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This article examines Edinburgh University Library’s experience of using OCLC’s Co‐operative Online Resource Catalogue (CORC). It discusses the project phase of CORC, its functionality, including automatic metadata harvesting and its ability to map between MARC and Dublin Core metadata formats. It also looks at how CORC fits into Edinburgh University Library’s policies regarding web resources, highlighting benefits and concerns associated with the system.

This paper aims to discuss the findings of recent surveys conducted around SUNCAT, the Serials Union Catalogue for the UK research community and the implications for the development of the service.

The results of two surveys are discussed: the first, a short print survey with a small group of inter‐library loan librarians, and the second, an online survey open to all SUNCAT users.

SUNCAT is used by both end users and a wide range of library and information professionals for a variety of purposes. The current level of satisfaction with the service is good but desirable developments include adding the serials' holdings data of more libraries, reducing the duplication on the catalogue and adding links to further information.

The findings are based on responses from mainly library and information professionals, with limited input from end users.

The paper focuses on SUNCAT, which is the most comprehensive source of serials' holdings information in the UK.

This issue of Vine tackles some of the big issues in web site management: design for accessibility and usability, content management and usage analysis, all discussed in information service contexts.

Peer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research's open peer review publishing platform.

PeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings.

PeerJudge can predict F1000Research judgements from negative evaluations in reviewers' comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision.

PeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments.

In the 1980s, as the United States encountered international economic and technological challenges, the very ability of the American educational system to produce a competitive labor force, able to learn and solve problems, was questioned. During this past decade, renewed concern about educational quality in the United States motivated over one hundred reports analyzing the shortcomings in our system of education and endorsing reform. All of the principal curriculum areas have been reviewed in this process; moreover, science education has been deemed particularly deficient. Major reports sponsored by the National Science Foundation (NSF) and the American Association for the Advancement of Science (AAAS) recommend both content revision of science courses and methodological changes in the way science is presented throughout the elementary and secondary grades.