The NDSA National Agenda for Digital Stewardship integrates the perspective of dozens of experts and hundreds of institutions, convened through the Library of Congress, to provide funders and executive decision‐makers insight into emerging technological trends, gaps in digital stewardship capacity, and key areas for funding, research and development to ensure that today's valuable digital content remains accessible and comprehensible in the future, supporting a thriving economy, a robust democracy, and a rich cultural heritage.
This new edition of the Agenda builds on earlier work, updating the 2014 report, and highlighting new areas of focus, specifically the selection and preservation of content at-scale. It also more clearly articulates the need for an evidence base for efficient and reliable digital preservation practice. Recent gains and observations on the technical infrastructure required for large-scale digital stewardship and the supporting policies and organizational structures required are also outlined. The report synthesizes the latest issues for funders, researcher and organizational leaders and provides actionable recommendations for practitioners.
Our work leads us to conclude that no one can have complete information and no single group can, on its own, create fair electoral maps. Legislative gerrymandering is not the answer, but as Americans turn toward independent commissions, why not deploy all technologies available to facilitate the widest possible participation in districting choices critical to American democracy?
Data citation is rapidly emerging as a key practice in support of data access, sharing, reuse, and of sound and reproducible scholarship. In this article we review the evolution of data citation standards and practices – to which Sue Dodd was an early contributor – and the core principles of data citation that have emerged through a collaborative synthesis. We then discuss an example of the current state of the practice, and identify the remaining implementation challenges.
Sound, reproducible scholarship rests upon a foundation of robust, accessible data. For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record. In other words, data should be considered legitimate, citable products of research. Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse.
In support of this assertion, and to encourage good practice, we offer a set of guiding principles for data within scholarly literature, another dataset, or any other research object.
Recent technological advances have enabled greater public participation and transparency in the United States redistricting process. We review these advances, with particular attention to activities involving open-source redistricting software.