A model and a systematic method for classifying digital data standards

Purpose Digital data standards are essential components of information infrastructures, shaping how information is represented, documented and exchanged. Despite the proliferation of standards across disciplines, there is limited work that systematically classifies digital data standards from an information-science perspective. Existing approaches are either domain-specific or conceptually narrow, which limits our understanding of the types of standards that exist and how they relate to one another. This study addresses this gap by proposing a general model and method for classifying digital data standards. Design/methodology/approach We conducted a review of existing classification models for data and metadata standards to identify their strengths and limitations. Building on this analysis, we developed a general six-criteria classification model for digital data standards and a systematic method to apply it. Each criterion is formally defined and supported by documentation procedures. To examine the utility of the model and method, we applied them to a corpus of 152 domain-agnostic digital data standards. Findings The analysis shows that the proposed model distinguishes categories of digital standards that existing schemes either merge or do not include. The method provides a description of each standard's informational and technical purpose and scope. It also produces an aggregated overview of the corpus that highlights structural patterns across its standards. Originality/value This work provides the first domain-agnostic classification model for digital data standards grounded in concepts from information-science and technical documentation. It offers researchers and practitioners a systematic analytical tool for comparing standards and identifying gaps and underrepresented areas within standards ecosystems.