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Volunteers are increasingly being recruited into citizen science projects to collect observations for scientific studies. An additional goal of these projects is to engage and educate these volunteers. Thus, there are few barriers to participation resulting in volunteer observers with varying ability to complete the project's tasks. To improve the quality of a citizen science project's outcomes it would be useful to account for inter-observer variation, and to assess the rarely tested presumption that participating in a citizen science projects results in volunteers becoming better observers. Here we present a method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding. Our method for indexing observer variability uses species accumulation curves, lines that describe how the total number of species reported increase with increasing time spent in collecting observations. We find that differences in species accumulation curves among observers equates to higher rates of species accumulation, particularly for harder-to-identify species, and reveals increased species accumulation rates with continued participation. We suggest that these properties of our analysis provide a measure of observer skill, and that the potential to derive post-hoc data-derived measurements of participant ability should be more widely explored by analysts of data from citizen science projects. We see the potential for inferential results from analyses of citizen science data to be improved by accounting for observer skill.

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.

New web-based models of scholarly communication have made a significant impact in some scientific disciplines, but chemistry is not one of them. What has prevented the widespread adoption of these developments by chemists — and what are the prospects for adoption over time?

The authors, who jointly serve as the Open Archives Initiative (OAI) executive, reflect on the three-year history of the OAI. Three years of technical work recently culminated in the release of a stable production version 2 of the OAI Protocol for Metadata Harvesting (OAI-PMH). This technical product, the work that led up to it, and the process that made it possible have attracted some favor from the digital library and information community. The paper explores a number of factors in the history of the OAI that the authors believe have contributed to this positive response. The factors include focus on a defined problem statement, an operational model in which strong leadership is balanced with solicited participation, a healthy dose of community building and support, and sensible technical decisions.

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.

The Fedora architecture is an extensible framework for the storage, management, and dissemination of complex objects and the relationships among them. Fedora accommodates the aggregation of local and distributed content into digital objects and the association of services with objects. This allows an object to have several accessible representations, some of them dynamically produced. The architecture includes a generic Resource Description Framework (RDF)-based relationship model that represents relationships among objects and their components. Queries against these relationships are supported by an RDF triple store. The architecture is implemented as a web service, with all aspects of the complex object architecture and related management functions exposed through REST and SOAP interfaces. The implementation is available as open-source software, providing the foundation for a variety of end-user applications for digital libraries, archives, institutional repositories, and learning object systems.[PUBLICATION ABSTRACT]

eBird is a citizen‐science project that takes advantage of the human observational capacity to identify birds to species, and uses these observations to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a human/computer learning network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both and thereby continually improves the effectiveness of the net‐ work as a whole. In this article we explore how human/computer learning networks can leverage the contributions of human observers and process their contributed data with artificial intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

For thousands of years, people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information in electronic form – and finding useful needles in the resulting haystacks has since become one of the most important problems in information management.

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.

The idea of Digital Libraries emerged in the early 1990s from a vision of a “library of the future”, without walls and open 24 hours a day. These digital libraries would leverage the substantial investments of federal funding in the Internet and advanced computing for the benefit of the entire population. The world’s knowledge would be a key press away for everyone no matter where their location. This vision led to substantial levels of funding from federal agencies, foundations, and other organizations for research into fundamental technical problems related to networked information and deployment of the results of this research in numerous digital library applications. The result was a number of exciting and influential technical innovations. But, the attempt to transplant the library to the online environment met with some unexpected obstacles. The funding agencies and many of the members of the digital library research community mainly focused on the technical issues related to online information. In general, they assumed that the new technology would be applied in a largely traditional (library) context, and largely ignored the profound social, economic, cultural, and political impact of turning “books (and other information resources) into bytes”. The extent of this impact was demonstrated by the concurrent evolution of the World Wide Web, a networked information system not bound by legacy institutional conventions and practices or funding agency mandates and, therefore, able to organically evolve in response to the profoundly democratizing effect of putting information online. This has provided the context for the recent revolution in the web known as Web 2.0, a participatory information environment that contradicts most of the core assumptions of the traditional library information environment. The overwhelming adoption of the Web 2.0 model for both popular culture and serious information exchange and the increased evidence of the efficacy of this model for activities such as learning and scholarship call into question the viability of the library information model and the digital libraries that were meant to instantiate that model online. In this dissertation I examine the almost two decade history of digital library research and analyze the relevance of the library information model, or meme, in relationship to the transformative Web 2.0 meme. I use my research results in digital library infrastructure and technology over this period as both a lens for viewing this historical relationship and a mirror for revealing its various facets. This analysis is particularly relevant as I, and fellow members of the research community, begin to engage in large-scale cyberinfrastructure projects that need to move beyond the largely technical focus of earlier digital library initiatives and recognize the sociotechnical nature of the work that lies ahead.