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944 results for “Human population”
LAGOS-US HUMAN v2: Data module of human population(1990-2020), urbanization classification, and lake access in the conterminous U.S.
The LAGOS-US HUMAN v1 data package is an extension module of the LAGOS-US research platform that includes data characterizing human population (population count, race, ethnicity, socioeconomic information), urbanization, and lake access of 479,950 lakes larger than or equal to 1 ha in the conterminous U.S. (48 states plus the District of Columbia). This data module contains four data tables linked through the unique lake identifier for the LAGOS-US research platform, lagoslakeid. Human population characteristics (race, ethnicity, and socioeconomic factors) were derived from U.S. census data for 1990, 2000, 2010, and 2020. Lakes were classified as urban or not using two different classifications: one based on the ‘Developed’ land category in the National Land Cover Dataset; and another based on the 2020 Census Urban Areas category. Metrics for lake access were developed from national datasets on public boat launches, transportation, and public lands. LAGOS-US HUMAN v1 provides a link between lake data and human contexts, facilitating interdisciplinary research in limnology, urban ecology, environmental justice, and conservation. To facilitate such studies, users are encouraged to use the other three core data modules of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds); GEO (geospatial ecological context at multiple spatial and temporal scales); and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.
Forest Change and Human Populations in New England 1600-2015
As part of retrospective studies of land-use across New England, information was compiled on forest cover and human population for the New England states. The states share a common history of deforestation for agriculture followed by farm abandonment and natural reforestation with the exception of northern Maine, which was never densely settled and largely remained forested. The extent to which these secondary forests differ in structure and function from permanently wooded areas or the forests of the pre-settlement period forms a major research emphasis of Harvard Forest studies.
Data and software supporting the manuscript 'The population frequency of human mitochondrial DNA variants is highly dependent upon mutational bias'
<p>Next-generation sequencing can quickly reveal genetic variation potentially linked to heritable disease. As databases encompassing human variation continue to expand, rare variants have been of high interest, since the frequency of a variant is expected to be low if the genetic change leads to a loss of fitness or fecundity. However, the use of variant frequency when seeking genomic changes linked to disease remains very challenging. Here, we explore the role of selection in controlling human variant frequency using the HelixMT database, which encompasses hundreds of thousands of mitochondrial DNA (mtDNA) samples. We find that a substantial number of synonymous substitutions, which have no effect on protein sequence, were never encountered in this large study, while many other synonymous changes are found at very low frequencies. Further analyses of human and mammalian mtDNA datasets indicate that the population frequency of synonymous variants is predominantly determined by mutational biases rather than by strong selection acting upon nucleotide choice. Our work has important implications that extend to the interpretation of variant frequency for non-synonymous substitutions. </p> <p> </p>
Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020
<p>The presented human thermal bioclimate dataset was created in the frame of the <a href="https://theheatalarm.wordpress.com/">HEAT-ALARM</a> ("Development of a heat-health warning system in Greece") research project.</p> <p><strong>Initially developed for Greece</strong>, it consists of hourly values of population-weighted mPET (modified physiologically equivalent temperature), simulated by the RayMan Pro model for the period 1991-2020 and for 10 population subsets in 72 regional units and combinations thereof, which are based on the NUTS-3 (Nomenclature of Territorial Units for Statistics-3) classification in Greece, using the Copernicus European Regional Reanalysis (CERRA) at 5.5 km spatial resolution. The dataset also includes the main environmental drivers of mPET (e.g. temperature) at the same spatiotemporal resolution.</p> <p>In the framework of <strong>replicating</strong> the original dataset, the current version includes population-weighted values of mPET and its environmental drivers for six populations in five districts of <strong>Cyprus</strong> at the LAU-1 (Local Administrative Units-1) level, covering the period from 1991 to 2020. </p> <p>The code used to produce the presented data is available at: <a href="https://doi.org/10.5281/zenodo.10793067">https://doi.org/10.5281/zenodo.10793067</a>. It can be used to replicate the dataset not only directly in Greece, but also in any other country included in the CERRA domain after appropriate adjustments, as in the case of Cyprus above.</p> <p><em>Compared to the previous version of the dataset for Greece, this version includes vapor pressure (VP) instead of relative humidity (see README.txt for more details), as VP is more relevant for human-biometerological and health-related studies.</em><em> </em></p> <p><strong>References</strong></p> <p>Giannaros, C., Agathangelidis, I., Galanaki, E. <em>et al.</em> Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece. <em>Sci Data</em> <strong>11</strong>, 76 (2024). <a href="https://doi.org/10.1038/s41597-024-02923-y">https://doi.org/10.1038/s41597-024-02923-y</a> </p>
Wittgenstein Center (WIC) Population and Human Capital Projections - 2023
<p>THIS IS VERSION WIC3.004 (Beta) (<strong>internal version V14,</strong> <strong>the last three versions were called V13, V12 and V11). </strong>This is a release version corresponding to the <a href="https://data.ece.iiasa.ac.at/ssp/#/login?redirect=%2Fworkspaces">SSP-Database 3.0</a> (will be updated) release and <a href="https://dataexplorer.wittgensteincentre.org/wcde-v3/">WIC-Data Explorer 2023</a></p> <p>Short Abstract</p> <p>We update the population and human capital components of the Shared Socio-Economic Pathways (SSPs) at the global level, considering the most recent baseline information. While the long-term assumptions based on extensive analysis and expert solicitations remain unchanged, we modify only the trend component. The first set of SSPs was based on demographic data through 2012. The population structures by age, sex, and education of the base year (2010) have since changed for most countries, mainly due to more recent data and reliable information. The mortality situation has improved in many countries affected by HIV/AIDS and among children in countries with higher mortality. The impact of COVID-19 on demographic trends must be addressed. In many countries, fertility rates have fallen faster than expected. International migration has been irregular and volatile as usual. These changes are reflected in the new update, with some improvements in operationalization.</p> <p>Changes in version "V14":</p> <p> We found a bug affecting the education distribution sex (interchanged) in 37 countries (with UN country code): 32 Argentina, 44 Bahamas, 48 Bahrain, 64 Bhutan, 76 Brazil, 108 Burundi, 132 Cabo Verde, 156 China, 158 China, Taiwan Province of China, 170 Colombia, 214 Dominican Republic, 218 Ecuador, 226 Equatorial Guinea, 288 Ghana, 296 Kiribati, 360 Indonesia, 392 Japan, 400 Jordan, 410 Republic of Korea, 414 Kuwait, 496 Mongolia, 528 Netherlands, 558 Nicaragua, 583 Micronesia (Fed. States of), 591 Panama, 626 Timor-Leste, 630 Puerto Rico, 634 Qatar, 643 Russian Federation, 662 Saint Lucia, 702 Singapore, 764 Thailand, 784 United Arab Emirates, 788 Tunisia, 792 Turkey, 840 United States of America, 894 Zambia. The impact on the total population is minimal.</p>
Data for: Patterns of shared signatures of recent positive selection across human populations
<p>Genome-wide summary stats for modified iHS scan in 1KG as reported in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/29459708/">Patterns of shared signatures of recent positive selection across human populations.</a></p> <p>Johnson KE, Voight BF.Nat Ecol Evol. 2018 Apr;2(4):713-720. doi: 10.1038/s41559-018-0478-6. Epub 2018 Feb 19.</p> <p>PMID: 29459708</p> <p>Code available at: https://github.com/bvoightlab/iHS_calc</p>
Genome-wide characterization of human minisatellite VNTRs: population-specific alleles and gene expression differences
<p>This repository consists of minisatellite VNTR genotypes for 2,800 samples (2,770 individuals). The raw VCF files were produced using <a href="https://github.com/yzhernand/VNTRseek">VNTRseek</a> on xxx data sources: <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/">30 high coverage WGS datasets</a> from the 1000 Genomes Project phase 3, <a href="https://www.internationalgenome.org/data-portal/data-collection/30x-grch38">2,504 unrelated genomes</a> from New York Genome Center (NYGC), <a href="https://www.internationalgenome.org/data-portal/data-collection/sgdp">253 genomes from Simons Diversity Genome Project</a> (SGDP), <a href="https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/tumor-normal.html">two tumor-normal breast cancer samples</a> from Illumina Basespace, haploid genomes <a href="https://www.ncbi.nlm.nih.gov/sra/SRX652547">CHM1 </a>and <a href="https://www.ncbi.nlm.nih.gov/sra/SRX1009644">CHM13</a>, and seven genomes from the Personal Genome Project from the Genome In A Bottle Consortium (GIAB). Raw VCF files are provided for each data source separately.</p> <p>The raw VCF files were preprocessed (preprocess.sh) to extract genotypes and provided in VNTRseek_preprocessed_data.tar.gz (uncompressed size 10G). The R Markdown code to analyze the preprocessed data and produce figures and tables is also provided (tables_and_figures.Rmd). For more information see the ReadMe file.</p> <p>This work was supported in part by NSF grants IIS-1423022 and DBI-1559829.</p>
IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm
FIG. 4 in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
FIG. 4. — Cranial bones of Alsophis antillensis (Schlegel, 1837) from La Désirade and Marie-Galante islands: A, right maxilla from Pointe Gros Rempart 6 (Dec. 7) located on La Désirade Island; B, right palatine from Blanchard Cave (Layer 8) located Marie-Galante Island; C, D, left pterygoid anterior (C) and posterior (D) fragments from Blanchard Cave (layers 8 and 10) located Marie-Galante Island; E, left compound bone from Blanchard Cave (Layer 8) located Marie-Galante Island; F, right dentary from Pointe Gros Rempart 6 (Dec. 3) located on La Désirade Island. Abbreviations: c. p., choanal process; d. n., dorsal notch; di., diastema; e. p. m., ectopterygoid process of the maxilla; e. p. p., ectopterygoid process of the pterygoid; f. m. n., foramen for the maxillary nerve; g. f., glenoid
FIG. 2 in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
FIG. 2. — Morphological variability among four specimens of Alsophis Fitzinger, 1843. From left-to-right: smallest and largest available specimens of Alsophis rijgersmaei Cope, 1869 and Alsophis antillensis (Schlegel, 1837) varieties A and B (of Duméril et al. 1854). The two figured vertebrae for each specimen correspond to the most different morphologies observed among the trunk vertebrae (anterior vertebra on the left and median vertebra on the right).
FIG. 7 in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
FIG. 7. — Results of statistical analyses of fossil and modern dipsadid snake vertebrae on the Guadeloupe Islands: A, two first axes of the PCA conducted on the maximum number of specimens (first analysis); B, Mahalanobis distance tree obtained from the results of the LDA (first analysis); C, two first axes of the PCA conducted on the maximum number of measurements (second analysis); D, Mahalanobis distance tree obtained from the results of the LDA (second analysis).
Run and output files from: Holocene population expansion of a tropical bee coincides with early human colonisation of Fiji rather than climate change
<p><span><span><span><span><span><span><span><span><span><span><span>There is substantial debate about the relative roles of climate change and human activities on biodiversity and species demographies over the Holocene. In some cases, these two factors can be resolved using fossil data, but for many taxa such data are not available. Inferring historical demographies of taxa has become common, but the methodologies are mostly recent and their shortcomings often unexplored. The bee genus <i>Homalictus</i> is developing into a tractable model system for understanding how native bee populations in tropical islands have responded to past climate change. We greatly expand on previous studies using sequences of the mitochondrial gene COI from 474 specimens and between 171 and 3,928 autosomal (DArTSeq) SNP loci from 19 specimens of the native Fijian bee, <i>Homalictus fijiensis</i> (Perkins & Cheesman, 1928), to explore its historical demography using coalescent and mismatch analyses. We ask whether past changes in demography were human- or climate-driven, while considering analytical assumptions. We show that inferred changes in population sizes are too recent to be explained by past climate change. Instead we find that a dramatic increase in population size for the main island of Viti Levu coincides with increasing occupation by humans and their modification of the environment. We found no corresponding change in bee population size for another major island, Kadavu, where human populations and agricultural activities have been historically very low. Our analyses indicate that molecular approaches can be used to disentangle the impacts of humans and climate change on a major tropical pollinator and that stringent analytical approaches are required for reliable interpretation of results. </span></span></span></span></span></span></span></span></span></span></span></p>
Varying genetic imprints of roads and human density in North American mammal populations
<p>Road networks and human density are major factors contributing to habitat fragmentation and loss, isolation of wildlife populations and reduced genetic diversity. Terrestrial mammals are particularly sensitive to road networks and encroachment by human populations. However, there are limited assessments of the impacts of road networks and human density on population-specific nuclear genetic diversity, and it remains unclear how these impacts are modulated by life history traits. Using generalized linear mixed models and microsatellite data from 1444 North American terrestrial mammal populations we show that taxa with large home range sizes, dense populations, and large body sizes had reduced nuclear genetic diversity with increasing road impacts and human density, but the overall influence of life history traits was generally weak. Instead, we observed a high degree of genus-specific variation in genetic responses to road impacts and human density. Human density negatively affected allelic diversity or heterozygosity more than road networks (13 versus 5-7 of 25 assessed genera, respectively); increased road networks and human density also positively affected allelic diversity and heterozygosity in 15 and 6-9 genera, respectively. Large bodied, human-averse species were generally more negatively impacted than small, urban-adapted species. Genus-specific responses to habitat fragmentation by ongoing road development and human encroachment likely depend on the specific capability to (i) navigate roads as either barriers or movement corridors, and (ii) exploit resource-rich urban environments. The non-uniform genetic response to roads and human density highlights the need to implement efforts to mitigate the risk of vehicular collisions, while also facilitating gene flow between populations of particularly vulnerable taxa.</p>
Human Capital-weighted population estimates for 185 countries from 1970 to 2100
<p>We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population's productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970-2020 and 2020-2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.</p>
Germline CpG methylation signatures in the human population inferred from genetic polymorphism
<p>This repository contains data released accompanying the manuscript "Germline CpG methylation signatures in the human population inferred from genetic polymorphism". </p>
Genomic footprints of (pre) colonialism: Population declines in urban and forest túngara frogs coincident with historical human activity
<p>Urbanisation is rapidly altering ecosystems, leading to profound biodiversity loss. To mitigate these effects, we need a better understanding of how urbanisation impacts dispersal and reproduction. Two contrasting population demographic models have been proposed that predict that urbanisation either promotes (facilitation model) or constrains (fragmentation model) gene flow and genetic diversity. Which of these models prevails likely depends on the strength of selection on specific phenotypic traits that influence dispersal, survival, or reproduction. Here, we a priori examined the genomic impact of urbanisation on the Neotropical túngara frog (<em>Engystomops pustulosu</em>s), a species known to adapt its reproductive traits to urban selective pressures. Using whole-genome resequencing for multiple urban and forest populations we examined genomic diversity, population connectivity and demographic history. Contrary to both the fragmentation and facilitation models, urban populations did not exhibit substantial changes in genomic diversity or differentiation compared to forest populations, and genomic variation was best explained by geographic distance rather than environmental factors. Adopting an a posteriori approach, we additionally found both urban and forest populations to have undergone population declines. The timing of these declines appears to coincide with extensive human activity around the Panama Canal during the last few centuries rather than recent urbanisation. Our study highlights the long-lasting legacy of past anthropogenic disturbances in the genome and the importance of considering the historical context in urban evolution studies as anthropogenic effects may be extensive and impact non-urban areas on both recent and older timescales. </p>
Data and code for "Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago"
<p>This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022).</p> <p>The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review).</p> <p>The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc).</p> <p>The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x).</p> <p>The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data.</p> <p>The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.</p>
Human Population-Averaged dMRI Templates (FIB Files, NIFTI Files)
<p>The templates were constructed by DSI Studio using q-space diffeomorphic reconstruction.</p> <p>Methods and Data Source: https://brain.labsolver.org/hcp_template.html</p> <p> </p> <p> </p>
Data from: Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains
<p>Protected areas are important in species conservation, but high rates of human-caused mortality outside their borders and increasing popularity for recreation can negatively affect wildlife populations. We quantified wolverine (<em>Gulo gulo</em>) population trends from 2011 to 2020 in >14 000 km2 protected and non-protected habitat in southwestern Canada. We conducted wolverine and multi-species surveys using non-invasive DNA and remote camera-based methods. We developed Bayesian integrated models combining spatial capture-recapture data of marked and unmarked individuals with occupancy data. Wolverine density and occupancy declined by 39 percent, with an annual population growth rate of 0.925. Density within protected areas was 3 times higher than outside and declined between 2011 (3.6 wolverines/1000 km2) and 2020 (2.1 wolverines/1000 km2). Wolverine density and detection probability increased with snow cover and decreased near development. Detection probability also decreased with human recreational activity. The annual harvest rate of 13% was above the maximum sustainable rate. We conclude that humans negatively affected the population through direct mortality, sub-lethal effects and habitat impacts. Our study exemplifies the need to monitor population trends for species at risk – within and between protected areas - as steep declines can occur unnoticed if key conservation concerns are not identified and addressed.</p>
The Optimal Species Richness Environments for Human Populations
<p>This supporting document should allow one to recreate the analysis performed as part of “The optimal species richness environments for human populations, ” By Freeman et al. 2018 Submitted to PNAS July 2018. The annotated scripts in this directory (RichnessSIScripts.pdf) contain code to replicate the analysis, as well as code for additional analyses not included in the main paper or supplemental information. To replicate the analysis, one can either analyze the data files provided or build their own data set. As discussed in the main body of the text, we built three data sets following the procedures outlined by Tallavaara et al. (2017) for linking species richness values, net primary productivity and pathogen stress to each ethnographic case. We do not replicate the scripts provided by Tallavaara et al.(2017) as these are available, clear and should be cited when used.To replicate our analysis, one needs to set their working directory in R to the file location that contains the data files. There are 11 files that follow the naming convention “name.csv.” The 11 files are “MainFinal.csv”. “AGPOP3Eco.csv”, “HGFEM4R.csv”, “AGPOPClass.csv”, “CountryMeansEco2.csv”, “AGPOP3EcoH.csv”, “AGPOP3EcoL.csv”, “HiHG.csv”, “LowHG,csv”, “CountryMeansEco2H.csv”, and “CountryMeansEco2L.csv”. The first five files are the main files, the second six files are divided into high and low species richness environments by economy type for convenience. In each file, the variables are defined as follows:</p> <p>1. Group/Country–name of the ethnographic society of country</p> <p>2. Latitude–the latitude at the geographic center of a group’s territory or a country’s territory.</p> <p>3. Longitude–the longitude at the geographic center of a group’s territory or a country’s territory.</p> <p>4. Class–an ordinal ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details)</p> <p>5. Class2–an binary ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details).</p> <p>6. ECI–The average economic complexity index since 1973 as measured among modern countries.</p> <p>7. DENSITY–Population density in people per square kilometer. This is a point in time estimatefor hunter-gatherer and agricultural groups and an average density since 1973 among nation states.</p> <p>8. LnDENSITY–The natural log of population density</p> <p>9. npp–net primary productivity estimated at the center of each group’s territory</p> <p>10. npp2-Net primary productivity squared</p> <p>11. biodiv–Standardized estimate of species richness at the center of each group’s range.</p> <p>12. biodiv2–Species richness *100 ad squared.</p> <p>13. pathos–Index of pathogen stress at the center of a group’s territory.</p> <p>14. DivDiff–The absolute value of species richness-the species richness value of peak population density (values identified in Fig. 2 of the main manuscript).1</p> <p>5. ID–A nominal variable that denotes economy type. HG=hunter-gatherer, AG=subsistence agriculturalist, IND=modern nation state</p> <p> </p> <p>Tallavaara, M., J. T. Eronen, and M. Luoto2017. Supporting data and script for ”productivity, biodiversity, and pathogens influence the global hunter-gatherer population density” (Tallavaara et al. pnas 2018).<a href="https://doi.org/10.5281/zenodo.1167852">https://doi.org/10.5281/zenodo.1167852</a></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.