3. Fake Data, Fake Research
http://www.bbc.com/news/science-environment-39357819
4.
5. Open Science (incl. Data) Defined
“Open Science is the practice of science in such a
way that others can collaborate and contribute,
where research data, lab notes and other
research processes are freely available, under
terms that enable reuse, redistribution and
reproduction of the research and its
underlying data and methods.” - FOSTER Project,
funded by the European Commission
7. Original Research Data Lifecycle image from University of California, Santa Cruz
http://guides.library.ucsc.edu/datamanagement/
Repositories
Repositories
Tools
Plan
Policy&Infrastructure
8. Benefits of Open Research Data (1)
• Predict trends, help make informed decisions, informs
policy
• Collaboration advances science, discovery
• Drives development, improves livelihoods of citizens
of countries
• Increases return on investment (funders), avoid
duplication – research is expensive
• More and more entrepreneurs are using data in
innovative ways, creating more jobs which is much
needed on our continent
9. Benefits of Open Research Data (2)
• Helps improve service delivery e.g. mobile apps,
robots, artificial intelligence (AI)
• Provides evidence for research conducted
• Data potentially has far more outcomes when open,
with a higher impact
• Use for tenure/promotion/measure contribution of
researchers (data citation)
14. Tunisia
Data Computing Centre el Khawarizmi
Kenya
Data Centre & Services @ Research & Education
Network (KENET)
South Africa
DIRISA, Data Intensive Research Cloud Infrastructure
Initiatives – ARC, SADIRC, Ilifu
High Performance Computing Centres
Botswana, Lesotho, Mozambique, SA, Tanzania,
Zambia, Zimbabwe
Open Data for Africa
African Development Bank
Initiatives,HPCCs,Services
15. Ilifu
• http://www.researchsupport.uct.ac.za/ilifu
• Consortium of 6 Western Cape institutions
• Data-centric, high-performance computing facility
for data-intensive research
• Proto-typing distributed, federated cloud-based
infrastructure as a platform for data-intensive
research (African Research Cloud)
• Data-processing pipelines and e-science research
tools for big data analysis, visualisation and analytics
• Development and implementation of research data
management systems and tools
• Development of platforms, portals and middleware
to support access and collaborative research by
distributed teams on data-intensive projects
18. Square Kilometre Array (SKA)
• Data collection on a massive scale
• Telescope array to consist of 250,000 radio
antennas between Australia & SA
• Investment in machine learning and artificial
intelligence software tools to enable data analysis
• 400+ engineers and technicians in infrastructure,
fibre optics, data collection
• Supercomputers to process data (IBM)
• To come: super computer 3x times power of
world’s current fastest computer (Tianhe-2) to cope
with SKA data
19. Testing Albert Einstein’s general theory of relativity; imaging
neutral hydrogen—the building blocks for stars – in the distant
universe; and examining galaxies that were formed billions of
years ago.
“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.”
20. African Open Science Platform (AOSP)
• Platform = opportunity to engage in dialogue,
create awareness, connect all, provide continental
view
• Funded by SA Dept. of Science & Technology
through National Research Foundation
• 3 years (1 Nov. 2016 – 31 Oct. 2019)
• Managed by Academy of Science of South Africa
(ASSAf)
• Through ASSAf hosting ICSU Regional Office for Africa
(ICSU ROA)
• Direction from CODATA
http://africanopenscience.org.za/
21. Accord on Open Data in a
Big Data World
• Proposes
comprehensive set of
principles
• FAIR Principles
• Data as open possible,
as closed necessary
• Provides framework &
plan for African data
science capacity
mobilization initiative –
AOSP
Call to Endorse
22. “Several open science activities are underway
across Africa, but a great deal will be gained if, in
the context of developing inter-regional links,
these activities were to be coordinated and
developed through such a coordinating
initiative.” - CODATA
23. Key Stakeholders
• Global Network of Science Academies (IAP)
• International Council for Science (ICSU)
• The World Academy of Sciences (TWAS)
• Research Data Alliance (RDA)
• NRENs (Internet Service Providers for Education)
• Association of African Universities (AAU)
• Network of African Science Academies (NASAC)
• African Research Councils (incl. DIRISA, funders)
• African Universities
• African Governments
• Other
24. AOSP Focus Areas (Frameworks &
Roadmaps)
Policy Infrastructur
Capacity
Building
Incentives
SA, Uganda,
Botswana,
Madagascar,
Kenya, Ethiopia
25. Intellectual Property Rights Policy
“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/
27. Africa Data Consensus Study
• Adopted in March 2015 at High Level Conference
on Data Revolution
• Strategy for implementing data revolution in Africa
• Plan of action to be guided by United Nations
Economic Commission for Africa (UNECA), African
Union Commission (AUC), African Development
Bank (AfDB), supported by UN Development
Programme (UNDP), UN Populations Fund
(UNFPA)
• Implemented in collaboration with partner
institutions from public & private sectors, civil
society organisations
29. Grid & Cloud-based ICT Infrastructure
Support
Activity Tools (some discipline specific)
Bandwidth
Internet Connectivity NetMap, Eduroam
Data Transfer Africa Globus Online
Data Organisation, Storage,
Access (Data Centres)
Spreadsheets, REDCap, H3APRDP, MIPAR,
Data Repositories e.g. Dataverse, Invenio,
Dspace, Zenodo, re3data.org. Also
FigShare (Ilifu), BaseGeo
Data Management Spreadsheets, REDCap, H3APRDP, MIPAR,
Data Repositories e.g. Dataverse, Invenio,
Dspace, Zenodo, re3data.org. Also
FigShare (Ilifu), BaseGeo, SQL, Hadoop(for
really large datasets), MySQL, PostgreSQL
30. Activity Tools (some discipline specific)
Data Analysis & Visualisation Galaxy, Job Management System,
WebProtege, eBiokits, R, R Markdown,
ggplot2, Python, MATLAB, C, C++
Data Processing Medical Image Processor and Repository
(MIPAR)
Data Execution
(pipelines/workflow)
Futuregateway, OAR, PHP, JSON, WAMP,
DAGMan, Pegasus, Makeflow
Data Collection Raspberry Pi, sensors & sensor networks,
electron & astronomical microscopes,
medical imaging
Data Mining
Data Cleaning OpenRefine
31. Activity Tools (some discipline specific)
Data Description Metadata standards incl. DOIs
Batch/Grid Computing Africa Grid Science Gateway, European
Grid Infrastructure, SURFSara (Dutch
National Grid)
Research Data Management
Planning
DMPTool (DMPRoadmap), FigShare
Supercomputers/High
Performance Computing
Tunisia (Data Computing Centre el Khawarizmi),
Kenya (Data Centre & Services), South Africa
(DIRISA, Data Intensive Research Cloud
Infrastructure Initiatives – ARC, SADIRC, Ilifu),
Countries: Botswana, Lesotho, Mozambique, SA,
Tanzania, Zambia, Zimbabwe.
Machine Learning R, Python
Artificial Intelligence (Artificial
Neural Networks)
R, Python, Tensorflow, deep
convolutional networks
32. Activity Tools (some discipline specific)
Identity Provider Federated Identity Services
Certification Authority Accredited by EUGRIDPMA. Digital
certificates to access eduGain resources
(including open research ID - ORCID)
Security
Data Licensing Copyright, Creative Commons, GNU
Data Citation
Command Line Interface UNIX Shell, bash (incl. editors e.g. Nano,
Emacs, Vim, etc.)
Software Development Git, GitHub, Mercurial
Other User controlled light paths,
videoconferencing, connecting e-resources
e.g. simulators, accelerators, dedicated
point-to-point Internet Protocol circuits
General Publishing Open Access Publications, Copyright, Creative Commons
Open Peer Review, Data Citations, DOIs, ORCID IDs, working with
data using LaTeX, XML, Markdown, BiBTeX, JSON, JabRef, Zotero
36. “You can have data without information, but you
cannot have information without data.” –
Daniel Keys Moran, an American computer programmer and science
fiction writer.
“Data is a precious thing and will last longer than
the systems themselves.” – Tim Berners-Lee, inventor of the
World Wide Web.
37. Thank you
Ina Smith
Project Manager, African Open Science Platform Project, Academy of
Science of South Africa (ASSAf)
ina@assaf.org.za
Visit http://africanopenscience.org.za
Notes de l'éditeur
The scale of "fake research" in the UK appears to have been underestimated, a BBC investigation suggests.
Official data points to about 30 allegations of research misconduct between 2012 and 2015.
However, figures obtained by the BBC under Freedom of Information rules identified hundreds of allegations over a similar time period at 23 universities alone.
There are growing concerns around the world over research integrity.
The House of Commons Science and Technology Committee has begun an inquiry into the issue to reassure the public that robust systems are in place in the UK.
Stephen Metcalfe, the committee's chairman, said it was vitally important that people have confidence in research that is paid for by public funds.
"Where research has been found to be fraudulent at a later point it has a big impact on the public - it leads to mistrust," he told BBC News.
"What we want to do is to investigate how robust the mechanisms are for ensuring that research is ethical, it is accurate, it is, to a degree, reproducible."
Growing pressures
Requests by the BBC under Freedom of Information rules show that at least 300 allegations were reported at 23 of the 24 research-intensive Russell Group universities between 2011 and 2016 among staff and research students.
About a third of allegations of plagiarism, fabrication, piracy and misconduct were upheld. More than 30 research papers had to be retracted.
Commenting, a spokesman for the Russell Group said: "Our universities take research integrity seriously and work continuously to help staff and students maintain high standards of research.
"The UK has a global reputation for the quality of our scientific research. This is not least because our members are rigorous in their approach to research integrity."
Mr Metcalfe said the figures obtained by the BBC demonstrated the importance of the MPs' inquiry, but they had to be put in the context of the overall number of papers published.
"We do need to have accurate figures that are available so we can all have confidence that the research is being conducted properly, and when it's not, there is a system that challenges that," he said.
Universities UK, which represents vice-chancellors of universities, was asked to comment on the data obtained by the BBC, but declined.
Research retractions
There are growing pressures on researchers to publish their work and obtain grants. Retractions of scientific papers have increased about ten-fold during the past decade.
The blog, Retraction Watch, reports on retractions of scientific papers.
Co-founder of Retraction Watch, Dr Ivan Oransky, told BBC News: "We do not have a good handle on how much research misconduct takes place, but it's become quite clear that universities and funding agencies and oversight bodies are not reporting even a reasonable fraction of the number of cases that they see."
He said one of the most widely cited surveys suggests 2% of researchers admit to committing something that would be considered misconduct.
"If that's a ball-park figure of 2%, well, the number of cases that we hear about is a miniscule fraction of that," said Dr Oransky.
"Clearly there's a lot that's happening that we don't know about. I would say that any steps that universities can take to begin being more honest and forthright and disclosing these cases would be wonderful."
Regulation
Deliberate research fraud is thought to be extremely rare. However, if it does happen it can have severe consequences, such as risking public health and undermining public trust in research.
There have been calls for a UK regulatory body to oversee publicly funded research, based on models in the US and Denmark.
Image copyrightSPLMr Metcalfe said the idea of some sort of regulator would be explored, although he said "there is no appetite for that in the wider community at the moment".
He said the committee would also be looking at why there is so little official data on research misconduct.
Figures from Research Councils UK are regarded as the most reliable, according to a source.
The body, which represents the UK's seven Research Councils, reported 33 allegations of research misconduct between 2012 and 2015. Of these, five were formally upheld, 20 were dismissed and eight are ongoing.
In addition, Universities UK looked at statements on research misconduct published by 19 universities for the year 2013-14. It found 29 allegations were reported, with seven cases upheld after investigation.
It is not clear whether the figures relate to the same or different cases.
Concordat
In 2012, universities signed up to a concordat to support research integrity.
Under the agreement, universities are encouraged to use transparent, robust and fair processes to handle allegations of misconduct.
However, they are not obliged to publish figures on breaches of research integrity, making the scale of the problem difficult to determine.
An audit by Universities UK found that about 35 of 131 universities published annual statements on allegations of research misconduct that were made available to the public.
The BBC investigation asked 24 universities in England, Wales, Scotland and Northern Ireland within the Russell Group, which focus heavily on research, to reveal figures on allegations of research misconduct for academic years between 2011 and 2016. All but one university complied in full or in part.
A total of 319 cases were reported between 2011 and 2016 among staff and research students. The actual number is likely to be higher as some universities did not provide full figures.
Of these 103 were upheld, 173 were dismissed and 43 are ongoing.
Allegations that were upheld after investigation included:
Falsification of research
Passing off others' work as one's own
Data in a published paper taken from other sources without due acknowledgement
The investigations led to at least 32 research papers being retracted as well as at least three PhD theses. These figures are likely to be an underestimate as some universities could not supply data on retractions.
“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
Information collected using specific methods for a specific purpose of studying or analyzing.
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.
"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.
"Research data is defined as recorded factual material commonly retained by and accepted in the scientific community as necessary to validate research findings; although the majority of such data is created in digital format, all research data is included irrespective of the format in which it is created.“
Source: https://www2.le.ac.uk/services/research-data/rdm/what-is-rdm/research-data
The UN 2030 Agenda for sustainable development describes the world today as one with immense challenges, with rising inequalities leaving people behind. Global health threats, more frequent and intense natural disasters, natural resource depletion and adverse impacts of environmental degradation are only a few of the challenges mentioned in this Agenda. Data generated through research offers many opportunities to address these challenges, maximising the benefit for both developed and developing countries. According to the international Accord on Open Data in a Big Data World the digital revolution created an unprecedented explosion in the capacity to acquire, store, manipulate and instantaneously transmit vast and complex data volumes, with profound implications for science. It further says that datasets can be semantically linked to create deeper meaning to address challenges faced by humanity, offering opportunities to natural, social and human sciences, including all the challenges referred to by the 2030 Agenda. There is however a condition for it to create a deeper meaning, which is: the data needs to be FAIR – findable, accessible, interoperable and reliable – in other words – open, transparent and of good quality.
Making sure that data remains open, and making it openly available – especially where funded with public money – is the responsibility of scientists, research institutions, universities, publishers, funding agencies, professional associations, scholarly societies, academies and libraries, archives and repositories. Once data has been generated, libraries – through managing data repositories and research data management support - have a responsibility towards the development and provision of services and technical standards, ensuring that the data are available to those who wish to use them and that data are accessible for the unforeseeable future. This support should be aligned with the needs and roles of all other role players, and specifically scientists.
During this workshop, librarians committed to supporting researchers and their research processes, and who would like to better understand the implications of Open Science incl. Open Data and their role in
the data research lifecycle, will be introduced to the necessary skills and knowledge on best practices involved in Open Science, towards innovation and economic growth.
More and more – both in the world of business and research – data is referred to as becoming the new oil. Artificial Intelligence – informed by data (incl. Big Data) and data instruments - is widely used to inform decisions, resolve problems, and predict trends. Social media such as facebook and Twitter are built around data only, and are billion dollar industries. Scientists internationally generate huge quantities of data on a daily basis, so that in turn it can be used to address challenges faced, including the challenges referred to in the 2030 Agenda for Sustainable Development. When data is FAIR – findable, accessible, interoperable, and reliable – it can be re-used, accelerating innovation and growth towards sustainable solutions. This paper will focus on the role of research librarians in making sure that data can be used for the unforeseeable future. The roles of the different role players in libraries will be addressed, as well as the knowledge, skills and services required to remain competent and relevant in an increasingly data driven world.
banana bacterial wilt in Uganda provided the government with real-time information on the spread of the disease. They were able to quickly identify the most affected areas and direct the limited treatments for the disease to prevent further advances. At the same time, they could disseminate information directly to the public via SMS on treatment options and how to protect their crops. Within five days of the first messages being sent out, 190,000 Ugandans had learned about the disease and knew how to save bananas on their farms.2
A particularly beneficial use of this data would be to build a global subnational map of the prevalence of underweight children that could be used by governments and aid groups to target nutrition interventions to where they are needed most.
It’s difficult to accurately measure the number of people who get malaria each year. This is because the malaria symptoms are shared with many other diseases that lead to death or illness, especially among young children.
However, there is a measure of malaria that is precise. Testing for the malaria parasite among large numbers of people provides a Parasite Rate, a useful measure of the quantity of malaria in any given area.
Surveys are done on a known number of people by malaria control programmes, non governmental organisations and researchers. Although they don’t tell us how many people are sick, the number of infected people in an area is indicated.
We spent the last 21 years tracking down malaria survey reports done across Africa. The greatest challenge was that they were mostly hidden in old government archives or curated by the World Health Organisation.
Most of the records were either poorly stored, burnt or were missing. In some countries like Kenya, Senegal, Tanzania, South Africa, Botswana, Namibia and Burkina Faso the surveys dated back 1950s. Conversely, recent surveys have been easier to locate through more modern web based searches.
To obtain village or school level data published in most journals or reports, scientists and government officials provided the raw data. This is a testament to a new era of data sharing where over 800 people have contributed finer resolution data.
The final report covers over 50,000 surveys dating back 115 years. This is the largest repository containing information on over 7.8 million blood tests for malaria. We analysed malaria infection prevalence for each of 520 administrative units across countries south of the Sahara and Madagascar for 16 time periods.
The study suggests that the prevalence of malaria infection in sub-Saharan Africa today is at the lowest point since 1900.
Declining malaria cases
Overall, there was a decline in the number of children infected with malaria at 24% between 2010 and 2015 compared to 40% between 1900 and 1929.
The biggest historical reduction in malaria coincided with the introduction of new tools to fight malaria. After the Second World War, the discovery of DDT for indoor spraying and chloroquine drugs made a difference in treating malaria.
Changing patterns of malaria in sub Saharan Africa. Bob SnowInvestment in malaria control in Africa has been sporadic in the past. The world has seen a reduction in malaria over the last 15 years, based largely on the use of treated bed nets and antimalarial drugs. If we take our eye off the ball then rising drug resistance and falling control will lead to the sorts of increases we saw in the 90s.
Again, in 2005 the rolling out of insecticide treated bed nets and new anti antimalarial drugs, led to a further drop of malaria cases.
The lowest periods of malaria prevalence were evident when the international community abandoned specific malaria control investment in Africa, during the late 1960s, through the 1970s and early 1980s. As a result, every fever was treated with chloroquine, an amazingly effective drug. There was a prolonged drought across the Sahel. This was the perfect lull.
However, from the late 1980s chloroquine resistance expanded across Africa. It was made worse in the 1990s when unprecedented rainfall led to flooding causing major malaria epidemics. Governments in Africa were unprepared because they did not have significant mosquito prevention and management strategies in place. Malaria cases increased and the prevalence was similar to those described before the Second World War. The perfect storm.
It took over five years for the international community to appropriately respond by providing free, and effective malaria treatments to vulnerable persons in the affected countries. They ensured access to effective malaria prevention tools which a decade earlier had reduced the malaria risk by half.
The Global Fund’s financial boost and the revisions of the 2005 world malaria report led to one of the largest drops in malaria infection prevalence witnessed.
More effective strategies needed
The gains made after 2005 have stalled since 2010. Declining malaria funding, insecticideand drug resistance are the obvious threats to the elimination of malaria in Africa.
Despite an impressive overall decline in malaria prevalence since 1900, Africa has the highest infection risks globally. Large parts of West through to Central Africa and down to Mozambique continue to have intense malaria transmission.
Unfortunately DDT, new insecticides, chloroquine and new combination treatments and insecticide treated bed nets have not been effective enough to shrink this high malaria burden. We need new tools.
What next?
There is an urgent need to focus on the high burden countries in Africa, they should not be left behind in a new global agenda for malaria elimination.
It is complex and predicting a future malaria landscape based on climate or economic development alone would be foolhardy. It needs a more integrated approach.
What we can say however is that the malaria map in Africa might shrink a bit at the margins but that middle belt isn’t going anywhere in our lifetimes with what we have at our disposal now – bed nets and drugs.
When insecticide and drug resistance becomes established, unless we have new classes of both drugs and insecticides or a natural period of drought, malaria will revert in large parts of Africa to what it was in the 1990s, another perfect storm.
The prevalence of malaria infection in sub-Saharan Africa today is at the lowest point since 1900.
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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Nature special: Ebola outbreak
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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
South Africa: Data Intensive Research Cloud Infrastructure Initiatives – ARC, SADIRC, Ilifu (cloud for researchers working in astronomy and bioinformatics in Western Cape & research data management system)
Tunisian Computing Centre el Khawarizmi manages Data Centre
Kenya Education Network (KENET) provides access to domain names, data center, cloud computing & science gateways, capacity building, security services
Data Intensive Research Initiative for South Africa (DIRISA) – component of SA National Cyber-Infrastructure System
Open Data for Africa platform (African Development Bank (AfDB)) – to boost access to quality data for managing & monitoring development results in African countries, incl. African Action Plan 2063 & 2030 SDGs
Ilifu is a consortium of Western Cape institutions that together will establish and operate a data-centric, high-performance computing facility for data-intensive research. The partner institutions are
Cape Peninsula University of Technology
Stellenbosch University
Sol Plaatje University
South African Radio Astronomy Observatory (SARAO, formerly SKA South Africa).
University of Cape Town (lead institute)
University of the Western Cape.
In addition to establishing and operating a data-intensive computing facility, the consortium will – in collaboration with local and international collaborators and partners – undertake research and development programmes for
Proto-typing a distributed, federated cloud-based infrastructure as a platform for data-intensive research, the African Research Cloud.
Development of data-processing pipelines and e-science research tools for big data analysis, visualisation and analytics.
Development and implementation of research data management systems and tools.
Development of platforms, portals and middleware to support access and collaborative research by distributed teams on data-intensive projects.
Collaborative projects in Biomedical Sciences – genomics research – catching up with outbreaks, ebola, malaria and more
Bioinformatics legs of H3Africa (Human Heridity and Health in Africa)
Work among 30 institutions, 15 Afrucan countries, 2 partners outside Africa
Ghana, Zambia, Madagascar, Botswana, Namibia, Kenya, Mauritius and Mozambique
The first fully assembled SKA dish was unveiled today at a ceremony in Shijiazhuang, China, by the Vice Minister of the Chinese Ministry of Science and Technology, in the presence of representatives from the countries involved and the SKA Organisation. The dish is one of two final prototypes that will be tested ahead of production of an early array.
To get Africa talking to one another
Engineers, Statisticians, Data Scientists, Librarians, Data Curators, Researchers, System Administrators, Policymakers, Auditors, Data Centre Managers, Data Architects – Wim Hugo
