Research Data Support: a Transformative Approach to Jointly Growing Services and Skills

May 20th, 2023

The phrase “data is the new currency of research” surfaces significant changes that academic libraries are grappling with in the scholarly information ecosystem.

The data landscape is teeming with overlapping issues such as data management, sharing, re-use, discovery, citation, FAIR- and CARE-based practices, reproducibility, bias and social justice concerns, community control and data sovereignty, evolving research methods, and more. Each of these areas implicate a plethora of evolving norms, recommended practices, skills, platforms, and tools.

Advancing library data services to meet the related evolving needs of researchers requires many things (e.g., service models, staffing, partnerships), and certainly it involves broadly advancing staff expertise in these data spaces. To accomplish this, many academic libraries have explored, studied, and piloted options and models for data-oriented organizational learning. An overview of different training and upskilling efforts is provided in Wittenberg, Sackmann, and Jaffe (2018).

Local conditions across libraries call for different approaches. At MIT Libraries, a recent initiative in this space is Digging into Data. With limited post-pandemic resources, our goal was to iterate forward with both learning and service development. Our overall thinking is that becoming better at research data support work is a skills-building situation for liaisons requiring ongoing integration into our everyday work and better ways of documenting and sharing disciplinary knowledge with data services staff. This learning effort was designed with that in mind.

Digging into Data for us is about learning to learn – together and efficiently – by blending the expertise of data services staff with the disciplinary knowledge of subject liaisons in a way that can be incorporated in regular work schedules. Our tactics included a guided learning plan, service-oriented deliverables, and strong project management.

Meeting periodically over the course of many months, each participating librarian used a common template to systematically explore and document the data landscape in a specific research field of their choice, gathering information on data collection practices and tools, data types, formats, analysis, metadata schemes, repositories, collaboration and sharing practices, and discipline-specific challenges. Throughout, attention was paid to issues of fairness and social justice as they surfaced differently across disciplines. The summary information gathered on these different research fields is being transferred to an extensible knowledge base that supports our data consultation services. As round one draws to a close, the expectation is that what liaisons have learned in this round can be applied going forward, both in their everyday work with researchers as it indicates which research fields should be next on their list for study, as well as in identifying research data topics and areas for future exploration.

Outcomes and Deliverables

Digging into Data unfolded in the context of other ongoing data-related projects and varied staff skills.

Outcomes for staff included:

• Ability to take the template and methods for systematically exploring the data landscape of a research field and apply them to additional research fields with increasing ease and confidence.
• Ability to translate what was being learned about the data landscape of a research field into actionable insights for both current and future service design, with particular attention to open research and scholarship and social justice issues.
• Creation of a shared knowledge base, useful for research support by both data services staff and liaisons.

The deliverables to support those outcomes were the reusable template for exploring the data landscape in a field, the learning plan, and the knowledge base.

Process

Attention to our multiple perspectives, different roles, and varied expertise was important from the start – we drew blended project leadership from both data services and liaisons to establish the outcomes, help design the learning plan, and develop a project plan and roadmap.

This learning experience was going to require a lot of commitment from the participants – keeping it on the radar while other projects and work demanded mindshare, maintaining momentum when many folks hit challenging learning curves. Consequently, the project team began by establishing explicit plans for communications and learning; creating a wiki space for the project and the deliverables; drafting pre- and post-surveys to better understand the experience at its completion; and structuring the kick-off meeting. This preparatory work allowed the team to thoughtfully consider the principles of andragogy and how to better support adult learning by creating a space where participants contributed to shaping goals, outcomes, and engagements with each other. An important part of this was a regular, required meeting where participants shared their work and saw the approaches of others. Through this process, the project team realized that another useful tool would be an optional working meeting that participants could use to consult or work companionably. This became an important element in peer learning, motivation and developing confidence in the liminal space of not having all the answers.

The learning plan was also key to the success of the learning experience. It served as a workbook and was carefully sequenced to guide the participants in systematically exploring data in their specific research field. The sequenced scaffolding in the learning plan (learning goals, hints, resources, considerations) took learners through these general modules:

• How to choose a sub-discipline (relevant to their work, not too broad or too arcane to start with, etc.)
• Finding specific examples of data used in that field, noting collection methods or sources of data, measurement units, file sizes, and other features of actual data types at different stages of the research effort.
• Tools and methods used for processing, managing, and analyzing data or creating more actionable data from raw data.
• Sharing and curating practices and challenges, funder requirements relevant to the field, repositories, metadata schemes, etc.
• Perspectives and skills that information professionals bring to data spaces and data challenges.

The templated worksheet helped participants capture their work for each module, making it easier to learn from each other and recognize their own increasing knowledge, while providing a central place from which to build the knowledge base to support future consultation services. Each module was also an opportunity to look at discipline specific issues of equity.

Experience & Adjustments

It surprised the team to discover that determining the right-sized field of research was work in itself, where the data and research were accessible enough for learners with varied levels of prior knowledge.

The process also uncovered a fair amount of personal investment, concerns, and desire to talk about how things were going. Especially for staff less familiar with the space, it was important to use group sessions to refocus on actual learnings and the process of exploration, rather than on what remained unknown.

We started to experience issues with other competing priorities and commitments, and the leadership of project managers in addressing challenges was key. We repeatedly chose to lengthen the timeline rather than lose the quality of the experience, and managed the tradeoffs accordingly. For example, lengthening the project timeline made it hard to maintain a coherent throughline for the experience. Multiple participants voiced challenges in setting aside time necessary to complete the preparatory and interim work for group sessions. To help manage this, the team added a weekly two-hour hangout session, where participants could pop-in and ask questions, or just use it as a hold on their calendar. We also added sections to the template to encourage participants to record their reflections and information in order to help with remembering what we accomplished as the timeline stretched out. As a group, we had to develop stronger norms about rescheduling sessions if participants had conflicts since group learning was key to the process.

Going forward

While none of our outcomes are fully met with round one of this work, our approach yielded many shorter-term successes. Here are a few:

• The shared vocabulary developed through the learning plan modules has enabled and prompted cross-disciplinary discussion of data use.
• Having more familiarity with the data & tools space enables liaisons to engage better in consultation work with data services staff.
• Data skills learned were embedded in domain knowledge that was already important to liaisons.
• Knowledge of disciplinary culture and practices that affect data discovery or sharing could be more usefully documented and shared with data services staff.
• Experts got a little more comfortable being in a space of not knowing all the things.

With much of the heavy lifting done (the design of the learning plan, template, and knowledge base), and given the positive feedback and insights from participants of this round, we expect to continue this learning work. Future rounds of Digging into Data will include liaisons who were not part of the original pilot, and will explore how this kind of systematic learning can become routine as liaisons increasingly engage with research data support. Other considerations for future work are choosing an interdisciplinary area as a theme for all participants that is important to the Institute (e.g., climate change), and figuring out a way to include students.

Acknowledgements

Resources

References

Wittenberg, J., Sackmann, A., & Jaffe, R. (2018). Situating expertise in practice: domain-based data management training for liaison librarians. The Journal of Academic Librarianship, 44(3), 323-329. ISSN: 0099-1333; DOI: https://doi.org/10.1016/j.acalib.2018.04.004