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6,298 results for “2022”
Urban habitat features and patterns of snake removals in the greater Phoenix, Arizona (USA) metropolitan area (March 2021 - March 2022)
In urban and suburban areas, wildlife and people are often in close quarters, leading to human-wildlife interactions (HWI). Understanding how wildlife interact with humans and the built environment is critical as urbanization contributes to habitat change and fragmentation globally. In our study, we partnered with a local business that removes and relocates snakes from homes and businesses in the Phoenix area. The most frequently removed were venomous (family Viperidae, e.g., rattlesnakes) and nonvenomous (family Colubridae, e.g., gophersnakes) snakes. Using these records, we investigated taxa-specific habitat trends at two spatial scales. The neighborhood scale focused on front yard measures of cover and vegetation classes and the landscape scale focused on variables related to vegetation indices and degree of urbanization. Both analyses compared areas where snakes were removed to random locations in the city to represent possible habitat available to snakes. At the neighborhood scale (n=60), we found that removals occurred in yards with abundant cover opportunities. At the landscape scale (n=764), we found species-specific differences with nonvenomous snakes removed from areas of higher urbanization compared to venomous snakes. Understanding these distinct habitat patterns in residential yards can identify areas with potential human-snake conflict.
Hierarchical herbivore exclosure vegetation canopy cover at 3 sites across grassland-shrubland ecotones, 2022
The goal of this dataset is to examine long-term effects of multiple herbivore groups on canopy cover of plants across a shrub encroachment gradient (i.e., Ecotone Study) using herbivore exclusion treatments. Plots (2x2-m) were controls (open to all herbivores), large herbivore exclusion (lagomorph, rodent access), or full exclusion (no herbivore access). Plots were established in 2001 across grassland-shrubland ecotones in patches of black grama (Bouteloua eriopoda with >75% cover). Biomass of B. eriopoda was physically removed from the center 40x40-cm2 patch of each treatment to simulate disturbance. We sampled the controls and herbivore exclosure plots in summer 2022 to evaluate the long-term influence of herbivore exclusion on B. eriopoda recovery and overall canopy cover.
Grass seedling survival and microhabitat vegetation cover among herbivore exclusion treatments across grassland-shrubland ecotones at 3 sites, 2022 and 2023
The aim of this study is to reveal how mammalian herbivores differentially affect the survival of grass seedlings depending on herbivore taxa (cattle, oryx, lagomorphs, rodents) and microhabitat vegetation structure surrounding grass seedlings. This dataset includes data tracking the survival of grass seedlings among herbivore exclusion treatments across grassland, ecotone, and shrubland habitats. Seedling survival trials were established at 3 spatial blocks associated with the Ecotone Study: JER Pastures 9 and 12, and CDRRC Pasture 3. Survival trials were conducted on Pasture 12 in 2022, and on Pastures 3, 9, and 12 in 2023. Each spatial block contained 3 sites (grassland, ecotone, shrubland) that were further subdivided into 5 replicate plots (n = 45 plots). Two trays (1 control open to all herbivores, 1 caged allowing only rodent access) of 25 seedlings each were buried at ground level at each plot and their condition (i.e., alive & undamaged, alive & herbivore damaged, senesced or absent via herbivory, senesced due to environmental stress, resprouted following herbivory, unknown fate, or herbivory following senescence) recorded every 3 days for a total of 15 days. Microhabitat vegetation cover surrounding the seedling trays was collected using ocular estimates of cover across plant functional types (e.g., perennial grasses, forbs, sub-shrubs, shrubs, etc.) within 1 square-meter PVC quadrats placed on both the east and west face of seedling trays established in the field. Maximum height of vegetative (non-reproductive) plant tissue of each functional type was additionally recorded to gauge the level of grass seedling concealment.
Seasonal Soil Sampling of Grass-dominated, Mesquite-dominated, and Ecotone Sites at the Jornada Basin LTER site for the Analysis of Microbial Community Variance, 2022-2023
Fungal and bacterial soil communities were analyzed to assess the influence of woody shrub encroachment on soil microbial communities. Three study sites in the Jornada Long Term Ecological Research Site were selected to represent a grass-dominated site, a woody shrub dominated site, and an ecotone of woody shrubs and grass. The field sampling began in October 2022 and concluded in July 2023 with five sampling periods that aimed to capture seasonal variation: October 2022, January 2023, March 2023, May 2023, and July 2023. This dataset includes data pertaining to the soil microbial composition, environmental characteristics, microbial sequence processing, and documentation of the code utilized for data processing and statistical analyses. Data on soil microbial composition was collected from Phospholipid Fatty-Acid composition data from soil samples. Data on environmental characteristics were collected from on-site temperature probes, laboratory assessments of soil properties, and Jornada meteorological stations. Information pertaining to microbial sequence processing is included in the documented code as well as in the record of the primers utilized.
Urban forest canopy cover, vegetation, and site characteristics, Twin Cities Metro Area, 2022 and 2023.
This data was primarily collected to assess forest quality within the Minneapolis-St. Paul (MSP) Metropolitan Area and to link above-ground and below-ground properties as part of the goals of the MSP-LTER Urban Tree Canopy research group. Here, we sampled vegetation on 48 circular plots with a 12.5 m radius distributed across 18 parks, registering the date of sampling, park and management agency names, the plot number, and geolocation (latitude, longitude, and elevation). The plots were randomly selected based on GEDI (Global Ecosystem Dynamics Investigation instrument) 2021 footprints in the MSP Metropolitan Area along accessible forested areas inside public parks, where the management agency allowed sampling. In each plot, we measured forest structure and diversity metrics, species names and abundance, DBH, height, distance from the plot center, the height where each individual canopy starts, and the relative position, exposure, and density of each canopy. We also measured understory plant structure and diversity in 4 subplots per plot, totaling 192 subplots. In these subplots, we surveyed all individual plants with heights over 20 cm, recording species names and abundance, plant basal diameter, plant height, and the total number of branches. Furthermore, we assessed the canopy openness above each subplot by calculating percent DIFN (diffuse non-interceptance) from fish eye pictures of the canopy at 1.3 meters over the subplot.
Vegetation and physical characteristics of Chesapeake Bay retreating Coastal Forests 2022-2024
This data set contains biomass and physical data across an upland forest to marsh transition. These measurements are taken at 5 sites around the Chesapeake and Delaware Bays. Data is collected at up to 5 ectones across the upland to marsh (High Marsh, Transition Zone, Low, Mid and High Forest). These ecotone definitions follow Smith et al. 2019, https://doi.org/10.6073/pasta/4524c22708628eb7f06d174edae89ff2).
Datasets of "Carbide coating on nickel to enhance the stability of supported metal nanoclusters" Nanoscale, 2022, 14, 3589-3598
<p>These are the datasets related to the publication "Carbide coating on nickel to enhance the stability of supported metal nanoclusters", Nanoscale, 2022, 14, 3589-3598 (<a href="https://doi.org/10.1039/D1NR06485A">https://doi.org/10.1039/D1NR06485A</a>). They are saved as NeXus/HDF5 files according to the nxstm NeXus application definition (<a href="https://doi.org/10.5281/zenodo.5792930">https://doi.org/10.5281/zenodo.5792930</a>).</p>
Proposições na Câmara dos Deputados de 1988 até 2022 classificadas por Tema
<p>Dados extraídos do <a href="https://dadosabertos.camara.leg.br/swagger/api.html#staticfile">Portal de Dados Abertos da Câmara dos Deputados</a> e processados para correlacionar as proposições com seus respectivos temas.</p> <p>Cada linha do dataset corresponde a uma Proposição apresentada na Câmara dos Deputados, com informações sobre sua identificação, conteúdo e andamento no processo legislativo, além da classificação temática. Com essa estrutura, seria possível realizar análises e visualizações dos dados, identificando padrões, tendências e evolução de temas ao longo do tempo.</p>
Figure 2a - Rossi et al. Design of Highly Efficient Semitransparent Perovskite/Organic Tandem Solar Cells RRL Solar (2022)
<p>The Data set is related to the<strong> figure 2a</strong> of the paper </p> <p>Design of Highly Efficient Semitransparent Perovskite/Organic Tandem Solar Cells by Daniele Rossi,Karen Forberich,Fabio Matteocci,Matthias Auf der Maur,Hans-Joachim Egelhaaf,Christoph J. Brabec,Aldo Di Carlo, Rapid Research Letter (2022) https://doi.org/10.1002/solr.202200242</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19 - APRIL 2022)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the eleventh dataset of the project, corresponding to April 2022, collected 24 months after the lockdown began in Mexico. Data collection was performed from March 17 to May 2, 2022.</p>
Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)
<p>These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are:</p> <ul> <li>Temperatures: <strong>Gilfordetal2024_BAMS-EEE_T2022.nc</strong></li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): <strong>Gilfordetal2024_BAMS-EEE_ChIP2022.nc</strong></li> </ul> <p>And an accompanying land-sea mask from ERA5 (<strong>Gilfordetal2024_BAMS-EEE_LandSeaMask.nc</strong>). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis.</p> <p>For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>).<br><br><em>Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.</em></p>
Metadata for the urbisphere-Paris campaign during 2022-2024: fieldwork maintenance log [L1]
<p>Machine-readable, formatted and redacted electronic fieldwork logs from the urbisphere-Paris observation campaign conducted between 2022-09-05 and 2024-07-22 in Paris, France. Provided in text format with comma separated columns (.csv) and in Microsoft Excel (.xlsx) format.</p> <p>The fieldwork logs are created from raw google form data submitted by campaign managers, scientists, technicinas and students. The formatting process is detailed in https://github.com/Urban-Meteorology-Reading/urbisphere-paris-fieldwork-log-format. The GitHub output has then been manually edited and adjusted.</p> <p>Contains maintenance information for the following observational sites operated as part of the urbisphere Paris campaign 2022 - 2024:</p> <table> <tbody> <tr> <td>PAARBO</td> <td>Paris – Arboretum de Vallée-aux-Loups </td> </tr> <tr> <td>PAAUNA</td> <td>Paris – Aunay-sous-Auneau</td> </tr> <tr> <td>PABOBI</td> <td>Paris – Bobigny</td> </tr> <tr> <td>PABONN</td> <td>Paris – Bonniel</td> </tr> <tr> <td>PABPAC</td> <td>Paris – Balloon Parc Andre Citroën</td> </tr> <tr> <td>PACHAM</td> <td>Paris – Chamant</td> </tr> <tr> <td>PACHAN</td> <td>Paris – Changis-sur-Marne </td> </tr> <tr> <td>PACHEM</td> <td>Paris – Chemin Vert Bobigny</td> </tr> <tr> <td>PACOMP</td> <td>Paris – Compiègne</td> </tr> <tr> <td>PACOUR</td> <td>Paris – Courdimanche-sur-Essonne</td> </tr> <tr> <td>PACRET</td> <td>Paris – Créteil</td> </tr> <tr> <td>PADENF</td> <td>Paris – Denfert Rocherau </td> </tr> <tr> <td>PADROU</td> <td>Paris – Droue Sur Drouette</td> </tr> <tr> <td>PAHOTE</td> <td>Paris – Hôtel de Ville</td> </tr> <tr> <td>PAJUSS</td> <td>Paris – Jussieu </td> </tr> <tr> <td>PALUPD</td> <td>Paris – Université Paris Diderot (LISA Platform)</td> </tr> <tr> <td>PAMEUD</td> <td>Paris – Meudon</td> </tr> <tr> <td>PANANG</td> <td>Paris – Nangis</td> </tr> <tr> <td>PANATI</td> <td>Paris – Rue Nationale</td> </tr> <tr> <td>PAPRUN</td> <td>Paris – Prunay-le-Temple</td> </tr> <tr> <td>PAROIS</td> <td>Paris – Roissy</td> </tr> <tr> <td>PAROMA</td> <td>Paris – Romainville</td> </tr> <tr> <td>PASIRT</td> <td>Paris – SIRTA Observatory Palaiseau</td> </tr> <tr> <td>PASTFE</td> <td>Paris – Saint Félix</td> </tr> <tr> <td>PAWYDT</td> <td>Paris – Wy-dit-Joli-Village</td> </tr> </tbody> </table> <p> </p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2022-01-01 to 2022-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.</p> <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2022.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRCHEM_2022_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2022-01-01 to 2022-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10.</p> <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2022.</li> <li>Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRWRTM_2022_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Umfragedaten Forschungsdatenmanagement 2022 der BUA-Einrichtungen
<p>Das 2020-22 von der Berlin University Alliance (BUA) geförderte Projekt „Concept Development for Collaborative Research Data Management Services“ hat sich die Konzeptentwicklung zum nachhaltigen Aufbau von Kompetenz und Expertise zum Thema FDM für Forschende und Multiplikator*innen sowie die Stärkung von Services rund um das FDM innerhalb der BUA zum Ziel gesetzt, um bereits bestehende Ressourcen bedarfsgetrieben bestmöglich nutzbar zu machen, Parallelentwicklungen zu vermeiden und Synergieeffekte zu ermöglichen. Hierfür wurde im Zeitraum 11/2021 bis 01/2022 an den vier BUA-Einrichtungen (Freie Universität Berlin, Humboldt-Universität zu Berlin, Technische Universität Berlin, Charité – Universitätsmedizin Berlin) eine Bestands- und Bedarfserhebung zum Umgang mit Forschungsdaten durchgeführt, deren Ergebnisse als Basis für die Entwicklung bedarfsorientierter standortspezifischer und standortübergreifender Beratungs-, Schulungs-, Kommunikations- und technischer Serviceleistungen dienen. Ziel der Befragungen war es, zu ermitteln, welche Services</p> <ol> <li> <p>aktuell an den jeweiligen Standorten bekannt sind und genutzt werden</p> </li> <li> <p>an allen Standorten gewünscht, aber noch nicht angeboten werden</p> </li> <li> <p>im Verbund als fruchtbar erachtet werden, um qualitätsvolle Forschung auch institutionsübergreifend zu ermöglichen.</p> </li> </ol>
Data for SARS-CoV-2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)
<p>This version contains a single file, with time series data for the most recent <a href="https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/sarscov2-reinfection-trends-in-south-africa-monthly-report/">monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa</a>:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> </ul> <p>Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data.</p> <p> </p> <p>Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. <a href="https://www.science.org/doi/10.1126/science.abn4947">Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa</a>. DOI: 0.1126/science.abn4947</p> <p>For code and more details see: <a href="https://github.com/jrcpulliam/reinfections/releases/tag/v3.0">https://github.com/jrcpulliam/reinfections/releases/tag/v3.0</a> or <a href="https://zenodo.org/record/6108448">10.5281/zenodo.6108448</a></p> <p>The version of this data set associated with the publication (available via the links above) included the following files:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> <li><code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or >0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>)</li> <li><code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript)</li> <li><code>sim_90_null.RDS</code> - simulation results (as used in the manuscript)</li> <li><code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)</li> </ul>
Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification
<p>Global maps at 1 km spatial resolution of the predicted soil types (0–100% probabilities) at 1 km resolution based on the <a href="https://www.fao.org/soils-portal/data-hub/soil-classification/world-reference-base/en/">WRB 2022</a> (<strong>World Reference Base</strong> the international standard for soil classification) classification system. The training data comes from the following 3 main sources:</p> <ol> <li>WOSIS points available via: <a href="https://www.isric.org/explore/wosis">https://www.isric.org/explore/wosis</a>;</li> <li>HWSD v2 (random draw of cca 20,000 points): <a href="https://iiasa.ac.at/models-tools-data/hwsd">https://iiasa.ac.at/models-tools-data/hwsd</a>;</li> <li>Other national datasets / data from publications and projects.</li> </ol> <p>Predictions are based on using Rando Forest algorithm as implemented in the <a href="https://www.randomforestsrc.org/">randomForestSRC package</a> with cca 190 covariate layers representing soil forming factors (CHELSA Climate, Global Lithological DB GLiM, MODIS EVI and LST long-term derivatives, Digital Terrain model parameters and similar).</p> <p>All TIF files are provided as <a href="https://www.cogeo.org/">COGs</a>, which means that you can open them directly in QGIS or similar. Publication explaining all modeling steps is pending.</p> <p>Update of the predictions takes about 4–5 hrs and will be regularly run provided that new training points are available. Disclaimer: These are initial results with limited accuracy and possible issues with quality of training points, location errors and harmonization issues. Use at own risk.</p> <p>Note: original list of soil types have been subset to classes that appear at least 10 times and at least in 2 countries. If you notice an error or artifact <strong>please report via <a href="https://github.com/OpenGeoHub/SoilTypeMapping">the Github repository</a></strong>. Help us improve this dataset by contributing training points.</p>
Dataset for: Mudrik, N., & Charles, A. S. (2022). Multi-Lingual DALL-E Storytime. arXiv preprint arXiv:2212.11985.
<p>This dataset represents the comprehensive collection of data generated during the study presented in the paper available at https://arxiv.org/abs/2212.11985.</p> <p>If your research incorporates this data and results in a publication - Please cite both the dataset and the paper.</p>
30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)
<p>Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (<em>Vv</em>) and bare soils (<em>Vs</em>) across China in 2014, 2018 and 2022. These pixel-wise <em>Vv</em> and <em>Vs</em> maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in <em>Vv</em> and <em>Vs</em> and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.</p>
Data Package for the 2022 Great Lakes Winter Grab
WARNINGS: 1. For Ice Thickness data, please use data in "WinterGrab_snow_ice_properties" file instead of data in Table 2 of the manuscript published in Limnology and Oceanography Letters! 2: In "WinterGrab_phytoplankton_abundance_McKay", EC1 has two sets of data records because it was sampled both on 2/28 and 3/10, both records are included in this data. --- The data package contains the results from a multi-institutional winter limnology sampling campaign on the Laurentian Great Lakes. Researchers from 19 institutions sampled 49 locations in all five of the Great Lakes over a period of 24 days in February-March 2022. This dataset contains information on diverse physical, chemical, and biological parameters. Great Lakes Winter Grab ArcGIS Storymap showing all locations of sampling sites and select photos: https://storymaps.arcgis.com/stories/8ff1c332dd944ba9a744dc0e0fc18906
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.