Publishing DNA-derived data through biodiversity data platforms

Abarenkov K, Andersson AF, Bissett A, Finstad AG, Fossøy F, Grosjean M, Hope M, Jeppesen TS, Kõljalg U, Lundin D, Nilsson RN, Prager M, Provoost P, Schigel D, Suominen S, Svenningsen C & Frøslev TG (2023) Publishing DNA-derived data through biodiversity data platforms, v1.3. Copenhagen: GBIF Secretariat. https://doi.org/10.35035/doc-vf1a-nr22.

Valuable discussions with members of ELIXIR, iBOL, GGBN, GLOMICON, and OBIS networks contributed to compilation of this draft. We are especially grateful for inputs and encouragement from Andrew Bentley, Matt Blissett, Pier Luigi Buttigieg, Kyle Copas, Camila A. Plata Corredor, Gabriele Dröge, Torbjørn Ekrem, Birgit Gemeinholzer, Quentin Groom, Tim Hirsch, Donald Hobern, Hamish Holewa, Corinne Martin, Raissa Meyer, Chris Mungall, Daniel Noesgaard, Corinna Paeper, Tim Robertson, Maxime Sweetlove, Andrew Young, John Waller, Ramona Walls, John Wieczorek, Lucie Zinger who have contributed to the GBIF community review process.

The document Publishing DNA-derived data through biodiversity data platforms is licensed under Creative Commons Attribution-ShareAlike 4.0 Unported License.

https://doi.org/10.35035/doc-vf1a-nr22

Version 1.3.0 released on 7 June 2023.

This version adds paragraph on marine datasets and the Ocean Biodiversity Information System (OBIS) along with some minor text edits throughout.

When genetic information is used to describe or classify a taxon, most users will foresee its use in the context of molecular ecology or phylogenetic research. It is important to realize that a sequence with coordinates and a timestamp is a valuable biodiversity occurrence which is useful in a much broader context than its original purpose. To realize this potential, DNA-derived data needs to be discoverable through biodiversity data platforms. This guide will teach you the principles and approaches of exposing “sequences with dates and coordinates” in the context of broader biodiversity data. The guide covers choices of particular schemas and terms, common pitfalls and good practice, without going into platform-specific details. It will benefit anyone interested in better exposure of DNA-derived data through general biodiversity data platforms, including national biodiversity portals.

The last 20 years have brought an increased understanding of the immense power of molecular methods for documenting the diversity of life on earth. Seemingly lifeless and mundane substrates such as soil and sea water turn out to abound with life–although perhaps not in a way that the casual observer may immediately appreciate. DNA-based studies have shown that organism groups such as fungi, insects, oomycetes, bacteria and archaea are everywhere, although we often cannot observe them physically (Debroas et al. 2017). The benefits of molecular methods are not restricted to the microscopic world: there are many organisms, such as some fish species, which can at least theoretically be observed physically but for which it is very costly, labour-intensive, and perhaps invasive to do so (Boussarie et al. 2018). In such situations, DNA data enable us to record the presence (and past presence) of these organisms non-invasively and with minimal effort. These developments mean that we do not always need tangible, physical manifestations of all organisms present at some site in order to record them. All organisms, whether or not they are physically observable, may be important when it comes to understanding biodiversity, ecology and biological conservation.

DNA-derived data enable us to record inconspicuous or otherwise unobservable taxa that fall below the radar of vetted protocols for field work, checklists, depositions into natural science collections, etc. The current maturity of DNA methodologies enables us to record the presence of these organisms to a level of detail that exceeds that of macroscopic observations of organisms in general. However, bearing in mind that DNA methodologies comes with their own problems and biases, it is important to use this moment to define and agree how we should record and report on an organism as present in some substrate or locality through molecular data. Doing so will help avoid significant inefficiencies that have been reported in other domains, in which the lack of standards and guidance has led to very heterogeneous and largely incomparable bodies of data (Berry et al. 2021; Leebens-Mack et al. 2006; Yilmaz et al. 2011; Nilsson et al. 2012; Shea et al. 2023). Moreover, clear documentation of the computational processing from raw sequence reads to deduced species observation, will enable reanalysis when improved methods appear.

DNA-derived occurrence data of species should be as standardized and reproducible as possible, regardless of whether or not the detected species have formal scientific names. In some cases, such occurrence records will hint at previously unknown geographical and ecological properties of described species, thus enriching our body of knowledge on these taxa. In other cases, the data may allow us to amalgamate and visualize information on currently undescribed species, potentially speeding up their eventual formal description. The ability to collect usable data even for unnamed species adds significantly to the many ways in which GBIF and other biodiversity data platforms index the living world, and make this knowledge available to all and for a variety of purposes, including biodiversity conservation. Recent estimates suggest that at least 85 per cent of all extant species are undescribed (Mora et al. 2011; Tedesco et al. 2014). Existing data standards have been designed for the minority of taxa that have been described. Good practices for dealing with DNA-derived data will help to characterize occurrences of all organisms, whether described or not.

This guide sets out the ways in which DNA-derived occurrence data should be reported for standardized inclusion in GBIF and other biodiversity data platforms. It does not express any view on the issue of access and benefit sharing for digital sequence information, the subject of extensive discussion through the Convention on Biological Diversity (CBD). However, it is worth noting that genetic barcodes and metabarcodes are typically genes or non-coding DNA fragments, which are not suitable for commercial exploitation. As the archiving of sequences through International Nucleotide Sequence Database Collaboration (INDSC) is a widespread norm in sequence-based research, publication of occurrence data originating from sequences does not involve publishing new sequences. In most cases these have already been placed in a public genetic repository. This guide therefore addresses the added value possible from deriving spatio-temporal occurrence data and dna-based names from dna data, rather than the value of the genetic information itself. In addition to dealing with sequence-derived data, this guide also includes suggestions for publishing occurrence data of species derived from qPCR or ddPCR analyses.

Reporting DNA-derived occurrences in an open and reproducible way brings many benefits: notably, it increases citability, highlights the taxa concerned in the context of biological conservation and contributes to taxonomic and ecological knowledge. Additionally, it also provides a mechanism to store occurrence records of undescribed species. When this yet to be described taxon is finally linked to a new Linnaean name, all these linked occurrence records will be immediately available. Each of these benefits provides a strong rationale for professionals to adopt the practices outlined in this guide, helping them to highlight a significant proportion of extant biodiversity, hasten its discovery and integrate it into biological conservation and policy-making.

This guide has been developed for multiple target audiences: students planning a first DNA-based study, researchers with old sequences and abundance tables they want to revive or preserve, biodiversity data specialists who are new to DNA-derived occurrences, and bioinformaticians familiar with sequence data but new to biodiversity data platforms. The guide does not directly target users of molecular data in biodiversity data platforms, but such users may find section 1.7 on Outputs particularly interesting. The authors' intention is to provide guidance on publishing data and associated attributes from genetic sequence through general biodiversity data platforms.

The flowchart outlines the processing steps involved in publishing amplicon-derived molecular biodiversity data in repositories such as GBIF and national biodiversity data platforms, including those built on the ALA platform. This guide’s focus is primarily on the steps following the arrival of raw FASTQ sequences from the sequencing step. By familiarizing themselves with the flowchart—and noting any steps that appear familiar or unclear—users will be able to navigate the content included in the guide.

We have done our best to present the information in this guide so that it is useful for each of the audiences described above, but background reading (e.g. GBIF quick guide to data publishing) may be required in certain cases.

DNA-derived biological occurrence data include information derived from DNA from individual organisms, but also from environmental DNA (eDNA, i.e. DNA extracted from environmental samples, Thomsen & Willerslev 2015) and from bulk samples comprising many individuals (e.g. plankton samples or Malaise trap samples consisting of multiple individuals from many species). Currently, the greatest volume of DNA-derived occurrence data derives from eDNA. Since analytical methods and end products are largely similar for all sample sources, the discussion below will focus on eDNA (§2.1.1 and §2.1.2), noting that the outline is applicable to the other sources. Surveys often utilize targeted sequencing of taxonomically and phylogenetically informative genetic markers, but can also use, for example, qPCR-based approaches that do not directly result in DNA sequence data (§2.1.3 and §2.2.2). This guide may appear heavy in DNA related terms; if this is the case, please consult the Glossary.

Publishing biodiversity data is largely a process of making species occurrence data findable, accessible, interoperable and reusable, in accordance with the FAIR principles (Wilkinson et al. 2016). Biodiversity data platforms help expose and discover genetic sequence data as biodiversity occurrence records alongside other types of biodiversity data, such as museum collection specimens, citizen science observations, and classical field surveys. The structure, management and storage for each original data source will vary according to the needs of each community. The biodiversity data platforms support data discovery, access and reuse by making these individual datasets compatible with each other, addressing taxonomic, spatial and other inconsistencies in the available biodiversity data. Making data available through single access points supports large-scale data-intensive research, management, and policy. The compatibility between datasets is reached through the process of standardization.

A number of data standards are in use for general biodiversity data (https://www.gbif.org/standards), and a separate set of standards for genetic sequence data (see MIxS and GGBN). This guide reflects some ongoing efforts to increase the compatibility between standards for general biodiversity and genetic data. Standards often highlight the subsets of fields which are most important or most frequently applicable. These subsets may be referenced as “cores”. The preferred format for publishing data in the GBIF and ALA networks is currently the Darwin Core Archive (DwC-A) using the Darwin Core (DwC) data standard. In practice, this is a compressed folder (a zip file) containing data files, in standard comma- or tab-delimited text format, a metadata file (eml.xml) that describes the data resource, and a metafile (meta.xml) that specifies the structure of files and data fields included in the archive. Standardized packaging ensures that the data can travel between systems using specific data exchange protocols. Section 2 of this guide provides recommendations for the mapping of the data files, while guidelines and tools for constructing the xml files can be found here: TDWG, GBIF, and ALA.

A central part of the standardization process is the mapping of fields, which is required to transform the original field (column) structure in a source-data export into a standard field structure. Standardization may also affect the content of the individual fields within each record, for example, by recalculating coordinates to a common system, rearranging date elements, or mapping the contents of fields a standard set of values, often called a vocabulary. The process of standardization also provides an opportunity to improve data quality, for example, by filling in omissions, correcting typos and extra spaces and handling inconsistent use of fields. Such improvements enhance the quality of data and increase its suitability for reuse, but at the same time, data published in any state are better than data that remain unpublished and inaccessible. Standartization is typically applied to a copy or to an export from the data source, leaving the original untouched.

Once a dataset has been through these standardization and data quality processes, it should be placed in an accessible online location and associated with relevant metadata. Metadata–data or information about the dataset includes key parameters that describe the dataset and further improve its discoverability and reuse. Metadata should include other important elements such as authorship, Digital Object Identifiers (DOIs), organizational affiliations and other provenance information, as well as procedural and methodological information about how the dataset was collected and curated. We encourage to provide a description of workflow details and versions including quality control in the methods section in the EML file.

Datasets and their associated metadata are indexed by each data portal: this process enables users to query, filter and process data through APIs and web portals. Unlike journal publications, datasets may be dynamic products that go through multiple versions, with an evolving number of records and mutable metadata fields under the same title and DOI.

Note that holders of genetic sequence data are expected to upload and archive genetic sequence data in raw sequence data repositories such as NCBI’s SRA, EMBL’s ENA or DDBJ. The sequence archival topic is not covered here, but e.g. Penev et al. (2017) provide a general overview of the importance of data submission and guidelines in connection with scientific publication. Biodiversity data platforms such as ALA, GBIF, and most national biodiversity portals are not archives or repositories for raw sequence reads and associated files. We do, however, stress the importance of maintaining links between such primary data and derived occurrences in Section 2.

Metabarcoding data can be produced from a number of different sequencing platforms (Illumina, PacBio, Oxford Nanopore, Ion Torrent, etc.) that rely on different principles for readout and generation of data that differ with respect to read length, error profile, whether sequences are single or paired-end, etc. Currently the Illumina short-read platform is the most widely adopted and as such is the basis of the descriptions here. However, the bioinformatics processing of the data follows the same general principles (QC, denoising, classification) regardless of the sequencing technology used (Hugerth et al. 2017, Figure 2).

Typically, the DNA sequences are first pre-processed by removing primer sequences and, depending on the sequencing method used, low quality bases, usually toward the 5’ and 3’ sequence ends. Sequences not fulfilling requirements on length, overall quality, presence of primers, tags etc. are removed.

The pre-processed sequences can then be assigned a taxon by comparing them against reference databases. When reference databases are incomplete, sequences classification can be done without taxonomic identifications, either by clustering sequences into operational taxonomic units based on their similarity (OTUs; Blaxter et al. 2005) or by denoising the data, i.e. explicitly detecting and excluding PCR/sequencing errors sequences to produce amplicon sequence variants (ASV; also referred to as zero radius OTU (zOTU)). Denoising attempts to correct errors that have been introduced in the PCR and/or sequencing steps, such that the denoised sequences are the set of unique biologically real sequences present in the original sequence mixture. In case of paired-end sequences, the forward and reverse sequences may be denoised separately before merging or else merged prior to denoising. ASVs in the resulting set can differ by as little as one base which is indicative of inter- or intraspecific sequence variation. Operationally, ASVs may be thought of as OTUs without defined radius and while denoising algorithms are typically very good, they do not entirely remove the problems of over-splitting or lumping sequences.

The PCR used for generating the sequencing library can result in the generation of artefactual sequences in the form of chimeras; a single sequence that originates from multiple parent sequences. Such sequences can be detected bioinformatically and removed, and this is typically done after denoising.

Finally, the pre-processed sequences, OTUs or ASVs, are taxonomically classified by comparing them to a database of annotated sequences (often referred to as reference libraries, see §1.6). As with the previous steps, several alternative methods are available. Most of these are either based on aligning the metabarcoding sequences to the reference sequences or on counting shared k-mers (short exact sequences).

Several open source tools and algorithms exist for bioinformatic processing of metabarcoding data (QIIME2 (Bolyen et al. 2019), DADA2 (Callahan et al. 2016), SWARM (Mahé et al. 2014), USEARCH (Edgar 2010), Mothur (Schloss et al. 2009), LULU (Frøslev et al. 2017), PROTAX (Somervuo et al. 2016), VSEARCH (Rognes et al. 2016)). Given the existence of many popular and well used workflows, we make some recommendations below on analysing data for submission to biodiversity data platforms. This is not to suggest that these are the best methods or most appropriate for all purposes but is an attempt to encourage submission of relatively standardized data that may readily be compared via the platforms. If possible, a well documented and maintained workflow should be used (e.g. nf-core/ampliseq pipeline). Metadata should include workflow details and versions either in the metadata method steps or as a reference in the SOP field in the DNA derived data extension (see mapping in Table 4). Sequence data should be deposited in an appropriate nucleotide archive (NCBI’s SRA: Leinonen et al. 2011) or EMBL’s ENA (Amid et al. 2020)) and data submitted to the biodiversity platform should include the biosample ID obtained from the archive (see data mapping in §2.2). Making use of these sample IDs will reduce the chances of duplication and ensure sequence data are readily obtainable should opportunities for re-analysis arise, as reference libraries and bioinformatic tools improve. The core end-product of these pipelines is typically a file of counts of individual OTUs or ASVs in each sample along with the taxonomy assigned to these. This is generated either in tabular format or in the BIOM format (McDonald et.al 2012). OTU or ASV sequences are also often provided in the FASTA format (Pearson & Lipman 1988).

Taxonomic annotation of sequences is a critical step in the processing of molecular biodiversity datasets, as scientific names are key to accessing and communicating information about the observed organisms. The accuracy and precision of such sequence annotation will depend on the availability of reliable reference databases and libraries across all branches of the tree of life, which in turn will require joint efforts from taxonomists and molecular ecologists. Public sequence databases should always be used knowingly of the fact that they suffer from various shortcomings related to, e.g., taxonomic reliability and lack of standardized metadata vocabularies (Hofstetter et al. 2019; Durkin et al. 2020).

Species, as described by taxonomists, are central to biology and attempts at characterizing biodiversity may therefore make use of the end products of taxonomic research. However, unlike DNA sequence data, taxonomic outputs are not always readily amenable to direct algorithmic or computational interpretation: classical taxonomy is a human-driven process which includes manual steps of taxon delimitation, description and naming, culminating in a formal publication in accordance to the international Codes of Nomenclature. As discussed in previous chapters, DNA sequence-based surveys are very effective at detecting hard to observe species and will often identify the presence of organisms currently outside traditional Linnaean taxonomic knowledge. While these guidelines do not address the publication of alternative species checklists derived from sequence data, the disconnection between traditional taxonomy and eDNA efforts is undesirable. Therefore we offer the following recommendations to readers of this guide.

As taxonomy is central to the discovery of biodiversity data, it is highly recommended that any eDNA sequencing efforts should seek to include relevant taxonomic expertise in their study. It will similarly be beneficial if eDNA sequencing studies are able to allocate a portion of their budget to generation and release of reference sequences from previously unsequenced type specimens or other important reference material from the local herbarium, museum, or biological collection. Taxonomists, too, can contribute towards this goal by always including relevant DNA sequences with each new species description (Miralles et al. 2020) and by targeting the many novel biological entities unravelled by eDNA efforts (e.g. Tedersoo et al. 2017).

Most current biodiversity data platforms are organized around traditional name lists and taxonomic indexes. As DNA sequence-derived occurrences are rapidly becoming a significant source of biodiversity data, and as official taxonomy and nomenclature for such data lags, it is recommended that data providers and platforms should continue to explore and include more flexible representations of taxonomy into their taxonomic backbones. These new representations include molecular reference databases (e.g., GTDB, BOLD, UNITE) that recognize sequence data as reference material for previously unclassified organisms. Additionally, we suggest other commonly used molecular databases (e.g., PR2, RDP, SILVA) should develop stable identifiers for taxa and make reference sequences available for those taxa, to allow their use as taxonomic references.

In contrast to classical taxonomy, which is a heavily manual process, clustering DNA sequences into taxonomic concepts relies on algorithmic analysis of similarity and other signals (such as phylogeny and probability), as well as some human editing. The resulting OTUs vary in stability, presence of reference sequences and physical material, alignments and cut-off values, and OTU identifiers such as DOIs (Nilsson et al. 2019). Even more importantly, they vary in scale, from local study- or project-specific libraries to global databases that enable broader cross-study comparison. In contrast to the centralization and codification of Linnaean taxa that are formally described in research publications, OTUs are distributed across multiple evolving digital reference libraries that differ in taxonomic focus, barcode genes and other factors. By associating standard sequences with identified reference specimens, BOLD and UNITE are establishing an essential mapping layer for linking ASVs and OTUs with Linnaean taxonomy. The GBIF backbone taxonomy includes identifiers for UNITE Species Hypotheses (SHs) as well as Barcode Index Numbers (BINs) which allows indexing of species occurrence data taxonomically annotated at the OTU level for primarily Fungi and Animals (GBIF secretariat 2018, Grosjean 2019).

Algorithms for taxonomic annotation of eDNA will typically assign each unique sequence to the nearest taxonomic group in a reference set, based on some criteria for relatedness and confidence. For poorly known groups of organisms, such as prokaryotes, insects and fungi, the annotation may be a non-Linnaean placeholder name for a (cluster-based) taxon (i.e. the ID/number of the relevant SH or BIN), and this taxon may represent a species or even a taxonomic unit above the species level. No reference database contains all species in a given group due to the many unknown, unidentified, and undescribed species on earth. Frequent ignorance of this fact has been the source of numerous taxonomic misidentifications during the last 30 years.

During import into the biodiversity platform (e.g. GBIF or OBIS), the taxonomic resolution for these DNA-based occurrences may be reduced even further, as the names/IDs obtained from comparing with the reference database (e.g. UNITE, BOLD) may not all be included in the taxonomic index of that platform at the time of publication. However, the inclusion of the underlying OTU or ASV sequence for each record will allow future users to potentially identify the sequence to a greater level of granularity, particularly as reference libraries improve over time. Therefore we also recommend to publish all sequences in a study - also those that are presently fully unclassified – as they may well be possible to identify with improved reference databases. In cases where the underlying sequence cannot be included as part of the submitted data, we advocate deposition of a (scientific or placeholder) name of the taxon (e.g. the BOLD BIN or UNITE SH) plus an MD5 checksum of the sequence as a unique taxon ID (see §2.2, “Data mapping”). MD5 checksums are unidirectional hash algorithms commonly used for verifying file integrity. In this case, they would provide a unique and repeatable representation of the original sequence that would nevertheless not allow the sequence itself to be recovered. This may be required in cases where sensitivity exists around access. MD5 checksums enable efficient query to determine whether the same exact sequence has been recovered in other eDNA efforts, but it is not a complete replacement of the sequence as MD5s do not enable further analyses. Two sequences differing by even a single base will get two completely different MD5 checksums, such that BLAST-style sequence similarity searches will not work.

The purpose of exposing DNA-derived data through biodiversity platforms is to enable reuse of these data in combination with other biodiversity data types. It is very important to keep this reuse in mind when preparing your data for publication. Ideally, the metadata and data should tell a complete story in such a way that new, uninformed users can use this evidence without any additional consultations or correspondence. Biodiversity data platforms provide search, filtering, browsing, visualizations, data access, and data citation functionality. For metabarcoding data we encourage users to configure filters for minimum absolute and relative read abundance to make a suitable filtering of data. Singletons or any occurrence with an absolute read count below some selected value can be filtered out by setting a minimum read abundance per OTU or ASV (using the field organismQuantity). Occurrences with a relative read abundance below a selected threshold can be filtered out by setting a minimum value of relative organism quantity, which is calculated from the detected reads (organismQuantity) and total reads in the corresponding sample (sampleSizeValue) (§2.2.1). Users can often choose data-output formats (e.g. DwC-A, CSV) and then process, clean and transform data into the shape and format needed for the analyses.

At GBIF.org or through the GBIF API, registered users can search, filter, and download biodiversity data in the following three formats:

Regardless of the selected format, each GBIF user download receives a reusable link to the query and a data citation that includes a DOI. This DOI-based citation system provides the means of recognizing and crediting uses to datasets and data originators, improving both the credibility and transparency of the findings based on the data. It is essential to follow data citation recommendations and use DOIs, as good data citation culture is not only the academic norm, but also a powerful mechanism for crediting acknowledging and, therefore, incentivizing data publishers.

For the purpose of this guide, we categorize data into five categories, linked by a key ID field (eventID), equivalent to the standards for general biodiversity data, and include fields relevant for DNA-derived data (see §2.2, “Data mapping”). These five categories seek to reflect the most commonly used molecular approaches to biodiversity characterization and are I) DNA-derived occurrences, II) enriched occurrences, III) targeted species detection, IV) name references and V) metadata only. Examine the decision tree and proceed to the correct section below.

While core files store ubiquitous data on the 'what, where and when' of a record, extension files are used to describe the specifics of a certain type of observation. We propose using the DNA derived data extension to complement occurrence data derived from either barcoding, metabarcoding (eDNA) or qPCR/ddPCR. The DNA derived data extension builds on the Minimum information standards developed by the Genomic Standards Consortium (GSC) and applied by the ENA for submission of eDNA sample metadata, for example. We are following and have contributed to the guidelines proposed by the Sustainable DwC-MIxS interoperability task group under TDWG. To improve indexing and search we have opted to split some MIxS terms, for instance separating forward and reverse primer sequences and names. Furthermore, some fields from the GGBN standard and fields from the MIQE (minimum information for the publication of quantitative real-time PCR) guidelines for qPCR and ddPCR data have been included to make it applicable for a wide range of DNA-derived data.

As a first step in preparing your data for publishing, you should make sure your field names / column headers follow the Darwin Core data standard. In many cases this is straightforward, such as renaming your lat or latitude field to decimalLatitude. However, the Darwin Core Standard is quite flexible and some terms are used in different ways, depending on the type of data. An example of this are the fields organismQuantity and organismQuantityType, which could be used to describe the number of individuals, per cent biomass or a score on the Braun-Blanquet Scale, as well as the number of reads of an ASV within a sample. Therefore, we here provide tables of required and recommended fields with descriptions and examples (Table 1, Table 2, Table 3 and Table 4). The recommendation to use the Occurrence core for DNA-derived data stems from the strong desire to share the sequence to help qualify the determination. Additional fields and extensions (such as extended Measurement or Fact (eMoF)) are applicable - both to occurrence cores and event core. When a sequence is derived from an organism (e.g. a parasite, gut contents, epibiont etc.) the observation may be linked to the observation of the host organism. This can be achieved using the (Resource Relation extension) of Darwin Core (e.g. https://www.gbif.org/species/143610775/verbatim). Perhaps the most important recommendation is to use globally unique (when available) and other permanent identifiers for as many data fields and parameters as possible (in all ID fields in the tables below).

When working with datasets originating in the marine environment, it is recommended that the information is published also in the Ocean Biodiversity Information System (OBIS) in addition to GBIF. OBIS is a global biodiversity database, which is specialized in providing reliable and accessible data related to marine life and is a part of the IOC-UNESCO. Like GBIF, and ALA, OBIS uses the DwC-A format for data indexing and publishing. By publishing marine datasets through OBIS in addition to other biodiversity databases, the data can reach a broader audience, and diverse groups working in the field of marine biodiversity, as datasets in OBIS are often used for UN processes. With the focus on marine datasets, stringent quality controls on the data increase the reliability of the data and lead to small differences in what information is required for publishing in OBIS as opposed to GBIF.

To ensure consistent taxonomic nomenclature OBIS uses the World Register of Marine Species (WoRMS) as the only taxonomic backbone. This is the case also for occurrences derived from genetic data; a scientific name linked to a scientific name ID from the WoRMS database is highly recommended information for publishing. If a scientific name ID is not provided, OBIS will try to match the scientific name with WoRMS during ingestion, but this should be avoided whenever possible. Scientific names not listed in WoRMS are acceptable, and will be submitted to WoRMS for review and possible inclusion in the register. Fully unclassified sequences are recommended to be classified as "incertae sedis", with the WoRMS scientificNameID urn:lsid:marinespecies.org:taxname:12. This will ensure correct interpretation by both GBIF and OBIS. Additionally, it is recommended that sequence identifiers from the used reference databases (e.g. Barcode index numbers: BINs from BOLD) be added in the taxonConceptID field of the occurrence core table. In this way OBIS will retain its taxonomic backbone based on WoRMS, while enabling linking to disparate reference sequence databases. Names from reference databases which are not strictly scientific names, can be added as verbatimIdentification. Automatic classification of species names can often be done through the WoRMS taxon match services and R packages like worrms and taxize. In the future, OBIS plans to periodically search and update the taxonomic assignments of submitted sequences as reference databases develop with time, so recording the sequence information linked to each occurrence is highly recommended.

Another required field in OBIS data submissions are geographic coordinates. OBIS performs additional quality checks related to marine data; e.g. that coordinates for strictly marine species are not on land, and that the depth value reported is in a reasonable range. Finally, it should be mentioned that in addition OBIS supports the use of the extended Measurement or Fact (eMoF). This extension allows linking environmental data and sampling facts to sampling events or occurrences, as well as biological measurements to occurrences in a flexible and standardized manner. OBIS has an example eDNA metabarcoding dataset with scripts for data formatting available at https://github.com/iobis/dataset-edna.