Overlay journals are a great concept – CCH is not affiliated with any publisher: it is a free resource run by scientists for scientists.  In addition to highlighting recently-published papers, I’m pretty sure it will also include highlights of non-journal resources like code, publicly available datasets, and papers available on preprint servers (e.g. arXiv, Nature Precedings).  It also allows non-anonymous comments on papers and will let authors respond to those comments.

Overlay journals are an interesting experiment. We’ll have to see how important they become, but I’m pretty happy to be included early on this one.

The American Heart Association has recently formed a task to explore and determine AHA’s role in open science.  Our specific interest at this time is focused on data repositories for finalized research data.  We are currently looking for a consultant to assist in gathering background information for this task force in the following areas:

• What open science is, and how it relates to the AHA.  This should be approached more broadly than just our data focus so the committee is aware of other research transparency opportunities.
• What other similar organizations are doing relative to open science and making data and publications available (e.g. NIH, HHMI, ACS, EU, etc.) The AHA can provide contact information within most these organizations.  We would like to see comparisons to about 6 similar organizations.  Our publishing area has looked at policies related to public access to publications, but this task force will be looking at how open access could impact our research program.
• Existing repositories in use which can hold the types of data that AHA funded grants produce.
• Legal, data design, or intellectual property issues with requiring researchers to store their data in a repository.
• Documenting issues that should be addressed by the task force if they decide to move forward (e.g. timing for providing data, retention of data, definition of data to store, requiring common terminology, issues related to the re-use of the stored data etc.)

We anticipate that these efforts may involve the consultant 5-10 hours a week for a 6 week time period.   The project will begin in mid-October and would ideally be completed by Dec 1st.  Consultants that are interested in this project should provide a summary of their qualifications by October 12^th.

 

Belinda Orland

Research Information Manager

Division of Research Administration

 

American Heart Association/American Stroke Association 

7272 Greenville Avenue
Dallas, TX 75231
214 360 6110
belinda.orland@heart.org

The advice I gave him was basically to make the committee’s job of measuring these contributions easier.  Here’s the advice (in a slightly edited form):

The audience for most tenure documents (and particularly the external letters) is a committee of non-specialists that advises the provost or other high-ranking administrator.  These committees are often somewhat skeptical of departments and candidates and are looking for external validation of what they are reading in the tenure dossier and the packet prepared by the departments.  They are swayed by real experts in the field (named chairs at other institutions, national academy members, people at top 10 institutions) and by things they can measure (publications, h-indices, grant money, citation counts). If you want to add a non-traditional contribution to a tenure dossier, you should also include a way of measuring the importance of that contribution.

First, if the rules of your institution allow it, make sure there is a strong defense of open ways of doing science in your dossier (1-2 paragraphs or so).  Make the case that it is important to consider non-standard contributions even though previous tenure committees did not.

Use as many metrics to back up your contributions as you can. Make a case that each of your software releases counts as much as a full publication, and use download statistics as if they were directly comparable to academic citations.  List external users of your software as if they were research collaborators, because they are!  If you can collect them, include download statistics on open contributions to sites like OpenWetware and Wikipedia.

If your institution’s rules allow it, make sections directly under your publications for  “Published Datasets”, “Contributed Software”, “Published Protocols & Notebooks”, “Scientific Videos”.  In each section, list authors, a title, description, and URL of the resource you have contributed along with a count of downloads or views, and a list of other groups using your data. Make this look as much like your publication section as possible, as you can then make the argument that these things should be treated with a similar weight to traditional academic publication.   Provide the metrics in the document so that your committees aren’t guessing about how important something is.  I can’t emphasize this enough – citation counts are easy for a committee to dig up – download stats are harder.  Do the measurement work for your committee and they’ll make the assumption that your metrics are important.

So that’s the advice.  I’ve been involved in a few internal tenure discussions, and the metrics are always important.  If there isn’t an easy analogy to something in my own experience, I look to the candidate’s documents and the external letters to tell me why something matters.

Open science is the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process.

The discussion on the list has been very interesting, but that particular “informal” definition is great because it gets at why we’re struggling with established social norms in science given the new technological methods of communicating results:

…when the journal system was developed in the 17th and 18th centuries it was an excellent example of open science.  The journals are perhaps the most open system for the dissemination of knowledge that can be constructed — if you’re working with 17th century technology.  But, of course, today we can do a lot better.

The CDK waterfall whiteboard

The CDK design session was a fun weekend. In retrospect, they were some of the purest days of collaborative creativity I’ve ever experienced. We spent many hours and a lot of coffee hashing out some of the basic classes of CDK. The final picture of the whiteboard shows a classic waterfall diagram of what we were going to implement.

I’m the first to admit that my contributions to CDK were minimal. Egon & Chris ran with the design, expanded and improved it, implemented all the missing pieces, and released it to the world. It has become an important piece of scientific software, particularly in the bioinformatics community. Beyond Egon & Chris, Rajarshi Guha has been one of the prime developers of the software.

CDK is, by all objective standards a fantastic success story of open source scientific software. It has a large and vibrant user community, active developers, and a number of people (including myself) who browse the code just to see how it does something difficult. Egon has written a thoughtful piece on where CDK should go from here.

Happy Birthday CDK!

Packmol is a cool program that appears to solve this problem. It creates an initial point for molecular dynamics simulations by packing molecules in defined regions of space. The packing guarantees that short range repulsive interactions do not disrupt the simulations. The great variety of types of spatial constraints that can be attributed to the molecules, or atoms within the molecules, makes it easy to create ordered systems, such as lamellar, spherical or tubular lipid layers. It works with PDB and XYZ files and appears to be available under the GPL. Very, very cool!

Computer code is also at the heart of a scientific issue. One of the key features of science is deniability: if you erect a theory and someone produces evidence that it is wrong, then it falls. This is how science works: by openness, by publishing minute details of an experiment, some mathematical equations or a simulation; by doing this you embrace deniability. This does not seem to have happened in climate research. Many researchers have refused to release their computer programs — even though they are still in existence and not subject to commercial agreements. An example is Professor Mann’s initial refusal to give up the code that was used to construct the 1999 “hockey stick” model that demonstrated that human-made global warming is a unique artefact of the last few decades. (He did finally release it in 2005.)

My favorite post so far: Why Open Source Will Rule Scientific Computing by Will Schroeder.