Data and Information Management
4 Issues per year
The continuous increase in the volume and detail of data captured by organizations has produced an overwhelming flow of data in either structured or unstructured format. Managing and gaining insights and, more precisely, knowledge from the produced data is a great challenge and key to competitive advantage. The ability to cross-relate private information with information publically available on the Web, and data from social networks, and the analytics solutions to mine structured and unstructured data open a wide range of possibilities for organizations to understand the needs of their customers, and optimize the use of resources, which symbolizes the Big Data era. However, collecting data from various interrelated sources to provide “complete” data, accurate information, and so to generate actionable knowledge to achieve the added value of Big Data also poses challenges for government, academia, and industry.
Aims and Scope
Data and Information Management (DIM) aims to promote cross-disciplinary data-driven information management research, especially targeting large-scale datasets in scientific/academic, government and business domains. The journal focuses on innovative theories and technologies related to data and information processing; creative applications of theories and techniques including patterns, models, and processes in various datasets; and compatible management processes and social systems required for the realization of the substantial value that data and information offers to organizations.
Specific topic areas may include but not limited to:
- Innovative theories and technologies in data-driven information analytics, including knowledge discovery and organization, cloud computing, machine learning, information visualization, and human-computer interaction
- Information organization and retrieval, and information search behaviors in data intensive environments such as social media
- Open data, information sharing, and information services
- Novel methods, algorithms, and processes of conducting data/text/Web mining
- Scalable semantic annotation and reasoning across distributed data repositories
- Digital humanities, and academic/scientific data analytics and evaluation
- Data curation, information privacy, and information security systems and networks
- New development and expansion of information literacy, including data literacy and media literacy
- Advanced data engineering in smart cities, smart government, smart health, smart education, and financial services
- Novel and impactful applications or case studies in big data and analytics for better managerial decision making in public and private sectors
- Social impact of data and algorithms on individuals and organizations
The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process.
Detailed description of the Similarity Check System can be found at:
- DE GRUYTER OPEN
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Submission of Manuscripts
Instructions for Authors
DIM encourages submissions which are able to advance the knowledge and practice on data-driven information management, including original research papers, literature reviews, case studies and surveys, and positioning papers and editorials. As a peer-reviewed academic journal, DIM will present 20 research papers in each volume.
We welcome you to submit your manuscript to DIM. The instructions below are structured so that you can quickly and easily answer the following questions:
1. Is my manuscript suitable for DIM? (see Aims and Scope)
2. How do I format my manuscript for DIM? (see Manuscript File Formats)
3. How do I submit my manuscript to DIM? (see Submission of Manuscripts)
4. What is DIM’s review system? (see Review System)
5. What is the Article publication charge? (see Cost)
Manuscript File Formats
For submission, acceptable manuscript file is expected to be Word format.
Article types and length
DIM primarily publishes original research papers, literature reviews，survey, empirical research papers，case studies, editorial and positioning papers, among which editorial and positioning papers are expected to be about 1,000-2,000 words, and the rest over 6000 words. Hopefully, a research paper should be grounded in theory, include substantive evidence from the literature for critical points raised in the submission, and offer original and significant contribution to information science and big data.
If a paper has already been published in a conference proceedings or a digital repository, the author(s) will need to revise the paper with approximately 30% different content and submit the manuscript to DIM under a new title.
Make spelling consistent with current editions of either Webster’s Dictionary (Am. Eng.) or Oxford English Dictionary (Br. Eng.).
The title page should give a concise but informative title, the first and last names and other initials of all authors, as well as their affiliations (but not degrees). The orders in which the contributors are listed should be agreed amongst the investigators, and should indicate that the first listed made the greatest contribution to the paper. Full contact details should be provided for the corresponding author.
Abstract and keywords
The abstract should be an unstructured narrative paragraph. It should be comprehensible to readers without their having read the paper, and abbreviations and reference citations within the abstract should be avoided. It should outline the purpose of the study, the basic procedures and the most important conclusions. Keywords, which may appear in the title, should be given below the abstract, each separated by a comma.
This should give a short, clear account of the background and reasons for undertaking the study by reference only to the pertinent literature. It should not be a review of all literature in the field but be limited to analysis of those aspects of previous work that raise questions that can be answered by the hypothesis addressed in the work being reported.
These should be brief, and should include sources of financial support, material (e.g. novel compounds, strains, etc.) not available commercially, personal assistance, advice from colleagues and gifts. Acknowledgements should be made only to those who have made a significant contribution to the study. Authors should submit to DIM written permission given by individuals named in this section.
Authors are responsible for the accuracy of the references. These may include published articles as well as those in press (but for these, state the name of the journal and enclose a copy of the manuscript).
In the text of the manuscript, references to the literature should be numbered consecutively and indicated by a superscript. Each reference should be numbered individually and listed at the end of the manuscript; DIM takes APA as its desired format.
Tables are useful for presentation of entire data-sets, and for results where little change between treatments occurs. (Figures here may show parallel lines that are difficult to distinguish from the overlapping symbols and error bars.) Tables must supplement, not duplicate, the data in the text or Figures. Tables should consist of at least two columns; columns should always have headings. They should have a title and be numbered sequentially as Table 1, Table 2, etc. and cited sequentially in the text. Tables should have a brief footnote that identifies all abbreviations used. Reference to Table footnotes should be made by means of Arabic numeral as superscripts. The data in the Tables should be consistent with those cited in the relevant places in the text. Check that totals add up correctly, that percentages have been calculated correctly and that the correct number of significant digits and decimal places are used.
Figures are useful to highlight clear differences between treatment groups, where lines may diverge and symbols and errors bars do not overlap. (Tables here require concentrated attention to locate the superscript indices of statistical differences between columns or rows.) Figures must supplement, not duplicate, the data in the text or in Tables. Illustrations must clearly convey their message and be of high quality and sufficient size and clarity (especially lettering, arrows and data points) to be interpretable when reduced for publication. Figures should be given a title and numbered sequentially as Figure 1, Figure 2, etc. and cited (as Fig. 1, Fig. 2 etc.) sequentially in the text. The position of Figures in the text should be referred to specifically in the paper but not embedded within the text. Scale bars (not magnifications) should be provided on all photomicrographs.
Presentation of reproduced material
If a Table or Figure has been published before, the authors must obtain written permission to reproduce the material in both print and electronic formats from the copyright owner and submit it with the manuscript. Prior written permission is also required for quotations, illustrations and other material taken from previously-published works not in the public domain. The original source should be cited in the Figure caption or Table footnote.
Accepted file formats
• Graphical image files (.gif or tif(f)). • JPEG image files (.jpg or .jpeg). • MS Word documents (.doc or .docx).
For all types of submission, word counts are inclusive of all textual matter (body of the manuscript, tables, captions, references and appendixes), not counting abstract and keywords.
Submission of Manuscripts
If you have not done so already, please register for an account on our online submission and review system with De Gruyter (http://www.editorialmanager.com/dim).
DIM adopts a double-blind peer review process to ensure quality.
Cost of Submission
DIM provides free access to its full-text articles as soon as they are available online. The open access fees (article-processing charges) are fully paid by Wuhan University, no charges are required from authors.
Abstracting & Indexing
Data and Information Management is covered by the following services:
- Baidu Scholar
- CNKI Scholar (China National Knowledge Infrastructure)
- EBSCO (relevant databases)
- EBSCO Discovery Service
- Google Scholar
- KESLI-NDSL (Korean National Discovery for Science Leaders)
- Naviga (Softweco)
- Primo Central (ExLibris)
- ProQuest (relevant databases)
- Summon (Serials Solutions/ProQuest)
- WanFang Data
- WorldCat (OCLC)
Xiaojuan Zhang, Wuhan University, China
Chowdhury, Gobinda, Department of Mathematics and Information Sciences, Northumbria University, United Kingdom
Janssen, Marijn, Faculty of Technology, Policy, and Management, Delft University of Technology, Netherlands
Seadle, Michael, Berlin School of Library and Information Science, Humboldt University, Germany
Hasle, Per Frederik Vilhelm, Royal School of Library and Information Science, University of Copenhagen, Denmark
Downie, Stephen, School of Library and Information Science, University of Illinois Graduate, USA
Ghose, Anindya, Information, Operations and Management Sciences, New York University's Stern School of Business, USA
He, Daqing, School of Information Sciences, University of Pittsburgh, USA
Liu, Xiaozhong, School of Informatics and Computing, Indiana University, USA
Ross, Seamus, Faculty of Information, University of Toronto, Canada
Zhang, Jin, School of Information Studies, University of Wisconsin Milwaukee, USA
Abrizah, Abdullah, Faculty of Computer Science & Information Technology, University of Malaya, Malaysia
Deng, Hepu, School of Business Information Technology and Logistics, Royal Melbourne Institute of Technology, Australia
Kauffman, Robert John, School of Information Systems, Singapore Management University, Singapore
Wei, Kwok Kee, Dean of the School of Continuing and Lifelong Education (SCALE) at the National University of Singapore, Singapore
Lee, Joong Seek, School of Convergence Science and Technology, Seoul National University, Korea
Sugimoto, Shigeo, Graduate School of Library, Information and Media Studies, University of Tsukuba, Japan
Chen, Guoqing, School of Economics and Management, Tsinghua University
Chen, Chuanfu, School of Information Management, Wuhan University
Cheng, Xueqi, Institute of Computing Technology, Chinese Academy of Science
Li, Yijun, Department of Management Sciences, National Natural Science Foundation of China
Mao, Jiye, School of Business, Renmin University of China
Shi, Yong, University of Chinese Academy of Sciences
Wang, Bin, Institute of Information Engineering, Chinese Academy of Science
Xue, Lan, School of Public & Management, Tsinghua University
Zeng, Dajun, Institute of Automation, Chinese Academy of Science
Zou, Lei, Institute of Computer Science and Technology, Peking University
Board of Assistant Editors
Hong, Liang, Editor in Computer Science
Wu, Dan, Editor in Library and Information Science
Wu, Jiang, Editor in Business and Management
School of Information Management
299 Bayi Rd., Wuhan, Hubei, P.R. China
Postal Code: 430072
De Gruyter Open
Bogumiła Zuga 32A Str.
01-811 Warsaw, Poland
T: +48 22 701 50 15