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. AOSP focuses on developing policy frameworks, building infrastructure and capacity, and providing incentives to support open data sharing across African countries. Some of its activities have included workshops in several nations to advance open data policies and training in research data management skills. AOSP also works with partners like research funders and universities to establish open data repositories and standards that can enable scientists across the continent to collaborate and make new discoveries from shared research.
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
Perspectives from the African Open Science Platform on Open Data
1. Perspectives from the African Open
Science Platform (AOSP)
Presented by Susan Veldsman
Director: Scholarly Publishing
ASSAf
2. About AOSP
• Funded by the National Research Foundation
(NRF) (SA Dept. of Science and Technology)
• 3 years (1 Nov. 2016 – 31 Oct. 2019)
• Open Data: Where are we? Where do we want to
be? What do we need to get there?
• Managed by Academy of Science of South Africa
(ASSAf)
• Through ASSAf hosting ICSU Regional Office for Africa
(ICSU ROA)
• Direction from CODATA
3. About ASSAf
• Recognise scholarly achievement & excellence
• Mobilise members in the service of society
• Conduct systematic & evidence-based studies on
issues of national importance (ASSAf OA Repository)
• Promote the development of an indigenous system of
South African research
• Publish science-focused journals (SAJS,
QUEST,SciELO SA)
• Training in Open Journal Systems (OJS)
• Criteria for high quality OA journals
• Ambassador for Directory of Open Access Journals (DOAJ)
4. • Develop productive partnerships with national,
regional and international organisations to build
capacity within the National System of Innovation
(NSI)
• Create diversified sources of funding for
sustainable functioning and growth of a national
academy
• Communicate with relevant stakeholders
• Association of African Universities (AAU) DATAD-R
harvester of OA repositories
• Evaluation instrument – harvesting IRs adhering to
criteria for best practice (ISO 16363, Data Seal of
Approval, WDS etc.)
6. AOSP Governance
• Advisory Council (Chair: Prof Khotso Mokhele)
• Terms of Reference
• Technical Advisory Board (Chair: Prof Joseph
Wafula)
• Terms of Reference
• Working Group
• Project Team
• Project Administrator
7. Key Stakeholders
• Global Network of Science Academies (IAP)
• International Council for Science (ICSU)
• Regional Office for Africa (ROA)
• Committee on Data for Science and Technology
(CODATA)
• World Data System (WDS)
• The World Academy of Sciences (TWAS)
• Research Data Alliance (RDA)
• Association of African Universities (AAU)
8. • Network of African Science Academies (NASAC)
• African Research Councils (incl. DIRISA, funders)
• African Universities
• African Governments
• NRENs (Internet Service Providers for
Education)
• Other
9. Progress re Openness in Africa
• Open Institutional Research Repositories
(Webometrics - 74)
• Open Educational Resources (E.g. UCT OER)
• Massive Open Online Courses (MOOCs)
• Open Access Journals (DOAJ) (11 SA
universities hosting their own)
11. Data Repositories vs Social Media
• Social media sites:
• Connect researchers sharing interests
• Marketing data
• Sites belong to third parties
• Repository:
• Supports export/harvesting of metadata
• Offers long-term preservation
• Non-profit – no advertisements
• Uses open standards and protocols
• Copyright
12. • OA2020 (2017)
• Dakar declaration on Open Science in Africa (2016)
• Open Data in a Big Data World Accord (2015)
• Open Science for the 21st century A declaration of ALL European
Academies (2012)
• Salvador Declaration on Open Access Cape Town Declaration
(2010)
• Cape Town Open Education Declaration (2008)
• Kigali Declaration on the Development of an Equitable
Information Society in Africa
Africa Supporting Open Science
14. “Open Science moves beyond open access
research articles, towards encompassing other
research objects such as data, software codes,
protocols and workflows. The intention is for
people to use, re-use and distribute content
without legal, technological or social
restrictions. In some cases, Open Science also
entails the opening up of the entire research
process from agenda-setting to the
dissemination of findings.” - Open and Collaborative
Science in Development Network project, funded by IDRC
Open Science Defined
15. Research data
Information collected using specific methods for a specific
purpose of studying or analysing.
"Data" implies accompanying metadata (e.g.
precise definitions of quantities, equations of
interrelationships, scientific units of measurement, error
analysis, etc.)
In experimental sciences the data is all the information
required to repeat the experiment and the
resulting data reported from that experiment.
• https://blogs.ch.cam.ac.uk/pmr/2010/07/25/pp01-what-is-
scientific-data/
• www.yourdictionary.com/scientific-data
16. Social Science?
• For social science, data is generally numeric
files originating from social research
methodologies or administrative records, from
which statistics are produced. It also includes,
however, more data formats such as audio,
video, geospatial and other digital content that
are germane to social science research.
21. "What 115 years of data tells us about Africa's
battle with malaria past and present" —
http://theconversation.com/what-115-years-of-
data-tells-us-about-africas-battle-with-malaria-
past-and-present-85482
22.
23. Intellectual Property
“In many African countries, intellectual property
protection is undeveloped, ineffective,
expensive and unenforced and in some African
countries there exists uncertainty on protection
of IP and the threat of innovation being stolen
away from inventors.”
https://ipstrategy.com/2016/12/05/a-new-look-at-intellectual-property-and-
innovation-in-africa/
24. Difficulties with sharing data
• One of the challenges of sharing data is to provide
enough information about
– Context – Measurement process (Metadata)
• Plus the data must be stored in a way that it is
“discoverable”
• Must adhere to standards an protocols
• All of this costs time and effort
• Fear of getting scooped
• Fear of someone else finding a path-breaking
application of the data that one hadn’t thought of
• Fear of problems/errors in the measurement
process being exposed
• Confidentiality/privacy of respondents - Ethics
clearance
• IPR
25. Accord on Open Data in a
Big Data World
• Values of open data in
emerging scientific culture
of big data
• Need for an international
framework
• Proposes comprehensive
set of principles
• FAIR Principles
• Provides framework & plan
for African data science
capacity mobilization
initiative
• Proposes African Platform
Call to Endorse
27. Policy Framework
• JKUT (Kenya) Institutional Open Data Policy
• Uganda Draft Open Data Policy
• Madagascar Lobbying for Open Data Policy
• Towards a White Paper on Open Research Data
Strategy in Botswana
• White Paper on Science , Technology and
Innovation in South Africa
• Funder Policy: National Research Foundation
(NRF)(SA)
• OECD Principles & Guidelines for Access to
Research Data from Public Funding
29. Infrastructure Framework
• NRENs –
• Richly connected at high speed to many other
networks/resources
• Value added services e.g. grid & cloud computing
resources, user controlled light paths,
videoconferencing, federated identity services, and
more
• Deep culture of collaboration
30. • Roadmaps needed
• Centres for High Performance Computing
Infrastructure Framework
34. “Construction of the SKA is due to begin in 2018 and finish
sometime in the middle of the next decade. Data acquisition will
begin in 2020, requiring a level of processing power and data
management know-how that outstretches current capabilities.
Astronomers estimate that the project will generate 35,000-DVDs-
worth of data every second. This is equivalent to “the whole world
wide web every day,” said Fanaroff.
The project is investing in machine learning and artificial
intelligence software tools to enable the data analysis. In advance
of construction of the vast telescope - which will consist of some
250,000 radio antennas split between sites in Australia and South
Africa - SKA already employs more than 400 engineers and
technicians in infrastructure, fibre optics and data collection.”
http://sciencebusiness.net/news/79927/Square-Kilometre-Array-
prepares-for-the-ultimate-big-data-challenge
36. Capacity Building Framework
• Data collector vs data user vs data manager
Therefore the following are core aspects to capacity building:
• Research Data Management Planning
• Repositories
• Command Line Interpretation
• Software Development
• Data Organisation
• Data Cleaning
• Data Management & Databases
• Data Analysis & Visualisation (incl. programming)
39. Incentives Framework
• Funder requirements changing
• Mechanisms that acknowledge publication of
datasets and to promote data sharing
• How do we deal with difficulties in sharing
data—what are the solutions
• Why is sharing essential
• How do we make sharing successful
• How do we lay the fears down and ensure buy-
in
41. Actions & Deliverables Year 1
• AOSP Side Event to the SFSA 2016 - Launch
• AOSP Workshop AAU 2017 (Ghana) - Policy
• Madagascar Meeting & Workshop 2017 –
Policy & Capacity Building
• Botswana National Open Data Open Science
Forum 2017 - Policy
• UbuntunetConnect Ethiopian Workshops 2017
– Policy & Infrastructure
• Database & Networks – Surveys etc
42. Actions & Deliverables Year 2
• Capacity Building – 3x AOSP Workshops
• Batch/Grid Computing: Condor, HTCondor, SGE,
PBS/Torque, LSF, SLURM
• Computing Workflow: DAGMan, Pegasus,
Makeflow
• Data Repositories: Figshare, Zenodo, re3data.org,
Dataverse, DSpace etc.
• RDM: DMPRoadmap
• Command Line Interface Interpretation: UNIX Shell,
bash (incl. editors e.g. Nano, Emacs, Vim, etc.)
43. • Software Development: Git, GitHub, Mercurial
• Data organization: Spreadsheets
• Data Cleaning: OpenRefine
• Data Management & Databases: SQL, Hadoop(for
really large datasets), MySQL, PostgreSQL
• Data Analysis & Visualisation: R, R Markdown,
ggplot2, Python, MATLAB, C, C++
• Machine Learning: R, Python
• Artificial Neural Networks: R, Python, Tensorflow,
deep convolutional networks
44.
45. Closing Remarks
• Collaborate & learn from one another –
strength in diversity
• Take ownership & collect/curate data in ethical
way
• Downloaders vs Uploaders
• Trusted & valid data managed in trusted way
• Exploit data for the benefit of society (Min
Naledi Pandor)
• Tell the African story, in an African way
The open source software is there, the infrastructure is improving, but we need to start manage our research output, data sets and more. Demonstrate impact of research conducted with tax payers money, funders money.
Last April, five months into the largest Ebola outbreak in history, an international group of researchers sequenced three viral genomes, sampled from patients in Guinea1. The data were made public that same month. Two months later, our group at the Broad Institute in Cambridge, Massachusetts, sequenced 99 more Ebola genomes, from patients at the Kenema Government Hospital in Sierra Leone.
Uncertainties over whether the information belongs to local governments or data collectors present further barriers to sharing. So, too, does the absence of patient consent, common for data collected in emergencies — especially given the vulnerability of patients and their families to stigmatization and exploitation during outbreaks. Ebola survivors, for instance, risk being shunned because of fears that they will infect others.
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We immediately uploaded the data to the public database GenBank (see go.nature.com/aotpbk). Our priority was to help curb the outbreak. Colleagues who had worked with us for a decade were at the front lines and in immediate danger; some later died. We were amazed by the surge of collaboration that followed. Numerous experts from diverse disciplines, including drug and vaccine developers, contacted us. We also formed unexpected alliances — for instance, with a leading evolutionary virologist, who helped us to investigate when the strain of virus causing the current outbreak arose.
The genomic data confirmed that the virus had spread from Guinea to Sierra Leone, and indicated that the outbreak was being sustained by human-to-human transmission, not contact with bats or some other carrier. They also suggested new probable routes of infection and, importantly, revealed where and how fast mutations were occurring2. This information is crucial to designing effective diagnostics, vaccines and antibody-based therapies.
What followed was three months of stasis, during which no new virus sequence information was made public (see 'Gaps in the data'). Some genomes are known to have been generated during this time from patients treated in the United States3. The number is likely to have been much larger: thousands of samples were transferred to researchers' freezers across the world.
Sources: Sequences, NCBI/virological.org; Ebola cases, WHO
Expand
In an increasingly connected world, rapid sequencing, combined with new ways to collect clinical and epidemiological data, could transform our response to outbreaks. But the power of these potentially massive data sets to combat epidemics will be realized only if the data are shared as widely and as quickly as possible. Currently, no good guidelines exist to ensure that this happens.
Speed is everything
Researchers working on outbreaks — from Ebola to West Nile virus — must agree on standards and practices that promote and reward cooperation. If these protocols are endorsed internationally, the global research community will be able to share crucial information immediately wherever and whenever an outbreak occurs.
The rapid dissemination of results during outbreaks is sporadic at best. In the case of influenza, an international consortium of researchers called GISAID established a framework for good practice in 2006. Largely thanks to this, during the 2009 H1N1 influenza outbreak, the US National Center for Biotechnology Information created a public repository that became a go-to place for the community to deposit and locate H1N1 sequence information4. By contrast, the publishing of sequence information in the early stages of the 2012 Middle East respiratory syndrome (MERS) outbreak in Saudi Arabia highlighted uncertainties about intellectual-property rights, and the resulting disputes hampered subsequent access to samples.
Hasan Jamali/AP
Pilgrims in Saudi Arabia try to protect themselves from Middle East respiratory syndrome (MERS) virus.
Sharing data is especially important and especially difficult during an outbreak. Researchers are racing against the clock. Every outbreak can mobilize a different mixture of people — depending on the microbe and location involved — bringing together communities with different norms, in wildly different places. Uncertainties over whether the information belongs to local governments or data collectors present further barriers to sharing. So, too, does the absence of patient consent, common for data collected in emergencies — especially given the vulnerability of patients and their families to stigmatization and exploitation during outbreaks. Ebola survivors, for instance, risk being shunned because of fears that they will infect others.
Nature special: Ebola outbreak
Fortunately, useful models for responsible data sharing have been developed by the broader genomics community. In 1996, at a summit held in Bermuda, the heads of the major labs involved in the Human Genome Project agreed to submit DNA sequence assemblies of 1,000 bases or more to GenBank within 24 hours of producing them5, 6. In exchange, the sequencing centres retained the right to be the first to publish findings based on their own complete data sets, by laying out their plans for analyses in 'marker' papers.
This rapid release of genomic data served the field well. New information on 30 disease genes, for instance, was published before the release of the complete human genome sequence. Since 1996, the Bermuda principles have been extended to other types of sequence data and to other fields that generate large data sets, such as metabolite research.
Guidelines for sharing
More-recent policies on data release similarly seek to align the interests of different parties, including funding agencies, data producers, data users and analysts, and scientific publishers. Since January, for example, the US National Institutes of Health has required grantees to make large-scale genomics data public by the time of publication at the latest, with earlier deadlines for some kinds of data7.
We urge those at the forefront of outbreak research to forge similar agreements, taking into account the unique circumstances of an outbreak.
First, incentives and safeguards should be created to encourage people to release their data quickly into the public domain. One possibility is to request that data users (and publishers) honour the publication intentions of data producers — the questions and analyses that they want to pursue themselves — for, say, six months. These intentions could be broadcast through several channels, including citable marker papers, disclaimer notices on data repositories such as GenBank, and online forums, such as virological.org and the EpiFlu database. Alternatively, data producers could publish an announcement about their data and their intentions on online forums as a resource that can be used by others as long as they cite the original source.
“We urge researchers working on outbreaks to embrace a culture of openness.”
Second, ethical, rigorous and standardized protocols for the collection of samples and data from patients should be established to facilitate the generation and sharing of that information. A global consortium involving the leading health and research agencies and the ministries of health of engaged nations should work together towards establishing these. Ethicists should be involved to safeguard subjects' privacy and dignity. Biosecurity experts will also be needed to address potential dual-use research and other safety concerns. A helpful analogue is the approach used by the Human Heredity and Health in Africa (H3Africa) Initiative, which aims to apply genomics to improving the health of African populations. Since August 2013, H3Africa has used standard consent-form guidelines8 for collecting DNA samples from subjects for genomic studies, regardless of their country of origin.
Toshifumi Kitamura/AFP/Getty
Quarantine officers rush to test passengers at Tokyo's Narita airport amid the 2009 swine-flu outbreak.
Lastly, any preparation for future outbreaks should include provisions for rapidly building new bridges and establishing community norms. Successful collaborations in genomics and historical data-sharing agreements have tended to involve a fairly stable group of individuals and organizations, making norms of behaviour relatively easy to establish and sustain. By contrast, outbreaks can involve a new cast of characters each time, and cases in which the pathogen is new to science call for whole new fields of research.
The Kenema way
As a first step, we call on health agencies such as the World Health Organization, the US Centers for Disease Control and Prevention and Médecins Sans Frontières, as well as genome-sequencing centres and other research institutions, to convene a meeting this year — similar to that held in Bermuda in 1996. Attendees must include scientists, funders, ethicists, biosecurity experts, social scientists and journal editors.
We urge researchers working on outbreaks to embrace a culture of openness. For our part, we have released all our sequence data as soon as it has been generated, including that from several hundred more Ebola samples we recently received from Kenema. We have listed the research questions that we are pursuing at virological.org and through GenBank, and we plan to present our results at virological.org as we generate them, for others to weigh in on. We invite people either to join our publication, or to prepare their own while openly laying out their intentions online. We have also made clinical data for 100 patients publicly available and have incorporated these into a user-friendly data-visualization tool, Mirador, to allow others to explore the data and uncover new insights.
Kenema means 'translucent, clear like a river stream' or 'open to the public gaze'9. To honour the memory of our colleagues who died at the forefront of the Ebola outbreak, and to ensure that no future epidemic is as devastating, let's work openly in outbreaks.
Nature 518, 477–479 (26 February 2015) doi:10.1038/518477a
Promote development & adoption of data policies, principles, practices, standards
Determine infrastructure available
Address issues of incentives, best practice, benefits
Foster training & capacity building activities
Create an awareness, stimulate dialogue (frontiers)
marks a fully mature NREN of the kind that characterises Europe, North America and comparable contexts. The NREN is richly connected at high speed to many other networks and resources. Numerous valueadded services are available, such as grid and cloud computing resources, usercontrolled lightpaths, videoconferencing, and federated identity services. The NREN’s value proposition lies primarily in these services, since bandwidth pricing in such contexts is transparently cost-related. Many institutions will purchase commodity bandwidth from a commercial provider in addition to NREN-specific bandwidth. A culture of collaboration is deeply established.
