Find research datasets worth reusing

opencc-by-4.0Dec 2018View details →

Figure 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

Figure 1 Map created using SimpleMappr (Shorthouse 2010) that illustrates geolocated specimens for Genus=Cicindela in California as found on iDigBio.

opencc-by-4.0Aug 2024View details →

Georeferenced and cropped "Quarter Inch" (1:253,440) maps of Burma (colonial period)

Georeferenced (to WGS1984) and cropped set of about 400 historic maps of Burma at a scale of 1 inch per four miles (1:253,440) covering most of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1896 and 1951, have been scanned and shared with the public as "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence. Each of the map sheet scans was georeferenced using the Latitude-Longitude corner coordinates in Everest 1830 projection. Those map sheets were cropped, keeping only the map area - to allow a seamless mosaic without the mapframe overlapping adjacent map sheets when several map sheets are put together in a GIS. Those cropped map sheets were projected from Everest 1830 to WGS1984 (EPSG4326) - standard GPS - projection to make them easier to use and combine with other GIS data. Most grid cells in this dataset are covered by 2 or more versions/editions of map sheets - produced in different years or with different map elements (grid type, hill shading, ...).  Those map sheets can be loaded directly in any GIS such as QGIS or ESRI ArcGIS. <ul> <li>The mm_QI_JBv2024_epsg4326 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl. <ul> <li>The mm_QI_JBv2024_epsg4326_kmlLinks contains a KML file for each map sheet facilitating their easy use in Google Earth byt linking them the georeferenced map sheet file located in the mm_QI_JBv2024_epsg4326 folder. </li> <li>The mm_historicQI_EPSG4326.gdb contains an ESRI mosaic datasets to easily use mapsheet in ArcGIS without the need to load each map sheet separately.</li> </ul> </li> <li>The mm_QI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.</li> <li>The mm_historicTopoQI_JBv2024 is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared via the "Old Survey Of India Maps” Community (e.g. to identify new mapsheets should new maps be released)</li> </ul> All georeferenced map scans are based on maps shared as part of the "Old Survey Of India Maps” via Zenodo. Links to each source file can be found in the above mentined excel file and most can be also accessed through the zenodo repository below. <ul> <li><a href="../records/8388423">https://zenodo.org/records/8388423</a> (253k/250k Maps of South Asia, version 7, Published September 28, 2023)</li> </ul> The file naming convention is to first give the number of the 4 degree x 4 degree block followed by the letter (A to P) of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D.  This Number Letter designation is followed by the year of the edition, followed by the map sheet title/name. The original files as shared as part of the "<a href="https://zenodo.org/records/11661876">Old Survey Of India Maps</a>” have been renamed to further standardize the file naming, sometimes correcting them and to make them unique in the case several editions of the same map sheet were available. Lineage: This version (1.01, Upload 2024-08-19) has some file attributes fixed.

dryad28/100

Asimina triloba georeferenced occurrence data and genetic data

Aim: Predictions of species' responses to accelerating global climate change require an understanding of historical range shifts. However, large-scale phylogeographic studies of Eastern North American understory plant taxa are relatively scarce. Here we employ ecological niche modelling and genetic analyses for inference of optimal pawpaw habitat in the past and future. Location: Twenty-six states in the eastern United States Taxon: Asimina triloba (L.) Dunal (Annonaceae) Methods: The present-day niche of Asimina triloba was modelled in Maxent using seven bioclimatic variables, elevation, and location data from field samples and herbarium specimens. To model historically optimal habitats, the present-day model was projected onto rasters of seven bioclimatic variables and elevation representing the last glacial maximum (~22,000 years before present [YBP]) and the mid-Holocene (~6,000 YBP). Predicted habitat suitability for 2070 was also modelled. Additionally, 62 populations were genotyped with nine nuclear microsatellite loci and statistically analyzed. Levels and partitioning of genetic variation within and among populations were estimated within a geographic context. Results: Models indicate that optimal habitat 22,000 YBP was severely restricted to now-submerged Gulf of Mexico and southeastern U.S. coastlines. By 2070 models suggest that optimal habitat will expand substantially northward relative to the present. Species-level genetic diversity (HE = 0.765) was high and genetic structure among populations was moderate (GST = 0.202). Structure indicates that there are two population clusters straddling the Appalachian Mountains. Main conclusions: Models suggest that 22,000 YBP A. triloba was restricted to two major refugia in narrow bands of now-submerged habitat and one small inland refugium in southeastern Alabama and southwestern Georgia. Molecular data are consistent and suggest that the two eastern refugia expanded to give rise to the eastern cluster which is characterized by higher genetic diversity. The Texas/Louisiana refugium likely gave rise to populations in the western cluster, characterized by lower genetic diversity.

opencc-zeroNov 2021View details →

Figure 4 from: Morales Rozo A, Valencia F, Acosta A, Parra J (2014) Birds of Antioquia: Georeferenced database of specimens from the Colección de Ciencias Naturales del Museo Universitario de la Universidad de Antioquia (MUA). ZooKeys 410: 95-103. https://doi.org/10.3897/zookeys.410.7109

Figure 4 - Distribution of specimens through time, showing two peaks in collection activity during the early 1970s and 2000s. Both time periods correspond to the timing of large-scale projects.

Figure 1 from: Morales Rozo A, Valencia F, Acosta A, Parra J (2014) Birds of Antioquia: Georeferenced database of specimens from the Colección de Ciencias Naturales del Museo Universitario de la Universidad de Antioquia (MUA). ZooKeys 410: 95-103. https://doi.org/10.3897/zookeys.410.7109

Figure 1 - Map of Colombia showing the collection localities for all bird specimens held in the MUA (white dots) and all specimens from Antioquia held in other collections (black dots). The upper right inset highlights Colombia within South America and the lower right inset provides a closer look at the distribution of points in Antioquia.

Figure 2 from: Morales Rozo A, Valencia F, Acosta A, Parra J (2014) Birds of Antioquia: Georeferenced database of specimens from the Colección de Ciencias Naturales del Museo Universitario de la Universidad de Antioquia (MUA). ZooKeys 410: 95-103. https://doi.org/10.3897/zookeys.410.7109

Figure 2 - Schematic flowchart of the steps taken to format the collection database according to the Darwin Core and submitting it for public access through GBIF.

publicOct 2021View details →

dryad28/100

Asimina triloba georeferenced occurrence data and genetic data

Open the record for dataset details and reuse information.

nasa28/100

Pre-Delta-X: UAVSAR Georeferenced Channel Maps, Atchafalaya Basin, LA, USA, 2016, V2

This dataset provides spatial data on water channels in the estuary of the Atchafalaya Basin of the Mississippi River Delta of coastal Louisiana. These Level-3 (L3) channel maps were developed from interferograms derived from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data collected on 2016-10-16 (low tides) and 2016-10-17 (high tides). The channel maps define open water paths in hydrodynamic models and are used to evaluate model performance. This is version 2 of this dataset. Data are provided in cloud optimized GeoTIFF format.

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VEMAP 1: Georeferencing

The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetationtype distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. The VEMAP data set includes three georeferencing and three cell area variables. Data Citation: This data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, T. H. Painter, D. S. Schimel, H. H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E. R. Hunt, Jr. 2002. VEMAP Phase I Database, revised. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

opencc-by-4.0Dec 2018View details →

zenodo24/100

Georeferenced genbank sequence accession numbers from GBIF

<blockquote> Request = I am interested in only occurrence records that have both coordinates and accessions. Unfortunately, it appears that while I can specify “Including coordinates” in Advance search, there is no “Including accessions”. </blockquote> GBIF: Custom HiveSQL query on the GBIF occurrence store. <pre><code class="language-sql">SELECT o.associatedSequences, o.gbifID, o.decimalLatitude, o.decimalLongitude, o.kingdom, o.phylum, o.class, o.order_, o.family, o.genus, o.species, o.infraspecificEpithet, o.basisOfRecord, o.v_geodeticdatum, o.coordinateuncertaintyinmeters, o.issue FROM prod_h.occurrence_hdfs o WHERE o.associatedSequences IS NOT NULL AND o.decimalLatitude IS NOT NULL AND o.hasgeospatialissues = false</code></pre> 98 GBIF datasets contributed to this data export. Total records = 9,181,930

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Figure 3 from: Morales Rozo A, Valencia F, Acosta A, Parra J (2014) Birds of Antioquia: Georeferenced database of specimens from the Colección de Ciencias Naturales del Museo Universitario de la Universidad de Antioquia (MUA). ZooKeys 410: 95-103. https://doi.org/10.3897/zookeys.410.7109

Figure 3 - Distribution of specimens in the collection according to families.

opencc-byOct 2021View details →

zenodo24/100

Old barn (georeferenced scan)

This barn was scanned using a similar DIY direct georeferencing 3D-scanner as shown in this youtube-video: https://www.youtube.com/watch?v=WE_fD6qsLKQ. If you are interested in this tech, please take a look at the video and it's description. Model shown in the video here: https://skfb.ly/6XUtD. In this scan also raster camera tracking was used, giving initial estimates for camera locations/orientations when taking pictures to be used as textures. Faking: * Roof was generated in point cloud-level because I couldn't reach up enough with the hand held scanner. * Few outliers/errors were removed from the mesh and point clouds. * Minor editing of the rasters. Data used to generate this model (relevant GNSS and lidar-data, point clouds, rasters and MeshLab project): https://github.com/GNSS-Stylist/3D_Scan_Barn (about 3.6 GB). When the location of the RTK-base used when scanning is known, this (including points in the scan) could be placed to it's original location/orientation with estimated accuracy of few cm. Source: Objaverse 1.0 / Sketchfab

Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100

The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.

Georeferenced Population Datasets of Mexico (GEO-MEX): Population Database of Mexico

The Population Database of Mexico contains geographically referenced population data for Mexican states, municipalities and localities from the 1990 Mexican population and housing census. The data include population by gender and age group for approximately 83.7% of the Mexican population. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Georeferenced Population Datasets of Mexico (GEO-MEX): Raster Based GIS Coverage of Mexican Population

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

Georeferenced Population Datasets of Mexico (GEO-MEX): Urban Place Time-Series Population of Mexico

The Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Georeferenced Population Datasets of Mexico (GEO-MEX): GIS of Mexican States, Municipalities and Islands

The GIS of Mexican States, Municipalities and Islands consists of attribute and boundary data for 1990. The attribute data include population, language, education, literacy, housing Units and land cover classification from the 1990 Mexican population and housing census. The boundary data associated with the United States-Mexico border are consistent with the U.S. Census Bureau TIGER95 data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).