African Open Science Platform
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The document discusses the African Open Science Platform, emphasizing its purpose to create a federated virtual space for scientists to manage and share data in an open science context. It outlines the guiding principles for open data, the roles of different stakeholders, and the challenges faced in implementing these practices. Additionally, the document highlights the need for capacity building among researchers and data professionals while fostering collaboration across various scientific ecosystems.
African Open Science Platform
- 1. African Open Science Platform Geoffrey Boulton CODATA Pretoria December 2016
- 2. 19 Exabytes280Exabytes Based on: http://www.martinhilbert.net/WorldOnfoCapacity.html 1 Exabyte=1018 bytes The digital revolution storage – analysis – communication Global information storage capacity In optimally compressed bytes Digital Storage Analogue Storage Explosion of the Digital revolution 1986 1993 2000 2007 2014-4000Exabytes The technological bases for open science if we choose to use them!
- 3. Why Open Data/Open Science? The international context What is a Platform, how should it be structured what is its value? How is it governed? Role of CODATA & ICSU Key Questions
- 4. • Identifies the opportunities and challenges of the data revolution as the dominant issue of policy for science • Sets out 12 guiding principles for the practice of open data • Outlines the responsibilities of all stakeholders in supporting such practice • Addresses the boundaries of openness, concluding that open data should be the default position for publicly funded science First statement on Open Data by the International Scientific Community International Accord on Open Data
- 5. EMBL-EBI services Labs around the world send us their data and we… Archive it Classify it Share it with other data providers Analyse, add value and integrate it …provide tools to help researchers use it A collaborative enterprise Discipline-driven Government-driven International Systemic Platforms/Commons European science cloud CODATA – ICSU COMMISSION ON ONTOLOGIES & METADATA FOR SCIENCE & TECHNOLOGY International Union of Crystallography DECADE OF DATA?
- 6. The Open Data Iceberg The Technical Challenge The Consent Challenge The Ecosystem Challenge The Funding Challenge The Support Challenge The Skills Challenge The Incentives Challenge The Mindset Challenge Processes & Organisation People motivation and ethos. National/Regional Infrastructure Technology
- 7. African Open Science Platform Purpose: • To provide a federated virtual space for scientists to find, deposit, manage, share and reuse data, software and metadata Functions: • Establishing common principles, policies and practices for data acquisition and use and Providing the facilitating tools in ways that are adapted to varying national, disciplinary and application priorities and approaches. • Recognising the roles and developing responsibilities of different actors at all levels in national scientific ecosystems. • Developing the technical capacities of researchers and data professionals. • Creating meaning from data: awareness and access to developing
- 8. a) Principles – Policies – Practices – Tools • Shared open data principles (Science International Accord on Open Data) • A computational environment for access, utilisation and storage • Common digital data compliance model that describes the properties of data that enable them to be Findable, Accessible, Interoperable and Reproducible (FAIR) • Publicly available datasets that adhere to accepted principles and practices • Software services and tools to facilitate access to data and their responsible use ! b) National Science Ecosystems • Governments: enunciate policy, create incentives • Funders: costs of open data as the costs of doing science; require FAIR data deposition from the projects they fund; collaborate in Platform evolution • Universities and Institutes: research data management; capacity building; research support; incentives for researchers • Publishers: require concurrent FAIR data deposition • Researchers: changing the mindset – data custodians not owners c) Capacity building amongst researchers and data professionals a) Principles – Policies – Practices – Tools • Shared open data principles (Science International Accord on Open Data) • A computational environment for access, utilisation and storage • Common digital data compliance model that describes the properties of data that enable them to be Findable, Accessible, Interoperable and Reproducible (FAIR) • Publicly available datasets that adhere to accepted principles and practices • Software services and tools to facilitate access to data and their responsible use ! b) National Science Ecosystems • Governments: enunciate policy, create incentives • Funders: costs of open data as the costs of doing science; require FAIR data deposition from the projects they fund; collaborate in Platform evolution • Universities and Institutes: research data management; capacity building; research support; incentives for researchers • Publishers: require concurrent FAIR data deposition • Researchers: changing the mindset – data custodians not owners c) Capacity building amongst researchers and data professionals Functions - 1
- 9. • Universities and Institutes: research data management; capacity building; research support; incentives for researchers • Publishers: require concurrent FAIR data deposition • Researchers: changing the mindset – data custodians not owners d) Creating Meaning from Data · Ensuring access to cutting edge analytic tools · Matching analytic tools for big data to project purpose · Using machine learning · Applying semantic methods to data integration · Developing/using relevant ontologies and vocabularies for discovery and integration · Linking with international efforts in data science and application areas c) Capacity building amongst researchers and data professionals · Coordination of technical capacity building exercises · Including scaled-up versions of existing CODATA training workshops and CODATA/RDA School of Research Data Science in Africa · Collaboration with disciplinary bodies in offering discipline-specific workshops · Discussions with universities about their longer-term adoption of data science curricula ! • Universities and Institutes: research data management; capacity building; research support; incentives for researchers • Publishers: require concurrent FAIR data deposition • Researchers: changing the mindset – data custodians not owners d) Creating Meaning from Data · Ensuring access to cutting edge analytic tools · Matching analytic tools for big data to project purpose · Using machine learning · Applying semantic methods to data integration · Developing/using relevant ontologies and vocabularies for discovery and integration · Linking with international efforts in data science and application areas c) Capacity building amongst researchers and data professionals · Coordination of technical capacity building exercises · Including scaled-up versions of existing CODATA training workshops and CODATA/RDA School of Research Data Science in Africa · Collaboration with disciplinary bodies in offering discipline-specific workshops · Discussions with universities about their longer-term adoption of data science curricula ! Functions - 2
- 10. Pilot Phase Funded by DST/NRF - Managed by Assaf - Directed by CODATA Interim Governance Advisory Council Technical Advisory Board Pilot Phase Priorities Developing the Partnership Developing Governance Creating a Roadmap Stimulating engagement