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85 results for “georeferencing”
Image of all georeferenced data mediated through GBIF
<p>A 512px image of the GBIF world data.</p>
UAS Trajectory Model Dynamics at different flight heights: An In-depth Analysis of PPK Georeferencing Results for an Urban Area
<p>In-depth analysis of the PPK georeferencing results when using three different Continuously Operating Reference Station (CORS) stations and one local base station.</p>
A georeferenced rRNA amplicon database of aquatic microbiomes from South America
<p>Here we present the samples and data used in Metz & Huber et al. (2022) to construct <strong>µSudAqua[db].</strong></p> <p><strong>µSudAqua[db]</strong> contains over 866 georeferenced samples with contextual environmental information manually revised. For its integration and validation, we constructed a curated database (<strong>µSudAqua[db.sp]</strong>) using the DADA2 pipeline (https://github.com/microsudaqua/usudaquadb). It comprised ~60% of the total georeferenced samples of the <strong>µSudAqua[db]</strong>.</p> <p>Here we provide five files compressed in a zip file .</p> <ul> <li>The <em>microsudaqua_metadata_Vx </em>presents the metadata associated with the samples used to build the <strong>µSudAqua[db] </strong>database. For samples included in the <strong>µSudAqua[db.sp]</strong> database, the number of high-quality reads and Amplicon Sequences Variants (ASVs) defined is also indicated.</li> <li>The archive <em>microsudaqua_rawtable_Vx </em>harvests the<em> </em>number of reads in each sample (<strong>µSudAqua[db.sp]</strong>).</li> <li>The archive <em>microsudaqua_rawseqs_Vx</em> harvests the nucleotide sequences of each ASV (<strong>µSudAqua[db.sp]</strong>).</li> <li>The archive <em>microsudaqua_rawtaxonomy_blast_silva132_nr99_Vx </em>harvests taxonomic classification of each ASV (<strong>µSudAqua[db.sp]</strong>).</li> <li>The archive <em>microsudaqua_bacteria_filtered_50reads_with_taxonomy_Vx</em> harvests the Bacterial filtered ASVs, with more than 50 reads in at least three samples (<strong>µSudAqua[db.sp]</strong>).</li> </ul> <p>Further information regarding data usage and processing is available in the @microsudaqua GitHub (https://github.com/microsudaqua/usudaquadb)</p> <h3><strong>Version history</strong></h3> <ul> <li>February 2025 / version V.1.1: microsudaqua_data_V1.1_Feb2025.zip <ul> <li>The metadata file has been updated: microsudaqua_metadata_V1.1_Feb2025.txt</li> </ul> </li> </ul> <ul> <li>July 2022 / version V.1.0: microsudaqua_data_V1.0_July2022.zip.</li> </ul> <p> </p>
Georeferenced phylogenetic analysis of a global collection of wild and cultivated Citrullus species
<p>The geographical origin of watermelon (Citrullus lanatus) remains debated. While a first hypothesis suggests the center of origin to be west Africa, where a sister endemic species C. mucosospermus thrives, a second hypothesis suggests north-eastern Africa where the white-fleshed Sudanese Kordophan melon is cultivated. In this study, we infer biogeographical and haplotype genealogy for C. lanatus, C. mucosospermus, C. amarus, and C. colocynthis using non-coding cpDNA sequences (trnT-trnL and ndhF-rpl32 regions) from a global collection of 135 accessions. In total, we identified 38 haplotypes in C. lanatus, C. mucosospermus, C. amarus, and C. colocynthis; of these, 21 were found in Africa and 17 appear endemic to the continent. The least diverse species was C. mucosospermus (5 haplotypes) and the most diverse was C. colocynthis (16 haplotypes). Some haplotypes of C. mucosospermus were nearly exclusive to West-Africa, and C. lanatus and C. mucosospermus shared haplotypes that were distinct from those of both C. amarus and C. colocynthis. The results support previous findings C. mucosospermus to be the closest relative to C. lanatus (including subsp. cordophanus). West Africa, as a center of endemism of C. mucosospermus, is an area of interest in the search of the origin of C. lanatus. This calls for further historical and phylogeographical investigations and wider collection of samples in West and North-East Africa.</p>
Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps
<p>Data and code supplement to our article revised submission to PeerJ.</p> <p> </p> <p>The file is compressed using standard zip. The uncompressed size is about 50 GB. There is a readme.md in the archive, which explains the structure of the contents.</p> <p> </p> <p>Abstract:</p> <p>We use natural language processing (NLP) to retrieve location data for cheilostome bryozoan species (text-mined occurrences [TMO]) in an automated procedure. We compare these results with data combined from two major public databases (DB): the Ocean Biogeographic Information System (OBIS), and the Global Biodiversity Information Facility (GBIF). Using DB and TMO data separately and in combination, we present latitudinal species richness curves using standard estimators (Chao2 and the Jackknife) and range-through approaches. Our combined DB and TMO species richness curves quantitatively document a bimodal global latitudinal diversity gradient for extant cheilostomes for the first time, with peaks in the temperate zones. 79% of the georeferenced species we retrieved from TMO (N = 1408) and DB (N = 4549) are non-overlapping. Despite clear indications that global location data compiled for cheilostomes should be improved with concerted effort, our study supports the view that many marine latitudinal species richness patterns deviate from the canonical latitudinal diversity gradient (LDG). Moreover, combining online biodiversity databases with automated information retrieval from the published literature is a promising avenue for expanding taxon-location datasets.</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
Supplementary material 1 from: Caudullo G, Barredo JI (2019) A georeferenced dataset of drought and heat-induced tree mortality in Europe. One Ecosystem 4: e37753. https://doi.org/10.3897/oneeco.4.e37753
The dataset contains 293 tree mortality occurrences induced by heat and/or drought in the period 1970—2017. The geographical domain of the dataset covers the EU, Switzerland, Norway and the Balkan countries. Tree mortality occurrences in the dataset were sourced from scientific and peer-reviewed literature as described in the paper. The dataset is georeferenced using latitude and longitude in decimal degrees (World Geodetic System: WGS84).
Redefining floristic zones in the Korean Peninsula using high-resolution georeferenced specimen data and self-organizing maps
<p>The use of biota to analyze the distribution pattern of biogeographic regions is essential to gain a better understanding of the ecological processes that cause biotic differentiation and biodiversity at multiple spatiotemporal scales. Recently, the collection of high-resolution biological distribution data (e.g., specimens) and advances in analytical theory have led to the quantitative analysis and more refined spatial delineation of biogeographic regions. This study was conducted to redefine floristic zones in the southern part of the Korean Peninsula and to better understand the eco-evolutionary significance of the spatial distribution patterns. Based on 309,333 distribution data of 2,954 vascular plant species in the Korean Peninsula, we derived floristic zones using self-organizing maps. We compared the characteristics of the derived regions with those of historical floristic zones and ecologically important environmental factors (climate, geology, and geography). In the clustering analysis of the floristic assemblages, four distinct regions were identified, namely, the cold floristic zone (Zone I) in high-altitude regions at the center of the Korean Peninsula, cool floristic zone (Zone II) in high-altitude regions in the south of the Korean Peninsula, warm floristic zone (Zone III) in low-altitude regions in the central and southern parts of the Korean Peninsula, and maritime warm floristic zone (Zone IV) including the volcanic islands Jejudo and Ulleungdo. Totally, 1,099 taxa were common to the four floristic zones. Zone IV showed the highest abundance of specific plants (those found in only one zone), with 404 taxa. Our study improves floristic zone definitions using high-resolution regional biological distribution data. It will help better understand and re-establish regional species diversity. In addition, our study provides key data for hotspot analysis required for the conservation of plant diversity.</p>
Georeferenced phylogenetic analysis of a global collection of wild and cultivated Citrullus species
Open the record for dataset details and reuse information.
Redefining floristic zones in the Korean Peninsula using high-resolution georeferenced specimen data and self-organizing maps
Open the record for dataset details and reuse information.
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.
Figure 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
Figure 2 This specimen record is an example from the University of California Collection Network Symbiota Portal. The large image is an edit of the record to include a medium size version of the image for easier viewing in this article. The portal software is open source and it is freely available for reuse through the Symbiota GitHub repository. The image is an example of a specimen record that includes an image of the specimen with label data. The image is contributed by the UCSB Invertebrate Zoology Collection at the Cheadle Center for Biodiversity and Ecological Restoration. The usage rights for the image is Creative Commons 0 (public domain).
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