Sharing data and code for reuse has become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible. Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets. This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code. The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.

Purpose: A definition of data calleddata as assemblageis presented. The definition accommodates different forms and meanings of data; emphasizes data subjects and data workers; and reflects the sociotechnical aspects of data throughout its lifecycle of creation and use. A scalable assemblage model describing the anatomy and behavior of data, datasets and data infrastructures is also introduced.Design/methodology/approach: Data as assemblage is compared to common meanings of data. The assemblage model's elements and relationships also are defined, mapped to the anatomy of a US Census dataset and used to describe the structure of research data repositories.Findings: Replacing common data definitions with data as assemblage enriches information science and research data management (RDM) frameworks. Also, the assemblage model is shown to describe datasets and data infrastructures despite their differences in scale, composition and outward appearance.Originality/value: Data as assemblage contributes a definition of data as mutable, portable, sociotechnical arrangements of material and symbolic components that serve as evidence. The definition is useful in information science and research data management contexts. The assemblage model contributes a scale-independent way to describe the structure and behavior of data, datasets and data infrastructures and supports analyses and comparisons involving them.

Objective: Investigate how different groups of depositors vary in their use of optional data curation features that provide support for FAIR research data in the Harvard Dataverse repository. Methods: A numerical score based upon the presence or absence of characteristics associated with the use of optional features was assigned to each of the 29,295 datasets deposited in Harvard Dataverse between 2007 and 2019. Statistical analyses were performed to investigate patterns of optional feature use amongst different groups of depositors and their relationship to other dataset characteristics. Results: Members of groups make greater use of Harvard Dataverse's optional features than individual researchers. Datasets that undergo a data curation review before submission to Harvard Dataverse, are associated with a publication, or contain restricted files also make greater use of optional features. Conclusions: Individual researchers might benefit from increased outreach and improved documentation about the benefits and use of optional features to improve their datasets' level of curation beyond the FAIR-informed support that the Harvard Dataverse repository provides by default. Platform designers, developers, and managers may also use the numerical scoring approach to explore how different user groups use optional application features.