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19,493 results for “Populism”
Tracking Selection using Temporal Population Genomics Data
<p>This repository contains the implementation of a pipeline to run the simulations and to produce a reference table for the ABC-RF inference of demography and selection. In its new release, this repository contains the whole-genome polymorphism of contemporary and museum specimens of <em>Apis mellifera</em> feral populations analyzed by Cridland et al. (2018).</p>
Synthetic business population containing simulated business variables
<p>This dataset contain simulated data for a fully synthetic business population. The dataset contains 900,000 records, each of which represents a simulated business. It resembles the real-world population of employing businesses in Australia in terms of the distribution of businesses across size categories, industry classes and geographic regions (state). The data for the population has been generated using a combination of published survey outputs available from the Australian Bureau of Statistics (ABS) website, and employee tax data and survey data sourced from the Business Longitudinal Analysis Data Environment (BLADE) in the ABS DataLab.</p>
Genotypes for Neurospora nested association mapping population
<p>Genotype file that contains genotypes all the Neurospora association mapping population developed in Kronholm lab. See https://github.com/ikron/Neurospora_NAM_population</p> <p>The file is a tab-delimited text file in hapmap format and contains first contains the columns: rs alleles chrom pos strand assembly center protLSID assayLSID panel GCcode and then the subsequent columns are strains identifiers. The column 'rs' is the name of the SNP, column 'alleles' shows which two alternative bases occur, the column 'chrom' is the chromosome, column 'pos' is the coordinate, columns 'strand', 'center', 'protLSID', 'assayLSID', 'panel' and 'GCcode' contain all missing data. They are included for hapmap format compatibility. The column 'assembly' indicates the version of the Neurospora crassa reference genome that the coordinates are based on. And in this case it is NC12 for all SNPs.</p> <p>Missing data is indicated by 'N'</p>
Supplementary material for "Dynamics of a goshawk population across half a century is driven by the variation of first-year survival"
<p><strong><span>Abstract</span></strong></p> <p><span>Population dynamics are driven by stochastic and density-dependent processes acting on demographic rates. Individuals differ demographically, and to capture these differences, models of population dynamics are usually structured by age and stage, rarely by sex. An effect of sex on population dynamics is expected if the dynamics of males and females differ, requiring an unequal sex ratio at birth and/or sex-specific survival probabilities. Goshawks (<em>Accipiter gentilis</em>) show large sexual size dimorphism and differential survival, but it is unknown whether males and females contribute differently to population dynamics. We studied a goshawk population in northern Germany over 47 years using brood monitoring data, collected feathers and nestling ringing data. We jointly analyzed the data using a two-sex integrated population model and performed retrospective and prospective population analyses to understand whether the demographic drivers of population change differ between the sexes. The population showed large fluctuations, during which the number of breeding pairs doubled, but the long-term trend of the population was slightly negative. Female survival exceeded male survival during the first year of life. Females started to reproduce at a younger age than males, productivity increased with female age, the sex ratio of nestlings was male biased and there was moderate male immigration. Despite these differences, temporal variation in sex ratio did not contribute to population dynamics and the contribution of temporal variation in survival was similar for both sexes. Variation in first-year survival was the strongest driver in this population, regulated by a weak density-dependent feedback acting through female first-year survival. Overall, the contributions of the two sexes to population dynamics were similar in this monogamous species with strong sexual size dimorphism.</span></p> <p><strong><span>Read me</span></strong></p> <p><span>Data files and code for carrying out all analyses and generating all figures presented in the paper. The six data files are provided in csv format. There are five code files written for R, but some of the main analyses require the NIMBLE software. The main code (IPM_Code.txt) contains a description of the data, code for loading and managing the data, and for fitting the integrated population models. The other files contain custom written functions (Functions.txt), code for performing posterior predictive tests (PPT_Code.txt), code for reporting results and performing various other analyses (Output_analyses_Code.txt), and code for generating figures (Figures_Code.txt).</span></p>
Variant dataset and code for "Population-level whole genome sequencing of Ascochyta rabiei identifies genomic loci associated with isolate aggressiveness"
<p>This dataset contains genetic variants (SNPs) of <em>Ascochyta rabiei</em> isolates and the R code used in their analysis to generate the results and figures described in the manuscript "<strong>Population-level whole genome sequencing of <em>Ascochyta rabiei</em> identifies genomic loci associated with isolate aggressiveness</strong>".</p> <div> <div> </div> </div>
Population Statistics on NCT02332590
<p>Data presented as mean or median (change), in parts with quantiles (Q1, Q3) published by</p> <table> <tbody> <tr> <td> <p>C. Gaby, G. Burmester, V. Strand, J. Msihid, M. Zilberstein, T. Kimura, B. van Hoogstraten, S. H. Boklage, J. Sadeh, M. H. G. Neil and A. Boyapati, "Sarilumab and adalimumab differential effects on bone remodelling and cardiovascular risk biomarkers, and predictions of treatment outcomes," <em>Arthritis Research & Therapy, </em>vol. 22, no. 1, p. 70, 2020.</p> </td> </tr> </tbody> </table>
Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023
<p>This dataset contains surveillance study estimates for population size, HIV prevalence, and ART coverage among female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), and transgender men and women (TGM/W) from 2010-2023. It was created to support the UNAIDS Estimates Key Population Workbook for use by HIV estimates teams in sub-Saharan Africa. Key population surveillance reports, including Ministry of Health-led biobehavioural surveys, mapping studies, and academic studies were used to populate the database.</p> <p>The dataset was populated using existing key population size estimate databases including:</p> <ul> <li>UNAIDS Key Population Atlas</li> <li>US Centers for Disease Control and Prevention surveillance database</li> <li>Global Fund against HIV/AIDS, TB, and Malaria surveillance database</li> <li>Global.HIV database</li> <li>Systematic review databases among MSM (<a href="https://pubmed.ncbi.nlm.nih.gov/31601542/" target="_blank" rel="noopener">Stannah et al, 2019</a> and <a href="https://pubmed.ncbi.nlm.nih.gov/37453439/" target="_blank" rel="noopener">Stannah et al., 2023</a>) and PWID (<a href="https://pubmed.ncbi.nlm.nih.gov/36996857/" target="_blank" rel="noopener">Degenhardt et al., 2023</a>)</li> </ul> <p><br>and was additionally supplemented by a literature review of peer-reviewed and grey literature sources.</p> <p>The data can be <a href="https://shiny.dide.ic.ac.uk/kp-data/" target="_blank" rel="noopener">explored in this web application</a> and the <a href="https://www.medrxiv.org/content/10.1101/2022.07.27.22278071v3" target="_blank" rel="noopener">accompanying manuscript can be found here</a></p>
Supplementary material for "Palaeo-demographic modelling supports a population bottleneck during the Pleistocene-Holocene transition in Iberia"
<p>This Supplementary material encompasses the archaeological radiocarbon dates for the Iberian Peninsula analyzed in the article “<em>Palaeo-demographic modelling supports a population bottleneck during the Pleistocene-Holocene transition in Iberia</em>". Specifically:</p> <ul> <li>‘<strong>PALEODEM_dataset.csv</strong>’ provides the dataset in comma-separated format. Contains the compiled raw data of each radiocarbon date used by every analytical procedure.</li> <li>‘<strong>PALEODEM_sites.xlsx</strong>’ is a spreadsheet containing the basic information of each archaeological site analyzed in this work. The columns include ID_site, name of Archaeological site, n<sup>o</sup> of <sup>14</sup>C dates, n<sup>o</sup> of assemblages, Regional Unit of correspondence, and bibliographical Reference.</li> <li>‘<strong>REFERENCES for PALEODEM_sites.docx</strong>’ is a text archive that collects the bibliographical references listed in the above mentioned ‘Reference’ column, that have been the main sources of archaeological information.</li> </ul>
Air Quality Index Scores by CBSA with Population
<p>The AQI describes the five main types of air pollution regulated by the Clean Air Act: sulfur dioxide, nitrogen dioxide, carbon monoxide, ground-level ozone, and particle pollution. The EPA and its partners take regular readings of these pollutants and converts the results into a number ranging from 0 to 500, along with a specific color corresponding to a level of health concern. Generally, if the air quality is good, the air quality index is low (0 to 50) or moderate (51-100), and the color associated with it is green or yellow. As the air quality gets worse, the numbers go up, and the color linked with it goes from orange, to red, to purple, all the way to a dark shade of maroon for hazardous (300+).</p> <p>This dataset contains the AQI scores by metropolitant area (CBSA) during 2017. I've enhanced some publically available data from the EPA's <a href="https://airnow.gov/">airnow</a> website with census data, to be able to provide context about the number of people who are actually impacted when an <a href="https://www.cleanairresources.com/resources/how-do-i-read-the-air-quality-index">AQI score</a> is high or low in a given area.</p> <p>Related datasets on <a href="https://www.cleanairresources.com/data#sources">relative composition of air pollution by source: typical distribution and during wildfire season</a> are available here.</p>
Retrotransposon-based genetic variation of Poa annua populations from contrasting climate conditions
<p>Raw photographs of agarose electrophoresis. Material: six Poa annua populations. Method: inter-Primer Binding Site (iPBS) markers This is the documentation of studies described in the manuscript entitled "Retrotransposon-based genetic variation of Poa annua populations from contrasting climate conditions" accepted for publication in PeerJ journal (decision received on 02.04.2019)</p>
Austrian Population by Bezirk and according altitude (in 2011)
<p>The dataset contains information of the Austrian Population in the year 2011. Is is grouped by Bezirk and the elevation of the main city of each Bezirk is added. This data was inted to be used for analysis on the average altitude at which Austrians live.</p>
Phenome-wide association studies across large population cohorts support drug target validation
<p>Summary-level data generated by Genomics plc as presented in:<br> Diogo, D. et al. Phenome-wide association studies across large population cohorts support drug target validation. Nat. Commun. 9, 4285 (2018). https://doi.org/10.1038/s41467-018-06540-3</p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a></p> <p>NOTES<br> -----------------------------<br> These analyses were carried out using the interim UK Biobank imputation data release. Analyses were restricted to a subset of "white-British" unrelated samples with a maximum sample size of 112,337 individuals. </p> <p>Case control phenotypes were defined based on categorical datafields as listed in the accompanying file. <br> Quantitative phenotypes were either rank-normalised before analysis, or beta/se values were standardised after analysis using the variance of the phenotype. The normalisation value is indicated in the accompanying file.<br> <br> All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. </p> <p>We used plink1.9 linear/logistic regression as appropriate. For chromosome X variants males were treated as having 0 or 2 alternative alleles. </p> <p>The results are not adjusted for genomic control.</p> <p>DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> CHR - Chromosome<br> SNP - Variant rsID<br> ALT - Alternative allele (effect allele)<br> REF - Reference Allele (non-effect allele)<br> BP - Position in base pairs (b37, 1-based)<br> NMISS - Number of samples with non-missing genotypes<br> BETA - Effect size (log odds ratio or standardised effect size)<br> SE - Standard error<br> P - P-value<br> F_MISS - genotype missing rate<br> P_hwe - Hardy-weinberg p-value<br> MAF - ALT allele frequency</p>
CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data: Codes and processed data
<p>Codes and processed data to reproduce the analysis discussed in: </p> <p>Wegmann <em>et Al.</em>,<strong> CellSIUS provides sensitive and specific detection of rare cell<br> populations from complex single cell RNA-seq data</strong>, Genome Biology 2019 (Accepted)<br> </p>
Microclimate predicts frost-hardiness of alpine Arabidopsis thaliana populations better than elevation
<p>In mountain regions, topological differences on the micro-scale can strongly affect microclimate and may counteract the average effects of elevation, such as decreasing temperatures. While these interactions are well understood, their effect on plant adaptation is understudied.</p> <p> </p> <p>We investigated winter frost hardiness of Arabidopsis thaliana accessions originating from 13 sites along altitudinal gradients in the Southern Alps during three winters on an experimental field station on the Swabian Jura and compared levels of frost damage with the observed number of frost days and the lowest temperature in eight collection sites.</p> <p> </p> <p>We found that frost-hardiness increased with elevation in a log-linear fashion. This is consistent with adaptation to a higher frequency of frost conditions, but also indicates a decreasing rate of change in frost hardiness with increasing elevation. Moreover, the number of frost days measured with temperature loggers at the collection sites correlated much better with frost-hardiness than the elevation of collection sites, suggesting that populations were adapted to their local microclimate. Notably, the variance in frost days across sites increased exponentially with elevation. Together, our results suggest that strong microclimate heterogeneity of high alpine environments can preserve functional genetic diversity among small populations.</p> <p> </p> <p>Synthesis. Here we tested how plant populations differed in their adaptation to frost exposure along an elevation gradient and whether microsite temperatures improve the prediction of frost hardiness. We found that local temperatures, particularly the number of frost days, is a better predictor of the frost hardiness of plants than elevation. This reflects a substantial variance in frost frequency between sites at similar high elevations. We conclude that high mountain regions harbor microsites that differ in their local microclimate and thereby can preserve a high functional genetic diversity among them. Therefore, high mountain regions have the potential to function as a refugium in times of global change.</p>
Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population - Supporting Material
<p>Supporting material for the study:</p> <p><strong>"Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population."</strong></p> <p><strong>Authors:</strong></p> <p>Cathleen Petit-Cailleux1, Hendrik Davi1, François Lefèvre1, Joseph Garrigue<strong>2</strong>, Jean-André Magdalou<strong>2</strong>, Christophe Hurson<strong>2,3</strong><strong>, </strong>Elodie Magnanou<strong>2,4</strong>, and Sylvie Oddou-Muratorio1.</p> <p> </p> <p>Adresses</p> <p>1INRA, UR 629 Ecologie des Forêts Méditerranéennes, URFM, Avignon, France</p> <p><strong>2</strong>Réserve Naturelle Nationale de la Forêt de la Massane, France</p> <p><strong>3</strong>Fédération des Réserves Naturelles Catalanes, Prades, France</p> <p><strong>4</strong>Sorbonne Université, CNRS, Biologie Intégrative des Organismes Marins, BIOM, F-66650 Banyuls-sur-Mer, France</p> <p><strong>ORCID:</strong></p> <p>Cathleen Petit-Cailleux: <a href="https://orcid.org/0000-0001-7714-6583">https://orcid.org/0000-0001-7714-6583</a></p> <p>François Lefèvre : <a href="https://orcid.org/0000-0003-2242-7251">https://orcid.org/0000-0003-2242-7251</a></p> <p>Sylvie Oddou-Muratorio <a href="https://orcid.org/0000-0003-2374-8313">https://orcid.org/0000-0003-2374-8313</a></p> <p> </p> <p>-------------</p> <p>Raw data of the Table_Massane_moratlity_trees.csv and climate can be obtained from Joseph Garrigue, Jean-André Magdalou and Christophe Hurson.</p> <p>The inventories files and daily climate are the input dataset to run CASTANEA models.</p> <p>All details are provided in the article.</p>
Incidences of community onset severe sepsis, Sepsis-3 sepsis, and bacteremia in Sweden – a prospective population-based study.
<p>Sepsis epidemiology study 2011-2012 Sweden</p> <p>Ljungström, Lars; Andersson, Rune; Jacobsson, Gunnar</p> <p> </p> <p>Data collected during the prospective "Sepsis Skaraborg study" performed 2011-2012 in the western region of Sweden. Adult patients admitted to the emergency department for suspicion of a community-onset sepsis were evaluated. The study was approved by the Regional Ethical Review Board of Gothenburg (376-11). The file includes data for patient characteristics, vital signs, biomarker measurements, cases of bacteremia, and patient classifications using Sepsis-2 and Sepsis-3 criteria.</p>
Example Dataset for npstat: Population genetics from Pooled NGS data NPStat v1: User guide
<p>Example Dataset for npstat to test the program and the different options.</p> <p>The example dataset contains a pileup file with sequences of of the 2L chromosome from fifteen pooled inbreed individuals of <em>Drosophila melanogaster </em>(<span>doi: 10.1038/nature10811</span>). The dataset also contains the sequence reference of the 2L chromosome in fasta format, an outgroup sequence in fasta format of <em>D. yakuba</em> (SRR26246471), a GFF3 annotation file and a file with a brief list of selected SNPs to be analyzed.</p>
The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover
<p>Zenodo deposit for Pozzi et al (2024) AJB</p> <p>The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover</p> <p><strong> _____________________________________________________________________________________________________________________________________</strong></p> <p><strong>The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover</strong></p> <p>Adrien C.M. Pozzi<sup>1,2</sup>, Ruth G. Shaw<sup>1</sup>, Georgiana May<sup>1,3</sup></p> <p><sup>1</sup> Department of Ecology, Evolution and Behavior, University of Minnesota Twin-Cities, St Paul, MN 55108. <sup>2</sup> Current affiliation: Universite Claude Bernard Lyon 1, Laboratoire d'Ecologie Microbienne, UMR CNRS 5557, UMR INRAE 1418, VetAgro Sup, 69622 Villeurbanne, France. ORCID: 0000-0001-6765-4293. <sup>3</sup> Correspondence: Georgiana May (gmay@umn.edu)</p> <p><em><strong>Keywords:</strong></em></p> <p>conservation; <em>Dalea</em> spp.; habitat fragmentation; mutualism; nitrogen-fixing symbiosis; population level variation; native prairie legume; restoration; rhizobia</p> <p><em><strong>Description:</strong></em></p> <p>This Zenodo deposit is part of the MN LCCMR Healthy Prairies project granted to R. Shaw and G. May, UMN Twin-Cities. It contains the following files:</p> <p><span><span>·<span> </span></span></span>“Metadata & data” spreadsheet. <em>Contains metadata and data about the Twin Valley experiment (including the position of plants, an intermediary census, growth and nodulation traits for harvested plants, data on bacterial isolates from root nodules, and source modifiers for GenBank accessions OQ732394-OQ732572).</em></p> <p><span><span>·<span> </span></span></span>“Blast alignments output.txt” text file. <em>Contains ouput of Blastn alignements for the 16S rRNA genes of bacterial isolates against the rRNA_typestrains/16S_ribosomal_RNA 16S ribosomal RNA (Bacteria and Archaea type strains) database, to determine the genus as recommended by GenBank during sequence submission.</em></p> <p><span><span>·<span> </span></span></span>“General script.R”. <em>R script of the general statistical analyses produced for publication.</em></p> <p><span><span>·<span> </span></span></span>“General environment.RData”.<em> The companion RData (environment) of the above R script.</em></p> <p><span><span>·<span> </span></span></span>“Trait model script.R”. <em>R script of the plant trait models produced for publication</em></p> <p><span><span>·<span> </span></span></span>“Trait model environment.RData”. <em>The companion RData (environment) of the above R script.</em></p> <p><strong><em>Acknowledgments:</em></strong></p> <p>The authors thank members of the May Lab (Mai Beauclaire, Em Daily, Mara Demers, Kane Keller, Cedric Ndinga-Muniania, Liam Vertal, Monica Watson) for their help in field and lab work. We thank members of the Healthy Prairies project (Shelby Flint, Anna Peschel, Bill Peterson) who provided much of the infrastructure that made this project possible, as well as help in setting up field experiments. We also thank volunteer undergrads from University of Minnesota Morris (Amelia Nelson, Emily Job, Lily Fulton) for assistance in measuring harvested plants and counting nodules. Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR) Project 00086965, Healthy Prairies.</p> <p> </p>
The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress: Data supporting the publication
This is the dataset on which the following publication is based: • Michel G, Baenziger J, Brodbeck J, Mader L, Kuehni CE, Roser K (2024). The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress. PLOS One. 19(7), e0305192. Doi: 10.1371/journal.pone.0305192, https://doi.org/10.1371/journal.pone.0305192 A description of the sample and the data collection procedure is available in the publication. The dataset contains the following variables: • Socio-demographic characteristics of the sample - Weights according to representative general population sample - Sex from Swiss Federal Statistical Office (SFSO) - Age at study (rounded to integer) - Age categories (10-year age groups) - Language questionnaire (German/Rumantsch, French, Italian) - Nationality from SFSO - Migration background - Education - Employment status • Original and prepared data on the Brief Symptom Inventory A detailed data dictionary is available in a separate excel file. Version • 1.0 (15 August 2024)
Galactic Populations of LISA DWD Binaries
<p>These COSMIC simulation outputs and assembled galactic populations of<br> Double White Dwarf (DWD) binaries<br> represent the work published by V. Delfavero et al,<br> in https://arxiv.org/abs/2409.15230</p> <p>The COSMIC version used in these simulations is 3.4.10</p> <p>The git repository for the accompanying code which built this dataset<br> will be available at https://gitlab.com/xevra/basil-cosmic<br> upon the publication of our article.</p> <p>Also useful is the fork of ldasoft used in this pipeline:<br> https://github.com/xevra/ldasoft</p> <p>Please cite this work if you use the included data in a publication.</p> <p>-------------------------------------------------------------------------------<br>Summary<br>-------------------------------------------------------------------------------<br>Params_[MODEL].ini<br> This initialization file was used for COSMIC, <br> for the runs tagged with [MODEL].</p> <p>[MODEL]_[KSTAR1]_[KSTAR2]_[METALLICITY_BIN].tar.gz<br> This tarball contains the outputs from one run of COSMIC,<br> using Params_[MODEL].ini, for a type of binary indicated by<br> [KSTAR1] and [KSTAR2] according to COSMIC syntax,<br> and for the [METALLICITY_BIN]'th metallicity bin.</p> <p>[MODEL]_COSMIC.hdf5<br> This hdf5 database holds the DWD population outputs for all 60<br> COSMIC simulations in a compressed format.<br> It only holds information about binary systems at the end<br> of evolution (conv, according to COSMIC syntax).<br> It does not contain the evolutionary history of individual binaries<br> (the bpp information, according to COSMIC syntax).</p> <p>[MODEL]_m12i-000_LISA.nal.hdf5<br> This hdf5 database holds the LISA "resolved" population of DWD binaries<br> for a single realization of a Milky-Way-like galaxy.<br> It also stores many attributes indicating assumptions and various<br> population statistics.<br> The "nal" extension indicates that this database contains<br> truncated Gaussian likelihood models for the resolved population<br> of LISA DWDs,<br> which can be loaded by the gwalk code (https://gitlab.com/xevra/gwalk).<br> This database can also be explored by any hdf5 library (such as h5py).</p> <p>[MODEL]_m12i-000_LISA.hdf5<br> This hdf5 database holds all of the LISA-band binaries (f_GW > 10^-4 Hz)<br> for a single galaxy realization.</p> <p>[MODEL]_m12i-000_LISA.txt.gz<br> This text file stores the inputs for ldasoft gbfisher analysis,<br> for a specific galaxy realization under assumptions<br> broadly described by [MODEL]<br><br>See README.txt for full description<br><br>-------------------------------------------------------------------------------<br>Funding<br>-------------------------------------------------------------------------------<br>VD is supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center administered by Oak Ridge Associated Universities under contract NPP-GSFC-NOV21-0031. ROS gratefully acknowledges support from NSF awards NSF PHY-1912632, PHY-2012057, PHY-2309172, AST-2206321, and the Simons Foundation. JB is supported by the NASA LISA Project Office.<br><br>We acknowledge software packages used in this publication, including NUMPY (Harris et al. 2020), SCIPY (Virtanen et al. 2020), MATPLOTLIB (Hunter 2007), ASTROPY (Astropy Collaboration et al. 2013, 2018), H5PY (Collette 2013), LEGWORK (Wagg et al. 2022), and ldasoft Littenberg et al. (2020). This research was done using resources provided by the Open Science Grid (Pordes et al. 2007; Sfiligoi et al. 2009), which is supported by the National Science Foundation awards #2030508 and #1836650, and the U.S. Department of Energy’s Office of Science</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.