Journal of Data and Information Science

Top Research Topics at Journal of Data and Information Science?

The journal primarily tackles Project management, Information technology, World Wide Web, Data science and Knowledge management. While work presented in it provided substantial information on Project management, it also covered topics in Bibliometrics, Information retrieval and Engineering management. The work tackled in the journal goes beyond the discipline of Information retrieval as it also encompasses Cluster analysis.

The journal aims to bridge the gap between the study of Information technology and disciplines such as Library science and Scientometrics. Library science research featured in it incorporates concerns from various other topics such as Questionnaire and Public relations. The journal explores issues in World Wide Web which can be linked to other research areas like Originality and Service (systems architecture).

The Originality study tackled is a key component of adjacent topics in the area of Sample (statistics). Data science research presented in Journal of Data and Information Science encompasses a variety of subjects, including Social network analysis and Citation. Journal of Data and Information Science holds forums on Knowledge management that merges themes from other disciplines such as Empirical research and Service (business).

• Project management (29.60%)
• Information technology (28.74%)
• World Wide Web (16.38%)

What are the most cited papers published in the journal?

• Science Mapping: A Systematic Review of the Literature (266 citations)
• A Criteria-based Assessment of the Coverage of Scopus and Web of Science (64 citations)
• Rediscovering Don Swanson: the Past, Present and Future of Literature-Based Discovery. (32 citations)

Research areas of the most cited articles at Journal of Data and Information Science:

The most cited publications cover a variety of subjects, including Data science, Project management, Information technology, Library science and Information retrieval. The journal publications address concerns in Data science which are intertwined with other disciplines, such as Science mapping, Big data, Computational thinking and Citation. The published articles with studies in Project management featured incorporate elements of Metadata, Cluster analysis and Literature-based discovery.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• The Internet
• Statistics

The previous edition focused in particular on these issues:

The journal mainly tackles studies in Artificial intelligence, Extraction (chemistry), Information retrieval, Natural language processing and Project management. Imbalanced data and Phrase studies in the realm of Artificial intelligence interact with fields like Structure (mathematical logic) and Scheme (programming language). It explores topics in Information retrieval which can be helpful for research in disciplines like Science mapping, Fractional counting, Harmonic mean and Embedding.

Issues in Natural language processing were discussed, taking into consideration concepts from other disciplines like Annotation, Semantic publishing, Word (computer architecture) and Knowledge representation and reasoning. Topics in Project management explored in the journal were investigated in conjunction with research in World Wide Web and Social web. The close relationship between Context (language use) and Public relations and Value (ethics) is one of the points of interest dissected in Scientometrics research.

The most cited articles from the last journal are:

• Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions—A Trial Dataset (4 citations)
• “My ADHD hellbrain”: A Twitter data science perspective on a behavioural disorder (3 citations)
• Extraction and Evaluation of Knowledge Entities from Scientific Documents (2 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in Journal of Data and Information Science (based on the number of publications) are:

• Ronald Rousseau (10 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Loet Leydesdorff (5 papers) published 1 paper at the last edition the same number as at the previous edition,
• Liu Xiwen (4 papers) absent at the last edition,
• Alan L. Porter (4 papers) absent at the last edition,
• Liying Yang (4 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of Data and Information Science (based on the number of publications) are:

• Chinese Academy of Sciences (23 papers) published 2 papers at the last edition, 3 less than at the previous edition,
• University of Antwerp (12 papers) published 2 papers at the last edition the same number as at the previous edition,
• Wuhan University (11 papers) published 5 papers at the last edition,
• Nanjing University (10 papers) published 1 paper at the last edition, 1 less than at the previous edition,
• Katholieke Universiteit Leuven (8 papers) published 2 papers at the last edition, 1 more than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 7.41% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 40.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 16.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.00% of all publications and 32.00% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Future Research Opportunities and Recommendations

The articles published in Journal of Data and Information Science prompt several intriguing and potentially transformative areas for future research. Given the prominence of data science and information technology fields in the journal, it would be interesting to see future studies exploring the crossover of these two areas. As well, drawing insights from Library Science could make a significant contribution towards the development of new methodologies in data presentation and management.

One promising area could be the application of data science and technology in the realm of the education sector, especially special education. For instance, harnessing data science to improve learning outcomes for students with special needs could be a focus of future research. Researchers looking to explore this path could find helpful information on becoming a special education teacher in South Dakota and the requirements at the special ed teacher requirements South Dakota page.

Another potential area for future research could be the exploration of Big Data's role in project management, especially given the growing demand for effective and efficient project management strategies in businesses.

In conclusion, the Journal of Data and Information Science offers a wealth of knowledge in various research areas, thereby placing it at the forefront when it comes to setting the research agenda in data science and related fields. It is hoped that future research can capitalize on the opportunities outlined above to revisit existing issues and uncover new insights.