Comments on Designing the Microbial Research Commons: Digital Knowledge Resources

1. Comments on Designing the Microbial Research Commons: Digital Knowledge Resources Katherine J. Strandburg New York University School of Law

2. PERSPECTIVE FOR COMMENTS: IMPORTANCE OF SOCIAL NORMS AND RESEARCHER PREFERENCES HOMO SCIENTIFICUS PREFERENCES: • Performingresearch • Autonomy in research direction • Learning results of the collective research enterprise Scarce resources needed to satisfy preferences: • Funding • Attention of others Access to these resources is mediated by publication – if OA is to succeed it must align with these preferences

3. I. OA JOURNALS THREE PATHS TO OA • Open Access Journals (perhaps based at universities) • Existing Journal Adoption of Open Access approach • Parallel OA manuscript repositories and “proprietary” journals

4. THE IMPORTANCE OF IMPACT FACTOR • Emphasis on high status publications exacerbated by recent trend to quantify publication records using impact factor • Table 3 (p. 67): • IF of OA journals: 4.0 (with range up to 9) • IF of restrictive journals: 5.77 (with range up to 50!!) • IF of 50 trumps long-term belief in value of OA • OA models cannot depend on scientists foregoing publication in high impact journals • IF is path dependent and sticky – network effects, preferential attachment, “Matthew effect” • Scientists unlikely to “vote with their feet” for the OA mode

5. OTHER BARRIERS TO UNIVERSITY PUBLISHED OA JOURNALS • Problems with the law review model • Proliferation of journals b/c each university needs 1 (or 2 or 5) • Overly fine-tuned ranking of journals (rather than post hoc ranking based on citation)  over-emphasis on “placement” • Grad students are not law students • No need for publication venue • No time for journal editing functions • Is law review publication really faster? • Anecdotally, physics is 3 to 6 months • Microbial research? • Not convinced of synergies with university educational mission

6. JOURNAL ADOPTION OF OA? • Unlikely b/c of bargaining power due to IF as discussed above • IP laws protect proprietary approaches and reform is difficult • Some movement is seen, but direct pressure on high impact journals is difficult • OA “tier” (e.g. Springer “Open Choice”) problematic if payment competes with spending on research • Journal versions of OA not entirely satisfactory

7. MANUSCRIPT REPOSITORIES • Circumvent the need to get journals to change their practices • Need journal acquiescence only • Separate things that universities can do easily and well from things that are more difficult or harder to dislodge • Good manuscript and good data mining, etc. • Hard copy printing, credentialing service • Deposit can be mandated by funding agencies to grant recipients • Solves collective action problem • Aligns incentives

8. MANUSCRIPT REPOSITORIES • NIH Experience • Journals do not prohibit deposit in such repositories • Federal Research Public Access Act of 2009 • Recently introduced in the Senate • Mandates agencies to ensure open access deposit of peer-reviewed manuscripts < 6 months after publication • Consistent with Obama administration Open Government push • Mitigates concerns with database protection statutes in Europe • No more sole source • Could integrate with material/data repositories • Users of data must deposit manuscripts • data and materials associated with manuscripts must be deposited

9. WHAT ABOUT PROPRIETARY JOURNALS? • May adapt to “service provider” role • page charges • Hard copies • Archival version • “better” or “premium” database services (competing with the OA repository) • May not be commercially viable • Scientific societies • Universities • “Knowledge hubs” Could replace them, take them over, partner Manuscript repositories = path to some OA outcome

10. II. DATA DEPOSITORIES • Similar to issue of material and research tool sharing (see earlier publications) • Collective action problem – temptation to withdraw w/o contributing

11. II. DATA DEPOSITORIES Other Scientists Share Don't Share Share U(N)+M+R-C U(1)+M+R-C Don't Share U(N)+E–P U(1) +E-P Scientist A U(.): value of the database, depends on N M: first mover advantage regarding A’s data E: incremental value of exclusive use of A’s data R: reputational value of contributing, including attribution P: penalty for not contributing C: cost (including opportunity cost) of contributing Contribute iff: R+P-C > E-M: U(N) doesn’t matter!

12. II. DATA DEPOSITORIES • Roughly speaking, then, success of depository depends on R+P-C > E-M • Reduce costs!! (Cf. “Empty Archives,” Nature, 9/10/09) • Easy formats • No direct fees • Provide rewards for contributing (e.g. attribution) • Note these rewards must compete w/ rewards for sharing informally with collaborators • Provide penalties for non-contribution (funder requirements to contribute) • Depositories work best for interdependent data

13. II. DATA DEPOSITORIES • Moral Hazard and Industry Scientists • Withdraw w/o contributing problems may be much greater for industry scientists • Different motivations • Less concern w/ reputation, funding, etc. • Greater access to secrecy • Should we be concerned? • If so, may want to consider semi-commons approach • Fee for service or data for data