Research Data Alliance/US Call for Fellows

I'm a co-PI on a project that provides a great opportunity to the early career researchers and professionals to engage with the Research Data Alliance and help to improve data practices and make data management and data sharing easier and more transparent. Below are the details from the call for fellows:

The Research Data Alliance (RDA) invites applications for its newly redesigned fellowship program. The program’s goal is to engage early career researchers in the US in Research Data Alliance (RDA), a dynamic and young global organization that seeks to eliminate the technical and social barriers to research data sharing.

The successful Fellow will engage in the RDA through a 12-18 month project under the guidance of a mentor from the RDA community. The project is carried out within the context of an RDA Working Group (WG), Interest Group (IG), or Coordination Group (i.e., Technical Advisory Board), and is expected to have mutual benefit to both Fellow and the group’s goals.

Fellows receive a stipend and travel support and must be currently employed or appointed at a US institution.

Fellows have a chance to work on real-world challenges of high importance to RDA, for instance:

• Engage with social sciences experts to study the human and organizational barriers to technology sharing
• Apply a WG product to a need in the Fellow’s discipline
• Develop plan and disseminate RDA research data sharing practices
• Develop and test adoption strategies
• Study and recommend strategies to facilitate adoption of outputs from WGs into the broader RDA membership and other organizations
• Engage with potential adopting organizations and study their practices and needs
• Develop outreach materials to disseminate information about RDA and its products
• Adapt and transfer outputs from WGs into the broader RDA membership and other organizations

The program involves one or two summer internships and travel to RDA plenaries during the duration of the fellowship (international and domestic travel). Fellows will receive a $5000 stipend for each summer of the fellowship. Fellows will be paired with a mentor from the RDA community.

Through the RDA Data Share program, fellows will participate in a cohort building orientation workshop offering training in RDA and data sciences. This workshop is held at the beginning of the fellowship. RDA Data Share program coordinators will work with Fellows and mentors to clarify roles and responsibilities at the start of the fellowship.

Criteria for selection: The Fellows engaging in the RDA Data Share program are sought from a variety of backgrounds: communications, social, natural and physical sciences, business, informatics, and computer science. The RDA Data Share program will look for a T-shaped skill set, where early signs of cross discipline competency are combined with evidence of teamwork and communication skills, and a deep competency in one discipline.

Additional criteria include: interest in and commitment to data sharing and open access; demonstrated ability to work in teams and within a limited time framework; and benefit to the applicant’s career trajectory.

Eligibility: Graduate students and postdoctoral researchers at institutions of higher education in the United States, and early career researchers at U.S.-based research institutions who graduated with a relevant master’s or PhD and are no more than three years beyond receipt of their degree. Applications from traditionally underserved populations are strongly encouraged to apply.

To apply: Interested candidates are invited to submit their resume/curriculum vitae and a 300-500 word statement that briefly describes their education, interests in data issues, and career goals to datashare-inquiry-l@list.indiana.edu. Candidates are encouraged to browse the RDA website https://rd-alliance.org/ and pages of interest and working groups to identify relevant topics and mutual interests.

Important dates:
April 16, 2015 – Fellowship applications are due
May 1, 2015 – Award notifications
June 18-19, 2015 – Fellowship begins with the orientation workshop in Bloomington, IN

---

RDA Data Share, funded by the Alfred P. Sloan Foundation under award G-2014-13746, engages students and early career researchers in the Research Data Alliance. This engagement builds on foundational infrastructure funded by the National Science Foundation grant # ACI-1349002.

4th RDA Plenary - Breakout session on engagement

Below is a summary from a breakout session on engagement that I co-chaired with Andrew Maffei at the Research Data Alliance 4th plenary in Amsterdam, the Netherlands (Monday, September 22, 2014).

Introduction / Overview

The session had about 25 people in attendance.

I provided an overview of the group and its activities. The group receives strong interest and support at plenaries, but in between the interest drops.
Activities to date include working on the model to connect technically oriented groups and domain interest groups (Domain Interest Group Form and Function model, or DIG-FF), a summer internship project, and participation in the RDA/US advisory committee.

DIG-FF Model: we need to observe inter-group interactions and support form and function of these groups as we can. It may be too early to propose a model. Rather, we can focus on small but practical things can facilitate inter-group communication (e.g., creating information-collection instruments, disseminating information, etc.).

Objectives for P4:

• Present the summer internship project
• Modify the case statement (create a charter)
• Attend breakout meetings of the domain-specific groups and collect information about their work and outcomes
• Find opportunities to work on the amplification and adoption theme promoted by the RDA/US within the group and through collaborations

RDA/US summer internship project
The project was done by the RDA/US intern Rene Patnode from the University of California San Diego under the mentorship of EIG chairs. Rene interviewed 16 chairs of the domain interest groups (DIGs) over the phone and email. The goal of the project was to understand the barriers for researchers to data sharing.
Observations and findings:

• There is a significant representation of information systems professionals rather than researchers in RDA
• Responses were consistent with the literature about barriers: sharing is extra work, user interfaces are poor, no fit with current research culture, no funding for data, lack of good data sources
• To remove those barriers we might try to make data sharing enjoyable and social (e.g., more interaction between researchers, etc.).
• Gamification (e.g., adding points, badges, etc.) is one possible approach. Citizen science is another mechanism for data collection and sharing.
• IT solutions need to better mirror the workflow that is currently in use
• Suggestions for RDA role: make processes of RDA engagement clear and transparent, support cross-pollination, take a political stance by lobbying, encourage better technical development

Discussion
Many interesting points and questions were raised during the discussion. Below are some of them:

• Collaborative virtual research environments are one way to improve inter-communication and incentivizing.
• Does funders requirement for data management plans and its implementation actually improve the outcomes of data stewardship and sharing?
• Data needs to be useful for someone else to create “an appetite” for removing burdens
• Cultural change usually means that you have to address ALL the stakeholders. Hence the idea for RDA to take a more political role.
• Grant budgets need to support data management plans, which need resources.
• Knowledge Exchange (http://www.knowledge-exchange.info/) is an organization that has interests similar to this group.
• Fun in sharing is good, but what are the other reasons for sharing? We might want to ask the question “What would you like?” and work on that. Dig into the benefits and show scientists in various areas how sharing data can be of benefit.
• We have talked a lot about domains. Another orthogonal axis is to look at organizations. Can you get universities, institutions, and membership organizations declare values around data sharing?
• Cultural differences in data sharing are often ignored. For example, there are different approaches to privacy and consent.

Next steps for the group

• Develop a form to collect stories about benefits and pains of data management / sharing 
• Start collecting stories Identify and reach out to champions of data sharing 
• Design an ISHARE t-shirt 
• Long term: build practical tools for engagement, pay attention to our own data practices, share the data from RDA, advocate for better RDA website, think about focusing on organizations instead of (or in addition to) domains, collaborate with the “Digital Practices in History and Ethnography” group on studying RDA as an organization 

Conclusion 
Engagement in RDA is very important, we need to keep going!

More about our group here: RDA Engagement Interest Group

Summary of drivers and barriers in data sharing

Nice summary of the drivers, barriers, and enablers that determine stakeholder engagement based on expert interviews in Dallmeier-Tiessen et al., 2014, Enabling Sharing and Reuse of Scientific Data (restricted access).

Drivers and benefits

• Societal benefits - economic/commercial benefits; continued education; inspiring the young; allowing the exploitation of the cognitive surplus in society; better quality decision making in government and commerce; citizens being able to hold governments to accountable.
• Academic benefits - the integrity of science; increased public understanding of science.
• Research benefits - validation of scientific results by other scientists; recognition of their contribution; reuse of data in meta-studies to find hidden effects/trends; testing new theories against past data; doing new science not considered when data was collected without repeating the experiment; easing discovery of data by searching/mining across large datasets with benefits of scale; easing discovery and understanding of data across disciplines to promote interdisciplinary studies; combining with other data (new or archived) in the light of new ideas.
• Organizational benefits - publication of high quality data and citation of data enhance organizational profile; preserved data linked to published articles adds value to the product; data preservation is more business; reputation of institution as “data holder with expert support” is increased; combining data from multiple sources helps to make policy decisions; reuse of data instead of new data collection reduces time and cost to new research results; use of data for teaching purposes.
• Individual contributor benefits - preserving data for the contributor to access later — sharing with your future self; peer visibility and increased respect achieved through publications and citation; increased research funding; when more established in their careers through increased control of organizational resources; the socio-economic impact of their research (e.g., spin-out companies, patent licenses, inspiring legislation); status, promotion and pay increase with career advancement; status conferring awards and honors.

Barriers and Enablers are Related to:

• Individual contributor incentives
• Availability of a sustainable preservation infrastructure
• Trustworthiness of the data, data usability, pre-archive activities
• Data discovery
• Academic defensiveness
• Finance
• Subject anonymity and personal data confidentiality
• Legislation/regulation

DLF forum notes: Data – sharing – libraries – culture

A recent Digital Library Federation (DLF) forum started with an inspiring keynote by R. D. Lankes and his challenging of the monopolies of content delivery in higher education and the neutrality of the library profession. He argued that librarians should improve society by facilitating knowledge creation, which includes not only providing access to resources, but also teaching literacy, genres, and communication, creating learning environments and motivating people to learn and research. He also emphasized that a librarian is somebody who has either training, professional experience, or spirit, thereby shifting the emphasis from degrees to actual knowledge and passion.

Of lesser success was my leading of a birds-of-a-feather session about Research Data Alliance (RDA). BoFs happened during lunch and it’s probably not a good time to learn about a new organization. Spreading the word about new initiatives is also hard because they are new and there is more anticipation and preparatory work, rather than something that is ready to use. I had several good conversations about RDA, but there could have been more.

All sessions were informative and productive, but the one that got me thinking was a session that I couldn’t attend "Creating the New Normal: Fostering a Culture of Data Sharing with Researchers". It’s a rich topic, so below is some food for thought - my interpretation of the session theme based on the materials prepared for the session by organizers and community notes taken by participants during the session.

The session was based on the Data Information Literacy (DIL) project that is looking to identify skills, capacities and toolkits for data management and leverage information literacy to bridge the disconnect between faculty, graduate students (who are often the data managers in scientific labs) and librarians.

The disconnect stems from different needs in relation to data – data collection vs analysis vs preservation and access. Even though researchers may recognize the importance of data management, they rarely consider it being part of the curriculum or articulated research culture. To simplify, graduate students collect data and both faculty and students analyze it and work on publications. The messiness of data collection and storage is something that bothers many researchers, but they don’t necessarily know what to do about it.

Librarians are increasingly interested in incorporating research data into their library collections and providing access to it as part of their service. They would like datasets to be better prepared for storage and sharing, and they are ready to help. They also have a mission of helping others to address their information needs, but they don’t necessarily have the power to do that. How could these disconnects be bridged?

By working through three scenarios proposed for discussion, the session seemed to approach disconnect bridging via embedding librarians/data specialists into research teams and projects, learning about researchers’ needs and helping them with their data, while teaching them good practices of data management. Librarians’ expertise can be useful in the areas of file organization, metadata, and tools for storage and sharing.

This approach is good, but it’s quite demanding in terms of resources. I agree that data literacy should be embedded in a larger teaching of proper information management (including ethics, security, authoritativeness, etc.) and this could help re-use existing channels of library instruction and minimize resources. At the same time, I wonder whether we should also think about the culture of data sharing in terms of private/public epistemic objects. Data is still a private object, which will constantly undermine the "new normal" of sharing, because we don’t typically share objects we consider private. The new norm then should be that data are "shareable" from the beginning.

In some organizations and domains data are already open and shareable, for example, data from the large observatories supported by the US government. Consequently, data producers in those organizations may have better data information literacy. For other "private" domains, particularly, in the social sciences, there are still a lot of barriers and fears. Would a public culture of data sharing mean peer review of data collection instruments? Survey respondents as co-owners of data? Data curators as independent decision-makers with regard to choosing and preparing data for public use? A lot of possibilities come with the idea of data sharing cultures.

Human infrastructure - build it bottom-up

An article by Procter et al "Fostering the human infrastructure of e-research" (2013, restricted access) discusses the challenges of embedding computing resources and systems into research. E-infrastructures (aka cyberinfrastructures in the US) are defined as digital information and communication technologies (ICTs) that can provide fast and scalable access to remote resources and increase discovery and innovation. Human infrastructure is arrangements of actors and organizations that make computer-research systems work. It is often acknowledged that human infrastructure is neglected compared to the investments in the technical infrastructure. And that's where the problem is. Cyberinfrastructures don't work without human adoption and use. As authors of the article state:

"So far, despite substantial investment, the desired transformative impact has yet to be achieved."

The article describes the Enabling Wider Uptake of e-Infrastructure Services project (ENGAGE/e-Uptake) that was designed to identify inhibitors and enablers of the adoption of e-Infrastructure services. The identification is based on interviews with ~50 researchers from higher education institutions and ~50 "intermediaries", or technical specialists who support researchers in their use of ICTs.

Findings

Obstacles in e-Infrastructure adoption and use:

• Lack of training - many researchers hear about research computing services, but they often don't know about the nature of the services and the benefits of using them.
• Lack of local research support - support is often basic, limited, fragmented and difficult to access.
• Poor project management - projects that involve technical and research personnel have their own managing needs, i.e., the need to manage collaborations between people with their own research agendas and temporarily aligned interests. Managers who don't have such skills may make biased decisions and favor one type of team members over others.

Conclusions

• There is complexity in divisions of labor and in organizational structures that may be historical. In cyberinfrastructure projects we may need more flexible and flatter approaches.
• More teaching and training is needed - not only teaching of e-Research methods in classes, but also lifecycle outreach from the collaborative formation of projects through the acquisition of skills and the appropriation of technologies to the dissemination of experiences back into the community (see, for example, eIUS project for a collection of use cases and tools used in them).
• User engagement can take a form of relying on "hybrids", i.e., people with both technical and domain expertise, or a form of co-locating technical experts and users throughout projects. More research is needed into how to do that plus how to leverage community engagement.
• New practices must be embraced not only by researchers, but by the organizations within which researchers work.

The article reinforces the idea that by default software and computing tools are hard to learn and use. Why is that? A common argument is that complex problems require complex solutions. Doesn't a simple fix sometimes work better? Or, perhaps, it's ok to have complex solutions, but they arise from a number of simple solutions combined and overlapped. I wonder whether we should start with building simple local systems ("recognized routes" or local roads) rather than large and multi-purpose systems (interstate highways, to continue the infrastructure metaphor). Once local needs are met and served well, we can move into connecting local systems (i.e., building bridges, gateways, etc.). It circles back to the investment in human infrastructure and bottom-up rather than top-down approaches.

Research Data Alliance (RDA) plenary thoughts

My notes from the Research Data Alliance plenary (RDA) are posted on the Digital Library Federation site - http://www.diglib.org/archives/5142/