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138 results for “Geospatial”
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
Multiple climatic factors co-regulate the accumulation and geospatial distribution of bufogenins in Bufo bufo gargarizans Cantor poison glands
<p>The eight main climatic factors included TEM_Avg, TEM_Max, TEM_Min, RHU_Avg, PRE_Time, PRE_Max, GST_Max and GST_Min. Data of related climatic factors were obtained from China Meteorological Administration.</p>
reproducible geospatial scientific workflows
<p>Additional material for submission to JSS.</p> <p>Manuscript ID: TJSS-2018-0191.r1</p>
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.
Dataset for "Geospatial segmentation of high-resolution photovoltaic production maps for Switzerland", Frontiers Energy
<p>## Notes<br> 1. The following tif files contain the Plain-Of-Array irradiation on south-facing solar panels with different tilts {20,30,40,50,60,70} and during different seasons {summer,winter}.<br> 2. The file pattern is poa\_{season}\_tilt\_{tilt}.tif<br> 3. Read Section 2.2 of the research article for details on how these files were created. <br> 4. These values are POA and need to be converted to energy production via an efficiency factor. This factor was chosen to be a fixed value of 20\% as noted in Section 2.2 of the research article.</p>
Data from: Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography
Open the record for dataset details and reuse information.
Data from: Zoogeographical regions and geospatial patterns of phylogenetic diversity and endemism of New World bats
Open the record for dataset details and reuse information.
Data from: A data-driven geospatial workflow to map species distributions for conservation assessments
Open the record for dataset details and reuse information.
Data from: Phylogeography of var gene repertoires reveals fine-scale geospatial clustering of Plasmodium falciparum populations in a highly endemic area
Open the record for dataset details and reuse information.
Data from: Breeding system and geospatial variation shape the population genetics of Triodanis perfoliata
Open the record for dataset details and reuse information.
FIGURE 1 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 1. Map of Australia with major clusters retrieved from the analysis.
FIGURE 8 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 8. The interim zoogeographic provinces of Australia.
Merged HLS2 (L30), ERA5-Land inputs and sample predictions of land surface temperature for the IBM granite-geospatial-land-surface-temperature model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and ERA5-Land data following the UTM CRS:WGS84. It has been assembled for predicting land surface temperature with a fine-tuned granite geospatial foundation model developed by IBM Research. In addition, we include sample predictions of land surface temperature derived from this model. Please see https://huggingface.co/ibm-granite/granite-geospatial-land-surface-temperature for more information on data preparation and model use.</p> <p><strong>HLS:</strong></p> <p>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <h4><strong>ERA5-Land:<br></strong></h4> <p>Copernicus Climate Change Service, Climate Data Store, (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p> <h4><strong>LST-predictions:</strong></h4> <p>These predictions of land surface temperature are derived from the IBM granite-geospatial-land-surface-temperature model and have been made available for Abidjan, Côte d’Ivoire and Johannesburg, South Africa for the period 2013-2023. </p> <h4>Attribution</h4> <p>Copernicus programme:</p> <p>Contains modified Copernicus Climate Change Service information [2024]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Data</strong></p> <p>Muñoz Sabater, J., Comyn-Platt, E., Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N., Cagnazo, C., Cucchi, M. (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p>
Quantification of spatio-temporal variation of aquaculture area in Satkhira, Bangladesh: Using Geospatial and social survey data
<p>This data shows the NDWI and MNDWI processed data of satkhira</p>
Figure 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
Figure 4 Initial expertise (color of the bar) vs final confidence (y-axis) after the GRU workshop for participants responding to final survey. Example for how to interpret this graphic: the blue color bar at the top indicates that before the workshop roughly 50% of respondents said their knowledge of GEOLocate was "neither high nor low" but after the workshop these same respondents selected "much higher" for their knowledge of GEOLocate.
Figure 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
Figure 3 An illustrative example of the two methods of uncertainty capture when georeferencing specimens. Method A, or polygon, creates a shape around the river (in blue). Method B, or point-radius, creates a circle of uncertainty around the origin. The illustration is based on output from GeoLocate software (Rios 2018) for both polygon and point-radius.
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.