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Caravan MultiMet (Part 2, Forecasts): Extending Caravan with Multiple Weather Nowcasts and Forecasts
<p>Caravan MultiMet is a novel extension to Caravan, focusing on enriching the meteorological forcing data. Our extension adds three precipitation nowcast products (CPC, IMERG v07 Early, and CHIRPS) and three weather forecast products (ECMWF IFS HRES, GraphCast, and CHIRPS-GEFS). Since all data is kept in it's original time zone (UTC+0) and the ERA5-Land data in the original Caravan data set is shifted to local time of each gauge, we also include ERA5-Land reanalysis data in this extension, matching the UTC-0 timezone for all gauges of the other forcings. This part of the extension includes the <strong>forecast</strong> products.</p> <p>The inclusion of diverse data sources, particularly weather forecasts, enables more robust evaluation and benchmarking of hydrological models, especially for real-time forecasting scenarios. To the best of our knowledge, this extension makes Caravan the first open large-sample hydrology dataset to incorporate weather forecast data.</p> <p>The data is also publicly available on Google Cloud Platform (GCP), and we provide below a colab with an example of how to access it which does not require downloading the entire dataset.</p> <p>Additional resources:</p> <ul> <li>The <a href="https://github.com/kratzert/Caravan">Caravan GitHub repository</a> includes further information and links to other extensions.</li> <li>The original <a href="https://www.nature.com/articles/s41597-023-01975-w">Caravan paper</a></li> </ul> <p>------</p> <p>Channel log:</p> <ul> <li>21 November 2024: Version 1.1 - Fixed bug in the FAO Penman-Monteith potential evaporation - values are now clipped to 0 (previously, some values were negative). Only the ERA5-Land dataset was changed.</li> </ul>
Caravan MultiMet (Part 1, Nowcasts): Extending Caravan with Multiple Weather Nowcasts and Forecasts
<p>Caravan MultiMet is a novel extension to Caravan, focusing on enriching the meteorological forcing data. Our extension adds three precipitation nowcast products (CPC, IMERG v07 Early, and CHIRPS) and three weather forecast products (ECMWF IFS HRES, GraphCast, and CHIRPS-GEFS). Since all data is kept in it's original time zone (UTC+0) and the ERA5-Land data in the original Caravan data set is shifted to local time of each gauge, we also include ERA5-Land reanalysis data in this extension, matching the UTC-0 timezone for all gauges of the other forcings. This part of the extension includes the <strong>nowcast</strong> products.</p> <p>The inclusion of diverse data sources, particularly weather forecasts, enables more robust evaluation and benchmarking of hydrological models, especially for real-time forecasting scenarios. To the best of our knowledge, this extension makes Caravan the first open large-sample hydrology dataset to incorporate weather forecast data.</p> <p>The data is also publicly available on Google Cloud Platform (GCP), and we provide below a colab with an example of how to access it which does not require downloading the entire dataset.</p> <p>Additional resources:</p> <ul> <li>The <a href="https://github.com/kratzert/Caravan">Caravan GitHub repository</a> includes further information and links to other extensions.</li> <li>The original <a href="https://www.nature.com/articles/s41597-023-01975-w">Caravan paper</a></li> </ul> <p>------</p> <p>Channel log:</p> <ul> <li>21 November 2024: Version 1.1 - Fixed bug in the FAO Penman-Monteith potential evaporation - values are now clipped to 0 (previously, some values were negative). Only the ERA5-Land dataset was changed.</li> </ul>
BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars
<p><em>BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 151 European weather radars in 18 countries, with varying coverage from 2012 to 2023. These data were created by processing weather radar data - provided by the Operational Programme for the Exchange of Weather Radar Information (<a href="https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/">OPERA</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=baltrad/">Aloft bucket</a>.</p> <div> <div>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</div> </div> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 2 radars in Belgium.</li> <li><strong>ch.tgz</strong>: VPTS data from 5 radars in Switzerland.</li> <li><strong>cz.tgz</strong>: VPTS data from 2 radars in Czechia.</li> <li><strong>de.tgz</strong>: VPTS data from 20 radars in Germany.</li> <li><strong>dk.tgz</strong>: VPTS data from 5 radars in Denmark.</li> <li><strong>ee.tgz</strong>: VPTS data from 2 radars in Estonia.</li> <li><strong>es.tgz</strong>: VPTS data from 15 radars in Spain.</li> <li><strong>fi.tgz</strong>: VPTS data from 13 radars in Finland.</li> <li><strong>fr.tgz</strong>: VPTS data from 26 radars in France.</li> <li><strong>hr.tgz</strong>: VPTS data from 7 radars in Croatia.</li> <li><strong>il.tgz</strong>: VPTS data from 1 radar in Israel.</li> <li><strong>nl.tgz</strong>: VPTS data from 3 radars in the Netherlands.</li> <li><strong>no.tgz</strong>: VPTS data from 11 radars in Norway.</li> <li><strong>pl.tgz</strong>: VPTS data from 8 radars in Poland.</li> <li><strong>pt.tgz</strong>: VPTS data from 3 radars in Portugal.</li> <li><strong>se.tgz</strong>: VPTS data from 22 radars in Sweden.</li> <li><strong>si.tgz</strong>: VPTS data from 2 radars in Slovenia.</li> <li><strong>sk.tgz</strong>: VPTS data from 4 radars in Slovakia.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands
<p><em>UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 24 weather radars in Belgium, Germany and the Netherlands, with varying coverage from 2008 to 2023. These data were created by processing weather radar data - provided by the Royal Meteorological Institute of Belgium (<a href="https://www.meteo.be/">RMI</a>), German Meteorological Service (<a href="https://www.dwd.de/">DWD</a>) and Royal Netherlands Meteorological Institute (<a href="https://www.knmi.nl/">KMNI</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=uva/">Aloft bucket</a>.</p> <p>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 3 radars in Belgium.</li> <li><strong>de.gz</strong>: VPTS data from 18 radars in Germany.</li> <li><strong>nl.gz</strong>: VPTS data from 3 radars in the Netherlands.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
Data for "Lithium isotope evidence shows Devonian afforestation may have significantly altered the global silicate weathering regime"
<p>This contains measured data for paper "Lithium isotope evidence shows Devonian afforestation may have significantly altered the global silicate weathering regime", under funding of ERC grant 682760 CONTROLPASTCO2. </p> <p>This consists all the Li isotope data obtained from brachiopods/bulk carbonate samples.</p>
Data & R-Code for "Weather and food availability additively affect reproductive output in an expanding raptor population"
<p><strong>Abstract</strong></p> <p>The joint effects of interacting environmental factors on key demographic parameters can exacerbate or mitigate the separate factors’ effects on population dynamics. Given ongoing changes in climate and land use, assessing interactions between weather and food availability on reproductive performance is crucial to understand and forecast population dynamics. By conducting a feeding experiment in 4 years with different weather conditions, we were able to disentangle the effects of weather, food availability and their interactions on reproductive parameters in an expanding population of the red kite (<em>Milvus milvus</em>), a conservation-relevant raptor known to be supported by anthropogenic feeding. Brood loss occurred mainly during the incubation phase, and was associated with rainfall and low food availability. In contrast, brood loss during the nestling phase occurred mostly due to low temperatures. Survival of last-hatched nestlings and nestling development was enhanced by food supplementation and reduced by adverse weather conditions. However, we found no support for interactive effects of weather and food availability, suggesting that these factors affect reproduction of red kites additively. The results not only suggest that food-weather interactions are prevented by parental life-history trade-offs, but that food availability and weather conditions are crucial separate determinants of reproductive output, and thus population productivity. Overall, our results suggest that the observed increase in spring temperatures and enhanced anthropogenic food resources have contributed to the elevational expansion and the growth of the study population during the last decades.</p>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Automatic weather station data from the debris-covered Kennicott Glacier, Alaska (May-Aug 2019)
<p>Data from an automatic weather station installed on the debris-covered<strong> Kennicott Glacier</strong> in the Wrangell St. Elias National Park, Alaska, USA, spanning most of the 2019 ablation season (28 May - 22 August 2019).</p> <p>Brief description of the uploaded datasets:</p> <ul> <li><em><strong>KEN_2019_60.csv</strong></em>: Hourly aggregated measurements of air temperature, relative humidity, wind speed and direction, atmospheric pressure, incoming and reflected shortwave radiation, incoming and outgoing longwave radiation and precipitation</li> <li><em><strong>KEN_2019_AWS_Metadata.pdf</strong></em>: Metedata file describing the station location, sensor setup and measurements</li> <li><em><strong>KEN_2019_debris.csv</strong></em>: Debris properties assumed to be potentially applicable for this site (not measured but taken from literature)</li> <li><strong><em>KEN_2019_VAL_debristemperature.csv</em></strong>: Hourly time series of temperature measurements at known depths within the debris cover close to the station location</li> <li><strong><em>KEN_2019_VAL_subdebrismelt.csv</em></strong>: Monthly stake measurements of surface height change (sub-debris ice melt) close to the station location spanning the ablation season</li> </ul> <p>We recorded additional meteorological data at the weather station (e.g. vertical profiles of wind speed/direction, relative humidity and temperature; 3D ultrasonic anemometer) as well as subdebris melt, dGPS-surface change and temperature profiles at additional transects for the same period. Please contact the authors in case of interest in any additional data.</p>
Soil carbon stock, litter decomposition, and weather data from Ethiopian forests
<p><strong>Introduction</strong></p> <p>100 sampling units (SU) were selected from the total of 631 SUs of the Forest Reference Level submission 2017 (FRL 2017). The sampling was designed unbiased for total growing stock per SU, altitude,and mean litter depth per SU. The actual field sampling succeeded on 98 of the pre-selected SUs due to accessibility restrictions.</p> <p><strong>Soil profile sampling</strong></p> <p>Soil sampling was performed from November 2017 till mid-January 2018. Samples were taken from undisturbed soil from depths of 0-10 cm, 10-20 cm, and 20-30 cm below the organic layer. Volumetric samples of 107.5 cm<sup>3</sup> were taken vertically, using a 10 cm long conically shaped corer with a cutting lower edge diameter of 37 mm and upper diameter of 40 mm. </p> <p>Composite samples were formed by combining the volumetric samples taken from different depths of two parallel soil profiles. The samples were transported to EEFRI Soil Laboratory in Addis Ababa after 1-4 weeks of sampling at distant locations. </p> <p> </p> <p><strong>Soil physical characteristics</strong></p> <p>The soil samples were air-dried, homogenized, and subjected to oven-drying at 105°C until constant mass. Total bulk density was determined using the total dry mass and volume of the composite samples. </p> <p>Organic carbon content (C % by wet oxidation method), and soil physical characteristics: moisture content, bulk density of the total sample, and bulk density of fine fraction (particles passing the 2 mm sieve). The mass of the coarse fraction was weighed. The soil fine fraction was also subjected to laser diffraction for more accurate particle size analysis for proportions of clay, silt, and sand. </p> <p> </p> <p>In addition to this 28 samples were also analyzed for C content in the laboratory of Natural Resources Institute Finland to determine C content by LECO CHN analyzer. This was done to calibrate the bulk of wet digestion-based estimates (Fig. 1). Before analysis, the soils were tested for the presence of inorganic C.</p> <p> </p> <p>For Figure 1. See Soil_C_Ethiopia.pdf</p> <p><strong>Figure 1</strong>. Comparison of results from wet oxidation (Walkley-Black) and dry oxidation (CHN analyzer). The dotted line shows the theoretical 1:1 match between the axis, the solid line shows linear regression (intercept = 0) between the methods. The estimated slope value of 1.165 was used in adjusting the wet digestion results to match those obtained by dry oxidation: OC<sub>adj</sub> = 1.165 * OC<sub>wet</sub>.</p> <p>Based on a linear regression between the wet and dry oxidation analysis results, a correction factor of 1.165 was applied to adjust the organic C% obtained by wet digestion. The adjusted data are shown in the file “SOC_Ethiopia_2017-2018.csv”.</p> <p> </p> <p>SOC stocks were calculated by multiplying the proportion of organic C with BD of fine earth, after which the result was corrected for stoniness, a visually estimated proportion of large stones (S, value from 0 to 1) in the soil profile that could not be included in the volumetric soil samples (FAO VS-FAST).</p> <p><span class="math-tex">\(SOCstock = C_{org} * BD_{fe} * (1-S)\)</span></p> <p><strong>Soil organic carbon stock data</strong></p> <p><strong>Files: “SOC_Ethiopia_2017-2018.csv” and “SOC_Ethiopia_2017-2018.xlsx”</strong></p> <p>The file includes soil characteristics from layers of 0-10 cm, 10-20 cm, and 20-30 cm below the loose organic layer on top of the soil. The data are used for SOC stock estimation in the respective layers as described above.</p> <p>In the .csv file individual columns are for </p> <p><strong>LAT</strong> is the latitude of the sampling site corresponding to <strong>FieldCode</strong> and <strong>SU_nr</strong></p> <p><strong>LON</strong> is the longitude of the sampling site corresponding to <strong>FieldCode</strong> and <strong>SU_nr</strong></p> <ul> <li>The coordinates are expressed as decimal degrees of the WGS84 system</li> </ul> <p><strong>FieldCode </strong>refers to the Region and Sampling Unit number of the Ethiopian NFI (see below) </p> <p><strong>SU_nr </strong>is the Sampling Unit number of the Ethiopian NFI</p> <p><strong>Region </strong>is the name of the administrative region where the sample was taken</p> <p><strong>Biome </strong>is the name of the forest biome type where the sample was collected</p> <p><strong>BiomeSimplified </strong>is the name of a biome with some close types combined</p> <p><strong>DepthRange </strong>is the upper and lower limit of the soil sample in the field, cm</p> <p><strong>StoninessVFAST </strong>is a percentage of stones (VS-FAST by FAO) in the ca. 40 cm deep soil profile exposed during the sampling</p> <p><strong>FreshMassInField </strong>is the mass of the total composite soil sample of the given layer, g, primarily indicative of checking the correct number of subsamples in composite</p> <p><strong>NrComposites </strong>is the number of subsamples included in the composite for each soil layer</p> <p><strong>CorerVolume </strong>is a constant of 107.5 cm<sup>3</sup> because only one type of corer was used for undisturbed, volumetric sampling</p> <p><strong>CompositeVolume </strong>is the volume of the composite sample for each soil depth layer</p> <p><strong>CoarseFractionMass </strong>is the dry mass, g of soil particles > 2mm that did not pass the sieve, but were included in the sample volume</p> <p><strong>FE_DryMass </strong>is oven-dry mass, g of the fine fraction that passed the 2 mm sieve.</p> <p><strong>BDtot </strong>is total bulk density, g m<sup>-3</sup>, calculated for the composite sample</p> <p><strong>BDfe </strong>is the bulk density of the fine earth fraction, g m<sup>-3</sup></p> <p><strong>OC_adj</strong> is organic carbon (OC) content (%) in the composite sample, adjusted according to the comparison between dry and wet oxidation methods (Fig. 1)</p> <p><strong>SOCfe </strong>is SOC stock calculated for soil fine earth fraction, t ha<sup>-1</sup> in the 10 cm deep soil layer</p> <p><strong>SOCfe_stoniness</strong> is SOC stock of the fine earth fraction, t ha<sup>-1</sup> in the 10 cm deep soil layer, adjusted for stoniness. The correction assumes that the volume occupied by larger stones would be void of OC. </p> <p> </p> <p><strong>Litter stock data</strong></p> <p><strong>File: “Litter_Ethiopia_2017-2018.csv”</strong></p> <p>The file includes measurements of litter layer on Ethiopian NFI Sampling Unit (SU) sites where sampling for SOC stock determination was done. The depth of the litter layer was measured in the SU’s of the NFI, and this data contains in addition to depth also a volumetric sample of the litter layer. The dry bulk density was used to calculate the carbon stocks in the litter pool.</p> <p> </p> <p>The depth of the litter layer was measured in the field. Litter from the respective spot was sampled quantitatively from a frame of 0.01m<sup>2</sup> of area for litter dry mass estimate.</p> <p>The organic C stock in a litter (L) was calculated as,</p> <p> </p> <p><span class="math-tex">\(L = {M\over z} * {C_{om}\over A}, \)</span></p> <p> </p> <p>where</p> <p><em>M</em> = Dry mass of the litter sample, g</p> <p><em>z</em> = Depth of the litter layer in the field, m</p> <p><em>C<sub>om</sub></em> = Conversion factor from dry organic matter to carbon (C), 0.5</p> <p><em>A</em> = area of quantitative collection of litter (0.01 m<sup>2</sup>)</p> <p> </p> <p>In the .csv file individual columns are for</p> <p><strong>LAT, LON</strong> is the GPS coordinates (decimal degrees of WGS84) for the Sampling Units (<strong>SU_ID</strong>)</p> <p><strong>SU_ID</strong> is the Sampling Unit identification number of the Ethiopian NFI</p> <p><strong>FieldCode </strong>refers to the Region and Sampling Unit number of the Ethiopian NFI (see below)</p> <p><strong>Region </strong>is the name of the administrative region where the sample was taken</p> <p><strong>Litter_dry</strong> is the dry mass, g of the litter sample</p> <p><strong>Area_m2</strong> is the area, m<sup>2</sup> of litter sampling</p> <p><strong>MeanLitterDepth </strong>is the mean depth of the litter layer at the sampling area</p> <p><strong>CDensityLitter </strong>is the dry bulk density of the litter, g m<sup>-2</sup> multiplied by the assumed organic C proportion of the oven-dry litter materials (0.50)</p> <p><strong>LitterCStock_tha</strong> is the litter stock, t ha<sup>-1</sup> calculated from the C density of the litter layer</p> <p> </p> <p><strong>Litter bag data (decomposition and quality)</strong></p> <p>The leaves and twigs were sampled from 2 species (Juniperus and Podocarpus) and 3 locations of the elevation gradient in the Chilimo forest (Table 1). The forest was considered an old-growth with <em>Juniperus procera</em> and <em>Podocarpus falcatus</em>being the main species forming the tree canopy. The sites form an elevation gradient (Table 1).</p> <p> </p> <p>Table 1. Geographical locations of the study sites in the Chilimo forest.</p> <p> </p> <table> <tbody> <tr> <td> <p>id</p> </td> <td> <p>Latitude (deg.)</p> </td> <td> <p>Longitude (deg.)</p> </td> <td> <p>Elevation</p> <p>(m a.s.l)</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>9.0672</p> </td> <td> <p>38.1443</p> </td> <td> <p>2500</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>9.0712</p> </td> <td> <p>38.1556</p> </td> <td> <p>2670</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>9.0869</p> </td> <td> <p>38.1684</p> </td> <td> <p>2800</p> </td> </tr> </tbody> </table> <p> </p> <p>The dying and dead leaves were sampled directly from the trees later referred to as “fresh” and from the branches found on the ground, referred to as “old”. The old leaves were assumed to be dead for around 3 months. The diameter of the branches/twigs was less than 1 cm in diameter. The samples were first sorted and air-dried in an elevated temperature of the greenhouse and thereafter oven-dried in the oven overnight at 45 °C. The samples were analyzed for acid, water, ethanol dissolved,and undissolved fractions (AWEN) (Table 2) and for the decomposition rates of the litter installed into the litter bags corresponding to each of the Chilimo sites. </p> <p> </p> <p>Table 2. Acid, water, ethanol (A, W, E, respectively) dissolved and undissolved fractions (N) from the litter components of the dominant tree species in the Chilimo forest.</p> <table> <tbody> <tr> <td> <p>Litter type</p> </td> <td> <p>Species</p> </td> <td> <p>A</p> </td> <td> <p>W</p> </td> <td> <p>E</p> </td> <td> <p>N</p> </td> </tr> <tr> <td> <p>leaves fresh</p> </td> <td> <p><em>Juniperus </em></p> </td> <td> <p>0.45</p> </td> <td> <p>0.13</p> </td> <td> <p>0.1</p> </td> <td> <p>0.33</p> </td> </tr> <tr> <td> <p>leaves fresh</p> </td> <td> <p><em>Podocarpus </em></p> </td> <td> <p>0.42</p> </td> <td> <p>0.28</p> </td> <td> <p>0.05</p> </td> <td> <p>0.25</p> </td> </tr> <tr> <td> <p>leaves old</p> </td> <td> <p><em>Juniperus </em></p> </td> <td> <p>0.44</p> </td> <td> <p>0.07</p> </td> <td> <p>0.08</p> </td> <td> <p>0.41</p> </td> </tr> <tr> <td> <p>leaves old</p> </td> <td> <p><em>Podocarpus </em></p> </td> <td> <p>0.44</p> </td> <td> <p>0.09</p> </td> <td> <p>0.05</p> </td> <td> <p>0.42</p> </td> </tr> <tr> <td> <p>twigs</p> </td> <td> <p><em>Juniperus </em></p> </td> <td> <p>0.61</p> </td> <td> <p>0.04</p> </td> <td> <p>0.02</p> </td> <td> <p>0.32</p> </td> </tr> <tr> <td> <p>twigs</p> </td> <td> <p><em>Podocarpus </em></p> </td> <td> <p>0.56</p> </td> <td> <p>0.15</p> </td> <td> <p>0.02</p> </td> <td> <p>0.27</p> </td> </tr> </tbody> </table> <p> </p> <p>A sufficient amount of litter was placed into the litter bags (polyurethane mesh 1 mm) and the mesh bags were installed on top of the soil surface under the forest canopy (later referred to as “canopy”) and in the forest gap caused by harvesting (later referred as “open”). The installation of the litter bags (for each species 3 replicates of each litter type for each site and canopy type for the 3 periods, in total 12 litter bags for leaves and 6 bags for twigs) was done on 22.9.2017. The mesh bags were left on the ground, protected from grazing by the fence, and retrieved subsequently on 12.10.2017, 31.10.2017, and 12.12.2017. Despite the efforts took few samples were lost. The retrieved samples were oven-dried and initial mass and mass loss data for each period and litter type with a detailed description of the variables can be found in the file “litter.chilimo_07.02.22.xlsx”.</p> <p> </p> <p><strong>Soil temperature data</strong></p> <p>During the period from 22.9.2017 to 12.12.2017, we monitored the soil temperature at 5 cm depth under the canopy and in the open canopy on all Chilimo sites continuously every 4 hours intervals with the Maxim iButton temperature loggers. However, some sensors were lost. Daily means and their standard deviation of the continuous temperatures can be found in the file “soil.temp.chilimo_07.02.22.xlsx”.</p> <p> </p> <p><strong>Processed weather data</strong></p> <p>The air temperature and precipitation data for 98 sampling units corresponding to soil carbon data originated from 73 weather stations located across Ethiopia and were obtained from Ethiopian Meteorological Agency (http://www.ethiomet.gov.et/). Sampling units were joined with weather data by the closest proximity to their corresponding weather stations. Precipitation was unaltered. The air temperature required correction by elevation is described in more detail in Lehtonen et al. (2020). The monthly values of air temperature and precipitation with an accompanied readme description of the variables can be found for 98 sampling units in the file “sampling.units98_meteo_07.02.22.xlsx” and the Chilimo study sites in the file “monthly.weather.chilimo_07.02.22.xlsx”. The monthly values in the file "sampling.units98_meteo_07.02.22.xlsx" correspond to long-term average over the period from 1986 to 2017.</p> <p> </p> <p> </p> <p><strong>References:</strong></p> <p> </p> <p>Lehtonen, A., Ťupek, B., Nieminen, T.M., Balázs, A., Anjulo, A., Teshome, M., Tiruneh, Y. and Alm, J., 2020. Soil carbon stocks in Ethiopian forests and estimations of their future development under different forest use scenarios. <em>Land Degradation & Development</em>, <em>31</em>(18), pp.2763-2774.</p> <p> </p> <p>FRL 2017. https://redd.unfccc.int/files/ethiopia_frel_3.2_final_modified_submission.pdf</p>
Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering
<p>A dataset for the publication: T. Puttonen, M. Salmi, J. Partanen, Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering, 2021.</p> <p>The paper studies the mechanical properties and fracture mechanics of Additive Manufacturing (AM) polyamide 12 (PA12) in two build orientations exposed to a 1500-hour accelerated weathering cycle (ISO-4982-3) followed by tensile testing (ISO-527). Fracture surfaces of X and Z build orientation AM PA12 and X build orientation AM glass-filled PA12 were studied with scanning electron microscopy. The tested AM materials were PA12, glass-filled PA12, and carbon-reinforced PA12. The reference materials cut from sheet included glass-filled and molybdenum disulfide-filled PA66, PMMA, ABS, PC, and cast PA12.</p> <p>The dataset contains:</p> <p>- Full tensile test results in PDF format, and individual CSV files</p> <p>- A python script for tensile CSV data plotting</p> <p>- Overall pictures of all samples after tensile tests</p> <p>- 3D models and drawings for tensile samples, manufacturing files for a custom QUV holder assembly</p> <p>- SEM images of fracture surfaces for AM polyamide 12 (SLS), X and Z build orientation, and glass-filled polyamide 12 (SLS) in the X build orientation</p> <p> </p> <p>Version history:</p> <p>1.0.1: A partially corrupted version of the tensile test results PDF file replaced (Tensile_test_results.pdf)</p>
Unprocessed data from the Jungle Weather Zooniverse citizen science project
<p>The <a href="https://www.zooniverse.org/projects/khufkens/jungle-weather">Jungle Weather project</a> aimed to transcribe weather observations recorded between 1949 and 1958 in the tropical rainforest of the Democratic Republic of the Congo. Long-term observations of tropical weather are rare. The Jungle Weather, as part of the COBECORE project, contains observations of three decades of data of weather in the central African tropical forest, and are therefore an extraordinary source of information to support our understanding of for example drought resilience of trees species.</p> <p><strong>Summary</strong></p> <p>Both input and output of the citizen science transcriptions are provided in this data set. This includes the original cut-outs as used in the Zoonivese project, and the output as generated by the Zooniverse data export routines. The data export routines provided CSV output with JSON subfields on the content of each classification made. In addition, we provided the exported subject list and the details of each workflow.</p> <p>In total the project output constitutes of four files:</p> <ul> <li>transcribe-climate-data-classifications.csv (annotations of the table cells)</li> <li>transcribe-meta-data-classifications.csv (annotations of table headers)</li> <li>jungle-weather-workflows.csv (description of the citsci workflow)</li> <li>jungle-weather-subjects.csv (list of all images transcribed, and their online location for validation / referencing)</li> </ul> <p>and roughly ~3GB in data volume.</p> <p><strong>Context</strong></p> <p>Our understanding of forest ecosystem responses to climate change relies on consistent long-term observations to provide baseline measurements. In the central Congo Basin established long-term observation programs are rare. In terms of meteorological observations, the central Congo Basin is currently represented by only a few rain gauges, limiting climate forecasts across the Congo Basin and the central African continent. This lack of long-term (historical) climatological data leaves the central Congo Basin spatially and temporally under-represented. However, old climate records could provide valuable information about previous growing conditions of the forest.</p> <p>Large amounts of ecological and climatological data, approximately five decades (~1910 – 1960), exists as unexplored heritage, stored in various Belgian federal archives and collections. As part of a larger project called Congo Basin eco-climatological data recovery and valorization (COBECORE, see) the Jungle Weather project will help transcribe historical climatological data as measured throughout the Congo Basin. These data will in part complement the completed <a href="https://www.zooniverse.org/projects/khufkens/jungle-rhythms">Jungle Rhythms Zooniverse project</a>, further valorizing these transcribed data.</p> <p><strong>Historical data</strong></p> <p>Within this project we will focus on data records as recorded throughout the tropical part of what is currently the Democratic Republic of the Congo (DRC). The area which we will cover is shown above in the map as an open polygon. The project will not cover the southern province of Katanga (red crosshatches) as this area transitions here from tropical to a humid subtropical climate.</p> <p>The historical data is archived and stored in the Belgian State Archives. The Belgian State Archive harbour almost all data regarding colonial affairs, ranging from communications about trade to the raw data as digitized within the context of the Jungle Weathers project. Row upon row of data is stored in the basement. Below you see a part of the INEAC (Institut National pour l’Etude Agronomique du Congo belge) archive, which holds all climatological records.</p> <p>These climatological records were noted rigorously on carbon copy paper. However, due to the hand written nature of the data (and the volume involved) automated processing is not possible. Although optical character recognition (OCR) works wonderfully on printed data the high variability in characters and the low contrast pencil markings contribute to the failure of current automated approaches. Similar to the <a href="https://www.oldweather.org/">Old Weather project</a> and in spirit of the Jungle Rhythms project, a keen eye is required to decipher the numbers written down on these sheets.</p> <p><strong>Pre-processing / digitization</strong></p> <p>The project provided citizen scientists with digital pictures of the original sheets. Scanning these climate data sheets was a laborious process. In total more than 70 000 records were digitized. Unlike the Old Weather project we did not require citizen scientists to outline valid sections of the sheet. This part of the processing has been automated. We refer to our<a href="https://doi.org/10.5281/zenodo.3378864"> Jungle Weather pre/post-processing repository </a>for more details and example code</p> <p>As such, once digitized and properly aligned the whole record was divided into an estimated 30 million cells and 70 000 header files. Below you find an example of a header file and a table cell. During the Jungle Weather project we selected a subset of ~300K table cells for transcription in efforts to validate further Machine Learning based, automated, transcriptions approaches. All data were transcribed by citizen scientists in the spring/summer of 2020.</p> <p><strong>Notes</strong></p> <p>The provided data is raw data, and expert knowledge is required for the correct interpretation of this data. Please contact the authors for the proper context if you are interested in using this data in your project.</p>
Paired Vegetation and Soil Burn Severity Metrics and Associated Climate, Weather, Topographical, and Land Cover Attributes
<p>This dataset pairs differenced Normalized Burn Ratio (dNBR) and soil burn severity (SBS) for 254 large (>400 ha in size) fires across the western US. Dataset also includes climate, weather, topography, physical and chemical soil characteristics, and land cover attributes of each burned pixel at the time of fire. This effort provided a table of 16.3 million burned pixels and their associated characteristics including dNBR, SBS, and 94 biological and physical covariates. After removing correlated features, the final data includes 18 fire covariates namely: dNBR, elevation, slope, aspect, land cover type, wind speed, energy release component, vapor pressure deficit, annual precipitation, and annual average daily max temperature, as well as the clay, sand and silt content of the soil and volumetric fraction of coarse fragments and soil organic carbon content. We also included spatial coherence metrices for dNBR, including DVAR, SHADE and SAVG. This data is provided as CSV files in Xtrain, Xvalidation, Xtest, as well as Ytrain, Yvalidation, and Ytest; in which X files (model input) provide all features except for SBS and Y files (model output) include SBS.</p><p>We also provided this data for an additional 16 large fires across the western US ("Extra Test" folder, including Dataset – X file – and Label – Y file).</p><p>Finally, the trained XGBoost model to translate dNBR to SBS using the associated features is also provided in this folder.</p>
Weather data Aglantzia, Cyprus 2015-2020
<p>Weather data from the meteorological station (Davis Vantage pro 2) from the 23/01/2015 until the 20/07/2020. </p> <p>The weather station was on the roof of the Novel Technologies Laboratory Building of the Cyprus Institute (coordinates <a href="https://maps.app.goo.gl/HBZAVfvxJCv61dFn7">35.141446, 33.380970</a>).</p>
Weather-related Disease Prediction Dataset
<p>This dataset integrates medical symptoms and weather conditions to facilitate research into the prediction of diseases influenced by meteorological factors. The data spans a range of weather parameters alongside reported medical symptoms from a sizable number of anonymized individuals.</p> <p> </p> <p><strong>Variables:</strong></p> <p>Age: Age of the patient.</p> <p>Gender: Gender of the patient (encoded numerically).</p> <p>Temperature (C): Daily average temperature in Celsius.</p> <p>Humidity: Daily average humidity percentage.</p> <p>Wind Speed (km/h): Daily average wind speed in kilometers per hour.</p> <p>Symptoms: Various symptoms such as nausea, joint pain, abdominal pain, high fever, chills, fatigue, runny nose, pain behind the eyes, etc., encoded as binary values (1 for present, 0 for absent).</p> <p>Pre-existing Conditions: Conditions like asthma history, high cholesterol, diabetes, obesity, HIV/AIDS, nasal polyps, high blood pressure, encoded as binary values.</p> <p><strong>Data Collection Method:</strong></p> <p>The data was collected from anonymous medical records and corresponding local weather stations. All personal identifiers have been removed to ensure patient confidentiality and data privacy, adhering to ethical standards for medical data handling.</p> <p><strong>Usage Notes:</strong></p> <p>This dataset is intended for use in academic and research settings, especially in studies focused on the impact of weather on human health. It could be particularly useful for developing machine learning models to predict the likelihood of disease outbreaks based on weather patterns.</p>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
<p>This Zenodo repository contains the runs data for the paper <strong>RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models </strong>[<a href="https://arxiv.org/abs/2408.16118" target="_blank" rel="noopener">https://arxiv.org/abs/2408.16118</a>] presented at the NeurIPS 2024 workshop on Tackling Climate Change with Machine Learning, as well as the Master of Research (MRes) report <strong>Towards improving weather and climate models using reinforcement learning</strong> at the University of Cambridge<strong>.</strong> For questions, please contact Pritthijit Nath, <a href="mailto:pn341@cam.ac.uk" target="_blank" rel="noopener">pn341@cam.ac.uk</a>. Full documentation is available in the README.md file of the associated <a href="https://github.com/nathzi1505/climate-rl" target="_blank" rel="noopener">GitHub repo</a>.</p>
Weather Data. University of Cantabria. Las Llamas Campus. 2016-17
<p>Data measured by a SETELSA weather station located on the rooftop of the Industrial and Telecommunications Engineering School of the University of Cantabria.</p> <p>Date.- day/month/year hour:minute</p> <p>WindSpeed.- [m/s]</p> <p>WindDir.- [degrees] North. clockwise</p> <p>SolarRadiation.- [W/m2]</p> <p>Temperature.- [celsius degrees]</p> <p>Humidity.- [%]</p> <p>Pressure.- [mbar]</p> <p>Rain.- [l/m2]</p> <p> </p>
Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements
<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p> </p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p> </p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p> </p> <p><strong>topo.nc</strong> – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask – basin mask for the Boise River Basin</li> <li>veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type – vegetation type from the National Land Cover Database</li> <li>veg_tau – vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k – vegetation emissivity derived from the vegetation type</li> </ul> <p> </p> <p><strong>maxus.nc</strong> – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p> </p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv – metadata for 40 stations</li> <li>air_temp.csv – 38 stations</li> <li>cloud_factor.csv – 7 stations</li> <li>precip.csv – 21 stations</li> <li>vapor_pressure.csv – 19 stations</li> <li>wind_direction.csv – 14 stations</li> <li>wind_speed.csv – 14 stations</li> </ul> <p> </p> <p><strong>smrf_config.ini</strong> – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>
Dataset for "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study"
<p>This archive corresponds to the source code, raw data, and results described in the article "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study" by Chrbolková et al. (2019) published in Icarus journal. See AA_README.txt for more information.</p>
Data archive associated with "Landscape age as a major control on the geography of soil weathering" (https://doi.org/10.1029/2019GB006266)
<p>(1) Table including parameter values and weathering model outputs associated with NASGLP sampling locations (SLP_data.csv). </p> <p>(2) List of rivers used for calibrating erosion estimates (river_list.csv).</p> <p>(3) R workspace with same data as (1), plus a data frame of global parameter values ("gm") and spatial polygons giving continent boundaries ("con").</p> <p>(4) Scripts with functions for running the single-compartment weathering model at individual point locations or running a global sample of locations and computing summary statistics by continent (run_soilgenesis.R; soilgenesis.R). </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.