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AI in Data Policy and Data Governance: Designing Research Methodologies for Crisis Situations

  • Creator
    Discussion
  • #133965

    Francis Crawley
    Participant

    1. An examination of research frameworks used in the Midterm Review of the Sendai Framework
    2. Examining the role of AI in research strategies for preparing for and responding to crisis situations
    3. The role of the WHO Handbook on Guidance for Research Methods in responding to crisis situations within an Open Science framework

    1. First group option
    Artificial Intelligence and Data Visitation (AIDV) WG

    Additional links to informative material

    CODATA, The Beijing Declaration on Research Data, 2019. Available at: https://codata.org/events/science-and-policy-workshops/codata-and-codata-china-high-level-international-meeting-on-open-research-data-policy-and-practice/the-beijing-declaration-on-research-data/ [Last accessed 06 December 2022]
    Eggleton, F and Winfield, K. 2020. Open Data Challenges in Climate Science. Data Science Journal, 19: 52, pp. 1–6. DOI: https://doi.org/10.5334/dsj-2020-052
    European Commission European Research Area Policy Agenda 2020-2024. Available at: https://ec.europa.eu/info/sites/default/files/research_and_innovation/strategy_on_research_and_innovation/documents/ec_rtd_era-policy-agenda-2021.pdf  [Last accessed 06 December 2022]
    FAIR principles. Available at: https://www.go-fair.org/fair-principles/  [Last accessed 06 December 2022]
    ISC Policy Brief: Using UNDRR/ISC Hazard Information Profiles to Manage Risk
    and implement the Sendai Framework for Disaster Risk Reduction, 2022. Available at: https://council.science/wp-content/uploads/2020/06/15050_ISC_policy_brief_UNDRR_ISC_Hazard_Information_Profiles.pdf  [Last accessed 06 December 2022]
    Kelly, JA, Farrell, SL, Hendrickson, LG, Luby, J and Mastel, KL. 2022. A Critical Literature Review of Historic Scientific Analog Data: Uses, Successes, and Challenges. Data Science Journal, 21: 14, pp. 1–11. DOI: https://doi.org/10.5334/dsj-2022-014
    Sendai Framework for Disaster Risk Reduction 2015-2030. Available at: https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030 [Last accessed 06 December 2022]
    Sendai Monitoring. Available at: https://sendaimonitor.undrr.org/  [Last accessed 06 December 2022]
    Sustainable Development Goals (SDGs). Available at: https://www.un.org/sustainabledevelopment/sustainable-development-goals/ [Last accessed 06 December 2022]
    The Paris Agreement. Available at: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement [Last accessed 06 December 2022]
    UNDRR/ISC Hazard definition and classification review, 2020. Available at: https://www.undrr.org/publication/hazard-definition-and-classification-review-technical-report [Last accessed 06 December 2022]
    UNESCO Open Science. Available at: https://www.unesco.org/en/natural-sciences/open-science [Last accessed 06 December 2022]
    WHO Health emergency and disaster risk management framework, 2019. Available at: https://apps.who.int/iris/handle/10665/326106 [Last accessed 06 December 2022]
    WHO Glossary of Health Emergency and Disaster Risk Management
    Terminology, 2020. Available at: https://www.who.int/publications/i/item/9789240003699 [Last accessed 06 December 2022]
    WHO Guidance on research methods for health emergency and disaster risk
    management, 2021. Available at: https://apps.who.int/iris/handle/10665/345591 [Last accessed 06 December 2022]
    WHO Technical guidance notes on Sendai framework reporting for ministries of health, 2020. Available at: https://www.who.int/publications/i/item/9789240003712 [Last accessed 06 December 2022]
    Zhang, L, Downs, RR, Li, J, Wen, L and Li, C. 2021. A Review of Open Research Data Policies and Practices in China. Data Science Journal, 20: 3, pp. 1–17. DOI: https://doi.org/10.5334/dsj-2021-003

    Avoid conflict with the following group (1)
    Artificial Intelligence and Data Visitation (AIDV) WG

    Brief introduction describing the activities and scope of the group
    Recent health emergencies, natural hazards, and geopolitical crises have demonstrated the need for evidence-informed decision-making in local, national, regional, and global preparedness and response measures. Recent pandemics/epidemics (COVID -19, Ebola, MERS), natural hazards/disasters (droughts in Europe, Africa, China, USA; floods in Europe, Pakistan, Bangladesh; earthquakes in Papua New Guinea, Peru, Japan), and geopolitical conflicts (Ukraine, Afghanistan, Syria, Yemen, Burkina Faso, Haiti) point to the need for increased data comprehensiveness, integrity, and transparency as well as for more robust ethics and scientific frameworks supporting data policy in crisis situations.
    Robust data policy contributes to more efficient interdisciplinary and cross-sector collaboration in disruptive or disaster situations that threaten lives and the public wellbeing. This data policy must be informed and reliable, developed according to ethics and human rights principles, and supported by sound scientific methodology. The role of AI in generating, processing, and curating data is critical. This is especially important if we want to understand how data will be shared and relied on for preparedness and response to crisis situations.
    Data policy plays a crucial role throughout the phases of a crisis situation: before, during and after. Data policy is critical to ensure better preparation, effective responses, and resilient recovery from a crisis. The effectiveness and acceptability of security and public health interventions in response to crises, as well as the need for democratic, inclusive, and informed debate – require stronger frameworks for developing and implementing data.
    Crucial in crisis situations multidisciplinary is collaboration through data incorporating an all-hazards approach for building comprehensive science-based evidence. Well-developed and robust data policy is essential to grounded and efficient decision-making in cases of system disruption and/or disaster.
    Data policy provides contributes to a governance framework where data gathering and data processing are managed for furthering the development of the science, for use by science, and as a contribution to economic, social, and political decision-making.
    Data policy must promote cross-discipline and cross-sector integration of data in crisis situations through implementing cross-cutting frameworks for data generation, data processing, and data sharing: the FAIR Data Principles, data integrity, data stewardship, data ethics and Open Science.

    Estimate of the required room capacity
    100

    Group chair serving as contact person
    Francis P. Crawley

    I declare that I have informed the chairs of all the Working / Interest groups included in this joint meeting application.
    Acknowledged

    If “Other,” Please specify:
    data policy

    Meeting presenters
    Francis P. Crawley, Perihan Elif Ekmekci, Virginia Murray, Burcak Basburg

    Privacy Policy
    1

    Target Audience
    The EOSC-Future & RDA Artificial Intelligence and Data Visitation Working Group (AIDV-WG) and the International Data Policy Committee (IDPC) of CODATA  (established under the auspices of the International Science Council) have developed this Joint Meeting within their respective inter-disciplinary groups to inform and be informed by, in the first place, the RDA and CODATA communities. Specific audiences that have an interest in this discussion are
    Data scientists
    Data users
    Open science and data commons researchers
    Ethicists
    Human rights experts
    Data policy experts
    FAIR Data Principles implementers
    Data Stewards
    Government, inter-governmental, and civil societies engaged with science in crisis situations
    Organisations providing platforms and tools to support data collection and processing in crisis situations
     

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