397
• Energy production and generation processes
• Production data in general, at the meso-scale
• PCII and secure Military and Homeland Security intelligence on critical
facilities
• Land availability and suitability, for example, for agriculture
14.5.2 Gaps
• Establishment data of all kinds
• Process data of all kinds, especially for factory processes and agribusiness
• Fine-resolution water use
• Water consumption, wastewater, and return flows
• Water availability
• Spatially explicit supply chain
• Product or process level performance benchmarks
• Storage data is patchy; water storage, in particular, is missing
• Electrical power grid function and utilization
• Transaction data in general
• Monthly data in general
• Hourly data in general
• Consumption data in general at all scales
• Legal data in general (e.g., water rights)
• Spatially or temporally explicit meso-scale input–output and life cycle
• Stocks and flows, with exceptions noted above
• Valid and verifiable meso-scale and global scale data using bottom-up methods
• Publicly available corporate supply chains
• Things we cannot see from space, like power plants, food processing facilities
• Differential privacy methods to efficiently work with PII and PCII data
• Standardized “reference” datasets describing the integrated system as a whole
• “Last Mile,” supply chain data, linking to retail and distribution facilities for
emergency response
• “Real-time” data without long lead times
• Linkages from the meso-scale to the establishment scale
Key Points
• Data comes from both observations and models and is useful to generate
metrics.
• Data quality has several aspects including but not limited to validity, completeness, precision, resolution, and provenance; this quality is relative to the
end use.
• Data management best practices must be followed throughout the Data Life
Cycle (e.g., FAIR (meta)data principles); this implies a central role for data
repositories, data sharing consortia, standards bodies, and search services.
14 Data
• Energy production and generation processes
• Production data in general, at the meso-scale
• PCII and secure Military and Homeland Security intelligence on critical
facilities
• Land availability and suitability, for example, for agriculture
14.5.2 Gaps
• Establishment data of all kinds
• Process data of all kinds, especially for factory processes and agribusiness
• Fine-resolution water use
• Water consumption, wastewater, and return flows
• Water availability
• Spatially explicit supply chain
• Product or process level performance benchmarks
• Storage data is patchy; water storage, in particular, is missing
• Electrical power grid function and utilization
• Transaction data in general
• Monthly data in general
• Hourly data in general
• Consumption data in general at all scales
• Legal data in general (e.g., water rights)
• Spatially or temporally explicit meso-scale input–output and life cycle
• Stocks and flows, with exceptions noted above
• Valid and verifiable meso-scale and global scale data using bottom-up methods
• Publicly available corporate supply chains
• Things we cannot see from space, like power plants, food processing facilities
• Differential privacy methods to efficiently work with PII and PCII data
• Standardized “reference” datasets describing the integrated system as a whole
• “Last Mile,” supply chain data, linking to retail and distribution facilities for
emergency response
• “Real-time” data without long lead times
• Linkages from the meso-scale to the establishment scale
Key Points
• Data comes from both observations and models and is useful to generate
metrics.
• Data quality has several aspects including but not limited to validity, completeness, precision, resolution, and provenance; this quality is relative to the
end use.
• Data management best practices must be followed throughout the Data Life
Cycle (e.g., FAIR (meta)data principles); this implies a central role for data
repositories, data sharing consortia, standards bodies, and search services.
14 Data