dataesr/scientific-paragraphs-categorization huggingface dataset:
g. listing all sequenced specimens with their corresponding taxonomic names and accession numbers), or a list of species or molecular taxonomic units identified from a metabarcoding survey (Figure  1d ). Clearly the advantages of having access to the taxonomic treatments and to the structured data tables embedded in the scientific papers, as this allows understanding the reasoning for creating a new species name or synonym, are numerous. This also provides access to cited specimens, permits the discovery of advanced species/taxa interactions such as viral hosts or plant pollinators, and promotes the development of a harmonised and complete list of taxonomic names tagged by unique taxIDs. Adoption more broadly of digital objects in biodiversity research will inform this taxonomic framework, and extend the scale and variety of connectable resources and datasets (specimen, ecosystem, species, gene, sampling event, trait, etc.
). Use case 2: Metagenomics exploration of the hidden diversity of the world's oceans Biodiversity data derived from marine metagenomics datasets have grown substantially during the last years and can serve as an excellent example of how molecular sequence data have expanded the insight and understanding of microbial biodiversity in the marine environment. Before the establishment of ELIXIR  (Harrow et al. 2021 ) and the Marine Metagenomics Community (MMC 2022), there was a lack of standards on how to process the data and deposit metagenomic and metagenomic-derived data into appropriate databases. As one of the first steps to address these gaps, the MMC published best practices  (ten Hoopen et al. 2017 ) that served as a foundation for a community standard to enable reproducibility and better sharing of metagenomic data along with comprehensive sampling metadata.
As a part of the work, the community built and benchmarked analysis pipelines, established domain-specific reference databases and established better procedures for deposition of metagenomic data. An example project that has been very successful in using molecular sequence data to inform and enrich our understanding of biodiversity is the work undertaken by the Tara Ocean Foundation (TARA 2022). Within this project, several major studies have been undertaken since 2009 using molecular sequencing techniques to characterise the life of the world's oceans. Tara Oceans has advanced our knowledge of all microbial kingdoms of life present in the ocean, from bacteria to small eukaryotes, as well as viruses (e. g. 150,000 eukaryotic taxa in the epipelagic ocean at 90% unknown, nearly 200,000 new double-stranded DNA viruses).
The approach uses meta-barcoding, metagenomic and meta-transcriptomic data sequencing  (Sunagawa et al. 2020)  to generate large numbers of raw sequence reads derived from organisms present in water samples. The Ocean Gene Atlas  (Villar et al. 2018 ) is a web service to explore the biogeography of marine genes (Figure  2 ) based on sequence similarities and consists of the Tara Ocean Microbiome -Reference Gene Catalog database (OM-RGC) and the Marine Atlas of Tara Ocean Unigenes (MATOU)  (Sunagawa et al. 2015; Tara Oceans Coordinators et al. 2018). The OM-RGC contains 46 million bacterial/archaeal genes, generated from metagenome raw data, while MATOU contains 117 million eukaryotic genes, generated from the metatranscriptome raw data. The raw data from Tara Oceans has also been submitted to MGnifya free to use resource for analysis, visualisation and discovery of metagenomic, metatranscriptomic, amplicon and assembly datasets  (Mitchell et al.
2019). Approximately 1,300 samples in eight studies have so far been analysed in MGnify, including 370 metatranscriptome and metagenome samples. Of these, 1,189 amplicon events have been registered in GBIF, giving rise to more than 750,000 biogeography occurrences (GBIF 2022). The sequence datasets analysed in MGnify are stored in the European Nucleotide Archive (ENA) and re-used in other marine reference databases such as METdb, a genomic reference database dedicated to micro-eukaryotic species, containing 348 organisms and 463 strains of micro-eukaryotic species derived from transcriptome sequence data  (Niang et al. 2020). The metagenome-assembled genomes (MAGs) generated from analyses of shotgun sequenced samples in MGnify have been included in the MAR databases  (Klemetsen et al. 2018) , a collection of richly annotated and manually curated contextual (metadata) and sequence databases for marine prokaryote species.
Context is captured through ensuring compliance with the Genomic Standards Consortium  (Field et al. 2014 ) recommendations for Minimum Information about any (x) Sequence (MIxS) standards, an overarching framework of sequence metadata  (Yilmaz et al. 2011 ). These resources are accessible through the Marine Metagenomics Portal (MMP 2022), with the MarRef containing nearly 1,000 complete microbial genomes, and MarDB hosting more than 13,000 non-complete genomes. The MAR database entries are cross-referenced with ENA and the World Register of Marine Species (WoRMS)  (Vandepitte et al. 2018)  to ease the access to additional and curated metadata. The data from the Tara Oceans project also provides links to several other 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.
databases such as UniProt (The UniProt Consortium 2019), a high-quality curated database of protein sequences and functional information, SILVA  (Quast et al. 2012) , a database for ribosomal RNA (rRNA) genes used for phylogenetic reconstruction, PR 2  (Guillou et al. 2012 ) a reference database of 18S rRNA protist sequences carefully curated by experts from each taxonomic group in the context of  EukRef project, and ITSoneDB (Santamaria et al. 2018) , a specialised collection of ribosomal RNA Internal Transcribed Spacer 1 (ITS1) sequences aimed at the taxonomic identification of eukaryotes. The Ocean Barcode Atlas (OBA) is a web service designed to explore the biodiversity and biogeography of marine organisms at planetary scale for Tara Oceans and other marine metabarcode datasets  (Vernette et al.
2021). Figure  2  illustrates how raw environmental sequence data derived from oceanic water samples are processed, annotated, and re-used, applying informatics tools and services to connect them with taxonomic information that helps link the data to knowledge about species biology and ecosystem variables. These sequence datasets therefore serve as a measure to determine diversity and abundance in a specific habitat, provide a means to quantify declines in biodiversity and climate change, and allow for efficient comparisons of datasets, e. g. time-series experiments, in environmental or species monitoring programmes. Other large-scale projects to analyse ocean biodiversity have also been undertaken in recent years, including the Malaspina expedition (Duarte 2015), Ocean Sampling Day initiatives  (Kopf et al.
2014 ). More generally, increasingly integrative approaches to diversity analysis are now favoured by the marine research community  (Canonico et al. 2019; Collins et al. 2020). Although marine metagenomics is relatively mature as a field, there are still many issues that need attention. There is a need to implement standardised procedures for processing and analysing datasets, including best practices for assembly, binning and annotation. Furthermore, the quality of reference databases, integration of new omics data, specific data warehouses, and long-term data management services are issues that warrant careful attention, e. g. in the context of moving from biodiversity snapshots to large-scale monitoring and discovery. Use case 3: Biodiversity genetics and genomics for food and agriculture Adaptation of agriculture has been based on fitting crop varieties and breeds to their production system, which includes farming systems and their natural environments.
Another possibility for archiving data on collections of genetic resources is to use the GBIF portal, which is often carried out in parallel as an alternative that does not require any clearance by governmental agencies (Figure  3 ; e. g. datasets at GBIF from The Netherlands Centre for Genetic Resources, (Menting 2022)). Since the 1980s, collections of accessions have been genotyped with a set of fast-evolving techniques, mainly to understand crops and breed evolution since domestication and for the identification of adaptive traits, e. g. (Wilkinson et al. 2013; Brozynska et al. 2016; Mascher et al. 2019). Sequence variation has also proved useful and is increasingly used for monitoring current maintained genetic diversity, e. g. detection of redundancy in collections, or assessment of threat levels facing small populations of breeds, forest trees or crop wild relatives  (Bélanger et al.
2019 ). International archives have been developed to store sequence variation data, such as dbSNP  (Sherry et al. 2001 ) focused on Sequence Nucleotide Polymorphisms (SNPs) or the European Variation Archive (EVA)  (Cezard et al. 2022 ) launched more recently to store any type of sequence variant that can be expressed in Variant Call Format (VCF) (Figure  3 ). Companion archives can be used to track the accession identifiers and collection provenance (BioSamples and BioStudies at EMBL-EBI or BioProjects at NCBI) while the reference genomes used for the detection of the variations must be stored in the INSDC archives prior to the submission of variation data (Figure  3 ). A key challenge to be addressed in the context of biodiversity genetics and genomics for food and agriculture rests with the identification of the accessions of genetic resources and their consistency across the catalogues and molecular archives.
) associated with a variant found in EVA or dbSNP. For crops, the United Nations FAO recently recommended adding a DOI to the three fields of the MultiCrop Passport Data standards that have ensured the unique identification of accessions to date (species name, holding institution name, and the accession identifier provided by the holding institute) and developed a dedicated service (GLIS: the Global Information System on Plant Genetic Resources for Food and Agriculture, Figure  3 ) to support the adoption of this new practice by genebanks. The communities working on crops, farm animals and forest trees are also actively working with EMBL-EBI to develop dedicated specifications for the metadata associated with the data archived Figure  3. Overview of the main information systems used for archiving data on genetic resources for food and agriculture.
In the green box at the bottom, the information systems used to manage the data collected and curate it. Some of these information systems are maintained by ELIXIR nodes in their national infrastructures. The data can then regularly be submitted and updated in international archives. The list of Genetic Resource accessions are archived in the European Search Catalogue for Plant Genetic Resources (EURISCO) and the European Farm Animal Biodiversity Information System (EFABIS) after clearance by National Focal Points appointed by country governments and then collected by global information systems, Genesys and the Domestic Animal Diversity Information System (DAD-IS). They can also publish their datasets at GBIF without any clearance. Genotyping and genomic data are archived in ELIXIR deposition databases and Core Data Resources (EMBL-EBI ENA, EVA and BioSamples).
Reciprocally, mechanisms for capturing and updating in genebank catalogues the identifiers associated with the samples that were genotyped or sequenced are needed (see e. g. in relation with domestic animals biological resources: (IMAGE 2022). These challenges are not necessarily unique to biodiversity genetics and genomics for food and agriculture, but they particularly highlight efforts required to informatically process and connect sequence data with sample metadata. Use case 4: Understanding biogeographical diversity through molecular mapping of global fungal distributions The GlobalFungi Database (GlobalFungi 2022) exemplifies efforts to connect sequencing data to biodiversity research infrastructures and advance data-driven research. Fungi play key roles in all terrestrial ecosystems, primarily as decomposers of organic matter but also as pathogens or symbionts.
Long-standing scientific interest in describing and understanding the patterns of global distributions and diversity of fungi mean that sequencing initiatives have led to an accumulating wealth of fungal molecular data from various geographical regions, ecosystems, and habitats. Large-scale studies focusing on soil fungi have used metabarcoding analysis to examine the ecological drivers and biogeographic patterns of fungal community composition and diversity  (Tedersoo et al. 2014; Egidi et al. 2019 ). However, coordinated global sampling at sufficient spatial and taxonomic resolution remains largely unfeasible for individual research studies. Instead, a meta-approach is needed to collect, collate, categorise, and centralise existing data using infrastructures that can continue to gather and include new and future genetic and genomic datasets.
The GlobalFungi Database was established as a platform to address these needs by providing public access to published data on fungal community composition obtained by next-generation-sequencing approaches through a web-based interface that promotes FAIR principles and allows various queries and visualisations of the results (Ve ˇtrovský et al. 2020). Release version 3. 0 contains over 1100 million observations of fungi from 367 manually curated studies with over 36,000 samples and 213 million ITS sequence variants (Figure  4 ). GlobalFungi allows searching for specific sequence variants, fungal genera, species, and molecular species (called 'species hypotheses') by performing BLASTn sequence searches and querying the local MySQL database. Annotation of taxa is based on UNITE, the database of fungal molecular taxa compiled using direct sequencing of known fungal species and environmental sequencing of targeted barcodes  (Nilsson et al.
2019). GlobalFungi contains data from high-throughput sequencing efforts including local abundance of fungi and complete sampling metadata, and allows querying of samples by location searches on maps or through the studies where they were published. These are complemented with extensive climatic data for sample locations retrieved from the CHELSA (Climatologies at High resolution for the Earth's Land Surface Areas) database  (Karger et al. 2017). The GlobalFungi Database aims to continue to grow by adding more records and by motivating the community to submit new datasets to help build the resource for research on the systematics, biogeography, and ecology of fungi. The utility of such a centralised resource connecting sequencing data to biodiversity research infrastructures is demonstrated generally through the characterisation of global patterns of fungal biodiversity (Ve ˇtrovský et al.
2019) or predicting the global biodiversity of fungi  (Baldrian et al. 2022 ) and specifically through the ability to identify fungi that are carried across continents along with introduced plants  (Vlk et al. 2020). Moreover, the metadata-rich resource helped to show that symbiotic fungi are more vulnerable to climate change than pathogens and that climate change thus represents a considerable threat for forestry production, agriculture, and food security (Ve ˇtrovský et al. 2019). Beyond community diversity and biogeography patterns, the distributions of individual fungal species are particularly important, e. g. for phytopathogenic fungi that may severely affect yields of agricultural crops such as the Fusarium pathogen of bananas  (Dale et al. 2017). Exploiting the GlobalFungi Database, mycologists, ecologists, or global climate change scientists are able to link fungal occurrence and diversity data with the panel of collected metadata, allowing for the characterisation of key environmental factors that are driving fungal diversity.
Such studies can be performed at different geographic levels, from country scales to biomes of the entire world, and for all identifiable fungal communities or for selected ecosystem compartments. Collating these data involves manual curation of information from published studies (367 studies in the latest release), but metadata heterogeneity means that attributes extracted from the publications that are common across the database are limited to just Longitude, Latitude, Continent, Sample type, Biome, Sampling year, Primers used, and pH, while additional metadata only exist for some of the studies. Nevertheless, these resources bring together different data types to enable assessments of fungal diversity across the globe and tracking of individual species or genera, leading to the development of a more comprehensive understanding of the biogeography of fungal diversity.
Importantly, this also facilitates assessments of potential threats faced by fungal communities and the ecosystems of which they form such a vital part. Informing strategies for large-scale national biodiversity programmes Now that large-scale regional, national, and global biodiversity genomics projects are a reality, it is vital to capitalise on the lessons learned and best practices developed through initiatives such as the example use cases presented above. The greatest impact that genetic and genomic data can have on biodiversity assessments, monitoring, conservation, and restoration will only be realised with the support of infrastructures that facilitate the finding, sharing, and connecting of increasingly large and diverse datasets. This requires efforts at all levels to be put into practice from the start, informing strategies for biodiversity programmes to ensure that the data they generate are findable and interoperable.
DNA barcoding contributes to rapid identification of biological material and, in terms of cost-benefit, knowing when to barcode and/or genome sequence a specimen could be seen to be a balancing act when considering how to efficiently make assessments of biodiversity. As noted in Use Case 1, barcoding provides a fast and cost-effective technical process to ascertain a provenance trail for a given organism with respect to its taxonomic lineage, an essential part of biodiversity studies. This becomes increasingly important where taxonomic identification is still uncertain due to conflicting or a lack of information, i. e. where an expert identification results in naming differences, lack of defined lineages of less well-studied organisms within the taxonomy databases, and discrepancies with taxonomic identifier allocation services such as the NCBI.
As part of DToL, specific metadata schemas are being prepared to assist with the collection of standardised barcoding data alongside methodologies to automate taxonomic identification based on amplicon sequences. Data management tools incorporate and link the deposited sample metadata and the subsequent genomes in the EMBL-EBI Biosamples and ENA databases, respectively, and will also submit to BOLD. Other national projects focus on within species diversity rather than between species. The national Swedish conifer programme to sequence the Norway spruce and Scots pine genomes serves as an example of what can be done with a wellassembled and annotated genome  (Nystedt et al. 2013). Around 75% of Sweden's area is covered with forest, and much of this is conifer. To improve production and to inform a sustainable forestry practice, the genomes will serve as a basis for a massive resequencing effort where thousands of individuals are sequenced using short read technologies.
They both comprise important first steps for future biodiversity studies, i. e. they develop fundamental genomic baselines on which to build future comparisons amongst organisms and populations through resequencing efforts. However, differences in sampling, naming conventions, sequencing dataset quality and coverage, and annotation quality can all lead to barriers to uptake within the FAIR data ecosystem. By using standardised methods and tools for metadata and data capture and processing, one of the key gaps in biodiversity data management is fulfilled and directly coupled to efforts to produce sequencing and barcoding data based on consistent rich metadata about the biological material from which data are derived. Technical tools, including COPO  (Shaw et al. 2020 ), an ELIXIR roadmapped data brokering resource, are being employed to aid consistent deposition of project-compliant data and metadata in DToL and other upcoming national and international programmes.
The use cases above highlight some examples of communities that are building brokering services to meet their own needs, but these probably reflect the exception rather than the rule. Brokering services support researchers by maintaining a technical infrastructure for aiding and automating data submission. For example, the Integrated Publishing Toolkit (IPT 2022) is a free open-source software used to publish and share biodiversity datasets through the GBIF network. Even when such data brokering tools exist for specific communities, users still need support to ensure that they are using the systems correctly: selecting the right standards; employing the right formats; correctly interpreting these standards and formats; obtaining useful feedback when metadata is not collected properly or missing; and most importantly, a human support mechanism that fits with their domain.
Without such frameworks, inconsistencies even within domains can hamper the ability to connect datasets from different studies. The next step, integrating data across different research domains, is often where the greatest metadata loss occurs. If a host resource cannot accommodate certain data types or structures, these remain with the submitter and risk being lost altogether even if provenance is recorded. Proactive communities have recognised many of these challenges and developed brokering tools to meet specific needs, and more generally it is clear that without such supporting services the end-value of the data for biodiversity research is greatly diminished. With the scaling up of production of high-quality sequence data collections and biodiversity research datasets, communities also recognise that ensuring high standards achieved normally through manually curating metadata will not be possible without efficient brokering.