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62 results for “geospatial data”
Geospatial data from: Identifying opportunity hot spots for reducing the risk of wildfire-caused carbon loss in western US conifer forests
<p>The geospatial dataset includes raster and vector data for visualizing the spatial distribution of risk of wildfire-caused carbon loss in Peeler et al. 2023. Raster data evaluate carbon exposure, sensitivity, and vulnerability at the pixel-level across western US carbon forests. Vector data aggregate pixel-level findings into project area and fireshed spatial units to identify target geographies (or "opportunity hot spots") where proactive forest management could reduce the greatest risk from wildfire to carbon. Vector data also identifies firesheds in which proactive forest management could simultaneously reduce the risk from wildfire to carbon and human communities.</p>
Data from: Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List
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Geospatial data from: Identifying opportunity hot spots for reducing the risk of wildfire-caused carbon loss in western US conifer forests
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Data from: A data-driven geospatial workflow to map species distributions for conservation assessments
<p>We developed a geospatial workflow that refines the distribution of a species from its extent of occurrence (EOO) to area of habitat (AOH) within the species range map. The range maps are produced with an inverse distance weighted (IDW) interpolation procedure using presence and absence points derived from primary biodiversity data (GBIF and eBird hotspots respectively). Here we provide sample data to run the geospatial workflow for nine forest species across Mexico and Central America.</p>
Data from: Breeding system and geospatial variation shape the population genetics of Triodanis perfoliata
<p><span>Both intrinsic and extrinsic forces work together to shape connectivity and genetic variation in populations across the landscape. Here we explored how geography, breeding system traits, and environmental factors influence the population genetic patterns of <em>Triodanis perfoliata</em>, a widespread mix-mating annual plant in the contiguous US. By integrating population genomic data with spatial analyses and modeling the relationship between breeding system and genetic diversity, we illustrate the complex ways in which these forces shape genetic variation. Specifically, we used 4,705 single nucleotide polymorphisms to assess genetic diversity, structure, and evolutionary history among 18 populations. Populations with more obligately selfing flowers harbored less genetic diversity (π: R<sup>2</sup> = 0.63, P = 0.01, n = 9 populations), and we found significant population structuring (F<sub>ST</sub> = 0.48). Both geographic isolation and environmental factors played significant roles in predicting the observed genetic diversity: we found that corridors of suitable environment appear to facilitate gene flow between populations, and that environmental resistance is correlated with increased genetic distance between populations. Last, we integrated our genetic results with species distribution modeling to assess likely patterns of connectivity among our study populations. Our landscape and evolutionary genetic results suggest that <em>T. perfoliata</em> experienced a complex demographic and evolutionary history, particularly in the center of its distribution. As such, there is no singular mechanism driving this species' evolution. Together, our analyses support the hypothesis that breeding system, geography, and environmental variables shape the patterns of diversity and connectivity of <em>T. perfoliata</em> in the US. </span></p>
Supplementary data- Detailed Geogenic Radon Potential Mapping Using Geospatial Analysis of Multiple Geo-variables. A Case Study from a High-Risk Area in SE Ireland.
<p>Supplementary Data: all the data produced and used, statistical analysis, mathematical proofs diagnostic tests, regression models and additional charts and tables</p> <p>Detailed Geogenic Radon Potential Mapping Using Geospatial Analysis of Multiple Geo-variables. A Case Study from a High-Risk Area in SE Ireland. </p>
Supplementary material 7 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.
Supplementary material 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.
Supplementary material 2 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
This document shows just the questions we asked the applicants who applied to participate in this Georeferencing for Research Use workshop. We used a Google Form to deliver these questions and collect responses. It is both an application and serves as our pre-workshop survey.
Supplementary material 8 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Summary of desired future workshop topics that were listed by participants on the last day of the workshop.
Supplementary material 6 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Summary of topics to be covered in an ideal workshop as identified by workshop applicants in the workshop call for participation. We incorporated as many as possible that also fit our scope.
Supplementary material 5 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Questions we asked in the Georeferencing for Research Follow Up Survey done 3 months after the workshop.
Supplementary material 4 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Three months after the workshop, participants were surveyed to assess what workshop-related knowledge and materials were being used and disseminated to others. This document summarized data collected in this particular survey.
Supplementary material 1 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Darwin Core Archive file downloaded from the iDigBio portal for use in the Georeferencing for Research Use workshop. Total 25,429 records, accessed on 2016-08-29. Collections contributing to the record set are listed in the archive records.citation.txt file. Dataset GUID: a69d1541-4726-465d-84ad-50c7ed556eee
Data from: Phylogeography of var gene repertoires reveals fine-scale geospatial clustering of Plasmodium falciparum populations in a highly endemic area
Plasmodium falciparum malaria is a major global health problem that is being targeted for progressive elimination. Knowledge of local disease transmission patterns in endemic countries is critical to these elimination efforts. To investigate fine-scale patterns of malaria transmission, we have compared repertoires of rapidly evolving var genes in a highly endemic area. A total of 3680 high quality DBLα sequences were obtained from 68 P. falciparum isolates from ten villages spread over two distinct catchment areas on the north coast of Papua New Guinea (PNG). Modeling of the extent of var gene diversity in the two parasite populations predicts more than twice as many var gene alleles circulating within each catchment (Mugil=906; Wosera=1094) than previously recognized in PNG (Amele=369). In addition, there were limited levels of var gene sharing between populations, consistent with local parasite population structure. Phylogeographic analyses demonstrate that while neutrally evolving microsatellite markers identified population structure only at the catchment level, var gene repertoires reveal further fine-scale geospatial clustering of parasite isolates. The clustering of parasite isolates by village in Mugil, but not in Wosera was consistent with the physical and cultural isolation of the human populations in the two catchments. The study highlights the micro-heterogeneity of P. falciparum transmission in highly endemic areas and demonstrates the potential of var genes as markers of local patterns of parasite population structure.
Data from: Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography
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Data from: Zoogeographical regions and geospatial patterns of phylogenetic diversity and endemism of New World bats
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Data from: A data-driven geospatial workflow to map species distributions for conservation assessments
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Data from: Phylogeography of var gene repertoires reveals fine-scale geospatial clustering of Plasmodium falciparum populations in a highly endemic area
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Data from: Breeding system and geospatial variation shape the population genetics of Triodanis perfoliata
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.