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245 results for “soil datasets”
Dataset for "Contrasting Effects of Organic and Mineral Nitrogen Challenge the N-Mining Hypothesis for Soil Organic Matter Priming"
<p>Dataset for the article:</p> <p>Mason-Jones, K., Schmücker, N., Kuzyakov, Y. (2018) Contrasting Effects of Organic and Mineral Nitrogen Challenge the N-Mining Hypothesis for Soil Organic Matter Priming. Soil Biology and Biochemistry 124, 38-46, https://doi.org/10.1016/j.soilbio.2018.05.024</p>
Soil moisture in Teutoburg forest/Bielefeld dataset
<p>Daten von Bodenfeuchte-Sensoren aus der Region des Teutoburger Waldes in Ostwestfalen-Lippe (Deutschland), die im Rahmen eines ehrenamtlichen Citizen Science Projektes von Code for Bielefeld e.V. gesammelt werden. Visualisierung auf <a href="https://bodenfeuchte.org" target="_blank" rel="noopener">bodenfeuchte.org</a></p> <p>Version 1 enthält Daten von August 2023 bis September 2024.</p> <h1><strong>Spaltenbeschreibung</strong></h1> <h2>Geräte-ID (device)</h2> <p>Diese Spalte enthält eine eindeutige Kennung für jedes Gerät. Die Geräte-ID ist ein alphanumerischer Code, der verwendet wird, um die Daten eines bestimmten Sensors eindeutig zu bestimmen</p> <h2>Gerätemarke (device_brand)</h2> <p>Marke des Bodenfeuchtesensors (z.B. dragino)</p> <h2>Gerätemodell (device_model)</h2> <p>Modell des Bodenfeuchtesensors (z.B. lse01)</p> <h2>Breitengrad (latitude)</h2> <p>Der Breitengrad des Standorts, an dem das Gerät installiert ist.</p> <h2>Längengrad (longitude)</h2> <p>Der Längengrad des Standorts, an dem das Gerät installiert ist.</p> <h2>Bodenfeuchtigkeit (soil_moisture)</h2> <p>Diese Spalte enthält den aktuellen Wert der Bodenfeuchtigkeit, gemessen in Prozent. Sie gibt an, wie viel Wasser im Boden enthalten ist, wobei höhere Werte auf eine höhere Bodenfeuchtigkeit hinweisen.</p> <h2>Bodenleitfähigkeit (soil_conductivity)</h2> <p>Dieser Wert gibt die elektrische Leitfähigkeit des Bodens in Mikrosiemens pro Zentimeter (µS/cm) an, was ein Indikator für die Menge an gelösten Salzen oder Nährstoffen im Boden ist.</p> <h2>Bodentemperatur (soil_temperature)</h2> <p>Die aktuelle Temperatur des Bodens in Grad Celsius gemessen.</p> <h2>Batteriestatus (battery)</h2> <p>Diese Spalte gibt die aktuelle Spannung der Batterie des Geräts in Volt an. Eine ausreichende Batteriespannung ist notwendig, um sicherzustellen, dass das Gerät weiterhin ordnungsgemäß funktioniert. </p> <h2>Letztes Update (last_update)</h2> <p>Das Datum und die Uhrzeit des letzten Datenupdates für den jeweiligen Sensor. Der Zeitstempel folgt dem ISO 8601-Format und enthält Informationen über das Datum, die Uhrzeit und die Zeitzone.</p> <h2>Tiefe der Messung (depth_cm)</h2> <p>Diese Spalte gibt an, in welcher Tiefe unter der Erdoberfläche der Sensor installiert wurde. Die Tiefe wird in Zentimetern angegeben.</p> <h2>Beschattung (shaded)</h2> <p>Ein boolescher Wert, der angibt, ob der Sensor in einem beschatteten Bereich installiert ist (`true` für ja, `false` für nein). Die Beschattung kann die gemessenen Temperaturwerte beeinflussen.</p> <h2>Versiegelter Boden (sealed_ground)</h2> <p>Diese Spalte gibt an, ob der Sensor in einem versiegelten Bodenbereich installiert ist (`true` für ja, `false` für nein). Versiegelter Boden beeinflusst Wasserinfiltration und Temperatur.</p> <h2>Notizen (note)</h2> <p>Freitextfeld für zusätzliche Informationen über den Standort oder die Umstände der Sensorinstallation. Hier können beispielsweise Hinweise auf besondere Bedingungen oder Standorte vermerkt sein.</p> <h2>Bodennotizen (soil_note)</h2> <p>Freitextfeld für zusätzliche Informationen über die Bodenbeschaffenheit.</p>
SERENA EJP Soil: Bundles' cookbook exemplary datasets
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p>
Dataset for "Combination of ryegrass (Lolium perenne L.) and tall fescue (Festuca aurundinacea Schreb.) improve yields in a low P soil."
<p>Dataset for the study "Combination of ryegrass (Lolium perenne L.) and tall fescue (Festuca aurundinacea Schreb.) improve yields in a low P soil."</p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach
<p>Abstract:</p> <p>A novel method of estimating the silica (SiO2) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to calculate first the minimum SiO2, water (H2O) and carbon dioxide (CO2) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO2. In a recursive step, a ‘consensus’ SiO2 is then established as the average between the two aforementioned estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Ti elemental concentrations + ‘consensus’ SiO2 + reported trace element concentrations converted to wt% + ‘normative’ H2O + ‘normative’ CO2) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H2O and CO2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO2 is linear, strong (R2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO2 concentrations with another publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO2 values measured on the ground over the same study area. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to validate results.</p> <p>Datasets:</p> <p>The original geochemical and mineralogical data for soils of the conterminous United States (A and C horizon datasets) were downloaded from <a href="https://mrdata.usgs.gov/ds-801/">https://mrdata.usgs.gov/ds-801/</a>.</p> <p>The ‘Shacklette and Boerngen’ dataset was downloaded from <a href="https://mrdata.usgs.gov/ussoils/">https://mrdata.usgs.gov/ussoils/</a>.</p> <p>A worked example for the five selected samples of Figure 5 is available as a Microsoft Excel spreadsheet (NALG_Ch_oxides_with_estimated_SiO2_LOI_worked example.xlsx) on Zenodo.org.</p> <p>The new datasets including sample identification, coordinates, converted major oxide concentrations, and the concentration estimates for SiO<sub>2</sub> and LOI in wt% for the A and C horizon datasets from the North American Soil Geochemical Landscapes (NASGL) project are available as comma separated value files (NALG_Ah_oxides_with_estimated_SiO2_LOI.csv and NALG_Ch_oxides_with_estimated_SiO2_LOI.csv) on Zenodo.org.</p>
Dataset Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial Pseudomonas in the wheat rhizosphere
<p>This dataset is related to the paper "<strong>Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial <em>Pseudomonas </em>in the wheat rhizosphere</strong>" (Garrido-Sanz et al., 2023, doi: 10.1186/s40168-023-01660-5) and contains the data obtained from bacterial competition asays and plant-growth measurements.</p> <p>Sequencing data used in this study has been deposited in the NCBI Sequence Read Archive (RSA) under the BioProject accession number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA948847">PRJNA948847</a>.</p> <p>The R script used to analyze the data generated in the paper is available at <a href="https://github.com/dgarrs/Pprotegens_proliferation_NatComs">GitHub </a>and <a href="https://doi.org/10.5281/zenodo.8322086">Zenodo</a>.</p>
Soil respiration dataset from abandoned croplands across China
Soil respiration, a critical component of the global carbon cycle, is highly sensitive to warming. Agricultural soils, including abandoned croplands, are large sources of carbon dioxide (CO2) to the atmosphere. Here, we report the responses of soil respiration and its components to warming from abandoned croplands spanning a large range in latitude (22.3 to 46.6°N) and elevation (2 to 3734 m) across China from three-year (2019 to 2021) in situ warming experiments. This dataset is a collation of soil respiration, heterotrophic respiration, and autotrophic respiration and their temperature sensitivity along with information on microclimates (e.g., soil temperature), plant biomasses, and soil carbon components (e.g., SOC) and quality under climate warming.
Photo-oxidation and photomineralization apparent quantum yield dataset for dissolved organic carbon leached from permafrost soils collected from the North Slope of Alaska, July 2018.
Dissolved organic carbon (DOC) was leached from permafrost soils near the Toolik Field Station in the Alaskan Arctic and then characterized for its photochemical properties. Oxygen (O2) consumed from photo-oxidation of permafrost DOC was measured as a function of sunlight wavelength, defined as the apparent quantum yield spectrum of photo-oxidation (O2 consumed per mol photon absorbed by DOC). Carbon dioxide (CO2) produced from photomineralization of permafrost DOC was measured as a function of sunlight wavelength, defined as the apparent quantum yield spectrum of photomineralization (CO2 produced per mol photon absorbed by DOC).
Radiocarbon and stable carbon isotope dataset for DOC leached from permafrost soils collected from the North Slope of Alaska in the summer of 2018
Dissolved organic carbon (DOC) was leached from permafrost soils near the Toolik Field Station in the Alaskan Arctic. The radiocarbon (14C) and stable carbon (13C) isotopic compositions of permafrost DOC were quantified.
Soil respiration and precipitation data at resin bag sites on canopy gradient sites at the Coweeta Hydrologic Laboratory in 1998 (dataset 1056)
We tested whether inputs from canopy herbivores would affect soil processes such as respiration, nutrient cycling, and decomposition along an elevation gradient. The five treatments we used were frass additions, throughfall additions, removal of all litter that fell during the study, removal of greenfall that fell during the study, and controls. Soil respiration was significantly reduced on low and mid elevation sites in litter exclusion, greenfall exclusion and throughfall addition treatments (from 0.846 g CO2/m2/h for controls to 0.618, 0.667, and 0.708 g CO2/m2/h, respectively, for the three treatments).
OSMO soil health dataset 2015-2018
<p>This is soil health data collected from 24 Finnish fields between 2015-2018. The fields were chosen to represent fields with poor and good productivity on a given farm. The dataset is coded based on farm code (two letters) and field code: 0 poor field, no interventions; 1 poor field, improved soil management; K good field.</p> <p>Fields were monitored for soil nutrients, soil physical quality and some biological parameters (aggregate stability, earthworms, arthropods, soil carbon dioxide burst respiration).</p> <p>The overview dataset contains the parameters used for monitoring development in soil health in the fields, the additional dataset present plant tissue nutrient concentrations and soil structure evaluation as well as a comparison of organic matter analysis from three laboratories and a comparison of two aggregate stability assessment methods. </p> <p>More information on the study can be found in the two reports (in Finnish, w. English abstracts):</p> <p>http://hdl.handle.net/10138/229450</p> <p>http://hdl.handle.net/10138/309062</p>
Soil Dataset for Land evaluation in southwestern of Egyptian Delta
<p>Soil Dataset for Land evaluation in southwestern of Egyptian Delta</p>
Dataset on soil hydraulic properties under contrasted plant covers and agricultural practices
<p>Complete dataset on the temporal variation of soil infiltrability along a homogeneous fluvisol, on bare soil or soil planted with two plant species with contrasted root systems (a Malvaceae with a tap-root system and a Poaceae with a fibrous root system), and impacted by three different management practices (burning, mowing, and chemical weeding). An original protocol, based on specific ring infiltrometers, able to measure the temporal dynamics of soil infiltrability was used. This dispositive takes into account the variability of the measurement across space.</p> <p> </p>
Dataset for "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland"
<p>This is the dataset (ver. 2017.02.13) for the manuscript "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland" submitted to the journal <em>Atmospheric Chemistry and Physics</em>.</p>
Dataset of physical, biological and chemical soil properties from 10 European long-term experiments
<p>This dataset contains all measurements that were conducted within the Workpackage #2 of the SoilX Project (2022-2024). The data contains physical, chemical and biological soil parameters that were measured in ten European long-term field experiments, as well as soil management indicators calculated with the SoilManageR packager for R. Each data table contains different parameters, and they can be linked by the identifying columns (LTE, treatment, depth, block, replicate). Further information can be found in the ReadMe.txt.</p> <pre> </pre>
A dataset of three vine water status indicators, weather records and soil water capacity components collected from a rain-fed Mediterranean vineyard
<p>This dataset contains three key indicators of vine water status: vine shoot growth index (iG-Apex), predawn leaf water potential (Ψpd), and carbon isotope ratio (δ13C). Additionally, it includes weather data and soil measurements. Spatial data files of studied fields, plots, and some vines’ locations are also provided. The data were collected from a rain-fed vineyard in Southern France, 4 km north of the Mediterranean Sea. Measurements were made at the plot level, with each plot consisting of 10 adjacent grapevines (Vitis Vinifera). The iG-Apex was recorded weekly from June 10 to August 22, 2022, across 70 vine plots. Ψpd was measured weekly between 3 a.m. and 5 a.m. in 12 of these plots using a pressure chamber. On August 23, 2022, 100 berries were sampled from each plot, and 1.5 mL of grape juice was extracted for δ13C analysis using a carbon analyzer and mass spectrometer. The dataset contains 761 iG-Apex measurements (70 time series), 720 Ψpd measurements (60 time series), and 70 single-date δ13C measurements. Weather data were recorded daily in 2022 from a weather station located at the vineyard's center, providing five parameters: cumulative rainfall, relative humidity, and three air temperatures (mean, minimum, and maximum). Soil available water capacity components (horizon thickness, field capacity, permanent wilting point, bulk density, and rock fragment content) were measured in 5 of the 70 plots after prior soil profile wall analyses. Monitoring vine water status is essential for optimizing grape yield and wine quality. While Ψpd and δ13C are considered reference methods, they are expensive and prone to logistical constrains. In contrast, iG-Apex can be collected with minimal time and financial investment. This dataset enables not only the exploration of statistical relationships between the plant-based and soil-based indicators, but also the modeling of Ψpd and/or δ13C using iG-Apex, while accounting for weather and soil influences. All data were georeferenced, allowing future integration of ancillary spatial data sources, like multi-spectral remote sensing images or yield data.</p>
Modelling CO2 emissions of cultivated and rewetted peat soils with SWAP-ANIMO - Dataset
<p><span>Three locations in Europe (wet river valley (Denmark), coastal peatland (The Netherlands) and broad river floodplain (Switzerland)) were selected for which two to three years of measurements of hydrological variables and CO2 exchange fluxes were available for some period between 2015 and 2023. The hydrology, grass growth and CO2 fluxes of these sites were modelled with the SWAP-ANIMO model using the available measurement period for model input and calibration. Model simulations were used to improve the understanding of the hydrological drivers of each site and to obtain estimates of the different pools contributing to the measured CO2 fluxes using a period of 10 years (2014-2023). Rewetting was considered either by calibration on direct measurements of an actual rewetting measure (Denmark, The Netherlands) or extrapolation of the reference simulation (Switzerland). Also, the potential impact of climate change on the rate of peat oxidation was modelled for these sites for both the reference and rewetting measure. </span></p> <p><span>The dataset contains the relevant detailed, daily model output of the 10 year simulation period and aggregated, yearly model output of the scenario simulations which are detailed in the corresponding report (van de Craats et al., 2024), available at <a href="https://doi.org/10.5281/zenodo.14041243">https://doi.org/10.5281/zenodo.14041243</a>.</span></p>
Soil-meteorological dataset collected at La Guette peatland (23 ha, Loiret, France)
<p>Data of meteorological and soil physics measured on La Guette peatland in two stations (lgt/bm1 and lgt/bm2). Measurements start on 12-10-2010 and are regularly updated with new data. The measured parameters are: Air temperature (°C), Air pressure (mbar), Precipitation (mm), Relative humidity (%), ShortWave/LongWave outgoing/incoming radiation (W/m2), Photosynthetic photon flux density incoming (μmol/m2/s), Wind direction (°), Wind speed (m/s), Soil temperature (°C), Soil water content (%) and Soil heat flux (W/m2).</p> <p>Zip file contain :</p> <ul> <li>metadata file (TOUR_en.json) which describe stations, sensors, variables and process</li> <li>csv file contain time series data for all variables by station</li> </ul> <p>Additional information on the measurement can be found in this website : <a href="https://data-snot.cnrs.fr/data-access/">https://data-snot.cnrs.fr/data-access/</a></p> <p>We also recommend to contact sno-tourbieres to talk about data acquisition and use : <a href="mailto:contact.sno-tourbieres@cnrs-orleans.fr">contact.sno-tourbieres@cnrs-orleans.fr</a></p>
Dataset for Aqueous habitats and carbon inputs shape the microscale geography and interaction ranges of soil bacteria
<p>This repository hosts data for the paper entitled: "<em>Aqueous habitats and carbon inputs shape the microscale geography and interaction ranges of soil bacteria</em>" by Samuel Bickel and Dani Or.</p> <p>The following files are provided:</p> <p><strong>Microcosm experiment:</strong></p> <p>- Fluorescence microscopy images of the microcosm experiment (*.tif)</p> <p>- Code used for extracting cell locations from images (image_analysis.py)</p> <p><strong>Global model estimates from the bacterial interactions heuristic model:</strong></p> <p>- Maps of estimated cell density and proportion of biomass associated with anoxic cell clusters (*.nc)</p> <p> </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.