Dudley and Butte on Software Skills

Via Titus Brown, a new PLoS paper titled “A Quick Guide for Developing Effective Bioinformatics Programming Skills” by Joel Dudley and Atul Butte. Their recommendations are:

1. Programming languages
2. Embracing open source
3. Unix command-line skills
4. Keeping projects documented and manageable
5. Preserving source code with version control
6. Embracing parallel computing paradigms
7. Structuring data for speed and scalability
8. Understanding the capabilities of hardware
9. Embracing standards and interoperability
10. Put a high value on your time

I think all these things matter, but:

1. How many hours do the authors think are needed to acquire these skills? We’ve tried very hard to fit Software Carpentry into 25 hours of lecture and 50-100 hours of practical work because we recognize that every one of those hours is time students aren’t spending doing science.
2. Shouldn’t testing be in the top 10? Or the top 5, or 3? These days, I care a lot more about how (and how well) someone tests than I do about their mastery of any particular programming language.

NSF Programs

I’d be interested in hearing from anyone who has enough direct experience of the following NSF programs to know whether they might be willing to support Software Carpentry:

1. Course, Curriculum, and Laboratory Improvement
2. Integrative Graduate Education and Research Traineeship
3. CISE Pathways to Revitalized Undergraduate Computing Education
4. Innovations in Engineering Education, Curriculum, and Infrastructure

Double Standards

Nicola Scafetta is refusing to release the software on which he bases his claims that the sun is responsible for much of terrestrial warming during the last century.  I obviously think that scientists should be required to do this as a condition of publication; coming as this does on the heels of Climategate, it will be interesting to see if journals finally start pushing in that direction. It also highlights the need to add more material to this course to cover packaging for release and data provenance.

Why Opening Up (Probably) Wouldn’t Help

Good post from Steve Easterbrook on why open-sourcing climate models probably wouldn’t make a difference.

Thanks, Jamie

The warmup tutorials for our grassroots Software Carpentry course started this week, and we’d like to send a “thank you” to Jamie Winter at The MathWorks, who has provided students with temporary licenses for MATLAB. It’s all been very last minute, and we’re grateful to Jamie for pulling this off on such short notice.

Caesar’s Wife

Improving the way scientists use computers isn’t just about making them more productive: it’s also essential to defending the integrity of their work. Stories like this one about a researcher struggling in vain for three years to replicate someone else’s results can only undermine public confidence at a time when we need to make a lot of hard decisions in a hurry. Sadly, we have no one to blame but ourselves…

Later: see also Victoria Stodden’s post.

Tutorials Start This Week

After a lot of hard work from Dominique and Jon, we’re kicking off warmup tutorials for Software Carpentry this week. 65 students from Psychology, Linguistics, Chemical Engineering, and a couple of other departments will get three weeks of review on basic programming, then start the regular material in January. Our thanks to MITACS, the MathWorks, SciNet, and DCS for their support.

Serendipitous and Unexpected

Via Ryan Lilian:

Most research effort does not produce what is thought of as a traditionally publishable result. That doesn’t mean, however, that nothing was gained by conducting the research. These results, whether they are failures or merely perplexing, can provide valuable insights into open problems and prevent other researchers from duplicating work. We started a journal that focuses on serendipitous (I have no idea why this worked) and unexpected (it seems like this technique should work on this problem but it doesn’t) results. The goal of the journal is to provide a venue where ideas can flow and be debated.

The Journal of Serendipitous and Unexpected Results (JSUR) is an open-access forum for researchers seeking to further scientific discovery by sharing surprising or unexpected results. These results should provide guidance toward the verification (or negation) of extant hypotheses. JSUR has two branches, one focusing on Computational Sciences and the other on the Life Sciences. JSUR submissions include, but are not limited to, short communications of recent research results, full-length papers, review articles, and opinion pieces.

Recently, we launched the beta version of the journal site at http://jsur.org. We would love to get your feedback and even better, a submission for the first issue.

To get the journal started, we’re looking to collect a large number of short (2-4 page) reports. I know you have something to publish. Please help us spread the word and forward this information to interested colleagues.

Sincerely,
The JSUR Editorial Board

Special Issue of Computing in Science & Engineering

A special issue of Computing in Science & Engineering that Andy Lumsdaine and I edited, devoted to software engineering in computational science, is now available. We’d like to thank everyone who contributed:

• Report on the Second International Workshop on Software Engineering for CSE, by Jeffrey Carver (University of Alabama)
• Managing Chaos: Lessons Learned Developing Software in the Life Sciences, by Sarah Killcoyne and John Boyle (Institute for Systems Biology)
• Scientific Computing’s Productivity Gridlock: How Software Engineering Can Help, by Stuart Faulk (University of Oregon), Eugene Loh and Michael L. Van De Vanter (Sun Microsystems), Susan Squires (Tactics), and Lawrence G. Votta, (Brincos)
• Mutation Sensitivity Testing, by Daniel Hook (Engineering Seismology Group Solutions) and Diane Kelly (Royal Military College of Canada)
• Automated Software Testing for MATLAB, by Steve Eddins (The MathWorks)
• The libflame Library for Dense Matrix Computations, by Field G. Van Zee, Ernie Chan, and Robert A. van de Geijn (University of Texas at Austin), and Enrique S. Quintana-Ortí and Gregorio Quintana-Ortí (Universidad Jaime I de Castellón)
• Engineering the Software for Understanding Climate Change, by Steve Easterbrook (University of Toronto) and Timothy Johns (Hadley Centre for Climate Prediction and Research)

Cloud Computing for Beginners

Ana Nelson has posted step-by-step instructions showing how to use Amazon’s EC2 cloud computing platform to run simulations. There are still a lot of fiddly details, but the barriers to entry are getting lower all the time…