The document summarizes an iODaV Data Workshop held at JKUAT in Kenya on open data and the JORD policy. It discusses why open data is important for reproducibility, innovation and scientific discovery. It outlines the FAIR principles for open data and metadata to make data findable, accessible, interoperable and reusable. It also discusses opportunities and challenges of open data for universities, including developing skills and infrastructure. Finally, it provides examples of open data initiatives at JKUAT including developing an open data policy, the iODaV program, contributions to national ICT policies, and the digital health applied research centre.
The African Open Science Platform aims to coordinate open science activities across Africa through engagement, awareness raising, and connecting stakeholders. It is managed by the Academy of Science of South Africa and funded by the South African Department of Science and Technology. Key focus areas include developing policy frameworks, building infrastructure, enhancing capacity, and providing incentives for open data practices. The platform seeks to establish principles like FAIR data, address issues around licensing and intellectual property, and mobilize data science capabilities on the continent. It will involve capacity building for various data roles, adapting curricula, and acknowledging data publication. The goal is to ethically collect, curate and manage trusted African data to drive benefits for society.
This document discusses open science and FAIR data principles. It begins by outlining the benefits of open data, including enabling reproducibility, avoiding replication gaps, and allowing data reuse and reinterpretation. Open data practices have transformed areas like genomics and astronomy. FAIR data principles help enable large-scale data use and machine analysis. The document then defines open science, including open access, open data, FAIR data principles, and engagement with society. It discusses frameworks for developing open data strategies at the national and institutional levels. These include developing policies, incentives, skills training, and data infrastructure. While open data brings benefits, it also requires investment and cultural changes to fully realize. Stakeholders like government and research institutions can benefit
This document discusses FAIR data principles and open data. It provides an overview of the FAIR data principles of Findable, Accessible, Interoperable and Reusable data. It outlines the benefits of open data in a big data world but also acknowledges the challenges of implementing open data practices. The document proposes establishing an African Open Data Forum and developing research data infrastructure, skills training, policies and strategies to support open science and FAIR data practices in Africa.
This document discusses infrastructure data sharing in Uganda. It outlines the benefits of data sharing such as improved infrastructure planning and resilience. However, there are also barriers like insufficient skills, data quality issues, legacy IT systems, and lack of data sharing policies and guidelines. The document proposes a strategic approach to infrastructure data sharing that involves establishing multi-institutional implementation, developing policies and standards, providing training, developing technical infrastructure like a national digital twin, and forming partnerships. The goal is to maximize benefits while mitigating risks and tradeoffs of wider data sharing.
Data Management Planning for researchersSarah Jones
This document provides information about creating a data management plan (DMP) for researchers. It begins with defining what a DMP is - a short plan that outlines what data will be created, how it will be managed and stored, and plans for sharing and preservation. It then discusses the common components of a DMP, including describing the data, standards and methodologies, ethics and intellectual property, data sharing plans, and preservation strategies. The document provides examples of DMP requirements and recommendations from funders. It offers tips for creating a good DMP, including thinking about the needs of future data re-users, consulting stakeholders, grounding plans in reality, and planning for sharing from the outset. Finally, it discusses tools and resources
This document summarizes a presentation on open science and open data. It discusses the importance of open research data for reproducibility and innovation. It outlines key policy developments promoting open data, including funder data policies and journal data policies. It also describes CODATA's activities related to data policies, frameworks for developing open data strategies, and components of the international open science ecosystem.
A presentation offering an introduction to managing and sharing research data given at the Czech Open Science days as part of the EC-funded FOSTER project.
The African Open Science Platform aims to coordinate open science activities across Africa through engagement, awareness raising, and connecting stakeholders. It is managed by the Academy of Science of South Africa and funded by the South African Department of Science and Technology. Key focus areas include developing policy frameworks, building infrastructure, enhancing capacity, and providing incentives for open data practices. The platform seeks to establish principles like FAIR data, address issues around licensing and intellectual property, and mobilize data science capabilities on the continent. It will involve capacity building for various data roles, adapting curricula, and acknowledging data publication. The goal is to ethically collect, curate and manage trusted African data to drive benefits for society.
This document discusses open science and FAIR data principles. It begins by outlining the benefits of open data, including enabling reproducibility, avoiding replication gaps, and allowing data reuse and reinterpretation. Open data practices have transformed areas like genomics and astronomy. FAIR data principles help enable large-scale data use and machine analysis. The document then defines open science, including open access, open data, FAIR data principles, and engagement with society. It discusses frameworks for developing open data strategies at the national and institutional levels. These include developing policies, incentives, skills training, and data infrastructure. While open data brings benefits, it also requires investment and cultural changes to fully realize. Stakeholders like government and research institutions can benefit
This document discusses FAIR data principles and open data. It provides an overview of the FAIR data principles of Findable, Accessible, Interoperable and Reusable data. It outlines the benefits of open data in a big data world but also acknowledges the challenges of implementing open data practices. The document proposes establishing an African Open Data Forum and developing research data infrastructure, skills training, policies and strategies to support open science and FAIR data practices in Africa.
This document discusses infrastructure data sharing in Uganda. It outlines the benefits of data sharing such as improved infrastructure planning and resilience. However, there are also barriers like insufficient skills, data quality issues, legacy IT systems, and lack of data sharing policies and guidelines. The document proposes a strategic approach to infrastructure data sharing that involves establishing multi-institutional implementation, developing policies and standards, providing training, developing technical infrastructure like a national digital twin, and forming partnerships. The goal is to maximize benefits while mitigating risks and tradeoffs of wider data sharing.
Data Management Planning for researchersSarah Jones
This document provides information about creating a data management plan (DMP) for researchers. It begins with defining what a DMP is - a short plan that outlines what data will be created, how it will be managed and stored, and plans for sharing and preservation. It then discusses the common components of a DMP, including describing the data, standards and methodologies, ethics and intellectual property, data sharing plans, and preservation strategies. The document provides examples of DMP requirements and recommendations from funders. It offers tips for creating a good DMP, including thinking about the needs of future data re-users, consulting stakeholders, grounding plans in reality, and planning for sharing from the outset. Finally, it discusses tools and resources
This document summarizes a presentation on open science and open data. It discusses the importance of open research data for reproducibility and innovation. It outlines key policy developments promoting open data, including funder data policies and journal data policies. It also describes CODATA's activities related to data policies, frameworks for developing open data strategies, and components of the international open science ecosystem.
A presentation offering an introduction to managing and sharing research data given at the Czech Open Science days as part of the EC-funded FOSTER project.
The African Open Science Platform (AOSP) aims to promote open science and open data practices in Africa. It is funded by the South African Department of Science and Technology and managed by the Academy of Science of South Africa (ASSAf). AOSP focuses on developing policies, building capacity, establishing infrastructure, and providing incentives to support open data sharing. It has held several workshops across Africa to engage stakeholders and has conducted surveys to assess the current landscape. AOSP's ultimate goal is to facilitate collaboration and ethical data practices to generate benefits for African society.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
This document provides an overview of a webinar on digital curation and research data management for universities. The webinar covers an introduction to digital curation, the benefits and drivers for research data management, current initiatives in UK universities, and the role of libraries in supporting research data management. Libraries are increasingly involved in developing institutional policies, providing training, and advising researchers on writing data management plans and sharing data. The webinar highlights training opportunities for librarians to develop skills in research data management and digital curation.
Presentation given at the European Research Council workshop on research data management and sharing in Brussels on 18th-19th September 2014. The presentation covers the benefits and drivers for RDM, points to relevant tools and resources and closes with some open questions for discussion.
Research Data Management in GLAM: Managing Data for Cultural HeritageSarah Anna Stewart
Presentation given at the 'Open Science Infrastructures for Big Cultural Data' - Advanced International Masterclass in Plovdiv, Bulgaria. Dec. 13-15, 2018
This document discusses data management, data intensive research, and the Australian National Data Service (ANDS). It provides examples of research data and outlines trends toward eResearch, open data, and data sharing. ANDS aims to transform disparate research data collections into a cohesive national resource. It is establishing the Australian Research Data Commons to make data findable, accessible, interoperable, and reusable. The document also discusses new roles for libraries in supporting data management and the research data lifecycle.
Presentación de Joy Davidson, Digital Curation Centre (UK) en FOSTER event: Data Management Plan and Social Impact of Research. Universitat Jaume I, 27 mayo 2016
Simon Hodson discusses key aspects of open science including open access to research outputs, FAIR data principles, and engaging society. Open science requires addressing technical, funding, skills, and mindset challenges. While data created with public funds should be open by default, legitimate exceptions exist for commercial interests, privacy, and security. Criteria for data appraisal, selection and preservation need input from disciplines. Barriers to data sharing include concerns over misuse and lack of credit, while benefits include advancing research and building institutional reputation. Open science governance is needed to balance openness with other priorities like intellectual property, and define roles and responsibilities among stakeholders.
This document discusses open data for digital development in Botswana. It outlines the importance of open data for transparency, participation, innovation and economic opportunities. It analyzes Botswana's open data readiness and compares it to international best practices from Korea. The document proposes establishing an open data portal and policy framework in Botswana to stimulate applications, startups and broadband usage to support digital development and economic diversification.
This document discusses data management plans (DMPs), which are brief plans that define how research data will be created, documented, stored, shared, and preserved. DMPs are often required as part of grant applications. The document provides an overview of why DMPs are important, how they benefit researchers and institutions, and key aspects to address in a DMP such as data organization, stakeholders, and making data FAIR (findable, accessible, interoperable, and reusable). Examples of DMPs from real projects are also presented.
Management of research data specifically for Engineering and Physical Science. Delivered by Stuart Macdonald at the "Support for Enhancing Research Impact" meeting at the University of Edinburgh on 22 June 2016.
This document summarizes Simon Hodson's presentation on open science and FAIR data developments globally. Some key points:
1) There is a growing policy push for open research data, with funders and organizations adopting data sharing policies based on FAIR data principles of findability, accessibility, interoperability, and reusability.
2) Initiatives are working to build the international ecosystem of open science, including components for reporting research outputs, persistent identifiers, data standards, data repositories, and criteria for trustworthy data.
3) The African Open Science Platform aims to lay the foundations for open science in Africa through frameworks for policy, incentives, training, and technical infrastructure development.
4) International
Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
The African Open Science Platform (AOSP) aims to promote open science and open data practices in Africa. It is funded by the South African Department of Science and Technology and managed by the Academy of Science of South Africa (ASSAf). AOSP focuses on developing policies, building capacity, establishing infrastructure, and providing incentives to support open data sharing. It has held several workshops across Africa to engage stakeholders and has conducted surveys to assess the current landscape. AOSP's ultimate goal is to facilitate collaboration and ethical data practices to generate benefits for African society.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
This document provides an overview of a webinar on digital curation and research data management for universities. The webinar covers an introduction to digital curation, the benefits and drivers for research data management, current initiatives in UK universities, and the role of libraries in supporting research data management. Libraries are increasingly involved in developing institutional policies, providing training, and advising researchers on writing data management plans and sharing data. The webinar highlights training opportunities for librarians to develop skills in research data management and digital curation.
Presentation given at the European Research Council workshop on research data management and sharing in Brussels on 18th-19th September 2014. The presentation covers the benefits and drivers for RDM, points to relevant tools and resources and closes with some open questions for discussion.
Research Data Management in GLAM: Managing Data for Cultural HeritageSarah Anna Stewart
Presentation given at the 'Open Science Infrastructures for Big Cultural Data' - Advanced International Masterclass in Plovdiv, Bulgaria. Dec. 13-15, 2018
This document discusses data management, data intensive research, and the Australian National Data Service (ANDS). It provides examples of research data and outlines trends toward eResearch, open data, and data sharing. ANDS aims to transform disparate research data collections into a cohesive national resource. It is establishing the Australian Research Data Commons to make data findable, accessible, interoperable, and reusable. The document also discusses new roles for libraries in supporting data management and the research data lifecycle.
Presentación de Joy Davidson, Digital Curation Centre (UK) en FOSTER event: Data Management Plan and Social Impact of Research. Universitat Jaume I, 27 mayo 2016
Simon Hodson discusses key aspects of open science including open access to research outputs, FAIR data principles, and engaging society. Open science requires addressing technical, funding, skills, and mindset challenges. While data created with public funds should be open by default, legitimate exceptions exist for commercial interests, privacy, and security. Criteria for data appraisal, selection and preservation need input from disciplines. Barriers to data sharing include concerns over misuse and lack of credit, while benefits include advancing research and building institutional reputation. Open science governance is needed to balance openness with other priorities like intellectual property, and define roles and responsibilities among stakeholders.
This document discusses open data for digital development in Botswana. It outlines the importance of open data for transparency, participation, innovation and economic opportunities. It analyzes Botswana's open data readiness and compares it to international best practices from Korea. The document proposes establishing an open data portal and policy framework in Botswana to stimulate applications, startups and broadband usage to support digital development and economic diversification.
This document discusses data management plans (DMPs), which are brief plans that define how research data will be created, documented, stored, shared, and preserved. DMPs are often required as part of grant applications. The document provides an overview of why DMPs are important, how they benefit researchers and institutions, and key aspects to address in a DMP such as data organization, stakeholders, and making data FAIR (findable, accessible, interoperable, and reusable). Examples of DMPs from real projects are also presented.
Management of research data specifically for Engineering and Physical Science. Delivered by Stuart Macdonald at the "Support for Enhancing Research Impact" meeting at the University of Edinburgh on 22 June 2016.
This document summarizes Simon Hodson's presentation on open science and FAIR data developments globally. Some key points:
1) There is a growing policy push for open research data, with funders and organizations adopting data sharing policies based on FAIR data principles of findability, accessibility, interoperability, and reusability.
2) Initiatives are working to build the international ecosystem of open science, including components for reporting research outputs, persistent identifiers, data standards, data repositories, and criteria for trustworthy data.
3) The African Open Science Platform aims to lay the foundations for open science in Africa through frameworks for policy, incentives, training, and technical infrastructure development.
4) International
Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
Open data-for-innovation-smart-and-sustainablegyleodhis
1) The document discusses how open data can support smart and sustainable development through enabling innovation, creative economies, and ICT applications in areas like disaster management and smart learning.
2) It provides examples of how open data principles and policies can be developed, highlighting the importance of context, content, and impact.
3) JKUAT's open research data policy and open data platform are presented as examples of enabling open data sharing and its benefits for research, transparency, and economic growth.
Open data for innovation, smart and sustainable prof muliarogyleodhis
1) The document discusses how open data can support smart and sustainable development through enabling innovation, creative economies, and ICT applications in areas like disaster management and smart learning.
2) It provides examples of how open data principles and policies can be developed, highlighting the importance of context, content, and impact.
3) JKUAT's open research data policy and open data platform are presented as case studies of enabling open data sharing and its benefits.
The document discusses policy, infrastructure, skills, and incentives related to data sharing in Africa. It provides information about the University of Botswana, including its faculties, research centers, and digital repository. It then discusses the upcoming International Data Week conference in Gaborone, Botswana, and themes related to digital science such as open data, data analysis, and data stewardship. Finally, it summarizes the proposed African Open Science Platform project to coordinate open science activities across Africa through a centralized initiative.
Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
Kenya open data case 7.7.17 prof wafulaTom Nyongesa
This document summarizes Kenya's open data initiatives and policies. It discusses Kenya's open data national case study, key pillars of open data policy development, best practices for open data implementation, JKUAT's open data initiatives including its open data platform and policy, the Digital Health Applied Research Centre project, open data in agricultural research, and Kenya's draft national ICT policy which supports open data principles and use of data for e-services like e-health and e-agriculture. The document provides an overview of how Kenya is using open data to support smart and sustainable development.
This document summarizes Kenya's open data initiatives and policies. It discusses Kenya's open data national case study, key pillars of open data policy development, best practices for open data implementation, JKUAT's open data initiatives including its open data platform and policy, the Digital Health Applied Research Centre project, open data in agricultural research, and Kenya's draft national ICT policy which supports open data principles and use in various sectors like health, agriculture and more. The document provides an overview of how Kenya is working to develop its open data ecosystem through projects, policies and stakeholder engagement.
Ross Wilkinson - Data Publication: Australian and Global Policy DevelopmentsWiley
Australia invests $AUD1-2B per annum in research data. Like most countries, it wants to get the best return possible on this data. Europe is spending E1.4B on their open data “pilot”. This means the data should be FAIR: findable, accessible, interoperable, and reusable. Part of this is that data should be routinely “published” and available in a “data repository”. But what does this mean?
Ross Wilkinson
CEO, Australian National Data Service
Presented at the 2015 Wiley Publishing Seminar, 5 November, Melbourne, Australia.
Making Data FAIR (Findable, Accessible, Interoperable, Reusable)Tom Plasterer
What to do About FAIR…
In the experience of most pharma professionals, FAIR remains fairly abstract, bordering on inconclusive. This session will outline specific case studies – real problems with real data, and address opportunities and real concerns.
·
Why making data Findable, Actionable, Interoperable and Reusable is important.
Talk presented at the Data Driven Drug Development (D4) conference on March 20th, 2019.
The document summarizes the Jisc Managing Research Data Programme which aims to support universities in improving research data management. It discusses why managing research data is important, highlighting funder policies and the benefits of open data. It provides an overview of Jisc's activities including training projects, guidance resources, and funding for institutional infrastructure services and repositories. The presentation emphasizes the importance of institutional policies, support services, skills development and cultural change to effectively manage research data in line with funder expectations.
Presentation given at Macquarie University in support of the ARDC 'institutional role in the data commons' project on "Implementing FAIR: Standards in Research Data Management" https://ardc.edu.au/news/data-and-services-discovery-activities-successful-applicants/
Curating the Scholarly Record: Data Management and Research LibrariesKeith Webster
The document discusses the role of research libraries in curating and managing scholarly data and the evolving scholarly record. It notes that rapid technological developments have opened up new applications for research data and increased data creation. It argues that full and open access to research data derived from publicly funded research should be adopted as an international norm. It also discusses challenges around data management, issues researchers face with eResearch, and the unclear role of academic librarians in supporting data management and curation.
Susanna Sansone's talk at the "Beyond Open" Knowledge Dialogues/Open Data Hong Kong event on research data, hosted at the Hong Kong Innocentre on Monday 20 November 2017.
NordForsk Open Access Reykjavik 14-15/8-2014:Finnish data-initiativeNordForsk
This document summarizes Finland's Research Data Initiative from 2009-2017. The initiative aimed to develop a sustainable research data infrastructure in Finland by providing services like data storage, metadata, and long-term preservation. It also sought to encourage open data sharing and reuse. The initiative progressed from early planning projects to establishing core services. Lessons learned include the importance of flexible governance, permanent preservation, embracing change through openness, and addressing cultural shifts around data sharing. The initiative aims to enhance research through improved access, collaboration and reuse of scientific data.
The document discusses the Enabling FAIR Data project, which aims to improve data sharing practices in earth and environmental sciences. It outlines the FAIR data principles, key stakeholders in the project including publishers and repositories, and outputs including a commitment statement, repository finder tool, and shared authoring guidelines. The next steps are to encourage more organizations to sign and implement the commitment statement and guidelines to promote open and interoperable data.
Open Data: Strategies for Research Data Management (and Planning)Martin Donnelly
The document provides information about facilitating open science training for European research. It discusses the Digital Curation Centre (DCC), which provides guidance and services on research data management and open science. The FOSTER project aims to spread open science practices through training resources, events, and online courses. The presentation then discusses research data management (RDM), including the benefits of managing data according to FAIR principles to make it findable, accessible, interoperable, and reusable. It also covers the importance of developing data management plans (DMPs) to document how research data will be handled and preserved over its lifecycle.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
1. iODaV Data Workshop.
iPiC Centre, JKUAT Main Campus, JUJA 19th September 2017
Open Data & JORD Policy
Prof Joseph Muliaro Wafula PhD, FCCS, FCSK.
Chair, iODaV & Director, iCEOD
Jomo Kenyatta University of Agriculture and Technology
Kenya
3. Why Data especially in this digital era?
• Science demands that you support your arguments with
evidence/data.
• Open research data are essential for reproducibility, self-
correction.
• Academic publishing has not kept up with age of digital data.
• Danger of an replication / evidence / credibility gap.
• Open data foster innovation and accelerate scientific discovery
through reuse of data.
Data for research should be intelligently open: accessible, assessible,
intelligible, useable.
FAIR: Findable, Accessible, Interoperable, Reusable.
Publications and data should be Open and available concurrently:
argues that not to make data concurrently open is scientific
malpractice
Science International Accord on Open Data in a Big Data World:
http://www.science-international.org/ (JKUAT has signed this
accord)
4. Open Data Guiding Principles-FAIR
• 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
5. FAIR Principles
• To be Findable:
• F1. (meta)data are assigned a globally unique and
persistent identifier
• F2. data are described with rich metadata (defined by R1
below)
• F3. metadata clearly and explicitly include the identifier
of the data it describes
• F4. (meta)data are registered or indexed in a searchable
resource
• To be Accessible:
• A1. (meta)data are retrievable by their identifier using a
standardized communications protocol
• A1.1 the protocol is open, free, and universally
implementable
• A1.2 the protocol allows for an authentication and
authorization procedure, where necessary
• A2. metadata are accessible, even when the data are no
longer available
• (Mons, B., et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, http://dx.doi.org/10.1038/sdata.2016.18)
• To be Interoperable:
• I1. (meta)data use a formal, accessible, shared, and
broadly applicable language for knowledge representation.
• I2. (meta)data use vocabularies that follow FAIR principles
• I3. (meta)data include qualified references to other
(meta)data
• To be Reusable:
• R1. meta(data) are richly described with a plurality of
accurate and relevant attributes
• R1.1. (meta)data are released with a clear and accessible
data usage license
• R1.2. (meta)data are associated with detailed provenance
• R1.3. (meta)data meet domain-relevant community
standards
• (Mons, B., et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, http://dx.doi.org/10.1038/sdata.2016.18)
6. Opportunities & Challenges for JKUAT/PAUSTI
Open and FAIR Research Data Presents Major Opportunities for Universities:
Research intensive universities will be data intensive universities.
Supporting researchers’ use of data is a key strategic mission and enabler: world
class research environment includes support for data stewardship.
A university’s reputation is increasingly built on all research outputs and wider
societal and economic impact: data is core to this.
Development of significant data collections of research intensive universities.
Leading departments / research groups will be characterised by excellence in
data, by Open FAIR data collections.
The way in which the contribution to research of both the individual researcher
and the institution will increasingly be measured on the basis of data outputs as
well as research articles.
Policies less and less ambiguous – data stewardship, RDM is necessary for grant
funding success.
Avoid reputational damage through data loss.
Challenges:
Policy development: unpicking Open
and FAIR data (JKUAT has JORD)
Supporting data through the
lifecycle.
Culture and incentives: what’s in it
for us?
Skills gaps: training and support.
Technical systems and infrastructure.
Developing culture of conscious data
stewardship: what to keep and what
to discard.
Supporting the long term
stewardship of research data
Sustainability and finance..
7. Boundaries of Open
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)
All these boundaries are fuzzy and need to be understood better!
There is a need to evolve policies, practices and ethics around
closed, shared, and open data.
9. Incentives: Data Citation
Out of Cite, Out of Mind
http://bit.ly/out_of_cite
Joint Declaration of Data Citation
Principles:
https://www.force11.org/datacitation
Background and Developments:
http://bit.ly/data_citation_principles
International Series of Data Citation
Workshops
http://bit.ly/data-citation-workshops
CODATA Task Group on
Data Citation
Principles and Practices
If publications are the stars and
planets of the scientific universe,
data are the ‘dark matter’ –
influential but largely unobserved
in our mapping process
10. Open Data Policy
Key Objectives:
1. Promote Data publication, preservation and reuse.
2. Promote multi-disciplined research capabilities and activities that are
ICT enabled
3. Accelerate ICT innovation through equipping innovators with
requisite skills and credible and quality data
4. Change culture of keeping data private to public by default
11. The long end of the tail…..has individual scientists data
• Much of this revolution is taking place at the top end
– at the head and neck
• Although ‘big data’ is all the rage….the vast majority
of data sets created through research fall into the
“Long Tail”
Source – Wagging the Long Tail, Kathleen Shearer et al, 2014
13. Data-driven
Innovation
successfully capitalizing on data
revolution requires public policies
and strategies designed to allow
data-driven innovation to
flourish(2013 WB).
These policies and strategies will
remove barriers , stimulate release,
use and impact assessment of
open data (Rininta et al., 2015).
Open
Data
Policy
Strategy
Action
Plan
14. Open Data Initiative(ODI) 1: JORD Policy
http://www.jkuat.ac.ke/directorates/iceod/wp-content/uploads/2017/06/JORD-Policy-ISO-ref-April-2016.pdf
JKUAT with the support of
CODATA, developed and
implemented an open research
data policy (JORD) Policy
(February 2016)
14
ROI
Encouragement of
diverse studies
and opinion
Promotion of new
areas of work not
envisioned by the
initial investigators
Development of
new products and
services
Strengthen the
credibility of
scholarly
publications
Development of
new products and
services
15. ODI 2: Innovative Open Data and Visualization (iODaV)-JKUAT and PAUST
The specific objectives of AFRICA
ai JAPAN Project Sub-Task Force
are as follows:
i See Link http://www.jkuat.ac.ke/wp-
content/uploads/2017/02/Innovation-
Research-Grants-AFRICA-ai-JAPAN-
Project.pdf
iODaV
Open
Research
Data-based
Innovation
Data
Analytics
Data,Info &
Scientific
Visualization
Smart
Learning-
ThinkBoard
S/W
Open Data
Principles,
Stds & JORD
Reuse of
Research
Data
15
16. ODI 3: DRAFT NATIONAL INFORMATION & COMMUNICATIONS TECHNOLOGY
(ICT) POLICY JUNE 2016 (http://icta.go.ke/pdf/National-ICT-Policy-20June2016.pdf)
• Article 5.10 –Data Centre:The government will:
Promote Data Centre infrastructure buildout carried out in cognizance of globally
approved standards for purposes of ensuring quality of service under open access,
carrier neutral model;
(b) Develop incentives to ensure and protect investment in the field of data centre;
(c) Facilitate the development and enactment of legislation on localization to support
growth in IT service consumption – as an engine to spur data centre growth;
(d) Ensure that Data is processed fairly and lawfully in accordance with the rights of
citizens and obtained only for specific, lawful purposes
In support of Kenya Open Data Initiative (http://www.opendata.go.ke/)
16
17. ODI 3….2
• Article 7.1- Digital Content
(a) Adopting Open Data principles: - in order to share historical/archive data that can be
a rich source for the creative and broadcast industry;
(b) Promoting Animation Labs (A-Lab):- Government will support incubation labs focused
on animation & film production that is largely computer generated;
(c) Content Ratings: - The Government will, develop policies and legislation that take into
consideration age appropriate content that upholds national values.
(d) Copyright Protection:- Government will recognise digital content as copyright
material and will actively protect the rights of copyright owners through law
enforcement to prevent digital content piracy.
17
18. ODI 3….3
• 15.4 Information Security
The government will develop information security policies and
guidelines to ensure protection of the confidentiality, integrity and
availability of information
18
19. ODI 4: DIGITAL HEALTH APPLIED RESEARCH CENTRE
(DHARC) -JKUAT
• DHARC is one of the deliverables of HIGDA Project funded by USAID 5 yr project
started Oct 2016
• DHARC -implementation of interoperability solutions informed by Open Data
Principles and Stds.
• DHARC will join a network of interoperability labs which have been established in
Canada (2007), South Africa (2010), and the Philippines (2016)
• It will provide examples of how key components (DHIS2, DATIM, MFL ver2, AMRS
and other mHealth solutions) interoperate, providing guidelines-based care
workflows, policies, and M&E mandates.
19
20. In 6: 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
20
21. 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
21
22. Policy Content Pillar
Key factors include:
Licensing
Access fee
Data restriction
Data presentation
Contact with user
Amount published
Processing before publishing
22
Cost of opening
Types of Data
Data Formats & stds
Data quality
Provision of metadata
23. 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 23
24. I 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
24
25. Open data implementation best practices
• Have an open data policy ( e.g. JORD-JKUAT)
• Ensure easy to understand content & formatting
• Release high-value and high-impact data first
• Ensure compatibility and interoperability of systems (e.g. Kenya
Health sector DHARC project –USAID/JKUAT)
• Establish data ownership
• Involve stakeholders
• Plan for open data advocacy (e.g. KALRO)
• Implement interaction and feedback mechanism
• Build communities of data producers and users
• Organize training programs
• Organize hackathons( eg CODATA, JAPAN ai AFRICA Project, IBM,
JKUAT, USAID have sponsored hackathon on Agriculture and Health
sector open data to promote innovations and data use in Kenya) 25