A Unified Framework for Measuring Stewardship Practices Applied to Digital Environmental Datasets

Abstract

This paper presents a stewardship maturity assessment model in the form of a matrix for digital environmental datasets. Nine key components are identified based on requirements imposed on digital environmental data and information that are cared for and disseminated by U.S. Federal agencies by U.S. law, i.e., Information Quality Act of 2001, agencies' guidance, expert bodies' recommendations, and users. These components include: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. A five-level progressive maturity scale is then defined for each component associated with measurable practices applied to individual datasets, representing Ad Hoc, Minimal, Intermediate, Advanced, and Optimal stages. The rationale for each key component and its maturity levels is described. This maturity model, leveraging community best practices and standards, provides a unified framework for assessing scientific data stewardship. It can be used to create a stewardship maturity scoreboard of dataset(s) and a roadmap for scientific data stewardship improvement or to provide data quality and usability information to users, stakeholders, and decision makers.

The Work of Art in the Age of Digital Fragility

“…The National Aeronautics and Space Administration (NASA) needs to keep running obsolete digital infrastructures and software to keep space missions and satellites launched decades ago (Cohn 2016). Scientists have to hire digital curators to ensure the authenticity of digital objects in the massive digital databanks containing DNA records, astronomical data, or climate-modeling records, and as a result, the kind of truth-claims that they can make about them (Peng et al 2015). Likewise, the juridical system and law enforcement have to figure out new ways of producing evidence in the digital realm (Gates 2016; Blanchette 2012); libraries have to create new infrastructures to cope with contemporary knowledge practices that are increasingly produced within this digital medium; while states (and corporations) have to figure out ways of managing and preserving the ever-growing volumes of digital datasets that mediate and define contemporary forms of citizenship (Isin and Ruppert 2015).…”

Practical Application of a Data Stewardship Maturity Matrix for the NOAA <i>OneStop</i> Project

“…NOAA's data capability is constantly improving with higher temporal and spatial resolutions, and improved observation systems. Data from new, unique, unconventional observing platforms such as unmanned water and air vehicles and instrumented animals are DSMM defines a graduated maturity scale for each of nine key components of scientific data stewardship to enable a consistent assessment of quantifiable stewardship practices applied to a given data product (Peng et al 2015; see Figure 1 for the conceptual model of the DSMM).…”

Curation as “Interoperability With the Future”: Preserving Scholarly Research Software in Academic Libraries

“…Thus far, existing work to articulate best practices, approaches, and innovations for working with digital research content have largely been driven by the scientific research community outside of the library space (Belhajjame et al, 2014;Stodden et al, 2016). In this article, we aim to address this question by uniting and synthesizing ongoing work from the digital preservation sphere on complex digital objects (Dietrich & Adelstein, 2015;Thibodeau, 2002) with parallel research in research data management and scientific communities (National Academy of Sciences, National Academy of Engineering, and Institute of Medicine Committee on Ensuring the Utility and Integrity of Research Data in a Digital Age, 2009;Peng, Privette, Kearns, Ritchey, & Ansari, 2015). Frameworks are often modeled on simple, static, and self-contained conceptions of data rather than reflecting complex, dynamic, and networked systems such as software.…”

“…How can research libraries support the use and reuse of research software, to ensure that modern research needs are met and persist over time? In this article, we aim to address this question by uniting and synthesizing ongoing work from the digital preservation sphere on complex digital objects (Dietrich & Adelstein, 2015;Thibodeau, 2002) with parallel research in research data management and scientific communities (National Academy of Sciences, National Academy of Engineering, and Institute of Medicine Committee on Ensuring the Utility and Integrity of Research Data in a Digital Age, 2009;Peng, Privette, Kearns, Ritchey, & Ansari, 2015). We introduce and describe a model and decision framework focused on six curation dimensions in support of preserving and providing access to software in research libraries.…”