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22,445 results for “diversity”
IPBES Assessment of the diverse values and valuation of nature - Figures presented in the summary for policymakers
<p>These figures are an integral part of the Summary for policymakers of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Tables presented in Chapter 4
<p>These tables are an integral part of Chapter 4 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Tables presented in Chapter 3
<p>These tables are an integral part of Chapter 3 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Tables presented in Chapter 6
<p>These tables are an integral part of Chapter 6 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Tables presented in Chapter 5
<p>These tables are an integral part of Chapter 5 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
The pan-genome of Aspergillus fumigatus provides a high-resolution view of its population structure revealing high-levels of lineage-specific diversity driven by recombination
<p><em>Aspergillus fumigatus </em>is a deadly agent of human fungal disease, where virulence heterogeneity is thought to be at least partially structured by genetic variation between strains. While population genomic analyses based on reference genome alignments offer valuable insights into how gene variants are distributed across populations, these approaches fail to capture intraspecific variation in genes absent from the reference genome. Pan-genomic analyses based on <em>de novo</em> assemblies offer a promising alternative to reference-based genomics, with the potential to address the full genetic repertoire of a species. Here, we use a combination of population genomics, phylogenomics, and pan-genomics to assess population structure and recombination frequency, phylogenetically structured gene presence-absence variation, evidence for metabolic specificity, and the distribution of putative antifungal resistance genes in <em>A. fumigatus</em>. We provide evidence for three distinct populations of <em>A. fumigatus</em>, structured by both gene variation (SNPs and indels) and distinct gene presence-absence variation with unique suites of accessory genes present exclusively in each clade. Accessory genes displayed functional enrichment for nitrogen and carbohydrate metabolism, hinting that populations may be stratified by environmental niche specialization. Similarly, the distribution of antifungal resistance genes and resistance alleles were often structured by phylogeny. Despite low levels of outcrossing, <em>A. fumigatus</em> demonstrated a large pan-genome including many genes unrepresented in the Af293 reference genome. These results highlight the inadequacy of relying on a single-reference based approach for evaluating intraspecific variation, and the power of combined genomic approaches to elucidate population structure, genetic diversity, and the putative ecological drivers of clinically relevant fungi.</p> <p>Accompanying manuscript is available as preprint at <a href="https://dx.doi.org/10.1101/2021.12.12.472145">https://dx.doi.org/10.1101/2021.12.12.472145</a> </p> <p>Lotus A. Lofgren, Brandon S. Ross, Robert A. Cramer, Jason E. Stajich. Combined Pan-, Population-, and Phylo-Genomic Analysis of <em>Aspergillus fumigatus</em> Reveals Population Structure and Lineage-Specific Diversity bioRxiv 2021.12.12.472145; doi: https://doi.org/10.1101/2021.12.12.472145</p>
Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population
<h1>Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population</h1> <h1> </h1> <p>These files contain data on bacteria present in the guts of wild great tit (Parus major) obtained from faecal samples and sequenced using Illumina MiSeq. These data resulted from an experiment which provided supplementary mealworms at the nest during the breeding season at number of woodland sites in Cork, Ireland. Approximately half of these nests were given mealworms covered in a freeze dried bacterial powder containing the bacteria Lactobacillus kimchicus, which had been isolated from great tit faeces from the previous season. This treatment aimed to disrupt the gut microbiota of the treatment birds in order to provide evidence for the gut microbiotas role in birds health and fitness. Included here are the 3 elements necessary to create a 'phyloseq object' containing the sample metadata, ASV (Amplicon Sequence Variant) count table and a taxonomy table. The metadata file includes the alpha diversity scores for each individual. The data include all negative control samples taken during sample collection and library preparation, which were removed before the main analyses. All analyses, except for the beta-diversity analyses, were conducted in R. All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p> <h2> </h2> <h2>## Description of the data and file structure </h2> <p>Taxonomy, ASV and metadata files required to create a phyloseq object in R. metadata.csv file contains data on individual birds (i.e. individual samples). The metadata includes descriptions of the bird itself and it's environment, namely:</p> <ul> <li>Rownames: unique sample ID for each sample, corresponds with asvTable.csv. </li> <li>Nest: unique identifier for the nest box associated with the bird being sampled. </li> <li>Sample.ID: unique identifier for the faecal sample or control sample.</li> <li>Bird.ID: Identity of the bird the sample came from, note some individuals sampled twice so some bird.ID's may reoccur in metadata with different Sample.ID.</li> <li>Date: Date the sample was taken dd/mm/yyyy.</li> <li>Day: Date the sample was taken, in days since 1st March.</li> <li>Ring.Mark: British Trust for Ornithology (BTO) metal ring ID where applicable. Birds only ringed at D15 so some young birds do not have IDRings.</li> <li>Site: ID of woodland site that bird was sampled at.</li> <li>Chick.LetterID: ID letter differentiates between different birds from the same nest. Either 'A'-'F' for nestlings, 'Fe' for females or 'M' for males.</li> <li>Age.code: BTO age code.</li> <li>Age.category: Age category that bird is in. D8 = 8 days post hatching, D15 = 15 days post hatching, adult = 1+ years post hatching.</li> <li>Sex: Bird's sex, only determined for adult birds. Fe = Female, M = Male.</li> <li>Wing_mm: Wing length in mm.</li> <li>Tarsus_mm: minimum tarsus length of bird in mm.</li> <li>Weight_g: bird's weight in grams.</li> <li>Faecal.Sample: bird's age at sampling.</li> <li>newRing: whether bird was fitted with a new BTO ring. Only relevant to adults.</li> <li>Treatment: the experimental treatment group that the bird was in. Either 'Treatment' when nest given L. kimchicus treated mealworms or 'Control' when nest given plain mealworms.</li> <li>Notes: field notes.</li> <li>Main.sample: indicates whether this sample was the main sample to be used for analysis, an alternative sample taken as a backup.</li> <li>Plate: the ID of the PCR plate which the sample was amplified on.</li> <li>Azenta_noPeriod: sample ID given to sequencing facility without special characters. Corresponds to fastq files and ASV table counts.</li> <li>Qubit_prePool: samples qubit score before pooling.</li> <li>Date_extracted: date the sample was extracted on dd/mm/yyyy.</li> <li>SampleType: whehther the sample was a 'main' sample intended for downstream analysis, a 'control' sample for detecting contamination during library preparation, a 'duplicate' for detecting PCR issues, a 'label_error' where sample was suspected of being mislabelled at some point, a 'repeat' sample intended to detect errors or issues, a 'contam' sample which was suspected of being contaminated, a 'common' sample used across different PCR plates to detect issues. Extraction_notes: notes regarding the DNA extraction of the sample. </li> <li>LibPrep_notes: notes regarding the library preparation of the sample.</li> <li>Ring.Mark.lab: the ring or sample ID written on the sample tube, recorded to help detect mislabelling.</li> <li>Post_lab_notes: notes regarding issues found post sequencing.</li> <li>NumberOfReads: number of sequence reads associated with the sample. </li> <li>DistanceToEdge: distance between nest and woodland edge in metres. </li> <li>BroodSize.D8: number of nestlings in the nest at day-8 post hatching. </li> <li>BroodSize.D15: number of nestlings in the nest at day-15 post hatching.</li> <li>firstEggLayDate: Date the first egg in the clutch was laid, in days since 1st March.</li> <li>lastEggLayDate: Date the last egg in the clutch was laid, in days since 1st March.</li> <li>Observed: number of unique ASV's (or taxa) detected in the sample.</li> <li>Chao1: Chao1 diversity of the sample.</li> <li>Shannon: Shannon diversity of the sample.</li> </ul> <p>The file 'taxonomy.csv' contains the taxonomic breakdown of each bacterial Amplicon Sequence Variant (ASV) found in the dataset from Phylum to Species. Obtained by using the Naive Bayes Classifier against the Silva (v138) taxonomic database.</p> <p>The file 'asvTable.csv' contains counts of each amplicon sequence variant's occurrence for each individual sample. Samples are rows and taxa are columns.</p> <p> </p> <h2>Sharing/Access information </h2> <p>All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p>
Cancer screening attendance rates in transgender and gender-diverse patients: a systematic review and meta-analysis
<p>Supplementary Data to support the findings of a systematic review investigating cancer screening rates in transgender and gender-diverse individuals.</p>
Metabarcoding reveals a high diversity of woody host-associated Phytophthora spp. in soils at public gardens and amenity woodlands in Britain
<p>This is the demultiplexed Illumina MiSeq raw sequencing data from two 96-well plates from the following recent publication, shared with permission of the corresponding author, Sarah Green:</p> <p>Riddell <em>et al.</em> (2019). Metabarcoding reveals a high diversity of woody host-associated <em>Phytophthora</em> spp. in soils at public gardens and amenity woodlands in Britain. https://doi.org/10.7717/peerj.6931<br> <br> It consists of 244 gzipped compressed plain text FASTQ format sequence files, grouped into 122 pairs by the widely used R1 and R2 suffix. The files have been renamed to use the anonymised site numbers (1 to 14) as in the paper, see also supplementary table one for site metadata. Additionally there are two negative controls, and positive control DNA mixtures of 10 and 15 species as described in the paper.<br> </p>
The impact of beech deadwood on soil properties and microbial diversity
<p><span><span>Our research is an attempt to determine the role of decaying wood in shaping the properties of forest soils in mountain ecosystems.</span></span><span><span> </span></span></p>
Inter-Chemical Correlation results for the study: HHEARx2018-2537 (Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort.)
Title: Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort. <br>Species: Homo sapiens <br>Number of samples: 630 <br>Number of named analytes: 16 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=66 <br>
plant diversity and terrain covariates at OAL-UK
<p>dataset containing information on plant diversity and terrain covariates retrieved from the slopes OAL-UK. Samples were collected using 1 m2 quadrants following a stratified random sampling approach. The data set casts light on the relationship between shallow landslides, terrain covariates and plant diversity. The data set is linked to the following publication <a href="https://doi.org/10.1007/s10346-017-0822-y">https://doi.org/10.1007/s10346-017-0822-y</a></p>
AMBON diversity & community composition, data & code
<p>Data and R code for analyzing diversity and community composition of eight assemblages in the Northeast Chukchi Sea in 2015 and 2017. For details of the analysis, results and interpretation, see:</p> <p><em>Mueter, F.J., Iken, K., Cooper, L.W., Grebmeier, J.M., Kuletz, K.J., Hopcroft, R.R., Danielson, S.L., Collins, R.E., Cushing, D. Changes in diversity and species composition across multiple assemblages in the northeast Chukchi Sea during two contrasting years are consistent with borealization. Oceanography (In Press).</em></p>
Expansion of the global RNA virome reveals diverse clades of bacteriophages
<p>This deposit is intended to contain the various data generated as part of the RNA Virus in MetaTranscriptomes project ("RVMT"). This initial version is released ahead of time, near the time of submission, in hopes of providing a long lasting resource for the general scientific community. Note well - The authors listed in this initial version release are a partial list only. The RNA Virus in MetaTranscriptomes consortium is a project with over 90 researches from various institutions (see below).</p> <p>High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum <em>Lenarviricota</em>, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.</p> <p>The RNA Virus in metatranscriptomes consortium:<br> Adrienne B. Narrowe, Alexander J. Probst, Alexander Sczyrba, Annegret Kohler, Armand Séguin, Ashley Shade, Barbara J. Campbell, Björn D. Lindahl, Brandi Kiel Reese, Breanna M. Roque, Chris DeRito, Colin Averill, Daniel Cullen, David A. C. Beck, David A. Walsh, David M. Ward, Dongying Wu, Emiley Eloe-Fadrosh, Eoin L. Brodie, Erica B. Young, Erik A. Lilleskov, Federico J. Castillo, Francis M. Martin, Gary R. LeCleir, Graeme T. Attwood, Hinsby Cadillo-Quiroz, Holly M. Simon, Ian Hewson, Igor V. Grigoriev, James M. Tiedje, Janet K. Jansson, Janey Lee, Jean S. VanderGheynst, Jeff Dangl, Jeff S. Bowman, Jeffrey L. Blanchard, Jennifer L. Bowen, Jiangbing Xu, Jillian F. Banfield, Jody W Deming, Joel E. Kostka, John M. Gladden, Josephine Z Rapp, Joshua Sharpe, Katherine D. McMahon, Kathleen K. Treseder, Kay D. Bidle, Kelly C. Wrighton, Kimberlee Thamatrakoln, Klaus Nusslein, Laura K. Meredith, Lucia Ramirez, Marc Buee, Marcel Huntemann, Marina G. Kalyuzhnaya, Mark P Waldrop, Matthew B Sullivan, Matthew O. Schrenk, Matthias Hess, Michael A. Vega, Michelle A. O’Malley, Monica Medina, Naomi E. Gilbert, Nathalie Delherbe, Olivia U. Mason, Paul Dijkstra, Peter F. Chuckran, Petr Baldrian, Philippe Constant, Ramunas Stepanauskas, Rebecca A. Daly, Regina Lamendella, Robert J Gruninger, Robert M. McKay, Samuel Hylander, Sarah L. Lebeis, Sarah P Esser, Silvia G. Acinas, Steven S. Wilhelm, Steven W. Singer, Susannah S. Tringe, Tanja Woyke, TBK Reddy, Terrence H. Bell, Thomas Mock, Tim McAllister, Vera Thiel, Vincent J. Denef, Wen-Tso Liu, Willm Martens-Habbena, Xiao-Jun Allen Liu, Zachary S. Cooper, Zhong Wang. For the full list of authors and related information, please see the spreadsheet tittle "Table S9 - Consortium coauthorship" available in this collection in the folder named "Tables".</p>
Experimental Factors Influence Diversity Metrics of the Gut Microbiome in Laboratory Mice
<p>Abstract<br> Introduction</p> <p>Gut microbiome studies often overlook experimental factors that could influence gut microbiome diversity and could impact findings. Large-scale studies investigating these experimental factors are lacking. Thus, we aimed to determine which experimental factors influence the gut microbiome diversity in pre-clinical animal model studies.</p> <p><br> Methods</p> <p>We extracted DNA and sequenced the V4 region of the 16S rRNA gene of a total of 538 samples from various sections of the gastrointestinal tract of 303 young and aged male and female C57BL/6J mice of three different genotypes on five diets from three animal house facilities. As a proof-of-concept in a disease model, some mice were treated with sham or angiotensin II, a commonly studied agent used as a hypertension model. Some samples were sequenced twice as a matched-comparison group.</p> <p>Results</p> <p>Using over 17 million sequencing reads, we found that experimental factors such as animal house facility, genotype, diet, age, sex, sampling site, and technical factor (i.e., sequencing batch) affected both α- and β-diversity (weighted and unweighted UniFrac), and were associated with compositional changes in the microbiome at varying magnitude, with diet and sampling site having the largest effect. After adjustment by these factors, treatment with angiotensin II had no impact on α-diversity and was only significant in unweighted UniFrac (presence/absence of bacteria) analyses.</p> <p><br> Conclusion</p> <p>Our data identified several key experimental and technical factors that affect the gut microbiome in laboratory mice. Our findings support that not accounting or adjusting for these factors may lead to false-positive discoveries and non-biologically relevant findings in the gut microbiome field.</p>
Results files for Land-free Bioenergy From Circular Agroecology -- A Diverse Option Space and Trade-offs
<p>This is the open data repository to support and reproduce results in the paper "<em>Land-free Bioenergy From Circular Agroecology -- A Diverse Option Space and Trade-offs</em>." There are <strong>three types </strong>of files here:</p> <p> </p> <p> </p> <p><strong>1. Ready-to-use final results files of all strategies and scenarios referred to in the paper. </strong>They can be downloaded and used directly without running any codes. They all have the same naming format for strategies/scenarios: `Org` = organic share, `ConcRed` = concentrate feeding reduction share, `WasteRed` = waste reduction share, and numbers refer to the share. E.g., `Org0_ConcRed50_WasteRed75` is a strategy with 0% organic share, 50% concentrate feeding reduction, and 75% waste reduction.</p> <p> </p> <ul> <li>`NationalAncillaryBioenergyPotential_EJ.csv`: The national potential of ancillary bioenergy in 2050 from all scenarios. (Units: EJ). Same in both pathways.</li> <li>`GlobalPotentialEnvironmentalImpacts_NutrientFirst.csv`: Environmental impacts of all scenarios from the pathway `<em>NutrientFirst</em>.` The first three rows refer to the combination of agroecological practices in places, which allow you to explore environmental impacts grouped by, e.g., different organic shares.</li> <li>`GlobalPotentialEnvironmentalImpacts_NegFirst.csv`: Same structure as the file above, but from another pathway, `<em>NegativeFirst</em>`.</li> </ul> <p> </p> <p><strong>2. `SOLmOutputs` contains all original output files from our model <a href="https://orgprints.org/id/eprint/38778/">SOLmV6</a>. </strong></p> <p> </p> <p><strong>3. `DataCleaningKit` has the Python codes and additional dataset of heat values to process 2. `SOLmOutputs` and spit 1. </strong>(Tip: One should adjust the `input_path` and `output_path` before running `DataCleaning.py.`)</p> <p> </p> <p> </p> <p>Fei Wu (fei.wu@usys.ethz.ch)</p> <p>Delft, August, 2023</p> <p> </p>
Supplementary data: Agro-morphological and molecular characterization reveal deep insights in promising genetic diversity and marker-trait associations in Fagopyrum esculentum and F. tataricum
<p>Our study focuses on the global/European buckwheat germplasm collected as part of the ECOBREDD project. The potential of this highly diverse collection for organic buckwheat breeding was evaluated at two complementary levels: phenotypic and genetic. Here, we characterized the phenotypic and genetic diversity of a global collection of the two cultivated buckwheat species <em>Fagopyrum esculentum</em> and <em>F. tataricum</em> (190 and 51 accessions, respectively) using 37 agro-morphological traits and 24 SSR markers (Simple Sequence Repeats) (see publication and info sheet of the data).</p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project
<p>Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Structural Diversity from the NEON Discrete-Return LiDAR Point Cloud in 2013-2022
Structural diversity, characterizing the volumetric capacity and physical arrangement of biotic components in an ecosystem, controls critical ecosystem functions like light interception, hydrology, and microclimate. This product generates structural diversity metrics for the NEON sites, sourced from the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform (DP1.30003.001; collected in March 2023). Using R programming, we computed the metrics detailing height, heterogeneity, and density at 30 m, aligned to the Landsat grids, for 243 site years in 57 NEON sites from 2013 to 2022.
Data for Forb diversity globally is harmed by nutrient enrichment but can be rescued by large mammalian herbivory
Forbs (“wildflowers”) are important contributors to grassland biodiversity and services, but they are vulnerable to environmental changes that affect their coexistence with grasses. In a factorial experiment at 94 sites on 6 continents, we tested the global generality of several broad predictions arising from previous studies: (1) Forb cover and richness decline under nutrient enrichment, particularly nitrogen enrichment, which benefits grasses at the expense of forbs. (2) Forb cover and richness increase under herbivory by large mammals, especially when nutrients are enriched as grazing will release forbs from decreased grass competition under fertilization. (3) Forb richness and cover are less affected by nutrient enrichment and herbivory in more arid climates, because water limitation reduces the impacts of competition with grasses. We found strong evidence for the first, partial support for the second, and no support for the third prediction. Forb richness and cover are reduced by nutrient addition, with nitrogen having the greatest effect; forb cover is enhanced by large mammal herbivory, although only under conditions of nutrient enrichment and high herbivore intensity; and forb richness is lower in more arid sites, but is not affected by consistent climate-nutrient or climate-herbivory interactions. We also found that nitrogen enrichment disproportionately affects forbs in certain families (Asteraceae, Fabaceae). Our results underscore that anthropogenic nitrogen addition is a major threat to grassland forbs and the ecosystem services they support, but grazing under high herbivore intensity can offset these nutrient effects. For associated r code that goes along with this dataset, please refer to the following Zenodo repository: https://zenodo.org/records/14207290
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.