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Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Deliverable 2.2: Biodiversity indicator database for focus regions
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km<sup>2 </sup>resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism<sup> </sup>index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a valuable first step in describing human-induced changes in vegetation and biodiversity patterns across both South America and Africa.</p>
A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled: Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study Iliana Chollett1, D. Ross Robertson2 1 Sea Cottage, Louisburgh, Co. Mayo, Ireland 2 Smithsonian Tropical Research Institute, Balboa, Panamá
<p><strong>A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled:</strong></p> <p><strong><em> </em></strong></p> <p><strong><em>Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study</em></strong></p> <p> </p> <p> Iliana Chollett, D. Ross Robertson</p> <p><strong> </strong></p> <p><strong> </strong></p> <p><strong>Database Authors: D Ross Robertson and Ernesto Peña, Smithsonian Tropical Research Institute, Panamá</strong></p> <p><strong> </strong></p> <p>This set of six databases contains georeferenced location records from six sources as described below.These six sources provided georeferenced records of occurrence of fishes found in the Greater Caribbean study area (6-33<sup>0</sup> N, 57-100<sup>0</sup> W). Each occurrence record consists of a species name and associated latitude and longitude. Databases included in the comparisons made here are from five major online aggregators. Since their content overlaps to some extent, and OBIS, iDigBio and FishNet collaborate with GBIF, their data might be expected to produce similar biogeographic patterns. STRI includes a curated compendium of data from those five aggregators, enriched with data from many additional sources.</p> <p> </p> <p>Only reef-associated fish species were included in the present analysis. These mostly represent demersal species known to occur on hard bottoms (coral, rock and oyster substrata), but also include species living on rubble, sand and vegetated bottoms within and around the immediate fringes of reefs, and pelagic species regularly found on reefs. All exotic and non-resident species and species other than reef-associated fishes were excluded from all databases prior to comparisons. Non-residents were defined as otherwise widespread species only rarely seen in the study area. Shore-fishes, including what are generally regarded as reef fishes, include those found in the waters of continental and insular shelves, i.e. between 0-200m. Reef-fish assemblages dominated by shallow-water taxa extend down to that depth in the study area (Baldwin <em>et al.</em> 2018). We used the shelf edge as a breakpoint and excluded records in areas deeper than 200m, identifying those areas using the General Bathymetric Chart of the Oceans (Kapoor, 1981; GEBCO Compilation Group, 2019).</p> <p> </p> <p>Before the analyses, for all databases, duplicate records were deleted. Subsequently, records in the Pacific or on land were deleted. We used the Global Self-consistent, Hierarchical, High-resolution Geography Database (Wessel & Smith, 1996) to identify these areas. The spatial distribution of species-records in each database is shown in Figure 1 of the publication.</p> <p><strong> </strong></p> <p><strong>Global Biodiversity Information Facility </strong>(GBIF, https://www.gbif.org/): GBIF is an international network and research infrastructure aimed at providing open access to data about all types of life on earth. GBIF works through participant nodes using common standards and open-source tools that enable them to share information. Data from among the 49,000+ datasets hosted by GBIF that were used here range from those on museum specimens collected since the 18th century, to published scientific checklists, to curated local checklists produced by trained science sources such as the Atlantic and Gulf Rapid Assessment Program (https://www.agrra.org/),to geotagged smartphone photos (that act as vouchers allowing verification) shared by amateur and scientific naturalists through iNaturalist (https://www.inaturalist.org/), to unvouchered, unverified and unverifiable observation records from untrained divers such as those contributing to DiveBoard (http://www.diveboard.com). GBIF data are standardized in Darwin Core format. GBIF data were obtained from a polygon of the region of study and subject to taxonomic review and selection after downloading. GBIF data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the GBIF portal, https://www.gbif.org/, on or about 2019-05-19).</p> <p> </p> <p><strong>Ocean Biogeographic Information System</strong> (OBIS, <a href="https://obis.org/">https://obis.org/</a>): OBIS is a global open-access data and information clearing-house on marine biodiversity (OBIS, 2019) that was adopted as a project of the Intergovernmental Oceanographic Data and Information Exchange of the Intergovernmental Commission of UNESCO . Its range of sources is similar to that of GBIF. OBIS hosts data from organizations or programs that join it as one of 13 “nodes”, and harvest the data from the IPT (Integrated Publishing Toolkit), where providers publish their data. The IPT is developed and maintained by the GBIF, and OBIS is a major contributor of marine data to GBIF. Data are standardized in Darwin Core format. OBIS data were obtained for the region of study by downloading data on each family, then retaining only data inside the study area, which were then subject to taxonomic review and selection (accessed through the OBIS portal, https://obis.org/, on or about 2019-05-19).</p> <p> </p> <p><strong>Integrated Digitized Biocollections</strong> (iDigBio, https://portal.idigbio.org/portal/search): iDigBio is sponsored by the a US National Science Foundation and run by the University of Florida that provides digital data from public, non-federal, US collections. Data are standardized in a Darwin Core format, and provided “as is”. IDigBio joined the GBIF network in 2017. IDigBio records were downloaded from a polygon of the region of study and subject to taxonomic review and selection (accessed through the iDigBio portal, https://portal.idigbio.org/portal/search, on or about 2019-05-19).</p> <p> </p> <p><strong>FishNet2 </strong>(http://www.fishnet2.net/): FishNet2 is a collaborative effort that aggregates data on fish collections around the world to share and distribute data on specimen holdings from ~75 museums, universities and other institutions. FishNet2 distributes data in Darwin Core, and data are provided “as is”. FishNet2 is part of the network VerNet, which has contributed to GBIF since 2013 and became part of IDigBio in 2016. While FishNet2 has made substantial efforts to georeference location-record data it hosts, many hosted records still lack georeferencing. FishNet2 data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the Fishnet2 Portal, www.fishnet2.org, 2019-05-19).</p> <p> </p> <p><strong>FishBase</strong> (<a href="http://www.fishbase.org/">http://www.fishbase.org</a>): FishBase is a global biodiversity information system supervise by a consortium of nine non-USA international institutions, and hosts data on fin fishes and elasmobranchs (Froese & Pauly, 2009). Information presented in FishBase is extracted from the scientific literature, reports and museum or aggregator (GBIF) databases, and standardized by a team of specialists. Data from Fishbase were downloaded for the following ecosystems: Caribbean Sea, Gulf of Mexico, Southeast U.S. Continental Shelf, Atlantic Ocean, Sargasso Sea and Bermuda, and subject to taxonomic review and selection after downloading (2019-05-19).</p> <p> </p> <p><strong>Smithsonian Tropical Research Institute</strong> (STRI; <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>): The STRI database was compiled by DRR and Ernesto Peña at STRI’s Naos Marine Laboratory, and represents about 15 years accumulation of curated data (see below) from the following sources: data downloaded at roughly two year intervals from the five aggregators; data from online databases of various museums that supply aggregators (data directly downloaded from a museum sometimes differs from that available in an aggregator from the same museum), including the Swedish Museum of Natural History, the American Museum of Natural History, the Natural History Museum of Denmark, the Gulf Coast Research Laboratory, the Colombian Museum of Natural Marine History, the United States National Museum, and the United States Geological Survey; data from national aggregators of Colombia (Sistema de Información Sobre Biodiversidad de Colombia (https://sibcolombia.net/), and Sistema de Información Ambiental Marina de Colombia, https://siam.invemar.org.co/), Mexico (La Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO; http://www.conabio.gob.mx/informacion/gis/), and Costa Rica (Museo de Zoologia de la Universidad de Costa Rica, http://museo.biologia.ucr.ac.cr/); verified (by DRR) underwater photographs of fishes taken at known locations; peer reviewed publications containing location information (species descriptions; taxonomic revisions of species, genera and families; regional and local checklists); fisheries reports; digital tagging data for species such as elasmobranchs; diving surveys and collections of local faunas by DRR (e.g. Robertson et al. 2019). In addition selected data from two sources that collect species lists at sites scattered throughout the Greater Caribbean are incorporated: from the Atlantic and Gulf Rapid Reef Assessment program (AGRRA, https://www.agrra.org/: Kramer & Lang, 2003) and from trained citizen scientists who contribute data on fishes to the Reef Environmental Education Foundation’s database (REEF: Pattengill-Semmens & Semmens, 2003). The bibliographic module (https://biogeodb.stri.si.edu/caribbean/en/library) of Robertson & VanTassel (2019) contains ~1700 publications linked to species names, among them the publications from which location data were extracted.</p> <p> </p> <p>Data from the aggregators is presented “as is” and the aggregators themselves do not do data curation. Duplicates (and occasionally triplicates and quaduplicates) of the same museum record often are included from multiple sources (e.g. the original museum source, derivative checklists, an aggregator), sometimes with slightly different georeferenced coordinates. Data available in one year may subsequently disappear from an aggregator, and different data may be available for the same species under different names (e.g. the old and new names when a species is reassigned to another genus). Errors, sometimes large errors (Robertson, 2008), are common in aggregator data, from museums as well as other sources, and longstanding errors can seem to take on a perpetual existence. For example the damselfish <em>Abudefduf saxatilis </em>is a common and widespread inhabitant of tropical reefs on both sides of the Atlantic, but does not naturally occur outside that ocean. Despite the fact that its taxonomic status and range were resolved ~30 y ago (e.g. see Allen, 1991) museum data presented by the all five aggregators that contributed to the multi-source database used in this study currently (December 10, 2019) show large numbers of records of this species throughout the entire tropical Indo-Pacific, as well as across its native range in the Atlantic. Since many of the databases accumulating on aggregators are derivative (lists derived from records and from other derivative lists) it will become increasingly difficult to eliminate such errors as corrections to data in primary sources do not automatically propagate through the chain of usage by different databases. Due to increasing limitations on resources for taxonomic work, museums themselves have difficulty dealing with errors in specimen identity and location, and old specimens become unidentifiable, specimens never get returned when loaned out, or simply vanish, and entire collections can get destroyed by hurricanes or fires, or get dumped when museums close or experience a major change in mission. Georeferenced location data on fish distributions in the neotropics (and presumably most other areas) hosted by aggregators, particularly GBIF and OBIS, which take data from a broad range of source types, might best be described as messy, and the significant potential for errors in location records and an inability to verify records always needs to be taken into account when incorporating data from aggregators, primary museum sources, and analog sources.</p> <p> </p> <p>Data considered for inclusion in the STRI database were screened as follows to exclude questionable records. Data from two databases hosted by OBIS and GBIF were excluded entirely due to lack of reliability: BioGoMx (https://www.gulfbase.org/project/biodiversity-gulf-mexico-biogomx-database) and Diveboard (http://www.diveboard.com). The only REEF data used were from “expert” REEF recorders on readily identifiable species that are unlikely to be confused with similar species (e.g. data for some genera of sparids, gerreids, labrisomids and gobies that include various sympatric species with very similar appearances, were not used). After data from aggregators and museum sources were combined into a single database duplicate records were filtered out by rounding all records to three decimal places and eliminating duplicates, a process that inevitably deleted some valid records as well as duplicates. The sizes of the databases and abundance of such duplicates precluded individual manual exclusion. Finally, all location data for each species were revised by DRR by examining the distribution of its georeferenced coordinates overlayed on a digital map of the current known distribution range of that species (for such range information see Carpenter & De Angelis, 2002; Ebert <em>et al.</em>, 2013; Last <em>et al.</em>, 2016; Robertson & Van Tassell, 2019; IUCN Redlist species accounts for most species considered here: https://www.iucnredlist.org/search). Such revision took into account any recent modifications to taxonomy and distributions due to new data and new publications, or as a result of discussions between DRR and experts in the taxonomy of particular species or genera. Source information of many individual questionable records provided by aggregators with the hosted data was inspected to try and assess their validity. Records thought likely to be erroneous were deleted. Those included inexplicable records lacking adequate documentation located well outside the known distribution range, and records in unlikely habitats (e.g. on land for marine species; in deep water for shallow-water species). This revision process reduced the number of records by about 30%.</p> <p> </p> <p>Data from the five individual aggregator databases that are used in the comparisons described here were all downloaded from their online portals during May, 2019. However, data from those five aggregators that were incorporated in the STRI database were downloaded in March 2017, with data from other sources described above added to the STRI database intermittently between then and May 2019, when the entire dataset was curated as described above. Hence the five individual aggregator databases analyzed in this study undoubtedly contain data not included in the version of the STRI database used in the present analyses.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p> </p> <p>Data acquisition and construction of the STRI database was supported by funds from STRI, the Smithsonian Marine Science Network, the Smithsonian Publications Fund, the Smithsonian’s Deep Reef Observation Project, the National Geographic Society, the IUCN Red List program, the Harte Research Institute, and CONABIO. We thank REEF and AGRRA for supplying species-location records, various people for taxonomic and location-record information used to construct that database (principal among them C Baldwin, S Brandl, K Conway B Frable, T Menut, T Munroe, R Robins, L Tornabene, J Van Tassell and B Victor), and hundreds of citizen-scientist submarine photographers whose images (see <a href="https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists">https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists</a>) acted as vouchers for location records.</p> <p> </p> <p><strong>References</strong></p> <p><strong> </strong></p> <p>Allen, G.R. (1991) <em>Damselfishes of the World</em>. Mergus, Melle, 271 p.</p> <p>Baldwin, C.C., Tornabene, L. & Robertson, D.R. (2018) Below the mesophotic. <em>Scientific Reports</em>, 8, 4920.</p> <p>Carpenter, K.E. (Ed) (2002) <em>The living marine resources of the Western Central Atlantic.</em> Vols 1-3, FAO, Rome, 2127 p.</p> <p>Ebert, D.A., Fowler, S., Compagno, L. (2013) <em>Sharks of the World: a fully illustrated guide</em>. Wild Nature Press, Plymouth. 528 p.</p> <p>GEBCO Compilation Group (2019) GEBCO 2019 Grid (doi:10.5285/836f016a-33be-6ddc-e053-6c86abc0788e).</p> <p>Kapoor, D.C. (1981) General bathymetric chart of the oceans (GEBCO). <em>Marine Geodesy</em>, 5, 73–80.</p> <p>Kramer, P.R. & Lang, J.C. (2003) Appendix one: The Atlantic and Gulf Rapid Reef Assessment (AGRRA) Protocols: Former Version 2. 2. <em>Atoll Research Bulletin</em>, 496, 611–624.</p> <p>Last, P. R., White, W.A., de Carvalho, M.R., Séret, B., Stehmann, F.W., & Naylor, J.P. (2016). <em>Rays of the World</em>. CSIRO, Clayton. 790 p.</p> <p>Pattengill-Semmens, C.V. & Semmens, B.X. (2003) <em>Conservation and management applications of the reef volunteer fish monitoring program</em>. <em>Coastal Monitoring through Partnerships: Proceedings of the Fifth Symposium on the Environmental Monitoring and Assessment Program (EMAP) Pensacola Beach, FL, U.S.A., April 24–27, 2001</em> (ed. by B.D. Melzian), V. Engle), M. McAlister), S. Sandhu), and L.K. Eads), pp. 43–50. Springer Netherlands, Dordrecht.</p> <p>Robertson, D. R. (2008) Global biogeographic databases on marine fishes: caveat emptor. <em>Diversity and Distributions, 14<strong>,</strong> 891-892</em></p> <p>Robertson, D.R,, Dominguez-Dominguez, O., Lopez Arollo, Y.M., Moreno Mendoza. R., Simoes, N. (2019) Reef-associated fishes from the offshore reefs of western Campeche Bank, Mexico, with a discussion of mangroves and seagrass beds as nursery habitats. <em>Zookeys </em>843: 71-115. <a href="https://doi.org/10.3897/zookeys.843.33873">https://doi.org/10.3897/zookeys.843.33873</a></p> <p>Robertson, D.R & Van Tassell, J. (2019) Shorefishes of the Greater Caribbean: online information system. Version 2.0. <em>Smithsonian Tropical Research Institute, Balboa, Panamá</em>. <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>.</p> <p>Wessel, P. & Smith, W.H.F. (1996) A global, self-consistent, hierarchical, high-resolution shoreline database. <em>Journal of Geophysical Research: Solid Earth</em>, 101, 8741–8743.</p>
Deliverable 2.4: Biodiversity impact database including characterization factors and documentation ready for use in Module C
<p><span>We quantify the impacts of agriculture and livestock, on biodiversity (including intensity of use), and translate the results of these modeling efforts which indicate current patterns of biodiversity indicators in both South America and Africa (as well as potential biodiversity loss) to data inputs to be further used in CLEVER. </span></p>
Deliverable 2.2: Biodiversity indicator database for focus regions (phylogenetic files)
<p>The files provided in this repository complements the previous version of the Deliverable 2.2: Biodiversity indicator database for focus regions (same DOI) by including:</p> <ul> <li>Phylodiversity maps</li> <li>Organized database in Excel</li> <li>Code for the results .txt</li> </ul>
20 GB in 10 minutes: Data linking across major biodiversity databases: Data supplements
<p>This supplementary data publication contains:</p> <p><strong>links-globi-wd-ott.tsv.gz:</strong> aggregate list of taxon graphs from Open Tree of Life Taxonomy (OTT), GloBI and Wikidata. This tab separated two column table, describe the taxonomic identifiers (e.g., NCBI:9606) that map into OTT, GloBI and Wikidata. For instance, the line "NCBI:9689{tab}WD:Q140" indicates that wikidata links their lion (<em>Panthera leo</em>, https://www.wikidata.org/wiki/Q140) to NCBI's lion (<em>Panthera leo</em>, https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=9689).</p> <p><strong>wikidata-taxon-info20171227.tsv.gz: </strong>a terse 5 column file in tab-separated format of taxon objects extracted from WikiData. (2018). Wikidata dump 2017-12-27 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1211767 . The columns contain the following:</p> <ol> <li>wikidata taxon item id (e.g., Q140 or https://www.wikidata.org/wiki/Q140)</li> <li>scientific name of taxon item id (e.g., Panthera leo, Mammalia)</li> <li>rank id of the taxon item id (e.g., Q7432 species or https://www.wikidata.org/wiki/Q7432). To retrieve a full list of wikidata taxon rank ids and their common names, you can use sparql to query wikidata (e.g., <a href="https://github.com/globalbioticinteractions/nomer/blob/c3a1f5a2ebfb87ffc67e3bace19b82d96c0d25e8/nomer/src/main/java/org/globalbioticinteractions/nomer/util/WikidataTaxonRankLoader.java">Nomer's WikidataTaxonRankLoader</a> ). </li> <li>parent ids if taxon item id using pipes "|" as separators if there's multiple parents. Please note that some taxon items have multiple parents (e.g., https://www.wikidata.org/wiki/Q774014).</li> <li>external taxonomic identifiers that taxon item link to (e.g. "ITIS:162532|EOL:8266|GBIF:2960|WORMS:125440") . If muliple are present, pipes "|" are used to separate the links. Only a selection of taxonomic schemes was used, namely: NCBI, GBIF, ITIS, WORMS, FISHBASE, IF (index fungorum) and EOL.</li> </ol> <p>The datasets can be recreated by scripts in https://github.com/bio-guoda/guoda-datasets/tree/master/wikidata or <a href="https://doi.org/10.5281/zenodo.1428949">https://doi.org/10.5281/zenodo.1428949</a> .</p>
Bibliographic dataset characterizing studies that use online biodiversity databases
<p>This dataset includes bibliographic information for 501 papers that were published from 2010-April 2017 (time of search) and use online biodiversity databases for research purposes. Our overarching goal in this study is to determine how research uses of biodiversity data developed during a time of unprecedented growth of online data resources. We also determine uses with the highest number of citations, how online occurrence data are linked to other data types, and if/how data quality is addressed. Specifically, we address the following questions:</p> <p>1.) What primary biodiversity databases have been cited in published research, and which</p> <p> databases have been cited most often?</p> <p>2.) Is the biodiversity research community citing databases appropriately, and are</p> <p> the cited databases currently accessible online?</p> <p>3.) What are the most common uses, general taxa addressed, and data linkages, and how </p> <p> have they changed over time?</p> <p>4.) What uses have the highest impact, as measured through the mean number of citations</p> <p> per year?</p> <p>5.) Are certain uses applied more often for plants/invertebrates/vertebrates?</p> <p>6.) Are links to specific data types associated more often with particular uses?</p> <p>7.) How often are major data quality issues addressed?</p> <p>8.) What data quality issues tend to be addressed for the top uses? </p> <p>Relevant papers for this analysis include those that use online and openly accessible primary occurrence records, or those that add data to an online database. Google Scholar (GS) provides full-text indexing, which was important to identify data sources that often appear buried in the methods section of a paper. Our search was therefore restricted to GS. All authors discussed and agreed upon representative search terms, which were relatively broad to capture a variety of databases hosting primary occurrence records. The terms included: “species occurrence” database (8,800 results), “natural history collection” database (634 results), herbarium database (16,500 results), “biodiversity database” (3,350 results), “primary biodiversity data” database (483 results), “museum collection” database (4,480 results), “digital accessible information” database (10 results), and “digital accessible knowledge” database (52 results)--note that quotations are used as part of the search terms where specific phrases are needed in whole. We downloaded all records returned by each search (or the first 500 if there were more) into a Zotero reference management database. About one third of the 2500 papers in the final dataset were relevant. Three of the authors with specialized knowledge of the field characterized relevant papers using a standardized tagging protocol based on a series of key topics of interest. We developed a list of potential tags and descriptions for each topic, including: database(s) used, database accessibility, scale of study, region of study, taxa addressed, research use of data, other data types linked to species occurrence data, data quality issues addressed, authors, institutions, and funding sources. Each tagged paper was thoroughly checked by a second tagger.</p> <p>The final dataset of tagged papers allow us to quantify general areas of research made possible by the expansion of online species occurrence databases, and trends over time. Analyses of this data will be published in a separate quantitative review.</p>
The Opportunistic Database of Biodiversity Databases
<p>The Opportunistic Database of Biodiversity Databases (ODBD) is an incomplete list of databases relevant to biodiversity research, including but not limited to databases with data on occurrence, range maps, radio-tracking, demography, functional traits, conservation status, genetic sequences, phylogenetic relationships, interspecific interactions, habitat affinities, and native/non-native status. Information on databases is populated and updated on an as-encountered basis.</p>
Linked collectors and determiners for: Database_Coleoptera_Madagascar Biodiversity Center PBZT.
Natural history specimen data linked to collectors and determiners held within, "Database_Coleoptera_Madagascar Biodiversity Center PBZT". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/22cd931a-95ab-4b68-be6c-e0eff9365eae">https://bionomia.net/dataset/22cd931a-95ab-4b68-be6c-e0eff9365eae</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/22cd931a-95ab-4b68-be6c-e0eff9365eae">https://gbif.org/dataset/22cd931a-95ab-4b68-be6c-e0eff9365eae</a>. Formatted as a Frictionless Data package.
Code and data used for the study: 'BioDeepTime: a database of biodiversity time series for modern and fossil assemblages'
<p>The repository includes code and data to reproduce the results in the manuscript ‘BioDeepTime: a database of biodiversity time series for modern and fossil assemblages' by Smith et al. (<code>analysis_biodeeptime.zip</code>).</p>
FIGURE 2 in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices
FIGURE 2 Taxonomy as a unifying key for ecological datasets. The two sides represent two exemplary datasets, with a containing conservation status of taxa (here species) and B their traits (colours show different traits). The datasets are indexed by taxon names 'Sp1' to 'Sp6'. The rounded rectangle in the middle depicts the taxonomic harmonization process: (a) the names are extracted from each dataset, respectively in the orange and purple rectangles; (b) both lists are then compared to a taxonomic database which harmonizes all names. Here the names 'Sp1' and 'Sp6' refer to the same taxon in the taxonomic database (as indicated by the dashed lines). Without taxonomic harmonization, the exact match of names would have resulted in the loss of Sp5 and Sp6 when merging both datasets. LC, NT, VU, and CR are abbreviations of Red List statuses, meaning least concern, not threatened, vulnerable, and critically endangered, respectively
FIGURE 1 in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices
FIGURE 1 Typology of taxonomic databases according to their taxonomic breadth and their spatial scale. The x-axis represents increasing taxonomic breadth from a single taxonomic group to no clear taxonomic restriction (e.g. considering all biota or all Eukaryota). The y-axis represents spatial scale from regional to global. Each box represents a specific type of taxonomic database, with examples. LCVP, Leipzig Catalogue of Vascular Plants; WorldFlora, World Flora Online; POWO, Plants of the World Online; GermanSL, German Simple List; Vascan, Database of Vascular Plants of Canada; WoRMS, World Register of Marine Species; CASD, Chinese Animal Scientific Database; COL, Catalogue of Life; GBIF, Global Biodiversity Information Facility; TAXREF, French Taxonomic Referential; FinBIF, Finnish Biodiversity Information Facility
FIGURE 4 in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices
FIGURE 4 Diagram of different taxonomic harmonization workflows. The workflows differ in the number of steps they consider and the databases they leverage on. Rounded rectangles are lists of taxon names while diamonds represent taxonomic databases against which the names are matched. The different colours used at step 2 represent different taxonomic groups
FIGURE 3 in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices
FIGURE 3 Screenshot showing the network view of taxharmonizexplorer. The left section shows a table of each of the nodes in the network to let the user select manually nodes of interest, the top part presents a summary of the information on the selected node in the network. The right section displays the relationships between packages (which depends on which other), between databases (how one populates another one) and between packages and databases (which packages access which databases)
Unlocking natural history collections to improve eDNA reference databases and biodiversity monitoring
Open the record for dataset details and reuse information.
BioTIME 2.0: expanding and improving a database of biodiversity time series
<p>Here we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies in the database, and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.</p> <p>We include here:</p> <ul> <li>SQL file of the database - biotime_v2_sql_15April25.sql</li> <li>RDS file of the query combining raw data with species names - biotime_v2_query_15April25.rds</li> <li>csv file for metadata information - biotime_v2_metadata_15April25.csv</li> <li>csv file of citations - references_biotime_v2_15April25.csv</li> <li>text file of BIB text citations - BIB_biotime_v2_15April25.csv.txt</li> </ul> <p>for issue of version 2.0 of the BioTIME database</p>
Resolved EXiobase (REX II) with regionalized biodiversity loss impact assessment of global mining – second version of a highly-resolved MRIO database for the year 2014
<p>This repository provides a new version of the highly-resolved global multi-regional input-output database called REX II (Resolved EXiobase) for the year 2014 with improved data quality for all mining and metals processing sectors, including a regionalized biodiversity impact assessment for all mining sectors. This regionalized impact assessment is based on the global mining area data set of Maus et al (2020). The database REX II is described in the study <em>"Hotspots of mining-related biodiversity loss in global supply chains and the potential for reduction by renewable electricity".</em></p> <p>Study: <a href="https://doi.org/10.1021/acs.est.2c04003">https://doi.org/10.1021/acs.est.2c04003</a></p> <p>Open-access preprint: <a href="https://doi.org/10.31223/X5T064">https://doi.org/10.31223/X5T064</a></p> <p> </p> <p>An earlier version of this database (REX I) with time series from 1995–2015 is provided under: <a href="http://doi.org/10.5281/zenodo.3993659">http://doi.org/10.5281/zenodo.3993659</a> and described here: <a href="https://doi.org/10.1016/j.scitotenv.2020.142587">https://doi.org/10.1016/j.scitotenv.2020.142587</a></p> <p> </p> <p>The repository REXIA_2014 contains the following files (<em>*.mat-files</em>) referring to the year 2014:<br> T_REXIA: transaction matrix<br> Y_REXIA: final demand matrix<br> Ext_REXIA and Ext_hh_REXIA: the satellite matrices of the economy and the final demand<br> The labels of all matrices are described in the excel file Labels_REXIA.xlsx</p>
A dark side of conservation biology: protected areas fail in representing subterranean biodiversity. Databases.
<p>We obtained distribution data for leiodids by digitizing the information provided by Fresneda and Salgado (2017). This information was updated and expanded with additional data from several sampling campaigns (unpublished data). For subterranean spiders, we included the entirety of the Alps range extending from France in the east westward through Switzerland, Italy, Liechtenstein, Austria, Germany, and Slovenia, by integrating available data in Global Biodiversity Information Facility (GBIF), Spider of Europe), Araneae.it and other literature sources.</p>
Supplemental Files to "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs"
<p>These are supplemental files to the manuscript, "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs". Supplements contain excel spreadsheets, DNA alignments, Newick tree files, FigTree files, and csv files.</p>
The Biodiversity Footprint Database
<p><strong>The Biodiversity Footprint Database</strong> contains global consumption-based, monetary, biodiversity impact factors for 44 countries and five rest of the world regions. The dataset has been compiled by combining information from EXIOBASE and LC-IMPACT databases. In addition, the EXIOBASE database has been analyzed with the pymrio analysis tool to determine the geographical location of the consumption-based biodiversity impacts. The mid-point impact factors from EXIOBASE are based on 2019 data, but the regional analysis with pymrio is based on 2011 data. EXIOBASE version 3.8.2 was used and LC-IMPACT version 1.3. <strong>The data is currently non peer-reviewed and under submission. The database will be open access after publication.</strong> The preprint of the manuscript can be found from: <a href="https://doi.org/10.48550/arXiv.2309.14186">https://doi.org/10.48550/arXiv.2309.14186</a></p> <p><strong>About the units</strong></p> <p>The unit used in the database is the biodiversity equivalent (BDe). The biodversity equivalent, as we call it, is more commonly known as the global potentially disappeared fraction of species (global PDF, Verones et al., 2020). Thus, the monetary biodiversity impact factors are presented in the form BDe/€.</p> <p>Prices are in basic prices and the conversion factors to transform purchaser prices (e.g. financial accounting prices) to basic prices are provided for Finland (and later for all regions), based on EXIOBASE supply and use tables (SUT).</p> <p><strong>Content of files</strong></p> <p><em>BiodiversityFootprintDatabase.xlsx</em></p> <p>The biodiversity impact factors, regional abbreviations and basic price conversion factors for Finland.</p> <p><em>BiodiversityFootprintDatabase_DetailedData.zip </em></p> <p>The detailed data used to combine EXIOBASE and LC-IMPACT data after the EXIOBASE data was analyzed with the pymrio tool. Contains folders for each driver of biodiversity loss according to the LC-IMPACT classification.</p> <p><em>20220406_Exio3stressorcode _2011.py </em>& <em>20220406_Exio3StressorAggregationCode_2011.py </em></p> <p>The pymrio codes that were used to analyze EXIOBASE and the geographical location of the drivers of biodiversity loss (mid-point indicators).</p>
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.