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Data and Information Management

4 Issues per year

Open Access
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Feicheng Ma / Gary Marchionini / Xiaojuan Zhang
Published Online: 2017-09-29 | DOI: https://doi.org/10.1515/dim-2017-0001

It is our great pleasure to welcome readers to the first issue of the Data and Information Management (DIM). The launch of a new journal is a critical point in the life of a research community. Like the arrival of a new child into the family, a journal must be nurtured and encouraged. We look forward to working with the global information science and related community to grow this journal into a mature vehicle for data and information management research and practice.

We might ask, why more journals when so many already exist? We believe we are in the midst of two Cambrian explosions of innovation and maximizing variety is essential in such times. First, the field of information science is expanding to apply data generation, analysis, and management techniques to all areas of human endeavor, especially in ways that build bridges across disciplines to address complex problems. It is important that the growing global population of data and information management scholars have venues for their work and their voices. Second, we are seeing changes in the way that scholarly ideas and results are shared and rewarded. DIM will provide an alternative and open access model of publishing that will maximize scholarly sharing and progress.

Data and information management is one of the crucial challenges of our time. Data is everywhere and it is famously high volume, high velocity, high variability, and IF properly curated and managed, high VALUE. This is however, a big IF. Statistical sciences will develop new ways to model, aggregate, summarize, and analyze data and computer science will develop new algorithms to process, store, transmit data and apply the statistical advances to those data sets. As information scientists, it is up to us to focus on not only data and information processing but also the related management and social issues: policies for data collection (e.g., informed consent), data cleaning/assurance (policies for managing and documenting errors, outliers, and subsets), retrieval (e.g., metadata ontologies, query and result presentation strategies, user behaviors), preservation (policies for balancing cost per bit per unit time with risk factors, preserving work flows as well as data), and usage (e.g., privacy, security, fairness). We are: most concerned with GOOD data rather than only BIG data, and how data manifests as information and in turn supports the advance of human knowledge.

In addition to articles reporting new statistical techniques and algorithms, DIM will provide a venue for scholars who address fundamental questions such as: What do we mean by good data and quality information? Can we establish measures for quality? How do we assess risk to data and information over time? What cost models are applicable to these risks? How do we optimize global sharing, reuse, and interpretation of data and information? What policies are: appropriate for balancing societal needs for data openness and information/knowledge access and individual needs for privacy or dignity? What kind of ethical policies are desirable and practical? DIM invites scholars to share technical breakthroughs as well as cogent arguments for answering a range of data and information challenges.

Publishing is changing in the 21st century. Paper journals that demand production costs such as physical printing, storing copies, and shipping are being supplanted by electronic journals that not only minimize production costs but also open up new possibilities for representation and usage. Electronic journals are less linear—they can support multimedia, hyperlinks out (and perhaps in), and secondary services such as lookups, citation analyses, and social media forums. They are not constrained by page limitations or by delivery costs and ultimately may change the nature of reading from a linear and sedentary process to a distributed (in time and space) process. They also allow new financial models such as Open Access rather than subscription. DIM is an open access electronic journal that will experiment with some of the trends in digital publishing. We aim to insure that contributions are peer reviewed and high quality but invite authors and readers to suggest features and ideas that may not be possible in paper journals.

We encourage you to send good papers to this journal and encourage your colleagues to do so. Please let us know if you are willing to review papers for the journal— we know that reviewer burden is a natural resource and we want to value it. We look forward to building a community that values the innovations in data and information management and in digital publishing and look forward to your active participation.

About the article

Published Online: 2017-09-29

Citation Information: Data and Information Management, Volume 1, Issue 1, Pages 1–2, ISSN (Online) 2543-9251, DOI: https://doi.org/10.1515/dim-2017-0001.

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© 2017 Feicheng Ma et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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