Scholarly HTML would be nice, but...

I'm quite interested in the idea of Scholarly HTML, as espoused in Pete Sefton's blog, and I've commented on some of Peter Murray Rust's hamburger PDF comments previously (although I do think a lot of people confuse wild PDF with well-made, should one say Scholarly PDF). I've always been slightly worried by one thing though.

A well-known advantage of PDF is that it pretty much assures I can save a document, share it, move it around etc and it will still be intact and readable. That's one of the reasons it's so popular.

Mostly we don't do that with HTML. Mostly we just point to it. But if I see an article these days, I want it on my computer if I'm allowed; this let's me study it at leisure, drop it in my Mendeley system, etc. As pointed out, that works a treat with PDF, and pretty well with Word or OpenOffice documents as well. This applies even where the document is quite heavily compound, with many embedded images, tables etc.

But if I try saving a HTML document to my hard disk, nothing very standard happens. OK, if I use Safari on my Mac, I get a .webarchive file, which is quite nice as I can do all the things with it that I could do with a PDF and Word etc, and when I open it later it will be as it was before, with all the images in place. But neither IE nor Firefox seem capable of opening a .webarchive file.

If I try saving the same article from Firefox, I get a .html file with the main article in it, and a directory with associated files in it (eg images). Safari does seem capable of opening this combination, but it's pretty ugly, and hard to move around. I haven't tried IE as I don't have easy access to it.

Is there in existence or development a standard approach to packaging the HTML and associated files that would be as convenient as the .webarchive, but usable across all browsers? If so, Scholarly HTML would be that little bit closer!

More activity on semantic publishing

If you saw tweets from @cardcc today, you might realise I’ve been very interested in a couple of recent developments in semantic publishing. I wrote earlier about linking data to journal articles, including David Shotton’s adventures in semantic publishing. David’s work was one of those included in the review article in the Biochemical Journal by Attwod, Kell, McDermott et al (2009). The article ranged over the place of ontologies and databases, science blogs, and various experiments. These included

• RSC and Project Prospect,
• The ChemSpider Journal of Chemistry,
• The FEBS Letters experiment,
• PubMed Central and BioLit,
• Shotton’s PLoS experiment,
• Elsevier Grand Challenge,
• Liquid Publications,
• The semantic Biochemical Journal experiment.

The latter was the real focus of the article, available in PDF, but which if read through a special reader called Utopia Document displayed some active capabilities. These included the ability to visualise and rotate 3-d images of proteins, to see tables represented as graphs (or vice versa) and to link to entries in nucleic acid databases. The capabilities were perhaps a bit awkward to spot and to manipulate, but still interesting. This article is (gold) open access. Other articles in the issue have also been instrumented in this way.

It’s clearly early days for Utopia, and I wasn’t wholly impressed with it as a PDF reader, but I was certainly very excited at some of what I read and saw.

I also read today a very different article (I think not available on open access), by Ruthensteiner and Hess (2008). They describe the processes in making 3-d models of biological specimens, and presenting them in PDF, readable by a standard Acrobat Reader. The 3-d capability was at least as good as if not better than the Utopia results.

Because it’s getting late, I’ll end with my last tweet:

“My head is spinning with semantic article possibilities. I hope some get picked up in new #jiscmrd proposals, see http://www.jisc.ac.uk/fundingopportunities/funding_calls/2009/12/1409researchdata.aspx"

Attwood, T. K., Kell, D. B., McDermott, P., Marsh, J., Pettifer, S. R., Thorne, D., et al. (2009). Calling International Rescue: knowledge lost in literature and data landslide! The Biochemical journal, 424(3), 317-33. doi: 10.1042/BJ20091474.

Ruthensteiner, B., & Hess, M. (2008). Embedding 3D models of biological specimens in PDF publications. Microscopy research and technique, 71(11), 778-86. doi: 10.1002/jemt.20618. Pubmed abstract.

New nature corresponding author policy on data

Thanks to Michael Jubb for pointing me to the editorial outlining the new Nature policy:

"Accordingly, we have modified the Nature journal policy on authorship, which is detailed on our website (http://tinyurl.com/dkgbf8). For papers submitted by collaborations, we now delineate the responsibilities of the senior members of each collaboration group on the paper. Before submitting the paper, at least one senior member from each collaborating group must take responsibility for their group's contribution. Three major responsibilities are covered: preservation of the original data on which the paper is based, verification that the figures and conclusions accurately reflect the data collected and that manipulations to images are in accordance with Nature journal guidelines (http://tinyurl.com/cmmrp7), and minimization of obstacles to sharing materials, data and algorithms through appropriate planning."

This sort of policy from respected journals is seriously good for data curation!

[Updated to enable the Nature tinyurls.]

Data publishing and the fully-supported paper

Cameron Neylon’s Science in the Open blog is always good value. He’s been posting installments of a paper on aspects of open science, and there’s lots of good stuff there. Of course, Cameron’s focus is indeed on open science rather than data, but data form a large part of that vision. In part 3:

“Making data available faces similar challenges but here they are more profound. At least when publishing in an open access journal it can be counted as a paper. Because there is no culture of citing primary data, but rather of citing the papers they are reported in, there is no reward for making data available. If careers are measured in papers published then making data available does not contribute to career development. Data availability to date has generally been driven by strong community norms, usually backed up by journal submission requirements. Again this links data publication to paper publication without necessarily encouraging the release of data that is not explicitly linked to a peer reviewed paper.”

“In other fields where data is more heterogeneous and particular where competition to publish is fierce, the idea of data availability raises many fears. The primary one is of being ’scooped’ or data theft where others publish a paper before the data collector has had the ability to fully analyse the data.”

In part 4, Cameron discusses the idea of the “fully supported paper”.

“This is a concept that is simple on the surface but very complex to implement in practice. In essence it is the idea that the claims made in a peer reviewed paper in the conventional literature should be fully supported by a publically accessible record of all the background data, methodology, and data analysis procedures that contribute to those claims.” He suggests that “the principle of availability of background data has been accepted by a broad range of publishers. It is therefore reasonable to consider the possibility of making the public posting of data as a requirement for submission.”

Cameron does remind us

“However the data itself, except in specific cases, is not enough to be useful to other researchers. The detail of how that data was collected and how it was processed are critical for making a proper analysis of whether the claims made in a paper to be properly judged.”

This is the context, syntax and semantics idea in another guise. Of course, Cameron wants to go further, and deal with “the problem of unpublished or unsuccessful studies that may never find a home in a traditional peer reviewed paper.” That’s part of the benefit of the open science idea.

Science publishing, workflow, PDF and Text Mining

… or, A Semantic Web for science?

It’s clear that the million or so scientific articles published each year contain lots of science. Most of that science is accessible to scientists in the relevant discipline. Some may be accessible to interested amateurs. Some may also be accessible (perhaps in a different sense) to robots that can extract science facts and data from articles, and record them in databases.

This latter activity is comparatively new, and (not surprisingly) hits some problems. Articles, like web pages, are designed for human consumption, and not for machine processing. We humans have read many like them; we know which parts are abstracts, which parts are text, which headings, which references. We can read the tables, find the intersections and think about what the data points mean. We can look at the graphs, the spectra etc, and relate them to the author’s arguments. Most of these tasks are hard for robots. But with a little bit of help and persistence, plus some added “understanding” of genre and even journal conventions, etc, robots can sometimes do a pretty good job.

However, most science articles are published in PDF. And PDF does not make the robot’s task easy; in fact, PDF often makes it very hard (not necessarily to be deliberately obscure, but perhaps as side-effects of the process leading to the PDF).

Peter Murray-Rust has been leading a number of one-man campaigns (actually they all involve many more than one man, but he is often the vocal point-person). One such campaign, based on attempts to robotically mine chemical literature can be summed up as “PDF is a hamburger, and we’re trying to turn it back into a cow” (the campaign is really about finding better semantic alternatives to PDF). I've referred to his arguments in the past, and we've been having a discussion about it over the past few days (see here, its comments, and here).

I have a lot of sympathy with this viewpoint, and it’s certainly true that PDF can be a hamburger. But since scientists and publishers (OK, mostly publishers) are not yet interested in abandoning PDF, which has several advantages to counter its problems, I’m also interested in whether and if so, how PDF could be improved to be more fit for the scientific purpose.

One way might be that PDF could be extended to allow for the incorporation of semantic information, in the same way that HTML web pages can be extended, eg through the use of microformats or RFDa, etc. If references to a gene could be tagged accordning to the Gene Ontology, references to chemicals tagged according to the agreed chemical names, InChis etc, then the data mining robots would have a much easier job. Maybe PDF already allows for this possibility?

PMR argues quite strongly that PDF is by design unfit for our purpose (in this case, holding scientific information such that it can reliably be extracted by text mining robots); that PDF's determined page-orientation and lack of structural and semantic significance doom such attempts to failure. He also argues strongly that the right current solution is to use XML… or perhaps XHTML for science writing.

I don’t know. He might be right. But being right is not necessarily going to persuade thousands of journal editors and hundreds of thousands of scientists to mend their ways and write/publish in XML.

I think we should tackle this in several ways:

• try to persuade publishers to publish their XML (often NLM XML) versions of articles as well as the PDFs
• try to persuade publishers who don’t have a XML format to release HTML versions as well as (or instead of PDFs)
• tackle more domain ontologies to get agreements on semantics
• work on microformats and related approaches to allow semantics to be silently encoded in documents
• try to persuade authors to use semantic authoring tools (where they exist), and publishers to accept these
• try to persuade Adobe to extend PDF to include semantic micro-metadata, and to help provide tools to incorporate it, and to extract it.

Might that work? Well, it’s a broad front and a lot of work, but it might work better than pursuing only one of them… But if we got even part way, we might really be on the way towards a semantic web for science...