The Beijing Declaration on Research Data

Date: Nov 8, 2019

Grand challenges related to the environment, human health, and sustainability confront science and society. Understanding and mitigating these challenges in a rapidly changing environment require data[1] to be FAIR (Findable, Accessible, Interoperable, and Reusable) and as open as possible on a global basis. Scientific discovery must not be impeded unnecessarily by fragmented and closed systems, and the stewardship of research data should avoid defaulting to the traditional, proprietary approach of scholarly publishing. Therefore, the adoption of new policies and principles, coordinated and implemented globally, is necessary for research data and the associated infrastructures, tools, services, and practices. The time to act on the basis of solid policies for research data is now.
The Beijing Declaration is intended as a timely statement of core principles to encourage global cooperation, especially for public research data. It builds on and acknowledges the many national and international efforts that have been undertaken in the policy and technical spheres on a worldwide basis.  These major contributions are listed in the Appendix. 
 Several emergent global trends justify and precipitate this declaration of principles:
  • Massive global challenges require multilateral and cross-disciplinary cooperation and the broad reuse of data to improve coherence concerning recent UN landmark agreements, such as the Paris Climate Agreement, the Sendai Framework for Disaster Risk Reduction, the Sustainable Development Goals (SDGs), the Convention on Biological Diversity, the Plant Treaty, the World Humanitarian Summit, and others. The comprehensive agendas for action provided by these agreements requires access to and reuse of all kinds of data.
  • Research and problem-solving, especially addressing the SDG challenges, are increasingly complex and driven by ‘big data’, resulting in the need to combine and reuse very diverse data resources across multiple fields. This poses an enormous challenge in the interoperability of data and responsible stewardship, with full respect for privacy.
  • Rapid advances in the technologies that generate and analyze data pose major challenges concerning data volume, harmonization, management, sharing, and reuse. At the same time, emerging technologies (including machine learning) offer new opportunities that require access to reusable data available in distributed, yet interoperable, international data resources.
  • Changing norms and ethics encourage high-quality research through greater transparency, promote the reuse of data, and improve trustworthiness through the production of verifiable and reproducible research results. Increasing the openness of research data is efficient, improving the public return on investment, and generating positive externalities.
  • Open Science initiatives are emerging globally, including in less economically developed countries. There consequently are opportunities for these countries to take advantage of technological developments to develop a greater share in scientific production. Without determined action, there is also a risk that the divide in scientific production will widen.
In September 2019, CODATA and its Data Policy Committee convened in Beijing to discuss current data policy issues and developed a set of data policies adapted to the new Open Science paradigm. The Declaration proposed below is the result of that meeting and is now put forward for public review.
The Beijing Declaration on Research Data:
[1] In the attached document we deliberately use the word data very broadly, to comprise data (stricto sensu) and the ecosystem of digital things that relate to data, including metadata, software and algorithms, as well as physical samples and analogue artefacts (and the digital representations and metadata relating to these things).

Message from the CODATA President, Barend Mons

Date: Nov 7, 2019

The field of research data and associated services is in a rapid - and epoch-making - phase transition from a data sparse to a data-overloaded ecosystem. Many national and international efforts are underway to try and deal with the enormous challenges posed by instrumentation and automation and the associated explosion in the volume and complexity of data. We all try and keep pace with this phenomenon by deploying the analytical processes and tools needed to enable data-intensive science, supported by machines. In order that high throughput data generation instruments and computers may effectively support the scientific and innovation process, both data and workflow components need to be machine-actionable. Building on and refining many earlier efforts, in 2014 the FAIR principles were formulated. These principles recommend that data (and services around them) should be Findable, Accessible, Interoperable and (thus) Reuseable, first and foremost by machines.

 In 21st century science, computers need to be fully enabled to do the hard work of processing, pattern identification and machine learning in relation to enormous amounts of heterogeneous, distributed data. Human researchers, and the science system as a whole, will benefit from machine-actionable data as less time will be spent data munging. When data is stewarded and processed properly, ambiguity and non-reproducibility will be less of a problem as well. In addition, many datasets and resources are now either too large or too privacy sensitive, or both, to be effectively routed around the globe for multidisciplinary and data-intensive science projects. Therefore, distributed machine learning is a new paradigm that I refer to as ‘data visiting’ rather than the classical model of ‘data sharing’.

 These rapid changes have in significant respects ‘taken science by surprise’ and many groups and infrastructures have great difficulties to adapt to this revolutionary new way of doing science. Rather than ‘excellence in silos’, and scholarly communication mainly designed for person-to-person information and knowledge transfer, we now need ‘excellence across silos’. We need to conceive of the underpinning ecosystem as -in essence- one computer with one, universal dataset. Workflows dealing with data and the data themselves are being reused over and over and need to be fully interoperable, reusable and reproducible. In particular when we address the major challenges facing our planet, as laid out in the Sustainable Development Goals, the data needed to gain the necessary insights come from many different domains and are frequently not purposefully generated for research. For an ‘Internet of FAIR Data and Services’ to emerge and flourish, all digital resources should be intrinsically FAIR and processable outside the environments and systems in which they were created. In other words, they need to be universally reusable. The good news is that computers can translate FAIR digital resources from one format to the other with high speed and minimal error rates as long as the machine has enough information about the resource. Another way of expressing the objective of FAIR is that when the resource is FAIR, ’machines know what it means’. In essence, the machine can answer three major questions for each FAIR digital object or resource they encounter: 

  1. What is this?,
  2. What operations can be performed on it? and,
  3. What operations are allowed?

With properly constructed FAIR digital resources, these questions can be answered, which enables machines (and thus also ultimately humans) to reuse them with full provenance outside their original context. Elusive as this may sound, I am very confident that the current international efforts in this exciting domain will soon yield the first scalable ecosystems that follow these principles, and major industries are already moving into this space as well. So be warned: the coming four years will not be ‘science as usual’!

CODATA has been around for roughly 50 years, and has lived in the data sparse times as well as now in the data rich era, which poses entirely different and daunting challenges, also for CODATA itself. CODATA, as a committee of the International Science Council (ISC), supporting the mission of ISC as the global voice of science and its role in the UN system, has the responsibility to fill a specific and strategic niche in the global ecosystem of research data related activities. Many other organisations have complementary roles that are either domain specific, national or regional or they are grass roots and community based. CODATA is actively engaging with these other international players in defining complementary and synergistic roles.

The data-intensive science and innovation challenge is obviously a global one, it should equitably involve all regions of the world and it cannot be solved sustainably within disciplinary or national silos. That is the niche in which CODATA should operate. CODATA also has a key role to play in the involvement of regions of the world that have been traditionally data and science-deprived. With the Internet of FAIR Data and Services emerging 'as we click’, we should not widen the digital divide but leap-frog to close it, such that the new research ecosystem is also fair in the traditional sense. Open Science, must also mean that no-one is left behind. The second bit of good news is that activities in the Global South are emerging at an early stage and some are ambitious enough to lead future developments. 

As the CODATA President I work with the Executive Director, with the officers and Executive Committee, and with CODATA’s core staff to serve this multi-organisational ecosystem in service of the global science community. We also work with regional organisations such as the European Commission and the EU Member states with their major leading initiative for the European Open Science Cloud, which has an increasing number of partner initiatives in other regions. We build on the excellent work of our predecessors in CODATA, including the intellectual leadership of the past President Geoffrey Boulton and in close collaboration our parent organisation, the International Science Council.

As of 2017, and extending for the duration of my CODATA presidency, I also serve on the US National Academy of Sciences Board for Research Data and Information. With my election as president of CODATA, I will gradually hand over operational leadership in GO FAIR to others, and I will seek to play an ambassadorial role for both, to help drive a joint, converging and balanced ecosystem for international policies supporting open, data driven science. We also work to consolidate and make explicit the key role for each of the internationally operating data organisations and in particular to bring RDA, GO FAIR, WDS and CODATA even closer together, with clear and complementary mandates. When we lock arms at all levels from institutional to international, I am optimistic that by the end of my term as President, the first phase of the Internet of FAIR data and services will be up and running.

For all this to happen, it will be of critical importance that each of the data supporting organisations is mandated and properly funded (although at the leanest necessary level) to serve the science and innovation communities, without competing for the same funds as the community they should serve. They should focus on those supra-level tasks that never make it to the top of the priority list of individual countries, regions, funders, researchers and innovators. In this set of partnerships, it is the CODATA mission to act strategically and globally to advance equitable Open Science, the FAIR ecosystem and to make data work for interdisciplinary global challenge research.

Research infrastructures have traditionally been almost an ‘afterthought’ or considered ‘other peoples’ problem’, which has resulted in a very dangerous situation where core resources, massively used by researchers, such as curated data bases and collections, mapping and standard services are ‘operating on a shoe string’ and go through a near-death experience each time funded projects run out. We, as the research community, should collectively speak with one voice, on these infrastructural and interoperability issues as trusted representatives of the real needs of the research community itself and society as a whole, towards policy makers, funders and unions dealing with the enormous data and analytics challenges we will face in the decades to come. It is an honour to be elected as the new president of CODATA and I hope to serve the community as expected.

Disaster Risk Reduction and Open Data Newsletter: November 2019 Edition

Date: Nov 6, 2019

UN High Commissioner for Refugees: Climate Change and Displacement Climate change and natural disasters can add to and worsen the threats that force people to flee across international borders. The interplay between climate, conflict, poverty and persecution greatly increases the complexity of refugee emergencies.

Victoria, Australia - National Climate Change and Agriculture Plan Agreed Australian ministers met in Melbourne at the Agricultural Ministers’ Forum to endorse a Victorian-led program that will facilitate collaboration between state and Commonwealth governments to meet the challenges of climate change and support the agriculture sector to adapt.

Flood forecasting a cyclone game-changer for Fiji The ground-breaking project has developed and implemented a Multi-Hazard Early Warning System (MHEWS) that delivers an integrated approach to forecasting, monitoring and warning for coastal flooding, no matter what the cause - river or ocean.

Bangladesh to move Rohingya to flood-prone island  Bangladesh will start relocating Rohingya Muslims to a flood-prone island off its coast as several thousand refugees have agreed to move. 

Tasman fire review finds shortfalls in New Zealand's preparedness for large-scale blazes A review of firefighting efforts during the Tasman fires last summer, which cost Fire and Emergency New Zealand $13 million, has found shortfalls in the number of skilled staff working in risk management.

UNSDSN TReNDS - SDG Financing Initiative In 2018, SDSN launched and became the Co-Chair of a Working Group on SDG Costing & Financing with the IMF, OECD, and World Bank. This group convenes sector experts to aggregate their respective costing models and data for SDG targets, especially for low-income countries.

Addressing the Challenges of Drafting Contracts for Data Collaboration Contracts for Data Collaboration (C4DC) is a new initiative seeking to address barriers to data collaboration. The partnership, launched in early 2019, has already yielded a number of outputs, including a project inception brief, the Contractual Wheel of Data Collaboration tool — which presents key considerations for the development of data sharing agreements — and an initial analytical framework.

October 2019: Publications in the Data Science Journal

Date: Nov 1, 2019

  Title: Different Preservation Levels: The Case of Scholarly Digital Editions Author: Elias Oltmanns, Tim Hasler, Wolfgang Peters-Kottig, Heinz-Günter Kuper
  Title: A Method for Extending Ontologies with Application to the Materials Science Domain
Author: Huanyu Li, Rickard Armiento, Patrick Lambrix URL:
  Title: Analysis of Several Years of DI Magnetometer Comparison Results by the Geomagnetic Network of China and IAGA
: ufei He, Xudong Zhao , Dongmei Yang, Fuxi Yang, Na Deng, Xijing Li

Call for Application - VizAfrica 2019 Botswana Summer School Program Deadline: Nov 1, 2019

Date: Oct 21, 2019

Date: 11-15 November 2019

Venue: Department of Computer Science, University of Botswana, Botswana

Objective: To introduce learners to data analytics and visualization

Learning Outcome: At the end of the course the learners will be able to apply data analytics and visualization tools in analyzing, visualizing, and making decisions in various situations.


  1. Big data  engineering
  2. Business Intelligence
  3. Computer aided engineering
  4. Geospital data visualization
  5. Basic introduction to Linux bash scripting
  6. Python for science and engineering and job submission using PBS pro at CHPC


  • Prof. Koji Koyamada  - Kyoto University (Japan)
  • Ms Becky Abraham - Pathways International (USA)
  • Dr. Mothsegwa Tshiamo - Univesity of Botswana (Botswana)
  • Prof. Muliaroo Wafula - Jomo Kenyatta University of Agriculture and Technology (Kenya)

Deadline - 1 November 2019

Apply here:

For inquiry: email at



Date: Oct 21, 2019

Conference website:
Conference hashtag: #IASSIST20

The 46th annual conference of the International Association for Social Science Information Services and Technology (IASSIST) will be held in Gothenburg, Sweden from May 19 to 22, 2020.

Data by Design: Building a Sustainable Data Culture We welcome submissions that showcase the various ways our IASSIST community is approaching “data by design” and tackling the challenges of building and sustaining data communities, practices and tools. In the tradition of Scandinavian design, characterized by simplicity, minimalism and functionality, we welcome modern or ambitious approaches that your organization has been looking at more recently to
keep pace with the ever-increasing amount of data, and new ways of publishing and accessing it. While a variety of submission topics are desired, we encourage you to think about if and how your topic may fit into one of the following tracks:

Partnerships and collaborations

What is the data culture like at your organization? What infrastructure – hardware, software, people or policies – are you leveraging, and is it enough? Who do you partner and collaborate with, both within and outside your own organization, and can we learn from these networking environments?

Data management and archiving

How can we build a community of data sharing that is equitable for all? How can we learn from each other’s approaches to demonstrating trust to lay a strong foundation? Have you designed any new and useful approaches and tools that can help in this space?

Data access, governance and ethics

As data practitioners we adhere to key principles of protecting human rights and high ethical standards. What principles, practices and tools have you worked on around data access, especially where there may be added risk in data publishing and use.

Data documentation and reproducibility

For a data community to persist, members need to share a common data language. What new approaches are you using to design documentation to facilitate our shared understanding? What strategies or tools have you designed that will help us respond best to the current reproducibility ‘crisis’?

Data literacy

A robust community includes not only experienced practitioners, but also newcomers. What innovative or successful approaches are you using around the topic of data literacy and how can we, as a community, better equip new practitioners with this important skill?

This year we also welcome suggestions for Special Interest Group and Birds of a Feather sessions, and require a short proposal and a meeting agenda/discussion points to support these. Also, panel proposals should be made up of speakers from multiple organizations to
encourage diversity of debate.

Finally, we expect to have many submissions, so we would kindly ask you to restrict submissions to one per person only. 

Submitting Proposals – DEADLINE: 6 December 2019

We welcome submissions for papers, presentations, panels, posters, and lightning talks.

The Call for Presentations, along with the link to the submission form, is at:

Questions about presentation submissions may be sent to the Program Co-Chairs (Stephanie Tulley, Stephanie Labou, and Louise Corti) at

The Call for Workshops, along with the link to the submission form, is at:

Questions about workshop submissions may be sent to the Workshop 
Coordinators, Eimmy Solis and Amber Sherman, at

Deadline for ALL submissions: 6 December 2019
Notification of acceptance: Mid-January 2020

Support for Attending Conference
IASSIST Fellows Program supports data professionals from underrepresented regions and countries with emerging economies. IASSIST Early Professional Fellows Program helps early career data professionals recognizing the value of innovative ideas. Applications can be made at and will close January 17, 2020.

Address questions about the Fellows Programs to Florio Arguillas (

We look forward to seeing you in Gothenburg in 2020! Contact for questions.


Date: Oct 3, 2019

Register Now for CODATA-Helsinki 2019 Workshop on FAIR RDM in Institutions:
The CODATA-Helsinki Workshop on FAIR RDM in Institutions will take place at the National Archives of Finland on 20-21 October 2019. It is a collocated event before the 14th RDA Plenary Meeting, Helsinki, Finland.

Research Data and Research Institutions
Research data are an asset for research institutions. Their creation, management and stewardship imposes considerable responsibilities and requires partnership and alignment with other institutions and research initiatives globally. All over the world universities and libraries have started the task of developing research data services, many aspiring to cover the entire research lifecycle: support in writing proposals and data management plans, repository infrastructures for the storage of data, support in publishing data, assignment of persistent identifiers, lecturing in data management, etc. This broad scope means that such services are often seen as requiring a joint effort from university, library, IT centre, faculties and other stakeholders.

FAIR Research Data Management involves robust planning, policies, infrastructure, training and support. Institutes that produce and consume data are required to ensure seamless accessibility to data and ensure practices that foster its reuse. Often institutes are less aware of existing good practices and progress in implementing Institutional Research Data Management. Improved awareness and knowledge sharing can help reduce duplication in initiatives, and avoid redundant and inefficient practices at various points in the data lifecycle. There is a widely recognised need to assist knowledge sharing between institutions and to do this in an increasingly structured way, by using (and where necessary refining) maturity models such as the DCC RISE. Equally, effective knowledge sharing can help reduce the gap between rich and poor institutions and between universities or research organisations in economically advantaged and disadvantaged contexts. Effective RDM practices can also make the process for sharing and reusing data more streamlined and efficient, thereby enabling research to be more efficient and driving greater impacts to be achieved out of research.
It is timely for actors in the various dimensions of such initiatives internationally to share their practical experiences, research and insights.

Workshop Programme

The event will start at 12:30 (TBC) on Sunday 20 October and will conclude at 18:00 (TBC) on Monday 21 October. the programme will feature at least two keynotes, parallel sessions of presentations on the workshop themes, and discursive workshop activities around the issues of benchmarking and maturity models for FAIR RDM in Institutions. Refreshments on both days and lunch on Monday 21 October will be provided. The programme is live now  at

Registration Fees

In order to contribute towards our costs (conference management system, refreshment breaks and lunch on Mon 21 October), but also to deter spurious and speculative registrations, which on previous events have greatly added to our workload, the programme committee has agreed to charge a registration fee of 40 euros.

Programme Committee

  • Jan Brase, University of Göttingen
  • Mercè Crosas, Harvard University
  • Claudia Engelhardt, University of Göttingen
  • Jane Greenberg, Drexel University
  • Simon Hodson, CODATA
  • Heidi Laine, CSC
  • Liu Chuang, Institute of Geography, Chinese Academy of Science
  • Devika Madalli, Indian Statistical Institute
  • Pekka Orponen, Aalto University
  • Limor Peer, Yale University
  • Jan Lucas van der Ploeg, University Medical Center Groningen
  • Robin Rice, University of Edinburgh
  • Annette Strauch, University of Hildesheim
  • Minglu Wang, York University
  • James Wilson, UCL
  • Keith Russell, ARDC
  • Michael Witt, Purdue University
  • Brian Westra, University of IOWA 

DEADLINE EXTENDED TO 31 OCTOBER - Call for Applications - The CODATA-RDA Research Data Science School, Pretoria 13- 24 January 2020

Date: Oct 2, 2019

In response to a number of requests, we have agreed to extend the deadline for applications for The CODATA-RDA Research Data Science School, Pretoria 13-  24 January 2020 to 31 October 2019.

University of Pretoria – Department of Information Science in collaboration with DIRISA, SADiLaR and NeDICC

Duration: 13–24 January 2020
Location: Pretoria, South Africa

The school provides early career researchers (M-level to postdoc) with the foundational data science skills, which include technical skills and responsible research practices, to enable them to work with their data in an effective and efficient manner required by 21st-century research.


The material covered by the programme is fundamental to all areas of research, and thus open to researchers and professionals from all disciplines that deal with signifi amounts of research data. The goal is to provide a practical introduction to these topics with some theory and extensive hands-on training.


  • Open Science
  • Introduction to Unix Shell
  • Introduction to Git
  • Open and Collaborative Research
  • Research Data Management
  • Data Cleaning – using Open Refine
  • Data Analysis and Visualisation – using R
  • Data Intensive Social Science
  • Author Carpentry
  • Information Security
  • Machine Learning and Neural Networks
  • Research Computational Infrastructure


The school is heavily sponsored. Successful applicants will be expected to pay a fee of R1  150 once the application is accepted. This fee includes all training and catering (lunches). Applicants are responsible for their own travel and accommodation arrangements.

How to Apply:

Application deadline: 31 October 2019
Online application and further information:

Females, Social Scientists and participants from developing countries are encouraged to apply

International Directors

  • H Shanahan (Royal Holloway University, UK)
  • L Bezuidenhout (University of Oxford, UK)

Local Organisers

  • A Vahed (DIRISA) | B Peterson, (NWU)
  • J Steyn (SADiLAR, NWU)
  • J van Wyk (NeDICC) M Holmner (UP)
  • M van Deventer (UP)

Disaster Risk Reduction and Open Data Newsletter: October 2019 Edition

Date: Oct 1, 2019


"If you ask us for ideas, act on them” - Youth call out UN, world leaders on climate action
The Youth Climate Action Summit brought youth climate champions together from more than 140 countries and territories to a platform to share their solutions on the global stage, and deliver a clear message to world leaders: we need to act now to address climate change. 

Global Partnership for Sustainable Development Data - Data for Now Initiative
The Data For Now initiative seeks to increase the sustainable use of robust methods and tools that improve the timeliness, coverage, and quality of SDG data through collaboration and partnership, technical and capacity support, and information sharing.

We could be losing the race against climate change, new UN report says
Scientists behind a landmark study of the links between oceans, glaciers, ice caps and the climate delivered a stark warning to the world on Wednesday: slash emissions or watch cities vanish under rising seas, rivers run dry and marine life collapse.

UNEP FI: Leading Financial Firms Commit to Improved Transparency on the Risks of Climate Change
The Global Commission on Adaption estimates over $7 trillion of climate change-related damages over the next ten years. To respond to these challenges, five leading banks and investors are committing to disclose risks and opportunities for their portfolios from the impacts of climate change by 2021.

UK Government: UK Aid to protect one billion people form impact of extreme weather
A new £175 million package will help make people safer and better prepared for disasters such as typhoons and hurricanes, as well as dealing with the aftermath.

Artificial Intelligence May Help Predict El Niño
Professor Yoo-Geun Ham and collaborators created a model using deep learning that forecasts El Niño and La Niña events 18 months in advance, beating current models that forecast only 1 year ahead.

Read the full Disaster Risk Reduction and Open Data Newsletter

DEADLINE APPROACHNG, 1 OCTOBER: Applications for CODATA-RDA School of Research Data Science, San José, Costa Rica, 2-13 December 2019

Date: Sep 27, 2019

This school provides early career researchers (at MSc-level to 3 years after their PhD) from the Latin American Region with the necessary set of foundational data science skills to enable them to analyse their data in an efficient and effective manner for the 21st century.


The material covered here is fundamental to all areas of data science and hence open to researchers and professionals from all disciplines that deal with significant amounts of data. The goal is to provide a practical introduction to these topics with extensive labs and seminars.


  • Open Science
  • Introduction to Unix Shell
  • Programming for Analysis
  • Git
  • Research Data Management
  • Author Carpentry
  • Data Visualization
  • Information Security
  • Machine Learning
  • Computational Infrastructures

How to apply:

Online application:

Deadline: 1st October 2019

Female students and scientists are encouraged to apply


The school has no cost, and a limited number of grants are available to support the attendance of selected participants from developing countries.

M. ALFARO CÓRDOBA, Universidad de Costa Rica, Costa Rica
R. COBE, UNESP, Brazil
R. QUICK, Indiana University, USA
H. SHANAHAN, Royal Holloway University, UK
L. BEZUIDENHOUT, University of Oxford, UK

Local Organizers:

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