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The Software and Data Licensing Solution: Not Your Dad’s UBMTA

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The Software and Data Licensing Solution: Not Your Dad’s UBMTA

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Presented at the Association of University Technology Managers (AUTM) Annual Conference 2018
Moderator: Arvin Paranjpe, Oregon Health & Science University
Speakers: Frank Curci, Ater Wynne LLP
Melissa Haendel, Oregon Health & Science University
Charles Williams, University of Oregon

Big data is an open frontier, and it’s quickly expanding. However, transaction costs and legal barriers stand squarely in the way of meaningful, far-reaching data integration. We’ll grapple with the issues regarding a large-scale data integration project across humans, model and non-model organisms. Without pointing fingers, we’ll also share a few highlights from the (Re)usable Data Project, which outlined a five-part rubric to evaluate data licenses with respect to clarity and the reuse and redistribution of data. In addition, the topic raises the question: How well-suited are off-the-shelf software and data licenses for universities? Data scientists and software programmers are all too quick to pick one when they release their technology on GitHub. What should technology transfer professionals
recommend? We’ll discuss the usefulness and attributes of a uniform software and data license for university researchers and software programmers.

Presented at the Association of University Technology Managers (AUTM) Annual Conference 2018
Moderator: Arvin Paranjpe, Oregon Health & Science University
Speakers: Frank Curci, Ater Wynne LLP
Melissa Haendel, Oregon Health & Science University
Charles Williams, University of Oregon

Big data is an open frontier, and it’s quickly expanding. However, transaction costs and legal barriers stand squarely in the way of meaningful, far-reaching data integration. We’ll grapple with the issues regarding a large-scale data integration project across humans, model and non-model organisms. Without pointing fingers, we’ll also share a few highlights from the (Re)usable Data Project, which outlined a five-part rubric to evaluate data licenses with respect to clarity and the reuse and redistribution of data. In addition, the topic raises the question: How well-suited are off-the-shelf software and data licenses for universities? Data scientists and software programmers are all too quick to pick one when they release their technology on GitHub. What should technology transfer professionals
recommend? We’ll discuss the usefulness and attributes of a uniform software and data license for university researchers and software programmers.

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The Software and Data Licensing Solution: Not Your Dad’s UBMTA

  1. 1. The Software and Data Licensing Solution Not Your Dad’s UBMTA
  2. 2. The Software and Data Licensing Solution Your Moderator and Panelists 2 MODERATOR and PANELIST Arvin Paranjpe, MS, JD, RTTP Sr. Technology Development Manager Technology Transfer Oregon Health & Science University PANELIST Melissa Haendel, Ph.D. Associate Professor Library & Dept. of Medical Informatics and Clinical Epidemiology Oregon Health & Science University PANELIST Chuck Williams, Ph.D, J.D., C.L.P Associate VP for Innovation University of Oregon PANELIST Frank X. Curci, JD IP Counsel and Partner Ater Wynne LLP
  3. 3. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 3 First Presenter: Frank X. Curci Key IP Issues To Consider When Sharing Data / Data Sets.
  4. 4. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 4 You should think about the following when your university considers sharing your Data /Data Sets (and the software that supports & runs that Data) with others: “You Cannot Share What You Don’t Own Or Have Rights To.”
  5. 5. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 5 • We all know that Data/Data Sets are increasingly critical to R&D • But, all parties may not fully appreciate the nuances of: – Whether / what degree is Data protectable as an “intellectual property” right (IP or IP Rights) ? – Who owns the IP Rights in that Data? – If another party owns the IP Rights in that Data • Can that other party try to control your access/use of that Data?
  6. 6. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights (Frank X. Curci) 6 • Data/Data Sets can be valuable IP assets for both research universities and for-profit companies they collaborate with: – Data/Data Sets could be the “secret ingredient” that’s critical to fully understanding and implementing the larger discovery – Analytics-derived data can be particularly important – Core-underlying data could have enhanced value in an aggregated form due to your selection criteria & organization of that data
  7. 7. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 7 • Even if your goal is to allow “open & free” sharing of your Data/Data Sets – You still need to address the underlying IP Rights / issues in that Data • But--you may still be asking? – Do you really need to care about these IP Rights if all you want to do is give others in the research community a free right to use your Data/Data Sets?
  8. 8. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 8 ANSWER: You normally cannot give away (ie: even openly share) something that you do not first own or have rights in.
  9. 9. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 9 • On the positive side---securing your IP Rights in your Data/Data Sets can actually give you more power / leverage to do what you want with your Data. – Including any goal/mission to allow open & free access to your Data/Data Sets by others in the research community • Think of IP Rights as your “tool” to help achieve your goal. • This “tool” helps you control the “destiny” of your Data, whether your goal is to: • “Commercialize” your Data, or • Allow your Data to be openly & freely used by others for more R&D
  10. 10. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 10 • If you need more convincing that you should address IP Rights before sharing Data: – For-profit companies --that collaborate with research universities-- increasingly view Data as an extremely valuable IP Right which they would like to own and / or control.
  11. 11. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 11 • Before “sharing” any Data/Data Sets, you should first: – Identify all proprietary Data: • used in their research • otherwise involved in any collaboration with others • otherwise under their control – Determine if that Data is protectable IP – Address who owns/controls that Data and each party’s right to access & use that Data
  12. 12. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 12 • Identify all potentially-protectable Data: – Clinical trial data – Any other data derived or gathered as part of any research • Research lab notes • Data & reports from a confidential pilot project • Analytics-derived data can be particularly important • Data from beta tests for software applications
  13. 13. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 13 • How Data is Protectable IP —Trade Secrets: – Trade Secret protection in USA: • “Trade secret” is confidential / proprietary information of an owner: • Has commercial value to the owner • Because that information is not generally available to the public • Owner takes reasonable measures to keep information secret – Examples of Data which may be protectable trade secrets: • Clinical trial data you organize in a special database • Research derived knowledge/data---think about your laboratory notes • Analytics-derived data from a research project or clinical trial
  14. 14. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 14 • How Data is Protectable IP---- Copyrights: – Copyright protection in USA: • A “work” is protectable under US copyright law if it’s: – An original “work of authorship” » ie: original to that creator/author – Fixed in a tangible medium of expression » Not an intangible “idea” • In other words: – original expressions of author are protected
  15. 15. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 15 • How Data is Protectable ----Copyrights: – Example of Data which may be Copyright-protectable: • Lab notes • Analytics-derived data may have certain copyright protection due to “creativity” in the analytical components • Databases may have certain copyright protection due to “creativity” in selection criteria & organization of the Data • Selection criteria & organization of aggregated core- underlying Data could give rise to certain copyright protection
  16. 16. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 16 • IP Ownership & Controlling Access to Data: – While some are starting to fully appreciate IP Rights in Data – For profit companies ---such as those your university collaborates with—already see tremendous IP value in Data which they want to own and/or control.
  17. 17. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 17 • IP Ownership and Controlling Access to Data: – Don’t underestimate the IP value of Data---because many others, including for profit companies, are increasingly focusing on: • Who owns the Data • Who controls the Data • Who has right to use the Data (under what rules)
  18. 18. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 18 • IP Ownership & Controlling Access to Data: – For-profit companies focusing more on ownership of Data and controlling access to Data: • Increasingly claim they own all IP Rights in the Data – then try to limit your researcher’s ability to use that Data – by inserting “rules” about that use • If University owns the IP Rights in the Data – For-profits try to obtain broad access rights to use that Data » With limited intervention by your university in the company’s use of that Data – May still try to impose restrictions on university’s use of your own Data
  19. 19. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 19 • Best Practices when giving your Data to Others: – Prudent to have a written agreement addressing • Ownership of Data • Rights to access and use the Data – Clearly preserve your institution’s IP ownership in the Data – You probably only want to give the other party a non-exclusive right to access/use your Data: • You do not want to limit your own use of your Data!
  20. 20. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 20 • Best Practices when giving your Data with others: – Clearly define other party’s right to access/use your Data: • Will other party get access to all--or just part--of the Data? • What can the other party do with your Data? • For how long? • Does the other party have to return your Data?
  21. 21. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 21 • Best Practices when giving your Data to others: – You may (again) be asking? • Why worry about these IP issues when giving “your Data” to another research university or non-profit research institution? • What’s wrong if that other university/institution suggests “informality” about how you share “your Data” with them? – Think about this: • Do you have all of the background IP rights and other rights necessary to allow the other institution to use “your Data”? • So, you may need to put parameters on the use of “your Data” by the other institution in order to respect the rights of prior rights-holders who have contributed to your Data source (“upstream party”).
  22. 22. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 22 • Recommendations when your institution seeks access to Data from other parties you collaborate with: – Ask party delivering the Data to clarify the “background IP” rights in that Data: • Does delivering party have the authority to give YOU the right to access and use that Data? • If not—ask delivering party to get you proper access / usage rights from the “upstream” parties • Need to avoid an upstream party trying to deny you the right to use that Data because delivering party did NOT actually have the proper authority to share that upstream party’s Data with you.
  23. 23. The Software and Data Licensing Solution You Cannot Share What You Don’t Own Or Have Rights To (Frank X. Curci) 23 • Recommendations when your institution seeks access to Data from other parties you collaborate with: – Clarify the scope of your rights to use that Data • Try to minimize restrictions on your university’s right to use the Data • Be realistic about your university’s ability to police these restrictions internally – You want to own IP rights in enhancements you develop while using that Data, such as: • Analytics-derived data you develop • Your selection criteria & organization of the underlying Data – If the delivering party enhances the Data they previously delivered to your university: • Does your university get access rights to use that enhanced Data?
  24. 24. MIA: Software Licensing for Academics Charles R. Williams, JD, PhD, CLP Associate Vice President for Innovation University of Oregon © University of Oregon. All rights reserved.
  25. 25. 25 We Aren’t The Bad Guys We all want to be open……. If you build it, they will come……. Information just wants to be free…… Most of what we do as universities is “Open”
  26. 26. 26 So how could Open be bad? Expectations for Attribution, Reputation and Participation Vary Between Academic Disciplines
  27. 27. IT’S ABOUT STEWARDSHIP 27 Providing incentives Providing curation Providing quality control
  28. 28. THE PUBLIC’S TRUST 28 • Perhaps at an all time low… • Distrust of government and science • High cost of education
  29. 29. ITS ABOUT IMPACT….. 29 NOT COMMERCIALIZATION AND PROFIT Commercialization is one of the channels that can achieve or accelerate impact. We need to showcase the adoption and use of our work. Demonstrating the importance of validation and quality control is critical.
  30. 30. BSD ATTRIBUTION 30 Copyright <YEAR> <COPYRIGHT HOLDER> Redistributions of source code must retain the above copyright notice……. Redistributions in binary form must reproduce the above copyright notice, …. in the Documentation and/or other materials provided with the distribution.
  31. 31. APACHE 2.0 ATTRIBUTION 31 Copyright [yyyy] [name of copyright owner] You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file….
  32. 32. GPL ATTRIBUTION • One line to give the program's name and a brief idea of what it does. Copyright (C) <year> <name of author> • appropriately publish on each copy an appropriate copyright notice • You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. 32
  33. 33. COMPUTER SCIENTIST ≠ ALL ACADEMIC RESEARCHERS • BSD 1999 Removal of attribution in advertisement. • Institutional affiliation important (© owner?) • Historical university position re: liability • Software is now ubiquitous (all disciplines) 33
  34. 34. TEACHING (IMPACT) Source Available Attribution Language • You agree to acknowledge the contribution Developers and Software make to your research, and cite appropriate references about the Software in your publications. The current citations for the Software can be found at: • http:_______________________ 34 Sharing of university software often is primarily about teaching what we have done. Making “Source Available” allows research colleagues to learn and test our work. Quality control is maintained where use and modification are allowed but distribution remains with the project steward.
  35. 35. RESEARCH COMMONS • A stewardship model that curates software projects. • Foundation: A unique agreement among member universities. The source code belongs to the Research Commons members and is a collaborative effort among research institutions, a model that promotes shared development and discoveries. Rosetta Commons RosettaCommons makes close collaboration between laboratories the norm, even with single code modules. This allows for rapid sharing of enhancements and promotes the values of team science. 35
  36. 36. Academic Commons • An iconography based open licensing system that has greater flexibility for academic researchers than CC: – Attribution: • Academic Credit Required • Approve & Update • No Attribution or reference – Sharing: • Research Commons • Hereditary • Redistribution allowed – Use: • Personal/Lab/Univ • Cost recovery • Commercial • Modification allowed Attribution RC
  37. 37. Thank You!
  38. 38. Reusable data for biomedicine: A data licensing odyssey Melissa Haendel Seth Carbon, Julie McMurry, Robin Champieux, Letisha Wyatt, Lilly Winfree @ontowonka #reusabledata Center for Data2Health
  39. 39. THERE >1500 PUBLIC BIOMEDICAL DATABASES IN NUCLEIC ACIDS RESEARCH DATABASE COLLECTION https://doi.org/10.1093/nar/gkw1188 @ontowonka 2
  40. 40. HOW MANY OF THESE DATA ARE TRULY REUSABLE? OPENNESS IS AN NAR REQUIREMENT, BUT … @ontowonka 3
  41. 41. MONARCH & THE NCATS BIOMEDICAL DATA TRANSLATOR www.ncats.nih.gov/translator www.monarchinitative.org @ontowonka 4
  42. 42. MONARCH’S LICENSING BURDEN @monarchinit 5
  43. 43. bit.ly/open-letter-licensing Additional signatories welcome @ontowonka 6
  44. 44. REUSABLEDATA.ORG Curate, evaluate, and provide guidance on legal and effective data reuse and redistributionWanna help? Join us bit.ly/reusabledata-forum github.com/reusabledata @ontowonka 7
  45. 45. reusabledata.org/criteria @ontowonka2018-01-07 8
  46. 46. CRITERION A: CLARITY 46% RECEIVE FULL STAR 25/54 Non Standard license (18/54) Multiple licenses (4/54) Missing license (7/54) @ontowonka 2018-01-07 9
  47. 47. CRITERION B: COMPREHENSIVE & FRICTIONLESS 59% RECEIVE FULL STAR 32/54 Reuse terms not clear 3/54 Doesn't apply to all data 10/54 Auto-fail due to missing/multiple license 9/54 2018-01-07 @ontowonka 10
  48. 48. CRITERION C: DATA IS ACCESSIBLE 87% RECEIVE FULL STAR 47/54 No “reasonable good-faith location” or single action 7/54 2018-01-07 @ontowonka 11
  49. 49. CRITERION D: FEW RESTRICTIONS ON TYPES OF REUSE: 35% RECEIVE FULL STAR 19/54 Restrictive but allows academic use 7/54 Restrictive, no academic provisions 19/54 Auto-fail due to missing/multiple license 9/24 2018-01-07 @ontowonka 12
  50. 50. CRITERION E: FEW RESTRICTIONS ON TYPES OF USER: 35% RECEIVE FULL STAR 19/54 Restrictive but allows academic use 11/54 Restrictive, no academic provisions 15/54 Auto-fail due to missing/multiple license 9/54 2018-01-07 @ontowonka 13
  51. 51. OVERVIEW OF 54 DATABASES 2018-01-07 @ontowonka 21
  52. 52. BALANCE OF, QUALITY, SUSTAINABILITY, AND (LEGAL) REUSABILITY @ontowonka 14
  53. 53. Findable Accessible Interoperable Reusable OPEN DATA IS FAIR-TLC Traceable Licensed Connected bit.ly/fair-tlc @ontowonka 15
  54. 54. THANKS TO: SETH CARBON JULIE MCMURRY ROBIN CHAMPIEUX LETISHA WYATT LILLY WINFREE ANDREW SU CASEY GREENE JOHN WILBANKS SEAN MCDONALD CHRIS AUSTIN NOEL SOUTHALL CHRISTINE COLVIS Center for Data2Health
  55. 55. The Data Licensing Solution
  56. 56. UNIVERSITY DATA LICENSING • Generally, TTOs do not: 1. Readily share data and related software under noncommercial licenses, 2. Allow rapid data aggregation, 3. Protect commercial licensing royalty streams, and 4. Safeguard provenance of research data 56
  57. 57. UNIVERSITY DATA LICENSING • What’s the root cause? – Too many data rightsholders (universities) preventing use of datasets • Data aggregation is impractical due to multiple license negotiations and conflicting data sharing restrictions and requirements 57
  58. 58. TRAGEDY OF THE COMMONS 58 • Overfishing, Atlantic NW Cod By Epipelagic - Own work, CC BY-SA 3.0, https://commons.wikim edia.org/w/index.php?c urid=19281989
  59. 59. TRAGEDY OF THE ANTICOMMONS 59 - The original Robber Barrons - Overprivatization - Gauntlet of tollbooths By lo Herodotus – Derivative, Nicolas Lardot - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/wiki/File:Bassin_de_la_Meuse.svg
  60. 60. TRAGEDY OF THE ANTICOMMONS 60 - Too many tolls, too little trade. By DerHexer; derivate work: Carschten - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=11052758 Château du Haut-Kœnigsbourg, Upper Rhine Plain
  61. 61. COMMONS VS. ANTICOMMONS Common Property • Overuse – English Language: ~1620s 61 Overprivatized Property • Underuse – Scrabble Dictionary: 2007
  62. 62. FULL SPECTRUM OF PROPERTY 62 Heller. “The Wealth of the Commons,” http://wealthofthecommons.org/essay/tragedy- anticommons Commons Anticommons
  63. 63. UNIVERSITY DATA LICENSING 63 CC BY Attribution Very permissive CC BY-SA Attribution- ShareAlike Like Copyleft CC BY-NC Attribution- NonCommercial No “commercial use,” but commercial R & D is fine CC BY-NC-SA Attribution- NonCommercial- ShareAlike Copyleft and no commercial use CC0 No Rights Reserved No restrictions
  64. 64. UNIVERSITY DATA LICENSING • CC0 – Pro’s 1. Anything is better than gridlock 2. Relatively quick and easy to implement 3. Digital data in the commons a. Inexhaustible, cannot be overused 4. Currently supported by the CC Science Commons initiative 5. NIH (tentatively) supports it 64
  65. 65. UNIVERSITY DATA LICENSING • CC0 – Con’s 1. Does not address software licensing a. Data without the related software will often render the data inaccessible/worthless b. Bayh-Dole Act implicitly protects software patent rights 2. Lose provenance on research data 3. Abolishes commercial royalty streams on datasets 4. Sustainability issue for data repositories 5. Does not solve the NIH NCBI Database problem of various data rightsholders submitting to those databases (and retaining their copyrights in their submissions) 65
  66. 66. PROPOSED DATA LICENSE • Universities / Non-profit Res. Inst. license to each other under: – Equivalent to a CC BY-NC-SA v. 4.0 (and limit to such “non- commercial licensees”). • NOT a Creative Commons license • de-identified data • expand non-commercial use to prohibit clinical use 66
  67. 67. DATA & SOFTWARE LICENSING SOLUTION • Aforementioned uniform data license and… • Open slot for software license – GPL v. 3.0, MIT Open Source License, etc. depending on backward compatibility issues – Whenever possible, use the uniform university software license agreement • Expandable slot for additional terms – Like the GPL 3.0 Section 7 on “Additional Terms,” allow custom language on: » Disclaiming warranties » Limiting liability » Declining to grant trademark rights » Others, TBD 67
  68. 68. DATA & SOFTWARE LICENSING SOLUTION • Rules of the game – Distributed aggregate licensors (i.e. various TTOs) authorized to license commercially BUT must share royalties with sources acc. to certain factors – Like the UBMTA, institutions enter a “master agreement” that includes the uniform data license – The master agreement also includes rules stating: • Who can claim to lead commercial licensing of a dataset • How royalties are shared, i.e. which factors determine royalty sharing and how they are quantified • NCBI Database data rightsholders (i.e. academic institutions, non-profits and government labs) hereby license their data rights under the uniform data license – More plausible than declaring the data CC0 and informing the submitters that NCBI unilaterally nuked their rights in their data 68
  69. 69. DATA & SOFTWARE LICENSING SOLUTION • AUTM Data & Software Licensing Committee to finalize and… – Adopt the master agreement, includes adopting the: • uniform data license • uniform software license – promulgate the rules of the game – Facilitate execution of the master agreement indicating that the institution will implement the uniform data license 69
  70. 70. CONTACT INFORMATION Arvin Paranjpe • paranjpe@ohsu.edu • 503-494-3210 70
  71. 71. The Software and Data Licensing Solution Models To Consider When Sharing Data/Data Sets (Frank X. Curci) 71 Closing Comments: Frank X. Curci Models To Consider When Sharing Data/Data Sets
  72. 72. The Software and Data Licensing Solution Models To Consider When Sharing Data/Data Sets (Frank X. Curci) 72 • In view of the IP issues and other critical considerations addressed today during our panel discussion, the following are some of the models or approaches that universities should consider when they want to share their Data/Data Sets (and the underlying software that supports & runs that Data) with others. • However, these models/approaches assume you have made sure you have the appropriate IP rights (as discussed at the beginning of this presentation) to share / license out your Data to others.
  73. 73. The Software and Data Licensing Solution Models To Consider When Sharing Data/Data Sets (Frank X. Curci) 73 • #1: UNILATERAL LICENSE OUT FROM YOUR UNIVERSITY: – Your university licenses out its Data/Data Sets and underlying software through your own license that sets your own rules of use • possibly royalty free—if you want – Allen Institute (Seattle) is an example: • royalty free licenses given in its brain atlas, but subject to their licensing provisions
  74. 74. The Software and Data Licensing Solution Models To Consider When Sharing Data/Data Sets (Frank X. Curci) 74 • #2: LICENSE OUT SOME OF YOUR DATA/SOFTWARE BY USING A THIRD PARTY LICENSING MODEL: – Could use Creative Commons license • Sounds attractive—but there are limitations – Could use GPL or other open source licensing model • May be too restrictive – There are pros and cons to relying on the terms & conditions dictated by a third party license agreement in order to govern the use and destiny of your Data
  75. 75. The Software and Data Licensing Solution Models To Consider When Sharing Data/Data Sets (Frank X. Curci) 75 • #3: BILATERAL DATA / SOFTWARE SHARING AGREEMENT BETWEEN TWO INSTITUTIONS: – Two institutions sign a written contract – Arguably easier to accomplish since only two institutions.
  76. 76. The Software and Data Licensing Solution Models To Consider When Sharing Data/Data Sets (Frank X. Curci) 76 • #4: CONSORTIUM BETWEEN MULTIPLE INSTITUTIONS VIA AGREEMENT: – Multiple parties sign a written contract – Need to address background IP/copyrights of the consortium members and how that is contributed by each member to this consortium effort – Also need to address copyright issues of parties who are not in consortium • may need to give notice to members that they may need license from these outsiders
  77. 77. The Software and Data Licensing Solution Models To Consider When Sharing Data/Data Sets (Frank X. Curci) 77 • #4: CONSORTIUM BETWEEN MULTIPLE INSTITUTIONS VIA AGREEMENT (continued): – Need to address the procedures by which the consortium members develop updates to the Data/Data sets and software – Need to address the rights of the consortium members to use the Data/Data sets and software (and updates) for their internal R&D – Need to address rights of each member (or just a lead member??) of the consortium to license out the Data/Data sets and software to 3rd parties and how to share the royalties among the consortium members (possibly similar to patent pools).
  78. 78. The Software and Data Licensing Solution Models To Consider When Sharing Data/Data Sets (Frank X. Curci) 78 • #5: CONSORTIUM BECOMES A STAND ALONE NON-PROFIT CORPORATION / LLC: – At some point it may make sense to incorporate this consortium effort into a standalone nonprofit entity – Often do this: • to have a more centralized way of managing the Data/Data Sets and software rights (ie: neutral repository) • for administrative/enforcement purposes
  79. 79. The Software and Data Licensing Solution 79 Question and Answer Period Doc #3002304

Hinweis der Redaktion

  • Abstract:
    Biomedical data integrators grapple with a fundamental blocker in research today: licensing for data use and redistribution. Complex licensing and data reuse restrictions hinder most publicly-funded, seemingly “open” biomedical data from being put to its full potential. Such issues include missing licenses, non-standard licenses, and restrictive provisions. The sheer diversity of licenses are particularly thorny for those that aim to redistribute data. Redistributors are often required to contact each sub-source to obtain permissions, and this is complicated by the fact that on each side of the agreement there may be multiple legal entities involved and some sub-sources may themselve already be aggregating data from other sub-sources. Furthermore, interpreting legal compliance with source data licensing and use agreements is complicated, as data is often manipulated, shared, and redistributed by many types of research groups and users in various and subtle ways. Here, we debut a new effort, the (Re)usable Data Project, where we have created a five-part rubric to evaluate biomedical data sources and their licensing information to determine the degree to which unnegotiated and unrestricted reuse and redistribution are provided. We have tested the (Re)usable Data rubric against various biomedical data sources, ranking each source on a scale of zero to five stars, and have found that approximately half of the resources rank poorly, getting 2.5 stars or less. Our goal is to help biomedical informaticians and other users navigate the plethora of issues in reusing and redistributing biomedical data. The (Re)usable Data project aims to promote standardization and ease of reuse licensing practices by dat providers.   
  • Clearly stated
    A clearly stated, unambiguous, and hopefully standard, license for data use is critical for any (re)use of data: if there is no license to be found, then rights are unclear and one needs to assume the default: all rights reserved. more »
    Comprehensive and non-negotiated
    Data that is mixed under different licenses, only partially available, or must be in some way negotiated creates barriers to the (re)use of data. more »
    Accessible
    Data must be accessible in a reasonable and manner to be useful to the broader community. more »
    Avoid restrictions on kinds of (re)use
    Data should be able to be copied, built upon, edited, and modified as freely as possible. more »
    Avoid restrictions who may (re)use
    Data should should be available to as many people as possible for their (re)use.
  • Overview, how many at which scores
  • TTOs are not able to address all four of the above bullets. There are no off the shelf noncommercial licenses that adequately protect our royalty streams, allow for rapid data aggregation between academic institutions, and safeguard the provenance of research data.

    In a broad sense, we TTOs are undergoing a coordination breakdown.


  • The root cause of the coordination breakdown is that there are too many rightsholders.

    It is subtle, but the end result is massive underuse of data. Commercial R & D that is less efficient and effective. Same goes for university R & D and for clinical informatics.

    A coordination breakdown like this is called an anticommons problem. But to unpack how an anticommons works, let’s start with the commons problem.

  • In 1992, Canada declared a moratorium on the Northern Cod fishery, which for the preceding 500 years had largely shaped the lives and communities of Canada's eastern coast.

    It has been over 20 years since the moratorium on fishing Atlantic cod in eastern Canada, but the fish stocks have not replenished. Or in other words, there are no fish.

    Commercial overfishing is why there are Somali pirates on the high seas. They used to be fisherman. When we use the term Tragedy, we are not being dramatic.
  • The Rhine River during the Middle Ages is the best example of a Tragedy of the Anticommons. The Rhine starts in the Swiss Alps, follows the Franco-German border, flows through the Netherlands and empties into the North Sea.

    It was an important waterway for commerce and Europe’s principal highway during the Middle Ages. The Roman Emperor protected the great European trade route. Merchant ships paid a modest toll to safeguard their transit. But after the empire weakened in the thirteenth century, German Robber Barrons built castles on the Rhine and began collecting their own illegal tolls.
  • Robber Barons built spectacular castles along the Rhine, including this one. There were hundreds of castles, including 35 within a 90 mile span. Far too many. The gauntlet of tollbooths made shipping impractical and commerce down the river halted.

    Today, the hundreds of ruined castles are a lovely tourist destination…But for hundreds of years, everyone suffered—even the barons. The European economic pie shrank. Wealth disappeared. Too many tolls meant too little trade.

    This is why I say we Universities are medieval.
  • As tech transfer professionals, we should be most fearful of gridlock, because it means rampant underuse. In the data sharing world, we have underuse due to high transaction costs.
  • The above continuum was introduced by Columbia Prof. Michael Heller, a thought leader in this field. He says there’s no effective ownership at the ends of the spectrum… Overfish the cod, and there’s effectively no cod to fish anymore. Too many data license restrictions, then data aggregation and integration is impractical and essentially impossible.

    So what’s the data licensing solution? Some say, nuke any and all IP rights in the data – essentially the copyrights in the databases. That’s called CC0. That involves waiving all interests in one’s works and thereby place them as completely as possible in the public domain. We’ll talk about that in a second, but first consider how governments deal with the commons problem. They can’t control the common resource like the ocean, but they can declare moratoria on how you access the ocean, i.e. how and where you fish. What it effectively does is move the resource closer to the middle of the continuum. I think that’s the real solution for data licensing. But first, let’s cover the off the shelf data licenses.
  • CC BY This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation.

    CC BY-SA This license lets others remix, tweak, and build upon your work even for commercial purposes, as long as they credit you and license their new creations under the identical terms. This license is often compared to “copyleft” free and open source software licenses.

    CC BY-NC This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.

    CC BY-NC-SA This license lets others remix, tweak, and build upon your work non-commercially, as long as they credit you and license their new creations under the identical terms.

    CC0 owners of copyright- or database-protected content to waive those interests in their works and thereby place them as completely as possible in the public domain, so that others may freely build upon, enhance and reuse the works for any purposes without restriction under copyright or database law.
  • NIH Scientific Data Council nearly adopted the plan to declare NIH-funded research data as CC0.

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