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Matthew Mayernik

Publications and source records attributed to Matthew Mayernik.

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Sustaining Knowledge Infrastructures: Asking the Right Questions and Listening for Answers

Sustaining knowledge infrastructures remains a persistent issue that requires continued engagement from diverse stakeholders as new questions and values arise in relation to KI maintenance. We draw on existing academic literature, practical experience with KI projects, and our discussions at a 2024 workshop for researchers and practitioners exploring KI evaluation to pose five questions for KI project managers to consider when thinking about how to make their KIs evolve sustainably over time. These questions include reflecting on sustainability throughout the life cycle of KIs, communicating evolving visions and values, engaging communities, right sizing a KI, and developing an iterative process for decision-making. Reflecting on these themes, we suggest, can support KI stakeholders to evolve, not necessarily grow, to meet the needs and values of their communities. How these themes are discussed will necessarily vary by funding sources, disciplines, governance, communities, and other contextual factors. However, adopting a deliberate and strategic approach to KI sustainability and aligning the invisible infrastructural work of KI maintenance with the outward-facing institutional work is, we argue, relevant to all KIs.

cs.DL

From Artifacts to Aggregations: Modeling Scientific Life Cycles on the Semantic Web

In the process of scientific research, many information objects are generated, all of which may remain valuable indefinitely. However, artifacts such as instrument data and associated calibration information may have little value in isolation; their meaning is derived from their relationships to each other. Individual artifacts are best represented as components of a life cycle that is specific to a scientific research domain or project. Current cataloging practices do not describe objects at a sufficient level of granularity nor do they offer the globally persistent identifiers necessary to discover and manage scholarly products with World Wide Web standards. The Open Archives Initiative's Object Reuse and Exchange data model (OAI-ORE) meets these requirements. We demonstrate a conceptual implementation of OAI-ORE to represent the scientific life cycles of embedded networked sensor applications in seismology and environmental sciences. By establishing relationships between publications, data, and contextual research information, we illustrate how to obtain a richer and more realistic view of scientific practices. That view can facilitate new forms of scientific research and learning. Our analysis is framed by studies of scientific practices in a large, multi-disciplinary, multi-university science and engineering research center, the Center for Embedded Networked Sensing (CENS).

cs.DL