The Internet Model = why Open Access is not enough
Publishing and peer review processes in academia are currently closed models. In my view, at least in the areas i operate in (social sciences and humanities), these processes should be far more, if not entirely, open, with a provision for privacy in special cases. I call this model Open-process academic publishing.The name deliberately distinguishes it fromOpen Access, which refers to only the final outcome of academic knowledge production being open. The suggestion is not to open the processes in random ways, but in ways in which this openness — fundamentally based on volunteer participation — brings/enables more structure, more internalized working discipline, more commitment, and more ability to improve cooperation/collaboration with deliberate precision – all with the goal of improving the outcomes. “[…] culture of open processes was essential in enabling the Internet to grow and evolve as spectacularly as it has”, hence, we could call it The Internet Model (software/FS + networking/IETF). Its potential screams for being reused, hacked, for other areas of production. Academia, especially its publishing side, seems to me capable of embracing such volunteer-core open-process cooperation …
DataSF is a central clearinghouse for datasets published by the City & County of San Francisco. The site allows you to find datasets in several ways: general search, tags/keywords, categories, and rating. The goal is to improve access to city data through open machine-readable formats. While the number and quality of datasets is increasing, we recognize there is much more that we can do. You can help by rating and commenting on existing datasets or by telling us what datasets we should make available to the public.
While there is plenty of room for improvement, our goal in releasing this site is:
(1) improve access to data (2) help our community create innovative apps (3) understand what datasets you’d like to see (4) get feedback on the quality of our datasets.
The latest campus revolutionaries are the so-called edupunks — and their mission is to break up the ivory tower so everyone can pile into the classroom. MIT was the first university to heed the edupunk call: it started posting syllabi, course notes and videotaped lectures onocw.mit.edu back in 2001. Harvard, Berkeley, Yale, Princeton and Stanford soon followed suit, with their own schemes for posting videos of their most popular courses. Now Academic Earth aggregates all this material so you can audit classes from the comfort of your computer.
… But what if there’s a better way? That’s the prospect we raise in “Who Needs Harvard?”. If you were starting a system of higher education from scratch today, would you still choose a campus-based model that charges hundreds of thousands of dollars for a degree? Or might the efficiency of the Web inspire a model in which classes would be remotely delivered via Web streaming, discussion groups conducted over Facebook, and testing handled electronically? This is already happening, as staff writer Anya Kamenetz reports, courtesy of a rising movement of tech-savvy “edupunks.” MIT, for instance, already posts online — for free — the full syllabi, lecture notes, class exercises, tests, and selected video and audio for every one of its classes. “Why is it that my kid can’t take robotics at Carnegie Mellon, linear algebra at MIT, law at Stanford?” asks David Wiley of Brigham Young University. “And why can’t we put 130 of those together and make it a degree?”
Now here’s the confession: I’m just as trapped by this model — and committed to it — as any tenured professor or university president. My son has already visited a couple of traditional four-year colleges, and we’ve got money stashed in a 529 account to help pay for one. Someone has to be progressive and pioneer a new way. But I’m not going to take that risk with my son’s future. If he gets into Harvard — or some other college — I’ll spend the money to send him. What kind of hypocrite does that make me?
Transparency in experimental methodology, observation, and collection of data.
Public availability and reusability of scientific data.
Public accessibility and transparency of scientific communication.
Using web-based tools to facilitate scientific collaboration.
The idea I’ve been most involved with is the first one, since granting access to source code is really equivalent to publishing your methodology when the kind of science you do involves numerical experiments. I’m an extremist on this point, because without access to the source for the programs we use, we rely on faith in the coding abilities of other people to carry out our numerical experiments. In some extreme cases (i.e. when simulation codes or parameter files are proprietary or are hidden by their owners), numerical experimentation isn’t even science. A “secret” experimental design doesn’t give skeptics the ability to repeat (and hopefully verify) your experiment, and the same is true with numerical experiments. Science has to be “verifiable in practice” as well as “verifiable in principle”.
In general, we’re moving towards an era of greater transparency in all of these topics (methodology, data, communication, and collaboration). The problems we face in gaining widespread support for Open Science are really about incentives and sustainability. How can we design or modify the scientific reward systems to make these four activities the natural state of affairs for scientists? Right now, there are some clear disincentives to participating in these activities. Scientists are people, and we’re motivated by most of the same things as normal people:
Money, for ourselves, for our groups, and to support our science.
Reputation, which is usually (but not necessarily) measured by citations, h-indices, download counts, placement of students, etc.
Sufficient time, space, and resources to think and do our research (which is, in many ways, the most powerful motivator).
Right now, the incentive network that scientists work under seems to favor “closed” science. Scientific productivity is measured by the number of papers in traditional journals with high impact factors, and the importance of a scientists work is measured by citation count. Both of these measures help determine funding and promotions at most institutions, and doing open science is either neutral or damaging by these measures. Time spent cleaning up code for release, or setting up a microscopy image database, or writing a blog is time spent away from writing a proposal or paper. The “open” parts of doing science just aren’t part of the incentive structure.