Fernando Rios

The Pathways of Research Software Preservation: An Educational and Planning Resource for Service Development

Research communities, funders, publishers, and academic libraries have put much effort towards ensuring that research data are preserved. However, the same level of attention has not been given to the associated software used to process and analyze it. As a guide to those tasked with preserving research outputs, a novel visual representation of preservation approaches relevant to research software, termed the Pathways of Research Software Preservation, is presented. The Pathways are discussed in the context of service development within the Data Management Services group at Johns Hopkins University.

The Software Preservation Network (SPN): A Community Effort to Ensure Long Term Access to Digital Cultural Heritage

The Software Preservation Network (SPN) is a National Forum grant project funded by IMLS which seeks to gather cultural heritage community input and develop a roadmap for actionable steps towards a national software preservation strategy. To achieve this, the project team conducted a needs assessment, partnered with legal experts, and convened a Forum, all focused on software preservation for cultural heritage. After the Forum produced a Community Roadmap, several Forum attendees gathered around the Roadmaps areas of focus, and coalesced into functioning working groups. The authors wish to acknowledge everyone who has contributed to the project, whether they were study participants, email correspondents, Forum attendees, blog post authors, or working group members. At its heart, the SPN project was a community building effort to address a community-wide challenge.

Manifestations of Metadata Structures in Research Datasets and Their Ontic Implications

ABSTRACT The proliferation of research datasets and their availability in various repositories require metadata that provides sufficient context and organizational clarity to enable their reuse. However, datasets come in myriad forms, structures, and relationships. As characteristics of datasets vary across disciplines, it is reasonable to suggest that the methods by which they are discoverable by metadata should be informed by the considerations unique to differing research areas. As such, for their internal unity to be accurately represented it is necessary that their ontological characteristics be documented reflectively along the same ontic spectrums. This article will explore the relationship between metadata and dataset structures in order to illuminate ontic alignments between them and how this impacts contextualization. We will examine common data structures for dataset metadata and implications each contains. A survey of the types of datasets from the Life Sciences, Social Sciences, Humanities, Geospatial Information Systems, and Research Software will be examined for their ability to be accurately documented by common standards, particularly in discipline-specific repositories. Similarly, an investigation into how metadata is being used in different contexts and how various systems help or hinder its fullest functioning may point towards challenges operating on a deep level.