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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. What is the data?.

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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. 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

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

  4. Different data have different characteristics • Example from 13,500 papers: • Highly tweeted stories focus on policy, gender, funding, ‘contentious science’ issues, mostly summaries on Nature News • Highly shared papers in Mendeley are hard core original research • Different platforms have discipline bias • Scholarly blogs both lead interest and respond • Data from Altmetric.com

  5. 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

  6. 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

  7. 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 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

  8. Buy-in, or bake-your-own • Buy-in: Altmetric.com and PLUM from Ebsco • Free-to-use: Impactstory.org, platforms that use Plos article-level-metrics code • Bake-your-own: Impactstory.org, Plos • Or a root-and-branch build

  9. Topics covered • Data sources • Providers • Different types of data, differences and similarities • Criticisms, weaknesses and strategies • Your next steps

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