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Many retrieval experiments have been based on inadequate test collections, and current research is hampered by the lack of proper collections. This short review does not attempt a fully documented survey of all the collections used in the past decade: hopefully representative examples have been studied to throw light on the requirements test collections should meet, to show how past collections have been defective, and to suggest guidelines for a future ‘ideal’ test collection. The specifications for this collection can be taken as an indirect comment on our present state of knowledge of major retrieval system variables, and experience in conducting experiments.

The purpose of this paper is to assess the efficacy of the brief test method and OCLC's WorldCat Collection Analysis (WCA) in analyzing strengths and weaknesses of collections in three academic libraries.

Monographic collections in zoology were assessed in the study, using both the brief test method and WCA at three institutions.

The paper finds that both the brief test and WCA provide valuable insight into collection strengths and weaknesses.

This is an analysis of only one subject area and one format. Further studies might focus on other disciplines and might take into account a variety of formats.

The study suggests ways in which collection managers might gain a better knowledge of their collection for such purposes as cooperative collection development and preservation.

Since WCA is a newly available tool, there are few studies using this methodology. This adds to the limited literature on the brief test methodology.

A major component in managing pandemic outbreaks involves testing the suspected individuals and isolating them to avoid transmission in the community. This requires setting up testing centres for diagnosis of the infected individuals, which usually involves movement of either patient from their residence to the testing centre or personnel visiting the patient, thus aggregating the risk of transmission to localities and testing centres. The purpose of this paper is to investigate and minimize such movements by developing a drone assisted sample collection and diagnostic system.

Effective control of an epidemic outbreak calls for a rapid response and involves testing suspected individuals and isolating them to avoid transmission in the community. This paper presents the problem in a two-phase manner by locating sample collection centres while assigning neighbourhoods to these collection centres and thereafter, assigning collection centres to nearest testing centres. To solve the mathematical model, this study develops a mixed-integer linear programming model and propose an integrated genetic algorithm with a local search-based approach (GA-LS) to solve the problem.

Proposed approach is demonstrated as a case problem in an Indian urban city named Kolkata. Computational results show that the integrated GA-LS approach is capable of producing good quality solutions within a short span of time, which aids to the practicality in the circumstance of a pandemic.

The COVID-19 pandemic has shown that the large-scale outbreak of a transmissible disease may require a restriction of movement to take control of the exponential transmission. This paper proposes a system for the location of clinical sample collection centres in such a way that drones can be used for the transportation of samples from the neighbourhood to the testing centres.

Epidemic outbreaks have been a reason behind a major number of deaths across the world. The present study addresses the critical issue of identifying locations of temporary sample collection centres for drone assisted testing in major cities, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust epidemic resistance.

Recently, the importance and effectiveness of Wikipedia Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic relatedness measurement, and research in this area has shown that Wikipedia can be used for semantic relatedness measurement. However, previous methods are facing two problems; accuracy and scalability. To solve these problems, the purpose of this paper is to propose an efficient semantic relatedness measurement method that leverages global statistical information of Wikipedia. Furthermore, a new test collection is constructed based on Wikipedia concepts for evaluating semantic relatedness measurement methods.

The authors' approach leverages global statistical information of the whole Wikipedia to compute semantic relatedness among concepts (disambiguated terms) by analyzing co‐occurrences of link pairs in all Wikipedia articles. In Wikipedia, an article represents a concept and a link to another article represents a semantic relation between these two concepts. Thus, the co‐occurrence of a link pair indicates the relatedness of a concept pair. Furthermore, the authors propose an integration method with tfidf as an improved method to additionally leverage local information in an article. Besides, for constructing a new test collection, the authors select a large number of concepts from Wikipedia. The relatedness of these concepts is judged by human test subjects.

An experiment was conducted for evaluating calculation cost and accuracy of each method. The experimental results show that the calculation cost of this approach is very low compared to one of the previous methods and more accurate than all previous methods for computing semantic relatedness.

This is the first proposal of co‐occurrence analysis of Wikipedia links for semantic relatedness measurement. The authors show that this approach is effective to measure semantic relatedness among concepts regarding calculation cost and accuracy. The findings may be useful to researchers who are interested in knowledge extraction, as well as ontology researches.

This article reviews the state of the art in automatic indexing, that is, automatic techniques for analysing and characterising documents, for manipulating their descriptions in searching, and for generating the index language used for these purposes. It concentrates on the literature from 1968 to 1973. Section I defines the topic and its context. Sections II and III consider work in syntax and semantics respectively in detail. Section IV comments on ‘indirect’ indexing. Section V briefly surveys operating mechanized systems. In Section VI major experiments in automatic indexing are reviewed, and Section VII attempts an overall conclusion on the current state of automatic indexing techniques.

Requests for tests and measuring instruments for use in class assignments and faculty and student research are both familiar and frustrating to most academic librarians. In typical scenarios, an education student wants to measure aggression in children or a nursing student needs a test for patient mobility. Even the faculty member who may know the name of a scale may not know its author or how to obtain a copy. All are looking for a measure applicable to a specific situation and each has come to the library in hopes of walking away with a copy of the measure that day. Those familiar with measurement literature know that accessing measures can be time consuming, circuitous, and sometimes impossible. The standard test reference books, such as the Mental Measurements Yearbook and Tests in Print (both of which are published by the Buros Institute, University of Nebraska, Lincoln, Nebraska), are of limited use. These books typically do not include actual instruments or noncommercial tests from the journal and report literature. While these standard reference books are essential to a test literature collection, sole use of them would mean bypassing large numbers of instruments developed and published only in articles, reports, papers, and dissertations. Sources are available to locate additional measurements, tests, and instruments, but they are widely dispersed in the print and electronic literature.

It would be very helpful in retrieval experiments if good retrieval performance for a test collection was known, so that performance for particular devices could be fully evaluated. This paper presents one performance yardstick, based on optimally weighted request terms, and illustrates its application to different test collections.

In today’s digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students’ data awareness.

The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students’ awareness and understanding of and reflection on data practices of data-driven digital artefacts.

The study’s findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour.

Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.

Many retrieval experiments are intended to discover ways of improving performance, taking the results obtained with some particular technique as a baseline. The fact that substantial alterations to a system often have little or no effect on particular collections is puzzling. This may be due to the initially poor separation of relevant and non‐relevant documents. The paper presents a procedure for characterizing this separation for a collection, which can be used to show whether proposed modifications of the base system are likely to be useful.

This study aims to examine the effects of the proposed mobile Web library application (MWLA) on the search experience and its impact on learners’ engagement, interaction and overall learning outcomes within an institutional repository. Furthermore, the study investigates learners’ acceptance of the MWLA system.

The study suggests implementing an MWLA with Algolia’s search service to improve the institutional repository and enhance learners’ access to reliable information. It involved an experiment with 85 undergraduate students divided into experimental and control groups (CGs), where the experimental group (EG) used MWLA for search tasks, and the CG used the traditional library website. The study evaluated the acceptance and learning behaviours of the EG towards MWLA, considering factors such as usefulness, ease of use, mobility, accessibility, satisfaction and intention to use.

The findings of this study provide empirical evidence that the EG, which used the MWLA, demonstrated superior performance compared to the CG across all institutional repository collections, resulting in improved learning outcomes. Participants were highly satisfied with MWLA and found it user-friendly and beneficial for improving search skills. MWLA’s portability and accessibility motivated active learner engagement.

The powerful search bar of MWLA significantly enhanced learners’ search efficiency, resulting in more effective retrieval of relevant materials. Moreover, learners who actively engaged with previews and full-text content, using appropriate keywords and syntax, achieved higher scores and were more likely to access previews, abstracts and full texts of documents using the sorting-by-year or by-advisor feature.