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128 results for “habitat type”
A global map of terrestrial habitat types
<p>We provide a global spatially explicit characterization of 47 (version 001) terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for assessing species’ Area of Habitat. We produced this novel habitat map by creating a global decision tree that intersects the best currently available global data on land cover, climate and land use. The maps broaden our understanding of habitats globally, assist in constructing area of habitat (AOH) refinements and are relevant for broad-scale ecological studies and future IUCN Red List assessments. We hope that these data and outlined framework will spur further development of biodiversity-relevant habitat maps at global scales. An interactive interface helping to navigate the map can be found at on the Naturemap website ( https://explorer.naturemap.earth/map).</p> <p>Provided is the code to recreate the map (to made available soon), the global composite image at native -100m Copernicus resolution for level 1 and level 2 and layers of aggregated fractional cover (unit: [0-1] * 1000) at 1km for level 1 and level 2.</p> <p>Starting with version 004 there changemasks for the years 2016, 2017, 2018 and 2019 are supplied. Changemasks for the composite masks show the changed grid cells and their new values with earlier years being nested in later years, e.g. using the changemask for 2019 includes all changes up to 2019. For the fractional cover estimates at ~1km resolution, new fractional cover changemasks are supplied as subtraction (before - after) between the previous and current year (unit range: [-1 to 1] * 1000).</p> <p>We highlight that only changes in land cover are considered since most of the ancillary layers (e.g. pasture, forest management, climate, etc...) are static and thus not all changes in habitats can be found. We therefore recommend end users to continue using the 2015 dataset unless specific habitat updates to habitat are needed.</p> <p><strong>Citation:</strong></p> <p>Please cite the published paper and state the used version of the habitat map</p> <p>Jung, M., Dahal, P.R., Butchart, S.H.M., Donald, P.F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C., Visconti, P., (2020). A global map of terrestrial habitat types. Sci. Data 7, 256. <a href="https://doi.org/10.1038/s41597-020-00599-8">https://doi.org/10.1038/s41597-020-00599-8</a></p>
Habitatquarries: distribution of underground marl quarries in the Flemish Region and border areas, with the Flemish distribution of Natura 2000 habitat type 8310
<p><strong>General</strong></p> <p>The data source is a geospatial collection of polygons that correspond with the presence or absence of the Natura 2000 Annex I habitat type 8310 (Caves not open to the public) in the Flemish Region (and border areas), Belgium. </p> <p>The dataset contains all known, not collapsed, underground marl quarries in Flanders. Several of these quarries have their entrance in or run underground to the neighboring regions/countries.</p> <p>In general, different polygons represent different quarry units with their own internal climatic environment. Units that cross Flemish borders have been split into separate polygons. Exceptionally they may overlap if such units are situated above each other. </p> <p>For safety reasons, the dataset only contains the contour of the quarries, and no details like floor plans or entrances. For admission to research the indoor climate, please contact the Quarries and Safety Department of the municipality of Riemst (<a href="https://www.riemst.be/nl/wonen/groeven">https://www.riemst.be/nl/wonen/groeven</a>; <a href="mailto:mike.lahaye@riemst.be">mike.lahaye@riemst.be</a>).</p> <p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, a scientific institute of the Flemish government).</p> <p> </p> <p><strong>Technical aspects</strong></p> <p>The data source is a GeoPackage that contains:</p> <ul> <li> <p>a spatial polygon layer ‘<code>habitatquarries</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>);</p> </li> <li> <p>a non-spatial table ‘<code>extra_references</code>’ with site-specific bibliographic references.</p> </li> </ul> <p>The data source has been based on an unpublished shapefile used in De Saeger & Lahaye (2019) and on a BibTeX bibliography file. See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/c0821eb/src/generate_habitatquarries">'n2khab-preprocessing' at commit c0821eb</a> for the creation.</p> <p>A reading function to return <code>habitatquarries</code> (this data source) in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The attributes of the spatial polygon layer ‘<code>habitatquarries</code>’ are: </p> <ul> <li> <p><code>polygon_id</code>: a unique number per polygon; </p> </li> <li> <p><code>unit_id</code>: a unique number for each quarry unit. Quarry units consisting of several polygons (= partly outside the Flemish region) have a number greater than 100;</p> </li> <li> <p><code>name</code>: name of the site;</p> </li> <li> <p><code>habitattype</code>: either:</p> <ul> <li> <p><code>8310</code> (habitat type 8310)</p> </li> <li> <p><code>gh</code> (no Natura 2000 type)</p> </li> <li> <p>missing (outside of the Flemish Region);</p> </li> </ul> </li> <li> <p><code>extra_reference</code>: extra reference with more information.</p> </li> </ul> <p>The non-spatial table <code>extra_references</code> provides the bibliography referred to by the spatial attribute <code>extra_reference</code>. It was derived from a BibTeX bibliography file by using the R-package <a href="https://docs.ropensci.org/bib2df">bib2df</a>, and it is back-convertible into one (see R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>). The original bibliography file is also available in the above linked ‘n2khab-preprocessing’ repository.</p>
Standardized map of habitat types and regionally important biotopes in Flanders
<p>The <code>habitatmap_stdized.gpkg</code> file is a processed version of the <a href="https://www.vlaanderen.be/datavindplaats/catalogus/biologische-waarderingskaart-en-natura-2000-habitatkaart-toestand-2023">Natura 2000 habitat map of Flanders</a> (De Saeger et al., 2023; see also De Saeger et al. 2017). It contains all polygons with Natura 2000 habitat types or regional important biotopes (RIB). This file is used as a basis for designing monitoring schemes in Flanders. </p> <p>In the original habitat map, every polygon can consist of maximum 5 different types (habitat (sub)types and regionally important biotopes). This information is stored in the columns <code>HAB1</code>, <code>HAB2</code>,..., <code>HAB5</code> of the attribute table. The fraction of each type within the polygons is stored in the columns <code>PHAB1</code>, <code>PHAB2</code>, ..., <code>PHAB5</code>.</p> <p>The <code>habitatmap_stdized.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>habitatmap_polygons</code>: a spatial layer with every habitat map polygon that contains a Natura 2000 habitat or RIB type.</li> <li><code>habitatmap_types</code>: a table with information on the habitat and RIB types (HAB1, HAB2,..., HAB5) that occur within each polygon of <code>habitatmap_polygons.</code></li> </ul> <p>The processing of the habitatmap_types table included following adjustments:</p> <ul> <li>For some polygons the type is uncertain, and the type code in the raw habitatmap data source consists of 2 or 3 possible types, separated with a ','. The different possible types are split up and one row is created for each of them, with <code>phab</code> for each new row simply set to the original value of <code>phab</code>. The variable <code>certain</code> will be <code>FALSE</code> if the original type code consists of 2 or 3 possible types, and <code>TRUE</code> if only one type is provided.</li> <li>Some polygons contain both a standing water habitat type and <code>rbbmr</code>: <ul> <li><code>3130_rbbmr</code>,</li> <li><code>3140_rbbmr</code>,</li> <li><code>3150_rbbmr</code>, and</li> <li><code>3160_rbbmr</code>.</li> </ul> </li> <li>Since <code>habitatmap_stdized_2020_v1</code>, the two types <code>31xx</code> and <code>rbbmr</code> are split up and one row is created for each of them, with <code>phab</code> for each new row simply set to the original value of <code>phab</code>. The variable certain in this case will be <code>TRUE</code> for both types.</li> <li>After those steps, a given polygon could contain the same type with the same value for <code>certain</code> repeated several times, e.g. when <code>31xx_rbbmr</code> is present with <code>phab</code> = yy% and <code>31xx</code> is present with <code>phab</code> = zz%. In that case the rows with the same <code>polygon_id</code>, <code>type</code> and <code>certain</code> were gathered into one row and the respective phab values were added up.</li> </ul> <p>The R-code for creating the <code>habitatmap_stdized</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/abf596e/src/generate_habitatmap_stdized">'n2khab-preprocessing' at commit abf596e</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p> <p>Attributes of <code>habitatmap_polygons</code>:</p> <ul> <li><code>polygon_id</code></li> <li><code>description_orig</code>: polygon description based on the original type codes in the raw habitatmap </li> </ul> <p>Attributes of <code>habitatmap_types</code>:</p> <ul> <li><code>polygon_id</code></li> <li><code>type</code>: the interpreted habitat or RIB type</li> <li><code>certain</code>: <code>TRUE</code> when type is certain and <code>FALSE</code> when type is uncertain</li> <li><code>code_orig</code>: original type code in raw habitatmap</li> <li><code>phab</code>: proportion of polygon covered by type, as a percentage.</li> </ul> <p>Since version <code>habitatmap_stdized_2020_v1</code>, rows are unique only by the combination of the <code>polygon_id</code>, <code>type</code> and <code>certain</code> columns.</p>
Redistribution of the Natura 2000 habitat map of Flanders, partim habitat type 3260 (version 2023)
<p>This is a redistribution of a subdataset of the data source '<a href="https://www.vlaanderen.be/datavindplaats/catalogus/biologische-waarderingskaart-en-natura-2000-habitatkaart-toestand-2023">Biologische Waarderingskaart en Natura 2000 Habitatkaart - Toestand 2023</a>', originally published by the Research Institute for Nature and Forest (INBO) and distributed by 'Digitaal Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p><p>The subdataset is a shapefile of line segments of the Natura 2000 habitat type 3260 (Watercourses of plain to montane levels with the <i>Ranunculion fluitantis</i> and <i>Callitricho-Batrachion</i> vegetation) that correspond with its presence in watercourses in the Flemish Region, identical to the shapefile Hab3260 in the original data source.</p><p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government).</p>
Map of standing water habitat types and regionally important biotopes in Flanders
<p>This map is a combination of the <a href="https://zenodo.org/records/13865531">standardized habitat map of Flanders</a> (version habitatmap_stdized_2023_v1) and <a href="https://zenodo.org/records/14203168">the watersurface map of Flanders</a> (version watersurfaces_2024). It contains standing water Natura 2000 habitat types (2190_a and 31xx) and regionally important biotopes (rbbah) in Flanders.</p> <p>The polygons with 2190_a habitat (dune slack ponds) are generated by selecting all watersurface polygons that overlap with dune habitat polygons (21xx) of the standardized habitat map.</p> <p>For each of the other aquatic habitat types (31xx and rbbah) we select the watersurface polygons that overlap with the selected habitat type polygons of the standardized habitat map. We also select polygons of the standardized habitat map containing standing water types but that do not overlap with polygons of the watersurface map.</p> <p>The <code>watersurfaces_hab.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>watersurfaces_hab_polygons</code>: a spatial layer with the selected polygons that contain standing water habitat types or regionally important biotopes. </li> <li><code>watersurfaces_hab_types</code>: a table with information on standing water habitat types and regionally important biotopes in each watersurface polygon.</li> </ul> <p>The R-code for creating the <code>watersurfaces_hab</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/58138a8/src/generate_watersurfaces_hab">'n2khab-preprocessing' at commit 58138a8</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p>
Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization
<p>Data and R code accompanying the publication "Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization"</p> <p>Bosco L, Xu Y, Deshpande P, Lehikoinen A</p> <p>2022</p> <p>---------</p> <p>The data and code to calculate range shifts based on the center of gravity are provided here.</p> <p>The RData files contains raw data from the winter bird counts with added average snow depth values downloaded from open source databases (described in the paper), 100x100km grid info (grid ID, centroid coordinates and average (geographical) coordinates).</p> <p>The csv file contains the route lengths from the winter bird count transects per habitat type.</p> <p>The R file contains the R code used to clean the data (see methods in the publication) and calculate the habitat specific center of gravity (based on bird densities) which were used to calculate shift direction and distance.</p>
Data from : Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning
<p>This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/. </p> <p>The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The *l1* in the file name indicates that the file was used for level 1 (wetland) classification and *l23* used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (<a href="https://github.com/Jean-Romain/lidR">https://github.com/Jean-Romain/lidR</a>) software package. Further details related to the lidar metrics extraction can be found at <a href="https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats">https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats</a> Github repository.</p> <p> </p>
Figure 3 in Investigating the influence of habitat type and weather conditions on the population dynamics of land snails Vertigo angustior Jeffreys, 1830 and Vertigo moulinsiana (Dupuy, 1849). A case study from western Poland
Figure 3. Diagram of one-way analysis of covariance test comparing a logarithmized number of individuals of Vertigo angustior (F = 92.16; p <0.01) and Vertigo moulinsiana (F = 8.165; p <0.01) in the Ilanka and Pliszka sites in 2009. Middle line: mean; box range: standard error; whiskers: standard deviation.
Figure 2 in Investigating the influence of habitat type and weather conditions on the population dynamics of land snails Vertigo angustior Jeffreys, 1830 and Vertigo moulinsiana (Dupuy, 1849). A case study from western Poland
Figure 2. Precipitation in the studied sites in consecutive months of 2009; dashed bars – sampling months. (B) and (C) Abundance of individuals: juveniles (white bars) and adults (black bars) of Vertigo angustior (B) and Vertigo moulinsiana (C) in each sampling event in the Ilanka and Pliszka sites in 2009.
FIGURE 4 in Specific damage recognised on land snail shells as a tool for studying predation intensity: differences related to habitat and predator types
FIGURE 4 Variation in the minimal rate of predation recorded in six samples collected in each of five habitat types. Different letters refer to significant differences (p <0.05) among habitat types, based on a generalised linear model. The central line of each box refers to the median value, box height to the interquartile range, whiskers to the non-outlier range (i.e., 1.5 times the interquartile range at each side), and small circles to outliers.
FIGURE 5 in Specific damage recognised on land snail shells as a tool for studying predation intensity: differences related to habitat and predator types
FIGURE 5 Relative representation of individual predation types recognised in six samples collected at each of five habitat types. The height of the column represents the average value, while the lines indicate the standard deviation.
FIGURE 2 in Specific damage recognised on land snail shells as a tool for studying predation intensity: differences related to habitat and predator types
FIGURE 2 Examples of the recorded evidence of predation caused by various predators. a) shell of Cochlicopa lubrica predated on by a snail; b) characteristic damage caused by a carabid beetle, here on a shell of Discus rotundatus; c) puparium of the parasitoid dipteran fly Pherbellia limbata within a Granaria frumentum shell (the shell was crushed manually); d) external damage to a Xerolenta obvia shell caused by bird predation; e) Drilus beetle larval exuviae within an Alinda biplicata shell (the shell was crushed manually).
Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis
<p>Natural pest and weed regulation are essential for agricultural production, but the spatial distribution of natural enemies within crop fields and its drivers are mostly unknown. Using 28 datasets comprising 1,204 study sites across eight Western and Central European countries, we performed a quantitative synthesis of carabid richness, activity densities and functional traits in relation to field edges (i.e. distance functions). We show for the first time that distance functions of carabids strongly depend on carabid functional traits, crop type and, to a lesser extent, adjacent non-crop habitats. Richness of both predators and granivores and activity densities of small and granivorous species decreased towards field interiors, whereas the densities of large species increased. We found strong distance decays in maize and vegetables whereas richness and densities remained more stable in cereals, oilseed crops and legumes. We conclude that carabid assemblages in agricultural landscapes are driven by the complex interplay of crop types, adjacent non-crop habitats and further landscape parameters with great potential for targeted agroecological management. In particular, our synthesis indicates that a higher edge-interior ratio can counter the distance decay of carabid richness per field and thus likely benefits natural pest and weed regulation, hence contributing to agricultural sustainability.</p>
Sexual dimorphism in subterranean amphipod crustaceans covaries with subterranean habitat type: data and R code
<p>The data and R code used for data analyses in the manuscript titled "Sexual dimorphism in <em>Niphargus </em>amphipods is predicted by surface-subterranean environmental gradient". The collection contains:</p> <ol> <li>A zipped folder "videos", where raw videos used in the study are stored.</li> <li>A zipped folder "tracking_results", where video-tracking results obtained from the raw videos are stored, along with supporting files and R code (Rscript_extract_behavior.Rmd) with custom functions (Behavior_custom_functions.Rmd) needed to analyze tracking results and retrieve final behavioral data.</li> <li>A README file with details on how the data and code is organized.</li> <li>Supplementary Material file including all raw data used in the main data analysis (SupplementaryMaterial.xlsx)</li> <li>A supporting file with data from another study used in the main data analysis (sex_ratio.xlsx, results from https://onlinelibrary.wiley.com/doi/full/10.1111/jeb.13917; https://zenodo.org/records/5175861)</li> <li>Two files containing phylogenetic trees: one complete phylogenetic tree of<em> Niphargus </em>(consensus_tree)<em> </em>and 100 randomly drawn phylogenetic trees from the stationary phase of the Bayesian analysis, pruned to focal species (100_pruned_trees), which were used in the main data analysis.</li> <li>The Rcode containing the code of the main data analysis to reproduce the reported results (RScript_data_analysis.Rmd), as well as some additional analyses not included in the manuscript.</li> </ol>
Climate and habitat type interact to influence contemporary dispersal potential in Prairie Smoke (Geum triflorum)
<p>Understanding dispersal potential, or the probability a species will move a given distance, under different environmental conditions is essential to predicting species' ability to move across the landscape and track shifting ecological niches. Two important drivers of dispersal ability are climatic differences and variation in local habitat type. Despite the likelihood these global drivers act simultaneously on plant populations, and thus dispersal potential is likely to change as a result, their combined effects on dispersal are rarely examined. To understand the effect of climate and varying habitat types on dispersal potential, we studied <em>Geum triflorum</em> - a perennial grassland species that spans a wide range of environments, including both prairie and alvar habitats. We explored how the climate of the growing season and habitat type (prairie vs alvar) interact to alter dispersal potential. We found a consistent interactive effect of climate and habitat type on dispersal potential. Across prairie populations, an increased number of growing degree days favored traits that increase dispersal potential, or the probability of dispersing farther distances. However, for alvar populations, dispersal potential tended to decrease as the number of growing degree days increase. Our findings suggest that under continued warming, populations in prairie habitats will benefit from increased gene flow, while alvar populations will become increasingly segregated, with reduced potential to track shifting fitness optima.</p>
Figure 2. Green and senescence N in Foliar resorption of nitrogen and phosphorus in Alcea apterocarpa (Malvaceae) in different habitats types
Figure 2. Green and senescence N:P ratio of A. apterocarpa in different habitat types. Means followed by the same letter are not significantly different at the 0.05 level using Tukey's HSD test.
Figure 1 in Foliar resorption of nitrogen and phosphorus in Alcea apterocarpa (Malvaceae) in different habitats types
Figure 1. Resorption proficiency and efficiency levels of A. apterocarpa according to all patterns. Habitat abbreviations are H1: riverbank, H2: meadow and H3: forest clearing. Minor letters followed by the same letter are not significantly different at the 0.05 level using Tukey's HSD test.
Distribution of the Natura 2000 habitat type 7220 (Cratoneurion) in Flanders and Brussels Capital Region, Belgium (version 2025)
<p>The dataset is a geospatial collection of points that correspond with the presence or absence of the Natura 2000 habitat type <code>7220</code> (Petrifying springs with tufa formation (<em>Cratoneurion</em>)) in springs and streaming water segments in the Flemish and Brussels Capital Region, Belgium. The dataset also contains a number of locations that were visited during mapping projects but where <code>7220</code> was found absent, or where additional survey is needed to decide on presence/absence of the habittype. The file is a GeoJSON format RFC7946 (WGS84).</p> <p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government).</p> <p>Headers are: </p> <ul> <li><code>id</code>; </li> <li><code>source</code>: original data source;</li> <li><code>validity status</code>: field inventory carried out (<code>gecontroleerd</code>) or not (<code>niet gecontroleerd</code>);</li> <li><code>name</code>: unique name of the site;</li> <li><code>system_type</code>: stream type (<code>rivulet</code>), mire type (<code>mire</code>), unknown (<code>unknown</code>) or na (<code>NA</code>);</li> <li><code>habitattype</code>: <code>7220</code>, no Natura 2000 type <code>(gh)</code>, unconfirmed <code>7220</code> <code>(7220, gh)</code>, alkaline fen<code>(7230)</code>;</li> <li><code>unit_id</code>: spatially related sites are identified by a common identifier</li> <li><code>area_m2</code>: area in square meters;</li> <li><code>year</code>: year of field inventory;</li> <li><code>sbz</code>: inside (1) or outside (0) special area of conservation;</li> <li><code>geometry</code>: latitude, longitude in decimal degrees.</li> </ul>
Correlation of urban avian species diversity present in heterogenous habitat types of the Silk city, Odisha, Eastern India
<p>This is the complete metadata and the R code required to do the analysis of the paper regarding birds of Berhampur city.</p>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
ScienceDex guides
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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.