20 Years of Data Science - DSJ Call for Submissions
Throughout 2022, the Data Science Journal invites submissions related to the evolution and future directions of data science (broadly defined), including recent advances, retrospective analyses, and community inspirations and provocations.
When the first issue of the Data Science Journal was published by CODATA in 2002, it was perhaps the first publication dedicated to the new concept of “data science.” Since the beginning, Data Science Journal papers have advanced understanding of many aspects of the science of data, including the capture of data, their analysis, metadata, retrieval, archiving, exchange, mining to find unexpected knowledge and data relationships, and visualization, along with intellectual property rights and other legal and ethical issues related to data.
Data science has evolved significantly over the past two decades, becoming a topic of significant interest in academic research, public and private sector workplaces, and in government policies and practices. The boom of data science has been stimulated by the large volumes and varieties of data being made public on the internet, via the explosive growth in digital technologies such as personal computers, cell phones, social media, smart devices, and sensor networks. Data science has also grown with a recognition that research integrity is enhanced with the increased availability of the data that underpins research. All this has resulted in a need for new infrastructure, skills, and support in the research process and the ability to work with data.
Data science has emerged as a panoply of techniques, tools, and skills that can be applied to derive value (economical, intellectual, cultural) out of the growing piles of data. We recognize that this includes advanced analytics and must also include concerns of ethics, infrastructure development, information theory, pragmatics, and more. Data science must consider the science of data and issues of data in science.
We encourage a broad range of contributions. Specific topics of interest include (but are not limited to):
1. What are the most significant advances in data science (broadly defined) over the past 20 years?
2. How does data science differ from its beginnings 20 years ago?
3. What are the main gaps and opportunities that must be addressed in data science going forward?
4. How have changes in the way that data are or should be shared influenced data science?
Submit articles at https://datascience.codata.org/
Matt Mayernik and Mark Parsons
Data Science Journal