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469 results for “spruce”
Lake Sediment Pollen from Spruce Pond in Harriman State Park NY from 13778 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen records from New England to explore how postglacial changes in the composition and spatial patterns of vegetation were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term trends and abrupt shifts in temperature and precipitation, as well as by
Survey of Alarka Laurel and Rich Mountain Red Spruce (Picea rubens) Overstory, Saplings, and Seedlings in western North Carolina in 2007, 2022, and 2023
In the southern Appalachians, disjunct red spruce (Picea rubens) populations persist at low latitudes at elevations above 1,370 m. However, research on the condition of these disjunct red spruce populations is limited. This study compared baseline health, recruitment, and stand dynamics of two of the southern-most red spruce populations in eastern North America, the Rich Mountain and Alarka Laurel spruce bog basins in Nantahala National Forest, North Carolina. We collected data on overstory (DBH>10 cm), saplings (DBH< 10 cm, and height >2 m), and seedlings (height<10 cm) from Alarka Laurel in 2007 and 2022. Data from Rich Mountain were collected in 2023. We used 10-m wide belt transects noted the species and diameter at breast height (DBH) of overstory species, counted and noted the DBH of red spruce saplings, and counted and noted the height of red spruce seedlings. In 2022 and 2023, we gave a health score from 0-3 for all three categories of trees (overstory, saplings, and seedlings), with 0 being dead and 3 being healthy with little to no signs of disease or stress. Overall, both stands did not yet appear affected by climatic warming, despite the southern latitude and relatively low elevation. Our findings reveal that red spruce is the dominant overstory species, comprising an average of 25.6% of all measured overstory trees, with seedlings and saplings making up 72.8% of the red spruce population, indicating sustainable recruitment. Red spruce basal area declined by 13.9% from 2007 to 2022 in Alarka Laurel, with a concomitant increase in some hardwood species. However, both Alarka Laurel and Rich Mountain showed high levels of sapling and seedling recruitment. Overall, red spruce trees are healthy, particularly seedlings, representing the healthiest age category. Our results suggest the stands are relatively stable and provide essential baseline data for monitoring of forest conditions in the context of intensifying climate change. This research contributes to b
Site Location and Environmental Characteristics for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Accoss Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains the GPS location, slope, orientation, elevation, forest type based on dominant tree biomass, Age, sampling year, total vascular cover, total lchen cover, deciduous index (Alexander et al. 2012, Ecosphere), basal area of black spruce, Alaska paper birch, trembling aspen, large deciduous shrubs and trembling aspen, organic layer depth, pH, heatload, approximated moisture class for all sites (Johnstone et al. 2008), gravimetric moisture content, and volumetric moisture content. This dataset was part of the site-level covariates used in a study of bryophyte post-fire succession in deciduous and coniferous successional trajectories. NOTE: there is some overlap between the >20 years old sites that were sampled by Heather Alexander with some of the data she submitted in relation to her 2012 Ecosphere paper.
Vegetation Data Collected with Point Frame for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains point frame data for vegetation less than 1.3 m, including vascular plants, bryophytes, lichens, leaf litter and bare ground, as well as species codes used, as described in Jean et al. 2017 Canadian Journal of Forest Research. Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
Bryophyte Cover Summary Data for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains the summarized bryophyte (percent cover) data obtained from point frame measurements, as used in Jean et al. 2017 Canadian Journal of Forest Research. ?Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
X-ray scattering data from Norway spruce at different moisture conditions
<p>This data includes small and wide-angle X-ray scattering (SAXS, WAXS) intensities measured for Norway spruce (<em>Picea abies</em>) wood.</p> <p>The experiments were done in perpendicular transmission geometry, with the wood fiber axis roughly vertical and the radial direction of the wood tissue parallel to the X-ray beam, using a Xenocs Xeuss 3.0 C SAXS/WAXS device and Cu K-alpha radiation (wavelength 1.542 Å). The scattering patterns were recorded using an EIGER2 R 1M detector (pixel size 75 µm). The wood sample was measured first in wet state (saturated with water; "Wet"), and then equilibrated at different relative humidities (RH) in the following order: 95% ("RH95_1st"), 85% ("RH85"), 70% ("RH70"), 50% ("RH50_1st"), 20% ("RH20"), 10% ("RH10"), 50% ("RH50_2nd"), 95% ("RH95_2nd"). The sample-to-detector distance was 0.4139 m in SAXS, and 0.1528 m for the first 4 conditions (until "RH70") and 0.1525 m for the remaining 5 conditions in WAXS. Beam center (in detector pixels) was at x=540.6, y=667.0 (except y=635.0 in "RH70") in SAXS and x=1540, y=1521 in WAXS.</p> <p>For each of the 9 moisture conditions, files corresponding to 3 different processing steps are provided:</p> <ul> <li>"_bgsub_saxs.txt" and "_bgsub_waxs.txt" are ASCII files that contain the normalized and background-subtracted detector images (intensity in units mm^-1) corresponding to SAXS and WAXS, respectively. Pixels to be masked have the value "nan".</li> <li>"_bgsub_saxs_pol90.txt" and "_bgsub_waxs_pol90.txt" contain azimuthally regrouped images (90 bins in azimuthal angle) based on "_bgsub_saxs.txt" and "_bgsub_waxs.txt", respectively. PNG image files "_bgsub_saxs_pol.png" and "_bgsub_waxs_pol.png" are provided for reference.</li> <li>"_bgsub_pol90_ibg.txt" and "_bgsub_waxs_pol90_vert_ibg.txt" contain the equatorial and meridional anisotropic intensities, respectively, which were obtained from the azimuthally regrouped images by subtracting the isotropic scattering from the equatorial or meridional intensity (sector width 25°) at each value of the scattering vector <em>q</em>. The equatorial anisotropic intensities from SAXS and WAXS were merged by scaling the SAXS intensity, and the meridional anisotropic intensity is provided for the WAXS range only. The files contain columns for the magnitude of the scattering vector (q, unit Å<sup>-1</sup>), anisotropic intensity (I_ani, unit mm<sup>-1</sup>), error of anisotropic intensity (dI_ani, unit mm<sup>-1</sup>), and isotropic intensity (I_iso, unit mm<sup>-1</sup>).</li> </ul> <p>More detailed descriptions of the sample, the measurement, and the data processing can be found in the following reference:<br> Antti Paajanen, Aleksi Zitting, Lauri Rautkari, Jukka A. Ketoja, Paavo A. Penttilä. Nanoscale mechanism of moisture-induced swelling in wood microfibril bundles. <em>Nano Letters</em> 2022, 22(13): 5143–5150, DOI: 10.1021/acs.nanolett.2c00822</p>
Data for estimating spruce tree health using drone-based RGB and multispectral imagery
<p>The dataset contains multispectral and RGB orthomosaics (.tif), and photogrammetric point clouds (.laz) of four study areas (about 25 ha each), where bark beetle-related decline of Norway spruce has been observed in Helsinki, Finland. The filenames refer to Area 1 (Männikkötie), Area 2 (Maunulanmaja), Area 3 (Hakuninmaa), and Area 4 (Paloheinä), described in detail in Junttila et al. 2022. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season, Remote Sensing 14(4), 909: <a href="https://doi.org/10.3390/rs14040909">https://doi.org/10.3390/rs14040909</a> </p> <p>The image data was acquired between 11th and 14th September 2020.</p> <p>RE = Red-Edge M multispectral data<br>RGB = RGB data (Phantom 4 Pro)<br>Altum = Altum multispectral data</p> <p>The ground sampling distances (GSD) were approximately 3 cm, 5 cm, and 8 cm for RGB, Altum, and RedEdge, respectively.</p> <p>The field reference data file contains 556 geolocated trees assessed in the field (between 11.9. and 17.9.2020), of which 203 were dead and 353 were alive. The data is in polygon format, representing the crown delineation done during the data processing. The file includes tree heights estimated from airborne laser scanning data, dbh (for a subset of trees), discoloration, defoliation, resin flow, bark structural damage, and canopy size estimates. More details are in the journal article mentioned above.</p> <p>Key for Field Reference:</p> <p>Z = tree height<br>dbh = diameter-at-breast-height (cm)<br>vari = Discoloration (score 0-5)<br>harsu = Defoliation (score 0-4)<br>pihka = Resin flows (score 0-2)<br>runko = Stem/bark structural damage (score 0-2)<br>latvus = Significantly decreased canopy size (score 0-1)</p>
Multi-year measurements of tree motion from an accelerometer on a spruce tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of a <em>Picea engelmannii</em> (engelmann spruce) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p> <p> </p>
Scanning electron microscope images of spruce needle homogenate and scanning electron microscope images of isolated small cellular particles from spruce needle homogenate
<p>Scanning electron microscope images of spruce needle homogenate and of isolated small cellular particles from spruce needle homogenate are presented. Each image is supplemented by description of the preparation of the sample and the data on the imaging technique and equipment. The data are curated by Veronika Kralj-Iglic and University of Ljubljana, Faculty of Health Sciences, Laboratory of Clinical Biophysics, and Anna Romolo, presently at University of Ljubljana, Faculty of Electrical Engineering, Laboratory of Physics, Ljubljana, Slovenia. Present address of Marko Jeran is: Department of Inorganic Chemistry and Technology, “Jožef Stefan” Institute, Ljubljana, Slovenia.</p>
White spruce trees tagged measured for total height and girth at 10 centimeter height, and leader length, Coldfoot, Alaska 2015, 2016
White spruce seedlings have colonized the site of the Coldfoot transplant garden (CF, 67°15′32″N, 150°10′12″W) since the original garden was established in 1982. Some trees are 2-3 meter tall. All seedlings and trees within the current (2014) garden were tagged, located with a Global Positioning System (GPS) receiver, and measured in 2015 and 2016 for total height and girth at 10 centimeter height and leader length.
Tree regeneration after fire: Wickersham Dome long-term vegetation study, bl. spruce height data
These data represent the most recent set of observations (made in 2002 by J. Johnstone) for several long-term vegetation monitoring plots near Wickersham Dome that were set up by Les Viereck and Joan Foote following the 1971 wildfire and 1978 experimental burns. Earlier records are available in the BNZ long-term vegetation database. This dataset documents tree seedling/sapling and shrub measurements made in 2002. Lists heights of all individual black spruce (Picea mariana) present in a plot.
Kuskokwim River Floodplain: White Spruce (Picea glauca) annual tree-ring width measurements (mm) at breast height from tree-core samples taken above Red Devil on the Kuskokwim River in July, 2007
This dataset contains annual raw ring width measurements in the Tucsan (decadal format) (.rwl file extension) of 14 large white spruce trees growing within 50m of the Kuskokwim River. Ring-widths were measured to 0.001mm on a velmex laser micrometer and accuracy was checked by crossdating using COFECHA. Annual values were measured from 1779-2006.
Local adaptation to light in Norway spruce
<p>Exome capture data of the 1654 trees involved in the study of local adaptation to light quality in Norway spruce:</p> <p>1. control_genes.vcf - Raw vcf file of the ten control genes that were not differentially expressed genes in response to SHADE (low R:FR light), between the southern and northern natural populations of Norway spruce in Sweden.</p> <p>2. degs.vcf - Raw vcf file of the 54 differentially expressed genes in response to SHADE (low R:FR light), between the southern and northern natural populations of Norway spruce in Sweden, that showed at least one missense SNP in coding region. Missense variations in coding regions of nine candidate genes followed a latitudinal cline in allele and genotype frequencies.</p>
Potential and realized distribution at 30m for Norway spruce (Picea abies) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Norway spruce (<em>Picea abies, </em>L. H. Karst.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_picea.abies_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>picea.abies</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_picea.abies_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_picea.abies_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_picea.abies_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_picea.abies_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Herbarium specimen image of Neea constricta Spruce ex J.A.Schmidt, part of the collection of Royal Botanic Gardens, Kew
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Mabea paniculata Spruce ex Benth., part of the collection of Royal Botanic Gardens, Kew
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Global Cluster Test Results: Pathogenic Fungi in Decayed Norway Spruce Stands
<p><strong>Accessing the Results:</strong> Users can retrieve the test results by opening the dataset <code>Global_cluster_test_results.RData</code> in an R session and using the functions inside the script of the same name.</p> <p><strong>Description: </strong>This dataset provides global cluster test results analyzing the spatial distribution of pathogenic fungi in 273 Norway spruce stands in Norway (Lara et al., 2024). The stands, composed mainly of Norway spruce (27% to 100%), also include Scots pine and birch. It focuses on spatial patterns of decayed spruce trees, offering p-values, clustering metrics, and other parameters from statistical analyses.</p> <p><strong>Analysis:</strong> The dataset includes results from three global cluster tests (Tango, 2010):</p> <ul> <li>Tango's Nearest Neighbors (TNN)</li> <li>Tango's Double Exponential Clinal (TCN)</li> <li>Diggle and Chetwynd’s (DC)</li> </ul> <p><strong>Methodology:</strong> Cluster testing employed 1,000 Monte Carlo simulations for each test across all stands to establish null distributions and adjusted p-values, ensuring robust statistical assessments under the random labeling hypothesis: H0: the observed n0 decayed trees are a random sample from the entire sample of size n = n0 + n1 (decayed trees + healthy trees) (Tango, 2010).</p> <p> </p>
Tower-based solar-induced fluorescence and vegetation index data for Southern Old Black Spruce forest
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from September 2019-December 2020 at the Southern Old Black Spruce site in Saskatchewan Canada. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and solar-induced fluorescence (SIF) and for a stand-representative mix of black spruce and larch. Additionally, we provide photosynthetically active radiation (PAR), absorbed photosynthetically active radiation (APAR), and the escape fraction of SIF photons (fesc). Version 2 also provides half-hourly averaged SIF and SIFrelative for black spruce (evergreen) and larch (deciduous) separately. </p>
Tree regeneration after fire: Delta 1994 burn surveys, live spruce seedling diameters
Data for this study were collected in 2001 and 2002 by Jill Johnstone (University of Alaska Fairbanks) and Eric Kasischke (University of Maryland). Sites were located within the perimeter of the 1994 burn southeast of Delta Junction Alaska, USA, bordering the Alaska Highway to the North and the Gerstle River to the West. Sites were selected from satellite classifications prepared by Eric Kasischke to represent different levels of burn severity and post-fire vegetation canopy greenness (NDVI). Site selection was constrained by road access, and only areas where all trees had been killed by the fire were selected. At each site, a central point was located in an area of visually homogeneous vegetation. Five parallel transects, each 50 m long, were laid out as follows: 1) the first transect started at the central point and followed a randomly-selected compass direction, 2) two additional transects were established parallel to the first, but at a random distance from the central transect up to 25 m distant. Vegetation was sampled in a 2-m wide belt centered on each transect, and soil samples were made at intervals along the transect line. Vegetation measurements included: a) basal diameters of all pre-fire trees greater than 1.3 m in height, b) counts of all post-fire tree seedlings, and c) basal diameters of tree seedlings and willows, measured in a randomly chosen 5x2 m portion of each transect. General notes were made on visual percent cover of different vegetation growth forms at the site. Destructive measurements of tree seedlings and willows made in 2001 were used to develop allometric equations to predict dry biomass from basal diameter. Measurements of soil organic layer depth were made at 5 m intervals with the use of a spade to excavate small chunks of sod. At one randomly-selected sample point per transect, a 10x10 cm sample of the organic layer was collected for bulk density measurements. Bulk density samples were dried in a 60degC oven for 48 hours and then w
Soil Water (Lysimeter) Chemistry for Mature Balsam Poplar and White Spruce for BCEF
This dataset includes soil water samples (lysimeter samples) collected from mature balsam poplar (BP1, BP2 and BP3) and white spruce (FP4A, FP4B, FP4C) stands during 2000 and 2001. Five Lysimeter were installed at 12 cm and four at the 40 cm in each stand type. Water from the Tanana River and small, non-silt, streams on the eastern portion of the floodplain were collected during many sampling periods.
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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.