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1,445 results for “species richness.”
Dataset: Inverse responses of species richness and niche specialization to human development
<p class="CxSpFirst">Humans impact biodiversity by altering land use and introducing nonnative species. Yet the extent to which coexistence processes, such as competition and niche shifts, mediate these relationships is not clear. This dataset was used in a study that aims to compare how human development influences wetland plant diversity by examining patterns of species richness, niche specialization, and nonnative species occurrences along a human development gradient.</p> <p class="CxSpFirst">This dataset can be used to analyzed species richness and niche specialization (a measure of the range of human development extents over which a species occurs) patterns from species occurrence data across 1582 wetlands in Alberta, Canada. Associations between human development extent and species richness, niche specialization, and nonnative species can be tested using linear mixed models. Also, nonmetric multidimensional scaling ordination can be applied from raw data (see usage notes) to examine whether community composition differed among wetlands surrounded by different human development extents.</p> <p class="CxSpFirst">Note that human development data are accessible only through a data sharing agreement with ABMI. See the readme document for more details on how to obtain assess to these data.</p> <p class="CxSpFirst">Results of these analyses can be found in the corresponding publication: Inverse responses of species richness and niche specialization to human development, Journal of Biogeography. https://doi.org/10.1111/jbi.14240</p> <ul> </ul>
Avian species richness and abundance shows stronger response to bison grazing intensity than to ecosystem productivity
Temperate grassland ecosystems are one of the most threatened ecosystems worldwide, and their loss endangers the grassland songbirds that rely upon them. This guild of birds has shown long-term declines in North America. At the same time, American bison (Bison bison) are becoming more common through reintroductions, and they may make significant modifications to grassland songbird habitat. To support conservation for this guild, we sought to understand the importance of bison grazing and ecosystem productivity to the species richness, occupancy, and abundance of this avian community. We conducted dependent double-observer bird counts, measured bison grazing intensity with patty counts, and used remote-sensed Normalized Difference Vegetation Index (NDVI) data to measure ecosystem productivity. Our work took place in the National Bison Range near Moiese, Montana and in Yellowstone National Park in Wyoming. We found that species richness was positively correlated with patty counts, and had a weak negative correlation with NDVI. Occupancy probability for six of seven grassland songbird species was positively correlated with patty counts, and for six of seven species was negatively correlated with NDVI. Abundance of vesper sparrow (Pooecetes graminueus) and western meadowlark (Sturnella neglecta) were positively correlated with patty counts, although for western meadowlark, this trend became less positive with increasing patty counts. Our work suggests that managers may want to encourage a broad range of bison grazing intensities to ensure that vegetative conditions related to bison grazing are present for all species.
Determinants of genetic diversity and species richness of North American amphibians
<p><strong>Aim:</strong> Ecological limits on population sizes and the number of species a region can sustain are thought to simultaneously produce spatial patterns in population genetic diversity and species richness due to the effects of random drift operating in parallel across population and community levels. Here, we test the extent to which resource-based environmental limits jointly determine these patterns of biodiversity in amphibians.</p> <p><strong>Location:</strong> North America.</p> <p><strong>Taxon:</strong> Amphibians.</p> <p><strong>Methods:</strong> We repurposed open, raw microsatellite data from 19 species sampled at 554 sites in North America and mapped nuclear genetic diversity at the continental scale. We then tested whether ecological limits defined by resource availability and environmental heterogeneity could simultaneously shape biogeographic patterns in genetic diversity and species richness with structural equation modeling.</p> <p><strong>Results:</strong> Spatial patterns of population genetic diversity run opposite patterns of species richness and genetic differentiation. However, while measures of resource availability and niche heterogeneity predict 89% of the variation in species richness, these landscape metrics were poor predictors of genetic diversity.</p> <p><strong>Main conclusions:</strong> Although heterogeneity appears to be an important driver of genetic and species biodiversity patterns in amphibians, variation in genetic diversity both within and across species makes it difficult to infer general processes producing spatial patterns of amphibian genetic diversity. This result differs from those found in endotherms and may be due to the considerable life history variation found across amphibians.</p>
Data from: Distributional trends and species richness of Maryland, USA stoneflies (Insecta: Plecoptera), with an emphasis on the Appalachian region
<p>Faunistic studies of regional biodiversity of aquatic insects are increasing in importance as declines are noted globally. Federal and state government conservation attempts for rare and threatened species are predicated upon the initial research of specialized taxonomists and trained field biologists. Reporting of aquatic insect occurrence data provides a baseline for conservation agencies to compare water quality monitoring studies. Updated fieldwork, literature reviews, and database queries for stoneflies from the mid-Atlantic USA state of Maryland necessitated an assessment of species diversity for the state. Seven new state records and one new literature record are presented, bringing the total number of species to 122. Chao1 estimates of species richness are presented for diversity hotspots and the state as a whole, indicating that increased sampling is still necessary to fully understand diversity patterns. Accompanying are assessments of elevation trends and adult presence patterns within nine families. Collections are predominantly restricted to the Appalachian region, herein we direct future efforts to focus on understudied regions. An outline of distribution knowledge for species is presented to inform upcoming State Wildlife Action Plans.</p>
Data from: Evaluating species richness using proteomic fingerprinting and DNA-barcoding – a case study on meiobenthic copepods from the Clarion Clipperton Fracture Zone
<p><span>The Clarion Clipperton Fracture Zone (CCZ) is a vast deep-sea region harboring a highly diverse benthic fauna, which will be affected by potential future deep-sea mining of metal-rich polymetallic nodules. Despite the need for conservation plans and monitoring strategies in this context, the majority of taxonomic groups remains scientifically undescribed. However, molecular rapid assessment methods such as DNA-barcoding and Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) provide the potential to accelerate specimen identification and biodiversity assessment significantly in the deep-sea areas. In this study, we successfully applied both methods to investigate the diversity of meiobenthic copepods in the eastern CCZ, including the first application of MALDI-TOF MS for the identification of these deep-sea organisms. Comparing several different species delimitation tools for both datasets, we found that biodiversity values were very similar, with Pielou's Evenness varying between 0.97 and 0.99 in all datasets. Still, direct comparisons of species clusters revealed differences between all techniques and methods, which are likely caused by the high number of rare species being represented by only one specimen, despite our extensive dataset of more than 2000 specimens. Hence, we regard our study as a first approach toward setting up a reference library for mass spectrometry data of the CCZ in combination with DNA-barcodes. We conclude that proteome fingerprinting, as well as the more established DNA-barcoding, can be seen as a valuable tool for rapid biodiversity assessments in the future, even when no reference information is available.</span></p>
Changing plant species composition and richness benefit soil carbon sequestration under climate warming
<p>Anthropogenic warming and land-use change are expected to accelerate global soil organic carbon (SOC) losses and change plant species composition and richness. However, how changes in plant composition and species richness mediate SOC responses to climate warming and land-use change remains poorly understood. Using data from a 7-year warming and clipping field experiment in an alpine meadow on the Qinghai-Tibetan Plateau, we examined the direct effects of warming and clipping on SOC storage versus their indirect effects mediated by plant functional type and species richness. We found that warming significantly increased SOC storage by 8.1% and clipping decreased it by 6.4%, which was closely correlated with the corresponding response of below-ground net primary productivity (BNPP). We also found a negative correlation between SOC storage and species richness, which was ascribed to the increased BNPP via enhancing the dominance of grasses and decreasing species richness under warming. The lower SOC storage under clipping was caused by the clipping-induced decrease in BNPP via weakening the dominance of grasses and increasing species richness. Our findings highlight that the SOC storage in this alpine meadow under climate warming and clipping was primarily governed by BNPP, which was mediated by changes in the dominance of grasses and species richness. Overall, our study demonstrates that shifting to the dominance of grasses and changing species richness would benefit soil C sequestration under climate warming, but this positive effect would be dampened by grazing or hay harvest.</p>
Plant species richness on the Tibetan Plateau: Patterns and determinants
<p><span><span>Whether current hypotheses for geographic patterns of species richness (SR) have a strong explanatory power for the Tibetan Plateau (TP) with extreme climatic conditions remains unclear. </span><span>In comparison with the classic "water–energy dynamics hypothesis", the unique climate factors (e.g., extreme low temperature and low oxygen partial pressure) on the TP likely significantly affect the spatial variation of SR. Here, </span></span><span>we investigate</span><span> geographic patterns and determinants of SR on the TP </span><span>through a systematic field investigation. We systematically analyzed a total of 2,013 plant communities covering 11 different vegetation types on the TP. The SR per 400 m<sup>2</sup> in the forests and shrubs and that per 1 </span><span>m<sup>2</sup></span><span> in alpine grasslands and deserts was 62.76 (±1.80 SE), 44.53 (±7.57 SE), 16.84 (±0.39 SE), and 3.62 (±0.55 SE), respectively. Unique climate factors, such as </span><span>extremely low temperature, mean diurnal temperature, and oxygen partial pressure,</span><span> act synergistically with water–energy dynamics and influence the spatial pattern of SR on the TP. </span><span>Our findings provide novel insights into the mechanisms underlying the spatial variation in plant diversity, especially on plateaus and in high-latitude regions. </span><span>Our findings and the SR map with 1 km resolution provide important benchmarks for biodiversity conservation and may help to improve predictions of the effect of climate change on biodiversity.</span></p>
Individual-level trait diversity predicts phytoplankton community properties better than species richness or evenness
<p>This archive includes the final summary tables used for the analyses.</p>
Dataset: Habitat suitability models to make conservation decisions based on areas of high species richness and endemism
<p>This repository contains the files associated with the following article:</p> <p>Hernández-Quiroz NS, EI Badano, F Barragán-Torres, J Flores & C Pinedo-Álvarez. Habitat suitability models to make conservation decisions based on areas of high species richness and endemism. Biodiversity and Conservation, 27, pp. 3185-3200. <a href="https://doi.org/10.1007/s10531-018-1596-9">https://doi.org/10.1007/s10531-018-1596-9</a></p> <p>The Microsoft Excel file (SM 01-Oak occurrences.xlsx) contains the occurrence points used to calibrate the habitat suitability model of each oak species (59 species in total). This file indicates the name of the species (column A), latitude and longitude of each occurrence point (columns B and C; in geographic coordinates) and the full set of bioclimatic variables (columns D-V) and topographic variables (columns W-Z) associated to each point. These later data are provided as they were gathered from the bioclimatic layers of WorldClim and the topographic layers of the Mexican National Institute of Statistics and Geography. The repository also contains interactive maps indicating the predicted and observed distributions of the 59 Mexican oak species (SM 02-Estimated oak distribution ranges.kmz), and the probability-based and occurrence-based map of oak richness and endemic species (SM 03-Oak richness maps.kmz). These geographic projections are provided in KMZ format to make them easy to visualize in Google Earth (freely available at www.google.com/earth). Details about these KMZ files can be consulted by accessing the file properties after opening them in Google Earth.</p>
Data from: Landscapes with higher crop diversity have lower aphid species richness but higher plant virus prevalence
<p>Diversifying agricultural systems by growing more than one crop species in an area can decrease pest and disease pressure and increase crop yields. However, there is a lack of information on how crop diversity at larger spatial scales influences pest and disease pressure. Here, we investigated how landscape-scale crop diversity affects aphid vector communities and prevalence of non-persistently transmitted potato virus Y (PVY). To test the influence of landscape-scale crop diversity on PVY prevalence and aphid communities, we conducted a field study during the 2020 and 2021 field seasons in the San Luis Valley, Colorado where we quantified aphid communities and PVY incidence at multiple sites. We then determined the association of aphid species richness and abundance and PVY incidence with landscape variables (crop diversity metrics and percentage cover of crop species) within 1, 2 and 3 km buffers from study sites. Higher crop diversity (measured as Shannon diversity index) led to decreased aphid species richness at a 3 km buffer in the 2021 field season. Percentage of alfalfa was positively associated with aphid species richness in 2020 and aphid abundance in 2021 within a 1 km buffer. Higher crop diversity led to increased PVY incidence at a 2 km buffer in 2021 and 3 km buffer in 2020 and 2021. At a 3 km buffer in 2021, we found a positive influence of crop species richness on PVY incidence and a negative influence of crop species evenness on PVY incidence. Also in 2021, we found a positive influence of percentage of potato (virus host) on PVY incidence and a negative influence of percentage of barley (virus non-host) on PVY incidence.</p> <p><strong>Synthesis and applications:</strong> In summary, we found that landscape-scale crop diversity impacts plant virus prevalence at spatial scales of >1 km. This suggests that potato growers could reduce PVY prevalence by geographically isolating potato fields from other potato or other PVY-hosts. Crop diversity had a negative influence on aphid vector communities so growers could reduce risk of virus spread by aphid vectors by using certified potato seed in a diversified landscape.</p>
Data and code from: Neighborhood habitat gains increase plant species richness in forest fragments - Rosenblad & Sullivan (2024)
<p>This repository contains all data and R code necessary to reproduce the results of Rosenblad & Sullivan (2024) <span>Neighborhood habitat gains increase plant species richness in forest fragments. README.md explains how the files fit together.</span></p>
The counteracting effects of human-driven speciation and extinction on mammal species richness and phylogenetic diversity
<p><span>Human activities are causing massive increases in extinction rates, but may also lead to drastic increases in speciation rates – for example following the human-mediated spread of species to otherwise unreachable landmasses. The long-term net anthropogenic effects on biodiversity, therefore, remain uncertain. The aim of this paper is to assess the combined anthropogenic effects of extinctions and speciations on biodiversity over geological time scales. </span><span>We estimate known anthropogenic and predicted future extinctions based on Red List categories from the International Union for Conservation of Nature. We infer potential anthropogenic speciations assuming that all introductions to isolated landmasses will over time evolve into distinct species. We then estimate changes in regional and global species richness and phylogenetic diversity due to these extinctions and speciations. </span><span>We show that if all species introduced into new landmasses develop into new species, the number of anthropogenic speciation and extinctions eventually become similar</span><span>. However, even after accounting for an anthropogenic increase in speciation, our estimates suggest recovery times for phylogenetic diversity of several million years</span><span>. </span><span>Our results highlight that while humans are causing drastic biodiversity losses, human-driven speciation could eventually counterbalance these losses in species numbers, while phylogenetic diversity at least within our simulation scenarios would remain permanently reduced. This conclusion, however, requires our pressures on biodiversity to cease soon and requires us to consider geological timescales rather than changes over this century.</span></p>
Fig. 4 in Species Accumulation Curves And Similarity Traits Of A Species-Rich Fly (Diptera) Community
Fig. 4. Jackknifed NESS indices relating to the first and kth group of 50, k = 1,2,…, 20
Fig. 3 in Species Accumulation Curves And Similarity Traits Of A Species-Rich Fly (Diptera) Community
Fig. 3. Quasi individual-based species accumulation curves after normalisation
Fig. 1 in Species Accumulation Curves And Similarity Traits Of A Species-Rich Fly (Diptera) Community
Fig. 1. Sample-based species accumulation curves without normalisation
Fig 8 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 8. Number of co-authored papers with authors from different continents from 1946 to 2012.
Fig. 7 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig. 7. Percentage of articles with co-authors from 1946 to 2012.
Stylized urban landscapes optimized for compactness, climate regulation and vascular plant species richness
<p>The data set provides the output of a genetic algorithm optimizing a stylized urban region with respect to three target functions: urban compactness, climate regulation as an exemplary ecosystem service and vascular plant species richness as a measure of biodiversity.</p> <p>The optimisation varies the spatial allocation of three types of land cover blocks in a stylized urban region: high- and low-density and park blocks which consist of green and/or built-up cells. We systematically vary landscape composition at the block level, but keep city size constant.</p> <p>The data set is related to a publication submitted to Frontiers in Environmental Science.</p>
Vertical variation in epiphytic cryptogam species richness and composition in a primeval Fagus sylvatica forest
<p>"Data description.docx " contains the description of the data-file " Data_JVS_Vertical variation.xlsx" used for analyses.<br> </p>
Species richness, composition and microhabitat characteristics of non-volant terrestrial mammals in disturbed habitats
<b>Description: </b><p>A study on the small mammals communities was carried out in disturbed habitats aroundsabah, namely university malaysia sabah (ums), klias peat swamp forest reserve(klias), kawang forest reserve (kawang), kalabakan forest reserve (safe) and maliaubasin conservation area (maliau). the objectives were (1) to determine the speciesrichness and composition of non-volant small mammal communities in disturbedhabitats; (2) to characterize the microhabitat-use patterns of the non-volant smallmammal communities in disturbed habitats; and (3) to determine the microhabitatpreferences of the non-volant small mammal communities in disturbed habitats. the aimof this study was to investigate how the habitat disturbance affects the species richness,community compositions and microhabitat-use pattern of the small mammals. this studywas conducted from october 2014 to march 2015 with a total sampling effort of 540trap-nights. overall, 71 individuals representing 14 species were successfully caughtduring this study. the species richness peaked at safe, and then declined at the rest ofthe study sites. habitat variables analysis showed that all study sites were divided intothree distinctive groups in terms of habitat types. canonical discriminant functionanalysis were used to analyze the microhabitat preferences and use-pattern of smallmammals and results showed the preferences of small mammals towards shrub cover(rattus rattus and callosciurus notatus), litter cover (callosciurus prevostii and echinorexgymnurus) and herbs limber (tupaia gracilis). The locations of the traps have not been given longitute and latitute as there were set on animal trails approximately 20metres from one another. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/150"><b>Species richness, composition and microhabitat characteristics of non-volant terrestrial mammals in disturbed habitats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Universiti Malaysia Sabah (Grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiverstiy Council (Research licence NA)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3265704">here</a></p><p><b>Files: </b>This consists of 1 file: Veg_Volent_mammals.xlsx</p><p><b>Veg_Volent_mammals.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Vegetation cover</b> (described in worksheet Vegetation_cover)</p><p>Description: The estimated canopy cover and percentage of ground cover of traps across disturbed habitats</p><p>Number of fields: 19</p><p>Number of data rows: 180</p><p>Fields: </p><ul><li><b>Date</b>: Date the vegetation cover was collected (Field type: Date)</li><li><b>Location</b>: Where data was collected (Field type: Location)</li><li><b>Habitat_type</b>: Habitat type (Field type: Categorical)</li><li><b>Trap_station</b>: Transect and trap number (Field type: ID)</li><li><b>Canopy_cover</b>: Percentage of canopy cover (Field type: Numeric)</li><li><b>Canopy_height</b>: Canopy height (Field type: Numeric)</li><li><b>Herbs_Climber</b>: Any climbers seen on trees for example vines and lianas (Field type: Numeric)</li><li><b>Tree_GBH_>10CM</b>: Tree girth at breast height of 10 cm (Field type: Numeric)</li><li><b>Tree_GBH_>30CM</b>: Tree girth at breast height of 30 cm (Field type: Numeric)</li><li><b>Tree_GBH_>60CM</b>: Tree girth at breast height of 60 cm (Field type: Numeric)</li><li><b>Tree_GBH_>90CM</b>: Tree girth at breast height of 90 cm (Field type: Numeric)</li><li><b>Fallen_trees_bran</b>: Percentage cover (Field type: Numeric)</li><li><b>Bareground</b>: Percentage cover (Field type: Numeric)</li><li><b>Shrub</b>: Percentage cover (Field type: Numeric)</li><li><b>grass</b>: Percentage cover (Field type: Numeric)</li><li><b>Rock</b>: Percentage cover (Field type: Numeric)</li><li><b>Litter</b>: Percentage cover (Field type: Numeric)</li><li><b>Water</b>: Percentage cover (Field type: Numeric)</li><li><b>Twig</b>: Percentage cover (Field type: Numeric)</li></ul></li><li><p><b>Non volent mammal abundance</b> (described in worksheet Non_volent_mammals)</p><p>Description: The abundance of non-volant terrestrails mammals caught across disturbed habitats. Growth and sex measurements takens</p><p>Number of fields: 17</p><p>Number of data rows: 490</p><p>Fields: </p><ul><li><b>Date</b>: Date the vegetation cover was collected (Field type: Date)</li><li><b>Location</b>: Where data was collected (Field type: Location)</li><li><b>Transect</b>: Transect number (Field type: ID)</li><li><b>Habitat</b>: Habitat type (Field type: Categorical)</li><li><b>Trap_station</b>: Transect and trap number (Field type: ID)</li><li><b>Species</b>: Species of non volent mammals caught in trap (Field type: Taxa)</li><li><b>Weight</b>: Weight of caught non volent mammal (Field type: Numeric)</li><li><b>Ear</b>: Ear measurment of caught non volent mammal (Field type: Numeric)</li><li><b>Hind_leg</b>: Hind leg measurement of caught non-volent mammal (Field type: Numeric)</li><li><b>Head_body</b>: Head to body measurement of caught non volent mammal (Field type: Numeric)</li><li><b>Tail</b>: Tail length of caught non volent mammal (Field type: Numeric)</li><li><b>Sex</b>: Sex of caught non volent mammal (Field type: Categorical)</li><li><b>Sexual_activity</b>: Sexual maturity of caught non volent mammal (Field type: Categorical)</li><li><b>Age</b>: Age class of caught non volent mammal (Field type: Categorical)</li><li><b>Trap_condition</b>: Trap condition (Field type: Categorical)</li><li><b>Trap_open_closed</b>: Trap open or closed (Field type: Categorical)</li><li><b>Bait</b>: Bait taken or intact (Field type: Categorical)</li></ul></li></ol><p><b>Date range: </b>2014-10-22 to 2015-03-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Erinaceomorpha<br> -  -  -  - Erinaceidae<br> -  -  -  -  - <i>Echinosorex</i><br> -  -  -  -  -  - <i>Echinosorex gymnura</i><br> -  -  - Rodentia<br> -  -  -  - Muridae<br> -  -  -  -  - <i>Lenothrix</i><br> -  -  -  -  -  - <i>Lenothrix canus</i><br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys rajah</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Niviventer</i><br> -  -  -  -  -  - <i>Niviventer cremoriventer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus rattus</i><br> -  -  -  - Sciuridae<br> -  -  -  -  - <i>Callosciurus</i><br> -  -  -  -  -  - <i>Callosciurus adamsi</i><br> -  -  -  -  -  - <i>Callosciurus notatus</i><br> -  -  -  -  -  - <i>Callosciurus prevostii</i><br> -  -  -  -  - <i>Sundasciurus</i><br> -  -  -  -  -  - <i>Sundasciurus lowii</i><br> -  -  - Scandentia<br> -  -  -  - Tupaiidae<br> -  -  -  -  - <i>Tupaia</i><br> -  -  -  -  -  - <i>Tupaia dorsalis</i><br> -  -  -  -  -  - <i>Tupaia glis</i><br> -  -  -  -  -  - <i>Tupaia gracilis</i><br></div><p></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.