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13,117 results for “Maps”
Probability maps of exceeding six soil thickness in mainland France
<p>This is the dataset of probability maps of exceeding six soil thickness (i.e. 5, 15, 30, 60, 100, 200 cm) in mainland France produced in the paper "Probability mapping of soil thickness by random survival forest at a national<br> scale" by Chen et al. (2019).</p> <p>Manuscript citation: Chen, S., Mulder, V.L., Martin, M.P., Walter, C., Lacoste, M., Richer-de-Forges, A.C., Saby, N.P., Loiseau, T., Hu, B. and Arrouays, D., 2019. Probability mapping of soil thickness by random survival forest at a national<br> scale. Geoderma, 344, 184-194.</p> <p>When using the data, please cite repositories as well as the original manuscript.</p> <p>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
OMOP2OBO Condition Occurrence Mappings
<p><strong>OMOP2OBO Condition Occurrence Mappings V1.0</strong></p> <p>These mappings were created by the OMOP2OBO mapping algorithm (see links below). OMOP2OBO - the first health system-wide, disease-agnostic mappings between standardized clinical terminologies and eight Open Biomedical Ontology (OBO) Foundry ontologies spanning diseases, phenotypes, anatomical entities, cell types, organisms, chemicals, vaccines, and proteins. These mappings are also the first to be explicitly created using standard terminologies in the Observational Medical Outcomes (OMOP) common data model (CDM), ensuring both semantic and clinical interoperability across a space of N conditions (and N relationships curated in these ontologies).</p> <p>The mappings in this repository were created between OMOP standard condition occurrence concepts (i.e., SNOMED CT) to the Human Phenotype Ontology (HPO) and the (Mondo). The National Library of Medicine's Unified Medical Language System (UMLS) Semantic Types are first used to filter out all concepts that did not have a biological origin (accidents, injuries, external complications, and findings without clear interpretations). Then, the Semantic Type was used to prioritize the mapping of HPO concepts to findings and symptoms and Mondo to Semantic Types indicative of disease. For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings.</p> <p><br> <strong>Mapping Details</strong><br> Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included.</p> <p><strong><em>Mapping Categories</em></strong></p> <ul> <li><strong>Automatic One-to-One Concept</strong>: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1</li> <li><strong>Automatic One-to-One Ancestor:</strong> Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1</li> <li><strong>Automatic One-to-Many Concept: </strong>Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li><strong>Automatic One-to-Many Ancestor:</strong> Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li><strong>Manual One-to-One: </strong>Hand mapping created using expert suggested resources; 1:1</li> <li><strong>Manual One-to-Many:</strong> Hand mapping created using expert suggested resources; 1:Many</li> <li><strong>Cosine Similarity:</strong> score suggested mapping -- manually verified</li> <li><strong>UnMapped:</strong> No suitable mapping or not mapped type</li> </ul> <p><br> <em><strong>Mapping Statistics</strong></em><br> Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology:</p> <table align="center"> <thead> <tr> <th scope="col">Mapping Category</th> <th scope="col">HPO</th> <th scope="col">Mondo</th> </tr> </thead> <tbody> <tr> <td>Automatic One-to-One Concept</td> <td>4767</td> <td>9097</td> </tr> <tr> <td>Automatic One-to-Many Concept</td> <td>150</td> <td>885</td> </tr> <tr> <td>Cosine Similarity</td> <td>1375</td> <td>667</td> </tr> <tr> <td>Automatic One-to-One Ancestor</td> <td>13595</td> <td>8911</td> </tr> <tr> <td>Automatic One-to-Many Ancestor </td> <td>38080</td> <td>40224</td> </tr> <tr> <td>Manual</td> <td>5131</td> <td>755</td> </tr> <tr> <td>Manual One-to-Many</td> <td>10326</td> <td>2835</td> </tr> <tr> <td>Unmapped</td> <td>36301</td> <td>46345</td> </tr> </tbody> </table> <p><br> <strong>Provenance and Versioning: </strong>The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020. </p> <p><strong>Caveats:</strong> The deposited files only contain the mappings that were generated automatically by the algorithm. The manually generated mappings will be deposited with the official preprint manuscript. Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.</p> <p> </p> <p><strong>Important Resources and Documentation</strong></p> <ul> <li>GitHub: <a href="https://github.com/callahantiff/OMOP2OBO">OMOP2OBO</a></li> <li>Project Wiki: <a href="https://github.com/callahantiff/OMOP2OBO/wiki">OMOP2OBO - wiki</a></li> <li>Zenodo Community: <a href="https://zenodo.org/communities/omop2obo">OMOP2OBO</a></li> <li>Preprint Manuscript: <a href="https://doi.org/10.5281/zenodo.5716421">10.5281/zenodo.5716421</a></li> </ul>
Supplementary material 1 from: Yemshanov D, Koch F, Ducey M, Haack R, Siltanen M, Wilson K (2013) Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker's perspective. NeoBiota 18: 193-218. https://doi.org/10.3897/neobiota.18.4002
Risk of out-of-state (out-of-province) locations to be the source of forest pests transported in firewood carried by campers. The risk rank values are based on the delineation of nested non-dominant sets via the first-degree stochastic dominance rule (FSD). The ranks close to 1.0 denote the highest risk of pest arrival and the ranks close to 0 denote the lowest risk. (doi: 10.3897/neobiota.18.4002.app1) File format: Adobe PDF File (pdf).:
Supplementary material 4 from: Yemshanov D, Koch F, Ducey M, Haack R, Siltanen M, Wilson K (2013) Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker's perspective. NeoBiota 18: 193-218. https://doi.org/10.3897/neobiota.18.4002
Summary of differences between risk rank classes, 0–0.05, 0.05–0.25, 0.25–0.5, 0.5–0.75, 0.75–0.95 and 0.95–1 in the delineations based on the FSD and SSD rules. (doi: 10.3897/neobiota.18.4002.app4) File format: Adobe PDF File (pdf).:
Theia OSO Land Cover Map 2106
<p>http://osr-cesbio.ups-tlse.fr/~oso/posts/2017-03-30-carte-s2-2016/</p>
Derivation of plant functional type (PFT) maps from the ESA CCI Land Cover product
<p><em>This package supplements the following paper submitted to ESSD: <strong>Gross and net land cover changes of the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015).</strong></em></p> <p><em>Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A. and Peng, S.: Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps, Earth Syst. Sci. Data Discuss., 1–23, doi:10.5194/essd-2017-74, 2017.</em></p> <p><em>This package contains the protocol of converting the original annual ESA CCI Land Cover product into plant functional types (PFTs) that can be used by land surface models and the corresponding cross-walking table.</em></p> <p><em>The original ESA LC class data and translated PFTs in 2000 as an example are attached in the .zip file. The annual ESA CCI PFT maps from 1992 to 2015 at half degree resolution are also added in a .zip file.</em></p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Topics, approaches and contributions of research about CRIS
<p>The size of the diamonds indicates the amount of articles belonging to each category.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Number of articles included during the search and qualitative evaluation process of the study
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Topics covered in the analyzed articles
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Process of systematic mapping
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications per country
<p>Publications per country. Map based on longitude and latitude. The circle’s colors show each country, while their size indicates the number of articles.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications by year and research areas
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Contributions of the studies
<p>The size of the rectangles shows the amount of articles in each category.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Research approaches
<p>The size of the diamonds shows the number of articles that belong to each category.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Input data for: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes input data used in the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard.</strong> Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>For this article, data have been processed with a R/GRASS script. The development version of this script is available on GitHub at https://github.com/ghislainv/deforestation-maps-Mada. The last release of this script is archived on Zenodo: [DOI: 10.5281/zenodo.1118484].</p>
Figure 1: The ECG model-MAPPING BETWEEN SEMANTIC GRAPHS AND SENTENCES IN GRAMMAR INDUCTION SYSTEM
<p>The following Figure 1 shows a sample semantic graph that describes a<br> simple test world.<br> During the processing of the ECG, the base units of the graph are the ECG<br> atoms. An ECG atom corresponds to a primitive statements related to one<br> predicate. It has a structure of one-level deep tree, where the root of the tree<br> is the predicate and the concepts linked to it are the leaves. The child concept<br> of the root predicate may be not only a single concept but it can be another<br> ECG atom.</p>
Output data from: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes output data from the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard</strong>. Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>This repository includes Madagascar forest cover (forXXXX.tif), forest density (fordensXXXX.tif), distance to forest edge (dist_edge_XXXX.tif) and forest fragmentation index (fragXXXX.tif) for the years 1953, 1973, 1990, 2000, 2005, 2010 and 2014. Data are available as GeoTIFF raster files at 30m resolution in the UTM 38S projection (EPSG:32738).</p>
Map of China Pumped Storage Hydropower Plant Geospatal Distribution
<p>A map of PSH geospatial distribution in China, updated as of April 2024.</p> <p>All information are collected by using open-source on Internet. If you want to cite this map, please retain the copyright of Jingcai Cai.</p> <p>This map will also be upload in ArcGIS online soon, also the dataset will be released soon.</p> <p>If you want to contact author, could sent e-mal to me to get the dataset and have deeper communication with him.</p>
Рис. 6. Карта распоΛожения гнезΑ ΑаΛьневосточного аиста в Амурской обΛасти в 2018– 2019 гг. по типам гнезΑовых опор Fig. 6. Map of Oriental stork nests in the Amur region in 2018–2019 by types of nesting supports in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис. 6. Карта распоΛожения гнезΑ ΑаΛьневосточного аиста в Амурской обΛасти в 2018– 2019 гг. по типам гнезΑовых опор Fig. 6. Map of Oriental stork nests in the Amur region in 2018–2019 by types of nesting supports
Рис. 3. Карта-схема пространственного распределениЯ биомассы Macoma balthica. Fig. 3. A schematic map of the spatial distribution of the Macoma balthica biomass. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 3. Карта-схема пространственного распределениЯ биомассы Macoma balthica. Fig. 3. A schematic map of the spatial distribution of the Macoma balthica biomass.
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International Brain Laboratory public data
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OpenNeuro
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