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152 results for “biodiversity information”
Data licences and organization type of contributors to the Global Biodiversity Information Facility as of 19 January 2016
<p>Data from the Global Biodiversity Information Facility were extracted using R (version 3.2.0) on 9 July 2015 using the rgbif package (version 0.9.0) (Chamberlain, S., Ram, K., Barve, V. & Mcglinn, D. (2015) Package ‘rgbif’: Interface to the Global 'Biodiversity' Information Facility 'API' http://cran.r-project.org/web/packages/rgbif/rgbif.pdf). The ‘rights’ statements was extracted for all occurrence datasets with one or more observations. A total of 12,458 datasets were extracted, but only about 11% of the datasets have an explicit data-useage-rights statement at the dataset level. However, some datasets use the occurrence level ‘rights’ and ‘accessRights’ fields. To extract these data the rights information was obtained from the first record of each dataset where a rights statement was missing at the dataset level.</p> <p>The datasets were categorized into 13 different types depending on the origin of the observations.</p> <ol> <li>Biodiversity Information Facility or data centre</li> <li>Botanical Garden or Herbarium</li> <li>Citizen science</li> <li>Commercial</li> <li>Data publisher</li> <li>Educational</li> <li>Government</li> <li>Museum</li> <li>Network</li> <li>Parks Authority or Nature Reserve</li> <li>Research institution</li> <li>Society</li> <li>Foundations</li> </ol>
A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)
<p>Publication date:<br> 2022-12-06T07:37:19-06:00</p> <p><br> A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> ---</p> <p>Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth.</p> <p>To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world.</p> <p>At time of writing (6 Dec 2022), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1].</p> <p>This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/current/simple.txt.gz to help facilitate access and improve performance of the creation of search indexes.</p> <p>Pre-process steps currently include:<br> 1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name</p> <p><br> Contents<br> ---</p> <p>README:<br> this file</p> <p>repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone.</p> <p>repackage-gbif-backbone.log:<br> log of repackaging of GBIF Simple Backbone.</p> <p>backbone-current-simple.txt.gz:<br> original GBIF backbone archive</p> <p>gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name </p> <p>gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz</p> <p>gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id</p> <p>gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz</p> <p>References<br> ---</p> <p>[1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2022-12-06.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18.</p> <p><br> Hash URIs<br> ---<br> This publication includes the following content uris:</p> <p>hash://sha256/82d5f2153b4533322692d95eeb18b0f103e1b2297e38bd9ea935b07ba86cd7d5<br> hash://sha256/50c155f66efb2efba0b8b624f8541e81cbe16a701d420a5073791fb993f72919<br> hash://sha256/9cd7d4c91292d86c726210446cd6fe45602505a7c0ea3b7c4f4f481f85f193ad (uncompressed)<br> hash://sha256/f950dde25cce9ba9cce67caa1c68ce0c99cb31fe2dc9658fec85a987d9f31654<br> hash://sha256/f21c6b90f17c6083fcfb4853f3c581dcc2aadd291691fa128392a205321f420b (uncompressed)<br> hash://sha256/5e0a4d1d2d1cccbdcc6b2c9831fafe61c54eb055f2d13ec40d9ac161889b9f89<br> hash://sha256/f6e477133d0585706ee5522963b204200cb3cd198f011cbf62be0fa8519763b5 (uncompressed)<br> </p>
Global Biodiversity Information Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130
<p>The Global Biodiversity Information Facility (GBIF) indexes thousands of biodiversity datasets from Natural History Collections, citizen science initiatives (e.g., iNaturalist, eBird), and other sources. As part of the index process, GBIF associates at least two identifiers with indexed records: a record id (aka gbifID) and a dataset id (aka dataset key). These ids are central to do lookup, reference data, and package interpreted data products.</p> <p>This publication contains an exhaustive list of GBIF IDs and ids associated by their data providers as derived from:</p> <p>GBIF.org (01 March 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.pk3trq</p> <p>The resource (size: ~260GB) provided by GBIF had content id hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 and was used to generate the resource included in this publication using</p> <pre><code class="language-bash">preston cat 'zip:hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97!/0015281-230224095556074.csv'\ | cut -f 1,2,3,37,38,39\ | gzip\ > gbifid.tsv.gz </code></pre> <p>with the content id of gbifid.tsv.gz (size: ~35GB) being hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8 .</p> <p>the first 10 lines of gbifid.tsv.gz as extracted via</p> <pre><code>preston cat --remote https://zenodo.org/record/7789866/files,https://linker.bio hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8\ | gunzip\ | head</code></pre> <p>are:</p> <pre><code>gbifID datasetKey occurrenceID institutionCode collectionCode catalogNumber 2997162320 c71c8000-9fc7-422c-804a-ce6abe751771 3399442 CEPEC CEPEC CEPEC00109669 2997162309 c71c8000-9fc7-422c-804a-ce6abe751771 2733085 CEPEC CEPEC CEPEC00000818 2997162317 c71c8000-9fc7-422c-804a-ce6abe751771 2733086 CEPEC CEPEC CEPEC00000888 2997162313 c71c8000-9fc7-422c-804a-ce6abe751771 3399443 CEPEC CEPEC CEPEC00109744 2997162306 c71c8000-9fc7-422c-804a-ce6abe751771 2733087 CEPEC CEPEC CEPEC00000889 2997162316 c71c8000-9fc7-422c-804a-ce6abe751771 3399440 CEPEC CEPEC CEPEC00109605 2997162324 c71c8000-9fc7-422c-804a-ce6abe751771 2733088 CEPEC CEPEC CEPEC00000890 2997162308 c71c8000-9fc7-422c-804a-ce6abe751771 3399441 CEPEC CEPEC CEPEC00109615 2997162303 c71c8000-9fc7-422c-804a-ce6abe751771 2733089 CEPEC CEPEC CEPEC00000891</code></pre> <p>Note that at time of writing, the html resource associated with the occurrence id 2997162320, and data set key c71c8000-9fc7-422c-804a-ce6abe751771 (extracted from of the first data row example above) are available via:</p> <p>https://gbif.org/occurrence/2997162320</p> <p>and</p> <p>https://gbif.org/dataset/c71c8000-9fc7-422c-804a-ce6abe751771</p> <p>respectively.</p> <p>This resource was initially created to help integrate with Bionomia (https://bionomia.net) to help associate people identifiers provided by bionomia to their original records via their GBIF ids. Bionomia re-uses GBIF records ids as a way to define links between records and the people (e.g., curators, collectors, identifiers) that worked on them. </p> <p>In other words, this resource provides a versioned translation table from the GBIF data universe (as defined by GBIF record ids, and dataset keys) to the data collections that exist (and evolve) independent of it. </p> <p>Note that the resource identified by hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 was not included in this publication it was too big (260GB) to fit. You may be able to retrieve the resource from its original location at https://api.gbif.org/v1/occurrence/download/request/0015281-230224095556074.zip .</p>
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Supplementary material 1: Global Biodiversity Information Facility: Taxa and Records from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
All records in GBIF with taxonomic ranks (kingdom, phylum, class, order, and species), basis of record (e.g., preserved specimen), and count of records, exported from GBIF on 7 December 2014.
Fig. 5 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?
Fig. 5: Das Suchportal des Botanik-Knotens von GBIF-Deutschland, einer der mehreren im Internet verfügbaren Zugangspunkte zu den Daten des GBIF-Netzwerks.
Fig. 2 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?
Fig. 2:Tatenflüsse über das Internet im GBIF-Netzwerk zwischen Nutzer, Suchportal und Datenlieferant.
Fig. 3 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?
Fig. 3: Die Zuordnung der Daten aus dem relationalen Datenschema der Sammlungsdatenbank zu den ABCD-Elementen wird im Mapping festgelegt und ist in einer komfortablen Oberfläche mit dem Internet- Browser möglich.
Fig. 1 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?
Fig. 1: Die Wrapper-Software umgibt die bestehenden Sammlungsdatenbanken mit einer zusätzlichen Abstraktionsschicht und bietet so eine definierte Schnittstelle zwischen den existierenden Datenbanksystemen und den GBIF-Suchportalen.
Fig.1 in Die Global Biodiversity Information Facility (GBIF) - Struktur, Aufgaben und Ziele
Fig.1: Das Knotensystem GBIF Deutschland und seine Anbindung an GBIF International. Die Daten fliessen aus den Teilprojekten in die Datenbanksysteme der einzelnen Knoten, denen ein BioCASE-Wrapper aufgesetzt ist, der auf dem ABCD-Datenmodell basiert. Damit ist es möglich, alle angebundenen Daten über das Datenportal von GBIF International im Internet abzurufen bzw. verfügbar zu machen. GBIF International stellt ausserdem die Wrapper-Software DiGIR, welche auf dem Darwin Core 2 aufbaut, zur Verfügung. Einzelne Teilprojekte, wie z.B. DIG mit BIODAT im Knoten Evertebraten I, setzen eigene Datenbanklösungen ein und fungieren daher als direkte GBIF Datenprovider. Die Angaben entsprechen dem Stand Anfang April 2005.
A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) - 2021-11-26
<p>A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> ---</p> <p>Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth.</p> <p>To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world.</p> <p>At time of writing (18 Aug 2021), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1].</p> <p>This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/backbone-current-simple.txt.gz to help facilitate access and improve performance of the creation of search indexes.</p> <p>Pre-process steps currently include:</p> <p>1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name</p> <p><br> Contents<br> ---</p> <p>README:<br> this file</p> <p>repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone.</p> <p>backbone-current-simple.txt.gz:<br> original GBIF backbone archive</p> <p>gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name</p> <p>gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz</p> <p>gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id</p> <p>gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz</p> <p>References<br> ---</p> <p>[1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2021-08-18.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18.</p> <p><br> Hash URIs<br> ---<br> This publication includes the following content uris:</p> <p>repackage-gbif-backbone.sh:<br> hash://sha256/073ac5490252c4ccbbd4f516d391faebe62c9fde9e4d75ae870441a86c382527</p> <p>backbone-current-simple.txt.gz:<br> hash://sha256/15cbfc038e666356af27248935f79e408ed51fd8c0b49a668fed8dbf72591502<br> hash://sha256/1f78788a4a046dcbcf1e36c7658a1e333ca60e7586a372238d58b938d91fde51 (uncompressed)</p> <p>gbif-backbone-by-name.tsv.gz:<br> hash://sha256/6e11ae9961a9498b60d4bdeb489d6c1f5da9c2732310edaecdc79bd287b79ef4<br> hash://sha256/934ce05dbd067abb209168bd1d9389f122d051e1b7374b5d757a12e86f8da9a5 (uncompressed)</p> <p>gbif-backbone-by-id.tsv.gz:<br> hash://sha256/c434c7d3622421b17dadcd119391b32a66edee59f484d4cab924d92fd17713e2<br> hash://sha256/e2cf9116a21966315b0482d391052223e21c8e916ae0c097dfd37bed017b815b (uncompressed)</p>
Fig. 4 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?
Fig. 4: Anzahl der im GBIF-Netzwerk verfügbaren Datensätze (Belege und Beobachtungen) in Millionen.
Supplementary information for: A continuous-score occupancy modeling framework for incorporating uncertain machine learning output in autonomous biodiversity surveys
<p><span>Ecologists often study biodiversity by evaluating species occupancy and the relationship between occupancy and other covariates. Occupancy models are now widely used to account for false absences in field surveys and to reduce bias in estimates of covariate relationships. Existing occupancy models take as inputs binary detection/non-detection observations of species at each visit to each site. However, autonomous sensing devices and machine learning models are increasingly used to survey biodiversity, generating a new type of observation record (i.e., continuous-score data) that reflects the model's confidence a species is present in each autonomously sensed file, instead of binary detection/non-detection data. These data are not directly compatible with traditional binary occupancy modeling methods.</span></p> <p><span>Here, we develop a new occupancy model that models continuous scores on a visit level as a Gaussian mixture, combining a distribution of scores for files that do contain the species of interest and a distribution of scores for files that do not. The model takes as input continuous scores for each autonomously sensed and classified file, along with an optional small number of binary, manually verified detection and non-detection annotations.</span></p> <p><span>We present a simulation study that shows that over a range of empirically realistic parameters, our model outperforms traditional occupancy models that are based on binary annotation alone. We also apply this new model to an empirical case study using data generated from five machine learning classifiers applied to autonomous acoustic recordings gathered in the eastern United States.</span></p> <p><span>Because our occupancy model generalizes allowable input data beyond binary observations, it is particularly well-suited to the increasing volume of machine learning classified data in ecology and conservation.</span></p>
Microevolutionary processes in a foundation tree inform macrosystem patterns of community biodiversity and structure
<p class="MDPI17abstract"><span>Despite an increased focus on multiscale relationships and interdisciplinary integration, few macroecological studies consider the contribution of genetic-based processes to landscape-scale patterns.<strong> </strong>We tested the hypothesis that tree genetics, climate, and geography jointly drive continental-scale patterns of community structure, using genome-wide SNP data from a broadly distributed foundation tree species (<a><em>Populus fremontii</em></a></span><span class="MsoCommentReference"><span> </span></span><span>S. Watson) and two dependent communities (leaf-modifying arthropods and fungal endophytes) spanning southwestern North America. Four key findings emerged: (1) Tree genetic structure was a significant predictor for both communities; however, the strength of influence was both scale- and community-dependent. (2) Tree genetics was the primary driver for endophytes, explaining 17% of variation in continental-scale community structure, whereas (3) climate was the strongest predictor of arthropod structure (24%). (4) Power to detect tree genotype<a><span>—</span></a></span><span>community phenotype associations changed with scale of genetic organization, increasing from individuals to populations to ecotypes, emphasizing the need to consider nonstationarity (i.e., changes in the effects of factors on ecological processes across scales) when inferring macrosystem properties. Our findings highlight the role of foundation tree species as drivers of macroscale community structure and provide macrosystems ecology with a theoretical framework for linking fine- and intermediate-scale genetic processes to landscape-scale patterns. Management of genetic diversity harbored within foundation species is a critical consideration for conserving and sustaining regional biodiversity.</span></p>
Species occurrence test set for "Using ChatGPT with Confidence for Biodiversity-Related Information Tasks" (hash://sha256/fd1c8a5304508981a02481a31dc88ad429356a26dff6f89d24fe47fc60a275a4)
Species occurrence test set described in "Using ChatGPT with Confidence for Biodiversity-Related Information Tasks", presented at TDWG 2023 in SYM05
Microevolutionary processes in a foundation tree inform macrosystem patterns of community biodiversity and structure
Open the record for dataset details and reuse information.
Supplementary information for: A continuous-score occupancy modeling framework for incorporating uncertain machine learning output in autonomous biodiversity surveys
Open the record for dataset details and reuse information.
Supplementary material 2 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Use cases were collected using a number of methods to maximise responses. Lead authors of papers published using data accessed via the Atlas of Living Australia (ALA) were contacted and asked to contribute their research data use cases, and a number of papers describing fitness for use determination were sent to the ALA Data Quality group. Fitness for use and quality check information from these papers were extracted and transferred to the use case library. These are the results of those surveys.
Supplementary material 4 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Description and specifications for the tests following the conventions of the Fitness For Use Framework. This supplement is a copy of https://github.com/tdwg/bdq/blob/master/tg2/core/TG2_tests.csv as of commit 941e774 2019-Aug-20.
Supplementary material 3 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Counts of occurrence records in 2019-04-15 snapshot of GBIF-mediated data that fit the three categories of expected responses for each of the event date-related validation tests.
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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)
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DANDI Archive for NWB datasets
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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.