Elsevier stopped Chris Hartgerink, a statistician, from downloading research papers in bulk from Sciencedirect for the purpose of content mining to detect potentially problematic research findings, despite having legal access through his university's subscription and only intending to extract facts without redistributing full papers; he had downloaded around 30GB of data over 10 days to mine psychology literature for test results, figures, tables and other information reported in papers. Hartgerink's research aims to investigate unreliable findings that can harm policy and research progress through an innovative content mining method.

1. NIH, Bethesda, US, 2016-11-15
High throughput mining of the
scholarly literature
Peter Murray-Rust1,2
[1]University of Cambridge
[2]TheContentMine
pm286 AT cam DOT ac DOT uk
Scientific knowledge is for everyone

2. Themes
• 500 Billion$ of funded STM research/year
• 85% of medical research is wasted (Lancet 2011)
• An Open mining toolset
• Wikidata as the semantic backbone
• Community involvement
• Sociopolitical issues
• My gratitude to NIH
• Offers of collaboration; data ingestion? Software?
Sources?

4. http://www.nytimes.com/2015/04/08/opinion/yes-we-were-warned-about-
ebola.html
We were stunned recently when we stumbled across an article by European
researchers in Annals of Virology [1982]: “The results seem to indicate that
Liberia has to be included in the Ebola virus endemic zone.” In the future,
the authors asserted, “medical personnel in Liberian health centers should be
aware of the possibility that they may come across active cases and thus be
prepared to avoid nosocomial epidemics,” referring to hospital-acquired
infection.
Adage in public health: “The road to inaction is paved with research
papers.”
Bernice Dahn (chief medical officer of Liberia’s Ministry of Health)
Vera Mussah (director of county health services)
Cameron Nutt (Ebola response adviser to Partners in Health)
A System Failure of Scholarly Publishing

5. CLOSED ACCESS
MEANS PEOPLE DIE

6. The Right to Read is the Right to Mine**PeterMurray-Rust, 2011
http://contentmine.org

7. (2x digital music industry!)

8. Scholarly publishing is “Big Data”
[2] https://en.wikipedia.org/wiki/Mont_Blanc#/media/File:Mont_Blanc_depuis_Valmorel.jpg
586,364 Crossref DOIs 201507 [1] per month
2.5 million (papers + supplemental data) /year [citation needed]*
each 3 mm thick
 4500 m high per year [2]
* Most is not Publicly readable
[1] http://www.crossref.org/01company/crossref_indicators.html
1 year’s scholarly output!

9. What is “Content”?
http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.01113
03&representation=PDF CC-BY
SECTIONS
MAPS
TABLES
CHEMISTRY
TEXT
MATH
contentmine.org tackles these

10. Demos of mining

11. http://chemicaltagger.ch.cam.ac.uk/
• Typical
Typical chemical synthesis

12. Automatic semantic markup of chemistry
Could be used for analytical, crystallization, etc.

13. AMI https://bitbucket.org/petermr/xhtml2stm/wiki/Home
Example reaction scheme, taken from MDPI Metabolites 2012, 2, 100-133; page 8, CC-BY:
AMI reads the complete diagram,
recognizes the paths and
generates the molecules. Then
she creates a stop-fram animation
showing how the 12 reactions
lead into each other
CLICK HERE FOR ANIMATION
https://bytebucket.org/petermr/xhtml2stm/wiki/animation.s
vg?rev=793a4d9ffa0616a84ff4aeabf80e657b5142ed33
(may be browser dependent)
Andy Howlett, Cambridge

14. ChemDataExtractor
• http://chemdataextractor.org/docs/intro
• http://chemdataextractor.org/demo
Swain, M. C., & Cole, J. M. "ChemDataExtractor: A Toolkit for Automated Extraction of
Chemical Information from the Scientific Literature", J. Chem. Inf. Model. 2016, 56 (10),
pp 1894–1904 http://pubs.acs.org/doi/abs/10.1021/acs.jcim.6b00207

15. Europe PubMedCentral

16. 2015
2016

17. Dictionaries!

18. catalogue
getpapers
query
Daily
Crawl
EuPMC, arXiv
CORE , HAL,
(UNIV repos)
Crossref PDF HTML
DOC ePUB
TeX XML
PNG
EPS CSV
XLSURLs
DOIs
crawl
quickscrape
norma
Normalizer
Structurer
Semantic
Tagger
Text
Data
Figures
ami
UNIV
Repos
search
Lookup
CONTENT
MINING
Chem
Phylo
Trials
Crystal
Plants
COMMUNITY
plugins
Visualization
and Analysis
PloSONE, BMC,
peerJ… Nature, IEEE,
Elsevier…
Publisher Sites
scrapers
queries
taggers
abstract
methods
references
Captioned
Figures
Fig. 1
HTML tables
100, 000 pages/day
Semantic ScholarlyHTML
(W3C community group)
Facts
Latest 20150908

19. abstract
methods
references
Captioned
Figures
Fig. 1
HTML tables
abstract
methods
references
Captioned
Figures
Fig. 1
HTML tables
Dict A
Dict B
Image
Caption
Table
Caption
MINING
with sections
and dictionaries
[W3C Annotation / https://hypothes.is/ ]

20. Disease Dictionary (ICD-10)
<dictionary title="disease">
<entry term="1p36 deletion syndrome"/>
<entry term="1q21.1 deletion syndrome"/>
<entry term="1q21.1 duplication syndrome"/>
<entry term="3-methylglutaconic aciduria"/>
<entry term="3mc syndrome”
<entry term="corpus luteum cyst”/>
<entry term="cortical blindness" />
SELECT DISTINCT ?thingLabel WHERE {
?thing wdt:P494 ?wd .
?thing wdt:P279 wd:Q12136 .
SERVICE wikibase:label {
bd:serviceParam wikibase:language "en" }
}
wdt:P494 = ICD-10 (P494) identifier
wd:Q12136 = disease (Q12136) abnormal condition that
affects the body of an organism
Wikidata ontology for disease

21. Example statistics dictionary
<dictionary title="statistics2">
<entry term="ANCOVA" name="ANCOVA"/>
<entry term="ANOVA" name="ANOVA"/>
<entry term="CFA" name="CFA"/>
<entry term="EFA" name="EFA"/>
<entry term="Likert" name="Likert"/>
<entry term="Mann-Whitney" name="Mann-Whitney"/>
<entry term="MANOVA" name="MANOVA"/>
<entry term="McNemar" name="McNemar"/>
<entry term="PCA" name="PCA"/>
<entry term="Pearson" name="Pearson"/>
<entry term="Spearman" name="Spearman"/>
<entry term="t-test" name="t-test"/>
<entry term="Wilcoxon" name="Wilcoxon"/>
</dictionary>
“Mann-Whitney” link to Wikipedia entry and Wikidata (Q1424533) entry

22. Annotation (entity in context)
prefix
surface
label
location
suffix
Lars Willighagen (NL) and Tom Arrow. visualisation of single facts and groups from
Corpus. https://tarrow.github.io/factvis/#cmid=CM.wikidatacountry136
Machine version

23. Wikidata demo
• Find all architecturally significant buildings in
Cambridge UK
• https://tools.wmflabs.org/wikishootme/#lat=52.204082366142&lng=0.11190176010131837&zoom=16&l
ayers=wikidata_image,wikidata_no_image&sparql_filter=%3Fq%20wdt%3AP1435%20wd%3AQ15700834
credit: Magnus Manske https://en.wikipedia.org/wiki/Magnus_Manske
Story: Magnus used FOI to get metadata for tens of thousands of “listed
buildings” [1] from English Heritage and put all data into Wikidata
[1] https://www.wikidata.org/wiki/Q570600

27. Is chemistry in Wikidata?

28. • https://pubchem.ncbi.nlm.nih.gov/

29. PubChem (P662) is a Wikidata “Property”
143347 PubChem items
Wikidata knows about PubChem
PubChem Item (Q27140241)
label O-acetylcarnitine

30. Wikidata

31. Identifiers
https://chemapps.stolaf.edu/jmol/jmol.php?&model=InChI=
1S/C9H13NO3/c1-10-5-9(13)6-2-3-7(11)8(12)4-6/h2-4,9-
13H,5H2,1H3/t9-/m0/s1

32. Search for “Zika” in EuropePMC and
Wikidata
• https://github.com/ContentMine/amidemos/blob/master/WIKIDATA.md#content
mine-demos (list of demos)
• https://rawgit.com/ContentMine/amidemos/master/zika/full.dataTables.html
• (datatables extracted - disease, gene, species, etc.)
• Lars Willighagen (NL) and Tom Arrow. visualisation of single facts and groups from
Corpus. https://tarrow.github.io/factvis/#cmid=CM.wikidatacountry136
• https://contentmine-demo.herokuapp.com/cooccurrences Coocurrence of
diseases - suggest select 25 and disease.

33. https://rawgit.com/ContentMine.amidemos/master/zika/full.dataTables.html
Search on publicly accessible papers on “Zika”

36. <dictionary title="tropicalVirus">
<entry term="ZIKV" name="Zika virus"/>
<entry term="Zika" name="Zika virus"/>
<entry term="DENV" name="Dengue virus"/>
<entry term="Dengue" name="Dengue virus"/>
<entry term="CHIKV" name="Chikungunya virus"/>
<entry term="Chikungunya" name="Chikungunya virus"/>
<entry term="WNV" name="West Nile virus"/>
<entry term="West Nile" name="West Nile virus"/>
<entry term="YFV" name="Yellow fever virus"/>
<entry term="Yellow fever" name="Yellow fever virus"/>
<entry term="HPV" name="Human papilloma virus"/>
<entry term="Human papilloma virus"
name="Human papilloma virus"/>
</dictionary>
Terms co-ocurring with “Zika”

37. Diagram mining
• TL; DR We can get high-precision scientific
data out of diagrams

38. PMR is collaborating with the European Bioinformatics
Institute to liberate metabolic information from journals

41. Chemical Computer Vision
Raw Mobile photo; problems:
Shadows, contrast, noise, skew, clipping

42. Binarization (pixels = 0,1)
Irregular edges

43. Ln Bacterial load per fly
11.5
11.0
10.5
10.0
9.5
9.0
6.5
6.0
Days post—infection
0 1 2 3 4 5
Bitmap Image and Tesseract OCR

44. http://www.slideshare.net/rossmounce/the-pluto-project-ievobio-2014

45. Ross Mounce (Bath), Panton Fellow
• Sharing research data:
http://www.slideshare.net/rossmounce
• How-to figures from PLOS/One [link]:
Ross shows how to bring figures to life:
• PLOSOne at http://bit.ly/PLOStrees
• PLOS at http://bit.ly/phylofigs (demo)

46. 4300 images

47. Note Jaggy and
broken pixels
NEW Bacteria must have a phylogenetic tree
Length
_________Weight
Binomial Name Culture/Strain GENBANK ID
Evolution
Rate

48. OCR (Tesseract)
Norma (imageanalysis)
(((((Pyramidobacter_piscolens:195,Jonquetella_anthropi:135):86,Synergistes_jonesii:301):131,Thermotoga
_maritime:357):12,(Mycobacterium_tuberculosis:223,Bifidobacterium_longum:333):158):10,((Optiutus_te
rrae:441,(((Borrelia_burgdorferi:…202):91):22):32,(Proprinogenum_modestus:124,Fusobacterium_nucleat
um:167):217):11):9);
Semantic re-usable/computable output (ca 4 secs/image)

50. Bacillus subtilis [131238]*
Bacteroides fragilis [221817]
Brevibacillus brevis
Cyclobacterium marinum
Escherichia coli [25419]
Filobacillus milosensis
Flectobacillus major [15809775]
Flexibacter flexilis [15809789]
Formosa algae
Gelidibacter algens [16982233]
Halobacillus halophilus
Lentibacillus salicampi [18345921]
Octadecabacter arcticus
Psychroflexus torquis [16988834]
Pseudomonas aeruginosa [31856]
Sagittula stellata [16992371]
Salegentibacter salegens
Sphingobacterium spiritivorum
Terrabacter tumescens
• [Identifier in Wikidata]
• Missing = not found with Wikidata API
20 commonest organisms (in > 30 papers) in trees from IJSEM*
Half do not appear to be in Wikidata
Can the Wikipedia Scientists comment?
*Int. J. Syst. Evol. Microbiol.

51. Supertree for 924 species
Tree

52. Supertree created from 4300 papers

53. But we can now
turn PDFs into
Science
We can’t turn a hamburger into a cow
Pixel => Path => Shape => Char => Word => Para => Document => SCIENCE

54. UNITS
TICKS
QUANTITY
SCALE
TITLES
DATA!!
2000+ points

55. Dumb PDF
CSV
Semantic
Spectrum
2nd Derivative
Smoothing
Gaussian Filter
Automatic
extraction

58. C) What’s the problem with this spectrum?
Org. Lett., 2011, 13 (15), pp 4084–4087
Original thanks to ChemBark

59. After AMI2 processing…..
… AMI2 has detected a square

62. http://www.lisboncouncil.net/publication/publication/134-text-and-data-mining-for-research-and-innovation-.html
Asian and U.S. scholars continue to show a huge interest in text and data mining
as measured by academic research on the topic. And Europe’s position is falling
relative to the rest of the world.
Legal clarity also matters. Some countries apply the “fair-use” doctrine, which
allows “exceptions” to existing copyright law, including for text and data mining.
Israel, the Republic of Korea, Singapore, Taiwan and the U.S. are in this group.
Others have created a new copyright “exception” for text and data mining – Japan,
for instance, which adopted a blanket text-and-data-mining exception in 2009, and
more recently the United Kingdom, where text and data mining was declared fully
legal for non-commercial research purposes in 2014. Some researchers worry that
the UK exception does not go far enough; others report that British researchers are
now at an advantage over their continental counterparts.
the Middle East is now the world’s fourth largest region for research on text and
data mining, led by Iran and Turkey.

63. @Senficon (Julia Reda) :Text & Data mining in times of
#copyright maximalism:
"Elsevier stopped me doing my research"
http://onsnetwork.org/chartgerink/2015/11/16/elsevi
er-stopped-me-doing-my-research/ … #opencon #TDM
Elsevier stopped me doing my research
Chris Hartgerink

64. I am a statistician interested in detecting potentially problematic research such as data fabrication,
which results in unreliable findings and can harm policy-making, confound funding decisions, and
hampers research progress.
To this end, I am content mining results reported in the psychology literature. Content mining the
literature is a valuable avenue of investigating research questions with innovative methods. For
example, our research group has written an automated program to mine research papers for errors in
the reported results and found that 1/8 papers (of 30,000) contains at least one result that could
directly influence the substantive conclusion [1].
In new research, I am trying to extract test results, figures, tables, and other information reported in
papers throughout the majority of the psychology literature. As such, I need the research papers
published in psychology that I can mine for these data. To this end, I started ‘bulk’ downloading research
papers from, for instance, Sciencedirect. I was doing this for scholarly purposes and took into account
potential server load by limiting the amount of papers I downloaded per minute to 9. I had no intention
to redistribute the downloaded materials, had legal access to them because my university pays a
subscription, and I only wanted to extract facts from these papers.
Full disclosure, I downloaded approximately 30GB of data from Sciencedirect in approximately 10 days.
This boils down to a server load of 0.0021GB/[min], 0.125GB/h, 3GB/day.
Approximately two weeks after I started downloading psychology research papers, Elsevier notified my
university that this was a violation of the access contract, that this could be considered stealing of
content, and that they wanted it to stop. My librarian explicitly instructed me to stop downloading
(which I did immediately), otherwise Elsevier would cut all access to Sciencedirect for my university.
I am now not able to mine a substantial part of the literature, and because of this Elsevier is directly
hampering me in my research.
[1] Nuijten, M. B., Hartgerink, C. H. J., van Assen, M. A. L. M., Epskamp, S., & Wicherts, J. M. (2015). The
prevalence of statistical reporting errors in psychology (1985–2013). Behavior Research Methods, 1–22.
doi: 10.3758/s13428-015-0664-2
Chris Hartgerink’s blog post

65. WILEY … “new security feature… to prevent systematic download of content
“[limit of] 100 papers per day”
“essential security feature … to protect both parties (sic)”
CAPTCHA
User has to type words

66. http://onsnetwork.org/chartgerink/2016/02/23/wiley-also-stopped-my-doing-my-research/
Wiley also stopped me (Chris Hartgerink) doing my research
In November, I wrote about how Elsevier wanted me to stop downloading scientific articles for my research. Today, Wiley
also ordered me to stop downloading.
As a quick recapitulation: I am a statistician doing research into detecting
potentially problematic research such as data fabrication and
estimating how often it occurs. For this, I need to download many scientific articles, because my research
applies content mining methods that extract facts from them (e.g., test statistics). These facts serve as my data to answer my research
questions. If I cannot download these research articles, I cannot collect the data I need to do my research.
I was downloading psychology research articles from the Wiley library, with a maximum of 5 per minute. I did this using the tool quickscrape,
developed by the ContentMine organization. With this, I have downloaded approximately 18,680 research articles from the Wiley library,
which I was downloading solely for research purposes.
Wiley noticed my downloading and notified my university library that they detected a compromised proxy, which they
had immediately restricted. They called it “illegally downloading copyrighted content
licensed by your institution”. However, at no point was there any investigation into whether my user credentials were
actually compromised (they were not). Whether I had legitimate reasons to download these articles was never discussed.
The original email from Wiley is available here.
As a result of Wiley denying me to download these research articles, I cannot collect data from
another one of the big publishers, alongside Elsevier. Wiley is more strict than Elsevier by immediately condemning the
downloading as illegal, whereas Elsevier offers an (inadequate) API with additional terms of use (while legitimate access
has already been obtained). I am really confused about what the publisher’s stance on content mining is, because Sage
and Springer seemingly allow it; I have downloaded 150,210 research articles from Springer
and 12,971 from Sage and they never complained about it.

67. Julia Reda, Pirate MEP, running ContentMine
software to liberate science 2016-04-16

68. WikiFactMine
• https://meta.wikimedia.org/wiki/Grants:Project/ContentMine/WikiFactMine

69. anyone can review the grant

70. comments help to refine proposal

71. (2x digital music industry!)

72. Themes
• 500 Billion$ of funded STM research/year
• 85% of medical research is wasted (Lancet 2011)
• An Open mining toolset
• Wikidata as the semantic backbone
• Community involvement
• Sociopolitical issues
• My gratitude to NIH
• Offers of collaboration; data ingestion? Software?
Sources?

73. Additional material

74. Typical medical paper
From http://journals.plos.org/plosmedicine/article/authors?id=10.1371/journal.pmed.1002150
(licence CC-BY)

75. Typical scholarly text
http://journals.plos.org/plosmedicine/article/authors?id=10.1371/journal.
pmed.1002150 (licence CC-BY)

76. Table in a scientific paper
http://dx.doi.org/10.1371/journal.pmed.1002150.t001

77. Typical scientific diagram
(bitmap, so not machine-understandable)