In this video, Boniface Akuku, co-chair of the Task Group lays out the activities and achievements in the past terms and details the TG objectives for the next two years.

Bapon Fakhruddin and Li Guoqing, co-chairs

After four terms of study, the award winning FAIR Data for Disaster Risk Research (FAIR-DRR) has focused itself on addressing enabling technology (i.e. decision support system, rapid damage mapping, etc.), scientific questions, technical challenges, and best practices of disaster data standards and applications in risk assessment across disciplines, development partners and governments.  FAIR-DRR also developed a data ecosystem by integrated other networks to work collaboratively (i.e. IRDR of UNDRR, GEO-DRR, NASA DRR working group, SDG, Disaster Statistic etc.) and applied data for cross-domain studies.  These activities closely allied with the ISC’s Decadal Programme “Making data work for cross-domain grand challenges”.

FAIR-DRR is an increasingly important activity linking and ensuring coherence of major global milestones – the Sendai Framework for Disaster Risk Reduction (SFDRR), Sustainable Development Goal (SDG), Paris Agreement for Climate Change and the New Urban Agenda (NUA)-Habitat III.

The experiences of the Covid-19 pandemic in the past year have made all disciplines keenly aware that solutions to complex and difficult problems require data to be readily assessable and actionable by machines using big data in combination with the most advanced hardware and software technologies. Our technology is advancing rapidly, however, our data systems are not able to achieve the same milestones. The fundamental enabler of data-driven science is an ecosystem of resources that enable data to be FAIR (Findable, Accessible, Interoperable, and Re-usable) for humans and machines. This ecosystem must include effective, maximally automated stewardship of data, and effective terminologies,  metadata specifications and partnerships.

Following these works the Fifth term of the FAIR-DRR task group proposed the following objectives for the 2022-2023 term.

• Volunteer Rapid Disaster Monitoring and Mapping in collaboration with Earth-GEO
• Enhance interdisciplinary data integration using FAIR-DRR’s sequence, partnership with other networks and documenting good practices.
• Engage with users and sectors for greater alignment and consistency of hazard definitions, standardisation of data loss quantification and risk assessment
• Demonstrate transdisciplinary approaches linking climate scientists, engineers and sectoral professionals to identify future emerging and complex research using data
• Capacity building using monthly newsletters, policy papers, conference, webinars and white papers.

In this video, Li Guoqing and Bapon Fakhruddin lay out the key activities and achievements of the Task Group in the past terms, detail their objectives for the next two years and invite participation in FAIR DRR.

Fred S. Roberts, Igor Sheremet Co-Chairs

Background: Resilience of Digitized Complex Systems
Today’s society has become dependent on complex systems, enabled by increased digitization of our world and the increasing availability of vast amounts of data, that have had a great impact on virtually all facets of our lives and our societies: enabling our financial transactions, running our power grid, underpinning our transportation systems, empowering our health care, supporting the rapid delivery of supplies and materials. Yet these changes have made us vulnerable to natural disasters, deliberate attacks, just plain errors. A challenge is to develop ways to make our complex systems more resilient. We propose to continue the work of the “Task Group Advanced Mathematical Tools for Data-Driven Applied System Analysis” to address this challenge through the development and refinement of a toolkit of advanced mathematical tools.

Mathematical Tools to Enhance Resilience
Modern technological and sociotechnological systems consist of numerous critical infrastructures that are strongly interconnected, which makes them vulnerable to multiple chain or cascading destructive impacts. Vast amounts of data need to be taken into account in understanding the performance of such infrastructures and their interconnections, and understanding how to make them resilient. Mathematical tools can assist with this and in particular the Task Group will study algorithms for responding to a disruption that will enhance resilience, i.e., minimize the departure from a previous state when things settle down after a disruption.

Mathematical Tools to Design Resilient Systems
In addition to helping us understand how to bring a system back to a normal state as rapidly as possible, mathematical methods can aid us in understanding how to design systems so as to make them more resilient in case of disruption. Modern complex systems may include millions of interconnected components (humans, devices, buildings, etc.), so to design a system with a predefined level of resiliency, it is necessary to represent in some formal way a system’s structure and logic of operation, and to develop an appropriate mathematical and algorithmic toolkit that can provide for efficient search for solutions over the extra-large volumes of data associated with digitized systems in today’s era of Big Data. This is a major goal of our proposed renewed Task Group.

The Task Group’s Approach
In the pages that follow, we describe the basic components of our approach. This consists of taking advantage of a multidisciplinary team, each bringing to the dialogue their own mathematical expertise and tools (whether it be graphs and networks, simulation tools, or the theory of algorithmic decision making), developing ways to share the tools, and studying how to relate them to an organizing component designed around a multiset-based (multigrammatical framework). Pilot software for components of the improved mathematical and knowledge engineering framework will be implemented in standard platforms and carefully documented. We also describe the connection to other Task Groups, to the CODATA Decadal Program, and the collaboration with the International Institute for Applied Systems Analysis (IIASA). The plans for the renewed Task Group are modeled after the successes of our first TG, namely webinars, a workshop, scientific papers, and a research monograph.

Read the full presentation

By Carolynne Hultquist and Peter Elias

The overall objective of the Task Group on Data from Participatory Mapping for the SDGs is to study data on environmental changes generated by participatory mapping projects and platforms for the specific requirements of the Result Framework proposed by the United Nations (UN) 2030 Agenda. Namely we focus on indicators associated with the Sustainable Development Goals (SDGs), especially Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA) and other high-level policy frameworks, such as the Sendai Framework for Disaster Risk Reduction and the post-2020 biodiversity monitoring framework proposed by the Convention for Biological Diversity (CBD). The alignment facilitates and encourages the inclusion of participatory mapping in the official monitoring of the SDGs and other policies at local, national, and global levels. Our group is particularly interested in evaluating the use of Participatory Geographic Information Systems (PGIS) data for underrepresented groups in relation to global environmental challenges.

Participatory GIS provides a powerful methodology in which open spatial data are contributed and in turn, accessible web-based tools enable all stakeholders to track progress at a local, regional, or even global level. However, data generated by participatory mapping projects are not yet included in the official framework to monitor the SDGs, despite the abundant literature illustrating that citizens can contribute high-quality data. Work previously supported by the CODATA–WDS TG on Citizen Science and the Validation, Curation, and Management of Crowdsourced data illustrated a wide range of actual practices. Growing support for Citizen Science also exists under the UN, with UN Environment recently supporting the establishment of a Citizen Science Global Partnership (http://www.citizenscienceglobal.org).

The TG seeks to facilitate and encourage the use of participatory mapping and Participatory Geographic Information Systems (PGIS) by envisaging a framework for evaluation and use that will facilitate the mapping of data to the specific requirements of the SDG framework. Participatory mapping is a sub-category of citizen science that involves spatial data while PGIS even more specifically involves user contributions and changes to spatial data being available in a public digital GIS environment. Surveying the platforms will provide visibility to participatory mapping data and their use in filling some of the official data gaps, while challenging the scientific community to identify targeted methods and data to tackle the remaining gaps. Sharing of ‘SDG-mapped’ data will produce benefits well beyond scientific results, strengthen the science-policy interface, and help amplify the societal impact of citizen science.

The activities of the TG will include the following tasks:

• Survey of participatory mapping data use by national statistical offices (NSO), health, environment, and humanitarian organizations, government agencies, and community groups
• Survey data practices of PGIS platforms/community science groups
• Develop a framework for evaluation of participatory mapping and share lessons learned for effective practices for metadata, stewardship, validation, and management
• Demonstrate the use of participatory mapping through case studies; e.g., underrepresented groups (slum, refugee, extreme poverty, isolated Pacific island communities) in relation to global challenges (e.g. health pandemic – COVID-19, climate change – flooding, Disaster Risk Reduction (DRR), biodiversity monitoring, etc.).
• Explore possible ways to map existing and historic participatory mapping data to the indicators framework, including the possibility to propose new indicators inferred by the data and more relevant to people’s life and experience.
• Explore the potential for data on human capital in volunteer activity from participatory mapping platforms; namely, data on the engagement of volunteers and subsequent learning/social/civic outcomes to support indicators. This incorporates issues of inclusiveness in monitoring and data collection, thus ensuring ‘leaving no one behind’.
• Collaborate with UN statistical offices and other UN stakeholders to gather requirements and develop shared glossaries to support the inclusion of participatory mapping in the list of accepted ‘non-official’ data providers for the SDGs.
• Work with the UN, including the UN Environment and Development Programmes,UN Habitat, and Convention for Biological Diversity (CBD), to continue to gain support for participatory mapping and strengthen the science–policy interface.

The outcomes of the above activities will include the study of concrete use cases that exemplifies the value of participatory mapping for a specific indicator by illustrating the creation and implementation of a participatory mapping project. The use cases will feature a complete ‘participatory mapping for SDGs’ cycle: identification of a data gap, design of the project, implementation, data collection, data analysis, and data sharing with UN officials. The final result in such an example could be a change in policy in the best-case scenario. The analysis of these use cases will help extract common practices and simple data policies that can be generalized to other projects and countries.

In this three-minute video, Bob Hanisch, chair of the DRUM (Digital Representation of Units of Measure) lays out the key activities and achievements of the Task Group 2018-2021 and details their objectives for the next two years.

This interview is with Alena Rybkina, a Vice President of the CODATA Executive Committee.

“Building foundation for a world of open data and open science” was posted in Options magazine (published by IIASA). The link to online magazine – https://iiasa.ac.at/web/home/resources/publications/options/2020_Summer.html

Read the full interview here

Bapon Fakhruddin is a specialist in climate and hydrological risk assessment with a focus on the design and implementation of hazard early warning systems and emergency communication. He is Technical Director, Disaster Risk Reduction and Climate Resilience at Tonkin + Taylor, New Zealand. He is also Co-Chair for the Open Data for Global Disaster Risk Research task force with CODATA.

Bapon Fakhruddin discusses why the COVID-19 pandemic requires thinking and decision making supported by a data ecosystem which looks much further into the future than previous short-term approaches.

The novel coronavirus disease (COVID-19) has created a human crisis globally, which has demanded an array of drastic, immediate responses. The United Nations (UN) Secretary-General has swiftly called for action, “for the immediate health response required to suppress transmission of the virus to end the pandemic and to tackle the many social and economic dimensions of this crisis[1]“. The pandemic also requires thinking and decision making supported by a data ecosystem that is more complete than currently, and which looks much further into the future than previous short-term approaches.

The COVID-19 outbreak has led to the proliferation of initiatives to facilitate open access to scientific research and databases and encourage research collaboration through digital platforms. However, there are concerns about the quality of data and publications provided in near real-time, leading to potentially poor decision making. These issues include comparability and interpretation of data, notably between countries, insufficient specification of methodology, and political acceptance of invalid results potentially biasing scientific methods. A call for data and research is necessary in relation to the discussion of the transmission of the disease.

Read more: https://council.science/current/blog/setting-up-a-data-ecosystem-to-defeat-covid-19/

CODATA and IIASA have co-convened a Workshop on Big (and FAIR) Data and Systems Analysis, 24-25 February. The workshop is organised by the CODATA Task Group on Advanced Mathematical Tools for Data-Driven Applied Systems Analysis which is working closely with IIASA (the International Institute for Applied Systems Analysis) on the interface of data, mathematical tools and systems analysis.

The workshop programme may be accessed here

The workshop also provides an important occasion to explore the opportunities for collaboration between CODATA and IIASA, on matters of FAIR data, data stewardship and the ISC CODATA Decadal Programme on Making Data Work for Cross-Domain Challenges.