Altmetrics: diversifying the understanding of influential scholarship

Konkiel, Stacy

doi:10.1057/palcomms.2016.57

Cited by 36 publications

(32 citation statements)

References 34 publications

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“…In recent years, altmetrics have increasingly been suggested as a means of understanding the broader impacts of research data. However, these highly heterogeneous data can mean many different things, and should be interpreted carefully, usually in tandem with other indicators (Konkiel, 2016).…”

Section: Altmetricsmentioning

confidence: 99%

“…The relative ease with which online attention for research can be fabricated or "gamed" is another concern often expressed by evaluators and researchers alike (Adie, 2013;Konkiel, 2016;Roemer & Borchardt, 2015). While altmetrics services rarely see cases of intentional gaming, automated "bots" can and do artificially inflate altmetrics (Haustein et al, 2016), including for research data (Lowenberg et al, 2019), obscuring organic, meaningful engagement.…”

Section: Altmetricsmentioning

confidence: 99%

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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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Section: Altmetricsmentioning

confidence: 99%

Section: Altmetricsmentioning

confidence: 99%

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

Konkiel

2020

SAR

Self Cite

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“…Las cifras más altas se encuentra con las lecturas en Mendeley. Konkiel (2016), en un repaso similar, coincide con la anterior valoración, ya que también señala que, en general, no hay correlación o que existen tan solo correlaciones moderadas entre las citas y la lectura en Mendeley, y altas valoraciones en Faculty of 1000 y menciones en blogs académicos. Por el contrario, existirían correlaciones débiles o incluso negativas para indicadores como los tuits, los post en Facebook o las menciones en LinkedIn.…”

Section: Correlación Con Calidad Científicaunclassified

Las altmétricas: aportaciones para la evaluación de publicaciones científicas en ciencias humanas y sociales

Abadal¹

2018

revistaph

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Se presentan las limitaciones del análisis de citas para evaluar las publicaciones en ciencias humanas y sociales. Se definen las altmétricas como unas métricas que permiten analizar la presencia de las publicaciones científicas en las redes sociales y académicas y que pueden ser un buen complemento a las citas. Se describen las fuentes así como los principales indicadores utilizados por las altmétricas y las principales aplicaciones en el ámbito de las revistas científicas y en bases de datos académicas. Finalmente se realiza una valoración global de los puntos fuertes y débiles de las altmétricas así como del interés que tienen para los nuevos modelos de evaluación de publicaciones científicas que se están configurando.

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“…El informe Horizon 2014, Library edition (Johnson et al, 2014), que ya comentamos, habla de las altmétricas como "Desde todas las instancias se aboga por una convergencia de servicios, difícil pero cada vez más necesaria" una tendencia en dos o tres años. Lo que ya sabemos tanto por los estudios realizados como por la propia realidad, es que no ofrecen un dibujo completo del impacto académico, como tampoco lo hacen las métricas tradicionales (Bornmann, 2014;Konkiel, 2016; González-Fernández-Villavicencio, 2016).…”

Section: Modelo Y Funcionesunclassified

Unidades de bibliometría y bibliotecas universitarias: hacia la transparencia

González-Fernández-Villavicencio¹

2017

ThinKEPI

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Altmetrics: diversifying the understanding of influential scholarship

Cited by 36 publications

References 34 publications

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

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

Las altmétricas: aportaciones para la evaluación de publicaciones científicas en ciencias humanas y sociales

Unidades de bibliometría y bibliotecas universitarias: hacia la transparencia

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