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1,024 results for “Edge”
Quantifying Growth and Structure along Forest Edges in the Northeastern USA 2010-2021
Fragmentation transforms the environment along forest edges. The prevailing narrative, driven by tropical research, suggests that edge environments increase tree mortality and structural degradation resulting in net decreases in ecosystem productivity. We show that temperate forest edges exhibit increased forest growth (basal area increment; BAI) and biomass (basal area; BA) with no change in total mortality relative to the forest interior. To assess forest edges, we analyzed more than 48,000 forest inventory plots (USDA FIA) across the north-eastern US using a quasi-experimental matching design. At forest edges adjacent to anthropogenic land covers, we report increases of 36.3% and 24.1% in forest growth and biomass, respectively. We then scale the edge impacts on growth (along anthropogenic edges only) across our study area using maps of land-cover and forest type. We find large variability in the effect of including edges on estimates of total forest growth, largely driven by differences in the prevalence of fragmentation. Estimated increases in forest growth range from a 23% increase in the agricultural-dominated western areas, a 2% increase in the least-fragmented northern regions, and a 15% increase within the metropolitan east coast. Finally, we also quantify forest fragmentation globally, at 30-m resolution, showing that temperate forests contain 52% more edge forest area than tropical forests. We provide two tables containing the post-matched dataset of FIA subplots, including subplot BA, BAI, and edge status. We include the associated environmental covariates, extracted from gridded raster data, and used in our matching and statistical analyses. Due to plot confidentiality restrictions we do not provide spatial locations of the FIA subplots, but we do provide unique plot identifiers that allow users to link each record to the publically-available data provided by the USDA FIA database (https://apps.fs.usda.gov/fia/datamart/). This dataset can be used to re
Soil Respiration at Forest Edges along an Urban to Rural Gradient in Massachusetts 2018-2019
As urbanization and forest fragmentation increase around the globe, it is critical to understand how rates of respiration and carbon losses from soil carbon pools are affected by these processes. This study characterizes soils in fragmented forests along an urban to rural gradient, evaluating the sensitivity of soil respiration to changes in soil temperature and moisture near the forest edge. While previous studies found elevated rates of soil respiration at temperate forest edges in rural areas compared to the forest interior, we find that soil respiration is suppressed at the forest edge in urban areas. At urban sites, respiration rates are 25% lower at the forest edge relative to the interior, likely due to high temperature and aridity conditions near urban edges. While rural soils continue to respire with increasing temperatures, urban soil respiration rates asymptote as temperatures climb and soils dry. Soil temperature- and moisture-sensitivity modeling show that respiration rates in urban soils are less sensitive to rising temperatures than those in rural soils. Scaling these results to Massachusetts (MA), which encompasses 0.25 Mha of urban forest, we find that failure to account for decreases in soil respiration rates near urban forest edges leads to an overestimate of growing-season soil carbon fluxes of greater than 350,000 MgC. This difference is almost 2.5 times that for rural soils in the analogous comparison (underestimate of less than 143,000 MgC), even though rural forest area is more than four times greater than urban forest area in MA. While a changing climate may stimulate carbon losses from rural forest edge soils, urban forests may experience enhanced soil carbon sequestration near the forest edge. These findings highlight the need to capture the effects of forest fragmentation and land use context when making projections about soil behavior and carbon cycling in a warming and increasingly urbanized world. We provide soil respiration, soil temp
Soil Carbon at Forest Edges along an Urban to Rural Gradient in Massachusetts since 2018
Global proliferation of forest edges through anthropogenic land-use change and forest fragmentation is well documented, and while forest fragmentation has clear consequences for soil carbon (C) cycling, underlying drivers of belowground activity at the forest edge remain poorly understood. Increasing soil C losses via respiration have been observed at rural forest edges, but this process was suppressed at urban forest edges. We offer a comprehensive, coupled investigation of abiotic soil conditions and biotic soil activity from forest edge to interior at eight sites along an urbanization gradient to elucidate how environmental stressors are linked to soil C cycling at the forest edge. Despite significant diverging trends in edge soil C losses between urban and rural sites, we did not find comparable differences in soil % C or microbial enzyme activity, suggesting an unexpected decoupling of soil C fluxes and pools at forest edges. We demonstrate that across site types, soils at forest edges were less acidic than the forest interior (p less than 0.0001), and soil pH was positively correlated with soil calcium, magnesium and sodium content (adj R2 = 0.37), which were also elevated at the edge. Compared to forest interior, forest edge soils exhibited a 17.8% increase in sand content and elevated freeze-thaw frequency with probable downstream effects on root turnover and decomposition. Using these and other novel forest edge data, we demonstrate that significant variation in edge soil respiration (adj R2 = 0.46; p = 0.0002) and C content (adj R2 = 0.86; p less than 0.0001) can be explained using soil parameters often mediated by human activity (e.g., soil pH, trace metal and cation concentrations, soil temperature), and we emphasize the complex influence of multiple, simultaneous global change drivers at forest edges. Forest edge soils reflect legacies of anthropogenic land-use and modern human management, and this must be accounted for to understand soil activity and C
Quantifying Forest Edge Area in the Northeastern USA 2016
Temperate forests are the most fragmented forest biome, yet current understanding of fragmentation effects on ecosystem processes, such as carbon cycling, is rooted in tropical forest research. In the associated manuscript, we review the effects of persistent fragmentation on temperate forest ecosystem processes and quantify the extent to which the US national forest inventory and land-cover maps represent forest edge area. We find a systematic underrepresentation of forest edges across all methods. Compared with very high resolution (1 m) maps, conventional 30 m resolution forest cover maps underestimate forest edge area by 16.4%, on average. Accounting for all forest edge area and edge effects on forest structure and growth results in a 14.8% median increase in aboveground forest carbon estimates with 23.8% and 74.2% increases in agriculturally and urban dominated counties, respectively. We conclude by proposing improvements to forest inventories, maps, and models to better represent the fragmented temperate forest landscape. We provide Google Earth Engine scripts (Gorelick et al. 2017; written in JavaScript) to calculate forest edge area and forest cover from commonly-used land cover maps, including the 2016 National Land Cover Database (NLCD), the 2016 Land Change Monitoring, Assessment, and Projection annual product (LCMAP), and the 2016 MODIS Land Cover IGBP annual product (Yang et al. 2018; Sulla-Menashe et al. 2019; Brown et al. 2020). We also include equivalent scripts to process a very-high resolution (1-m pixel size; VHR) land cover map of the Chesapeake Bay Watershed in 2014 (Pallai and Wesson 2017). We provide an R script to calculate forest edge proportion from the US national forest inventory (USDA FIA), following methods to identify inventory plots containing forest edge as described in Morreale et al. (2021) and using the R library rFIA to access the FIA data (Stanke et al. 2020). We also provide a data table containing the estimated forest area and
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons.
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons. The ice season duration is defined as the time elapsed between day of ice-edge advance and day of ice-edge retreat within a given sea ice year, which begins mid-February (mean minimum of summer sea ice extent for the Southern Ocean) and ends the following mid-February. See Stammerjohn et al (2008, JGR) for further details.
Extreme Drought in Grassland Ecosystems (EDGE) Net Primary Production Quadrat Data at the Sevilleta National Wildlife Refuge, New Mexico
EDGE is located at six grassland sites that encompass a range of ecosystems in the Central US - from desert grasslands to short-, mixed-, and tallgrass prairie. We envision EDGE as a research platform that will not only advance our understanding of patterns and mechanisms of ecosystem sensitivity to climate change, but also will benefit the broader scientific community. Identical infrastructure for manipulating growing season precipitation will be deployed at all sites. Within the relatively large treatment plots (36 m2), we will measure with comparable methods, a broad spectrum of ecological responses particularly related to the interaction between carbon fluxes (NPP, soil respiration) and species response traits, as well as environmental parameters that are critical for the integrated experiment-modeling framework, as well as for site-based analyses. By designing EDGE as a research platform open to the broader scientific community, with subplots in all replicates (n = 180 plots) set-aside for additional studies, and by making data available to the broader ecological community EDGE will have value beyond what we envision here.
Extreme Drought in Grassland Ecosystems (EDGE) Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico
Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots collected in SEV297, "Extreme Drought in Grassland Ecosystems (EDGE) Net Primary Production Quadrat Data" and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Hydrodynamic, sediment, and bivalve data from seagrass edges in South Bay, VA, 2021 to 2022
The northern edge of the South Bay seagrass meadow was studied for two years to quantify flow characteristics, sediment movement, and bivalve abundance. ADCPs (Aquadopp, Vector, Vectrino) and wave gauges were used to measure hydrodynamic conditions, sediment sensors and sediment traps were used to measure sediment movement, and sediment cores were used to measure bivalve abundance. Data were collected across seagrass edges in vegetated and unvegetated locations, or along transects spanning the natural edge of meadow vegetation. Manmade bare patches were also created in the study area to collect data along patch edges. Study sites 1 and 2 were approximately 100m apart along the northern edge of the seagrass meadow. A PDF figure describing the locations is included as Site_Figure.pdf along with the data tables.
Hydrodynamic field data near Galveston, Texas wetland edges to help assess storm impacts and erosion
<p>Water free surface elevation measurements via submerged pressure transducers along transects near Galveston Bay wetland edges</p>
Experimental data and software for: Defaults: a double-edged sword in governing common resources
<p>Experimental data and software for the paper: <strong>Defaults: a double-edged sword in governing common resources</strong></p> <p>The experiment consisted in three treatments of the Common Pool Resource Dilemma, where three default interventions were applied: pro-social, self-serving and no default. Plus, the participants had to complete an SVO task and a Risk assessment task.</p> <h4>Description of the data and file structure</h4> <p>In the file called <code>all_participants.csv</code> is the full dataset of all participants that took part of the experiment. This includes participants who will end up excluded and dropouts.</p> <p>The experimental data files come in two formats: wide and long. The wide version, called <code>data_wide_format.csv</code> contains one row per participant and a column for all the fields, including rounds from 1 to 10 of the CPR task. Also, this file includes all demographic information of the participants, times and payments. The ID shown is generated internally and has no relationship with the participants' Prolific ID.</p> <p>The long version, called <code>data_long_format.csv</code>, contains 10 rows per participant, and columns for the extraction and other variables necessary for analysis. This version contains the necessary data to reproduce all the figures and statistics detailed in the main manuscript.</p> <p>In both of the previous files, the participants taken into account were the ones who completed the whole experiment. Those who did not complete the comprehension test, dropped out or did not sign the Informed Consent Form were excluded from the experimental data used. More details in the Methods below.</p> <p>In the file <code>default_opinions.csv</code>, we manually classified the responses by participants to whether they were influenced by the default presented.</p> <p>The file "<code>Instructions of the experiment.pdf</code>" contains the instructions of the experiment as shown to participants, also screenshots of the platform.</p>
Synthetic time series data generation for edge analytics
<p>In this research, we create synthetic data with features that are like data from IoT devices. We use an existing air quality dataset that includes temperature and gas sensor measurements. This real-time dataset includes component values for the Air Quality Index (AQI) and ppm concentrations for various polluting gas concentrations. We build a JavaScript Object Notation (JSON) model to capture the distribution of variables and structure of this real dataset to generate the synthetic data. Based on the synthetic dataset and original dataset, we create a comparative predictive model. Analysis of synthetic dataset predictive model shows that it can be successfully used for edge analytics purposes, replacing real-world datasets. There is no significant difference between the real-world dataset compared the synthetic dataset. The generated synthetic data requires no modification to suit the edge computing requirements. The framework can generate correct synthetic datasets based on JSON schema attributes. The accuracy, precision, and recall values for the real and synthetic datasets indicate that the logistic regression model is capable of successfully classifying data</p>
AutoML for Video Analytics with Edge Computing - Dataset
<p>Latency and confidence measurements obtained from an edge-assisted object recognition system.</p> <p>The records are obtained by tuning the image encoding rate and Neural Network input layer size and measuring the latency on completing each system operation, i.e. encoding the image, transmiting it wirelessly, decoding and rotating at the server, and performing object recognition with YOLO on the server's GPU. Moreover, we document the achievable frame rate as a result of the total latency, as well as the object recognition confidence and cumulative confidence for all identified objects of each image.</p>
Data for "Traceable X-ray focal spot reconstruction by circular edge analysis: From sub-microfocus to mesofocus"
<p>Raw data used to create figures for the paper "Traceable X-ray focal spot reconstruction by circular edge analysis: From sub-microfocus to mesofocus" <a href="https://doi.org/10.1088/1361-6501/ac6225">https://doi.org/10.1088/1361-6501/ac6225</a></p>
Classification of New Caledonian Forests According to Edge and Elevation Effects
<h1>Description</h1> <p>This map represents a classification of forest types based on the influence of the edge effect (distance to the forest edge) and elevation effect (temperature and area) on tree community richness.</p> <ul> <li>The edge effect influences tree diversity through an environmental aridity filter. In New Caledonia, the maximum temperature recorded at the forest edge is 41°C in February, while it never exceeds 24°C beyond 100 meters from the edge. This temperature difference induces a selection for species that tolerate the most arid conditions, leading to a reduction in the biological richness of tree communities (<a href="https://doi.org/10.1007/s10980-017-0534-7" target="_blank" rel="noopener">Ibanez et al., 2017</a>; <a href="https://cnrt.nc/wp-content/uploads/2022/12/CNRT-rappsc-RELIQUES_Tome-ENV-Edition-2022-cp.pdf" target="_blank" rel="noopener">Birnbaum et al., 2022</a>; <a href="https://doi.org/10.1111/1365-2745.14105" target="_blank" rel="noopener">Blanchard et al., 2023</a>).</li> <li>Altitude also affects tree diversity due to temperature variation and available area (<a href="https://doi.org/10.1111/avsc.12070" target="_blank" rel="noopener">Ibanez et al., 2014</a>; <a href="https://doi.org/10.1093/aobpla/plv075" target="_blank" rel="noopener">Birnbaum et al., 2015</a>; <a href="https://doi.org/10.1111/ddi.12374" target="_blank" rel="noopener">Pouteau et al., 2015</a>; <a href="https://doi.org/10.1111/jvs.12396" target="_blank" rel="noopener">Ibanez et al., 2016</a>; <a href="https://doi.org/10.1093/aob/mcx107" target="_blank" rel="noopener">Ibanez et al., 2018</a>). In New Caledonia, observed tree community richness ranges from 35 to 121 species per hectare within the NC-PIPPN network, peaking at mid-altitude ranges (refer to figure '<a title="1ha Plot Tree Richness Distribution Along Elevation" href="../records/12739730/files/amap_elevation_richness.png?download=1&preview=1" target="_blank" rel="noopener">amap_elevation_richness.png</a>'). Potential richness was assessed using the S-SDM model, with the 80th percentile used as a threshold to distinguish low and high potential richness across three elevation classes: [0 - 400m[, [400 - 900m[, and [900 - 1628m[.</li> </ul> <p>The classification of forest types combines distance from the forest edge and potential richness by elevation into three major categories, as illustrated in the figure '<a title="Illustration of the three forest types" href="../records/12739730/files/amap_forest_types_nc.png?download=1&preview=1" target="_blank" rel="noopener">amap_forest_types_nc.png</a>':</p> <ol> <li><strong>Edge Forest:</strong> Parts of the forest located less than 100 meters from the forest edge.</li> <li><strong>Mature Forest:</strong> Parts of the forest located beyond 100 meters from the edge with a lower potential richness of tree communities.</li> <li><strong>Core Forest:</strong> Parts of the forest located more than 300 meters from the edge with a higher potential richness of tree communities.</li> </ol> <h1>Content</h1> <p>The map is computed from the Forest Map of New Caledonia (v2024) and the Potential Tree Species Richness in the Forests of New Caledonia (v2024). This dataset was produced, analyzed, and verified using a combination of open-source software, including QGIS, PostgreSQL, PostGIS, Python, R, and the GDAL library, all running on Linux. </p> <ul> <li>amap_forest_types_nc.png is a picture illustrating the forest type classification </li> <li>amap_forest_types_nc.zip is a compressed file contains the six essential files for an ESRI-format GIS system, using the WGS84 international coordinate system, and can be uploaded to a spatial database such as PostgreSQL/PostGIS. Each row of the attribute table represents a forest type (a multi-polygon) with associated fields :</li> </ul> <table> <tbody> <tr> <td><strong>Field</strong></td> <td><strong>Type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>type</strong></td> <td>TEXT</td> <td>One of the three forest types ("Edge Forest", "Mature Forest", "Core forest")</td> </tr> <tr> <td><strong>area_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the multi-polygon in hectares</td> </tr> <tr> <td><strong>description<br></strong></td> <td>TEXT</td> <td>Description of the three forest types</td> </tr> <tr> <td><strong>geom</strong></td> <td>GEOMETRY (MULTIPOLYGON, 4326))</td> <td>Geometry with datum EPSG: 4326 (WGS 84 – World Geodetic System 1984)</td> </tr> </tbody> </table> <h1>Limitations</h1> <p>We caution users that the distinction between the three classes is based on an ecological interpretation and does not reflect directly perceptible breaks in the forest. The ecological transition from the edge to the core of the forest follows multiple gradient modulated by environmental conditions.</p> <p>Moreover, this classification is based on local observations and measurements, which are complex to generalize and extrapolate across a territory as environmentally diverse as New Caledonia. Nevertheless, it allows us to address the impact of fragmentation at the scale of New Caledonia.</p>
Empirical laws of plasmapause and plasmasphere outer edge location from the Van Allen Probes
<p>This webpage provides access to empirical laws of the plasmapause position and the dense outer edge of the plasasphere position (i.e. location of the 100 #/cc electron density) established from spacecraft charging of the Van Allen Probes (RBSP), using data from Probe B from 26 September 2012 to 16 July 2019. These empirical laws are published of the following article:</p> <p>Ripoll, J.-F., Thaller, S. A., Hartley, D. P., Cunningham, G. S., Pierrard, V., Kurth, W. S., et al. (2022). Statistics and empirical models of the plasmasphere boundaries from the Van Allen Probes for radiation belt physics. Geophysical Research Letters, 49, e2022GL101402. https://doi.org/10.1029/2022GL101402.</p> <p>Please refer to the article and this link if you make any use of the data.</p> <p>We first deliver 3 files which contain the plasmapause position versus a given geomagnetic index, either Kp, AE, or Dst. These data files are MLT-averaged. The filename is Lpp_v_XX_stats_1.txt with XX the index name. </p> <p>We then deliver 3 files which contains the plasmapause position versus a geomagnetic starred index (i.e. a max taken during the last 24 hours for Kp and AE and a min for Dst), either Kp*, AE*, or Dst* for 4 MLT sectors written successively in each file and then for all sectors averaged together. These data files are MLT-dependent for the 4 first blocks and then MLT-averaged in the fifth block. The filename is Lpp_v_XX_star_MLT_stats_2-1.txt with XX the starred index name. </p> <p>The MLT range is given in the last two columns. When the plasmapause location is undetermined (i.e. Lpp = 0 or no value), there is no MLT, so that in the file we just have the total number of points (all real) and not the three values of total, number of real, number of Nans.</p> <p>Similarly, we deliver 3 files which contains the plasmapause position versus a geomagnetic non starred index, either Kp, AE, or Dst for 4 MLT sectors written successively in each file. Filenames have the form "Lpp_v_XX_MLT_stats_1.txt" with XX the name of the index. Figures associated to this data were not given in the article and have been added here with a filename of the form Lpp_by_mlt_XX.pdf with XX the name of the index.</p> <p>Finally, the zip file "RBSP-A Figures and Laws" contains Figures (same format as each figure of Figure 4 in the article) and empirical laws (same format as above) for RBSP-A data (10/2012-04/2016). Being more limited in time, we rather recommend to use RBSP B data. They are provided to confirm both RBSP A and B data agree when statistics are converged.</p>
Generalised Oscillator Strengths for the simulation of EELS spectra, with a broader coverage of high energy and minor edges
<p>This deposit contains a tabulated set of generalised oscillator strengths, which are required to compute the double differential cross sections for the inelastic scattering of fast electrons by atoms, i.e. for the simulation of EELS spectra.</p> <p>These tabulated values are calculated self-consistently within the local density approximation using the exchange correlation potential after Perdew [1]. For this a modified version of a program by Hamann is used [2]. Using this atomic potential the wave function of the ejected free electron is calculated, which is normalised by matching it to spherical Bessel and Neumann functions at large distances from the core [3]. The remaining integral constitutes a spherical Bessel transform. Using the convolution theorem this integral is solved with the fast Fourier transformation routine as done in [4]. A further discussion is available along with the code (see below), or more in-depth (but in German) in the <a href="https://www.uni-muenster.de/imperia/md/content/physik_pi/kohl/abschlussarbeiten/lsegger-bsc-arbeit.pdf">Thesis of L. Segger</a>.</p> <p><strong>This updated version offered here greatly expands the number of available edges</strong>, but is otherwise identical to the earlier version uploaded at <a href="https://zenodo.org/record/6599071">https://zenodo.org/record/6599071</a>.</p> <p> </p> <p>The data offered here is in the GOSH file format, a file format developed for the distribution of such datasets. A description of the file format as used here is included in the file `gosh.md`, while an up to date version can be found at:</p> <p><a href="https://gitlab.com/gguzzina/gosh">https://gitlab.com/gguzzina/gosh</a></p> <p>The code used to compute the GOS is publicly available, along with a discussion of the approach and methods, at:</p> <p><a href="https://github.com/Br0Fi/goscalc">https://github.com/Br0Fi/goscalc</a></p>
CKN Edge AI Dataset for Image inference at the Edge (CEAD)
<p>This synthetic workload models camera device requests for resource constrained inference requests at the Edge for Campaign Knowledge Network evaluation. </p> <p>The workload is a deterministic and pre-ordered set of time windows containing close to 5 million individual data points belonging to 1500 time windows, each time window with a number of requests between 100-1000. Composed of independent inference requests (events), the workload is structured to reflect sudden changes in need as reflected by the user-perceived quality of experience (e.g., accuracy and latency). </p>
Shrub Edge Community and Microclimate Data from Hog Island, VA 2021-2022
This data was collected from 2021-2023 on Hog Island, VA in the swale along the south end of the island where there is active shrub expansion of Morella cerifera (Southern wax myrtle). The microclimate data includes water table depth, soil moisture, soil nutrients, PAR (Photosynthetically active radiation), and temperature in (℃). These data were collected along the shrub edge and in the open grassland plots (n=10). The vegetation community data includes percent cover, stem density, and height measured in a 0.25 m2 plot. The 4 species identified to dominate in both communities were: Andropogon virginicus, Spartina patens, Panicum amarum, and Solidago sempervirens. These species were used for trait analyses. The trait data includes: specific leaf area (SLA), stem specific density (SSD), leaf dry matter content (LDMC), and leaf nutrients.
Experimental data for bulk valley transport and Berry curvature spreading at the edge of flat bands
<p>This dataset was used in our study of bulk valley transport and Berry curvature spreading at the edge of flat bands in twisted double bilayer graphene.</p>
Relations in the Biographical Dictionary of Republican China - Node & Edge lists and Attribute File
<p>This dataset contains the node and edge lists of relations in the BDRC. They are based on the "Relations in the Biographical Dictionary of Republican China - Standardized output" file in this repository. The attributes of the nodes refer only to the main 589 figures in the BDRC.</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.