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141 results for “research database”
Emissions Database for Global Atmospheric Research, version v4.3.2 part I Greenhouse gases
<p>The Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2, partim Greenhouse gases compiles anthropogenic emissions data for CO2, CH4 and N2O based on international statistics and emission factors. The version v4.3.2 of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of EU’s first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emission sources of the multiple gases include all human activities except the land-use, land-use change and forestry sector and are compiled following a bottom-up and IPCC-compliant approach. The dataset provides in addition to the complete timeseries 1970-2012 also annual and global gridmaps of 0.1 degree by 0.1 degree resolution for each source-sector and each year. For 2010 also 12 monthly gridmaps per source-sector are provided.</p>
SEEtheSkills resources database from the Interregional research on the status of energy skills
<p>This dataset includes a list of resources identified during the interregional research on the status of energy skills, done in the frame of SEEtheSkills project. The comprehensive overview of the information created in the area of Energy Efficiency (EE) and Renewable Energy Systems (RES), goes both wide, by trying to identify as many different examples as possible, and deep, by digging into the examples themselves. The key areas the research focused on: skills defined in national roadmaps; skills developed as part of previous BUS projects; developed training schemes; the number of trained workers and professionals; companies that design and produce EE materials; status of Recognition of Previous Learning (RPL); status of demand for energy skills; level of awareness of energy skills; available certification schemes; legal obligations promoting the use of energy skills and their timelines, predictions for future development of energy skills. The survey covers mainly the five countries participating in the project Slovenia, Spain, Netherlands, Slovakia and North Macedonia, but also beyond their geographical coverage.</p>
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
The Hearpiece database of individual transfer functions of an openly available in-the-ear earpiece for hearing device research
<p>We present a database of acoustic transfer functions of the Hearpiece, an openly available multi-microphone multi-driver in-the-ear earpiece for hearing device research. The database includes HRTFs for 87 incidence directions as well as responses of the drivers, all measured at the four microphones of the Hearpiece as well as the eardrum in the occluded and open ear. The transfer functions were measured in both ears of 25 human subjects and a KEMAR with anthropometric ears for five reinsertions of the device. We describe the measurements of the database and analyse derived acoustic parameters of the device. All regarded transfer functions are subject to differences between subjects as well as variations due to reinsertion into the same ear. Also, the results show that KEMAR measurements represent a median human ear well for all assessed transfer functions. The database is a rich basis for development, evaluation and robustness analysis of multiple hearing device algorithms and applications.</p>
COVRIN D0.3.1: Database of COVID-19 research activities
<p>OHEJP project: COVRIN "One Health research integration on SARS-CoV-2 emergence, risk assessment and preparedness".</p> <p>Since the start of the pandemic in early 2020, a huge number of research projects have been initiated on SARS-CoV-2/COVID-19; additionally, many pre-existing networks and infrastructures have turned their attention to the virus, setting up SARS-CoV-2-specific services. To avoid overlaps and ensure optimal use of resources, a scoping review was performed of European Union-supported SARS-CoV-2 research activities that overlap with COVRIN in terms of focus.</p> <p>This database is associated with OHEJP Deliverable report "D0.3.1: Scoping review of European Union-supported COVID-19 research activities" available at https://doi.org/10.5281/zenodo.5537781</p>
The IHA database of human geometries including torso, head and complete outer ears for acoustic research
<p>This is the first version of the IHA database, which is being created in the project HAPPAA C1 funded by the Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 352015383 - SFB 1330 C1. (https://uol.de/en/sfb-1330-hearing-acoustics)</p> <p>The database includes a subsample of 10 human geometries comprising the torso, head and the entire outer ear including the ear canal and eardrum. The data are available in two different 3D object formats: ply binary file, stl binary file.</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>
MongoDB database dump for the analysis of the current sustainability state of research software
<p>This data set is the MongoDB dump (bson files) of the data created and analyzed with the rsps framework. In the first step, a research subject is assigned to the research software repositories. Afterwards, the current sustainability state is evaluated. The data set comprises the following six bson files:</p> <p><strong>repositories: </strong>metadata, received from the GitHub REST API, for repositories containing the search terms "doi+10" or "doi+10+in:readme", additional information are the request date, the contained search term, and the repository hosting service, in this case for all repositories "github". For repositories the Readme files are available.</p> <p><strong>publications:</strong> metadata of publications, published on arXiv and ACM, that contain the search term "github.com".</p> <p><strong>rs_repositories:</strong> research software candidates containing a DOI or that are referenced by the publications contained in the publications data set.</p> <p><strong>rs_artifacts: </strong>research software artifacts that are referenced in the harvested GitHub repositories by a DOI and the harvested publications.</p> <p><strong>publication_subjects:</strong> All Science Journal Classification (ASJC) of Scopus combined with the Scopus source list and Scopus book title list (https://www.scopus.com/home.uri)</p> <p><strong>arxiv_subjects:</strong> arXiv taxonomy complemented with the ASJC research subject.</p> <p> </p>
India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research
<p>This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.</p> <p>v4.0: Development of District Flood Severity Index (DFSI)</p> <p>v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district. </p> <p>v1.0: India Flood Inventory (IFI) 1967-2016.</p> <p>v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.</p> <p><strong>REFERENCES</strong></p> <p>Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a></p> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>
Search results for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)
<p>This datasets is the result of the search for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)</p>
Research Organization Registry (ROR) Data in Sqlite Database
<p>Version 1.1 of the Research Organization Registry data translated into a SQLite database using ROR2DB (https://github.com/Metadata-Game-Changers/ROR2DB).</p> <p>This version adds country, country_code, and status to the ror table.</p>
Dataset for 'A Resource Hub For Interoperability And Data Integration In Heritage Research: The H-Setis Database'
<p> Data and scripts for charts and maps published in "A Resource Hub For Interoperability And Data Integration In Heritage Research: The H-Setis Database".</p>
Database of movement behavior and EEG in virtual audiovisual everyday-life environments for hearing aid research
<p>This database contains movement behavior (head, eye, torso) and EEG signals of 21 young normal-hearing (11 male, 11 female, mean age 25 +/- 3.6 years) and 19 elderly normal-hearing subjects (9 male, 12 female, mean age 69 +/- 5.4 years) measured in virtual audiovisual listening environments in the laboratory. The virtual audiovisual environments that were used are: a living room, a lecture hall, a cafeteria, a street and a train station. The video and audio material for the environments is also available (see Related identifiers). The methods and an analysis of the movement behavior are described in Hendrikse et al. (2019). The supplementary materials to this paper that are published here include plots of the gaze trajectories of the subjects in all environments, plotted separately for the young and elderly subjects so that they can be compared, and histograms of the head-, eye- and torso-rotation for the environments that were not included in the paper.</p>
Combat-TB-NeoDB: fostering Tuberculosis research through integrative analysis using graph database technologies.
<p>NeoDB is a free and open source integrated M.tuberculosis ‘omics’ knowledge-base. NeoDB is based on Neo4j and enables researchers to execute complex federated queries by linking well-known, curated and widely used biological data resources, and supplementary TB variants data from published literature.</p> <p>Documentation can be found at https://combat-tb-db.readthedocs.io</p>
Linked collectors and determiners for: Plant Specimen Database of Tama Forest Science Garden, Forestry and Forest Products Research Institute, Japan.
Natural history specimen data linked to collectors and determiners held within, "Plant Specimen Database of Tama Forest Science Garden, Forestry and Forest Products Research Institute, Japan". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/38e8b720-9074-4471-a016-73cae18a6c1c">https://bionomia.net/dataset/38e8b720-9074-4471-a016-73cae18a6c1c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/38e8b720-9074-4471-a016-73cae18a6c1c">https://gbif.org/dataset/38e8b720-9074-4471-a016-73cae18a6c1c</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Research database of Madagascar grasses compiled by Maria Vorontsova.
Natural history specimen data linked to collectors and determiners held within, "Research database of Madagascar grasses compiled by Maria Vorontsova". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/15d76f87-6d16-4948-b4d5-f7d50a168aa6">https://bionomia.net/dataset/15d76f87-6d16-4948-b4d5-f7d50a168aa6</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/15d76f87-6d16-4948-b4d5-f7d50a168aa6">https://gbif.org/dataset/15d76f87-6d16-4948-b4d5-f7d50a168aa6</a>. Formatted as a Frictionless Data package.
Database research on the performance of Brazilian teachers in the pandemic and the burnout syndrome
<p>The research developed at the Federal University of Santa Catarina aimed to analyze the levels of Burnout Syndrome in teaching activities during the pandemic, seeking to identify factors, conditions and levels of education that could be associated. Burnout Syndrome is associated with professional activity including exhaustion, emotional exhaustion, chronic stress and dehumanization. The research foresees two procedures for data collection. At first, the instrument was built and validated with specialists. The questionnaire applied online included sociodemographic questions and health conditions, as well as personal and institutional conditions for acting as a teacher during the pandemic and includes the adaptation of a standardized instrument on Burnout Syndrome.</p>
Database Publication - Exploring adult age-at-death research in anthropology: Bibliometric mapping and content analysis, Forensic Sciences, 2023
<p>The dataset contains the publications' ID and DOI numbers used in the bibliometric analysis for the paper "Exploring adult age-at-death research in anthropology: Bibliometric mapping and content analysis," which was published in Forensic Sciences. Dimensions only granted the authors permission to disclose the publication IDs and DOIs for this study. However, using the identification codes, it is possible to extract titles' full metadata through Dimensions (<a href="https://www.dimensions.ai/">https://www.dimensions.ai/</a>).</p>
Using Open Citation Databases for Snowballing in Software Engineering Research
<p>Dataset for our study on the coverage of software engineering articles in open citation databases:</p> <ul> <li>a list of the 23 sampled venues with their respective CORE ranks and publishers, <ul> <li>01-venues.csv,</li> </ul> </li> <li>a list of the 204 sampled articles with their respective number of references/citations per citation database, <ul> <li>02-articles.csv (articles with publication information),</li> <li>03-references-absolute.csv (number of references in published PDF & absolute numbers for reference coverage in databases),</li> <li>04-references-relative.csv (relative numbers for reference coverage in databases),</li> <li>05-citations-absolute.csv (absolute numbers for citation coverage in databases),</li> <li>06-citations relative.csv (relative numbers for citation coverage in databases),</li> </ul> </li> <li>a list of the 8 articles analyzed in more detail with complete references data from the citation databases, <ul> <li>07-selected-articles.csv (articles with publication information),</li> <li>08A–08H (comparison of references found in databases for each article),</li> </ul> </li> <li>and additional statistical measures and plots <ul> <li>09-Statistics.{pdf,xlsx} (statistical measures – i.e., minimum, maximum, median, average, variance – for the whole dataset and for subsets by publisher, CORE rank, or year of publication),</li> <li>10-Figures.zip (figures for references as shown in the study and additional figures for citations – each in EPS and PNG format).</li> </ul> </li> </ul>
The ePEStemology database: a meta-analysis of 15 years of research on knowledge claims of Payments for Ecosystem Services (PES)
<p><strong>Introduction</strong></p> <p>Payments for ecosystem services (PES) are a leading conservation finance tool to encourage land-users to deliver ecosystem services in exchange for financial incentives (Blundo-Canto et al., 2018; Kaiser et al., 2021; Salzman et al., 2018; Schomers and Matzdorf, 2013). They have generated considerable interest over the past two decades, following the publication of the UN’s Millennium Ecosystem Assessment (MEA, 2005). Yet, there has been a dearth of analysis that reveals the outcomes and evolution of the PES research body itself, with most studies having a very specific or limited focus on the theoretical and empirical development of these PES programmes. The ePEStemology database assembles a systematic literature review into a meta-analysis aimed at understanding knowledge generation in PES research. The database allows for an analysis of key trends in how the body of literature on PES has informed scholars and practitioners since the emergence of this conservation tool.</p> <p><strong>Research questions and main objectives:</strong></p> <p>The ePEStemology database is the largest review of PES scientific research to date and addresses the following research questions:</p> <ul> <li>In the history of 15 years of PES research, what are the dominant criteria used to define and assess success of PES in practice? Are there geographical patterns in defining those criteria? Where does research take place? And by whom?</li> <li>What does 15 years of PES research illustrate in terms of the social and political consequences that arise in the implementation of PES? To what extent does literature engage with territorial dynamics and conflicting frameworks around nature’s values?</li> <li>How has PES research evolved? Do research recommendations feed into future research objectives over time?</li> <li>How does the epistemological position, including methods used, geographic location and disciplinary focus of authors among other variables, influence PES research outcomes?</li> </ul> <p>A core objective of the database is to ensure it remains a living compendium of PES research. While the database does not account for every peer-reviewed published article on PES, it facilitates the possibility of iteratively adding new publications or retroactively adding in missing articles as well as those that use cognate terms to define PES (e.g. as rewards or compensation for ecosystem services). The database also aims to serve as an important basis for future research questions on overall or regional trends emerging from PES research.</p> <p><strong>Search strategy</strong></p> <p>Using these key objectives and questions, we defined a set of variables (see attached) and search strategy to construct the ‘ePEStemology’ database. The database is populated by International Scientific Indexed (ISI) peer-reviewed journal articles. It includes Anglophone articles in Scopus and Web of Science (WoS), using all of the search terms “PES”, “Payments for Ecosystem Services,” “Payment for Ecosystem Services,” “Payment for Environmental Service,” or Payments for Environmental Services” either in the title, abstract, or keywords of queried articles. </p> <p>Articles were excluded if they were produced in a language other than English or if they were book chapters, books, conference papers, reviews, or webpages. While recognizing the caveats of excluding research articles in other languages as well as in other media (e.g. as books or conference papers), we justify our approach in order to ensure consistency and comparability of the body of research literature.</p> <p>Following the different steps prescribed by the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol (Moher et al., 2009), we identified a total of 1,067 published articles between 2005 and 2019. We took the year 2005 as the starting point for published research on PES, following the Millennium Ecosystem Assessment (Millennium Ecosystem Assessment, 2005) of the same year as well as an initial seminal publication on the subject (Wunder, 2005). Initial screening of articles began in September, 2018 with a total of 1,215 ISI-peer reviewed research articles on PES identified between both the Scopus and WoS queries. Additional articles were included to account for the year 2019 in October, 2020 to total 1,439 articles.</p> <p>It is also worth noting that Scopus providing the greatest coverage of published articles on PES, accounting for 78% of the total identified. WoS accounted for 66% of the total, but also included 305 articles that did not fall within the Scopus search.</p> <p>The screening procedure for selecting articles was contingent on the time in which the search in SCOPUS and WoS were conducted. It should be noted that this systematic review does not account for every ISI-research article published on PES during the time period considered. Since the database search tools are continuously updated retroactively, the query date may alter the number of articles retrieved in the search, increasing them over time despite being limited to specific dates. However, even if the database gets adapted over time, we can safely assume that the corpus represents a representative account of peer-reviewed published research on PES. In addition to the language exclusions made (i.e. our database only includes Anglophone publications), there are various permutations on the PES terminology, including “payments for hydrological services,” “payments for watershed services”, “conservation payments,” “rewards for ecosystem services,” or “agro-ecological incentives,” and many others that make it challenging to fully account for every relevant article. However, the protocol does not include all these possible variations, but can easily allow these variations to be included and brought into the analysis at a later stage.</p> <p><strong>Coding strategy</strong></p> <p>For the initial database (2005-2019) four independent reviewers to review each abstract and full text of each article were selected (BT, CJ, GVH, VK). Each reviewer had experience in (empirical) PES research and have published peer-reviewed research on PES or other market-like transactions for ecosystem services (e.g. Tabaichount et al., 2019; Jacob et al., 2016; Van Hecken et al., 2015; Kolinjivadi and Sunderland, 2012). Broad variables of interest that guide our analysis (e.g. research objectives, disciplines, methods, author affiliations and positionality, geographical base, research outcomes, and others) served as the basis for coding. However, in order to establish a foundation of codes for each of these categories of interest and further refine each of those broader categories, the research team randomly selected 100 articles of the final 1,067 articles identified and applied a grounded theory open-coding process (e.g. Strauss and Corbin, 1990; Thornberg and Charmaz, 2014). The latter implies a process in which any preconceived identification for each category of interest was open to be further adapted prior to the final coding process. The iterative process of initial coding determined the most relevant sets of codes for each identified variable of interest, as well as possible values for each of them. Additional codes were added to the list as more articles were reviewed and until saturation was reached.</p> <p>After discussing and deciding on the final categories/variables with the whole group, all remaining articles in the corpus were evenly and randomly divided among all reviewers. On average, each of the four reviewers coded between 500 and 600 articles. </p> <p>Subsequently, and for over a period of 15 months, the coding team revised and coded all articles based on the abstracts and a review of the full text in case the abstract did not allow to accurately code some of the variables. Each article was independently (blindly) coded by two different reviewers. To ensure robustness of coding, pairs of reviewers for each set compared results of the coding process for each article through a triangulation process and resolved discrepancies through collective deliberation and cross-checked consensus.</p> <p><strong>Variable list</strong></p> <p>The variable list and description of variables and their corresponding values can be found in the attached Word-document</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.