|
 Data collection and analysis tools for food security and nutritioncc1865enDocument Outline - Cover page
- Table of contents
- List of tables
- Table 1: FAIR data principles
- Annex Table 1: Examples of existing FSN data-related initiatives (including databases, repositories, data systems and analysis tools), organized by dimension of food security and nutrition
- Annex Table 2: Summary of risks, associated digital technologies, key stakeholders and risk mitigation measures
- Annex Table 3: List of countries grouped by date of last agricultural census on record*
- Annex Table 4: Care principles for indigenous data governance
- List of figures
- Figure 1: Framework for a systemic view of fsn to guide data collection and analysis
- Figure 2: Data-informed decision-making cycle
- Figure 3: How to structure a data-informed, decision-making process matrix
- Figure 4: Example of how to use the conceptual framework (theoretical guidance) and data-informed decision-making cycle (methodological guidance) for FSN
- List of boxes
- Box 1: FAO statistical system
- Box 2: The Agricultural Market Information System (AMIS)
- Box 3: Improving the analysis of fish data
- Box 4: GIEWS and other information systems
- Box 5: FAO's Hand-in-hand initiative
- Box 6: The 50 × 2030 Initiative to close the agricultural data gap
- Box 7: FAO's approach to mapping territorial markets
- Box 8: Data collection in conflict settings
- Box 9: FSN and the SDG monitoring frameword
- Box 10: Countdown to 2030
- Box 11: Global open data for agriculture and nutrition (GODAN)
- Box 12: An example of affordable, global data management platform: REDCap
- Box 13: The integrated food security phase classification (IPC) initiative
- Box 14: Exemplars in global health
- Box 15: The food system dashboard
- Box 16: The POSHAN Network
- Box 17: The high cost of FSN-relevant surveys
- Box 18: The complexity of nutrition assessments
- Box 19: On food safety data
- Box 20: The women empowerment in agriculture index
- Box 21: Satellite technologies for improved drought assessment (SATIDA)
- Box 22: Opportunities and risks in the use of automated data analysis
- Box 23: A critical view of FAO statistical support to Member Nations
- Box 24: SATIDA COLLECT
- Box 25: Tackling constraints in food composition data availability and quality
- Box 26: Definitions of new and emerging digital technologies
- Box 27: Examples of efforts that support data consolidation
- Box 28: Examples of the application of blockchain technology to FSN data
- Box 29: Challenges with digitalizing services and access: the case of India’s Aadhaar identification number
- Box 30: Personal data protection and the right to privacy
- Box 31: The EAF-Nansen Programme
- Box 32: Nepal's nutrition-sensitive livestock introduction programme
- Box 33: The Global agriculture and food security programme (GAFSP)
- Foreword
- Acknowledgments
- Abbreviations and acronyms
- Key messages
- Introduction
- Chapter 1. Setting the stage
- Defining key terms
- Data
- Analysis tools
- Data governance
- Conceptual framework
- Figure 1: Framework for a systemic view of fsn to guide data collection and analysis
- Data-informed decision-making cycle
- Figure 2: Data-informed decision-making cycle
- Using the conceptual framework and the data-informed decision-making cycle to address issues relevant for FSN
- Figure 3: How to structure a data-informed, decision-making process matrix
- Example 1: How to increase population-level fruit and vegetable (FV) consumption based on local FV supply chains?
- Figure 4: Example of how to use the conceptual framework (theoretical guidance) and data-informed decision-making cycle (methodological guidance) for FSN
- Chapter 2: A review of existing FSN data collection and analysis initiatives
- Illustrative overview of existing FSN data
- FSN data and information systems relevant at the distal (macro) level
- Box 1: FAO statistical system
- Box 2: The Agricultural Market Information System (AMIS)
- FSN data and information systems at the proximal (meso) level
- Box 3: Improving the analysis of fish data
- Box 4: GIEWS and other information systems
- Box 5: FAO's Hand-in-hand initiative
- Box 6: The 50 × 2030 Initiative to close the agricultural data gap
- FSN data and information systems at the immediate (micro) level
- Box 7: FAO's approach to mapping territorial markets
- Box 8: Data collection in conflict settings
- Challenges and opportunities for FSN data-informed decision making
- Challenges and opportunities for FSN data-informed decision making
- Box 9: FSN and the SDG monitoring frameword
- Set priorities for data
- Box 10: Countdown to 2030
- Gather, curate and disseminate date
- Box 11: Global open data for agriculture and nutrition (GODAN)
- Data analysis
- Poorly conceived or inappropriate measures, indicators or scales
- Inadequate data-collection designs
- Box 12: An example of affordable, global data management platform: REDCap
- Lack of harmonization and poor data quality
- Timeliness
- Box 13: The integrated food security phase classification (IPC) initiative
- Data protection
- Heavy reliance on quantitative data
- Box 14: Exemplars in global health
- Translate data and use for decision-making
- Box 15: The food system dashboard
- Using data for decision-making requires buyinand involvement on the part of those with theresponsibility to make decisions, and clarity onthe decisions to be made
- Box 16: The POSHAN Network
- Chapter 3. Constraints, bottlenecks (and some solutions) for effective use of FSN data
- Insufficient resources for data collection and analysis
- Financial constraints
- Box 17: The high cost of FSN-relevant surveys
- Inadequate research infrastructure
- Box 18: The complexity of nutrition assessments
- Box 19: On food safety data
- Box 20: The women empowerment in agriculture index
- Box 21: Satellite technologies for improved drought assessment (SATIDA)
- Human-resource constraints
- Constraints related to data collection
- Constraints related to the lack of data processing, analytical and dissemination capabilities
- Box 22: Opportunities and risks in the use of automated data analysis
- Inadequate institutional arrangement and data governance
- Constraints that limit stakeholder engagement
- Constraints related to the lack of coordination among agencies
- Box 23: A critical view of FAO statistical support to Member Nations
- Constraints that create a lack of transparency and of appropriate regulatory frameworks
- Box 24: SATIDA COLLECT
- Box 25: Tackling constraints in food composition data availability and quality
- Chapter 4. New and emerging digital technologies for FSN data
- Landscape and relevance of new and emerging digital technologies to FSN
- Landscape and relevance of new and emerging digital technologies to FSN
- Box 26: Definitions of new and emerging digital technologies
- Define/refine evidence priorities and questions
- Review, consolidate, collect and curate data
- Box 27: Examples of efforts that support data consolidation
- Box 28: Examples of the application of blockchain technology to FSN data
- Translate data into results, insights and conclusions
- Disseminate, share, review, discuss results, refine insights and conclusions*
- Use results, insights and conclusions to make decisions
- Risks associated with digital technologies for FSN and their mitigation
- Ethics, data protection, trust, justice and identity
- Box 29: Challenges with digitalizing services and access: the case of India’s Aadhaar identification number
- Quality of data
- Interoperability of data
- Capacity, equity, scalability and sustainability
- Chapter 5. Institutions and governance for FSN data collection, analysis, and use
- Issues of relevance for data governance
- The debate on the nature of data and the role of data markets
- The questions of data ownership and the social value of data
- Box 30: Personal data protection and the right to privacy
- Priority objectives for FSN data-governance initiatives
- Achieving adherence to global standards and harmonization of data
- Ensuring adequate mechanisms are in place to protect individual and collective rights
- Relevant recent initiatives on data governance for FSN
- World Bank open data
- Open science initiatives and the FAIR and CARE data principles
- Table 1: FAIR data principles
- Global strategy to improve agricultural and rural statistics
- Initiatives in stakeholder collaboration
- Box 31: The EAF-Nansen Programme
- Box 32: Nepal's nutrition-sensitive livestock introduction programme
- Box 33: The Global agriculture and food security programme (GAFSP)
- Greater attention to data quality issues
- Challenges to data governance from data-driven technologies
- Solutions to enhance FSN data governance
- Streamlining transnational and national data governance for FSN
- Inclusive approach to data governance
- Increasing transparency and governance of official statistics for FSN
- Partnership agreements to manage and share digital data
- Chapter 6. Final reflections and recommendations
- Create greater demand for data for decision-making among governments, policymakers and donors
- Optimize and, if needed, repurpose current data-related investments, while increasing collaboration between international organizations, governments, civil society, academia and the private sector, to harmonize and maximize the sharing of existing FSN data
- Invest in human capital and in the needed infrastructures to ensure the sustainability of data processing and analytic capacity
- Improve data governance at all levels, promoting inclusiveness to recognize and enhance agency among data users and data generators
- References
- Glossary
- Annexes
- Annex Table 1: Examples of existing FSN data-related initiatives (including databases, repositories, data systems and analysis tools), organized by dimension of food security and nutrition
- Annex Table 2: Summary of risks, associated digital technologies, key stakeholders and risk mitigation measures
- Annex Table 3: List of countries grouped by date of last agricultural census on record*
- Annex Table 4: Care principles for indigenous data governance
- Blurb
Dostları ilə paylaş: |
|
|
