Carl Lagoze’s research while affiliated with University of Michigan and other places

To examine some of the ways platforms facilitate knowledge brokerage within the field of education, this chapter establishes the concept of platformed knowledge brokerage and critically compares four case examples: EdArXiv, Marginal Syllabus, Teachers Pay Teachers, and What Works Clearinghouse. Analytic questions focus on who is involved in the brokerage process, the nature of the knowledge objects exchanged, the ways platforms organize knowledge, and the functions available to platform users in the brokerage process. The chapter discusses the implications these platforms have for the movement and transformation of knowledge through networks in education, highlighting questions concerning whose voices are amplified by online platforms, how, and to whom. It concludes by setting the stage for future research in this area.

We propose a metadata package that is intended to provide academic journals with a lightweight means of registering, at the time of publication, the existence and disposition of supplementary materials. Information about the supplementary materials is, in most cases, critical for the reproducibility and replicability of scholarly results. In many instances, these materials are curated by a third party, which may or may not follow developing standards for the identification and description of those materials. As such, the vocabulary described here complements existing initiatives that specify vocabularies to describe the supplementary materials or the repositories and archives in which they have been deposited. Where possible, it reuses elements of relevant other vocabularies, facilitating coexistence with them. Furthermore, it provides an “at publication” record of reproducibility characteristics of a particular article that has been selected for publication. The proposed metadata package documents the key characteristics that journals care about in the case of supplementary materials that are held by third parties: existence, accessibility, and permanence. It does so in a robust, time-invariant fashion at the time of publication, when the editorial decisions are made. It also allows for better documentation of less accessible (non-public data), by treating it symmetrically from the point of view of the journal, therefore increasing the transparency of what up until now has been very opaque.

Structured Data Transformation Language (SDTL) provides structured, machine actionable representations of data transformation commands found in statistical analysis software. The Continuous Capture of Metadata for Statistical Data Project (C2Metadata) created SDTL as part of an automated system that captures provenance metadata from data transformation scripts and adds variable derivations to standard metadata files. SDTL also has potential for auditing scripts and for translating scripts between languages. SDTL is expressed in a set of JSON schemas, which are machine actionable and easily serialized to other formats. Statistical software languages have a number of special features that have been carried into SDTL. We explain how SDTL handles differences among statistical languages and complex operations, such as merging files and reshaping data tables from “wide” to “long”.

The C2Metadata (“Continuous Capture of Metadata for Statistical Data”) Project automates one of the most burdensome aspects of documenting the provenance of research data: describing data transformations performed by statistical software. Researchers in many fields use statistical software (SPSS, Stata, SAS, R, Python) for data transformation and data management as well as analysis. The C2Metadata Project creates a metadata workflow paralleling the data management process by deriving provenance information from scripts used to manage and transform data. C2Metadata differs from most previous data provenance initiatives by documenting transformations at the variable level rather than describing a sequence of opaque programs. Scripts used with statistical software are translated into an independent Structured Data Transformation Language (SDTL), which serves as an intermediate language for describing data transformations. SDTL can be used to add variable-level provenance to data catalogs and codebooks and to create “variable lineages” for auditing software operations. Better data documentation makes research more transparent and expands the discovery and re-use of research data.

Research syntheses provide one means of managing the proliferation of research knowledge by integrating learnings across primary research studies. What it means to appropriately synthesize research, however, remains a matter of debate: Syntheses can assume a variety of forms, each with important implications for the shape knowledge takes and the interests it serves. To help shed light on these differences and their stakes, this chapter provides a critical comparative review of six research synthesis infrastructures, entities that support research syntheses through investments they make in synthesis production and/or publication—enabling (and constraining) the ways knowledge takes shape. Identifying our critical cases through purposive selection, we examined research synthesis infrastructure variations with respect to four different kinds of investments they make: in the genres of synthesis they support, in their promotion of synthesis quality, in sponsoring stakeholder engagement, and in creating the conditions for collective work. We draw on this comparison to suggest some of the potential changes and challenges in store for education researchers in future years.

Many obstacles must be overcome to generate new biomedical knowledge from real-world data and then directly apply the newly generated knowledge for decision support. Attempts to bridge the processes of data analysis and technical implementation of analytic results reveal a number of gaps. As one example, the knowledge format used to communicate results from data analysis often differs from the knowledge format required by systems to compute advice. We asked whether a shared format could be used by both processes. To address this question, we developed a data-to-advice pipeline called ScriptNumerate. ScriptNumerate analyzes historical e-prescription data and communicates its results in a compound digital object format. ScriptNumerate then uses these same compound digital objects to compute its advice about whether new e-prescriptions have common, rare, or unprecedented instructions. ScriptNumerate demonstrates that data-to-advice pipelines are feasible. In the future, data-to-advice pipelines similar to ScriptNumerate may help support Learning Health Systems.

Web-based platforms play an increasingly important role in managing and sharing research data of all types and sizes. This article presents a case study of the data storage, sharing, and management platform Figshare. We argue that such platforms are displacing and reconfiguring the infrastructure of norms, technologies, and institutions that underlies traditional scholarly communication. Using a theoretical framework that combines infrastructure studies with platform studies, we show that Figshare leverages the platform logic of core and complementary components to re-integrate a presently splintered scholarly infrastructure. By means of this logic, platforms may provide the path to bring data inside a scholarly communication system still optimized mainly for text publications. Yet the platform strategy also risks turning over critical scientific functions to private firms whose longevity, openness, and corporate goals remain uncertain. It may amplify the existing trend of splintering infrastructures, with attendant effects on equity of service.

Introduction Health systems are challenged by care underutilization, overutilization, disparities, and related harms. One problem is a multiyear latency between discovery of new best practice knowledge and its widespread adoption. Decreasing this latency requires new capabilities to better manage and more rapidly share biomedical knowledge in computable forms. Knowledge objects package machine‐executable knowledge resources in a way that easily enables knowledge as a service. To help improve knowledge management and accelerate knowledge sharing, the Knowledge Object Reference Ontology (KORO) defines what knowledge objects are in a formal way. Methods Development of KORO began with identification of terms for classes of entities and for properties. Next, we established a taxonomical hierarchy of classes for knowledge objects and their parts. Development continued by relating these parts via formally defined properties. We evaluated the logical consistency of KORO and used it to answer several competency questions about parthood. We also applied it to guide knowledge object implementation. Results As a realist ontology, KORO defines what knowledge objects are and provides details about the parts they have and the roles they play. KORO provides sufficient logic to answer several basic but important questions about knowledge objects competently. KORO directly supports creators of knowledge objects by providing a formal model for these objects. Conclusion KORO provides a formal, logically consistent ontology about knowledge objects and their parts. It exists to help make computable biomedical knowledge findable, accessible, interoperable, and reusable. KORO is currently being used to further develop and improve computable knowledge infrastructure for learning health systems.

Two theoretical approaches have recently emerged to characterize new digital objects of study in the media landscape: infrastructure studies and platform studies. Despite their separate origins and different features, we demonstrate in this article how the cross-articulation of these two perspectives improves our understanding of current digital media. We use case studies of the Open Web, Facebook, and Google to demonstrate that infrastructure studies provides a valuable approach to the evolution of shared, widely accessible systems and services of the type often provided or regulated by governments in the public interest. On the other hand, platform studies captures how communication and expression are both enabled and constrained by new digital systems and new media. In these environments, platform-based services acquire characteristics of infrastructure, while both new and existing infrastructures are built or reorganized on the logic of platforms. We conclude by underlining the potential of this combined framework for future case studies.

The Knowledge Grid (KGrid) is a research and development program toward infrastructure capable of greatly decreasing latency between the publication of new biomedical knowledge and its widespread uptake into practice. KGrid comprises digital knowledge objects, an online Library to store them, and an Activator that uses them to provide Knowledge-as-a-Service (KaaS). KGrid's Activator enables computable biomedical knowledge, held in knowledge objects, to be rapidly deployed at Internet-scale in cloud computing environments for improved health. Here we present the Activator, its system architecture and primary functions.