PurposeA definition of data called data as assemblage is 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/approachData 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.FindingsReplacing 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/valueData 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.

Data as assemblage

Purpose Budgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management. Design/methodology/approach We make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work. Findings We identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics. Originality/value The different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed. 

Measuring the time spent on data curation

Maintaining Repositories, Databases, and Digital Collections in Memory Institutions: An Integrative Review

Database maintenance and migration are critical but under‐supported activities in libraries, archives, museums (LAMs), and other scholarly spaces. Existing guidelines for digital curation rarely account for the maintenance needed to keep digital curation infrastructures functioning over time. Though many case studies have been published describing individual instances of migration, there has been little generalizable research done in this area. Thus, it is challenging to understand overall trends or best practices in this space. We bridge this gap by conducting an integrative literature review of papers describing database migrations and maintenance in LAMs and other scholarly contexts. By qualitatively coding 75 articles from 58 publication venues, we identify common motivations for database migrations and maintenance actions. We find that databases are migrated to support changing user needs as well as to ward off technological obsolescence; we also find that common challenges include schema crosswalking and a need for data cleaning. Practitioners describe community collaboration as key in surmounting these challenges. Through this integrative review, we build a base for further best practices development and identify a need to better model database curation as part of the digital curation lifecycle.

INTRODUCTION As funders and journals increasingly create policies that require effective data management and data sharing, many institutions have developed research data management (RDM) programs to help researchers meet these mandates. While there is not a standard set of services for these RDM programs, some institutions, particularly those with repositories that accept data deposits, provide data curation services as a way to add value to research data and help make data more accessible and reusable. Stakeholder communities within the field, such as the Data Curation Network (DCN), are also developing guidelines, procedures, and best practices to support and expand data curation practices. DESCRIPTION OF PROJECT This paper examines the data curation activities defined by the DCN, and describes an activity undertaken by library staff at Duke University and the University of North Carolina at Chapel Hill to create a structured model of these tasks to more easily conceptualize and communicate data curation within these two institutional settings. The purpose of this paper is to describe how this model provided a basis for the implementation and expansion of data curation services at each institution and concludes with overall lessons learned. NEXT STEPS As we develop our services, libraries have an opportunity to make the often-invisible work of curation more transparent. This paper aims to provide a point of reference for other libraries as they consider how to scale up their data curation programs as well as contribute to discussions around prioritization of services, program assessment, and communication with stakeholders.

https://www.iastatedigitalpress.com/jlsc/article/12867/galley/12555/download/

Julie Rudder

Papers

Conceptualizing Data Curation Activities Within Two Academic Libraries