About - Archives of Data Science, Series B

About

Concept

Archives of Data Science, Series B (Data Sets, Algorithms, Processes, and Services) addresses the problems of data science. The journal covers scientific articles which improve methods, algorithms, and processes over the whole data life cycle. In addition, papers on data analysis processes, services, and (scientific) infrastructures are welcome. Prototypes of processes and services should be available on the web site of the journal. Examples of such processes are benchmarks, resampling processes, …

The journal is organized around data sets and it follows the traditional structure of volumes and numbers, however with a non-traditional interpretation of what constitutes a number and a volume. A number is a an open ended stream of articles which starts with a seminal article on a data set (head article) and continues with articles which propose innovative ways of “handling” the data set (tail articles).

The organization of the journal requires that a number always starts with an article about a data set, followed by papers with methods applied to the data set in the head article. A purely theoretic paper (without reference and application to a head article) is not suitable for the Archives of Data Science, Series B. The editors reserve the right to delegate such articles to other Data Science journals.

All publications are available both as free OpenAccess articles as well as printed version orderable via KIT Scientific Publishing (KSP).

Contributions

Head Article
Describes a data set and provides access to it. All data sets must be open. Such a seminal article must provide at least the structure of the data set and the interfaces available to access it. Data sets are also defined in a wide sense: E.g. comma-separated files, relational data-bases, open linked data, data-harvesting processes, data generators, and interfaces to data streams. In addition, the measurement process for the data set must be explained in detail, restrictions and possible problems of the measurement process should be covered. The authors of such a seminal article are also expected to provide problems and questions (ideally a challenge) that they would like to see solved by analyzing the data set.
Tail Articles
They describe innovative ways of generating, accessing, storing, distributing, analyzing, visualizing, and using data in the broadest sense. In addition, each article must be complemented by a well-documented open source version of a software package which implements the methods described in the tail article.

Peer Review Policy

Every submitted paper is reviewed by at least two reviewers.

Accepted final papers will be published as fully reviewed online-first version that are freely available and already citable with the note Online-First in the reference. Final papers are published in cooperation with KIT Scientific Publishing (KSP) as an electronic version. Each issue of the journal can also be ordered as print-on-demand version.

Copyright

Copyright for articles published in this journal is retained by the authors, with first publication rights granted to the journal. By virtue of their appearance in this open access journal, articles are free to use, with proper attribution, in educational and other non-commercial or commercial settings.
By submitting an article the authors agree that their work is published (on acceptance) under the terms of the Creative Commons Attribution-ShareAlike (CC CY-SA) license.
For details, we refer to the FAQ of KIT Scientific Publishing (in German). For further information on this license, see: http://creativecommons.org/licenses/by-sa/4.0/

Editorial Board

In alphabetic order:

  • N. Dean (University of Glasgow)
  • A. Geyer-Schulz (KIT, Karlsruhe)
  • C. Hennig (UCL, London)
  • F. Leisch (University of Natural Resources and Life Sciences, Vienna)
  • I. van Mechelen (KU Leuven)

Contact details

Technical Support
For problems when preparing a submission and with the submission and reviewing processes of the journal please contact:
Technical Support Team
em-journals@iism.kit.edu
KIT Scientific Publishing
c/o KIT-Bibliothek
Stra├če am Forum 2
D-76131 Karlsruhe
Germany
Contact
Principal Contact: Prof. Dr. Andreas Geyer-Schulz
Karlsruhe Institute of Technology
Institute of Information Systems and Marketing
Information Services and Electronic Markets
Kaiserstra├če 12
76131 Karlsruhe
Germany

Phone: +49 721 608 48402
Fax: +49 721 608 48403
andreas.geyer-schulz@kit.edu