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2,672 results for “Service”
Interagency Ecological Program and US Fish and Wildlife Service: San Francisco Estuary Enhanced Delta Smelt Monitoring Program Data, 2016-2024
The Enhanced Delta Smelt Monitoring Program (EDSM) was initiated by the U.S. Fish and Wildlife Service in 2016. The main purpose of EDSM is to provide information about endemic Delta Smelt (Hypomesus transpacificus) population sizes and distributions within the upper San Francisco Estuary. To track the life cycle of this annual species, larval trawling with a fine-mesh (20 mm) net is conducted during the spring months, and Kodiak trawling for juveniles and adults occurs during the summer, fall, and winter months. Sampling sites are chosen via a stratified random sampling design. A minimum of two tows are conducted at each site, and field staff typically sample between 18 and 41 sites weekly. All fish collected are identified and enumerated, and a subset are measured for body length. Environmental data (water temperature, conductivity, dissolved oxygen, turbidity, depth) are also measured. In addition to Hypomesus spp., this long-term monitoring dataset can also be useful in evaluating the status and trends of other species of interest, especially pelagic fishes. For more information: https://www.fws.gov/office/lodi-fish-and-wildlife
Land-Use Impacts on Ecosystem Services Provisioning in Massachusetts 2001-2011
Meeting fundamental human needs while also maintaining ecosystem function and services is the central challenge of sustainability science. In the densely populated state of Massachusetts, USA, abundant forests and other natural land cover convey a range of ecosystem services. However, after more than a century of reforestation following an agrarian past, Massachusetts is again losing forests, this time to housing and commercial development. We used land-cover maps, ecosystem process models, and land-use data bases to map changes (2001, 2006, 2011) in eight ecosystem service variables and to identify “hotspots,” or areas that produce a high value of five or more services, at three policy-relevant spatial scales. Water-related services (clean water provisioning and flood regulation) experienced local declines in response to shifting land uses, but changed little when measured at the state-level. General habitat quality for terrestrial species declined state-wide during the study period as a consequence of forest loss. In contrast, climate regulation (carbon storage) and cultural services (outdoor recreation) increased, driven by continued forest biomass accrual and land protection, respectively. Timber harvest volume had high inter-annual variability, but no temporal trend. The scale at which hotspots are delineated greatly affects their quantity and spatial configuration, with a higher density in eastern Massachusetts and 10–12% more hotspots overall when they are identified at a town scale as compared to a watershed or state scale. Ecosystem service hotspots cover a small percentage of land area in Massachusetts (2.5–3.5% of the state), but are becoming more abundant as urbanization concentrates ecosystem service provisioning onto a diminished natural land base. This suggests that while ecosystem service hotspots are valuable targets for conservation, more are not necessarily better since hotspot proliferation can reflect the bifurcation of the landscape into servic
WSC 2006: Spatial interactions among ecosystem services in the Yahara Watershed
Understanding spatial distributions, synergies and tradeoffs of multiple ecosystem services (benefits people derive from ecosystems) remains challenging. We analyzed the supply of 10 ecosystem services for 2006 across a large urbanizing agricultural watershed in the Upper Midwest of the United States, and asked: (i) Where are areas of high and low supply of individual ecosystem services, and are these areas spatially concordant across services? (ii) Where on the landscape are the strongest tradeoffs and synergies among ecosystem services located? (iii) For ecosystem service pairs that experience tradeoffs, what distinguishes locations that are win win exceptions from other locations? Spatial patterns of high supply for multiple ecosystem services often were not coincident locations where six or more services were produced at high levels (upper 20th percentile) occupied only 3.3 percent of the landscape. Most relationships among ecosystem services were synergies, but tradeoffs occurred between crop production and water quality. Ecosystem services related to water quality and quantity separated into three different groups, indicating that management to sustain freshwater services along with other ecosystem services will not be simple. Despite overall tradeoffs between crop production and water quality, some locations were positive for both, suggesting that tradeoffs are not inevitable everywhere and might be ameliorated in some locations. Overall, we found that different areas of the landscape supplied different suites of ecosystem services, and their lack of spatial concordance suggests the importance of managing over large areas to sustain multiple ecosystem services. Documentation: Refer to the supporting information of the follwing paper for full details on data sources, methods and accuracy assessment: Qiu, Jiangxiao, and Monica G. Turner. "Spatial interactions among ecosystem services in an urbanizing agricultural watershed." Proceedings of the National Academy
German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid
<h1>Overview</h1> <p>These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.</p> <p>The base rasters from DWD are:</p> <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> <p>To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.</p> <p>Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.</p> <p>The following image illustrates the generation of the refined rasters on a small example window:</p> <p></p>
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC1 SET Surface Water level data from in Biscayne National Park, Florida, USA (2016-2025)
Surface water level data (m) was collected in Biscayne National Park (BISC) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2016 to 2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 1, known as BISC-SET-1 or BISC1. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC2 SET Surface Water level data from in Biscayne National Park, Florida, USA (2017-2025)
Water level data (m) was collected in Biscayne National Park (BISC) by the National Park Service - South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017-2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 2, known as BISC-SET-2 or BISC2. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.
Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Mary's Point SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Mary's Point (MARY) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Water Creek SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Water Creek (WACR) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
Insights on global rangeland ecosystem services shaped by grazing and fertilization (2007-2021)
The Nutrient Network (NutNet) is a globally coordinated research initiative aimed at investigating the impacts of human-induced changes in nutrient availability and consumer presence on grassland ecosystems. In this study, we used data from 79 grassland sites participating in NutNet, which includes a factorial experiment involving herbivory exclusion and/or nutrient addition. Standardized methodologies were applied across all sites to facilitate direct comparisons of response variables. We used ecosystem variables to quantify three provisioning ecosystem services (forage quantity, forage chemical quality, and forage physical quality), three supporting services (forage stability, soil fertility, and soil stability), and eight regulating services (erosion control, control of soil acidification, regulation of water quantity and quality, carbon storage, resistance to plant invasion, pest control, and pollination). Additionally, we identified three plant biodiversity variables that are closely related to the provisioning of ecosystem services (alpha richness, beta diversity, and native diversity). Using this data, we quantified key ecosystem services provided by rangelands, assessed both short- and long-term impacts of grazing exclusion and fertilization on these services, and identified synergies and trade-offs between them.
Interagency Ecological Program and US Fish and Wildlife Service: Juvenile/Larval Fish and Zooplankton collections at Liberty Island, California 2002-2005 & 2013-2019
The U.S. Fish and Wildlife Service (USFWS) Lodi Fish and Wildlife Office (LFWO) Delta Juvenile Fish Monitoring Program (DJFMP) has intermittently sampled Liberty Island with various equipment since 2002. Liberty Island was a reclaimed agricultural island until it flooded in 1997-1998 and we subsequently left to passively restore as a tidally influenced wetland. Larval trawls and beach were the only sampling methods used during both early (2002-2005) and late (2009-2019) sampling periods. The main purpose of the sampling was to gather information about fish presence and abundance in Liberty Island during the passive restoration, with an emphasis on reproductive and early life-stages of native species. Larval trawls, or tow nets, were used to catch larval fish in 2004-2005 and again from 2013-2019. Zooplankton nets were used 2013-2019. Water quality measurements were collected alongside each tow.
Meta-analysis of ecosystem services associated with oyster restoration on the Eastern and Gulf coasts of the US
We conducted a meta-analysis to systematically quantify the success and uncertainty of oyster reef restoration for a suite of biological, biogeochemical, and physical ecosystem services relative to both degraded and natural reference habitats. We focused on the eastern oyster, Crassostrea virginica. To evaluate whether restored eastern oyster reefs enhance ecosystem services relative to unaltered, degraded habitats and whether restored reefs provide ecosystem services equivalent to reference reefs, we synthesized data and calculated log response ratios for 245 restored-degraded reef pairs and 136 restored-reference reef pairs from 106 publications collected along 3500 km of U.S. Gulf of Mexico and Atlantic coastlines.
Accessibility Indicators to services at EU scale - 1km grid indicators
<p>This archive makes available <strong>accessibility indicators at EU scale from populated 1km EU grid to towns and cities at EU scale</strong> (512 million travel time by car calculated between origins and destinations). It follows a reproducible, transparent and updatable framework. It uses <strong>only open source and free routing engines (OSRM)</strong>, based on OpenStreetMap (OSM) network. This routing engine makes possible the creation of travel time indicators for a large set of origins and destinations.</p> <p>The EU towns and cities layer has been recently made available and named by the European Commission. This layer is based on a <a href="https://ec.europa.eu/regional_policy/information-sources/maps/urban-centres-towns_en">common methodology</a> for all Europe. Within GRANULAR activities, we consider the towns and cities layer as <strong>a proxy</strong> to discuss on little and medium commercial centralities in Europe.</p> <p>This methodological framework, <strong>implemented with open source solutions (data and code) only and documented in a reproducible way in R notebooks</strong>, could be easily extended to other origins and destinations, if a relevant layer will be identified in the future.</p> <p>Based on travel time matrix, it is possible to compute a large set of indicators. This archive (see readme at the root folder) <strong>describes the input data used, summarises the data processing and provide information and metadata on output indicators created at 1km grid cells.</strong></p> <p>All the output data is also available. </p> <p> </p>
Synthetic Dataset of Emergency Healthcare Services
<p>Synthetic dataset of emergency services comprised of several CSV files that we have generated using a simulation software. This dataset is open for public use; please cite our work if used in research or applications.</p> <p>## File Overview</p> <ol> <li>**CheckBloodPressure.csv** - (9 KB): Contains blood pressure Server records of patients.</li> <li>**CheckPatientType.csv** - (19 KB): Identifies the type of each patient (e.g., 1 or 3).</li> <li>**Fill_Information.csv** - (2 KB): Fill information records for new patients.</li> <li>**MedicalRecord1.csv** - (10 KB): Medical record dataset for patient type 1.</li> <li>**MedicalRecord2.csv** - (4 KB): Medical record dataset for patient type 2.</li> <li>**MedicalRecord3.csv** - (2 KB): Medical record dataset for patient type 3.</li> <li>**MedicalRecord4.csv** - (13 KB): Medical record dataset for patient type 4.</li> <li>**OutPatientDepartment.csv** - (18 KB): Data related to the satisfaction and length of stay of an given patient.</li> <li>**Triage.csv** - (13 KB): Data related to the triage process.</li> <li>**README.txt** - (4 KB): Documentation of the dataset, including structure, metadata, and usage.</li> </ol> <p> </p> <p>## Common Fields Across Files</p> <ol> <li>**Patient ID** *(Integer)*: Unique identifier for each patient.</li> <li>**Patient Type** *(Integer)*: Classification of patient (e.g., 1, 4).</li> <li>**Medical Records Arrival Time** *(DateTime)*: Timestamp of the patient's first arrival in the medical record department.</li> <li>**Exiting Time** *(DateTime)*: Timestamp when the patient exits a Server.</li> <li>**Waiting Time (min)** *(Real)*: Total waiting time before being attended to.</li> <li>**Resource Used** *(String)*: Resource (e.g., Operator) allocated to the patient.</li> <li>**Utilization %** *(Real)*: Utilization rate of the resource as a percentage.</li> <li>**Queue Count Before Processing** *(Integer)*: Number of patients in the queue before processing begins.</li> <li>**Queue Count After Processing** *(Integer)*: Number of patients in the queue after processing ends.</li> <li>**Queue Difference** *(Integer)*: Difference between the before and after queue counts.</li> <li>**Length of Stay (min)** *(Real)*: Total time spent in the simulation by the patient.</li> <li>**LOS without Queues (min)** *(Real)*: Length of stay excluding any queuing time.</li> <li>**Satisfaction %** *(Real)*: Patient satisfaction rating based on their experience.</li> <li>**New Patient?** *(String)*: Indicates if this is a new patient or a returning one.</li> </ol> <p>Ferreira, M. (2024). Synthetic Dataset of Emergency Healthcare Services [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14212812</p> <p> </p>
Dataset to manuscript: Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems
<p>Raw data to the manuscript entitled "Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems" by Severin-Luca Bellè, Jean Riotte, Norman Backhaus, Muddu Sekhar, Pascal Jouquet and Samuel Abiven. </p> <p>Data files include all raw data of farmer interviews (20211209_Biochar_India_rawdata_Bellè_Abiven_farmer_interviews) and all raw data from the soil incubation study (20211209_Biochar_India_rawdata_Bellè_Abiven_soil_incubation). </p> <p>File ending with var_names is the README file. </p>
Data on ecosystem services in mountain pastures
<p><strong>Datasets used for analyses in Ecosystem services in mountain pastures: a complex network of site conditions, climate and management:</strong></p> <p><strong>(1) Plot information data</strong></p> <table> <tbody> <tr> <td>Plot-ID</td> <td>Plot-ID</td> </tr> <tr> <td>Farm-ID</td> <td>Farm-ID</td> </tr> <tr> <td>Region-ID</td> <td>Region-ID</td> </tr> <tr> <td>X_WGS84</td> <td>X coordinate</td> </tr> <tr> <td>Y_WGS84</td> <td>Y cooordinate</td> </tr> <tr> <td>Plant-biomass-g_m2</td> <td>Plant biomass (g DM m-2)</td> </tr> <tr> <td>Digestibility_Perc</td> <td>Digestibility (%)</td> </tr> <tr> <td>SOC-kg_m2</td> <td>Soil C content (kg m-2)</td> </tr> <tr> <td>Color-abundance-Perc</td> <td>Share of coloured species (%)</td> </tr> <tr> <td>Pollinator-ress_Ind</td> <td>Floral reward indicator</td> </tr> <tr> <td>Plant-species-richness</td> <td>No. of plant species</td> </tr> <tr> <td>P-conten</td> <td>Soil P (mg/kg)</td> </tr> <tr> <td>pH</td> <td>Soil pH</td> </tr> <tr> <td>Slope_Perc</td> <td>Terrain slope (%)</td> </tr> <tr> <td>SummerTemp</td> <td>Mean Temperature May-Sept (°C)</td> </tr> <tr> <td>SummerPrecip</td> <td>Mean Precipitation May-Sept (mm)</td> </tr> <tr> <td>logLU_total</td> <td>Cattle presence (LU ha-1 a-1)</td> </tr> <tr> <td>Distance</td> <td>Distance to farm centre (m)</td> </tr> </tbody> </table> <p> </p> <p><strong>(2) Vegetation data</strong></p> <p>Presence-absence cross-table</p> <p>columns: Plots</p> <p>rows: Plant species</p> <p>Taxonomic names according to Lauber et al. (2001) Flora Helvetica</p>
Deliverable T2.2 soil threats and soil ecosystem services of interest in SERENA.xlsx
<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> <p>In T2.2 we discussed the definitions of soil threats (ST) and soil-based ecosystem services (SES) to be further analysed in SERENA. In this process we kept information from the literature review and the results of the national prioritisation of ST and SES. This dataset description is an Excel with sheets of literature search and national prioritisation from the participating countries. Please read metadata at the fist sheet. Furthermore, the last sheet shows the results of our discussion on definitions that we had to clarify before the prioritisation. </p>
Review of existing modelling studies focusing on specific soil-based ecosystem services (SES) and threats (ST) including climate change, management and land use change scenarios.
<p><span>We </span><span>reviewed existing modelling studies focusing on soil ecosystem services (SES) and soil threats (ST) including climate change, land use change and management scenarios. A publication has been submitted and is currently being reviewed. The title of the manuscript is: </span><span>Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review. </span></p> <p><span>Work was split between various authors. All Co-authors were working on either one or more SES or one ST. Excel sheets were prepared by INRA and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The final list of related publications is reported here. <br></span></p>
Global Ionosphere Maps of vertical electron content combined in real-time from the RT-GIMs of CAS, CNES, UPC-IonSAT, and WHU International GNSS Service (IGS) centers (from Dec 1, 2020, to March 1, 2021)
<p>The datasets consists on 91 daily files, in IONEX format (<a href="http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf">http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf</a>) , corresponding to three months of global ionospheric maps (GIM) of vertical total electron content (VTEC) computed in real-time from the assessed and combined real-time GIMs generated by four analysis centers. Indeed, the Real-Time Working Group (RTWG) of International GNSS Service (IGS) is dedicated to providing high-quality data, high-accuracy products for Global Navigation Satellite System (GNSS) navigation, positioning, timing, and Earth observations. As one of the important part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) have been generated by real-time weighting technique with the help of RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d’Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU). Compared with IGS rapid Global Ionosphere Maps (GIMs) (corg, ehrg, emrg, esrg, igrg, jprg, uhrg, uprg, uqrg, whrg) and IGS final combined GIM (igsg), the IGS combined RT-GIM (irtg) is equivalent to the post-processed GIMs and even better than some rapid GIMs. The IGS RT-GIMs are reliable sources of real-time global VTEC information and has great potential for real-time applications including range error correction for transionospheric radio signals (such as GNSS positioning, search and rescue, air traffic, radar altimetry, and radioastronomy), the monitoring of space weather (such as geomagnetic and ionospheric storms, ionospheric disturbance) and detection of natural hazards on a global scale (such as hurricanes/typhoons, ionospheric anomalies associated with earthquakes)</p>
Mobile Service Robots Crash Testing with Pedestrians: Safety Assessment with Child and Adult Dummies
<p>Data published with the manuscript: “<em>Estimating risks posed by personal mobility devices and service robots to pedestrians: comparative crash testing of adult versus child dummies</em>”. 2021 (Paez-Granados & Billard, 2021)<br> <strong>Summary:</strong></p> <p>This dataset contains injury measures during collisions between a mobile service robot - Qolo - (Paez-Granados, et al, 2018) and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present multiple collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children. In these tests, we followed known methods of safety analysis used in car crash testing and used a standing wheelchair robot "Qolo" as a representative system of mobile service robots, such as delivery bots (robot without occupant), person carrier robots, autonomous wheelchairs, standing mobility vehicles, and other transport robots expected to operate in pedestrian and public areas.</p> <p>The robot was equipped with an experimental front structure allowing different bumper heights and measurement of reaction forces. On the other hand, the human dummies were equipped with standard instrumentation calibrated in accordance with SAE J211-1 for impact tests, thus, the child dummy, Q3 provided head accelerations, neck forces and moments, chest deflections, and accelerations; and pelvis accelerations. The dummy H3 provided forces and moments at the tibia and femur, and accelerations at the pelvis, chest, and head. You will find scripts to read and plot the data, as well as, analysis of the injury risk based on standard crash testing metrics: Head Injury Criteria (HIC-15), head acceleration (a_3ms), Neck Injury (Nij), Chest deflection (CD), and tibia injury (TI).</p> <p><strong>Instructions: </strong></p> <p><em>This dataset contains the following main files:</em></p> <ol> <li><strong><em>Data Description.pdf</em>: </strong>Highly recommended to read through this file for understanding the setup of the collected dataset, as well as, the submitted manuscript.</li> <li><em><strong>collision_test_rawdata.zip</strong>: </em>This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2.<em> ‘test_name’/01_values/’testName’_CFC1000.xlsx</em></li> <li><em><strong>collision_test_analysis.zip</strong>: </em>This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.<em>‘test_name’/01_values/’testName’_Analysis_v2.xlsx --> </em>Dataset with filtered sensor data accordingly to SAEJ21.</li> <li><em><strong>collision_data_matlab_structure.zip</strong>:</em><em> Matlab containers with all data - also available as .mat files for easy reading from Code Ocean capsule.</em></li> <li><em><em><strong>scripts-crash-test-service-robots.zip</strong>:</em> processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: <a href="https://github.com/epfl-lasa/crash-tests-service-robots">https://github.com/epfl-lasa/crash-tests-service-robots</a></em></li> </ol>
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.