Consulta las condiciones de publicación y los listados de títulos de los diferentes Acuerdos vigentes en la UBU para publicar en acceso abierto.
Elsevier
Big Data Research
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.
The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.
Benefits to authors
We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services .
Computational Statistics and Data Analysis
Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas:
I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article.
II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures.
Statistical methodology includes, but not limited to: bootstrapping, classification techniques, clinical trials, data exploration, density estimation, design of experiments, pattern recognition/image analysis, parametric and nonparametric methods, statistical genetics, Bayesian modeling, outlier detection, robust procedures, cross-validation, functional data, fuzzy statistical analysis, mixture models, model selection and assessment, nonlinear models, partial least squares, latent variable models, structural equation models, supervised learning, signal extraction and filtering, time-series modelling, longitudinal analysis, multilevel analysis and quality control.
III) Special Applications - Manuscripts at the interface of statistics and computing (e.g., comparison of statistical methodologies, computer-assisted instruction for statistics, simulation experiments). Advanced statistical analysis with real applications (social sciences, marketing, psychometrics, chemometrics, signal processing, medical statistics, environmentrics, statistical physics).
IV) Annals of Statistical Data Science - The manuscripts concern with well-founded theoretical and applied data-driven research, with a significant computational or statistical methodological component for data analytics. Emphasis is given to comprehensive and reproducible research, including data-driven methodology, algorithms and software.
Data & Knowledge Engineering
Database Systems and Knowledgebase Systems share many common principles. Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems. DKE achieves this aim by publishing original research results, technical advances and news items concerning data engineering, knowledge engineering, and the interface of these two fields.
DKE covers the following topics:
1. Representation and Manipulation of Data & Knowledge: Conceptual data models. Knowledge representation techniques. Data/knowledge manipulation languages and techniques.
2. Architectures of database, expert, or knowledge-based systems: New architectures for database / knowledge base / expert systems, design and implementation techniques, languages and user interfaces, distributed architectures.
3. Construction of data/knowledge bases: Data / knowledge base design methodologies and tools, data/knowledge acquisition methods, integrity/security/maintenance issues.
4. Applications, case studies, and management issues: Data administration issues, knowledge engineering practice, office and engineering applications.
5. Tools for specifying and developing Data and Knowledge Bases using tools based on Linguistics or Human Machine Interface principles.
6. Communication aspects involved in implementing, designing and using KBSs in Cyberspace.
Springer
Advances in Data Analysis and Classification
The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.
Supported by the International Federation of Classification Societies, and funded by the Italian, German, and Japanese Classification Societies (CLADAG, GfKl, JCS).
Officially cited as: Adv Data Anal Classif
- Presents research and applications on the extraction of knowable aspects from many types of data
- Topics include structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets
- Shows how new domain-specific knowledge can be made available from data by skillful use of data analysis methods
Behaviormetrika
Behaviormetrika is issued twice a year to provide an international forum for new theoretical and empirical quantitative approaches in data science. When Behaviormetrika was launched in 1974, the journal advocated data science, as an interdisciplinary field that included the use of statistical methods to extract meaningful knowledge from data in its various forms: structured or unstructured. Behaviormetrika is the oldest journal addressing the topic of data science. The first editor-in-chief of Behaviormetrika, Dr. Chikio Hayashi, described data science in this way:
“Data science is not only a synthetic concept to unify statistics, data analysis, and their related methods; it also comprises its results. Data science is intended to analyze and understand actual phenomena with ‘data.’ In other words, the aim of data science is to reveal the features or the hidden structure of complicated natural, human, and social phenomena using data from a different perspective from the established or traditional theory and method.”
Behaviormetrika is a fully refereed international journal, which publishes original research papers, notes, and review articles. Subject areas suitable for publication include but are not limited to the following methodologies and fields.
Methodologies
- Data science
- Mathematical statistics
- Survey methodologies
- Artificial intelligence
- Information theory
- Machine learning
- Knowledge discovery in databases (KDD)
- Graphical models
- Computer science
- Algorithms
Fields
- Medicine
- Psychology
- Education
- Economics
- Marketing
- Social science
- Sociology
- Political science
- Policy science
- Cognitive science
- Brain science
Data Mining and Knowledge Discovery
The premier technical publication in the field, Data Mining and Knowledge Discovery is a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities.
The journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications.
Coverage includes:
- Theory and Foundational Issues
- Data Mining Methods
- Algorithms for Data Mining
- Knowledge Discovery Process
- Application Issues.
- The premier technical journal focused on the theory, techniques and practice for extracting information from large databases.
- Publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications.
Wiley
Statistical analysis and data mining
Statistical Analysis and Data Mining is primarily a statistical journal focusing on novelty, intellectual importance, and relevance in current topics of contemporary research in statistics, statistical machine learning, and statistical data mining. This is interpreted broadly to include innovative analytic techniques and applications provided they have a secure grounding in mathematical rigor. Aside from the emphasis on novelty, methodologies are expected to have been developed to the point that their key general properties can be established and their applicability can be assessed. In principle, any topic in any emerging areas of statistical thinking is welcome.
Statistical Analysis and Data Mining also welcomes review articles on recent topics of broad contemporary interest in statistics research.
Guidelines for Reviewers
Submitted research papers should have a clear focus stated succinctly in an introduction. The introduction should provide enough background that a typical reader will be able to see why the contribution is important. The development of the theory, methodology, computational approach, or other aspects of the focus should proceed in a logically streamlined way. When a new method is given, a statement of when it is better than existing methods should be made and supported via theory, systematic simulations, or other argumentation. An application demonstrating the feasibility of any new technique is also usually desirable...
Wires Data Mining and Knowledge Discovery
The award-winning WIREs (Wiley Interdisciplinary Reviews) series combines some of the most powerful features of encyclopedic reference works and review journals in an innovative online format. They are designed to promote a cross-disciplinary research ethos while maintaining the highest scientific and presentational standards, but should be viewed first and foremost as evolving online databases of cutting-edge reviews.
Journal Aims and Scope
The objectives of WIREs Data Mining and Knowledge Discovery are to (a) present the current state of the art of data mining and knowledge discovery through an ongoing series of reviews written by leading researchers, (b) capture the crucial interdisciplinary flavor of the field by including articles that address the key topics from the differing perspectives of data mining and knowledge discovery, including a variety of application areas in technology, business, healthcare, education, government and society and culture, (c) capture the rapid development of data mining and knowledge discovery through a systematic program of content updates, and (d) encourage active participation in this field by presenting its achievements and challenges in an accessible way to a broad audience. The content of WIREs DMKD will be useful to upper-level undergraduate and postgraduate students, to teaching and research professors in academic programs, and to scientists and research managers in industry.
The techniques of data mining and knowledge discovery (DMKD) are now being applied in many areas of business and government, such as banking and finance, market research, risk analysis, and counterterrorism. In the sciences, DMKD has become pervasive in such fields as bioinformatics, medical diagnosis, epidemiology, drug discovery, environmental modeling, and meteorological data analysis.
Readership
Each WIREs title was established in response to the urgent need to publish current, comprehensive reviews of the pioneering research that is being done in an interdisciplinary and complementary set of fields. Our goal is to support the research and teaching needs of advanced students, scientists, healthcare providers, governmental and policy analysts, and other professionals in these rapidly developing areas.
Reviews are structured into different Article Categories, each with its own description and intended audience. All articles are tagged with Topics and Subtopics to facilitate browsing.
Additionally, Wiley participates in the Research4Life initiative, which provides people at more than 7,700 institutions in the developing world with free or low cost access to scientific content.
Abstracting and Indexing Information
- Advanced Technologies & Aerospace Database (ProQuest)
- Current Contents: Engineering, Computing & Technology (Clarivate Analytics)
- Science Citation Index Expanded (Clarivate Analytics)
- SciTech Premium Collection (ProQuest)
- SCOPUS (Elsevier)
- Technology Collection (ProQuest)
- The DBLP Computer Science Bibliography (University of Trier)
- Web of Science (Clarivate Analytics)