Love Data Week 2018 Roundup

Last week Facet participated in Love Data Week, a 5-day online international event ‘to raise awareness and build a community to engage on topics related to research data management, sharing, preservation, reuse, and library-based research data services.’ We asked our authors  to share their data stories and each responded with a different approach. You’ll find a summary of each post published during the week below.

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David Haynes, author of brand new Metadata for Information Management and Retrieval, 2nd edition, began the week with an exploration of the ethical implications for metadata gathering and uses in his post Metadata – have we got the ethics right?

Next up, Selena Killick, co-author of the forthcoming Putting Library Assessment Data to Work, explained how library data can be used to measure student success in Data is the new oil

Sara Mannheimer and Ryer Banta, co-contributors to The Complete Guide to Personal Digital Archiving, shared their data success story in Building Bridges – in which they offer advice for introducing students to research data management skills through something everyone can relate to—organizing personal digital files.

Bringing the week to a close Andrew Cox, co-author of the forthcoming Exploring Research Data Management, took a look at the future for academic libraries in The Growing Importance of Data in Academic Libraries

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New research on the architecture, design and evaluation of online information systems and services

Facet Publishing have announced the release of the second book in the iResearch series, Information Systems: Process and practice, edited by Christine Urquhart, Dr Faten Hamad, Dr Dina Tbaishat  and Alison Yeoman

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Design and evaluation of information systems and services have remained an area of study and research in many disciplines ranging from computing and information systems, information and library studies, to business management. Each discipline aims to address a set of unique challenges as they are seen from their disciplinary background and perspectives. This results in research that often fails to take a holistic view of information systems including technologies, people and context. This second title in the iResearch series addresses this challenge by bringing together different viewpoints and perspectives of information systems design and evaluation from the contributors’ own diverse and yet complimentary areas of teaching and research interests.

Co-editor Christine Urquhart said, “This book attempts to bridge some of the gaps between discrete areas of research that information professionals could use to design helpful and effective information systems and services.  Our aim is to provide a critical analysis, with supporting case studies of library and information service and systems architecture – in a very broad interpretation of the term architecture”.

The book will be essential reading for researchers in information science, specifically in the areas of digital libraries, information architecture and information systems. It will also be useful for practitioners and students in these areas seeking to understand research issues and challenges and to discover how they have been handled in practice elsewhere.

iResearch series editor G G Chowdhury said,

‘This is not just another book on information architecture that focuses on content architecture alone; the research and development activities reported in this book also cover the other end of the spectrum concerned with service evaluation, performance management and library assessment. The 14 chapters in this book, written by academics and researchers from different research backgrounds and viewpoints, offer a significant contribution to research and practices in the architecture, design and evaluation of online information systems and services.’

About the authors

Christine Urquhart was a full-time member of staff in the Department of Information Studies, Aberystwyth University. Since retiring from full-time teaching she has continued to pursue her research interests.

Dr Faten Hamad is an Assistant Professor in the Library and Information Science Department, University of Jordan.

Dr Dina Tbaishat is an Assistant Professor at the University of Jordan, Library and Information Science Department.

Alison Yeoman was formerly a Research Officer in the Department of Information Studies, Aberystwyth University and is now an independent researcher.

With contributions from: Sally Burford, Catherine M. Burns, Karen Colbron, Adam Euerby, Fernando Loizides, Aekaterini Mavri, Paula Ormandy and Cristina Vasilica.

 

For more information about the book and to read a sample chapter click here

 

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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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Building Bridges

This guest post from Sara Mannheimer and Ryer Banta is about introducing undergraduates to the foundations of research data management through something everyone can relate to—organizing personal digital files. You can read more about their experience in The Complete Guide to Personal Digital Archiving which features their co-authored chapter, “Personal Digital Archiving as a Bridge to Research Data Management”

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In 2016, we were both working at Montana State University Library, but working in totally different divisions. Sara was the Data Management Librarian with a focus on research data management and data management planning for faculty and graduate students. Ryer was the Undergraduate Experience Librarian, focusing on information literacy instruction and support for undergraduate students. In many ways it would seem that we were living in very different parts of the library.

Although our jobs were quite different, we connected over our shared conviction that undergraduates would benefit from learning fundamental research data management skills. Undergraduates are entering a data-driven job market, where skills related to data management are in high demand. In industry, data scientists are working in a wide variety of sectors, and in academia, researchers are increasingly required to publish research data. Tailoring research data management lessons to undergraduates also served a key student population at MSU. Montana State University is a mid-sized university with about 16,500 total students, about 14,000 of whom are undergraduates. So we knew that there could be a big potential impact if we could figure out a meaningful way to help undergraduates build research data management skills.

As we began to think about creating a useful and meaningful research data management related lesson for undergraduates, our immediate challenge was figuring out how to get over the hurdle of making research data management relevant. Most undergraduates do not encounter research data on a regular basis, and we wanted to connect research data management to their daily life, their current schoolwork, or ideally both. The instructional principle of making lessons relevant may seem to be fairly common sense, but it is also supported by constructivist learning theory. We dipped our toes into this rich area of scholarship while developing our lesson, focusing on a couple of aspects of constructivist learning theory. For anyone developing learning experiences, we highly recommend dipping your toes, and even diving headlong, into constructivist learning theory and related theories.

Constructivist learning theory encompasses several principles, but we focused on the principles related to active, student-focused discovery. Two core tenets of constructivist learning theory specify that:

  • New learning builds on prior knowledge. By tapping into students’ past experiences, educators can create a learning sequence that extends from prior knowledge to the current lesson to a lifelong pattern of curiosity and learning.
  • Meaningful learning develops through “authentic” tasks. Activities conducted in class should simulate activities that students will use in their class assignments and in their real lives. This strategy ensures that the skills students learn in the classroom have direct relevance to their lives outside of the classroom.

Applying these tenets provided us with new insights about how to make research data management relevant for undergraduates. Given that new learning builds on prior knowledge, we aimed to understand students’ prior knowledge regarding data, tap into students’ past learning experiences, and then build upon that knowledge in the classroom. Given that meaningful learning develops through “authentic” tasks, we aimed to teach concrete, relatable skills that could be practiced both during instruction and afterwards. We wanted to position research data management skills in the context of students’ current lives, rather than promising a theoretical applicability to an abstract future career.

Taking a cue from constructivist learning theories, we realized that we could start with data that students already use and manage on a daily basis, specifically their digital files on their computers. At the same time, we also realized that many of the basic principles of research data management are also found in personal digital archiving practice. These dual realizations helped us focus our lesson on principles and practices that could be immediately applied to students’ digital files. In fact, in our lesson, we designed activities that got students started on reorganizing their files following personal digital archiving best practices. We organized our lesson into four key sections:

  • Set the stage. Students describe the use, importance, and challenges of data within their discipline or other personally relevant contexts. This step helps prepare students to apply the lesson to their own lives.
  • Basics of personal digital archiving. Students discover basic personal digital archiving strategies and principles that are also used to manage research data. This step provides a foundation of knowledge that informs in-class activities.
  • Apply learning with activities. Students apply personal digital archiving strategies and principles to organize and document their own files and data. This step provides students with hands-on experience with personal digital archiving strategies.
  • Debrief to connect personal digital archiving to research data management. Students reflect upon the value of the personal digital archiving principles and practices for their own personal data and discover the connection and similarities between personal digital archiving and research data management. This step allows students to process the lesson and consider future applications of the skills they learned.

We have had success with this lesson, and we have found that teaching personal digital archiving practices can act as a bridge that connects key practices of research data management to students’ everyday lives. Personal digital archiving builds on students’ prior knowledge of their digital belongings, and allows students to learn through authentic tasks that have immediate relevance to their daily lives. We hope that other librarians and educators can adapt and reuse the basic instructional strategies that we developed in their own learning contexts. Critical thinking about managing digital materials—whether personal files or research data—is a foundational skill that will benefit students during their undergraduate education and in their future careers.

Ryer Banta is the information literacy and technology librarian at Centralia College (WA), where he manages digital resources and services, and helps learners develop information literacy and lifelong learning skills. His research interests include open education, instructional design, educational technology, information literacy, and user experience.

You can follow Ryer on Twitter @RyerBanta

Sara Mannheimer is the data librarian at Montana State University in Bozeman, where she facilitates research data management and sharing, and promotes digital scholarship using library collections and “big data” sources. Her research focuses on data management practices, data discovery, digital preservation, and the social, ethical, and technical issues surrounding data-driven research.

You can follow Sara on Twitter @saramannheimer

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The Complete Guide to Personal Digital Archiving helps information professionals break down archival concepts and best practices into teachable solutions. Whether it’s an academic needing help preserving their scholarly records, a student developing their data literacy skills or someone backing up family photos and videos to protect against hard-drive failure, this book will show information professionals how to offer assistance.

Find out more about the book and read a sample chapter here.

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Data is the new oil

Guest post by Selena Killick, co-author of the forthcoming book Putting Library Assessment Data to Work

With the move to increasing online provision within the education sector, and by association academic libraries, we have seen an explosion in big data sets. The initial COUNTER compliant statistics containing electronic journal title downloads seem quite quaint now as the granularity and scale of data harvesting has continued to expand. It is not uncommon for an academic library to have usage data for its electronic resources at an item level on an individual customer basis. The University of Huddersfield Library Impact Data Project were the first exploration into how library analytics could be used to identify the impact the library was having on student success. The researchers successfully identified a correlation between library usage and student success (Stone & Ramsden 2013). Coincidentally this research was being replicated in Australia and the USA at the same period, all with similar results (Cox & Jantti 2012) (Soria et al. 2013). Five years on, where are we now? And, if data really is the new oil, where should we drill next…?

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The Ethical Debate

Before we look at where we could drill next, the question on whether we should drill at all needs to be considered. The librarian field is divided on this issue with strong views on both sides. Quantitative data is not the only source of information and it should not be used in isolation to evaluate library performance. That being said, it is a source of insight available to us and we should consider carefully if and how we use it. Personally, I think that if we are able to use library analytics anonymously, ethically, transparently, legally and with the goal of improving learner success we should exploit the data to benefit our students. I know some will disagree with me, and I’m happy to debate the subject. As data analytics becomes increasing part of the academic institutional infrastructure libraries need to identify the role they will play in this arena or risk becoming obsolete and ultimately redundant.

The Next Drilling Expedition

Until now, library analytics research has focussed on student satisfaction, library usage and student success. The Jisc Library Data Labs project has worked with librarians to combine and visualise various data sets. SCONUL Statistics and National Student Survey (NSS) scores have been combined to see if students who are the most satisfied with library provision are studying at the universities with the largest library budgets (Baylis & Burke 2017). LibQUAL+ satisfaction scores have been combined with Association of College and Research Libraries (ACRL) statistics to see if there is a correlation between satisfaction, usage and expenditure in academic libraries (Hunter & Perret 2011).

As an increasing number of library services move online the ability to harvest and analyse user data in the areas of enquiry handling and information literacy training is growing. Libraries are starting to use customer relationship management systems to manage enquiries received at help points (Killick 2017). Webchat transcripts between customers and librarians can be analysed to identify common enquiries with a view of improving the customer experience (Mungin 2017). Within the area of information literacy training provision, live online tuition and webinars are now being used by academic libraries. Delivery tools such as Adobe Connect can provide the library with data at the individual student level for live attendance and subsequent video views. With regards to data drilling, services are the new content, and it is only a matter of time before someone breaks ground.

Learning Analytics

Within the wider academic sphere the field of learning analytics has emerged, using big data to understand the characteristics of successful students with a view to optimise the learning environment (Rienties et al. 2017). As an important stakeholder in the learning environment, libraries are considering their role in supporting the learning analytics agenda (Oakleaf et al. 2017). The Library Integration in Institutional Learning Analytics (LIILA) Project is currently reviewing how libraries can support learning analytics. This one-year Institute of Museum and Library Services National Forum grant is working with a variety of international stakeholders, including librarians, system vendors and policy makers. The project team hope to get to the position where libraries are culturally ready and technically able to engage in this arena. This is our opportunity to shape the use of library data in this field, ensuring its use is anonymous, legal, ethical, and transparent; with the goal of improving learner success. If we fail to engage in the debate I suspect our publishers will bypass the library and pipe their usage data Killick_blog jacket cover.jpgto the learning analytics community directly.

Selena Killick is co-author of the forthcoming book Putting LibraryAssessment Data to Work alongside Frankie Wilson. She has presented, published, and provided consultancy services to academic libraries on an international basis on library assessment for over 15 years. She is currently an editorial board member of the International Conference on Performance Measurement in Libraries. In 2003 she was part of the team that introduced LibQUAL+ to the UK in partnership with the Association of Research Libraries. Previously she has worked on the SCONUL Value & Impact Measurement Programme (VAMP) and the SCONUL Statistics e-measures pilot.

 

 

 

You can follow Selena on Twitter @selenakillick

References

Baylis, L. & Burke, S., 2017. Insights from Jisc & HESA Analytics Labs: An Agile, cross-institutional approach. In 12th International Conference on Performance Measurement in Libraries. Oxford.

Cox, B.L. & Jantti, M., 2012. Capturing business intelligence required for targeted marketing, demonstrating value, and driving process improvement. Library and Information Science Research, 34(4), pp.308–316.

Hunter, B. & Perret, R., 2011. Can Money Buy Happiness? A Statistical Analysis of Predictors for User Satisfaction. The Journal of Academic Librarianship, 37(5), pp.402–408.

Killick, S., 2017. Exploiting customer relationship management analytics to improve the student experience. In 12th International Conference on Performance Measurement in Libraries. Oxford.

Mungin, M., 2017. Stats Don’t Tell the Whole Story: Using Qualitative Data Analysis of Chat Reference Transcripts to Assess and Improve Services. Journal of Library and Information Services in Distance Learning, 11(1–2), pp.25–36.

Oakleaf, M. et al., 2017. Academic Libraries & Institutional Learning Analytics: One Path to Integration. Journal of Academic Librarianship, 43(5), pp.454–461.

Rienties, B. et al., 2017. A review of ten years of implementation and research in aligning learning design with learning analytics at the Open University UK. Interaction Design and Architecture(s) Journal, 33, pp.134–154.

Soria, M.K., Fransen, J. & Nackerud, S., 2013. Library Use and Undergraduate Student Outcomes: New Evidence for Students’ Retention and Academic Success. portal: Libraries and the Academy, 13, pp.147–164.

Stone, G. & Ramsden, B., 2013. Library Impact Data Project: looking for the link between library usage and student attainment. College and Research Libraries, 74(6), pp.546–559.

 

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Delivering a Data Strategy in the Cauldron of Business As Usual

Guest post by the co-authors of The Chief Data Officer’s Playbook, Caroline Carruthers (Group Director of Data Management, Lowell Group) and Peter Jackson (Chief Data Officer, Southern Water).

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Being a Chief Data Officer in the current climate is a rather interesting place to be, it can feel a little like dancing on quicksand while you have to learn to juggle wriggling snakes. So in order to help people interested in this area, whether you are a new CDO, well established data hero or just wondering what all the fuss is about, we have worked on a set of articles to answer some of the questions we are asked at nearly every conference we go to. While we can’t promise you a solution to all your data related problems handed to you on a plate, we can promise that once a week you can look forward to another concise, interesting and easy to read article to help you on your data and information related journey.

One of the most difficult tasks for the new CDO is developing a Data Strategy whilst the organisation continues to operate (and must continue to operate) using and abusing data, continuing with bad habits around data and often with a lack of governance and planning. This has been likened to performing open heart surgery on a runner while they are in the middle of a marathon, in reality it’s more like patching them up, giving them water to keep them going and a clear map to get them to the end of the race. In most situations for a new CDO the organisation probably feels that it has been operating quite happily without this new person for a very long while. So, for the new CDO it may feel like they are sitting in the corner talking to themselves. Alternatively the CDO may be met with comments like ‘Yes, we tried that before and it didn’t work’ or ‘ IT/ Finance/ Procurement/ Marketing (delete as appropriate) won’t like you doing that’ or my personal favourite ‘that’s not how we do that here’.

What is the context of Business As Usual? In most cases (unless the organisation is a start-up) it will be:

  • a legacy data environment: siloes of data, multiple records, ‘duplicates’, weak data governance, no useful meta data, heavy MI and no BI.
  • legacy systems: burning platforms, bespoke developments, hard to maintain and manage, reporting systems remote from end-users, no true data management systems
  • legacy business processes: evolved over time, limited by technology and data available at the point in time, containing many work-arounds
  • multiple suppliers: of software and systems
  • legacy IT department: focused on building stuff rather than delivering and supporting software-as-a-service, internal networks as opposed to cloud
  • legacy ‘transformation’ process: based on project governance and waterfall, struggling with agile and innovation. Not able to adapt to transformation being data driven rather than technology driven

The task for the new CDO is how to steer their way through this bubbling cauldron and deliver a data strategy. One approach is to break the task down into two parts: an Immediate Data Strategy (IDS), a tactical approach to deliver support for BAU, gain quick wins and temporary fixes and to prepare the way for the second part. The additional benefit of the IDS is the delivery of incremental value to the organisation through its data, avoiding the hypecycle on the way (the next article deals with this in more detail). The second part is the Target Data Strategy (TDS), the strategic approach. The new CDO cannot sit back and deliver the TDS over a two to three year window, the organisation will probably be expecting some results now, so it is just as important to set realistic expectations as it is to provide some tactical delivery through the IDS. One piece of advice, don’t call these tactical deliveries ‘Projects’ instead refer to them as ‘Initiatives’, this might engender a more agile approach.

The IDS should listen to the organisation’s data pain and try to deliver high profile quick wins. The tactical initiatives of the IDS should blend into the strategy of the TDS, and not run down a rabbit hole or blind alley. The IDS should help build up the narrative and vision of the TDS.

The six key elements of the IDS could be:

  1. Stability and rationalisation of the existing data environment
  2. Data culture and governance
  3. Existing and immediate data and IT development projects
  4. Data exploitation and integration
  5. Data performance, quality, integrity, assurance and provenance
  6. Data security (especially with GDPR in mind).

Whilst the new CDO is delivering the IDS they should be pushing the TDS through business engagement, the organisation needs to be prepared, ready and believe in the changes that are coming. The CDO should also be using the IDS to show the ‘art of the possible’ to a data illiterate business to help the business engage with the new data possibilities. Through the IDS they should be running Proof of Concepts, feasibility studies, data science initiatives and building a narrative around the vision of the TDS for all levels of the business.

Finally, six tips on how to succeed using the IDS and TDS approach:

  1. Use internal communications to sell the vision, don’t allow a vacuum to form
  2. Seek every opportunity to communicate the vision. Do not be frightened of becoming a data bore.
  3. Socialise the data visons and the changes that could be coming, especially the controversial ideas, locate the data champions to support you
  4. Engage the organisation’s leadership and find your senior sponsors, they will be crucial
  5. If you can’t explain it, you’re doing something wrong, ‘it’s me not you’
  6. Win hearts and minds, often a good argument is not enough to win the day.

The book is available to purchase now

Playbook