1. “Enabling Seamless Data Sharing in Industry and Academia”
September 29th-30th
Back in September, I took part in a two-day workshop here at Drexel, sponsored by the NSF Northeast Big Data Innovation Hub. The workshop is part of a connection to a new NSF/Northeast Spokes award, entitled “A Licensing Model and Ecosystem for Data Sharing,” in which the Metadata Research Center is a key partner, with MIT and Brown. As of January, I am the Metadata Research Center research associate on the project.
There were 53 workshop participants, from all over the country, from both industry and academia so that we could get many varying perspectives. The idea behind the workshop was to motivate the data sharing community to get involved in the discussion around what we can do to make the data sharing process faster, easier, and more frequent.

2. Workshop Goals What data is not being shared that should be, and why?
Data sharing failures: what went wrong?
Data sharing successes: Identify best practices
Identify data sharing challenges
The workshop had several overall goals.
The first goal was to discuss the data sharing problem: what data is not being shared that should be, and why is it not being shared?
The second goal was to share specific case studies of real life data sharing failures, to assess what went wrong, and what would be a potential solution to that problem.
The third goal was to share examples of successful data sharing partnerships, to identify best practices that could be replicated again in the future.
The fourth goal was to discuss the scope of potential solutions to the challenges that organizations face regarding data sharing. The challenges for industry were frequently different than the challenges faced by academia, so solutions have to be innovative. For example, developing a set of standardized data sharing licenses could help to expedite the data sharing process by as much as 6 months.

3. Data Sharing Challenges
The sharing of private and competitive information
Lack of incentives (especially in industry)
Overhead costs (money & resources)
Liability, preventing mistakes or data misuse
Data as a living entity
Complex regulations governing use
The workshop brainstorming sessions identified several key data sharing challenges, which included:
-Serious concern around sharing private and competitive information.
-A lack of incentives. In the academic world, data sharing might seem like a no-brainer, but for industry, many have a hard time finding a clear rationale for taking the risks involved with sharing data.
-There’s a tremendous overhead cost of both money, resources, and time
-Concerns about liability and preventing mistakes or data misuse. People are afraid of lawsuits.
-There’s also the notion of data as a living entity that can't just be thrown over the wall. Sometimes it needs to be updated or even redacted. How do we ensure that other organizations will comply with this “back and forth” process?
-In addition, data sharing is fraught with complex regulations governing data use. Sometimes datasets can only be used for a specific purpose, for a specific period of time, and under very specific conditions.

4. Potential Solutions Standardized data sharing licenses
Integrated policy/technology framework with access controls & usage protection
Incentivizing data sharing: Money, innovation, awards, building relationships, making data sharing easier
Some of the potential solutions discussed in the workshop included:
-Creating a set of standardized data sharing licenses, using pre-defined clauses, and in comprehendible language
-An integrated policy/technology framework, or “sandbox environment” for data sharing which would control access to data sets, as well as track data usage using watermarks so as to prevent data misuse.
-With regards to incentivizing the data sharing process, incentives could include money, awards, innovation, reducing duplication of effort, and the idea of involving people with similar mindsets to facilitate the incremental building of trust between organizations, over time.
-Another obvious key incentive is the general concept of making data sharing an easier process.

5. Broad Action items Data gathering and inquiry Community building
Educational resource development and outreach
Moving forward, some of the phase 1 broad action items to result from this workshop include:
data gathering and inquiry
community building
And educational resource development and outreach.
To find out more, the workshop agenda and final report can be found on the Metadata Research Center website.
So, thanks for listening, and let me know if you have any questions.
Workshop Agenda & Final Report: