Data management challenges in analysis and synthesis in the ecosystem sciences

Open-data has created an unprecedented opportunity with new challenges for ecosystem scientists. Skills in data management are essential to acquire, manage, publish, access and re-use data. These skills span many disciplines and require trans-disciplinary collaboration. Science synthesis centres support analysis and synthesis through collaborative 'Working Groups' where domain specialists work together to synthesise existing information to provide insight into critical problems. The Australian Centre for Ecological Analysis and Synthesis (ACEAS) served a wide range of stakeholders, from scientists to policy-makers to managers. This paper investigates the level of sophistication in data management in the ecosystem science community through the lens of the ACEAS experience, and identifies the important factors required to enable us to benefit from this new data-world and produce innovative science. ACEAS promoted the analysis and synthesis of data to solve transdisciplinary questions, and promoted the publication of the synthesised data. To do so, it provided support in many of the key skillsets required. Analysis and synthesis in multi-disciplinary and multi-organisational teams, and publishing data were new for most. Data were difficult to discover and access, and to make ready for analysis, largely due to lack of metadata. Data use and publication were hampered by concerns about data ownership and a desire for data citation. A web portal was created to visualise geospatial datasets to maximise data interpretation. By the end of the experience there was a significant increase in appreciation of the importance of a Data Management Plan. It is extremely doubtful that the work would have occurred or data delivered without the support of the Synthesis centre, as few of the participants had the necessary networks or skills. It is argued that participation in the Centre provided an important learning opportunity, and has resulted in improved knowledge and understanding of good data management practices.