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.
6,246
datasets available to search
ShareScore release 0.7.1
Dataset results
6,246 results for “Biodiversity”
Data and R-scripts for "Land-use trajectories for sustainable land system transformations: identifying leverage points in a global biodiversity hotspot" (V2)
<p>Sustainable land system transformations are necessary to avert biodiversity and climate collapse. However, it remains unclear where entry points for transformations exist in complex land systems. Here, we conceptualize land systems along land-use trajectories, which allows us to identify and evaluate leverage points; i.e., entry points on the trajectory where targeted interventions have particular leverage to influence land-use decisions. We apply this framework in the biodiversity hotspot Madagascar. In the Northeast, smallholder agriculture results in a land-use trajectory originating in old-growth forests, spanning forest fragments, and reaching shifting hill rice cultivation and vanilla agroforests. Integrating interdisciplinary empirical data on seven taxa, five ecosystem services, and three measures of agricultural productivity, we assess trade-offs and co-benefits of land-use decisions at three leverage points along the trajectory. These trade-offs and co-benefits differ between leverage points: two leverage points are situated at the conversion of old-growth forests and forest fragments to shifting cultivation and agroforestry, resulting in considerable trade-offs, especially between endemic biodiversity and agricultural productivity. Here, interventions enabling smallholders to conserve forests are necessary. This is urgent since ongoing forest loss threatens to eliminate these leverage points due to path-dependency. The third leverage point allows for the restoration of land under shifting cultivation through vanilla agroforests and offers co-benefits between restoration goals and agricultural productivity. The co-occurring leverage points highlight that conservation and restoration are simultaneously necessary. Methodologically, the framework shows how leverage points can be identified, evaluated, and harnessed for land system transformations under the consideration of path-dependency along trajectories.</p>
Dataset for "Fire disturbance promotes biodiversity of plants, lichens and birds in the Siberian subarctic tundra"
<p>Data that support the findings of the study "<strong>Fire disturbance promotes biodiversity of plants, lichens and birds in the Siberian subarctic tundra</strong>".</p>
Global knowledge and use of soil biodiversity: Results of an expert survey
<p>A global survey on soil biodiversity (see file Global Biodiversity Survey.pdf provided as an attachment) was conducted over a three-week period in March 2022 by the Global Soil Partnership (GSP) of the Food and Agriculture Organization (FAO) of the United Nations, as part of the activities of the International Network on Soil Biodiversity (NETSOB). The survey intended to obtain information on the current status of knowledge and use of soil organisms worldwide, i.e., to identify who is doing what, where, and how, as well as the main gaps, pitfalls, and opportunities across existing national initiatives and research.</p> <p>The survey included 122 questions that characterized the work undertaken by experts regarding microbes, fauna, and their activity in soils, community & functional assessments, inventories, mapping and monitoring activities, ecosystem services, applications, and threats to soil biodiversity, education, and communication activities, as well as public policies related to soil biodiversity. The online survey was created using the software Survey Monkey v. 11 and was sent out to over 70 thousand e-mail addresses with a link to complete the survey. </p> <p>Over 2,600 responses were received, representing >1,350 institutions from 135 countries, mainly from experts active in research and academia. The number of respondents was not equal for all questions, as the survey guided the respondents to different parts, depending on their replies.</p> <p>The 122 questions and the replies of the respondents are presented as separate tabs in the attached Excel file (Results survey for Zenodo.xlsx). The respondents and their identities, as well as their e-mails and any personal websites were removed in the current file to maintain anonymity. Institutional websites were maintained as long as they did not identify the respondent(s) directly. </p> <p>A detailed written description of the survey results was prepared as a manuscript for a special issue of the journal Soil Organisms, volume 97 (Brown et al., 2025). The survey was prepared by a team of scientists from the Brazilian Corporation for Agricultural Research (Embrapa) and collaborating institutions, with assistance from the board of the International Network on Soil Biodiversity (NETSOB), and with funding provided by the FAO. The work was further supported by the Fundação de Apoio a Pesquisa e Desenvolvimento Agropecuário Edmundo Gastal (FAPEG), Brazil, a grant of CNPq (Processo No. 312824/2022-0) to GGB, and of the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant program (# 05901–2019) to ZL, who was also supported by Western University.</p>
Dataset: Comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research.
<p>Supplementary material for a comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research. We conducted a relevance evaluation with 6 users over 19 search questions in two search interfaces.</p> <p>The users provided up to five search questions and relevant keywords from their research background. We setup a dataset search over a corpus of ~92,000 randomly selected metadata files from GFBio (<a href="https://www.gfbio.org">https://www.gfbio.org</a>). For each of their own search queries, the users got two result sets presented. The first one displayed results obtained from a keyword search. The second panel contained dataset results from a prototypical semantic search. Instead of results with exact mentions of the query terms, the semantic search also presented related results with synonyms and more specific terms or terms obtained from concept nodes of a higher hierarchy level.</p> <p>Each user rated the relevance of his/her own search queries on a 7-point Likert scale for both search results.<br> In addition, users also assessed the expanded keywords for each question.</p> <p>More information can be found in our publication:</p> <p>Löffler, F. and Klan, F. (2016): Does Term Expansion Matter for the Retrieval of Biodiversity Data? in Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS'16), co-located with the 12th International Conference on Semantic Systems (SEMANTiCS 2016),2016, <a href="http://ceur-ws.org/Vol-1695/paper2.pdf">http://ceur-ws.org/Vol-1695/paper2.pdf</a></p> <p> </p>
Sampling metadata for the publication: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem "
<p>Meta data of sampling location, time and depth of eDNA samples used in the study: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem". As well as taxonomic identification of sponges, their microbial abundance and growth form.</p>
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>
Dataset of micro-phytoplankton biodiversity in the Eastern Channel area from 1987 to 2019.
<p>Micro-phytoplankton communities are widely influenced by the environment and seasonal cycling. The French-English Channel is a system between the East Atlantic and the North Sea with contrasted coastal areas along the French coast. In a context of changing environment, it is crucial to understand the causes and consequences of environmental variability on marine compartments whose phytoplankton represent the first link.</p> <p>Thanks to various monitoring programs such as REPHY (REPHY – French Observation And Monitoring Program For Phytoplankton And Hydrology In Coastal Waters, 2021) and RHLN (Regional Observation and Monitoring programs for Phytoplankton and Hydrology in the eastern English Channel), datasets of micro-phytoplankton diversity are available since the late 80s at a national scale.</p> <p>Here, we present a subset of these datasets on micro-phytoplankton community. It gathers diversity and abundance from 1987 to 2019. Compared to the main dataset, a taxonomical review was done to ensure a correct spatial and temporal homogenization of the taxonomical denomination (description below). We also focus on 6 monitored stations along the French Channel coast; Antifer ponton pétrolier, Cabourg, Géfosse, Donville, At so and St Cast – Les Hébihens (gathering of two times series geographically close). A graphical visual of the sampling history (for the FLORTOT standard protocol) is available in the uploaded image below.</p> <p>The dataset comes from a national-wide standard protocol of water sampling (Neaud-Masson, 2016). All records are stored in the Quadrige² platform, the reference information system for coastal waters in France. A compilation of the various results obtained at the French national scale was done on the last 30 years of monitoring (Belin and Soudant, 2018). The sampling is done at water surface (0-1 meter) around the high tide period (+/- 2 hours), it is fixed through lugol acid on arrivals at the lab and analysed through reversed microscopy by experts trained through a similar 2 years learning process. The taxa are identified to the lowest taxonomic level possible and names are updated according to the WORMs’ denomination base. Since 2015, evaluations (International Phytoplankton Intercomparison - IPI) are being implemented at the European scale, coordinated by the Marine Institute of Galway, to reduce identification deviations and biases between laboratories.</p> <p>The columns are described as following:</p> <ul> <li><strong>#1 : Taxa_Name</strong></li> </ul> <p>Due to the extent of the initial dataset, both in space and time, a work of homogenization in the taxa’s denomination was applied. Therefore, associated with this dataset, we made available a table that summarizes changes applied in the nominations. It displays all the taxa initially present in the REPHY database (first column), the change in denomination applied if needed as they are not at the same resolution between stations’ laboratories, or because two species are now grouped into a common denomination. There is an “X” if the taxa is not relevant, rare, or not a phytoplankton or protist of interest and therefore it means the taxa was deleted and is not in the given dataset (second column) and potential comments or justifications (third column). It has been build and reviewed several times by the phytoplankton experts from the three Ifremer laboratories covering the stations (Dinard, Port en Bessin and Boulogne sur Mer).</p> <ul> <li><strong>#2 to 5 : Date, Day, Month, Year</strong></li> </ul> <p>Temporal description of the samples. Details of the sampling history are given in the image below.</p> <ul> <li><strong>#6 : Season</strong></li> </ul> <p>Defined as Spring = March to May; Summer = June to August; Autumn = September to November; Winter = December (Y) to February (Y+1)</p> <ul> <li><strong>#7 and 8 : Julian_days and week number</strong></li> <li><strong>#9 and 10 : Station_FullName and Station_Code</strong></li> </ul> <p><strong></strong>In the REPHY monitoring program, stations are both describe by full name locations and codes. The same level of detail was kept available in this version, with : Antifer ponton pétrolier (010-P-001), Cabourg (010-P-109), Géfosse (014-P-023), Donville (018-P-054), At so (006-P-001) and St Cast – Les Hébihens (022-P-002)</p> <ul> <li><strong>#11: Parameter</strong></li> </ul> <p>FLORTOT (standard samples, all micro-phytoplanktonic species are identified). FLORPAR (partially identified samples in area identified as under any sanitary risk). FLORIND (additional samples taken for toxic species monitoring, or for species reaching 100 000 cells per liter or producing toxins are identified).</p> <ul> <li><strong>#12 : Results </strong></li> </ul> <p>Abundance (in cells.L<sup>-1</sup>), whose level of detection (lowest value) through the microscopy approach is at 100 cells per liter.</p> <ul> <li><strong>#13 : Class.worms2019</strong></li> </ul> <p>The taxa’s class according to the World Register of Marine Species 2019 (https://www.marinespecies.org/)</p> <ul> <li><strong>#14 : Rank</strong></li> </ul> <p>Taxa's taxonomical rank : Species, Genus, Family, e-species (aka: group of similar species), e-genus (aka : group of similar genus), Class, subclass, Order, Phylum, Other.</p> <p> </p>
Sustaining Cities, Naturally Webinar: Ecology Session - Ecological management – let's bring biodiversity to cities
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This film was shown as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Dutch grid map layers for biodiversity surveys
<p>This dataset contains map layers in GeoPackage format with the following grids used in biodiversity surveys in the Netherlands:</p> <table> <tbody> <tr> <td><strong>Layer name</strong></td> <td><strong>Size (m)</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>IvonGrid Kwartierhok</td> <td>1250x1042 </td> <td>Used between 1902 and 1950 by IVON botanical surveys.</td> </tr> <tr> <td>IvonGrid Uurhok</td> <td>5000x4168 </td> <td>Used between 1902 and 1950 by IVON botanical surveys. Each square contains 16 'kwartierhok' squares with corresponding first four characters in column name.</td> </tr> <tr> <td>RdGrid Kilometerhok</td> <td>1000</td> <td>Used since 1950 in biodiversity surveys. </td> </tr> <tr> <td>RdGrid Uurhok</td> <td>5000</td> <td>Used since 1950 in biodiversity surveys. Each square contains 25 kilometer squares.</td> </tr> </tbody> </table> <p> </p> <p>The RdGrid maps are available in the WGS84 and RD New (EPSG:28992) spatial reference systems, as they are aligned to rounded RD coordinates. IvonGrid is only available in WGS84.</p> <p><strong>Grid square codes</strong></p> <p>Kilometerhok (1x1 km) and Uurhok (5x5) grids have corresponding grid square codes. A code consists of three concatenated numbers for map sheat (1-62), 5 km square (11-58) and 1 km square (11-55). E.g. a 5 km square code looks like 4017. In literature and on collection labels this often written as 40.17 or 40-17.</p> <p><strong>Maintanance</strong></p> <p>This dataset is being curated by FLORON Plant Conservation Netherlands since 1988 for use in botanical surveys (previously by Theo Peterbroers, Bart Vreeken and Ruud Beringen).</p> <p><strong>History</strong></p> <p>IVON stands for "Instituut voor Vegetatie-onderzoek Nederland", an NGO founded in 1930 was most active until the 1950's and then succeeded by FLORON in 1988. More information about the repartition of the IVON grid can be found in Atlas van de Nederlandse Flora part 1 (Mennema et al. 1989). The original map was hand drawn on topographical maps by J.W.C. Goethart en W. J. Jongmans (Van Ooststroom 1956) originally following the Bessel 1841 projection (EPSG:7004). The grid cells are equal in size, although not rounded to kilometers. The size was probably chosen to fit maps that were commercially available at that time.</p> <p> </p> <p><strong>References</strong></p> <p>Mennema, J., Quene-Boterenbrood, Plate, C. L., Van der Meijden, R., & Weeda, E. J. (1989). Atlas van de Nederlandse flora. Kosmos, Amsterdam.</p> <p>van Ooststroom, S.J. (1956). Het I.V.O.N. – Werk. Correspondentieblad ten dienste van de floristiek en het vegetatie-onderzoek van Nederland 1: 2–3.</p>
Data on public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland
<p>A public participatory GIS -survey dataset detailing public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland.</p>
Automated monitoring of biodiversity in the tropics: A pilot study at Barro Colorado Island
<p>Automated monitoring of biodiversity using camera systems and acoustic devises is becoming increasingly common practise. The development of camera traps for insects, however, is relatively new, and has not been tested in tropical environments. To understand both the challenges and opportunities for automated monitoring in the tropics we deployed three newly developed cameras systems for monitoring insects, with a focus on night-flying insects. In addition we deployed audible and ultrasound recording equipment. The locations of each device was changed over the 5 day pilot study, and the configuration of each system was changed to allow an assessment of the relative importance of position, light, etc. The study was undertaken on Barro Colorado Island, Panama, in January 2023.</p> <p>Data are aggregated into <em>.zip</em> files, and details of their contents, and metadata are given in <em>read_me.txt</em> and <em>metadata.csv</em>.</p>
The Effect of Soundscape Composition on Bird Vocalization Classification in a Citizen Science Biodiversity Monitoring Project
<p>This archive includes sound clips (.wav files) and associated mel-scale spectrograms of bird vocalizations for 54 species in Sonoma County, California, USA. These data were used for training and validating convolutional neural network (CNN) models for bird species detection. We also include xeno-canto training and validation mel spectrograms used to pretrain CNNs. Details on these data are explained in the paper by Clark et al. (2023) titled "The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project". These data are available for use without restrictions, with no warranty on data quality or utility for a given application. We request that any work that does use these data cite the Clark et al. (2023) paper.<br> <br> Clark, M.L., Salas, L., Baligar, S., Quinn, C., Snyder, R.L., Leland, D., Schackwitz, W., Goetz, S.J., Newsam, S. (2023). The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project. <em>Ecological Informatics</em>. <a href="https://doi.org/10.1016/j.ecoinf.2023.102065">https://doi.org/10.1016/j.ecoinf.2023.102065</a></p> <p>Associated code for training CNN models, performing inference, and applying post-classification corrections can be found in the GitHub archive <a href="https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species">https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species</a></p> <p>Raw sound data from the Soundscapes to Landscapes project are available upon request: Dr. Matthew Clark, matthew.clark@sonoma.edu</p> <p>These data were collected as part of the Soundscapes to Landscapes project (<a href="https://soundscapes2landscapes.org/">soundscapes2landscapes.org</a>), funded by NASA’s Citizen Science for Earth Systems Program (CSESP) 16-CSESP 2016-0009 under cooperative agreement 80NSSC18M0107.<br> <br> ----------------------------<br> This depository includes the following archives:</p> <ul> <li> <p>mel_specs.zip: contains 2-sec mel spectrograms split into training (“tr”), validation (“val”), testing (“test”) data for each target bird species (n = 54) used to fine-tune the CNNs. Select spectrogram files are appended with “aug” if they are augmented versions for the training data.</p> </li> <li> <p>wav.zip: contains the associated wav-format sound recordings used to generate the training, validation, testing mel spectrograms found in mel_specs.zip.</p> </li> <li> <p>Xeno-canto_pretrain.tar: contains 2-sec mel spectrograms split into training and validation data for 40 bird species used for CNN pre-training that were generated using a warbleR segmentation methodology described in the paper. The sound files used to generate these mel spectrograms came from the Kaggle competition, <a href="https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset">https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset</a><br> Mel spectrogram naming reflects the XC number used for cataloging on Xeno-canto in the format XC123456_2.png. The six numbers following the XC characters can be used to search for unique recordings on Xeno-canto (<a href="https://xeno-canto.org/">https://xeno-canto.org/</a>) using the search query “nr:123456” in the search tool or queried using the Xeno-canto API (<a href="https://xeno-canto.org/explore/api">https://xeno-canto.org/explore/api</a>). Unique recording names can be extracted from the mel spectrogram filenames.</p> </li> <li> <p>soundscape_test_wavs.zip: the wav-format sound recordings used to perform soundscape testing.</p> </li> </ul>
Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity
<p>The repository contains the data and codes supporting the findings of the study:<strong> </strong>Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity, which can be found in the zip file "<strong>euclimate_vs_natpolicy-main.zip"</strong>. </p> <p>Further, the repository includes the raw forest simulation data used as input for the multi-objective optimizations and the raw optimization outputs of each study region. The codes to run the national optimization can be retrieved from <a href="http://doi.org/10.5281/zenodo.6631109">https://doi.org/10.5281/zenodo.6631109</a>.</p> <p>Abstract:</p> <p>The European Union (EU) set clear climate change mitigation targets to reach climate neutrality, accounting for forests and their woody biomass resources. We investigated the consequences of increased harvest demands resulting from EU climate targets. We analysed the impacts on national policy objectives for forest ecosystem services and biodiversity through empirical forest simulation and multi-objective optimization methods. We show that key European timber-producing countries – Finland, Sweden, Germany (Bavaria) – cannot fulfil the increased harvest demands linked to the ambitious 1.5°C target. Potentials for harvest increase only exists in the studied region Norway. However, focusing on EU climate targets conflicts with several national policies and causes adverse effects on multiple ecosystem services and biodiversity. We argue that the role of forests and their timber resources in achieving climate targets and societal decarbonization should not be overstated. Our study provides insight for other European countries challenged by conflicting policies and supports policymakers.</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>
Dataset - Drying out fish ponds, for an entire growth season, as an agroecological practice: maintaining primary producers for fish production and biodiversity conservation
<p>This dataset is based on samples taken from fish ponds in the Dombes region between 2007 and 2014. It includes sediment and water physio-chemistry data, as well as primary producer diversity, benthic invertebrate density and fish yield for 85 different ponds. All these data are linked to the distance to the last dry-out, a major practice in extensive fish farming in this region.</p> <p>There are two .tab and .csv files:<br> One containing the dataset<br> One containing the description of the different variables (Metadata)</p>
Mapping the planet's critical areas for biodiversity and people
<p>Data associated with "Mapping the planet's critical areas for biodiversity and people"</p> <p>Abstract: Meeting global commitments to conservation, climate, and sustainable development requires consideration of synergies and tradeoffs among targets. We evaluate the spatial congruence of ecosystems providing globally high levels of nature’s contributions to people, biodiversity, and areas with high development potential across several sectors. We find that conserving approximately half of global land area through protection or sustainable management could provide 90% of the current levels of ten of nature’s contributions to people and meet minimum representation targets for 26,709 terrestrial vertebrate species. This finding supports recent commitments by national governments under the Global Biodiversity Framework to conserve at least 30% of global lands and waters, and proposals to conserve “half Earth”. More than one-third of areas required for conserving nature’s contributions to people and species are also highly suitable for agriculture, renewable energy, oil and gas, mining, or urban expansion. This indicates potential conflicts among conservation, climate and development goals.</p> <p>This dataset contains code and outputs of spatial optimizations run using prioritizr (https://prioritizr.net/index.html). R code used to run the spatial optimizations is contained in a zipfile named "code.zip".</p> <p>Output data includes raster files (TIF format). Raster values are 0-1, where 1 means the grid cell was selected to achieve a particular target, 0 means the grid cell was not selected, and values between 0 and 1 indicate a grid cell was partially selected.</p> <p>Three variations of the spatial optimization were run. Each TIF or ZIP file contains the outputs from one of these variations:</p> <ol> <li>NCP (Nature's contributions to people) only <ol> <li>File name: NCP_only_2km.zip (ZIP file)</li> </ol> </li> <li>NCP and biodiversity, prioritization run at 10km then masked to natural and semi-natural habitat at 2km <ol> <li>File names: es00bio1_nathab_mask.tif, es05bio1_nathab_mask.tif, etc. (TIF files)</li> </ol> </li> <li>NCP and biodiversity, with protected areas and OECM (WDPA) locked in <ol> <li>File name: NCP_biod_WDPA_nathab.zip (ZIP file)</li> </ol> </li> </ol> <p>Within each variation, 19 different spatial optimizations were run, with NCP targets ranging from 5%-95% in 5% increments.</p> <ul> <li>Raster filenames within ZIP files indicate the NCP (ecosystem service) target (for example, es05 indicates a target of 5%)</li> <li>Whether biodiversity was included or not (for example, bio1 indicates biodiversity was included, bio0 indicates it was not)</li> </ul> <p>Two additional TIF files were included, which are the result of summing the rasters from the above scenarios. Raster values range from 0-19, where 19 indicates grid cells selected in all scenarios, 0 indicates grid cells selected in 0 scenarios. Higher values (e.g. 19) indicate cells with the highest levels of NCP globally in the least amount of area. These rasters were used to create Figure 2 in the paper.</p> <ol> <li>NCP_only_2km_sum - NCP only scenario, all rasters summed.</li> <li>NCP_biod_nathab_sum - NCP and biodiversity scenario, masked to natural habitat, all rasters summed.</li> </ol> <p>Additional files include:</p> <ol> <li>dpi.tif - Development Potential Index raster</li> <li>dpi_key.csv - legend describing the DPI raster values</li> <li>HDP_DriverCats.tif - High Development Potential areas disaggregated by sector (raster)</li> <li>HDP_DriverCats_key.csv - legend describing the HDP raster values</li> <li>es90bio1_hdp_drivers_multiply.tif - raster resulting from the combination of the prioritized areas for NCP and biodiversity combined with High Development Potential areas for each economic sector (key is the same as for HDP raster)</li> <li>nathab_2km_WGS84.tif - raster with natural and semi-natural habitat mask (based on ESA 2015 land cover) (2 km)</li> </ol> <p> </p>
Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases
<p>These data were used for the development of the paper "<strong>Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases</strong>". Especifically, we added ecological and environmental data that were used for modeling.</p>
Data from: Species delimitation in endangered groundwater salamanders: implications for aquifer management and biodiversity conservation
Open the record for dataset details and reuse information.
Genome size influences plant growth and biodiversity responses to nutrient fertilization in diverse grassland communities
Experiments comparing diploids with polyploids and in single grassland sites show that nitrogen and/or phosphorus availability influences plant growth and community composition dependent on genome size; specifically plants with larger genomes grow faster under nutrient enrichments relative to those with smaller genomes. However, it is unknown if these effects are specific to particular site localities with speciifc plant assemblages, climates, and historical contingencies. To determine the generality of genome size dependent growth responses to nitrogen and phosphorus fertilisation, we combined genome size and species abundance data from 27 coordinated grassland nutrient addition experiments in the Nutrient Network that occur in the Northern Hemisphere across a range of climates and grassland communities. We found that after nitrogen treatment, species with larger genomes generally increased more in cover compared to those with smaller genomes, potentially due to a release from nutrient limitation. Responses were strongest for C3 grasses and in less seasonal, low precipitation environments, indicating that genome size effects on water-use-efficiency modulates genome size-nutrient interactions. Cumulatively the data suggest that genome size is informative and improves predictions of species’ success in grassland communities.
Biodiversity - Fauna - Bird Survey (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-bes/543/170. The abstract below was extracted from the Level 0 data package and is included for context: This dataset is associated with BES Bird Monitoring Bird Monitoring Project: ================= The BES Bird Monitoring Project is a breeding bird survey designed to find out what birds are found in the breeding season in Baltimore and where. Our monitoring efforts will show associations among block group socioeconomic variables, land cover, land use, and habitat features with breeding bird abundance, to provide information for land managers on possible consequences of land use changes on bird communities. A distinguishing feature of the bird monitoring at BES LTER, relative to other urban bird work, is the capacity for long-term monitoring of features at multiple scales through links to other parts of the project. Different processes influence habitat for birds at different scales, e.g. ongoing household level human decision-making at lot scale vs. block or neighborhood scale abandonment/re-development. Our project seeks to understand how these processes impact bird occurrence, abundance, and composition differ at the lot, block and neighborhood scale. The database consists of four tables. Sites, Surveys, Taxalist, and Birds. Sites records thje sites and their characteristics. Surveys describe the actual outings or sampling sessions. They describe the weather, the temperature, the sites visited. Taxalist provides the integration of speciaies abbreviations and common names, and Birds describes the actual sightings, linking to the other three tables. Attribute information: The tables form a set. Here are the fields and relationship information: Surveys: site_id FK->Sites[site_id
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.