Biodiversity Information Standards Research Articles

Sharing insect data through GBIF: novel monitoring methods, opportunities and standards.

Technological advancements in biological monitoring have facilitated the study of insect communities at unprecedented spatial scales. The progress allows more comprehensive coverage of the diversity within a given area while minimizing disturbance and reducing the need for extensive human labour. Compared with traditional methods, these novel technologies offer the opportunity to examine biological patterns that were previously beyond our reach. However, to address the pressing scientific inquiries of the future, data must be easily accessible, interoperable and reusable for the global research community. Biodiversity information standards and platforms provide the necessary infrastructure to standardize and share biodiversity data. This paper explores the possibilities and prerequisites of publishing insect data obtained through novel monitoring methods through GBIF, the most comprehensive global biodiversity data infrastructure. We describe the essential components of metadata standards and existing data standards for occurrence data on insects, including data extensions. By addressing the current opportunities, limitations, and future development of GBIF's publishing framework, we hope to encourage researchers to both share data and contribute to the further development of biodiversity data standards and publishing models. Wider commitments to open data initiatives will promote data interoperability and support cross-disciplinary scientific research and key policy indicators. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Ten simple rules for pushing boundaries of inclusion at academic events.

Inclusion at academic events is facing increased scrutiny as the communities these events serve raise their expectations for who can practically attend. Active efforts in recent years to bring more diversity to academic events have brought progress and created momentum. However, we must reflect on these efforts and determine which underrepresented groups are being disadvantaged. Inclusion at academic events is important to ensure diversity of discourse and opinion, to help build networks, and to avoid academic siloing. All of these contribute to the development of a robust and resilient academic field. We have developed these Ten Simple Rules both to amplify the voices that have been speaking out and to celebrate the progress of many Equity, Diversity, and Inclusivity practices that continue to drive the organisation of academic events. The Rules aim to raise awareness as well as provide actionable suggestions and tools to support these initiatives further. This aims to support academic organisations such as the Deep Learning Indaba, Neuromatch Academy, the IBRO-Simons Computational Neuroscience Imbizo, Biodiversity Information Standards (TDWG), Arabs in Neuroscience, FAIRPoints, and OLS (formerly Open Life Science). This article is a call to action for organisers to reevaluate the impact and reach of their inclusive practices.

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

AbstractCamera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.

Aligning Standards Communities for Omics Biodiversity Data: Sustainable Darwin Core-MIxS Interoperability.

The standardization of data, encompassing both primary and contextual information (metadata), plays a pivotal role in facilitating data (re-)use, integration, and knowledge generation. However, the biodiversity and omics communities, converging on omics biodiversity data, have historically developed and adopted their own distinct standards, hindering effective (meta)data integration and collaboration. In response to this challenge, the Task Group (TG) for Sustainable DwC-MIxS Interoperability was established. Convening experts from the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) alongside external stakeholders, the TG aimed to promote sustainable interoperability between the Minimum Information about any (x) Sequence (MIxS) and Darwin Core (DwC) specifications. To achieve this goal, the TG utilized the Simple Standard for Sharing Ontology Mappings (SSSOM) to create a comprehensive mapping of DwC keys to MIxS keys. This mapping, combined with the development of the MIxS-DwC extension, enables the incorporation of MIxS core terms into DwC-compliant metadata records, facilitating seamless data exchange between MIxS and DwC user communities. Through the implementation of this translation layer, data produced in either MIxS- or DwC-compliant formats can now be efficiently brokered, breaking down silos and fostering closer collaboration between the biodiversity and omics communities. To ensure its sustainability and lasting impact, TDWG and GSC have both signed a Memorandum of Understanding (MoU) on creating a continuous model to synchronize their standards. These achievements mark a significant step forward in enhancing data sharing and utilization across domains, thereby unlocking new opportunities for scientific discovery and advancement.

No Pain No Gain: Standards mapping in Latimer Core development

Latimer Core (LtC) is a new proposed Biodiversity Information Standards (TDWG) data standard that supports the representation and discovery of natural science collections by structuring data about the groups of objects that those collections and their subcomponents encompass (Woodburn et al. 2022). It is designed to be applicable to a range of use cases that include high level collection registries, rich textual narratives and semantic networks of collections, as well as more granular, quantitative breakdowns of collections to aid collection discovery and digitisation planning. As a standard that is (in this first version) focused on natural science collections, LtC has significant intersections with existing data standards and models (Fig. 1) that represent individual natural science objects and occurrences and their associated data (e.g., Darwin Core (DwC), Access to Biological Collection Data (ABCD), Conceptual Reference Model of the International Committee on Documentation (CIDOC-CRM)). LtC’s scope also overlaps with standards for more generic concepts like metadata, organisations, people and activities (i.e., Dublin Core, World Wide Web Consortium (W3C) ORG Ontology and PROV Ontology, Schema.org). LtC represents just an element of this extended network of data standards for the natural sciences and related concepts. Mapping between LtC and intersecting standards is therefore crucial for avoiding duplication of effort in the standard development process, and ensuring that data stored using the different standards are as interoperable as possible in alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles. In particular, it is vital to make robust associations between records representing groups of objects in LtC and records (where available) that represent the objects within those groups. During LtC development, efforts were made to identify and align with relevant standards and vocabularies, and adopt existing terms from them where possible. During expert review, a more structured approach was proposed and implemented using the Simple Knowledge Organization System (SKOS) mappingRelation vocabulary. This exercise helped to better describe the nature of the mappings between new LtC terms and related terms in other standards, and to validate decisions around the borrowing of existing terms for LtC. A further exercise also used elements of the Simple Standard for Sharing Ontological Mappings (SSSOM) to start to develop a more comprehensive set of metadata around these mappings. At present, these mappings (Suppl. material 1 and Suppl. material 2) are provisional and not considered to be comprehensive, but should be further refined and expanded over time. Even with the support provided by the SKOS and SSSOM standards, the LtC experience has proven the mapping process to be far from straightforward. Different standards vary in how they are structured, for example, DwC is a ‘bag of terms’, with informal classes and no structural constraints, while more structured standards and ontologies like ABCD and PROV employ different approaches to how structure is defined and documented. The various standards use different metadata schemas and serialisations (e.g., Resource Description Framework (RDF), XML) for their documentation, and different approaches to providing persistent, resolvable identifiers for their terms. There are also many subtle nuances involved in assessing the alignment between the concepts that the source and target terms represent, particularly when assessing whether a match is exact enough to allow the existing term to be adopted. These factors make the mapping process quite manual and labour-intensive. Approaches and tools, such as developing decision trees (Fig. 2) to represent the logic involved and further exploration of the SSSOM standard, could help to streamline this process. In this presentation, we will discuss the LtC experience of the standard mapping process, the challenges faced and methods used, and the potential to contribute this experience to a collaborative standards mapping within the anticipated TDWG Standards Mapping Interest Group.

Mapping across Standards to Calculate the MIDS Level of Digitisation of Natural Science Collections

The Minimum Information about a Digital Specimen (MIDS) standard is being developed within Biodiversity Information Standards (TDWG) to provide a framework for organisations, communities and infrastructures to define, measure, monitor and prioritise the digitisation of specimen data to achieve increased accessibility and scientific use. MIDS levels indicate different levels of completeness in digitisation and range from Level 0: not yet meeting minimal required information needs for scientific use to Level 3: fulfilling the requirements for Digital Extended Specimens (Hardisty et al. 2022) by inclusion of persistent identifiers (PIDs) that connect the specimen with derived and related data. MIDS Levels 0–2 are generic for all specimens. From MIDS Level 2 onwards we make a distinction between biological, geological and palaeontological specimens. While MIDS represents a minimum specification, defining and publishing more extensive sets of information elements (extensions) is readily feasible and explicitly recommended. The MIDS level of a digital specimen can be calculated based on the availability of certain information elements. The MIDS standard applies to published data. The ability to map from, to and between TDWG standards is key to being able to measure the MIDS level of the digitised specimen(s). Each MIDS term is being mapped across TDWG standards involving Darwin Core (DwC), the Access to Biological Collections Data (ABCD) Schema and Latimer Core (LtC, Woodburn et al. 2022), using mapping properties provided by the Simple Knowledge Organization System (SKOS) ontology. In this presentation, we will show selected case studies that demonstrate the implementation of the MIDS standard supplemented by MIDS mappings to ABCD, to LtC, and to the Distributed System of Scientific Collections' (DISSCo) Open Digital Specimen specification. The studies show the mapping exercise in practice, with the aim of enabling fully automated and accurate calculations. To provide a reliable indicator for the level of digitisation completeness, it is important that calculations are done consistently in all implementations.

Leveraging AI in Biodiversity Informatics: Ethics, privacy, and broader impacts

Artificial Intelligence (AI) has been heralded as a hero by some and rejected as a harbinger of destruction by others. While many in the community are excited about the functionality and promise AI brings to the field of biodiversity informatics, others have reservations regarding its widespread use. This talk will specifically address Large Language Models (LLMs) highlighting both the pros and cons of using LLMs. Like any tool, LLMs are neither good nor bad in and of themselves, but AI does need to be used within the appropriate scope of its ability and properly. Topics to be covered include model opacity (Franzoni 2023), privacy concerns (Wu et al. 2023), potential for algorithmic harm (Marjanovic et al. 2021) and model bias (Wang et al. 2020) in the context of generative AI along with how these topics differ from similar concerns when using traditional ML (Machine Learning) applications. Potential for implementation and training to ensure the most fair environment when leveraging AI and keeping FAIR (Findability, Accessibility, Interoperability, and Reproducibility) principles in mind, will also be discussed. The topics covered will be mainly framed through the Biodiversity Information Standards (TDWG) community, focusing on sociotechnical aspects and implications of implementing LLMs and generative AI. Finally, this talk will explore the potential applicability of TDWG standards pertaining to uniform prompting vocabulary when using generative AI and employing it as a tool for biodiversity informatics.

Data Standards and Interoperability Challenges for Biodiversity Digital Twin: A novel and transformative approach to biodiversity research and application

The Biodiversity Digital Twin (BioDT) project (2022-2025) aims to create prototypes that integrate various data sets, models, and expert domain knowledge enabling prediction capabilities and decision-making support for critical issues in biodiversity dynamics. While digital twin concepts have been applied in industries for continuous monitoring of physical phenomena, their application in biodiversity and environmental sciences presents novel challenges (Bauer et al. 2021, de Koning et al. 2023). In addition, successfully developing digital twins for biodiversity requires addressing interoperability challenges in data standards. BioDT is developing prototype digital twins based on use cases that span various data complexities, from point occurrence data to bioacoustics, covering nationwide forest states to specific communities and individual species. The project relies on FAIR principles (Findable, Accessible, Interoperable, and Reusable) and FAIR enabling resources like standards and vocabularies (Schultes et al. 2020) to enable the exchange, sharing, and reuse of biodiversity information, fostering collaboration among participating research infrastructures (DiSSCo, eLTER, GBIF, and LifeWatch) and data providers. It also involves creating a harmonised abstraction layer using Persistent Identifiers (PID) and FAIR Digital Object (FDO) records, alongside semantic mapping and crosswalk techniques to provide machine-actionable metadata (Schultes and Wittenburg 2019, Schwardmann 2020). Governance and engagement with research infrastructure stakeholders play crucial roles in this regard, with a focus on aligning technical and data standards discussions. In addition to data, models and workflows are key elements in BioDT. Models in the BioDT context are formal representations of problems or processes, implemented through equations, algorithms, or a combination of both, which can be executed by machine entities. The current twin prototypes are considering both statistical and mechanistic models, introducing significant variations in (1) data requirements, (2) modelling approaches and philosophy, and (3) model output. The BioDT consortium will develop guidelines and protocols for how to describe these models, what metadata to include, and how they will interact with the diverse datasets. While discussions on this topic exist within the broader context of biodiversity and ecological sciences (Jeltsch et al. 2013, Fer et al. 2020), the BioDT project is strongly committed to finding a solution within its scope. In the twinning context, data and models need to be executed within a computing infrastructure and also need to adhere to FAIR principles. Software within BioDT includes a suite of tools that facilitate data acquisition, storage, processing, and analysis. While some of these tools already exist, the challenge lies in integrating them within the digital twinning framework. One approach to achieving integration is through workflow representation, encompassing standardised procedures and protocols that guide the acquisition, packaging, processing, and analysis of data. The project is exploring Research Object Crate (RO-Crate) implementation for this (Soiland-Reyes et al. 2022). Implementing workflows can ensure reproducibility, scalability, and transparency in research practices, enabling scientists to validate and replicate findings. The BioDT project offers a novel and transformative approach to biodiversity research and application. By leveraging collaborative research infrastructures and adhering to data standards, BioDT aims to harness the power of data, software, supercomputers, models, and expertise to provide new insights. The foundation provided by the data standards, including those of Biodiversity Information Standards (TDWG), is crucial in realising the full potential of digital twins, facilitating the seamless integration of diverse data sources and combinations with models.

FAIR but not Necessarily Open: Sensitive data in the domain of biodiversity

In the framework of implementing the European Open Science Cloud (EOSC), there is still confusion between the concept of data FAIRness (Findable, Accessible, Interoperable and Re-usable, Wilkinson et al. 2016) and the idea of open and freely accessible data, which are not necessarily the same. Data can indeed comply with the requirements of FAIRness even if their access is moderated or behind a paywall. Therefore the motto of EOSC is actually “As open as possible, as closed as necessary”. This confusion or misinterpretation of definitions has raised concerns among potential data providers who fear being obligated to make sensitive data openly accessible and freely available, even if there are valid reasons for restrictions, or to forfeit any charges or hamper profit making if the data generate revenue. As a result, there has been some reluctance to fully engage in the activities related to FAIR data and the EOSC. When addressing sensitive data, what comes to mind are personal data governed by the General Data Protection Regulation (GDPR), as well as clinical, security, military, or commercially valuable data protected by patents. In the domain of biodiversity or natural history collections, it is often reported that these issues surrounding sensitive data regulations have less impact, especially when contributors are properly cited and embargo periods are respected. However, there are cases in this domain where sensitive data must be considered for legal or ethical purposes. Examples include protected or endangered species, where the exact geographic coordinates might not be shared openly to avoid poaching; cases of Access and Benefit sharing (ABS), depending on the country of origin of the species; the respect of traditional knowledge; and a desire to limit the commercial exploitation of the data. The requirements of the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization to the Convention on Biological Diversity, as well as the upcoming Digital Sequence Information regulations (DSI), play an important role here. The Digital Services Act (DSA) was recently adopted with the aim of the protection of the digital space against the spread of illegal content, which sets the interoperability requirements for operators of data spaces. This raises questions on the actual definition of data spaces and how they would be affected by this new European legislation but with a worldwide impact on widely used social media and content platforms such as Google or YouTube. During the implementation and updating activities in projects and initiatives like Biodiversity Community Integrated Knowledge Library (BiCIKL), it became clear that there is a need to offer a secure data repository and management system that can deal with both open and non-open data in order to effectively include all potential data providers and mobilise their content while adhering to FAIR requirements. In this talk, after a general introduction about sensitive data, we will provide several examples in the biodiversity and natural sciences domains on how to deal with sensitive data and their management, such as recommended by GBIF. Last, but not least, we will highlight how important it is to use internationally accepted standards such as those from Biodiversity Information Standards (TDWG) to achieve such developments in the context of the Biodiversity Knowledge Hub (BKH) implemented by BiCIKL. Notably, by providing clear metadata about the terms of use, citation requirements and licensing, actual re-use of the data is made possible both legally and efficiently.

Connecting the Dots: Aligning human capacity through networks toward a globally interoperable Digital Extended Specimen (DES) infrastructure

Thanks to substantial support for biodiversity data mobilization in recent decades, billions of occurrence records are openly available, documenting life on Earth and enabling timely research, awareness raising, and policy-making. Initiatives across local to global scales have been separately funded to serve different, yet often overlapping audiences of data users, and have developed a variety of platforms and infrastructures to meet the needs of these audiences. The independent progress of biodiversity data providers has led to innovations as well as challenges for the community at large as we move towards connecting and linking a diversity of information from disparate sources as Digital Extended Specimens (DES). Recognizing a need for deeper and more frequent opportunities for communication and collaboration across the globe, an ad-hoc group of representatives of various international, national, and regional organizations have been meeting virtually since 2020 to provide a forum for updates, announcements, and shared progress. This group is provisionally named International Partners for the Digital Extended Specimen (IPDES), and is guided by these four concepts: Biodiversity, Connection, Knowledge and Agency. Participants in IPDES include representatives of the Global Biodiversity Information Facility (GBIF), Integrated Digitized Biocollections (iDigBio), American Institute of Biological Sciences (AIBS), Biodiversity Collections Network (BCoN), Natural Science Collections Alliance (NSCA), Distributed System of Scientific Collections (DiSSCo), Atlas of Living Australia (ALA), Biodiversity Information Standards (TDWG), Society for the Preservation of Natural History Collections (SPNHC), National Specimen Information Infrastructure of China (NSII), and South African National Biodiversity Institute (SANBI), as well as individuals involved with biodiversity informatics initiatives, natural science collections, museums, herbaria, and universities. Our global partners group strives to increase representation from around the globe as we aim to enable research that contributes to novel discoveries and addresses the societal challenges leading to the biodiversity crisis. Our overarching mission is to expand on the community-driven successes to connect biodiversity data and knowledge through coordination of a globally integrated network of stakeholders to enable an extensible technical and social infrastructure of data, tools, and working practices in support of our vision. The main work of our group thus far includes publishing a paper on the Digital Extended Specimen (Hardisty et al. 2022), organizing and hosting an array of activities at conferences, and asynchronous online work and forum-based exchanges. We aim to advance discussion on topics of broad interest to our community such as social and technical capacity building, broadening participation, expanding social and data networks, improving data models and building a backbone for the DES, and identifying international funding solutions. This presentation will highlight some of these activities and detail progress towards a roadmap for the development of the human network and technical infrastructure necessary to support the DES. It provides an opportunity for feedback from and engagement by stakeholder communities such as TDWG and other initiatives with a focus on data standards and biodiversity informatics, as we solidify our plans for the future in support of integrated and interconnected biodiversity data and credit for those doing the work.

The Role of the OLS Program in the Development of echinopscis (an Extensible Notebook for Open Science on Specimens)

Starting in early 2022, biodiversity informatics researchers at Kew have been developing echinopscis: an "extensible notebook for open science on specimens". This aims to build on the early experiments that our community conducted with "e-taxonomy": the development of tools and techniques to enable taxonomic research to be conducted online. Early e-taxonomic tools (e.g., Scratchpads Smith et al. 2011) had to perform a wide range of functions, but in the past decade or so the move towards open science has built better support for generic functionality, such as reference management (Zotero) and document production (pandoc), skills development in automation and revision control to support reproducible science, as documented by the Turing Way (The Turing Way Community 2022), and an awareness of the importance of community building. We have developed echinopscis at Kew via a cross-departmental collaboration between researchers in biodiversity informatics and accelerated taxonomy. We have also benefitted from valuable input and advice from our many colleagues in associated projects and organisations around the world. OLS (originally Open Life Sciences) is a training and mentoring program for Open Science leaders with a focus on community building. The name was recently (2023) made more generic—"Open Seeds"—whilst retaining their well-known acronym "OLS"*1. OLS is a 16-week cohort-based mentoring program. Participants apply to join a cohort with a project that is developed through the 16 weeks. Each week of the syllabus alternates between time with a dedicated Open Science mentor and cohort calls, which are used to develop skills in project design, community building, open development & licencing, and inclusivity. Over 500 practitioners, experts and learners have participated across the seven completed cohorts of OLS' Open Seeds training and mentoring. Through this programme, over 300 researchers and open leaders from across six continents have designed, lauched and supported 200 projects from different disciplines worldwide. The next cohort will run between September 2023 and January 2024, and will be the eighth iteration of the program. This talk will briefly outline the work that we have done to setup and experiment with echinopscis, but will focus on the impact that the OLS program has had in its development. We will also include the use of techniques learned through OLS in other biodiversity informatics projects. OLS acknowledges that their program receives relatively few applications from project leads in biodiversity and we hope that this talk will be informative for Biodiversity Information Standards (TDWG) participants and can be used to build productive links between these communities.

Retiring TDWG Standards and How Mapping Standards Could Support Agility

Since its genesis in September 1985, TDWG (formerly Taxonomic Databases Working Group now Biodiversity Information Standards) has become the steward of standards for the biodiversity informatics community; however, there is not yet a process for retiring standards. This talk will educate the community members on TDWG Standard categories of "Current Standard", "Prior Standard", "2005 Standard", and the history and context of how these categories came to be. It will also report on the progress the TAG (Technical Architecture Group) has made on moving towards creating a process for retiring standards through auditing, community participation, and other methods. Mapping TDWG standards can provide an agility to address overlaps, gaps, contradictions, and/or inconsistencies between standards in a proactive manner. Mapping standards' relationships provides infrastructure to support decision-making, combat information overload, and give context to the community as it continues to progress at a rapid pace. More specifically for TDWG, it gives a clear picture how updating, ratifying, and/or retiring a singular standard impacts the greater TDWG information ecosystem and how to update adjacent standards to preserve clarity and consistency for the community as a whole.

A Novel Part in the Swiss Army Knife for Linking Biodiversity Data: The digital specimen identifier service

Digital specimens are new information objects on the internet, which act as digital surrogates of the physical objects they represent. They are designed to be extended with data derived from the specimen like genetic, morphological and chemical data, and with data that puts the specimen in context of its gathering event and the environment it was derived from. This requires linking the digital specimens and their related entities to information about agents, locations, publications, taxa and environmental information. To establish reliable links and (re-)connect data to specimens, a new framework is needed, which creates persistent identifiers (PIDs) for the digital specimen and its related entities. These PIDs should be actionable by machines but also can be used by humans for data citation and communication purposes. The framework that enables this is a new PID infrastructure, produced by the European Commission-funded BiCIKL project (Biodiversity Community Integrated Knowledge Library), creates persistent and actionable identifiers. It is a generic PID infrastructure that will be used by the Distributed System for Scientific Collections research infrastructure (DiSSCo), but it can also be used by other infrastructures and institutions. PIDs minted by DiSSCo will be linked to the digital specimens and samples provided through DiSSCo. The new PIDs are a key element in enabling the concept of Digital Extended Specimens (Webster et al. 2021) and provide unique and resolvable references to enable bidirectional linking. DiSSCo has done extensive work to select the most appropriate PID scheme (Hardisty et al. 2021) and to design a PID infrastructure for the pan-European specimens. The draft design has been discussed with technical specialists in the joint DiSSCo and Consortium of European Taxonomic Facilities (CETAF) community, with international stakeholders like the Global Biodiversity Information Facility (GBIF) and Integrated Digitized Biocollections (iDigBio) and was discussed at the 2022 conference of the Society for the Preservation of Natural History Collections (SPNHC). A first implementation was demonstrated in the Biodiversity Information Standards (TDWG) annual conference in 2022 and illustrated key elements in the design. To be able to provide digital specimen identifiers as DOIs (Digital Object Identifiers), a pilot project was started in 2023 with DataCite to investigate if Digital Specimen DOIs in the new PID infrastructure can be created using the DataCite service. The pilot aim was to create metadata crosswalks to the DataCite schema in consultation with the DataCite Metadata Working Group, to evaluate synergies with the IGSN (International Generic Sample Number) metadata schema, to develop and test PID kernel metadata registration, and to evaluate performance and the impact of using DataCite services. There are around two billion specimens and creating PIDs for them as DOIs requires creating DOIs at an unprecedented scale. Also, PID kernel metadata registration is new for DOIs. The included metadata for specimens will complement existing Biodiversity Information Standards such as Darwin Core, and supports the new MIDS (Minimum Information about a digital specimen) standard that is under development. The design, development and testing of the new PID infrastructure is being done as part of the BiCIKL project that aims to foster collaboration between infrastructures and develop bidirectional connections (Penev et al. 2022). In the session, we will demonstrate the results in development of the PID infrastructure as part of the BiCIKL toolbox to link biodiversity data and to discuss the progress with creating digital specimen DOIs.

Harmonised Data is Actionable Data: DiSSCo’s solution to data mapping

Predictability is one of the core requirements for creating machine actionable data. The better predictable the data, the more generic the service acting on the data can be. The more generic the service, the easier we can exchange ideas, collaborate on initiatives and leverage machines to do the work. It is essential for implementing the FAIR Principles (Findable, Accessible, Interoperable, Reproducible), as it provides the “I” for Interoperability (Jacobsen et al. 2020). The FAIR principles emphasise machine actionability because the amount of data generated is far too large for humans to handle. While Biodiversity Information Standards (TDWG) standards have massively improved the standardisation of biodiversity data, there is still room for improvement. Within the Distributed System of Scientific Collections (DiSSCo), we aim to harmonise all scientific data derived from European specimen collections, including geological specimens, into a single data specification. We call this data specification the open Digital Specimen (openDS). It is being built on top of existing and developing biodiversity information standards such as Darwin Core (DwC), Minimal Information Digital Specimen (MIDS), Latimer Core, Access to Biological Collection Data (ABCD) Schema, Extension for Geosciences (EFG) and also on the new Global Biodiversity Information Facility (GBIF) Unified Model. In openDS we leverage the existing standards within the TDWG community but combine these with stricter constraints and controlled vocabularies, with the aim to improve the FAIRness of the data. This will not only make the data easier to use, but will also increase its quality and machine actionability. As the first step towards this the harmonisation of terms, we make sure that similar values use the same term in a standard as key. This enables the next step in which we harmonise the values. We can transform free-text values into standardised or controlled vocabularies. For example: instead of using the names J. Doe, John Doe and J. Doe sr. for a collector, we aim to standardise these to J. Doe, with a person identifier that connects this name with more information about the collector. Biodiversity information standards such as DwC were developed to lower the bar for data sharing. The downside of including minimal restraints and flexibility is that they provide room for ambiguity, leading to multiple ways of interpretation. This limits interoperability and hampers machine actionability. In DiSSCo, data will come from different sources that use different biodiversity information standards. To cover this, we need to harmonise terms between these standards. To complicate things further, different serialisation methods are used for data exchange. Darwin Core Archives (DwC-A; GBIF 2021) use Comma-separated values (CSV) files. ABCD(EFG) exposed through Biological Collection Access Service (BioCASe) uses XML. And most custom formats use JavaScript Object Notation (JSON). In this lightning talk, we will dive into DiSSCo’s technical implementation of the harmonisation process. DiSSCo currently supports two biodiversity information standards, DwC and ABCD(EFG), and maps the data to our openDS specification on a record-by-record basis. We will highlight some of the more problematic mappings, but also show how a harmonised model massively simplifies generic actions, such as the calculation of MIDS levels, which provide information about digitisation completeness of a specimen. We will conclude by having a quick look at the next steps and hope to start a discussion about controlled vocabularies. The development of high quality, standardised data based on a strict specification with controlled vocabularies, rooted in community accepted standards, can have a huge impact on biodiversity research and is an essential step towards scaling up research with computational support.

Can Biodiversity Data Scientists Document Volunteer and Professional Collaborations and Contributions in the Biodiversity Data Enterprise?

The collection, archiving and use of biodiversity data depend on a network of pipelines herein called the Biodiversity Data Enterprise (BDE) and best understood globally through the work of the Global Biodiversity Information Facility (GBIF). Efforts to sustain and grow the BDE require information about the data pipeline and the infrastructure that supports it. A host of metrics from GBIF, including institutional participation (member countries, institutional contributors, data publishers), biodiversity coverage (occurrence records, species, geographic extent, data sets) and data usage (records downloaded, published papers using the data) (Miller 2021), document the rapid growth and successes of the BDE (GBIF Secretariat 2022). Heberling et al. (2021) make a convincing case that the data integration process is working. The Biodiversity Information Standards' (TDWG) Basis of Record term provides information about the underlying infrastructure. It categorizes the kinds of processes*1 that teams undertake to capture biodiversity information and GBIF quantifies their contributions*2 (Table 1). Currently 83.4% of observations come from human observations, of which 63% are of birds. Museum preserved specimens account for 9.5% of records. In both cases, a combination of volunteers (who make observations, collect specimens, digitize specimens, transcribe specimen labels) and professionals work together to make records available. To better understand how the BDE is working, we suggest that it would be of value to know the number of contributions and contributors and their hours of engagement for each data set. This can help the community address questions such as, "How many volunteers do we need to document birds in a given area?" or "How much professional support is required to run a camera trap network?" For example, millions of observations were made by tens of thousands of observers in two recent BioBlitz events, one called Big Day, focusing on birds, sponsored by the Cornell Laboratory of Ornithology and the other called the City Nature Challenge, addressing all taxa, sponsored jointly by the California Academy of Sciences and the Natural History Musuems of Los Angeles County (Table 2). In our presentation we will suggest approaches to deriving metrics that could be used to document the collaborations and contribution of volunteers and staff using examples from both Human Observation (eBird, iNaturalist) and Preserved Specimen (DigiVol, Notes from Nature) record types. The goal of the exercise is to start a conversation about how such metrics can further the development of the BDE.

The Journey to a TDWG Mappings Task Group and its Plans for the Future

Some Biodiversity Information Standards (TDWG) standards have had mappings to other standards for years or even decades. However each standard is using its own approach to documenting those mappings, some are incomplete and often hard to find. There is no TDWG recommended approach for how mappings should be documented, like the Standards Documentation Standard (SDS) does for the standards themselves. During TDWG 2022 in Sofia, Bulgaria, the topic of mapping between standards was mentioned several times throughout the conference, which led to an impromptu discussion about standards mappings at the Unconference slot on the last conference day. Afterwards a dedicated Slack channel within the TDWG Slack workspace was added to continue the conversation (#mappings-between-standards). During further discussions, both within the Technical Architecture Group (TAG) of TDWG and during separate video conferences on the topic, it was decided to form a dedicated task group under the umbrella of the TAG. This task group is still in the process of formation. The goal of the group is to review the current state of mappings for TDWG standards, align approaches by the different standards to foster interoperability and give recommendations for current and future standards on how to specify mappings. Further work to define the strategy and scope for achieving these goals is needed, particularly to gain community input and acceptance. Consideration has been given to a range of possible types of mappings, which serve the different use cases and expectations for mappings such as machine actionability and improved documentation of the TDWG standards landscape to aid user understanding and implementation. In this talk we will show the work that has already been done, outline our planned steps and invite the community to give input on our process.

Promoting High-Quality Data in OBIS: Insights from the OBIS Data Quality Assessment and Enhancement Project Team

The Ocean Biodiversity Information System (OBIS) (Klein et al. 2019) is a global database of marine biodiversity and associated environmental data, which provides critical information to researchers and policymakers worldwide. Ensuring the accuracy and consistency of the data in OBIS is essential for its usefulness and value, not only to the scientific community but also to the science-policy interface. The OBIS Data Quality Assessment and Enhancement Project Team (QCPT), formed in 2019 by the OBIS steering group, aims to assess and enhance data quality. It has been working on three categories of activities for this purpose: Data quality enhancement and management The OBIS QCPT organized data laundry events to identify and address data quality issues of published OBIS datasets. Furthermore, individual OBIS nodes were invited to give their data-processing presentations in the monthly meetings to foster knowledge sharing and collaborative problem-solving focused on data quality. Data quality issues and solutions highlighted in the presentations and data laundry events were documented in a dedicated GitHub repository as GitHub issues. The solutions for data quality issues and marine-specific pre-publication quality control tools, designed to identify the data quality issues, were provided as feedback to the OBIS Capacity Development Task Team. These inputs were used to create training resources (see OBIS manual, upcoming OBIS training course hosted on OceanTeacher Global Academy) aimed at preventing these issues. Standardization of OBIS data processing pipeline As OBIS uses the Darwin Core standard (Wieczorek et al. 2012), the use of standardized tests and assertions in the data processing pipeline is encouraged. To achieve this, the OBIS QCPT aligned OBIS quality checks with a subset of core tests and assertions (Chapman et al. 2020) developed by the Biodiversity Information Standards (TDWG) Biodiversity Data Quality (BDQ) Task Group 2 (TG2) (Chapman et al. 2020) as tracked in this GitHub issue. Not all default parameters of the core tests and assertions are optimal for marine biodiversity data. The OBIS QCPT met monthly to determine suitable parameters for customizing the tests. The pipeline produces a data quality report for each dataset with quality flags that indicate potential data quality issues, enabling node managers and data providers to review the flagged records. Community engagement The OBIS QCPT led a survey among data users to gather insights into OBIS data quality issues and bridge the gap between the current implementation and user expectations. The survey findings enabled OBIS to prioritize issues to be addressed, as summarized in Section 2.2.2 of the 11th OBIS Steering Group meeting report. In addition to engaging with data users, the OBIS QCPT also served as a platform to discuss questions related to the use of Darwin Core from the nodes and provided feedback for the term discussions. In summary, the OBIS QCPT improves marine species data reliability and usability through transparent and participatory approaches, fostering continuous improvement. Collaborative efforts, standardized procedures, and knowledge sharing advance OBIS' mission of providing high quality biodiversity data for research, conservation, and ocean management.

FAIR Principles and TDWG Standards: The case of morphological description of taxa and specimens

Sharing data is crucial in biodiversity research as well as in all scientific domains. Biodiversity Information Standards (TDWG) validates and makes available a set of standards to facilitate the sharing of biodiversity data. Of the 23 standards listed in alphabetical order, each has a status, a category, and a short description. But these standards are designed for very different purposes, which we will discuss by linking them to the FAIR principles (Findable, Accessible, Interoperable, and Reusable) . The FAIR principles (Wilkinson et al. 2016) focus on the ability of machines to automatically find and use the digital data. It is therefore crucial that software for editing, acquiring and using data, shares defined standards that are made available to all. TDWG has been working in this direction for over 30 years. Pioneers in biodiversity informatics, such as Richard Pankhust (Pankhurst 1970), Mike Dallwitz (Dallwitz 1974, Dallwitz 1980) and Jacques Lebbe (Lebbe et al. 1987) worked specifically on taxon identification with computers and how to represent morphological descriptions of taxa and specimens. Some TDWG standards, such as ABCD (Access to Biological Collection Data; Access to Biological Collections Data Task Group 2005), TCS (Taxonomic Concept Transfer Schema; Taxonomic Names Subgroup 2006) or SDD (Structured Descriptive Data; Structure of Descriptive Data (SDD) Subgroup 2006) are expressed by an XML schema covering a formal data model. Other standards, as Floristic Regions of the World (Takhtajan 1986), or Vocabulary Maintenance Standard (VMS; Vocabulary Maintenance Specification Task Group 2017) concern vocabularies or a collection of standardized terms. The Plant Occurrence and Status Scheme (POSS; World Conservation Monitoring Centre 1995) provides both, a list of accepted terms, and a data model (list of fields). In case of morpho-anatomical data describing taxa or specimens, TDWG offers two standards: DELTA (DEscription Language for TAxonomy, Dallwitz 2006) and SDD (Structured Descriptive Data, Hagedorn 2007). In order to further the discussion on morphological description data sharing, we would like to clarify what is meant by the term standard. We'll be looking at the concepts of guidelines, rules, defined format, referential list of terms, data schema, model, metamodel, protocols, which are all terms linked to this notion of standard and FAIR principles. Perhaps this reflection will lead us to propose criteria for better classifying TDWG standards.

Reaching Further with Earth Science Data

Earth Sciences cover a broad spectrum of research fields such as petrology, sedimentology, structural geology, seismology, and geomorphology, to name a few, which aim to understand interrelated processes on the surface and in the interior of our planet. Many of the research questions studied in the Earth sciences, such as, understanding past climates, the human impact on the Critical Zone*1, or the co-evolution of the geo- and biosphere, require interdisciplinary approaches, employing methodologies and integrating observations from diverse subdisciplines such as geochemistry, mineralogy, geophysics, and paleontology, but also increasingly from other disciplines, specifically biology and genomics. The use of data and samples in multi- and interdisciplinary research is substantially facilitated by consistent metadata including schemas and vocabularies, data formats, and data exchange protocols. The association of Biodiversity Information Standards (TDWG) with the Earth Science part of the Natural Sciences has, until recently, been largely limited to palaeontology, primarily due to its close affinity with biology. Standards have been developed that support the taxonomy of fossils and include terms for "deep time" intervals (chrono- and bio-stratigraphy) and "paleo-surface environment" (sedimentology). Other important and highly relevant fields such as mineralogy, geochemistry, meteoritics, and soil sciences have not been included or coordinated with so far. Although some progress is being made with the ongoing development of the Mineral Extension for Darwin Core and the inclusion of some geological terms in Latimer Core (Woodburn et al. 2022), more effort should be made to link and align with existing and emerging data standards in these Earth Science communities. These Earth Science communities are very active, building their own domain-specific FAIR (Findable, Accessible, Interoperable, Reusable) data standards and infrastructures with data systems such as EarthChem, the Astromaterials Data System, Mindat, and WoSIS. We will introduce the users, data systems, and community standards and best practices, including metadata, vocabularies, and persistent identifiers for data and samples being used and developed in the subdisciplines of the Earth Sciences mentioned above. We advocate for collaboration and coordination with these fields to make TDWG more inclusive, expand accessible resources, and provide an opportunity for TDWG to link to these other communities. Broader networking within an interdisciplinary research framework has the potential to address more fundamental and over-arching issues of both scientific and public relevance.

A Community-Driven Strategy for Addressing Fossil Taxonomy Challenges

Taxonomy has long been a cornerstone issue in the paleontological collections community, especially as digitization has enabled us to share data at a global scale. Communities of practice such as the Paleo Data Working Group (PDWG, Krimmel et al. 2021) have made concerted efforts to understand and evaluate challenges with the taxonomic information published via fossil occurrence records (Little et al. 2021, Little et al. 2022). Since 2020, PDWG has been leading discussions to define the nuances of challenges associated with fossil taxonomy, to look at the landscape of cyberinfrastructure tools available to tackle these challenges, and to develop community guidelines. This work has led to a deeper understanding of the full range of challenges surrounding the use of fossil taxonomy, from local practices and systems to representation at the data aggregator level. Here we share our initial strategy for tackling challenges with fossil data, particularly taxonomy, centered on developing a reusable framework for assessing and addressing issues so that data can be made more discoverable and usable for transformative and translational research. An essential element of this approach is the coordination of efforts across stakeholder communities. Through these collaborations, we ultimately seek to improve systems for handling and curating taxonomic data, increase the quality and discoverability of taxonomic data presented by aggregators, and better integrate data provided by fossil occurrence records into the research lifecycle. The required coordination will be driven through the development of a partner network from across the global bio- and geo- science data ecosystem. This network is composed of representatives with a variety of perspectives and roles, including participation from Catalogue of Life, Global Biodiversity Information Facility, and Biodiversity Information Standards (TDWG), as well as paleontology collections professionals (led by PDWG), informaticians, researchers, and other end-users. Integrating the existing PDWG community of practice into this larger network allows us to learn from work already underway in the landscape. In addition, recognizing that the paleo community is not alone in these challenges, we believe that a successful strategy for this network is to create processes and envision solutions that can be adapted by other disciplines with similar needs. Our strategy underscores the need for the combined efforts of both community engagement and evaluation of the existing technical landscape. Through this work we aim to establish an enhanced network of FAIR (Findable, Accessible, Interoperable, Reusable; Wilkinson et al. 2016) and research-ready fossil data, improving the support for and quality of fossil taxonomy data along the way.