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21 results for “Community Mapping”
Dataset: Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery
<div> <div> <div> <p>This repository containts the datasets from the article "Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery" (Submitted). The dataset was used in a workflow used to create saltmarsh maps for the Algarve region (South Portugal), focused on the 4 main costal systems of the region: Alvor, Arade, Ria Formosa and Guadiana.</p> <p> </p> <p>For a description of the methodology see the article [link] and Github repo [link].</p> <p> </p> <h1>Repository content</h1> <h2>1. system-masks.zip</h2> <p>Contains 4 <code>geojson</code>files with a polygon which delimits the areas included in the study. The files are named after the respective systems that they delimit. Any region outside of these polygons were not used in the analysis.</p> <p><strong>CRS</strong> - EPSG:4326</p> <h2>2. manual-clean-up-masks.gpkg</h2> <p>Polygons which were manually created to mask out (exclude) pixels which were classified as saltmarsh, but are clearly not.</p> <p>File contains a single layer with 52 polygons and one variable.</p> <p><strong>Variables:</strong></p> <ul> <li>system [<em>string</em>] - Which system the polygon delimits</li> </ul> <h2>3. saltmarsh-training-data.gpkg</h2> <p>Data used for supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, target classes, or predictor classes.</p> <p>File contains a single layer with 2448 points and 18 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the quadrat was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>train [<em>boolean</em>] - Was quadrat used in the train (TRUE) or test (FALSE) stage of model training?</li> <li>ndvi [<em>numerical</em>] - Normalized Difference Vegetation Index, calculated from the satellite image mosaic as (nir – red) / (nir + red).</li> <li>ndwi_high [<em>numerical</em>] - Normalized Difference Water Index estimated from images at high tide, calculated as (green – nir) / (green + nir)</li> <li>ndwi_low [<em>numerical</em>] - Normalized Difference Water Index estimated from images at low tide, calculated as (green – nir) / (green + nir)</li> <li>subtime [<em>numerical</em>] - Fraction of time that a cell is estimated to be submerged in water over one year.</li> <li>coastal_blue [<em>numerical</em>] - Surface reflectance values at 443 nm.</li> <li>blue [<em>numerical</em>] - Surface reflectance values at 490 nm.</li> <li>green_i [<em>numerical</em>] - Surface reflectance values at 531 nm.</li> <li>green [<em>numerical</em>] - Surface reflectance values at 565 nm.</li> <li>yellow [<em>numerical</em>] - Surface reflectance values at 610 nm.</li> <li>red [<em>numerical</em>] - Surface reflectance values at 665 nm.</li> <li>rededge [<em>numerical</em>] - Surface reflectance values at 705 nm.</li> <li>nir [<em>numerical</em>] - Surface reflectance values at 865 nm.</li> </ul> <h2>4. saltmarsh-transect-metadata.csv</h2> <p>Comma-delimited file with information about vegetation sampling transects. Each row represents one transect.</p> <p>File contains 6 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>site [<em>string</em>] - Name of the site within the study system. This was used exclusively to plan transects.</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>date [<em>date yyyy-mm-dd</em>] - Date of transect sampling.</li> <li>notes [<em>string</em>] - Notes taken during transect sampling and which might be relevant to understand data issues.</li> </ul> <h2>5. saltmarsh-vegetation-quadrats.gpkg</h2> <p>Data used for to create vegetation clusters (<code>cluster</code>) and saltmarsh community types (<code>marsh_type</code>). The later was used as the target class in the supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, or presence/absence of species.</p> <p>File contains a single layer with 2448 points and 32 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>distance_from_water <em>[integer]</em> - Distance from start of quadrat, which was the point closes to the water where saltmarsh was found for that transect.</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>Arthrocaulon.macrostachyum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Tripolium.pannonicum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.halimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Cistanche.phelypaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.portulacoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limbarda.crithmoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Juncus.effusus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limoniastrum.monopetalum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Myriolimon.ferulaceum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limonium.vulgare [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Phragmites.australis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Polygonum.maritimum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Puccinellia.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.procumbens [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.europaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Caroxylon.vermiculatum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.fruticosa [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.perennis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Bolboschoenus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Spergularia.bocconei [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Suaeda.vera [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Triglochin.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.montevidensis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> </ul> <h2>6. predicted-map.tif</h2> <p>Geotiff file with a single layer for predicted saltmarsh community. Values are:<br> - <em>no data</em> - Not saltmarsh<br> - <em>1</em> - Low saltmarsh<br> - <em>2</em> - Middle saltmarsh<br> - <em>3</em> - High saltmarsh</p> <p><strong>CRS</strong> - EPSG:32629</p> </div> </div> </div>
Mapping practices of online community management
<p>Results of an online survey conducted during the period 13-31st March 2018. Responses were collected through a Google form; instructions and context were made available on a <a href="http://www.cottica.net/2018/03/13/mapping-online-community-management-practices-can-i-have-a-little-help-with-my-thesis/">web page</a>, the link of which was disseminated through Facebook, Twitter and on the e-mint mailing list (dedicated to professional online community managers on Yahoo. </p> <p>Each row of the file represents one questionnaire; each column represents one question.</p> <ul> <li>The first 9 questions are all of the format "To manage your online community, which of these courses of actions do you take, and how often?". The answers were given on a Likert-4 scale.</li> <li>The 10th question was "Do you want to add any other activity that uses up significant chunks of your community management time?". The answers were given in free form text.</li> <li>The 11th question was "How old is the community you manage? If you manage more than one, refer to the oldest." The answers were given as multiple choice, with three possible choices.</li> <li>The 12th question was "How large is the community you manage? If you manage more than one, refer to the largest.". The answers were given as multiple choice, with five possible choices.</li> </ul> <p>This work is part of my PhD Thesis.</p>
Mapping Jewish Communities of the Byzantine Empire
<p>The aim of the project was to map the Jewish presence in the Byzantine empire using GIS (Geographical Information Systems). All references (published and unpublished) to Jewish communities in the Byzantine Empire were gathered and collated. The data were incorporated in a GIS which will be made freely available to the general public using web maps at www.byzantinejewry.net.</p> <p> </p>
Mobilitätsberichterstattung Erhebungsdaten der qualitative Methoden Community Mapping und Teilnehmende Beobachtungen
<p>Als Forschungsdaten der qualitativen Methoden in der Mobilitätsberichterstattung werden die Interviewmitschnitte der Proband*innen aus den neun Community Mappings und 18 Teilnehmende Beobachtungen zur Verfügung gestellt. Aus Datenschutzgründen wurden die Interviews anonymisiert. Die Dateinamen zeigen die Personenmerkmale, aufgrunddessen die Proband*innen für die Erhebungen ausgewählt wurden. Dies umfasste eine gemischte Gruppen aus den drei Stadtraumtypen in Pankow: Innenstadt, Innenstadtrand und suburbanes Gebiet. Darüber hinaus wurden in allen drei Gebieten Schulkinder beteiligt: Primarstufe in der Innenstadt (PS), Sekundarstufe I im Innenstadtrand (ISS) und Sekundarstufe II (OSZ) im suburbanen Gebiet. Zusätzlich wurden Personengruppen mit Mobilitätseinschränkungen und besonderen Mobilitätsbedarfen beteiligt: Menschen mit Gehbehinderungen in der Innenstadt, Senior*innen im Innenstadtrand und zur Arbeit Pendelnde im suburbanen Gebiet. Über die Dateiendung "TB" wird angezeigt, dass das Interview nach einer Teilnehmenden Beobachtung mit den Proband*innen auf ihren Alltagswegen durchgeführt wurde. Diese wurden meistens auf Hinwegen am Morgen und Rückwegen am Nachmittag durchgeführt, wodurch meistens zwei Audiodateien pro Person entstanden sind, die in der Dateibezeichnung nummeriert wurden. Alle anderen Interviews wurden mithilfe des Community Mappings durchgeführt.</p> <p>Genauere Informationen zur Methodik können dem Leitfaden Mobilitätsberichterstattung und auf der Webseite <a href="http://mobilbericht.de">mobilbericht.de</a> entnommen werden. Die Ergebnisse wurden in der Erstellung des 1. Pankower Mobilitätsberichts aufgearbeitet.</p>
Text-fig. 1. Map of Primorye region with early angiosperm localities near Bolshoy Kamen, Partizansk and Jixi. in An Angiosperm Dominated Herbaceous Community From The Early - Middle Albian Of Primorye, Far East Of Russia
Text-fig. 1. Map of Primorye region with early angiosperm localities near Bolshoy Kamen, Partizansk and Jixi.
Plant Survey of Current Vegetation: MAP OF SONORAN DESERT PLANT COMMUNITY DISTRIBUTION IN THE CAPLTER STUDY AREA, PHOENIX, ARIZONA
This study represents an effort to map the distribution of plant community types across the Central Arizona - Phoenix Long Term Ecological Research (CAP-LTER) site centered in metropolitan Phoenix using Landsat ETM data. Vegetation classification was carried out using field data collected from within the study area describing woody plant species. A system was devised which represented a compromise between providing floristic information and enabling maximum spectral discrimination between community types. Image classification used reference spectra derived from training sites in the field and was carried out on subsets defined by soil surface texture in order to control for the strong background soil signature inherent to arid regions. While groundtruthing revealed that vegetation on clayey soils was mapped to 91% accuracy, other sections produced maps with less accuracy. The results of this study demonstrate that image classification of desert vegetation using only Landsat ETM data is problematic and may not be practical without other supporting data, such as radar imaging.This project attempts to produce a vegetation distribution map across undeveloped parcels of outlying desert wilderness, as well as remnant mountain parks throughout the city, contained within the Central Arizona Phoenix Long Term Ecological Research (CAPLTER) study area. This effort seeks to create the first successful classification map of Sonoran Desert vegetation derived from satellite imagery. The map would also be the first fine-scale map of plant community types in the Phoenix region. The depiction would allow for a calculation of the land area covered by each vegetation class, and which communities are exposed to development pressures. This map potentially provides a basis from which researchers can measure vegetative biomass distribution across the landscape and attempt to incorporate this component into ecological models of energy flows and biogeochemical cycling in the CAP-LTER site. I
Fragebogen und Datensätze zur Evaluation von Community Mapping und Teilnehmende Beobachtung
<p>Der Datensatz umfasst zwei Fragebögen zur Evaluation von Teilnehmenden der qualitativen Erhebungsmethoden Community Mapping sowie Teilnehmende Beoabchtung im Rahmen des <a href="http://mobilbericht.de">Forschungsprojekts MobilBericht </a>zur Erfassung von Mobilitätsbedarfen. Neben der Bewertung aus der Sicht der Teilnehmenden wurde auch ihre Bewertung der kommunalen Verkehrspolitik in Berlin-Pankow erfragt. Die Ergebnisse der Daten werden als Rohdaten in Excel angeboten.</p>
Meta-data and coding for Resilience of Coastal Communities Nexus Evidence Map
<p>Data extracted and coded for studies relevant to the Evidence Map associated with the manuscript "Mapping research at the nexus of resilience, wellbeing and environmental sustainability in UK marine systems"</p>
Mapping trait versus species turnover reveals spatiotemporal variation in functional redundancy and network robustness in a plant‐pollinator community
<p>1. Functional overlap among species (redundancy) is considered important in shaping competitive and mutualistic interactions that determine how communities respond to environmental change. Most studies view functional redundancy as static, yet traits within species – which ultimately shape functional redundancy – can vary over seasonal or spatial gradients. We therefore have limited understanding of how trait turnover within and between species could lead to changes in functional redundancy or how loss of traits could differentially impact mutualistic interactions depending on where and when the interactions occur in space and time.</p> <p>2. Using an Arctic bumblebee community as a case study, and 1,277 individual measures from 14 species over three annual seasons, we quantified how inter- and intraspecific body-size turnover compared to species turnover with elevation and over the season. Coupling every individual and their trait with a plant visitation, we investigated how grouping individuals by a morphological trait or by species identity altered our assessment of network structure and how this differed in space and time. Finally, we tested how the sensitivity of the network in space and time differed when simulating extinction of nodes representing either morphological trait similarity or traditional species groups. This allowed us to explore the degree to which trait-based groups increase or decrease interaction redundancy relative to species-based nodes.</p> <p>3. We found that i) groups of taxonomically and morphologically similar bees turn over in space and time independently from each other, with trait turnover being larger over the season; ii) networks composed of nodes representing species versus morphologically similar bees were structured differently; and iii) simulated loss of bee trait groups caused faster coextinction of bumblebee species and flowering plants than when bee taxonomic groups were lost. Crucially, the magnitude of these effects varied in space and time, highlighting the importance of considering spatiotemporal context when studying the relative importance of taxonomic and trait contributions to interaction network architecture.</p> <p>4. Our finding that functional redundancy varies spatiotemporally demonstrates how considering the traits of individuals within networks is needed to understand the impacts of environmental variation and extinction on ecosystem functioning and resilience.</p>
Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level
<p>Data and codes for the XGBoost model training in our manuscript titled "Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level"</p>
Data from: Community Credit mapping of trust in consumer financial services
<p class="MsoNormal">The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we mapped the landscape of financial services providers and advertisements in low-income neighborhoods in Orange County. Through documenting the presence of alternative financial services (AFS) providers and fringe financial advertisements, alongside traditional financial services providers, we investigated the spatial relationship between these businesses, as well as the factors that create consumers' sense of (dis)trust in them. This data set contains photographs taken as part of this mapping research.</p> <p class="MsoNormal">All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.</p>
Data from: Predictive mapping to identify refuges for plant communities threatened by earthworm invasion
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Mapping trait versus species turnover reveals spatiotemporal variation in functional redundancy and network robustness in a plant‐pollinator community
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Data from: Community Credit mapping of trust in consumer financial services
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COS-1m: the first community-scale open space maps of 31 major cities in China created with the layered occlusion perception model
<p>Correspondence to:<br>Shihong Du<br>Peking University<br>Beijing 100871, China<br>E-mail: shdu@pku.edu.cn</p> <p>COS-1m (Community Open Spaces) offers detailed, high-resolution maps of community-level open spaces across 31 major cities in China. Developed using the latest very high-resolution satellite imagery and a novel Layered Occlusion Perception Model (LOPM), this dataset ensures precise representation with a spatial resolution of 1 meter. In total, the COS-1m dataset covers an area of 46727.46 square kilometers.</p> <p>The COS-1m dataset addresses the critical challenge of layered occlusions in remote sensing imagery by accurately modeling and reconstructing the intricate layered structures of COS. This approach allows for the detailed delineation of occluded ground elements, overcoming the limitations of traditional single-layer mapping methods. The dataset reveals that, on average, 60.51 km² of COS area per city is occluded, constituting 10.18% of the total COS area, which had previously been overlooked.</p> <p>Key features of the COS-1m dataset include:</p> <ul> <li><strong>Four Types of Public Open Space Elements</strong>: Green spaces (1), blue spaces (2), squares (4), and walkways (6).</li> <li><strong>Three Types of Coupled Open Spaces</strong>: waterfront woodlands (3), shaded squares (5), and shaded walkways (7).</li> </ul> <p>The dual-layer mapping technique used in COS-1m provides a comprehensive overview of urban open spaces, capturing the vertical overlaps of various elements such as trees, water bodies, squares, and walkways. This dataset is a significant advancement in COS monitoring technology, filling a critical gap in community-scale Sustainable Development Goal (SDG) assessments. It provides urban planners and policymakers with valuable insights to promote more effective and sustainable urban development.</p> <p>COS-1m achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across the 31 cities, demonstrating its robustness and reliability as a tool for urban space analysis and planning.</p> <p>If you use this dataset, please cite: "Lei, Y., Zhang, X., Xiong, S., Tan, G., & Du, S. (2025). Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities. Remote Sensing of Environment, 316, 114498."</p> <p> </p>
Supplementary material 1 from: Picanço A, Gil A, Rigal F, Borges PAV (2017) Pollination services mapping and economic valuation from insect communities: a case study in the Azores (Terceira Island). Nature Conservation 18: 1-25. https://doi.org/10.3897/natureconservation.18.11523
Supporting information : Explanation note: Description of the landscape disturbance index methodological approach according to Cardoso et al. (2013).
Data from: Ecological genomics meets community-level modeling of biodiversity: mapping the genomic landscape of current and future environmental adaptation
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Data from: High resolution mapping of community structure in three glass sponge reefs (Porifera, Hexactinellida).
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HiPR-FISH Spatial Mapping of Cheese Rind Microbial Communities
<p>This dataset is associated with this HiPR-FISH Spatial Mapping of Cheese Rind Microbial Communities pub from Arcadia Science.</p> <p>HiPR-FISH spatial imaging was used to look at the distribution of microbes within five distinct microbial communities growing on the surface of aged cheeses. Probe design and imaging was performed by Kanvas Biosciences. </p> <p>This dataset includes the following:</p> <ul> <li>For each field of view (roughly 135µm x 135µm; 7 FOVs per each cheese specimen): <ul> <li>A fluorescence intensity image (*_spectral_max_projection.png/.tif).</li> <li>A pseudo-colored microbe-labeled image (*_identification.png/.tif).</li> <li>A data frame contains each identified microbe's identity, position, and size (*_cell_information.csv).</li> <li>A segmented mask for microbiota (*_segmentation.png/.tif)</li> <li>A spatial proximity graph for each species close to each other, showing the spatial enrichment over random distribution (*_spatialheatmap.png).</li> <li>A corresponding data frame used to generate the spatial proximity graph (*_absolute_spatial_association.csv) and dataframe for the average of 500 random shuffles of the taxa (*_randomized_spatial_association_matrix.csv). </li> </ul> </li> <li>For each cheese specimen: <ul> <li>A widefield image with FOVs located on the image (*_WF_overlay.png).</li> </ul> </li> <li>In general: <ul> <li>A png showing the color legend for each species. (ARC1_taxa_color_legend.png)</li> <li>A data frame showing the environmental location of each FOV in the cheese (RIND/CURD) and the location of each FOV relative to FOV 1. (ARC1_Cheese_Map.csv).</li> <li>A vignette showing an example of each cell and its false coloring according to its taxonomic identification (ARC1_detected_species_representative_cell_vignette.png).</li> <li>Sequences used as input in probe design (16S_18S_forKanvas.fasta).</li> <li>A CSV file containing the sequences that belong to each ASV (ARC1_sequences_to_ASVs.csv).</li> <li>Plots of log-transformed counts for each microbe detected across all FOVs, and broken down for each cheese (*detected_species_absolute_abundance.png).</li> <li>CSVs containing pairwise correlation of FOVs based on spatial association (ARC1_spatial_association_FOV_correlation.csv) and microbial abundance (ARC1_abundance_FOV_correlation.csv).</li> <li>Plots of spatial association matrices, aggregated for different cheeses and different locations (RIND vs CURD) (*samples_*loc_relative_spatial_association.png).</li> <li>CSV containing the principle component coordinates for each FOV (ARC1_abundance_FOV_PCA.csv, ARC1_spatial_association_FOV_PCA.csv).</li> <li>CSV containing the mean fold-change in number of edges between each ASV and the corresponding p-value when compared to the null state (random spatial association matrices) (ARC1_spatial_enrichment_significance.csv).</li> </ul> </li> </ul>
LBA-ECO LC-01 City, Community, and Road Maps, Northern Ecuadorian Amazon: 1990-2002
This data set contains the boundaries of the four major cities in the Northern Ecuadorian Amazon, the locations of primary communities in the colonist settlement area, and the locations of the road network, circa 2002. This area in northeastern Ecuador, know as the northern Oriente of Ecuador, borders the Andes Mountains and contains the headwaters of the Amazon River.The road network was originally digitized from 1:50,000 scale topographic maps from 1990. The surface attributes for the majority of the roads have been updated based on later remote sensing and field observations from 1999 and 2002. There are three compressed (*.zip) files with this data set.
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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.