| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 386 | 72 | 76 | 15 |
The journal generally zeroes in on subjects such as Data quality, Data mining, Data science, Information quality and Information retrieval. Aside from research in Data quality, the journal also discusses topics such as Database, Big data, Data integration, Linked data and Risk analysis (engineering). The research on Data mining featured in it combines topics in other fields like Synthetic data, Artificial intelligence, Machine learning and Set (abstract data type).
Research on Artificial intelligence addressed in the journal frequently intersections with the field of Natural language processing. The journal dives deep in exploring the relationship between the study of Machine learning and Crowdsourcing. It discusses concepts in Analytics under Data science and how they intertwine with disciplines like Digital library.
Studies in Information quality were the highlight in Journal of Data and Information Quality but it also discussed other topics like Knowledge management and World Wide Web.
The journal articles tackle a plethora of topics, such as Data quality, Information quality, Knowledge management, Data science and World Wide Web. The journal articles with studies in Knowledge management featured incorporate elements of Relation (database) and Interoperability. The most cited articles explore themes in Data mining like Data warehouse and link them with other fields of study like Risk analysis (engineering), Currency and Campaign management.
The discussions in Journal of Data and Information Quality mainly cover the fields of Artificial intelligence, Data mining, Big data, Data science and Data quality. Journal of Data and Information Quality explores research in Machine learning and overlapping concepts in Interpretation (philosophy) to expand the discourse in Artificial intelligence. The work on Data mining tackled in it brings together disciplines like Segmentation, Similitude and Outlier.
It facilitates discussions on Data science that incorporate concepts from other fields like Hybrid approach, Biomedical data, Metadata and Knowledge graph. Attendees participated in lively discussions that mix various fields of study, including Data quality and Crowdsourcing, Secondary source, Consistency (database systems), General Data Protection Regulation and Data breach. In addition to Deep learning research, Journal of Data and Information Quality aims to explore topics under Language model, Matching (statistics), Task (project management), Data integration and Blocking (computing).
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.
The top authors publishing in Journal of Data and Information Quality (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in Journal of Data and Information Quality (based on the number of publications) are:
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.
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, 13.04% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.00% of all publications and 70.00% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
With the vast array of research topics covered by Journal of Data and Information Quality, numerous career opportunities and roles are available for those interested in this field. These opportunities are not limited to academia and research but also extend to industries that heavily rely on quality data information, such as technology, finance, health care, and government sectors.
For individuals with a keen interest in the field, positions such as Data Quality Analyst, Information Quality Manager, and Data Governance Specialist are some of the roles to consider. These roles require specialized knowledge and skills in areas like data quality, information retrieval, and knowledge management, which were extensively covered by the journal's articles.
Furthermore, the journal’s engagements with current and emerging areas such as artificial intelligence, machine learning, and big data also open doors for roles like AI Specialist, Machine Learning Engineer, and Big Data Analyst. The intersection of these technologies with the field of data and information quality provides a unique perspective and a competitive edge for professionals in these roles.
Besides these roles, the field of data and information quality also benefits educators, especially those who impart knowledge on these topics to the next generation of experts. For example, one could aim to become an elementary school teacher who introduces basic concepts related to this field. If you are interested in such a role, this guide on {anchor} provides a detailed path to becoming an elementary school teacher in New York.
With evolving technological advancements, new roles and opportunities are emerging in the field of data and information quality. Hence, continuous learning and staying abreast with latest research trends, such as those discussed in the Journal of Data and Information Quality, can contribute to success in this profession.
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(2023)Leopoldo Bertossi;Floris Geerts
(2020)Yuliang Li;Jinfeng Li;Yoshihiko Suhara;Jin Wang
(2021)Nelson Novaes Neto;Stuart Madnick;Anchises Moraes G. De Paula;Natasha Malara Borges
(2021)Evaggelia Pitoura
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