Introduction
Open Science, especially open data and services, is increasingly being recognized as a critical driver for achieving the United Nations’ 2030 Sustainable Development Goals (SDGs) and implementing the Sendai Framework on Disaster Risk Reduction. These components of Open Science serve as bridges in science, technology, and innovation (STI), fostering global collaborations and ultimately contributing to the transformative actions from science to policies and practices. The pivotal role of STI in disaster risk reduction (DRR) has been widely recognized. For example, the United Nations’ 2030 Agenda has underscored the indispensable role of STI as an essential tool for SDG implementation, advocating worldwide collaborations to ensure equitable access to STI and facilitate knowledge exchange to support the SDGs (Walsh, Murphy and Horan, 2020). The Sendai Framework for Disaster Risk Reduction (UNDRR, 2015) has emphasized the role of data and information-related technologies in understanding disaster risks and taking actions accordingly. Scientists and policymakers are urged to actively participate in evidence-based risk assessment processes, promote risk-informed sustainable development, and take actions for emergency and disaster risk management (UN, 2015). The Committee on Data of the International Science Council (CODATA) has stressed the need for data across DRR, climate change, and the SDGs (Fakhruddin, et al., 2019; CODATA, 2022). Most countries recognize increased investment in data technology, capacity building on local and national data, and improved access to high-quality, disaggregated data as keys to effective DRR (ISC, 2023; UNDRR, 2023). Successful DRR data cases include efforts to develop multi-hazard early warning systems (Rogers et al., 2020) and access DRR information services (Muhamad, Arshad and Pereira, 2021), etc.
However, DRR data still face challenges, like complex data management, interoperable data standardization, and mechanisms of sustained data sharing (Fakhruddin et al., 2022). To tackle various DRR data challenges, Open Science, particularly open data and services, plays an essential role throughout the stages of disaster risk management, including mitigation, preparedness, response, and recovery (UNESCO, n.d.), which have been clarified by the UNISDR Terminology on Disaster Risk Reduction (2016) as:
Mitigation
“The lessening or minimizing of the adverse impacts of a hazardous event.” (p.20).
Preparedness
“The knowledge and capacities developed by governments, response and recovery organizations, communities and individuals to effectively anticipate, respond to and recover from the impacts of likely, imminent or current disasters.” (p. 21).
Response
“Actions taken directly before, during or immediately after a disaster in order to save lives, reduce health impacts, ensure public safety and meet the basic subsistence needs of the people affected.” (p. 22).
Recovery
“The restoring or improving of livelihoods and health, as well as economic, physical, social, cultural and environmental assets, systems and activities, of a disaster affected community or society, aligning with the principles of sustainable development and ‘build back better’, to avoid or reduce future disaster risk.” (p. 21).
By facilitating timely and unlimited access to all kinds of research deliverables, such as scholarly publications, research data and algorithms, software and platforms, and others (EU, n.d.), Open Science helps connect data and other research resources for better disaster preparedness. Besides, developing robust Open Science Infrastructures (Cousijn, Hendricks and Meadows, 2021) and cutting-edge ICT adoptions support better responses to DRR situations by providing compound solutions. Open and collaborative models also facilitate dialogues between knowledge systems and encourage diversified community engagements for disaster recovery and future mitigation (Yen and Chiang, 2018). Overall, Open Science has the potential to provide transparent, cooperative, and inclusive solutions to bridge the capacity gaps in data and resources and push the boundaries of knowledge transfer in the science-policy-practice interface (UNESCO, 2021; Albris, Lauta and Raju, 2020).
In addition, adopting recognized principles, standards, and practices fosters the development of an Open Science ecosystem to enable the availability, and usability of open data that are timely and of sufficient quality to support DRR (Fakhruddin et al., 2022; Mitchell et al., 2022; Peng et al., 2022). The FAIR Principles for scientific data have advanced understanding of the benefits and challenges of offering findable, accessible, interoperable, and reusable data (Wilkinson et al., 2016; Mazimwe, Hammouda and Gidudu, 2021). The TRUST Principles for Digital Repositories have contributed to approaches for adopting transparency, responsibility, user focus, sustainability, and technology to improve practices for managing and sharing data (Lin et al., 2020; Shijin, Wenli and Qiaoxia, 2023). Similarly, the CARE Principles for Indigenous Data Governance have provided guidance for managing and sharing data to improve collective benefit, authority to control, responsibility, and ethics when collecting, managing, and sharing data about indigenous peoples and the lands that they steward (Carroll et al., 2020; O’Brien et al., 2024). Publication of the Open Archival Information Systems (OAIS) framework by the International Standards Organization as ISO 14721:2012 (CCSDS 2012), has led to the identification of challenges, recommended practices, cultural changes, and approaches for data facilities to sustainably manage and share open data (Brown 2021; Downs 2021; Strecker et al., 2023). Likewise, building on such efforts as well as on related international policies that are applicable to Open Science, PROTECT recommendations have been offered as opportunities to consider people, resources, operation, technology, ethics, communication, and trust as necessary elements for crisis management (Zhang et al., 2024).
In light of these drivers, the Global Open Science Cloud (GOSC) Initiative1,2 and the Integrated Research on Disaster Risk (IRDR)3 jointly organized a session titled ‘Open Data and Open Services for Disaster Risk Reduction’ at the 2023 International Data Week (IDW 2023). This parallel session brought together a diverse range of communities, including data scientists, domain researchers, industry leaders, entrepreneurs, and policymakers, to review the progress of data resilience for DRR in the context of Open Science and to share their experiences, insights, and suggestions to advance these new cross-sectoral endeavors. The discussions focused on selected reports, such as the UN-level flagship DRR efforts, the IRDR actions, the regional GOSC SDG-13 case study, the SEDAC DRR applications, and the CASEarth SDG Big Data platform. Drawing from the insights shared by the speakers and the enriched panel discussions, this paper reports the available open data and open services based on selected case studies and captures the highlights within and beyond this conversation. The next section discusses the resilience of DRR data and services, followed by a discussions of relevant United Nations’ activities, case studies for DRR, summary of topics raised by the case studies, and recommendations.
Towards Resilience of DRR Data Resources and Services Delivery
Resilience (UNISDR, 2016) depicts the ability to ‘resist, absorb, accommodate, adapt to, transform and recover from the effects of a hazard in a timely and efficient manner’. Data resilience is critical for effective disaster risk reduction (DRR). As disasters become more frequent and intense due to climate change and other factors, the ability to collect, analyze, share, and apply high-quality data must keep pace throughout the preparedness, response, and recovery phases. To better illustrate such a resilient framework for DRR data and services, we use selected case studies within and beyond the IDW ‘Open Data and Open Services for Disaster Risk Reduction’ session to cast light on DRR data resources and services delivery across domains and regions.
Leading roles and flagships in the United Nations
The United Nations has been actively leveraging open platforms and tools to bolster DRR extensively. Among these, the United Nations Office for Disaster Risk Reduction (UNDRR) has championed this progress. Since adopting the Sendai Framework in 2015, UNDRR has launched various platforms to support its implementation. Examples include the Sendai Framework Monitor (SFM) and the Sendai Framework Voluntary Commitments (SFVC). Sendai Framework Monitor is designed to oversee global reporting on the Sendai indicators while SFVC functions as a mechanism to mobilize, monitor, and assess commitments from multiple stakeholders. Furthermore, UNDRR adapted the DesInventar database to ensure reliable, event-based data to save disaster losses (Mizutori, 2020) and established the Prevention Web to facilitate global knowledge sharing for DRR and resilience. In addition to UNDRR, multiple branches of the UN system, such as the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) and the United Nations Development Programme (UNDP), are actively offering open services for improving DRR from diverse perspectives. Table 1 showcases some of the best practices implemented by the UN system and the IRDR case study is described in the next section.
Table 1
Examples of UN DRR data tools and platforms.
| NAME | ORGANIZATION | SERVICE DESCRIPTION | URL |
|---|---|---|---|
| Sendai Framework Monitor (SFM) | UNDRR | The Sendai Framework Monitor is a tool to help countries develop DRR strategies, make risk-informed policy decisions, and allocate resources to reduce disaster risks. | https://sendaimonitor.undrr.org/ |
| UNDRR Prevention Web | UNDRR | Prevention Web is the global knowledge-sharing platform for DRR and resilience. | https://www.preventionweb.net/ |
| DesInventar Sendai | UNDRR | DesInventar Sendai is a system for collecting, documenting, and analyzing data about losses caused by disasters associated with natural hazards. | https://www.desinventar.net/ |
| Sendai Framework Voluntary Commitments (SFVC) | UNDRR | The SFVC online platform incentivizes stakeholders to inform the public about their work, provides a vehicle for sharing commitments and initiatives, and motivates them toward implementing the Sendai Framework. | https://sendaicommitments.undrr.org/ |
| Humanitarian Data Exchange (HDX) | OCHA | HDX is an open platform for sharing humanitarian data for crises management. | https://data.humdata.org/ |
| Crisis Risk Dashboard | UNDP | The Crisis Risk Dashboard is a tool for data aggregation and visualization to support contextual risk analysis. | https://sdgintegration.undp.org/crisis-risk-dashboard |
IRDR actions toward Open Science for disaster risk reduction
Co-sponsored by the International Science Council (ISC) and the United Nations Office for Disaster Risk Reduction (UNDRR), the Integrated Research on Disaster Risk (IRDR1) is an interdisciplinary research program to mobilize science and technology for DRR. In alignment with its commitment to advancing Open Science, IRDR has undertaken a series of actions to harness the potential of open data in the realm of DRR for enhanced resilience development. Integrated Research on Disaster Risk launched A Framework for Global Science in Support of Risk-informed Sustainable Development and Planetary Health with ISC and UNDRR. The Framework sets nine research priorities to advance risk science, among which the Priority Five highlights the importance of harnessing data, technology, and knowledge for DRR (ISC, UNDRR and IRDR, 2021). The necessity of developing an open-access digital platform with crowd-sourcing capabilities has been further recommended (ISC, UNDRR and IRDR, 2021). Integrated Research on Disaster Risk has also initiated several research projects and workshops to implement this priority. One of the best practices is an IRDR Pilot Study, ‘Open Science Cloud for Disaster Risk Reduction,’ initiated in early 2023, jointly supported by the GOSC Initiative. Expected deliverables include dialogues among global stakeholders to bridge knowledge gaps, flagship training programs for capacity building, and policy briefings that conceptualize resilient DRR development in tailored open-science platforms, especially featuring community efforts on supporting the young generation and vulnerabilities from the Global South. The next case study focuses on the Global Open Science Cloud Initiative.
Global Open Science Cloud initiative SDG-13 case study
Robust and resilient infrastructures sustain the supply of critical services and safeguard society by acting as a buffer against extreme events (UNDRR, 2022). One of the best practices of co-building research e-infrastructures for DRR is the Global Open Science Cloud (GOSC) Initiative. In line with the UNESCO Recommendation on Open Science (2021), GOSC has endeavored to co-build a trustworthy Open Science ecosystem to bridge worldwide research e-infrastructures towards international accessibility, interconnectivity, interoperability, and intelligibility. Initiated in 2021, GOSC has now grown into an influential initiative with more than 200 registered members coming from over 40 countries and regions (Zhang and Li, 2023). Four working groups, six case studies, and one international program office (GOSC IPO) have been set up to support its implementation.
The GOSC SDG-13 case study primarily focuses on research regarding climate change and natural hazards. Supported by multiple sources of data, models, algorithms, and tools, this case study seeks to investigate the large-scale heterogeneity of climate change and related disasters through a dynamic monitoring and risk assessment model system. It aims to explore comprehensive assessment and seasonal prediction on the temporal changes, understand spatial patterns of extreme climate events and natural hazards, and investigate the causes and frequency of extreme regional climate and related, offering science-based support to mitigate disasters (Chen et al., 2023).
A key challenge in this example is to explore interoperable solutions across various sectors and domains. Cross-sectoral interoperability involves aligning policies, data resources, services, and technologies to ensure seamless collaboration and data exchange. The GOSC SDG-13 case study underscores the importance of establishing long-lasting and sustainable collaborative research models, as exemplified by a Southeast Asian pilot study. Trainings and conferences were conducted to strengthen the international alignment (GOSC IPO and AIT BRRC, 2023).
Regarding data and service interoperability, the GOSC SDG-13 case study co-built a user-friendly system to leverage data from multiple sources and utilizes algorithms contributed by collaborative parties. A GOSC testbed (https://goscloud.net/) is under construction to address technical interoperability issues, providing an integrated virtual collaborative research environment for the transparent sharing and delivery of DRR data and services for global users.
To further enhance cross-sectoral interoperability, the initiative is exploring the integration of diverse data types and standards from different sectors, such as environmental, social, and economic data. This involves developing common data standards and protocols that facilitate data sharing and integration across sectors. Additionally, fostering partnerships between public and private sectors, academia, and non-governmental organizations is crucial to adopt the holistic approach for DRR.
By promoting cross-sectoral interoperability, the GOSC SDG-13 case study aims to create a more cohesive and comprehensive framework for addressing complex challenges posed by climate change and natural hazards, ultimately enhancing global resilience and sustainability.
Next, the NASA SEDAC case study describes data and tools for DRR.
Open data services at the NASA socioeconomic data and applications center
The NASA Socioeconomic Data and Applications Center (SEDAC) is one of twelve Distributed Active Archive Centers (DAACs) operated within the NASA Earth Observing System Data and Information System (EOSDIS). The Socioeconomic Data and Applications Center focuses on improving capabilities for understanding human interactions in the environment and supporting integrating Earth Science data with Social Science data to foster the generation of transdisciplinary knowledge for research, education, and practice. The Socioeconomic Data and Applications Center’s research e-infrastructure currently disseminates over 290 datasets across over fifty collections. In accordance with Open Science practices, data acquired and developed by SEDAC for dissemination are distributed and supported as free and open data products and services, covering various themes to foster interdisciplinary research, applications, and decision-making by researchers, planners and decision-makers, emergency and disaster responders, educators and students, and the public.
Current tools for DRR research and management include the NASA SEDAC Hazards Mapper, the NASA Hazards and Population Mapper (HazPop) application, the POPGRID Viewer,4 the SEDAC Global COVID-19 Viewer, and the Population Estimation Service. These tools serve various purposes before, during, and after crises for locating, accessing, and visualizing hazard data, comparing population data sets, visualizing trends of infections and mortality rates, and estimating at-risk populations globally. These and other SEDAC tools are accessible through various platforms, such as browser-based interfaces and mobile apps for iOS and Android operating systems. These SEDAC resources are also designed to facilitate users in exploring and analyzing data on hazards, populations, and COVID-19 trends for disaster preparation, assessment, mitigation, response, and recovery. See descriptions of SEDAC tools for disaster applications in Table 2. The next case study describes the big data platform for attaining the SDGs offered by CAS.
Table 2
Selected NASA SEDAC tools for DRR.
| NAME | SERVICE DESCRIPTION | URL |
|---|---|---|
| NASA SEDAC Hazards Mapper | Browser-based web application for locating and visualizing recent hazard data | https://sedac.ciesin.columbia.edu/mapping/hazards/ |
| NASA Hazards and Population Mapper (HazPop) | Free mobile app for iPhone and Android for the latest hazard data visualization | https://software.nasa.gov/software/GSC-17605-1 |
| NASA SEDAC POPGRID Viewer | A browser-based tool with a four-panel display for comparing global population data sets | https://sedac.ciesin.columbia.edu/mapping/popgrid/ |
| SEDAC Global COVID-19 Viewer | Browser-based tool to visualize trends of infections and mortality rates by age and sex | https://sedac.ciesin.columbia.edu/mapping/popest/covid-19/ |
| Population Estimation Service | Browser-based web application for estimating populations around the Earth | https://sedac.ciesin.columbia.edu/mapping/popest/pes-v3/ |
CASEarth data services
Funded by the Chinese Academy of Sciences (CAS), particularly the ‘Big Earth Data Science Engineering Project (CASEarth),’ the SDG Big Data Platform (https://sdg.casearth.cn/en) is an integrated data infrastructure for SDGs research, monitoring, prediction, and decision making, with research focuses on SDG 2, 6, 11, 13, 14, and 15. This SDG Big Data Platform combines various research resources, such as supercomputing, big data, data storage, and high-speed networks for SDG research. Currently, 10 PB SDGs data resources covering Earth Science, Social Science, and others are converged through this platform. Beyond data collection, the SDGs Workbench neatly integrates various SDGs data resources, tools, and software for SDG monitoring and research. It also adopts a virtual collaborative research environment and uses cloud service models to ensure the open-science way of data service delivery for DRR and other SDG research in the long run.
The following sections contain discussions of DRR issues for data that are based on these case studies and the previous issues that have been introduced.
Discussion
Data Science explores ways to develop data resources, robust digital technologies and data infrastructures, and data literacy empowerment (Zhang, 2023). In the face of crises, resilient data efforts should be expanded within the context of Open Science and tailored to address specific crises and challenges at hand. Thus, focused data actions may include the design and development of a comprehensive data landscape throughout the crisis lifecycle to guide crisis data work systematically, leveraging supporting digital technologies, running robust and sustained service models, promoting interconnected and interoperable data infrastructures, and fostering an inclusive environment for open collaboration to support such efforts.
Data preparedness throughout the entire crisis management cycle
Crisis management is subject to different stages, such as preparedness, response, and recovery before, during, and after certain hazards. Uncertainty in crises calls for unceasing data preparedness. Such data preparedness should prioritize tailored scenarios with essential factors ranging from data scale, type, source, timeliness, discipline, quality, trustworthiness, analysis readiness, and usability. As illustrated in UNDRR’s Desinventar platform, data preparedness includes exploiting generic rules for data resources development, such as data classification, prioritization, on-demand consolidation, and segmentation, and alternative methods to deal with data in scenarios, like the management of non-disaggregated, long-duration, chained data. Besides, as exemplified in SEDAC and CASEarth’s SDG Big Data Platform, preparing crisis data also includes continuously scalable data fusion for use and reuse promptly. Preparedness also requires dynamic monitoring and evaluation of the effectiveness of open data progress, thus fitting into the changing crisis stages.
Robust digital technologies to address DRR data challenges
Digital technologies and data-driven approaches should be fully exploited to address data challenges in hazardous scenarios. The availability of commonly used and interoperable standards, shared ontologies, and standardized metadata should be prioritized to ensure efficient and effective data integration, thus fostering a better understanding of disaster risks. The Hazard Definition and Classification Review (UNDRR and ISC, 2020; UNDRR and ISC, 2022) and Hazard Information Profiles (Murray et al., 2021) are good examples.
Moreover, extensive adoptions of cutting-edge technologies boost resilient DRR, such as data computing for intelligent crisis monitoring on the CASEarth SDG Big Data Platform, data visualization for crisis prediction and knowledge dissemination in SEDAC, artificial intelligence, blockchain technologies for trustworthy crisis data sharing in the GOSC SDG-13 demonstration. Examples beyond may also include adopting the Humanitarian eXchange Language to enable data sharing between crisis platforms (UN OCHA, 2022), automated translation to unlock insights from multilingual social media streams during disasters (Imran et al., 2020), using blockchain technologies to significantly improve crisis management by multiple engagements (Wang and Chen, 2022), among others.
Furthermore, considering the urgent needs for sundry data services ranging from data resources, tools, algorithms, and knowledge, the deployment of cloud services and XAAS (anything as a service) (Duan et al., 2015; Khalil, Ghani and Khalil, 2016) schema should be ready. They will enhance crisis management’s efficiency by providing diverse on-demand services on a pay-as-you-go basis (Kumari and Kaur, 2021). For instance, Database as Service (DBaaS) streamlines the access and analysis of cloud databases with a more cost-effective and time-efficient approach to mitigate potential disaster risks (Al Shehri, 2013). Collaborative Knowledge as a Service (CKaaS) integrates disparate knowledge, creating customized solutions to specific mitigation strategies (Grolinger et al., 2015). Infrastructure as a Service (IaaS) backs up critical crisis data and facilitates quick restoration of services for disaster recovery (Andrade et al., 2017).
Trustworthy and interoperable data infrastructures
Open Science underpins infrastructures as consolidated foundations (UNESCO, 2021). Thus, to support resilient DDR, those e-infrastructures should be ready for adaptive data governance to balance security, privacy, transparency, and public good (Rizza, Büscher and Watson, 2017). Given the high scale and complexity involved in data computation, exchange, and sharing during any crises, it is challenging for a single e-infrastructure to handle these demands effectively. Therefore, strengthening connections between e-infrastructures becomes crucial. For example, open data services in DRR foster dialogues among potential stakeholders, highlighting the interests of potential data-vulnerable groups, such as youth and people from the Global South. By engaging these stakeholders, IRDR aims to co-build a trusted ecosystem as the foundation for open services. Building upon connections with worldwide research e-infrastructures in Africa, Europe, Southeast Asia, and other regions, the Global Open Science Cloud (GOSC) Initiative showcases federated solutions promoting better flow of data and other resources. Further exemplified in the China-Europe cloud federation testbed, the interconnected OpenStack clusters and smart orchestration of data storage, computing, and other resources together serve as a joint regional testbed to support China-Europe scientists in domain demonstrations (Chen et al., 2021; Chen et al., 2023). Under the GOSC umbrella, regional demonstrations in Africa are also in progress, such as the efforts in Kenya and others to conceptualize the framework of coupled cross-domain interoperability into real practice.
Community-centered approaches and open collaborations
Open data and open services in resilient DRR would be impossible without an inclusive, community-centered environment that encourages open collaborations among diverse stakeholders. Building such an environment entails careful consideration of external and internal factors. Externally, formulating and implementing national, regional, and global policies are essential to support risk assessment and communication, facilitating stakeholder engagement among policymakers and practitioners. Several good practices are currently dedicated to addressing transboundary risks and bridging gaps and inequalities in data and technologies. Notably, all UN member states are encouraged to engage with global agreements such as the Sendai Framework (UNDRR, 2015), the SDGs, and the Paris Agreement (UNFCCC, 2015). In particular, the Target F of the Sendai Framework (UNDRR n.d.) indicates the necessity to substantially enhance international cooperation with developing countries. Equally important is the establishment of robust information networks at the local level. Such engagement can empower individuals and communities to effectively mitigate the impact of disasters.
To ensure the benefits of Open Science for DRR are widely shared across communities, community-centered approaches should be embraced throughout the life cycle of disaster risk management. These approaches should extend beyond external efforts and be applied internally to foster common trust and mitigate overall disaster impacts. Embracing cultural changes across the scientific community, including exposure to open data norms and recognition for open data sharing practices (Borycz et al., 2023; Downs, 2021). Besides, open dialogues also contribute to resilient DRR development across disciplines and sectors, especially by breaking silos and transforming isolated standards to community agreed protocols. The key to community-centered approaches lies in integrating various scientific methods and resources to comprehensively understand the characteristics of hazards, exposures, vulnerabilities, and risks. Considering the issues that have been presented, recommendations for improving DRR capabilities are discussed in the following sections.
Recommendations
The cascading, complex, and systemic nature of sundry hazards requires Open Science and open data for disaster mitigation. Based on the examples and discussions above, recommended actions on resilient data and services for DRR are offered:
Enhance policy governance and regulatory frameworks for DRR. We should expand the discussion on policy frameworks, including international agreements, national legislation, or local governance structures. Developing enhanced regulatory frameworks to ensure data quality, security, and privacy. Regulations are crucial for maintaining trust and accountability in data sharing and usage, especially in crisis situations. We may also analyze the roles of public-private partnerships and develop commonly agreed and shared protocols, standards, ontologies, and standardized metadata to ensure efficiency and effectiveness in sharing data resources across platforms for timely DRR adoption. The policy governance and regulatory frameworks provide structured approaches to managing data, ensuring that they are used ethically and responsibly. These policies and regulatory frameworks may also facilitate international cooperation by aligning standards and practices across borders, making it easier to share and utilize data globally.
Be consistent with the FAIR (Wilkinson et al., 2016), CARE (Carroll et al., 2020), TRUST (Lin et al., 2020) principles, and PROTECT essentials (Zhang et al., 2024) for DRR data management. Hazardous data should be reliable to support timely decision-making in crises, trustworthy for appropriate crisis response based on mutual consensus, and sustained in disaster scenarios and through the crisis management cycle. Among these, FAIRness ensures data of high quality. CARE principles protect sovereign data rights and insist on data ethics in emergencies. TRUST principles highlight the sustained and reliable operation of data and data facilities, while PROTECT identifies data essentials for crisis preparedness. These principles can convergently guide appropriate data management before, during, and after crises, thus co-building a resilient DRR data ecosystem.
Understand resilient DRR data by developing a crisis data management landscape guiding crisis data work throughout the crisis lifecycle. Implementation following such data landscape may include descriptions of policy constraints associated with data strategies, and plans preparing enriched datasets through crisis stages in tailored crisis scenarios, comprehensive monitoring and assessments of hazardous data and policies simultaneously. It is essential to explore the ethical implications of STI in DRR, such as issues related to data privacy, equity in technology access, and the potential for data misuse and suggested frameworks or guidelines for ethical STI deployment in DRR contexts.
Unleash the full potential of digital technologies and promote necessary standardization to unlock DRR data value. Data challenges persist in unexpected situations, preventing the full exploration of data value. To address these hazardous data challenges, we should pursue STI opportunities by adopting integrated networks, cloud storage, big data and other cutting-edge technologies appropriately. These adoptions may include network optimization, data recovery, reconstruction planning, and open data exchange and sharing. Similarly, infrastructures are needed to enable crisis data capture, cleaning, computation, and analysis with federated clouds and flexible XAAS service schemas to ensure versatile DDR data service delivery. Explore cutting-edge technologies, such as artificial intelligence, blockchain, quantum computation, and other innovative methods, thus contributing to robust, entrusted, and sustained data work throughout crisis stages.
Link existing data facilities towards Open Science Infrastructures. The sufficiency of data preparedness and the efficiency of digital technology adoption are tightly tied to data infrastructures. However, e-infrastructures can hardly fulfill these objectives independently without addressing interoperability and interconnectivity that scale up their service capabilities for DRR. Such research e-infrastructures, also known as Open Science Infrastructures (UNESCO, 2021), are vital to support seamless flow of open data and the application of open services within a mutually trustworthy ecosystem in crises. Linked Open Science Infrastructures may exchange larger scale of data resources, share different algorithms and tools across platforms timely, drive data facility development intelligibly, and nourish a healthy Open Science ecosystem with expanded involvement of stakeholders aligned to those facilities.
Promote interdisciplinary and international research collaboration and knowledge exchange to bridge gaps and disseminate data, technologies, and knowledge for DRR as much as possible. Foster multi-stakeholder dialogues and cooperation to establish Open Science Infrastructures and platforms, providing data and information, expertise and methodologies, technologies, and services required for DRR research and practices. Cooperations may include work streams that break silos, embracing active partners and providing insightful deliverables at large promptly. Courses, training workshops, and seminars should be developed towards Open Science, especially to empower data vulnerable communities. Moreover, cultural changes should embrace Open Science and promote open data and open collaborations as norms in research ecosystems and others.
Conclusions
This paper emphasizes the importance of open data and services for resilient disaster mitigation. Resilient DRR must be built upon preparedness of dynamic data resources, robust digital technologies, and interoperable data facilities that follow community-centered approaches and open collaborations. Such efforts will contribute to cooperative development of a trustworthy ecosystem that involves the scientific communities across disciplines and societal contributions across sectors. It is inspiring to see these global, regional, national, and institutional joint efforts serve as exemplars for DRR management. While facing the grand challenges ahead, we believe that practicing Open Science for data development and sharing offers vital opportunities for continuing progress on global resilient DRR.
The critical importance of STI in enhancing DRR is evident and well-supported by international frameworks. Data availability, quality control, and standardization challenges persist, but the commitment to improving these areas is strong across nations and institutions. We must foster Open Science principles to bridge gaps between data capacity and resource availability as we progress. Doing so can improve transparency, collaboration, and inclusivity in the science-policy-practice interface, ensuring a resilient future for sustainable development. The recommendations aim to inspire continued advancement and careful consideration of STI in the realm of DRR, emphasizing the need for ethical practices, equitable access, and a sustained focus on local and global collaborations.