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1,868 results for “Spatial Data”
Data from: Spatial and seasonal variation in thermal sensitivity within North American bird species
<p>Responses of wildlife to climate change are typically quantified at the species level, but physiological evidence suggests significant intraspecific variation in thermal sensitivity given adaptation to local environments and plasticity required to adjust to seasonal environments. Spatial and temporal variation in thermal responses may carry important implications for climate change vulnerability; for instance, sensitivity to extreme weather may increase in specific regions or seasons. Here, we leverage high-resolution observational data from eBird to understand regional and seasonal variation in thermal sensitivity for 20 bird species. Across their ranges, most birds demonstrated regional and seasonal variation in both thermal peak and range, or the temperature and range of temperatures of greatest occurrence. Some birds demonstrated constant thermal peaks or ranges across their geographic distributions and while others varied according to local and current environmental conditions. Across species, birds typically invested in either geographic or seasonal adaptation to climate. Local adaptation and phenotypic plasticity are likely important but neglected aspects of organismal responses to climate change.</p>
Data for: Drivers of wood decay in tropical ecosystems: Termites vs. microbes along spatial, temporal and experimental precipitation gradients
<ol> <li>Models estimating decomposition rates of dead wood across space and time are mainly based on studies carried out in temperate zones where microbes are dominant drivers of decomposition. However, most dead wood biomass is found in tropical ecosystems, where termites are also important wood consumers. Given the dependence of microbial decomposition on moisture with termite decomposition thought to be more resilient to dry conditions, the relative importance of these decomposition agents is expected to shift along gradients in precipitation that affect wood moisture.</li> <li>Here, we investigated the relative roles of microbes and termites in wood decomposition across precipitation gradients in space, time and with a simulated drought experiment in tropical Australia. We deployed mesh bags with non-native pine wood blocks, allowing termite access to half the bags. Bags were collected every six months (end of wet and dry seasons) over a four-year period across 5 sites along a rainfall gradient (ranging from savanna to wet sclerophyll to rainforest) and within a simulated drought experiment at the wettest site. We expected microbial decomposition to proceed faster in wet conditions with greater relative influence of termites in dry conditions.</li> <li>Consistent with expectations, microbial-mediated wood decomposition was slowest in dry savanna sites, dry seasons, and simulated drought conditions. Wood blocks discovered by termites decomposed 16% to 36% faster than blocks undiscovered by termites regardless of precipitation levels. Concurrently, termites were 10 times more likely to discover wood in dry savanna compared with wet rainforest sites, compensating for slow microbial decomposition in savannas. For wood discovered by termites, seasonality and drought did not significantly affect decomposition rates.</li> <li>Taken together, we found that spatial and seasonal variation in precipitation are important in shaping wood decomposition rates as driven by termites and microbes, although these different gradients do not equally impact decomposition agents. As we better understand how climate change will affect precipitation regimes across the tropics, our results can improve predictions of how wood decomposition agents will shift with potential for altering carbon fluxes.</li> </ol>
Data and code from: Spatial ecology of the Turks & Caicos boa, Chilabothrus c. chrysogaster Cope, 1871 (Serpentes: Boidae)
<p><span>Obtaining ecological and natural history data from cryptic squamates can be challenging, but is crucial to understanding species' biology, particularly in the context of conservation. In the Greater Antilles, this challenge is especially apparent, particularly among the West Indian boas (genus <em>Chilabothrus</em>). Most species have had only minimal natural history study, with a few exceptions. The Turks & Caicos boa (<em>C. chrysogaster</em>) has been studied intensively for over 16 years on the small privately owned island of Big Ambergris Cay, Turks and Caicos Islands. We conducted a multi-year radio-tracking study on the species to generate information relevant to spatial habitat use and movement that will inform conservation decision-making in the face of increasing development pressure. We tracked a total of 19 female snakes using surgically implanted transmitters, enabling us to obtain between 16 and 40 location observations per boa over the lifetime of each transmitter. We estimated home ranges, the core space used by an animal, using range distributions, finding that females have a home range of 0.70 ha to 1.2 ha. We also estimated occurrence distributions, the use of space between specific time intervals, finding an average occurrence area of 1.62 ha. Several females overlapped in their spatial habitat use, and we observed female boas using two novel habitats for the species (iron shore wrack and red mangrove). This study provides valuable information on the spatial ecology of an endangered boa and will serve to inform conservation work that is currently underway. </span></p>
Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics (codes and data files)
<p>This dataset includes all the relevant codes and data files associated with the paper ("Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics") in Clinical and Translational Medicine journal.</p>
Data from: A species' response to spatial climatic variation does not predict its response to climate change
<p>The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to <em>in situ</em> climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.</p>
Data from: Whole-brain spatial organization of hippocampal single-neuron projectomes
<p>Mapping hippocampal single-neuron projections is essential for understanding brain-wide circuit organization and diverse functions of the hippocampus, a brain structure underlying episodic memory and cognition. Here, we reconstructed 10,100 single-neuron projectomes of the mouse hippocampus, identified rostral and caudal axon pathways that preferentially innervated cortical vs. subcortical areas, and classified 43 projectome subtypes with distinct axon targeting patterns. Notably, the soma locations along hippocampal longitudinal and transverse axes determined the number of their target areas and the spatial distribution and complexity of their axon arbors within the targets. We defined selective hippocampal subdomains based on spatial transcriptomic profiles and found that many projectome subtypes were enriched in specific subdomains. Next, we defined the wiring diagram for hippocampal neurons exclusively projecting to hippocampal formation (HPF) and those projecting to both intra- and extra-HPF targets with coordinated projection strengths. Furthermore, bi-hemispheric projecting hippocampal neurons generally projected to one pair of homologous targets with ipsilateral preference. These organization principles of single-neuron projectomes provide a structural basis for understanding diverse but coordinated functions of hippocampal neurons.</p>
Data for: Biomechanical adaptations enable phoretic mite species to occupy distinct spatial niches on host burying beetles
<p>Niche theory predicts that ecologically similar species coexist by minimising interspecific competition through niche partitioning. Therefore understanding the mechanisms of niche partitioning is essential for predicting interactions and coexistence between competing organisms. Here we study two phoretic mite species, <em>Poecilochirus carabi, </em>and <em>Macrocheles nataliae</em> that coexist on the same host-burying beetle <em>Nicrophorus vespilloides </em>and use it to 'hitchhike' between reproductive sites. Field observations revealed clear spatial partitioning between species in distinct host body parts. <em>P. carabi</em> preferred the ventral side of the thorax, whereas <em>M. nataliae </em>were exclusively found ventrally at the hairy base of the abdomen. Experimental manipulations of mite density showed that each species preferred these body parts, largely regardless of the density of the other mite species on the host beetle. Force measurements indicated that this spatial distribution is mediated by biomechanical adaptations, because each mite species required more force to be removed from their preferred location on the beetle. While <em>P. carabi</em> attached with large adhesive pads to the smooth thorax cuticle, <em>M. nataliae</em> gripped abdominal setae with their chelicerae. Our results show that specialist biomechanical adaptations for attachment can mediate spatial niche partitioning among species sharing the same host.</p>
Clonally resolved spatial transcriptomics data of mouse spleen
<p>The BGI Stereo-seq strategy was applied to a mouse spleen sample containing SPLINTR barcoded AML cells.</p> <p>Data generated with <a href="https://github.com/DaneVass/bartools_manuscript_code/blob/main/spatial-analysis/data_preprocessing_m4_paper.py" target="_blank" rel="noopener">https://github.com/DaneVass/bartools_manuscript_code/blob/main/spatial-analysis/data_preprocessing_m4_paper.py</a>.</p> <p>mouse4_bin*_bc_counts.tsv:<br>Binned barcode counts across whole slide.<br>Can be merged with AnnData file by `cell_id`.<br>Contains all barcodes detected in a bin (`barcode`) and UMI counts summed by bin (`count_binned`).<br>`isin_adata` marks whether the bin is on the manually segmented tissue section.</p> <p>mouse4_bin*_bc_counts_top1.tsv:<br>Binned barcode counts on tissue section, barcode with most UMI per bin is selected. </p> <p>mouse4_bin*_bc.h5ad:<br>Binned stereo-seq data with barcode information.</p> <p>mouse4_bin*_bc_clustered.h5ad:<br>Filtered, log1p transformed, scaled, clustered stereo-seq data.<br>Data is not zero centered for bin10 for memory efficiency.</p>
Data from: Developing spatially explicit and stochastic measures of ecological departure
<p>Background: Ecological departure is a metric applied to mapped ecological systems measuring dissimilarity between the distributions of observed and expected proportions of non-stochastic reference vegetation classes within an area.</p> <p>Aims: We created spatially explicit measures of ecological departure incorporating stochasticity for each ecological system and all ecological systems from a central Nevada USA landscape.</p> <p>Methods: Spatially explicit ecological departures were estimated from a radius from each pixel governed by a distance-decay function within a moving window. Variability was introduced by simulating replicate climate time series for each spatial reference condition and calculating departure per replicate.</p> <p>Key results: Single system spatial ecological departure was highly and extensively departed, except for one area of low-elevation groundwater-dependent systems. Variance of spatial ecological departure was extensively low, except in areas of lower ecological departure, despite vegetation differences among replicates. The multiple-system ecological departure exhibited lower ecological departure.</p> <p>Conclusions: Spatial ecological departure was warranted for efficient land management as results were concordant between non-spatial and spatial metrics; however, rapid coding languages will be required.</p>
Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<p>Global ~10km (5 arcmin) output data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual and monthly temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Discharge (m3 s-1)</li> <li>Channel storage (m3) </li> <li>Water temperature (K)</li> <li>Total dissolved solids (TDS) load (g s-1)</li> <li>Biological oxygen demand (BOD) load (g s-1)</li> <li>Fecal coliform (FC) load (million cfu s-1)</li> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Note. a minimum discharge threshold of 0.1 m3 s-1 was used when computing salinity (TDS), organic (BOD) and pathogen (FC) concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Thus, if the the average discharge for the month was below 0.1 m3 s-1, concentrations are not calculated (assigned as NA).</p> <p>In-stream water quality aggregated to 0.5 degree (i.e. 30 arcmin) spatial resolution (daily, monthly and annual) can be found at: <a href="https://zenodo.org/records/14675270">https://zenodo.org/records/14675270</a>. </p>
Global surface water quality data from 1980 - 2019, derived from the dynamical surface water quality model (DynQual) at 30 arcmin spatial resolution
<p>Global ~50km (30 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual, monthly and daily temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Simulations were originally made at 5-arcmin resolution and aggregated to 30 arcmin 0.5 degree by summing the in-stream (routed) loadings and channel storage over the aggregated area (at daily, monthly and annual timesteps), and subsequently calculating in-stream concentrations. Please note the aggregation technique is provisional and thus the data is subject to change.</p> <p>Note. A minimum discharge threshold of 0.1 m3 s-1 was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.</p> <p>Hydrology and water quality simulations made at DynQuals native spatial resolution (5 arcmin) can be found at: <a href="https://zenodo.org/records/14673871">https://zenodo.org/records/14673871</a>.</p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Data from: Disentangling the drivers of ground-dwelling macro-arthropod metacommunity structure at two different spatial scales
<p>The goal of this study was to explore the community assembly rules at local and regional scales.</p> <p> </p> <p><strong><em>Site description </em></strong></p> <p>All sampling locations were selected within the black soil region (Fig. 1), which is predominantly located in the temperate continental monsoon climatic zone in North China. It is characterized by a dry and cold winter and warm and humid summer. The soil was classified as black soil following the Chinese Soil Classification System, which is equivalent to a Typic Hapludoll in the USDA Soil Taxonomy. More specific details for this soil (such as black soil coverage area, geographical and ecological resources, etc.) can be obtained from Wen and Liang (2001). Samples were collected from three municipal districts: Bei'an, Hulan and Dehui.</p> <p> </p> <p><strong><em>Sampling design and setup</em></strong></p> <p>We conducted field sampling of ground-dwelling macro-arthropods and measured a set of environmental and spatial variables across all sampling locations three times: in May, July and September 2015. In total, 15 plots (five plots in each of the three municipal districts) were selected and sampled. At each plot, we further selected five sampling sites (approximately 10 m away from each other).</p> <p>We collected additional samples for estimating soil abiotic parameters at each site. Soil samples (5 × 5 cm and 10 cm depth) were collected near each pitfall trap site. The exact geographic coordinates of each sampling site were obtained by GPS.</p> <p>Ground-dwelling macro-arthropods were sampled by a pitfall trapping method. For pitfall traps, we used plastic cups (7 cm in diameter and 12 cm deep), which were partially filled with saturated salt water. The traps were exposed for one week in each sampling month. All collected ground-dwelling macro-arthropods were removed from the pitfall traps, sorted and preserved in a 95% alcohol solution. All adult macroarthropods from pitfalls were identified at the species or genus level using appropriate keys (e.g., Simon (1879), Martens (1978) and Barrientos (2004) for Opiliones; Roberts (1993, 1995) for Lycosidae; and Forel and Leplat (2001) and Ortuño and Marcos (2003) for Carabidae) and then were counted. Juvenile ground-dwelling arthropods were excluded from all analyses due to difficulties with their identification (Gao et al., 2016).</p> <p> </p> <p><strong><em>Environmental and spatial variables</em></strong></p> <p>Environmental variables used in our analysis included soil organic matter, soil total nitrogen, water content, pH, temperature. Soil water content (SWC%) was measured in the laboratory after the fresh soil was loaded into an aluminium box. Prior to estimating soil total nitrogen (TN) (Kjeldahl's method described by Duchaufour (1975)), soil organic matter (SOM) (Anne's method described by Duchaufour (1975)) and pH (Pansu and Gautheyrou, 2003), the collected soil samples were air-dried at 25℃ for one week and sieved (1 mm mesh size). Local temperature values were obtained from the publicly available datasets (The Local Chronicles of Bei’an, Hulan and Dehui). Geographic coordinates were recorded for further spatial modelling analysis.</p> <p> </p> <p>We have seven data files:</p> <p>env BAHLDH may.csv</p> <p>env BAHLDH july.csv</p> <p>env BAHLDH september.csv</p> <p>sp BAHLDH may.csv</p> <p>sp BAHLDH july.csv</p> <p>sp BAHLDH september.csv</p> <p>Geospatial coordinates.csv</p> <p> </p> <p>Explanation of the variables in the datasets:</p> <p>Site: Bei’an, Hulan, Dehui represent sampling district; I-V represent sampling plot; 1-5 represent replicate</p> <p>SOM: soil organic matter</p> <p>pH: soil pH</p> <p>SWC: Soil water content</p> <p>TN: soil total nitrogen</p>
State of biodiversity documentation in the Philippines: Metadata gaps, taxonomic biases, and spatial biases in the DNA barcode data of animal and plant taxa in the context of species occurrence data
<p>These files can be categorized into three groups: (1) raw datasets obtained from public databases (i.e., GBIF, BOLD, and GenBank), (2) manually edited files needed for parsing and analysis, and (3) supplementary files for spatial analysis. All are used in the examination of gaps and biases present in Philippine biodiversity data, which can direct research on the taxa and spatial regions that need more sampling.</p>
Spatial patterns of extreme precipitation and their changes under ~2 °C global warming: A large-ensemble study of the western US: Data Release
<p>This dataset supports the analysis in Rupp et al. (2022). The dataset consists of 17,223 data files containing the water year (WY) maximum of the daily-averaged precipitation rate simulated with the HadRM3p regional climate model configured for the western United States. Each file contains the WY maxima across the model domain for a single WY, single model parameterization, and single set of initial conditions. Please refer to Hawkins et al. (2019) and Rupp et al. (2022) for a description of how the climate model data were generated.</p>
Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds
<p>For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel sub-sites in the Florida Bay (application site) under a different time period (application period). We used foraging Little Blue Herons (<em>Egretta caerulea</em>) as the ecological unit with which to constrain the model's 'water depth window', i.e., range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by sub-site and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially-explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow-water has implications for monitoring habitat conditions for tidally-influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.</p>
Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines
<p>This repository contains the processed data and plotting routines associated with the article</p> <p>Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914</p> <p>The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots.</p> <p>The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.</p>
Global patterns of current and future road infrastructure - Supplementary spatial data
<p><strong>Global patterns of current and future road infrastructure - Supplementary spatial data</strong></p> <p><strong>Authors:</strong> Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper</p> <p><strong>Research paper summary: </strong>Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted <em>R</em>2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.</p> <p><strong>Contents:</strong> The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a <a href="https://creativecommons.org/publicdomain/zero/1.0/deed.en">Creative Commons License (CC-0)</a> and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the <a href="https://www.globio.info/global-patterns-of-current-and-future-road-infrastructure">paper by Meijer et al (2018)</a>. Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.</p> <p>Regional coding of the other vector datasets in shapefile and ESRI fgdb format:</p> <ul> <li>Region 1: North America</li> <li>Region 2: Central and South America</li> <li>Region 3: Africa</li> <li>Region 4: Europe</li> <li>Region 5: Middle East and Central Asia</li> <li>Region 6: South and East Asia</li> <li>Region 7: Oceania</li> </ul> <p>Road density raster data:</p> <ul> <li>Total density, all types combined</li> <li>Type 1 density (highways)</li> <li>Type 2 density (primary roads)</li> <li>Type 3 density (secondary roads)</li> <li>Type 4 density (tertiary roads)</li> <li>Type 5 density (local roads)</li> </ul> <p><strong>Keyword:</strong> global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios</p>
Fig. 4 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data
Fig. 4. Distribution of resources (light bars) and distribution of resources used by P. major (grey bars).
Fig. 5 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data
Fig. 5. Distribution of pseudo absence cells: a — the distance to the presence cells is not less than 1000 meters; b — the distance to the presence cells is not less than 500 meters; c — the distance to the presence cells is not less than 250 meters; d — distance to the presence cells is not less than 100 meters.
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.