The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field‐specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through the examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping subcommunities within a research specialty, and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set to adequately represent a research specialty and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.

In this paper we describe eBird, a citizen science project that takes advantage of human observational capacity and machine learning methods to explore the synergies between human computation and mechanical computation. We call this model 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 network as a whole. Human/Computer Learning Networks leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

SUMMARY Digital scholarship offers the opportunity to move beyond the limitations of traditional scholarly publication. Rather than limiting scholarly communication to text‐based static documents, the Web makes it possible for scholars to expose and share the full evidence of their research including data, images, video, and other genre of materials. These aggregations of evidence, or compound documents, can then be integrated into a linked data cloud, the basis of Scholarship 2.0—an open environment in which scholars collaborate and build new knowledge on the existing scholarship. We present Open Archives Initiative–Object Reuse and Exchange (OAI–ORE), a set of standards to identify and describe aggregations of WebResources, thereby making the Scholarship 2.0 vision possible. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper we study how to discover the evolution of topics over time in a time-stamped document collection. Our approach is uniquely designed to capture the rich topology of topic evolution inherent in the corpus. Instead of characterizing the evolving topics at fixed time points, we conceptually define a topic as a quantized unit of evolutionary change in content and discover topics with the time of their appearance in the corpus. Discovered topics are then connected to form a topic evolution graph using a measure derived from the underlying document network. Our approach allows inhomogeneous distribution of topics over time and does not impose any topological restriction in topic evolution graphs. We evaluate our algorithm on the ACM corpus.

The oreChem project, funded by Microsoft Research, is investigating the design and deployment of a semantic-based eScience infrastructure for chemistry. The results of the project include the creation of an ontology that provides the basis for describing the entities and relationships for a scientific experiment, and the implementation of a workflow to combine extracted and in situ information from multiple sources, which provides a framework for computational enhancement of the data and querying thereof.

In this paper we address the problem of detecting topics in large-scale linked document collections. Recently, topic detection has become a very active area of research due to its utility for information navigation, trend analysis, and high-level description of data. We present a unique approach that uses the correlation between the distribution of a term that represents a topic and the link distribution in the citation graph where the nodes are limited to the documents containing the term. This tight coupling between term and graph analysis is distinguished from other approaches such as those that focus on language models. We develop a topic score measure for each term, using the likelihood ratio of binary hypotheses based on a probabilistic description of graph connectivity. Our approach is based on the intuition that if a term is relevant to a topic, the documents containing the term have denser connectivity than a random selection of documents. We extend our algorithm to detect a topic represented by a set of terms, using the intuition that if the co-occurrence of terms represents a new topic, the citation pattern should exhibit the synergistic effect. We test our algorithm on two electronic research literature collections,arXiv and Citeseer.Our evaluation shows that the approach is effective and reveals some novel aspects of topic detection.

There has been a lot of recent interest in mining patterns from graphs. Often, the exact structure of the patterns of interest is not known. This happens, for example, when molecular structures are mined to discover fragments useful as features in chemical compound classification task, or when web sites are mined to discover sets of web pages representing logical documents. Such patterns are often generated from a few small subgraphs (cores), according to certain generalization rules (GRs). We call such patterns "generalized patterns "(GPs). While being structurally different, GPs often perform the same function in the network. Previously proposed approaches to mining GPs either assumed that the cores and the GRs are given, or that all interesting GPs are frequent. These are strong assumptions, which often do not hold in practical applications. In this paper, we propose an approach to mining GPs that is free from the above assumptions. Given a small number of GPs selected by the user, our algorithm discovers all GPs similar to the user examples. First, a machine learning-style approach is used to find the cores. Second, generalizations of the cores in the graph are computed to identify GPs. Evaluation on synthetic data, generated using real cores and GRs from biological and web domains, demonstrates effectiveness of our approach.

The term "application profile" has recently become highly topical. Heery and Patel [1] define application profiles as metadata schemas which consist of metadata elements drawn from one or more namespaces, combined together by implementers and optimised for a particular local application. They state that the principal characteristics of an application profile are that: it may draw on one or more existing namespaces; does not introduce new metadata elements; it can specify permitted schemes and values; and it can refine standard metadata elements. Significant new initiatives such as TV-Anytime [2], MPEG-21 [3] and the Open Archives Initiative (OAI) [4] are demanding application profiles which combine elements from a number of different existing standardized metadata schemas whilst maintaining interoperability and satisfying their own specific requirements through refinements, extensions and additions.

The World Wide Web provides unprecedented access to globally distributed content. The extent and uniform accessibility of the Web has proven beneficial for research, education, commerce, entertainment, and numerous other uses. Ironically, the fact that the Web is an information space without boundaries has also proven its biggest flaw. Key aspects of libraries, such as selectivity of content, customization of tools and services relative to collection and patron characteristics, and management of content and services are noticeably absent. Over the past four years, we researched the technology and deployment of a digital library architecture that makes it possible to create managed information spaces, digital libraries, within the World Wide Web. Our work has taken place in the context of NCSTRL,1a digital library of computer science research reports. The technical foundation of NCSTRL is Dienst, a protocol and architecture for distributed digital libraries that we developed as part of the DARPA-funded Computer Science Technical Reports Project. At the time of the writing of this paper, the NCSTRL collection consisted of papers from more than 100 research institutions residing in servers distributed across the United States, Europe, and Asia. In addition, the Dienst protocol and implementation has been successfully adopted by a number of other distributed collections. In this paper, we review our experiences with NCSTRL and Dienst, describe some of the lessons we have learned from the deployment experience, and define some directions for the future.

The goal of drop-in publishing is to simplify digital publishing over the Internet. We would like digital publishing of non-commercial matter (e.g. technical reports, course notes, brochures) be as easy as sending e-mail is now, but with the virtues of archival storage and easy searching that we associate with electronic libraries. We propose a protocol, Dienst, to allow communication between clients and document servers by encoding object-oriented messages within URL's. A preliminary version of this protocol now runs at eight sites, and we deseribe some of its features. Next we present tools for automating the maintenance of document collections. Finally, we discuss the problems we've had with the Web as it stands, hoping to motivate changes that would improve performance of digital library systems such as ours.