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Under the data-driven research paradigm, research software has come to play crucial roles in nearly every stage of scientific inquiry. Scholars are advocating for the formal citation of software in academic publications, treating it on par with traditional research outputs. However, software is hardly consistently cited: one software entity can be cited as different objects, and the citations can change over time. These issues, however, are largely overlooked in existing empirical research on software citation. To fill the above gaps, the present study compares and analyzes a longitudinal dataset of citation formats of all R packages collected in 2021 and 2022, in order to understand the citation formats of R-language packages, important members in the open-source software family, and how the citations evolve over time. In particular, we investigate the different document types underlying the citations and what metadata elements in the citation formats changed over time. Furthermore, we offer an in-depth analysis of the disciplinarity of journal articles cited as software (software papers). By undertaking this research, we aim to contribute to a better understanding of the complexities associated with software citation, shedding light on future software citation policies and infrastructure. [ABSTRACT FROM AUTHOR]

This research examines the prevalence of research software as independent records of output within UK academic institutional repositories (IRs). There has been a steep decline in numbers of research software submissions to the UK's Research Excellence Framework from 2008 to 2021, but there has been no investigation into whether and how the official academic IRs have affected the low return rates. In what we believe to be the first such census of its kind, we queried the 182 online repositories of 157 UK universities. Our findings show that the prevalence of software within UK Academic IRs is incredibly low. Fewer than 28% contain software as recognised academic output. Of greater concern, we found that over 63% of repositories do not currently record software as a type of research output and that several Universities appeared to have removed software as a defined type from default settings of their repository. We also explored potential correlations, such as being a member of the Russell group, but found no correlation between these metadata and prevalence of records of software. Finally, we discuss the implications of these findings with regards to the lack of recognition of software as a discrete research output in institutions, despite the opposite being mandated by funders, and we make recommendations for changes in policies and operating procedures. [ABSTRACT FROM AUTHOR]

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Citation analysis can uncover ways to apply the use of software in different research synergies. A cited reference search on MATLAB in the Web of Science database retrieved 6,873 citing articles from 2000 to 2020. These articles were analyzed by research areas to reveal the use of MATLAB in unexpected subject-specific disciplines. The VOSviewer software tool was used to generate and analyze keyword co-occurrence clusters by research area. The study found that MATLAB not only holds applications in the Technology and the Physical Sciences disciplines but also extends to the Life Sciences & Biomedicine, Social Sciences, and Arts & Humanities. [ABSTRACT FROM AUTHOR]

Software is essential for scientific research and is applied in research methodology, data analysis, and knowledge dissemination. Scientists believe that software plays a crucial role in their research process, so it is necessary to recognize the contribution of software developers. This study aims to investigate the current situation of software citation in scientific research and explore possible solutions to improve its sustainability via full-text content analysis. There is a gap between the use and the citation of software in scientific publications, and the lack of information in software citations has been a source of both failure and improvement of software in scientific research. We found various deficiencies that hinder the durability of software citations and put forward some suggestions for their stability and development, such as designing software citation standards, promoting free open licensing of scientific software, and implementing strict peer review of software citations. [ABSTRACT FROM AUTHOR]

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision; while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI).

In this paper, we investigate progress toward improved software citation by examining current software citation practices. We first introduce our machine learning based data pipeline that extracts software mentions from the CORD-19 corpus, a regularly updated collection of more than 280,000 scholarly articles on COVID-19 and related historical coronaviruses. We then closely examine a stratified sample of extracted software mentions from recent CORD-19 publications to understand the status of software citation. We also searched online for the mentioned software projects and their citation requests. We evaluate both practices of referencing software in publications and making software citable in comparison with earlier findings and recent advocacy recommendations. We found increased mentions of software versions, increased open source practices, and improved software accessibility. Yet, we also found a continuation of high numbers of informal mentions that did not sufficiently credit software authors. Existing software citation requests were diverse but did not match with software citation advocacy recommendations nor were they frequently followed by researchers authoring papers. Finally, we discuss implications for software citation advocacy and standard making efforts seeking to improve the situation. Our results show the diversity of software citation practices and how they differ from advocacy recommendations, provide a baseline for assessing the progress of software citation implementation, and enrich the understanding of existing challenges.

Science across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research data and insights, is a prerequisite for reproducibility and can enable macro-analysis of the evolution of scientific methods over time. However, missing rigor in software citation practices renders the automated detection and disambiguation of software mentions a challenging problem. In this work, we provide a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles. Our information extraction approach distinguishes different types of software and mentions, disambiguates mentions and outperforms the state-of-the-art significantly, leading to the most comprehensive corpus of 11.8 M software mentions that are described through a knowledge graph consisting of more than 300 M triples. Our analysis provides insights into the evolution of software usage and citation patterns across various fields, ranks of journals, and impact of publications. Whereas, to the best of our knowledge, this is the most comprehensive analysis of software use and citation at the time, all data and models are shared publicly to facilitate further research into scientific use and citation of software.

Software is as integral as a research paper, monograph, or dataset in terms of facilitating the full understanding and dissemination of research. This article provides broadly applicable guidance on software citation for the communities and institutions publishing academic journals and conference proceedings. We expect those communities and institutions to produce versions of this document with software examples and citation styles that are appropriate for their intended audience. This article (and those community-specific versions) are aimed at authors citing software, including software developed by the authors or by others. We also include brief instructions on how software can be made citable, directing readers to more comprehensive guidance published elsewhere. The guidance presented in this article helps to support proper attribution and credit, reproducibility, collaboration and reuse, and encourages building on the work of others to further research.

Software is as integral as a research paper, monograph, or dataset in terms of facilitating the full understanding and dissemination of research. This article provides broadly applicable guidance on software citation for the communities and institutions publishing academic journals and conference proceedings. We expect those communities and institutions to produce versions of this document with software examples and citation styles that are appropriate for their intended audience. This article (and those community-specific versions) are aimed at authors citing software, including software developed by the authors or by others. We also include brief instructions on how software can be made citable, directing readers to more comprehensive guidance published elsewhere. The guidance presented in this article helps to support proper attribution and credit, reproducibility, collaboration and reuse, and encourages building on the work of others to further research.

Nearly all research today has a digital component, and typically, scholarly results are strongly dependent on software. For the research results to be fully understood, the software that is used must be uniquely identified. Research software is frequently developed by researchers themselves, often initially to solve a single problem, and then later generalized to solve additional problems. Ideally, the software is shared so that other researchers can also benefit and avoid the duplicate work required for development and maintenance. The researchers must expect and receive value for their contribution and sharing. Because publishing is a key element of our existing scholarly structures, the research that was done must be clearly explained in papers. This can be used to create incentives for researchers not only to share their software, but also to contribute to community software, in both cases through software citation. Contributors to software that is used in papers and is cited by those papers can become authors of the software as it is tracked by indexes, which also track how often the software is cited. [ABSTRACT FROM AUTHOR]

Great strides have been made to encourage researchers to archive data created by research and provide the necessary systems to support their storage. Additionally it is recognised that data are meaningless unless their provenance is preserved, through appropriate meta-data. Alongside this is a pressing need to ensure the quality and archiving of the software that generates data, through simulation, control of experiment or data-collection and that which analyses, modifies and draws value from raw data. In order to meet the aims of reproducibility we argue that data management alone is insufficient: it must be accompanied by good software practices, the training to facilitate it and the support of stakeholders, including appropriate recognition for software as a research output.

Following the widespread digitalization of scholarship, software has become essential for research, but the current sociotechnical system of citation does not reflect this sufficiently. Citation provides context for research, but the current model for the respective research citation graphs does not integrate software. In this article, I develop a directed graph model to alleviate this, describe challenges for its instantiation, and give an outlook of useful applications of research citation graphs, including transitive credit. [ABSTRACT FROM AUTHOR]

Software is a fundamental pillar of modern scientific research, across all fields and disciplines. However, there is a lack of adequate means to cite and reference software due to the complexity of the problem in terms of authorship, roles, and credits. This complexity is further increased when it is considered over the lifetime of a software that can span up to several decades. Building upon the internal experience of Inria, the French research institute for digital sciences, we provide in this article a contribution to the ongoing efforts in order to develop proper guidelines and recommendations for software citation and reference. Namely, we recommend: first, a richer taxonomy for software contributions with a qualitative scale; second, to put humans at the heart of the evaluation; and third, to distinguish citation from reference. [ABSTRACT FROM AUTHOR]

• We analyze the prevalence of research software citation in Clarivate Analytics' Data Citation Index (DCI). • To date, most software citations have been limited to research software hosted by a small number of repositories. • Research software remains highly uncited in the DCI, or the reuse of research software is not well reflected in the DCI. • The use of persistent identifiers, which can help to identify research software did not necessarily result in higher citation rates. 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. [ABSTRACT FROM AUTHOR]

The CodeMeta Project recently proposed a vocabulary for software metadata. ISO Technical Committee 211 has published a set of metadata standards for geographic data and many kinds of related resources, including software. In order for ISO metadata creators and users to take advantage of the CodeMeta recommendations, a mapping from ISO elements to the CodeMeta vocabulary must exist. This mapping is complicated by differences in the approaches used by ISO and CodeMeta, primarily a difference between hard and soft typing of metadata elements. These differences are described in detail and a mapping is proposed that includes sixty-four of the sixty-eight CodeMeta V2 terms. The CodeMeta terms have also been mapped to dialects used by twenty-one software repositories, registries and archives. The average number of terms mapped in these cases is 11.2. The disparity between these numbers reflects the fact that many of the dialects that have been mapped to CodeMeta are focused on citation or dependency identification and management while ISO and CodeMeta share additional targets that include access, use, and understanding. Addressing this broader set of use cases requires more metadata elements.

Highlights • This study examines the distribution of citations to different citable objects related to lme4. • Multiplicity of citable objects connected to software is a challenge to measuring its impact. • The reassignment of the citation format of lme4 catalyzed its changing citation behavior. Abstract The rise of software as a research object is mirrored by increasing interests in quantitative studies of scientific software. However, inconsistent citation practices have led most existing studies of this type to base their analysis of software impact on software name mentions, as identified in full-text publications. Despite its limitations, citation data exists in much greater quantities and covers a broader array of scientific fields than full-text data, and thus can support investigations with much wider scope. This paper aims to analyze the extent to which citation data can be used to reconstruct the impact of software. Specifically, we identify the variety of citable objects related to the lme4 R package and examine how the package's impact is dispersed across these objects. Our results shed light on a little-discussed challenge of using citation data to measure software impact: even within the category of formal citation, the same software object might be cited in different forms. We consider the implications of this challenge and propose a method to reconstruct the impact of lme4 through its citations nonetheless. [ABSTRACT FROM AUTHOR]

Highlights • This paper investigates the contributions of software to LIS research. • We explore the extent to which researchers follow software citation instructions. • Nearly 30% of the LIS articles in our sample explicitly mention and use software. • A substantial proportion of scholars do not follow software citation instructions. Abstract We investigate the contributions of scientific software to library and information science (LIS) research using a sample of 572 English language articles published in 13 journals in 2008, 2011, 2014, and 2017. In particular, we examine the use and citation of software freely available for academic use in the LIS literature; we also explore the extent to which researchers follow software citation instructions provided by software developers. Twenty-seven percent of the LIS journal articles in our sample explicitly mention and use software. Yet although LIS researchers are becoming increasingly reliant on software that is freely available for academic use, many still fail to include formal citations of such software in their publications. We also find that a substantial proportion of researchers, when documenting software use, do not cite the software in the manner recommended by its developers. [ABSTRACT FROM AUTHOR]

This study investigates the use, citation and diffusion of three bibliometric mapping software tools (CiteSpace, HistCite and VOSviewer) in scientific papers. We first conduct a content analysis of a sample of 481 English core journal papers—i.e., papers from journals deemed central to their respective disciplines—in which at least one of these tools is mentioned. This allows us to understand the predominant mention and citation practices surrounding these tools. We then employ several diffusion indicators to gain insight into the diffusion patterns of the three software tools. Overall, we find that researchers mention and cite the tools in diverse ways, many of which fall short of a traditional formal citation. Our results further indicate a clear upward trend in the use of all three tools, though VOSviewer is more frequently used than CiteSpace or HistCite. We also find that these three software tools have seen the fastest and most widespread adoption in library and information science research, where the tools originated. They have since been gradually adopted in other areas of study, initially at a lower diffusion speed but afterward at a rapidly growing rate. [ABSTRACT FROM AUTHOR]

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision; while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI).