Show me the data!
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[Update: I’ve submitted this idea as a FORCE11 £1K Challenge research proposal 2015-01-13. I may be unemployed from April 2015 onwards (unsolicited job offers welcome!), so I certainly might find myself with plenty of time on my hands to properly get this done…!]

Inspired by something I heard Stephen Curry say recently, and with a little bit of help from Jo McIntyre I’ve started a project to compare EuropePMC author manuscripts with their publisher-made (mangled?) ‘version of record’ twins.

How different are author manuscripts from the publisher version of record? Or put it another way, what value do publishers add to each manuscript? With the aggregation & linkage provided by EuropePMC – an excellent service – we can rigorously test this.

 

In this blog post I’ll go through one paper I chose at random from EuropePMC:

Sinha, N., Manohar, S., and Husain, M. 2013. Impulsivity and apathy in parkinson’s disease. J Neuropsychol 7:255-283.  doi: 10.1111/jnp.12013 (publisher version) PMCID: PMC3836240 (EuropePMC version)

Method

A quick & dirty analysis with a simple tool that’s easy to use & available to everyone:

pdftotext -layout     (you’re welcome to suggest a better method by the way, I like hacking PDFs)

(P) = Publisher-version , (A) = Author-version

Manual Post-processing – remove the header and footer crud from each e.g. “262
Nihal Sinha et al.” (P) and “J Neuropsychol. Author manuscript; available in PMC 2013 November 21.” (A)

Automatic Post-processing – I’m not interested in numbers or punctuation or words of 3-letters or less so I applied this bash-one-liner:

strings $inputfile | tr ‘[A-Z]’ ‘[a-z]’ | sed ‘s/[[:punct:]]/ /g’ | sed ‘s/[[:digit:]]/ /g’ |  sed s/’ ‘/\\n/g | awk ‘length > 3’ | sort | uniq -c | sort -nr > $outputfile

Then I just manually diff’d the resulting word lists – there’s so little difference it’s easy for this particular pair.

 

Results

The correspondence line changed slightly from this in the author version:

Correspondence should be addressed to Nuffield Department of Clinical Neurosciences and Department Experimental Psychology, Oxford University, Oxford OX3 9DU, UK (masud.husain@ndcn.ox.ac.uk). . (A)

To this in the publisher version (I’ve added bold-face to highlight the changes):

Correspondence should be addressed to Masud Husain, Nuffield Department of Clinical Neurosciences and Department Experimental Psychology, Oxford University, Oxford OX3 9DU, UK (e-mail: masud.husain@ndcn.ox.ac.uk). (P)

 

Reference styling has been changed. Why I don’t know, seems a completely pointless change. Either style seems perfectly functional to me tbh:

Drijgers RL, Dujardin K, Reijnders JSAM, Defebvre L, Leentjens AFG. Validation of diagnostic criteria for apathy in Parkinson’s disease. Parkinsonism & Related Disorders. 2010; 16:656–660. doi:10.1016/j.parkreldis.2010.08.015. [PubMed: 20864380] (A)

to this in the publisher version:

Drijgers, R. L., Dujardin, K., Reijnders, J. S. A. M., Defebvre, L., & Leentjens, A. F. G. (2010). Validation of diagnostic criteria for apathy in Parkinson’s disease. Parkinsonism & Related Disorders, 16, 656–660. doi:10.1016/j.parkreldis.2010.08.015 (P)

In the publisher-version only (P) “Continued” has been added below some tables to acknowledge that they overflow on the next page. Arguably the publisher has made the tables worse as they’ve put them sideways (landscape) so they now overflow onto other pages. In the author-version (A) they are portrait-orientated and so hence each fit on one page entirely.

 

Finally, and most intriguingly, some of the figure-text comes out only in the publisher-version (P). In the author-version (A) the figure text is entirely image pixels, not copyable text. Yet the publisher version has introduced some clearly imperfect figure text. Look closely and you’ll see in some places e.g. “Dyskinetic state” of figure 2 c) in (P), the ‘ti’ has been ligatured and is copied out as a theta symbol:

DyskineƟc state

 

Discussion

 

I don’t know about you, but for this particular article, it doesn’t seem like the publisher has really done all that much aside from add their own header & footer material, some copyright stamps & their journal logo – oh, and ‘organizing peer-review’. How much do we pay academic publishers for these services? Billions? Is it worth it?

I plan to sample at least 100 ‘twinned’ manuscript-copies and see what the average difference is between author-manuscripts and publisher-versions. If the above is typical of most then this will be really bad news for the legacy academic journal publishers… Watch this space!

 

Thoughts or comments as to how to improve the method, or relevant papers to read on this subject are welcome. Collaboration welcome too – this is an activity that scales well between collaborators.

I’m proud to announce an interesting public output from my BBSRC-funded postdoc project:
PLUTo: Phyloinformatic Literature Unlocking Tools. Software for making published phyloinformatic data discoverable, open, and reusable

MOAR PHYLOGENY!

Screenshot of some of the PLOS ONE phylogeny figure collection on Flickr

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I’ve made openly available my first-pass filter of PLOS ONE phylogeny figures (I’m not in any way claiming this is *all* of them).

This curated & tagged image collection is on Flickr for easy browsing: http://bit.ly/PLOStrees

As well as on Github for version control, open archiving, and collaboration (I have remote collaborators):

https://github.com/rossmounce/P1-phylo-part1

https://github.com/rossmounce/P1-phylo-part2

https://github.com/rossmounce/P1-phylo-part3

https://github.com/rossmounce/P1-phylo-part4

(Github doesn’t like repositories over 1GB so I’ve had to split-up the content between 4 separate repositories)

 

Why?

The aim of the PLUTo project is to re-extract & liberate phylogenetic data & associated metadata from the research literature. Sadly, only ~4% of modern published phylogenetic analysis studies make their underlying data available. Another study finds that if you ask the authors for this data, only 16% will be kind enough to reply with the requested data!

This particular data type is a cornerstone of modern evolutionary biology. You’ll find phylogenetic analyses across a whole host of journal subjects – medical, ecological, natural history, palaeontology… There are also many different ways in which this data can be re-used e.g. supertrees  & comparative cladistics. Not to mention, simple validation studies &/or analyses which extend-upon or map new data on to a phylogeny. It’s really useful data and we should be archiving it for future re-use and re-analysis. To my great delight, this is what I’m being paid to attempt to do for my first postdoc; on a grant I co-wrote – finding & liberating phylogenetic data for everyone!

 

Why PLOS ONE?

 

  •  It’s a BOAI-compliant open access journal that publishes most articles under CC BY, with a few under CC0.
    • This means I can openly re-publish figures online (provided sufficient attribution is given) — no need to worry about DMCA takedown notices or ‘getting sued’! This makes the process of research much easier. Private, non-public, access-restricted repositories for collaboration are a hassle I’d rather do without.
  • It’s a high-volume ‘megajournal’ publishing ~200 articles per day, many of which include phylogenetic analyses.
    • Thus its worthwhile establishing a regular daily or weekly method for parsing-out phylogenetic tree figures from this journal
  • Killer feature: as far as I know, PLOS are the only publisher to embed rich metadata inside their figure image files.
    • This makes satisfying the CC BY licence trivially easy — sufficient attribution metadata is already embedded in the file. Just ensure that wherever you’re uploading the file to doesn’t wipe this embedded data, hence why I chose Flickr as my initial upload platform.

 

What does this enable or make easier?

 

On it’s own, this collection doesn’t do much, this is still an early stage – but it gives us an important insight into the prevalence of certain types of visual display-style that researchers are using:

‘radial’ phylogenies

https://www.flickr.com/search?user_id=123621741%40N08&sort=relevance&text=radial

Source: Zerillo et al 2013 PLOS ONE. Carbohydrate-Active Enzymes in Pythium and Their Role in Plant Cell Wall and Storage Polysaccharide Degradation

Source: Zerillo et al 2013 PLOS ONE. Carbohydrate-Active Enzymes in Pythium and Their Role in Plant Cell Wall and Storage Polysaccharide Degradation

 

 

 

 

 

 

 

 

 

 

 

 

 

‘geophylogeny’ (phylogeny displayed relative to a map of some sort, 2D or 3D)

https://www.flickr.com/search?user_id=123621741%40N08&sort=relevance&text=geophylogeny

Source: Guo et al 2012 PLOS ONE. Evolution and Biogeography of the Slipper Orchids: Eocene Vicariance of the Conduplicate Genera in the Old and New World Tropics

Source: Guo et al 2012 PLOS ONE. Evolution and Biogeography of the Slipper Orchids: Eocene Vicariance of the Conduplicate Genera in the Old and New World Tropics

 

 

 

 

 

 

 

 

 

 

‘timescaled’ (phylogenies where the branch lengths are proportional to units of time or geological periods)
https://www.flickr.com/search?user_id=123621741%40N08&sort=relevance&text=timescaled

Source: Pol et al 2014 PLOS ONE. A New Notosuchian from the Late Cretaceous of Brazil and the Phylogeny of Advanced Notosuchians

Source: Pol et al 2014 PLOS ONE. A New Notosuchian from the Late Cretaceous of Brazil and the Phylogeny of Advanced Notosuchians

 

 

 

 

 

 

 

 

 

‘splitstrees’

https://www.flickr.com/search?user_id=123621741%40N08&sort=relevance&text=splitstree

Source: McDowell et al 2013 PLOS ONE. The Opportunistic Pathogen Propionibacterium acnes: Insights into Typing, Human Disease, Clonal Diversification and CAMP Factor Evolution

Source: McDowell et al 2013 PLOS ONE. The Opportunistic Pathogen Propionibacterium acnes: Insights into Typing, Human Disease, Clonal Diversification and CAMP Factor Evolution

 

 

 

 

 

 

 

 

 

 

 

Arguably it also facilitates complex searches for specific types of phylogeny

e.g. analyses using cytochrome b
https://www.flickr.com/search/?w=123621741@N08&q=%22cyt%20b%22%20OR%20%22cytochrome%20b%22
(you could use PLOS’s API to do this, particularly their figure/table caption search field — but you’d get a lot of false positives — this is an expert-curated collection that has filtered-out non-phylo figures)

In my initial roadmap, the plan is to do PLOS ONE, the other PLOS journals, then BMC journals, then possibly Zootaxa & Phytotaxa (Magnolia Press). There will be a Github-based website for the project soon, lots still to do…!

 

Want to know more / collaborate / critique ?

Conferences:

I’ve got an accepted lightning talk at iEvoBio in Raleigh, NC later this year about the PLUTo project.

As well as an accepted lightning talk at the Bioinformatics Open Source Conference (BOSC) in Boston, MA.

Elsewise, contact me via twitter @rmounce , the comment section on this blog post, or email ross dot mounce <at> gmail dot com

Setting-up AMI2 on Windows

October 6th, 2013 | Posted by rmounce in Content Mining - (1 Comments)

I’ve been rather preoccupied in the last few months hence the lack of blog posts. (Apologies!)

Here’s a quick recap of some things I’ve done since July:

  • Got married in China (in September)
  • Successfully proposed that the Systematics Association (of which I’m a council member) should sign DORA
  • Gave an invited talk on open science at an INNGE workshop at INTECOL 2013
  • Completed and handed-in my PhD thesis last Thursday!

So yeah, I really didn’t have time blog until now.

But now my PhD thesis is handed-in I can concentrate on the next step… Matthew Wills, myself, and Peter Murray-Rust have an approved BBSRC grant to work on further developing AMI2 to extract phylogenetic trees from the literature (born-digital PDFs).

At the moment it is in alpha stage so it doesn’t extract trees perfectly – it needs work. But in case you might want to try it out I thought I’d use this post to explain how to get a test development of it running on Windows (I don’t usually use Windows myself, I much prefer linux). These notes are thus as much an open notebook science ‘aide memoire’ for myself as they are instructions for others!

Dependencies and IDE:

1.) You’ll need Java JDK, Eclipse, Mercurial and Maven for starters.

If you haven’t got this setup already you may need to set your environment variables e.g. JAVA_HOME

2.) Within Eclipse you need to install the m2e (maven integration) plugin

(from within the Eclipse GUI) click ‘Help’ -> Install New Software -> All available sites (from the dropdown) -> select m2e

 

3.) Using mercurial, clone the AMI2 suite to a clean workspace folder. The suite includes:

 

[euclid-dev itself has many dependencies which are indicated in its POM file which you shouldn’t need to worry about – they should be pulled-in automatically. These include:  commons-io, log4j, xom, joda and junit.]

4.) From within the Eclipse GUI import your workspace of AMI2 tools:

click ‘File’ -> Import -> Maven -> select ‘Existing Maven Projects’ -> Next -> select your workspace

 

5.) Test if it works. In the package explorer side-pane window you should now see folders corresponding to the six AMI2 tools listed above.

Right-click on svg2xml-dev -> select ‘Run-as’ -> JUnit Test

and sit back and watch the test run in the console at the bottom of the Eclipse GUI.

(The tests are a little slow, have patience, it may take a few minutes – it took me 175 seconds)

To view the results, in the package explorer pane, navigate inside the svg2xml-dev document tree into /target/output/multiple-1471-2148-11-312 and click ont TEXT.0 to see what the text-extraction looks like. You should see something like this below (note it successfully gets italics, bold, and superscripts)

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Gene conversion and purifying selection shape nucleotide variation in gibbon L/M opsin genes

Tomohide Hiwatashi 1 , Akichika Mikami 2,8 , Takafumi Katsumura 1 , Bambang Suryobroto 3 , Dyah Perwitasari-Farajallah 3,4 , Suchinda Malaivijitnond 5 , Boripat Siriaroonrat 6 , Hiroki Oota 1,9 , Shunji Goto 7,10 and Shoji Kawamura 1*

 

Abstract Background: Routine trichromatic color vision is a characteristic feature of catarrhines (humans, apes and Old World monkeys). This is enabled by L and M opsin genes arrayed on the X chromosome and an autosomal S opsin gene. In non-human catarrhines, genetic variation affecting the color vision phenotype is reported to be absent or rare in both L and M opsin genes, despite the suggestion that gene conversion has homogenized the two genes. However, nucleotide variation of both introns and exons among catarrhines has only been examined in detail for the L opsin gene of humans and chimpanzees. In the present study, we examined the nucleotide variation of gibbon (Catarrhini, Hylobatidae) L and M opsin genes. Specifically, we focused on the 3.6~3.9-kb region that encompasses the centrally located exon 3 through exon 5, which encode the amino acid sites functional for the spectral tuning of the genes.

Results: Among 152 individuals representing three genera ( Hylobates ,  Nomascus and  Symphalangus ), all had both L and M opsin genes and no L/M hybrid genes. Among 94 individuals subjected to the detailed DNA sequencing, the nucleotide divergence between L and M opsin genes in the exons was significantly higher than the divergence in introns in each species. The ratio of the inter-LM divergence to the intra-L/M polymorphism was significantly lower in the introns than that in synonymous sites. When we reconstructed the phylogenetic tree using the exon sequences, the L/M gene duplication was placed in the common ancestor of catarrhines, whereas when intron sequences were used, the gene duplications appeared multiple times in different species. Using the GENECONV program, we also detected that tracts of gene conversions between L and M opsin genes occurred mostly within the intron regions.

Conclusions: These results indicate the historical accumulation of gene conversions between L and M opsin genes in the introns in gibbons. Our study provides further support for the homogenizing role of gene conversion between the L and M opsin genes and for the purifying selection against such homogenization in the central exons to maintain the spectral difference between L and M opsins in non-human catarrhines.

 

Background In catarrhine primates (humans, apes and Old World monkeys) the L and M opsin genes are closely juxta-posed on the X chromosome and, in combination with the autosomal S opsin gene, enable routinely trichro-matic color vision [1,2]. The L and M opsin genes have a close evolutionary relationship and are highly similar in nucleotide sequence (~96% identity). Among 15

* Correspondence: kawamura@k.u-tokyo.ac.jp 1 Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa 277-8562, Japan Full list of author information is available at the end of the article

amino acid differences between the human L and M opsin genes, three account for the main shifts in spectral sensitivities and tuning [3-9]. The organization of the L and M opsin genes among humans is known to be variable and includes the absence of an L or M opsin gene or the presence of L/M hybrid genes with an intermediate spectral sensitivity. A high incidence (approximately 3-8%) of color vision   deficien-cies in males results as a consequence [10].
Hiwatashi et al . BMC Evolutionary Biology 2011, 11 :312 http://www.biomedcentral.com/1471-2148/11/312
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If you’d like to try your own PDFs with it you’ll need to do two things:

A.) place the PDF(s) to be tested within the folder:    svg2xml-dev/src/test/resources/pdfs

B.) edit the file:    svg2xml-dev/src/test/java/org/xmlcml/svg2xml/pdf/PDFAnalyzerTest.java

so that

new PDFAnalyzer().analyzePDFFile(new File(” …

points at your file(s).

 

You can then right-click ‘multipletest’ from within PDFAnalyzerTest.java and select Run As -> JUnit Test

 

We’re working with BMC journal content for the moment, and when we perfect it on this, we will expand our scope to include subscription access content too.

 

 

In the last 2 weeks I’ve given talks in Brussels & Amsterdam.

The first one was given during a European Commission (Brussels) working group meeting on Text & Data Mining. There were perhaps only ~30 people in the room for that.

The second presentation was given just a few days ago at Beyond The PDF 2 (#btpdf2) in Amsterdam.

I uploaded the slides from both of these talks to Slideshare just before or after I gave each talk to help maximize their impact. Since then they’ve had nearly 1000 views according to my Slideshare analytics dashboard.

It’s not just the view count I’m impressed with. The global reach is also pretty cool too (see below, created with BatchGeo):

View My Slideshare Impact 08/Mar/2013 to 22/Mar/2013 in a full screen map

Now obviously, these view counts don’t always mean that the viewers always went through all the slides, and a minority of the view-count are bots crawling the web but still I’m pretty pleased. Imagine if I hadn’t uploaded my Content Mining presentation to the public web? I would have travelled all the way to Brussels and back again (in the same day!) for the benefit of *just* ~30 people (albeit rather important people!). Instead, over 800 people have had the opportunity to view my slides, from all over the world (although, admittedly mostly just US & Europe).

The moral of this short story: upload your slides & tweet about them whenever you give a talk!
You may not appreciate just how big your potential audience could be. Something academics sceptical of Open Access should perhaps think about?

Particular thanks should go to @openscience for helping disseminate these slides far and wide. During just a 60 minute period, upon first release, thanks to @openscience and others my PDF metadata slidedeck got over 100 views this Wednesday!

Next step… must work on getting these stats into an ImpactStory widget for the next version of my CV!