This dataset is an inventory of existing quantitative resilience frameworks, indicators, and measures that have been evaluated and entered into a database according to a standardized methodology. The inventory is a broad inventory of existing resilience indicators (whether proposed or applied) and key information for each indicator. The indicators span all systems likely to be included in the assessment methodology, including physical systems (e.g., buildings and infrastructure), social and economic systems, and natural systems (e.g., natural environment). The inventory is a foundational component of the Community Resilience Program's project that is aimed at developing a first-generation methodology to assess resilience at the community-scale based on community functions, supported by buildings and infrastructure systems, and the recovery of those functions following a disruptive hazard event. One aspect of this work is to identify the types of indicators that should be used as proxies to represent system attributes, dimensions, and dependencies.

Maps of relative classifications (low to high) for six resilience indicators and two anthropogenic stressors and a map of final relative resilience scores for 78 sites in the Commonwealth of the Northern Mariana Islands. The six resilience indicators are: bleaching resistance, coral diversity, coral recruitment, herbivore biomass, macroalgae cover and temperature variability. The two anthropogenic stressors are fishing access and nutrients and sediments. The resilience score map compares sites across all four of the surveyed islands: Saipan, Tinian, Aguijan, and Rota.

Open Data for Resilience Initiative (OpenDRI) applies the concepts of the global open data movement to the challenges of reducing vulnerability to natural hazards and the impacts of climate change. OpenDRI supports World Bank Regional Disaster Risk Management Teams to build capacity and long-term ownership of open data projects with client countries that are tailored to meet specific needs and goals of stakeholders around three main areas of Sharing Data, Collecting Data, Using Data. All data is published under an open license. Projects include Open Cities Africa, with national projects in: Niger (flood hostpots and mitigation), Uganda (drought risk information and disaster risk financing), Zanzibar (vunlerability to natural disasters), Pacific Islands (Natural Disasters and Climate Change), Sri Lanka (evidence based methods for natural disaster response), Afghanistan (disaster risk decisionmaking), St Vincent and the Grenadines (hydroclimatic disasters), Saint Lucia (post disaster rehabilitation), Jamaica (storm even impact), Serbia (disaster preparedness), Indonesia (disaster management especially flooding), Seychelles (site specific risks of floods, earthquakes, cyclones, storm surge and tsunamis), Muaritius (under development), Madagascar (under development), Vietnam (natural hazards especially flood risks and climate change impacts), Bangladesh (under development), Pakistan (earthquakes and monsoon floods), Nepal (Seismic risk), Haiti (storms, flooding, landslides, environmental degradation), Guyana (under development), Grenada (under development), Dominica (extreme weather events), Colombia (flooding, landslides, increased vulnerability due to insufficient urban planning), Antigua and Barbuda (cyclones, fires and flooding), Belize (storm, flood and tsunami risks), Bolivia (natural hazards and climate change), Kyrgyz Republic (risk data on meteorological, geological, geophyical and boilogical hazards), Philippines (typhoones and monsoon floods recovery data), Tanzania (flood maps), Mozambique (flood, cyclone and windstorms), Comoros (flood, storm, volcanic eruption), Malawi (information to develop schools, healthcare and agriculture against floods and droughts), Armenia (earthquakes, drought, hailstorms, landslides)

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

coastal and river flooding (Ward et al, 2020)

extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)

tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

population (Schiavina et al, 2023)

built-up area (Pesaresi et al, 2023)

roads (OpenStreetMap, 2023)

railways (OpenStreetMap, 2023)

power plants (Global Energy Observatory et al, 2018)

power transmission lines (Arderne et al, 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

snkit helps clean network data



nismod-snail is designed to help implement infrastructure
exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the
global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo.
DOI: 10.5281/zenodo.3628142



Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical
cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI:
10.4121/12705164.v3



Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.;
et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI:
10.4121/14510817.v3



Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources
Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine;
resourcewatch.org/



Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries
– Final Report. Available online:
https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries



Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme
climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI:
10.1029/2020EF001616



Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online:
www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details



OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived
from OpenStreetMap. [Dataset] Available at
global.infrastructureresilience.org



Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and
Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID:
data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA



Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived
from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI:
10.5281/zenodo.8147088



Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal
(1975-2030). European Commission, Joint Research Centre (JRC) PID:
data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE



Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020)
Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at:
www.wri.org/publication/aqueduct-floods-methodology.

The dataset contains several equity-related data types: socioeconomic, demographic, health, and environmental inequities. Specifically, it includes data layers for low income, race and ethnicity, linguistic isolation, disability, age (children and older adults), renting population, populations without health insurance, proximity to brownfields and hazardous sites, lack of green space, and tax base per capita.The dataset is a compilation of equity-related data compiled from publicly available screening tools, including the Climate and Economic Justice Screening Tool (CEJST) developed by the White House’s Council on Environmental Quality in 2022, EJ Screen developed by USEPA, and Social Vulnerability Index (SVI) developed by the CDC/Agency for Toxic Substances and Disease Registry (ATSDR) Geospatial Research, Analysis, and Services Program, and CMAP. Find more information on data sources in the data dictionary.

The EX_Genome_Assemblies.zip file contain the contig sequences (i.e. assembly) of fifteen isolates used for genomic and antibiotic resistance genes (ARG) analysis. EX_OTU.fasta file contain the sequences of the bacterial 16S rRNA-encoding V4 region gene (≈250 nt) for each Operational Taxonomic Unit (OTU). This dataset is associated with the following publication: Gomez-Alvarez, V., S. Pfaller, J. Pressman, D. Wahman, and R. Revetta. Resilience of microbial communities in a simulated drinking water distribution system subjected to disturbances: role of conditionally rare taxa and potential implications for antibiotic-resistant bacteria. Environmental Science: Water Research & Technology. Royal Society of Chemistry, Cambridge, UK, 2: 645-657, (2016).

NEMAC Climate Resilience/Risk Raw DataLandslide, Flood and Wildfire Risk, 7 layers in total.Assessment Report: https://drive.google.com/file/d/1X_Gr4eUCmkXPOzAcvyxCe-uZPkX84Byz/viewThe assessment report give field names, data source information and metadata.For Asheville's Climate Resource guide please visit: https://www.ashevillenc.gov/news/asheville-climate-change-guide-release-and-renewable-energy-initiative-draft-plan/Open Data - to download, use the Download Filtered Dataset option to download the individual layers.

The report covers Global Data Resiliency Market Share and it is segmented by Deployment (On-premise, Cloud), End-user Vertical (BFSI, IT & Telecommunication, Government), and Geography.

The statistic represents the impact of various digital transformation initiatives on business resilience and the ability of companies to adequately ensure data security, as of March 2016. According to the survey, 42 percent of respondents reported that the adoption of mobile payments had had a strong impact on data security and business resilience, while 28 percent identified digital identity management as having had a critical impact.

This dataset comprises various geospatial and tabular layers within the Tahoe Basin that supports the Climate Resilience Dashboard at https://climate.laketahoeinfo.org/. Spatial layers include Forest Management Zone, Forest Management Zone Identity Tree Mortality 2023, ForestManagementZone_Identity_HighSeverityProbability, ForestManagementZone_Identity_LowSeverityProbability, Parcel BMP, Areawide Stormwater Treatment, Parcel Development History, Housing Deed Restrictions, Transit Stops, Transit Stop Walkshed Quarter Mile, Transit Stop Walkshed One Mile, Transit Stop Walkshed Half Mile, Transit Stop Buffer Quarter Mile, Transit Stop Buffer Half Mile, Community Priority Zones, Existing Active Transportation Facilities, Bicycle Level of Traffic Stress Intersections, Bicycle Level Traffic Stress Segments, Public Beach Access and ZVH, Healthcare and ZVH, Food and ZVH, Electric Vehicle Charging Station, Block Group Centroid, and Tahoe Block Group TDC Values. Tables include GHG Emissions Summary, Secchi Depth, SEZ Enhanced or Restored, SEZ Restoration, Census Data, Probability of High Severity Fire by Forest Management Zone, Probability of Low Severity Fire by Forest Management Zone, Total VMT, Transit Monthly Ridership by Route, Mode Share, Transient Occupancy Tax Revenue, Impervious Covered by Areawide Stormwater Treatment, Origin Destination LODES Data, Work Locations LODES Data, Air Quality, Energy Mix, Central Sierra Snow Lab All Data, Real Estate Sale Price Monthly Average, Rental Rates All Beds, and Deed Restricted Housing Units.Spatial Reference: NAD83 / UTM zone 10N (26910)Area Covered: Tahoe Basin, Nevada, California

According to Cognitive Market Research, the global data resiliency market size is USD 22.87 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 17.6% from 2024 to 2031. Market Dynamics of Data Resiliency Market Key Drivers for Data Resiliency Market Growing Cybersecurity Threat Environment and the Requirement for Securing Information- An important factor driving the global data resilience market is the growing security environment of cyberattacks. Cyber-attacks involving ransomware and other evil actions have become more frequent, sophisticated, and devastating. To protect their vital information assets, organizations in a variety of industries are finding that they increasingly require strong data protection solutions. The need for data resiliency solutions has increased as a result of the growing awareness that any infrastructure is entirely secure against cyberattacks. This driver emphasizes how important it is for companies to strengthen their data protection plans at a time when cybersecurity threats are ever-present and changing. Technological advancement also drives the market. Key Restraints for Data Resiliency Market The data resilience market is constrained by complex data management. The system's exorbitant cost will also hinder the market's expansion. Introduction of the Data Resiliency Market Data resilience refers to the systems and offerings that facilitate the efficient operation and management of data in an environment of interruptions such as blackouts, cyberattacks, computer or software errors, or similar occurrences. Developing plans and techniques that enable information-management systems to endure such occurrences without losing important data or suffering protracted downtime is required. Data resilience solutions are becoming more and more necessary to safeguard and effectively administer the growing amount of data generated through growing digitization. Data resilience is essential to protecting private details, and the increase in cyberattacks like extortion and data breaches is fueling the need for data resilience solutions.

Reason for Selection Resilience scores quantify a combination of landscape diversity and local connectedness, stratified by geophysical setting and ecoregion. These measures represent the number of microclimates available to species and the current state of the landscape. This builds on research from Anderson and Ferree (2010), who showed geophysical diversity and elevation range were associated with biodiversity in the Eastern United States. Resilience emphasizes diverse landscapes where species are likely to be able to move and adjust to changing conditions. Input DataBase Blueprint 2022 extentThe Nature Conservancy’s (TNC) Resilient Land (latest version of the terrestrial resilience that incorporated an updated wetlands layer and the 2019 National Land Cover Database); download the data; read more about the analysisSoutheast Blueprint 2023 extentMapping StepsClassify the VALUE field into the final indicator values as shown below. This translates the original continuous layer into the standard deviation-based classes that TNC uses to display the resilience data in their viewer.Clip to the spatial extent of Base Blueprint 2022.As a final step, clip to the spatial extent of Southeast Blueprint 2023.Note: For more details on the mapping steps, code used to create this layer is available inthe Southeast Blueprint Data Download under > 6_Code.Final Indicator Values Indicator values are assigned as follows:7 = Most resilient6= More resilient5 = Slightly more resilient4 = Average/median resilience 3 = Slightly less resilient2 = Less resilient1 = Least resilient 0 = DevelopedKnown IssuesThis indicator does not account for the occurrence and timing of disturbance processes, particularly fire. Without fire, resilient sites in many terrestrial ecosystems will not serve as biodiversity hotspots. This is particularly problematic in pine and prairie ecosystems in the Piedmont and Coastal Plain.Resilience scores on indigenous lands are still under review and are not included in this indicator.This indicator is derived from an interim data product provided by TNC prior to its official release. The final version has since been publicly released. While we are not aware of any significant changes, it’s possible there may be slight differences between the preliminary data used in this indicator and the final published data.TNC no longer runs the terrestrial resilience analysis in the coastal zone, coding some areas as NoData that they scored in past iterations of TNC Resilient Land. Areas within the coastal zone receive a NoData score in this indicator because TNC’s analysis estimates coastal resilience only for areas that are currently marsh or expected to become marsh under various sea-level rise scenarios.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature Cited Anderson, M.G., A. Barnett, M. Clark, C. Ferree, A. Olivero Sheldon, J. Prince. 2016. Resilient Sites for Terrestrial Conservation in Eastern North America. The Nature Conservancy, Eastern Conservation Science. [https://easterndivision.s3.amazonaws.com/Resilient_Sites_for_Terrestrial_Conservation.pdf].

Anderson, M.G., Ferree, C.E., 2010. Conserving the stage: climate change and the geophysical underpinnings of species diversity. PLoS One 5, e11554. [https://doi.org/10.1371/journal.pone.0011554].

The Coastal Resilience Open Data Platform hosted by NFWF provides access to the monitoring data collected or generated by NFWF grantees and partners through our programs that fund conservation for coastal resilience, including the Hurricane Sandy Coastal Resiliency Competitive Grant Program. This searchable data platform is designed to store metadata and data files for discovery and sharing among NFWF grantees, partners and the wider conservation community. Datasets include both spatial and tabular data and span ecological and socioeconomic topics for coastal restoration activities at locations funded by each program. A list of the NFWF programs currently participating in this platform is available on the Programs page. This data platform is part of a broader effort to provide a transparent means of reporting the measurable impacts of coastal resilience projects. A related metrics database and reporting tool is being developed that will be used to track and communicate the impacts and cost-effectiveness of funding different types of resilience projects. Funding for development of these systems is provided under the Hurricane Sandy Coastal Resiliency Competitive Grant Program. 1625 Eye Street NW, Suite 300 Washington, DC 20006 T 202-857-0166 | F 202-857-0162 Please send questions or comments to ResilienceData@nfwf.org Developed by Industrial Economics, Inc. through the Hurricane Sandy Response Fund Grant 2300.16.055.13

Maps of relative classifications (low to high) for six resilience indicators and two anthropogenic stressors and a map of final relative resilience scores for 78 sites in the Commonwealth of the Northern Mariana Islands. The six resilience indicators are: bleaching resistance, coral diversity, coral recruitment, herbivore biomass, macroalgae cover and temperature variability. The two anthropogenic stressors are fishing access and nutrients and sediments. The resilience score map compares sites across all four of the surveyed islands: Saipan, Tinian, Aguijan, and Rota.

Data Resiliency Market Outlook

In 2023, the global data resiliency market size was valued at approximately USD 16.9 billion, and it is projected to reach around USD 45.8 billion by 2032, reflecting a robust CAGR of 11.6% over the forecast period. The significant growth in market size can be attributed to the increasing awareness of data security, the rapid digital transformation of businesses, and the growing significance of data as a critical asset for organizations.

The demand for data resiliency solutions is being driven by the rising number of cyber-attacks and data breaches, which have highlighted the importance of ensuring business continuity and data protection. Enterprises are increasingly investing in data resiliency solutions to safeguard their data against various threats, including malware, ransomware, natural disasters, and human errors. Moreover, the stringent regulatory requirements mandating data protection and compliance have further propelled the need for robust data resiliency strategies.

Another major growth factor in the data resiliency market is the proliferation of big data and the adoption of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies generate vast amounts of data, necessitating efficient data management and protection solutions. As organizations strive to leverage data for competitive advantage, ensuring the availability and integrity of data becomes paramount, thereby driving the demand for data resiliency solutions.

The shift towards cloud computing is also contributing significantly to the market's growth. Cloud-based data resiliency solutions offer several advantages, such as scalability, cost-efficiency, and ease of deployment, making them an attractive option for businesses of all sizes. With the increasing adoption of cloud services, there is a growing emphasis on integrating data resiliency measures to protect cloud-stored data. This trend is expected to continue, further fueling the growth of the data resiliency market.

In terms of regional outlook, North America holds a dominant position in the data resiliency market, driven by the presence of major technology companies and widespread adoption of advanced data protection solutions. The Asia Pacific region is anticipated to witness significant growth during the forecast period, owing to the rapid digitalization, increasing cyber threats, and rising investments in IT infrastructure. Additionally, Europe is also expected to see substantial growth due to stringent data protection regulations and growing awareness of data security among enterprises.

Component Analysis

The data resiliency market is segmented into solutions and services. The solutions segment encompasses various technologies and software designed to ensure data protection and recovery, including data backup and recovery, disaster recovery, and data archiving. The growing complexity of data management and the increasing volumes of data generated by organizations are driving the adoption of advanced data resiliency solutions. Companies are seeking comprehensive solutions that can provide end-to-end data protection and quick recovery in case of data loss or disruptions.

Services, as a component, play a critical role in supporting the implementation and management of data resiliency strategies. This segment includes professional services such as consulting, integration, and support services, as well as managed services. The demand for these services is rising as organizations lack the in-house expertise to handle complex data resiliency initiatives. Managed services are particularly gaining traction as they offer continuous monitoring and management of data resiliency processes, thereby allowing companies to focus on their core business activities.

Within the solutions segment, data backup and recovery solutions are widely adopted as they provide a crucial layer of security by ensuring that data can be restored in the event of data loss. Disaster recovery solutions, on the other hand, are essential for maintaining business continuity by enabling organizations to quickly recover their IT infrastructure and resume operations after a disruption. Data archiving solutions help in managing the lifecycle of data, ensuring that critical information is stored securely and can be retrieved when needed.

The increasing sophistication of cyber-attacks has led to the development of advanced data resiliency solutions that incorporate features such as encryption, anomaly

This upload contains the data and scripts used for a research paper in preparation for publication. The paper presents a probabilistic resilience assessment framework where failure scenarios and network disruptions are generated using weather profile data from climate prediction models with component-level fragility functions. A case study is then carried out to quantify the resilience of Great Britain’s railway passenger transport system to high-temperature-related track buckling under the Representative Concentration Pathway 8.5 (RCP8.5) climate change scenario.

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

• coastal and river flooding (Ward et al, 2020)
• extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)
• tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

• population (Schiavina et al, 2023)
• built-up area (Pesaresi et al, 2023)
• roads (OpenStreetMap, 2023)
• railways (OpenStreetMap, 2023)
• power plants (Global Energy Observatory et al, 2018)
• power transmission lines (Arderne et al, 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

• snkit helps clean network data
• nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

• Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142
• Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3
• Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3
• Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/
• Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
• Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616
• Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
• OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org
• Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
• Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088
• Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
• Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.

This data is part of a project that aimed to (1) assess and then (2) co-develop with local stakeholders and communities multipronged interventions for fostering resilience through schools. This data was collected as part of the assessment phase, 40 months after a devastating 2018 disaster in Central Sulawesi, Indonesia. Surveys included measures related to risk and resilience assessment from disaster-affected teenage girls (n = 47), teachers (n = 45) and caregivers (n = 38) from three school sites in the region.

The database contain a set of indicators extracted from publicly available data, providing a holistic view of vulnerabilities (i.e. features that can exacerbate the negative impact of crises and transitions, or obstacles that may hinder the achievement of long-term strategic goals) and capacities (i.e. enablers or abilities to cope with crises and structural changes and to manage the transitions.) along four different, but intertwined dimensions: social and economic, green, digital and geopolitical. They are developed for the EU and its MS, to help countries assess areas for intervention and further analysis. They are also extended to selected extra-EU countries, to assess how the EU as a whole is doing in the global scene. Moreover the database includes synthetic resilience indices illustrating the “aggregate” relative situation of vulnerabilities and resilience capacities along each of the four dimensions

The database contain a set of indicators extracted from publicly available data, providing a holistic view of vulnerabilities (i.e. features that can exacerbate the negative impact of crises and transitions, or obstacles that may hinder the achievement of long-term strategic goals) and capacities (i.e. enablers or abilities to cope with crises and structural changes and to manage the transitions.) along four different, but intertwined dimensions: social and economic, green, digital and geopolitical.

They are developed for the EU and its MS, to help countries assess areas for intervention and further analysis. Moreover the database includes synthetic resilience indices illustrating the “aggregate” relative situation of vulnerabilities and resilience capacities along each of the four dimensions.