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Project Snowball – sharing data for cross-institutional benchmarking Lisa Colledge, Anna Clements, Mhamed el Aisati, Scott Rutherford euroCRIS 2012 with.

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Presentation on theme: "Project Snowball – sharing data for cross-institutional benchmarking Lisa Colledge, Anna Clements, Mhamed el Aisati, Scott Rutherford euroCRIS 2012 with."— Presentation transcript:

1 Project Snowball – sharing data for cross-institutional benchmarking Lisa Colledge, Anna Clements, Mhamed el Aisati, Scott Rutherford euroCRIS 2012 with modifications for JISC RIM Meeting Bristol Jun 28 th 2012

2 The aims of Snowball Higher education institutions – Agree a common set of metrics to support institutional decision making – Reach consensus on standard methodologies for calculating these metrics – Publish the recipe book as open standard definitions These metrics will cover the entire landscape of research activity These metrics will become global sector standards

3 The origins of these aims... Growing recognition of value of metrics to support strategies Dissatisfaction with the tools available Frustration over availability of metrics to make sensible measurements Institutions and funders should work more collaboratively, and develop stronger relationships with suppliers An agreed national framework for data and metric standards is needed Suppliers should participate in the development of data and metric standards BACKGROUND Joint Imperial-Elsevier JISC-funded study of research information management, available via RECOMMENDATIONS

4 Snowball has evolved from these recommendations Agree methodologies for a standard set of metrics to support strategic decision making Driven by higher education institutions - with recognised common challenges and goal - working with a supplier (Elsevier), with everyone contributing voluntarily The goal? To enable cross- institutional benchmarking

5 Comprehensive metrics landscape Metrics require institutional, proprietary and third party data

6 Test 1 to calculate the metrics landscape Approach: institution and Elsevier contribute data on 10 chemistry researchers as proxy for the whole university Definitions of metrics Data availability across landscape Sensitivity of some data types (next slide) Researcher-level data Manual labour in data collection

7 Data types with high sensitivity

8 Test 2 of metric calculation feasibility Definitions of metrics Data availability across landscape Sensitivity of some data types Researcher-level data Manual labour in data collection Experts group formed to select and define phase 1 metrics – impactful, do- able, require data from 3 sources Data agreement prepared by partners Most sensitive data types not phase 1 Used minimally Metric granularity Institution and Elsevier supply data as close to native as possible Approach: institution and Elsevier test scalability by contributing data on whole university for a smaller set of metrics

9 Test 2 of metric calculation feasibility

10 Metrics require institutional, proprietary and third party data Test 2 of metric calculation feasibility HESA cost centre HESA FTE research, reserch & teaching

11 Project Snowball recap Driven by sector Facilitated and supported by Elsevier Public service The project has demonstrated feasibility of scalably inputting data from 3 sources to generate metrics and benchmarks Institutional Proprietary Third party

12 Next steps Publish the phase 1 metrics recipe book as open standards – Sep 2012 Refine phase 1 metrics as global standards, and extend same approach to more metrics CERIFy metrics – meeting scheduled Sep 2012 Spread the word – Russell Group, 94 Group, Vendors, Funders

13 Thank you for your attention


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