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58 results for “mapping database”
Derby database for mapping secondary to primary HGNC gene symbols
<p>The datasets (hgnc_complete_set and withdrawn) used to create this ID mapping database were downloaded from HGNC (<em>HUGO Gene Nomenclature Committee at the European Bioinformatics Institute, </em>website URL: https://www.genenames.org/) on 09/05/2022. </p> <p>This database was used for the <a href="https://github.com/tabbassidaloii/BridgeDbDemoBioSB2022">BridgeDb demo at BioSB 2022</a> conference.</p> <p>The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary</p> <p>This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</p>
Africa Tree Database: use mapping
There are three main goals for this project. 1. To compile information on plant traits, seed dispersal, seed predation, and frugivory in Africa. 2. To store the information in a way that enables analyses of plant-animal interactions. 3. To share information about plants and animals in the database. We are compiling information from published literature. Our team uses a password-protected data entry form. If you would like to add published information that is not currently in our database, please contact us. One of the scientific goals of this project is to be able to find broad patterns and trends in seed dispersal relationships across Africa. The database is structured in a way to enable analysis of plant traits along with dispersers/predators and location information. Information about plants and the animals associated with them is viewable through this database, as well as through the Encyclopedia of Life (ATD is a content partner). Several hundred photographs of plants are viewable in Flickr and the Encyclopedia of Life.<p></p>
Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database
<p>The database of organizational actors, "Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database" analyzed in "<span>Whose Priorities? Examining Inequities in Earth </span><span>Observation Advancements Across Africa" </span>this study, is available on Zenodo, an open-access repository developed under the European OpenAIRE program. The dataset comprises information on 310 space-centric earth observation organizations, including headquarters locations. For the 31 organizations in our sample, we provide additional details including the African countries where their projects are active, the type of initiative or program, other focus areas, organizational classification (commercial, government, or nongovernmental), funding source (public or private), organizational type (research, startup, or established industry), capabilities (data analysis, data storage, image labeling, competition platforms), involvement in early warning systems, data accessibility, availability of global products, and whether they build commercial satellites.</p> <p>This open sharing of the compiled organizational data aims to promote transparency, reproducibility, and additional investigations into the evolving landscape of earth observation activities globally and across Africa. Analyses of this dataset's relationships, funding flows, and priorities can provide further insights to guide equitable advancement of earth observation capabilities.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl Metazoa 52)
<p>Mapping databases derived from Ensembl Metazoa 52. These files can be used with BridgeDb.<br> The scripts which were used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p> <p>This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</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>
Mapping an Impact Network for Transformational Change in CLEVER - Stakeholder Database
<p>How does international trade in agricultural and forest products affect biodiversity? CLEVER (Creating leverage to enhance biodiversity outcomes of global biomass trade) investigates this question, particularly for animal feed, energy crops, tropical timber and aquacultures.<br> <br> This stakeholder database has been created in the CLEVER project to provide a comprehensive but not exhaustive list of stakeholders who may have an interest in CLEVER. The database includes information such as names and types of organisations and data about their role in the value chains concerned (timber and soy in Brazil, timber in Cameroon, fishmeal and oil globally). Particular attention has been paid to the involvement of local communities and vulnerable groups directly or indirectly affected by trade. </p>
Additional Data: Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023
<p>The following is a selection of figures and tables from a bibliometric study that will be released later. The title of this study is Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023.</p> <p>In the online listing of the appendix, we will find three figures (Figure 5, Figure 6, and Figure 12) and three tables (Table 3, Table 4, and Table 4), also several references related to this research. It was important to us that the core of the study that is now being carried out not be diminished in any way, which is why we chose the photos and tables we did. This study was conceived and supported by the Indonesia Endowment Fund for Education (LPDP), which the Ministry of Finance administers in the Republic of Indonesia, to evaluate current trends and research problems in computational thinking for education. The Scopus database was used, and its range of coverage was from 1987 to 2023.</p> <p> </p>
Database of Geographic Information: Topographic Map of Central Arizona
This dataset has been created to meet the needs of the research community of Arizona State University. Apart from purely vizualization purposes (i.e. displaying the data on various maps) it can potentially be used for spatial modeling. The data consist of engineering-quality contours, also known as isolines, created from the NED 10-meter Digital Elevation Model subset to the extent somewhat exceeding Cetral Arizona - Phoenix LTER. Contours ( lines connecting points of equal height above sea level) are drawn at 15 meter intervals with the base set at 145 m of elevation. Contours are an exact interpretation of the grid surface model and may sometimes appear blocky looking, may cross, appear to intersect, or form an unclosed branching line. All these are valid engineering-quality interpretations of the elevation surface that cartographers typically modify (smooth) for aesthetic purposes.
Gene/Protein BridgeDb ID Mapping Database (Ensembl 85)
<p>Ensembl 85 derived ID mapping database for use with <a href="https://bridgedb.github.io/">BridgeDb</a> and was created with <a href="https://github.com/bridgedb/create-bridgedb-genedb">custom code</a>.</p>
BridgeDb: Human and SARS-related corona virus gene/protein mapping database derived from Wikidata
<p>Gene/Protein mappings for human and SARS-related coronaviruses. Data is derived from Wikidata. Mappings from Wikidata to NCBI Gene, RefSeq, UniProt, and Guide to Pharmacology Target are provided in the .bridge file.</p> <ul> <li>179 Wikidata (Wd) identifiers</li> <li>82 Uniprot-TrEMBL (S) identifiers</li> <li>70 NCBI Gene (L) identifiers</li> <li>66 RefSeq (Q) identifiers</li> <li>9 Guide to Pharmacology Target (Gpt) identifiers</li> </ul> <p>Virus name was added as attribute ("virus") to Wikidata Xrefs.</p>
wikidata mapping to taxonomic ids from 11 other databases
Open the record for dataset details and reuse information.
UNICITY: A depth maps database for people detection in security airlocks
<p><strong>UNICITY: A depth maps database for people detection in security airlocks.</strong></p> <p>UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries, like for instance tailgating (when a person walks very close to another to get into a restricted area). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</p> <p><strong>Main Features:</strong></p> <ul> <li>UNICITY consists of 58k images using two depth sensors.</li> <li>65 recorded sequences with one or two people performing different behaviors such as attacks and tailgating.</li> <li>UNICITY also provides code for evaluation and visualization, and full annotation of people such as the location of head and shoulders.</li> <li>This new dataset is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</li> </ul> <p><strong>Citation</strong>:</p> <p>Please cite the following paper if you use the UNICITY dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>UNICITY: A depth maps database for people detection in security airlocks. J. Dumoulin, O. Canevet, M. Villamizar, H. Nunes, O.A. Khaled, E. Mugellini, F. Moscheni, and J.M Odobez. International Conference on Advanced Video and Signal-based Surveillance Workshop (AVSSW). November 2018.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>Joël Dumoulin, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Olivier Canévet, Idiap Research Institute, Martigny, Switzerland.</li> <li>Michael Villamizar, Idiap Research Institute, Martigny, Switzerland.</li> <li>Hugo Nunes, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Omar Abou Khaled, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Elena Mugellini, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Fabrice Moscheni, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Jean-Marc Odobez, Idiap Research Institute, Martigny, Switzerland.</li> </ul> <p><strong>Acknowledgement:</strong></p> <p>The work was supported by Innosuisse, the Swiss innovation agency, through the UNICITY (3D scene understanding through machine learning to secure entrance zones) project.</p> <p><strong>Links:</strong></p> <p>Next links contain additional information about the dataset:</p> <ul> <li>Innosuisse UNICITY project: <a href="https://www.idiap.ch/en/scientific-research/projects/UNICITY">[link]</a></li> <li>Paper describing the dataset: <a href="http://publications.idiap.ch/index.php/publications/show/3939">[link]</a></li> <li>Video presenting the dataset: <a href="https://www.youtube.com/watch?time_continue=2&v=pGrnI12OhmA">[link]</a></li> <li>Paper using the dataset for counting people and detecting intrusions: <a href="http://michael-villamizar.com/avss18.html">[link]</a> <ul> <li>WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems.<br> M. Villamizar, A. Martinez-Gonzalez, O. Canevet and J-M. Odobez.<br> International Conference on Advanced Video and Signal-based Surveillance (AVSS) - 2018.</li> </ul> </li> </ul> <p><strong>Contact</strong>:</p> <p>For any questions, please contact:</p> <ul> <li>Michael Villamizar, Idiap Research Institute, Martigny -Switzerland</li> </ul>
Supplementary tables for the paper: "Comprehensive Mapping of the AOP-Wiki Database: Identifying Biological and Disease Gaps"
<p><strong>Supplementary tables for the paper: "Comprehensive Mapping of the AOP-Wiki Database: Identifying Biological and Disease Gaps"</strong></p> <p><em>Original Research Article</em><br><strong>Frontiers in Toxicology</strong>, March 8, 2024<br>Section: Regulatory Toxicology<br><strong>Volume 6 - 2024</strong> | <a href="https://doi.org/10.3389/ftox.2024.1285768" target="_new" rel="noopener">https://doi.org/10.3389/ftox.2024.1285768</a></p> <p><strong>Authors</strong>:<br>Thomas Jaylet, Thibaut Coustillet, Nicola M. Smith, Barbara Viviani, Birgitte Lindeman, Lucia Vergauwen, Oddvar Myhre, Nurettin Yarar, Johanna M. Gostner, Pablo Monfort-Lanzas, Florence Jornod, Henrik Holbech, Xavier Coumoul, Dimosthenis A. Sarigiannis, Philipp Antczak, Anna Bal-Price, Ellen Fritsche, Eliska Kuchovska, Antonios K. Stratidakis, Robert Barouki, Min Ji Kim, Olivier Taboureau, Marcin W. Wojewodzic, Dries Knapen, Karine Audouze</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl Fungi 52)
<p>Mapping databases derived from Ensembl Fungi 52. These files can be used with BridgeDb.<br> This version doesn't have the issue of not could be searched using gene names (e.g. in PathVisio).</p> <p>The scripts which were used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl 106)
<p>Ensembl 106 derived ID mapping databases for use with BridgeDb.<br> This version doesn't have the issue of not could be searched using gene names (e.g. in PathVisio).</p> <p>The scripts used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl Plants 52)
<p>Mapping databases derived from Ensembl Plants 52. These files can be used with BridgeDb.<br> This version doesn't have the issue of not could be searched using gene names (e.g. in PathVisio).</p> <p>The scripts which were used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl 107)
<p>Ensembl 107 derived ID mapping databases for use with BridgeDb.<br> This version doesn't have the issue of not could be searched using gene names (e.g. in PathVisio).</p> <p>The scripts used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p> <p> </p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl 108)
<p>Ensembl 108 derived ID mapping databases for use with BridgeDb.<br> <br> The scripts used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p>
D6.1 - Report on non-technical barrier and legal issues - Literature-mapping database
<p>This data set presents a literature map for D6.1 "Report on non-technical barrier and legal issues" considering relevant non-technical and legal challenges for consideration during the deployment of a pay-per-wash business model in European.</p>
DATABASE OF THE SYSTEMATIC MAPPING OF SOCIAL SUPPORT ANS STIGMA IN POPULATION LIVING WITH HIV
<p>DATABASE OF THE SYSTEMATIC MAPPING OF SOCIAL SUPPORT ANS STIGMA IN POPULATION LIVING WITH HIV. IT WAS USED THE PRISMA (2020) METHODOLOGY.</p>
ScienceDex guides
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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.