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42 results for “TREES Project”
Tree Canopy Leaf Area Index in CRUI Land Use Project at Harvard Forest 1997
Numerous variables related to land use disturbance and recovery processes can influence forest composition and structure. We’ve measured differences in forest communities in six sites that were formerly plowed, pastured, or continuously forested woodlots in Prospect Hill. None of the sites had noticeable canopy gap disturbance at the time of the measurements. Leaf area index (LAI) was measured with an LAI-2000 plant canopy analyzer (Li-Cor, Inc., Lincoln, NE) at all 77 edge and interior intersection points in the 30 m x 50 m permanent plot (7 columns x 11 rows) in 5 of our 6 land use sites. Under-canopy measurements were made in each site over 25-30 minutes during midday hours (11:00-2:30 EST) on overcast days near solstice (June 13, 18). The under-canopy readings were contrasted with an open-sky measurement taken in an open field near the Harvard Forest headquarters just before beginning data collection in each site. LAI averaged 3.98 and ranged from 2.28 to 5.93 across all sites. W1 had the highest site-level mean (4.62) and maximum (5.93) LAI while S2 had the lowest values (mean = 3.41, max = 4.55). The woodlot also showed the greatest spatial variation as measured by C.V., while plow #1 showed the least variation.
Mapped Trees in CRUI Land Use Project at Harvard Forest 1996-2006
Numerous variables related to land use disturbance and recovery processes influence forest composition, structure, and growth. We measured forest communities in six sites that were formerly plowed, pastured, or continuously forested woodlots in Prospect Hill to test predictions about species composition, stand structure, and productivity in response to agricultural land use legacies. A permanent 30 x 50 m plot gridded in 5 x 5 m sub-plots was established in each of the 6 sites. All trees at least 2.5 cm DBH (diameter at breast height, 1.3 m) were mapped visually in the field, marked with an aluminum tag, and their DBH recorded in summer 1996. Individual boles of multi-stemmed trees were measured separately and a composite single DBH was calculated. Standing dead trees were also mapped and their DBH recorded as well. The 1996 data were used to determine above ground woody biomass using allometric equations for individual trees. The permanent plots were re-surveyed ten years later in fall 2006. The status of each tree mapped in 1996 was recorded (alive, dead standing, dead fallen, forked) and DBH’s were re-measured. Additional trees that grew across the 2.5 cm DBH threshold during the ten-year period were mapped and their DBH’s measured. Composite diameters and above ground woody biomass were determined for forked trees as in 1996. The 2006 re-measurements were used to analyze changes in species composition, stand structure (density, diameter distribution), and mortality patterns among species and size classes, and to calculate net changes in above ground woody biomass across the ten-year period.
Tree Seedlings in CRUI Land Use Project at Harvard Forest 1996
Forests recovering from agricultural legacies differ in many ways that influence tree seed dispersal and seedling establishment patterns. We recorded the number of seedlings (less than 0.5 m tall) of all tree species on a 1 x 1 m resolution across six land use legacy sites (2 plowed, 2 pastured, 2 permanent woodlot) in summer 1996 to test several predictions about seedling abundance, diversity, and dispersion patterns, and their relationships to forest structure, microclimates, and soil resources.
Tree Growth in Macrosystems Biodiversity Project at Harvard Forest 2011-2013
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This dataset contains annual growth measurements of trees along a series of transects using the measures of diameter at breast height and/or diameter and ground height at the five Gentry plots set up at Harvard Forest. These plots were set up by the Enquist Lab (PI, Brian Enquist) from the University of Arizona as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
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>
Individual tree data of the temporary test plot Neusorgefeld 5138 - VERMOS project
<p>This dataset is an artificial dataset of the dataset of the temporary trial plot in Neusorgefeld 5138 from the VERMOS project. The original dataset is significantly larger, the adaptation was made for a planned publication by Chris Wudel and was also carried out by him.</p>
The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life. Data file underpinning Figure 2A and Figure 2B
<div>These datasheets accompany the article "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life" in Frontiers in Science</div> <div>This file contains data processed from Catalog of Life on 31 December 2023. The catalog was downloaded and post-processed to</div> <div>remove prokaryotic taxa</div> <div>remove extinct and fossil taxa</div> <div>remove taxon names that were listed as junior synonyms</div> <div>remove taxon names listed as "invalid"</div> <div>Total living, valid eukaryotic genera 167,085</div> <div>The taxa were sorted by the nomenclatorial Code under which they were declared (to avoid namespace clashes)</div> <div>International Code for Algae, Fungi and Plants https://www.iapt-taxon.org/nomen/main.php</div> <div>Algal, Fungal, Plant code genera 31,076</div> <div>International Code of Zoological Nomenclature https://www.iczn.org/the-code/the-code-online/</div> <div>Zoological code genera 136,009</div> <div>The Code-sorted taxa were aggregated by the generic portion of their names, and two plots were generated:</div> <div>a plot aggregating the cumulative number of species in genera sorted by species number (Figure 2A)</div> <div>a plot illustrating the distribution of the size of genera (Figure 2B)</div> <div>This data file gives access to these processed data for</div> <div>Figure 2 A Data</div> <div>Figure 2 B Data</div> <div>The original data including the intermediate calculations of values, and the plotted graphs, are available as a GoogleDoc at https://docs.google.com/spreadsheets/d/1V-bTtWjIRasC3AgID0jGlyToKqI-H9h1aPeSxUpNrjk/edit?usp=sharing</div>
Projection of potential future tree cover persistence for 2029 based on the global model
<p>Tree cover persistence projection results for 2029 based on the global model under a business-as-usual scenario.</p>
Projection of potential future tree cover persistence for 2029 based on the six regional models
<p>Tree cover persistence projection results for 2029 based on the six regional models under a business-as-usual scenario.</p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects. Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1, dated using <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with nodes contain the mean and variance of tsdate's posterior distribution on node time. To access these values, we can use:</p> <pre><code>import json node = ts.node(10000) metadata_dict = json.loads(node.metadata) print("The mean of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["mn"])) print("The variance of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["vr"]))</code></pre> <p>Age estimates for each variant site can be derived from the mean of the age estimates of the upper and lower bounding nodes of the oldest mutation associated with a site. tsdate includes <a href="https://tsdate.readthedocs.io/en/latest/python-api.html?highlight=sites_time_from_ts#tsdate.sites_time_from_ts">a function to find the age estimates of all sites in the tree sequence</a>:</p> <pre><code>import tsdate site_times = tsdate.sites_time_from_ts(ts, node_selection='arithmetic')</code></pre> <p>This returns a numpy array which has a length equal to the number of sites.</p> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>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>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>
Tree growth (1999-2012) for the Functional Diversity project, Coweeta Hydrologic Laboratory, Otto, North Carolina
The importance of the herbaceous layer in regulating ecosystem processes in deciduous forests is generally unknown. We use a manipulative study in a rich, mesophytic cove forest in the southern Appalachians to test the following hypotheses: (i) the herbaceous functional group (HFG) in mesophytic coves accelerates carbon and nutrient cycling, (ii) high litter quality input and rapid nutrient turnover associated with HFG will have a positive effect on overstory tree growth, and (iii) the HFG regulates tree regeneration with negative effects on seedling establishment due to competition for resources. We established treatment plots in a mesic, cove-hardwoods forest and removed the herbaceous flora (HR, removed twice per year) or added herbaceous organic material (OMA, once per year) for comparison to a no removal (NR) reference for a total of 14 years. The OMA treatment stimulated soil N-mineralization and increased litterfall mass and N content. OMA N-mineralization rates were more than two times greater than both the NR and HR treatments; however, we did not detect significant differences in soil CO2 efflux among treatments. Higher overstory litterfall mass and N in the OMA treatment plots indicated that overstory trees were benefiting from the enhanced soil N-mineralization. Higher overstory leaf mass and N suggests an important linkage between HR and aboveground net primary production even though this did not translate into greater tree basal area increment. We found an increase in regeneration of all tree species with HFG removal, and the response was particularly evident for Acer rubrum seedlings.
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture.</p> <p>Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1 and <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4, as described in <a href="http://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. The files were compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>import json site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>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>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>
'Does crown sheltering effect the vulnerability of trees to wind damage in tropical forests? ' project dataset
<p>The manually delineated tree crowns, polygon-based sheltering indices, canopy height model (CHM) and digital surface model (DSM) generated to investigate the effects of local crown sheltering on wind vulnerability in Barro Colorado Island, Panama. Files include both circular and directional indices at 10m, 20m, 50m and 2 x canopy radius.</p>
Tree, shrub, and herb data from 10 Hillslope Project sites located in Macon County, North Carolina, within the Upper Little Tennessee River Basin
Vegetation was surveyed on 10 sites in the upper Little Tennessee River watershed in Macon County, North Carolina during the summer of 2012. The sites represent a gradient of development, including forested sites, agricultural sites, and exurban sites. All woody vegetation >2.5 cm diameter at breast height (DBH) was measured and identified within twelve 10 x 10 m plots at each site. Woody shrubs >0.5 m in height and <2.5 cm DBH were counted and identified within a 1x10 m transect in each plot. Herbs and woody plants <0.5m in height were identified and percent cover estimated in 3 1 x 1 m subplots located within each of the larger 10 x 10 m plots.
Projected future autumn leaf phenology of deciduous trees
<p><span>Autumn leaf phenology (i.e. leaf colouring or leaf senescence) marks the end of the growing season, during which trees assimilate atmospheric CO<sub>2</sub>. Since autumn leaf phenology responds to climatic conditions, climate change affects the length of the growing season. Thus, autumn phenology is often modelled to assess possible climate change effects on future CO<sub>2</sub> mitigating capacities and species compositions of forests.</span></p> <p><span>Here, we give access to the entire dataset of projected autumn phenology analyzed in Meier and Bigler (2023). The data was derived from different combinations of 21 process-oriented phenology models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. The dataset contains the average autumn phenology per site and for the years 2080-2099 according to each combination that led to a successful calibration. Calibration and validation were based on >45 000 observations for common beech (<em>Fagus sylvatica L.</em>), pedunculate oak (<em>Quercus robur L.</em>), and European larch (<em>Larix decidua Mill</em>.) from 500 Central European sites each.</span></p> <p><span>Cite as Meier, M., & Bigler, C. (2023). Process-oriented models of autumn leaf phenology: Ways to sound calibration and implications of uncertain projections. <em>Geoscientific Model Development</em>, 16(23), 7171–7201. https://doi.org/10.5194/gmd-16-7171-2023</span></p>
360° video (repo 2) of ancient oak trees from the SCATTER project
<p>This repository contains Insta360 video data recorded of ancient oak trees and surrounds as part of the <a href="https://doi.org/10.5281/zenodo.11658042">SCATTER project</a>.</p> <p>See <a href="https://zenodo.org/records/11658042">https://zenodo.org/records/11658042</a> for a list of trees and other available datasets. See <a href="https://zenodo.org/records/12543267">https://zenodo.org/records/12543267</a> for additional videos.</p> <p>Individual videos were processed with Insta360 Studio and exported in .mp4 format. </p> <h2>Funding, licence and usage</h2> <p>This research was funded by the Woodland Trust Conservation Research Programme. By accessing or using this dataset, you agree to the terms of the relevant licence agreement(s). You will ensure that this dataset is cited in any publication that describes research in which the data have been used.</p>
Mobile phone video of individual ancient oak trees stems from the SCATTER project
<p>This repository contains video data recorded of tree stems as part of the <a href="https://doi.org/10.5281/zenodo.11658042">SCATTER project</a>.</p> <p>See <a href="https://zenodo.org/records/11658042">https://zenodo.org/records/11658042</a> for a list of trees and other available datasets. </p> <p>Individual video files are in .mp4 format and were recorded with a Google Pixel 7 phone. </p> <h2>Funding, licence and usage</h2> <p>This research was funded by the Woodland Trust Conservation Research Programme. By accessing or using this dataset, you agree to the terms of the relevant licence agreement(s). You will ensure that this dataset is cited in any publication that describes research in which the data have been used.</p>
360° video (repo 1) of ancient oak trees from the SCATTER project
<p>This repository contains Insta360 video data recorded of ancient oak trees and surrounds as part of the <a href="https://doi.org/10.5281/zenodo.11658042">SCATTER project</a>.</p> <p>See <a href="https://zenodo.org/records/11658042">https://zenodo.org/records/11658042</a> for a list of trees and other available datasets. See <a href="https://zenodo.org/records/12580682">https://zenodo.org/records/12580682</a> for additional videos.</p> <p>Individual videos were processed with Insta360 Studio and exported in .mp4 format. </p> <h2>Funding, licence and usage</h2> <p>This research was funded by the Woodland Trust Conservation Research Programme. By accessing or using this dataset, you agree to the terms of the relevant licence agreement(s). You will ensure that this dataset is cited in any publication that describes research in which the data have been used.</p>
Earth BioGenome Project: Authors and author contributions for publication "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life"
<p>The zipped file cntains three tab-separated datasets (worksheets). These worksheets list</p> <p>The contributing authors for the manuscript "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life" and the roles of these authors in the manuscript.</p> <p>The funding sources for these authors</p> <p>A list of the authors contributing to the "EBP Community of Scientists" collective authorship for the same manuscript.</p>
Two decades of annual landscape-scale tree growth and dynamics in old-growth tropical rainforest in the CARBONO Project, La Selva Biological Station, 1997-2018
<p>Here we present the complete data series from a 21-yr study of the annual growth and dynamics of trees, palms and lianas in the old-growth tropical rainforest at the La Selva Biological Station in Costa Rica. These observations were part of the CARBONO Project, a multidisciplinary team study of forest carbon cycling. The project was designed to assess forest processes at the landscape scale by sampling with replication across the within-landscape edaphic heterogeneity typical of tropical forests. Through more than two decades, forest growth and dynamics were assessed annually. The annual time-step used in the CARBONO Project effectively captured forest responses to major disturbances and to interannual and climatic variation. Annual measurements also enhanced the accuracy and long-term consistency of the data. To our knowledge, the resulting records are unique for tropical forests, where the dominant approach to studying the dynamics of a given forest has been to use a single plot and multi-year inter-census intervals. To date these CARBONO Project data have revealed: multi-decadal forest stability in spite of the short-term changes in forest structure resulting from major natural disturbances (e.g., the 1997-1998 Strong El Niño, and the extreme windstorm of May 2018); the dynamics and importance of large trees; and the responses of a major component of ecosystem productivity, aboveground wood production, to interannual and long-term climatic and atmospheric change. These data have also contributed to many remote-sensing studies.</p> <p>The data set consists of annual observations through the period 1997–2018 of the floristics, survival, recruitment, and growth of all woody stems (diameter <u>></u> 10 cm) in a landscape-scale plot network. At completion of the study, the data spanned 6705 individuals and 21 years. The data set is complete and has been through extensive internal checks for quality assurance. Detailed data documentation and an emphasis on measurement repeatability were prioritized through the study. The metadata include an extensive README file describing the data files and the methods, a document detailing the data management and qa/qc, and the scanned original field data-sheets for the 22 annual censuses.</p> <p>We gratefully acknowledge the careful long-term field work and data entry and checking by paraforesters Leonel Campos Otoya and William Miranda Conejo. Logistical support and the long-term protection of the La Selva reserve were provided by the Organization for Tropical Studies. The Ministerio de Ambiente y Energía of Costa Rica granted permits to carry out this study through the years of the study (most recently: Resolución No. 037-2018-ACCVC-PI).</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.