ACM/IMS Journal of Data Science : Inaugural Issue Editorial

Data Science (JDS) is a joint journal of the Association for Computing Machinery (ACM) and the Institute of Mathematical Statistics (IMS), publishing high-impact research from all areas of data science, across foundations, applications, and systems.The scope of the journal is multi-disciplinary and broad, spanning statistics, machine learning, computer systems, and the societal implications of data science.JDS accepts original papers and novel surveys that summarize and organize critical subject areas, as well as opinion papers.The journal bridges communities across the two scientific societies, representing diverse areas of research expertise.By combining elements of journal and conference publishing, the journal aims to serve the needs of a rapidly evolving research landscape.The journal accepts submissions three times a year.Each submission receives three expert reviews from a standing reviewing board, and the three-month initial reviewing process includes author feedback, review quality analysis, and reviewer discussions to reach a decision and provide constructive feedback.After the initial review process, which proceeds on a fixed schedule typical of conferences, authors prepare revisions, taking as much time as required.Accepted papers are published online on the JDS website immediately after the camera-ready sources have been prepared and checked, followed by full publication in the first available issue.This inaugural issue of the journal includes four research papers that intersect the areas of machine learning, artificial intelligence, databases, and data management systems.The papers were submitted upon invitation by the editors to area experts.Following JDS guidelines, the papers were reviewed by experts in the relevant communities.The topics and perspectives seen in this work are signs of the diversity, impact, and high standards that JDS aims to achieve as the journal ramps up.The subsequent two issues will also include invited submissions, representing additional research at the interface of statistics, machine learning, computer systems, and other areas that make up the growing landscape of data science.