Persistent Identifiers Research Articles

On a BiCIKL to Wikidata: Harmonizing the chaotic universe of natural history collectors

People play a key role in science and have been getting increased recognition for this work, initially by the efforts of libraries to harmonize attribution of authors of creative works, such as in the Virtual International Authority File (VIAF). Now, in the realm of scientific publishing, the international ORCID (Open Researcher and Contributor ID) organization allows scientists to mint globally unique and self-maintained identifiers for themselves, which tie all of their scientific output together. This has been fairly successful for formal publications, but less so for other scientific contributions, such as samples or specimens collected from organisms to serve as vouchers or references for long-term study. Such specimens may end up in natural history collections, where their metadata and, in particular, their taxonomic identity is refined over the years by experts. As the cost of digitization has decreased and the methods become more refined, digital versions of these specimens are now being published online in vast numbers, making attribution with identifiers such as ORCID easier. However, many of these specimens are only linked to the people who worked on them by name strings, often using a variety of syntaxes, transliterations and abbreviations. Recently, some collections have made an effort to disambiguate these names by enriching them with an explicit link to a persistent identifier, such as an ORCID (Groom et al. 2022, Little et al. 2022, von Mering et al. 2022, Meeus et al. 2023). In the European Union-funded BiCIKL (Biodiversity Community Integrated Knowledge) project, one of the goals is to develop better workflows that aid and streamline this enrichment process. This approach focuses primarily on Wikidata, as a fall-back resource for cases where ORCID identifiers are not available. Wikidata is suitable for people who are unable to register for an ORCID identifier, but also acts as a broker to harmonize between other authority sources such as the fore-mentioned VIAF. In this presentation, we will show the results achieved so far in this BiCIKL effort. First, we will provide a landscape analysis of different person identifier enrichment efforts so far in the natural history sector. Infrastructures such as the Global Biodiversity Information Facility (GBIF), the related Bionomia and the Botany Pilot (Güntsch et al. 2021) will play a key role. Building on those results, we will identify improved methods to link name strings to unique biographical records in resources such as Wikidata and ORCID. The focus will primarily be on properties of these records that make them more identifiable as collectors of specimens, and that can systematically be harnessed to rank multiple possible candidates or reject false positives. Finally, these findings will be incorporated into workflows that streamline the disambiguation and enrichment process. These workflows will build on existing pipelines and platforms, such as Bionomia and the Botany Pilot, focusing in particular on data roundtripping from these resources into other infrastructures, such as local collection management systems, but also specimen data aggregators such as GBIF and the Distributed System of Scientific Collections (DiSSCo).

Data-Sharing Across Otolaryngology: Comparing Journal Policies and Their Adherence to the FAIR Principles.

Data-sharing plays an essential role in advancing scientific understanding. Here, we aim to identify the commonalities and differences in data-sharing policies endorsed by otolaryngology journals and to assess their adherence to the FAIR (findable, accessible, interoperable, reusable) principles. Data-sharing policies were searched for among 111 otolaryngology journals, as listed by Scimago Journal & Country Rank. Policy extraction of the top biomedical journals as ranked by Google Scholar metrics were used as a comparison. The FAIR principles for scientific data management and stewardship were used for the extraction framework. This occurred in a blind, masked, and independent fashion. Of the 111 ranked otolaryngology journals, 100 met inclusion criteria. Of those 100 journals, 79 provided data-sharing policies. There was a clear lack of standardization across policies, along with specific gaps in accessibility and reusability which need to be addressed. Seventy-two policies (of 79; 91%) designated that metadata should have globally unique and persistent identifiers. Seventy-one (of 79; 90%) policies specified that metadata should clearly include the identifier of the data they describe. Fifty-six policies (of 79; 71%) outlined that metadata should be richly described with a plurality of accurate and relevant attributes. Otolaryngology journals have varying data-sharing policies, and adherence to the FAIR principles appears to be moderate. This calls for increased data transparency, allowing for results to be reproduced, confirmed, and debated.

Artificial Intelligence in The Healthcare System; A Cross-Sectional Study Involving Medical Students

Introduction: Artificial Intelligence has brought revolutionary changes in the medical field in terms of diagnosis, surgeries, and rehabilitation. This study aims to assess the knowledge and perceptions of medical students regarding Artificial Intelligence. Methods: This is a cross-sectional study having a sample size of n= 210. The study was conducted in a medical university of Rawalpindi, among all the years of medicine. A pre-made questionnaire to assess the knowledge and perceptions of the students. The data was collected from September to December 2022. SPSS version 26 and Microsoft Excel were used for data analysis. The data were deposited in a repository of Zenodo with the persistent identifier. Results: Only 13% of students understood what is meant by neural networks. With 65.4% believing that AI will be able to help to establish a prognosis, 60.1% expressed their confidence in AI to replace humans in performing surgery, and 40.3% found it a threat to physicians’ jobs being replaced by AI. The majority (73.9%) of students thought that health equity will face quite a lot of new challenges if AI steps into medicine but they also agreed that AI skills should be ingrained in medical training. Conclusion: Medical students lack an understanding of AI but are quite optimistic that it has the potential to transform existing healthcare practices. Students believe that training in AI competencies should be added to their curriculum so that they can be well equipped with upcoming challenges.

From principles to reality. FAIR implementation in the nanosafety community

Developing safe and sustainable nanomaterials-based solutions to current global challenges including clean energy, sustainable food production and water security requires access to high quality data and appropriate analysis and modelling approaches. Achieving these challenges requires increased re-use of research data to accelerate progress and support development of new materials that are safe and sustainable for energy capture and storage, nano-agriculture and environmental remediation. The principles of Findability, Accessibility, Interoperability and Reusability (FAIR) provide a roadmap to enhanced data sharing and re-use, but require consensus within the nanosafety community on metadata, ontologies and persistent identifiers (among other things) and guidance to support implementation and achieve machine-readability. Here, we highlight the main focus of the AdvancedNano GO FAIR Implementation Network in supporting the nanosafety community with implementation of FAIR to maximize data-driven safe and sustainable application of nano- and advanced materials.

Teaching FAIR in computational chemistry: managing and publishing data using the twin tools of compute portals and repositories

The history of the emerging area of tools for managing research resources and the data produced from them is summarised from the perspective of two decades of use in teaching and research at one institution. These tools are a portal or electronic laboratory notebook for computational chemistry interfaced in one direction to a high-performance computing resource and in the other direction to a modern research data repository. The essential features of both these tools are described over two generations of each, with examples of student work cited as examples using persistent identifiers or PIDs, better known as DOIs. Underpinning this is the metadata describing the data being processed. The article outlines the evolution of managing such metadata-rich data and its progress towards what can now be summarised by the acronym FAIR data, itself enabling future emerging areas such as knowledge graphs.

Making Research FAIR With a PID-centric Workflow

Persistent identifiers (PIDs) are unique, machine-readable codes assigned to research entities that allow them to be easily discoverable. PIDs, along with their accompanying metadata, are crucial enablers of the FAIR principles (Findable, Accessible, Interoperable, and Reusable). PIDs ensure that digital objects can be located, accessed, and reused by humans and machines alike, while metadata provides essential information about research objects, including their origin, content, and format. In the research ecosystem, each stakeholder has a role to play in integrating PIDs into their workflows. Publishers, for example, can assign DOIs (Digital Object Identifiers) to articles, books, and other publications, making them easily findable and citable. Repositories can assign PIDs to datasets, making them discoverable and accessible. Researchers can use PIDs to link their data to their publications, ensuring that their data is discoverable and can be reused in future research. Despite the importance of PIDs and metadata, it's not always clear to researchers how to take advantage of the existing infrastructure and make their outputs FAIR. Being aware of the available PIDs, such as DOIs, ORCIDs, and RORs, and how they can be used to identify, connect, and cite various types of outputs and resources can help researchers plan and execute sensible data management, sharing, and publishing decisions that are efficient and beneficial in the long term. In the Implementing FAIR Workflows Project, DataCite works with a team of researchers at the Max Planck Institute for Empirical Aesthetics to follow along a neuroscience PhD project from the beginning, to design and plan for a series of workflows that put the FAIR principles into practice, so that they become an inherent part of the research process, instead of an afterthought. The FAIR workflows researcher is undertaking in the project include data management planning, experiment preregistration, domain-specific metadata capturing and archiving, data and code sharing, preprinting, and open access publishing. We have also been tracking the time spent on various types of FAIR and Open activities, hoping to shed a light on the actual time commitment expected for a FAIRly conducted research project. We share our experience so far implementing these workflows with the Edinburgh Open Science community - the approach we used, the steps we’ve taken, and the outcomes and challenges that surfaced during the process. We are also preparing a guide for researchers to take on FAIR research workflows in their day-to-day work based on the lessons learned in the project, we look forward to taking the opportunity to hear from the community whether it resonates, and how can we format it in a way that’s most useful.

Measuring the Concept of PID Literacy: User Perceptions and Understanding of PIDs in Support of Open Scholarly Infrastructure

Abstract The increasing centrality of persistent identifiers (PIDs) to scholarly ecosystems and the contribution they can make to the burgeoning “PID graph” has the potential to transform scholarship. Despite their importance as originators of PID data, little is known about researchers’ awareness and understanding of PIDs, or their efficacy in using them. In this article, we report on the results of an online interactive test designed to elicit exploratory data about researcher awareness and understanding of PIDs. This instrument was designed to explore recognition of PIDs (e.g. Digital Object Identifiers [DOIs], Open Researcher and Contributor IDs [ORCIDs], etc.) and the extent to which researchers correctly apply PIDs within digital scholarly ecosystems, as well as measure researchers’ perceptions of PIDs. Our results reveal irregular patterns of PID understanding and certainty across all participants, though statistically significant disciplinary and academic job role differences were observed in some instances. Uncertainty and confusion were found to exist in relation to dominant schemes such as ORCID and DOIs, even when contextualized within real-world examples. We also show researchers’ perceptions of PIDs to be generally positive but that disciplinary differences can be noted, as well as higher levels of aversion to PIDs in specific use cases and negative perceptions where PIDs are measured on an “activity” semantic dimension. This work therefore contributes to our understanding of scholars’ “PID literacy” and should inform those designing PID-centric scholarly infrastructures that a significant need for training and outreach to active researchers remains necessary.

Improving Domain Repository Connectivity

ABSTRACTDomain repositories, i.e. repositories that store, manage, and persist data pertaining to a specific scientific domain, are common and growing in the research landscape. Many of these repositories develop close, long-term communities made up of individuals and organizations that collect, analyze, and publish results based on the data in the repositories. Connections between these datasets, papers, people, and organizations are an important part of the knowledge infrastructure surrounding the repository.All these research objects, people, and organizations can now be identified using various unique and persistent identifiers (PIDs) and it is possible for domain repositories to build on their existing communities to facilitate and accelerate the identifier adoption process. As community members contribute to multiple datasets and articles, identifiers for them, once found, can be used multiple times.We explore this idea by defining a connectivity metric and applying it to datasets collected and papers published by members of the UNAVCO community. Finding identifiers in DataCite and Crossref metadata and spreading those identifiers through the UNAVCO DataCite metadata can increase connectivity from less than 10% to close to 50% for people and organizations.

Scaling Identifiers and their Metadata to Gigascale: An Architecture to Tackle the Challenges of Volume and Variety

Persistent identifiers are applied to an ever-increasing variety of research objects, including software, samples, models, people, instruments, grants, and projects, and there is a growing need to apply identifiers at a finer and finer granularity. Unfortunately, the systems developed over two decades ago to manage identifiers and the metadata describing the identified objects no longer scale. Communities working with physical samples have grappled with these three challenges of the increasing volume, variety, and variability of identified objects for many years. To address this dual challenge, the IGSN 2040 project explored how metadata and catalogues for physical samples could be shared at the scale of billions of samples across an ever-growing variety of users and disciplines. In this paper, we focus on how we scale identifiers and their describing metadata to billions of objects and who the actors involved with this system are. Our analysis of these requirements resulted in the definition of a minimum viable product and the design of an architecture that not only addresses the challenges of increasing volume and variety but, more importantly, is easy to implement because it reuses commonly used Web components. Our solution is based on a Web architectural model that utilises <a href="http://Schema.org">Schema.org</a>, JSON-LD, and sitemaps. Applying these commonly used architectural patterns on the internet allows us to not only handle increasing variety but also enable better compliance with the FAIR Guiding Principles.

The current landscape of author guidelines in chemistry through the lens of research data sharing

Abstract As the primary method of communicating research results, journals garner an enormous impact on community behavior. Publishing the underlying research data alongside journal articles is widely considered good scientific practice. Ideally, journals and their publishers place these recommendations or requirements in their author guidelines and data policies. Several efforts are working to improve the infrastructure, processes, and uptake of research data sharing, including the NFDI4Chem consortium, working groups within the RDA, and IUPAC, including the WorldFAIR Chemistry project. In this article, we present the results of a large-scale analysis of author guidelines from several publishers and journals active in chemistry research, showing how well the publishing landscape supports different criteria and where there is room for improvement. While the requirement for deposition of X-ray diffraction data is commonplace, guidelines rarely mention machine-readable chemical structures and metadata/minimum information standards. Further evaluation criteria included recommendations on persistent identifiers, data availability statements, data deposition into repositories as well as of open analytical data formats. Our survey shows that publishers and journals are starting to include aspects of research data in their guidelines. We as authors should accept and embrace the guidelines with increasing requirements for data availability, data interoperability, and re-usability to improve chemistry research.

FAIR digital objects, persistent identifiers and machine actionability

Based on the work of the Research Data Alliance and FAIR Digital Objects Forum, and examples from three different domains, this article highlights the importance and effectiveness of PID level metadata for FAIR implementation and machine actionability.

Entity Management Using Wikidata for Cultural Heritage Information

Entity management in a Linked Open Data (LOD) environment is a process of associating a unique, persistent, and dereferenceable Uniform Resource Identifier (URI) with a single entity. It allows data from various sources to be reused and connected to the Web. It can help improve data quality and enable more efficient workflows. This article describes a semi-automated entity management project conducted by the “Wikidata: WikiProject Chinese Culture and Heritage Group,” explores the challenges and opportunities in describing Chinese women poets and historical places in Wikidata, the largest crowdsourcing LOD platform in the world, and discusses lessons learned and future opportunities.

Organization IDs in Germany—Results of an Assessment of the Status Quo in 2020

Persistent identifiers (PIDs) for scientific organizations such as research institutions and research funding agencies are a further decisive piece of the puzzle to promote standardization in the scholarly publication process—especially in light of the already established digital object identifiers (DOIs) for research outputs and ORCID iDs for researchers. The application of these PIDs enables automated data flows and guarantees the persistent linking of information objects. Moreover, PIDs are fundamental components for the implementation of open science. For example, the application of PIDs for scientific organizations is of crucial importance when analyzing publications and the costs of the transition to open access at an institution. To find out more about the status quo of the use and adoption of organization IDs in Germany, a ‘Survey on the Need for and Use of Organization IDs at Higher Education Institutions and Non-University Research Institutions in Germany’ was conducted among 548 scientific institutions in Germany in the period from July 13 to December 4, 2020, as part of the DFG-funded project ORCID DE. One hundred and eighty-three institutions participated in what was the largest survey to date on organization IDs in Germany. The survey included questions on the knowledge, adoption, and use of organization IDs at scientific institutions. Moreover, respondent institutions were asked about their needs with regard to organization IDs and their metadata (e.g., in terms of relationships and granularity). The present paper provides a comprehensive overview of the results of the survey conducted as part of the aforementioned project and contributes to the promotion and increased awareness of organization IDs.

From Research Evaluation to Research Analytics. The digitization of academic performance measurement

One could think that bibliometric measurement of academic performance has always been digital since the computer-assisted invention of the Science Citation Index. Yet, since the 2000s, the digitization of bibliometric infrastructure has accelerated at a rapid pace. Citation databases are indexing an increasing variety of publication types. Altmetric data aggregators are producing data on the reception of research outcomes. Machine-readable persistent identifiers are created to unambiguously identify researchers, research organizations, and research objects; and evaluative software tools and current research information systems are constantly enlarging their functionalities to make use of these data and extract meaning from them. In this article, we analyse how these developments in evaluative bibliometrics have contributed to an extension of indicator-based research evaluation towards data-driven research analytics. Drawing on empirical material from blogs and websites as well as from research and policy papers, we discuss how interoperability, scalability, and flexibility as material specificities of digital infrastructures generate new ways of data production and their assessment, which affect the possibilities of how academic performance can be understood and (e)valuated.

FAIRifying a scholarly publishing service: Methodology based on the OpenEdition’s internal FAIR audit

The FAIR principles (Findability, Accessibility, Interoperability, and Reusability) constitute a guide whose aim is to improve the management of digital scholarly resources. Nevertheless, the literature regarding data services other than data repositories is still scarce.OpenEdition is a digital infrastructure for open scholarly communication in the Social Sciences and Humanities (SSH) that carried out an internal full review to assess the degree of FAIRness of its activities. The objective of this paper is to present the methodology employed by OpenEdition’s team and the recommendations for the FAIRification of a publishing system, and hence, the elements for the FAIR Publishing Toolkit. The FAIR review was conducted in three main phases: preparation, assessment, and result phase, which listed the recommendations for the FAIR principles implementation. The preparation phase gathered the available information to define the perimeter of the FAIR review. It comprised two steps: the landscape study and the exam of actual use cases. The assessment phase contextualized the FAIR principles according to the scholarly publishing context, defined the datasets to be analyzed, carried outa FAIR maturity review per dataset, and analyzed the state of the art of some important FAIR-related elements. The result phase produced the recommendations, organized as priorities and extended objectives. The priority recommendations regard persistent identifiers and licensing policies. The extended objectives focus on authors' information management, controlled vocabularies, machine-actionability, and Digital Management Plans.

Use of Research Organizations Registry (ROR) identifiers in author academic profiles: the case of Google Scholar Profiles

Research organizations' persistent identifiers allow for reducing affiliation ambiguities, enable accurate institutional analyses and favor the design of modern online scholarly databases suited for research discovery and research evaluation. However, few studies have attempted to quantify their degree of use. Precisely, the purpose of this work is to determine the use of Research Organizations Registry (ROR) IDs in author academic profiles, specifically in Google Scholar Profiles (GSP). To do this, all the Google Scholar profiles including the term ROR in any of the public descriptive fields were collected and analyzed. The results evidence a low use of ROR IDs (1,033 profiles), mainly from a few institutions (e.g. Pontificia Universidad Javeriana in Colombia, and Escuela Superior Politécnica del Litoral in Ecuador hold 55.7% of all profiles), from low citation-based impact authors (45.1% of profiles attain less than 100 citations each), belonging mainly to Social Sciences (26.3%), Engineering fields (25.3%), and Natural Sciences (22.2%). Although Google Scholar does not facilitate the inclusion of identifiers, it seems that the world's leading research institutions are not recommending their researchers include these identifiers in their profiles yet.

Emerging uses of the Research Organization Registry

Since its launch in 2019, the Research Organization Registry (ROR) has seen tremendous support from key players in the scholarly communication system, and the past year has seen a particular increase in adoption of ROR. Crossref, DataCite, and ORCID now all support ROR identifiers in their metadata schemas, and many open access publishers and data repositories are using ROR to exchange organizational affiliation information across systems. This poster will give a brief introduction to ROR and feature some notable uses of the ROR ID in order to show what is possible when systems track and share consistent organizational identifiers as open metadata in the teeming ecosystem of scholarly information. Crossref has articulated its ideal scholarly communication system in its aspirational vision of the Research Nexus, which it imagines as "A rich and reusable open network of relationships connecting research organizations, people, things, and actions; a scholarly record that the global community can build on forever, for the benefit of society." These building blocks of "research organizations, people, things, and actions" must generally be represented by machine-readable persistent identifiers in order to be made visible and useful: ROR IDs identify and disambiguate research organizations just as ORCIDs identify researchers and DOIs identify research outputs ("things") such as articles and datasets. DOIs and ORCIDs are now widely understood to be central to the scholarly communication system, but because ROR is a newer initiative, its value may not be as clearly understood. Only recently has ROR begun to be visible in the scholarly landscape, especially but not only in open access and open data projects. For instance, The Open Access Monitor &lt;https://open-access-monitor.de&gt;, a project to improve data about open access in Germany, uses ROR IDs to describe and manage organizations, which allows a user to get very rich data about the open access status of publications issued by researchers affiliated with a particular German research organization (e.g., the Max Planck Institute). OpenAlex &lt;https://openalex.org&gt;, a fully open catalog of the global research system, also uses ROR IDs to track author affiliation information: 94% of the 109,000 institutions in OpenAlex have a ROR ID. The Dryad data repository &lt;https://datadryad.org&gt; asks for institutional affiliation when a user submits a dataset and uses ROR to generate an institutional browse feature in its interface. As we try to improve the system of scholarly communication, we need to remember that research organizations should not be represented by inconsistent text strings or closed identifiers. We can ask and answer more interesting questions of the scholarly record when we can trust its organizational information.

Recommendations for use of annotations and persistent identifiers in taxonomy and biodiversity publishing

The paper summarises many years of discussions and experience of biodiversity publishers, organisations, research projects and individual researchers, and proposes recommendations for implementation of persistent identifiers for article metadata, structural elements (sections, subsections, figures, tables, references, supplementary materials and others) and data specific to biodiversity (taxonomic treatments, treatment citations, taxon names, material citations, gene sequences, specimens, scientific collections) in taxonomy and biodiversity publishing. The paper proposes best practices on how identifiers should be used in the different cases and on how they can be minted, cited, and expressed in the backend article XML to facilitate conversion to and further re-use of the article content as FAIR data. The paper also discusses several specific routes for post-publication re-use of semantically enhanced content through large biodiversity data aggregators such as the Global Biodiversity Information Facility (GBIF), the International Nucleotide Sequence Database Collaboration (INSDC) and others, and proposes specifications of both identifiers and XML tags to be used for that purpose. A summary table provides an account and overview of the recommendations. The guidelines are supported with examples from the existing publishing practices.

ORCID coverage in research institutions-Readiness for partially automated research reporting.

Reporting and presentation of research activities and outcome for research institutions in official, normative standards are more and more important and are the basis to comply with reporting duties. Institutional Current Research Information Systems (CRIS) serve as important databases or data sources for external and internal reporting, which should ideally be connected with interfaces to the operational systems for automated loading routines to extract relevant research information. This investigation evaluates whether (semi-) automated reporting using open, public research information collected via persistent identifiers (PIDs) for organizations (ROR), persons (ORCID), and research outputs (DOI) can reduce effort of reporting. For this purpose, internally maintained lists of persons to whom an ORCID record could be assigned (internal ORCID person lists) of two different German research institutions-Osnabrück University (UOS) and the non-university research institution TIB-Leibniz Information Center for Science and Technology Hannover-are used to investigate ORCID coverage in external open data sources like FREYA PID Graph (developed by DataCite), OpenAlex and ORCID itself. Additionally, for UOS a detailed analysis of discipline specific ORCID coverage is conducted. Substantial differences can be found for ORCID coverage between both institutions and for each institution regarding the various external data sources. A more detailed analysis of ORCID distribution by discipline for UOS reveals disparities by research area-internally and in external data sources. Recommendations for future actions can be derived from our results: Although the current level of coverage of researcher IDs which could automatically be mapped is still not sufficient to use persistent identifier-based extraction for standard (automated) reporting, it can already be a valuable input for institutional CRIS.

DataverseNO

DataverseNO (https://dataverse.no/) is a national, generic repository for open research data, owned and operated by UiT The Arctic University of Norway. The technical infrastructure of the repository is based on Dataverse, a community-driven open-source application, which is developed by an international developer and user community led by Harvard University. DataverseNO supports the FAIR Guiding Principles for scientific data management and stewardship, and is CoreTrustSeal certified (Conzett 2020). DataverseNO is based on collaboration between Norwegian research organizations. Datasets from partner institutions are published in institutional collections. Currently, 14 research organizations are partnering with DataverseNO. UiT is responsible for training and support for institutional collection managers, whereas the partner institutions are responsible for the curation of their institutional data and local user support. To ensure that data deposited into DataverseNO comply with the DataverseNO policies and guidelines they are curated by qualified research data service staff before they are published. The DataverseNO curators join forces in the DataverseNO curation network. Datasets in DataverseNO are equipped with persistent identifiers (DOIs, Digital Object Identifiers) at dataset and file level. DOIs serve as unique references to datasets and files and make them searchable in discovery services like B2FIND – EUDAT, Bielefeld Academic Search Engine (BASE), DataCite Search, Google Dataset Search, and Oria/Primo. On December 1, the last day of this year’s Munin Conference, DataverseNO will be turning 5 years!