Guest post by Angus Whyte, co-author of Delivering Research Data Management Services
Librarians have grown to love research data so much they can’t get enough of it! Well some at least have, and Love Your Data Week will help spread the love. Of course nobody loves data more than the researchers who produce it. Funders love it too; after all they pay for it to come into the world. If data is loved so much, why is so much of it running around loose, dirty and in no fit state to get a job? Is all that is needed a little more discipline?
Three years ago when Delivering Research Data Management Services was first published, my co-authors Graham Pryor and Sarah Jones were working with colleagues in the Digital Curation Centre and in universities across the UK to help them get support for research data off the ground and into the roster of institutional service development. At the time, as Graham said in his introduction, institution-wide RDM services had “at last begun to gain a foothold”.
The (now open access) chapter titled “a pathway to sustainable research data services: from scoping to sustainability”described six phases, from envisioning and initiating, through discovering requirements, to design, implementation and evaluation. Across the UK sector as a whole, few institutions had got beyond the discovery phase. Some of the early adopters in the UK, US and Australia have case studies featured in the book, providing more fully-fledged examples of the mix of soft and hard service components that a ‘research data management service’ typically comprises. Broadly these include support for researchers to produce Data Management Plans, tools and storage infrastructure for managing active data, support for selection and handover to a suitable repository for long-term preservation, and support for others to discover what data the institution has produced.
So what has changed? The last three years have seen evolution, consolidation and growth. According to one recent survey of European academic research libraries almost all will be offering institutional RDM services within two years. The mantra of FAIR data (findable, accessible, interoperable and reusable) has spurred a flurry of data policy-making by funders, journals and institutions. Many organisations have yet to adopt one,but policy harmonisation is now a more pressing need than formulation. Data repositories have mushroomed, with re3data.org now listing about three times the number it did three years ago. Training materials and courses are becoming pervasive, and data stewardship is increasingly recognised as essential to data science.
The burgeoning development in each of these aspects of RDM does not hide the immaturity of the field; each aspects is the subject of international effort by groups like COAR (Confederation of Open Access Repositories), and the Research Data Alliance, to consolidate and codify the organisational and technical knowledge needed to further join up services. European initiatives to establish ‘Research Infrastructures’ have demonstrated how this can be done, at least for some disciplines.
Over the same period, many institutions have learned to love ‘the cloud’; gaining scalability and flexibility by integrating cloud storage and computation services with their IT infrastructure. The same is not yet true of the higher-level RDM services that require academic libraries to collaborate with their IT and research office colleagues. Shared services are a trend that has seen some domain-focused data centres spread their disciplinary wings. Ambitious initiatives like the European Open Science Cloud pilot, will tell us how far ‘up the stack’ cloud services to support open science can go to offer better value to science and society.
The biggest challenges in 2013 are still big challenges now. Political and cultural change is messy, for a number of reasons.There is high-level political will to fund data infrastructure as it’s seen as essential for innovation, as well as for research integrity. But the economic understanding to direct resources to where they are most needed, to ensure data is not only loved but properly cared for? That requires better understanding of what kinds of care produce good outcomes, like citation and reuse. Evaluation studies have been thin on the ground and, perhaps as a result, funding for data infrastructure still tends to be short-term and piecemeal.
The book offers a comprehensive grounding in the issues and sources to follow up. Its basic premise is as true now as when it was published: keeping data requires a mix of generic and domain-specific stewardship competencies, together with organisational commitments and basic infrastructure. The basic challenge is as true now as then; research domains are fluid and tribal, crossing national and international boundaries and operating to norms that tend to resist institutional containers. But that has always been the case, and yet institutions and their libraries continue to adapt and survive.
By happy coincidence the International Digital Curation Conference (IDCC17) is happening the week after Love Your Data Week. You can follow it as it happens on twitter at #idcc17
Dr Angus Whyte is a Senior Institutional Support Officer at the Digital Curation Centre, University of Edinburgh. He is responsible for developing online guidance and consultancy to research organisations, to support their development of research data services. This is informed by studies of research data practices and stakeholder engagement in research institutions.
 Research Data Services in Europe’s Academic Research Libraries by Liber Europe
 Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., … others. (2016). The FAIR Guiding Principles for scientific data management and stewardship
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Guest post by Starr Hoffman, editor of Dynamic Research Support for Academic Libraries.
Similar to the confusion between open access as opposed to open source, the terms research data and secondary data are sometimes confused in the academic library context. A large source of confusion is that the simple term “data” is used interchangeably for both of these concepts.
What is Research Data?
As research data management (RDM) has become a hot topic in higher education due to grant funding requirements, libraries have become involved. Federal grants now require researchers to include data management plans (DMPs) detailing how they will responsibly make taxpayer-funded research data 1) available to the public via open access (for instance, depositing it in a repository) and 2) preserve it for the future. Because there are often gaps in campus infrastructure around RDM and open access, many academic libraries have stepped in to provide guidance with writing data management plans, finding appropriate repositories, and in other good data management practices.
This pertains to original research data–that is, data that is collected by the researcher during the course of their research. Research data may be observational (from sensors, etc), experimental (gene sequences), derived (data or text mining), among other type, and may take a variety of forms, including spreadsheets, codebooks, lab notebooks, diaries, artifacts, scripts, photos, and many others. Data takes many forms not only in different disciplines, but in different methodologies and studies.
Example: For instance, Dr. Emmett “Doc” Brown performs a series of experiments in which he notes the exact speed at which a DeLorean will perform a time jump (88 MPH). This set of data is original research data.
What is Secondary Data?
Secondary data is usually called simply “data” or “datasets.” (For the sake of clarity, I prefer to refer to it as “secondary data.”) Unlike research data, secondary data is data that the researcher did not personally gather or produce during the course of their research. It is pre-existing data on which the researcher will perform their own analysis. Secondary data may be used either to perform original analyses or for replication (studies which follow the exact methodology of a previous study, in order to test the reliability of the results; replication may also be performed by following the same methodology but gathering a new set of original research data). Secondary data can also be joined to additional datasets, including datasets from different sources or joining with original research data.
Example: Let’s say that Marty McFly makes a copy of Doc Brown’s original data and performs a new analysis on it. The new analysis reveals that the DeLorean was only able to time-jump at the speed of 88 MPH due to additional variables (including a power input of 1.21 jigowatts). In this case, the dataset is secondary data.
Reuse of Research Data
Another potential point of confusion is that one researcher’s original research data can be another researcher’s secondary data. For instance, in the example above, the same dataset is considered original research data for Doc Brown, but is secondary data for Marty McFly.
Data Services: RDM or Secondary Data?
The phrase “data services” can also be confusing, because it may encompass a variety of services. A potential menu of data services could include:
- Assistance locating and/or accessing datasets.
o This might pertain to vendor-provided data collections, consortial collections (such as ICPSR), locally-produced data (in an institutional repository), or with publically-accessible data (such as the U.S. census).
o Because this service specifically focuses on accessing data, it by default pertains to secondary data.
- Data management plan (DMP) assistance.
o Typically only applies to original research data.
- Data curation and/or RDM services.
o These may include education on good RDM practices, assistance depositing data into an institutional repository (IR), assistance (or full-service) creating descriptive or other metadata, and more.
o Typically only provided for original research data. However, if transformative work has been done to a secondary dataset (such as merging with additional datasets or transforming variables), data curation / RDM may be necessary.
- Assistance with data analysis.
o This service is more often provided for students than for faculty, but may include both groups.
o Services may include providing analysis software, software support, methodological support, and/or analytical support.
o May include support for both original research data and secondary data.
You Say “Data Are,” I Say “Data Is” …Let’s Not Call the Whole Thing Off!
So in the end, what does all this matter? The primary takeaway is to be clear, particularly when communicating about services the library will or won’t provide, about specific types of data. In many cases this will be obvious–for instance, “RDM” contains within it the term “research data” and is thus clear. Less clear is when a library department decides to provide “assistance with data.” What does this mean? What kind of assistance, and for what kind of data? Is the goal of the service to support good management of original research data? Or is the goal to support the finding and analysis of secondary data that the library has purchased? Or another goal altogether?
Clarity is key both to understanding each other and to clearly communicating emerging services to our researchers.
Starr Hoffman is Head of Planning and Assessment at the University of Nevada, Las Vegas, where she assesses many activities, including the library’s support for and impact on research. Previously she supported data-intensive research as the Journalism and Digital Resources Librarian at Columbia University in New York. Her research interests include the impact of academic libraries on students and faculty, the role of libraries in higher education and models of effective academic leadership. She is the editor of Dynamic Research Support for Academic Libraries. When she’s not researching, she’s taking photographs and travelling the world.
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Starr Hoffman has made two videos to support her new book Dynamic Research Support for Academic Libraries, published this month by Facet. The first video describes how academic libraries can support the research lifecycle for faculty and students and the second introduces the book and defines ‘research support’.
Facet are pleased to announce the release of two new books, Practical Tips for Facilitating Research and Dynamic Research Support for Academic Libraries.
Higher education is in a period of rapid evolution and academic libraries must continually evaluate and adjust their services to meet new needs. Librarian roles are changing and new specialisms, such as data librarians are emerging. Activities are being driven by researcher requirements such as the demand for wider dissemination and the impact of research.
Two new books from Facet Publishing, Practical Tips for Facilitating Research and Dynamic Research Support for Academic Libraries, will provide inspiration and practical guidance to enable LIS staff developing their role in the research environment to evaluate their current provision and develop services to meet the evolving needs of the research community.
Practical Tips for Facilitating Research offers innovative tips and reliable best practice to assist academic liaison librarians, research support librarians and all library and information professionals who work with research staff and students.
Author Moira Bent said, “my book bridges the gap between theory and practice, grounding the very practical ideas garnered from library and information staff around the world in current research in the library and information science discipline.”
Dynamic Research Support for Academic Libraries provides inspiration through illustrative examples of emerging models of research support and is contributed to by library practitioners from across the world.
Editor Starr Hoffman said, “Dynamic Research Support for Academic Libraries is designed to inspire librarians and administrators to think of ‘research support’ not merely as Reference 2.0, but as an innovative, holistic activity that should be distributed throughout the organization.”
A preview chapter for each book is available on the Facet website, along with information about how to order.