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Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports
<p>This is the dataset for paper "Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports"</p>
Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution
<h2>Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution</h2><p><br>This dataset uses regional atmospheric climate model output to simulate wind and solar power generation across the entire contiguous US. This data is particularly useful for obtaining power production profiles at new locations or locations where only short records exist, or to study climate change impacts. We assume that a generic power plant exists at each grid cell and model the power output for solar at the surface and wind at 80, 100 and 125 meter hub heights. The data consists of a historical period 1980-2022 and several future scenarios that extend from 2020-2099.</p><p><br>- Code: <a href="https://github.com/GODEEEP/tgw-gen/">https://github.com/GODEEEP/tgw-gen/</a><br>- Underlying climate data and description of the future scenarios: <a href="https://tgw-data.msdlive.org/">https://tgw-data.msdlive.org/</a><br>- The NREL reV model was used to produce these profiles: <a href="https://github.com/NREL/reV">https://github.com/NREL/reV</a></p><h3>Data</h3><p>The dataset consists of a series of netcdf files, one per year, grouped together based on resource type, climate scenario, and year range. The data is available as capacity factors which can be scaled to any desired plant size.</p><p> </p><p>Please see the data directory below to find the appropriate record for your purposes.</p><ul><li>solar<ul><li>historical<ul><li><a href="https://doi.org/10.5281/zenodo.10138040">solar historical 1980-2022</a></li></ul></li><li>rcp45cooler<ul><li><a href="https://doi.org/10.5281/zenodo.10138850">solar rcp45cooler 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10139076">solar rcp45cooler 2060-2099</a></li></ul></li><li>rcp45hotter<ul><li><a href="https://doi.org/10.5281/zenodo.10139819">solar rcp45hotter 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10139994">solar rcp45hotter 2060-2099</a></li></ul></li><li>rcp85cooler<ul><li><a href="https://doi.org/10.5281/zenodo.10140410">solar rcp85cooler 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10140517">solar rcp85cooler 2060-2099</a></li></ul></li><li>rcp85hotter<ul><li><a href="https://doi.org/10.5281/zenodo.10140685">solar rcp85hotter 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10140758">solar rcp85hotter 2060-2099</a></li></ul></li></ul></li><li>wind<ul><li>historical<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10182689">wind 80m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10182848">wind 80m historical 2001-2022</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10143911">wind 100m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10144158">wind 100m historical 2001-2022</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10161120">wind 125m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10161276">wind 125m historical 2001-2022</a></li></ul></li></ul></li><li>rcp45cooler<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10183152">wind 80m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10183308">wind 80m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10183360">wind 80m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10192154">wind 80m rcp45cooler 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10145097">wind 100m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10145630">wind 100m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10145720">wind 100m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10145987">wind 100m rcp45cooler 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10161377">wind 125m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10161494">wind 125m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10161683">wind 125m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10162136">wind 125m rcp45cooler 2080-2099</a></li></ul></li></ul></li><li>rcp45hotter<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10193740">wind 80m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10198849">wind 80m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10198912">wind 80m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10198962">wind 80m rcp45hotter 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10146056">wind 100m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10146108">wind 100m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10146118">wind 100m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10146159">wind 100m rcp45hotter 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10162368">wind 125m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10162463">wind 125m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10162561">wind 125m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10162689">wind 125m rcp45hotter 2080-2099</a></li></ul></li></ul></li><li>rcp85cooler<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10199074">wind 80m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10199103">wind 80m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10199362">wind 80m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10199411">wind 80m rcp85cooler 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10146346">wind 100m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10150299">wind 100m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10152763">wind 100m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10152830">wind 100m rcp85cooler 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10162728">wind 125m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10163251">wind 125m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10171504">wind 125m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10178654">wind 125m rcp85cooler 2080-2099</a></li></ul></li></ul></li><li>rcp85hotter<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10199444">wind 80m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10199543">wind 80m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10199587">wind 80m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10199638">wind 80m rcp85hotter 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10152864">wind 100m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10152945">wind 100m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10160579">wind 100m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10160776">wind 100m rcp85hotter 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10182205">wind 125m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10182349">wind 125m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10182514">wind 125m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10182574">wind 125m rcp85hotter 2080-2099</a></li></ul></li></ul></li></ul></li></ul><p> </p><p>This research was supported by the <a href="https://godeeep.pnnl.gov">Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP)</a> Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Chesley et al., 2023 - Gofar Oceanic Transform Fault CSEM data from fault-perpendicular profiles (collected 2022)
<p>This repository contains processed and edited controlled-source electromagnetic amplitude and phase data from the Gofar oceanic transform fault with corresponding bathymetry files. The files beginning "dataFile.." are the amplitudes and phases and the files beginning "topo..." are the bathymetry information. Each profile in this repository crosses the fault approximately perpendicularly.</p>
Standrad Household Load Profile
<p>BDEW standard load profile generated using demandlib python library with an annual consumption of 4.7 MWh/a. </p>
Dataset for paper "Global method for gender profile estimation from distribution of first names"
<p>Full dataset for paper "<i>Global method for gender profile estimation from distribution of first names</i>". See https://arxiv.org/abs/2305.07587</p>
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - June 2023
<p>June 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - July 2023
<p>July 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - August 2023
<p>August 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma
<p>Processed data:</p><p>inferCNV_output_WM4237.zip, inferCNV_output_WM4007.zip - InferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_cnv_WM4237.h5ad, ad_all_human_clustered_cnv_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of the inferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_im_st_WM4237.h5ad, ad_all_human_clustered_im_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for 12 Visium samples of model WM4237 or WM4007. "_im" for imaging, "_st" for Visium.</p><p>ad_all_human_clustered_im_ad_WM4237_m.h5ad, ad_all_human_clustered_im_ad_WM4007_m.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for additional H&E slides (non-Visium) of model WM4237 or WM4007. "_im" for imaging, "_m" for Macenko normalization of the H&E slides with STQ.</p><p>ids_WM4237_AD.txt, ids_WM4007_AD.txt, ids_WM4237_ST.txt, ids_WM4237_ST.txt - list of identifiers of all samples (tissue sections).</p><p>ad_all_human_clustered_st_WM4237.h5ad, ad_all_human_clustered_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of RNA profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>CNV_burden_WM4237.csv, CNV_burden_WM4007.csv - Per-spot values of InferCNV-derived CNV burden for samples of models WM4237 or WM4007.</p><p>ad_all_scaled_filtered_st_WM4237.h5ad, ad_all_scaled_filtered_st_WM4007.h5ad - AnnData objects containing concatenated and pre-processed samples of WM4237 or WM4007.</p><p>WM4237_3_AD_m-Imaging-STQ.zip, WM4007_3_AD_m-Imaging-STQ.zip - Imaging portion of STQ pipeline output derived from additional (non-Visium) H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237_ST-Imaging-STQ.tar.gz, WM4007_ST-Imaging-STQ.tar.gz - Imaging portion of STQ pipeline output derived from Visium H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237-ST-downstream-output.tar.gz, WM4007-ST-downstream-output.tar.gz - ST-downstream-processing pipeline output for Visium samples of model WM4237 or WM4007.</p><p>WM4237-STQ-sequencing.tar.gz, WM4007-STQ-sequencing.tar.gz - RNA sequencing portion of STQ pipeline output derived from Visium samples of model WM4237 or WM4007.</p><p>rna-pseudotime-ordered.zip - Pseudotime ordering of RNA profiles of spots for each time point (T0, T1, T2, T3, T4, TC) for samples of models WM4237 and WM4007.</p>
Moving vessel profiler (MVP) transect from PolarFront 2023-08 cruise
<p><span>The Moving Vessel Profiler (MVP) was deployed to collect data with high-spatial resolution along our </span><span>transect crossing the front. Instruments contained in the profiling unit ("fish") of the winch were a CTD </span><span>(AML µCTD), a fluorescence sensor (WETLABS) and a laser optical plankton counter (LOPC). </span></p> <p><span>Together the instruments yield data on the physical environment, phytoplankton, zooplankton and other </span><span>particle distributions</span></p> <p><span>During the entire deployment of the MVP, weather conditions were optimal with very low wind speeds </span><span>and calm sea. Apart from one stop at the surface due to a fishing vessel in our transect (20 min stop), </span><span>no sampling issues whatsoever were experienced. A few breaks along the transect were taken to collect </span><span>water samples and to set out the Sailbuoy and Seaglider.</span></p> <p><span>Transect start: 75N, 29 30' E, 17 August at 02:13 UTC</span></p> <p><span>Transect end: 78N, 29 30' E, 18 August at 11:09 UTC</span></p> <p><span>In total the transect was 333 km long and 382 profiles to 25 m above bottom were collected, resulting </span><span>in a spatial resolution of, on average, 1 profile every 870 m.</span></p>
Oxygen concentration profiles data and code from: Langet et al., 2023, Single-celled bioturbators: benthic foraminifera mediate oxygen penetration and prokaryotic diversity in intertidal sediment
<p>This archive contains the data of measured oxygen profile and the scripts used to generate the oxygen surface fluxes for the article</p><p>Langlet, D., Mermillod-Blondin, F., Deldicq, N., Bauville, A., Duong, G., Konecny, L., Hugoni, M., Denis, L., and Bouchet, V. M. P.: Single-celled bioturbators: benthic foraminifera mediate oxygen penetration and prokaryotic diversity in intertidal sediment, Biogeosciences, 20, 4875–4891, https://doi.org/10.5194/bg-20-4875-2023, 2023.</p>
ChromatoShiny: an interactive R/Shiny App for plotting chromatography profiles
<p>Raw data for the manuscript.</p> <p>"011221_Run15_533.tif" - an uncropped gel scan (Fig. 1A)</p> <p>"CPC_SurvK62A_preps_CPC_SP_IE512.tif" - a scan of the marker used in the gel image in Fig. 1A (the gel on the right)</p> <p>"Fig. 1B.pdf" - a raw plot for Fig. 1B exported from the shiny app</p> <p>"Fig. 1C.pdf" - a raw plot for Fig. 1C exported from the shiny app</p> <p>"Fig. 2A.pdf" - a raw plot for Fig. 2A exported from the shiny app</p> <p>"Fig. 2B.png" - a raw screenshot from the shiny app for Fig. 2B </p> <p>"Fig. 3.pdf" - a raw plot for Fig. 3 exported from the shiny app</p> <p> </p>
Ocean bottom seismometer data of the OBS2020-1 profile at the northern continental margin of the South China Sea
<p> Ocean bottom seismometer hydrophone data in SEG-Y format of 11 OBS stations along the OBS2020-1 profile and travel times used for the forward modeling and tomographic inversion. The travel times include the primary phases and secondary phases which are seawater layer multiples at the receiver side. Meanwhile, the OBS locations are also submitted to this repository.</p>
Polski frontendu and American Back-end: GitHub Profile Recruitment Bias in GPT-4
<p>This repository serves as the online appendix for the paper "Polski frontendu and American Back-end: GitHub Profile Recruitment Bias in GPT-4".</p>
Stratosphere-Troposphere wind profiler radar data
<p>Horizontal wind profiles from ST radar data at Cochin (10.04N, 76.33 E) during mosoon seasons (June to September) for three years (2019-2021)</p>
Konsulivske Hillfort — Pit No. 4 profile
The pit discovered near on of the dwellings of Konsulivske Hillfort excavated during 2018 field season (trench 4). It contained a number of bones and pottery framgents, lot of burned clay and coil. The profile has been reconstructed from SfM-photogrammetry (35 images) This has been complicated due to the lack of space in the pit, however, such a workflow reveals additional opportunities in the analysis of profile, stratigraphy and pit's refill process. Source: Objaverse 1.0 / Sketchfab
Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Validation Data Sets
<p>Two public validation cohorts were utilized in the MT-Pilot study, the Cancer Genome Atlas (TCGA) and cohort-1 Johns Hopkins University (JHU). These datasets included DNA, RNA, clinical data, and tissue protein analytes analyzed for survival outcome prediction using AI/Machine Learning modeling. </p>
Replication data for global population profile of tropical cyclone exposure during 2002 and 2019
<p>Tropical cyclones have far-reaching impacts on livelihoods and population health that often persist years after the event. Characterizing the demographic and socioeconomic profile and the vulnerabilities of the exposed populations is essential to assess health and other risks associated with future tropical cyclone events. Estimates of exposure to tropical cyclones are often regional rather than global and do not consider population vulnerabilities. Here, we combine spatially resolved annual demographic estimates with tropical cyclone wind fields estimates to construct a global profile of the populations exposed to tropical cyclones between 2002 and 2019. We find that approximately 560 million people are exposed yearly and that the number of people exposed has increased across all cyclone intensities over the study period. The age distribution of those exposed has shifted away from children (under-5) and towards older people (over-60) in recent years compared to the early 2000s. Populations exposed to tropical cyclones are more socioeconomically deprived than those unexposed within the same country, and this relationship is more pronounced for people exposed to higher intensity storms. By characterizing the patterns and vulnerabilities of populations exposed to tropical cyclones, our results can help identify mitigation strategies and assess the global burden and future risks of tropical cyclones.</p>
Data from: Metabolomic profiles of acute and chronic ambient hydrogen sulfide exposure in a mouse model
<p>Hydrogen sulfide (H<sub>2</sub>S) is an environmental toxicant of health concern following acute or chronic human exposures. Male 6-8 week-old C57BL/6J mice were exposed by whole-body inhalation to 1000 ppm H<sub>2</sub>S for 45 min and euthanized at 5 min and 72 h for acute exposure. For subchronic study, mice were exposed to 5 ppm H<sub>2</sub>S 2 h/day, 5 days/week for 5 weeks. The brainstem was removed for metabolomic analysis. The metabolomics analyses consisted of three assays, (1) primary metabolism by GC-TOF MS, (2) biogenic amines (hydrophilic compounds) by HILIC-MS/MS and (3) lipidomics by RPLC-MS/MS. Metabolomics were performed in West Coast Metabolomics Center, University of California at Davis, CA, USA. 348, 311, and 565 known metabolites were detected and analyzed by primary metabolism, biogenic amines, and lipidomic metabolomics assays. 33, 19, and 46 metabolites were increased at 5 min and 72 h post acute H<sub>2</sub>S exposures and subchronic ambient H<sub>2</sub>S exposures, respectively, compared to room air control group. 22, 17, and 32 metabolites were decreased at 5 min and 72 h post acute H<sub>2</sub>S exposures and subchronic ambient H<sub>2</sub>S exposures, respectively, compared to room air control group. Acute H<sub>2</sub>S exposure decreased excitatory neurotransmitters aspartate and glutamate concentrations while the inhibitory neurotransmitter serotonin was increased. Glutamate and serotonin were also decreased after ambient H<sub>2</sub>S exposure. Branched-chain amino acids, fructose, and glucose were increased by acute H<sub>2</sub>S exposure. In ambient H<sub>2</sub>S exposure, glucose was decreased while MUFAs, PUFAs, inosine, and hypoxanthine were increased. Collectively, these results provide important mechanistic clues of acute and subchronic ambient H<sub>2</sub>S poisonings and show that H<sub>2</sub>S alters neurotransmission homeostasis.</p>
Soil profile pits, carbon and vegetation data of an old Anogeissus grove in Mole National Park, Ghana
<p>Formation of forest islands in West Africa has been linked to anthropogenic soil improvement resulting in luxuriant tree growth in otherwise open savanna landscapes. However, there is limited understanding of how such unique ecosystems modulate soil carbon (C) dynamics and nutrient cycling. In this study, we report soil nutrient characteristics and two distinct soil organic carbon pools of Anogeissus grove (forest island) associated with abandoned village sites of the Mole National Park in the Guinea savanna or tropical continental climatic zone of Ghana, taking opportunity of a previously published study in Biotropica in 1978. We compared present-day differences in soil characteristics between the previously studied forest grove and adjoining open savanna in the Park and evaluated vegetation dynamics since first measurement in 1974. Overall, we see changes related to self-thinning and expansion of the grove on a decadal timescale. Soil organic matter and available phosphorus contents were greater in the grove and increased by 19.6 and 18.7%, respectively over time, showing persistence after four decades. Mineral associated organic carbon (MAOC) differed significantly (p<0.05) between the vegetation types, being 3.44% in the grove and 2.34% in the savanna. The grove was ca. 25% greater in particulate organic carbon (POC) content than the savanna. In both vegetation types, >55% of carbon was stabilized in the mineral fraction. Our study demonstrates long-term human impacts on soil and vegetation and offers a clear nature-based solution for climate change mitigation through sustainable land management by indigenous people towards achievement of the '4p1000' initiative.</p>
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.