Publishing Data Workflows

[Guest post from Angus Whyte, Digital Curation Centre]

In the first week of March the 7th Plenary session of the Research Data Alliance got underway in Tokyo. Plenary sessions are the fulcrum of RDA activity, when its many Working Groups and Interest Groups try to get as much leverage as they can out of the previous 6 months of voluntary activity, which is usually coordinated through crackly conference calls.

The Digital Curation Centre (DCC) and others in Edinburgh contribute to a few of these groups, one being the Working Group (WG) on Publishing Data Workflows. Like all such groups it has a fixed time span and agreed deliverables. This WG completes its run at the Tokyo plenary, so there’s no better time to reflect on why DCC has been involved in it, how we’ve worked with others in Edinburgh and what outcomes it’s had.

DCC takes an active part in groups where we see a direct mutual benefit, for example by finding content for our guidance publications. In this case we have a How-to guide planned on ‘workflows for data preservation and publication’. The Publishing Data Workflows WG has taken some initial steps towards a reference model for data publishing, so it has been a great opportunity to track the emerging consensus on best practice, not to mention examples we can use.

One of those examples was close to hand, and DataShare’s workflow and checklist for deposit is identified in the report alongside workflows from other participating repositories and data centres. That report is now available on Zenodo. [1]

In our mini-case studies, the WG found no hard and fast boundaries between ‘data publishing’ and what any repository does when making data publicly accessible. It’s rather a question of how much additional linking and contextualisation is in place to increase data visibility, assure the data quality, and facilitate its reuse. Here’s the working definition we settled on in that report:

Research data publishing is the release of research data, associated metadata, accompanying documentation, and software code (in cases where the raw data have been processed or manipulated) for re-use and analysis in such a manner that they can be discovered on the Web and referred to in a unique and persistent way.

The ‘key components’ of data publishing are illustrated in this diagram produced by Claire C. Austin.

Data publishing components. Source: Claire C. Austin et al [1]

Data publishing components. Source: Claire C. Austin et al [1]

As the Figure implies, a variety of workflows are needed to build and join up the components. They include those ‘upstream’ around the data collection and analysis, ‘midstream’ workflows around data deposit, packaging and ingest to a repository, and ‘downstream’ to link to other systems. These downstream links could be to third-party preservation systems, publisher platforms, metadata harvesting and citation tracking systems.

The WG recently began some follow-up work to our report that looks ‘upstream’ to consider how the intent to publish data is changing research workflows. Links to third-party systems can also be relevant in these upstream workflows. It has long been an ambition of RDM to capture as much as possible of the metadata and context, as early and as easily as possible. That has been referred to variously as ‘sheer curation’ [2], and ‘publication at source [3]). So we gathered further examples, aiming to illustrate some of the ways that repositories are connecting with these upstream workflows.

Electronic lab notebooks (ELN) can offer one route towards fly-on-the-wall recording of the research process, so the collaboration between Research Space and University of Edinburgh is very relevant to the WG. As noted previously on these pages [4] ,[5], the RSpace ELN has been integrated with DataShare so researchers can deposit directly into it. So we appreciated the contribution Rory Macneil (Research Space) and Pauline Ward (UoE Data Library) made to describe that workflow, one of around half a dozen gathered at the end of the year.

The examples the WG collected each show how one or more of the recommendations in our report can be implemented. There are 5 of these short and to the point recommendations:

  1. Start small, building modular, open source and shareable components
  2. Implement core components of the reference model according to the needs of the stakeholder
  3. Follow standards that facilitate interoperability and permit extensions
  4. Facilitate data citation, e.g. through use of digital object PIDs, data/article linkages, researcher PIDs
  5. Document roles, workflows and services

The RSpace-DataShare integration example illustrates how institutions can follow these recommendations by collaborating with partners. RSpace is not open source, but the collaboration does use open standards that facilitate interoperability, namely METS and SWORD, to package up lab books and deposit them for open data sharing. DataShare facilitates data citation, and the workflows for depositing from RSpace are documented, based on DataShare’s existing checklist for depositors. The workflow integrating RSpace with DataShare is shown below:

RSpace-DataShare Workflows

RSpace-DataShare Workflows

For me one of the most interesting things about this example was learning about the delegation of trust to research groups that can result. If the DataShare curation team can identify an expert user who is planning a large number of data deposits over a period of time, and train them to apply DataShare’s curation standards themselves they would be given administrative rights over the relevant Collection in the database, and the curation step would be entrusted to them for the relevant Collection.

As more researchers take up the challenges of data sharing and reuse, institutional data repositories will need to make depositing as straightforward as they can. Delegating responsibilities and the tools to fulfil them has to be the way to go.

 

[1] Austin, C et al.. (2015). Key components of data publishing: Using current best practices to develop a reference model for data publishing. Available at: http://dx.doi.org/10.5281/zenodo.34542

[2] ‘Sheer Curation’ Wikipedia entry. Available at: https://en.wikipedia.org/wiki/Digital_curation#.22Sheer_curation.22

[3] Frey, J. et al (2015) Collection, Curation, Citation at Source: Publication@Source 10 Years On. International Journal of Digital Curation. 2015, Vol. 10, No. 2, pp. 1-11

http://doi:10.2218/ijdc.v10i2.377

[4] Macneil, R. (2014) Using an Electronic Lab Notebook to Deposit Data http://datablog.is.ed.ac.uk/2014/04/15/using-an-electronic-lab-notebook-to-deposit-data/

[5] Macdonald, S. and Macneil, R. Service Integration to Enhance Research Data Management: RSpace Electronic Laboratory Notebook Case Study International Journal of Digital Curation 2015, Vol. 10, No. 1, pp. 163-172. http://doi:10.2218/ijdc.v10i1.354

Angus Whyte is a Senior Institutional Support Officer at the Digital Curation Centre.

 

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Lorna M. Campbell, a Digital Education Manager with EDINA and the University of Edinburgh, writes about the ideas shared and discussed at the open.ed event this week.

 

Earlier this week I was invited by Ewan Klein and Melissa Highton to speak at Open.Ed, an event focused on Open Knowledge at the University of Edinburgh.  A storify of the event is available here: Open.Ed – Open Knowledge at the University of Edinburgh.

“Open Knowledge encompasses a range of concepts and activities, including open educational resources, open science, open access, open data, open design, open governance and open development.”

 – Ewan Klein

Ewan set the benchmark for the day by reminding us that open data is only open by virtue of having an open licence such as CC0, CC BY, CC SA. CC Non Commercial should not be regarded as an open licence as it restricts use.  Melissa expanded on this theme, suggesting that there must be an element of rigour around definitions of openness and the use of open licences. There is a reputational risk to the institution if we’re vague about copyright and not clear about what we mean by open. Melissa also reminded us not to forget open education in discussions about open knowledge, open data and open access. Edinburgh has a long tradition of openness, as evidenced by the Edinburgh Settlement, but we need a strong institutional vision for OER, backed up by developments such as the Scottish Open Education Declaration.

open_ed_melissa

I followed Melissa, providing a very brief introduction to Open Scotland and the Scottish Open Education Declaration, before changing tack to talk about open access to cultural heritage data and its value to open education. This isn’t a topic I usually talk about, but with a background in archaeology and an active interest in digital humanities and historical research, it’s an area that’s very close to my heart. As a short case study I used the example of Edinburgh University’s excavations at Loch na Berie broch on the Isle of Lewis, which I worked on in the late 1980s. Although the site has been extensively published, it’s not immediately obvious how to access the excavation archive. I’m sure it’s preserved somewhere, possibly within the university, perhaps at RCAHMS, or maybe at the National Museum of Scotland. Where ever it is, it’s not openly available, which is a shame, because if I was teaching a course on the North Atlantic Iron Age there is some data form the excavation that I might want to share with students. This is no reflection on the directors of the fieldwork project, it’s just one small example of how greater access to cultural heritage data would benefit open education. I also flagged up a rather frightening blog post, Dennis the Paywall Menace Stalks the Archives,  by Andrew Prescott which highlights the dangers of what can happen if we do not openly licence archival and cultural heritage data – it becomes locked behind commercial paywalls. However there are some excellent examples of open practice in the cultural heritage sector, such as the National Portrait Gallery’s clearly licensed digital collections and the work of the British Library Labs. However openness comes at a cost and we need to make greater efforts to explore new business and funding models to ensure that our digital cultural heritage is openly available to us all.

Ally Crockford, Wikimedian in Residence at the National Library of Scotland, spoke about the hugely successful Women, Science and Scottish History editathon recently held at the university. However she noted that as members of the university we are in a privileged position in that enables us to use non-open resources (books, journal articles, databases, artefacts) to create open knowledge. Furthermore, with Wikpedia’s push to cite published references, there is a danger of replicating existing knowledge hierarchies. Ally reminded us that as part of the educated elite, we have a responsibility to open our mindsets to all modes of knowledge creation. Publishing in Wikipedia also provides an opportunity to reimagine feedback in teaching and learning. Feedback should be an open participatory process, and what better way for students to learn this than from editing Wikipedia.

Robin Rice, of EDINA & Data Library, asked the question what does Open Access and Open Data sharing look like? Open Access publications are increasingly becoming the norm, but we’re not quite there yet with open data. It’s not clear if researchers will be cited if they make their data openly available and career rewards are uncertain. However there are huge benefits to opening access to data and citizen science initiatives; public engagement, crowd funding, data gathering and cleaning, and informed citizenry. In addition, social media can play an important role in working openly and transparently.

Robin Rice

James Bednar, talking about computational neuroscience and the problem of reproducibility, picked up this theme, adding that accountability is a big attraction of open data sharing. James recommended using iPython Notebook   for recording and sharing data and computational results and helping to make them reproducible. This promoted Anne-Marie Scott to comment on twitter:

@ammienoot: "Imagine students creating iPython notebooks... and then sharing them as OER #openEd"

“Imagine students creating iPython notebooks… and then sharing them as OER #openEd”

Very cool indeed.

James Stewart spoke about the benefits of crowdsourcing and citizen science.   Despite the buzz words, this is not a new idea, there’s a long tradition of citizens engaging in science. Darwin regularly received reports and data from amateur scientists. Maintaining transparency and openness is currently a big problem for science, but openness and citizen science can help to build trust and quality. James also cited Open Street Map as a good example of building community around crowdsourcing data and citizen science. Crowdsourcing initiatives create a deep sense of community – it’s not just about the science, it’s also about engagement.

open._ed_james

After coffee (accompanied by Tunnocks caramel wafers – I approve!) We had a series of presentations on the student experience and students engagement with open knowledge.

Paul Johnson and Greg Tyler, from the Web, Graphics and Interaction section of IS,  spoke about the necessity of being more open and transparent with institutional data and the importance of providing more open data to encourage students to innovate. Hayden Bell highlighted the importance of having institutional open data directories and urged us to spend less time gathering data and more making something useful from it. Students are the source of authentic experience about being a student – we should use this! Student data hacks are great, but they often have to spend longer getting and parsing the data than doing interesting stuff with it. Steph Hay also spoke about the potential of opening up student data. VLEs inform the student experience; how can we open up this data and engage with students using their own data? Anonymised data from Learn was provided at Smart Data Hack 2015 but students chose not to use it, though it is not clear why.  Finally, Hans Christian Gregersen brought the day to a close with a presentation of Book.ed, one of the winning entries of the Smart Data Hack. Book.ed is an app that uses open data to allow students to book rooms and facilities around the university.

What really struck me about Open.Ed was the breadth of vision and the wide range of open knowledge initiatives scattered across the university.  The value of events like this is that they help to share this vision with fellow colleagues as that’s when the cross fertilisation of ideas really starts to take place.

This report first appeared on Lorna M. Campbell’s blog, Open World:  lornamcampbell.wordpress.com/2015/03/11/open-ed

P.S. another interesting talk came from Bert Remijsen, who spoke of the benefits he has found from publishing his linguistics research data using DataShare, particularly the ability to enable others to hear recordings of the sounds, words and songs described in his research papers, spoken and sung by the native speakers of Shilluk, with whom he works during his field research in South Sudan.

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Dancing with Data

I went to an interesting talk yesterday by Prof Chris Speed called “Dancing with Data”, on how our interactions and relationships with each other, with the objects in our lives and with companies and charities are changing as a result of the data that is now being generated by those objects (particularly smartphones, but increasingly by other objects too). New phenomena such as 3D printing, airbnb, foursquare and iZettle are giving us choices we never had before, but also leading to things being done with our data which we might not have expected or known about. The relationships between individuals and our data are being re-defined as we speak. Prof Speed challenged us to think about the position of designers in this new world where push-to-pull markets are being replaced by new models. He also told us about his research collaborations with Oxfam, looking at how technology might enhance the value of the second-hand objects they sell by allowing customers to hear their stories from their previous owners.   Logo for the Tales of Things project

All very thought-provoking, but what about the implications for academic research, aside from those working in the fields of Design, Economics or Sociology who must now develop new models to reflect this changing landscape? Well, the question arises, if all this data is being generated and collected by companies, are the academics (and indeed the charity sector) falling behind the curve? Here at the University of Edinburgh, my colleagues in Informatics are doing Data Science research, looking into the infrastructure and the algorithms used to analyse the kind of commercial Big Data flowing out of the smartphones in our pockets, while Prof Speed and his colleagues are looking at how design itself is being affected. But perhaps academics in all disciplines need to be tuning their antennae to this wavelength and thinking seriously about how their research can adapt to and be enhanced by the new ways we are all dancing with data.

For more about the University of Edinburgh’s Design Informatics research and forthcoming seminars see www.designinformatics.org. Prof Chris Speed tweets @ChrisSpeed.

Pauline Ward is a Data Library Assistant working at the University of Edinburgh and EDINA.

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Data journals – an open data story

Here at the Data Library we have been thinking about how we can encourage our researchers who deposit their research data in DataShare to also submit these for peer review.

Why? We hope the impact of the research can be enhanced with the recognised added-value of peer review. Regardless whether there is a full-blown article to accompany the data.

We therefore decided recently to provide our depositors with a list of websites or organisations where they could do this.

I pulled a table together, from colleagues’ suggestions, from the PREPARDE project and the latest RDM textbook. And, very much in the Open Data spirit, I then threw the question open on Twitter:

“[..]does anyone have an up-to-date list of journals providin peer review of datasets (without articles), other than PREPARDE? #opendata

…and published the draft list for others to check or make comments on. This turned out to be a good move. The response from the Research Data Management community on Twitter was very heartening, and colleagues from across the globe provided some excellent enhancements for the list.

That process has given us confidence to remove the word ‘Draft’ from the title – the list, this crowd-sourced resource, will need to be updated from time-to-time, but we are confident that we’ve achieved reasonable coverage of the things we were looking for.

Another result of this search was the realisation that what we had gathered was in fact quite clearly a list of Data Journals. My colleague Robin Rice has now added a definition of that term to the list, and we will be providing all our depositors with a link to it:

https://www.wiki.ed.ac.uk/display/datashare/Sources+of+dataset+peer+review

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