Data Technologies & Applications focusses on the management of digital information, mostly covering Information Science and Information System aspects. Covers all aspects of the data revolution brought about by the Internet and the World-Wide-Web.
Because you never know what data will be useful to someone else,
Data Science for Transportation publishes high-quality original research and reviews in a wide range of topics related to Data Science for Transportation. This includes classical approaches when data sources are used to unravel underlying physical mechanisms leading to general laws and new modelling frameworks. It also includes new data-driven approaches when AI plays a central role.
The goal of the journal is to showcase the latest methodological advances and applications of data science methods in transportation and appropriate implications for policy making. The journal is also interested in the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where big data intelligence can augment other methods in transportation systems planning, operations, freight, safety analysis, transit, safe and sustainable cities and emergency management. There are many emerging questions of relevance on ethical, social and privacy, that are also relevant in this domain. The focus is primarily on analytical data driven methods. High quality application based studies will also be considered.
Journal closed. Available from 1978 volume: 1 until 1999 volume: 22 issue: 3
Huge volumes of primary data are currently archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories, large datasets will become even more prevalent than today. The lasting archiving, accurate curation, efficient analysis and precise interpretation of all of these data are a challenge. Collectively, database development and biocuration are at the forefront of the endeavor to make sense of this mounting deluge of data.