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CC BY and data: Not always a good fit

Simba dans le carton, jacme31, CC BY-SA 2.0

This post was originally published on the University of California Office of Scholarly Communication blog.

Last post I wrote about data ownership, and how focusing on “ownership” might drive you nuts without actually answering important questions about what can be done with data. In that context, I mentioned a couple of times that you (or your funder) might want data to be shared under CC0, but I didn’t clarify what CC0 actually means. This week, I’m back to dig into the topic of Creative Commons (CC) licenses and public domain tools — and how they work with data. (more…)

Building a user-friendly RDM maturity model

UC3 is developing a guide to help researchers assess and progress the maturity of their data management practices.

What are we doing?

Researchers are increasingly faced with new expectations and obligations in regards to data management. To help researchers navigate this changing landscape and to complement existing instruments that enable librarians and other data managers to assess the maturity of data management practices at an institutional or organizational level, we’re developing a guide that will enable researchers to assess the maturity of their individual practices within an institutional or organizational context.

Our aim is to be descriptive rather than prescriptive. We do not assume every researcher will want or need to achieve the same level of maturity for all their data management practices. Rather, we aim to provide researchers with a guide to specialist knowledge without necessarily turning researchers into specialists. We want to help researchers understand where they are and, where appropriate, how to get to where they want or need to be.

Existing Models

As a first step in building our own guide, we’ve researched the range of related tools, rubrics, and capability models. Many, including the Five Organizational Stages of Digital Preservation, the Scientific Data Management Capability Model, and the Capability Maturity Guide developed by the Australian National Data Service, draw heavily from the SEI Capability Maturity Model and are intended to assist librarians, repository managers, and other data management service providers in benchmarking the policies, infrastructure, and services of their organization or institution.  Others, including the Collaborative Assessment of Research Data Infrastructure and Objectives (CARDIO), DMVitals, and the Community Capability Framework, incorporate feedback from researchers and are intended to assist in benchmarking a broad set of data management-related topics for a broad set of stockholders – from organizations and institutions down to individual research groups.

We intend for our guide to build on these tools but to have a different, and we think novel, focus. While we believe it could be a useful tool for data management service providers, the intended audience of our guide is research practitioners. While integration with service providers in the library, research IT, and elsewhere will be included where appropriate, the the focus will be on equipping researchers to assess and refine their individual own data management activities. While technical infrastructure will be included where appropriate, the focus will be on behaviors, “soft skills”, and training.

Our Guide

Below is a preliminary mockup of our guide. Akin to the “How Open Is It?” guide developed by SPARC, PLOS, and the OASPA, our aim is to provide a tool that is comprehensive, user-friendly, and provides tangible recommendations.

Obviously we still have a significant amount of work to do to refine the language and fill in the details. At the moment, we are using elements of the research data lifecycle to broadly describe research activities and very general terms to describe the continuum of practice maturity. Our next step is to fill in the blanks- to more precisely describe research activities and more clearly delineate the stages of practice maturity. From there, we will work to outline the behaviors, skills, and expertise present for each research activity at each stage.

Next Steps

Now that we’ve researched existing tools for assessing data management services and sketched out a preliminary framework for our guide, our next step is to elicit feedback from the broader community that works on issues around research support, data management, and digital curation and preservation.

Specifically we are looking for help on the following:

• Have we missed anything? There is a range of data management-related rubrics, tools, and capability models – from the community-focused frameworks described above to frameworks focused on the preservation and curation of digital assets (e.g. the Digital Asset Framework, DRAMBORA). As far as we’re aware, there isn’t a complementary tool that allows researchers to assess where they are and where they want to be in regards to data management. Are there efforts that have already met this need? We’d be grateful for any input about the existence of frameworks with similar goals.
• What would be the most useful divisions and steps within our framework? The “three legged stool” developed by the Digital Preservation Management workshop has been highly influential for community and data management provider-focused tools. Though examining policies, resources, and infrastructure are also important for researchers when self-assessing their data management practices, we believe it would be more useful for our guide to be more reflective of how data is generated, managed, disseminated in a research context. We’d be grateful for any insight into how we could incorporate related models – such as those depicting the research data lifecycle – into our framework.

Who “owns” your data?

Tug of war, Kathleen Tyler Conklin, CC BY-NC 2.0

This post was originally published on the University of California Office of Scholarly Communication blog.

Which of these is true?

“The PI owns the data.”

“The university owns the data.”

“Nobody can own it; data isn’t copyrightable.”

You’ve probably heard somebody say at least one of these things — confidently. Maybe you’ve heard all of them. Maybe about the same dataset (but in that case, hopefully not from the same person). So who really owns research data? Well, the short answer is “it depends.”

A longer answer is that determining ownership (and whether there’s even anything to own) can be frustratingly complicated — and, even when obvious, ownership only determines some of what can be done with data. Other things like policies, contracts, and laws may dictate certain terms in circumstances where ownership isn’t relevant — or even augment or overrule an owner where it is. To avoid an unpleasant surprise about what you can or can’t do with your data, you’ll want to plan ahead and think beyond the simple question of ownership. (more…)