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STREAMS Project: Emergent landscape patterns in stream ecosystem processes resulting from groundwater/surface water interactions
This Data Set is hosted by the Luquillo LTER Program (LUQ) and owned by a LUQ's investigator. Our primary objective is to understand the linkage between surface-subsurface water interactions and ecosystem processes in neotropical lowland streams over an extended time frame (>25 yrs). Proposed research will occur at La Selva Biological Reserve in Costa Rica, which is owned and operated by the Organization for Tropical Studies In tectonically active regions of Central America, it is common for solute-rich groundwater to emerge at gradient breaks within the complex volcanic topography of mountains and foothills which intergrade with the coastal plain. These groundwaters can significantly influence solute chemistry and related ecological and ecosystem-level processes in receiving surface waters. Many solute-rich groundwaters are associated with underlying volcanic activity which has altered the chemistry of receiving streams throughout Central America. Geothermally-modified groundwaters, surfacing at the gradient break between the Central Mountain range and the coastal plain at La Selva Biological Station, have high levels of P (up to 400 mg SRP L-1) and other solutes (Ca, Cl, Mg, SO4) but are not elevated in temperature. Spatial patterns in stream solute chemistry are determined by geomorphic features of the volcanic landscape that include: upland lavas drained by P-poor streams; a gradient break (~50 m.a.s.l.), at or near where P-rich springs emerge; and lowland alluvial areas drained by streams that are both P-rich and P-poor depending on whether they receive the input of solute-rich springs. Our project is the first to determine long-term effects of nutrient enrichment in a detrital-based stream within the wet tropics. We will continue to build upon our long-term(1988-present) data set on stream solute chemistry, which is the only one that we are aware of for lowland primary rainforest of Central America. The proposed project will build on 18 years of past resear
Perceptual decisions result from the continuous accumulation of memory and sensory evidence
Open the record for dataset details and reuse information.
Repetition of Computer Security Warnings Results in Differential Repetition Suppression Effects as Revealed with Functional MRI
Open the record for dataset details and reuse information.
Field-lab data and analyses results West Kalimantan, Indonesia
<p>Field data included in this compressed folder were collected over an area of approximately 23,500 hectares in West Kalimantan, Indonesia. The folder includes Excel and txt files with the following contents: field measurements of the peat thickness at 63 coring sites, laboratory analyses results of the samples collected in the field, field and lab measurements of electrical conductivity. Moreover, there is a file containing all the measurements of peat thickness and soil elevation extracted from the figures contained in previous studies. Finally, the folder also includes the results obtained from the inversion of the Airborne Electromagntic (AEM) data collected with the SkyTEM instrument over the study site, and specifically the resistivity of the soil layers obtained from the inversion and the peat thickness corresponding to the 45 Ohmm threshold. The folder also includes the Python codes used for the statistical analyses explained in the paper.</p>
Impact of urban and shipping emissions on NASA-Unified Weather Research and Forecasting model results
<p>This dataset supports Huang et al. (2019, JGR-Atmospheres): "Impact of aerosols from urban and shipping emission sources on terrestrial carbon uptake and evapotranspiration: a case study in East Asia". The file named "NUWRFout.tar.gz" contains NUWRF base and sensitivity simulation results on 31 May 2016. The file named "LIS_soil_LAI.zip" contains model grid information, soil conditions and leaf area index (LAI) at NUWRF initialization times in late May 2016.</p>
Figure 2. Comparison bar of statistic results SC 2015 – 2017
<p>The theme of the Summer Reading and Creativity Campaign 2018, in which 148 members of the GLN participated, focused on “Favorite Data”. From June 20th to September 7th, children were called on to observe, record, comprehend, and report data through 46 different workshops tailored accordingly to innovative methodological approaches to pedagogy. </p>
Dataset containing the results of the selection process of DSH and CSS articles 2018-2020
<p>Dataset containing the results of the selection process of Digital Scholarship of Humanities and Computational Social Science articles. It shows which articles use data and have a clear data section. These are used to create a corpus to help build and validate and evaluate the data model of data scopes.</p>
Test results and analysis of whitelisted URLs in Jammu and Kashmir, January 2020
<p><strong>Version 3: </strong>In this version, the tab entitled "New entries Jan 31 order" was added to version 2 of the spreadsheet. This tab contains new entries from the whitelist accompanying the order dated 31 January 2020 [Order number: Home-08 (TSTS) of 2020]. A separate tab was required because the "field" category present in previous versions of the whitelist was removed in the 31 January order. </p> <p><strong>Version 2:</strong> This dataset contains an analysis of a whitelist comprising 301 entries issued by the Home Department, Government of Jammu and Kashmir on 24 January 2020 [<a href="http://jkhome.nic.in/Home-05(tsts)%20of%202020_0001.pdf">Order number: Home-05 (TSTS) of 2020</a>]. The department issued an order with the first version of this whitelist in response to a Supreme Court judgement dated 10 January 2020 (<em><a href="https://indiankanoon.org/doc/82461587/">Anuradha Bhasin vs. Union of Indian and Ors</a>.</em>) that directed the Government of India to review the blanket suspension of Internet services in Jammu and Kashmir since 5 August 2019.</p> <p><strong>Version 1:</strong> The first version of the whitelist (dated <a href="https://www.scribd.com/document/443380803/Temporary-Suspension-of-Telecom-Services#download&amp;from_embed">18 January 2020</a>), and this dataset by extension, comprised 153 entries. The Home Department states in its orders that this whitelist will be continually updated; the next update may be issued on 31 January or earlier.</p> <p>This preliminary analysis was conducted by Rohini Lakshané and Prateek Waghre from 22 and 26 January 2020 IST, to empirically determine whether the whitelisted websites and services would be practically usable for an ordinary resident of Jammu and Kashmir at the time of writing.</p> <p>A Chrome browser extension was used to simulate access to only those URLs that are mentioned in the government order.</p> <p>A detailed description of the method, its limitations, and the full analysis of the findings was published on Medianama at <a href="https://www.medianama.com/2020/01/223-analysis-of-whitelisted-urls-in-jammu-and-kashmir-how-usable-are-they/">Even the 301 whitelisted sites in Jammu and Kashmir are not entirely accessible: An analysis </a>on 28 January 2020.</p> <p>For information on how to read this dataset, refer to the tab entitled "About this sheet". A numerical summary of the findings of this analysis is present in the tab entitled "Summary of findings".</p> <p>Data provided AS-IS, without warranty as to accuracy or completeness.</p> <p>This dataset has been released under the <a href="https://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons-Attribution-Share Alike (CC-BY-SA) 4.0 International License</a>. All uses of the accompanying data and modifications and derivatives thereof must contain the following attribution: "By Rohini Lakshané and Prateek Waghre (2020)".</p> <p>All versions have been uploaded in 3 file formats: PDF, XLSX and ODS.</p>
Figures S1-S7. SMR-HEIDI analysis results for 8q24.21 locus between BP and selected phenotypes.
<p><strong>Supplementary Figures S1-S7. </strong><strong>SMR-HEIDI analysis results for rs6651255 between BP and selected phenotypes.</strong></p> <p>This project contains the following figures:</p> <ul> <li> <p>Figure S1. SMR-HEIDI analysis results for rs6651255 between BP and LDH.</p> </li> <li> <p>Figure S2. SMR-HEIDI analysis results for rs6651255 between BP and <em>GSDMC</em> expression in skeletal muscle (GTEx v6).</p> </li> <li> <p>Figure S3. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in Brain anterior cingulate cortex BA24 (GTEx v6).</p> </li> <li> <p>Figure S4. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in CD8 cell line (CEDAR).</p> </li> <li> <p>Figure S5. SMR-HEIDI analysis results for rs6651255 between BP and heel bone mineral density (UKBB).</p> </li> <li> <p>Figure S6. SMR-HEIDI analysis results for rs6651255 between BP and disc problem phenotype (UKBB)</p> </li> <li> <p>Figure S7. SMR-HEIDI analysis results for rs6651255 between BP and height (UKBB).</p> </li> </ul> <p> </p> <p><strong>Figures legend:</strong></p> <p>Each figure consists of four parts (1 – top left; 2- top right; 3- bottom left; 4 – bottom right):</p> <ol> <li> <p>Regional association plots for GWAS-1 (in our case BP GWAS) and GWAS-2 (expression or complex trait). Blue triangles represent SNPs used to calculate HEIDI test. Crossed triangle is leading SNP for which SMR test was computed.</p> </li> <li> <p>Z-Z plot (GWAS-1 on y-axis and GWAS-2 on x-axis).</p> </li> <li> <p>Visualization of LD matrix for SNPs used in calculation of HEIDI test.</p> </li> <li> <p>Plot of SMR regression coefficient estimates. The plot visualizes the heterogeneity of SMR coefficient. Blue color represents SNPs used to calculate HEIDI test.</p> </li> </ol>
Processed data and analysis results for 104 RBPs
<p>This repository makes available the processed data and the results of our SURF paper. </p> <p>The paper presents the <strong>S</strong>tatistical <strong>U</strong>tility for <strong>R</strong>BP <strong>F</strong>unctions (SURF) for integrative analysis of RNA-seq and CLIP-seq data. The goal of SURF is to identify alternative splicing (AS), alternative transcription initiation (ATI), and alternative polyadenylation (APA) events regulated by individual RBPs and elucidate protein-RNA interactions governing these events. We applied the SURF pipeline to analyze 104 RBP data sets (from <a href="https://www.encodeproject.org">ENCODE</a>) and performed downstream analysis. Check out the browsable results from this <a href="http://www.statlab.wisc.edu/shiny/surf/">shiny</a> app!</p> <p>The current repository includes:</p> <ul> <li>meme.326.input.zip -- input of 326 MEME runs on SURF-inferred location features</li> <li>meme.326.output.zip -- output of 326 MEME runs on SURF-inferred location features</li> <li>surf_inferred_feature.gtf -- SURF-inferred location features for 52 RBPs</li> <li>gencode.v24.annotation.filtered.gtf -- filtered genome annotation used for ENCODE data analysis</li> <li>Homo_sapiens.GRCh37.71.primary_assembly.protein_coding.gtf -- genome annotation used for simulation study</li> <li>simulation_truth.txt -- truth parameters used for RNA-seq simulation</li> <li>[RBP].results.rds -- SURF output (each an R object) for 104 RNA-binding proteins ([RBP] the protein name). </li> </ul> <p>For reproducing the results, the source code is available at DOI: <a href="https://doi.org/10.5281/zenodo.3779853">10.5281/zenodo.3779853</a>.</p>
NAQPMS simulation results for analysis the effects of regional transport on haze in the North China Plain
<p>This directory contains data generated and used for paper "Effects of regional transport on haze in the North China Plain: transport of precursors or secondary inorganic aerosols".</p> <p>This dataset contains the observed and simulated aerosols components of Beijing and surrounding cities; regional sources of secondary inrganic aerosols in Beijing; sources of secondary inorganic aerosols under relative clean and polluted conditions; and average differences of regional contribution to secondary inorganic aerosols between clean and polluted conditions; and data for supporting figures.</p>
mom6 cobalt model result for oceanic carbon response to El Niños
<p>GEOS_Chem atmospheric transport model, monthly, 2.5 degree resolution in tropical Pacific Ocean (120-300E, 30N-30S)<br> 1. GEOS_Chem_tropical_pacific_monthly_clim_1992_2017.nc<br> 2. GEOS_Chem_tropical_pacific_monthly_iav_1992_2017.nc<br> 1 and 2 are GEOS_Chem atmospheric transport model results</p> <p>MOM6 COBALT model results<br> Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1<br> Note2: if the file name has a label "_detrend_deseason", this file is detrended and deseasonized using full value in 1982-2018 with CDO<br> "cdo -ymonsub -detrend $fin -ymonmean -detrend $fin $fout"<br> Note3: ocean/sea surface is the first layer which is 1 meter deep<br> Note4: MLD_001_temp/salt/dic/alk/kd is the vertical mean value in the mixing layer depth (criterial of 0.01 kg/m3)</p> <p>MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface)<br> MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018.nc<br> (delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface)<br> MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized air-sea CO2 flux, positive to ocean)<br> MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018.nc<br> (air-sea CO2 flux, positive to ocean)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_1982_2018.nc<br> (Mixing layer depth with a density criterial of 0.01 kg/m3)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_1982_2018_detrend_deseason.nc<br> (first compute vertical mean alkilinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_zgradient_1982_2018_detrend_deseason.nc<br> (first compute vertical mean alkilinity gradient (dalk/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_1982_2018_detrend_deseason.nc<br> (first compute vertical mean dissolved inorganic carbon (DIC) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_zgradient_1982_2018_detrend_deseason.nc<br> (first compute vertical mean DIC (ddic/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_Kd_interface_1982_2018_detrend_deseason.nc<br> (first compute vertical mean Diapycnal diffusivity at interfaces layers (kd_interface) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_salt_1982_2018_detrend_deseason.nc<br> (first compute vertical mean salinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_temp_1982_2018_detrend_deseason.nc<br> (first compute vertical mean temperature in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_001_u_1982_2018_detrend_deseason.nc<br> (first compute vertical mean velocity u in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_v_1982_2018_detrend_deseason.nc<br> (first compute vertical mean velocity v in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_003_1982_2018.nc<br> (Mixing layer depth with a density criterial of 0.03 kg/m3)<br> MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized sea surface pCO2 (ocean pCO2))</p> <p>MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018.nc<br> (sea surface pCO2 (ocean pCO2))<br> MOM6_COBALT_tropical_pacific_monthly_sfc_chl_1982_2018.nc<br> (sea surface chlorophyll)<br> MOM6_COBALT_tropical_pacific_monthly_sfc_dic_1982_2018.nc<br> (sea surface dissolved inorganic carbon (DIC))<br> MOM6_COBALT_tropical_pacific_monthly_sfc_no3_1982_2018.nc<br> (sea surface nitrate, NO3)<br> MOM6_COBALT_tropical_pacific_monthly_sfc_po4_1982_2018.nc<br> (sea surface phosphate, PO4)<br> MOM6_COBALT_tropical_pacific_monthly_SSS_1982_2018.nc<br> (sea surface salinity)<br> MOM6_COBALT_tropical_pacific_monthly_SST_1982_2018.nc<br> (sea surface temperature)<br> MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized wind stress in zonal direction)<br> MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018.nc<br> (wind stress in zonal direction)<br> MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized wind stress in meridional direction)<br> MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018.nc<br> (wind stress in meridional direction)<br> MOM6_COBALT_tropical_pacific_monthly_tc_depth_1982_2018.nc<br> (thermocline depth defined as depth where the temperature equals 20oC)</p> <p>JRA_rain_tropical_pacific_monthly_prrn_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized JRA rainfall)<br> </p> <p>Budget terms based MOM6 COBALT model results<br> Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1<br> Note2: this is the vertical mean result in the mixing layer depth (criterial of 0.01 kg/m3) after detrend and deseasonalize</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_001_pco2w_budget_1982_2018_detrend_deseason.nc<br> (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> pco2_hadv_hdif: horizontal transport term, H_circ;<br> dpco2_hadvx: zonal advection term;<br> dpco2_hadvy: meridional advection term;<br> dpco2_hdif: horizontal diffusivity term;<br> dpco2_vadv_vdif: vertical transport term;<br> dpco2_dic_vdif_vdif: vertical transport term induced by DIC;<br> dpco2_alk_vdif: vertical transport term induced by Alk;<br> dpco2_bio: biological term<br> dpco2_rain: surface freshwater term<br> dpco2_sst: thermal term<br> dpco2_dt: pco2 response term<br> dpco2_flux: CO2 flux response term<br> </p>
CitieS-Health Barcelona Survey Results
<p>This dataset contains the data collected using an online survey on knowledge, perceptions and preferences on topics to be investigated around the theme of air pollution and health in Barcelona, Spain. The data collected are for the CitieS-Health project in Barcelona. A scientific paper based on the online survey results is under review. </p>
TESTAR Test results extracted while executing MyThaiStar as web system under test
<p>TESTAR test results datasets extracted with TESTAR tool using MyThaiStar web application as System Under Test (SUT). These datasets have been generated to be used as an example to be automatically generated and introduced locally in DECODER PKM, from H2020 DECODER Project.</p> <p>TESTAR tool is an open source tool (www.testar.org) for automated testing through graphical user interface (GUI) currently being developed by the Universitat Politecnica de Valencia and the Open University of the Netherlands.</p> <p>MyThaiStar (<a href="https://github.com/devonfw/my-thai-star">github.com/devonfw/my-thai-star</a>) is the reference application that Capgemini uses internally to promote best programming practices and the correct use of last technologies. It’s is developed with Devon Framework, the standard tool for development at the company.</p> <p>PKM is the Persistent Knowledge Monitor developed as main infrastructure from H2020 DECODER Project (www.decoder-project.eu) under grant agreement number 824231.</p> <p>As TESTAR explores automatically the SUT, it will apply a couple of oracles to automatically check if any error or exception is detected at the Document Object Model (DOM) level extracted from MyThaiStar SUT.</p> <p>- MyThaiStar_TestResults_dataset.rar: All the information obtained through the DOM is used graphically and semantically to create screenshots, logs and html reports that indicate how TESTAR has navigated in the exploration and indicates if any error has been detected. </p> <p>- ArtefactTestResults_MyThaiStar_2020.1_2020-06-15_12h14m24s.json: for DECODER project purposes, TESTAR test results knowledge has been summarized and referenced in an artifact JSON file to adapt to PKM input requirements.</p> <p> </p> <p> </p> <p> </p>
Supplementary material for "A drop in immigration results in the extinction of a local woodchat shrike population"
<p>Data files and code for all analyses and figures presented in the paper. The three data files are provided either in ASCII format (WoodchatCount.txt, WoodchatReproduction.txt, WoodchatCMR.txt) or in csv format (WoodchatCount.csv, WoodchatReproduction.csv, WoodchatCMR.csv). The code file (WoodchatCode.txt) is a space delineated text file. The code file is written for R, but some models are run in JAGS from R. The code file also contains the description of the data files and code for data management.</p>
Rbbt SINTEF workflow bliss and hsa task results
<p>The two datasets have the results of <strong>rbbt's</strong> <strong>SINTEF</strong> workflow tasks <strong>bliss</strong> and <strong>hsa </strong>and the rbbt docker image used for the analysis.</p> <p>See the analysis results of the SINTEF dataset (Flobak et. al 2019) in: <a href="https://druglogics.github.io/sintef-obs-synergies/">https://druglogics.github.io/sintef-obs-synergies/</a></p>
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
Self-efficacy questionnaire for children - Results
<p>The dataset (csv) contains data from a self-efficacy questionnaire for children between the age of 6 and 16. This questionnaire (also attached) was specifially developed for the purposes of the DOIT project, which aimed at bringing entrepreneurial education, maker skills and social innovation together and at developing an educational programme lasting for at least 15 hours.</p> <p>Children participating in the DOIT progamme filled in the survey two times (pre and post survey). The dataset contains the questionnaire results from 633 children from 10 different European countries, some basic demographic (age, gender, country, disability) and some variables regarding the DOIT programme (attended hours, Age of facilitators, gender ratio of facilitator, etc.). For context information also a related DOIT report has been attached.</p>
Tooling, Data and Results for "Components in Probabilistic Systems: Suitable by Construction"
<p>The tooling, data and results for the racetrack case study in the paper <em> Components in Probabilistic Systems: Suitable by Construction, ISoLA 2020, <a href="https://doi.org/10.1007/978-3-030-61362-4_13">DOI</a></em></p>
Dataset related to article "Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures"
<p>This dataset is related to the article entitled: Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures. This article is published in the Journal GLIA.</p> <p>Bres EE et al.<br> Lipoprotein receptor loss in forebrain radial glia results in<br> neurological deficits and severe seizures. Glia. 2020;1–33.</p>
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.