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.
179
datasets available to search
ShareScore release 0.9.0
Dataset results
179 results for “Series 2”
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>
A 2-minute rainfall (12 locations) and discharge time series at the Vallon de Nant catchment, Switzerland, for 2018 summer seasons
<p>The data set contains rainfall time series within the experimental 13.4 km² Vallon de Nant catchment, Switzerland (Michelon et al., 2020), from June 30th to September 23rd 2018 at 12 locations. A network of <em>Pluvimate</em> drop-counting raingauges (www.driptych.com) measured continuously the rainfall intensity at a 2-minute resolution. Operation and characteristics of the raingauges are detailed in Benoit et al. (2018) and Michelon et al. (2020), and the rating curve is described by Ceperley et al. (2018).</p> <p>Description of the files:</p> <ul> <li><em><strong>data.csv</strong></em> contain the rainfall intensities for the observation period, along with the main river discharge measured at the <a href="https://map.geo.admin.ch/?lang=fr&topic=ech&bgLayer=ch.swisstopo.pixelkarte-farbe&layers=ch.swisstopo.zeitreihen,ch.bfs.gebaeude_wohnungs_register,ch.bav.haltestellen-oev,ch.swisstopo.swisstlm3d-wanderwege,KML%7C%7Chttps:%2F%2Fpublic.geo.admin.ch%2FaLKDanGXRPGMpB_D51f2Tg&layers_visibility=false,false,false,false,true&layers_timestamp=18641231,,,,&E=2574619.27&N=1122462.26&zoom=8">outlet</a> over the same 2-minutes time step as the rainfall intensity. We also provide areal rainfall intensity aggregated over the whole catchment:<br> Columns: <ul> <li>year [-]</li> <li>month [-]</li> <li>day [-]</li> <li>hour [-]</li> <li>minute [-]</li> <li>specific discharge 95% inf. [mm/day]: inferior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge 95% sup. [mm/day]: superior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge mean [mm/day]: mean value of the specific discharge over 2 minutes</li> <li>specific discharge median [mm/day]: median value of the specific discharge over 2 minutes</li> <li>P St. #X [mm]: rainfall amount measured at the station X over 2 minutes</li> <li>P stochastic mean [mm/h]: rainfall amount interpolated over the whole catchment over 2 minutes</li> <li>P stochastic std [mm/h]: standard deviation of the stochastic rainfall interpolation, over 2 minutes</li> </ul> </li> <li><strong><em>stations.csv</em></strong> describes the raingauge locations.<br> Columns: <ul> <li>Station ID [-]</li> <li>lon [WGS84]: decimal longitude of the station into WGS84</li> <li>lat [WGS84]: decimal latitude of the station into WGS84</li> <li>E [CH1903]: east coordinate into Swiss Coordinate System</li> <li>N [CH1903]: north coordinate into Swiss Coordinate System</li> <li>elevation [m asl]: altitude of the station in meters above the sea level</li> <li>data in 2017 [-]: flag if the station was working over the 2017 observation period</li> <li>data in 2018 [-]: flag if the station was working over the 2018 observation period</li> </ul> </li> <li><em><strong>rainfall_viewer.m</strong></em> is a <em>MatLab</em> script (created with <em>MatLab 2017b</em>) which allows the joint visualization of the rainfall intensities and river discharge. It produces a composite figure with the following plots: <ul> <li>On top the general hydrograph over the whole observation period [mm/day]. The red dashed lines mark out period that the other plots are focus on. The shaded orange areas correspond to when the river stage data was not available.</li> <li>Below, the zoomed hydrogram show a detailed view of the river discharge (and uncertainty). In case a river reaction is associated, the discharge event is marked out by red dashed lines. Between these vertical lines is drawn a line joining the initial and final baseflow, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff part.</li> <li>In the middle a zoomed magnification of the hydrograph that shows a detailed view of the discharge in the river [mm/day]. When a river response is associated, the discharge event is marked with dashed red lines. Between these vertical lines a line joining the initial and final baseflow is drawn, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff.</li> <li>At the bottom are shown the rainfall recorded by each of the 12 rain gauges (the y-axis scale between 2 stations is about 20 mm/h). The rainfall event is marked out by green dashed lines.</li> <li>Above is shown the rainfall amount (and uncertainty) interpolated over the catchment using the stochastic method. The rainfall event is marked out by green dashed lines.</li> <li>On the left, a map with the 12 raingauge locations show the total amount of rainfall recorded by each station during the event (a red cross shows missing data).<br> <br> It is possible to zoom in the plots by clicking with the left and right mouse buttons to define respectively the starting and ending of the visualization window. The middle button defines a third time reference used to identify rainfall intensity peaks or discharge peaks. Statistics concerning the visualization period are displayed on the MatLab console.<br> Pressing [enter] will save the figure into a PNG file named with the starting and ending dates of the visualization window.</li> </ul> </li> <li><strong><em>Q_stats.m </em></strong>is a MatLab function used by the main code rainfall_viewer.m</li> <li><strong><em>print_figure.m </em></strong>is a MatLab function used by the main code rainfall_viewer.m</li> <li><strong>data.mat</strong> is a MatLab data file with all data required by the main code rainfall_viewer.m</li> </ul>
Sentinel-5P Methane Density at 2 km from 2021-12 to 2023-11 Monthly Aggregation Time-series Reconstructed
<p><strong>General Description</strong></p><p>The <i>monthly aggregated Methane Volume Mixing Ratio </i>dataset is derived from Sentinel-5P to generate a time-series reconstructed monthly aggregated map. The dataset time spans from December 2021 to November 2023 and provides data that covers the entire globe. The mission is still underway and expected to update periodically.</p><p>For more info about the s5p Methane product see: <a href="">https://maps.s5p-pal.com/ch4/</a>.</p><p>The dataset can be used in many applications like emission tracing, livestock monitor, and greenhouse gas monitor.</p><ul><li><strong>Monthly time-series:</strong></li></ul><p>Methane monthly average value December 2021 – November 2023. Derived using the <a href="https://eumap.readthedocs.io/en/latest/">eumap</a> and <a href="https://github.com/openlandmap/scikit-map">scikitmap</a> package in Python . We derived three standard statistics: (1) 10th percentile (p10), median (p50), and 90th percentile (p90).</p><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> December 2021 – November 2023</li><li><strong>Type of data:</strong> Methane Volume Mixing Ratio (Unit: ppbv)</li><li><strong>How the data was collected or derived:</strong> Derived from 2km Sentinel-5P Menthane using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> and <a href="https://eumap.readthedocs.io/en/latest/">eumap </a>Python package.</li><li><strong>Statistical methods used:</strong> percentiles 10, 50, and 90.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset is not completed gap-filled. Certain areas have no data in the whole time series</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -61.9966697, 180.0000072, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/60 d.d. = 0.016666667 (2km)</li><li><strong>Image size:</strong> 21,600 x 8,962</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p><ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li></ul><p><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li><strong>generic variable name:</strong> ch4.vmr = methane density methane volume mixing ratio</li><li><strong>variable procedure combination:</strong> m.seacov = monthly aggregated and gap filled by seasonal convolution</li><li><strong>Position in the probability distribution / variable type:</strong> p10/p50/p90 = 10th/50th/90th percentile</li><li><strong>Spatial support:</strong> 2km</li><li><strong>Depth reference:</strong> a = above surface</li><li><strong>Time reference begin time:</strong> 20211201 = 2021-12-01</li><li><strong>Time reference end time:</strong> 20231131 = 2023-11-31</li><li><strong>Bounding box:</strong> go = global (without Antarctica)</li><li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li><li><strong>Version code:</strong> v20230628 = 2023-12-08 (creation date)</li></ol>
Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 2: 2005 - 2009) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7434376">https://zenodo.org/record/7434376</a></p>
ERA5-Land weekly: Air temperature at 2 meter above surface, weekly time series for Europe at 1 km resolution (2016 - 2020)
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Air temperature (2 m):<br> Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Processing steps:<br> The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.<br> Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of air temperature (2 m).</p> <p>File naming:<br> Average of daily average: <code>era5_land_t2m_avg_weekly_YYYY_MM_DD.tif</code><br> Max of daily max: <code>era5_land_t2m_max_weekly_YYYY_MM_DD.tif</code><br> Min of daily min: <code>era5_land_t2m_min_weekly_YYYY_MM_DD.tif</code></p> <p>The date in the file name determines the start day of the week (Saturday).</p> <p>Pixel value:<br> °C * 10<br> Example: Value 44 = 4.4 °C</p> <p>The QML or SLD style files can be used for visualization of the temperature layers.</p> <p>Coordinate reference system:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 1km</p> <p>Temporal resolution:<br> weekly</p> <p>Time period:<br> 01/01/2016 - 12/31/2020</p> <p>Format: GeoTIFF</p> <p>Representation type: Grid</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Lineage:<br> Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Original ERA5-Land dataset license:<br> <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Other resources:<br> <a href="https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b">https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact:<br> mundialis GmbH & Co. KG, info@mundialis.de</p>
Temperature and wind speed time series on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains weather timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000-2018. It has been generated directly from the MERRA-2 reanalysis dataset.</p> <p>This data serves as an input to the Sector-Coupled Euro-Calliope model and has been generated to match the spatial resolution of the renewable energy generation capacity factors found at https://doi.org/10.5281/zenodo.3891480.</p> <p><em>temperature.nc:</em> air temperature at 2m above ground in degrees C.</p> <p><em>tsoil5.nc</em>: soil temperature at layer 5 in degrees C.</p> <p><em>wind10m.nc</em>: wind speed at 10m above ground in m/s.</p> <p><em>grid.nc:</em> provides the latitude and longitude of each grid (a.k.a. "site") centroid. This data is also given in every other dataset, but is provided here as a lightweight alternative to align other datasets to the same grid spacing.</p> <p> </p> <p><strong>Changelog</strong></p> <p>2024-06-07:</p> <ul> <li>Moved data variables to dataset attributes where the same data was duplicated per gridcell.</li> <li>Added units to file attributes.</li> <li>Updated `tsoil5` variable name from `soil_temperture_5` to `tsoil5`. Now all timeseries data variables have names that match the filename. </li> <li>Updated `tsoil5` variable from Kelvin to degrees C.</li> <li>Updated `tsoil5` variable empty data (when gridcell is not over land) from zero to NaN.</li> <li>Added `grid.nc`.</li> <li>Removed `electricity` variable from `wind10m.nc`, leaving only wind speed as the available timeseries data in the file.</li> </ul>
Series data for the 22PN nonlinear memory effect in the l=2, m=0 mode.
<p>Dataset associated with the preprint arXiv:2407.19017, “Waveform models for the gravitational-wave memory effect: Extreme mass-ratio limit and final memory offset” by Arwa Elhashash and David A. Nichols. It contains the 22 post-Newtonian-order series data for the l=2, m=0 spin-weighted spherical harmonic mode of the gravitational-wave memory signal from an extreme-mass ratio inspiral with nonspinning black holes.</p>
Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 2: 2005 - 2009) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2005 - 2009. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 1000 m</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6342822">https://doi.org/10.5281/zenodo.6342822</a></p>
EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 2: Motivations and objectives
<p>This document is Part 2 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe.</p> <p>EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.</p>
Text-fig. 9. Wataria kvacekii sp. nov., UF 279-24556. a: Wood ring-porous, earlywood with 2–3 rows of wide pores, vessels solitary and in radial multiples of 2, axial parenchyma scanty vasicentric and some apotracheal diffuse-in-aggregates, TS. b: Series of vessel elements with simple perforations, axial parenchyma strands adjacent to vessels, RLS. c: Alternate intervessel pitting, vessel element end walls horizontal, RLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e, f: Rays with tile cells, storied axial parenchyma, some strands chambered crystalliferous, TLS. g: Detail of ray, TLS. h: Storied imperforate elements. Scale bars: 200 µm in a; 100 µm in b, e; 50 µm in c, d, f, h; 20 µm in g. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 9. Wataria kvacekii sp. nov., UF 279-24556. a: Wood ring-porous, earlywood with 2–3 rows of wide pores, vessels solitary and in radial multiples of 2, axial parenchyma scanty vasicentric and some apotracheal diffuse-in-aggregates, TS. b: Series of vessel elements with simple perforations, axial parenchyma strands adjacent to vessels, RLS. c: Alternate intervessel pitting, vessel element end walls horizontal, RLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e, f: Rays with tile cells, storied axial parenchyma, some strands chambered crystalliferous, TLS. g: Detail of ray, TLS. h: Storied imperforate elements. Scale bars: 200 µm in a; 100 µm in b, e; 50 µm in c, d, f, h; 20 µm in g.
Emily Dickinson's Poems: Series #1, #2, and #3
I did the quickest of readings against Emily Dickinson's three series of poems, and, quite frankly, I did not learn a whole lot.
Fig. 8 in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 8. The camenellan tommotiid Dailyatia sp. 1 A sclerites and fragments from the Cambrian Series 2, Stages 3, 4, Shackleton Limestone, Holyoake Range, Transantarctic Mountains, East Antarctica. A. A sclerite, NRM X10006, apical (A1), anterior (A2), oblique posterior (A3), and posterior (A4) views, magnification of radial plicae on the lateral field (A5), magnification of single plica showing the densely stacked growth series of the radial plicae (A6). B. Fragment of A sclerite anterior field, NRM X10007, general view (B1), oblique view showing cross section of plicae (B2). C. Fragment of A sclerite anterior field, NRM X10008, top of specimen (C1), general view (C2), detail showing plicae (C3). D. Fragment of A sclerite with partial lateral and anterior field, NRM X10009. E. Fragment of sclerite lateral field, NRM X10010. F. Fragment of A sclerite lateral field, NRM X10011. G. Fragment of unknown sclerite type, with chaotically distributed plicae, NRM X10012, general view (G1), detail (G2). Scale bars 200 µm.
Fig. 11. The tommotiid Shetlandia multiplicata Wrona, 2004, unknown type sclerite fragments from the Cambrian Series 2, Stages 3, 4 in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 11. The tommotiid Shetlandia multiplicata Wrona, 2004, unknown type sclerite fragments from the Cambrian Series 2, Stages 3, 4, Shackleton Limestone, Holyoake Range, Transantarctic Mountains, East Antarctica. A. NRM X10020, general view (A1), detail showing arched growth series (A2). B. NRM X10021, general view (B1), detail of broken margin (B2). C. NRM X10022, general view (C1), detail of comarginal ribs and broken edge (C2). D. NRM X10023. E. NRM X10024. Scale bars 200 µm, except B2, 100 µm. A–C taken with backscattered electron detector.
Fig. 5 in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 5. The camenellan tommotiid Dailyatia icari sp. nov. C1 sclerites and sclerite fragments from the lower Cambrian Schneider Hills limestone, Schneider Hills, Argentina Range, East Antarctica. A. Dextral sclerite, KUMIP 585059, apical view (A1), view of dorsal (A2) and ventral (A3) surfaces, view of distal (A4) and proximal (A5) edges. B. Sinistral sclerite, KUMIP 585060, apical view (B1), oblique view of proximal edge and dorsal surface (B2), oblique view of distal edge and ventral surface (B3), view of distal edge (B4). C. Dextral sclerite, KUMIP 585061, dorsal (C1), apical (C2), and oblique dextral (C3) views. D. Dextral sclerite, KUMIP 585062, apical (D1), apical-dorsal (D2), ventral (D3), and lateral (D4) views. E. Large fragment from unknown sclerite type, KUMIP 585063. F. Sclerite of unknown chirality, KUMIP 585064, apical (F1) and lateral (F2) views. G. Sclerite fragment from unknown sclerite type, KUMIP 585065, detail showing distinctive pseudoplicae (G1), general view (G2). H. Sclerite of unknown chirality, KUMIP 585066, detail of apertural margin (H1), oblique views of the aperture (H2, H3). I. Fragment of unknown sclerite type showing pseudoplicae, KUMIP 585067. Scale bars 200 µm.
Fig. 2 in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 2. Stratigraphic columns of the sampled sections in the Holyoake Range (HRA) and Churchill Mountains (CM2). Grain size: B, boundstone; G, grainstone; M, mudstone; P, packstone; W, wackestone.
Fig. 1. A in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 1. A. Topographic map of the Central Transantarctic Mountains (adapted from the USGS 2008) with the main geographic features and the sites visited by the Kansas University expeditions (study areas marked by section names GM, 87-L2, M, H, S). The locality GM is the type locality for Dailyatia braddocki, which is close to the location of the CM2 section. The locality M is the type locality of Dailyatia odyssei). B. The area of the Churchill Mountains visited by the Swedish-Australian expedition, showing the location of the CM2 section. C. The area of the Holyoake Range visited by the Swedish-Australian expedition, showing the location of the HRA section. D. Overview of Antarctica showing the extent of the Transantarctic Mountains, including the the location of the Argentina Range, and IC (location on sections unknown) in the Schneider Hills (sampled by the Kansas Expeditions).
Fig. 10. The tommotiid Kennardiidae indet. from the Cambrian Series 2, Stages 3, 4 in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 10. The tommotiid Kennardiidae indet. from the Cambrian Series 2, Stages 3, 4, Shackleton Limestone, Holyoake Range, Transantarctic Mountains, East Antarctica. A. Symmetrical first sclerite type, KUMIP 585077, apical view (A1), oblique view of posterior field (A2), lateral field (A3). B. Third sclerite type, KUMIP 585078, apical (B1) and lateral (B2) views. C. Third sclerite type, KUMIP 585079, apical view (C1), ventral side (C2), dorsal side (C3). D. Third sclerite type, KUMIP 585080, ventral side (D1), apical view (D2). E. Third sclerite type, KUMIP 233002, apical view (E1), detail of dorsal side (E2), detail of ventral side (E3), ventral side (E4). F. Third type of sclerite, KUMIP 585081, ventral side (F1), detail showing plicae on ventral surface (F2), distal edge (F3). G. Second type of sclerite, KUMIP 585082, apical view (G1), oblique view of posterior field (G2), lateral field (G3). H. Third type of sclerite, KUMIP 585083, posterior side (H1), distal edge (H2), apical view (H3). I. Third type of sclerite, KUMIP 585084, ventral side (I1), detail showing plicae on the ventral surface (I2). Scale bars 200 µm, except E2, E3, I2, 100 µm.
Fig. 7. The camenellan tommotiids from from the Cambrian Series 2, Stages 3, 4 in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 7. The camenellan tommotiids from from the Cambrian Series 2, Stages 3, 4, Shackleton Limestone, Transantarctic Mountains, East Antarctica. Dailyatia braddocki Evans and Rowell, 1990, Churchill Mountains (A, B), Dailyatia decobruta Betts in Betts et al., 2019, Holyoake Range (C–F), and Dailyatia cf. odyssei, Holyoake Range (G–I). A. C sclerite, NRM X10001, ventral view (A1), detail showing fold in the margin of the ventral field (A2). B. Sclerite of unknown chirality, NRM X10002, dorsal view (B1), detail showing apex (B2). C. Dextral C sclerite, KUMIP 585071, details showing micro-ornament on the anterior-lateral (C1) and the posterior (C3) fields, apical view (C2). D. Dextral C sclerite, KUMIP 585072, apical view (D1), view of proximal edge and dorsal field (D2). E. Sclerite of unknown chirality, KUMIP 585073, oblique view of ventral field (E1), view of apical area showing perforated apex (E2). F. Sclerite of unknown chirality, KUMIP 585074, dorsal (F1) and oblique apical (F2) views. G. Dextral C sclerite, NRM X10003, view of broken dorsal field (G1), detail showing fine growth series (G2), apex (G3), oblique basal view showing plicae at distal edge (G4). H. Sinistral C sclerite, NRM X10004, apical view (H1), oblique view of distal edge and part of ventral field (H2). I. Unknown sclerite type, NRM X10005, ventral view (I1), detail showing fine growth series (I2). Scale bars 200 µm, except E2, 50 µm.
Fig. 4 in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 4. The camenellan tommotiid Dailyatia icari sp. nov. A sclerites from the lower Cambrian Schneider Hills limestone, Schneider Hills, Argentina Range, Antarctica. A. KUMIP 585054, view of anterior field (A1), enlarged view of the anterior apical area (A2), oblique view of the lateral field (A3), apical view with anterior to the top (A4). B. KUMIP 585055, anterior field of broken sclerite. C. KUMIP 585056 anterior field of broken sclerite. D. KUMIP 585057, view of anterior field (D1), apical view with anterior to the top (D2), lateral view (D3). E. KUMIP 585058, view of anterior field (E1), apical view with anterior to the top (E2). Scale bars 200 µm.
Fig. 3 in Camenellan tommotiids from the Cambrian Series 2 of East Antarctica: Biostratigraphy, palaeobiogeography, and systematics
Fig. 3. Palaeobiogeographic maps of the distribution of Dailyatia and other camenellan tommotiids from East Gondwana (dark grey). New occurrences described in this paper in bold. Maps show the Cambrian (A) Terreneuvian Series to basal Series 2, Stage 3 (i.e., upper range of the Micrina etheridgei Zone for South Australia, Betts et al. 2016, 2017) and (B) Series 2 (from the base of the Dailyatia odyssei Zone for South Australia, Betts et al. 2016, 2017). Data for previous occurrences: 1 Laurie 1986; 2 Skovsted et al. 2015; 3 Betts et al. 2016; 4 Betts et al. 2017; 5 Betts et al. 2019; 6 Evans and Rowell 1990; 7 Wrona 2004. Abbreviations: CTM, Central Transantarctic Mountains; KGI, King George Island; AR, Argentina Range. Map adapted from Torsvik and Cocks (2013) and Yang et al. (2015).
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.