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Using SciVal responsibly: a guide to interpretation and good practice
This guide is designed to help those who use SciVal to source and apply bibliometrics in academic institutions. It was originally devised in February 2018 by Dr Ian Rowlands of King’s College London as a guide for his university, which makes SciVal widely available to its staff. King’s does this bec...
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| Main Authors: | , , |
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| Format: | Default OER |
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2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/2134/11812044.v1 |
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| Summary: | This guide is designed to help those who use SciVal to source and apply bibliometrics in academic institutions. It was originally devised in February 2018 by Dr Ian Rowlands of King’s College London as a guide for his university, which makes SciVal widely available to its staff. King’s does this because it believes that bibliometric data are best used in context by specialists in the field. A small group of LISBibliometrics committee members reviewed and revised the King’s guide to make it more applicable to a wider audience. SciVal is a continually updated source and so feedback is always welcome at LISBibliometrics@jiscmail.ac.uk. LIS-Bibliometrics is keen that bibliometric data should be used carefully and responsibly and this requires an understanding of the strengths and limitations of the indicators that SciVal publishes. The purpose of this Guide is to help researchers and professional services staff to make the most meaningful use of SciVal. It includes some important `inside track’ insights and practical tips that may not be found elsewhere. The scope and coverage limitations of SciVal are fairly widely understood and serve as a reminder that these metrics are not appropriate in fields where scholarly communication takes place mainly outside of the journals and conference literature. This is one of the many judgment calls that need to be made when putting bibliometric data into their proper context. One of the most useful features of SciVal is the ability to drill down in detail using various filters. This allows a user to define a set of publications accurately, but that may mean generating top level measures that are based on small samples with considerable variance. Bibliometrics distributions are often highly skewed, where even apparently simple concepts like the `average’ can be problematic. So one objective of this Guide is to set out some advice on sample sizes and broad confidence intervals, to avoid over-interpreting the headline data. Bibliometric indicators should always be used in combination, not in isolation, because each can only offer partial insights. They should also be used in a `variable geometry’ along with other quantitative and qualitative indicators, including expert judgments and non-publication metrics, such as grants or awards, to flesh out the picture. |
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