dataesr/scientific-paragraphs-categorization huggingface dataset:
Data integration beyond organisms To facilitate more accurate and deeper integration of data, it is important to standardize metadata accompanying DNA sequences, orthologous gene relationships among organisms, phenotypic properties of organisms, inter-species and organismenvironment interactions including host-pathogen relationships. We describe some of these efforts now. Metadata on samples. DDBJ, EBI and NCBI are jointly hosting the BioSample database as an international collaboration. In this resource, metadata are accumulated on the samples from which DNA sequence in the INSDC database was collected and/or on which other research projects were conducted. The metadata includes species, type of samples (cell types etc. ) and phenotypic or environmental information, and therefore it is valuable for data integration if the metadata is available as RDF.
A group from DDBJ generated an RDF version of BioSample metadata during the BioHackathon 2014, using as a starting point 14,362 entries stored in the DDBJ BioSample database in XML format. In addition, existing terminologies and ontologies for geological, archeological and morphological data were explored during the 2014 BioHackathon. For example, there are several resources for geolocations such as W3C Geospatial Ontologies, GeoRSS, GeoNames and Global Biodiversity Information Facility (GBIF). The National Aeronautics and Space Administration (NASA) has developed the Global Change Master Directory (GCMD) and the Semantic Web for Earth and Environmental Terminology (SWEET) which can be used to describe archaeological time scales. For morphology, the Foundational Model of Anatomy (FMA)  24  , Anatomy Reference Ontology (AEO)  25  , Vertebrate Skeletal Anatomy Ontology (VSAO)  26  and other domain specific ontologies  27  were surveyed.
These ontologies are essential for encoding RDF data in environmental biology, such as biodiversity and biomolecular archeology. As a case study, a group developed a semantic resource with information about corals by integrating taxonomic, genomic, environmental, disease and coral bleaching information. Ontologies for integration of microbial data. Within the field of microbiology, genomic and metagenomic data are expanding rapidly due to advances in next generation sequencing technologies. To effectively analyze these huge amounts of data, it is necessary to integrate various microbial data resources available on the Internet. Orthology can play an important role in summarizing such data by grouping corresponding genes across different organisms, and by annotating genes by transferring knowledge from highly curated model organism to newly sequenced genomes.
Therefore RDF models were developed for representing the orthology data stored in the Microbial Genome Database for Comparative Analysis (MBGD)  8  , and these were used to construct an RDF version of MBGD. This also required the development of the OrthO ontology  28  for representing orthology and aligning concepts with the existing OGO ontology  29  , with additional definitions mapped from OrthoXML  30. Orthology RDF data can now be linked with other databases published as RDF such as UniProt  31  , allowing the integrated dataset to be queried using SPARQL. When searching these data, ontologies can be utilized to specify complex search conditions. To assist making such precise queries, the Microbial Phenotype Ontology (MPO) was developed for describing microbial phenotypes such as microbial morphology, growth conditions, biochemical or physiological properties.
During the hackathon, the ontology was updated to comply with a better classification of the hierarchical (is-a) and partonomical (part-of) structure. In addition, the Pathogenic Disease Ontology (PDO) was developed to describe pathogenic microbes that cause diseases in their hosts. An RDF dataset that describes pathogenic information relating to each microbial genome sequence was created using the PDO. Since the genes within these genomes are connected to the ortholog information in the MBGD ortholog database, it is possible to calculate the set of orthologous gene groups that is enriched in the disease related microbes. Knowledge extraction of factors related to diseases. Information and knowledge of the relationships between genes/ mutations/ lifestyle/ environment and diseases is required in order to predict the risk of a disease and for prognosis after the onset of a disease.
In practice, it will also be necessary to collect individual lifestyle and environmental profiles as well as personal genetic data such as genome sequences to allow such predictions for individual people. The necessary underlying relationships are often described in the literature, but are not yet systematically collected in a database. To extract these relationships from the literature, there are two key steps that need to be addressed. First, entities must be annotated automatically using text mining software and, second, these annotations must be represented in a curation interface to allow confirmation that the information has been extracted accurately. Genes, genetic variants, diseases, environmental factors and lifestyle factors are the entity types that need to be annotated on the corpus.
Existing software for extracting genes (e. g. GNAT  32  , GenNorm 33 etc. ), mutations (e. g. tmVar  34  , MutationFinder  35  etc. ) and diseases (e. g. BANNER  36  with disease model) are openly available, along with existing datasets such as BioContext  37  and EVEX DB  38. Before environmental factors and lifestyle factors can be extracted systematically it is necessary to decide on a controlled vocabulary (whether existing or not) to represent them. Pregnancy Induced Hypertension (PIH) was chosen as a case study and 86 relevant open access PubMed Central articles identified. It was possible to extract genes from 32 of these articles using the BioContext dataset, while the other 54 articles were published more recently than BioContext. Attempts were made to extract mutations from 86 articles.
For lifestyle and environmental factors, controlled vocabularies were collected in preparation for entity recognition. After obtaining all the entities in the 86 articles, they were curated using interfaces such as PubAnnotation  39  , and the curated relationships represented as an RDF graph. Tools for semantic genome data Genome annotations have historically been represented and distributed in non-standard domain-specific data formats (e. g. , INSDC, GFF3, GTF). The data formats themselves often include implicit semantics, making automatic interpretation and integration of the data with other resources challenging. Therefore, tools to convert those data into RDF and ontologies to support semantic representation of data need to be developed. BioInterchange is a tool to convert those file formats into RDF and was originally developed in the BioHackathon 2012  4  , with its functionalities and ontologies being enhanced over successive hackathons.
Other tools for high-throughput data processing of Sequence Alignment/Map format (SAM), Binary SAM format (BAM)  40  , Variant Call Format (VCF)  41  , Genome Variation Format (GVF)  42  , Header-Dictionary-Triples (HDT)  43  files have also been developed and a middleware to enable SPARQL queries directly against these huge files on-the-fly for scalability was explored and results were incorporated into integrated semantic genome databases such as TogoGenome and MicrobeDB. jp.
Utilization of domain specific data formats in semantic web. In BioHackathon 2013, VCF2RDF was developed and subsequently published as a Ruby program to convert VCF files into RDF, which represent positions in FALDO and alleles in its own ad hoc ontology terms. The resulting RDF data was loaded into Fuseki and queries were tested in the Jena framework, taking three minutes for the cow genome on a laptop to plot quality scores of variant calls for a million base pairs. During BioHackathon 2014, a group developed middleware to interpret SPARQL queries against SAM/BAM/VCF files on the fly. The first implementation was prototyped in JRuby so that the Java library for samtools can be used in a Ruby program. The resulting application, VCFotf, is packaged as a Docker image that serves a query interface on the Web page.
Use of compressed RDF for large scale genomic data. BioInterchange was used in a Genomic HDT project as a feasibility study to convert a variety of genomic data files (e. g. GVF) containing coordinate-annotated genomic features into an ontology-annotated RDF representation. The RDF data file is then processed into an RDF/HDT file, which is a compressed, indexed, and queryable data archive. Using Ensembl's human somatic variation data (81MB, 9MB gzipped), it was found that the RDF/HDT archive is only 20MB (1. 5M triples; 15MB data + 5MB index), which is a significant reduction from the 313MB RDF N-triples representation. A JSON RESTful API was made available using Sinatra to provide access to the RDF/ HDT file, and this allowed a demonstration of genome-based browsing of the RDF/HDT data file using the JBrowse genome browser.
Integrated semantic genome databases. TogoGenome 9 was developed to integrate heterogeneous biomedical data using Semantic Web technologies. This utilizes the representation of genomic data in the standard RDF format, enabling interoperation with any other Linked Open Data (LOD) around the world. To support these efforts we have collaborated with DDBJ, UniProt, and the EBI RDF group to develop ontologies for representing locations and annotations of genome sequences and used these developments for all prokaryotic genomes and, later, eukaryotic genomes. To complement the above work we developed ontologies for taxonomies, phenotypes, environments and diseases related to organisms, so enabling faceted browsing of the entire datasets. Every TogoGenome report page is made up of modular components called TogoStanza, which is a generic framework to generate Web components querying SPARQL endpoints and rendering them as HTML elements.
Visualization of semantic annotations in JBrowse. JBrowse  45  was used by several projects within the BioHackathon as a demonstration platform. JBrowse running on top of the SPARQL endpoints, e. g. TogoGenome or a prototype InterMine  46  endpoint, or from indexed files produced by GenomicHDT, were comparable in performance with typical RDB back-ended settings. In addition, an unusual use of JBrowse was to view text instead of DNA sequence, with the annotation viewed being the output of natural language processing. TogoGenome: JBrowse was extended to support the TogoGenome's SPARQL endpoint as a data source to retrieve and visualize genes on a chromosomal track (Figure  2 ). This enhancement is already merged into the official JBrowse release since version 1. 10 in 2013. SPARQL queries are customizable in the JBrowse configuration file as long as they return start, end, strand, type (label), uniqueID and parentUniqueID of the annotation objects in a given range within a sequence.
When scrolling to neighboring regions, the performance is good enough for browsing. InterMine: Representatives from the InterMine project  46  produced proof-of-concept demonstrations of semantic extensions to InterMine data-warehouses. These included on the one hand a draft of how to model InterMine data as linked data, producing both an ontology of relationships and triples that conform to that ontology, and on the other hand a draft of a very limited SPARQL engine capable of operating on an InterMine data source directly. Together these investigations indicate that given some development effort, it is likely that significant progress can be made to integrating InterMine into the semantic web. An area that needs work, and is receiving attention, is the production of stable URIs for InterMine entities.
Text-mining: In the community of BioHackathon, text mining resources were developed around PubAnnotation, a public repository of literature annotation data sets. Usually text mining requires its own set of tools, e. g. viewers or editors. However, an interesting experiment was carried out during BioHackathon 2013 and 2014 to use JBrowse as a viewer of text annotation data. The idea behind the experiment was that both genomic data and text data are represented as character sequences, and that annotations of both type of data are attached to specific regions on the sequences. A simple script was developed to convert annotations in PubAnnotation to JBrowse format, and it was observed that text annotations can be nicely viewed in JBrowse. The result raises the possibility of further interoperability between tools for genomics and text mining.
Protein structures, interactions and expressions The European Bioinformatics Institute's (EBI) SIFTS "Structure Integration with Function, Taxonomy and Sequences" resource provides regularly updated residue-level mappings between UniProt and PDB entries  47. SIFTS has been distributed in Comma Separated Values (CSV) and Extensible Markup Language (XML) formats. Like many other proteome-related databases, SIFTS uses the classical protein chain ID specified by the author. However, in 2016, the Worldwide Protein Data Bank (wwPDB) will abolish the conventional PDB format and instead will distribute RDF/XML based on the PDB exchange dictionary / macromolecular Crystallographic Information Format (PDBx/mmCIF) [PDBx/mmCIF]. At the same time wwPDB will start assigning protein chain identifiers, which will also be encoded as URIs in the wwPDB/RDF.
In BioHackathon 2014, a Perl library was developed to standardize the metabolomics data obtained from MasterHands software  58. MasterHands is a proprietary software for the analysis of CE-MS-based metabolomics used in the Institute for Advanced Biosciences, Keio University, and at Human Metabolome Technologies Inc. The library allows the annotation of KEGG compound information using the KEGG REST API, and also allows the annotation of Reactome and MetaCyc information. In the age of systems biology and data integration, proteomics data represent a crucial component to understand the "whole picture" of life. In this context, well-established databases for proteomics data include the Global Proteome Machine Database (GPMDB), PeptideAtlas, ProteomicsDB, and the Proteomics Identification (PRIDE) database among others  59.
In addition, at BioHackathon 2014, the "omics" group worked on the standardization to RDF of different web services and APIs for proteomics and protein expression data. The GPMDB2RDF and PRIDE2RDF library allow the export of expression data from the GPMDB Database  60  and PRIDE Database 61 respectively. The development of a standard interface for providing protein expression data will allow, in the future, exchange and proper reuse of public proteomics data. To this end, the "omics" group in the BioHackathon 2014 made the first steps towards the development of the ProteomeXchange Interface (PROXI) for protein expression data exchange  59.
As it would be impossible, in a week, to create an ontology that could cover the full spectrum of glycomics information and experimental data, it was decided that the group would limit the first version to the data that currently exists in glycomics databases. On the other hand, the developers also attempted to define the ontology so that it could be easily extended with additional predicates and classes if needed, in case more data or more glyco-related databases utilize the proposed RDF format. As a result, by the end of BioHackathon 2013, the first version of the GlycoRDF ontology was agreed upon and is currently available at the GlycoRDF repository at 63. In 2014, work progressed to the point where all glyco-scientists who attended previous BioHackathons had now generated GlycoRDF-formatted versions of their databases.
The whole-PubMed-scale dataset, Allie, triggered the issue of scalability. However, integration of the two corpora, CRAFT and GRO, into PubAnnotation, demonstrated significantly improved utility. Natural language query SPARQL is a standard language for querying triple stores. However, SPARQL queries can be difficult to write, even for experts. Usability studies have shown natural language interfaces to SPARQL to be the preferred method of SPARQL query formation assistance  80. For this reason, software developers are encouraged to create applications that allow users to ask biomedical questions against triple stores using natural (i. e. human) language. Building on the work in BioHackathon 2012 on querying Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), during BioHackathon 2013 effort was focused on the Online Mendelian Inheritance in Man (OMIM) SPARQL endpoint, with a simultaneous focus on building an evaluation data set.
Accordingly, a TUI concept recognizer was implemented into the open source LODQA system for automatic generation of SPARQL queries from natural language queries. Efforts to develop a natural language interface were continued in BioHackathon 2014, during which the LODQA system was configured for two large scale RDF datasets, Bio2RDF and Bio-Gateway. In this way, it was demonstrated that technology like LODQA can answer a question like, "Which genes are involved in calcium binding?", based on RDF data sets like Bio2RDF. However, it also revealed remaining performance issues.
Metadata about RDF data resources Because there is so far no solid guideline on publication of RDF data available, it is not clear for a researcher who wants to develop and release RDF data, how to create the associated metadata, how to describe the provenance of the data and how to assess the quality of the data/service. Also, understanding a dataset is not easy for users of data because there are so many classes, relations and possibilities. To resolve these issues, minimum requirements to represent statistics and characteristics of RDF data and services, including SPARQL endpoints, were discussed.
Dataset metadata The International Society for Biocuration (ISB), in collaboration with the BioSharing forum, developed the BioDBCore  82  which is a community-defined, uniform, generic description of the core attributes of biological databases. However, when it comes to the RDF datasets, one of the difficulties reported by users is that they find it difficult to figure out what data are in a dataset and how things are connected. Vocabulary of Interlinked Datasets (VoID) is a small vocabulary to describe key schemata style information about a dataset. It also includes key metadata such as when a dataset has been updated and under which license it falls. In this section, we propose a guideline for database providers, to provide useful extended VoID files for their users. Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data -this is the core of the FAIR Data Principles 83.
However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently capture all the necessary metadata. Towards providing guidance for producing a high-quality description of biomedical datasets, we identified RDF vocabularies that could be used to specify common metadata elements and their value sets. The resulting guidelines, finalized under the auspices of the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG), cover elements of description, identification, versioning, attribution, provenance, and content summarization. This guideline reuses existing vocabularies, and is expected to meet key functional requirements including discovery, exchange, query, and retrieval. Big data presents an exciting opportunity to pursue largescale analyses over collections of data in order to uncover valuable insights across a myriad of fields and disciplines.
Yet, as more and more data are made available, researchers are finding it increasingly difficult to discover and reuse these data. One problem is that data are insufficiently described to understand what they are or how they were produced. A second issue is that no single vocabulary provides all key metadata fields required to support basic scientific use cases. For instance, the Data Catalog Vocabulary (DCAT) is used to describe datasets in catalogs, but does not deal with the issue of dataset evolution and versioning. A third issue is that data catalogs and data repositories all use different metadata standards, if they use any standard at all, and this prevents easy search, aggregation, and exchange of data descriptions. Thus, there is a need to combine these vocabularies in a comprehensive manner that meets the needs of data registries, data producers, and data consumers.
We developed a specification for the description of a dataset that meets key functional requirements (dataset description, linking, exchange, change, content summary), reuses 18 existing vocabularies, and is expressed using RDF. The specification covers 61 metadata elements pertaining to data description, identification, licensing, attribution, conformance, versioning, provenance, and content summary. Each metadata element includes a description and an example of use. The specification presents a three component model for modular description depending on whether specific files and versions are known (Figure  3 ). The summary level description focuses on release-independent information that mirrors the one captured by dataset registries; the distribution level description focuses on specific data files, their formats and downloadable location; and the version level description links summary descriptions with distribution descriptions.
VoID for InterMine and UniProt As VoID is a vocabulary for describing datasets that can be used to generate documentation and assist users in finding key knowledge on how to write analytical data queries, the InterMine group worked on automatically generating VoID files for InterMinebased Model Organism Databases, while the UniProt group worked on the same for showing classes and predicates used in the named graphs on the UniProt SPARQL endpoint. InterMine  86  is an open source graph-based data warehouse system built on top of PostgreSQL. Through a collaboration (the InterMOD consortium  87  ), with most of the main animal Model Organism Databases (MODs) there are now InterMine databases available for budding yeast (SGD)  88  , rat (RGD  89  ), zebrafish (ZFIN  90  ), mouse (MGI  91  ), nematode (WormBase, unpublished), Fly (InterMine group)  92  , and Arabidopsis 93 with further MOD InterMine instances expected.
Extensive data from the modEN-CODE project  94  are also available through modMine  95. As a step towards exposing these rich data as RDF, code was developed that uses existing InterMine RESTful web services to interrogate the FlyMine database and to generate a VoID description of the database. Further work is required to adjust the core InterMine data model to include additional database metadata items. This will then allow the automatic generation of VoID descriptions for any InterMine database. Further work is also required to ensure that appropriate standards are adhered to, especially for RDF predicates. In addition to the above developments, progress was made in creating a Sesame-based SPARQL endpoint for InterMine databases to complement the existing web application and web services.
At the moment the endpoint only supports a small range of simple queries. It is hoped that in the future such endpoints will make available the rich data assembled and curated by the world wide Model Organism Database community. In the process this should provide opportunities for interoperation and also a mechanism for federation across the different resources. UniProt  31  is available as RDF and can be queried via SPARQL and REST services. UniProt is a large and complicated database, that is difficult to explore due to its size. During the hackathon we implemented a procedure to generate a VoID file to describe UniProt data. The VoID file, now available on FTP and via the uniprot. org SPARQL Service Description (application/rdf+xml), is updated every release in synchrony with our production, and show users what types of data (and how much) are available in the UniProt datasets.