,age}^{\text{gpw}}\)</span> is the fraction of the population in a given age band for 2010, for the grid cell <em>c</em>.</p> <p>The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.</p> <p> </p> <p><strong>Method - demographic totals</strong></p> <p>Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.</p> <p>The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.</p> <p>Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.</p> <p> </p>
MeteoSerbia1km: the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period
<p>MeteoSerbia1km is the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea level pressure, and total precipitation. Besides daily summaries, it contains monthly and annual summaries, daily, monthly, and annual long term means (LTM). Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology based on Random Forest and using nearest observations and distances to them as spatial covariates, together with environmental covariates.</p> <p>Complete script in R and datasets used for modelling, tuning, validation, and prediction of daily meteorological variables are available <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km">here</a>.</p> <p>If you discover a bug, artifact or inconsistency in the MeteoSerbia1km maps, or if you have a question please use <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km/issues">this channel</a>.</p> <p>File naming convention of .zip files and containing MeteoSerbia1km files:</p> <ul> <li>Daily summaries per year: day_<em>yyyy</em>_<em>proj</em>.zip <ul> <li><em>var</em>_day_<em>yyyymmdd</em>_<em>proj</em>.tif</li> </ul> </li> <li>Monthly summaries: mon_<em>proj</em>.zip <ul> <li><em>var</em>_mon_<em>yyyymm</em>_<em>proj</em>.tif</li> </ul> </li> <li>Annual summaries: ann_<em>proj</em>.zip <ul> <li><em>var</em>_ann_<em>yyyy</em>_<em>proj</em>.tif</li> </ul> </li> <li>Daily, monthly and annual LTM: ltm_<em>proj</em>.zip <ul> <li>daily LTM: <em>var</em>_ltm_day_mmdd_<em>proj</em>.tif</li> <li>monthly LTM: <em>var</em>_ltm_mon_mm_<em>proj</em>.tif</li> <li>annual LTM: <em>var</em>_ltm_ann_<em>proj</em>.tif</li> </ul> </li> </ul> <p>where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection - wgs84 or utm34</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> </p>
MAGIC Deliverable D6.5: Shale gas development in the EU 10Km radius well grid scenario
<p>Geo data set of escenario of shale gas implementation in Europe. Developed for WP 6 of the <a href="https://magic-nexus.eu/">MAGIC-Nexus project</a>. It derives from a Geomodel of wells and a database of shale gas played developed by the <a href="https://ec.europa.eu/jrc/sites/jrcsh/files/pl1-britze.pdf">EUOGA </a>project. </p> <p><strong>DB Fields------------------------------------------</strong></p> <p>WELLid: Id of the well</p> <p>RBid: Id of the River Basin in which the well is located</p> <p>RBtxtINT: Name of the River Basin - English</p> <p>RBtxt: Name of the River Basin - Country's Name</p> <p>GWid: Groundwater basin ID</p> <p>PADid: ID of the extraction pad</p> <p>Formation: Shale formation</p> <p>Age: of the well </p> <p>Depth_avg: Average depth of the shale (inherited)</p> <p>Mature_avg: Average matureness of the shale (inherited)</p> <p>TOC_avg: Average Organic content of the shale (inherited)</p> <p>ThickGross: Gross Thickness of the shale play in meters (inherited)</p> <p>ThickNet_m: Net Thickness of the shale play in meters (inherited)</p> <p>EUOGA_Basi: Basin of the well according ot the EUOGA project database (inherited)</p> <p>Basin_inde: Id of the shale basin (inherited)</p> <p>NGS_Basin: Id of the BAsin as stated by the national geological service</p> <p>Shale_CP: Shale country </p> <p>RF_Maturit: Reference Maturity</p> <p>RF_Depth: Reference Depth</p> <p>CNTR_CODE, Country code</p> <p>NUTS_NAME: Name of the NUTS region</p> <p>NUTid: ID of the NUTS region</p> <p>x,y Coordinates of the well</p>
ICESat-2 monthly gridded winter Arctic sea ice thickness
<p>Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below.</p> <p>Temporal range: November 2018 - April 2019, October 2019 to April 2020.</p> <p>Data: A single netCDF file is included for each month. Variables include:</p> <ul> <li>Sea ice freeboard (from ATL10)</li> <li>Snow depth (redistributed NESOSIM)</li> <li>Snow density (redistributed NESOSIM)</li> <li>Bulk sea ice density</li> <li>Sea ice type (from OSI SAF)</li> <li>Sea ice thickness uncertainty</li> <li>Mean day of month in a given grid cell</li> <li>Number of freeboard segments in a given grid cell.</li> </ul> <p>A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication.</p> <p>Key changes from version 1 (Petty et al., 2020) to version 2 include:</p> <ul> <li>Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020).</li> <li>Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (<a href="https://github.com/akpetty/NESOSIM">https://github.com/akpetty/NESOSIM</a>).</li> <li>Use of all three strong beams (instead of just strong beam #1).</li> </ul> <p>The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: <em>gs://sea-ice-thickness-data/v2/</em></p>
NAPv1.0: A seasonal hydrographic gridded data set for the Northern Antarctic Peninsula, Southern Ocean
<p>The Northern Antarctic Peninsula (NAP) climatology version 1 (NAPv1.0) was built by optimally interpolate hydrographic data sets from the CTD, MEOP and Argo floats profiles sampled in the NAP and adjacent regions during the period of 1990-2019. The database consists of data from the World Ocean Database, Pangaea, Hutchinson et al. (2020), Brazilian High Latitude Oceanography Group (GOAL; http://goal.furg.br/), Marine Mammals Exploring the Oceans Pole to Pole consortium (MEOP), and Argo floats. The climatology has outputs for summer (Jan-Mar), autumn (Apr-Jun), winter (Jul-Sep) and spring (Oct-Dec). The profiles were first linearly interpolated onto 90 depth levels, and then optimally interpolated in space using a grid of ~10 km resolution. The grid spacing is 0.09˚ along latitudes and 0.2˚ along longitudes (i.e., 0.09˚ latitude x 0.09˚/cos(63˚S) longitude, where 63˚S is the mean latitude of our domain). A series of tests were made to find the appropriate smoothing lengthscale and the a priori relative error in order to find a balance between smoothness and feature representativeness. The final smoothing lengthscale (i.e. the radius of influence of the interpolation) chosen was 1˚ in latitude and longitude, and the a priori relative error allowed was set to 0.2 for the objective interpolation algorithm. The same constants were set for all depth levels and all variables. The regions where the mapping relative error was higher than 0.5 were excluded. The NAPv1.0 climatology can be used for several applications, including input data for ocean and climate models initialization/assessment and ocean reanalysis evaluation, as well as to produce and reconstruct biogeochemical properties. The NAPv1.0 climatology represents the ocean mean-state for the NAP for the end of the 20th and early 21st-century.</p> <p> </p> <p><strong>Reference: </strong><br> Dotto, T. S., Mata, M. M., Kerr, R., and Garcia, C. A. E.: A novel hydrographic gridded data set for the northern Antarctic Peninsula, Earth Syst. Sci. Data, 13, 671–696, https://doi.org/10.5194/essd-13-671-2021, 2021.</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.