Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
20
datasets available to search
ShareScore release 0.9.0
Dataset results
20 results for “Point occurrence”
Harmonized Tree Species Occurrence Points for Europe
<p>This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.</p> <p>The <em>RDS </em>file is created from an sf-object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p><strong>The code producing this data set is <a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">publicly available on GitLab.</a></strong></p> <p>Data sets were last updated in September 2021.</p> <p>Variables:</p> <ul> <li><strong>id</strong> = unique point identifier</li> <li><strong>easting</strong> = x coordinate</li> <li><strong>northing </strong>= y coordinate</li> <li><strong>country </strong>= ISO country code</li> <li><strong>species </strong>= Latin species name</li> <li><strong>genus </strong>= genus name</li> <li><strong>scientific_name </strong>= long species name</li> <li><strong>gbif_taxon_key </strong>= taxon key from GBIF</li> <li><strong>gbif_genus_key </strong>= genus key from GBIF</li> <li><strong>taxon_rank </strong>= species or genus</li> <li><strong>year </strong>= year of observation</li> <li><strong>accessed_through </strong>= database through which data was accessed (GBIF, LUCAS, EU-Forest)</li> <li><strong>dataset_info </strong>= data set name (individual sub-data-set)</li> <li><strong>citation </strong>= DOI citation of the individual data set</li> <li><strong>license </strong>= distribution license</li> <li><strong>location_accuracy </strong>= spatial accuracy of observation (meters)</li> <li><strong>flag_location_issue </strong>= known location issues present</li> <li><strong>flag_date_issue </strong>= known date issues present</li> <li><strong>eoo </strong>= Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)</li> <li><strong>dbh </strong>= Diameter Breast Height (only recorded for observations from the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>))</li> <li><strong>lc1 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS</a> land cover type 1 (only recorded for observations from LUCAS data)</li> <li><strong>lc2 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS </a>land cover type 2 (only recorded for observations from LUCAS data)</li> <li><strong>landmask_country </strong>= land mask overlay 30 meters (NA = not on land)</li> <li><strong>corine </strong>= <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018">CORINE 2018</a> land cover type (extracted from the 100 meter raster data set)</li> <li><strong>nightlights</strong> = <a href="https://eogdata.mines.edu/download_dnb_composites.html">light pollution</a> observed by VIIRS (proxy for remoteness / distance to human structures)</li> <li><strong>canopy_height </strong>= <a href="https://zenodo.org/record/4057883#.X3sx4-2xW9I">canopy height</a> derived from GEDI waveform LiDAR point data</li> <li><strong>natura_2000 </strong>= Natura 2000 site code (if a point falls inside a protected area (<a href="https://www.eea.europa.eu/data-and-maps/data/natura-11">GIS-layer</a>) this variable contains the site identification code; all sites can be explored on an <a href="https://natura2000.eea.europa.eu/">interactive map</a>)</li> <li><strong>freq_location </strong>= number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)</li> <li><strong>geometry </strong>= point geometry in ETRS89 / LAEA Europe</li> </ul> <p>See <strong><a href="https://docs.google.com/spreadsheets/d/1WM0BIaVEKxTsCISEaF76RJ8F1iWiZlDfuyQCBiF2Sxw/edit?usp=sharing">this detailed documentation</a></strong> for more insights into each variable and individual GBIF data set citations.</p> <p>If you would like to know more about the creation of this data set, see</p> <ol> <li>the R-Markdown documenting the process (<a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">GitLab repository</a>)</li> <li>the talk at OpenGeoHub Summer School 2020 (<a href="https://www.youtube.com/watch?v=5HhmLGcqXLs&list=PLXUoTpMa_9s0Ea--KTV1OEvgxg-AMEOGv&index=40">Youtube</a>)</li> </ol> <p>Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.</p> <p> </p> <p>This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (<a href="https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095">https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095</a>).</p>
European Ivies (Hedera L., Araliaceae) Point Occurrence Database with Taxonomic Certainty
<p>We present two databases and six spatial layers recording biodiversity information of the six species of ivies (<em>Hedera </em>L., Araliaceae) native to W Europe (<em>Hedera azorica, H. canariensis, H. helix, H. hiberncia, H. iberica, H. maderensis)</em>. Each database covers the entire native distribution of each species. Therefore, the databases document the distribution and occurrence of all the European <em>Hedera </em>taxa except for <em>H. pastuchovii </em>subsp.<em> cypria</em> which is a restricted endemic of the south-west of the island of Cyprus. </p> <ul> <li>The first database (TaxRev) includes taxonomic, geographic and habitat information from the morphological revision of 2,276 individuals from 1,280 populations. 866 of the records also included point-occurrence data. This database represents the entire native distribution and the morphological variation of each species.</li> <li>The second database (MixOcc) includes the spatial-point occurrence of the six species across their entire native distribution ranges. This database was compiled with the 880 records from the TaxRev database (records with high taxonomic certainty, as they all were examined by the taxonomist of the genus) plus 2,372 records from curated online databases selected from the European regions with low expected taxonomic uncertainty (C and E Europe and the Macaronesian Islands). As a result the database have high taxonomic quality (certainty and coverage) and good geographical coverage for Europe at a large-scale except for France and Ireland.</li> <li>The uploaded files related to the TaxRev database are as follows:</li> </ul> <p>Hedera_TaxRevDatabase_Field description: a cvs file with the description of the 71 variables included in the database</p> <p>Hedera_TaxRevDatabase_Records: a cvs file with the database (71 variables, 1,280 records)</p> <ul> <li>The uploaded files related to the MixOcc database are as follows:</li> </ul> <p>Hedera_MixOccDatabase_Field description: a cvs file with the description of the 11 variables included in the database</p> <p>Hedera_MixDatabase_Records: a cvs file with the database (11 variables, 3,252 records)</p> <p>Finally, we also upload 20 layers including the point-occurrence maps obtained from the MixOcc database. Six species maps (one per species), five additional maps of <em>H. canariensis</em> (one per island), eight additional maps of <em>H. azorica</em> (one per island) and a combined map including the six species. In all of them, we distinguish the records from individuals morphologically reviewed by the taxonomist of the genus and those obtained from online repositories and not reviewed by the taxonomist:</p> <ul> <li>Hedera azorica_MixOccDatabase_Map</li> <li>Hedera_azorica_map_Corvo</li> <li>Hedera_azorica_map_Faial</li> <li>Hedera_azorica_map_Flores</li> <li>Hedera_azorica_map_Graciosa</li> <li>Hedera_azorica_map_Pico</li> <li>Hedera_azorica_map_Santa Maria</li> <li>Hedera_azorica_map_Sao Jorge</li> <li>Hedera_azorica_map_Sao Miguel</li> <li>Hedera_azorica_map_Terceira</li> <li>Hedera canariensis_MixOccDatabase_Map</li> <li>Hedera_canariensis_map_El Hierro</li> <li>Hedera_canariensis_map_Gran Canaria</li> <li>Hedera_canariensis_map_La Gomera</li> <li>Hedera_canariensis_map_La Palma</li> <li>Hedera_canariensis_map_Tenerife</li> <li>Hedera helix_MixOccDatabase_Map</li> <li>Hedera hibernica_TaxRevDatabase_Map</li> <li>Hedera iberica_TaxRevDatabase_Map</li> <li>Hedera maderensis_MixOccDatabase_Map</li> <li>Hedera_MixOccDatabase_Map</li> </ul> <p>The records which allow us to improve geographic coverage without compromising taxonomic certainty. The databases and the resulting spatial layers have high taxonomic and geographic certainty and a good geographic coverage for ivies in Europe.</p>
Amazona obscured occurrences, background points, and environmental data
<p><strong>Aim:</strong> Introduced species offer insight on whether and how organisms can shift their ecological niches during translocation. The genus <em>Amazona</em> offers a clear test case, where sister species Red-crowned (<em>A. viridigenalis</em>) and Lilac-crowned Parrots (<em>A. finschi</em>) have established breeding populations in southern California following introduction via the pet trade from Mexico where they do not coexist. After establishment in the 1980s, introduced population sizes have increased, with mixed species flocks found throughout urban Los Angeles. Here, we investigate the differences between the environmental conditions of the native and introduced ranges of these now co-occurring species.</p> <p><strong>Location:</strong> Southern California and Mexico.</p> <p><strong>Method</strong>s: Using environmental data on climate and habitat from their native and introduced ranges, we tested whether Red-crowned and Lilac-crowned Parrots have divergent realized niches between their native ranges, and whether each species has significantly shifted its realized niche to inhabit urban southern California. We also analyzed data from Texas and Florida introductions of Red-crowned Parrots for comparative analysis.</p> <p><strong>Results: </strong>There are significant differences in the native-range niches of both parrot species, but a convergence into a novel, shared environmental niche into urban southern California, characterized by colder temperatures, less tree cover, and lower rainfall. Texas and Florida Red-crowned Parrots also show evidence for niche shifts with varying levels of niche conservatism through the establishment of somewhat different realized niches.</p> <p><strong>Main Conclusions: </strong>Despite significant niche shifts, introduced parrots are thriving, suggesting a broad fundamental niche and an ability to exploit urban resources. Unique niche shifts in different U.S. introductions indicate that <em>Amazona</em> parrots can adapt to diverse environmental conditions, with cities offering a resource niche and the timing of introduction playing a crucial role. Cities can potentially serve as refugia for threatened parrot species, but the risk of hybridization between species emphasizes the need for ongoing monitoring and genetic investigations.</p>
Text-fig. 2. Nymphaea sp. from the Miocene Clarkia Lake flora, Locality P-33. a: Photograph of the fossil leaf. b: Sketch of leaf showing the salient features of shape, basal lobes and margin, eccentric insertion point of the abaxial petiole, and primary actinodromous venation. Dashed lines represent torn edge of lamina; dotted line is outline of right basal lobe. Line drawing by P. Martin Sander. Scale bar applies to both photo and drawing. in First Water Lily, A Leaf Of Nymphaea Sp., From The Miocene Clarkia Flora, Northern Idaho, Usa: Occurrence, Taphonomic Observations, Floristic Implications
Text-fig. 2. Nymphaea sp. from the Miocene Clarkia Lake flora, Locality P-33. a: Photograph of the fossil leaf. b: Sketch of leaf showing the salient features of shape, basal lobes and margin, eccentric insertion point of the abaxial petiole, and primary actinodromous venation. Dashed lines represent torn edge of lamina; dotted line is outline of right basal lobe. Line drawing by P. Martin Sander. Scale bar applies to both photo and drawing.
Text-fig. 2. Eospondylus primigenius (STÜRTZ) Bundenbach, Eschenbach-Bocksberg quarry, Lower Devonian, Lower Emsian (Zlichovian), Hunsrück Slate,, NM S 4764, x 3. Specimen with ventral arm coiling. The specimen is on its dorsum in slate with all five rays curled ventrally inward toward mouth area on underside of disk. Barely visible are tips of two jaws; slightly exposed are proximal parts of rays in oral view extending outward from disk. The location of abrupt ventral bending of rays is indicated by emergence from slate of five rays in aboral view that point inward toward buried disk. Based on ventral bending of rays and intimate association with crinoids Eospondylus has been interpreted as stratigraphic first occurrence of Order Euryalida, which contains epizoic gorgonocephalid and euryalid basket-stars of modern oceans. This status is rejected using new evidence from isolated vertebrae. [Photo by Alexander Glass]. in Isolated Ossicles Of The Family Eospondylidae Spencer Wright, 1966, In The Lower Devonian Of Bohemia (Czech Republic) And Correction Of The Systematic Position Of Eospondylid Brittlestars (Echinodermata: Ophiuroidea: Oegophiurida)
Text-fig. 2. Eospondylus primigenius (STÜRTZ) Bundenbach, Eschenbach-Bocksberg quarry, Lower Devonian, Lower Emsian (Zlichovian), Hunsrück Slate,, NM S 4764, x 3. Specimen with ventral arm coiling. The specimen is on its dorsum in slate with all five rays curled ventrally inward toward mouth area on underside of disk. Barely visible are tips of two jaws; slightly exposed are proximal parts of rays in oral view extending outward from disk. The location of abrupt ventral bending of rays is indicated by emergence from slate of five rays in aboral view that point inward toward buried disk. Based on ventral bending of rays and intimate association with crinoids Eospondylus has been interpreted as stratigraphic first occurrence of Order Euryalida, which contains epizoic gorgonocephalid and euryalid basket-stars of modern oceans. This status is rejected using new evidence from isolated vertebrae. [Photo by Alexander Glass].
Fig. 1. Occurrence points for 12 in Projected Climate Change Effects On Nuthatch Distribution And Diversity Across Asia
Fig. 1. Occurrence points for 12 Sitta species and one Tichodroma species used in this study. Sitta solangiae and S. victoriae each had fewer than 5 occurrence points and were excluded from the analysis.
Text-fig. 10. List of Siwalik murine rodents. a) Stratigraphic occurrence of fossil localities of the Potwar Plateau, Pakistan. b) Murine species recovered from the Siwaliks, updated from Kimura et al. (2015). All localities but DP 13 have Y as prefix. in Early Late Miocene Murine Rodents From The Upper Part Of The Nagri Formation, Siwalik Group, Pakistan, With A New Fossil Calibration Point For The Tribe Apodemurini (Apodemus/Tokudaia)
Text-fig. 10. List of Siwalik murine rodents. a) Stratigraphic occurrence of fossil localities of the Potwar Plateau, Pakistan. b) Murine species recovered from the Siwaliks, updated from Kimura et al. (2015). All localities but DP 13 have Y as prefix.
Puma concolor occurrence points (filtered data)
<p>Puma concolor occurrence points (duplicates removed) in Canada until December 2021. Used in Maxent habitat suitability model (performed in R).</p>
Amazona obscured occurrences, background points, and environmental data
Open the record for dataset details and reuse information.
Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models
<p><span>Digital point-occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time-consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. This study examined the extent to which cleaned data from six pipelines using data cleaning tools (e.g., the GBIF web application, different R packages) affect downstream species distribution models. In addition, we assessed how the pipeline data differ from expert data. From 13,889 North American <i>Ephedra</i> observations in GBIF, the pipelines removed 31.7% to 62.7% false-positives, invalid coordinates, and duplicates, leading to data sets that included between 9,484 (GBIF application) and 5,196 records (manual-guided filtering). The expert data consisted of 703 thoroughly handpicked records, comparable to data from field studies. Although differences in the record numbers were relatively large, stacked species distribution models (sSDM) from the pipelines and the expert data were strongly related (mean Pearson's <i>r</i> across the pipelines: 0.9986, versus the expert data: 0.9173). The ever-stronger correlations resulted from occurrence information that became increasingly condensed in the course of the workflow (from individual occurrences to collectivized occurrences in grid cells to predicted probabilities in the sSDMs). In sum, our results suggest that the <i>R</i> package-based pipelines reliably identified invalid coordinates. In contrast, the GBIF-filtered data still contained both spatial and taxonomic errors. However, major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of expert taxonomic knowledge. We conclude that application-filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high-quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.</span></p>
Bird species occurrence point data(2015-2019)
<p>The data comes from BirdReport.cn(http://www.birdreport.cn/),including the numbers and geographic location of six bird species (<em>Falco tinnunculus, Ardea alba, Aix galericulata, Dendrocopos canicapillus, Streptopelia chinensis, Turdus mandarinus</em>) in 2015-2019.</p>
Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models
Open the record for dataset details and reuse information.
Fossil and occurrence points of Rhinocerotidae, Elephantidae, Equus, and Camelus in China from the Pleistocene to the present
Open the record for dataset details and reuse information.
Environmental variables and occurrence points of Solenopsis geminata
<p>Occurrence points of Solenopsis geminata was obtained from GBIF which is global database website. </p> <p>Optimal data was constructed by spatial filtering and removing duplicate coordinates.</p> <p>Environmental variables (bio1~19) was obtained from Worldclim.</p> <p>Optimal variables was selected by considering correlation analysis and species characteristics. </p>
FIGURE 1 in The Wild Silkmoths (Lepidoptera: Bombycoidea: Saturniidae) of Colombia: a database of occurrence points and taxonomic checklist
FIGURE 1. Distribution records for Saturniidae moths in Colombia. Warm colours indicate areas with higher densities of occurrences records, while colder colours and white areas represent a lower number of records and lack thereof, respectively.
Deep learning for the occurrence of tipping points: training data
<p>This data accompanies the manuscript by Chengzuo Zhuge et al. “Deep learning for the occurrence of tipping points” and the Github repository <a href="https://github.com/zhugchzo/dl_occurrence_tipping">https://github.com/zhugchzo/dl_occurrence_tipping</a>. It contains the model time series data that are used to train the deep learning algorithm. The directory <br>increased_bifurcation contains 150k time series (50k Fold, Hopf, Transcritical respectively) with parameter increasing and the directory decreased_bifurcation contains 150k time series (50k Fold, Hopf, Transcritical respectively) with parameter decreasing. The directory pitchfork contains 100k time series (50k supercritical and subcritical pitchfork respectively) with parameter increasing. Both directories contain files labels.csv and groups.csv which provide numbers corresponding to the labels (The tipping points) and groups (Training, Validation, Test) for each time series respectively.</p>
Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence
Open the record for dataset details and reuse information.
Environmental variables and occurrence points of Solenopsis geminata
Open the record for dataset details and reuse information.
Anacanthotermes ochraceus occurrence points
<p>Anacanthotermes ochraceus occurrence points </p>
FIGURE 2 in The Wild Silkmoths (Lepidoptera: Bombycoidea: Saturniidae) of Colombia: a database of occurrence points and taxonomic checklist
FIGURE 2. Distribution of surveyed Colombian Saturniidae voucher specimens, including type specimens (i.e., holotypes, paratypes, and lectotypes), in 13 national (grey) and 18 international (black) biological collections examined in this study (Table S1).
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.