Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Museum impact: linking-up specimens with research published on them


Published on

An informal talk for the monthly #SciFri series of talks at the Natural History Museum, London. 2015-09-18

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Museum impact: linking-up specimens with research published on them

  1. 1. Museum Impact Linking-up our specimens with research published on them Dr Ross Mounce @rmounce
  2. 2. Talk Structure ● Background: the collections, the research literature ● Interesting things you should know about access to research ○ The costs of knowledge $$$ ● Examples of content mining ○ Including a video demo! ● My work (in progress) on finding NHM specimens in recent literature
  3. 3. Source: © The Trustees of the Natural History Museum, London
  4. 4. ● New ● Open Data ● Easy-to-use ● Quick ● Images ● Audio ● Interactive Maps ● Citable ● API access ● Open Source Infrastructure It’s not KE Emu :)
  5. 5. What I want to do: link specimen records to their mentions in the literature “Micro-computed tomography scan slice through four bat skulls, displaying the relative position of the three semicircular canals within the skull. Scans are from the following species: (A) Pteropus rodricensis (BMNH.; …” NHM Data Portal Link (Stable, Unique Identifier) Article DOI (Stable, Unique Identifier)
  6. 6. 114,000,000 scholarly papers available online 36,000,000 of which are ‘Biology’ / ‘Environmental Studies’ / ‘Geosciences’ / ‘Multidisciplinary’ Khabsa, M. and Giles, C. L. 2014. The number of scholarly documents on the public web. PLoS ONE
  7. 7. Sadly, the vast majority of papers are only ‘available’ online to paying subscribers and no institution in the world has access to everything. Not even close to everything!
  8. 8. Cheryl Hall (2014) FOI request We rent access to knowledge. Companies profiteer from it 2004/05 £357,197.79 2005/06 £383,214.29 2006/07 £340,690.33 2007/08 £381,526.57 2008/09 £441,706.36 2009/10 £437,539.71 2010/11 £430,105.08 2011/12 £449,515.12 2012/13 £469,007.50 2013/14 £494,913.01 10-year-total: £4,185,415.76 Tax Year Revenue Profit Profit Margin 2004 £1363m £460m 33.75% 2005 £1436m £449m 31.25% 2006 £1521m £465m 30.57% 2007 £1507m £477m 31.65% 2008 £1700m £568m 33.41% 2009 £1985m £693m 34.91% 2010 £2026m £724m 35.74% 2011 £2058m £768m 37.30% 2012 £2063m £780m 37.81% 2013 £2126m £826m 38.85% Source: RELX Group (Parent company of Elsevier) Company Reports
  9. 9. Actually, the NHM’s annual bill isn’t bad compared to others Source: Lawson S and Meghreblian B. (2015) Journal subscription expenditure of UK higher education institutions. F1000Research
  10. 10. Content Mining provides more bang for your buck Making fuller use of our expensively provisioned access ● If the NHM is going to pay £500,000 per year to rent journals, why not use the access to this resource to its fullest? ● I can’t read everything with my human eyes but… computers can! ● If you can process one document with a computer, you can process a million: content mining
  11. 11. Recent examples of Content Mining Fig. 6 from the paper Brachiopod body-size estimates Red-line humans Grey bars machines (PaleoDeepDive) Better than PaleoDB ? I think so. PDD more clearly-linked to evidence than PDB Provenance matters.
  12. 12. Recent examples of Content Mining (Images) 3-second image analysis source: 10.1099/ijs.0.65212-0 (Zymobacter_palmae:261,((((Chromohalobacter_canadensis:42, (Chromohalobacter_sarecensis:96,Chromohalobacter_nigrandesensls:154):41):80, (Chromohalobacter_marismortui:125,Chromohalobacter_beijerinckii:103):164):61, (Chromohalobacter_israelensis:11,Chromohalobacter_salexigens:11):92):293, ((Halomonas_halodurans:328,(Halomonas_ventosae:100,(Halomonas_pacifica:116, (Halomonas_halophila:223,(Halomonas_eurihalina:27,Halomonas_elongate:58):236): 79):41):46):72,(Halomonas_desiderata:187,(Halomonas_pantelleriensis:173, Halomonas_muralis:190):70):30):110):187); outputs re-usable Newick & NeXML no manual input required Can replot data, re-analyse, combine many to make a supertree! PLUTo Project Mounce, Murray-Rust, Wills (in prep.)
  13. 13. How to get a sufficient volume of journal articles? ● The ContentMine (CM) team are actively developing new tools & training workshops to help researchers get into content mining: be it text, data, or image mining ● CM are a not-for-profit Shuttleworth-funded project led by Peter Murray-Rust ● All the software tools are open source and available on github: ● I’m a Scientific Advisor with the ContentMine ● Try getpapers OR quickscrape to get journal content en masse
  14. 14. ● No problem. PMC to the rescue! ● PMC has a full text Open Access-only subset which you can download easily for free ● >1,100,000 full texts in XML (compressed) is just 16.6GB Want to download more than a million (OA) papers? Source: Neil Saunders (2014)
  15. 15. Are there NHM specimens in the PMC OA subset? PMC is medically-focused, so one wouldn’t expect it to be rich in organismal biology, however …some relevant content ALL of PLOS ONE is in the PMC OA subset. Over 100,000 articles in that journal alone!
  16. 16. Version-controlled data on github open for scrutiny & collaboration
  17. 17. Searching ALL full texts is not enough!!! A significant number of specimens are probably ‘hiding-out’ in supplementary data files of all sorts of formats. Google Scholar does not index SI Web of Science doesn’t either Nor does Scopus At scale, journal-held supplementary data files are the ‘darkest corners’ of science“Specimens were deposited in the collections of the California Academy of Sciences' Department of Herpetology (CAS), the British Museum of Natural History (BMNH) and of author GJM (Table S1)” 10.1371/journal.pone.0104628
  18. 18. I don’t just find in-text mentions. I’m trying to match them up to our NHM Data Portal records too! Specimens in RED do not appear to be on the Data Portal ...yet Blue globe represents a PLOS ONE paper
  19. 19. Blue globe represents a PLOS ONE paper Very few specimens occur in more than one paper Can you guess what BMNH 37001 is? Hint: it’s famous! Grey represents an NHMUK specimen
  20. 20. Mining over 200 subscription access / non-PMC journals from 2000 <-> 2015 inclusive Nature + Science + PNAS + Phytotaxa + Zootaxa BioOne Journals (131) Springer Journals (32) Wiley Journals (22) Taylor & Francis Journals (14) Elsevier Journals (12) Oxford University Press Journals (8) SciELO Journals (7) [Open Access but not in PMC] Ecological Society of America Journals (6) Geological Society Journals (4) CSIRO Journals (4) Cambridge University Press Journals (3) Royal Society Journals (2) Journal-omics!
  21. 21. Thanks to a recent change in UK copyright law: text and data mining for non- commercial research purposes is legal (in the UK), (provided that you have legitimate access to the resource you want to mine e.g. a paid-for institutional subscription)
  22. 22. Image credit: Ubiquity Press
  23. 23. So far… (very much still in progress)
  24. 24. Almost nothing in Nature & Science ‘full (short) text’ Context: 15 years worth of full text research in Nature & Science examined. Science: only 11 NHM specimens found in ~39,600 texts. Nature: similar story. <30 specimens in 14,132 ‘full’ texts. Clearly there are more, but it’s all buried in supplementary materials :(
  25. 25. Shoving all the research details into non-searchable supplementary materials is bad for science ● For the avoidance of doubt, this is not a criticism of authors. This is squarely aimed at journals that artificially restrict the ‘length’ of research articles online. e.g. Prufer, K. et al. 2014. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 2014, 505, 43-49. 7-pages (in paper), 12-pages (in PDF, with extra data tables & figures) The supplementary data file? 249 pages!
  26. 26. Someone needs to build a searchable index of supplementary data. ASAP
  27. 27. Interactive plot:
  28. 28. “Micro-computed tomography scan slice through four bat skulls, displaying the relative position of the three semicircular canals within the skull. Scans are from the following species: (A) Pteropus rodricensis (BMNH.; …” NHM Data Portal Link (Stable, Unique Identifier) Article DOI (Stable, Unique Identifier) Huge potential to go beyond mere linking-up of identifiers. This specimen & others have been CT scanned in the PLOS ONE paper. We could do data, media and knowledge ‘repatriation’ back to the museum/portal.
  29. 29. Credit: Davies KTJ, Bates PJJ, Maryanto I, Cotton JA, Rossiter SJ (2013) The Evolution of Bat Vestibular Systems in the Face of Potential Antagonistic Selection Pressures for Flight and Echolocation. PLoS ONE 8 (4): e61998. doi:10.1371/journal.pone.0061998 Openly-licensed data on specimens, published elsewhere, could be re-incorporated back into the online museum catalogue. A one-stop shop for information. Beyond-linking: repatriation of knowledge This is a CT-scan of “BMNH”. Without mining, I wouldn’t know this data exists. Perhaps it could also be made available on the portal? 0275-4a82-9fa6-cf1c3949f408
  30. 30. Does published info make it back ‘home’ to the collections? BMNH 2013.2.13.3 on the portal as “Petrochromis nov.sp. Takahashi” I found it (by text mining) here: It’s now called: Petrochromis horii n. sp. , according to the paper. What mechanisms are there to update newer information back into the collection? Content mining could definitely help keep collections data up-to-date!
  31. 31. Acknowledgements ● Sincere thanks to: ○ The NHM Library staff, particularly Sarah Vincent for actively supporting my content mining ○ Nancy Chillingsworth (IPR, NHM London) ○ Mark Wilkinson (Life Sciences, NHM London) ○ Peter Murray-Rust & the ContentMine team ○ Vince Smith (Life Sciences, NHM London) ○ Ben Scott (NHM Data Portal Lead Architect) ○ Rod Page (University of Glasgow) ○ All of the Biodiversity Informatics team
  32. 32. Please ask me questions! Feedback appreciated :) @rmounce