Find research datasets worth reusing

opencc-by-4.0Jun 2022View details →

Data from: Robust Estimation of Field Inhomogeneity Map Following Magnitude-Based Water-Fat Separation with Resolved Ambiguity

These data have been uploaded and shared as part of "Robust Estimation of Field Inhomogeneity Map Following Magnitude-Based Water-Fat Separation with Resolved Ambiguity". The data may be used for field inhomogeneity mapping and PDFF/R2* reconstruction.  These data were acquired by Perspectum Ltd (https://perspectum.com/) on a healthy volunteer. Informed consent was obtained from the participant. The dataset includes a localizer series and a multi-slice series (magnitude and phase) acquired from a volunteer covering the dome of the liver, heart and lungs: <ul> <li>1-localizer_haste_bh</li> <li>2-I_6_Echo_3D_32_Slice_IDEAL</li> <li>3-I_6_Echo_3D_32_Slice_IDEAL</li> </ul> These data were gathered using a Siemens Prisma 3 Tesla scanner. The main dataset comprises an acquisition with thirty-two slices including the abdominal region, with slices placed away from the isocenter. The acquisition consisted of a 6‐echo (TE1=1.3 ms, ΔTE=1 ms) gradient-recalled echo (GRE) protocol designed to minimize T1 bias (3° flip angle), Pixel Bandwidth = 1565 Hz, and 232 x 256 reconstructed image size, with 5 mm slice thickness and 1.72 x 1.72 mm^2 in-plane resolution.

opencc-by-4.0Jun 2022View details →

Maps of ecosystem multifunctionality and ecological connectivity for identifying Green Infrastructure networks in the European Alps

High resolution raster datasets (20 meters) containing the results of an ecological connectivity and an ecosystem multifunctionality assessment for identifying Green Infrastructure networks in 10 pilot regions of the European Alps, modelled as part of the LUIGI Interreg Alpine Space project. Pilot regions include: department of Isère (FR), departments of Savoie and Haute-Savoie (FR), Munich Metropolitan Region (DE), Central Area of Salzburg (AT), South Burgenland (AT), Gori&scaron;ka region (SI), South Tyrol (IT), canton of Grisons (CH), Metropolitan City of Milan (IT), and Metropolitan City of Turin (IT). For a preview of the data and the results available for each pilot region <a href="https://www.alpine-space.org/projects/luigi/en/project-results/d.t1.2.1-pilot-regions-policy-briefs">click here</a> Further information on the LUIGI project is available at: <a href="https://www.alpine-space.org/projects/luigi/en/home">https://www.alpine-space.org/projects/luigi/en/home</a> <a href="https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf">https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf </a> The datasets include: <ul> <li>a map for ecosystem service-based multifunctionality calculated out of the average of 11 standardized ecosystem service indicators: water provision, crop potential, timber production, fodder provision, pollination potential, carbon sequestration, nitrogen retention, natural hazard mitigation, runoff retention, outdoor recreation, and landscape aesthetics.</li> <li>a map of the modelled Ecological Network composed of core areas and ecological corridors. Corridors are modelled for medium-large forest mammal species and represent least-cost pathways connecting core areas. Different classes indicate areas with different levels of current ecological connectivity starting from core areas to areas in cities or anthropized land with no connectivity. Modeled corridors are presented in two classes to mirror different levels of prioritization and management actions.</li> <li>a map of the resistance of the landscape to the movement of forest mammal species. The landscape resistance raster has been developed by reclassifying and aggregating a high resolution (5m) land use and land cover map. Resistance values have been determined in relation to the naturalness of different land use and land cover classes. In this context, land use or landscape resistance is intended as the opposite of habitat suitability.</li> </ul>

FOODRUS Cooperation Map

FOODRUS has built a Cooperation and Collaboration Network (CCN) with other projects and initiatives which have worked on, or are currently working in the field of food losses and food waste. With these projects FOODRUS aims to develop thematic discussions on tools and approaches for food loss and food waste prevention. Through these dedicated working groups project partners from the involved projects share experiences and lessons learnt and identify gaps and barriers as well as best practices for the transition towards a more sustainable food system.

opencc-by-4.0Jun 2022View details →

Derby database for mapping secondary to primary HMDB identifiers

The data (hmdb_metabolites, released on 17/11/2021) used to create this ID mapping database was downloaded from HMDB (Human Metabolome Database, website URL: https://hmdb.ca/).  This database was used for the <a href="https://github.com/tabbassidaloii/BridgeDbDemoBioSB2022">BridgeDb demo at BioSB 2022</a> conference. The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary 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>).

opencc-by-2.0Jun 2022View details →

Derby database for mapping secondary to primary HGNC gene symbols

The datasets (hgnc_complete_set and withdrawn) used to create this ID mapping database were downloaded from HGNC (HUGO Gene Nomenclature Committee at the European Bioinformatics Institute, website URL: https://www.genenames.org/) on 09/05/2022.  This database was used for the <a href="https://github.com/tabbassidaloii/BridgeDbDemoBioSB2022">BridgeDb demo at BioSB 2022</a> conference. The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary 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>).

Fig. 4 in Molecular phylogenies map to biogeography better than morphological ones

Fig. 4 The number of morphological and molecular trees most congruent with biogeography. Comparison of the number of trees in each sample (morphological or molecular) with a greater biogeographic fit than its counterpart. a Consistency index (CI), grey bars show totals for the whole sample, coloured bars indicate totals in the subset significantly different from the expected null (CI & RI p value <0.05). b Retention index (RI), grey bars show totals for the whole sample, coloured bars indicate totals in the subset significantly different from the expected null (CI & RI p value <0.05). c P values for the CI & RI random permutations (CI & RI p value), where grey bars show totals for the whole sample, coloured bars are clades with values <0.05. d biogeographic HER (bHER), counts are for the whole dataset. Bars show the number of clades in each subset, with binomial confidence intervals calculated using the approach of Clopper and Pearson103. N = 48 biologically independent pairs of morphological and molecular phylogenies.

Fig. 1 in Molecular phylogenies map to biogeography better than morphological ones

Fig. 1 Testing the biogeographic congruence of phylogenetic trees. a Defining biogeographic regions and coding taxon presences and absences. 1. Occurrence data on the distribution of extant species is used to produce a list of biogeographic regions for the clade and to summarise ranges for the taxa in the published phylogenies. 2. This distributional information is converted into a matrix of binary characters representing taxa in biogeographic regions, where 0 indicates the taxon is absent and 1 indicates the taxon is present. 3. Characters in the occurrence matrix are mapped onto the morphological and molecular phylogeny selected for each clade, allowing standard measures of character fit (CI, RI) to be calculated for each tree. b Presence and absence codings in each matrix are randomly reassigned to taxa, keeping the presence and absence codings fixed for each row. Characters form the new randomly permuted matrix are mapped onto the original trees and both CI and RI are recalculated. The entire randomisation process is performed 10,000 times. c The 10,000 CI and RI values from matrices' biogeographic region reassignments form a null distribution of expected congruence values if taxa in the clade were randomly distributed in biogeographic regions. The observed CI and RI of region characters for a given tree is compared to the null distribution for that same tree to determine whether the observed biogeographic congruence value lies outside of the 95% confidence interval.

Fig. 2 in Molecular phylogenies map to biogeography better than morphological ones

Fig. 2 Biogeographic congruence in morphological and molecular phylogenies. Binary biogeographic region characters mapped onto paired morphological and molecular phylogenies. a Placental mammals (Eutheria) from O'Leary et al. 2013100. b Caribbean boas (Chilabothrus/Epicrates), with the morphological tree taken from Kluge 1989101 and the molecular tree taken from Tolson 1987102. Regions for which the terminal taxon is coded present are represented as coloured pie slices. Consistency index (CI), retention index (RI) and biogeographic HER (bHER) values given are for the matrix of biogeographic region presences and absences, while CI & RI p value is calculated using 10,000 randomly permuted region matrices.

Geo-Harmonized PM2.5 maps over Europe for the years 2018-2020

A space-time extremely randomised trees model was used to estimate daily (between 10 a.m. and 2 p.m.) PM2.5 concentrations with 1 km spatial resolution for a three-year period 2018–2020 over Europe. Satellite remote sensing, meteorological data, and land variables were used as the independent variables, PM2.5 ground-observations were used as the dependent variable while building the model.

Identification and fine mapping of gummy stem blight resistance gene Gsb-7(t) in Melon

Gummy stem blight (GSB), caused by the Didymella bryoniae (Auersw.) Rehm, is a devastating fungal disease of melon worldwide. Breeding GSB-resistant cultivars with host resistance genes is considered to be the most economic and effective strategy to control this disease. In this study, 260 melon germplasm resources were screened for resistance to GSB, and an inbred line H55R exhibited immunity to GSB was identified. To further understand the resistance mechanism of H55R against GSB, an F2 population was obtained from a cross between the GSB-susceptible line A15 and H55R, and genetic analysis indicated that the resistance in H55R was controlled by a single dominant gene, tentatively named Gsb-7(t). The Gsb-7(t) gene was finally delimited to a 140 kb interval on chromosome 7 using bulked-segregant analysis and chromosome walking strategies. Ten putative genes were annotated in this region that contains a wall-associated receptor kinase (WAK) gene MELO3C010403. The MELO3C010403 gene contains two alternative transcripts, T1 and T2, with five and seven non-synonymous mutation sites, respectively. Gene expression analysis showed that expression of T1 but not the T2 was significantly induced by the D. bryoniae at 24 hours post-inoculation (hpi), indicating that the T2 transcript of MELO3C010403 was the most likely candidate gene of Gsb-7(t). Our results offer new genetic resources and will be helpful for the development of GSB-resistant melon cultivars in the future.

A Priority Map for Vision-Language Navigation - Datasets

This archive contains full versions of the datasets and additional data presented in the following paper: A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues A priority map module (PM-VLN) boosts the performance of transformer-based architectures in navigation tasks by combining temporal sequence alignment and feature-level localisation in cross-modal inputs. The module is pretrained on trajectory estimation and a multi-objective task that pairs location estimation with cross-modal sentence prediction. Two datasets are introduced for the auxiliary tasks:  - TR-NY-PIT-central - a set of path traces for routes in two urban locations.  - MC-10 - a set of samples with multimodal inputs representing landmarks in 10 US cities. Full details and links for this research are available at the following link: https://jasonarmitage-res.github.io/projects/priority_map/ Additional data comprising path traces for routes in Manhattan and language tokens for the Touchdown task are provided for training and evaluating the PM-VLN and framework on the Touchdown benchmark. Please refer to the following link for details on the Touchdown dataset and StreetLearn environment: https://sites.google.com/view/streetlearn/touchdown

The Story Map of the Royal Netherlands Standardization Institute (NEN) on Institutionalizing Responsibility

The Royal Netherlands Standardization Institute (NEN) has started various research projects to explore how standardization can be made more socially responsible. The Delft University of Technology has explored what factors motivate, obstruct, and facilitate social responsibility in standardisation processes. As such, it supports NEN in understanding how it can better align its activities with the values, needs, and expectations of society.

Fig. 4. Distribution maps. A in A new semi-cryptic Filistata from caves in the Levant with comments on the limits of Filistata insidiatrix (Forsskål, 1775) (Arachnida: Araneae: Filistatidae)

Fig. 4. Distribution maps. A. Filistata insidiatrix (Forsskål, 1775) in the Mediterranean and Middle East (note the species has been recorded from Angola, Venezuela and the islands of Socotra, Azores and Cabo Verde, but these records seem to be introduced); literature records have been taken from Benoit (1968), Brignoli (1982), Marusik & Zonstein (2014), Marusik & Zamani (2015a) and Zonstein & Marusik (2019). B. Filistata insidiatrix (circles) and F. betarif sp. nov. (star) in Israel and Palestine; only specimens examined by us are included in the map. Darker shades of orange are records that include at least one male specimen. C. Other species of Filistata Latreille, 1810 in Israel (except F. insidiatrix).

OER World Map data dump

This is a data dump in CSV and JSON format from the OER World Map as of 2022-04-29, the date of its shutdown. It comprises 6615 metadata records which utilize the schema.org vocabulary to describe these kind of things:   <ul> <li>organizations (type "Organization")</li> <li>services (type "Service")</li> <li>persons (type "Person)</li> <li>projects (type "Action")</li> <li>events (type "Event")</li> <li>stories (type "Article")</li> <li>tools (type "Product")</li> <li>publications (type "WebPage")</li> <li>policies (type "Policy")</li> </ul> The OER Worldmap was hosted and developed by the North Rhine-Westphalian Library Service Centre (hbz) from 2014 to 2022. See for more background information: <ul> <li>Project blog: <a href="https://oerworldmap.wordpress.com">https://oerworldmap.wordpress.com</a></li> <li>Software repos & issue tracking: <a href="https://github.com/hbz/oerworldmap">https://github.com/hbz/oerworldmap</a> (backend), <a href="https://github.com/hbz/oerworldmap-ui">https://github.com/hbz/oerworldmap-ui</a> (frontend)</li> <li>Twitter: <a href="https://twitter.com/oerworldmap/">@oerworldmap</a></li> </ul>

Dataset for Cloud Identification in Mars Daily Global Maps with Deep Learning

Overview: This repository stores cloud masks and MDGMS for Martian Years (MYs) 28-33. MDGMs were obtained from Harvard Dataverse (<a href="https://doi.org/10.7910/DVN/U3766S">https://doi.org/10.7910/DVN/U3766S</a>), and cloud masks were created using the cloudmask model (<a href="https://github.com/03kalven/cloudmask">https://github.com/03kalven/cloudmask</a>). The cloud masks contained in the binary folders have already been binarized using the threshold of 0.912. This dataset is considerably smaller in size than the floating-point cloud mask dataset and is suited for researchers that prefer to use the default threshold of 0.912.   Quick breakdown of the files and folders: <ul> <li>phase folders contain the complete set of MDGMs and cloud masks for Mars Reconnaisance Orbiter mission phases P, B, G, D, F, and J (MYs 28-33)</li> <li>phase_binary folders contain the complete set of binary MDGMs and cloud masks for Mars Reconnaisance Orbiter mission phases P, B, G, D, F, and J (MYs 28-33)</li> <li>view_masks.ipynb has a few handy methods to plot cloud masks and MDGMs</li> </ul>   Phase folders: Each folder is organized based on phase (P, B, G, D, F, J) and subphase (_01 to _23). In each subphase, there are cloudmasks and mdgms folders, as well as a .txt file with MY and solar longitude (Ls) data for each day.   MDGM and cloud mask formats: <ul> <li>mdgm: JPEG, 3600x1801</li> <li>cloudmask: (NETCDF4_CLASSIC data model, file format HDF5): <ul> <li>dimensions(sizes): x(3600), y(1801)</li> <li>variables(dimensions): float32 longitude(x), float32 latitude(y), float32/int16 cloudmask(y, x)</li> </ul> </li> </ul> The cloud masks' values for any pixel are -999 for NaN and a float from 0 to 1 reporting the model's confidence in that pixel being a cloud. The cloud masks can be binarized using get_cloudmask() included in view_masks.ipynb. A binarized mask would report -999 for NaN, 0 for no cloud, and 1 for cloud. The default threshold is 0.912, but this value can be adjusted if desired. The cloud masks' (0,0) coordinate is the lower left corner of the map, so it may be needed to flip the cloudmask vertically before plotting on a Martian map. The cloud mask NetCDF files are constructed the same way as Wang and González Abad's (<a href="https://doi.org/10.7910/DVN/WU6VZ8">https://doi.org/10.7910/DVN/WU6VZ8</a>).

openmit-licenseSep 2020View details →

OMOP2OBO Measurement Mappings

OMOP2OBO Measurement Mappings V1.0 The mappings in this repository were created between OMOP standard measurement concepts (i.e., LOINC) to the Human Phenotype Ontology (HPO), Chemical Entities of Biological Interest (CheBI), Vaccine Ontology (VO), National Center for Biotechnology Information Taxon Ontology (NCBITaxon), Protein Ontology (PRO), Cell Ontology (CL), and the Uber-anatomy Ontology (UBERON). For each measurement, all levels of the test result (results above, below, and within a reference range) were mapped, not only those deemed clinically relevant. Results outside of a reference range, but not currently deemed clinically relevant (as advised by the literature or consultation via domain expert), were annotated to the nearest relevant ontology concept ancestor. For example, when annotating the results of a test for Asparagus IgE Ab RAST class [Presence] in Serum (LOINC:15547-3), a result above a reference range would be annotated with an increased anti-plant-based food allergen IgE antibody level (HP:0410228). While a low level of this antibody may not be deemed clinically relevant, it is still outside of the provided reference range and thus was annotated to the nearest applicable concept ancestor, abnormal immunoglobulin level (HP:0010701). There is one exception to this rule: all measured drugs and toxins (entities not normally found in the human body) with normal results (results that were not outside of a given reference range) were annotated to the same HP concept as the clinically relevant result and logically negated. For example, Amphetamine [Presence] in Urine by Screen (LOINC:19343-3), a positive finding was mapped to a positive urine amphetamine test (HP:0500112) and a negative finding was mapped to a positive urine amphetamine test and logically negated (NOT HP:0500112). LOINC2HPO currently aligns LOINC to HP. The current work extends existing LOINC2HPO annotations to match the OMOP2OBO mappings in the following two ways: (1) annotations were updated if new and/or more specific concepts had been added to the HP; and (2) existing mappings were expanded to include the measurement substance (body fluids, tissues, and organs via Uberon), the entity being measured (chemicals, metabolites, or hormones via ChEBI; cell types via CL; and proteins and protein complexes via PR), and the species of the measured entities (organism taxonomy via NCBITaxon). Consistent with LOINC2HPO, all measurements lacking sufficient specimen detail (those measured in non-specific body substances) were annotated as “Unspecified Sample” and all measurements without a valid result type were annotated as “Not Mapped test Type”. All modifications to the original LOINC2HPO annotations were meticulously recorded in the mapping evidence field enabling users to easily identify when an original LOINC2HPO annotation had been updated. For this OMOP domain, the owl:complementOf (“not” and was used to model normal test results), owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings. Mapping Details 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. Mapping Categories <ul> <li>Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1</li> <li>Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1</li> <li>Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Manual One-to-One: Hand mapping created using expert suggested resources; 1:1</li> <li>Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many</li> <li>Cosine Similarity: score suggested mapping -- manually verified</li> <li>UnMapped: No suitable mapping or not mapped type</li> </ul> Mapping Statistics 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: <table align="center"> <thead> <tr> <th scope="col">Mapping Category</th> <th scope="col">HPO</th> <th scope="col">UBERON</th> <th scope="col">ChEBI</th> <th scope="col">CL</th> <th scope="col">PR</th> <th scope="col">NCBITaxon</th> </tr> </thead> <tbody> <tr> <td>Automatic One-to-One Concept</td> <td>20</td> <td>1981</td> <td>268</td> <td>129</td> <td>19</td> <td>286</td> </tr> <tr> <td>Automatic One-to-Many Concept</td> <td>49</td> <td>5</td> <td>0</td> <td>24</td> <td>0</td> <td>0</td> </tr> <tr> <td>Automatic One-to-One Ancestor</td> <td>43</td> <td>426</td> <td>1149</td> <td>5</td> <td>5</td> <td>207</td> </tr> <tr> <td>Automatic Constructor - Ancestor  </td> <td>0</td> <td>1</td> <td>12</td> <td>1</td> <td>0</td> <td>0</td> </tr> <tr> <td>Cosine Similarity</td> <td>113</td> <td>50</td> <td>160</td> <td>35</td> <td>45</td> <td>56</td> </tr> <tr> <td>Manual</td> <td>10663</td> <td>319</td> <td>1446</td> <td>185</td> <td>1590</td> <td>2357</td> </tr> <tr> <td>Manual One-to-Many</td> <td>49</td> <td>1118</td> <td>528</td> <td>18</td> <td>133</td> <td>196</td> </tr> <tr> <td>UnMapped</td> <td>184</td> <td>184</td> <td>529</td> <td>3688</td> <td>2296</td> <td>982</td> </tr> </tbody> </table> Provenance and Versioning: 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.  Caveats: 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.   Important Resources and Documentation <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>

openmit-licenseSep 2020View details →

OMOP2OBO Drug Exposure Ingredient Mappings

OMOP2OBO Drug Exposure Ingredient Mappings V1.0 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]. The mappings in this repository were created between OMOP standard drug exposure concepts at the ingredient-level (i.e., RxNorm) to the Chemical Entities of Biological Interest (ChEBI), the National Center for Biotechnology Information Taxon Ontology (NCBITaxon), the Protein Ontology (PRO), and the Vaccine Ontology (VO). All concepts were aligned to at least one ChEBI concept and the remaining ontologies (NCBITaxon, PR, and VO) were mapped by their drug class and/or type (e.g., biologics versus vaccines). For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings. Mapping Details 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. Mapping Categories <ul> <li>Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1</li> <li>Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1</li> <li>Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Manual One-to-One: Hand mapping created using expert suggested resources; 1:1</li> <li>Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many</li> <li>Cosine Similarity: score suggested mapping -- manually verified</li> <li>UnMapped: No suitable mapping or not mapped type</li> </ul> Mapping Statistics 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: <table align="center"> <thead> <tr> <th scope="col">Mapping category</th> <th scope="col">ChEBI</th> <th scope="col">NCBITaxon</th> <th scope="col">PRO</th> <th scope="col">VO</th> </tr> </thead> <tbody> <tr> <td>Automatic One-to-One Concept</td> <td>3151</td> <td>155</td> <td>43</td> <td>108</td> </tr> <tr> <td>Automatic One-to-Many Constructor</td> <td>404</td> <td>1</td> <td>1</td> <td>0</td> </tr> <tr> <td>Automatic One-to-One Ancestor</td> <td>147</td> <td>17</td> <td>20</td> <td>4</td> </tr> <tr> <td>Automatic One-to-Many Ancestor</td> <td>210</td> <td>3</td> <td>2</td> <td>2</td> </tr> <tr> <td>Cosine Similarity</td> <td>109</td> <td>4241</td> <td>18</td> <td>17</td> </tr> <tr> <td>Manual</td> <td>322</td> <td>230</td> <td>157</td> <td>21</td> </tr> <tr> <td>Manual One-to-Many</td> <td>72</td> <td>14</td> <td>8</td> <td>2</td> </tr> <tr> <td>UnMapped</td> <td>7392</td> <td>7146</td> <td>11558</td> <td>11653</td> </tr> </tbody> </table> Provenance and Versioning: 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.  Caveats: 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.   Important Resources and Documentation <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>

opencc-by-4.0Aug 2022View details →

Geological Map of the Derain (H10) Quadrangle of Mercury (3 crater class version)

Geological (morphostratigraphic) map recognising 3 crater degradation classes. We also have a 5 crater class version that is otherwise identical. This version is slightly revised after review for publication in J Maps (3 Aug 2022).

opencc-by-4.0Aug 2022View details →

Geological Map of the Derain (H10) Quadrangle of Mercury (5 crater class version)

Geological (morphostratigraphic) map recognising 5 crater degradation classes. We also have a 3 crater class version, that is otherwise identical. This version is slightly revised after review for publication in J Maps 3 Aug 2022.

EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping

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