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DataSpace

DataSpace. A Funding and Operational Model for Long-Term Preservation and Sharing of Research Data. Special Thanks To. Mark Ratliff Digital Repository Architect Implemented DataSpace at Princeton Princeton University Library and OIT Marvin Bielawski

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DataSpace

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  1. DataSpace A Funding and Operational Model for Long-Term Preservation and Sharing of Research Data EDUCAUSE Live!, 9/1/2010

  2. Special Thanks To • Mark Ratliff • Digital Repository Architect • Implemented DataSpace at Princeton • Princeton University Library and OIT • Marvin Bielawski • Joyce Bell (Metadata) and Dan Santamaria (archives) • http://arks.princeton.edu/ark:/88435/dsp01w6634361k EDUCAUSE Live!, 9/1/2010

  3. Goal of DataSpace Model • Store data “forever” • Preserve and protect • Share data with everyone • Make available • Make accessible • Make it Work! • Financially • Operationally EDUCAUSE Live!, 9/1/2010

  4. What is “Forever”? • Longer than a typical project? • Longer than a typical career? • Longer than a typical institution? • 5 years, 10 years, 25 years, 100 years? • Suggestion: treat data same way library treats books • Intent is to preserve indefinitely • As long as practical, feasible • Cannot be precisely defined EDUCAUSE Live!, 9/1/2010

  5. Why Save Data “Forever” • Because we want to make it: • Available to ourselves and our students and colleagues • Where are the data sitting today? On a departmental server? On a computer under your desk? On a CD or DVD somewhere? • Where is your dissertation data? • Available to future scholars, including ourselves EDUCAUSE Live!, 9/1/2010

  6. Why Save Data “Forever” • Because we need to: • Encourage honesty? • Gregor Mendel probably cheated • Like open-source: help uncover mistakes, bugs? • Open Data Movement • Mostly library/catalog data, map data, WordNet • Open Access Movement • Mostly publications • Because it’s not “our” data EDUCAUSE Live!, 9/1/2010

  7. Why Save Data “Forever” Because we have to: Science Insider, May 5 reports: ” Edward Seidel, acting head of NSF's mathematics and physical sciences directorate, described NSF's intention to require all applicants to submit a data management plan along with their grant application in a presentation this morning to the National Science Board, NSF's oversight body. …NSF's current policy requires grantees to share their data within a reasonable length of time so long as the cost is modest. "That's nice, but it doesn't have much teeth," said Seidel. Under the new policy, which is expected to be unveiled this fall, a researcher would submit a data management plan as a two-page supplement to any regular grant proposal. That would make it an element of the merit review process.” EDUCAUSE Live!, 9/1/2010

  8. Data Sharing Policies • New NSF policy continues trend • NIH Data Sharing Policy (2003): http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html “all investigator-initiated applications with direct costs greater than $500,000 in any single year will be expected to address data sharing in their application.” EDUCAUSE Live!, 9/1/2010

  9. NIH Data Sharing Policy • “Applicants may request funds for data sharing and archiving. The financial issues should be addressed in the budget section of the application.” • Specifics depend on grant, published in RFP, RFA or PA EDUCAUSE Live!, 9/1/2010

  10. NSF Data Archiving Policy • Division of Social and Economic Scienes • http://www.nsf.gov/sbe/ses/common/archive.jsp • “Grantees from all fields will develop and submit specific plans to share materials collected with NSF support, except where this is inappropriate or impossible.” EDUCAUSE Live!, 9/1/2010

  11. NSF Data Archiving • From Grant Proposal Guide • NSF “expects PIs to share with other researchers, at no more than incremental cost and within a reasonable time, the data, samples, physical collections and other supporting materials created or gathered in the course of the work.” • Specifics depend on grant and program officer EDUCAUSE Live!, 9/1/2010

  12. Other agency Policies • See Gary King’s Page on “Data Sharing and Replication” • http://gking.harvard.edu/replication.shtml • See National Academy of Sciences “Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age”, July, 2009 • http://www.nap.edu/catalog/12615.html EDUCAUSE Live!, 9/1/2010

  13. Current Storage Models • Let someone else do it • Government agency/lab/bureau • NOAA National Geophysical Data Center • GenBank (DNA data) • fMRIDC (fMRI publications and data) • NCSA Astronomy Digital Image Library EDUCAUSE Live!, 9/1/2010

  14. Current Storage Models • Professional society/Journals • Global Ocean Observing System: coordinates distributed data • Dryad: ecology/evolutionary biology • Nice folks at another University • ICPSR, University of Michigan (political/social) • Dryad: ecology/evolutionary biology • Protein Data Bank (PDB): 3-D protein data • NCSA Astronomical Image Library • The “Cloud” EDUCAUSE Live!, 9/1/2010

  15. Current Funding Models • Institution/department pays • Grants pay monthly/yearly • Haphazard • Some grant money • Some departmental money • Use whatever is available • Don’t worry, someone will pay EDUCAUSE Live!, 9/1/2010

  16. Current Funding Models • Most require some form of on-going payment • Advantages • Capitalist approach to data storage • If someone wants to pay, data gets saved • “Natural” expiration process • Disadvantages • Capitalist approach to data storage • Who pays to save rarely used data? EDUCAUSE Live!, 9/1/2010

  17. Different Approach PAY ONCE, STORE ENDLESSLY (POSE) • Why Pay Once? • Grants expire often and quickly • Researchers retire/move/move-on pretty often • How Store Forever? • Administrators expire slowly • Institutions expire rarely • Cost is FINITE (how is that possible?) • Magic of Math EDUCAUSE Live!, 9/1/2010

  18. The Business Model (1) • I = Initial cost of storage • D = rate at which storage costs decrease yearly, expressed as a fraction (e.g., 20% would be 0.2) • R = How often, in years, storage is replaced • T= Cost to store the data “forever” T = I + (1-d)r * I + (1-d)2r * I + …. If d=20%, r = 4: T = I + (.84 )* I + (.88)* I + …. EDUCAUSE Live!, 9/1/2010

  19. The Business Model (2) If d >0, T = I + (1-d)r * I + (1-d)2r * I + …. CONVERGES = I * 1/(1-(1-d)r) For d=20%, r = 3: T=I * (1/.83): T ~= I * 2 Charge 2x initial storage cost, save half, store forever! To simplify: Let S = 1/(1-(1-d) r) : “Storage Factor”, “S factor” Then: T = I * S : Total Cost = Initial Cost * S Factor EDUCAUSE Live!, 9/1/2010

  20. “S” Factor is very stable • “S” factor small, between 1 and 2 for broad range of reasonable values: EDUCAUSE Live!, 9/1/2010

  21. Rate of Decrease in Storage • Is 20%/year too much? • 1981: 10 meg drive costs $3,000 • 2010: 500 gig drive costs $600 • 250,000 fold decrease over 30 years, or an average of 35% per year. • 2000: IBM 20 gig drive costs $280 • 2010: 500 gig drive costs $600 • 12-fold decrease over 10 years, or about 23%/year. EDUCAUSE Live!, 9/1/2010

  22. An Example: DataSpace at Princeton • FC costs decrease by about 16% per year • SATA costs decrease by about 17% per year EDUCAUSE Live!, 9/1/2010

  23. The “S” factor for Princeton • SATA cost = $1.81/gb • Replace every four years • Costs decrease by 20% year “S” = $1.81/(1-.8 **4) = $3/gb Adding tape backup jumps this to $6/gb $6K one-time to store a terabyte forever $6K one-time to store a terabyte forever EDUCAUSE Live!, 9/1/2010

  24. But what about Other Costs? • Cost of disk drives only (small) part of cost of saving/sharing data • What about costs of people, buildings, electronics, software • Some claim these costs “swamp” the actual cost of the disks EDUCAUSE Live!, 9/1/2010

  25. Model can handle other costs • People, building, infra-structure, software costs MUCH higher than disk costs …. • … but NOT if pro-rated across storage units. • Storage administrator in 2010 can manage 1000 times more storage than in 2000. • Have to look at “marginal costs”; how much more in people costs to store an extra terabyte? EDUCAUSE Live!, 9/1/2010

  26. Model can handle other costs • On a per unit of storage basis, people and electronics and software and infra-structure costs also decreasing rapidly • These costs can be built in to the “S” factor (just as we built the tape backup costs into the “S” factor at Princeton). • As long as a cost is decreasing over time on a pro-rated basis, model applies. EDUCAUSE Live!, 9/1/2010

  27. When does model break down? • If costs increase on a per-unit basis over time, then you must have on-going income stream • Administrative, data-translation, data-delivery costs are candidates • Need to minimize such costs • Keep administrative overhead and service overhead to a minimum EDUCAUSE Live!, 9/1/2010

  28. Organizational facet of model • So far, only looked at financial facet • Financial facet needs to be combined with organizational facet to keep costs down • Write Once, Read Forever (WORF) • Set of principles aimed at minimizing operational costs • Also implements the “sharing” requirement of current grant policies EDUCAUSE Live!, 9/1/2010

  29. WORF Principles • Storage may not be “re-used”; it is archival, not working • Permanent URL assigned; data cannot be changed. Can be made unavailable or super-ceded by new data. • All data permanently publicly accessible (minus “embargo” period) • Repository only provides storage and web access EDUCAUSE Live!, 9/1/2010

  30. WORF Principles (2) • POSF • Ancillary services (data conversion) separately charged We propose that a repository implementing the above set of principles be called a “DataSpace Repository” EDUCAUSE Live!, 9/1/2010

  31. DataSpace at Princeton • OIT and the Library have implemented a DataSpace repository • Uses “Dspace” repository software developed at MIT and the Archival Resource Key (ARK) system for permanent URLs • Just started: • http://dataspace.princeton.edu EDUCAUSE Live!, 9/1/2010

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