David de Roure on "the new e-Science"

I was at the eScience All Hands meeting last week, and unfortunately missed a presentation by David de Roure on the New e-Science, an update on a talk he gave 10 months ago. The slides are available on Slideshare, but David has agreed I can share his summary:

"1. Increasing scale and diversity of participation
Decreasing cost of entry into digital research means more people, data, tools and methods. Anyone can participate: researchers in labs, archaeologists in digs or schoolchildren designing antimalarial drugs. Citizen science! Improved capabilities of digital research (e.g. increasing automation, ease of collaboration) incentivises this participation. "You're letting the oiks in!" people cry, but peer review benefits from scale of participation too. "Long Tail Science"

2. Increasing scale and diversity of data
Deluge due to new experimental methods (microarrays, combinatorial chemistry, sensor networks, earth observation, ...) and also (1). Increasing scale, diversity and complexity of digital material, processed separately and in combination. New digital artefacts like workflows, provenance, ontologies and lab books. Context and provenance essential for re-use, quality and trust. Digital Curation challenge!

3. Sharing
Anyone can play and they can play together. Anyone can be a publisher as well as a consumer - everyone's a first class citizen. Science has always been a social process, but now we're using new social tools for it. Evidenced by use of wikis, blogs, instant messaging. The lifecycle goes faster, we accelerate research and reduce time-to-experiment.

4. Collective Intelligence
Increasing participation means network effects through community intelligence: tagging, reviewing, discussion. Recommendation based on usage. This is in fact the only significant breakthrough in distributed systems in the last 30 years. Community curation: combat workflow decay!

5. Open Research
Publicly available data but also the open services and software tools of open science. Increasing adoption of Science Commons, open access journals, open data and linked data (formerly known as Semantic Web), PLoS, ... Open notebook science

6. Sharing Methods
Scripts, workflows, experimental plans, statistical models, ... Makes research repeatable, reproducible and reusable. Propagates expertise. Builds reputation. See Usefulchem, myExperiment.

7. Empowering researchers
Increasing facility with new tools puts the researchers in control - of their software/data apparatus and their experiments. Empowerment enables creativity and creation of new, sharable methods. Tools that take away autonomy will be resisted. Beware accidental disempowerment! Ultimately automation frees the researcher to do what they're best at, but can also be disempowering.

8. Better not perfect
Researchers will choose tools that are better than what they had before but not necessarily perfect. This force encourages bottom-up innovation in the practice of research. It opposes the adoption of over-engineered computer science solutions to problems researchers don't know they have and perhaps never will.

9. Pervasive deployment
Increasingly rich intersection between the physical and digital worlds through devices and instruments. Web-based interfaces not software downloads. Shift towards devices and the cloud. REST architecture coupling components that transcend their application.

10. Standing on the shoulders of giants
e-Science is now enabling researchers to do some completely new stuff! As the pieces become easy to use, researchers can bring them together in new ways and ask new questions. Boundaries are shifting, practice is changing. Ease of assembly and automation is essential."

The presentation is well worth looking at as well for the extra material David includes. I thought the more open and inclusive approach to e-Science (or Cyber-infrastructure) was well worth including here. The word "heroic" appears on his slides in relation to the Grid, which sums up my concerns, I think!

Many citations flow from data...

I've been at the UK e-Science All Hands Meeting in Edinburgh over the past few days (easy, since it's being held in the bulding in which I work!). Lots of interesting presentations; far too many to go to, let alone blog about. But I can't resist mentioning one short presentation (PPT), from Prof Michael Wilson of STFC. His pitch was simple: publishing data is good for your career, especially now. And he has evidence to back up his claims!

Michael and colleagues put together a Psycholinguistic database many years ago, with funding from the MRC. At first (1981) it was available via postal request, then (1988) via ftp from the Oxford Text Archive, now via web access from STFC and from UWA(since 1994). Not much change over the years, little effort, free data, no promotion.

The database is now publicly available, eg at the link above. You may see that users are requested to cite the relevant paper:

Wilson, M.D. (1988) The MRC Psycholinguistic Database: Machine Readable Dictionary, Version 2. Behavioural Research Methods, Instruments and Computers, 20(1), 6-11.

The vital piece of evidence was a plot of citations over the years (data extracted from Thomson ISI, I hope they don't mind my re-using it):

You'll notice that citations flowed very slowly in the early days, picked up a little after ftp access was available (it takes a few years to get the research done and published), and then really started to climb after web access was provided. Now Michael and his colleagues are getting around 80 citations per year!

To ram home his point, Michael did some quick investigations, and found "At least 7 of the top 20 UK-based Authors of High-Impact Papers, 2003-07 ranked by citations ... published data sets".

There was some questioning on whether citing data through papers (rather than the dataset itself) was the right approach. Michael is clear where he is on this question: paper citations get counted in the critical organs by which he and others will be measured, so citations should be of papers.

Summary of the ptich: data publishing is easy, it's cheap, and it will boost your career when the new Research Evaluation Framework comes into effect.