Open data-for-innovation-smart-and-sustainable

1. Open Data for Innovation, Smart and Sustainable
Development
Prof Muliaro Wafula, PhD, FCCS, FCSK
Director, iCEOD and Associate Prof Computing Department JKUAT
muliaro@icsit.jkuat.ac.ke
ARFICA ai JAPAN Project Innovation Seminar Presentation April 6, 2017 at JKUAT

2. Open Data support for Smart and Sustainable
Development (SSD)
Open Data Value (EU ,2016)

3. Creative Economy based on Open Data
Source: Asia-Pacific Regional Forum on e-Government, Smart Cities, and Digital
Societies for Sustainable Development, August, 2015, Bangkok, Thailand

4. ICT application for SSD (Source: World Economic Forum Report, March 2015)

5. Use of ICT for Disaster Management
Develop early warning systems
 Enhancing coordination, cooperation, and logistics;
Develop databases of key disaster response agents
Adopt and implement Tempere Convetio
 calls for streamlining of disaster relief process by waiving regulatory barriers & and putting in place
procedures that maximize access life saving ICT systems (SSDM Report, 2015)

6. Smart learning
Make learning process adaptive, effective, efficient, engaging, flexible and accessible
(Spector, 2014).
Provide learner‐centred environments that are intelligent and open, and integrate digital
virtual reality learning (Zhong & Zhang, 2006)
Use quality education to achieve SDGs- Poverty, hunger, good health, quality education,
gender equality, clean water & energy, economic growth, smart cities etc

7. Smart Sustainable Cities
Deploy IoTs and sensors
Install instruments for data collection and processing in support for efficient & effective
city mgt & planning
Build human capacity needed to support proper use of ICT
Build reliable heterogeneous ICT systems that support interoperability
Enable use and aggregation of data by systems and services

8. The Value of Open Data Sharing
 Benefits in the areas of:
 Economic Benefits
 Social Welfare Benefits
 Research and Innovation Opportunities
 Education
 Governance
 Available at http://dx.doi.org/10.5281/zenodo.33830

9. FAIR Data recommended ?
• FAIR Data
• Findable: have sufficiently rich metadata and a unique and persistent identifier.
• Accessible: retrievable by humans and machines through a standard protocol; open
and free; authentication and authorization where necessary.
• Interoperable: metadata use a ‘formal, accessible, shared, and broadly applicable
language for knowledge representation’.
• Reusable: metadata provide rich and accurate information; clear usage license;
detailed provenance.
• FAIR Guiding Principles for scientific data management and stewardship,
http://dx.doi.org/10.1038/sdata.2016.18
• Guiding Principles for FAIR Data: https://www.force11.org/node/6062

10. Open Data limits
 For data created with public funds or where there is a strong demonstrable public
interest, Open should be the default.
 As Open as Possible as Closed as Necessary.
 Proportionate exceptions for:
 Legitimate commercial interests (sectoral variation)
 Privacy (‘safe data’ vs Open data – the anonymisation problem)
 Public interest (e.g. endangered species, archaeological sites)
 Safety, security and dual use (impacts contentious)

11. Open Data Policy Development
• Open Data policy development need to be based on the following three pillars:
1. C-context
2. C-content
3. I-impact
11

12. Policy Context Pillar
Key factors include:
Level of Gov organization
Key motivations, policy objectives
Open data platform launch
Resource allocation & economic context
Legislation
Social, cultural & Political context
Drivers for open data
Forces against Opening data
12

13. Policy Content Pillar
Key factors include:
Licensing
Access fee
Data restriction
Data presentation
Contact with user
Amount published
Processing before publishing
Cost of opening
Types of Data
Data Formats & stds
Data quality
Provision of metadata
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14. Policy Impact Pillar
Key factors include:
Re-use of published data
Possible predicted risks
Benefits aligned with motivation
Public value
Transparency & accountability
Economic growth
Entrepreneurial open data use/ innovation
Efficiency
Environmental sustainability
Inclusion of marginalized
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15. Key Strategic Pillars of Sustainable Open Data Programs
Support open data infrastructure build based on open data policies standards and
supportive legal and licensing frameworks
Make data publishing and access available and easy
Create feedback channels for data users
Prioritize dataset that users want
Address quality issues of datasets
Protect privacy rights
Provide clear, consistent, and useful metadata
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16. JORD Policy
 JKUAT developed and implemented an open research data policy (JORD) Policy (February
2016)
 JORD expected benefits include
1. ROI
2. Encouragement of diverse studies and opinion
3. Promotion of new areas of work not envisioned by the initial investigators.
4. Strengthen the credibility of scholarly publications
5. Development of new products and services
6. Support JKUAT open data platform (https://opendata.jkuat.ac.ke
16

17. Designed in
consultation with
IBM Expert
(Ben Mann)

18. Innovative Open Data and Visualization (iODaV)
The specific objectives of AFRICA ai JAPAN Project Sub-Task Force are as follows:
Enable and promote innovations based on open research data published, preserved and
accessible for sharing and reuse.
Enable and promote innovations and decision making process through use of data,
information and scientific visualization
Support Smart Learning
Support and promote conformity with open data principles, standards and JKUAT Open
Research Data (JORD) Policy
Support and enable multidisciplinary research activities.
Enable use and reuse of research data to accelerate innovations and achievement of the
Kenya Vision 2030, and the UN Sustainable Development Goals (SDGs) in Kenya and the
region
Link and be linked to other open data platforms.

19. Kyoto University –Viz Lab & iCEOD Collaboration
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4661
5593
6525
7457
8389
9321
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11185
12117
13049
13981
14913
15845
16777
17709
18641
19573
20505
21437
22369
23301
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25165
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Building collaboration:
 Scientific, Data and Information Visualization
technologies for manufacturing, innovation, insight and
policy making
 High Performance Computing-HP Simulate Complex
problems and solve (Kyoto has HPC, Kenya has 4
undersea Internet Cables-Good Internet connectivity)
 Capacity Building-train short JICA training, MSc and PhD
 Student & Staff Exchange
 Joint research projects of mutual/common interest
 Use FEMAP Engineering Software to improve design and
innovation in manufacturing sector and Building
Construction Industries
 Causality Determination

20. Thank You

21. Bibliography
• Spector, J. M. (2014). Conceptualizing the emerging field of smart learning environments.
Smart Learning Environments, 1 (2), http://www.slejournal.com/content/1/1/2
• Smart and Sustainable Development (SSD) Report 2015
• World Economic Forum Report, March 2015
• Zhong, G., & Zhang, X. (2006). A building of the current intelligent learning environment
mode. Computer Science, 1, 170‐171.
• Simon Hodson. RDM-Policy, High-Level Implementation and Best Practice. Danish Agency
for Science and Higher Education. Copenhagen, Denmark. 21 February 2017