UK Secretariat to support BF Collaborative Research Action on Data Management and e-Infrastructure

点击翻译按钮获取中文摘要

End-to-end decision support systems are imperative in helping society respond to global environmental change. Development of these systems are impaired by numerous social and technological challenges however, and include the lack of coordinated research strategies and objectives between individual infrastructures and across disciplines, the growth of "big and complex data" to scales well beyond the capabilities of traditional data handling tools, limited access to data and software tools by mainstream scientists, and a lack of interoperability of technical architecture and of governance across individual infrastructures, disciplines and scales for data, e-infrastructures and data analytics. Many nations have instituted parallel e-infrastructure activities that can be used to develop end-to-end decision support systems. COOPEUS in the European Union, EarthCube in the US, the Environmental Virtual Observatory pilot in the UK and the Australian National Data Service, are a few of the most note-worthy examples. Most of these have concentrated on serving data, whereas information systems also need to serve models and tools, where standards are much less developed. Cross-disciplinary, cross-national collaborations focused on addressing serving all types of environmental information require a coordinated approach to planning, implementation and management if they are to be efficient and maximise the time scientists can spend on their science. A concerted international effort to develop a more coordinated international approach has the potential to produce greater sustainability in data curation, storage and sharing and to integrate and leverage existing resources, expertise and tools; increase the rate at which standards are created, adopted and shared; promote open data and open source programmes and enhance capability, capacity and trust, leading to creation and promulgation of an international community of practice in data and computational sciences for global environmental change.