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1,838 results for “location”
Simulated daily weather dynamics and gross primary production in 3 locations for 100,000 years
<p>IMPORTANT NOTE: The data in version 1 of this record, due to an error of units in the precipitation, had a non-physical vegetation growth and gross primary production. This has been fixed in version 2 of the record/dataset. Further, version 2 of the dataset contains 3 locations because the sites called "Grassland" and "Temperate" site produced the same type of vegetation (just grasses) in version 2, that contains therefore only a "Temperate" site.</p> <p>-------------------------------------------------</p> <p>The dataset reports daily temperature, precipitation, radiation and gross primary production in 3 different geographic locations (denoted as Temperate, Boreal and Tropical), representative of different climates and vegetation distributions, for 100,000 years. Each of the .nc files contains the dataset corresponding to one particular site.</p> <p>The weather data was produced using the AWE-GEN stochastic weather generator model ( Fatichi et al., Water Resources, 34(4):448–467 (2011) ). Vegetation dynamics and gross primary production are simulated via the dynamic global vegetation model LPX-Bern v1.4 ( Lienert and Joos, Biogeosciences, 15(9):2909–2930 (2018) ). The foliar projective cover is also reported at an annual scale.</p> <p> </p>
Artificial reefs geographical location matters more than shape, age and depth for sessile invertebrate colonization in the Gulf of Lion (NorthWestern Mediterranean Sea)
<p>Artificial reefs (ARs) have been used to support fishing activities. Sessile invertebrates are essential components of trophic networks within ARs, supporting fish productivity. However, colonization by sessile invertebrates is possible only after effective larval dispersal from source populations, usually in natural habitat. While most studies focused on short term colonization by pioneer species, we propose to test the relevance of geographic location, shape, age and depth of immersion on the ARs long term colonization by species found in natural stable communities in the Gulf of Lion. We recorded the presence of five sessile invertebrates species, with contrasting life history traits and regional distribution in the natural rocky habitat, on ARs with different shapes deployed during two immersion time periods (1985 and the 2000s) and in two depth ranges (<20m and >20m). At the local level (~5kms), neither shape, depth nor immersion duration differentiated ARs assemblages. At the regional scale (>30kms), colonization patterns differed between species, resulting in diverse assemblages. This study highlights the primacy of geographical positioning over shape, immersion duration and depth in ARs colonization, suggesting it should be accounted for in maritime spatial planning.</p>
Potentially toxic trace metal (Cu, Cd) threshold concentrations for phytoplankton at given open and coastal locations
<p>This data compilation contains previously published threshold concentrations of copper and cadmium of phytoplankton in open and coastal oceans. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper and cadmium as 63.546 and 112.411, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilise different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included, along with the name of the phytoplankton. The oceans were divided into geographical sections, namely the Atlantic Ocean, Indian Ocean, Pacific Ocean and Southern Ocean, and subsequently further subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
Concentrations of trace metals (Cu, Cd, Zn) in the ocean at given open and coastal locations
<p>This data compilation contains previously published concentrations of copper, cadmium and zinc in open and coastal oceans of the world. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper, cadmium and zinc as 63.546, 112.411 and 65.380 g/mol, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilize different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included. The oceans were divided into different geographical regions, namely the Atlantic Ocean, Pacific Ocean, Indian Ocean and Southern Ocean, and subsequently subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
UAV multispectral imagery dataset over a vineyard affected by Botrytis in 'Tomiño', Pontevedra, Spain. It includes GPS location of vine trunks, diseases and GCP points.
<p>This dataset contains a set of ground data and four flights captured on grape harvest over a vineyard affected by Botrytis cinerea. UAV flights took place on 16 September 2021, at 30 m height and using different angles (0, 30, 45 degrees). Pictures were taking using a Micasense RedEdge 3 sensor and were calibrated using the provided Micasense reflectance panel. The flight path was programmed to fly in autonomously, following manufacturer’s instructions (DJI). The dataset includes a shapefile with the GPS location of vine trunks, bunches affected by Botrytis and GCP points.</p>
LOCATION DOMESTIC ARCHITECTURE AUGUSTA EMERITA (MERIDA, SPAIN)
<p>This dataset is based on houses from the Roman colony of <em>Augusta Emerita </em>(ca. B.C 25- A.D. 382)<sup> <a href="#_ftn1"><sup>[1]</sup></a></sup>. These dates are not chosen randomly: 25 B.C. saw the foundation <em>ex nihilo </em>of the settlement, according to Cass. Dio., <em>Hist</em>. 53.26.1<a href="#_ftn2"><sup><sup>[2]</sup></sup></a>, and A.D. 382, the last time a name of a <em>vicarius</em> is attested in the epigraphy<a href="#_ftn3"><sup><sup>[3]</sup></sup></a>. The remains from <em>Augusta Emerita </em>are particularity well suited to GIS analysis of this type because the site was occupied for more than two thousand years and was never abandoned, while also being occupied by several cultures over that time.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> All documentation based on La arquitectura doméstica de Augusta Emerita (2015 Phd) https://dehesa.unex.es/handle/10662/2670# </p> <p><a href="#_ftnref2">[2]</a> The Duoviri's first couple is evidenced ca.20 B.C. from the fragment of the Fasti duovirales: A. Stylow and A. Ventura, “Los hallazgos epigráficos”, in R. Ayerbe, T. Barrientos and F. Palma (edd.), <em>El foro de Augusta Emerita. </em><em>Génesis y evolución de sus recintos monumentales</em> (Mérida 2009) 453-523.</p> <p><a href="#_ftnref3">[3]</a> L. Hidalgo and G. Méndez, “Octavius Clarus, un nuevo Vicarius Hispaniarum en Augusta Emerita”, <em>Mérida. Excavaciones Arqueológicas </em>8 (2005) 547-64.</p>
[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria
<p><strong>Highlights:</strong></p> <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul> <p> </p> <p><strong>Collector array description: </strong>The data is from a flat plate collector array with a total gross collector area of 516 m<sup>2</sup> (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.</p> <p> </p> <p><strong>Data files:</strong></p> <ul> <li><strong>FHW_ArcS__main__2017.csv</strong> – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li><strong>FHW_ArcS__main__2017.parquet</strong> – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li><strong>FHW_ArcS__parameters.json</strong> – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li><strong>FHW_ArcS__raw__2017.csv </strong>– Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul> <p> </p> <p><strong>Scripts: </strong></p> <ul> <li><strong>make_data.py</strong> – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li><strong>make_plots.py</strong> – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul> <p> </p> <p><strong>Data collection and preparation</strong>: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria</p> <p> </p> <p><strong>Data owner</strong>: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria</p> <p> </p> <p><strong>Additional information</strong> is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.</p> <p> </p> <p><strong>Note: </strong>A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a></p> <p> </p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna, Italy.
<p>This dataset contains all raw geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna. The dataset is divided in three separated excel worksheets: </p> <p>- <strong>Focus Area</strong>. Sediment composition of the 21 sediment samples collected in 2022 in the Focus Area. Refer to Figs. 1 and 2 in the manuscript Giambastiani et al., 2024 for the sample locations. Listed are also other information related to sampling, such as depositional facies (BR: beach ridge deposits; IF: Interfluvial floodplain deposits), distance from the sea, altimetry, amount of fertilizer applied based on the land use, and EC of drainage water. <br>The sediment samples were collected in March 2022 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by the authors.</p> <p>- <strong>LRC, Land Reclamation Consortium</strong>. PTEs composition of the sediment samples of the Land Reclamation Consortium dataset. Refer to Fig. 1 and 2 in the manuscript Giambastiani et al., 2024 for the location. Listed are also other information related to sampling, such as distance from the sea, altimetry, and amount of fertilizer applied based on the land use. <br>The sediment samples were collected since 2010 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by The Land Reclamation Consortium of Romagna (Italy). No other uses apart from scientific purpose is allowed without notice to the authors.</p> <p>- <strong>Wells</strong>. Physical and chemical groundwater parameters of 4 wells localted within the Focus Area. Refer to Fig.2 in the manuscript Giambastiani et al., 2024 for the location. <br>Data were collected during previous studies by Greggio et al. (2020) and reprocessed to obtain vertical profiles of EC, pH, Eh, and chemical concentrations along the coastal aquifer depth.</p> <p>More informations regarding the source, ownership, collection methodologies and analytical techniques are in Giambastiani et al., 2024.</p>
The global water resources and use model WaterGAP v2.2e: location and attributes of reservoirs and regulated lakes
<p>This dataset contain the location and attributes of the reservoirs and regulated lakes in WaterGAP v2.2e. This dataset is provided to be transparent how the reservoirs are included in this WaterGAP version and e.g. to check deviations from the locations as provided by ISIMIP (www.isimip.org).</p> <p>Please see the readme.md for furhter details and please consider the license terms from the data sources listed in the readme.md.</p>
NLL-SSST-coherence high-precision earthquake location catalog for the 2023 Ojai, California earthquake sequence
<p><strong>Hypocenter catalog files and visualizations of high-precision, NLL-SSST-coherence earthquake locations for the 2023 M5.1 Ojai, California earthquake sequence and background seismicity (2128 events, 1980-01-01 to 2023-08-25).</strong></p> <p>NLL-SSST-coherence (<a href="https://doi.org/10.1029/2021JB023190">Lomax and Savvaidis, 2022</a>; <a href="https://doi.org/10.26443/seismica.v2i1.324">Lomax and Henry, 2023</a>) is an enhanced, absolute-timing earthquake location procedure which 1) iteratively generates spatially varying travel-time corrections to improve multi-scale location precision and 2) uses waveform similarity to improve fine-scale location precision.</p> <p>Relocations performed with phase arrival data available from <a href="http://service.scedc.caltech.edu">http://service.scedc.caltech.edu</a></p> <p>Visualizations include topography from <a href="https://opentopography.org">https://opentopography.org</a> and surface fault traces from <a href="https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=5a6038b3a1684561a9b0aadf88412fcf">https://usgs.maps.arcgis.com</a></p> <p> </p> <p>This repository contains:</p> <p><strong>Full catalog in CSV format</strong>:<br> CSV file data columns correspond to selected fields of the of NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Full catalog in NonLinLoc hyp format</strong>:<br> NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Key NLL-SSST-coherence configuration files</strong>: NLL-SSST-coherence_config/*</p> <p><strong>Selected Visualization images</strong></p> <p> </p>
ECHAM6-wiso nudged simulation water isotopes and precipitation for the period 1990-2020 at the EastGRIP drilling location, Greenland
<p>This model dataset contains output produced with the isotope-enabled atmosphere GCM ECHAM6-wiso at T127L95 spatial resolution, nudged to the ERA-5 reanalysis product. The 6-hourly model output is provided for the period 01/1990-12/2020 for the grid cell containing EastGRIP drilling location in Greenland, centered at 75.27N, -36.57 E.</p> <p>The complete description of the simulation can be found in:</p> <p><em>Cauquoin, A. and Werner, M., 2021. High‐Resolution Nudged Isotope Modeling With ECHAM6‐Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data. Journal of Advances in Modeling Earth Systems, </em><a href="https://doi.org/10.1029/2021MS002532">https://doi.org/10.1029/2021MS002532</a></p> <p>The provided files (netCDF) contain the ECHAM6-wiso model data used as input for the SNOWISO snow pack model in:</p> <p><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M.S. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, </em><a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a><em>.</em></p> <p>The provided variables are:</p> <p> d18O_vapor: delta value for <sup>18</sup>O (‰) in the vapor of the lowest atmospheric layer (ECHAM level 95).<br> dD_vapor: delta value for H<sub>2</sub> (D) (‰) in the vapor of the lowest atmospheric layer (ECHAM level 95).<br> aprt: total precipitation (mm water equivalent per month)<br> d18O_precip: delta value for <sup>18</sup>O (‰) in the precipitation<br> dD_precip: delta value for H<sub>2</sub> (D) (‰) in the precipitation</p> <p><strong>Data usage notice:</strong></p> <p>If you use<strong> any of these data</strong> you should refer to:</p> <p><em>Cauquoin, A. and Werner, M., 2021. High‐Resolution Nudged Isotope Modeling With ECHAM6‐Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data. Journal of Advances in Modeling Earth Systems, </em><a href="https://doi.org/10.1029/2021MS002532">https://doi.org/10.1029/2021MS002532</a></p>
Lake ecosystem metabolism estimates from 3 locations in Lake Sunapee, NH, USA during the summer stratified period from June to September 2018
Surface water lake ecosystem metabolism daily estimates during the 2018 summer stratified period (04 June - 22 Sept) at three locations within Lake Sunapee (NH, USA). Estimates at each site used previously published data from high-frequency temperature and dissolved oxygen sensors deployed in the lake: the Deep Site (LSPA et al., 2021a: full citation in Methods) and the Herrick Cove and Georges Mills sites (Ward et al., 2021: full citation in methods). The Deep Site was located near Loon Island in the main basin of the lake with 12 m total depth and the dissolved oxygen sensor was deployed 1 m below surface. The Herrick Cove site was in the north east cove of the lake with 6.5 m total depth at site and the dissolved oxygen sensor was deployed 1.75 m below surface. The Georges Mills site was in the northwest cove of the lake with 7 m total depth at site and the dissolved oxygen sensor deployed 1.75 m below surface. We used an inverse modeling approach, where the lake ecosystem model predicted diel changes in dissolved oxygen to estimate daily volumetric rates of gross primary production (GPP), respiration (R), and net ecosystem metabolism (NEM) using the in-lake buoy measurements at each site and wind and surface PAR from the meteorological station at the Deep Site buoy (LSPA et al., 2021b). Raw metabolism estimates were QA/QC'd to generate this final metabolism estimate dataset following protocols described in the Methods section of this dataset.
Weather data for the period 2009 to 2022 from the Open Field location at University Farms, Case Western Reserve University
Data from the Open Field weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. From 10/20/2009 to 10/30/2014, the weather station was located at N 41.496883, W 81.436117, when it was relocated to N 41.49759, W81.43738. Data include date/time (in 15-minute intervals), wind speed, wind gust speed, wind direction, air temperature, relative humidity, solar radiation, rainfall, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.
Weather data for the period 2009 to 2022 from the North Woodlot location at University Farms, Case Western Reserve University
Data from the North Woodlot weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. The North Woodlot weather station is at N 41 29.969, W 81 25.234. Data include Date and time (in 15-minute intervals), wind speed, wind gust speed, air temperature, relative humidity, solar radiation, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.
Groundwater nitrate concentrations in wells located at the shoreline of West Falmouth Harbor from 2006 to 2023
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000's. As part of a long-term study into the effects of this nitrogen enrichment, we have been measuring groundwater chemistry in wells installed along the shoreline of the harbor to monitor the spatial and temporal patterns in N loading. Water samples were collected approximately annually from 2006 through 2009, and less frequently in subsequent years, and processed for nitrate + nitrite. Full analysis details are available in Hayn et al. 2025 in Estuaries and Coasts (doi: 10.1007/s12237-025-01630-0).
LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S.
This data package, LAGOS-US LOCUS v1.0, is one of the core data modules of the LAGOS-US platform that provides an extensible research-ready platform to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). This data module contains information on the location, identifiers, and physical characteristics of lakes and their watersheds. The characteristics in this module include: variables that can be obtained from GIS data such as location and geometry; variables that can be derived using GIS processing such as lake watersheds and their geometry, lake glaciation history, and lake connectivity; and commonly used identifiers from GIS and other data products useful for linking with LAGOS-US. LOCUS is based on a snapshot of the high-resolution National Hydrography Dataset product available at the initiation of the project that provided the basis for locating, identifying, and characterizing the geometry of all lakes in LAGOS-US. The database design that supports the LAGOS-US research platform was created based on several important design features. Lakes are the fundamental unit of consideration, all lakes in the spatial extent must be represented (above a minimum size) and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other 2 core data modules that are part of the LAGOS-US platform: GEO (which includes geospatial ecological context at multiple spatial and temporal scales for lakes and their watersheds) and LIMNO (in situ lake surface-water physical, chemical, and biological measurements through time) that are each found in their own data packages.
Historic salvage sale locations (1954 - 1974), Andrews Experimental Forest
Historic salvage sale areas (with buffered roads) are reconstructed. Historic salvage timber sales in the H J Andrews from 1954 - 1974 were outlined on a variety of hard copy maps. These manuscripts were digitized into four non-overlapping coverages. Each coverage was turned into a region and the UNION command was used to combine the data into a single coverage of salvage sale regions. The road layer was buffered by 50 yards (45.72 meters)and these areas were added to the coverage.
Experimental watershed boundaries and gaging station locations, Andrews Experimental Forest, 2011
This dataset contains locations of the HJ Andrews hydrology and watershed studies features including small experimental watersheds, all gaged watersheds, gaging station locations, and watershed boundaries generated from digital elevation models. Data sources are identified within the metadata for each GIS layer.
Spot fire locations (1991), Andrews Experimental Forest
1991 Spot Fire Locations for the HJ Andrews Experimental Forest. This data documents the locations of fires during the 1991 fire season. There is little additional information about the fires and suppression efforts.
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.