One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Journal of Applied Data Sciences is a forum for exchange of research findings, analysis, information, and knowledge in areas that include but are not limited to:
- Predictive Modelling - Journal of Applied Data Sciences encourages research endeavours that identify organizational risks and opportunities by exploiting patterns found in historical and transactional data.
- Simulation Modelling - Journal of Applied Data Sciences promotes application of simulation in enterprise and organizational context for examining and comparing options and scenarios prior to implementation.
- Optimization Modelling - Journal of Applied Data Sciences promotes research that can help decision-makers make the best choice by means of various optimization models.
- Prescriptive Methods - Journal of Applied Data Sciences invites research that supports the joint application of predictive models and optimization technology to create better solutions for decision-makers.
- Business Intelligence - Journal of Applied Data Sciences invites research that utilizes the latest techniques in data mining, analysis, and performance management to help decision-makers gain and sustain a competitive edge.