How to Choose the ‘Right’ Repository for Research Data

Shawn A Ross1, Steven McEachern2, Peter Sefton3, Brian Ballsun-Stanton4

1Macquarie University, Sydney, Australia, shawn.ross@mq.edu.au

2Australian National University, Canberra, Australia,steven.mceachern@anu.edu.au

3University of Technology Sydney, Australia, peter.sefton@uts.edu.au

4Macquarie University, Sydney, Australia, brian.ballsun-stanton@mq.edu.au

DESCRIPTION

In Australia, multi-institutional, domain-specific information infrastructure projects, such as those funded through the Australian National eResearch Collaboration Tools and Resources (NeCTAR) program, are typically open-source software (OSS). National infrastructure such as AARNet’s Cloudstor, built on OwnCloud, is also OSS. Even publications repositories and data management planning tools are often OSS (DSpace, DMPOline, DMPTool, RedBox, etc.). The trend in institutional research data repository software amongst institutions who prefer not to build an in-house solution, however, appears to favour proprietary software (e.g., Figshare). In comparison to Europe and North America, OSS is much less popular in Australia (e.g., Dataverse, CKAN). Dataverse, for example, has 33 installations on five continents containing 76 institutional ‘Dataverses’ (some installations house more than one) – but Australia has only one installation or institutional Dataverse, (the Australian Data Archive) [1]. By contrast, Figshare has been or is being implemented by at least five Australian universities [2], with others actively considering it.

This BoF session examines the reasons why institutions choose proprietary versus OSS for research data infrastructure. We compare the practical advantages, disadvantages, and considerations around each approach. We propose for discussion the idea that the advantages of proprietary software are overstated, as is the burden of implementing and administering OSS. For example, costs like requirements analysis, systems integration and engagement, outreach, and training – which together likely account for the majority of a software project’s budget – are similar whether proprietary or OSS. Deployment and maintenance of modern OSS platforms, facilitated by approaches like containerisation and automation, is lower than in the past. SaaS options for OSS are also sometimes overlooked. Proprietary software, moreover, is not always an ‘out-of-the-box’ turn-key solution for software at universities, especially regarding specialised software for research (as opposed to commodity). As such, it may require the creation of separate but interoperable systems to fill gaps in capacity, dramatically raising costs. Conversely, the flexibility and capabilities of an OSS solution are neglected: if a feature is missing or inadequate, it can be built (often with support from the community) and made available for reuse, without having to work around the edges of a proprietary system. The Australian Data Archive, for example, has added significant new features to Dataverse to support mediated access to sensitive data, which are available to other users. However, a deeper exploration of the tradeoffs and demands of both approaches in the context of specialised academic software is warranted. The focus of the discussion will be practical, but it may extend to the potential impact of various software business models on research data, a core output and asset of universities.

The session will be 60 minutes in duration. It will include brief presentations by the organisers based on their experience, followed by open discussion. Audience participation is essential – we encourage a candid exchange of experience with either proprietary or OSS for research data management at various institution, so that we can learn from each others’ successes and challenges. The outcome will be information to guide decision making around repository platform procurement at universities.

REFERENCES

  1. The Dataverse Project. Available from: https://dataverse.org/ accessed 22 June 2018. See also https://dataverse.org/metrics accessed 22 June 2018.
  2. The University of Adelaide. Available from: https://www.adelaide.edu.au/figshare/. The University of Melbourne. Available from: https://melbourne.figshare.com/. Monash University. Available from: https://monash.figshare.com/. La Trobe University. Available from: https://latrobe.figshare.com/. Federation University Australia (planned). Available from: https://federation.edu.au/staff/governance/projects/current-projects.

Biographies:

Shawn Ross (Ph.D. University of Washington, 2001) is Associate Professor of History and Archaeology and Director of Data Science and eResearch at Macquarie University. A/Prof Rossʼs research interests include the history and archaeology of pre-Classical Greece and the Balkans, and the application of information technology to research. He supervises a large-scale landscape archaeology and palaeo-environmental study in central and southeast Bulgaria. Since 2012, he has also directed a large information infrastructure project developing data capture and management systems for field research. Previously, A/Prof Ross worked at the University of New South Wales (Syndey, Austrlalia) and William Paterson University (Wayne, New Jersey).

Steve McEachern is Director and Manager of the Australian Data Archive at the Australian National University, where he is responsible for the daily operations and technical and strategic development of the data archive. He has high-level expertise in survey methodology and data archiving, and has been actively involved in development and application of survey research methodology and technologies over 15 years in the Australian university sector. Steve holds a PhD in industrial relations from Deakin University, as well as a Graduate Diploma in Management Information Systems from Deakin University, and a Bachelor of Commerce with Honours from Monash University. He has research interests in data management and archiving, community and social attitude surveys, organisational surveys, new data collection methods including web and mobile phone survey techniques, and reproducible research methods. Steve has been involved in various professional associations in survey research and data archiving over the last 10 years.

Peter Sefton is the Manager, eResearch Support at the University of Technology, Sydney (UTS). Before that he was in a similar role at the university of Western Sydney (UWS). Previously he ran the Software Research and development Laboratory at the Australian Digital Futures Institute at the University of Southern Queensland. Following a PhD in computational linguistics in the mid-nineties he has gained extensive experience in the higher education sector in leading the development of IT and business systems to support both learning and research. At UTS Peter is leading a team which is working with key stakeholders to implement university-wide eResearch infrastructure, including an institutional data repository, as well as collaborating widely with research communities at the institution on specific research challenges. His research interests include repositories, digital libraries, and the use of The Web in scholarly communication.

Brian Ballsun-Stanton is Solutions Architect (Digital Humanities) for the Macquarie University Faculty of Arts with a PhD from UNSW in Philosophy. He is working with researchers from across Australia to deploy digital technologies and workflows for their research projects. He has developed a new methodology (The Social Data Flow Network) to explore how individuals in the field understand the nature of data. Brian’s current research interests are in exploring the Philosophy of Science’s interactions with the Open Data movement, and building tools for rapid analysis and bulk manipulation of large ancient history corpora.

About the conference

eResearch Australasia provides opportunities for delegates to engage, connect, and share their ideas and exemplars concerning new information centric research capabilities, and how information and communication technologies help researchers to collaborate, collect, manage, share, process, analyse, store, find, understand and re-use information.

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