Zenodo in the Spotlight of Traditional and New Metrics

Peters, Isabella; Kraker, Peter; Lex, Elisabeth; Gumpenberger, Christian; Gorraiz, Juan

doi:10.3389/frma.2017.00013

Cited by 41 publications

(33 citation statements)

References 26 publications

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“…However, the specific way each aggregator queries the API using specific or multiple identifiers can have an effect on the final reported readership counts. For instance, Altmetric.com only queries Mendeley readership when other altmetric event has been reported for the publication [ 26 ; 27 ]. This largely explains the strong discrepancies between this aggregator and the others in the reporting of Mendeley readership.…”

Section: Resultsmentioning

confidence: 99%

General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators

Zahedi

Costas

2018

PLoS ONE

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The data collection and reporting approaches of four major altmetric data aggregators are studied. The main aim of this study is to understand how differences in social media tracking and data collection methodologies can have effects on the analytical use of altmetric data. For this purpose, discrepancies in the metrics across aggregators have been studied in order to understand how the methodological choices adopted by these aggregators can explain the discrepancies found. Our results show that different forms of accessing the data from diverse social media platforms, together with different approaches of collecting, processing, summarizing, and updating social media metrics cause substantial differences in the data and metrics offered by these aggregators. These results highlight the importance that methodological choices in the tracking, collecting, and reporting of altmetric data can have in the analytical value of the data. Some recommendations for altmetric users and data aggregators are proposed and discussed.

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Section: Resultsmentioning

confidence: 99%

General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators

Zahedi

Costas

2018

PLoS ONE

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“…Plum Analytics also takes this approach, and supplements it by providing data-specific metrics from data sharing platforms. Comparative analyses have shown differences in altmetrics between these two providers (Enkhbayar et al, 2020;Meschede & Siebenlist, 2018;Peters et al, 2016Peters et al, , 2017Zahedi et al, 2015). Moreover, metrics services sometimes collect data from the same sources in different ways.…”

Section: Discipline Shares Data? Explanationmentioning

confidence: 99%

“…PlumX Metrics has collected metrics for over 450,000 datasets sourced from repositories like Dryad and Independent studies have found that PlumX Metrics has indexed 16% of datasets shared on Zenodo (Peters et al, 2017), and between 4% and 9% of datasets indexed in the Data Citation Index (Peters et al, 2016). For Zenodo-hosted datasets indexed in PlumX Metrics, around 6.2% of attention comes in the form of "captures" (e.g.…”

Section: Altmetricsmentioning

confidence: 99%

Assessing the Impact and Quality of Research Data Using Altmetrics and Other Indicators

Konkiel

2020

SAR

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Research data in all its diversity-instrument readouts, observations, images, texts, video and audio files, and so on-is the basis for most advancement in the sciences. Yet the assessment of most research programmes happens at the publication level, and data has yet to be treated like a first-class research object. How can and should the research community use indicators to understand the quality and many potential impacts of research data? In this article, we discuss the research into research data metrics, these metrics' strengths and limitations with regard to formal evaluation practices, and the possible meanings of such indicators. We acknowledge the dearth of guidance for using altmetrics and other indicators when assessing the impact and quality of research data, and suggest heuristics for policymakers and evaluators interested in doing so, in the absence of formal governmental or disciplinary policies. Policy highlights • Research data is an important building block of scientific production, but efforts to develop a framework for assessing data's impacts have had limited success to date. • Indicators like citations, altmetrics, usage statistics, and reuse metrics highlight the influence of research data upon other researchers and the public, to varying degrees. • In the absence of a shared definition of "quality", varying metrics may be used to measure a dataset's accuracy, currency, completeness, and consistency. • Policymakers interested in setting standards for assessing research data using indicators should take into account indicator availability and disciplinary variations in the data when creating guidelines for explaining and interpreting research data's impact. • Quality metrics are context dependent: they may vary based upon discipline, data structure, and repository. For this reason, there is no agreed upon set of indicators that can be used to measure quality. • Citations are well-suited to showcase research impact and are the most widely understood indicator. However, efforts to standardize and promote data citation practices have seen limited success, leading to varying rates of citation data availability across disciplines. • Altmetrics can help illustrate public interest in research, but availability of altmetrics for research data is very limited. • Usage statistics are typically understood to showcase interest in research data, but infrastructure to standardize these measures have only recently been introduced, and not all repositories report their usage metrics to centralized data brokers like DataCite. • Reuse metrics vary widely in terms of what kinds of reuse they measure (e.g. educational, scholarly, etc). This category of indicator has the fewest heuristics for collection and use associated with it; think about explaining and interpreting reuse with qualitative data, wherever possible. • All research data impact indicators should be interpreted in line with the Leiden Manifesto's principles, including accounting for disciplinary variation and data availability. • Assessing research...

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“…We consulted the Altmetric Explorer in May 2018 using the advanced search option in its main menu and analyzed total figures retrieved by type of output. With few exceptions (e.g., Peters et al, 2016Peters et al, , 2017 most of the studies related to the use of altmetrics are based on the analysis of journal articles. However, Altmetric.com retrieves mentions also to datasets, books, book chapters, clinical trials and news stories.…”

Section: General Overview: Research Outputs Mentions and Evolutionmentioning

confidence: 99%

The insoluble problems of books: what does Altmetric.com have to offer?

Torres‐Salinas

Gorraiz

Robinson-García

2018

AJIM

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Purpose: Analyze the capabilities, functionalities and appropriateness of Altmetric.com as a data source for the bibliometric analysis of books in comparison to PlumX. Methodology:We perform an exploratory analysis on the metrics the Altmetric Explorer for Institutions platform offers for books. We use two distinct datasets of books. On the one hand, we analyze the Book Collection included in Altmetric.com. On the other, we use Clarivate's Master Book List, to analyze Altmetric.com's capabilities to download and merge data with external databases. Finally, we compare our findings with those obtained in a previous study performed in PlumX. Findings:Altmetric.com combines and orderly tracks a set of data sources combined by DOI identifiers to retrieve metadata from books, being Google Books its main provider. It also retrieves information from commercial publishers and from some Open Access initiatives, including those led by university libraries such as Harvard Library. We find issues with linkages between records and mentions or ISBN discrepancies. Furthermore, we find that automatic bots affect greatly Wikipedia mentions to books. Our comparison with PlumX suggests that none of these tools provide a complete picture of the social attention generated by books and are rather complementary than comparable tools. Practical implications:This study targets different audiences which can benefit from our findings. First, bibliometricians and researchers who seek for alternative sources to develop bibliometric analyses of books, with a special focus on the Social Sciences and Humanities fields. Second, librarians and research managers who are the main clients to which these tools are directed. Third, Altmetric.com itself as well as other altmetric providers who might get a better understanding of the limitations users encounter and improve this promising tool.Originality/value: This is the first study to analyze Altmetric.com's functionalities and capabilities for providing metric data for books and to compare results from this platform, with those obtained via PlumX.

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Zenodo in the Spotlight of Traditional and New Metrics

Cited by 41 publications

References 26 publications

General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators

General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators

Assessing the Impact and Quality of Research Data Using Altmetrics and Other Indicators

The insoluble problems of books: what does Altmetric.com have to offer?

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