How is R cited in research outputs? Structure, impacts, and citation standard

Li, Kai; Yan, Erjia; Feng, Yuanyuan

doi:10.1016/j.joi.2017.08.003

Cited by 34 publications

(33 citation statements)

References 26 publications

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“…These citable objects include not only published software papers and software project pages, but also unpublished manuscripts created along the history of the software package. Despite the fact that we only analyzed a single software entity, lme4, the existence of multiple citable objects for the same software package is by no means a special case based on past research (Li et al, 2017). As a result, our results are applicable to many other software packages.…”

Section: Diversity Of Software Citation Practice and Its Real-world Imentioning

confidence: 96%

“…This question aims to examine the variety of citable objects related to lme4. The question is directly driven by a clear gap between the official citation format that is available for every R package and the inconsistent citation practice to follow this instruction (Li et al, 2017). This inconsistency, however, cannot be easily categorized into the formal-informal citation dichotomy (Park, You, & Wolfram, 2017), because authors could be citing a CRAN page in a formal way (i.e., offering citations and references) even though what they are cited is not the official citable object.…”

Section: Question 1: Which Citable Objects Related To Lme4 Are Cited mentioning

confidence: 99%

“…Citation data has been proven to be an inconsistent indicator of software mentions in scientific publications (Howison & Bullard, 2015;Li et al, 2017;Pan et al, 2015). However, as a research object, its coverage is much broader than that of full-text data.…”

Section: Quantitative Studies Of Scientific Softwarementioning

confidence: 99%

“…The rising status of software in science is a major motivation for recent scholarly interests in the scientific impact of software from a quantitative perspective. Studies have reported that software tends to be inconsistently represented in publications: authors do not cite software in consistent ways or offer enough metadata information to support the functions of software citation (Howison & Bullard, 2015;Li, Greenberg, & Lin, 2016;Li, Yan, & Feng, 2017). This reality makes it difficult to identify software from publications just based on citation data, which makes full text scientific publications a preferable data source to pursue related questions (Li & Yan, 2018;Pan, Yan, & Hua, 2016;Pan, Yan, Wang, & Hua, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The following considerations contribute to the selection of this package as the research object of this study. First, it is one of the most highly-mentioned R packages based on a previous study (Li et al, 2017). Second, a number of software papers have been published focusing on this package, and its CRAN page is also a highly cited object; this offers rich data to understand how a software package is cited in different manners and disciplinary contexts over time.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Challenges of measuring software impact through citations: An examination of the lme4 R package

Chen

Yan

2019

Journal of Informetrics

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The rise of software as a research object is mirrored in the increasing interests towards quantitative studies of scientific software. However, due to the inconsistent practice of citing software, most of the existing studies analyzing the impact of scientific software are based on identification of software name mentions in full-text publications. Despite its limitations, citation data have a much larger quantity and broader coverage of scientific fields than full-text data and thus could support findings in much larger scopes. This paper presents an analysis aiming to evaluate the extent to which citations data can be used to reconstruct the impact of software. Specifically, we identified the variety of citable objects related to the lme4 R package and examined how the package's impact is scattered across these objects. Our results reveal a littlediscussed challenge of using citation data to measure the impact of software, that even within the category of formal citation, there might be different forms in which the same software object is cited. This challenge can be mitigated by more carefully selecting objects as the proxy of software. However, it cannot be fully solved until we have one-software-one-proxy policy for software citation.

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Section: Diversity Of Software Citation Practice and Its Real-world Imentioning

confidence: 96%

Section: Question 1: Which Citable Objects Related To Lme4 Are Cited mentioning

confidence: 99%

Section: Quantitative Studies Of Scientific Softwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Challenges of measuring software impact through citations: An examination of the lme4 R package

Chen

Yan

2019

Journal of Informetrics

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Measuring the impact of R packages

2017

Proc. Assoc. Info. Sci. Tech.

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In this poster, we describe a framework to measure the impact of R packages from an altmetrics perspective. This framework includes search statistics on Google, the number of downloads on CRAN (the official R package repository), the number of posts on Stack Overflow and the count of papers on the Public Library of Science (PLoS) journals mentioning or citing the packages. These measures are normalized into monthly units. Six visualization packages were selected to test the method. Results are presented and discussed, particularly in light of the levels of engagement represented by these measures. The next steps of this project are also explained at the end of this poster proposal.

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SOMD@NSLP2024: Overview and Insights from the Software Mention Detection Shared Task

Krüger,

Karmakar,

Dietze

2024

Lecture Notes in Computer Science

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Software is a central part of the scientific process and involved in obtaining, analysing, visualising and processing research data. Understanding the provenance of research requires an understanding of the involved software. However, software citations in scientific publications often are informal, what creates challenges when aiming at understanding software adoption. This paper provides an overview of the Software Mention Detection (SOMD) shared task conducted as part of the 2024 Natural Scientific Language Processing Workshop, aiming at advancing the state-of-the-art with respect to NLP methods for detecting software mentions and additional information in scholarly publications. The SOMD shared task encompasses three subtasks, concerned with software mention recognition (subtask I), recognition of additional information (subtask II) and classification of involved relations (subtask III). We present an overview of the tasks, received submissions and used techniques. The best submissions achieved F1 scores of 0.74 (subtask I), 0.838 (subtask II) and 0.911 (subtask III) indicating both task feasibility but also potential for further performance gains.

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How is R cited in research outputs? Structure, impacts, and citation standard

Cited by 34 publications

References 26 publications

Challenges of measuring software impact through citations: An examination of the lme4 R package

Challenges of measuring software impact through citations: An examination of the lme4 R package

Measuring the impact of R packages

SOMD@NSLP2024: Overview and Insights from the Software Mention Detection Shared Task

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