Dataset Information

Improving e-book access via a library-developed full-text search tool.

ABSTRACT:

Purpose

This paper reports on the development of a tool for searching the contents of licensed full-text electronic book (e-book) collections.

Setting

The Health Sciences Library System (HSLS) provides services to the University of Pittsburgh's medical programs and large academic health system.

Brief description

The HSLS has developed an innovative tool for federated searching of its e-book collections. Built using the XML-based Vivísimo development environment, the tool enables a user to perform a full-text search of over 2,500 titles from the library's seven most highly used e-book collections. From a single "Google-style" query, results are returned as an integrated set of links pointing directly to relevant sections of the full text. Results are also grouped into categories that enable more precise retrieval without reformulation of the search.

Results/evaluation

A heuristic evaluation demonstrated the usability of the tool and a web server log analysis indicated an acceptable level of usage. Based on its success, there are plans to increase the number of online book collections searched.

Conclusion

This library's first foray into federated searching has produced an effective tool for searching across large collections of full-text e-books and has provided a good foundation for the development of other library-based federated searching products.

SUBMITTER: Foust JE

PROVIDER: S-EPMC1773047 | biostudies-literature | 2007 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving e-book access via a library-developed full-text search tool.

Foust Jill E JE Bergen Phillip P Maxeiner Gretchen L GL Pawlowski Peter N PN

Journal of the Medical Library Association : JMLA 20070101 1

<h4>Purpose</h4>This paper reports on the development of a tool for searching the contents of licensed full-text electronic book (e-book) collections.<h4>Setting</h4>The Health Sciences Library System (HSLS) provides services to the University of Pittsburgh's medical programs and large academic health system.<h4>Brief description</h4>The HSLS has developed an innovative tool for federated searching of its e-book collections. Built using the XML-based Vivísimo development environment, the tool en ...[more]

PMID: 17252065

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Project description:BackgroundThe problem of access to medical information, particularly in low-income countries, has been under discussion for many years. Although a number of developments have occurred in the last decade (e.g., the open access (OA) movement and the website Sci-Hub), everyone agrees that these difficulties still persist very widely, mainly due to the fact that paywalls still limit access to approximately 75% of scholarly documents. In this study, we compare the accessibility of recent full text articles in the field of ophthalmology in 27 established institutions located worldwide.MethodsA total of 200 references from articles were retrieved using the PubMed database. Each article was individually checked for OA. Full texts of non-OA (i.e., "paywalled articles") were examined to determine whether they were available using institutional and Hinari access in each institution studied, using "alternative ways" (i.e., PubMed Central, ResearchGate, Google Scholar, and Online Reprint Request), and using the website Sci-Hub.ResultsThe number of full texts of "paywalled articles" available using institutional and Hinari access showed strong heterogeneity, scattered between 0% full texts to 94.8% (mean = 46.8%; SD = 31.5; median = 51.3%). We found that complementary use of "alternative ways" and Sci-Hub leads to 95.5% of full text "paywalled articles," and also divides by 14 the average extra costs needed to obtain all full texts on publishers' websites using pay-per-view.ConclusionsThe scant number of available full text "paywalled articles" in most institutions studied encourages researchers in the field of ophthalmology to use Sci-Hub to search for scientific information. The scientific community and decision-makers must unite and strengthen their efforts to find solutions to improve access to scientific literature worldwide and avoid an implosion of the scientific publishing model. This study is not an endorsement for using Sci-Hub. The authors, their institutions, and publishers accept no responsibility on behalf of readers.

Project description:BackgroundComputational methods have been used to find duplicate biomedical publications in MEDLINE. Full text articles are becoming increasingly available, yet the similarities among them have not been systematically studied. Here, we quantitatively investigated the full text similarity of biomedical publications in PubMed Central.Methodology/principal findings72,011 full text articles from PubMed Central (PMC) were parsed to generate three different datasets: full texts, sections, and paragraphs. Text similarity comparisons were performed on these datasets using the text similarity algorithm eTBLAST. We measured the frequency of similar text pairs and compared it among different datasets. We found that high abstract similarity can be used to predict high full text similarity with a specificity of 20.1% (95% CI [17.3%, 23.1%]) and sensitivity of 99.999%. Abstract similarity and full text similarity have a moderate correlation (Pearson correlation coefficient: -0.423) when the similarity ratio is above 0.4. Among pairs of articles in PMC, method sections are found to be the most repetitive (frequency of similar pairs, methods: 0.029, introduction: 0.0076, results: 0.0043). In contrast, among a set of manually verified duplicate articles, results are the most repetitive sections (frequency of similar pairs, results: 0.94, methods: 0.89, introduction: 0.82). Repetition of introduction and methods sections is more likely to be committed by the same authors (odds of a highly similar pair having at least one shared author, introduction: 2.31, methods: 1.83, results: 1.03). There is also significantly more similarity in pairs of review articles than in pairs containing one review and one nonreview paper (frequency of similar pairs: 0.0167 and 0.0023, respectively).Conclusion/significanceWhile quantifying abstract similarity is an effective approach for finding duplicate citations, a comprehensive full text analysis is necessary to uncover all potential duplicate citations in the scientific literature and is helpful when establishing ethical guidelines for scientific publications.

Dataset Information

Improving e-book access via a library-developed full-text search tool.

Purpose

Setting

Brief description

Results/evaluation

Conclusion

Publications

Improving e-book access via a library-developed full-text search tool.

Similar Datasets

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