Data Science, Statistics & Visualisation (DSSV) is a forum to discuss recent progress and emerging ideas in these different disciplines that contribute to data science, statistics, and visualization. The conference welcomes contributions to practical aspects of data science, statistics and visualization, and in particular those which are linking and integrating these subject areas. Presentations should thus be oriented towards a very wide scientific audience, and can cover topics such as machine learning and statistical learning, the visualization and verbalization of data, big data infrastructures and analytics, interactive learning, advanced computing, and other important themes. We encourage informal contacts and discussions among all the participants.
The European Conference on Data Analysis (ECDA) provides a forum for scientific exchange in the realm of data science, whereby theory and application are of equal interest. The conference offers scientists and practitioners the opportunity to present their research questions and latest results and to discuss them with a qualified expert audience. Among the special characteristics of the conference is its strong emphasis on interdisciplinarity: in addition to participants with a rather theoretical focus, especially from mathematics, statistics, and computer science, the conference is also visited by a wide circle of users of statistical and data-analytical methods, for example from econometrics, marketing, bioinformatics, or psychology.
Journal of Data Science, Statistics, and Visualisation
This international refereed journal creates a forum to present recent progress and ideas in the different disciplines of data science, statistics, and visualization. It welcomes contributions to data science, statistics, and visualization, in particular, those aspects which link and integrate these subject areas. Articles should be oriented towards a wide scientific audience, and can cover topics such as machine learning and statistical learning, the visualization and verbalization of data, visual analytics, big data infrastructures and analytics, interactive learning, and advanced computing. Papers that discuss two or more research areas of the journal are favored. Scientific contributions should be of a high standard. The journal explicitly welcomes contributions that include software with the aim of reproducibility of the results and application of the proposed methodology to other data by the reader. It is expected that data used in a paper are provided. JDSSV is an open access journal that charges no author fees.
Archives of Data Science
Archives of Data Science currently publishes two series: Series A covers regular research articles from the field of Data Science and special issues on conferences, workshops and joint activities of the German Classification Society/Gesellschaft für Klassifikation (GfKL) and its cooperating partners and organizations. Series B (Data Sets, Algorithms, Processes, and Services) covers scientific articles which improve methods, algorithms, and processes over the whole data life cycle. It is organized around data sets and 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.
All publications are available both as free OpenAccess articles as well as printed version orderable via KIT Scientific Publishing (KSP).