1. Altmetrics - How do I rate thee? Let me count the tweets! Mike Taylor Research Specialist http://orcid.org/0000-0002-8534-5985

2. Mike Taylor of Elsevier Labs discusses altmetrics and how it plays with open access, social impact, Orcid and why it's got a long journey to become truly significant

3. Elsevier Labs “Researchers” and “developers”, ie, experimental architects Researchers’ specialities include text-mining, NLP, semantics, ontologies, etc

4. Elsevier Labs (and me) Research Specialist My projects are: altmetrics, contributorship, networks, identity / Orcid, author profiles Not an academic (although…) Not entirely commercially focused

5. In relation to altmetrics: Work with and support academic researchers Support movement towards integrated model of reference / citation / mentions inc bibliometrics Support and encourage innovation in this area in Elsevier Publish data and findings Develop position as thought leader

6. Previous credits include: ORCID Collaborate on technical architecture Previously working on similar project with EU university End of year 1 – great success www.orcid.org/statistics www.orcidlive.org

7. Why bother with Orcid? Disambiguation is a growing problem Importance of the personal Permanent labels are good things: DOIs, ISSNs, ISBNs, Orcids 430,000 minted 102,000 contain ‘works’

8. The problem Disambiguation technologies are reliant on good meta-data, and are focused towards western/northern names Poor meta-data / Asian names are difficult Eg, 3 Korean family names cover >50% population c/w US, several thousand, “Spangler” being the tipping point

9. Elsevier infrastructure #1 to integrate with Scopus > Orcid (free to use, no need to have Scopus account) Scopus display uses Orcid api Editorial submission system Integration into metadata hub Searchable field on Sciencedirect

10. Infrastructure / cultural issues Brazil, Korea, Denmark = excellent metadata China, Italy, India = dreadful metadata

11. Infrastructure / cultural issues Brazil, Korea, Denmark = excellent metadata China, Italy, India = dreadful metadata Chinese attitude towards relative reward Research tools are primarily English language Poverty of infrastructure… …which impacts on altmetrics

12. Altmetrics The collection of social network data Term coined in 2009 by Jason Priem on Twitter http://altmetrics.org/manifesto/ Ambitions: filtering, hidden impact, replacement for peer review

13. Altmetrics: a potentially brilliant development with a terrible name “Alt” to what – not really “metrics” either The data in altmetrics is … from whatever is available. Calling it “alt” potentially alienates “metrics” people

14. Pragmatic and technocratic Eg, Mendeley is included, Zotero isn’t, Colwiz isn’t. Big old pile of data: Twitter, Github, Dryad, Facebook, blogs, usage data (sometimes), re-use data (sometimes) Is mostly reliant on DOIs (caveats apply) Collect what you can, how you can (not the best basis for clear activity)

15. Research! Papers! Start-ups! Very exciting: Altmetric.com, Plum Analytic, Grow Kudos, impactstory.org Lots of papers published (though not research heavy, this is starting to happen) Several “special issues” Couple of PhDs in progress I’ve heard a book might get published NISO

16. Research findings Seems to be a correlation between Mendeley adds and citation rates There are definitely patterns of things that happen together (“impact flavors” – Piwowar, Priem et al) There are definitely differences between disciplines No OA advantage obvious (yet?) N is too small

17. Altmetric.com Owned by Digital Science / Macmillan The “donut” Quick demo of Altmetric.com Appears on all Scopus articles (with Altmetric.com data) since June 2012 Trialing on Sciencedirect Lots of criticism

18. Altmetrics data is variable Which is why I favour small, low-judgment buckets of data classes (what does it take to…): Social activity Component re-use Scholarly commentary Scholarly activity Mass media

19. Nuclear error editorial, Nature

20. Supervolcano, Nature Earth

21. Right handed vs left handed tail wagging (Current Biology)

22. Altmetrics – 7 use cases 1.Prediction of ultimate citation, identifying potentially impactful authors 2.Measuring / recognizing component re-use / preparatory work, reproducibility 3.Hidden impact (impact without citation) 4.Real-time filtering, real-time evaluation 5.Platform / publisher / institution comparison 6.Measuring social reach, estimating social impact 7.Altmetrics is of interest by itself

23. Data classes vs use cases Social activity Scholarly activity Scholarly comment Mass-mediaRe-use Predictionxx Re-usex Hidden impact xxxxx Real-time filtering xx Comparisonxxx Social reachxx Guesswork that needs verification and data!

24. Research project Are the classes internally viable? Do they survive disruption by uptake, new contributors, how do we normalize? Are the classes distinct and discrete? How (and when and why) do they interact? (Questions I hope to address in articles over next few years and in PhD)

25. Wider role – in community Support open standards Support researchers with data (etc) Form links between bibliometricians and altmetricians Be a generator of ideas Support special editions, workshops etc

26. Wider role – in Elsevier Encourage support for my community work Champion open standards for metrics (everyone is doing this) Support product development and outreach For example:

27. Things that we can do Real-time suggestions (allied to much improved search) Hidden research Social impact statements for researchers Re-use indicators support open data ‘Evaluation / impact’ network with Fundref, Orcid, DOIs, etc