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3,575 results for “2009”
Anastasia Mosquito Control District entomological monitoring 2009
<p>Mosquito surveillance from the Anastasia Mosquito Control District Vector Surveillance program to survey mosquito populations.</p>
CLDF dataset derived from Haspelmath and Tadmor's "World Loanword Database" from 2009
<p>Cite the source of the dataset as:</p> <blockquote> <p>Haspelmath, Martin & Tadmor, Uri (eds.) 2009. World Loanword Database. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://wold.clld.org)</p> </blockquote>
DOI's with SDG labels on Target level | 1.4M research articles (2009-2020) related to Sustainable Development Goals
<p>Table content: This data set contains 1.4 million publication DOI's related to the <a href="http://metadata.un.org/sdg/">Targets of the Sustainable Development Goals</a> in the period 2009 - 2020.</p> <p>Table dimensions: rows: 1.4 million, columns: 4 / rows: 1.4 million, columns: 180</p> <p>Table columns: <a href="https://en.wikipedia.org/wiki/Digital_object_identifier">doi</a> | date | <a href="http://metadata.un.org/sdg/ontology#Target">sdg_target</a> | <a href="http://metadata.un.org/sdg/ontology#Goal">sdg_goal</a> / <a href="https://en.wikipedia.org/wiki/Digital_object_identifier">doi</a> | date | <a href="http://metadata.un.org/sdg/ontology#Target">169 sdg_targets</a> | <a href="http://metadata.un.org/sdg/ontology#Goal">17 sdg_goalsl</a></p> <p>Table formats: <a href="https://en.wikipedia.org/wiki/Comma-separated_values">.csv</a> | <a href="https://en.wikipedia.org/wiki/Microsoft_Excel">.xlsx</a> | <a href="https://en.wikipedia.org/wiki/Apache_Parquet">.parquet</a></p> <p><em>How we made this data:</em></p> <p>We have made a search on <a href="https://scopus.com">Scopus </a>using the <a href="https://aurora-network-global.github.io/sdg-queries/">Aurora SDG queries version 5</a> for each of the targets, with a limited year range from 2009 till 2020.</p> <p>Good to know: don't be alarmed if you can find a doi that is labeled with more than one target (~16%). This is not a bug, this is a feature... We used 169 queries, one for each target, a publication can appear in more han one result set.</p> <p>Read this <a href="https://zenodo.org/record/4964606/files/Evaluation_on_accuracy_of_mapping_science_to_the_United_Nations__Sustainable_Development_Goals__SDGs__of_the_Aurora_SDG_queries.pdf?download=1">report to learn more about the accuracy</a> of the queries and the data result sets.</p> <p><em>How can you use this data:</em></p> <p>You can use this data to 1. quickly match your existing publication lists to this list to see how that your publications are related to the targets of the SDG's. 2. use these as a basis / seed set / gold set to train more advanced text / graph classifiers (after you have extracted title, abstract or even full-text using crossref.org, unpaywall.org, etc)</p> <p><em>How can you help:</em></p> <p><a href="https://sites.google.com/vu.nl/aurora-sdg-research-dashboard/sdg-knowledge-base#h.d2pd3c39k276">Let us know</a> how you use this data. We'll put your project on the list in our <a href="https://sites.google.com/vu.nl/aurora-sdg-research-dashboard/sdg-knowledge-base">SDG matching knowledge base.</a></p>
Filmografía occidental entre 2009-2015. Títulos y fichas técnicas
<p>Dataset incluye los títulos de películas más su ficha técnica para el periodo comprendido entre el 2009 y el 2015 obtenidos a partir de Web Scraping utilizando Python y librerías como BeautifulSoup. Se eligieron las películas pertenecientes a los mercados cinematográficos más influyentes del mundo occidental. Así, el dataset contiene películas de EE.UU, Francia, Italia, España, México, Argentina.</p> <p>Las fuentes de las que extrae esta información son <a href="https://www.culturaydeporte.gob.es/cultura/areas/cine/mc/catalogodecine/descargas-catalogo.html">catálogo del Ministerio de Cultura y Deporte</a> y <a href="https://en.wikipedia.org/">Wikipedia</a>.</p> <p>Este dataset está pendiente de limpieza pero los campos con mayor relevancia son:</p> <ul> <li> <p>spa_title: Título en español de la película</p> </li> <li> <p>eng_title: Título en inglés de la película</p> </li> <li> <p>year: Año de publicación de la película</p> </li> <li> <p>name: Nombre de la película.</p> </li> <li> <p>image: Imagen significativa o poster de la película.</p> </li> <li> <p>alt: Texto alternativo a la imagen.</p> </li> <li> <p>caption: <em>Caption</em> de la imagen.</p> </li> <li> <p>native_name: Título original de la película.</p> </li> <li> <p>director: Director de la película.</p> </li> <li> <p>writer: Guionista de la película. </p> </li> <li> <p>screenplay: Director de escenografía.</p> </li> <li> <p>story: Escritor de la historia.</p> </li> <li> <p>based_on: Obra en la que está basada la película.</p> </li> <li> <p>Producer: Productor de la película.</p> </li> <li> <p>Starring: Elenco de actores/actrices.</p> </li> <li> <p>narrator: Narrador de la película.</p> </li> <li> <p>cinematography: Director de fotografía.</p> </li> <li> <p>editing: Editor de la película.</p> </li> <li> <p>music: Director musical.</p> </li> <li> <p>animator: Equipo de animadores o animador.</p> </li> <li> <p>layout_artist: Artista conceptual/Boceto</p> </li> <li> <p>background_artist: Artista de fondos y escenarios.</p> </li> <li> <p>color_process: Gama cromática de la película (Ej. blanco y negro, sepias, etc.)</p> </li> <li> <p>studio or production_companies: Estudio o productora.</p> </li> <li> <p>distributor: Distribuidora.</p> </li> <li> <p>released: Fecha de estreno.</p> </li> <li> <p>runtime: Duración de la película.</p> </li> <li> <p>country: País de producción.</p> </li> <li> <p>language: Lenguaje principal.</p> </li> <li> <p>budget: Presupuesto de la película.</p> </li> <li> <p>gross: Recaudación de la película.</p> </li> </ul> <p> </p> <p> </p>
Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 2: 2005 - 2009) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2005 - 2009. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 1000 m</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6342822">https://doi.org/10.5281/zenodo.6342822</a></p>
Ridgecrest, CA 2009-2019 microseismicity catalog
<p>Catalog of microseismicity between 2009-01-01 and 2019-07-04 in the Ridgecrest, California area (latitudes: 35.50 -- 36.00, longitudes: -117.80 -- -117.30). This catalog captures 10 years of seismic activity before the July 2019 Ridgecrest sequence, which started with a M6.4 on 2019-07-04 and was followed by a M7.1 on 2019-07-06.</p> <ul> <li><strong>v1.0.0</strong>: Earthquake catalog from "Enhanced Tidal Sensitivity of Seismicity Before the 2019 Magnitude 7.1 Ridgecrest, California Earthquake" (in press at Geophysical Research Letters). The catalog contains 191,569 events and was built with the BPMF workflow (v2.0.0alpha, <a href="https://github.com/ebeauce/Seismic_BPMF">https://github.com/ebeauce/Seismic_BPMF</a>). v1.0.0 is available at <a href="https://doi.org/10.5281/zenodo.8127880">https://doi.org/10.5281/zenodo.8127880</a>.</li> <li><strong>v2.0.0</strong>: Minorly updated catalog accompanying "BPMF: A BackProjection and Matched-Filtering Workflow for Automated Earthquake Detection and Location" (submitted to Seismological Research Letters). The catalog contains 183,269 events, slightly revised locations and magnitudes with respect to v1, and was built with the BPMF workflow (v2.0.0beta, <a href="https://github.com/ebeauce/Seismic_BPMF">https://github.com/ebeauce/Seismic_BPMF</a>).</li> <li><strong>v2.0.1</strong>: Removal of some regional (particularly at the edges of the study region) and teleseismic events and slightly updated event magnitudes. 155,794 events.</li> </ul> <p>The catalog is a table with 11 columns:</p> <ul> <li>event_id: a unique event identifier,</li> <li>origin_time: origin time, UTC, of the event,</li> <li>longitude: longitude in decimal system,</li> <li>latitude: latitude in decimal system,</li> <li>depth: depth in km,</li> <li>hmax_unc: maximum horizontal uncertainty in km (from uncertainty covariance ellipsoid),</li> <li>hmin_unc: minimum horizontal uncertainty in km (from uncertainty covariance ellipsoid),</li> <li>az_hmax_unc: azimuth (angle from north) of maximum horizontal uncertainty in degrees,</li> <li>vmax_unc: maximum vertical horizontal uncertainty in km (from uncertainty covariance ellipsoid),</li> <li>Mw: moment magnitude, !! this column is mostly empty since most events were not well enough recorded to estimate a moment magnitude !!</li> <li>Ml: local magnitude.</li> </ul>
Woody biomass flows in the EU Member States for the years 2009-2017
<p>This dataset, available in the file JRC_Forestry_Sankey_2009_2017_vers2022.csv, contains the amounts of woody biomass flowing across the different sectors of the forest-based bioeconomy, for all the Member States of the European Union, from 2009 to 2017. These amounts, here expressed in thousand cubic meters solid wood equivalent, represent the arrows of a Sankey diagram per each Member State and per each year.<br>The interactive graphical visualisation of this dataset is available at: https://knowledge4policy.ec.europa.eu/visualisation/interactive-sankey-diagrams-woody-biomass-flows-eu-member-states_en.<br>The codes of the arrows (flows) and the codes of the starting and end nodes, together with the type of biomass that flows from one node to the other, are reported in detail in the file: arrow_codes.csv.<br>All the definitions are available in the file: Definitions.txt.</p> <p>© European Union, 1995-2023</p>
Role of fluid on earthquake occurrence: Example of the 2019 Ridgecrest and the 1997, 2009 and 2016 Central Apennines sequences
<p>This repository contains files needed to reproduce the b-value times series and stress change modeling related to the Central Apennines and Ridgecrest earthquake sequences (paper under revision, preprint available at <a href="https://doi.org/10.31223/X5MH1J">https://doi.org/10.31223/X5MH1J</a>). </p>
Lake Sunapee Gloeotrichia echinulata density near-term hindcasts from 2015-2016 and meteorological model driver data, including shortwave radiation and precipitation from 2009-2016
Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.
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.
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.
Chlorophyll data from experiments testing for nitrogen, phosphorus, and thiamine limitation of phytoplankton in 39 Ohio lakes of varying trophic status during the growing seasons (April – October) of 2008–2009
Although nitrogen and phosphorus deficiency of algal blooms have been the focus of substantial attention, organic nutrients can limit algal growth in aquatic systems. Growing evidence indicates thiamine (vitamin B1) can influence the community of primary producers in marine systems, but comparatively little is known about the effect of thiamine on freshwater algal productivity. We conducted 106 nutrient deficiency experiments with water from 39 Ohio lakes of varying trophic status during the growing seasons (April – October) of 2008–2009. Specifically, we tested the response of phytoplankton biomass (as chlorophyll a, chl-a) relative to controls to added nitrogen (N), phosphorus (P), thiamine (Th), or combinations of N+P and N+P+Th in integrated surface water collected from the inflow and outflow of each lake. The data presented here show the average chl-a of two replicate samples of each treatment (control, N, P, Th, N+P, N+P+Th), ratio of treatment/control response, and growth response ratio as ln(treatment chl-a/control chl-a). Each entry also includes lake surface water pH at time of collection and the initial chl-a concentration at time of experiment start.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Aboveground plant biomass, 2009-2017. (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/275/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-bnz/501/17. The abstract below was extracted from the Level 0 data package and is included for context: The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes aboveground plant biomass from winter warming, summer warming, and control treatment plots at CiPEHR.
Adminstrative boundary, Andrews Experimental Forest, 1997 survey, 2009 update
This database contains the spatial representations for the administrative boundary of the HJ Andrews Experimental Forest. This database was derived from ground based surveys of the Andrews in 1997, based on the Experimental Forest Establishment report. Field personnel determined ridge tops. The original data was updated in 2009 (entity 2) and contains the boundaries of watershed 9, 10. and 11(which are outside Lookout Basin) as updated in 2009, in addition to the Lookout Creek Basin.
Plant community typing (2009 update), Andrews Experimental Forest
Plant Communities of the HJ Andrews Experimental Forest (revised 2009). A total of 23 forest communities have been identified and characterized in a preliminary manner. Data used in formatting the classification had previously been collected on 300 reconnaissance plots located on the H. J. Andrews Forest and surrounding area. Vegetation classification was facilitated by similarity analysis and stand ordination procedures developed by Dr. Will Moir, formerly of Colorado State University. Results of stand ordination indicate the presence of strong moisture and temperature gradients along which forest stands array themselves. The forest communities recognized in this classification are listed in the following internal report: http://andrewsforest.oregonstate.edu/pubs/pdf/pub1741.pdf
Stream nutrient sampling during winter baseflow conditions in the Andrews Forest and Willamette River Basin, February 2009
To better understand the impact of land use on stream nutrient export, a synoptic sampling of streams draining 57 sub-basins within the Willamette River basin was conducted during winter baseflow conditions. The objective of the study was to assess how streamwater values of stream dissolved organic carbon (DOC), NO3- and Cl- and specific ultra-violet absorbance (SUVA) were related with the proportion of land in the watershed in urban areas, agriculture, and forest. Stream water samples were collected during baseflow conditions in February 2009 throughout the Willamette River basin, including the experimental watersheds (1,2,6,7,8,9,10), Lookout and Mack Creeks at the HJ Andrews Forest. Land use for each point-delineated watershed were determined from published values from the PNWERC dataset (http://www.fsl.orst.edu/pnwerc/wrb/access.html).
Meteorological data collected on Toolik Lake during the ice free season since 1989 to 2009, Arctic LTER, Toolik Research Station, Alaska.
Yearly file describing the metological conditions on Toolik Lake (named the Toolik Lake Climate station), adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This is a floating climate station and should not be confused with the Toolik Field Station Climate site (TFS Climate Station or Met Station) which is a terrestrial station (located on land). Note that this land station has been called the "Toolik Main Climate Station", and the station on the lake is located where the main lake sampling site is located so it has also been called the Toolik Lake Main Climate Station. Measurements include air temperature, relative humidity, wind direction, wind speed, and radiation.
Water chemistry data for various lakes near Toolik Research Station, Arctic LTER. Summer 2000 to 2009.
Decadal file describing the water chemistry in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 2000 to 2009. Chemical analyses were conducted on samples from various depths in the sample lakes either once, or multiple times during the spring, summer and fall months (May to September). Chemical analyses for the samples include alkalinity, dissolved organic and inorganic carbon (DIC/DOC), inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP), particulate carbon, nitrogen and phsphorous (PC, PN, PP), cations (Ca, Mg, K, Na and Si) and anions (SO4, Cl). See methods for the yearly datsets which were combined into this data set.
Sedimentation rate, concentration of macronutrients and flux for NE14, Toolik, Dimple, Perched during Summer 2009.
We measured the flux of bulk material and major macronutrients (carbon, nitrogen and phosphorus) from the water column to the benthos in four separate lakes during the summer of 2009. The lakes were chosen to investigate the impacts of disturbance on lake sedimentation. Two of the lakes, Dimple and Perched, were within catchments that were burned by the 2007 Anaktuvuk River wildfire. Two of the lakes, NE-14 and Perched, were receiving elevated sediment loads from thermokarst failures on their shorelines, and Toolik Lake was used as a reference lake. As such, the lakes were organized by disturbance regime: Dimple = fire only, Perched = fire + thermokarst, NE-14 = thermokarst only, and Toolik = undisturbed reference.
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.