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Altmetrics : a primer Where does the data come from? Can it be gamed? Buy in or build your own?

Altmetrics : a primer Where does the data come from? Can it be gamed? Buy in or build your own?. Mike Taylor Research Specialist http://orcid.org/0000-0002-8534-5985 mi.taylor@elsevier.com. Some words on terms.

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Altmetrics : a primer Where does the data come from? Can it be gamed? Buy in or build your own?

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  1. Altmetrics: a primerWhere does the data come from?Can it be gamed?Buy in or build your own? Mike Taylor Research Specialist http://orcid.org/0000-0002-8534-5985 mi.taylor@elsevier.com

  2. Some words on terms • Alternative metrics, altmetrics, article data, usage data, assessments, metrics, impact, understanding, attention, reach… • If this seems confusing… • Altmetrics is at the big bang stage – this universe has not yet cooled down and coalesced

  3. What is the data? • A set of altmetric data is about a common document and represents usage, recommendation, shares, re-usage • Identified by DOI, URL, shortened URL, other ID (egArxiv, Pubmed) • It does not show common intent: a tweet is not the same as a Mendeley share is not the same as a Data Dryad data download is not the same as mass media coverage or a blog

  4. Various providers… • Altmetric.com • Kudos • Plum Analytics • PLOS / PLOS code • Impactstory.org • Altmetrics is not Altmetric.com Each has strengths and weaknesses, no canonical source

  5. Elsevier uses Altmetric.com • Scopus donut • Sciencedirect • Scival and Pure • We’re looking to improve the ‘donut’ • “Altmetric.com score” can probably be more useful

  6. Bringing together sources… • Altmetrics isn’t one thing, so attempting to express it as one thing will fail. • We favour intelligent clusters of data: social activity, mass media, scholarly activity, scholarly comment, re-use • Elsevier believes that more research is needed, and that best indicators are scholarly activity and scholarly comment

  7. The more data we can see • The bigger the picture

  8. Gaming / cheating • If people take this data seriously, will they cheat? • Eg, Brazilian citation scandal, strategies used by people to increase IF of journals • Expertise in detecting fraudulent downloads (eg, SSRN), self-tweeting – when is ‘normal’ corrupt? • One thing to buy 1000 tweets, another to buy 10 blogs, or mass media coverage • Do those twitter accounts have scholarly followers? • Pattern analysis, usage analysis, network analysis • Public data = public analysis = public response

  9. Other criticisms • Biggest criticisms are when people try and conflate all the data into a single thing • Easy point of attack – tweets are all about “sex and drugs and rock ‘n’ roll papers”* • Using clusters is more intelligible to academic community – eg, re-use, scholarly activity, scholarly comment (blogs, reviews, discussions) • * this isn’t true anyway

  10. Making altmetrics work • Altmetrics has got where it is today on the basis of standards • Without ISSNs and DOIs, the world is a harder place x 1000 • Elsevier is supporting research to discover scholarly impact in areas that don’t use DOIs • (Other standards exist: PubMed IDs, Arxiv IDs)

  11. Expanding views of altmetrics • Increasingly, we’re seeing altmetrics being used to describe objects other than articles, but also institutions, books, journals, data and people • For institutions, Snowball Metrics has recently adopted the same formulation for grouping altmetricsas Elsevier (www.snowballmetrics.com)

  12. Making data count • More funders are insisting on open data • And the way to understand whether its being used … is data metrics – combining altmetrics and traditional (web-o-)metrics • Downloads, citations, shares, re-uses… • Downside: data repository is fragmented, 600+ repositories registered at databib.org • Upside:Datacite, ODIN, ORCID, DOI, RDA, Draft Declaration of Data Citation Principles

  13. Measuring the effect of research on society • Governments don’t operate like scholars • Rhetoric, argument, polemics • Personal reputation is important • Laws don’t contain citations • The relationship is fuzzy – less a chain of evidence, more a miasma of influence • Elsevier is sponsoring work to understand this relationship

  14. Elsevier and alternative metrics • Being deployed across our platforms • Engaging with thought leadership • Promoting open metrics • Acceptance research • Academic research • Sponsoring research in cutting-edge technology • LibraryConnect, www.researchtrends.com

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