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13,117 results for “Maps”
OpenEye PAINS Mapping Python Script
Open the record for dataset details and reuse information.
Map PAINS substructures according to PAINS class and find the highest PAINS violation
Open the record for dataset details and reuse information.
Phase-Based Antarctica Ice Velocity Map
Open the record for dataset details and reuse information.
Land use map of Maricopa County, central Arizona: period 1995
The 1995 Land Use coverage was created as a joint effort of MAG (Maricopa Association of Governments) and MAG member agency staff. Land Use components were classified into 24 categories. The 1995 Land Use coverage is used for a variety of planning purposes including socioeconomic forecasting and air quality modeling.
Bedrock geologic map of Hubbard Brook Experimental Forest and maps of fractures and geology in roadcuts along Interstate-93, Grafton County, New Hampshire.
Bedrock Geologic Map of Hubbard Brook Experimental Forest and maps of fractures and geology in roadcuts along Interstate-93, Grafton County, New Hampshire. Two maps are included in this data set. Interstate I-93 map includes plots summarizing data for fractures shown on map roadcuts. Citation: Barton, C. C., R. H. Camerlo and S. W. Bailey. 1997. Bedrock Geologic Map of Hubbard Brook Experimental Forest and maps of fractures and geology in roadcuts along Interstate-93, Grafton County, New Hampshire. Sheet 1, Scale 1:12,000; Sheet 2, Scale 1:200. U.S. Geological Survey, Miscellaneous Investigations Series, Map I-2562 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Baseline maps and description of Watershed 5 treatment
A commercial whole-tree harvest was carried out on the Hubbard Brook Experimental Forest's Watershed 5 in 1983-4. This dataset includes a description of this harvest (in methods), and associated maps and tables for reference when using data from this experiment. Long-term research continues on Watershed 5, and includes the collection of data on soils, vegetation, water chemistry, streamflow and precipitation.
Integrated Terrain Unit Map (ITUM) for Niwot Saddle, Niwot Ridge LTER Project Area, Colorado
A 350 x 500 m integrated terrain unit map (ITUM) was produced at 1:500 scale inside the 350 x 500 m saddle grid, and the 1:500 digital elevation model (DEM). Vegetation was mapped using Komarkova's (1979) classification system (Braun-Blanquet) units. All map units were mapped to 1/8-inch minimum map-polygon-size resolution. The map is part of the Saddle grid geographic information system (GIS). Many GIS projects use an approach in which existing mapped information is digitized into the GIS database directly from the original sources. The maps may have different map scale, map-unit resolutions, dates of data collection, and classification systems. When these different sources are combined in a GIS, artifacts may arise due to boundary mismatches and scale incompatibility (Dangermond and Harnden 1990). Integrated geobotanical mapping can minimize many of these problems. This method simultaneously maps vegetation and other terrain features that are interpreted on a common air-photo base (Everett et al. 1978, Walker et al. 1980). We use the term geobotany in its traditional European sense to refer to the study of plant communities and their relationships to geology, landforms, and soils (Braun-Blanquet 1932). Terrain geomorphic boundaries are used to guide the delineation on aerial photographs of most major vegetation boundaries similiar to the landscape-guided vegetation mapping approach developed in Europe (Zonneveld 1988) and the integrated terrain unit mapping approach developed by the Environmental System Research Institute in Redlands, CA (Dangermond and Harnden 1990). Additional information concerning the Niwot Ridge LTER GIS can be found in Walker et al. (1993). [1]Braun-Blanquet, J. 1932. Plant sociology: The study of plant communities. New York: McGraw-Hill, 439 pp. [2]Everett, K.R., P.J. Webber, D.A. Walker, R.J. Parkinson, and J. Brown. 1978. A geoecological mapping scheme for Alaskan coastal tundra. Third International Conference on Permafrost, 10-13 July 19
Aquarius CAP Level 3 Wind Speed Standard Mapped Image Monthly Data V5.0
Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the monthly wind speed V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.
Aquarius CAP Level 3 Sea Surface Salinity Rain Corrected Standard Mapped Image 7-Day Data V5.0
Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean sea surface salinity (SSS) rain corrected V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.
Aquarius CAP Level 3 Sea Surface Salinity Rain Corrected Standard Mapped Image Monthly Data V5.0
Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the monthly sea surface salinity (SSS) rain corrected V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.
Aquarius CAP Level 3 Wind Speed Standard Mapped Image 7-Day Data V5.0
Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean wind speed V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.
Aquarius CAP Level 3 Sea Surface Salinity Standard Mapped Image 7-Day Data V5.0
Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean sea surface salinity (SSS) V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.
Aquarius CAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly Data V5.0
Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the monthly sea surface salinity (SSS) V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.
MAP 2 in Four new species of Myrmecium Latreille, 1824 and complementary description of M. machetero Candiani & Bonaldo, 2017 (Araneae: Corinnidae: Castianeirinae)
MAP 2. Distribution records of Myrmecium quasimodo sp. nov.
Supplementary material 1 from: Vogiatzakis IN, Zotos S, Litskas VD, Manolaki P, Sarris D, Stavrinides MC (2020) Towards implementing Mapping and Assessment of Ecosystems and their Services in Cyprus: A first set of indicators for ecosystem management. One Ecosystem 5: e47715. https://doi.org/10.3897/oneeco.5.e47715
Suplementary file
Significance map of V404 Cygni region in 20-40 keV band.
<p><a href="https://www.astro.unige.ch/cdci/astrooda_?src_name=V404%20Cygni&RA=306.015917&DEC=33.867194&T1=2015-06-18T00:00:00.0&T2=2015-06-28T23:59:59.0&T_format=isot&instrument=isgri&osa_version=OSA10.2&radius=15&use_scws=no&E1_keV=20&E2_keV=40&query_type=Real&detection_threshold=100&product_type=isgri_image">https://www.astro.unige.ch/cdci/astrooda_?src_name=V404%20Cygni&RA=306.015917&DEC=33.867194&T1=2015-06-18T00:00:00.0&T2=2015-06-28T23:59:59.0&T_format=isot&instrument=isgri&osa_version=OSA10.2&radius=15&use_scws=no&E1_keV=20&E2_keV=40&query_type=Real&detection_threshold=100&product_type=isgri_image</a></p>
IGV-results of Bowtie mapping
<p> Visualize sequence read alignment data.</p>
1000 Disease Ontology terms and their Wikidata mappings to 17 mostly Indian languages and English
<p>This dataset contains the result of a SPARQL query run on the <a href="https://query.wikidata.org/">Wikidata Query Service</a> on 13 February 2020 around 22:25 UTC. They are archived here as a means to determine progress with the coverage of disease-related terms in languages other than English, particularly in languages of India.</p> <p>The <a href="https://query.wikidata.org/#%23%20Wikidata%20items%20for%20concepts%20that%20have%20a%20Disease%20Ontology%20ID%20%28P699%29%0A%23%20sorted%20by%20number%20of%20sitelinks%0A%23%20optionally%20with%20their%20Wikidata%20label%20in%20English%0A%23%20optionally%20with%20their%20Wikidata%20label%20in%20Hindi%2C%20Bangla%20and%20Swahili%0A%23%20optionally%20with%20their%20Wikidata%20label%20in%20Marathi%2C%20Telugu%2C%20Eastern%20Punjabi%2C%20Western%20Punjabi%2C%20Gujarathi%2C%20Maithili%2C%20Kannada%2C%20Odia%2C%20Bhojpuri%2C%20Tamil%2C%20Nepali%2C%20Urdu%2C%20Malayalam%2C%20Esperanto%0A%0ASELECT%20DISTINCT%20%3Fitem%20%0A%23%20English%20Wikidata%20label%0A%20%3FLabelEN%0A%0A%23%20%20%20%20English%20Wikipedia%20article%20title%0A%3FPageTitleEN%20%0A%0A%23%20Hindi%2C%20%20%20Bangla%20and%20Swahili%20Wikidata%20labels%0A%3FLabelHI%20%3FLabelBN%20%20%20%20%3FLabelSW%20%0A%0A%23%20Marathi%2C%20%20Telugu%2C%20Eastern%20Punjabi%2C%20Western%20Punjabi%2C%20Gujarathi%2C%20Maithili%2C%20Kannada%2C%20%20%20%20Odia%2C%20Bhojpuri%2C%20%20%20Tamil%2C%20%20Nepali%2C%20%20%20%20Urdu%2C%20Malayalam%2C%20Esperanto%0A%20%3FLabelMR%20%3FLabelTE%20%20%20%20%20%20%20%20%20%3FLabelPA%20%20%20%20%20%20%20%20%3FLabelPNB%20%20%20%3FLabelGU%20%3FLabelMAI%20%3FLabelKN%20%3FLabelOR%20%20%3FLabelBH%20%3FLabelTA%20%3FLabelNE%20%3FLabelUR%20%20%20%3FLabelML%20%20%20%3FLabelEO%0A%0A%3Fsitelinks%20%0A%0AWHERE%20%7B%0A%20%20%3Fitem%20wdt%3AP699%20%5B%5D%20.%20%20%0A%20%20%3Fitem%20wikibase%3Asitelinks%20%3Fsitelinks%20.%0A%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3F%3FLabelEN%20filter%20%28lang%28%3FLabelEN%29%20%3D%20%22en%22%29%20.%20%7D%20%0A%20%20OPTIONAL%20%7B%20%3Farticle%20schema%3Aabout%20%3Fitem%20%3B%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20%3B%20%20schema%3Aname%20%3FPageTitleEN%20.%20%7D%0A%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelHI%20filter%20%28lang%28%3FLabelHI%29%20%3D%20%22hi%22%29%20.%20%7D%20%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelBN%20filter%20%28lang%28%3FLabelBN%29%20%3D%20%22bn%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelSW%20filter%20%28lang%28%3FLabelSW%29%20%3D%20%22sw%22%29%20.%20%7D%0A%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelMR%20filter%20%28lang%28%3FLabelMR%29%20%3D%20%22mr%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelTE%20filter%20%28lang%28%3FLabelTE%29%20%3D%20%22te%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelPA%20filter%20%28lang%28%3FLabelPA%29%20%3D%20%22pa%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelPNB%20filter%20%28lang%28%3FLabelPNB%29%20%3D%20%22pnb%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelGU%20filter%20%28lang%28%3FLabelGU%29%20%3D%20%22gu%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelMAI%20filter%20%28lang%28%3FLabelMAI%29%20%3D%20%22mai%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelKN%20filter%20%28lang%28%3FLabelKN%29%20%3D%20%22kn%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelOR%20filter%20%28lang%28%3FLabelOR%29%20%3D%20%22or%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelBH%20filter%20%28lang%28%3FLabelBH%29%20%3D%20%22bh%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelTA%20filter%20%28lang%28%3FLabelTA%29%20%3D%20%22ta%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelNE%20filter%20%28lang%28%3FLabelNE%29%20%3D%20%22ne%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelUR%20filter%20%28lang%28%3FLabelUR%29%20%3D%20%22ur%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelML%20filter%20%28lang%28%3FLabelML%29%20%3D%20%22ml%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelEO%20filter%20%28lang%28%3FLabelEO%29%20%3D%20%22eo%22%29%20.%20%7D%0A%7D%0AORDER%20BY%20DESC%28%3Fsitelinks%29%0ALIMIT%201000">SPARQL query</a></p> <ul> <li>was for <ul> <li> <p>Wikidata items for concepts that have a <a href="https://www.wikidata.org/wiki/Property:P699">Disease Ontology ID (P699)</a></p> <ul> <li> <p>sorted by number of sitelinks</p> </li> <li> <p>optionally with their Wikidata label in English</p> </li> <li> <p>optionally with their Wikipedia article title in English</p> </li> <li> <p>optionally with their Wikidata label in Hindi, Bangla and Swahili</p> </li> <li> <p>optionally with their Wikidata label in Marathi, Telugu, Eastern Punjabi, Western Punjabi, Gujarathi, Maithili, Kannada, Odia, Bhojpuri, Tamil, Nepali, Urdu, Malayalam, Esperanto</p> </li> </ul> </li> </ul> </li> <li>is contained in the file SPARQL.txt,</li> </ul> <p>whereas the results are available in several formats, as provided by the Wikidata Query Service:</p> <ul> <li>query.csv</li> <li>query.tsv<br> query.html</li> <li>query.json.txt (Zenodo produced an error upon trying to upload the file as query.json, so I renamed it, which worked fine).</li> </ul> <p>A simplified version of the SPARQL query can also be fed into the TABernacle tool that <a href="https://tools.wmflabs.org/tabernacle/#/tab/sparql/SELECT%20%09%0A%09%3Fitem%0AWHERE%20%09%0A%7B%0A%20%20%3Fitem%20wdt%3AP699%20%5B%5D%20.%20%20%0A%20%20%3Fitem%20wikibase%3Asitelinks%20%3Fsitelinks%20.%0A%0A%7D%0AORDER%20BY%20DESC(%3Fsitelinks)/Len%2Chi%2Cbn%2Csw%2Cmr%2Cte%2Cpa%2Cpnb%2Cgu%2Cmai%2Ckn%2Cor%2Cbh%2Cta%2Cne%2Cur%2Cml%2Ceo">represents</a> the live data in a way that facilitates editing the missing pieces.</p>
Data and analysis accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p>This repository contains files, instructions to replicate, and internal scripts for the manuscript "A pan-plant protein complex map reveals deep conservation and novel assemblies"</p> <p>See also <a href="http://plants.proteincomplexes.org/">http://plants.proteincomplexes.org</a></p>
Figures 1–6. Big Bend maps and dung beetles. 1–3 in The dung beetle fauna of the Big Bend region of Texas (Coleoptera: Scarabaeidae: Scarabaeinae)
Figures 1–6. Big Bend maps and dung beetles. 1–3) Location of study area and collection sites. 1) Extension of the Chihuahuan Desert (dark green) in north-central Mexico and southwestern United States. 2) [A] Trans-Pecos Texas showing counties and Big Bend region (shaded); [B] Tri-county Big Bend region (numbers indicate primary collection sites described in Materials and Methods; dots mark secondary sites). 3–6) Beetles in natural settings. 3) Aggregation of Canthon imitator, C. praticola and C. mixtus at cow dung. 4) Canthon imitator rolling ball of cow dung. 5) Stick nest of Neotoma micropus at base of mesquite brush, potential habitat for inquilinous Onthophagus. 6) Canthon blumei feeding directly from javelina carcass.
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.