dataesr/scientific-paragraphs-categorization huggingface dataset:
The ELIXIR contextualised portfolio of biodiversity informatics resources and services provides a starting point to bringing visibility to existing infrastructures as well as stimulating improved integration. In order to better understand the challenges concerning emerging technologies, scaling up workflows, and ensuring that standards evolve in a coherent manner, we recommend that the community develops a curated, shared, and public understanding of the different types of emerging data, dataflows, repositories, and portals that are necessary to steward up-to-date, comprehensive, complete, and interoperable reference datasets on biodiversity. Such a catalogue of use cases would be a natural output of the Communities of Practice described above in our recommendation on improved collaborations.
4: "These examples help to formulate more formal definitions: (i) molecular sequence data collection initiatives are producing and collating reference catalogues of genetic and genomic biodiversity on Earth; and (ii) biodiversity research infrastructures are capturing knowledge from scientific collections, observations, and the literature, and building resources of biodiversity information for all Earth's organisms. Here we identify opportunities to connect these. " -I find the definition (i) incomplete: in addition to producing reference catalogues (e. g. genomes or marker genes) they, importantly, also contain sequencing datasets from the field that hold information on species occurrences (and sometimes intra-specific diversity) in samples (i. e. metagenomic and metabarcoding datasets). The description of MGnify on page 7 illustrates this.
Here we identify opportunities to connect these. " -I find the definition (i) incomplete: in addition to producing reference catalogues (e. g. genomes or marker genes) they, importantly, also contain sequencing datasets from the field that hold information on species occurrences (and sometimes intra-specific diversity) in samples (i. e. metagenomic and metabarcoding datasets). The description of MGnify on page 7 illustrates this. Maybe this is what is meant by "collating" but that was not clear to me.
Response: We agree that the definition should be more clearly broadened to encompass other sequencing datasets and have updated the text accordingly. 1  are focused on international projects and umbrella initiatives producing (meta) genomes, (meta) transcriptomes, and/or DNA barcodes. GTDB seems to fit more the profile of a consumer of such data and therefore we do not think it represents an example of the type of project we wish to highlight here. p. 6: Add a brief description of GTDB, for example to the first paragraph of page 6. GTDB is rapidly establishing itself as the standard for cataloguing prokaryotic diversity and a good example of how (meta)genomics can aid in improving taxonomies. Response: We agree that this is a good example and we have added GTDB in the discussion of microbe-focused sequencing initiatives.
It may be better to reference "(NCBI, COL or GBIF)". Page 7 -"For example, connecting GBIF with the UNITE database" -Note that GBIF has integrated both BOLD and UNITE in this way -both now contribute molecular OTUs (in BOLD's case BINs) that appear as part of the GBIF taxonomic framework. Page 7 -"Reciprocally, traditional biodiversity data and resources can help inform" -while this is true, it is not clear how the authors expect the benefits to be developed in this direction. Navigation from molecular data to a corpus of knowledge about associated taxa is *relatively* simple to achieve, but the published literature is insufficiently structured or parsed to support meaningful inference to support machine-driven analysis of genetic and genomic data. Page 8 -"The NCBI taxonomy database" -The listed number of synonyms is important, but should be put in context of the current version of COL including 1.
95 million accepted species names and another 2 million synonyms. Page 10 -"An additional source of information on taxon names" -It is important to highlight the scale and diversity of information resources and datasets relating to biodivesity, but these are so heterogeneous that it is unhelpful to treat them monolithically. Lumping them together leaves taxonomic identifiers as the only possible connection point. In practice, the field of biodiversity informatics needs to digest these resources into digital objects that fall into more precise classes (specimen, ecosystem, species, gene, sampling event, trait, etc. ). Page 13 -Figure  3  caption -"They can also archive their datasets at GBIF without any clearance. " -Unless I am missing something, this would be better expressed as "publish their datasets to GBIF" since GBIF does not currently assume responsibility for archival of data published to the network (although such archival often de facto occurs).
The first sentence, ("Creating a comprehensive taxonomy linked. ") does not make sense and certainly needs to be rewritten. Page 11 -"The MAR database entries are cross-referenced with ENA and the World Register of Marine Species (WoRMS)  (Vandepitte et al. 2018)  records" -no need for the word "records". Page 11 -"On the one hand, the large and growing variety of observations taken during oceanic sampling (Gorsky et al. 2019) have posed many data management challenges. " -"has posed". Page 13 -"Long-standing scientific interests" -"interest". Page 15 -"This species, experiment and sample metadata" -may be clearer as "This metadata on species, experimennts and samples". Page 18 -"It is clear that from barcodes to reference genomes, sequencing hundreds of thousands of species in the near future will generate the foundational data for most biodiversity molecular studies for decades to come.
Response: Updated accordingly. Page 6 -"(e. g. NCBI or GBIF)" -as noted above, COL is the core of the GBIF backbone and is used in many other contexts. It may be better to reference "(NCBI, COL or GBIF)". Response: Updated accordingly. Page 7 -"For example, connecting GBIF with the UNITE database" -Note that GBIF has integrated both BOLD and UNITE in this way -both now contribute molecular OTUs (in BOLD's case BINs) that appear as part of the GBIF taxonomic framework. Response: Our second example in this section (Canadian invertebrate fauna) presents a specific case of how BOLD integrates with GBIF, so although somewhat implicit this integration is already noted and we prefer to leave it as it is rather than expand this section. Page 7 -"Reciprocally, traditional biodiversity data and resources can help inform" -while this is true, it is not clear how the authors expect the benefits to be developed in this direction.
Navigation from molecular data to a corpus of knowledge about associated taxa is *relatively* simple to achieve, but the published literature is insufficiently structured or parsed to support meaningful inference to support machine-driven analysis of genetic and genomic data. Response: We agree that currently there are many challenges that still need to be overcome to achieve this and have edited the text to reflect this as a future goal rather than a current reality. Page 8 -"The NCBI taxonomy database" -The listed number of synonyms is important, but should be put in context of the current version of COL including 1. 95 million accepted species names and another 2 million synonyms. Response: For context we have added this after the reference to Figure  1. Page 10 -"An additional source of information on taxon names" -It is important to highlight the scale and diversity of information resources and datasets relating to biodivesity, but these are so heterogeneous that it is unhelpful to treat them monolithically.
Lumping them together leaves taxonomic identifiers as the only possible connection point. In practice, the field of biodiversity informatics needs to digest these resources into digital objects that fall into more precise classes (specimen, ecosystem, species, gene, sampling event, trait, etc. ). Response: Although this case study is focused on taxonomy we agree that highlighting the role of digital objects is important and have updated the text accordingly. Page 13 -Figure  3  caption -"They can also archive their datasets at GBIF without any clearance. " -Unless I am missing something, this would be better expressed as "publish their datasets to GBIF" since GBIF does not currently assume responsibility for archival of data published to the network (although such archival often de facto occurs).
Figure2. Overview of the processing of marine environmental sequence information. A simplified flowchart of the processing steps of information from the Tara Oceans datasets (metagenomics, metatranscriptomics, and amplicons) to integrate data with primary and secondary resources and other biodiversity platforms as GBIF and WoRMS. Processing of sequence data from oceanic water samples using informatics tools and services connects them with taxonomic information and links them to knowledge about species biology and ecosystem variables. PR 2 , pr2-primers: an 18S rRNA primer database for protists; SILVA, an on-line resource for quality checked and aligned ribosomal RNA sequence data; ITSoneDB, a curated collection of eukaryotic ribosomal RNA Internal Transcribed Spacer 1 (ITS1) sequences; UniProt, the universal protein resource of sequence and functional information; ENA, European Nucleotide Archive; MGnify, the microbiome analysis resource; OGA, Ocean Gene Atlas; OBA, Ocean Barcode Atlas; GBIF, Global Biodiversity Information Facility; WoRMS, the World Register of Marine Species; MetDB, a genomic reference database for marine species; MAR databases, a collection of richly annotated and manually curated contextual and sequence resources for marine species.
Grant information: DA was supported by  Arcadia  -a charitable fund of  Lisbet Rausing  and  Peter Baldwin. RMW was supported by  Swiss National Science Foundation   PP00P3_170664. RPD was supported by the  Biotechnology and Biological Sciences Research Council (BBSRC) , part of  UK Research and Innovation (UKRI) , through the  Core Strategic Programme Grant   BB/CSP1720/1 ,  BBS/E/T/000PR9817 ,  Designing Future Wheat  grant  BB/P016855/1 ,  BBS/E/T/000PR9783  and  Core Capability Grant   BB/CCG1720/1 ,  BBS/E/T/000PR9814  at the  Earlham Institute. PB was supported by the  Czech Science Foundation  ( 21-17749S ) and by the  Ministry of Education, Youth and Sports of the Czech Republic  ( LM2015047 ). NPW was supported by the  Research Council of Norway  ( 270068 ). A-F A-B was supported by the  GenRes Bridge  project that received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  817580.
2019; 6(308): 308. PubMed Abstract|Publisher Full Text DiSSCo: Reference Source DivSeek: Reference Source Droege G, et al. : The Global Genome Biodiversity Network (GGBN) Data Standard specification. Database. 2016:baw125. 2016; 2016 PubMed Abstract|Publisher Full Text|Free Full Text DToL: Darwin Tree of Life -Reading the genomes of all life: a new platform for understanding our biodiversity. 2022. (Accessed June 30, 2022). Reference Source Duarte CM: Publisher Full Text EBP: Earth BioGenome Project. Proj: Earth Biog. 2022. (Accessed June 30, 2022). Reference Source Egidi E, Reference Source ELIXIR: Reference Source ENVRI: ENVRI Community: environmental research infrastructures. ENVRI Community. 2022. (Accessed June 30, 2022). Reference Source ERGA: Field D, et al. : Genomic Standards Consortium Projects.
The IntAct molecular interaction database (https:// www. ebi. ac. uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.
In 2017, the IMEx Consortium became an ELIXIR core data resource  (2) , recognising it as part of the fundamental infrastructure for life sciences. For an in-depth review of the current IMEx data model, curation strategies and collaborations, see  (3). The IntAct database of molecular interactions is used by all currently active IMEx partners (In-tAct, DIP (4), UniProt (5), MINT  (6) , MatrixDB  (7) , UCL ICS, IID (8)) as a common curation platform, and also acts as a common data dissemination platform, in parallel to the partners' own websites. While the detailed IMEx interaction data has always been available through download in the feature-rich PSI-MI XML format  (9, 10) , many annotation details were not conveniently accessible through Nucleic Acids Research, 2022, Vol. 50, Database issue D649 the IntAct website, and often users are not aware of the depth of available annotations.
Data Content Since the last IntAct NAR publication  (11) , data content has grown from 408 000 (Jamuary 2014) to 1 114 500 (June 2021)) interaction evidences, and the number of referenced publications has risen from 12 500 to 22 500. This rapid increase is based on the integration of previously curated data from IMEx partners, as well as the ongoing curation work. The faster rise in interaction numbers compared to publication numbers reflects the increasing trend towards largescale interaction studies. In the same period, interactions from 21 publications have been retracted, usually due to retraction of the supporting publication. Several new datasets have been released, including two key collections: the 'Mutations dataset' (6) and the 'Coronavirus interactome'  (7).
Mutations dataset This dataset contains annotations describing the effect of small sequence changes on protein interactions. Captured changes comprise both natural variants and experimentally introduced sequence changes. This dataset is continuously maintained and updated, and since the original publication in February 2019  (12)  it has grown from 28 000 to 72 000 mutation annotations. In order to fully reflect the importance of this data and to improve accessibility to it for users, in addition to web interface changes (see below), we have also introduced a dedicated tab-delimited download file format (https://www. ebi. ac. uk/ intact/download/datasets#mutations).
Coronavirus interactome After the outbreak of the COVID19 pandemic in Europe in early March 2020, we initiated an IMEx-wide initiative to record molecular interaction data related to SARS-CoV-2 and other members of the Coronaviridae family of viruses, along with human protein interactions of potential relevance for the disease's aethiopathology. Since its publication in November 2020  (13) , the Coronavirus interactome dataset has grown from 4400 interaction evidences derived from 151 publications to 9100 interaction evidences from 332 publications in June 2021, and is accessible at https://www. ebi. ac. uk/intact/resources/ datasets#coronavirus. Work is still actively ongoing to capture novel interactions, and details of known interactions such as the effects of variants, to further enhance this dataset.
Curation Policies Given the fast pace at which COVID19-related data has been generated, the IMEx Consortium decided to allow the curation of preprints when the scientific interest contained in these publications justifies it. We will periodically review and update these datasets to ensure only data from peerreviewed publications is maintained in the database longterm. More information about IMEx's curation policy regarding preprints is provided at www. imexconsortium. org/ curation/. Curation practices and controlled vocabularies/ontologies are continuously updated, driven by the development of new methods like BioID  (14)  (term in PSI-MI Ontology https://www. ebi. ac. uk/ols/ontologies/mi/terms?iri = http% 3A%2F%2Fpurl. obolibrary. org%2Fobo%2FMI 1314). The IntAct sibling resource, the Complex Portal  (15) , now provides a reference resource for biomolecular complexes, and we are annotating complexes with Complex Portal identifiers as interacting objects where possible, in addition to interactions of proteins with small molecules, nucleic acids and polysaccharides such as glycosaminoglycans.
Web Site We have redeveloped the IntAct web site (https://www. ebi. ac. uk/intact) to provide efficient, user-friendly access to In-tAct data content, with a focus on filter and display functionality to make the detailed interaction data accessible and useful through the user interface. The quick search provides autocompletion to facilitate selection of molecules of interest based on gene names, protein names, and accession numbers. The batch search supports multiple simultaneous query terms and subsequent result refinement. Results are shown both graphically and in tabular format, can be modified through comprehensive filter and visualisation options, and exported in both tabular and graphical formats. Figure  1  provides a view of the new IntAct web interface and its functionality, Figure  2  demonstrates the high level of detail provided for a single interaction.
Implementation The new IntAct public instance is deployed on the EMBL-EBI cloud using Kubernetes to manage the different containerized applications (the images have been built with Docker). The IntAct public interface is based on a Neo4j graph database and Apache Solr to enable the search and navigation features. An externally accessible API (https://www. ebi. ac. uk/intact/documentation/ technical corner#apis), developed in Java™ with the Spring framework to ease the implementation of the microservices architecture, serves data to both the web application and the Cytoscape app  (17). The web frontend is a single page application implemented with the Angular framework together with the EMBL-EBI Visual framework for general styling (https://www. ebi. ac. uk/style-lab/websites/). The network display (Figure  1 ) is based on Cytoscape.
Rare diseases dataset. As part of our commitment to the clinical community, we are currently populating a rare disease dataset, with a focus on interactions affected by rare disease mutations. Approximately 5500 rare disease interactions have been annotated to date, assigning details such as kinetic parameters, variable experimental conditions or construct details, including binding surfaces and mutations that affect the interactions. The data features information about the amino acid changes, their effect over the inter- action and full reference to the experimental interaction evidence from which it was extracted. Currently, around 98% of the annotations are mapped to human proteins, providing high-quality experimental evidence of sequence change effects which directly relate to existing variation data.
Box text: key concepts IMEx: The International Molecular Exchange Consortium (IMEx), founded in 2005, is an international collaboration of twelve interaction data resources which coordinate their curation strategies. IntAct is an IMEx founding member, and provides the web-based curation platform used by all current IMEx partners. Interaction evidence: Interactions may have two or more participating molecules, and the number of observable interactors may depend on both biological and experimental constraints. As an example, the yeast-two-hybrid array technology  (27)  typically identifies only pairs of interactors (binary interactions), while techniques like tandem affinity purification (TAP)  (28)  and BioID (29) may identify two or more interacting molecules (n-ary interactions). Observed n-ary interactions are stored as such in the IntAct database, but for some download files and for visualisation, counting and comparison purposes, they are expanded into multiple binary interactions.
FUNDING European Molecular Biology Laboratory ,  European Bioinformatics Institute (EMBL-EBI) ;  Open Targets  [ OTAR-044 ,  OTAR02-048 ,  OTAR02-066 ];  Wellcome Trust  [Biomedical Resources grants INVAR # 3367  and  218294 , and Centre grant 203149]; the MINT database is supported by a grant from the  Italian Association for Cancer Research  [ AIRC IG 2017  no. 20322  to G. C. ]; the MatrixDB database is supported by  Institut Francais de Bioinformatique  [ ANR-11-INBS-0013 ,  Glycomatrix  project to S. R. B. ]; the DIP database is supported by  NIH   NIGMS award  [ GM123126  to M. P. ]. Funding for open access charge:  European Molecular Biology Laboratory ,  European Bioinformatics Institute  ( EMBL-EBI ).
With the addition of metaXplor: an interactive viral and microbial metagenomic data manager new projects, the methodical storage and query of such heterogeneous data, including metabarcoding and shotgun data, become increasingly difficult and stable tools that provide means to manage, share, and search them are required. While tools and platforms such as Qiime  [10] , Mothur  [11] , Megan  [12] , IMG/VR  [13] , Anvi'o  [14] , Qiita  [15]  or Metavir  [16]  offer extensive sets of tools to analyse and compare datasets obtained from distinct metagenomic projects, they are not specifically designed for the identification of distant homologies or tracking newly discovered sequence/gene families across projects. Thus, metaXplor was developed to ease the search of viral sequences within existing projects using similarity-based search algorithms and phylogenetic tools.
Application description metaXplor is a sequence-centric web-interfaced application that is designed for managing, sharing, and exploring metagenomic datasets. Being distributable, its main features are to (i) centralize them at the laboratory or institute level, (ii) share them with local collaborators or partner scientists, (iii) easily filter on provided metadata to quickly get hold of sequences of interest at any time, (iv) compare external sequences with those contained in the system, and (v) refine provided taxonomic assignments using phylogenetic placement. The application is accessible via a web browser. It can handle multiple database hosts (defined via a configuration file), each of them being likely to point to several databases. An administration interface previously proven in Gigwa v2  [17]  allows for managing databases, projects, users, and permissions.
For compatibility with various processing methods that may be used for generating data, several assignment lines may be provided for a single sequence, and/or several accession IDs may be supplied (as CSV) on each assignment line. A bash script for con-verting tabular BLAST outputs to the appropriate format can be downloaded from the documentation page. Imported sequences are thus divided into 2 categories: assigned to known accessions or unassigned. For each imported project, nucleotide and protein BLAST  [19]  banks are automatically created using all associated sequences, in order to allow for subsequent query. The contents of all fields present in the sample and assignment files are stored and indexed in a NoSQL database. The system caches relationships between NCBI accessions and taxonomy IDs in order to link each assigned sequence to a taxon.