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1,103 results for “moisture”
Observations of groundwater fluctuations and surface moisture content on a medium-grained, planar beach (Sand Engine, the Netherlands)
<p>These data are groundwater and beach surface moisture values collected during the MegaPex campaign between October 11 and 20, 2014 at the Sand Engine, The Hague, the Netherlands by MSc students and staff of the Coastal Research Group at Utrecht University, the Netherlands. The data were obtained at 8 locations in a cross-shore array on the intertidal and upper beach. During the measurements the beach was planar (1:30) and the median grain size was 0.365 mm. The data are supplemented with bed profiles along the instrument array. For further information and meta-data, please consult the readme.txt and the header of the individual text files in the zip-file.</p>
Database Manuscript Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics - Gonzalez Dominguez et al
<p>The database contained the data used in the manuscript <strong>Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics, by Gonzalez Dominguez et al. </strong></p>
Moisture content and total aflatoxin content of the freshly harvested maize samples
<p>Moisture content and total aflatoxin content of the freshly harvested maize samples. </p> <p>Moisture content of the samples were determined on-site in triplicate using Superpoint handheld moisture analyzer (Supertech Agroline, Hestchaven 5, DK-5400 Bogense, Denmark; ±0.5% accuracy) following the manufacturer’s instructions.</p> <p>Total AF in the samples were quantified by a single step lateral flow immunoassay utilizing the developed Reveal Q+ test strip for Aflatoxin (Neogen Item 8085) read on a calibrated AccuSan Gold reader (Neogen Corporation, 620 Lesher Place, Lansing, MI 48912 USA) (Neogen item 9595) at 18-22<sup>o</sup>C</p>
Fig. 3 in Biological responses of Hypothenemus hampei (Coleoptera: Curculionidae) on Cenibroca artificial diet at different moisture content levels and relative humidities
Fig. 3. Coffee berry borer external feeding and reproduction behavior on Cenibroca artificial diet with 60% moisture content at 75% relative humidity. Notice how the external reproduction offers a simple separation of the coffee berry borer immature and adults from the diet for coffee berry borer parasitoid production. (A) Diet pellet 10 to 15 d afer infestation showing external feeding and development of first-generation offspring, arrows showing eggs of first generation. (B) Diet pellet 25 to 30 d afer infestation showing external feeding and development of first-generation offspring. Stages suitable for the African ectoparasitoids (Cephalonomia stephanoderis and Prorops nasuta) reproduction. (C) Diet pellet 35 to 40 d afer infestation showing external feeding and development of first-generation offspring. Notice the presence of mature and teneral females, arrows showing oviposition of second generation. (D) Diet pellet> 50 d afer infestation showing female production, suitable for the reproduction of the African ectoparasitoid P. nasuta.
Fig. 2 in Biological responses of Hypothenemus hampei (Coleoptera: Curculionidae) on Cenibroca artificial diet at different moisture content levels and relative humidities
Fig. 2. Loss of moisture content level percentage on a Cenibroca diet pellet with a 50, 60, and 70% moisture content level maintained at 65, 75, and 85% relative humidity. (95% confidence limits of the mean; n = 7 per evaluation time per treatment).
Fig. 1 in Biological responses of Hypothenemus hampei (Coleoptera: Curculionidae) on Cenibroca artificial diet at different moisture content levels and relative humidities
Fig. 1. Mean brood production of coffee berry borer per Cenibroca diet pellet with 50, 60, and 70% moisture content level maintained at 65, 75, and 85% relative humidity at different time periods. (95% confidence limits of the mean; n = 7 per evaluation time per treatment).
Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model
<p>This repository contains:</p> <ol> <li>A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (<a href="../api/records/13309165/draft/files/SMAP_NN_36km_20150331_20220326.nc/content" target="_blank" rel="noopener noreferrer">SMAP_NN_36km_20150331_20220326.nc</a>)</li> <li>Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (<a href="../api/records/13309165/draft/files/SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc/content" target="_blank" rel="noopener noreferrer">SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc</a>)</li> </ol> <p>Summary:</p> <p>The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted.</p> <p>The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals. </p> <p>Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3.</p> <p>The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing.</p> <p>For more details, please contact me (wotp12@unist.ac.kr)</p>
Field measurements of moisture content of dead leaves of Pinus pinaster (2014-2023)
<p>The Association for the Development of Industrial Aerodynamics - Forest Fire Research Centre (ADAI-CEIF) has developed a daily measurement program for the moisture content of a set of representative fine fuels in forests of Central Portugal since 1987. The sampling site is located is in Lousã, and samples are collected daily during the main fire season (15<sup>th</sup> of May - 15<sup>th</sup> of October) and twice a week for the rest of the year. These samples are then analyzed at the Laboratory for Forest Fire Studies (LEIF), located less than 1km from the sampling plot.</p> <p>As part of a study named "The role of field measurements of fine dead fuels moisture content in the Canadian Fire Weather Index System – a case study in the Central Region of Portugal" (https://doi.org/10.3390/f15081429) we present the field measurements of surface litter (<em>Pinus pinaster</em>) carried out from 2014 to 2023. Based on this dataset, the study proposes a correction for the moisture factor (<em>m<sub>f</sub></em>) in the Fine Fuel Moisture Code (FFMC) of the Canadian Fire Weather Index System (CFWIS). This moisture correction was used to directly determine the Initial Spread Index (ISI) and, subsequently, the Fire Weather Index (FWI).</p> <p>Acknowledgments:</p> <p>We gratefully acknowledge the researchers and laboratory technicians of the ADAI-CEIF team, who dedicated part of their time to the field collection of samples to determine the moisture content of forest fuels in Lousã (Coimbra, Portugal). In particular, we would like to thank the MCFIRE project (PCIF/MPG/0108/2017, https://mcfire.adai.pt/) for coordinating and supporting these measurements from 2019 to 2023. We also gratefully acknowledge the ongoing efforts of Nuno Luís and João Carvalho in ensuring the collection and processing of samples in the laboratory.</p>
Sample of Reanalysis Dead Fuel Moisture Content Dataset of California (2000-2020)
Open the record for dataset details and reuse information.
Atmospheric moisture recycling in Mediterranean-type climate regions across the world
<p>Please cite the corresponding manuscript when using this data:</p> <p>... (information will follow as soon as the manuscript is published)</p> <p>This dataset includes the local precipitation recycling ratios and the regional moisture recycling ratios for five major Mediterranean-type climate regions across the globe. Below we list these five regions and explain the concepts of local precipitation recycling and regional moisture recycling. </p> <p> </p> <p><strong>Mediterranean-type climate regions</strong></p> <p>Region 1: South West Australia</p> <p>Region 2: West coast of the US (California)</p> <p>Region 3: Central Chile</p> <p>Region 4: Mediterranean Basin (region around the Mediterranean Sea)</p> <p>Region 5: The Cape region of South Africa</p> <p> </p> <p><strong>Local precipitation recycling ratio</strong></p> <p>The local precipitation recycling ratio is the fraction of precipitation that originated within approximately 50 km from where it rains out, i.e., it evaporated from the grid cell where it rains out and the 8 surrounding grid cells. The grid cells have a resolution of 0.5DEGx0.5DEG. A more detailed explanation is provided in the journal article Theeuwen et al. (2024). </p> <p>The files that include local precipitation recycling ratios are:</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Study region</strong></td> <td><strong>Time dimension (month)</strong></td> <td><strong>Latitude range</strong></td> <td><strong>Longitude range</strong></td> </tr> <tr> <td>PLMR_SWAustralia.nc</td> <td>South West Australia</td> <td>January-December</td> <td>-15:-48 DEGN</td> <td>106:154 DEGE</td> </tr> <tr> <td>PLMR_California.nc</td> <td>West coast of the US (California)</td> <td>January-December</td> <td>52:20 DEGN</td> <td>-131:-105 DEGE</td> </tr> <tr> <td>PLMR_CentralChile.nc</td> <td>Centra Chile</td> <td>January-December</td> <td>-10:-54 DEGN</td> <td>-80:-60 DEGE</td> </tr> <tr> <td>PLMR_Med_Basin.nc</td> <td>Mediterranean Basin</td> <td>January-December</td> <td>48:23 DEGN</td> <td>-20:-45 DEGE</td> </tr> <tr> <td>PLMR_SWCapeSA.nc</td> <td>The Cape region of South Africa</td> <td>January-December</td> <td>-26:-40 DEGN</td> <td>10:38 DEGE</td> </tr> </tbody> </table> <p> </p> <p><strong>Regional moisture recycling ratio</strong></p> <p>The regional moisture recycling ratio data includes both regional evaporation recycling ratios as well as regional precipitation recycling ratios. </p> <p>The regional evaporation recycling ratio is the fraction of evaporated water that rains out within the Mediterranean region it evaporated from. </p> <p>The regional precipitation recycling ratio is the fraction of precipitation that originated from the Mediterranean region it rains out in. </p> <p>This data has a resolution of 0.5DEGx0.5DEG and is a multi-year average (years: 2008-2017). A more detailed description is provided in the journal article Theeuwen et al. (2024). </p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Type of recycling</strong></td> <td><strong>Study region</strong></td> </tr> <tr> <td>ERMR_SWAustralia.nc</td> <td>Regional evaporation recycling</td> <td>South West Australia </td> </tr> <tr> <td>ERMR_California.nc</td> <td>Regional evaporation recycling</td> <td>West coast of the US (California)</td> </tr> <tr> <td>ERMR_CentralChile.nc</td> <td>Regional evaporation recycling</td> <td>Centra Chile</td> </tr> <tr> <td>ERMR_Med-Basin.nc</td> <td>Regional evaporation recycling</td> <td>Mediterranean Basin</td> </tr> <tr> <td>ERMR_CapeSA.nc</td> <td>Regional evaporation recycling</td> <td>The Cape region of South Africa</td> </tr> <tr> <td>PRMR_SWAustralia.nc</td> <td>Regional precipitation recycling</td> <td>South West Australia </td> </tr> <tr> <td>PRMR_California.nc</td> <td>Regional precipitation recycling</td> <td>West coast of the US (California)</td> </tr> <tr> <td>PRMR_CentralChile.nc</td> <td>Regional precipitation recycling</td> <td>Centra Chile</td> </tr> <tr> <td>PRMR_Med-Basin.nc</td> <td>Regional precipitation recycling</td> <td>Mediterranean Basin</td> </tr> <tr> <td>PRMR_CapeSA.nc</td> <td>Regional precipitation recycling</td> <td>The Cape region of South Africa</td> </tr> </tbody> </table>
Research data for Structure and function of skin barrier lipids: Effects of hydration and natural moisturizers in vitro
<p>Research data for 10.1016/j.bpj.2024.10.006 Research data for Structure and function of skin barrier lipids: Effects of hydration and natural moisturizers in vitro. Biophysical Journal 2024.</p>
SOIL-WATERGRIDS v1, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014, dataset and modelling
<p>SOIL-WATERGRIDS is a comprehensive data product of the monthly estimates of volumetric soil water content at three depths within the root zone and the depth of the water table globally gridded at a resolution of 0.25x025 degree per grid cell from 1970 to 2014. The SOIL-WATERGRIDS data product also provides the full-scale global model (BRTSim, https://sites.google.com/site/thebrtsimproject/home) that allows third party users to assess the entire volumetric soil water content and water table dynamics from land surface to 50 m depth. </p> <p>This package includes a Technical Documentation with the details about the use of the data product.</p>
Structure-Dependent Influence of Moisture on Resistive Switching Behavior of ZnO Thin Films - Dataset
<p>This is the dataset of "Structure-Dependent Influence of Moisture on Resistive Switching Behavior of ZnO Thin Films"</p>
SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States
<p><a href="https://waterai.earth/smaphb/">SMAP-HydroBlocks (SMAP-HB)</a> is a hyper-resolution satellite-based surface soil moisture product that combines NASA's Soil Moisture Active-Passive (SMAP) L3 Enhance product, hyper-resolution land surface modeling, radiative transfer modeling, machine learning, and in-situ observations. The dataset was developed over the continental United States at 30-m 6-hourly resolution (2015–2019), and it reports the top 5-cm surface soil moisture in volumetric units (m3/m3).</p> <p>This repository contains the following two versions of the SMAP-HydroBlocks dataset:</p> <ol> <li><strong>SMAP-HB_hru_6h.zip</strong>: SMAP-HydroBlocks data in the Hydrological Response Unit (HRU) space. Storing the data in the HRU space enables the entire 30-m 6-h dataset to be compressed to 33.8 GB. A python script and instructions to post-process and remap the data from the HRU-space into geographic coordinates (latitude, longitude) is provided at <a href="https://github.com/NoemiVergopolan/SMAP-HydroBlocks_postprocessing">GitHub</a>. After post-processed, files are stored in netCDF4 format with a Plate Carrée projection.</li> <li><strong>SMAP-HB_1km_6h.zip</strong>: SMAP-HydroBlocks data at 1-km 6-h resolution. This aggregated version is already post-processed, and thus it is already in geographic coordinates (latitude, longitude), stored in netCDF4 format, with a Plate Carrée projection, and comprising 31.5 GB of data. </li> </ol> <p>Different subsets of the original dataset can be made available on request from Noemi Vergopolan (noemi.v.rocha@gmail.com). Data visualization, updates, and more information is available at <a href="http://waterai.earth/smaphb/">https://waterai.earth/smaphb/</a> </p> <p> </p> <p>Please cite the following paper when using the dataset in any publication:</p> <p>Vergopolan, N., Chaney, N.W., Pan, M. <em>et al.</em> SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US. <em>Sci Data</em> 8<strong>, </strong>264 (2021). <a href="https://doi.org/10.1038/s41597-021-01050-2">https://doi.org/10.1038/s41597-021-01050-2</a></p> <p>Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan, S., & Wood, E. F. (2020). Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 242, 111740. <a href="https://doi.org/10.1016/j.rse.2020.111740">https://doi.org/10.1016/j.rse.2020.111740</a></p> <p> </p> <p>To download all the files via the command line, please try <a href="https://zenodo.org/record/1261813">zenodo_get</a>:</p> <pre><code>pip install zenodo-get zenodo_get 5206725</code></pre>
High-resolution soil moisture data (1km)
<p>High-resolution soil moisture data based on ESA CCI surface soil moisture data in southwestern Europe (Iberia Peninsula).</p> <p>Refs:</p> <p>He, K., Zhao, W., Brocca, L., and Quintana-Seguí, P.: SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation, Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, 2023.</p> <p> </p>
IODP Expedition 350 Moisture and Density
<p>Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances. Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D). Dry volume (C or D) is measured by helium pycnometry. Submethods A and B are not recommended by IODP. Submethod C is suitable for saturated materials such as fine-grained sediments. Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.</p>
Grid-to-Grid daily simulated soil moisture 1964-2018, at selected UK Soil Moisture Databank sites.
<p>This dataset contains Grid-to-Grid (G2G) daily simulated soil moisture time-series at selected UK Soil Moisture Databank (UKSMD) sites. It was created to facilitate an evaluation of G2G simulated soil moisture against the UKSMD neutron probe soil moisture observations (Bell et al., 2022). </p> <p>G2G (Bell et al., 2009) is a national-scale gridded hydrological model, which has been widely applied to simulate river flows and more recently soil moisture. Here, the model was run at 1km resolution from 01/01/1964 - 16/12/2019 across Great Britain. Simulated soil moisture time-series are provided for the 1km grid-cells closest to selected UKSMD site locations. The G2G simulates vertically-integrated soil moisture in units of mm/m. For further explanation of G2G soil moisture, please see Kay et al., 2022 (https://iopscience.iop.org/article/10.1088/1748-9326/ac7a4e). </p> <p>The data is provided as two plain text files:</p> <p>1) g2g_soilmoist_1964_2018.txt contains the simulated soil moisture values. The first three columns specify the simulation date (day, month, year). Subsequent columns are soil moisture (mm/m) time-series at each site, with the UKSMD site ID given as column headers. </p> <p>2) site_locations.csv contains the locations of the UKSMD sites. In some cases there were multiple tubes with slightly different locations within a larger site, and here we are providing the location of the specific tube used. Columns specify: SITE_NAME (the site ID), TUBE_NAME (the tube number), EASTING and NORTHING (easting and northing in British National Grid). The site ID and tube names used in this document are consistent with the UKSMD documentation. </p> <p>References:</p> <p>Bell, V. A., Kay, A. L., Jones, R. G., Moore, R. J., & Reynard, N. S. (2009). Use of soil data in a grid-based hydrological model to estimate spatial variation in changing flood risk across the UK. Journal of Hydrology, 377(3-4), 335-350.</p> <p>Bell, V.A.; Davies, H.N.; Fry, M.; Zhang, T.; Murphy, H.; Hitt, O.; Hewitt, E.J.; Chapman, R.; Black, K.B. (2022). Collated neutron probe measurements and derived soil moisture data, UK, 1966-2013. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/450bb14b-c711-47af-8792-f9bd88482cd4</p> <p>Kay, A. L., Lane, R. A., & Bell, V. A. (2022). Grid-based simulation of soil moisture in the UK: future changes in extremes and wetting and drying dates. Environmental Research Letters, 17(7), 074029.</p>
Dataset: Remotely sensed soil moisture can capture dynamics relevant to plant water uptake
<p><strong>Dataset Description</strong><br> Stable isotope water uptake profiles were consulted across 45 datasets to determine the primary zone of root water uptake ("Uptake Range Top" to "Uptake Range Bottom"), whether the uptake increases in proportion nearer to the surface ("Decay of Water Uptake With Depth"), and whether uptake temporarily switches to shallow soils ("Temporary Uptake of Upper Layers"). More details on the data collection are shared in our Water Resources Research publication (in revision).</p> <p>Correlation length scales, or the effective depth of representation of L-band satellite soil moisture, are estimates in Short Gianotti et al. 2019 using SMAP surface soil moisture and GPM precipitation retrievals.</p> <p><strong>Citations</strong><br> Those that use the stable isotope table are asked to cite our Water Resources Research publication (in revision) as well as the 45 references contributing to the table.<br> Those that use the correlation length scale dataset are asked to cite:<br> Short Gianotti, D.J., Salvucci, G.D., Akbar, R., McColl, K.A., Cuenca, R., Entekhabi, D., 2019. Landscape water storage and subsurface correlation from satellite surface soil moisture and precipitation observations. Water Resour. Res. 9111–9132. https://doi.org/10.1029/2019wr025332</p>
Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS ecosystem sites
<p>Standardized drought indices to support research at ICOS ecosystem sites. Dataset to Nature Scientific Data submission.</p> <p>"The dataset comprises four files for each of the 101 sites: "[site_name]_input" contains the observational data extracted from E-OBS, PET estimates as well as the simulated soil water storage and actual evapotranspiration from mHM; and threemore files for each of the standardized drought indices ("SSMI_[site_name]", "SPI_[site_name]", "SPEI_[site_name]"). Details on the variables, their units and their origin are given in Tab. 1. For the SPI and SPEI, the file of each site contains the estimates for various aggregation times, ranging from 5 to 730 days in steps of 5 days from 5 to 365 and steps of 10 days from 370 to 730. Each data file has a daily temporal resolution and covers the time span from 1950 to 2021."</p>
Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau
<p>Current remote sensing techniques fail to observe and generate large scale multi-layer soil moisture (SM) due to the inherent features of the satellite sensors. The lack of comprehensive understanding of multi-layer SM hinders the sustainable development of agriculture, hydrology, and food security. In order to overcome the depth barrier of traditional SM assimilation and downscaling methods, we developed a Two-step Multi-layer SM Downscaling (TMSMD) framework by fusing multi-source remotely sensed, reanalysis, and in-situ data through both machine learning and state-of-the-art deep learning models to generate multi-layer SM. The produced multi-layer SM was characterized by high resolution (1 km), high spatio-temporal continuity (cloud-free and daily), and high accuracy (i.e., 3H data). Firstly, the coarse resolution SMAP SM was downscaled to 1 km spatial resolution using LightGBM to weaken the effects of scale mismatch issue and provide high-resolution input for the subsequent calibration. Results indicated that the downscaled SMAP SM remained high consistency with the original SMAP SM product. With the high-resolution inputs, we calibrated the downscaled SMAP SM using multi-layer in-situ SM through state-of-the-art attention-based LSTM. Results demonstrated that the average PCC, RMSE, ubRMSE, and MAE were improved by 22.3%, 50.7%, 26.2%, and 56.7% compared to SMAP L4 SM while 38.5%, 52.1%, 29.5%, and 58.7% compared to downscaled SMAP SM. Further spatio-temporal and comparative analysis confirmed that the multi-layer SM produced by the TMSMD framework had excellent performance in capturing the spatial and temporal dynamics. In conclude, the proposed TMSMD framework successfully generated 3H multi-layer SM data and is promising for accurate assessment and monitoring in agriculture, water resources, and environmental domains.</p> <p> </p> <p>The remaining data will be uploaded soon.</p>
ScienceDex guides
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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)
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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.