Research Data Storage Infrastructure consultation University of Sydney response

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1 Research Data Storage Infrastructure consultation University of Sydney response From: Prof Jill Trewhella Deputy Vice-Chancellor (Research) Date: 14 May Summary of response The University is grateful for the opportunity to provide a response to the Super Science consultation on the $50M Research Data Storage Infrastructure. We also endorse the response Intersect is submitting. The Implementation Discussion Paper affords an excellent overview and plan for addressing the organisational and technical questions involved in establishing the infrastructure. Nonetheless, two primary challenges are not fully covered by the paper: 1. Financial sustainability of model: Before proceeding to build, it is essential that a clear understanding be reached on how operational costs for the infrastructure will be met, and where the responsibility for future capital replacement funding will lie. 2. Incentives for researchers and institutions: To encourage researchers to shift culture and practice so as to use the infrastructure, and to make institutional participation worthwhile, incentives and benefits for all stakeholders are important. This can be achieved by applying a combination of Federal funding and co-investment to allow common infrastructure covering all stages in the research data life-cycle: research project; retention; sharing for re-use; and longterm curation. The response discusses these issues first. It then turns to the questions posed in the consultation announcement, on which our feedback can be summarised as follows: 3. Data storage infrastructure model: The distinction between the three models described in the paper is not a sharp one. Our preference is for an approach based on Model 2 Regional strength, but with thorough national coordination (from Model 1), and including an investigation of the benefit versus cost and risk of commercial cloud-based storage (Model 3). We suggest a way of considering the choice which will allow for evolution towards cloud storage over time, and also flexibly address subsidiary questions raised in the discussion paper concerning co-location of storage with HPC analysis, and progressive development of discipline-specific collections. 4. Criteria for assigning data storage capacity: These criteria would flow from the incentives discussed in point 2 above, and would include: willingness and readiness of researchers to share the data; extent of collaboration (especially cross-institutional) in the initial research project; value for re-use in future research; and, where local co-investment is contributing, a degree of devolved institutional prioritisation. 5. Other sector developments impacting feasibility of options: Translational research, such as in the University s $385M Centre for Obesity, Diabetes and Cardiovascular Disease, which is currently under construction, requires good data connectivity at different sites, including across firewall boundaries to area health services. The response also includes a final point: 6. Lead agent: The University would welcome an opportunity to express interest in undertaking a lead agent or contracting entity role in relation to the Research Data Storage Infrastructure initiative. Research Data Storage Infrastructure consultation response Page 1 of 7

2 1. Financial sustainability of model Provision of $50M EIF Super Science funding towards research data storage infrastructure is a strong first step, and its significance cannot be overstated. However, if the infrastructure is to fulfil its promise as an ongoing component of support for Australian research, we need to face head on the challenge of building from the EIF investment by finding first operational funding, and then a budget mechanism for capital replacement, so that the infrastructure can be a permanent resource. The discussion paper does not address this challenge directly, although it alludes to it often for example: over time, the research sector will need to devote more resources to data, taken as a whole, regardless of how funding is provided (p3) the way in which necessary operating costs will be met [is a question] to be explored (p5) requirements include governance capable of addressing longer term issues beyond the life of this funding (p6) the nature and growth of operating costs needs to be better understood, as is the means by which this funding support can be provided (p7) under Model 1 some form of operating cost contribution would be required (p8) a key benefit of [Model 2] would be the leveraging of local co-investment (p8) co-investment or provision of appropriate facilities may need to be included in the mechanism for determining priority (p11). The University of Sydney sees the benefits to its researchers, the University and society overall that will come from appropriate storage, management, sharing and re-use of research data. The University acknowledges the contribution initial EIF funding will make, and recognises the attractions of economy of scale and lower environmental impact from large-scale, shared data centres. However, the question of co-investment is not straightforward for us. Increasingly many schemes require co-investment, and as there is no mechanism for funding the full cost of research, these economics are becoming more and more difficult. The Summary of Interim Agreement Negotiations with Higher Education Providers 1 mentioned the general problem in the following terms: While funding from the Education Investment Fund and Super Science Initiative were welcome, the issue of funding scientific equipment and national facilities more broadly (including funding to operate equipment and funding for routine infrastructure) was raised. That said, co-investment in some form can be envisaged. University of Sydney ICT, in anticipation of the necessity for supporting research data, is already making a degree of interim provision for research data storage out of its regular infrastructure budget, and also may be able to offer (in association with Intersect) tier-three-plus data-centre space in kind to house part of a NSW component of the national infrastructure. The University is keen to participate, in partnership with Intersect, in the initial focus on those communities that are likely to benefit from early engagement proposed on page 10 of the discussion paper. Overall, before proceeding to build, it is essential that a clear understanding be reached on how operational costs for the infrastructure will be met, and where the responsibility for future capital replacement funding will lie. 2. Incentives for researchers and institutions To achieve broad sharing and re-use of data across disciplines, an even more challenging task than infrastructure and service provision is engendering concomitant cultural change in researcher attitudes and practice. Experience in the University in attempting to encourage researchers to share data for ANDS Data Capture projects has shown the necessity for offering not only long-term research and social benefit from re-use, but also more immediate improvements for research groups themselves in their day-today project activities which create the data. In addition, resources are needed to provide education 1 Research Data Storage Infrastructure consultation response Page 2 of 7

3 and support to researchers on data management. ANDS Seeding the Commons program will contribute here, but provision of support will be a continuing requirement. Demonstration of short- and medium-term benefits to institutions is also important for achieving participation. While the principal driver for the research data storage infrastructure investment is, as described in the discussion paper on page 3, the contributions of data-intensive research and data re-use to innovation excellence and effective collaboration, and while the primary purpose should be to hold data collections which have national applicability and national governance arrangements (p5), it is good to see that the paper also identifies further potential benefits to be considered: 2.1. the resulting data environment should also be easily augmented by institutions, disciplines and research facilities, on a needs or interest basis, at an incremental cost (p3, emphasis added) 2.2. assisting the sector develop a shared collaboration environment in which institutions and researchers can easily manage many separate collaborations (p5) 2.3. it may be possible to allow some of the storage capacity to [assist institutions to] sustain a quality of research in the digital age that includes the reproducibility of results; and comply with the research data provisions of University Australia s Australian Code for the Responsible Conduct of Research (p5, emphasis in original) 2.4. a key question concerns the degree to which resources should be directed to [inter alia] the provision of robust archive and retention capability for selected data-sets (p7). The paper also notes (p7) that the presumption of ownership of data present in the sector is likely to influence the rate and manner in which data services will be taken up. The model needs to be able to respond to this issue. These points from the discussion paper pave a way for the EIF funding and co-investment to combine in providing incentives for researchers and institutions to participate in use and development of the storage infrastructure. In order to delineate benefits for researcher, institutional and government/funder stakeholders from research data storage infrastructure, it is helpful to characterise stages in the life-cycle of the data as follows: a) storage and management for research data during projects, to prevent loss and provide convenient access for the research teams themselves, including teams collaborating across institutional boundaries; b) retention of data as required of institutions by the ACRCR and State records legislation after projects end; c) enabling appropriately open access to data for re-use and further collaboration (the principal driver for the Super Science $50M investment); d) long-term curation of retained data to allow continuing re-use if relevant, including appraisal for eventual disposal if appropriate, and attention to preservation in the face of formats becoming obsolete. While the main purpose of the Super Science initiative is re-use (c), points 2.1 and 2.2 above have a bearing on data during projects (a), point 2.3 on retention (b), and point 2.4 on long-term curation (d). To encourage all institutions, and researchers from all areas, to participate fully in the initiative, it will be beneficial if a combination of the Super Science funding, future Federal funding and institutional co-investment can support not just stage (c), but (a), (b) and (d) as well. Such an approach would provide for all three data domains depicted in Andrew Treloar s 2007 diagram (Figure 1); but instead of the different repositories of the title, it would provide a single environment for appropriately collaborative and re-usable research data through its whole life-cycle, with suitable access regimes at each stage. In this way, the research data storage infrastructure could provide the required incentives for all stakeholders: for researchers, a single system (at least as seen from their point of view) for managing their data during projects and for appropriate sharing from a suitable point in time Research Data Storage Infrastructure consultation response Page 3 of 7

4 for institutions, return on their co-investment by virtue of assisting research projects and also by fulfilling ACRCR and other data retention requirements for government and funding bodies, infrastructure which would not only making sharing and re-use possible, but also encourage incorporation of sharing into research culture and practice. Figure 1 3. Data storage infrastructure model 3.1. Overlap and rivalry between models The discussion paper presents three potential implementation models. The distinction between these is not clear-cut, and there may be overlap between them. For example, the four or five major facilities envisaged for Peak National Model 1 in the paper do not seem very different from half a dozen regional nodes under Regional Strength Model 2, whereas the cloud storage Industry Partnerships Model 3 might compete with or complement either Model 1 or Model 2 for provision of part or all of the storage. In any case, the first target should be not to make a definite choice of model, but rather since any model will require national data-centric governance to establish the governance and management mechanism and the process which will design the implementation model. We agree with the judgements on page 10 of the discussion paper that a regional but integrated solution offers the best opportunities, and that more highly distributed models are not attractive Model 2 preferred to Model 1 Our overall preference is towards Model 2 Regional Strength. As the discussion paper points out, this would help to leverage local co-investment (potentially helping to address the challenges described in section 1 above). In NSW, it would build on cooperation between institutions which is Research Data Storage Infrastructure consultation response Page 4 of 7

5 being realised through Intersect. Local institutional caches in addition to regional storage nodes may assist performance and access speed. Such a model would nonetheless need to offer a level of national coordination akin to that of Model 1. While cooperating technical operational teams could be distributed across regional nodes, the potential risk of fragmentation or divergence of approach this might present would need to be addressed by a central level of management, funded (as the paper suggests) by a partial flow of regional co-invested budget to the centre which might perhaps be virtual rather than geographically all in one place, given sufficient attention to team communications Cloud considerations: evolving towards Model 3 It seems reasonable to posit that: industry-partnership cloud storage models are not ready for exclusive use in the research data storage infrastructure at the present moment; but they are likely to become increasingly relevant and attractive over say the next decade. A 2009 ECAR study of alternative sourcing in higher education 2 noted (p2) expectations that cloud adoption will grow, but modestly, with only 6.5% of respondents outsourcing data storage so far (p4). Indications from the Association of Pacific Rim Universities suggest that take-up may be more rapid than the ECAR assessment. Instead of a choice of Model 2 alone, it therefore seems important to develop a model which will allow a mix of provision approaches for research data, potentially involving some cloud storage from the start, and with more added over time. In doing so, questions and requirements to be addressed will need to include: addressing viability and trust issues as pointed out in the discussion paper (p9) access through a common interface independent of underlying storage origin (p2) flexibility and control over modes of use of all storage, including cloud storage, to allow development over time of rich, discipline-specific tools for data display and analysis (as described on page 11 of the discussion paper, and equivalent to the topmost data grid layer in Robin Rice s Data Sharing Continuum 3 ). In particular, at what point may intensive cloud computing, supplementing or even replacing various level of HPC facility, be worth consideration as well as cloud storage? The benefit described in the paper (p9) of cost-effective additional cloud storage for other institutional purposes will be worth examining, with the proviso that for other kinds of data the trust considerations may differ Proximity The paper draws attention (e.g. p4, p5) to the relationship between data storage and data analysis, and the question of the degree to which they need to be co-located. To the extent required, this may be achieved by building storage at existing HPC facilities, but also by adding a computational component at new data storage nodes. Consultation and cooperation beyond the scope of the EIF $50M investment itself on both HPC considerations and network bandwidth to allow data movement will be needed to address these questions. Rather than a one size fits all answer, comparisons of costs between storage in a particular location on one hand and increased network capacity on the other may be determinative case by case. There are further potential proximity requirements to be considered, e.g., for visualisation, where sometimes it may be beneficial for data such as large images to be present at a local workstation. 2 Alternative IT Sourcing Strategies: From the Campus to the Cloud Key Findings, ECAR Key Findings, August or September PDF available at Research Data Storage Infrastructure consultation response Page 5 of 7

6 3.5. Discipline collections The discussion paper (p8, p10) refers to a need to allow data to migrate over time to build disciplinespecific collections. The requirement to build such collections over time is an important one: ASSDA 4 is a significant existing example. The paper notes (p10) that slices of collections may be in different regional stores, and that this could lead to significant complexity. However, there may be an alternative to the idea that data would migrate. Instead thinking of collections vertically, in silos, consider instead a stack model with discipline collections situated in a separate, higher horizontal layer of infrastructure. Each collection would have its own appropriate structure and services (including top-level data grid functionality), but rather than managing its own storage, would rely on the common services of the research data storage infrastructure. In this way, the location of data would not be relevant at the collection level. Such an approach might also be designed to address the question of determination of a funding model acceptable to the sector for storage of the discipline collections raised in the paper (p10) Path forwards On page 7, the discussion paper notes that the manner in which [analysis] facilities should be associated with the national data environment will likely evolve, so that an implementation model needs to provide that flexibility. This principle of flexibility can valuably be extended, to apply also to evolution of the extent of cloud storage employed, and to the development of discipline collections. Instead of predetermining these choices, either of overall model or on subsidiary points, it may be helpful to picture the structure to be implemented in the following way: Discipline collections and services Common management and services Near- HPC storage Regional storage (Model 2) Cloud storage (Model 3) Here the key will be to specify the common management and services layer first. Storage types, whether regional, near to HPC analysis facilities, or in the cloud, must all be designed to support the common services and to afford a common interface. The balance between these storage types will then be able to evolve over time according to requirements, capabilities and cost. Also in time, a layer for discipline-specific collections and services can be developed above, using the common data storage as an infrastructural service provider. A path to establishing such a structure could have the following components: 1. determine a lead agent (see section 6 below) 2. establish a governance body (possibly interim to start, but with a plan for full development; to include an independent chair, institutional/academic/diisr/ands/arcs2/nci representation, and IT/data-storage knowledge; and with provisions for handling conflicts of interest) 3. recruit professional project management resources 4. for speed, issue an early general call for expressions of interest from commercial cloudstorage providers, to assess potential cost per storage capacity, flexibility, service levels, administrative control options, location of hosting, and approach to trust issues around access and confidentiality 5. define a high-level project plan, including defined deliverables, communication and further consultation with all stakeholders, and risk management 6. establish management and the beginnings of an establishment and operations team (see 3.2 above) 4 Australian Social Science Data Archive: Research Data Storage Infrastructure consultation response Page 6 of 7

7 7. in line with point E on page 7 of the paper, develop a scoping document characterising requirements for tiers of storage, management techniques, proximity for various purposes, etc 8. on the basis of cloud EOIs received, decide what proportion of industry-provided cloud storage to incorporate from the start of implementation 9. staged implementation, with gradual build up of capacity to match data availability, and deferring decisions on balance between types and tiers of storage until provisioning time. 4. Criteria for assigning data storage capacity Criteria for how data storage capacity would be assigned, and what datasets would be supported, would flow out of the discussion on incentives in section 2 above, and should include the following: willingness and readiness of researchers to share the data (after an embargo period until publication or commercialisation where relevant; in de-identified form as necessary; and, where researchers specify, on the basis of formal collaboration or co-authorship) extent of collaboration (especially cross-institutional) in the initial research project value for re-use in future research where local co-investment is contributing, a degree of devolved institutional prioritisation association of data with other national activities, e.g. ANDS Data Capture projects. 5. Other sector developments impacting feasibility of options The University s $385M Centre for Obesity, Diabetes and Cardiovascular Disease, which is currently under construction, is an example of a major research initiative which will generate significant amounts of data: about 40% of the Centre s space will be dedicated to dry laboratories focussed on computerised data acquisition and analysis. In particular, the analysis of extremely large data sets, from clinical trials and from imaging, genomic, proteomic and similar technologies, will require highcapacity data storage, fast communication channels to different sites where the data is acquired and analysed, and ready access to appropriate computing capacity. Furthermore, collaboration around CODCD and other medical research data, particularly for translational research, occurs beyond University boundaries, in partnership with clinical staff in hospitals on area health service data networks with tight firewall restrictions. Attention during development of the research data storage infrastructure to connectivity across such boundaries will be an important requirement. 6. Lead agent As a leading research-intensive institution, nationally and at a global level, the University of Sydney recognises the benefits that eresearch, including advanced research data storage and management, will bring to its own researchers and to all research communities. In collaboration and partnership with Intersect which we helped to establish, we are committed to the development and promotion of eresearch. University of Sydney ICT has experience in large-scale data centre management, and the University has long practice and expertise in establishment of organisational structures. These characteristics could potentially permit the University to contribute to the Research Data Storage Infrastructure initiative by carrying out the role of lead contracting entity, in consultation with all stakeholders, for this nationally important endeavour to develop sustainable eresearch infrastructure for the benefit of researchers across the nation. We would therefore welcome an opportunity, if one becomes available, to express interest in acting in the role of lead agent for the initiative. Research Data Storage Infrastructure consultation response Page 7 of 7