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6,569 results for “Long-term”
Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars
<p>The datasets here correspond to a paper by Dawkins et al., “Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars”, originally submitted in November 2022.</p> <p>The following datasets are sufficient to produce Figure 2 and 3 in the main manuscript.</p> <p>Figure 2:</p> <ul> <li>Please use the 12 individual files with filenames, “Dawkins_et_al_2022__meteor_peak_altitude__*_data.txt”. Here the asterisk should be replaced by one of the following station abbreviations: CAR, COL, CPa, DAV, KIR, KSS, ROT, SMa, SOD, SVA, TdF, and TRO.</li> <li>Each file contains 5 columns: Column 1 is year (from 1999 to 2022), Column 2 is the time series of the annual peak altitude residuals (no units), Column 3 is the corresponding standard error, Column 4 is the multilinear model fit, and Column 5 is the normalized annual solar flux (F10.7) in arbitrary units.</li> </ul> <p>Figure 3:</p> <ul> <li>Please use “Dawkins_et_al_2022__meteor_peak_altitude_trends.txt”. For ease, a description of the different columns is included within this file.</li> </ul> <p> </p>
The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020)
<p>Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas > 1 km<sup>2</sup>, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine (GEE) cloud platform, a 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) was developed using Landsat series imagery and a robust water-color-parameter-based SD model. <strong><em>Noted, the LAWSD30 has been updated to 2021 using Landsat data from 2020 to 2022.</em></strong> The details are described in "<em>30 m long-term LAke Water Secchi depth (SD) dataset (LAWSD30) of China (1985–2021)_Readme_V1.1.docx</em>".</p>
Data for Figures in "Unexpected Long-Term Variability in Jupiter's Tropospheric Temperatures
<p>This repository provides the data submitted by the primary author that were used to create Figures 1, 2, 3, 4 and 5 in the text for the article: <strong>"Unexpected Long-Term Variability in Jupiter's Tropospheric Temperatures", by </strong> Glenn S. Orton, Arrate Antuñano, Leigh N. Fletcher, James Sinclair, Thomas Momary, Takuya Fujoyoshi, Padma Yanamandra-Fisher, Padraig T. Donnelly, Jennifer Greco, Anna Payne, Kimberly Boydstun, Laura Wakefield.</p> <p>The repository consists of five files in text (ASCII) format: (1) User Guide to Data (16 kb), (2) figure1_data.txt (1.5 MB), (3) figure2_data.txt (142 kb), figure4_data.txt (61 kb), and figure5_data.txt (59 kb). Data for Figure 3 are provided in the User Guide.</p>
Data for: Detecting Long-Term Balancing Selection Using Allele Frequency Correlation
<p>Genome-wide and top 1% scores for 1KG project data output from BetaScan reported in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/28981714/">Detecting Long-Term Balancing Selection Using Allele Frequency Correlation.</a></p> <p>Siewert KM, Voight BF. Mol Biol Evol. 2017 Nov 1;34(11):2996-3005. doi: 10.1093/molbev/msx209.</p> <p>PMID: 28981714</p> <p>Code available at: https://github.com/ksiewert/BetaScan</p>
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations
<p>This dataset contains a comprehensive set of quality-controlled remote sensing observations of low-level mixed-phase clouds collected at the high Arctic site of Ny-Ålesund, between 10 October 2021 and 31 December 2022. Cornerstones of the dataset are observations from a 35-GHz polarimetric scanning Doppler cloud radar and a 94-GHz zenith-pointing Doppler cloud radar. Radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer and wind fields from large-eddy simulations. All data have undergone extensive quality control, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter and range folding. This dataset is especially suited for cloud microphysical studies, and the high number of events included allows for the compiling of robust statistics. The dataset is accompanied by a data descriptor article, which is available at <a href="https://doi.org/10.5194/essd-15-5427-2023" target="_blank" rel="noopener">doi.org/10.5194/essd-15-5427-2023</a>.</p> <p> </p> <p><strong>Dataset overview</strong><br>The files include only low-level mixed-phase cloud (LLMPC) events, as well as the 2 hours preceding and following events. Each file contains an individual event, unless multiple events are less than 4 hours apart, in which case they are combined into the same file. LLMPC events are detected by requiring that ice and liquid phase coexist in a cloud layer with top below 2500 m for at least one hour. All radar variables observed in zenith (Doppler moments at 35 and 94 GHz, linear depolarization ratio (LDR) at 35 GHz), as well as microwave radiometer retrievals (temperature (T), liquid water path (LWP), integrated water vapor (IWV)), liquid base height from the ceilometer, and model data (horizontal wind speed and direction) are brought to the same time and range grids (respectively named ‘time_zen’ and ‘range_zen’ in the files). Off-zenith radar variables (reflectivity, differential reflectivity (ZDR), maximum spectral ZDR (sZDRmax), correlation coefficient (RhoHV), differential phase shift (PhiDP), and specific differential phase (KDP)) are stored on separate coordinates (named ‘time_slant’ and ‘range_slant’). All derived corrections are already applied to the data, and stored in the files, in case the user is interested in reconstructing the original data. A number of flags have been included in the files: in particular ‘MPC_detected’ indicates whether a LLMPC event was detected, and ‘liquid_attenuation_correction_flag_zen’ and ‘liquid_attenuation_correction_flag_slant’ indicate whether radar reflectivities were corrected for attenuation due to liquid hydrometeors. Liquid attenuation corrections should be especially taken into account when computing the dual-wavelength ratio (i.e., the difference between reflectivity at 35 GHz and at 94 GHz, both expressed in dBZ), and performing quantitative analyses of reflectivity fields.</p>
Functional potential and evolutionary response to long-term heat selection of bacterial associates of coral photosymbionts
<p>Sequencing reads were assembled using the genome assembler pipeline Shovill v1.1.0. Briefly, the Shovill pipeline included read trimming using Trimmomatic v0.39, de novo assembly with SPAdes v3.15.5 and genome polishing with Pilon v1.24. After the pipeline, additional polishing was performed by mapping the reads back to the contigs with BWA v0.7.17 and sorting the resulting SAM/BAM files using SAMtools v1.15.1. Pilon v1.24 was then used to correct bases, fix mis-assemblies and fill gaps. The reformat.sh script from the Bbmap package v38.76 (-minlength=1000) was used to filter out contigs less than 1000bp. The draft genome assemblies were then annotated with Bakta v1.7.0. </p> <p>Single nucleotide polymorphism (SNP) detection between WT (WTref) and SS (SSref) samples were then performed using snippy v4.6.0, where both WT and SS samples were inputted as the reference genome in turn.</p> <p>A subset of the snippy output files are uploaded here and contain all variants found.</p>
Dataset for "Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects"
<p>Data and Code for 'Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects' by Graf et al. (Communications Earth and Environment)</p>
Database of a study on long-term land cover changes in Thừa Thien Huế Province, Central Vietnam
<p>Data provided here forms the basis of a study which is published in the journal Land Use Policy (year 2023) under the title 'The nature of a ‘forest transition’ in Thừa Thien Huế Province, Central Vietnam – A study of land cover changes over five decades'. The database contains the image files of the maps (and associated legends) shown in the publication. Further data (currently under use for other related research) will eventually be added to the repository.</p>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</p>
IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database
<h2>Abstract</h2><p>Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis.</p><h2>Article</h2><p><a href="https://www.nature.com/articles/s41597-023-02621-1">https://www.nature.com/articles/s41597-023-02621-1</a></p><h2>Repository</h2><p><a href="https://github.com/cedricgilon/iridia-af">https://github.com/cedricgilon/iridia-af</a></p><h2>Versions history</h2><ul><li>2023-10-04: v1.0.1 – remove hidden files from .zip archive</li><li>2023-07-26: v1.0.0 – initial release</li></ul><h2>Keywords</h2><p>Paroxysmal Atrial Fibrillation, AF, long-term electrocardiogram, ECG, Holter monitoring, Database, Dataset, IRIDIA, IRIDIA-AF</p>
Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
<p><strong>General Description</strong></p> <p>The dataset contains composites at 250 m spatial resolution of (1) monthly potential FAPAR for the year 2021 from ensemble ML model predictions, (2) the model deviance for each prediction, (3) the yearly average of potential FAPAR, (4) the yearly average of actual FAPAR and (5) the yearly average of the difference between actual and potential (actual minus potential) FAPAR. The dataset is based on the <a href="https://zenodo.org/record/8392976">95th percentile of the monthly aggregated FAPAR</a> derived from <a href="http://glass.umd.edu/Overview.html">250 m 8 d GLASS V6 FAPAR</a>. Potential FAPAR was predicted by fitting an ensemble ML model using globally distributed training points (cca 3 Mio) and a set of 52 biophysical covariates including several layers related to human pressure. The code for modeling potential FAPAR is openly available at <a href="http://github.com/Open-Earth-Monitor/Global_FAPAR_250m">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m</a>. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. </p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2021 - December 2021</li> <li><strong>Type of data: </strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR</li> <li><strong>Statistical methods used: </strong>Ensemble machine learning</li> <li><strong>Limitations or exclusions in the data: </strong>The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size: </strong>172,800 x 71,698</li> <li><strong>File format: </strong>Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p> </p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> pot.fapar = Potential Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination: </strong>eml = ensemble machine learning</li> <li><strong>Position in the probability distribution / variable type:</strong> m = mean</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference: </strong>s = surface</li> <li><strong>Time reference begin time:</strong> 20210101 = 2021-01-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2021-12-31</li> <li><strong>Bounding box: </strong>go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230924 = 2023-09-24 (creation date)</li> </ol>
Long-term response of wetland plant communities to management intensity, grazing abandonment, and prescribed fire
Isolated, seasonal wetlands within agricultural landscapes are important ecosystems. However, they are currently experiencing direct and indirect effects of agricultural management surrounding them. Because wetlands provide important ecosystem services, it is crucial to determine how these factors affect ecological communities. Here, we studied the long-term effects of land use intensification, cattle grazing, prescribed fires, and their interactions on wetland plant diversity, community dynamics, and functional diversity. To do this, we used vegetation and trait data from a 14-year-old experiment on 40 seasonal wetlands located within semi-natural and intensively managed pastures in Florida. These wetlands were allocated different grazing and prescribed fire treatments (grazed vs. ungrazed; burned vs. unburned). Our results showed that wetlands within intensively managed pastures have lower native plant diversity, floristic quality, evenness, higher non-native species diversity, and exhibited the most resource-acquisitive traits. Wetlands embedded in intensively managed pastures were also characterized by lower species turnover over time. We found that 14 years of cattle exclusion reduced species diversity in both pasture management intensities and had no effect on floristic quality. Fenced wetlands exhibited lower functional diversity and experienced a higher rate of community change both due to an increase in tall, clonal, and palatable grasses. The effects of prescribed fires were often dependent on grazing treatment. For instance, prescribed fires increased functional diversity in fenced wetlands but not in grazed wetlands. Our study suggests that cattle exclusion and prescribed fires are not enough to restore wetlands in intensively managed pastures and further highlights the importance of not converting semi-natural pastures to intensively managed pastures. Our study also suggests that grazing levels applied in semi-natural pastures maintained high plant dive
Short- And Long-Term Micro & Nano Particles Exposure to Estuarine Model Species at Variable Salinities
Micro (< 5mm) & Nano (1-1000 nm) plastic (MNP) particles are ubiquitous in the environment and have been shown to have a variety of effects on aquatic organisms. The effects of MNP exposure can vary depending on the type of MNP, the concentration of MNP, the duration of exposure, and the salinity of the water. This study used 5 plastic types including polyester (PE), polypropylene (PP), polylactic acid (PLA), polyethylene terephthalate (PETE) and tire particles (TP) in two forms, solid plastic particles and microfibers. To assess potential impacts on exposed organisms, early life stages of the estuarine indicator species Inland Silverside (Menidia beryllina) and mysid shrimp (Americamysis bahia) were exposed to three concentrations at micro- and nano-size fractions, and separately to leachate, across a 5-25 PSU salinity gradient. This exposure study was performed in longer term (21 days for Inland Silverside and 28 days for mysid shrimp) and shorter term (4 days for Inland Silverside and 7 days for mysid shrimp). Following MNP exposures of 7d (A. bahia) and 96 h (M. beryllina), behavioral assays were performed post-exposure from each treatment using a Danio Vision Observation Chamber (Noldus, Wageningen, the Netherlands) for the dark: light cycle as described previously (Siddiqui et al., 2022; Siddiqui et al., 2023; Mundy et al., 2021; Segarra et al., 2021). These behavior studies provide important information for risk assessments and policy making that can establish knowledge for MNPs risk.
Compiled long-term community composition datasets of primary producers and consumers in both freshwater and terrestrial communities
This data package consists of two files to study long-term changes in communities across a range of systems (freshwater and terrestrial) and organism types (from short-lived (sub annual) to long-lived species) that are both primary producers and consumers. We compiled many datasets from publicly available archives (41 datasets are from 14 LTER sites). All datasets must have had a measure of species level abundance to calculate more derived community ecological metrics beyond species richness. These data can be used to study community dynamics over space and time.
FISHPASS ASSESSMENT PLAN LONG-TERM MONITORING OF HYDROLOGIC AND WATER QUALITY DATA
The Great Lakes Fishery Commissions’ (GLFC) FishPass project seeks to reconnect the waterscape for only desired species (i.e., selective passage) by integrating a multitude of existing and novel passage techniques and technologies. The probability of a fish passing through a sorting system is dependent on environmental conditions and a fish’s motivation ─ its internal state in relation to environmental stimuli. While fish decision making abilities introduce complexity to the sorting operations, they also provide an opportunity to exploit behavioral tendencies and abilities to achieve selective sorting. The FishPass Assessment Plan details a monitoring program aimed at quantifying fish movement and sorting capabilities associated with both individual mechanisms and integrated sorting systems. The results of the monitoring program will be used to inform future adjustments to the selection of techniques and technologies and their configuration to optimize passage of desirable species while blocking and/or removing undesirable species. A key component to the Assessment Plan is the long-term monitoring of abiotic variables in and around FishPass. This data set contains the hydrologic (e.g., river discharge, water level) and water quality data (e.g., temperature, specific conductivity, conductivity, and turbidity) collected at mostly static stations throughout the Boardman/Ottaway River. The dataset is updated annually. These data are collected until the initiation and/or substantial completion of the FishPass structure. Collection of this type of data are expected to continue after FishPass construction completion but modifications to the extent and location of monitoring stations are anticipated. As a result, a new dataset will be updated in the future containing all long term hydrologic and water quality monitoring post construction. R. Swanson, GLFC Assessment Biologist, is primarily responsible for maintaining the monitoring equipment, data retrieval, quality assuran
Transplanted Sapling Long-term Survivability with Experimental Fungicide and Fencing at Multiple Michigan Sites (2009-2023)
Long-term transplant sapling recruitment (2009-2023) in Michigan, seeking to understand the effects of transplanting northern, southern, and locally sourced tree seeds. Regional sapling transplants simulate the effects of climate change as well as different tree species' ability to survive human-assisted northward migration. This dataset includes >25 species of trees and woody plants with approximately 500 surviving saplings out of ~24,000 transplanted throughout 2009-2017. Saplings are also experimentally treated with fungicide, some fenced to prevent deer browse, and planted under differing levels of canopy cover. Saplings are censused yearly, most recently 2023, recording survival and height.
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
[DEPRECATED] CFC01 Kings Creek long-term fish and crayfish community sampling at Konza Prairie (Reformatted to ecocomDP Design Pattern)
This data package has been deprecated due to several issues in the L0 source dataset that prohibits the creation of an L1 ecocomDP dataset. This data package is formatted according to the "ecocomDP", a data package design pattern for ecological community surveys, and data from studies of composition and biodiversity. For more information on the ecocomDP project see https://github.com/EDIorg/ecocomDP/tree/master, or contact EDI https://environmentaldatainitiative.org. This Level 1 data package was derived from the Level 0 data package found here: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-knz&identifier=130&revision=4 The abstract below was extracted from the Level 0 data package and is included for context:
Meiobenthos abundance. Long-term variability and dynamics of estuarine meiobenthic populations for North Inlet Estuary, South Carolina, from 1972 to 1992, North Inlet LTER (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-nin/6/1. The abstract below was extracted from the Level 0 data package and is included for context: The original purpose of this research was to determine if natural meiobenthic assemblages exhibited continuity over time and to monitor several physical variables to determine if these influenced long-term temporal patterns. The most recent study focused on variation and the relations of meiobenthos abundance with environmental factors over 11 years. Typically marine benthic community studies are limited temporally and the majority of previously published 'longterm' meiofauna results (all taxa) were based on about a year's duration.
Lake browning generates a spatiotemporal mismatch between DOC and limiting nutrients, 2018 spatial survey, modeled light limitation and whole-lake productivity changes in long-term Adirondack lake survey 1994-2012
This data set contains information on a spatial survey of dissolved organic matter (DOM) across lakes and wetlands in the Northeast and Midwest, USA and modeled long-term changes in light limitation and whole-lake productivity in a suite of lakes in the Adirondack State Park, New York, USA. Widespread long-term increases in DOM have been observed in many lakes in a process known as browning. This data set enables the assessment of potential changes in dissolved absorbance and dissolved organic nutrients associated with browning. This data set accompanies a manuscript in review at Limnology and Oceanography: Letters.
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)
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.