Artikel: Workflow for building interoperable food and nutrition security (FNS) data platforms
Trends in food science & technology. 2022 Mar. 21,
2022
Abstract: In response to growing needs for the integration of heterogeneous data on food and nutrition security (FNS), and the current fragmentation of interoperability resources, the ‘FNS-Cloud project’ aims to develop a cross-domain, interoperable ‘food-cloud’ ... ...
Abstract | In response to growing needs for the integration of heterogeneous data on food and nutrition security (FNS), and the current fragmentation of interoperability resources, the ‘FNS-Cloud project’ aims to develop a cross-domain, interoperable ‘food-cloud’ that integrates diverse FNS data. Currently, there is insufficient guidance on how to develop such an FNS data platform and integrate a variety of FNS data types that differ in both their syntax and semantics. In the present paper, we propose a generalizable workflow to guide data managers in building interoperable, cross-domain FNS data platforms, which centres around the definition of interoperability criteria (IC) that capture standardized data structures, terminologies and reporting formats for key variables across FNS data types. Information technology tools for automating different workflow steps are discussed. Finally, we include an illustrative case study, where we manually harmonize and link branded food datasets based on pre-defined IC to answer an example research question. Our work highlighted the unique harmonization requirements within the FNS field. We provide two examples of how generic and domain-specific IC addressing these requirements can be defined. Incoming FNS data must comply with defined IC in order to enable their (semi-)automated integration into a data platform. Our case study reinforced the importance of semantic annotation of FNS data, and the need for clear mapping rules to be included into a platform's internal semantic data model. The proposed workflow can be applied to any setting in which data managers strive towards harmonized and linked FNS data, and, thus, promotes an open-data and open-science environment. |
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Schlagwörter | automation ; case studies ; data collection ; food science ; information technology ; models ; nutrition |
Sprache | Englisch |
Erscheinungsverlauf | 2022-0321 |
Erscheinungsort | Elsevier Ltd |
Dokumenttyp | Artikel |
Anmerkung | Pre-press version |
ZDB-ID | 1049246-x |
ISSN | 1879-3053 ; 0924-2244 |
ISSN (online) | 1879-3053 |
ISSN | 0924-2244 |
DOI | 10.1016/j.tifs.2022.03.022 |
Datenquelle | NAL Katalog (AGRICOLA) |
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