Data Citation

The study systematically analyzes Research Support Services (RSS) in academic libraries through a comprehensive review of scholarly literature. The objective is to evaluate publication trends, geographical distribution, sample populations, journal coverage, categorization models, core components, implementation challenges, and developmental insights. A systematic review methodology was adopted, utilizing databases Scopus and the Web of Science. The search strategy employed keyword combinations (e.g., "research support services," "research services and library," and "academic library"), supplemented by forward and backward citation tracking. This resulted in a final selection of 43 peer-reviewed journal articles published between 1992 and 2024. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The analysis reveals a progressive increase in literature on RSS, with recent focus areas including research data management, bibliometric services, and open access initiatives. Core services identified encompass citation management, data curation, bibliometric analysis, and support for scholarly communication. Key challenges include limited resources, technological barriers, and professional skill gaps. The study emphasizes the need for strategic innovation, including integration of artificial intelligence and collaborative service models. The findings offer significant implications for library administrators, researchers, and policymakers, and represent a pioneering global synthesis of RSS trends and practices in academic libraries.

Considers the promotion of data citation and attribution standards by: 1) establishing an education and training mandate, drawing on existing resources and standards, and engaging researchers early in their careers; and 2) exploring the role of scholarly organizations in creating and disseminating such standards. Presented at the first workshop on Developing Standards for Data Citation and Attribution for Reproducible Research in Linguistics, held at the University of Colorado at Boulder from 09/18/15-09/20/15.

Artificial Intelligence (AI) presents substantial opportunities to advance library operations and user experiences. However, its integration in academic libraries in Ghana has been relatively unexplored. This study investigates the adoption and implementation of AI tools in academic libraries within Ghana, employing a quantitative approach. Focusing on library staff from the University of Ghana (UG), Kwame Nkrumah University of Science and Technology (KNUST), and the University of Cape Coast (UCC), the research sampled 40% of the total population of 450 staff members, resulting in a sample size of 180. Findings indicate that AI-driven search engines and automated cataloging are the most widely adopted tools. UCC shows a broader adoption of AI applications, including chatbots and AI-based recommender systems, while predictive analytics remains less common. The study highlights key factors for successful AI implementation: adequate funding, staff training, technical support, and user acceptance. Despite concerns about job displacement and the necessity for policy adjustments, librarians view AI as a valuable asset for enhancing library services and user engagement. The study underscores the need for academic libraries to prioritize securing resources, investing in staff training, and updating policies to effectively integrate AI technologies and address the evolving demands of library users.

Poster: TROLLing (opendata.uit.no) is an international archive for open linguistic data and statistical code (e.g. R scripts), launched in 2014 at UiT The Arctic University of Norway. With the increasing demand for archiving and sharing research data, as well as the problem of improper attribution, TROLLing aims to meet researchers’ needs by proposing safe storage of data files, and metadata templates based on international standards. Retrieval, sharing, and reuse of data is further facilitated by TROLLing being part of a global open data network. As regards attribution, the system automatically provides a dataset citation, comprising among other things the author name(s) and a persistent identifier (doi). A version control allows researchers to update their datasets at any time, previously published versions still being available open access. TROLLing is available to all subfields of linguistics, but is limited to structural data. The metadata template, however, allows linking to p...

This paper discusses many of the issues associated with formally publishing data in academia, focusing primarily on the structures that need to be put in place for peer review and formal citation of datasets. Data publication is becoming increasingly important to the scientific community, as it will provide a mechanism for those who create data to receive academic credit for their work and will allow the conclusions arising from an analysis to be more readily verifiable, thus promoting transparency in the scientific process. Peer review of data will also provide a mechanism for ensuring the quality of datasets, and we provide suggestions on the types of activities one expects to see in the peer review of data. A simple taxonomy of data publication methodologies is presented and evaluated, and the paper concludes with a discussion of dataset granularity, transience and semantics, along with a recommended human-readable citation syntax.

La transmisión del conocimiento debería verse favorecida por las oportunidades que brindan las TIC como medio de acceso y distribución de objetos digitales. Sin embargo, las barreras que impiden el acceso y reutilización de los trabajos derivados de la actividad científica y académica, ya sean económicas o de derechos de explotación, inhiben compartir un bien común como es el conocimiento. El movimiento por el acceso abierto a la ciencia promueve la eliminación de estas barreras y aboga por una cultura que permita compartir y reutilizar materiales, siempre con el reconocimiento de la autoría y con un uso responsable. Si el artículo científico ha sido históricamente una forma esencial de la comunicación de la ciencia, en la era digital cobran relevancia sus fundamentos, entre ellos los datos observacionales, descriptivos o experimentales que subyacen al artículo. Los datos pueden reutilizarse, transformarse mediante nuevos métodos o agregarse a otras fuentes. Los datos en abierto evitan la duplicidad de ensayos, dan transparencia a su forma de obtención y permiten su validación. En este trabajo se presentan algunas iniciativas y recomendaciones de cómo compartir, gestionar y promocionar el acceso abierto a los datos generados durante la investigación científica, como vía de colaboración entre grupos o personas con afinidad en sus temas de trabajo. Palabras clave: Acceso abierto; datos; repositorios de datos; citación de datos; políticas de acceso abierto. Abstract: Knowledge transfer should be facilitated by the opportunities offered by information technologies, as they affect the access and distribution of digital objects. However barriers to the access and reuse of scholarly research, whether economic or copyright-related, inhibit the sharing of this valuable common good. The open access movement promotes the elimination of these barriers and advocates for an open access culture of sharing and reusing materials, while guaranteeing that authors be duly acknowledged and that the data be used responsibly. If scientific papers have historically been essential for the communication of science, in the digital age it is now their building blocks that have gained greater importance, especially the observational, descriptive or experimental data that underpin the articles. Open research data can be reused, transformed by new methods or aggregated to other sources. Open access to research data avoids redundancy, provides transparency on how they have been obtained and allow for their validation. This work provides an overview of some initiatives and recommendations on how to share and manage research data and foster open access to it, as a means of collaboration between groups or individuals working in similar disciplines.

Detailed, accessible methods are essential for reproducibility, trust in science and scientific advancement; yet, many studies suggest that the reporting of methodological details in life sciences research publications is often incomplete. This may be due to a lack of incentives or reporting standards, or other cultural or educational factors. Promoting Reusable and Open Methods and Protocols (PRO-MaP) aims to increase and improve the reporting of detailed, reusable and open methods and reusable step-by-step protocols in the life sciences. This initiative began with a workshop convened by the EU Reference Laboratory for alternatives to animal testing (EURL ECVAM) at the European Commission's Joint Research Centre, which included representatives from various stakeholder groups. Our draft recommendations outline actions that four stakeholder groups, researchers, research institutions and departments, publishers and editors, and funders, can take to achieve these goals. While some recommendations address study design and reporting guidelines, the primary focus is on capturing clear, accurate, methodological detail, e.g. with re-usable step-by-step protocols. We welcome feedback on these draft recommendations from members of each stakeholder group, including experts in improving methodological reporting. The final recommendations will be released after incorporating feedback received during consultation sessions. Once the recommendations are finalized, we hope that organizations and individuals from each stakeholder group will join us in collaboratively working to improve the reporting of detailed methods and reusable step-by-step protocols in the life sciences.

In this case study, we aim to explore the characteristics and the reception of files uploaded to Zenodo, and the role the repository plays itself in generating usage. To this end, we first apply descriptive statistics on Zenodo's full set of data record metadata with digital object identifiers (DOIs) until and including January 2017 (n = 141,777 records). Second, we estimate the coverage of Zenodo datasets in the Data Citation Index as well as of Zenodo journal articles in the Science Citation Index, Social Science Citation Index, and Arts and Humanities Citation Index, Zenodo books and book chapters in the Book Citation Index, and Zenodo conference papers in the Proceedings Citation Index, and analyze their citedness according to the different data record types in Zenodo (e.g., journal article, dataset, book, or conference paper). Third, we provide a bibliometric analysis of Zenodo records by using different metrics for citedness, including citation, usage, and altmetrics. Altmetrics data are gathered from two of the most popular tools for altmetric analyses, PlumX and Altmetric.com, and we compare the results. Moreover, we study how open access and DOIs provided by Zenodo influence the impact of Zenodo data records and we find a tendency toward a positive relationship between permissive access rights and altmetrics in certain data records.

The aim of this study is to explore the phenomenon of research software citation and, in particular, to draw attention to the increasing importance of this form of citation in scholarly communication. This research sheds light on the current status of formal software citation that is captured by citation databases. Data for the study were gathered from more than 67,000 research software records available in public repositories indexed by Clarivate Analytics' Data Citation Index (DCI). The metadata characteristics of the indexed records and citation data were then analyzed. Research software was rarely cited in the DCI, suggesting that the documented reuse of research software rarely occurs or is not well documented. Institutional repositories attracted few citations and had low rate of citation. It proved impossible, however, using the available data to isolate specific identifiers that can promote formal software citation. The findings presented here offer insights into research software citation that will be of interest to funding agencies, publishers, researchers, and research organizations.

Preservation, Discoverability, and Access (1) What specific Federal policies would encourage public access to and the preservation of broadly valuable digital data resulting from federally funded scientific research, to grow the U.S. economy and improve the productivity of the American scientific enterprise? ICPSR advocates Federal policies in these areas to improve the access and preservation of scientific data:

International audienceMitochondrial bioenergetics contributes to important biological processes and its dysfunction underlies some diseases, but its assessment requires invasive methods involving intracellular staining and chemical inhibitors. In this study, we introduce microwave dielectric spectroscopy (MWDS) as a new non-invasive and label-free method to detect mitochondrial activity in live cells. We show that under electromagnetic radiation with microwaves (0.4-40 GHz), the dielectric properties of living cells are determined by their mitochondrial activity. MWDS instantly detects the mitochondrial depolarization induced by drugs targeting electron transport chain complexes or during the earliest events of the apoptotic process, from a cell suspension of a single microliter. MWDS also discriminates cancer cells with higher mitochondrial activity, an aspect often related to therapeutic resistance in cancer. Thereby, MWDS represents a highly innovative method for non-invasive det...

The enormous growth in research data generated today has highlighted the value of data management (RDM) to make research FAIR (Findable, Accessible, Interconnected and Reusable). Appropriate data instructs researchers to use and reuse that data within appropriate citations and attribute it to the author. And Data citation refers to the process of presenting a reference to data in the same way as a bibliographic reference to printed resources is regularly provided by researchers. In this regard, the objective of this paper is to investigate the activities of the Datacite website in managing research data. Methodology The study approached the Datacite website, a non-profit organization that provides analysis with persistent identifiers (DOIs). The research examines the Statistics systems and other critical resources. Registrations by the Collective group and most involved repositories are included in the statistical approaches. The basic resources include top executives, OAI-PMH, DataCite Public Roadmap, DataCite Commons, DataCite/ORCID Auto-update and Service Providers. The outcomes were analysed by MS Excel. Results It is noted that there were 293 members of the registry from different countries. The USA was at the top of the 137 members according to registration, while at least one was located in India, Finland, Spain, etc. Germany was listed as the top member and most of the repository holding companies. Datafirst is the only server found in an Indian context. DataCite Commons found as a discovery tool which allows simple searches by works, individuals and organisations, while providing users with a detailed overview of the relationships between the entities in the research setting. Using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), the DataCite service exposes metadata stored in the DataCite Metadata Store (MDS). Datacite Auto-update unambiguously categorises researchers and provides tools to automate the link between researchers and their creative work.

This is the published version of the Scholix metadata scheme The goal of the Scholix initiative is to establish a high level interoperability framework for exchanging information about the links between scholarly literature and data. It aims to enable an open information ecosystem to understand systematically what data underpins literature and what literature references data. Scholix is an evolving lightweight set of guidelines to increase interoperability. It consists of: A consensus among a growing group of publishers, datacentres, and global/ domain service providers to work collaboratively and systematically to improve exchange of data-literature link information Information model: conceptual definition of what is a Scholix scholarly link Scholix is the “wholesaler to wholesaler” exchange framework, to be implemented by existing hubs or global aggregators of data-literature link information such as DataCite, CrossRef, OpenAIRE, or EMBL-EBI. These hubs in turn work with their nat...

VAMDC bridged the gap between atomic and molecular (A&M) producers and users by providing an interoperable e-infrastructure connecting A&M databases, as well as tools to extract and manipulate those data. The current paper highlights how the new paradigms for data citation produced by the Research Data Alliance in order to address the citation issues in the data-driven science landscape, have successfully been implemented on the VAMDC e-infrastructure.

The present paper examines the developments in the Information and Communication Technology and its application to the Library and Information Science. As it developed the library's functions and services, there is a need for training of the library professionals in these applications. The paper briefly explains the role of the library professionals and applications of the Information and Communication Technology applications to the libraries. The applications may be grouped as Basic Computerization, Library Automation, Electronic Services, Strategic Management, Digital Rights Management, Information Systems and Networks, etc. These are identified as key areas in which, present library and information professionals need training and competency development.

In recent years we have seen a growing adoption of Archival Resource Key (ARK) identi ers in France and in French-speaking countries, a growing reliance on National Library of France (BnF) ARKs for data dissemination, and a growing demand for data permanence from the BnF community-the French libraries. e BnF has acknowledged these changes through renewed e orts in education, communication, and development of good practices. is progress report describes the recent upsurge of interest in ARKs, the main di culties posed by identi er management for the BnF and its community, and nally the "ARK Summit 2018", a major event in the construction of a francophone community of interest around ARK identi ers.

The role of DataCite and other large-scale infrastructures is evolving from identifying things to connecting things and DataCite metadata includes many ways to make connections. We will concentrate on relatedIdentifiers (and citations), nameIdentifiers and affiliationIdentifiers. We will explore how these connectors are being used in DataCite metadata. Adding these connectors to your DataCite metadata provides great opportunities for you to improve connectivity for your datasets and your users. A recording of the presentation can be found at: https://youtu.be/5WIBGY-Z7E8

The role of DataCite and other large-scale infrastructures is evolving from identifying things to connecting things and DataCite metadata includes many ways to make connections. We will concentrate on relatedIdentifiers (and citations), nameIdentifiers and affiliationIdentifiers. We will explore how these connectors are being used in DataCite metadata. Adding these connectors to your DataCite metadata provides great opportunities for you to improve connectivity for your datasets and your users. A recording of the presentation can be found at: https://youtu.be/5WIBGY-Z7E8

Since the 2011 workshop, the Task Group has undertaken a series of activities designed to build upon the international body of knowledge on data citation and attribution practices. The report presented here represents the next step identified by the Task Group: to document the current state of practice for data citation and attribution, noting emerging trends, successes, and challenges.

PARSEC Introduction: Advances in science, both today and in the future, depend on the openness, accessibility and reusability of data, software, samples, and data products. As environmental, ecological, and geological data represent single observations of dynamics they are critically important in aggregate. Global data on many phenomena and systems are essential for many applications, including weather prediction, hazardmitigation, water use, and have major economic benefits. Yet large amounts of research data related to the Earth and its ecosystems are either not well preserved or preserved at all. And when they arepreserved, the creator of those data is not always identified, resulting in difficulties in attributing the data resulting in proper credit. In some scientific domains, the practice of preserving data in a trusted repository is improving. During the last 18 months the Enabling FAIR Data project has made significant improvements in the common policies across the Earth, sp...

The Inter-university Consortium for Political and Social Research (ICPSR)'s Bibliography of Data-Related Literature was established in 2000 to match datasets held in ICPSR topical archives to works resulting from data analyses. As a librarian-bibliographer working with ICPSR's criminal justice topical archive, it has been my responsibility to employ effective methodologies for browsing social science literature and establishing continuing email alerts detecting when ICPSR data is used, whether formally cited with a DOI or informally mentioned in an article's abstract or methods. This presentation will showcase the typical work of an ICPSR bibliographer, highlight the usefulness of the ICPSR Bibliography for data librarians, and emphasize the importance of standardized dataset citation in scholarly publications.

This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles, a synopsis and harmonization of the recommendations of major science policy bodies. The roadmap was developed by the Repositories Expert Group, as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH BioCADDIE (https://biocaddie.org) program. The roadmap makes 11 specific recommendations, grouped into three phases of implementation: a) required steps needed to support the Joint Declaration of Data Citation Principles, b) recommended steps that facilitate article/data publication workflows, and c) optional steps that further improve data citation support provided by data repositories.

Data citation to reflect instances of data sharing and re-use is becoming more common, although it is not yet widely adopted. We investigate how common formal and informal data citation are in bioscience/biomedical research. We found that informal data citation (i.e., acknowledgments embedded in publications outside of formal references) is far more common than formal citations. Informal citations go unrecorded, and therefore authors of datasets largely do not receive credit for having informed or influenced the research of others.

Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation-where data references are included alongside bibliographic references in the reference section of a publication-is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined.

This study examines characteristics of data sharing and data re-use in Genetics and Heredity, where data citation is most common. This study applies an exploratory method because data citation is a relatively new area. The Data Citation Index (DCI) on the Web of Science was selected because DCI provides a single access point to over 500 data repositories worldwide and to over two million data studies and datasets across multiple disciplines and monitors quality research data through a peer review process. We explore data citations for Genetics and Heredity, as a case study by examining formal citations recorded in the DCI and informally by sampling a selection of papers for implicit data citations within publications. Citer-based analysis is conducted in order to remedy self-citation in the data citation phenomena. We explore 148 sampled citing articles in order to identify factors that influence data sharing and data re-use, including references, main text, supplementary data/information, acknowledgments, funding information, author information, and web/author resources. This study is unique in that it relies on a citer-based analysis approach and by analyzing peer-reviewed and published data, data repositories, and citing articles of highly productive authors where data sharing is most prevalent. This research is intended to provide a methodological and practical contribution to the study of data citation.

Recommended Citation Champieux, Robin; Kramer, Bianca; Bosman, Jeroen; Bruno, Ian; Buckland, Amy; Callaghan, Sarah; Chapman, Chris; Hagstrom, Stephanie; Martone, MaryAnn E.; and O'Donnell, Daniel Paul (2016) "Finding the Principles of the Commons: A Report of the Force11 Scholarly Communications Working Group," Collaborative Librarianship: Vol. 8 : Iss. 2 , Article 5. Available at: https://digitalcommons.du.edu/collaborativelibrarianship/vol8/iss2/5

Poster: TROLLing (opendata.uit.no) is an international archive for open linguistic data and statistical code (e.g. R scripts), launched in 2014 at UiT The Arctic University of Norway. With the increasing demand for archiving and sharing research data, as well as the problem of improper attribution, TROLLing aims to meet researchers’ needs by proposing safe storage of data files, and metadata templates based on international standards. Retrieval, sharing, and reuse of data is further facilitated by TROLLing being part of a global open data network. As regards attribution, the system automatically provides a dataset citation, comprising among other things the author name(s) and a persistent identifier (doi). A version control allows researchers to update their datasets at any time, previously published versions still being available open access. TROLLing is available to all subfields of linguistics, but is limited to structural data. The metadata template, however, allows linking to p...

Since the 2011 workshop, the Task Group has undertaken a series of activities designed to build upon the international body of knowledge on data citation and attribution practices. The report presented here represents the next step identified by the Task Group: to document the current state of practice for data citation and attribution, noting emerging trends, successes, and challenges.

Call for papers for the Special Issue "Data reuse: What new information can we learn from used data?" One of the most critical tasks of executing empirical research in Business Administration is to collect reliable data. This is usually costly, time-consuming and, sometimes, demands legal agreements along the process to guarantee the rights of those who own the data. Thus, the open access to empirical data is of extreme value for researchers of several fields (Piwowar & Vision, 2013; Van Raaij, 2018; Wallis, Rolando, & Borgman, 2013; Wegener & Rüping, 2011; Zimmerman, 2008). In the past few years, the worldwide community (Wilkinson, Dumontier, Aalbersberg, Appleton, Axton, Baak, et al., 2016) initiated a process to make research data FAIR (findable, accessible, interoperable, and reusable) throughout Open Science practices. RAC supports this movement and is committed to boosting open science practices (including open data) in the local community. One of the cornerstones of...

Considers the promotion of data citation and attribution standards by: 1) establishing an education and training mandate, drawing on existing resources and standards, and engaging researchers early in their careers; and 2) exploring the role of scholarly organizations in creating and disseminating such standards. Presented at the first workshop on Developing Standards for Data Citation and Attribution for Reproducible Research in Linguistics, held at the University of Colorado at Boulder from 09/18/15-09/20/15.

Working group reports from the four communities represented at the workshop: (1) Archivists, (2) Journal Editors, (3) IT/Big Data, (4) Ordinary Working Linguists. Presented at the first workshop on Developing Standards for Data Citation and Attribution for Reproducible Research in Linguistics, held at the University of Colorado at Boulder from 09/18/15-09/20/15.

oAs stated in NSF's "Information about the Data Management Plan Required for all Proposals" for Biological Sciences, the Federal government defines data (OMB Circular A-110) as: "…the recorded factual material commonly accepted in the scientific community as necessary to validate research findings." This definition includes both original data (observations, measurements etc.) as well as metadata (e.g., experimental protocols, software code for statistical analysis etc.).

In an effort to lead our community in following modern data citation practices by formally citing data used in published research and implementing standards to facilitate reproducible research results and data, while also producing meaningful metrics that help assess the impact of our services, the National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL) has implemented the use of Digital Object Identifiers (DOIs) (DataCite 2017) for both physical objects (e.g., research platforms and instruments) and datasets. We discuss why this work is important and timely, and review the development of guidelines for the use of DOIs at EOL by focusing on how decisions were made. We discuss progress in assigning DOIs to physical objects and datasets, summarize plans to cite software, describe a current collaboration to develop community tools to display citations on websites, and touch on future plans to cite workflows that document dataset processing and quality control. Finally, we will review the status of efforts to engage our scientific community in the process of using DOIs in their research publications.

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier How to cite this article Starr et al. (2015), Achieving human and machine accessibility of cited data in scholarly publications. PeerJ Comput. Sci. 1:e1; DOI 10.7717/peerj-cs.1 resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.