Recent policy shifts on the part of funding agencies and journal publishers are causing changes in the acknowledgment and citation behaviors of scholars. A growing emphasis on open science and reproducibility is changing how authors cite and acknowledge "research infrastructures"-entities that are used as inputs to or as underlying foundations for scholarly research, including data sets, software packages, computational models, observational platforms, and computing facilities. At the same time, stakeholder interest in quantitative understanding of impact is spurring increased collection and analysis of metrics related to use of research infrastructures. This article reviews work spanning several decades on tracing and assessing the outcomes and impacts from these kinds of research infrastructures. We discuss how research infrastructures are identified and referenced by scholars in the research literature and how those references are being collected and analyzed for the purposes of evaluating impact. Synthesizing common features of a wide range of studies, we identify notable challenges that impede the analysis of impact metrics for research infrastructures and outline key open research questions that can guide future research and applications related to such metrics.

In this commentary, the authors, an international group data curation researchers and educators, reflect on some of the challenges and opportunities for data curation in the wake of the COVID‐19 pandemic. We focus on some topics of particular interest to the information science community: data infrastructures for scholarly communication and research, the politicization of data curation and visualization for public‐facing “dashboards,” and human subjects research and policies. We conclude with some areas of opportunity and need, including broader and richer data curation education in the information schools, the establishment of better data management policy implementations by research funders, the award of formal academic credit for data curation activities and data sharing, and engagement in cooperative action around data ethics and security.

Self-adjusting, or adaptive, systems have gathered much recent interest. We present a model for self-adjusting systems which treats the control parameters of the system as slowly varying, rather than constant. The dynamics of these parameters is governed by a low-pass filtered feedback from the dynamical variables of the system. We apply this model to the logistic map and examine the behavior of the control parameter. We find that the parameter leaves the chaotic regime. We observe a high probability of finding the parameter at the boundary between periodicity and chaos. We therefore find that this system exhibits adaptation to the edge of chaos.

While problems related to the curation and preservation of scientific data are receiving considerable attention from the information science and digital repository communities, relatively little progress has been made on approaches for evaluating the value of data to inform investment in acquisition, curation, and preservation. Adapting Hjørland's concept of the "epistemological potential" of documents, we assert that analytic potential, or the value of data for analysis beyond its original use, should guide development of data collections for repositories aimed at supporting research. Three key aspects of the analytic potential of data are identified and discussed: preservation readiness, potential user communities, and fit for purpose. Based on evidence from research from the Data Conservancy initiative, we demonstrate how the analytic potential of data can be determined and applied to build large-scale data collections suited for grand challenge science.

“Context” is an elusive concept in Information Science –often invoked, and yet rarely explained. In this paper we take a domain analytic approach to examine five sub‐disciplines within Earth Systems Science to show how the contexts of data production and use impact the value of data. We argue simply that the value of research data increases with their use. Our analysis is informed by two economic perspectives: first, that data production needs to be situated within a broader information economy; and second, that the concept of anti‐fragility helps explain how data increase in value through exposure to diverse contexts of use. We discuss the importance of these perspectives for the development of information systems capable of facilitating interdisciplinary scientific work, as well as the design of sustainable cyberinfrastructures.