About the Journal

Data Science and Business Review (DSBR) is an academic journal created and maintained by the Graduate Program in Data Science for Business, at UFPB, aiming to disseminate knowledge in Data Science among researchers, professionals, entrepreneurs, managers of public and private organizations, and public policy makers.

ISSN (online) 2664-2682

Vision

DSBR aims (1) to foster the development of a community engaged and participative in Data Science for business; (2) cover specific and in-depth topics of scope, being a position of reference with the community; and (3) achieve active impact, exercising academic and managerial/practical influence, by publishing content of interest to the community with the addition of new knowledge.

Contribution

DSBR proposes to evaluate, improve and publish content that addresses sufficiently well-formulated problems. Potential authors should dedicate  to the elaboration of arguments to support the research, so that the answer to the problem reveals relevant. A good contribution comes from (1) a well-defined and positioned problem in relation to existing knowledge and (2) the expectation of a response that adds relevant new knowledge. Therefore, contents that merely report the application of methods and techniques (even sophisticated) do not meet the journal's expectations. In order to achieve a contribution, potential authors must answer three questions:

  1. What is known about the topic;
  2. What is not yet known about the topic; and
  3. Why it is worth searching this new knowledge. 

Scope

DSBR publishes academic and professional content, in English, Portuguese and Spanish, according to the following thematic sections:

  • Discussing Data Science: section focused on philosophical, ontological and epistemological analysis, in the form of theoretical essays;
  • Data Science in Education: aimed at disseminating materials and useful content to teachers, such as teaching cases, experiences of implementing courses with Data Science, etc.;
  • Data Science in Scientific Research: aimed at publishing results and analysis of theoretical-empirical research and methodological work;
  • Data Science and Solutions: technological and applied articles for practitioners;
  • Data Science and Open Source: dissemination of open source tools (as defined by OPI) applied to open data and which have a high social impact. 

Scientific rigor, methodological robustness, data reliability and respect for ethical standards are preconditions for submission to DSBR. Authors who intend to submit to DSBR must also emphasize the practical relevance of their study, especially how this study has contributed, or may come to contribute, in improving decision-making in organizations, even in the case of theoretical essays. DSBR favors analysis of real data. However, contributions on simulated data can also be welcome, as long as they prove to have the potential to impact real decisions. 

Data science is an essentially interdisciplinary field. DSBR recognizes this interdisciplinarity and accepts original contributions using theoretical, methodological or conceptual contributions from different fields. In particular, contributions that mix contributions from more than one field are preferred. Accepted fields include, but are not limited to:

  • Administration: works related to the traditional functional areas of Administration (marketing, human resources, production management, information systems) analyzed from the perspective of Data Science;
  • Finance and actuarial: works in the areas of public and corporate finance, and actuarial sciences with applications in / of Data Science;
  • Statistics: works with theoretical and methodological content of statistics in Data Science applied to business;
  • Computing: works with theoretical and methodological content of computer science in Data Science applied to business;
  • Economics: works with thematic and methodological applications of microeconomics, macroeconomics and econometrics in Data Science applied to business;
  • Operational Research: works with thematic and methodological applications of operational research and management science in Data Science applied to business.

Eventually, special issues can be organized specifically for one of these fields, for a new field, or for a theme of common interest to several of them.

Topics

  • Business problems and solutions in Data Science;
  • Innovations in the use of data to extract useful business knowledge;
  • Machine learning;
  • Artificial intelligence;
  • Engineering and data-driven decision making;
  • Data management (structured, unstructured, distributed, cloud);
  • Data quality;
  • Data governance;
  • Training of data analysts and scientists;
  • Human, social and cultural factors and their influence on Data Science processes for business;
  • Management of professionals and teams in Data Science projects;
  • Measurement theory, metrics and KPI for business. 

Evaluation Process

Submitted articles are initially evaluated by the editor-in-chief or by a designated senior editor (desk review). If the manuscript meets the DSBR format and scope standards, the text will be forwarded to at least two ad hoc reviewers, under the coordination of an associate editor. The evaluations will take place through double-blind and peer review process. The editor-in-chief is in charge of the final decision for acceptance, major/minor revision or rejection. In the case of opinions with opposite recommendations, the editorial board may designate a third reviewer before making a decision. Acceptance or rejection decisions are final. Authors are encouraged not to submit a new version of a previously rejected article. 

Authors are responsible for the authorship and originality of the work. DSBR will reject the potential publication and will portray the publication of eventually published works that are considered to have any form of plagiarism, whether external plagiarism or self-plagiarism, in any magnitude and considering any section of the work.

At the end of each year/volume, the editor-in-chief and associate editors may nominate the best reviewer of the year/volume for the DSBR Reviewer award. The award will consist of certificate and disclosure on the DBSR website. The award criteria are quality and punctuality of the opinions. Only reviewers with at least two opinions sent per year will be considered for the award. 

Frequency

DSBR is published every six months, with publications in January and July of each year. Accepted articles will be made available on the journal's website in advance (ahead of print), even if they are not yet associated with a specific number.

Open Access Policy

DSBR offers immediate free access to its content, following the principle that making scientific knowledge freely available to the public provides greater worldwide democratization of knowledge.

No fees

Submission, processing and publication of articles in the DSBR are free and do not generate any material or pecuniary rights for the authors regarding the publication of the work in this journal.

Deposit Policy

Directory of Open Access Policies for Brazilian Scientific Journals.