African Open Science Platform

Geoffrey Boulton

CODATA

Pretoria

December 2016

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The digital revolution

storage – analysis – communicationGlobal information storage capacity

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Digital

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The technological bases for open scienceif we choose to use them!

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

• 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 CommunityInternational Accord on Open Data

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 COMMISSIONON ONTOLOGIES & METADATAFOR SCIENCE & TECHNOLOGY

International Union of Crystallography

DECADE OF DATA?

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

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

A Roadmap for Implementation A current priority is the creation of a Technical Advisory Board that will produce the road map to

determine how the above functions will be prioritised and how they will be implemented in ways

that adapt to current national priorities and research initiatives.

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

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 !

A Roadmap for Implementation A current priority is the creation of a Technical Advisory Board that will produce the road map to

determine how the above functions will be prioritised and how they will be implemented in ways

that adapt to current national priorities and research initiatives.

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

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 - 1

A Roadmap for Implementation A current priority is the creation of a Technical Advisory Board that will produce the road map to

determine how the above functions will be prioritised and how they will be implemented in ways

that adapt to current national priorities and research initiatives.

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

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 !

A Roadmap for Implementation A current priority is the creation of a Technical Advisory Board that will produce the road map to

determine how the above functions will be prioritised and how they will be implemented in ways

that adapt to current national priorities and research initiatives.

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

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

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