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As an emerging discipline, data science represents a vital new current of school of library and information science (LIS) education. However, it remains unclear how it relates to information science within LIS schools. The purpose of this paper is to clarify this issue.

Mission statement and nature of both data science and information science are analyzed by reviewing existing work in the two disciplines and drawing DIKW hierarchy. It looks at the ways in which information science theories bring new insights and shed new light on fundamentals of data science.

Data science and information science are twin disciplines by nature. The mission, task and nature of data science are consistent with those of information science. They greatly overlap and share similar concerns. Furthermore, they can complement each other. LIS school should integrate both sciences and develop organizational ambidexterity. Information science can make unique contributions to data science research, including conception of data, data quality control, data librarianship and theory dualism. Document theory, as a promising direction of unified information science, should be introduced to data science to solve the disciplinary divide.

The results of this paper may contribute to the integration of data science and information science within LIS schools and iSchools. It has particular value for LIS school development and reform in the age of big data.

With the continuous change of research contents and methods of intelligence science, its integration with other disciplines is also deepening. The purpose of this paper is to further explore the interdisciplinary research characteristics of intelligence science in theoretical depth and application value.

This paper summarizes and explores in two aspects. The first is a large number of literature review, mainly combined with the historical characteristics of the development of intelligence science researches in China and international comparison. The second is to refine the discipline construction ideas suitable for the development of contemporary intelligence science.

From the perspective of the historical development of discipline relevance, the development characteristics and positioning of intelligence science in China are introduced, with the comparison of many disciplines including information technology, library science, information science, data science, management science and other disciplines. In order to better meet the practical needs of intelligence service in the new era, this paper mainly analyzes the construction method of intelligence science research system and the relocation of intelligence science research content.

This paper summarizes the historical characteristics and international comparison of the development of intelligence science in China. It proposes the development characteristics and orientation of intelligence science in China from the perspective of historical development of discipline relevance. It also proposes the discipline construction ideas suitable for the development of contemporary intelligence science.

Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective.

Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective?

The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences.

The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.”

The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.

In recent years, data science has become a high-demand profession, thereby attracting transmuters (individuals who want to change their profession due to industry trends) to this field. The primary purpose of this paper is to guide transmuters in becoming data scientists.

An exploratory study was conducted to uncover the challenges faced by data scientists according to their educational backgrounds. An extensive set of responses from 31 countries was received.

The results reveal that skill requirements and tool usage vary significantly with educational background. However, regardless of differences in academic background, the data scientists surveyed spend more time analyzing data than operationalizing insight.

The collected data are available to support replication in various scenarios, for example, for use as a roadmap for those with an educational background in art-related disciplines. Additional empirical studies can also be conducted specific to geographical location.

The current work has categorized data scientists by their fields of study making it easier for universities and online academies to suggest required knowledge (courses) according to prospective students' educational background.

The conducted study suggests the required knowledge and skills for transmuters to acquire, based on their educational background, and reports a set of motivational factors attracting them to adopt the data science field.

This paper aims to selects 59 journals that focus on data science research in 14 disciplines from the Ulrichsweb online repository. This paper analyzes the aim and scope statement using both quantitative and qualitative methods to identify the research types and the scope of research promoted by these journals.

Multiple disciplines are involved in data science research and publishing, but there lacks an overview of what those disciplines are and how they relate to data science. In this study, this paper aims to understand the disciplinary characteristics of data science research. Two research questions are answered: What is the population of journals that focus on data science? What disciplinary landscape of data science is revealed in the aim and scope statements of these journals?

Theoretical research is mainly included in journals that belong to statistics, engineering and sciences. Almost all data science journals include applied research papers. Keywords analysis shows that data science research in computers, statistics, engineering and sciences appear to share characteristics. While in other disciplines such as biology, business and education, the keywords are indicative of the types of data to be used and the special problems in these disciplines.

This is the first study to use journals as the unit of analysis to identify the disciplines involved in data science research. The results provide an overview of how researchers and educators from different disciplinary backgrounds understand data science research.

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

Data science promises new opportunities for organizational decision-making. Data scientists arguably play an important role in this regard and one can even observe a certain “buzz” around this nascent occupation. This paper enquires into how data scientists construct their occupational identity and the challenges they experience when enacting it.

Based on semi-structured interviews with data scientists working in different industries, the authors explore how these actors draw on their educational background, work experiences and perception of the contemporary digitalization discourse to craft their occupational identities.

The authors identify three main components of data scientists’ occupational identity: a scientific mindset, an interest in sophisticated forms of data work and a problem-solving attitude. The authors demonstrate how enacting this identity is sometimes challenged through what data scientists perceive as either too low or too high expectations that managers form towards them. To address those expectations, they engage in outward-facing identity work by carrying out educational work within the organization and (paradoxically) stressing both prestigious and non-prestigious parts of their work to “tame” the ambiguity and hype they perceive in managers’ expectations. In addition, they act upon themselves to better appreciate managers’ perspectives and expectations.

This study contributes to research on data scientists as well as the accounting literature that often refers to data scientists as new competitors for accountants. It cautions scholars and practitioners alike to be careful when discussing the possibilities and limitations of data science concerning advancements in accounting and control.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

This study investigates differences and commonalities in data production, sharing and reuse across the widest range of disciplines yet and identifies types of improvements needed to promote data sharing and reuse.

The first authors of randomly selected publications from 2018 to 2019 in 20 Scopus disciplines were surveyed for their beliefs and experiences about data sharing and reuse.

From the 3,257 survey responses, data sharing and reuse are still increasing but not ubiquitous in any subject area and are more common among experienced researchers. Researchers with previous data reuse experience were more likely to share data than others. Types of data produced and systematic online data sharing varied substantially between subject areas. Although the use of institutional and journal-supported repositories for sharing data is increasing, personal websites are still frequently used. Combining multiple existing datasets to answer new research questions was the most common use. Proper documentation, openness and information on the usability of data continue to be important when searching for existing datasets. However, researchers in most disciplines struggled to find datasets to reuse. Researchers' feedback suggested 23 recommendations to promote data sharing and reuse, including improved data access and usability, formal data citations, new search features and cultural and policy-related disciplinary changes to increase awareness and acceptance.

This study is the first to explore data sharing and reuse practices across the full range of academic discipline types. It expands and updates previous data sharing surveys and suggests new areas of improvement in terms of policy, guidance and training programs.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2021-0423.