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287 results for “Ontologies”
The Semantic Turkey metadata registry ontology
<p>An application profile of DCAT combining it with other metadata vocabularies (e.g. VoID, DCTERMS, LIME) to meet requirements elicited in various use cases of the Semantic Web platform Semantic Turkey</p>
Alignment between type of landmark in different sources and the concept in the spatial reference objects ontology
<p>The five datasets represent a manually alignment between the landmark type of five different datasets archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a> and a common vocabulary extracted from an application ontology defined for mountain rescue purposes, named <a href="https://hamac.ign.fr/owa/redir.aspx?C=cjlWje9SCaYsVOTLbxbOoIBLZUCS56nVb248cRSMTEDSENDFzybaCA..&URL=http%3a%2f%2fchoucas.ign.fr%2fdoc%2fontologies%2foor.owl%2f">Ontology of landmarks</a> (OOR).</p> <p>Each file represents the alignment for features belonging to a data source with the same OOR ontology.</p> <p>For example, the type «bivouac» from camptocamp.org source is aligned with the uri <a href="http://purl.org/choucas.ign.fr/oor#abri">http://purl.org/choucas.ign.fr/oor#abri</a> of the corresponding class «Shelter » in the ontology of landmark. The alignments models can be considered as a ground truth data.</p> <p>The alignments results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
Dataset for paper: A Systematic Literature Review and Recommendations for Ontology-based Support of Digital Forensics
<p>PLEASE, READ THE README.TXT FILE</p> <p>This document describes how to interpret the data and metadata files, and it is licensed under Creative Commons CC BY-NC-AS (https://creativecommons.org/licenses).</p> <p>The file "primary_studies_final_set-DATA.csv" is a CSV file format and contains the raw data extracted from our systematic literature review primary studies. Such data were extracted based on the research questions defined for our study.<br> The file "primary_studies_final_set-METADATA.csv" is a CSV file format and contains the following:<br> - the first row contains two pieces of information: the data type, which might be original or reused;<br> - the second row contains the reused data URL/DOI, which should inform the URL or DOI from which the data was reused, or n/a if the data is original;<br> - the third row contains the date of data generation in the format mm/dd/yyyy;<br> - the fourth row contains 11 elements describing each of the fields of the file "primary_studies_final_set-DATA.csv": the study ID, title, objective, six research questions, and an observation field; and<br> - the fifth row describes the data type of each field of the file "primary_studies_final_set-DATA.csv".<br> The .bib files contain the bibtex entry for the final set of studies.<br> The license.txt file describes the Creative Commons license for this material.</p> <p>We hope you have an excellent read!!</p> <p>Cheers!<br> Thiago, Edson, and Avelino</p>
The ExaMode Ontology (full version, v.2)
<p>The goal of this document is to define an OWL 2 ontology for the ExaMode project whose overall goal is to build predictive algorithms to help pathologists in the diagnosis of cancer cases. The starting point of ExaMode are medical diagnostic reports associated with WSIs of examined tissues.</p> <p>The present ontology models the diagnostic reports associated with a (series of) WSI and enable a structured encoding of the main concepts of a diagnosis. These concepts and their relations can be used to automatically annotate WSI as well as to do some reasoning over diagnostic reports about the cervix, colon and lung cancer, and celiac disease.</p> <p>The full documentation is available here: http://examode.dei.unipd.it/ontology/</p>
EVI: Evidence Graph Ontology v1.0
<p>The Evidence Graph ontology (EVI v1.0) extends core concepts from the W3C Provenance Ontology PROV-O to describe evidence for correctness of findings in biomedical publications. The semantic data model in EVI is expressed using OWL2 Web Ontology Language (OWL2).</p> <p>The core PROV ontology concepts used in EVI are Entity, Activity, and Agent, with two sub-classes Person and Organization. The object properties in EVI are used to establish relations among instances, most of which are of type DigitalObject. Computations are activities performed on instances of other DigitalObjects, Software or Services.</p> <p>The latest release of EVI is v1.0 that can be accessed here at <a href="https://w3id.org/EVI">https://w3id.org/EVI.</a></p>
Example models and queries for Flow Systems Ontology
<p><strong>Example models and queries for Flow Systems Ontology</strong></p> <p>This repository contains the files used to produce the examples in the article manuscript introducing the <a href="https://w3id.org/fso">Flow Systems Ontology (FSO)</a>. This includes both the triples of the example models, and the SPARQL queries used to demonstrate the use cases.</p> <p>The manuscript has been published as "<a href="https://doi.org/10.1016/j.autcon.2021.104067">An ontology to support flow system descriptions from design to operation of buildings</a>" in Automation in Construction.</p> <p>For further details, see the included README.md.</p>
ICOPS Workshop Series - Standard Vocabularies and Ontologies
<p><strong>This is the fifth workshop in the International Committee on Open Phytolith Science (ICOPS) workshop series on Open Research Skills. </strong></p><p>In this workshop we had multiple speakers:</p><ul><li>Introduction - Henriette Harmse - slides in the main presentation</li><li>Case study and demo of image database - Frances Wong - slides attached as pdf.</li><li>Phytolith standarised nomenclature ICPN 2.0 - Luc Vrydaghs [*presentation not included*]</li><li>Phytolith ontology - Celine Kerfant and Zach Dunseth - slides in the main presentation</li></ul><p>Youtube video of the workshop:</p><p><a href="https://youtu.be/qZaWkJlVXvE">https://youtu.be/qZaWkJlVXvE</a></p>
Supporting Data: ontophylo: Reconstructing the evolutionary dynamics of phenomes using new ontology-informed phylogenetic methods
<p>This dataset contains all scripts and data for reproducing the analyses of the paper. The README files contain additional information.</p>
Supporting Data for "Exploring ChatGPT-4 for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development"
<p>Data from the trials with ChatGPT to generate OWL files for taxonomic data from the GBIF Backbone Taxonomy.</p> <p>Updates of version 2: additional prompts from the experiments with Gemini and DeepSeek.</p>
Polifonia Ontology Network, v0.1
<p>The Polifonia Ontology Network, a set of OWL ontology modules to describe the content and context of tangible and intangible musical cultural heritage assets from Europe.</p> <p>Included modules are: <a href="https://github.com/polifonia-project/core/">Core</a>, <a href="https://github.com/polifonia-project/musical-performance/">Musical Performance</a>, <a href="https://github.com/polifonia-project/musical-composition/">Musical Composition</a>, <a href="https://github.com/polifonia-project/musical-feature/">Musical Feature</a>, <a href="https://github.com/polifonia-project/source">Source</a>, <a href="https://github.com/polifonia-project/instrument">Instrument</a>, <a href="https://github.com/polifonia-project/comparative-measure">Comparative Measure</a>, <a href="https://github.com/polifonia-project/music-emotion">Music Emotion</a>, <a href="https://github.com/polifonia-project/metadata">Metadata</a>, <a href="https://github.com/polifonia-project/bell">Bell</a></p>
SeaLiT Knowledge Graphs - Maritime History Data in RDF using a CIDOC-CRM extension (SeaLiT Ontology)
<p><strong>SeaLiT Knowledge Graphs</strong> is an RDF dataset of maritime history data that has been transcribed (and then transformed) from original archival sources in the context of the <a href="http://www.sealitproject.eu/">SeaLiT Project</a> (Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s). The underlying data model is the <a href="https://zenodo.org/record/5964240">SeaLiT Ontology</a>, an extension of the ISO standard <strong>CIDOC-CRM</strong> (ISO 21127:2014) for the modelling and integration of maritime history information. </p> <p>The knowledge graphs integrate data of totally 16 different types of archival sources:</p> <ul> <li>Crew Lists <ul> <li>Crew and displacement list (Roll)</li> <li>Crew List (Ruoli di Equipaggio)</li> <li>General Spanish Crew List</li> </ul> </li> <li>Registers / Lists <ul> <li>Students Register</li> <li>Civil Register</li> <li>Register of Maritime Personnel</li> <li>Register of Maritime Workers (Matricole della gente di mare)</li> <li>Sailors Register (Libro de registro de marineros)</li> <li>Naval Ship Register List</li> <li>Seagoing Personnel</li> <li>Lists of ships</li> </ul> </li> <li>Censuses <ul> <li>Census La Ciotat</li> <li>First National all-Russian Census of the Russian Empire</li> </ul> </li> <li>Payrolls <ul> <li>Payrolls of private archives and libraries in Greece</li> <li>Payrolls of Russian Steam Navigation and Trading Company</li> </ul> </li> <li>Employment records <ul> <li>Shipyards of Messageries Maritimes, La Ciotat</li> </ul> </li> </ul> <p>More information about the archival sources are available through the <a href="https://sealitproject.eu/dictionary-of-source-types-list">SeaLiT website</a>. Data exploration applications over these sources are also publicly available (<a href="https://catalogues.sealitproject.eu/">SeaLiT Catalogues</a>, <a href="http://rs.sealitproject.eu/">SeaLiT ResearchSpace</a>). </p> <p>Data from these archival sources has been transcribed in tabular form and then curated by historians of SeaLiT using the <a href="https://www.ics.forth.gr/isl/fast-cat">FAST CAT</a> system. The transcripts (records), together with the curated vocabulary terms and entity instances (ships, persons, locations, organizations), are then transformed to RDF using the SeaLiT Ontology as the target (domain) model. To this end, the corresponding schema mappings between the original schemata and the ontology were defined using the <a href="https://github.com/isl/x3ml">X3ML</a> mapping definition language, that were subsequently used for delivering the RDF datasets. </p> <p>More information about the FAST CAT system and the data transcription, curation and transformation processes can be found in the following paper:</p> <blockquote> <p>P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. <a href="https://doi.org/10.1145/3461460">https://doi.org/10.1145/3461460</a> [<a href="http://users.ics.forth.gr/~fafalios/files/pubs/fafaliosJOCCH2021.pdf">pdf</a>, <a href="http://users.ics.forth.gr/~fafalios/files/bibs/fafaliosJOCCH2021.bib">bib</a>]</p> </blockquote> <p>The RDF dataset is provided as a set of TriG files per record per archival source. For each record, the dataset provides: i) one trig file for the record's data (<em>records.trig</em>), ii) one trig file for the record's (curated) vocabulary terms (<em>vocabularies.trig</em>), and iii) four trig files for the record's (curated) entity instances (<em>ships.trig, persons.trig, persons.trig, organizations.trig</em>).</p> <p>We also provide the RDFS files of the used ontologies (SeaLiT Ontology verson 1.0, CIDOC-CRM version 7.1.1). </p>
Adapting the Harmonized Data Quality Framework for Ontology Quality Assessment
<p>Ontologies play an important role in the representation, standardization, and integration of biomedical data, but are known to have data quality (DQ) issues. We aimed to understand if the Harmonized Data Quality Framework (HDQF), developed to standardize electronic health record DQ assessment strategies, could be used to improve ontology quality assessment. A novel set of 14 ontology checks was developed. These DQ checks were aligned to the HDQF and examined by HDQF developers. The ontology checks were evaluated using 11 Open Biomedical Ontology Foundry ontologies. 85.7% of the ontology checks were successfully aligned to at least 1 HDQF category. Accommodating the unmapped DQ checks (n=2), required modifying an original HDQF category and adding a new Data Dependency category. While all of the ontology checks were mapped to an HDQF category, not all HDQF categories were represented by an ontology check presenting opportunities to strategically develop new ontology checks. The HDQF is a valuable resource and this work demonstrates its ability to categorize ontology quality assessment strategies.</p>
The use of Foundational Ontologies in Bioinformatics - Supplementary Material
<p>Supplementary material for the paper "The use of Foundational Ontologies in Bioinformatics".</p>
Hypertension - Florida Annotated Corpus for Translational Science (FACTS), Vital Sign Ontology Annotations
<p>Florida Annotated Corpus for Translational Science (FACTS), which currently consists of 20 case reports about hypertension annotated with Vital Sign Ontology (VSO) classes (version 2012-04-25). </p>
Gold standard corpus, ontologies, and Entity-Quality ontology annotations for evolutionary phenotypes
<p>This data set includes a gold-standard corpus of evolutionary phenotype descriptions (in the form of character state descriptions pulled from a variety of phylogenetic systematics studies), and their corresponding expert-curated annotations with ontology terms in the form of Entity-Quality (EQ) statements. EQ annotatons allow machine-reasoning (through the semantics encoded in the requisite ontologies from which the ontology terms are drawn), and machine-reasoning in turn enables computing metrics for quantifying the semantic similarity between different phenotype descriptions as represented by their EQ annotations.</p> <p>Also included are the ontologies, and the human expert-generated and Semantic Charaparser (i.e., machine) generated EQ annotations used to assess Semantic Charaparser performance relative to inter-curator variation and to the effect of having access to external knowledge. The ontologies include those used as input, the "augmented" ontologies created by human curators in each experiment round, and the merged ontology used to maximize Semantic Charaparser's performance.</p> <p>The production of the gold standard corpus, annotation experiments, and evaluation of the results are described in detail in the following manuscript:</p> <blockquote> <p>Dahdul et al (2018) Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems. BioRxiv https://doi.org/10.1101/322156. Submitted to Database.</p> </blockquote> <p>The analysis code for evaluating the gold standard corpus (and the input data and ontologies for that) are available separately from the following:</p> <blockquote> <p>Manda et al (2018) Code and data for analysis of evolutionary phenotype ontology annotations and gold standard corpus. Zenodo. https://doi.org/10.5281/zenodo.1218010</p> </blockquote> <p>In comparison to the previous version (v1.0.0), this record includes a file of MD5 checksums of the Gold Standard data files. The data files themselves are unchanged.</p>
Ontology based text mining of gene-phenotype associations: application to candidate gene prediction
<p>Gene-phenotype associations play an important role in understanding<br> the disease mechanisms which is a requirement for treatment<br> development. A portion of gene-phenotype associations are observed<br> mainly experimentally and made publicly available through several<br> standard resources such as MGI. However, there is still a vast<br> amount of gene--phenotype associations buried in the biomedical<br> literature. Given the large amount of literature data, we need<br> automated text mining tools to alleviate the burden in manual<br> curation of gene-phenotype associations and to develop<br> comprehensive resources. We developed an ontology based<br> approach in combination with statistical methods to text mine<br> gene-phenotype associations from literature. Our method achieved<br> AUC values of 0.90 and 0.75 in recovering known gene-phenotype<br> associations from HPO and MGI respectively. We posit that candidate<br> genes and their relevant diseases should be expressed with similar<br> phenotypes in publications. Thus, we demonstrate the utility of our<br> approach by predicting disease candidate genes based on the semantic<br> similarities of phenotypes associated with genes and diseases. We evaluated our disease candidate prediction model on<br> the gene-disease associations from MGI. Our model achieved AUC<br> values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of<br> gene-disease associations respectively. Our manual analysis on the<br> text mined data revealed that, our method can accurately extract<br> gene-phenotype associations which are not currently covered by the<br> existing public gene-phenotype resources. Overall, results indicate<br> that our method can precisely extract known as well as new<br> gene-phenotype associations from literature. This released dataset at Zenodo covers our gene-phenotype extracts from the literature. All the methods used to extract the data are available at https://github.com/bio-ontology-research-group/genepheno.</p>
The terrestrial carnivorous plant Utricularia reniformis sheds light on environmental and life-form genome plasticity: Annotation, Gene Ontology and raw data
<p><strong>Description:</strong> In this work, we deeply sequenced (genome and transcriptome of different organs), assembled, and analyzed the 311-Mbp genome of the terrestrial carnivorous plant <em>U. reniformis</em> (Lentibulariaceae). This project presents great importance to the understanding of genomic, evolutive and functional aspects of<em> U. reniformis</em>, which may, with the next-generation sequencing and computational biology approaches shed light to a better understanding not only for the biology and evolution of <em>Utricularia</em> genus, but also for other genera and lineages of the Lentibulariaceae family. Here we present all the raw data generated, including annotation and gene ontology files.</p> <p><strong>External Information</strong></p> <p><a href="https://genomevolution.org/coge/GenomeInfo.pl?gid=54799">Genome Browser</a> avaliable at CoGe Portal (https://genomevolution.org/coge/GenomeInfo.pl?gid=54799)</p> <p><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">GenBank </a><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">Bioproject</a> (https://www.ncbi.nlm.nih.gov/bioproject/290588) for raw genomic and transcriptomic reads</p> <p><a href="https://bv.fapesp.br/en/auxilios/84264/genomics-and-transcriptomics-of-utricularia-reniformis-lentibulariaceae-an-evolutive-and-function/">FAPESP grant website</a> contaning the project abstract and other information.</p> <p><strong>Papers published related to <em>Utricularia reniformis</em> genome</strong></p> <pre><strong>[1]</strong> Silva SR, Diaz YC, Penha HA, Pinheiro DG, Fernandes CC, Miranda VF, MichaelTP, Varani AM. <strong>The Chloroplast Genome of Utricularia reniformis Sheds Light on the Evolution of the ndh Gene Complex of Terrestrial Carnivorous Plants from the Lentibulariaceae Family</strong>. PLoS One. 2016 Oct 20;11(10):e0165176. doi:<strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/27764252">10.1371/journal.pone.0165176</a></strong>. </pre> <pre><strong>[2] </strong>Silva SR, Alvarenga DO, Aranguren Y, Penha HA, Fernandes CC, Pinheiro DG, Oliveira MT, Michael TP, Miranda VFO, Varani AM. <strong>The mitochondrial genome of the terrestrial carnivorous plant Utricularia reniformis (Lentibulariaceae): Structure, comparative analysis and evolutionary landmarks.</strong> PLoS One. 2017 Jul19;12(7):e0180484. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/28723946">10.1371/journal.pone.0180484</a></strong>.</pre> <pre><strong>[3] </strong>Silva SR, Moraes AP, Penha HA, Julião MHM, Domingues DS, Michael TP, Miranda VFO, Varani AM. <strong>The Terrestrial Carnivorous Plant Utricularia reniformis Sheds Light on Environmental and Life-Form Genome Plasticity.</strong> Int J Mol Sci. 2019 Dec 18;21(1). pii: E3. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/31861318">10.3390/ijms21010003</a></strong>.</pre> <p><strong>Acknowledgements</strong></p> <p>This work was supported by Sao Paulo Research Foundation FAPESP, Grant ID: [1325164-6]</p> <p> </p> <p><strong>---------------------------------------------------------</strong><br> <strong>FILES DESCRIPTION</strong><br> <strong>---------------------------------------------------------</strong><br> <br> ----------------<br> <strong>ANNOT-vFinal.sql: </strong>MySQL database containing all integrated annotation information of Urenif and Ugibba<br> ----------------<br> <strong>TABLE fields description</strong><br> gene_name gene name generated by EVidence Modeler + PASA<br> length gene lenght<br> status duplicate_gene_classifier status (0:singleton, 1:dispersed, 2:proximal, 3: tandem, 4:WGD)<br> product gene product <br> GOterms Blast2GO/OmicsBox GOterms<br> GO_mapping Blast2GO/OmicsBox GOterms derived from direct mapping (UniProt)<br> GO_annotation Blast2GO/OmicsBox annotated GOterms<br> GO_interpro Blast2GO/OmicsBox derived from InterProScan<br> EC Blast2GO/OmicsBox EC number<br> EC_name Blast2GO/OmicsBox enzyme name<br> NOG_annot EggNOG annotation description<br> NOG_EC EggNOG EC number<br> NOG_GO EggNOG GOterms<br> NOG_class EggNOG COG/KOG classfication<br> KEGG_Pathway EggNOG KEGG pathyways<br> KEGG_ko EggNOG KEGG ko<br> CAZy EggNOG CAZy enzymes<br> TAIR_gene Closest A. thaliana gene name (homologous) TAIR database lasted version<br> TAIR_annot Closest A. thaliana gene product (homologous) TAIR database lasted version <br> ortho MCL clustering among Vvinifera, Athaliana, and Slycopersicum (S:singleton, C: clustered, Y: shared)<br> ortho_two MCL clustering among Urenif and Ugibba (S:singleton, C: clustered, Y: shared)<br> -<br> -<br> ----------------<br> <strong>CEGs.zip </strong> 336 shared and concatenated CEGs from Urenif, U. gibba, Genlisea nigrocaulis, G. hispidula, G. aurea, G. pygmaea, and G. repens.<br> ----------------</p> <p><strong>ProcessRepeats_mod</strong> Modified version of RepeatMasker, ProcessRepeats script for detection of plant evolutionary lineages<br> ----------------</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia gibba</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Ugibba</strong><strong>-no-masked.fa </strong> Ugibba genome excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ugibba-softmasked.fa</strong> Ugibba genome RepeatMasker softmasked and excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ug.collinearity </strong> MCScanX collinearity file<br> <strong>Ug-duplicates.txt</strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ug.gene_type </strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ug.tandem </strong> Ugibba tandem genes generated by MCScanX tool<br> <strong>Ugibba_annot.annot </strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim) <strong>Ugibba_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong><strong> </strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Ugibba</strong><strong>.cDNA</strong> Ugibba cDNAs fasta file<br> <strong>Ugibba</strong><strong>.CDS </strong> Ugibba CDSs fasta file<br> <strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Ugibba GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Ugibba GFF3 file fully annotated (genes only)<br> <strong>Ugibba_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Ugibba_fasta.fasta</strong> Blast2GO/OmicsBox Ugibba fasta proteins containg annotation (product and GO terms)<br> <strong>ugibba_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>ugibba_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>ugibba_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)</p> <p><strong>Ugibba_GAF.txt</strong> GAF file<br> <strong>Ugibba</strong><strong>.gene</strong> Ugibba gene fasta file<br> <strong>Ugibba_GOstat.txt </strong> GOstat file<br> <strong>Ugibba</strong><strong>-PASA-assemblies.fasta </strong> Ugibba PASA assemblies<br> <strong>Ugibba</strong><strong>-PASA.stats </strong> Ugibba annotation STATS<br> <strong>Ugibba</strong><strong>.</strong><strong>prot</strong><strong> </strong> Ugibba protein fasta file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff </strong> Ugibba RepeatMasker gff file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff3 </strong> Ugibba RepeatMasker gff3 file<br> <strong>Ugibba</strong><strong>-RepeatMasker.tbl </strong> Ugibba RepeatMasker results<br> <strong>Ugibba</strong><strong>-RepeatMasker-v2.gff3</strong> Ugibba RepeatMasker gff3 second version file<br> <strong>Ugibba</strong><strong>-RNAseq-assembled.fasta </strong> Ugibba RNAseq assembled transcriptome (Trinity)<br> <strong>Ugibba_TEs_DANTE_2019.fa </strong> Ugibba TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Ugibba_WEGO.txt </strong> WEGO file</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia reniformis</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Urenif</strong><strong>-no-masked.fa </strong> Urenif genome excluding organellar genomes<br> <strong>Urenif</strong><strong>-</strong><strong>softmasked</strong><strong>.fa</strong> Urenif genome RepeatMasker softmasked and excluding organellar genomes<br> <strong>Ur.collinearity </strong> MCScanX collinearity file<br> <strong>Ur-duplicates.txt </strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ur.gene_type</strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ur.tandem</strong> Urenif tandem genes generated by MCScanX tool<br> <strong>Urenif_annot.annot</strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim)<br> <strong>Urenif_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Urenif</strong><strong>.cDNA</strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>.CDS </strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Urenif GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Urenif GFF3 file fully annotated (genes only)<br> <strong>Urenif_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Urenif_fasta.fasta</strong> Blast2GO/OmicsBox Urenif fasta proteins containg annotation (product and GO terms)<br> <strong>urenif_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>urenif_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>urenif_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)<br> <strong>Urenif_GAF.txt </strong> GAF file<br> <strong>Urenif</strong><strong>.gene</strong> Urenif gene fasta file<br> <strong>Urenif_GOStat.txt </strong> GOstat file<br> <strong>Urenif</strong><strong>-PASA-assemblies.fasta</strong> Urenif PASA assemblies<br> <strong>Urenif</strong><strong>-PASA.stats </strong> Urenif annotation STATS<br> <strong>Urenif</strong><strong>.</strong><strong>prot</strong><strong> </strong> Urenif protein fasta file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff </strong> Urenif RepeatMasker gff file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff3 </strong> Urenif RepeatMasker gff3 file<br> <strong>Urenif</strong><strong>-RepeatMasker.tbl </strong> Urenif RepeatMasker results<br> <strong>Urenif</strong><strong>-RepeatMasker-v2.gff3 </strong> Urenif RepeatMasker gff3 second version file<br> <strong>Urenif</strong><strong>-RNAseq-assembled.fasta </strong> Urenif RNAseq assembled transcriptome (Trinity)<br> <strong>Urenif_TEs_DANTE_2019.fa </strong> Urenif TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Urenif_WEGO.txt </strong> WEGO file<br> <strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong></p>
EOL v3 data model Ontologies: stylesheet (.css)
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: stylesheet (.xsl)
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
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.