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.
1,168
datasets available to search
ShareScore release 0.9.0
Dataset results
1,168 results for “Carbon data”
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude</p> <p>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual.</p> <p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).</p> <p><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al
<p>CO2 and CH4 emissions and several associated environmental variables were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>
Monthly global ocean carbonyl sulfide and carbon disulfide flux data (2000–2019)
<p>This data product reports simulated monthly global ocean–atmosphere fluxes of carbonyl sulfide (OCS) and carbon disulfide (CS2) at 0.5° × 0.5° resolution (equivalent to 55 km × 55 km at the equator) between January 2000 and December 2019.</p> <p>Data are contained in two NetCDF files:</p> <ul> <li>ocs-flux-monthly-2000-to-2019.nc: Monthly global ocean OCS fluxes, 2000–2019</li> <li>cs2-flux-monthly-2000-to-2019.nc: Monthly global ocean CS2 fluxes, 2000–2019</li> </ul> <p>Data characteristics</p> <ul> <li>Version: 1.0.1 (2025-04-07)</li> <li>Spatial coverage: global</li> <li>Spatial resolution: 0.5° longitude × 0.5° latitude</li> <li>Temporal coverage: 2000-01-15 thru 2019-12-15 (nominal timestamps fall on the 15th day of each month)</li> <li>Temporal resolution: monthly</li> </ul> <p>Related manuscript</p> <p>Sun, W., Merder, J., Zhao, G., Lennartz, S. T., & Michalak, A. M. (2025). Tropical sources dominate the ocean carbonyl sulfide budget. Under consideration in <em>Global Biogeochemical Cycles.</em></p>
Detrital Carbonate Minerals in Earth's Element Cycles (Data & Scripts)
<p>Earth surface conditions, including climate and sea level, are largely controlled by the cycling of carbon and biogeochemically coupled elements. However, most elemental budgets cannot be consentaneously balanced for the present state. Here, we investigate the possible role of riverine carbonate minerals in biogeochemical cycles. We derive individual river basin export fluxes, the global export flux to the ocean and its reduction by human influence, utilizing state-of-the-art regression techniques and published global-scale datasets. Results point to a significance of riverine detrital carbonates for the global mass balances of carbon, calcium, alkalinity and strontium, which might help solving this long-standing problem. </p> <p>[Plain Language summary from: Müller et al. 2022, Detrital Carbonates in Earth's Element Cycles, GBC, <a href="https://doi.org/10.1002/essoar.10508409.1">https://doi.org/10.1002/essoar.10508409.1</a> ].</p> <p>Here data and scripts on which these investigations are based can be accessed.</p> <p> </p> <p>Funding:<br> This work was carried out under the umbrella of the Netherlands Earth System Science Centre (NESSC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie, grant agreement No 847504. Funding was also provided by BMBF-project PALMOD (Ref 01LP1506C) through the German Federal Ministry of Education and Research (BMBF) as Research for Sustainability inititative (FONA). AS thanks the European Research Council for Consolidator Grant 771497.</p>
Data Sources for the World Atlas of late Quaternary Foraminiferal Oxygen and Carbon Isotope Ratios 2021
<p>A tabulated text file containing all data sources used for the World Atlas of late Quaternary Foraminiferal Oxygen and Carbon Isotope Ratios 2021 (WA_Foraminiferal_Isotopes_2021), https://doi.org/10.1594/PANGAEA.936747 (Mulitza et al. 2021)</p>
Data from: Radial stem growth of the clonal shrub Alnus alnobetula at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring Pinus cembra
<p><strong>Data are documented in the following article:</strong></p> <p>Oberhuber W., G Wieser, F. Bernich, A. Gruber (2022) Radial stem growth of the clonal shrub <em>Alnus alnobetula</em> at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring <em>Pinus cembra</em>. Forests 2022, 13, 440. doi: 10.3390/f13030440.</p> <p> </p> <p><strong>Summary:</strong></p> <p>Global change is affecting species areal distribution in many regions. A better understanding of how land-use change and climate warming affects shrub growth is essential for improved predictions of forest dynamics at the alpine treeline. Evaluation of radial stem growth of the clonal shrub <em>Alnus alnobetula</em> (= <em>Alnus viridis</em>) and the co-occurring tree species Swiss stone pine (<em>Pinus cembra</em>) within an alpine treeline ecotone revealed that mean ring width of nitrogen fixing <em>A. alnobetula</em> was about four times lower compared to <em>P. cembra</em>. Our findings are based on ring width data from <em>A. alnobetula</em> and <em>P. cembra</em> stems sampled at the alpine treeline ecotone on Mt. Patscherkofel (47°12’N, 11°27’E, Central European Alps, Austria, elevation range 2050 to 2190 m asl). Ring width time series include 86 radii from 51 stems of <em>A. alnobetula</em> (stems had mean age of 18±7 yrs) and 24 radii from 16 stems of <em>P. cembra </em>(18±4 yrs). We explain our findings by different carbon allocation strategies, i.e., preference of “vertical” stem growth in late successional <em>P. cembra</em> vs. favoring “horizontal” spread in the pioneer shrub<em> A. alnobetula.</em> By favouring clonal propagation over individual stem growth <em>A. alnobetula</em> is able to quickly spread at the alpine treeline ecotone.</p>
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Globally-gridded data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon
<p>Supporting globally-gridded data products for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415) along with ancillary data on climate, vegetation, and soil characteristics to produce spatially-explicit global estimates of mineral-associated soil organic carbon stocks (MOC) and mineralogical carbon capacity (MOC<sub>max</sub>) in non-permafrost, non-desert mineral soils. Globally-gridded datasets are given in kgC/m<sup>2</sup> for topsoil (0-30cm) and subsoil (30-100cm) at 0.5 degree by 0.5 degree spatial resolution.</p>
Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon
<p>Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022.</p> <p>We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.</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>
Underlying Data for Manuscript titled "Hydrothermal Carbonization (HTC) of Dairy Waste: Effect of Temperature and Initial Acidity on the composition and quality of solid and liquid products"
<p>The embodied files include the raw data and initial calculations used to generate the extended data for Manuscript titled "Hydrothermal Carbonization (HTC) of Dairy Waste: Effect of Temperature and Initial Acidity on the composition and quality of solid and liquid products". The files include calculations for Phosphorus Recovery from Hydrochar, as well as Heavy Metals Fractions retrieved by the hydrochar (solid product of Hydrothermal Carbonization).</p>
Data associated with the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data".
<p>This dataset refers to the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data". https://doi.org/10.5194/acp-2022-15.</p> <p> </p>
Soil carbon and nitrogen stock data for dominant geomorphological terrain units in Qarlikturvik Valley, Bylot Island, Arctic Canada
<p>Dataset for the manuscript 'The distribution of soil carbon and nitrogen stocks among dominant geomorphological terrain units in Qarlikturvik Valley, Bylot Island, Arctic Canada.' to appear in the 'Journal of Geophysical Research: <em>Biogeosciences'.</em></p>
Raw data for the journal article "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide"
<p>This data set corresponds to the article by Kong et al. entitled "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide", published in Small Methods</p>
Data for 'Microbial carbon use efficiency along an altitudinal gradient'
<p>This dataset is related to the manuscript “Microbial carbon use efficiency along an altitudinal gradient“ by Kevin Mganga, Outi-Maaria Sietiö, Nele Meyer, Christopher Poeplau, Sylwia Adamczyk, Christina Biasi, Subin Kalu, Matti Räsänen, Per Ambus, Hannu Fritze, Petri Pellikka, and Kristiina Karhu.</p> <p>Corresponding author: Outi-Maaria Sietiö (<a href="mailto:outi-maaria.sietio@helsinki.fi">outi-maaria.sietio@helsinki.fi</a>)</p>
Processing and Data for "Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats"
<p><strong>Description: </strong></p> <p>These files contain processed BGC-Argo float data, figure data, the radiocarbon productivity subset, bootstrapping results, and the associated Python/Matlab code to calculate net primary productivity from daily cycles of optical backscatter and dissolved oxygen.</p> <p>The raw float data used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis) and Monterey, California (ftp://usgodae.org/pub/outgoing/argo/dac/coriolis). The raw MODIS satellite-based productivity data is available from the Oregon State University Ocean Productivity site (<a href="http://orca.science.oregonstate.edu/npp_products.php">http://orca.science.oregonstate.edu/npp_products.php</a>). The raw MODIS satellite-based euphotic depth estimates are available from the NASA L3 browser (<a href="https://oceancolor.gsfc.nasa.gov/l3/">https://oceancolor.gsfc.nasa.gov/l3/</a>). The original ship-based estimates of net primary productivity are available from the Pangaea (<a href="https://doi.pangaea.de/10.1594/PANGAEA.932417">https://doi.pangaea.de/10.1594/PANGAEA.932417</a>) and the British Oceanography Data Centre (<a href="https://www.bco-dmo.org/dataset/814803">https://www.bco-dmo.org/dataset/814803</a>).</p> <p><strong>Please cite as: </strong></p> <p>Stoer, A., and Fennel, K. 2022. Processing and Data for Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats. Zenodo. doi: 10.5281/zenodo.6977161.</p> <p><strong>Python/MATLAB Software Description: </strong></p> <p>dielFit_GOPeqCR.m: This code is from Johnson and Bif (2021). We have added outputs for standard errors for linear and PvE models and sunrise/sunset times. To run this code with the associated Python software a MATLAB engine needs to be installed. Please see: <a href="https://www.mathworks.com/help/matlab/matlab-engine-for-python.html">https://www.mathworks.com/help/matlab/matlab-engine-for-python.html</a></p> <p>argo_so_processing_20220815.py: This code is the first of two pieces of software for estimating net primary productivity from floats in the Southern Ocean. The program below obtains the data from the BGC Argo database (Argo, 2021) and processes it. Simple data quality control, interpolation, biogeochemical calculations, and data binning occur. The processed float data is located in the folder 'Processed Argo Transects'.</p> <p>argo_daily_npp_20220815.py: This code using processed Argo float data that contains oxygen and particle backscatter measurements to infer net primary production. The code combines the float that meet the criteria of sampling at all local hours of the day throughout its lifetime. Then, it constructs diel cycles from this data by finding the median value of each hour and uses the code from Johnson and Bif (2021), which is a modified version from Barone et al. (2019). The algorithm used to convert particle backscatter to particulate organic carbon is from Graff et al. (2015). We assume that dissolved primary productivity accounts for 30% of total primary productivity (Moran et al., 2022).</p> <p>argo_daily_npp_bootstrap_20220815.py: This code using processed Argo float data that contains co-located oxygen and particle backscatter measurements to infer net primary production. This code is very similar to argo_daily_npp_20220815.py but randomly samples a subset of the co-located profiles at different sample sizes before calculating net primary productivity. Productivity is calculated at each sample size 1000 times. The results of this analysis is located in the folder 'Bootstrapped Results'. </p> <p>More details can be found in the code itself. </p> <p><strong>Data Descriptions: </strong></p> Data from 'Processed Argo Transects' Folder | Description for each variable <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>depth</td> <td>Average depth of depth bin</td> <td>m</td> </tr> <tr> <td>mid_depth</td> <td>Center of depth bin</td> <td>m</td> </tr> <tr> <td>pressure</td> <td>Average pressure in depth bin</td> <td>dbar</td> </tr> <tr> <td>profile_index</td> <td>Profile number or index</td> <td> </td> </tr> <tr> <td>profile_longitude</td> <td>Average longitude of profile</td> <td>degE</td> </tr> <tr> <td>profile_latitude</td> <td>Average latitude of profile</td> <td>degN</td> </tr> <tr> <td>profile_time</td> <td>Average UTC time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_time</td> <td>Average local time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_hour</td> <td>The hour of the local timestamp</td> <td> </td> </tr> <tr> <td>salinity</td> <td>Seawater salinity</td> <td>PSU</td> </tr> <tr> <td>temperature </td> <td>Seawater temperature</td> <td>degC</td> </tr> <tr> <td>oxygen</td> <td>Dissolved oxygen concentration</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_saturation</td> <td>Saturated dissolved oxygen concentration calculated from the Garcia and Gordon (1992) equation.</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_anom</td> <td>The difference between observed dissolved oxygen concentration and saturated oxygen </td> <td>umol kg-1</td> </tr> <tr> <td>bbp470</td> <td>Optical backscatter coefficient at 470 nm. Particulate organic carbon is calculated in argo_daily_npp_20220815.py</td> <td>m-1</td> </tr> </tbody> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>wmo</td> <td>WMO number of float</td> <td> </td> </tr> <tr> <td>profile_index</td> <td>Profile index or profile number taken by float</td> <td> </td> </tr> <tr> <td>profile_latitude</td> <td>Average profile latitude</td> <td>degN</td> </tr> <tr> <td>profile_longitude</td> <td>Average profile longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>fod</td> <td>Fraction of day</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Sinusoidal curve fit to oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc</td> <td>Sinusoidal curve fit to particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>oxy_med</td> <td>Hourly median oxygen</td> <td>mol m-3</td> </tr> <tr> <td>oxy_sem</td> <td>Hourly standard error of oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc_med</td> <td>Hourly median particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>poc_sem</td> <td>Hourly standard error of particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N, co-located)</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N) </td> <td> </td> </tr> <tr> <td>depth</td> <td>Depth of profile</td> <td>m</td> </tr> <tr> <td>zeu</td> <td>1% euphotic depth from Lee et al. (2013) algorithm from NASA (2022) L3 satellite products. </td> <td>m</td> </tr> <tr> <td>n_profiles_bpp</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>n_profiles_oxy</td> <td>Number of oxygen profiles</td> <td> </td> </tr> <tr> <td>n_floats_bbp</td> <td>Number of floats with backscatter measurements</td> <td> </td> </tr> <tr> <td>n_floats_oxy</td> <td>Number of floats with oxygen measurements</td> <td> </td> </tr> <tr> <td>gop_do</td> <td>Gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_serr</td> <td>Standard error of gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly oxygen data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly oxygen data</td> <td> </td> </tr> <tr> <td>oxy_sr</td> <td>The calculated sunrise time as a fraction of the day</td> <td> </td> </tr> <tr> <td>oxy_ss</td> <td>The calculated sunset time as a fraction of the day</td> <td> </td> </tr> <tr> <td>gpp_bbp</td> <td>Gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gpp_bbp_serr</td> <td>Standard error of gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_bbp</td> <td>Gross oxygen productivity calculated from gross carbon productivity (gpp_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_bbp_serr</td> <td>Standard error of gross oxygen productivity calculated from gross carbon productivity (gpp_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp</td> <td>Net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp_serr</td> <td>Standard error of net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do</td> <td>Net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do_serr</td> <td>Standard error of net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do_serr)</td> <td>mol m-3 yr-1</td> </tr> </tbody> </table> <table> </table> Data for Fig. S1 | Description for number_of_bbp_profiles_in_each_year.csv and number_of_oxy_profiles_in_each_year.csv <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>year</td> <td>Year</td> <td> </td> </tr> <tr> <td>bbp470</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>oxygen_anom</td> <td>Number of oxygen profiles</td> <td> </td> </tr> </tbody> </table> <table> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>mid_depth</td> <td>Depth of NPP profile</td> <td>m</td> </tr> <tr> <td>mean</td> <td>Mean volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>median</td> <td>Median volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>min</td> <td>Minimum volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>maximum</td> <td>Maximum volumetric 14C-NPP</td> <td>mmol m-3 yr-1</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th><strong>Variable</strong></th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>subset</td> <td>Number of profiles randomly sampled from the co-located dataset</td> <td> </td> </tr> <tr> <td>int_npp_do</td> <td>Euphotic-depth-integrated net primary productivity calculated from oxygen-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>int_npp_bbp</td> <td>Euphotic-depth-integrated net primary productivity calculated from backscatter-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>gop_do_r2</td> <td>R-squared of the sinusoidal curve to the diel cycle of oxygen anomaly</td> <td> </td> </tr> <tr> <td>gpp_bbp_r2</td> <td>R-squared of sinusoidal curve to the diel cycle of particulate organic carbon</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>ROSE</td> <td>Topographic (negative values are below sea level)</td> <td>m</td> </tr> <tr> <td>ETOPO05_Y</td> <td>Latitude</td> <td>degN</td> </tr> <tr> <td>ETOPO05_X</td> <td>Longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>database</td> <td>Database the data was extracted from</td> <td> </td> </tr> <tr> <td>Month</td> <td>Month of NPP measurement</td> <td>month of year</td> </tr> <tr> <td>npp_14c</td> <td>Net primary productivity estimated from the radiocarbon method</td> <td>mmol m-3 y-1</td> </tr> <tr> <td>depth</td> <td>depth of 14C-NPP measurement</td> <td>m</td> </tr> </tbody> </table> <table> </table>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Data on carbon, nitrogen, and phosphorus forms in a north temperate river, Rivière du Nord, Québec, Canada, from 2017 to 2019
<p>The Rivière du Nord was sampled at 13 sites along its mainstem once per season from summer 2017 to winter 2020. Sites are numbered by river kilometer (RKm) with the outlet being RKm 0. The dataset includes concentrations (ug/L or mg/L as noted) of total organic carbon, dissolved organic carbon, particulate organic carbon, total nitrogen, total dissolved nitrogen, nitrate, ammonium, dissolved organic nitrogen, total phosphorus, total dissolved phosphorus and particulate phosphorus. It also includes fluorescence metrics derived from PARAFAC EEMs: fluorescence intensities (in Raman units) of 5 dissolved organic matter components (C1-C5), and 5 indices (SUVA-254, CDOM, FI, b:a, HIX). We sampled an additional 12 sites to act as endmembers (5 mainly forested sites, 5 mainly agricultural sites, and 2 wastewater treatment plant measures).</p> <p>Data were used to calculate C:N:P stoichiometry in the paper "Different forms of carbon, nitrogen, and phosphorus influence ecosystem stoichiometry in a north temperate river across seasons and land uses" (<a href="https://doi.org/10.1002/lno.11960">https://doi.org/10.1002/lno.11960</a>).</p> <p>Data were used to quantify changes in organic matter composition in the paper "Contrasting seasons and land uses alter riverine dissolved organic matter composition" (<a href="https://doi.org/10.1007/s10533-022-00979-9">https://doi.org/10.1007/s10533-022-00979-9</a>). </p>
Data set for the paper: Intercomparison of ocean colour algorithms for picophytoplankton carbon in the ocean
<p>This dataset contains the phytoplankton carbon,Cphy, obtained from in situ counts of phytoplankton cells using ow cytometry presented in the paper [13]. The location and time of the samples have been matched with the satelllite data in the Ocean Colour Climate Change Initiative (OCCCI) dataset. This dataset is the match between the in situ Cphy and the products from using the OCCCI inputs (i.e. chlorophyll concentration, backscattering coecient, phytoplankton absorption) with 6 different algorithms. This document describes the dataset details: data sources, computation of Cphy, selected data.</p>
Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"
<p>Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires, and highlights the need for more field data to further validate this initial assessment.</p> <p>Here we provide the post-fire SOC erosion dataset ("Post-fire SOC erosion rates" file) used for calculating the SOC ratio of eroded sediments implemented in the RUSLE modelling; as well as the list of data sources ("List of data sources" file).</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.