research topics in library science

newjerseystatemuseum.org |

Research paper topics in library and information science

A systematic approach is best when undertaking research in the library and information science. Not only should you have an in-depth knowledge of major themes in the area, but you should also be aware of current research methods and topics of influence, such as library systems, cooperation between libraries, and the flow of information between libraries.

Finding a good research paper topic can greatly depend upon your interests and what you took away from your coursework. Paying attention in classes and taking adequate notes makes it easier to assimilate that knowledge into a coherent research paper topic. Take a look at the following research paper topics for some ideas:

• A critical analysis of student attitudes towards cataloguing and classification in college campus libraries
• The Impact of Public Libraries at the state level
• The implementation of information and communication technology in academic libraries in Brazil
• Evaluating the effect of feminization and professionalization on librarianship
• The challenges involved in running private libraries in Nigeria
• Defining comparative and international library and information science
• An assessment of international cultural exchange through libraries
• The role of international librarianship in promoting freedom of information and expression
• International issues faced by librarians and information science professionals with regard to the knowledge society
• Exploring the relationship between government schools and public libraries in the context of South Asia
• The importance of resource-sharing in an international library network: bridging gaps using modern technology
• Tackling indigenous knowledge by adopting innovative tools and strategies
• The influence of library aid in developing countries during globalization
• A critical comparison of American librarianship and information science research in European countries
• Learnings from major book acquisitions in American academic libraries
• The expanding purview of American ideas in German public libraries
• The British Council and its critical role in building bridges across the developing world

Browsing through sample topics in library and information science can help you brainstorm your own ideas more effectively. Take the time to scan such resources and choose a topic that you can convincingly discuss and analyze. A good source for potential research paper topics and paper help is mypaperwriter.com , also papers written by past students as well as reputed works in the field.

Copyright ©2017 - newjerseystatemuseum.org

Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.

We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.

Brief introduction to this section that descibes Open Access especially from an IntechOpen perspective

Want to get in touch? Contact our London head office or media team here

Our team is growing all the time, so we’re always on the lookout for smart people who want to help us reshape the world of scientific publishing.

Home > Books > Qualitative versus Quantitative Research

Research Methods in Library and Information Science

Submitted: 28 October 2016 Reviewed: 23 March 2017 Published: 28 June 2017

DOI: 10.5772/intechopen.68749

Cite this chapter

There are two ways to cite this chapter:

From the Edited Volume

Qualitative versus Quantitative Research

Edited by Sonyel Oflazoglu

To purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www.cbspd.com | [email protected]

Chapter metrics overview

6,022 Chapter Downloads

Impact of this chapter

Total Chapter Downloads on intechopen.com

Total Chapter Views on intechopen.com

Overall attention for this chapters

Library and information science (LIS) is a very broad discipline, which uses a wide rangeof constantly evolving research strategies and techniques. The aim of this chapter is to provide an updated view of research issues in library and information science. A stratified random sample of 440 articles published in five prominent journals was analyzed and classified to identify (i) research approach, (ii) research methodology, and (iii) method of data analysis. For each variable, a coding scheme was developed, and the articles were coded accordingly. A total of 78% of the articles reported empirical research. The rest 22% were classified as non‐empirical research papers. The five most popular topics were “information retrieval,” “information behaviour,” “information literacy,” “library services,” and “organization and management.” An overwhelming majority of the empirical research articles employed a quantitative approach. Although the survey emerged as the most frequently used research strategy, there is evidence that the number and variety of research methodologies have been increased. There is also evidence that qualitative approaches are gaining increasing importance and have a role to play in LIS, while mixed methods have not yet gained enough recognition in LIS research.

• library and information science
• research methods
• research strategies
• data analysis techniques
• research articles

Author Information

Aspasia togia *.

• Department of Library Science & Information Systems, Technological Educational Institute (TEI) of Thessaloniki, Greece

Afrodite Malliari

• DataScouting, Thessaloniki, Greece

*Address all correspondence to: [email protected]

1. Introduction

Library and information science (LIS), as its name indicates, is a merging of librarianship and information science that took place in the 1960s [ 1 , 2 ]. LIS is a field of both professional practice and scientific inquiry. As a field of practice, it includes the profession of librarianship as well as a number of other information professions, all of which assume the interplay of the following:

information content,

the people who interact with the content, and

the technology used to facilitate the creation, communication, storage, or transformation of the content [ 3 ].

The disciplinary foundation of LIS, which began in the 1920s, aimed at providing a theoretical foundation for the library profession. LIS has evolved in close relationship with other fields of research, especially computer science, communication studies, and cognitive sciences [ 4 ].

The connection of LIS with professional practice, on one hand, and other research fields on the other has influenced its research orientation and the development of methodological tools and theoretical perspectives [ 5 ]. Research problems are diverse, depending on the research direction, local trends, etc. Most of them relate to the professional practice although there are theoretical research statements as well. LIS research strives to address important information issues, such as these of “ information retrieval, information quality and authenticity, policy for access and preservation, the health and security applications of data mining ”(p. 3) [ 6 ]. The research is multidisciplinary in nature, and it has been heavily influenced by research designs developed in the social, behavioral, and management sciences and to a lesser extent by the theoretical inquiry adopted in the humanities [ 7 ]. Methods used in information retrieval research have been adapted from computer science. The emergence of evidence‐based librarianship in the late 1990s brought a positivist approach to LIS research, since it incorporated many of the research designs and methods used in clinical medicine [ 7 , 8 ]. In addition, LIS has developed its own methodological approaches, a prominent example of which is bibliometrics. Bibliometrics, which can be defined as “ the use of mathematical and statistical methods to study documents and patterns of publication ” (p. 38) [ 9 ], is a native research methodology, which has been extensively used outside the field, especially in science studies [ 10 ].

Library and information science research has been often criticized as being fragmentary, narrowly focused, and oriented to practical problems [ 11 ]. Many authors have noticed limited use of theory in published research and have advocated greater use of theory as a conceptual basis in LIS research [ 4 , 11 – 14 ]. Feehan et al. [ 13 ] claimed that LIS literature has not evolved enough to support a rigid body of its own theoretical basis. Jarvelin and Vakkari [ 15 ] argued that LIS theories are usually vague and conceptually unclear, and that research in LIS has been dominated by a paradigm which “ has made little use of such traditional scientific approaches as foundations and conceptual analysis, or of scientific explanation and theory formulation ” (p. 415). This lack of theoretical contributions may be associated with the fact that LIS emanated from professional practice and is therefore closely linked to practical problems such as the processing and organization of library materials, documentation, and information retrieval [ 15 , 16 ].

In this chapter, after briefly discussing the role of theory in LIS research, we provide an updated view of research issues in the field that will help scholars and students stay informed about topics related to research strategies and methods. To accomplish this, we describe and analyze patterns of LIS research activity as reflected in prominent library journals. The analysis of the articles highlights trends and recurring themes in LIS research regarding the use of multiple methods, the adoption of qualitative approaches, and the employment of advanced techniques for data analysis and interpretation [ 17 ].

2. The role of theory in LIS research

The presence of theory is an indication of research eminence and respectability [ 18 ], as well as a feature of discipline’s maturity [ 19 , 20 ]. Theory has been defined in many ways. “ Any of the following have been used as the meaning of theory: a law, a hypothesis, group of hypotheses, proposition, supposition, explanation, model, assumption, conjecture, construct, edifice, structure, opinion, speculation, belief, principle, rule, point of view, generalization, scheme, or idea ” (p. 309) [ 21 ]. A theory can be described as “ a set of interrelated concepts, definitions, and propositions that explains or predicts events or situations by specifying relations among variables ” [ 22 ]. According to Babbie [ 23 ], research is “ a systematic explanation for the observed facts and laws that related to a particular aspect of life ” (p. 49). It is “ a multiple‐level component of the research process, comprising a range of generalizations that move beyond a descriptive level to a more explanatory level ” [ 24 ] (p. 319). The role of theory in social sciences is, among other things, to explain and predict behavior, be usable in practical applications, and guide research [ 25 ]. According to Smiraglia [ 26 ], theory does not exist in a vacuum but in a system that explains the domains of human actions, the phenomena found in these domains, and the ways in which they are affected. He maintains that theory is developed by systematically observing phenomena, either in the positivist empirical research paradigm or in the qualitative hermeneutic paradigm. Theory is used to formulate hypotheses in quantitative research and confirms observations in qualitative research.

Glazier and Grover [ 24 ] proposed a model for theory‐building in LIS called “circuits of theory.” The model includes taxonomy of theory, developed earlier by the authors [ 11 ], and the critical social and psychological factors that influence research. The purpose of the taxonomy was to demonstrate the relationships among the concepts of research, theory, paradigms, and phenomena. Phenomena are described as “ events experienced in the empirical world ” (p. 230) [ 11 ]. Researchers assign symbols (digital or iconic representations, usually words or pictures) to phenomena, and meaning to symbols, and then they conceptualize the relationships among phenomena and formulate hypotheses and research questions. “ In the taxonomy, empirical research begins with the formation of research questions to be answered about the concepts or hypotheses for testing the concepts within a narrow set of predetermined parameters ” (p. 323) [ 24 ]. Various levels of theories, with implications for research in library and information Science, are described. The first theory level, called substantive theory , is defined as “ a set of propositions which furnish an explanation for an applied area of inquiry ” (p. 233) [ 11 ]. In fact, it may not be viewed as a theory but rather be considered as a research hypothesis that has been tested or even a research finding [ 16 ]. The next level of theory, called formal theory , is defined as “ a set of propositions which furnish an explanation for a formal or conceptual area of inquiry, that is, a discipline ” (p. 234) [ 11 ]. Substantive and formal theories together are usually considered as “middle range” theory in the social sciences. Their difference lies in the ability to structure generalizations and the potential for explanation and prediction. The final level, grand theory , is “ a set of theories or generalizations that transcend the borders of disciplines to explain relationships among phenomena ” (p. 321) [ 24 ]. According to the authors, most research generates substantive level theory, or, alternatively, researchers borrow theory from the appropriate discipline, apply it to the problem under investigation, and reconstruct the theory at the substantive level. Next in the hierarchy of theoretical categories is the paradigm , which is described as “ a framework of basic assumptions with which perceptions are evaluated and relationships are delineated and applied to a discipline or profession ” (p. 234) [ 11 ]. Finally, the most significant theoretical category is the world view , which is defined as “ an individual’s accepted knowledge, including values and assumptions, which provide a ‘filter’ for perception of all phenomena ” (p. 235) [ 11 ]. All the previous categories contribute to shaping the individual’s worldview. In the revised model, which places more emphasis on the impact of social environment on the research process, research and theory building is surrounded by a system of three basic contextual modules: the self, society, and knowledge, both discovered and undiscovered. The interactions and dialectical relationships of these three modules affect the research process and create a dynamic environment that fosters theory creation and development. The authors argue that their model will help researchers build theories that enable generalizations beyond the conclusions drawn from empirical data [ 24 ].

In an effort to propose a framework for a unified theory of librarianship, McGrath [ 27 ] reviewed research articles in the areas of publishing, acquisitions, classification and knowledge organization, storage, preservation and collection management, library collections, and circulations. In his study, he included articles that employed explanatory and predictive statistical methods to explore relationships between variables within and between the above subfields of LIS. For each paper reviewed, he identified the dependent variable, significant independent variables, and the units of analysis. The review displayed explanatory studies “ in nearly every level, with the possible exception of classification, while studies in circulation and use of the library were clearly dominant. A recapitulation showed that a variable at one level may be a unit of analysis at another, a property of explanatory research crucial to the development of theory, which has been either ignored or unrecognized in LIS literature ” (p. 368) [ 27 ]. The author concluded that “explanatory and predictive relationships do exist and that they can be useful in constructing a comprehensive unified theory of librarianship” (p. 368) [ 27 ].

Recent LIS literature provides several analyses of theory development and use in the field. In a longitudinal analysis of information needs and uses of literature, Julien and Duggan [ 28 ] investigated, among other things, to what extent LIS literature was grounded in theory. Articles “ based on a coherent and explicit framework of assumptions, definitions, and propositions that, taken together, have some explanatory power ” (p. 294) were classified as theoretical articles. Results showed that only 18.3% of the research studies identified in the sample of articles examined were theoretically grounded.

Pettigrew and McKechnie [ 29 ] analyzed 1160 journal articles published between 1993 and 1998 to determine the level of theory use in information science research. In the absence of a singular definition of theory that would cover all the different uses of the term in the sample of articles, they operationalized “theory” according to authors’ use of the term. They found that 34.1% of the articles incorporated theory, with the largest percentage of theories drawn from the social sciences. Information science itself was the second most important source of theories. The authors argued that this significant increase in theory use in comparison to earlier studies could be explained by the research‐oriented journals they selected for examination, the sample time, and the broad way in which they defined “theory.” With regard to this last point, that is, their approach of identifying theories only if the author(s) describe them as such in the article, Pettigrew and McKechnie [ 29 ] observed significant differences in how information science researchers perceive theory:

Although it is possible that conceptual differences regarding the nature of theory may be due to the different disciplinary backgrounds of researchers in IS, other themes emerged from our data that suggest a general confusion exists about theory even within subfields. Numerous examples came to light during our analysis in which an author would simultaneously refer to something as a theory and a method, or as a theory and a model, or as a theory and a reported finding. In other words, it seems as though authors, themselves, are sometimes unsure about what constitutes theory. Questions even arose regarding whether the author to whom a theory was credited would him or herself consider his or her work as theory (p. 68).

Kim and Jeong [ 16 ] examined the state and characteristics of theoretical research in LIS journals between 1984 and 2003. They focused on the “theory incident,” which is described as “an event in which the author contributes to the development or the use of theory in his/her paper.” Their study adopted Glazier and Grover’s [ 24 ] model of “circuits of theory.” Substantive level theory was operationalized to a tested hypothesis or an observed relationship, while both formal and grand level theories were identified when they were named as “theory,” “model,” or “law” by authors other than those who had developed them. Results demonstrated that the application of theory was present in 41.4% of the articles examined, signifying a significant increase in the proportion of theoretical articles as compared to previous studies. Moreover, it was evident that both theory development and theory use had increased by the year. Information seeking and use, and information retrieval, were identified as the subfields with the most significant contribution to the development of the theoretical framework.

In a more in‐depth analysis of theory use in Kumasi et al. [ 30 ] qualitatively analyzed the extent to which theory is meaningfully used in scholarly literature. For this purpose, they developed a theory talk coding scheme, which included six analytical categories, describing how theory is discussed in a study. The intensity of theory talk in the articles was described across a continuum from minimal (e.g., theory is discussed in literature review and not mentioned later) through moderate (e.g., multiple theories are introduced but without discussing their relevance to the study) to major (e.g., theory is employed throughout the study). Their findings seem to support the opinion that “ LIS discipline has been focused on the application of specific theoretical frameworks rather than the generation of new theories ” (p. 179) [ 30 ]. Another point the authors made was about the multiple terms used in the articles to describe theory. Words such as “framework,” “model,” or “theory” were used interchangeably by scholars.

It is evident from the above discussion that the treatment of theory in LIS research covers a spectrum of intensity, from marginal mentions to theory revising, expanding, or building. Recent analyses of the published scholarship indicate that the field has not been very successful in contributing to existing theory or producing new theory. In spite of this, one may still assert that LIS research employs theory, and, in fact, there are many theories that have been used or generated by LIS scholars. However, “ calls for additional and novel theory development work in LIS continue, particularly for theories that might help to address the research practice gap ” (p. 12) [ 31 ].

3. Research strategies in LIS

3.1. surveys of research methods.

LIS is a very broad discipline, which uses a wide range of constantly evolving research strategies and techniques [ 32 ]. Various classification schemes have been developed to analyze methods employed in LIS research (e.g., [ 13 , 15 , 17 , 33 – 35 , 38 ]). Back in 1996, in the “research record” column of the Journal of Education for Library and Information Science, Kim [ 36 ] synthesized previous categories and definitions and introduced a list of research strategies, including data collection and analysis methods. The listing included four general research strategies: (i) theoretical/philosophical inquiry (development of conceptual models or frameworks), (ii) bibliographic research (descriptive studies of books and their properties as well as bibliographies of various kinds), (iii) R&D (development of storage and retrieval systems, software, interface, etc.), and (iv) action research, it aims at solving problems and bringing about change in organizations. Strategies are then divided into quantitative and qualitative driven. In the first category are included descriptive studies, predictive/explanatory studies, bibliometric studies, content analysis, and operation research studies. Qualitative‐driven strategies are considered the following: case study, biographical method, historical method, grounded theory, ethnography, phenomenology, symbolic interactionism/semiotics, sociolinguistics/discourse analysis/ethnographic semantics/ethnography of communication, and hermeneutics/interpretive interactionism (p. 378–380) [ 36 ].

Systematic studies of research methods in LIS started in the 1980s and several reviews of the literature have been conducted over the past years to analyze the topics, methodologies, and quality of research. One of the earliest studies was done by Peritz [ 37 ] who carried out a bibliometric analysis of the articles published in 39 core LIS journals between 1950 and 1975. She examined the methodologies used, the type of library or organization investigated, the type of activity investigated, and the institutional affiliation of the authors. The most important findings were a clear orientation toward library and information service activities, a widespread use of the survey methodology, a considerable increase of research articles after 1960, and a significant increase in theoretical studies after 1965.

Nour [ 38 ] followed up on Peritz’s [ 37 ] work and studied research articles published in 41 selected journals during the year 1980. She found that survey and theoretical/analytic methodologies were the most popular, followed by bibliometrics. Comparing these findings to those made by Peritz [ 37 ], Nour [ 38 ] found that the amount of research continued to increase, but the proportion of research articles to all articles had been decreasing since 1975.

Feehan et al. [ 13 ] described how LIS research published during 1984 was distributed over various topics and what methods had been used to study these topics. Their analysis revealed a predominance of survey and historical methods and a notable percentage of articles using more than one research method. Following a different approach, Enger et al. (1989) focused on the statistical methods used by LIS researchers in articles published during 1985 [ 39 ]. They found that only one out of three of the articles reported any use of statistics. Of those, 21% used descriptive statistics and 11% inferential statistics. In addition, the authors found that researchers from disciplines other than LIS made the highest use of statistics and LIS faculty showed the highest use of inferential statistics.

An influential work, against which later studies have been compared, is that of Jarvelin and Vakkari [ 15 ] who studied LIS articles published in 1985 in order to determine how research was distributed over various subjects, what approaches had been taken by the authors, and what research strategies had been used. The authors replicated their study later to include older research published between 1965 and 1985 [ 40 ]. The main finding of these studies was that the trends and characteristics of LIS research remained more or less the same over the aforementioned period of 20 years. The most common topics were information service activities and information storage and retrieval. Empirical research strategies were predominant, and of them, the most frequent was the survey. Kumpulainen [ 41 ], in an effort to provide a continuum with Jarvelin and Vakkeri’s [ 15 ] study, analyzed 632 articles sampled from 30 core LIS journals with respect to various characteristics, including topics, aspect of activity, research method, data selection method, and data analysis techniques. She used the same classification scheme, and she selected the journals based on a slightly modified version of Jarvelin and Vakkari’s [ 15 ] list. Library services and information storage and retrieval emerged again as the most common subjects approached by the authors and survey was the most frequently used method.

More recent studies of this nature include those conducted by Koufogiannakis et al. [ 42 ], Hildreth and Aytac [ 43 ], Hider and Pymm [ 32 ], and Chu [ 17 ]. Koufogiannakis et al. [ 42 ] examined research articles published in 2001 and they found that the majority of them were questionnaire‐based descriptive studies. Comparative, bibliometrics, content analysis, and program evaluation studies were also popular. Information storage and retrieval emerged as the predominant subject area, followed by library collections and management. Hildreth and Aytac [ 43 ] presented a review of the 2003–2005 published library research with special focus on methodology issues and the quality of published articles of both practitioners and academic scholars. They found that most research was descriptive and the most frequent method for data collection was the questionnaire, followed by content analysis and interviews. With regard to data analysis, more researchers used quantitative methods, considerably less used qualitative‐only methods, whereas 61 out of 206 studies included some kind of qualitative analysis, raising the total percentage of qualitative methods to nearly 50%. With regard to the quality of published research, the authors argued that “ the majority of the reports are detailed, comprehensive, and well‐organized ” (p. 254) [ 43 ]. Still, they noticed that the majority of reports did not mention the critical issues of research validity and reliability and neither did they indicate study limitations or future research recommendations. Hider and Pymm [ 32 ] described content analysis of LIS literature “ which aimed to identify the most common strategies and techniques employed by LIS researchers carrying out high‐profile empirical research ” (p. 109). Their results suggested that while researchers employed a wide variety of strategies, they mostly used surveys and experiments. They also observed that although quantitative research accounted for more than 50% of the articles, there was an increase in the use of most sophisticated qualitative methods. Chu [ 17 ] analyzed the research articles published between 2001 and 2010 in three major journals and reported the following most frequent research methods: theoretical approach (e.g., conceptual analysis), content analysis, questionnaire, interview, experiment, and bibliometrics. Her study showed an increase in both the number and variety of research methods but lack of growth in the use of qualitative research or in the adoption of multiple research methods.

In summary, the literature shows a continued interest in the analysis of published LIS research. Approaches include focusing on particular publication years, geographic areas, journal titles, aspects of LIS, and specific characteristics, such as subjects, authorship, and research methods. Despite the abundance of content analyses of LIS literature, the findings are not easily comparable due to differences in the number and titles of journals examined, in the types of the papers selected for analysis, in the periods covered, and in classification schemes developed by the authors to categorize article topics and research strategies. Despite the differences, some findings are consistent among all studies:

Information seeking, information retrieval, and library and information service activities are among the most common subjects studied,

Descriptive research methodologies based on surveys and questionnaires predominate,

Over the years, there has been a considerable increase in the array of research approaches used to explore library issues, and

Data analysis is usually limited to descriptive statistics, including frequencies, means, and standard deviations.

3.2. Data collection and analysis

Articles published between 2011 and 2016 were obtained from the following journals: Library and Information Science Research, College & Research Libraries, Journal of Documentation, Information Processing & Management, and Journal of Academic Librarianship ( Table 1 ). These five titles were selected as data sources because they have the highest 5‐year impact factor of the journals classified in Ulrich’s Serials Directory under the “Library and Information Sciences” subject heading. From the journals selected, only full‐length articles were collected. Editorials, book reviews, letters, interviews, commentaries, and news items were excluded from the analysis. This selection process yielded 1643 articles. A stratified random sample of 440 articles was chosen for in‐depth analysis ( Table 2 ). For the purpose of this study, five strata, corresponding to the five journals, were used. The sample size was determined using a margin of error, 4%, and confidence interval, 95%.

Table 1.

Profile of the journals.

Table 2.

Journal titles.

Each article was classified as either research or theoretical. Articles that employed specific research methodology and presented specific findings of original studies performed by the author(s) were considered research articles. The kind of study may vary (e.g., it could be an experiment, a survey, etc.), but in all cases, raw data had been collected and analyzed, and conclusions were drawn from the results of that analysis. Articles reporting research in system design or evaluation in the information systems field were also regarded as research articles . On the other hand, works that reviewed theories, theoretical concepts, or principles discussed topics of interest to researchers and professionals, or described research methodologies were regarded as theoretical articles [ 44 ] and were classified in the no‐empirical‐research category. In this category, were also included literature reviews and articles describing a project, a situation, a process, etc.

Each article was classified into a topical category according to its main subject. The articles classified as research were then further explored and analyzed to identify (i) research approach, (ii) research methodology, and (iii) method of data analysis. For each variable, a coding scheme was developed, and the articles were coded accordingly. The final list of the analysis codes was extracted inductively from the data itself, using as reference the taxonomies utilized in previous studies [ 15 , 32 , 43 , 45 ]. Research approaches “ are plans and procedures for research ” (p. 3) [ 46 ]. Research approaches can generally be grouped as qualitative, quantitative, and mixed methods studies. Quantitative studies aim at the systematic empirical investigation of quantitative properties or phenomena and their relationships. Qualitative research can be broadly defined as “ any kind of research that produces findings not arrived at by means of statistical procedures or other means of quantification ” (p. 17) [ 47 ]. It is a way to gain insights through discovering meanings and explaining phenomena based on the attributes of the data. In mixed model research, quantitative and qualitative approaches are combined within or across the stages of the research process. It was beyond the scope of this study to identify in which stages of a study—data collection, data analysis, and data interpretation—the mixing was applied or to reveal the types of mixing. Therefore, studies using both quantitative and qualitative methods, irrespective of whether they describe if and how the methods were integrated, were coded as mixed methods studies.

Research methodologies , or strategies of inquiry, are types of research models “ that provide specific direction for procedures in a research design ” (p. 11) [ 46 ] and inform the decisions concerning data collection and analysis. A coding schema of research methodologies was developed by the authors based on the analysis of all research articles included in the sample. The methodology classification included 12 categories ( Table 3 ). Each article was classified into one category for the variable research methodology . If more than one research strategy was mentioned (e.g., experiment and survey), the article was classified according to the main strategy.

Table 3.

Coding schema for research methodologies.

Methods of data analysis refer to the techniques used by the researchers to explore the original data and answer their research problems or questions. Data analysis for quantitative researches involves statistical analysis and interpretation of figures and numbers. In qualitative studies, on the other hand, data analysis involves identifying common patterns within the data and making interpretations of the meanings of the data. The array of data analysis methods included the following categories:

Descriptive statistics,

Inferential statistics,

Qualitative data analysis,

Experimental evaluation, and

Other methods,

Descriptive statistics are used to describe the basic features of the data in a study. Inferential statistics investigate questions, models, and hypotheses. Mathematical analysis refers to mathematic functions, etc. used mainly in bibliometric studies to answer research questions associated with citation data. Qualitative data analysis is the range of processes and procedures used for the exploration of qualitative data, from coding and descriptive analysis to identification of patterns and themes and the testing of emergent findings and hypotheses. It was used in this study as an overarching term encompassing various types of analysis, such as thematic analysis, discourse analysis, or grounded theory analysis. The class experimental evaluation was used for system and software analysis and design studies which assesses the newly developed algorithm, tool, method, etc. by performing experiments on selected datasets. In these cases, “experiments” differ from the experimental designs in social sciences. Methods that did not fall into one of these categories (e.g., mathematical analysis, visualization, or benchmarking) were classified as other methods . If both descriptive and inferential statistics were used in an article, only the inferential were recorded. In mixed methods studies, each method was recorded in the order in which it was reported in the article.

Ten percent of the articles were randomly selected and used to establish inter‐rater reliability and provide basic validation of the coding schema. Cohen’s kappa was calculated for each coded variable. The average Cohen’s kappa value was κ = 0.60, p < 0.000 (the highest was 0.63 and lowest was 0.59). This indicates a substantial agreement [ 48 ]. The coding disparities across raters were discussed, and the final codes were determined via consensus.

3.3. Results

3.3.1. topic.

Table 4 presents the distribution of articles over the various topics, for each of which a detailed description is provided. The five most popular topics of the papers in the total sample of 440 articles were “information retrieval,” “information behavior,” “information literacy,” “library services,” and “organization and management.” These areas cover over 60% of all topics studied in the papers. The least‐studied topics (covered in less than eight papers) fall into the categories of “information and knowledge management,” “library information systems,” “LIS theory,” and “infometrics.”

Table 4.

Article topics.

Figure 1 shows how the top five topics are distributed across journals. As expected, the topic “information retrieval” has higher publication frequencies in Information Processing & Management, a journal focusing on system design and issues related to the tools and techniques used in storage and retrieval of information. “Information literacy,” “information behavior,” “library services,” and “organization and management” appear to be distributed almost proportionately in College & Research Libraries. “Information literacy” seems to be a more preferred topic in the Journal of Academic Librarianship, while “information behavior” is more popular in the Journal of Documentation and Library & Information Science Research.

Figure 1.

Distribution of topics across journals.

3.3.2. Research approach and methodology

Of all articles examined, 343 articles, which represent the 78% of the sample, reported empirical research. The rest 22% (N = 97) were classified as non‐empirical research papers. Research articles were coded as quantitative, qualitative, or mixed methods studies. An overwhelming majority (70%) of the empirical research articles employed a quantitative research approach. Qualitative and mixed methods research was reported in 21.6 and 8.5% of the articles, respectively ( Figure 2 ).

Figure 2.

Research approach.

Table 5 presents the distribution of research approaches over the five most famous topics. The quantitative approach clearly prevails in all topics, especially in information retrieval research. However, qualitative designs seem to gain acceptance in all topics (except information retrieval), while in information behavior research, quantitative and qualitative approaches are almost evenly distributed. Mixed methods were quite frequent in information literacy and information behavior studies and less popular in the other topics.

Table 5.

Topics across research approach.

The most frequently used research strategy was survey, accounting for almost 37% of all research articles, followed by system and software analysis and design, a strategy used in this study specifically for research in information systems (Jarvelin & Vakkari, 1990). This result is influenced by the fact that Information Processing & Management addresses issues at the intersection between LIS and computer science, and the majority of its articles present the development of new tools, algorithms, methods and systems, and their experimental evaluation. The third‐ and fourth‐ranking strategies were content analysis and bibliometrics. Case study, experiment, and secondary data analysis were represented by 15 articles each, while the rest of the techniques were underrepresented with considerably fewer articles ( Table 6 ).

Table 6.

Research methodologies.

3.3.3. Methods of data analysis

Table 7 displays the frequencies for each type of data analysis.

Table 7.

Method of data analysis.

Almost half of the empirical research papers examined reported any use of statistics. Descriptive statistics, such as frequencies, means, or standard deviations, were more frequently used compared to inferential statistics, such as ANOVA, regression, or factor analysis. Nearly one‐third of the articles employed some type of qualitative data analysis either as the only method or—in mixed methods studies—in combination with quantitative techniques.

3.4. Discussions and conclusions

The patterns of LIS research activity as reflected in the articles published between 2011 and 2016 in five well‐established, peer‐reviewed journals were described and analyzed. LIS literature addresses many and diverse topics. Information retrieval, information behavior, and library services continue to attract the interest of researchers as they are core areas in library science. Information retrieval has been rated as one of the most famous areas of interest in research articles published between 1965 and 1985 [ 40 ]. According to Dimitroff [ 49 ], information retrieval was the second most popular topic in the articles published in the Bulletin of the Medical Library Association, while Cano [ 50 ] argued that LIS research produced in Spain from 1977 to 1994 was mostly centered on information retrieval and library and information services. In addition, Koufogiannakis et al. [ 42 ] found that information access and retrieval were the domain with the most research, and in Hildreth and Aytac’s [ 43 ] study, most articles were dealing with issues related to users (needs, behavior, information seeking, etc.), services, and collections. The present study provides evidence that the amount of research in information literacy is increasing, presumably due to the growing importance of information literacy instruction in libraries. In recent years, there is an ongoing educational role for librarians, who are more and more actively engaging in the teaching and learning processes, a trend that is reflected in the research output.

With regard to research methodologies, the present study seems to confirm the well‐documented predominance of survey in LIS research. According to Dimitroff [ 49 ], the percentage related to use of survey research methods reported in various studies varied between 20.3 and 41.5%. Powell [ 51 ], in a review of the research methods appearing in LIS literature, pointed out that survey had consistently been the most common type of study in both dissertations and journal articles. Survey reported the most widely used research design by Jarvelin and Vakkari [ 40 ], Crawford [ 52 ], Hildreth and Aytac [ 43 ], and Hider and Pymm [ 32 ]. The majority of articles examined by Koufogiannakis et al. [ 42 ] were descriptive studies using questionnaires/surveys. In addition, survey methods represented the largest proportion of methods used in information behavior articles analyzed by Julien et al. [ 53 ]. There is no doubt that survey has been used more than any other method in LIS research. As Jarvelin and Vakkari [ 15 ] put it, “it appears that the field is so survey‐oriented that almost all problems are seen through a survey viewpoint” (p. 416). Much of survey’s popularity can be ascribed to its being a well‐known, understood, easily conducted, and inexpensive method, which is easy to analyze results [ 41 , 42 ]. However, our findings suggest that while the survey ranks high, a variety of other methods have been also used in the research articles. Content analysis emerged as the third‐most frequent strategy, a finding similar to those of previous studies [ 17 , 32 ]. Although content analysis was not regarded by LIS researchers as a favored research method until recently, its popularity seems to be growing [ 17 ].

Quantitative approaches, which dominate, tend to rely on frequency counts, percentages, and descriptive statistics used to describe the basic features of the data in a study. Fewer studies used advanced statistical analysis techniques, such as t‐tests, correlation, and regressions, while there were some examples of more sophisticated methods, such as factor analysis, ANOVA, MANOVA, and structural equation modeling. Researchers engaging in quantitative research designs should take into consideration the use of inferential statistics, which enables the generalization from the sample being studied to the population of interest and, if used appropriately, are very useful for hypothesis testing. In addition, multivariate statistics are suitable for examining the relationships among variables, revealing patterns and understanding complex phenomena.

The findings also suggest that qualitative approaches are gaining increasing importance and have a role to play in LIS studies. These results are comparable to the findings of Hider and Pymm [ 32 ], who observed significant increases for qualitative research strategies in contemporary LIS literature. Qualitative analysis description varied widely, reflecting the diverse perspectives, analysis methods, and levels of depth of analysis. Commonly used terms in the articles included coding, content analysis, thematic analysis, thematic analytical approach, theme, or pattern identification. One could argue that the efforts made to encourage and promote qualitative methods in LIS research [ 54 , 55 ] have made some impact. However, qualitative research methods do not seem to be adequately utilized by library researchers and practitioners, despite their potential to offer far more illuminating ways to study library‐related issues [ 56 ]. LIS research has much to gain from the interpretive paradigm underpinning qualitative methods. This paradigm assumes that social reality is

the product of processes by which social actors together negotiate the meanings for actions and situations; it is a complex of socially constructed meanings. Human experience involves a process of interpretation rather than sensory, material apprehension of the external physical world and human behavior depends on how individuals interpret the conditions in which they find themselves. Social reality is not some ‘thing’ that may be interpreted in different ways, it is those interpretations (p. 96) [ 57 ].

As stated in the introduction of this chapter, library and information science focuses on the interaction between individuals and information. In every area of LIS research, the connection of factors that lead to and influence this interaction is increasingly complex. Qualitative research searches for “ all aspects of that complexity on the grounds that they are essential to understanding the behavior of which they are a part ” (p. 241) [ 59 ]. Qualitative research designs can offer a more in‐depth analysis of library users, their needs, attitudes, and behaviors.

The use of mixed methods designs was found to be rather rare. While Hildreth and Aytac [ 43 ] found higher percentages of studies using combined methods in data analysis, our results are analogous to those shown by Fidel [ 60 ]. In fact, as in her study, only few of the articles analyzed referred to mixed methods research by name, a finding indicating that “ the concept has not yet gained recognition in LIS research ” (p. 268). Mixed methods research has become an established research approach in the social sciences as it minimizes the weaknesses of quantitative and qualitative research alone and allows researchers to investigate the phenomena more completely [ 58 ].

In conclusion, there is evidence that LIS researchers employ a large number and wide variety of research methodologies. Each research approach, strategy, and method has its advantages and limitations. If the aim of the study is to confirm hypotheses about phenomena or measure and analyze the causal relationships between variables, then quantitative methods might be used. If the research seeks to explore, understand, and explain phenomena then qualitative methods might be used. Researchers can consider the full range of possibilities and make their selection based on the philosophical assumptions they bring to the study, the research problem being addressed, their personal experiences, and the intended audience for the study [ 46 ].

Taking into consideration the increasing use of qualitative methods in LIS studies, an in‐depth analysis of papers using qualitative methods would be interesting. A future study in which the different research strategies and types of analysis used in qualitative methods will be presented and analyzed could help LIS practitioners understand the benefits of qualitative analysis.

Mixed methods used in LIS research papers could be analyzed in future studies in order to identify in which stages of a study, data collection, data analysis, and data interpretation, the mixing was applied and to reveal the types of mixing.

As far as it concerns the quantitative research methods, which predominate in LIS research, it would be interesting to identify systematic relations between more than two variables such as authors’ affiliation, topic, research strategies, etc. and to create homogeneous groups using multivariate data analysis techniques.

• 1. Buckland MK, Liu ZM. History of information science. Annual Review of Information Science and Technology. 1995; 30 :385-416
• 2. Rayward WB. The history and historiography of information science: Some reflections. Information Processing & Management. 1996; 32 (1):3-17
• 3. Wildemuth BM. Applications of Social Research Methods to Questions in Information and Library Science. Westport, CT: Libraries Unlimited; 2009
• 4. Hjørland B. Theory and metatheory of information science: A new interpretation. Journal of Documentation. 1998; 54 (5):606-621. DOI: http://doi.org/10.1108/EUM0000000007183
• 5. Åström F. Heterogeneity and homogeneity in library and information science research. Information Research [Internet]. 2007 [cited 23 April 2017]; 12 (4): poster colisp01 [3 p.]. Available from: http://www.informationr.net/ir/12-4/colis/colisp01.html
• 6. Dillon A. Keynote address: Library and information science as a research domain: Problems and prospects. Information Research [Internet]. 2007 [cited 23 April 2017]; 12 (4): paper colis03 [6 p.]. Available from: http://www.informationr.net/ir/12-4/colis/colis03.html
• 7. Eldredge JD. Evidence‐based librarianship: An overview. Bulletin of the Medical Library Association. 2000; 88 (4):289-302
• 8. Bradley J, Marshall JG. Using scientific evidence to improve information practice. Health Libraries Review. 1995; 12 (3):147-157
• 9. Bibliometrics. In: International Encyclopedia of Information and Library Science. 2nd ed. London, UK: Routledge; 2003. p. 38
• 10. Åström F. Library and Information Science in context: The development of scientific fields, and their relations to professional contexts. In: Rayward WB, editor. Aware and Responsible: Papers of the Nordic‐International Colloquium on Social and Cultural Awareness and Responsibility in Library, Information and Documentation Studies (SCARLID). Oxford, UK: Scarecrow Press; 2004. pp. 1-27
• 11. Grover R, Glazier J. A conceptual framework for theory building in library and information science. Library and Information Science Research. 1986; 8 (3):227-242
• 12. Boyce BR, Kraft DH. Principles and theories in information science. In: W ME, editor. Annual Review of Information Science and Technology. Medford, NJ: Knowledge Industry Publications. 1985; pp. 153-178
• 13. Feehan PE, Gragg WL, Havener WM, Kester DD. Library and information science research: An analysis of the 1984 journal literature. Library and Information Science Research. 1987; 9 (3):173-185
• 14. Spink A. Information science: A third feedback framework. Journal of the American Society for Information Science. 1997; 48 (8):728-740
• 15. Jarvelin K, Vakkari P. Content analysis of research articles in Library and Information Science. Library and Information Science Research. 1990; 12 (4):395-421
• 16. Kim SJ, Jeong DY. An analysis of the development and use of theory in library and information science research articles. Library and Information Science Research. 2006; 28 (4):548-562. DOI: http://doi.org/10.1016/j.lisr.2006.03.018
• 17. Chu H. Research methods in library and information science: A content analysis. Library & Information Science Research. 2015; 37 (1):36-41. DOI: http://doi.org/10.1016/j.lisr.2014.09.003
• 18. Van Maanen J. Different strokes: Qualitative research in the administrative science quarterly from 1956 to 1996. In: Van Maanen J, editor. Qualitative Studies of Organizations. Thousand Oaks, CA: SAGE; 1998. pp. ix‐xxxii
• 19. Brookes BC. The foundations of information science Part I. Philosophical aspects. Journal of Information Science. 1980; 2 (3/4):125-133
• 20. Hauser L. A conceptual analysis of information science. Library and Information Science Research. 1988; 10 (1):3-35
• 21. McGrath WE. Current theory in Library and Information Science. Introduction. Library Trends. 2002; 50 (3):309-316
• 22. Theory and why it is important - Social and behavioral theories - e-Source Book - OBSSR e-Source [Internet]. Esourceresearch.org. 2017 [cited 23 April 2017]. Available from: http://www.esourceresearch.org/eSourceBook/SocialandBehavioralTheories/3TheoryandWhyItisImportant/tabid/727/Default.aspx
• 23. Babbie E. The practice of social research. 7th ed. Belmont, CA: Wadsworth; 1995
• 24. Glazier JD, Grover R. A multidisciplinary framework for theory building. Library Trends. 2002; 50 (3):317-329
• 25. Glaser B, Strauss AL. The discovery of grounded theory: Strategies for qualitative research. New Brunswick: Aldine Transaction; 1999
• 26. Smiraglia RP. The progress of theory in knowledge organization. Library Trends. 2002; 50 :330-349
• 27. McGrath WE. Explanation and prediction: Building a unified theory of librarianship, concept and review. Library Trends. 2002; 50 (3):350-370
• 28. Julien H, Duggan LJ. A longitudinal analysis of the information needs and uses literature. Library & Information Science Research. 2000; 22 (3):291-309. DOI: http://doi.org/10.1016/S0740‐8188(99)00057‐2
• 29. Pettigrew KE, McKechnie LEF. The use of theory in information science research. Journal of the American Society for Information Science and Technology. 2001; 52 (1):62-73. DOI: http://doi.org/10.1002/1532‐2890(2000)52:1<62::AID‐ASI1061>3.0.CO;2‐J
• 30. Kumasi KD, Charbonneau DH, Walster D. Theory talk in the library science scholarly literature: An exploratory analysis. Library & Information Science Research. 2013; 35 (3):175-180. DOI: http://doi.org/10.1016/j.lisr.2013.02.004
• 31. Rawson C, Hughes‐Hassell S. Research by Design: The promise of design‐based research for school library research. School Libraries Worldwide. 2015; 21 (2):11-25
• 32. Hider P, Pymm B. Empirical research methods reported in high‐profile LIS journal literature. Library & Information Science Research. 2008; 30 (2):108-114. DOI: http://doi.org/10.1016/j.lisr.2007.11.007
• 33. Bernhard, P. In search of research methods used in information science. Canadian Journal of Information and Library Science. 1993;18(3): 1-35
• 34. Blake VLP. Since Shaughnessy. Collection Management. 1994; 19 (1‐2):1-42. DOI: http://doi.org/10.1300/J105v19n01_01
• 35. Schlachter GA. Abstracts of library science dissertations. Library Science Annual. 1989; 1 :1988-1996
• 36. Kim MT. Research record. Journal of Education for Library and Information Science. 1996; 37 (4):376-383
• 37. Peritz BC. The methods of library science research: Some results from a bibliometric survey. Library Research. 1980; 2 (3):251-268
• 38. Nour MM. A quantitative analysis of the research articles published in core library journals of 1980. Library and Information Science Research. 1985; 7 (3):261-273
• 39. Enger KB, Quirk G, Stewart JA. Statistical methods used by authors of library and infor- mation science journal articles. Library and Information Science Research. 1989; 11 (1): 37-46
• 40. Jarvelin K, Vakkari P. The evolution of library and information science 1965-1985: A content analysis of journal articles. Information Processing and Management. 1993; 29 (1):129-144
• 41. Kumpulainen S. Library and information science research in 1975: Content analysis of the journal articles. Libri. 1991; 41 (1):59-76
• 42. Koufogiannakis D, Slater L, Crumley E. A content analysis of librarianship research. Journal of Information Science. 2004; 30 (3):227-239. DOI: http://doi.org/10.1177/0165551504044668
• 43. Hildreth CR, Aytac S. Recent library practitioner research: A methodological analysis and critique on JSTOR. Journal of Education for Library and Information Science. 2007; 48 (3):236-258
• 44. Gonzales‐Teruel A, Abad‐Garcia MF. Information needs and uses: An analysis of the literature published in Spain, 1990‐2004. Library and Information Science Research. 2007; 29 (1):30-46
• 45. Luo L, Mckinney M. JAL in the past decade: A comprehensive analysis of academic library research. The Journal of Academic Librarianship. 2015; 41 :123-129. DOI: http://doi.org/10.1016/j.acalib.2015.01.003
• 46. Creswell JW. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 3rd ed. Thousand Oaks, CA: SAGE; 2009
• 47. Strauss A, Corbin J. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Newbury Park, CA: SAGE Publications; 1990
• 48. Neuendorf KA. The Content Analysis Guidebook. 2nd ed. Thousand Oaks, CA: SAGE Publications; 2016
• 49. Dimitroff A. Research for special libraries: A quantitative analysis of the literature. Special Libraries. 1995; 86 (4):256-264
• 50. Cano V. Bibliometric overview of library and information science research in Spain. Journal of the American Society for Information Science. 1999; 50 (8):675-680. DOI: http://doi.org/10.1002/(SICI)1097‐4571(1999)50:8<675::AID‐ASI5>3.0.CO;2‐B
• 51. Powell RR. Recent trends in research: A methodological essay. Library & Information Science Research. 1999; 21 (1):91-119. DOI: http://doi.org/10.1016/S0740‐8188(99)80007‐3
• 52. Crawford GA. The research literature of academic librarianship: A comparison of college & Research Libraries and Journal of Academic Librarianship. College & Research Libraries. 1999; 60 (3):224-230. DOI: http://doi.org/10.5860/crl.60.3.224
• 53. Julien H, Pecoskie JJL, Reed K. Trends in information behavior research, 1999-2008: A content analysis. Library & Information Science Research. 2011; 33 (1):19-24. DOI: http://doi.org/10.1016/j.lisr.2010.07.014
• 54. Fidel R. Qualitative methods in information retrieval research. Library and Information Science Research. 1993; 15 (3):219-247
• 55. Hernon P, Schwartz C. Reflections (editorial). Library and Information Science Research. 2003; 25 (1):1-2. DOI: http://doi.org/10.1016/S0740‐8188(02)00162‐7
• 56. Priestner A. Going native: Embracing ethnographic research methods in libraries. Revy. 2015; 38 (4):16-17
• 57. Blaikie N. Approaches to social enquiry. Cambridge: Polity; 1993
• 58. Johnson RB, Onwuegbuzie AJ. Mixed methods research: A research paradigm whose time has come. Educational Researcher. 2004; 33 (7):14-26
• 59. Westbrook L. Qualitative research methods: A review of major stages, data analysis techniques, and quality controls. Library & Information Science Research. 1994; 16 (3):241-254
• 60. Fidel R. Are we there yet?: Mixed methods research in library and information science. Library and Information Science Research. 2008; 30 (4):265-272. DOI: http://doi.org/10.1016/j.lisr.2008.04.001

© 2017 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Continue reading from the same book

Edited by Sonyel Oflazoglu Dora

Published: 28 June 2017

By Seyma Demir and Yasemin Yildirim Usta

1964 downloads

By Maria Cecília de Souza Minayo

2168 downloads

By Yusuf Bilgin

3267 downloads

IntechOpen Author/Editor? To get your discount, log in .

Discounts available on purchase of multiple copies. View rates

Local taxes (VAT) are calculated in later steps, if applicable.

Support: [email protected]

ALA Research & Library Topics

Ala library and information resource center, frequently asked questions about books and reading.

Answers to the questions that we receive most often about books and reading.

Resource Guides

Providing information on the work of the American Library Association, as well as subject-specific resources both for and about libraries.

Professional Resources: A to Z Index of Topics

Professional resources for librarians on a wide range of topics, in alphabetical order.

ALA Publications

Share This Page

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals

Library science articles from across Nature Portfolio

Latest research and reviews.

The effect of prosocial behavior and its intensity on doctors’ performance in an online health community

• Yuguang Xie
• Shuping Zhao

What factors influence the intention to adopt blockchain technology in accounting education?

• Hamood Mohammed Al-Hattami

Digital financial inclusion in micro enterprises: understanding the determinants and impact on ease of doing business from World Bank survey

• Mohammad Asif
• Mohammad Wasiq

Big data visualisation in regional comprehensive economic partnership: a systematic review

What determines digital accounting systems’ continuance intention? An empirical investigation in SMEs

• Faozi A. Almaqtari

What makes deceptive online reviews? A linguistic analysis perspective

News and Comment

Pandemic publishing poses a new COVID-19 challenge

The scientific community’s response to COVID-19 has resulted in a large volume of research moving through the publication pipeline at extraordinary speed, with a median time from receipt to acceptance of 6 days for journal articles. Although the nature of this emergency warrants accelerated publishing, measures are required to safeguard the integrity of scientific evidence.

• Adam Palayew
• Ole Norgaard
• Jeffrey V. Lazarus

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

University of Washington Information School

• Advising & Support
• Capstone Projects
• Upcoming Info Sessions
• Videos: Alumni at Work
• Request more information

Library and Information Science

Researchers

• Marika Cifor
• Chris Coward
• Lorcan Dempsey
• Tracie D. Hall
• Joseph Janes
• Chris Jowaisas
• Michelle H. Martin
• Carole Palmer
• Joseph Tennis
• Melanie Walsh
• Nicholas Weber
• Stacey Wedlake
• Helene Williams

Current Projects and Grants

Empowering neurodivergent librarians to lead inclusion in libraries.

PIs: Hala Annabi, Michelle Martin Funding: Institute of Museum and Library Services, $491,500 Dates: 2022-2024

Valuing Library and Archives Labor: Assessing Internship and Fellowship Implications for the Library and Archives Community

PI: Marika Cifor Funding: Institute of Museum and Library Services, $318,989 Dates: 2022-2024

Misinformation Escape Room: Supporting Libraries as Hubs for Misinformation Education

PIs: Chris Coward, Jin Ha Lee Funding: Institute of Museum and Library Services, $249,691 Dates: 2021-2023

The Community Archives Center for Tacoma

PIs: Jason Young, Chris Jowaisas, Marika Cifor Funding: Institute of Museum and Library Services, $56,423 Dates: 2021-2023

Project VOICE

PIs: Michelle Martin, Ricardo Gomez Funding: Institute of Museum and Library Services, $169,201 Dates: 2019-2022

TASCHA Legacy Grant

PI: Chris Coward Funding: Bill & Melinda Gates Foundation, $15,918,283 Dates: 2017-2049

Open Data for Public Good: Data Literacy Education for Public Information Professionals

PI: Carole Palmer Funding: Institute of Museum and Library Services (IMLS), $690,858 Dates: 2016-2020

Full Results

Customize your experience.

Library and Information Science

What are libguides, how to add and edit guide content, examples of libguides.

• Developing Your Research Projects and Search Strategies
• Sources for Finding Articles and More Content
• Evaluating Sources
• Citation Managers and Citation Writing Style Guides This link opens in a new window
• iSchool Literature and Systematic Reviews This link opens in a new window
• School Media This link opens in a new window
• LIS Student Publication Venues
• SU Librarian Career Series Webinars
• Resources for Online & Distance Students
• Syracuse University Libraries Campus & Virtual Events
• Contact your Librarian and SU Libraries
• A guide is a webpage where you add and share content. To the public, they are referred to as research guides instead of LibGuides.
• Each guide can be organized into pages
• Each page contains content boxes
• Each box holds content items, like text, links, images, and databases
• Integrated reusability means you can create content once in LibGuides and reuse it again and again
• Colleges and university libraries' LibGuides are consistent in color, font, and headings, and follow the college or university's accessibility standards (e.g. adding alt text for images, confirming that files are accessible documents, captioning videos and tutorials, etc.)

Excerpted from  https://www.springshare.com/libguides/ .

• Getting Started with LibGuides

Contact Brenna with any questions about creating LibGuides.

Besides the guide for LIS which you are currently in, feel free to take a look at any of the guides from my liaison librarian colleagues here:

SU Libraries Research Guides

• Next: Developing Your Research Projects and Search Strategies >>
• Last Updated: Sep 4, 2024 2:29 PM
• URL: https://researchguides.library.syr.edu/library_science
• Print This Page

• Library of Congress
• Research Guides
• Main Reading Room

Library and Information Science: Online Resource Guide

Introduction.

• Subscription Databases
• Library of Congress Website
• External Resources
• Frequently Asked Questions
• Using the Library of Congress

History, Humanities & Social Sciences : Ask a Librarian

Have a question? Need assistance? Use our online form to ask a librarian for help.

Chat with a librarian , Monday through Friday, 12-4pm Eastern Time (except Federal Holidays).

Authors: Peter Armenti, Reference Specialist, Researcher and Reference Services Division

Elizabeth Brown, Reference Specialist, Researcher and Reference Services Division

Created: June 17, 2021

Last Updated: December 16, 2022

This guide provides an introduction to library and information science resources available at the Library of Congress and on the Web. Its intended audience is librarians, library technicians, and library science students. Special emphasis is given to freely available online resources that can be used by librarians and library science students without ready access to print resources and subscription databases.

The guide's Frequently Asked Questions page provides answers to a number of the most common library and information science questions, and questions in related fields, received by the Library of Congress through its Ask a Librarian service.

• Next: Subscription Databases >>
• Last Updated: Jul 3, 2024 11:51 AM
• URL: https://guides.loc.gov/library-science

• Thesis Proposal
• Engineering dissertation
• Medical dissertation
• Technology dissertation
• Planning a Dissertation
• Strategy to buy dissertation
• Method to order your paper
• Motivation for Coursework

Dissertation Topics In Library And Information Science

Introduction.

Over and above, when you need to write something for a particular subject, it makes sense to consider what you want to write about. Writing may not be for you, but as a student, this is a very crucial requirement that you need to accomplish in order for you to graduate on time.

Are you in need of profound topics to write about in your scholarly thesis that has something to do with library and information science? It is true that deciding for the appropriate topic that you can consider for your paper is an exhausting stage. However, this can be outlasted if you will give it your best shot in researching for the best possible subjects that you know are worth talking about. Luckily, you can go over various offline and online resources and unveil from there the topic that precisely matches your writing skills.

Here is a collection of dissertation topics in Library and Information Science that you can delve into:

• Tackle change management in the library environment that is especially intended for organizational renewal
• The role and accreditation of the academic library in undergraduate, graduate as well as other teaching programs
• Benchmarking as an approach to obtain results; your library’s use of benchmarking and the outcomes, issues and opportunities
• Discuss collection development strategies for scholastic programs
• Time study or cost of services, programs and collections in the library, including the detailed description of the approaches and results at your library
• Developing a yearly scholastic agenda for the library. Discuss the benchmarks and performance measure
• Talk about electronic resources and their significant impact on scholastic library as the intellectual and social core of the school
• Development programs and fund raising for libraries
• Tackle the remarkable impact of cultural and demographic changes on library services
• Discuss the linkage between life-long learning and libraries: what this conveys and what are the necessary steps that need to be taken?
• The changing role of the library in the information economy
• Talk about the literacy programs carried out in the library environment

Students nowadays do not need to suffer from choosing what suitable topic to write about. If you are tasked to discuss something that is connected with library and information science, you will surely have countless of possible options to pick from. Take a look at the topic ideas provided in the list above and from there you can decide which one is right for you.

• Buy Your Dissertation

©Copyright 2010 - 2024 All Rights Reserved | IBC-research.org

Knowledge structure transition in library and information science: topic modeling and visualization

• Open access
• Published: 12 August 2020
• Volume 125 , pages 665–687, ( 2020 )

Cite this article

You have full access to this open access article

• Yosuke Miyata   ORCID: orcid.org/0000-0002-5239-5396 1 ,
• Emi Ishita   ORCID: orcid.org/0000-0002-1398-8906 2 ,
• Fang Yang 3 ,
• Michimasa Yamamoto 3 ,
• Azusa Iwase 4 &
• Keiko Kurata   ORCID: orcid.org/0000-0002-8486-2438 1  

5337 Accesses

23 Citations

Explore all metrics

The purpose of this research is to identify topics in library and information science (LIS) using latent Dirichlet allocation (LDA) and to visualize the knowledge structure of the field as consisting of specific topics and its transition from 2000–2002 to 2015–2017. The full text of 1648 research articles from five peer-reviewed representative LIS journals in these two periods was analyzed by using LDA. A total of 30 topics in each period were labeled based on the frequency of terms and the contents of the articles. These topics were plotted on a two-dimensional map using LDAvis and categorized based on their location and characteristics in the plots. Although research areas in some forms were persistent with which discovered in previous studies, they were crucial to the transition of the knowledge structure in LIS and had the following three features: (1) The Internet became the premise of research in LIS in 2015–2017. (2) Theoretical approach or empirical work can be considered as a factor in the transition of the knowledge structure in some categories. (3) The topic diversity of the five core LIS journals decreased from the 2000–2002 to 2015–2017.

Similar content being viewed by others

Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study

Mapping the evolution of library and information science (1978–2014) using topic modeling on lisa.

Terminological (di) Similarities between Information Management and Knowledge Management: a Term Co-Occurrence Analysis

Explore related subjects.

• Artificial Intelligence

Avoid common mistakes on your manuscript.

Introduction

Investigating the kind of research being done in a field of research involves understanding the knowledge structure of that field and, in turn, revealing the identity of that field. In library and information science (LIS), such investigations have been undertaken since the 1970s using a variety of approaches. However, the topic modeling approach has recently garnered considerable attention. This approach is a type of big data analysis of words in articles that can reveal hidden relationships between them and can sometimes find non-thematic topics. This article uses this topic modeling approach to clarify the knowledge structure of LIS.

Literature Review

Content analysis.

Content analysis is used to identify and record the meanings of documents in a systematic and quantitative way (Allen and Reser 1990 ). In LIS, surveys to examine trends of research based on content analysis began in the 1970s. Atkins ( 1988 ) conducted a content analysis of research articles published from 1975 to 1984 and found a list of 58 subjects in LIS. As recently as the 2000s, information retrieval (IR) was consistently the subject of approximately 30% of research articles in LIS (Jarvelin and Vakkari 1993 , Pettigrew and McKechnie 2001 , Koufogiannakis et al. 2004 , Miyata et al. 2010 ).

A survey by Tuomaala et al. ( 2014 ), which was a follow-up to the analysis by Jarvelin and Vakkari ( 1993 ), examined data from 718 articles in 2005. They found that information storage and retrieval (30%) was the most common subdomain of LIS, followed by scientific and professional communication (24%), library and information service activities (17%), and information seeking (12%). This survey also revealed that other research topics in LIS had been rarely studied (Tuomaala et al. 2014 ).

Some studies have conducted a content analysis of LIS research by country, such as Denmark (Kajberg 1996 ), Japan (Sugiuchi et al. 2011 ), Spanish-speaking countries (Kawalec 2013 ), and India (Dora and Kumar 2017 ).

Content analysis is the method of reading articles to identify topics and assigning prepared subject headings or classification numbers to them. It is necessary to prepare an appropriate classification system to this end. Jarvelin and Vakkari ( 1993 ) proposed a classification system, but Tuomaala et al. ( 2014 ) showed that generative subjects needed to be added to it.

Content analysis is limited in tracking long-term transitions in research. Furthermore, because it is based on manual work, the number of articles analyzed is always limited.

Citation Analysis

White and Griffith ( 1981 ) mapped information science (IS) using authors as units of analysis and the co-citation of pairs of authors as the variable. They chose 39 authors, formed author pairs using a citation index to determine the number of co-cited articles, and plotted a co-citation matrix in two dimensions using MDSCAL. They found that “information science lacks a strong central author, or group of authors, whose work orients the work of others across the board, and the field consists of several specialties around a weak center” (White and Griffith 1981 , p. 343). Also, they successfully identified and visualized specialties that constitute IS. Their results were confirmed by the consensus among researchers on the correctness of the knowledge structure that they had attributed to IS. Moreover, the study has been appreciated as pioneering the use of a quantitative approach to identify the knowledge structure of a research field.

Since then, a large number of studies have used author co-citation analysis. For example, Zhao and Strotmann ( 2008 ) introduced author bibliographic coupling analysis. They observed that research on webometrics was active in the years 2001–2005 but appeared to have declined since. Similarly, research on information retrieval was no longer an active research area although it had attracted a number of researchers in the years 1996–2000. Yang et al. ( 2016 ) proposed author keyword coupling analysis (AKCA) to visualize the intellectual structure of information science and used the data analyzed by Zhao and Strotmann ( 2008 ). They labeled factors obtained by the AKCA as bibliometrics, IR, and information behavior, mapping of science, research performance, impact and ranking, patent analysis, and digital library. Citation analysis has been most frequently used to elucidate trends in a research field, and is becoming more sophisticated over time.

Another approach to co-citation is document co-citation. Hou et al. ( 2018 ) analyzed emerging trends and new developments in information science in the years 2009–2016 through document co-citation. In their study, they found that the positions of certain core topics found in the previous studies (i.e., information retrieval, webometrics, and citation behavior) had been replaced by other topics (i.e., scientometric indicators, citation analysis, scientific collaboration, and information behavior) in the recent period.

Analysis of Co-occurring Words

In addition to analyzing the structure of a field by grouping authors through citation information in articles, some studies have used co-occurrence word analysis. Co-occurrence word analysis has been used to clarify the relatedness of co-occurring words from different articles and has often been used to analyze a combination of co-citation and co-author relations to identify topics. Milojevic et al. ( 2011 ) identified three main branches of LIS—LS (academic/public/school librarianship, information literacy, technology, policy, the Web, knowledge management, and others), IS (information retrieval, Web search, catalogs, and databases), and scientometrics/bibliometrics (SCI-BIB).

Topic Modeling and LDA

The last 20 years have witnessed a rise in the number of studies using topic modeling in a large number of articles. Probabilistic Latent Semantic Analysis (PLSA) is a traditional method used to classify a large amount of bibliographic data. Wang and McCallum ( 2006 ) presented Topics over Time (TOT), which is a topic modeling method that models timestamp values in order to discuss the topics’ occurrence and correlation changes over time.

Blei et al. ( 2003 ) proposed latent Dirichlet allocation (LDA) as an approach that represents topic in documents by using a mixture of words to analyze how topics had changed over time. Since then, topic modeling has focused on LDA. Blei and Lafferty ( 2006 ) applied LDA to analyze trends in the journal Science . They collected 30,000 articles and gleaned 7.5 million words from them by stemming each term to its root. They also removed function terms as well as terms occurring fewer than 25 times. LDA has also been used for topic modeling in computer linguistics (Hall et al. 2008 ), statistics (De Battisti et al. 2015 ), international speech communication (Liu et al. 2015 ), and software engineering (Dam and Ghose 2016 ).

Some studies have examined trends of research in LIS using LDA, as shown in Table  1 . Sugimoto et al. ( 2011 ) indicated that the main topics in LIS had changed significantly from those in the initial period (1930–1969) to what was then 2000 through 2009. The main topics from 2000 through 2009 were information use; the Internet; information-seeking behavior; information retrieval and user centeredness; and information retrieval and classification. The study showed that LDA can be used to map trends in LIS over long periods.

Lu and Wolfram ( 2012 ) identified 20 topics and presented an LDA map consisting of informetric laws, scientific impact evaluations, webometrics and search engine analysis, and information retrieval. They concluded that “the overall layout of the clusters in the LDA map is more distinctive than the word-based maps” (Lu and Wolfram 2012 , p. 1981).

Yan ( 2014 ) found research topics as follows: Web information retrieval, citation and bibliometrics, system and technology, health science, the h-index, online communities, data preservation, social media, and Web analysis. Yan ( 2015 ) also stated that topics related to online technologies, informetrics, information retrieval systems, health communication and informetrics, and online social networks have become popular over the last few decades. On the contrary, topics concerning books, collections, and cataloging have declined in popularity.

Figuerola et al. ( 2017 ) applied LDA to identify and label the main topics and categories in the corpus. Their quantitative results identified 19 important topics that were grouped into four areas: processes, information technology, library, and specific areas of information application.

Kurata et al. ( 2018 ) analyzed LDA results for five LIS journals by the ratio of articles. They showed that a few topics were stable in the periods and others were influenced by journals’ orientation (i.e. library science or information science) and publication periods. Lamba and Madhusudhan ( 2019 ) mapped the topics in DESIDOC Journal of Library and Information Technology for the period of 1981–2018 using LDA and found that bibliometrics, information and communication technology (ICT), information retrieval, and user studies were highly researched areas in India during the period.

Studies using LDA in subdomains of LIS have also been conducted, including information retrieval (Chen et al. 2017a ), knowledge organization (Joo et al. 2018 ), and electronic health records (Chen et al. 2017b ).

Research Questions

Understanding the knowledge structure of research fields using traditional research methods was intended to provide a big picture of these areas (Borner et al. 2003 ). In other words, the aim was to draw a map of the given research area consisting of subfields. On the contrary, research using LDA is intended to clarify the changes in topics over time, and it can help reveal new aspects of research on LIS. The purpose of this research is to understand the knowledge structure of LIS using specific topics identified by LDA and visualize the big picture of the field consisting of them. Moreover, it describes the transition of the knowledge structure between specific periods (2000–2002 and 2015–2017).

We chose academic articles published in core journals as a source of information to reflect the knowledge structure of LIS. As a result, our datasets are not very large. Then we decided to use the full text, not the article title and abstract, although all previous studies using LDA to investigate research trends in LIS have used titles and abstracts. This is because Syed and Spruit ( 2017 ) applied LDA to four kinds of datasets of articles; two of them were title and abstracts, the other two were full texts. This showed that terms of topics obtained by LDA had not been appropriate in small datasets constructed with titles and abstracts.

We selected the two periods (2000–2002 and 2015–2017) to understand the transition of the knowledge structure in LIS. The period 2015–2017 coincides with the beginning of this research, and the period 2000–2002 (15 years ago) is a sufficient amount of time for a marked transition to have occurred. This is especially true given that 10–20 years has been chosen as the period to observe changes in previous research (Jarvelin and Vakkari 1993 , Tuomaala et al. 2014 ). Moreover, the time around 2000–2002 was when the Internet was becoming popular and its influence was becoming noticeable.

We refer not only to its top five most frequently used terms but also its title, abstract, and full text because topic label in previous studies was difficult to understand. Additionally, we categorize the topics and analyze the knowledge structure of the field by visualizing the distance between pairs of labeled topics using a two-dimensional (2D) map. Next, we examine the transition of the knowledge structure in the two periods mentioned. Finally, we analyze the relationship between journals and topics, which has not been considered in previous research.

Summarizing the above, the research questions for this study are as follows.

Which categories are identified as research areas using the 2D map?

What kinds of transitions are seen in the two periods among categories and topics?

What kinds of relationships are observed between topics and journals, and what are the transitions in this relationship between the periods?

Data Collection

The data used for topic modeling were derived from research articles published in core LIS journals. We selected journals that were peer-reviewed, had high prestige among researchers, belonged to the LIS domain, were not narrowly specialized, and continually published a sufficient number of articles per year. Although it is possible to obtain metadata for a large number of articles from databases, such databases include not only peer-reviewed journals, but also magazine articles and other non-peer-reviewed sources. Moreover, the journals selected using the Journal Citation Report ( JCR ) include those that are said not to be considered core journals in LIS. We thus selected five journals given that previous studies identified core journals.

Nixon ( 2014 ) reviewed tiered or ranked lists of LIS journals, and proposed an expert opinion study and a citation study for such research. Kohl and Davis ( 1985 ) asked deans of library schools accredited by the American Library Association and directors of the Association of Research Libraries about representative journals in LIS. Follow-up studies were published by Blake ( 1996 ), Nisonger and Davis ( 2005 ), and Manzari ( 2013 ). The ranking by deans of LIS faculty in each result are summarized in Table  2 . Because of the newness and the sample size, we selected the top five journals according to the result by Manzari ( 2013 ) as our datasets.

Information Processing & Management (IPM) , J ournal of Documentation (JDOC) , Journal of the Association for Information Science and Technology ( JASIST ), Library & Information Science Research (LISR) , and Library Quarterly (LQ) were selected. The articles that were included in special issues such as “Special Issue; Digital Libraries” (Volume 51, Issue 4) were excluded from our sample because of their negative influence on topic extraction. We acquired the full text of 1648 articles from HTML files from each journal’s online platform. This number of articles is similar to the small number of articles surveyed by Syed and Spruit ( 2017 ). We also considered it appropriate to use the full text of the articles. The number of the articles in two periods is shown in Table  3 .

Experimental Settings

Preprocessing for the full text was performed as follows: (1) All letters were converted to lower case. (2) Stop words from the NLTK library (available at  https://www.nltk.org/nltk_data/ ) (e.g., a, it, not, etc.), functional words, words containing numbers, and words frequently used in research articles (e.g., table , figure ) were removed. (3) The remaining words were stemmed by Porter’s algorithm; (4) Frequently used words that had appeared in more than 90% of our datasets and rare words appearing fewer than nine times were removed.

LDA was performed using Python’s gensim library (available at  https://radimrehurek.com/gensim/index.html ) for each period. The number of iterations was set to 500 and the other parameters were set according to the standard setting of gensim. The number of topics was set to 30 based on previous research.

Labels were assigned to the 30 topics in each period. Topic labels were determined by the agreement of the authors based on the top 10 most frequently used terms as well as the metadata and full text with a probability of over 0.5 for a given topic. In this study, preliminary analysis was conducted with multiple parameter settings and different numbers of topics. The most interpretable settings were then selected.

LDAvis was then used to interpret the results visually. It can plot topics on a 2D scale and the sizes of topics in the plot represent the ratios of topic probabilities. Topic probabilities are calculated by aggregate of the probability of all articles in each topic. We used pyLDAVis (available at  https://github.com/bmabey/pyLDAvis ), which is a Python implementation of LDAVis (available at  https://CRAN.R-project.org/package=LDAvis ).

Based on their location and proximity in the plots, the topics were categorized. We analyzed changes in the categories and topics between 2000–2002 and 2015–2017. By comparing the distributions of topic probabilities, changes in the specializations of the journals were explored.

Labeling the Topics

The process of labeling is explained using the topic Modeling student information - seeking behavior in the period 2000–2002 as an example. First, we examined the top five most frequently used terms in this topic ( student , search , device , user , and database ) indicated by LDA. Considering the topic labels from these five words, student or users searching databases or some device was assumed to be appropriate. Then, we examined the titles, abstracts, and full texts of the articles with probabilities of higher than 0.5 for this topic (eight articles). The titles and authors of articles with a probability of higher than 0.7 for this topic are shown in Table  4 . Half of the articles focused on students and search behavior in various search systems was targeted rather than specific databases. Moreover six out of eight articles conducted theoretical modeling of information behavior than an empirical survey. For example, the first article in Table  4 modeled the user’s coding of information received from an IR system using Kintsch’s theory. Based on the above features, this topic was labeled Modeling student information - seeking behavior . In this example, the top five most frequently used terms did not represent this topic.

All thirty topics were similarly labeled. The results are as shown in Table  5 for 2000–2002 and Table  6 for 2015–2017.

Categorization of Topics

Thirty topics were placed on a 2D plot for each of the two periods and were categorized based on their locations and contents. Starting from where similar topics were overlapped, the extent to which the topics around the starting point could be included in one category was examined. If two topics were located in the same place but had different content, they were classified into different categories. We have reported briefly categories between periods in Miyata et al. ( 2018 ), but here we added description of each category and discussed insights in transition of categories between periods.

Categories in 2000 – 2002. Figure  1 shows a 2D plot of thirty topics and categories in 2000–2002. The topics were grouped into the following six categories: Information Retrieval , Information Search and User , Library , Scholarly Communication , Library and Information Science , and Bibliometrics .

Topics and categories in 2000–2002

The Information Retrieval category was used to identify the place (location) where several topics ( Multilingual IR , Image retrieval , and Word similarity ) overlapped. This category denoted various types of IR, and topics on theories and methods supporting the IR systems. The topics Query expansion and database compression and Stemming and lemmatization were also included into the category because they are related methods to IR. Although the topic Document analysis overlapped with Modeling student information - seeking behavior , only Document analysis was included in the Information Retrieval category according to the labels.

The Bibliometrics category consisted of only the topic Bibliometrics and statistical method .

The Information Search and User category was used to identify overlapping topics related to search behavior, such as Interaction in information seeking behavior . Despite this, the category also included topics focusing on system development (e.g., Information architecture and UI ). Therefore, rather than Information Search Behavior , Information Search and User was chosen as the name of the category to denote the broader context.

The Library category was used to identify the overlap between Roles of public library and Library and print media . The topic Research and education in LIS which did not focus on libraries was included in the Library category. This is because in the map, it overlapped almost completely with the Library category but was significantly far from the LIS category, which would otherwise be considered its natural abode based on the content.

The Scholarly Communication category was used to identify the overlap between the topics Internet impact on scholarly communication and Social network in discipline . The topic Economy of digital academic publishing was close to the Library category, but its contents were unrelated to the library and instead pertained to scholarly communication.

As Topic Epistemology in LIS had the unique feature in that it philosophically examines LIS, the Library and Information Science category was created as an independent category.

Categories in 2015 – 2017. Figure  2 shows a 2D plot of the thirty topics and the categories in 2015–2017. The topics in 2015–2017 were divided into the following five categories: Information Retrieval , Information Search and User , Library , Scholarly Communication , and Tweet Analysis .

Topics and categories in 2015–2017

The Information Retrieval category was based on two topics, IR algorithm and Classification and selection algorithm , related to the IR algorithm.

The Tweet Analysis category was centered on the topic Twitter . It included topics analyzing big data, with tweets (posts to SNS) such as those on Feature extraction from the Web , for example. The topic Recommendation system overlapped with the topic IR algorithm in Information Retrieval category but did not deal with an IR algorithm. It consisted of articles on the analysis of tweets. Therefore, the topic Recommendation system was included in the Tweet Analysis category.

The Information Search and User category spanned from the topic Health information search behavior , located on the left, to the topic Search strategies during task , located at the center. This category included topics on typical information behavior research and ones focusing on systems for searching, such as News sites and business intelligence , the effects of using SNS ( Emotion in social media ), and the attention or interests in search in diverse environments ( Motivation ). Therefore, the category was called Information Search and User instead of Information-seeking Behavior.

The Scholarly Communication category summarized topics dealing with the structure of academic communication ( Network of academic knowledge ), and those related to the evaluation and analysis of research achievements ( Research evaluation ). The topic Research data sharing was close to Information Search and User category but was included in Scholarly Communication because it had been a latest topic of that.

Library was a category consisting only of the topic Philosophical approach to the library and document . This topic deals with libraries and documents from a philosophical perspective (e.g., public space and publicity) and, according to its content, this category is different from Information Search and User or Scholarly Communication.

Transition of Categories Between Periods

Overview of changes. The categories Library and Information Science and Bibliometrics were only identified in the 2000–2002 period. The category Bibliometrics included only the topic of bibliometrics and statistical method , which contained theoretical articles on bibliometrics, in 2000–2002. In 2015–2017, research applying bibliometric approaches, such as the topic of research evaluation belonged to the category Scholarly Communication and the category Bibliometrics was no longer independent.

The category identified only in the 2015–2017 period was Tweet analysis. In 2000–2002, there was no work on the analysis of big data, and so this topic was not identified.

Changes in the same category . In the category Information Retrieval, the number of topics decreased significantly in 2015–2017. This category in 2000–2002 contained theoretical views on IR system development, and empirical analyses of different kind of IR systems. However, the category in 2015–2017 contained only two topics, both focusing on more abstract algorithms.

The category Library in 2000–2002 contained topics focusing on library services and functions of the library. This category in 2015–2017 contained only one topic focusing on the library function. The category Users and Information Search in 2000–2002 contained 10 topics and then 16 in 2015–2017. This category was altered to cover broader concepts in the periods 2000–2002 and 2015–2017. In 2000–2002, it included topics focusing on information search using a new technology or system on the Web in the traditional framework of information search behavior (e.g., Topic Web information-seeking behavior of children and students). On the other hand, in this category in 2015–2017, there is no longer a topic that emphasizes the use of the Web for information behavior. As the Web has become the premise of research, focus of topics became a specific context, such as health information or various communities online. Furthermore, the category included topics that focused on emotions and motivation on the Web and was not limited to traditional information search behavior directed toward a clear goal.

In the category Scholarly Communication, there were four topics in 2000–2002 and six in 2015–2017. In 2000–2002, three of its four topics focused on changes to scholarly communications through the Internet and digital environments. In 2015–2017, the topics included new features and systems of scholarly communication, such as open access and data sharing, due to the influence of the Internet and digital environments (e.g., Scholarly communication and OA ). Furthermore, it included topics related to the evaluation of research results as scholarly communication using bibliometrics. (e.g., Analysis of authors ).

Relationships Between Topics and Journals

We analyzed the transitions in the journals based on the topics. For each topic, the characteristics of the journals were viewed in terms of probability distributions of topics per journal and calculated as follows: (1) sum of topic probability for each article per journal and (2) standardize the value by dividing by the number of articles. Tables  7 and 8 show the relationships between topics and journals using probability distributions of topics per journal.

Topics with a probability higher than 0.1 were regarded as those with which the journal mainly dealt. In 2000–2002, the probability of Roles of public library , (Topic 25) for LQ was 0.42, the highest value among all topics in all journals. Approximately 40% of the contents of the journal as a whole had some relationship with the topic Roles of public library . It was followed by the topics Research and education in LIS (Topic 24) and Epistemology in LIS  (Topic 26). LQ had strong relationships with topics on theoretical and philosophical approaches to LIS. On the contrary, LQ had a near zero probability for IR-related topics and those focusing on empirical approaches to library services, such as Library services on the Internet  (Topic 23).

The topic specialties were also seen in other journals. The topic Roles of public library also had the highest probability in LISR (0.22). In IPM , IR-related topics, such as Word similarity (for document retrieval) (Topic 11) (0.10) and Query expansion and database compression (Topic 8) (0.13) had probabilities higher than 0.1. In JDOC , only Economy of digital academic publishing (Topic 27) (0.15) had a probability higher than 0.1.

On the contrary, JASIST in 2000–2002 did not contain a topic exceeding a probability of 0.1 but had no topic with a near-zero probability ( p  < 0.01). It can be concluded that JASIST uniformly treated various research topics in LIS.

In 2015–2017, LQ was highly biased toward Health information search behaviors (Topic 6) (0.45) and Philosophical approach to the library and document (Topic 19) (0.26). The topic of Health information search behaviors was also highly rated in JDOC and LIS R (0.25 for both).

IPM had the same tendency in 2000–2002, whereby IR-related topics such as IR algorithm (Topic 2) (0.13) and SNS-related topics such as Twitter (Topic 30) (0.12) had a probability of over 0.1.

The topic Research evaluation (Topic 24) had the highest probability of 0.17 in JASIST , which did not have any other topic over 0.1 in 2000–2002. However, JASIST also maintained topic generality because it did not have a topic with a near-zero probability.

We measured topical diversity by calculating the standard deviation of topic distribution for each journal in the two periods. A large standard deviation indicated a large bias in the topics, and a small standard deviation indicated a diversity of topics. Figure  3 shows the transition of standard deviation in the two periods.

Comparison of standard deviation in the two periods by each journal

For each journal, LQ had the largest standard deviation in the two periods, which increased in 2015–2017. Its bias toward topics was the largest of all journals. Meanwhile, JASIST in 2000–2000 had the smallest standard deviation and bias for topics. JASIST had a smaller standard deviation in each period and the largest diversity of topics.

In 2000–2002, the standard deviation of JDOC had a similar value to that of IPM. But in 2015–2017, the bias of JDOC became very large and followed that of LQ . In other words, the diversity of JDOC decreased the most in five journals. IPM and LISR were more or less stable.

The standard deviations for all journals in 2015–2017 were higher than those in 2000–2002. This means that bias in topic distribution increased. This result indicates that topic diversity in core LIS journals decreased compared with that in 2000–2002.

For the categorization of topics into research areas (RQ1), we found some commonalities with the results of previous studies using co-citation analysis and content analysis. For example, the classic research by White and Griffith ( 1981 ) clarified the knowledge structure in IS by author co-citation analysis. They identified the five categories in IS, and the three thematic research areas of it (i.e. IR, Bibliometrics, Scientific Communication) which are all included in the six categories found in the 2000–2002 period in our results. Furthermore, the other two areas excluded Bibliometrics persisted in 2015–2017. Thus, research areas discovered 40 years ago have persisted to the present in some form.

Comparing with other LDA studies in LIS, the declining library topics in our results was similar to the study by Sugimoto et al. ( 2011 ) and that by Figuerola et al. ( 2017 ). Concerning information retrieval and informetric laws, which were two of the main clusters in a study by Lu and Wolfram ( 2012 ), our two-period and journal-based analysis revealed a drop in the LIS domain. In contrast to Yan ( 2015 ), Journal based analysis indicated a decline of the diversity of topics in all five journals. The difference could be attributed to the fact that Yan analyzed long-term macro trends from the early days of LIS, while our analysis concerned trends after 2000. Especially, JASIST ’s specialization in bibliometrics coincidenced with a citation analysis by Nicolaisen and Frandsen ( 2015 ).

The key points of the transition of the knowledge structure in LIS from 2000–2002 to 2015–2017 (RQ2) are as follows:

The Internet became the premise of research in LIS.

A relationship was established between research on theoretical modeling and its application.

First, in 2000–2002, Internet impact and Web information seeking had already been identified as keywords for topics. By 2015–2017, the use of the Web had become a premise and ceased to be identified as a topic. Instead of discussing the holistic effects of the Internet, the use of new services and means of communication, such as Twitter in the Tweet Analysis category and open access in the topic Scholarly communication and OA , were emphasized as research issues.

Second, the question of whether given research is a theoretical approach or an empirical work can be considered a factor in the transition of the knowledge structure in the categories Information Retrieval, Bibliometrics, as well as various topics related to IR which were identified in Information Retrieval in 2000–2002. However, only one topic focused on IR algorithms in 2015–2017. Hjørland ( 2017 ) cited Bawden’s blog post about IR following: “[Thirty] years ago, it [IR] was clearly part of LIS, and very few computer scientists took it seriously; 15 years ago it was spread across the boundary lines of the disciplines; now, the party line is that it is an integral part of computer science.” This indicates that research on IR is published in journals dedicated to fields other than the core LIS journals. We identified the category Tweet Analysis as a new category in 2015–2017. This can be considered an applied research from of IR in the Web environment. Bibliometrics in 2000–2002 contained only one topic, and in 2015–2017 was no longer identified. This indicates that research applying bibliometric methods came to be part of scholarly communication. With the spread (generalization) of the bibliometric method, there is no topic on which bibliometrics itself focuses.

Regarding the relationship between topics and journals (RQ3), we found that the topic diversity of the five core LIS journals decreased from 2000–2002 to 2015–2017. One reason for a decline in topic diversity is that these journals have become highly competitive, and the range of topics for which it is easier to obtain acceptance for publication in these journals has narrowed regardless of the subject.

We explored the transition of knowledge structure of LIS in the years 2000–2002 and 2015–2017, using LDA. Our results indicated that there were drastic changes in topics while there were slight changes in categories. Technological advances and new digital environments have generated changes in topics. Because LIS was established before the millennium, the categories were less variable. Therefore, the more the digital environment was introduced to LIS, the more the changes in topics would accelerate. Thus, we comprehend the transition of topics that shape the core of LIS.

Our results were based on an analysis of 1648 articles published in five core LIS journals. Because of the difficulty in obtaining full text data, our experiment utilized a relatively small dataset, but using full text can lead to extracting more detailed topics. Journal selection for bibliometric analysis is always a difficult task. Articles published in JASIST accounts for 52% of our dataset. Although we thought that was the actual state of the core LIS area and used it as our dataset, we may be missing out on the diversity of LIS in the broad sense. Notably, articles about bibliometrics and informetrics were published in specialized journals such as Scientometrics and Journal of Informetrics . Including such journals might give us insights into the relationship between core topics and specialized topics. Future research may examine a broader range of journals and a greater amount of full text data to get a more in-depth understanding of the knowledge structure of LIS.

Allen, B., & Reser, D. (1990). Content analysis in library and information science research. Library & Information Science Research, 12 (3), 251–262.

Google Scholar  

Atkins, S. E. (1988). Subject trends in library and information science research 1975–1984. Library Trends, 36 (4), 633–658.

Blake, V. L. (1996). The perceived prestige of professional journals, 1995: A replication of the Kohl–Davis study. Education for Information, 14 (3), 157–179.

Article   MathSciNet   Google Scholar  

Blei, D. M., & Lafferty, J. D. (2006). Dynamic topic models. In Proceedings of the 23rd international conference on machine learning (pp. 113–120). Pittsburgh: ACM.

Blei, D. M., Ng, Y. A., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

MATH   Google Scholar  

Borner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37 (1), 179–255.

Article   Google Scholar  

Chen, B., Tsutsui, S., Ding, Y., & Ma, F. (2017a). Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval. Journal of Informetrics, 11 (4), 1175–1189.

Chen, J., Wei, W., Guo, C., Tang, L., & Sun, L. (2017b). Textual analysis and visualization of research trends in data mining for electronic health records. Health Policy and Technology, 6 (4), 389–400.

Dam, H. K., & Ghose, A. (2016). Analyzing Topics and Trends in the PRIMA Literature. In International conference on principles and practice of multi - agent systems (pp. 216–229). Puckett: Springer International Publishing.

De Battisti, F., Ferrara, A., & Salini, S. (2015). A decade of research in statistics: a topic model approach. Scientometrics, 103 (2), 413–433.

Dora, M., & Kumar, H. A. (2017). An empirical analysis of the research trends in the field of library and information science in India—2004–2015. COLLNET Journal of Scientometrics and Information Management, 11 (2), 361–378.

Figuerola, C. G., Marco, F. J. G., & Pinto, M. (2017). Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA. Scientometrics, 112 (3), 1507–1535.

Hall, D., Jurafsky, D., & Manning, C. D. (2008). Studying the history of ideas using topic models. In Proceedings of the conference on empirical methods in natural language processing (pp. 363–371). Honolulu: Association for Computational Linguistics.

Hjørland, B. (2017). Theory development in the information sciences. Journal of the Association for Information Science & Technology, 68 (7), 1796–1801.

Hou, J., Yang, X., & Chen, C. (2018). Emerging trends and new developments in information science: A document co-citation analysis (2009–2016). Scientometrics, 115 (2), 869–892.

Jarvelin, K., & Vakkari, P. (1993). The evolution of library and information science 1965–1985: A content analysis of journal articles. Information Processing and Management, 29 (1), 129–144.

Joo, S., Choi, I., & Choi, N. (2018). Topic analysis of the research domain in knowledge organization: A latent Dirichlet allocation approach. Knowledge Organization, 45 (2), 170–183.

Kajberg, L. (1996). A citation analysis of LIS serial literature published in Denmark 1957–1986. Journal of Documentation, 52 (1), 69–85.

Kawalec, A. (2013). Information science based on Spanish scientific publication 2000 to 2010. Malaysian Journal of Library & Information Science, 18 (2), 1–13.

Kohl, D. F., & Davis, C. H. (1985). Ratings of journals by ARL library directors and deans of library and information science schools. College and Research Libraries, 46 (1), 40–47.

Koufogiannakis, D., Slater, L., & Crumley, E. (2004). A content analysis of librarianship research. Journal of Information Science, 30 (3), 227–239.

Kurata, K., Miyata, Y., Ishita, E. Yamamoto, M., Yang, F., & Iwase, A. (2018). Analyzing library and information science full-text articles using a topic modeling approach. In Proceedings of the association for information science and technology (pp. 847–848). Hoboken, NJ: Wiley.

Lamba, M., & Madhusudhan, M. (2019). Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a Study. Scientometrics, 120 (2), 477–505.

Liu, P., Jameel, S., Lam, W., Ma, B., & Meng, H. M. (2015) Topic modeling for conference analytics. In 16th annual conference of the international speech communication association (pp. 707–711). Dresden: INTERSPEECH.

Lu, K., & Wolfram, D. (2012). Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches. Journal of the Association for Information Science and Technology, 63 (10), 1973–1986.

Manzari, L. (2013). Library and information science journal prestige as assessed by library and information science faculty. The Library Quarterly, 83 (1), 42–60.

Milojević, S., Sugimoto, C. R., Yan, E., & Ding, Y. (2011). The cognitive structure of library and information science: Analysis of article title words. Journal of the American Society for Information Science and Technology, 62 (10), 1933–1953.

Miyata, Y., Habu, E., Sugiuchi, M., Koizumi, M., Kurata, K., & Ueda, S.(2010). Toshokan ohogaku kenkyu no trend: kaigai zasshi keisai ronbun no naiyo bunseki wo chushin toshite (The trend of library and information science research: A content analysis of research articles in LIS core journals). In Proceedings of Japan society of library and information science annual meeting , (pp. 141–144). Sapporo: Fuji Women’s University.

Miyata, Y., Yamamoto, M., Yang, F., Iwase, A., Ishita, E., Kurata, K., & Ueda, S. (2018). LDA wo mochiita toshokan johogaku no kenkyu topic no henka: 2 kikan no zasshi ronbun no zenbun wo taisho ni (Topic transition in library and information science research using LDA: A full-text analysis in two periods). In Proceedings of the 2018 the Mita society for library and information science (pp. 41–44). Tokyo: Mita Society for Library and Information Science.

Nicolaisen, J., & Frandsen, T. F. (2015). Bibliometric Evolution: Is the journal of the association for information science and technology transforming into a specialty Journal? Journal of the Association for Information Science and Technology, 66 (5), 1082–1085.

Nisonger, T. E., & Davis, C. H. (2005). The perception of library and information science journals by LIS education deans and ARL library directors: A replication of the Kohl–Davis study. College & Research Libraries, 66 (4), 341–377.

Nixon, J. M. (2014). Core journals in library and information science: Developing a methodology for ranking LIS journals. College & Research Libraries, 75 (1), 66–90.

Pettigrew, K. E., & McKechnie, L. (2001). The use of theory in information science research. Journal of the American Society for Information Science and Technology, 52 (1), 62–73.

Sugimoto, C. R., Li, D., Russell, T. G., Finlay, S. C., & Ding, Y. (2011). The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation. Journal of the Association for Information Science and Technology, 62 (1), 185–204.

Sugiuchi, M., Habu, E., Ueda, S., Kurata, K., Miyata, Y., & Koizumi, M. (2011). Ronbun kara mita nihon no toshokanjohogaku kenkyu no doko (The trend of library and information science research in Japan: A content analysis of research articles). Library and Information Science, 66, 127–151.

Syed, S., & Spruit, M. (2017). Full-text or abstract? Examining topic coherence scores using latent Dirichlet allocation. In 2017 IEEE international conference on data science and advanced analytics (DSAA) . Tokyo: IEEE.

Tuomaala, O., Jarvelin, K., & Vakkari, P. (2014). Evolution of library and information science, 1965–2005: Content analysis of journal articles. Journal of the Association for Information Science and Technology, 65 (7), 1446–1462.

Wang, X., & McCallum, A. (2006). Topics over time: A non-Markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 424–433). New York: ACM.

White, H. D., & Griffith, B. C. (1981). Author co-citation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32 (3), 163–171.

Yan, E. (2014). Research dynamics: Measuring the continuity and popularity of research topics. Journal of Informetrics, 8 (1), 98–110.

Yan, E. (2015). Research dynamics, impact, and dissemination: A topic-level analysis. Journal of the Association for Information Science and Technology, 66 (11), 2357–2372.

Yang, S., Han, R., Wolfram, D., & Zhao, Y. (2016). Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis. Journal of Informetrics, 10 (1), 132–150.

Zhao, D., & Strotmann, A. (2008). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic coupling analysis. Journal of the American Society for Information Science and Technology, 59 (13), 2070–2086.

Download references

Acknowledgements

The authors thank Shuichi Ueda, professor emeritus at Keio University, for his support and invaluable advice. We also thank the editors and anonymous reviews for their meaningful comments.

This work was supported by JSPS KAKENHI Grant Number 19K12702 and 19H04423.

Author information

Authors and affiliations.

Faculty of Letters, Keio University, Tokyo, Japan

Yosuke Miyata & Keiko Kurata

Research and Development Division, Kyushu University Library, Kyushu University, Fukuoka, Japan

Tokyo, Japan

Fang Yang & Michimasa Yamamoto

Graduate School of Letters, Keio University, Tokyo, Japan

Azusa Iwase

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yosuke Miyata .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Miyata, Y., Ishita, E., Yang, F. et al. Knowledge structure transition in library and information science: topic modeling and visualization. Scientometrics 125 , 665–687 (2020). https://doi.org/10.1007/s11192-020-03657-5

Download citation

Received : 11 March 2020

Published : 12 August 2020

Issue Date : October 2020

DOI : https://doi.org/10.1007/s11192-020-03657-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Library and information science
• Latent Dirichlet allocation
• Topic modeling
• Visualization
• Research trend
• Find a journal
• Publish with us
• Track your research

College & Research Libraries News  ( C&RL News ) is the official newsmagazine and publication of record of the Association of College & Research Libraries,  providing articles on the latest trends and practices affecting academic and research libraries.

C&RL News  became an online-only publication beginning with the January 2022 issue.

ALA JobLIST

Advertising Information

• Preparing great speeches: A 10-step approach (240000 views)
• The American Civil War: A collection of free online primary sources (209435 views)
• 2018 top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education (78231 views)

2021–22 ACRL Research Planning and Review Committee

Top trends in academic libraries

A review of the trends and issues

This article summarizes trending topics in academic librarianship from the past two years–a time of tremendous upheaval and change, including a global pandemic, difficult reflections concerning racial justice, and war between nation states. Rapid changes and uncertainty from these events have created a significant amount of shifts to academic libraries, higher education, and society in general. Such shifts have yielded new perspectives and innovations in how librarians approach delivering services, supporting student success, managing staff and physical spaces, embracing new technology, and managing data. This report attempts to provide a snapshot of developments worth noting.

COVID-related trends

The impact of the COVID-19 pandemic on academic library services has been significant, and these changes, in many cases, are ongoing. The issues identified below transcend the period covered by this review as libraries face a fundamental shift that will extend far into the future and beyond the pandemic. In early 2020, nearly all academic 1 and public 2 libraries closed temporarily and shifted towards virtual services. Despite in-person closures, libraries continued online services, 3 kept their communities informed, 4 and adopted innovative collaborations 5 and technologies to adapt to changing circumstances. These closures and the institutional changes that enabled them had a significant and diverse impact on librarianship, including rethinking long-held paradigms, 6 increased professional stress around institutional budgets, 7 and the ability to work remotely. 8 The pandemic also surfaced long-standing issues of inequality 9 and inaccessibility 10 in libraries. Despite the challenges raised by closures, libraries continued to deliver core services and creative solutions, including virtual reference with increasing complexity, 11 a renewed focus on digital literacy with the rise in online learning, 12 and born-digital collection development. 13

Library staffing challenges

Since early 2020, the pandemic and racial justice protests have drawn increased attention to several trends in library and higher education staffing. Inequities between librarians and other library staff were heightened. One study found many lower-income and lower-status staff were required to work in-person to a greater degree than librarians and administrators. 14 The contradiction between the necessity of these “frontline” and “essential” positions and their lower-rates of financial compensation has become difficult to ignore. Such staff, often quite ably, perform duties that had previously been the purview of credentialed librarians. 15 Additionally, the Bureau of Labor Statistics note that while workers of color represent 25% of the higher education workforce, more than half of individuals who lost jobs during COVID-19 have been nonwhite. 16 Lingering pandemic impacts, such as budget cuts and hiring freezes, have led to inadequate staffing, 17 even as services have largely returned to pre-pandemic levels. Of note is that, partly in response to university pandemic austerity measures, Northwestern University’s library workers announced their unionization with SEIU Local 73, which has been recognized by the university. 18, 19

Low morale continues to be an area of concern in librarianship, 20, 21 particularly for librarians of color, nonlibrarian staff, 22 and members of underrepresented groups. Stress from caretaking responsibilities for workers who lost childcare or other forms of support during the pandemic has exacerbated issues of esprit de corps and well-being in the workplace. For academic librarians, who are pre-tenure or otherwise expected to contribute publications and presentations to library scholarship (and already at a high-stress time in their careers), 23 the pandemic has created additional challenges to those with caretaking responsibilities, particularly women. 24, 25 These workers already experience burnout at higher rates. 26 Future research concerning recruitment and retention may also investigate the benefits and repercussions of library staff working remotely and wanting to continue doing so.

Space utilization

COVID has thrust the physical spaces of libraries, along with most campus facilities, into the forefront of faculty, staff, and student consciousness over the past two years. Balancing demands for the use of current spaces, increases to construction pricing that may extend projects into 2023, and aligning current capital budgets to this reality continue to impact decisions about how existing spaces will evolve in the near future.

Beyond maintaining appropriate distancing or providing adequate sanitization, librarians operating physical facilities are asking questions, including whether and how to operate in-person collaboration spaces safely (for both users and staff), how to provide resources consistently during waves of openings and closures, and how to assess and address patron and staff levels of comfort interacting in a physical space. Rapidly changing methods of service delivery, information access, and materials storage are continuing to generate questions that may possibly reshape the reliance on centralized, in-person settings as more options move online or become embedded externally. 27

Simultaneously, key trends in library design continue to be reassessed and may help inform librarians in the near future. For instance, one study from the University of Nebraska-Lincoln argues that recent trends in library space have overcommitted to collaborative learning spaces at the cost of providing valued space for intensive academic work. 28 Daejin Kim, Sheila Bosch, and Jae Hwa Lee investigated, pre-COVID, how collaboration spaces are used and found that furniture or spaces intended for multiple people are frequently being used by single individuals. 29 Similar studies looking at more nuanced patron needs find that, much like evolving workplace trends, users want a wide variety of space types with different acoustical, privacy, and technology needs according to the work undertaken. 30 Recent studies in other academic settings point to taking this moment of change to pilot new layouts or space configurations that align better with current service needs and that are more activity-based rather than based on type of occupant or user. 31 A multiplicity of trends dominate discussions surrounding space. Accordingly, it is clear that local institutional factors ranging from budget to different use cases will continue to influence how space is allocated, constructed, and used.

Collaborative collections and growth of shared print

While there is a long history of libraries working together to preserve and provide access to rich collections, collaboration around shared print programs has rapidly accelerated in recent years. By creating a collaborative collection, which “elevates the concept of a library collection to scales above a single institution, extending its boundaries to encompass the resources concentrated among a group of libraries,” 32 these programs help research libraries to fulfill their mission to preserve the scholarly record in an era of changing usage, limited funding, and space constraints.

With an initial focus on print journals, shared print programs have matured and evolved to include print monographs more recently. According to Susan Stearns and Alison Wohlers, 33 “over 300 academic and research libraries in the U.S. and Canada participate in some form of shared print program, committing to archive or retain tens of millions of monographs and hundreds of thousands of serial and journal print titles.” A major factor in the growth of shared print monograph initiatives was the launch of the HathiTrust Shared Print Program, which “has now secured commitments on more than 5.4 million individual titles held in the HathiTrust Digital Library.” 34

However, as these programs have grown, so has the need for more coordination, standards, and infrastructure. Several groups have been launched in recent years to tackle these issues. In 2015, the Rosemont Shared Print Alliance was founded to coordinate among regional shared print journal programs in order to archive more titles and ensure sufficient copies are preserved. 35 As a complementary organization, the Partnership for Shared Book Collections was founded in 2019 to collaborate around shared print monographs, aiming to “reduce the cost of retaining the scholarly record” and “develop and promote evidence-based best practices.” 36 Recently the California Digital Library, the Center for Research Libraries, and HathiTrust announced a collaboration around shared print infrastructure intended to develop standards, workflows, and tools to support collaborative efforts and embed shared print work into the lifecycle of collection development and management. 37 In addition, groups such as the Big Ten Academic Alliance, the University of California Libraries, and the Canadian Collective Print Strategy Working Group have embarked on their own initiatives to take more strategic and intentional approaches to collection development and management in light of their shared print collaborations. 38

Finally, it is worth noting that controlled digital lending (CDL) is an emerging trend where libraries “circulate temporary digital copies of print books they own in a one-to-one ratio of ‘loaned to owned,’ removing the print copy from circulation while the digital copy is in use.” 39 ACRL has signed a statement in support of CDL. 40 CDL advocates argue that reasonable interpretation of copyright law should insulate libraries from legal exposure; however, the legality of CDL remains an open question. 41

Open everything

The open access (OA) movement to “make scholarly works both freely available and reusable” continues to be important for librarians, educators, and administrators in higher education. 42 Yet, as Ángel Borrego, Lluís Anglada, and Ernest Abadal, state, the “landscape of scholarly communication is characterized by increasing costs and limited access to research output.” 43 Numerous barriers exist ranging from economics to policy that prevent wide-scale adoption in higher education of executing scholarly communication strategies that would be considered open access. Issues with increasing subscription costs for academic journals are well documented. 44 While librarians typically report favorable beliefs about OA there is a noted lack of OA policy. 45 A report from Hannah Rosen and Jill Grogg, states “while both formal and informal policies exist. . .” regarding OA scholarship, data, and open educational resources, most institutions do not have policies in place “resulting in a scatter-shot approach to open content of all types and less than cohesive institutional strategies.” 46

In addition to further opportunities regarding OA training and outreach, librarians also have opportunities to help with the “identification of, and sometimes deposit into the institutional repository of works that are sitting outside the peer reviewed literature,” often called gray literature. 47 Barriers continue to exist for accessing and using open access information. Some scholars are concerned that open access materials are not understandable to the general public, defeating the point of making such materials open and accessible in the first place. 48 For such reasons there is an increasing call for articles to use a “significance statement,” which describes an article concisely in plain language understandable to a lay audience. 49

The COVID-19 pandemic has provided opportunities for various types of OA content to become more widely available and served as “proof of concept” for what is possible. 50 For instance, OA resources were viewed as important for providing off-campus access to library materials in some developing countries. 51 Some publishers recognized the public health importance of providing timely information related to COVID-19 and committed to open access publication of articles relating to it. 52 Worthy of note were the use of preprint servers by scientists, which “in effect [were] crowdsourcing rapid expert peer-review.” 53 Europe developed an open access publishing initiative—Plan S—in 2018 with support from national research agencies and 12 European countries. As of 2020, notable journals like Nature announced they would facilitate Plan S committing to publishing with full open access in the future. 54

The Scholarly Publishing and Academic Resources Coalition (SPARC) tracks “Big Deal” cancellations, which continue to occur. For instance, Purdue University canceled a $3.3 million contract for 2020 opting instead for a one-year, title-by-title contract for 2021, while New Mexico State University pointed out both inflationary journal prices and COVID-19 considerations while cutting their collections budget by $800,000 for fiscal year 2021. 55 Some universities and consortia are seeking “transformative agreements,” which promote open access publishing by their authors and allow those authors to maintain copyright. Transformative agreements facilitate a more transparent journal licensing process and aim to shift the focus of “scholarly journal licensing from cost containment towards open access publication.” 56

Many facets of the OA movement continue to develop. As libraries continue more aggressive journal subscription negotiations, which may include transformative agreements, as well as possible Big Deal cancellations, 57 more questions will develop about the future of access to scholarly materials. This is multivariable including open data, open educational resources, and OA policies, tools, and advocacy. Combined with the results of unanticipated experiments born from COVID-19, OA continues to be a focal point for academic librarians and administrators.

Artificial intelligence

Artificial intelligence (AI) is being increasingly embedded in academic libraries tools and services. Pattern recognition, 58 AI-powered text recognition, transcription, and searching of historical documents 59 are prime examples that facilitate search and discovery. Keenious, cofunded by the Horizon 2020 program of the European Union, is a research tool for document and writing analysis, attempting to make online research easier. 60 Cactus Communications (CACTUS) recently announced a new AI-powered tool, Paperpal Preflight, “to improve the scholarly publishing experience for researchers, peer reviewers, and journal editors” during the manuscript submission process. 61

The adoption of AI in virtual reference services provides a new online model for libraries by using “chatbots.” 62 Recent attempts to automate standard library operations, such as cataloging, through expert systems have focused on simpler tasks like descriptive cataloging. 63 A team of researchers from the National Library of Norway describes an experiment that uses AI methods to automatically group articles and assign Dewey Decimal numbers to aid in cataloging. 64

The Library of Congress is experimenting with neural networks and the use of computer vision. The intent is to create new online search prototypes that can sort through large amounts of data in new ways, such as examining and contextualizing millions of digitized items that humans could not do alone. 65 Other experimental work like the Newspaper Navigator aims to explore the visual and textual content via AI. 66 At Yale’s Digital Humanities Lab, data-mining techniques are used to illuminate the conventions of portraiture and other visual genres in the 19th century. 67 Leaders, such as Eun Seo Jo and Timnit Gebru, have drawn archives as a model for data collection and annotation in order to inform how decisions that surround fairness, accountability, transparency, and ethics are addressed in machine learning systems. 68

In the Netherlands, concerns that surround data, information ethics, and data-driven public management have been captured under the Data Ethics Decision Aid (DEDA) to use a deliberative rather than rule-based approach to ethical concerns and advance the development of responsible data practices. 69 It is also important to acknowledge cybersecurity concerns as AI becomes more and more embedded in systems routinely used in libraries. 70

While AI technologies could be harnessed to provide more tailored search results, monitor social distancing, and integrate the library into personal assistants, 71 it can also help academic libraries demonstrate real value to institutions if it is used judiciously. Asaf Tzachor et al. expressed concerns stemming from urgency in adopting these technologies along with the challenging ethical issues and risks that can arise in a crisis—the COVID-19 pandemic prevention and response is one example. 72 At the same time, AI’s potential has remained largely untapped among research libraries. A recent Ex Libris survey revealed that while nearly 80 percent of research librarians are exploring the use of AI and machine learning, only about 5 percent are currently leveraging the technology. 73

Higher education faces increased challenges with the surging interest in big data. The need to invest in training skilled employees, increase repository capacity, and assign and clarify responsibilities 74 remains critical as libraries and librarians continue to take on leadership roles 75 and provide data services. Those vanguard libraries that were the first to offer services have begun to evaluate programs, 76 services, 77 and tools 78 and make adjustments focusing both on usability for the owner of the data to upload and share data sets and discoverability of those data sets for the end user. The body of literature associated with research data management services in libraries and skill development has reached the point where literature reviews and scoping reviews are looking back in time to draw conclusions and offer suggestions to advance the field and the libraries’ role. 79

Data mining proves itself as an emerging field as well, especially when linked to the Internet of Things (IoT). A recent study using both Clarivate Analytics Web of Science and Sciverse Scopus revealed that knowledge discovery in databases are paving the way to make data increasingly more meaningful. 80 Along these same lines, data analytic methods are constantly changing with the ever-increasing volume of data generated. As a result, “cloud-based AI activities are expected to increase five-fold by 2023,” 81 which could translate into a greater capacity “to store data in a cost-effective manner and glean more actionable insight from IoT data.” 82

Data curation remains an overarching role for the library. 83 The term active curation, involvement of the curator from collection and development of the data set to its final analysis and storage, 84 will continue to expand as librarians become more embedded in the data life cycle. Additionally, institutions of higher education continue to show a growing interest in data science education. Based on the study conducted at Purdue University in 2017 to examine the roles of academic libraries to support data science education curriculum, results showed that “hard-core” scientific courses for third- and fourth-year STEM students were most common as opposed to offerings in data-oriented skills, such as data management, data ethics, and data communications. 85 At schools of information, a group of instructors who teach data curation have expressed the importance of integrating both research and teaching in the curriculum. The objective would give students opportunities to develop core competencies, learn about data librarianship and practices to support preservation and access, and broaden their professional horizons by gaining a greater awareness with multidimensional problems of working with data. 86

Finally, in light of the growing prominence of data, data visualization skills continue to be highly valued, and visual results can be interpreted as a research product and form of expression. Libraries are taking a greater interest in data visualization as they seek to tell their own story, including assessment, value of the library, collection analysis, and internal capacity building. 87

Critical librarianship

Critical librarianship continues to be an important theoretical perspective for information professionals. Rooted in critical theory (originally denoting a group of Marxist philosophers but over time scholars in many fields now employ critical theory or critical approaches), critical librarianship challenges traditional concepts in librarianship. 88 For instance, critical librarianship argues that libraries are not neutral and challenges librarians to take active steps toward antiracist and antioppresive practices both for the benefit of users but also for the benefit of the profession itself. 89 As libraries continue to aim for accessibility and more welcoming spaces, scholars familiar with critical librarianship, urge library workers to take meaningful action to include its teachings in their daily practice–referred to as praxis. 90 With little diversity in the library professions, 91 and many critiques of popular approaches to information literacy, for instance the ACRL Framework for Information Literacy for Higher Education’s lack of acknowledgement of the underlying power structures in which academia operates, 92 critical librarianship argues that there are numerous opportunities for librarians to fight inequity, racism, sexism, and other problems through concrete action.

One facet of critical librarianship and critical pedagogy is critical information literacy (CIL). CIL literature discusses why and how information professionals should ask questions about power dynamics within academia, equal access to information, and the economic incentives around how information and data are created, stored, and used. CIL scholarship also critiques academia itself. As with other teaching and learning theories, CIL is constantly evolving and must be adapted for students in different course levels and in different course subjects. 93 Margaret Rose Torrell examined implementing CIL when using a writing across the curriculum approach with undergraduates, and highlighted the benefits of having more than a one-shot session with students. 94 Marcia Rapchak employed CIL with graduate students who were “eager to engage in discussion and material,” such as case studies, essays, and self assessments. 95 L Sofia Y. Leung and Jorge R. López-McKnight taught LIS students and found that including and centering intersectionalities such as race and gender in their pedagogical approach allowed them to be better teachers. 96 Erin Fields and Adair Harper incorporated CIL and open pedagogy into a university course and found that by using nonacademic sources and student work, their students were more empowered to work within and assess the current information landscape. 97

Critical approaches to librarianship and information literacy will likely continue to be an area of exploration for LIS scholars.

We foresee numerous challenges in the next few years, including potential budget reductions as well as questions about returning to the physical office after an extended period of virtual work. We are also excited that new opportunities for collaboration, additional interest in critical perspectives, and incorporation of different approaches to manage shared collections will allow academic librarians to continue leading the way in student success and learning, organizational impact, and rigorous scholarly inquiry.

• Lisa Janicke Hinchliffe and Christine Wolff-Eisenberg, “Academic Library Response to COVID19,” Ithaka S+R, March 13, 2020, https://sr.ithaka.org/blog/academic-library-response-to-covid19/ .
• Ting Wang and Brady Lund, “Announcement Information Provided by United States’ Public Libraries during the 2020 COVID-19 Pandemic,” Public Library Quarterly 39, no. 4 (2020): 283–94, https://doi.org/10.1080/01616846.2020.1764325 .
• Marie L. Radford, Laura Costello, and Kaitlin Montague, “Surging Virtual Reference Services: COVID-19 a Game Changer,” College & Research Libraries News 82, no. 3 (2021), https://doi.org/10.5860/crln.82.3.106 .
• Bibi M. Alajmi and Dalal Albudaiwi, “Response to COVID-19 Pandemic: Where Do Public Libraries Stand?” P ublic Library Quarterly 40, no. 6 (2021): 540–56, https://doi.org/10.1080/01616846.2020.1827618 .
• Liladhar R. Pendse, “Academic Libraries and Research in Flux: Global Conversations in Times of COVID-19,” College & Research Libraries News 82, no. 1 (2021): 36, https://doi.org/10.5860/crln.82.1.36 .
• Sara Jones, “Optimizing Public Library Resources in a Post COVID-19 World,” Journal of Library Administration 60, no. 8 (2020): 951–57, https://doi.org/10.1080/01930826.2020.1820281 .
• Lily Todorinova, “One Year In: A Survey of Public Services Librarians on the Effects of the COVID-19 Pandemic,” Journal of Library Administration 61, no. 7 (October 3, 2021): 776–92, https://doi.org/10.1080/01930826.2021.1972728 .
• Anna R. Craft, “Remote Work in Library Technical Services: Connecting Historical Perspectives to Realities of the Developing COVID-19 Pandemic,” Serials Review 46, no. 3 (2020): 227–31, https://doi.org/10.1080/00987913.2020.1806658 .
• Amelia N. Gibson et al., “Struggling to Breathe: COVID-19, Protest and the LIS Response,” Equality, Diversity and Inclusion an International Journal 40, no. 1 (2020): 74–82, https://doi.org/10.1108/EDI-07-2020-0178 .
• J. J. Pionke, “COVID-19, Accessibility, and Libraries: A Call to Action,” College & Research Libraries News 81, no. 8 (2020): 398, https://doi.org/10.5860/crln.81.8.398 .
• Raeda Anderson, Katherine Fisher, and Jeremy Walker, “Library Consultations and a Global Pandemic: An Analysis of Consultation Difficulty during COVID-19 across Multiple Factors,” The Journal of Academic Librarianship 47, no. 1 (2021): 102273, https://doi.org/10.1016/j.acalib.2020.102273 .
• Konstantina Martzoukou, “Academic Libraries in COVID-19: A Renewed Mission for Digital Literacy,” Library Management, 2020, https://doi.org/10.1108/LM-09-2020-0131 .
• Anna L. Neatrour, Jeremy Myntti, and Rachel J Wittmann, “Documenting Contemporary Regional History: The Utah COVID-19 Digital Collection,” Digital Library Perspectives ahead-of-print, no. ahead-of-print (2020), https://doi.org/10.1108/DLP-04-2020-0025 .
• Lindsey Wharton and Emily Zoe Mann, “Transitioning Online Reference Staffing Models: Assessing and Balancing Needs of Patrons and Practitioners,” T he Reference Librarian 61, no. 1 (2020): 15–24, https://doi.org/10.1080/02763877.2019.1678217 .
• Kevin R. McClure, “Higher Ed, We’ve Got a Morale Problem—And a Free T-Shirt Won’t Fix It—EdSurge News,” EdSurge, September 27, 2021, https://www.edsurge.com/news/2021-09-27-higher-ed-we-ve-got-a-morale-problem-and-a-free-t-shirt-won-t-fix-it .
• Isabel Funk, “Northwestern University Library Workers Union Seeks Recognition,” The Daily Northwestern (blog), October 15, 2021, https://dailynorthwestern.com/2021/10/15/campus/northwestern-university-library-workers-union-seeks-formal-recognition/ .
• Colleen Flaherty, “When Librarians Unionize,” Inside Higher Ed, January 12, 2022, https://www.insidehighered.com/news/2022/01/12/northwestern-librarians-unionize-following-furloughs-cuts .
• Christina Heady et al., “Contributory Factors to Academic Librarian Turnover: A Mixed-Methods Study,” Journal of Library Administration 60, no. 6 (August 17, 2020): 579–99, https://doi.org/10.1080/01930826.2020.1748425 .
• Ann Glusker, Celia Emmelhainz, Natalia Estrada, and Bonita Dyess, “Viewed as Equals”: The Impacts of Library Organizational Cultures and Management on Library Staff Morale,” Journal of Library Administration 62, no. 2 (2022): 153-189, https://doi.org/10.1080/01930826.2022.2026119 .
• Ann Glusker et al., “‘Viewed as Equals’: The Impacts of Library Organizational Cultures and Management on Library Staff Morale,” Journal of Library Administration 62, no. 2 (February 17, 2022): 153–89, https://doi.org/10.1080/01930826.2022.2026119 .
• Laura Cameron, Stephanie Pierce, and Julia Conroy, “Occupational Stress Measures of Tenure-Track Librarians,” Journal of Librarianship and Information Science 53, no. 4 (2021): 551–58, https://doi.org/10.1177/0961000620967736 .
• Alessandra Minello, “The Pandemic and the Female Academic,” Nature, April 17, 2020, 1135–39, https://doi.org/10.1038/d41586-020-01135-9 .
• Vicki L. Baker, “Women in Higher Education: Re-Imagining Leadership in the Academy in Times of Crisis,” The Journal of Faculty Development 35, no. 1 (2021): 57–62.
• Jason Martin, “Job Satisfaction of Professional Librarians and Library Staff,” Journal of Library Administration 60, no. 4 (May 2020): 365–82, https://doi.org/10.1080/01930826.2020.1721941 .
• Lindsay Blake, Darra Ballance, Kathy Davies, Julie K. Gaines, Kim Mears, Peter Shipman, Maryska Connolly-Brown, and Vicki Burchfield, “Patron Perception and Utilization of an Embedded Librarian Program,” Journal of the Medical Library Association 104, no. 3 (2016): 226-230, https://doi.org/10.3163/1536-5050.104.3.008 .
• Erica Lynn DeFrain, Jennifer Thoegersen, and Miyoung Hong, “Standing Out or Blending In: Academic Libraries in the Crowded Informal Learning Space Ecosystem,” College & Research Libraries 83, no. 1 (January 3, 2022): 45, https://doi.org/10.5860/crl.83.1.4 5.
• Daejin Kim, Sheila Bosch, and Jae Hwa Lee, “Alone with Others: Understanding Physical Environmental Needs of Students within an Academic Library Setting,” The Journal of Academic Librarianship 46, no. 2 (2020): 102098, https://doi.org/10.1016/j.acalib.2019.102098 .
• Camille Andrews, Sara E. Wright, and Howard Raskin, “Library Learning Spaces: Investigating Libraries and Investing in Student Feedback,” Journal of Library Administration 56, no. 6 (2016): 647-672, https://doi.org/10.1080/01930826.2015.1105556 .
• Lisa Wesel, “Unassigned Space at Colleges and Universities,” Tradeline, Inc. (2021), accessed December 20, 2021, https://www.tradelineinc.com/reports/2021-8/unassigned-space-colleges-and-universities .
• Brian Lavoie, Lorcan Dempsey, and Constance Malpas, “Reflections on Collective Collections,” College & Research Libraries 81, no. 6 (2020): 981–96, https://doi.org/10.5860/crl.81.6.981 .
• Susan Stearns and Alison Wohlers, “Shared Print on the Threshold: Looking Back and Forging the Future,” Collaborative Librarianship 12, no. 2 (2020): 159, https://digitalcommons.du.edu/collaborativelibrarianship/vol12/iss2/5/ .
• Heather Weltin and Natalie Fulkerson, “Old Texts, New Networks: HathiTrust and the Future of Shared Print,” in Transforming Print: Collection Development and Management for Our Connected Future (ALA Editions, 2021), 65–79, http://deepblue.lib.umich.edu/handle/2027.42/169166 .
• Susan Stearns and Alison Wohlers, “Shared Print on the Threshold: Looking Back and Forging the Future,” Collaborative Librarianship 12, no. 2 (2020): 159–69, https://digitalcommons.du.edu/collaborativelibrarianship/vol12/iss2/5/ .
• Lisa Peet, “Big Ten Academic Alliance Plans BIG Collection,” Library Journal, March 2021, https://www.libraryjournal.com/?detailStory=big-ten-academic-alliance-plans-big-collection-across-15-libraries ; Canadian Collective Print Strategy Working Group, “Final Report of the Canadian Collective Print Strategy Working Group,” Canadian Association of Research Libraries, September 2020, https://www.carl-abrc.ca/wp-content/uploads/2020/09/CCPSWG_final_report_EN.pdf .
• Association of Research Libraries, “Association of Research Libraries Signs Statement in Support of Controlled Digital Lending,” accessed March 31, 2022, https://www.arl.org/news/association-of-research-libraries-signs-statement-in-support-of-controlled-digital-lending/ .
• Chad Currier and Alissa Centivany, “Controlled Digital Lending” Proceedings of the Association for Information Science and Technology 58, no. 1 (2021): 80–91, https://doi.org/10.1002/pra2.438 .
• Open Access (n.d.), Electronic Frontier Foundation, retrieved January 30, 2022, http://eff.org/issues/open-access .
• Ángel Borrego, Lluís Anglada, and Ernest Abadal, “Transformative Agreements: Do They Pave the Way to Open Access?” Learned Publishing 34, no. 2 (2021): 216–32, https://doi.org/10.1002/leap.1347 .
• Christine Fruin, LibGuides: Scholarly Communication Toolkit: Economics of Publishing, Acrl.libguides.com (n.d.), https://acrl.libguides.com/scholcomm/toolkit/economics .
• Rachel Elizabeth Scott, Caitlin Harrington, and Ana Dubnjakovic, “Exploring Open Access Practices, Attitudes, and Policies in Academic Libraries,” portal: Libraries and the Academy 21, no. 2 (2021): 365–88, https://doi.org/10.1353/pla.2021.0020 .
• Hannah Rosen and Jill Grogg, “LYRASIS 2020 Open Content Survey Report,” (June 2020): 1-53, www.lyrasis.org/programs/Pages/open-content-survey-report.aspx .
• Danny Kingsley, “The ‘Impact Opportunity’ for Academic Libraries through Grey Literature,” The Serials Librarian 79, no. 3–4 (November 16, 2020): 281–89, https://doi.org/10.1080/0361526X.2020.1847744 .
• May R. Berenbaum, “On COVID-19, Cognitive Bias, and Open Access,” Proceedings of the National Academy of Sciences 118, no. 2 (January 12, 2021), https://doi.org/10.1073/pnas.2026319118 .
• Willa Tavernier, “COVID-19 Demonstrates the Value of Open Access: What Happens Next?,” College & Research Libraries News 81, no. 5 (May 2020), https://doi.org/10.5860/crln.81.5.226 .
• Muhammad Rafiq, Syeda Hina Batool, Amna Farzand Ali, and Midrar Ullah, “University Libraries Response to COVID-19 Pandemic: A Developing Country Perspective,” The Journal of Academic Librarianship 47, no. 1 (2021), https://doi.org/10.1016/j.acalib.2020.102280 .
• Tavernier, “COVID-19 Demonstrates.”
• Holly Else, “Nature Journals Reveal Terms of Landmark Open-Access Option,” Nature 588 (November 2020): 19–20, https://doi.org/10.1038/d41586-020-03324-y .
• “Big Deal Cancellations,” accessed February 4, 2022, https://bigdeal.sparcopen.org/cancellations .
• Borrego et al., “Transformative agreements.”
• “Project Launch: Canceling the Big Deal,” Ithaka S+R, May 2020, https://sr.ithaka.org/blog/project-launch-canceling-the-big-deal/ .
• Garima Gujral, “Perceptions and Prospects of Artificial Intelligence Technologies for Academic Libraries: An Overview of Global Trends,” in 12th International CALIBER-2019, n.d., https://ir.inflibnet.ac.in/handle/1944/2337 .
• “Transkribus,” READ coop,accessed December 15, 2021, https://readcoop.eu/transkribus/9 .
• “Keenious,” accessed December 15, 2021, https://keenious.com/ .
• Society for Scholarly Publishing, “New AI-Powered Tool Improves Screening for Journals and Authors,” Member news and release (December 2, 2021), https://www.sspnet.org/community/news/new-ai-powered-tool-improves-manuscript-screening-for-journals-and-authors/ .
• Souvick Ghosh, “Future of AI in Libraries,” SJSU, Center for Information Research and Innovation, March 15, 2021, https://ischool.sjsu.edu/ciri-blog/future-ai-libraries .
• S. Vijay Kumar, K. N. Sheshadri, “Applications of Artificial Intelligence in Academic Libraries,” International Journal of Computer Sciences and Engineering 7 (16) (2019): 136-140, https://doi.org/10.26438/ijcse/v7si16.136140 .
• Ryan Cordell, “Machine Learning and Libraries: A Report on the State of the Field,” Library of Congress (2020): https://apo.org.au/node/307049 .
• Sara Castellanos, “Library of Congress Looks to AI to Help Users Sift Through Its Collection,” June 24, 2021, https://www.wsj.com/articles/library-of-congress-looks-to-ai-to-help-users-sift-through-its-collection-11624552197 .
• Library of Congress, “LABS,” Library of Congress, https://labs.loc.gov/work/experiments/newpaper-navigator (n.d.).
• “Neural Neighbors,” accessed December 14, 2021, https://dhlab.yale.edu/neural-neighbors/ .
• Eun Seo Jo and Timnit Gebru, “Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning,” In FAT* ’20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (2020): 306–16, https://doi.org/10.1145/3351095.3372829 .
• Aline Shakti Franzke, Iris Muis, and Mirko Tobias Schäfer, “Data Ethics Decision Aid (DEDA): A Dialogical Framework for Ethical Inquiry of AI and Data Projects in the Netherlands,” Ethics and Information Technology 23 (2021): 551–67, https://doi.org/10.1007/s10676-020-09577-5 .
• Clayton Davis, “Does a Lack of AI Security Mean More Cyberattacks Are Coming?” November 1, 2021, https://www.dbta.com/Editorial/Trends-and-Applications/Does-a-Lack-of-AI-Security-Mean-More-Cyberattacks-are-Coming-149731.aspx .
• C. C. Cox, “Changed, Changed Utterly,” Inside Higher Ed, 2020, https://www.insidehighered.com/views/2020/06/05/academic-libraries-will-change-significant-ways-result-pandemic-opinion .
• Asaf Tzachor et al., “Artificial Intelligence in a Crisis Needs Ethics with Urgency,” Nature Machine Intelligence 2 (2020): 365–66, https://doi.org/10.1038/s42256-020-0195-0 .
• Ellen Prokop et al., “AI and the Digitized Photoarchive,” Journal of the Art Libraries Society of North America 40, no. 1 (2021): 1–20, https://doi.org/10.1086/714604 .
• Bradley Wade Bishop et al., “Potential Roles for Science Librarians in Research Data Management: A Gap Analysis,” Issues in Science & Technology Librarianship, no. 98 (Summer 2021): 21–37, https://doi.org/10.29173/istl2602 .
• Jeonghyun Kim, “Determining Research Data Services Maturity: The Role of Library Leadership and Stakeholder Involvement,” Library & Information Science Research 43, no. 2 (2021): 101092, https://doi.org/10.1016/j.lisr.2021.101092 ; Cas Laskowski, “Structuring Better Services for Unstructured Data: Academic Libraries Are Key to an Ethical Research Data Future with Big Data,” The Journal of Academic Librarianship 47, no. 4 (2021): 102335, https://doi.org/10.1016/j.acalib.2021.102335 .
• Fred Willie Zametkin LaPolla, Nicole Contaxis, and Alisa Surkis, “Piloting a Long-Term Evaluation of Library Data Workshops,” Journal of the Medical Library Association 109, no. 3 (October 5, 2021), https://doi.org/10.5195/jmla.2021.1047 .
• Jeonghyun Kim, “Determining Research Data Services Maturity: The Role of Library Leadership and Stakeholder Involvement,” Library & Information Science Research 43, no. 2 (2021): 101092, https://doi.org/10.1016/j.lisr.2021.101092 .
• Sagar Bhimrao Gajbe et al., “Evaluation and Analysis of Data Management Plan Tools: A Parametric Approach,” Information Processing & Management 58, no. 3 (2021): 102480, https://doi.org/10.1016/j.ipm.2020.102480 ; Sara Mannheimer et al., “Dataset Search: A Lightweight, Community-Built Tool to Support Research Data Discovery,” Journal of EScience Librarianship 10, no. 1 (January 19, 2021), https://doi.org/10.7191/jeslib.2021.1189 ; Kasey Soska et al., “(Hyper)Active Data Curation: A Video Case Study from Behavioral Science,” Journal of EScience Librarianship 10, no. 3 (August 11, 2021), https://doi.org/10.7191/jeslib.2021.1208 .
• Angeliki Andrikopoulou, Jennifer Rowley, and Geoff Walton, “Research Data Management (RDM) and the Evolving Identity of Academic Libraries and Librarians: A Literature Review,” New Review of Academic Librarianship, 2021, 1–17, https://doi.org/10.1080/13614533.2021.1964549 ; Nedelina Tchangalova et al., “Research Support Services in STEM Libraries: A Scoping Review,” Issues in Science and Technology Librarianship, no. 97 (May 7, 2021), https://doi.org/10.29173/istl2574 .
• Sumeer Gul and Shohar Bano, “Exploring Data Mining: Facets and Emerging Trends,” Digital Library Perspectives, 2021, http://dx.doi.org/10.1108/DLP-08-2020-0078 .
• Raghavendra Singh, “Top Data and Analytics Trends for 2021,” Data Science Central, n.d., https://www.datasciencecentral.com/profiles/blogs/top-data-and-analytics-trends-for-2021 .
• Seth Erickson, “Plain Text and Character Encoding: A Primer for Data Curators,” Journal of Escience Librarianship 10, no. 3 (August 11, 2021), https://doi.org/10.7191/jeslib.2021.1211 ; F. Rios and C. Ly, “Implementing and Managing a Data Curation Workflow in the Cloud,” Journal of EScience Librarianship 10, no. 3 (2021): e1205, https://doi.org/10.7191/jeslib.2021.1205 ; Kasey Soska et al., “(Hyper)Active Data Curation: A Video Case Study from Behavioral Science,” Journal of eScience Librarianship 10, no. 3 (August 11, 2021), https://doi.org/10.7191/jeslib.2021.1208 .
• Inna Kouper et al., “Active Curation of Large Longitudinal Surveys: A Case Study,” Journal of EScience Librarianship 10, no. 3 (August 11, 2021), https://doi.org/10.7191/jeslib.2021.1210 .
• Gang Shao et al., “Exploring Potential Roles of Academic Libraries in Undergraduate Data Science Education Curriculum Development,” The Journal of Academic Librarianship 47, no. 2 (2021), https://doi.org/10.1016/j.acalib.2021.102320 .
• Amelia Acker, Devan Ray Donaldson, Adam Kriesburg, Andrea Thomer, and Nicholas Weber, “Integrating Research and Teaching for Data Curation in ISchools,” In Proceedings of the Association for Information Science and Technology 57, (October 2020), https://doi.org/https://doi.org/10.1002/pra2.285 .
• Negeen Aghassibake, Justin Joque, and Matthew L. Sisk, “Supporting Data Visualization Services in Academic Libraries,” The Journal of Interactive Technology & Pedagogy, 2020, https://jitp.commons.gc.cuny.edu/supporting-data-visualization-services-in-academic-libraries/ .
• James BohmanJeffrey Flynn, and Robin Celikates, “Critical Theory,” in The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Spring 2021, Metaphysics Research Lab, Stanford University, 2021, https://plato.stanford.edu/archives/spr2021/entries/critical-theory/ .
• Marcia Rapchak, “Introducing Critical Librarianship to Information Professionals: Using Critical Pedagogy and Critical Information Literacy in an LIS Graduate Course,” Communications in Information Literacy 15, no. 1 (June 21, 2021), https://doi.org/10.15760/comminfolit.2021.15.1.8 ; Suzanne M. Stauffer, “Educating for Whiteness: Applying Critical Race Theory’s Revisionist History in Library and Information Science Research: A Methodology Paper,” Journal of Education for Library and Information Science 61 no. 4 (October 2020): 452–62, https://doi.org/10.3138/jelis.61.4.2019-0042 .
• Marcia Rapchak, “Introducing Critical Librarianship to Information Professionals: Using Critical Pedagogy and Critical Information Literacy in an LIS Course,” Communications in Information Literacy 15, no. 1 (2021): 140-157, https://doi.org/10.15760/comminfolit.2021.15.1.8 .
• Amy VanScoy and Kawanna Bright, “Articulating the Experience of Uniqueness and Difference for Librarians of Color,” The Library Quarterly 89, no. 4 (2019): 285-297, https://doi.org/10.1086/704962 .
• Sofia Y. Leung and Jorge R. López-McKnight, “Dreaming Revolutionary Futures: Critical Race’s Centrality to Ending White Supremacy,” Communications in Information Literacy 14, no. 1 (2020): 12-26, http://doi.org/10.15760/comminfolit.2020.14.1.2 .
• Margaret Rose Torrell, “That Was Then, This Is Wow: A Case for Critical Information Literacy Across the Curriculum,” Communications in Information Literacy 14, no. 1 (2020): 118-133, http://doi.org/10.15760/comminfolit.2020.14.1.9 .
• Torrell, 119.
• Rapchak, 146.
• Leung and López-McKnight, 17.
• Erin Fields and Adair Harper, “Opening Up Information Literacy: Empowering Students Through Open Pedagogy,” Scholarship of Teaching and Learning, Innovative Pedagogy 2, no. 1 (2020): 4-15, https://digitalcommons.humboldt.edu/sotl_ip/vol2/iss1/ 1.

Article Views (Last 12 Months)

Contact ACRL for article usage statistics from 2010-April 2017.

Article Views (By Year/Month)

© 2024 Association of College and Research Libraries , a division of the American Library Association

Print ISSN: 0099-0086 | Online ISSN: 2150-6698

ALA Privacy Policy

ISSN: 2150-6698