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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Fostering open science in social science

FOSTER_logoOn 10th of June, the Data Library team ran two workshops in association with the EU Horizon 2020 project, FOSTER (Facilitate Open Science Training for European Research), and the Scottish Graduate School of Social Science.

The aim of the morning workshop, “Good practice in data management & data sharing with social research,” was to provide new entrants into the Scottish Graduate School of Social Science with a grounding in research data management using our online interactive training resource MANTRA, which covers good practice in data management and issues associated with data sharing.

The morning started with a brief presentation by Robin Rice on ‘open science’ and its meaning for the social sciences. Pauline Ward then demonstrated the importance of data management plans to ensure work is safeguarded and that data sharing is made possible. I introduced MANTRA briefly, and then Laine Ruus assigned different MANTRA units to participants and asked them to briefly go through the units and extract one or two key messages and report back to the rest of the group. After the coffee break we had another presentation on ethics, informed consent and the barriers for sharing, and we finished the morning session with a ‘Do’s and Dont’s exercise where we asked participants to write in post-it notes the things they remembered, the things they were taking with them from the workshop: green for things they should DO, and pink for those they should NOT. Here are some of the points the learners posted:

DO
– consider your usernames & passwords
– read the Data Protection Act
– check funder/institution regulations/policies
– obtain informed consent
– design a clear consent form
– give participants info about the research
– inform participants of how we will manage data
– confidentiality
– label your data with enough info to retrieve it in future
– develop a data management plan
– follow the certain policies when you re-use dataset[s] created by others
– have a clear data storage plan
– think about how & how long you will store your data
– store data in at least 3 places, in at least 2 separate locations
– backup!
– consider how/where you back up your data
– delete or archive old versions
– data preservation
– keep your data safe and secure with the help of facilities of fund bodies or university
– think about sharing
– consider sharing at all stages. Think about who will use my data next
– share data (responsibly)

DON’T
– unclear informed consent
– a sense of forcing participants to be part of research
– do not store sensitive information unless necessary
– don’t staple consent forms to de-identified data records/store them together
– take information security for granted
– assume all software will be able to handle your data
– don’t assume you will remember stuff. Document your data
– assume people understand
– disclose participants’ identity
– leave computer on
– share confidential data
– leave your laptop on the bus!
– leave your laptop on the train!
– leave your files on a train!
– don’t forget it is not just my data, it is public data
– forget to future proof

Robin Rice presenting at FOSTERing Open Science workshop

Our message was that open science will thrive when researchers:

  • organise and version their data files effectively,
  • provide comprehensive and sufficient documentation for others to understand and replicate results and thus cite the source properly
  • know how to store and transport your data safely and securely (ensuring backup and encryption)
  • understand legal and ethical requirements for managing data about human subjects
  • Recognise the importance of good research data management practice in your own context

The afternoon workshop on “Overcoming obstacles to sharing data about human subjects” built on one of the main themes introduced in the morning, with a large overlap of attendees. The ethical and regulatory issues in this area can appear daunting. However, data created from research with human subjects are valuable, and therefore are worth sharing for all the same reasons as other research data (impact, transparency, validation etc). So it was heartening to find ourselves working with a group of mostly new PhD students, keen to find ways to anonymise, aggregate, or otherwise transform their data appropriately to allow sharing.

Robin Rice introduced the Data Protection Act, as it relates to research with human subjects, and ethical considerations. Naturally, we directed our participants to MANTRA, which has detailed information on the ethical and practical issues, with specific modules on “Data protection, rights & access” and “Sharing, preservation & licensing”. Of course not all data are suitable for sharing, and there are risks to be considered.

In many cases, data can be anonymised effectively, to allow the data to be shared. Richard Welpton from the UK Data Archive shared practical information on anonymisation approaches and tools for ‘statistical disclosure control’, recommending sdcMicroGUI (a graphical interface for carrying out anonymisation techniques, which is an R package, but should require no knowledge of the R language).

DrNiamhMooreFinally Dr Niamh Moore from University of Edinburgh shared her experiences of sharing qualitative data. She spoke about the need to respect the wishes of subjects, her research gathering oral history, and the enthusiasm of many of her human subjects to be named in her research outputs, in a sense to own their own story, their own words.

Links:

Rocio von Jungenfeld & Pauline Ward
EDINA and Data Library

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open.ed report

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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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Open up! On the scientific and public benefits of data sharing

Research published a year ago in the journal Current Biology found that 80 percent of original scientific data obtained through publicly-funded research is lost within two decades of publication. The study, based on 516 random journal articles which purported to make associated data available, found the odds of finding the original data for these papers fell by 17 percent every year after publication, and concluded that “Policies mandating data archiving at publication are clearly needed” (http://dx.doi.org/10.1016/j.cub.2013.11.014).

In this post I’ll touch on three different initiatives aimed at strengthening policies requiring publicly funded data – whether produced by government or academics – to be made open. First, a report published last month by the Research Data Alliance Europe, “The Data Harvest: How sharing research data can yield knowledge, jobs and growth.”  Second, a report by an EU-funded research project called RECODE on “Policy Recommendations for Open Access to Research Data”, released last week at their conference in Athens.  Third, the upcoming publication of Scotland’s Open Data Strategy, pre-released to attendees of an Open Data and PSI Directive Awareness Raising Workshop Monday in Edinburgh.

Experienced so close together in time (having read the data harvest report on the plane back from Athens in between the two meetings), these discrete recommendations, policies and reports are making me just about believe that 2015 will lead not only to a new world of interactions in which much more research becomes a collaborative and integrative endeavour, playing out the idea of ‘Science 2.0’ or ‘Open Science’, and even that the long-promised ‘knowledge economy’ is actually coalescing, based on new products and services derived from the wealth of (open) data being created and made available.

‘The initial investment is scientific, but the ultimate return is economic and social’

John Wood, currently the Co-Chair of the global Research Data Alliance (RDA) as well as Chair of RDA-Europe, set out the case in his introduction to the Data Harvest report, and from the podium at the RECODE conference, that the new European commissioners and parliamentarians must first of all, not get in the way, and second, almost literally ‘plan the harvest’ for the economic benefits that the significant public investments in data, research and technical infrastructure are bringing.

CaptureThe report’s irrepressible argument goes, “Just as the World Wide Web, with all its associated technologies and communications standards, evolved from a scientific network to an economic powerhouse, so we believe the storing, sharing and re-use of scientific data on a massive scale will stimulate great new sources of wealth.” The analogy is certainly helped by the fact that the WWW was invented at a research institute (CERN), by a researcher, for researchers. The web – connecting 2 billion people, according to a McKinsey 2011 report, contributed more to GDP globally than energy or agriculture. The report doesn’t shy away from reminding us and the politicians it targets, that it is the USA rather than Europe that has grabbed the lion’s share of economic benefit– via Internet giants Google, Amazon, eBay, etc. – from the invention of the Web and that we would be foolish to let this happen again.

This may be a ruse to convince politicians to continue to pour investment into research and data infrastructure, but if so it is a compelling one. Still, the purpose of the RDA, with its 3,000 members from 96 countries is to further global scientific data sharing, not economies. The report documents what it considers to be a step-change in the nature of scientific endeavour, in discipline after discipline. The report – which is the successor to the 2010 report also chaired by Wood, “Riding the Wave: How Europe can gain from the rising tide of scientific data,” celebrates rather than fears the well-documented data deluge, stating,

“But when data volumes rise so high, something strange and marvellous happens: the nature of science changes.”

The report gives examples of successful European collaborative data projects, mainly but not exclusively in the sciences, such as the following:

  • Lifewatch – monitors Europe’s wetlands, providing a single point to collect information on migratory birds. Datasets created help to assess the impact of climate change and agricultural practices on biodiversity
  • Pharmacog – partnership of academic institutions and pharmaceutical companies to find promising compounds for Alzheimer’s research to avoid expensive late-stage failures of drugs in development.
  • Human Brain Project – multidisciplinary initiative to collect and store data in a standardised and systematic way to facilitate modelling.
  • Clarin – integrating archival information from across Europe to make it discoverable and usable through a single portal regardless of language.

The benefits of open data, the report claims, extends to three main groups:

  • to citizens, who will benefit indirectly from new products and services and also be empowered to participate in civic society and scientific endeavour (e.g. citizen science);
  • to entrepeneurs, who can innovate based on new information that no one organisation has the money or expertise to exploit alone;
  • to researchers, for whom the free exchange of data will open up new research and career opportunities, allow crossing of boundaries of disciplines, institutions, countries, and languages, and whose status in society will be enhanced.

‘Open by Default’

If the data harvest report lays out the argument for funding open data and open science, the RECODE policy recommendations focus on what the stakeholders can do to make it a reality. The project is fundamentally a research project which has been producing outputs such as disciplinary case studies in physics, health, bioengineering, environment and archaeology. The researchers have examined what they consider to be four grand challenges for data sharing.

  • Stakeholder values and ecosystems: the road towards open access is not perceived in the same way by those funding, creating, disseminating, curating and using data.
  • Legal and ethical concerns: unintended secondary uses, misappropriation and commercialization of research data, unequal distribution of scientific results and impacts on academic freedom.
  • Infrastructure and technology challenges: heterogeneity and interoperability; accessibility and discoverability; preservation and curation; quality and assessibility; security.
  • Institutional challenges: financial support, evaluating and maintaining the quality, value and trustworthiness of research data, training and awareness-raising on opportunities and limitations of open data.

Capture1RECODE gives overarching recommendations as well as stake-holder specific ones, a ‘practical guide for developing policies’ with checklist for the four major stakeholder groups: funders, data managers, research institutions and publishers.

‘Open Changes Everything’

The Scottish government event was a pre-release of the  open data strategy, which is awaiting final ministerial approval, though in its final draft, following public consultation. The speakers made it clear that Scotland wants to be a leader in this area and drive culture change to achieve it. The policy is driven in part by the G8 countries’ “Open Data Charter” to act by the end of 2015 on a set of five basic principles – for instance, that public data should be open to all “by default” rather than only in special cases, and supported by UK initiatives such as the government-funded Open Data Institute and the grassroots Open Knowledge Foundation.

Capture

Improved governance (or public services) and ‘unleashing’ innovation in the economy are the two main themes of both the G8 charter and the Scotland strategy. The fact was not lost on the bureaucrats devising the strategy that public sector organisations have as much to gain as the public and businesses from better availability of government data.

The thorny issue of personal data is not overlooked in the strategy, and a number of important strides have been taken in Scotland by government and (University of Edinburgh) academics recently on both understanding the public’s attitudes, and devising governance strategies for important uses of personal data such as linking patient records with other government records for research.

According to Jane Morgan from the Digital Public Services Division of the Scottish Government, the goal is for citizens to feel ownership of their own data, while opening up “trustworthy uses of data for public benefit.”

Tabitha Stringer, whose title might be properly translated as ‘policy wonk’ for open data, reiterated the three main reason for the government to embrace open data:

  • Transparency, accountability, supporting civic engagement
  • Designing and delivering public services (and increasingly digital services)
  • Basis for innovation, supporting the economy via growth of products & services

‘Digital first’

The remainder of the day focused on the new EU Public Service Information directive and how it is being ‘transposed’ into UK legislation to be completed this year. In short, the Freedom of Information and other legislation is being built upon to require not just publication schemes but also asset lists with particular titles by government agencies. The effect of which, and the reason for the awareness raising workshop is that every government agency is to become a data publisher, and must learn how to manage their data not just for their own use but for public ‘re-users’. Also, for the first time academic libraries and other ‘cultural organisations’ are to be included in the rules, where there is a ‘public task’ in their mission.

‘Digital first’ refers to the charging rules in which only marginal costs (not full recovery) may be passed on, and where information is digital the marginal cost is expected to be zero, so that the vast majority of data will be made freely available.

keep-calm-and-open-data-11Robin Rice
EDINA and Data Library

 

 

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