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58 results for “mapping database”
EUKulele databases for Phaeocystis mapping (Krinos et al. 2023)
<p>Supplementary dataset for Krinos et al. 2023, <a href="https://www.biorxiv.org/content/10.1101/2023.07.30.551153v1">Missing microbial eukaryotes and misleading meta-omic conclusions</a>, <em>bioRxiv</em>. Each zipped folder is a different database containing some subset of the <em>Phaeocystis</em> references used to map against the <em>Tara</em> Oceans metagenomes. These folders contain a combined database and taxonomy table derived from other sequencing dataset resources which can be used directly with the <a href="https://github.com/akrinos/2022-euk-diversity">EUKulele software</a>.</p> <ul> <li>`marmmetsp_all_phaeo.tar.gz` - full database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014), and all <em>Phaeocystis</em> references as listed in Krinos et al. (2023)</li> <li>`marmmetsp_all_colonies.tar.gz` - database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014), and <em>Phaeocystis</em> references known to form colonies (<em>Phaeocystis antarctica</em>, <em>Phaeocystis pouchetii</em>, <em>Phaeocystis globosa</em>)</li> <li>`marmmetsp_all_free.tar.gz` - database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014), and <em>Phaeocystis</em> references known <strong>not</strong> to form colonies (<em>Phaeocystis rex</em>, <em>Phaeocystis jahnii</em>, <em>Phaeocystis cordata</em>)</li> </ul>
Public database of multilingual map reading test
<p>The Excel file contains the filtered data records of the map-reading study of the Research Group on Experimental Cartography at the Eötvös Loránd University (ktk.elte.hu). The data collection started in the autumn of 2015 and lasted until April 2022. The file contains three sheets: demographic_questions; correct_answers; map_reading_database. The first two sheets contain the questions asked, the answer codes, and the correct answers. The third one has 511 records, which is the result of a filtering of the original 805 fills. The filtering excluded the unfinished tests, and the ones with fill time below 2.5 minutes and above 15 minutes.</p>
SDMapCH (v1.3): a Comprehensive database of modelled species habitat suitability maps for Switzerland
Open the record for dataset details and reuse information.
Mapping the Landscape of Open Source Health Economic Models: A Systematic Database Review and Analysis
<p><span>Health economic models are crucial for health technology assessment (HTA) to evaluate the value of medical interventions. Open source models (OSMs), where source code and calculations are publicly accessible, enhance transparency, efficiency, credibility, and reproducibility. This study systematically reviews databases to map the landscape of available OSMs in health economics.</span></p>
BridgeDb: pathway identifier mapping database derived from Wikidata
<p>First release of a BridgeDb pathway identifier mapping database. Currently supports Wikidata and WikiPathways identifiers. CCZero.</p> <pre><code>[INFO]: Database finished. INFO: old database is Wikidata 1.0.0 (build: 20211211) INFO: new database is Wikidata 1.0.0 (build: 20211211) INFO: Number of ids in Wd (Wikidata): 905 (unchanged) INFO: Number of ids in Wp (WikiPathways): 900 (unchanged) INFO: new size is 2 Mb (changed +0.0%) INFO: total number of identifiers is 1805 INFO: total number of mappings is 1810 </code></pre> <p> </p>
SSHOC - National Gallery - Grounds Database CIDOC CRM Mapped Dataset
<p>In 2018 the <a href="https://doi.org/10.5281/zenodo.5838339">IPERION-CH Grounds Database</a> was presented to examine how the data produced through the scientific examination of historic painting preparation or grounds samples, from multiple institutions could be combined in a flexible digital form. Exploring the presentation of interrelated high resolution images, text, complex metadata and procedural documentation. The original <a href="https://research.ng-london.org.uk/iperion/">main user interface</a> is live, though password protected at this time. Work within the <a href="https://www.sshopencloud.eu/">SSHOC project</a> aimed to reformat the data to create a more <a href="https://www.go-fair.org/fair-principles/">FAIR</a> data-set, so in addition to mapping it to a standard ontology, to increase Interoperability, it has also been made available in the form of <a href="http://en.wikipedia.org/wiki/Linked_Data">open linkable data</a> combined with a <a href="http://en.wikipedia.org/wiki/SPARQL">SPARQL</a> end-point. A draft version of this live data presentation can been found <a href="https://rdf.ng-london.org.uk/sshoc/">Here</a>.</p> <p>This is a draft data-set and further work is planned to debug and improve its semantic structure.This deposit contains the CIDOC-CRM mapped data formatted in XML and an example model diagram representing some of the key relationships covered in the data-set.</p>
BridgeDb Complex Identifier Mapping Database
<p>BridgeDb identifier mapping databases for complexes extracted from Wikidata. Code to generate the database (https://github.com/bridgedb/Wikidata2Bridgedb - ComplexIdentifiers.java). </p>
BridgeDb: pathway identifier mapping database derived from Wikidata
<p>Release of a BridgeDb gene identifier mapping database between Wikidata and Ensembl.</p> <pre>[INFO]: Database finished. INFO: old database is Wikidata 1.0.0 (build: 20230506) INFO: new database is Wikidata 1.0.0 (build: 20230506) INFO: Number of ids in Wd (Wikidata): 153715 (unchanged) INFO: Number of ids in En (Ensembl): 153637 (unchanged) INFO: new size is 95 Mb (changed +0.0%) INFO: total number of identifiers is 307352 INFO: total number of mappings is 307430 </pre>
Data from: High-resolution and large-extent mapping of plant species richness using vegetation-plot databases
Open the record for dataset details and reuse information.
Database for Kansei and Design: A Systematic Mapping Review
<p>Dadaset used for the development of the Systematic Mapping Review "Kansei and Design".</p>
Geographic range maps for Mammal Diversity Database v1.2 taxonomy from "Expert range maps of global mammal distributions harmonised to three taxonomic authorities"
<p>Data mirroring for long-term integrity of these critical geospatial resources. Included here are expert geographic range maps aligned to the taxonomy of the Mammal Diversity Database (MDD) version 1.2, which was published on 24 Sept 2020 https://zenodo.org/record/4139818. That taxonomy includes 6,485 total species, of which 103 are considered recently extinct, 20 are considered domestic extant, and 6,362 are considered wild extant (this corrects for 1 species, <em>Capra hircus</em>, that was incorrectly coded as 'domestic=0' rather than 'domestic=1' in the MDD v1.2). For this mapping project, only 6,362 species from MDD v1.2 have maps -- this total:</p> <ul> <li>excludes all extinct and domestic species;</li> <li>excludes 2 species for which no spatial information was available (<em>Nycticeius aenobarbus</em> and <em>Phoniscus aerosus</em>); and</li> <li>includes 2 species<em> </em>(<em>Elaphurus davidianus</em> and <em>Oryx dammah</em>) that are extinct in the wild (EW) in IUCN, have recent range information and were included in the MDD as extant.</li> </ul> <p><strong>### File inventory ###</strong></p> <ul> <li>Order-level zipped files (27 total), one for each extant order of mammals, unzips to geopackage (*.gpg) format;</li> <li>Mammalia-wide zipped file (1: "MDD_Mammalia.zip"), includes maps for all 27 orders, unzips to gpg format;</li> <li>Full taxonomy for MDD v1.2 (as published on https://zenodo.org/record/4139818) in csv format ("MDD_v1.2_all_6485species.csv"); and</li> <li>Subset of MDD v1.2 taxonomy for which range maps are here provided (6,362 species) in csv format ("mdd_spList_wFamilieswOrders_mapped_6362species.csv").</li> </ul> <p><br> <strong>### Full citation ###</strong></p> <p>Marsh, C.J., Sica, Y.V., Burgin, C.J., Dorman, W.A., Anderson, R.C., del Toro Mijares, I., Vigneron, J.G., Barve, V., Dombrowik, V.L., Duong, M., Guralnick, R., Hart, J.A., Maypole, J.K., McCall, K., Ranipeta, A., Schuerkmann, A., Torselli, M.A., Lacher Jr, T., Mittermeier, R.A., Rylands, A.B., Sechrest, W., Wilson, D.E., Abba, A.M., Aguirre, L.F., Arroyo-Cabrales, J., Astúa, D., Baker, A.M., Braulik, G., Braun, J.K., Brito, J., Busher, P.E., Burneo, S.F., Camacho, M.A., Cavallini, P., de Almeida Chiquito, E., Cook, J.A., Cserkész, T., Csorba, G., Cuéllar Soto, E., da Cunha Tavares, V., Davenport, T.R.B., Deméré, T., Denys, C., Dickman, C.R., Eldridge, M.D.B., Fernandez-Duque, E., Francis, C.M., Frankham, G., Franklin, W.L., Freitas, T., Friend, J.A., Gadsby, E.L., Garbino, G.S.T., Gaubert, P., Giannini, N., Giarla, T., Gilchrist, J.S., Gongora, J., Goodman, S.M., Gursky-Doyen, S., Hackländer, K., Hafner, M.S., Hawkins, M., Helgen, K.M., Heritage, S., Hinckley, A., Hintsche, S., Holden, M., Holekamp, K.E., Honeycutt, R.L., Huffman, B.A., Humle, T., Hutterer, R., Ibáñez Ulargui, C., Jackson, S.M., Janecka, J., Janecka, M., Jenkins, P., Juškaitis, R., Juste, J., Kays, R., Kilpatrick, C.W., Kingston, T., Koprowski, J.L., Kryštufek, B., Lavery, T., Lee Jr, T.E., Leite, Y.L.R., Novaes, R.L.M., Lim, B.K., Lissovsky, A., López-Antoñanzas, R., López-Baucells, A., MacLeod, C.D., Maisels, F.G., Mares, M.A., Marsh, H., Mattioli, S., Meijaard, E., Monadjem, A., Morton, F.B., Musser, G., Nadler, T., Norris, R.W., Ojeda, A., Ordóñez-Garza, N., Pardiñas, U.F.J., Patterson, B.D., Pavan, A., Pennay, M., Pereira, C., Prado, J., Queiroz, H.L., Richardson, M., Riley, E.P., Rossiter, S.J., Rubenstein, D.I., Ruelas, D., Salazar-Bravo, J., Schai-Braun, S., Schank, C.J., Schwitzer, C., Sheeran, L.K., Shekelle, M., Shenbrot, G., Soisook, P., Solari, S., Southgate, R., Superina, M., Taber, A.B., Talebi, M., Taylor, P., Vu Dinh, T., Ting, N., Tirira, D.G., Tsang, S., Turvey, S.T., Valdez, R., Van Cakenberghe, V., Veron, G., Wallis, J., Wells, R., Whittaker, D., Williamson, E.A., Wittemyer, G., Woinarski, J., Zinner, D., Upham, N.S., Jetz, W., 2022. Expert range maps of global mammal distributions harmonised to three taxonomic authorities. Journal of Biogeography 49 (5): 979-992. <a href="https://doi.org/10.1111/jbi.14330">https://doi.org/10.1111/jbi.14330</a><br> </p> <p><strong>###</strong><strong> Data downloads on Map of Life ###</strong></p> <p>All range maps for the three taxonomic sources are openly available for non-commercial use through https://mol.org/datasets or at species-level at https://mol.org/species, or for bulk download at https://doi.org/10.48600/mol-7r3j-8066 (HMW), https://doi.org/10.48600/mol-zzrs-q778 (CMW) and https://doi.org/10.48600/mol-48vz-p413 (MDD).</p> <p> </p> <p><strong>###</strong><strong> Abstract ###</strong></p> <p><strong>Aim: </strong> Comprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the <em>Handbook of the Mammals of the World</em> (HMW) and the <em>Illustrated Checklist of the Mammals of the World</em> (CMW).</p> <p><strong>Location: </strong> Global.</p> <p><strong>Taxon: </strong> All extant mammal species.</p> <p><strong>Methods: </strong> Range maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species).</p> <p><strong>Results: </strong> Range maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use.</p> <p><strong>Main conclusion: </strong> Expert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control.</p> <p><strong>Keywords: </strong> GIS; Mammalia; biodiversity; biogeography; conservation planning; mapping; species distributions.</p>
Database of ionospheric rate of TEC change index (ROTI) map derived from Indonesian GNSS receiver network
<blockquote> <p><strong>USERS CAN ALSO ACCESS THE ROTI MAPS IN THIS DATABASE LINK:</strong></p> <p><strong>https://gatotkaca.brin.go.id/petaionosfer/</strong></p> </blockquote> <p> </p> <p><strong>Introduction</strong></p> <p>Indonesia, located near the magnetic equator in Southeast and East Asia, is essential for studying ionospheric phenomena, particularly equatorial plasma bubbles (EPBs). The Indonesian Geospatial Information Agency (BIG) has deployed a network of Global Navigation Satellite System (GNSS) receivers as part of the Indonesia Continuously Operating Reference Stations (Ina-CORS) across this country. This network has enabled the creation of detailed ionospheric irregularities maps based on the Rate of Total Electron Content (TEC) Change Index (ROTI). These maps are crucial for understanding EPBs in Southeast and East Asia.</p> <p><strong>GNSS network and ROTI map generation</strong></p> <p>The Ina-CORS consists of over 300 GNSS receivers strategically placed across Indonesia (see attached figure "geographic map_receivers.jpg"), spanning from 95°E to 140°E and from 5°N to 10°S. These receivers continuously gather GNSS observable data with a time resolution of 30 seconds in a Receiver Independent Exchange (RINEX) file. This data is then processed to generate the ROTI maps from the magnetic equator to the southern low-latitude region in Southeast/East Asia.</p> <p>The GNSS data in the RINEX file is processed to calculate the Total Electron Content (TEC) using the open software developed by Seemala (2023). The software can be found at https://seemala.blogspot.com/2020/12/gps-tec-program-version-3-for-rinex-3.html. ROTI is derived by measuring the standard deviation of the rate of change of TEC over a 5-minute interval (Pi et al., 1997). This index is a critical indicator of ionospheric irregularities with kilometers of spatial scales inside the EPBs.<br>Using ROTI data plotted at the Ionospheric Pierce Point (IPP) altitude of 350 km, 2-dimensional (2D) latitude-longitude ROTI maps are generated. The grid size of the ROTI map is 0.25° × 0.25°. The ROTI map is smoothed by a boxcar average of 5 × 5 grid data regarding geographic latitude and longitude. In the map, sunset and sunrise terminators at altitudes of 110 km (red curve), 350 km (green curve), and 650 km (black curve) are plotted. The ROTI map is generated at each interval of 10 minutes from 9:00 to 23:50 UT. The name file of the zipped map in one day indicates the year and day of the year. For example, s_2024122_map.rar indicates the maps on day 122 in 2024.</p> <p><strong>Purpose</strong></p> <p>Sharing GNSS data in RINEX files from the CORS could be strictly limited. Sharing the ROTI map derived from the CORS of Southeast Asian countries can be an alternative solution. This database aims to store the ROTI maps over Indonesia derived from GNSS data of the Ina-CORS network. The ROTI map database is also freely accessible and can be used for educational and scientific purposes. It is an academic/scientific resource and promotes a deeper understanding of EPB phenomena and their impact on navigation and communication systems. This database enables continuous monitoring and analysis of ionospheric conditions, particularly EPB occurrence. The database supports scientific research, enhances GNSS applications, and contributes to space weather forecasting by providing a high-resolution ROTI map. This ROTI map database has been developed to encourage research collaboration between researchers globally and in Indonesia.</p> <p><strong>Attribution</strong></p> <p>Users must appropriately credit the data source in this database in any publications, presentations, or products derived from it. When using the ROTI maps in this database, please cite the database. The users acknowledge the Indonesian Geospatial Information Agency (Badan Informasi Geospasial, BIG) for providing the GNSS data to make the ROTI map. Users are also encouraged to collaborate with ionospheric researchers in Indonesia.</p> <p><strong>References</strong></p> <p>Gopi K. Seemala (2023), Chapter 4 - Estimation of ionospheric total electron content (TEC) from GNSS observations, Editor(s): A.K. Singh, S. Tiwari, In Earth Observation, Atmospheric Remote Sensing, Elsevier, 2023, Pages 63 - 84, doi.org/10.1016/B978-0-323-99262-6.00022-5.</p> <p>Pi, X., Mannucci, A. J., Lindqwister, U. J., and Ho, C. M. (1997), Monitoring of global ionospheric irregularities using the worldwide GPS network, Geophys. Res. Lett., 24, 2283–2286.</p> <p> </p> <p><strong>More Information</strong><br>Feel free to reach out via email for more information. Your feedback is invaluable to us, and we encourage users to share their experiences and suggestions for research ideas and further improvements.<br>Correspondence: P. Abadi, Dr.; Researcher at Research Center for Climate and Atmosphere, BRIN; email: pray001[at]brin.go.id (replace "[at]" with "@").</p> <p> </p> <p> </p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl 91)
<p>Ensembl 91 derived ID mapping database for use with BridgeDb.</p>
China Dimensions Data Collection: China Maps Bibliographic Database
The China Maps Bibliographic Database is an historical collection of bibliographic information for more than 400 maps of China. The information resides in a searchable database and includes title, author/editor, publisher, location, projection, year, elevation, land cover type (forest, desert, marsh/swamp, grassland), vegetation, transportation (roads, railroads), rivers and lakes, spatial coverage (provincial, county, township), and language for maps published from 1765 to 1994. The information is available in both English and Chinese (GB Code for Chinese Characters). This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Center for International Earth Science Information Network (CIESIN).
Multi-Dimensional Simulation of the Volumetric Database by Video - Mapping Technology of Sea surface temperature remotley sensed (Algerian basin) ( SST : 2003 - 2007 - 2015 - 2019 ) (L4, MUR,GHRSST)
<p><span>Marine data, which are volumetric data, contain a variety of information about a water body (e.g., seawater temperature, salinity, density, and current) (Feng Zhang et all.,2019),due to its large scale, random variation and multi-resolution in nature, are hard to be visualized and analyzed (Jerry, 2001; Claes, 2004). Moreover, people can not observe the internal characteristics and phenomena of the ocean directly and comprehensively. ‘Digital Ocean’, which emerged after ‘Digital Earth’, focuses on analyzing ocean phenomena and serves for ocean applications. ‘Digital Earth’ is an information system including massive, multi-resolution, multi-temporal marine databases and analysis algorithms (Shi and Lei, 2011). Nowadays, constructing an ocean model and visualizing volumetric data have become some of the most important and critical research topics of ‘Digital Ocean’. The study of marine volumetric data visualization can be valuable in many other research areas (Coelho et al., 2004).</span></p> <p><span>Volume visualization, one of the most important fields of scientific visualization, is the process of generating meaningful and visual information on a two-dimensional image plane from three dimensional datasets. It has been increasingly important in geographical information systems to improve the ocean modeling (Gonzato and Saec, 2000; Djurcilov et al., 2002)</span> </p> <p><strong><span>REF :</span></strong></p> <p><span>Feng Zhang, Ruichen Mao, Zhenhong Du, Renyi Liu,Spatial and temporal processes visualization for marine environmental data using particle system,Computers & Geosciences, Volume 127,2019,Pages 53-64,ISSN 0098-3004,https://doi.org/10.1016/j.cageo.2019.02.012.</span></p> <p><span>Jerry, T., 2001. Simulating ocean water. ACM SIGGRAPH (Special Interest Group on Computer Graphics) 2001 Course Notes, Los Angeles, <a href="http://home1.get.net/tssndrf/"><span>http://home1.get.net/tssndrf/</span></a>.</span></p> <p><span>Claes, J., 2004. Real time water rendering. Master thesis. Department of Computer Science, Lund University</span></p> <p><span>Shi, S. X., and Lei, B., 2011. Theory and Practice on China Digital Ocean. Ocean Press, Beijing, 80-100.</span></p> <p><span><span>Coelho, A., Nascimento, M., Bentes, C., de Castro, M. C. S.,and Farias, R., 2004. Parallel volume rendering for ocean visualization in a cluster of PCS. In: <em>Proceeding of VI Brazilian</em> <em>Symposium on Geoinformatics</em>, Campos do Jordão, São Paulo, Brazil, 22- 24.</span></span></p> <p><span>Gonzato, J. C., and Saec, B. L., 2000. On modeling and rendering ocean scenes. <em>Journal of Visualisation and Computer</em> <em>Animation</em>, <strong>11 </strong>(1): 27-37.</span></p> <p><span>Djurcilov, S., Kim, K., Lermusiaux, P. F. J., and Pang, A., 2002.Visualizing scalar volumetric data with uncertainty. <em>Computers</em> <em>and Graphics</em>, <strong>2 </strong>(26): 239-248.</span></p>
Database of publication "Diagnostic accuracy of Quantitative Susceptibility Mapping in Multiple System Atrophy: the impact of echo time and the potential of histogram analysis". NeuroImage: Clinical, 2022, 102989.
<p>The file data_MSA_NICL2022.mat contains the data from Lancione et al., NeuroImage: Clinical 2022.</p> <p>If you use these data, please cite:</p> <p>Lancione, M., et al. Diagnostic accuracy of Quantitative Susceptibility Mapping in Multiple System Atrophy: the impact of echo time and the potential of histogram analysis. NeuroImage: Clinical, 2022, 102989.</p> <p><br> The data are structured as follows:</p> <p>- age: age in years of all 44 subjects<br> - sex: "1": male; "-1": female<br> - diagnosis: "HC": healthy controls; "MSAp": Multiple System Atrophy with Parkinsonian phenotype; "MSAc": Multiple System Atrophy with cerebellar phenotype<br> - roi_name: names of the 20 ROIs; the abbreviations are defined in the original paper<br> - stats_name: names of the 9 histogram features<br> - stats: [9 histogram features x 44 subjects x 20 ROIs x 5 TEs (4 acquired TEs + the average of all TEs)]</p>
Gene expression profiling coupled with the Connectivity Map database-mining reveals potential therapeutic drugs for Hirschsprung disease
GEO Series GSE98502. Homo sapiens. 16 samples. Type: Expression profiling by array.
Analysis of differentially expressed genes in placental tissues of Pre-eclampsia Using microarray combined with Connectivity Map database
GEO Series GSE47187. Homo sapiens. 5 samples. Type: Expression profiling by array.
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.