Data and ethics

As an academic support person, I was surprised to find myself invited onto a roundtable about ‘The Ethics of Data-Intensive Research’. Although as a data librarian I’m certainly qualified to talk about data, I was less sure of myself on the ethics front – after all, I’m not the one who has to get my research past an Ethics Review Board or a research funder.

The event was held last Friday at the University of Edinburgh as part of the project Archives Now: Scotland’s National Collections and the Digital Humanities, a knowledge exchange project funded by the Royal Society of Edinburgh. This event attracted attendees across Scotland and had as its focus “Working With Data“.

I figured I couldn’t go wrong with a joke about fellow ‘data people’ with an image from flickr that we use in our online training course, MANTRA.

Binary-by-Xerones-CC-BY-NC

‘Binary’ by Xerones on Flickr (CC-BY-NC)

Appropriately, about half the people in the room chuckled.

So after introducing myself and my relevant hats, I revisited the quotations I had supplied on request for the organiser, Lisa Otty, who had put together a discussion paper for the roundtable.

“Publishing articles without making the data available is scientific malpractice.”

This quote is attributed to Geoffrey Boulton, Chair of the Royal Society of Edinburgh task force which published Science as an Open Enterprise in 2012. I have heard him say it, if only to say it isn’t his quote. The report itself makes a couple of references to things that have been said that are similar, but are just not as pithy for a quote. But the point is: how relevant is this assertion for scholarship that is outside of the sciences, such as the Humanities? Is data sharing an ethical necessity when the result of research is an expressive work that does not require reproducibility to be valid?

I gave Research Data MANTRA’s definition of research data, in order to reflect on how well it applies to the Humanities:

Research data are collected, observed, or created, for the purposes of analysis to produce and validate original research results.

When we invented this definition, it seemed quite apt for separating ‘stuff’ that is generated in the course of research from stuff that is the object of research; an operational definition, if you will. For example, a set of email messages may just be a set of correspondences; or it may be the basis of a research project if studied. It all depends on the context.

But recently we have become uneasy with this definition when engaging with certain communities, such as the Edinburgh College of Art. They have a lot of digital ‘stuff’ – inputs and outputs of research, but they don’t like to call it data, which has a clinical feel to it, and doesn’t seem to recognise creative endeavour. Is the same true for the Humanities, I wondered? Alas, the audience declined to pursue it in the Q&A, so I still wonder.

“The coolest thing to do with your data will be thought of by someone else.”                          – Rufus Pollock, Cambridge University and Open Knowledge Foundation, 2008

My second quote attempted to illustrate the unease felt by academics about the pressure to share their data, and why the altruistic argument about open data doesn’t tend to win people over, in my experience. I asked people to consider how it made them feel, but perhaps I should have tried it with a show of hands to find out their answers.

Information Wants to Be Free

Quote by John Perry Barlow, image by Robin Rice

I swiftly moved on to talk about open data licensing, the choices we’ve made for Edinburgh DataShare, and whether offering different ‘flavours’ of open licence are important when many people still don’t understand what open licences are about. Again I used an image from MANTRA (above) to point out that the main consideration for depositors should be whether or not to make their data openly available on the internet – regardless of licence.

By putting their outputs ‘in the wild’ academics are necessarily giving up control over how they are used; some users will be ‘unethical’; they will not understand or comply with the terms of use. And we as repository administrators are not in a position to police mis-use for our depositors. Nevertheless, since academic users tend to understand and comply with scholarly norms about citing and giving attribution, those new to data sharing should not be unduly alarmed about the statement illustrated above. (And DataShare provides a ‘suggested citation’ for every data item that helps the user comply with the attribution requirements.)

Since no overview of data and ethics would be complete without consideration given to confidentiality obligations of researchers towards their human subjects, I included a very short video clip from MANTRA, of Professor John MacInnes speaking about caring for data that contain personally identifying information or personal attributes.

For me the most challenging aspect of the roundtable and indeed the day, was the contribution by Dr Anouk Lang about working with data from social media. As an ethical researcher one cannot assume that consent is unnecessary when working with data streams (such as twitter) that are open to public viewing. For one thing, people may not expect views of their posts outside of their own circles – they treat it as a personal communication medium. For another they may assume that what they say is ethereal and will soon be forgotten and unavailable. A show of hands indicated only some of the audience had heard of the Twitter Developers and API, or Storify, which can capture tweets and other objects in a more permanent web page, illustrating her point.

While this whole area may be more common for social researchers – witness the Economic and Social Research Council’s funding of a Big Data Network over several years which includes social media data – Anouk’s work on digital culture proves Humanities researchers cannot escape “the plethora of ethics, privacy and risk issues surrounding the use (and reuse) of social media data.” (Communication on ESRC Big Data Network Phase 3.)

Robin Rice
Data Librarian

IDCC 2014 – take home thoughts

A few weeks ago I attended the 9th International Digital Curation Conference in San Francisco.  The conference was spread over four days, with two days for workshops, and two for the main conference.  The conference was jointly run by the Digital Curation Centre and the California Digital Library.  Unsurprisingly it was an excellent conference with much debate and discussion about the evolving needs for digital curation of research data.

San Francisco

The main points I took home from the conference were:

Science is changing: Atul Butte gave an inspiring keynote that contained an overview of the ways in which his own work is changing.  In particular he explained how it is now possible to ‘outsource’ parts of the scientific process. The first is the ability to visit a web site to buy tissue samplesfor specific diseases which were previously used for medical tests, but which have now been anonymised and collected rather than being discarded.  Secondly it is also now possible to order mouse trials to be undertaken, again via a web site.  These allow routine activities to be performed more quickly and cheaply.

Big Data: This phrase is often used and means different things to different people.  A nice definition given by Jane Hunter was that curation of big data is hard because of its volume, velocity, variety and veracity.  She followed this up by some good examples where data have been effectively used.

Skills need to be taught: There were several sessions about the role of Information Schools in educating a new breed of information professionals with the skills required to effectively handle the growing requirements of analysing and curating data.  This growth was demonstrated by how we are seeing many more job titles such as data engineer / analyst / steward / journalist.  It was proposed that library degrees should include more technical skills such as programming and data formats.

The Data paper: There was much discussion about the concept of a ‘Data Paper’ – a short journal paper that describes a data set.  It was seen as an important element in raising the profile of the creation of re-usable data sets.  Such papers would be citable and trackable in the same ways as journal papers, and could therefore contribute to esteem indicators.  There was a mix of traditional and new publishers with varying business models for achieving this.  One point that stood out for me was that publishers were not proposing to archive the data, only the associated data paper.  The archiving would need to take place elsewhere.

Tools are improving: I attended a workshop about Data Management in the Cloud, facilitated by Microsoft Research.  They gave a demo of some of the latest features of Excel.  Many of the new features seem to nicely fit into two camps, but equally useful and very powerful to both.  Whether you are looking at data from the perspective of business intelligence or research data analysis, tools such as Excel are now much more than a spreadsheet for adding up numbers.  They can import, manipulate, and display data in many new and powerful ways.

I was also able to present a poster that contains some of the evolving thoughts about data curation systems at the University of Edinburgh: http://dx.doi.org/10.6084/m9.figshare.902835

In his closing reflection of the conference, Clifford Lynch said that we need to understand how much progress we are making with data curation.  It will be interesting to see the progress made and what new issues are being discussed at the conference next year which will be held much closer to home in London.

Stuart Lewis
Head of Research and Learning Services
Library & University Collections, Information Services

Using an electronic lab notebook to deposit data into Edinburgh DataShare

This is heads up about a ‘coming attraction’.  For the past several months a group at Research Space has been working with the DataShare team, including Robin Rice and George Hamilton, to make it possible to deposit research data from our new RSpace electronic notebook into DataShare.

I gave the first public preview of this integration last month in a presentation called Electronic lab notebooks and data repositories:  Complementary responses to the scientific data problem  to a session on Research Data and Electronic Lab Notebooks at the American Chemical Society conference in Dallas.

When the RSpace ELN becomes available to researchers at Edinburgh later this spring, users of RSpace will be able to make deposits to DataShare directly from RSpace using a simple interface we have built into RSpace.  The whole process only takes a few clicks, and starts with selecting records to be deposited into DataShare and clicking on the DataShare button as illustrated in the following screenshot:b2_workspaceHighlightedYou are then asked to enter some information about the deposit:

c2_datashareDialogFilledAfter confirming a few details about the deposit, the data is deposited directly into DataShare, and information about the deposit appears in DataShare.

h2_viewInDatashare2We will provide details about how to sign up for an RSpace account in a future post later in the spring.  In the meantime, I’d like to thank Robin and George for working with us at RSpace on this exciting project.  As far as we know this is the first time an electronic lab notebook has ever been integrated with an institutional data repository, so this is a pioneering and very exciting experiment!  We hope to use it as a model for similar integrations with other institutional and domain-specific repositories.

Rory MacNeil
Chief Executive, Research Space

How can you improve your data management skills?

A range of training courses on research data management (RDM) in the form of half-day courses and seminars have been created to help you with research data management issues, and are now available for booking on the MyEd booking system:

  • Research Data Management Programme at the University of Edinburgh
  • Good practice in research data management
  • Creating a data management plan for your grant application
  • Handling data using SPSS (based on the MANTRA module)
  • Handling data with ArcGIS (based on the MANTRA module)

RDM trainingThese courses and seminars aim to equip researchers, postgraduate research students and research support staff with a grounded understanding in data management issues and data handling.

If you manage research data, provide support for research, or are interested in finding out more about efficient and effective ways of managing your research data these course will be for you.

For detailed information about these courses please go to: http://www.ed.ac.uk/schools-departments/information-services/research-support/data-management/rdm-training

We are also happy to arrange tailored sessions for researchers and research support staff in aspects of research data management from planning through to depositing.  Please contact us at IS.Helpline@ed.ac.uk if you would like to arrange a training session.

Cuna Ekmekcioglu
Senior Research Data Officer
Library & University Collections, IS