The first version of the gap analysis on data standards focused primarily on the overall methodology and criteria, and gave an initial tentative overview of the partial gap analysis results (based on the content of the map as of 20/11/2016) especially on technical issues of openness and usability.
This second version, in line with the focus the project has in 2017 on weather data and related use cases, examines especially the situation in the area of data standards for weather data (and closely related geospatial data) and in particular weather data for use in farm management services. And it analyzes gaps both from the point of view of the usability and openness of the standards (as we did in the first version) and from the point of view of their adoption, authoritativeness and endorsement.
The report starts with a broad review of the relevant types of data, then reviews the standardisation landscape for weather data as a whole before narrowing down to the use of weather data in farm management information systems (FMIS) specifically.
The main conclusions drawn from the report, for weather data standards as well as (and especially) for the use of weather data in FMIS are:
Need for application-friendly and possibly harmonized data formats
Need for common controlled values, especially variable naming conventions (this might also mean a need for better use of semantic technologies, but feedback from experts hasn’t highlighted this and indeed there doesn’t seem to be much perceived demand for and work on Linked Data in this area)
Need for web services / APIs that allow for querying (time, space but also selected variables)
Need for standards to implement ways to clarify and enforce data rights and data ownership at each stage of the data value chain (especially important for FMIS)
One strongly felt gap not strictly related to data standards but worth noting is the lack of quality (reliable, granular, timely) data from free public services, especially for certain geographic regions
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