Caliper: Interoperability and management of statistical classifications

The Food and Agriculture Organization of the United Nations (FAO) and other agencies of the UN Family of Organizations regularly collect and publish a wealth of official data according to the Open and FAIR data and metadata principles (Findability, Accessibility, Interoperability, Reusability). The role played by statistical classifications in these data activities cannot be overestimated, as they provide the “meaning” of the data collected and disseminated. In this paper we describe Caliper, a methodological and infrastructural framework that enhances the interoperability of statistical classifications, as well as their creation and maintenance. Caliper was launched in 2020, as the FAO platform for the dissemination of statistical classifications. Since 2024, it is a reusable suite of tools supporting the entire life cycle of statistical classifications, including creation, maintenance and publication. Moreover, Caliper is now the infrastructural and methodological basis for a partnership of custodians sharing the vision of an international community of data producers and users devoted to producing better statistics. In this paper we present Caliper as a suite of tools and the Linked Open Data publication style adopted by Caliper. We also discuss the contribution of our endeavor to the achievement of syntactic and semantic interoperability of data. Our work benefits custodians of statistical classifications, data producers, policy makers, researchers, public administration and other agents that are involved in the use, integration and interpretation of data.

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Publication Details

Journal
Statistical Journal of the IAOS
Published
2026-05-08
DOI
https://doi.org/10.1177/18747655261445388
Primary Topic
Research Data Management Practices
Type
article
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article

Caliper: Interoperability and management of statistical classifications

Carola Fabi, Caterina Caracciolo, José Rosero Moncayo, Veronica Gianfaldoni et al.
Statistical Journal of the IAOS
Research Data Management Practices
article

Caliper: Interoperability and management of statistical classifications

Carola Fabi, Caterina Caracciolo, José Rosero Moncayo, Veronica Gianfaldoni, Craig Matadeen
article en

Abstract

The Food and Agriculture Organization of the United Nations (FAO) and other agencies of the UN Family of Organizations regularly collect and publish a wealth of official data according to the Open and FAIR data and metadata principles (Findability, Accessibility, Interoperability, Reusability). The role played by statistical classifications in these data activities cannot be overestimated, as they provide the “meaning” of the data collected and disseminated. In this paper we describe Caliper, a methodological and infrastructural framework that enhances the interoperability of statistical classifications, as well as their creation and maintenance. Caliper was launched in 2020, as the FAO platform for the dissemination of statistical classifications. Since 2024, it is a reusable suite of tools supporting the entire life cycle of statistical classifications, including creation, maintenance and publication. Moreover, Caliper is now the infrastructural and methodological basis for a partnership of custodians sharing the vision of an international community of data producers and users devoted to producing better statistics. In this paper we present Caliper as a suite of tools and the Linked Open Data publication style adopted by Caliper. We also discuss the contribution of our endeavor to the achievement of syntactic and semantic interoperability of data. Our work benefits custodians of statistical classifications, data producers, policy makers, researchers, public administration and other agents that are involved in the use, integration and interpretation of data.

Statistical Journal of the IAOS
Food and Agriculture Organization of the United Nations (IT)
Openalex Percentile: Top 2%
Research Data Management Practices
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Caliper: Interoperability and management of statistical classifications — Carola Fabi, Caterina Caracciolo, et al. · Statistical Journal of the IAOS (2026) | TGRS Research Map | TGRS