Tagged: Research Data

Our new catalogue is out now

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Our new catalogue, featuring all our new and forthcoming titles as well as bestsellers and key backlist, is out now.

Download a PDF of the catalogue here

Browse the catalogue online here

If you would like a printed copy, send an email to info@facetpublishing.co.uk and we will post one out to you.

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The Growing Importance of Data in Academic Libraries

Guest post by Andrew Cox, co-author of the forthcoming book, Exploring Research Data Management

In a globalising world the future is complicated. It is not simply a matter of new trends impacting library work in clearly defined ways. Rather change seems to be impacting our work in complex ways. In a recent report on the future of academic libraries (Pinfield, Cox and Rutter, 2017) we sought to address this complexity by proposing that we think in terms of nexuses of change. Two major ones we identified were:

  • “The dataification of research” -combining trends such as open access, open science, text and data mining, artificial intelligence and machine learning, the internet of things, digital humanities and academic social networking services, and
  • “Connected learning” -incorporating changing pedagogies, learning analytics, students as customers, social media, mobile computing, maker spaces and blurring of space uses.

A story that seems to be threaded through these changes is the growing importance of data.

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Photo by Joshua Sortino on Unsplash

Research data management

Thus one aspect of change in research practice is the way that the valued outputs of research are increasingly not confined to written outputs, but also seen to lie in the underlying research data. If made discoverable and useable these data can be the foundation for new research. Research Data Management is the emergent set of professional practices that supports this emphasis. Librarians have had a big part in how this story has unfolded.

Text Data Mining

Another data related change in research is the way that texts in the library are increasingly to be seen as data for Text and Data Mining (TDM). When there are literally hundreds of thousands of research papers on a topic, a manually conducted “comprehensive literature review” becomes an impossibility. Rather we will need the help of text mining algorithms that seek out patterns in the corpus of texts. It is libraries that are seeking to create the legal and technical infrastructure in which TDM can be carried out.

Learner Analytics

Learning is also undergoing a data revolution. Usage behaviour as library or learning analytics is another key area of development. If we can connect the data we have about user visits to the library, book issues and resource downloads, activities in the virtual learning environment and in the classroom, we can produce a better understanding of learner behaviour to help customise services. Librarians could be at the forefront of mining this data to improve services.

Data: structured or unstructured?

Granted, this storm of interest in “data”, may disguise different usages of the term. Research data could be qualitative data, though they are perhaps most valuable and vulnerable when derived from data intensive science. TDM is concerned with text as unstructured data. Library and Learning analytics are based primarily on structured, log file data.

The ethical issues

At the same time our sensitivity to the ethical issues around data is rising. How can users be given appropriate control over their own data? What restrictions need to be placed on commercial companies’ use of data about our online behaviour? Can libraries themselves retain users’ trust while exploiting the benefits of learning data analysis for service improvement? There are also massive challenges around data preservation in all these contexts. The professional knowledge of librarians and archivists need to be translated to meet the challenges of data curation. Data literacy, as an aspect of information literacy, will also need to be part of librarians’ training offer.

All these trends suggest that our skills in managing, interpreting and visualising, and curating data, in ethical and legally safe ways, will be at the heart of our profession’s work in the next decades. One of our future stories is as a data profession.

 

Andrew Cox is a senior lecturer at the Information School, University of Sheffield and led the RDMRose Project. His research interests include virtual community, social media and library responses to technology. He coordinates Sheffield’s MSc in Digital Library Management.

You can follow Andrew on Twitter @iSchoolAndrew

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Exploring Research Data Management is an accessible introduction to RDM with engaging tasks for the reader to follow and build their knowledge. It will be useful reading for all students studying librarianship and information management, and librarians who are interested in learning more about RDM and developing Research Data Services in their own institution.

Find out more about the forthcoming book here

 

References

Pinfield, S., Cox, A.M., Rutter, S. (2017). Mapping the future of academic libraries: A report for SCONUL. https://sconul.ac.uk/publication/mapping-the-future-of-academic-libraries

 

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Love Your Data Week Roundup

Last week Facet participated in Love Your Data Week, a 5-day international event to help reasearchers take better care of their data. We have gathered all the resources we published during the week below

data-scrabble-turned

Image source: data (scrabble) by Flickr user justgrimes

New Open Access chapters

During the week, we made several new chapters from our research data management titles available Open Access. All the chapters can be downloaded below.

Supporting data literacy by Robin Rice and John Southall from The Data Librarian’s Handbook

Training researchers to manage data for better results, re-use and long-term access by Heather Coates from Dynamic Research Support for Academic Libraries

Specific interventions in the research process or lifecycle by Moira J Bent from Practical Tips for Facilitating Research

The lifecycle of data management by Sarah Higgins from Managing Research Data

A pathway to sustainable research data services: from scoping to sustainability by Angus Whyte from Delivering Research Data Management Services

Blogposts from Facet authors

Starr Hoffman explored the difference between research data and secondary data using the speed at which the DeLorean in Back to the Future will time jump as an example in her blogpost, Data Services and Terminology: Research Data versus Secondary Data

Robin Rice and John Southall provided practical advice for data librarians undertaking a reference consultation or interview to match users to the data required in their blogpost, Top tips for a data reference interview

Gillian Oliver talked about practical ways of ensuring you have a successful relationship with data in her blogpost, Five ways to love your data

Angus Whyte looked at what has changed in the world of research data management in the past three years in his blogpost, 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?

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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?

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?

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Image source: data (lego) by Flickr user justgrimes

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.[1] The mantra of FAIR data (findable, accessible, interoperable and reusable) has spurred a flurry of data policy-making by funders, journals and institutions.[2] 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.[3]

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Image source: 3D Cloud Computing by ccPix.com

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.

[1] Research Data Services in Europe’s Academic Research Libraries by Liber Europe

[2] 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

[3] European Open Science Cloud pilot

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The lifecycle of data management

As Love Your Data Week continues, today we have made a new chapter from Managing Research Data available Open Access. The chapter, The lifecycle of data management by Sarah Higgins, is available to download here.

We will be releasing more Open Access chapters 9781856047562throughout the week and publishing blogposts from our authors. For a chance to win one of our research data management books, share a tweet about why you (or your institution) are participating in Love Your Data Week 2017 using #WhyILYD17. More details about the prize draw are available here.

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Data Services and Terminology: Research Data versus Secondary Data

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.

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Image source: Back to the Futue by Graffiti Life from Flickr user MsSaraKelly

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.

Back to the Future

Image source: Back to the Future by Flickr user Garry Knight

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 LibrariesWhen she’s not researching, she’s taking photographs and travelling the world.

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