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The Data Science Journal values the perspectives of all data professionals and practitioners and explicitly wants to recognize the insight that comes from Indigenous knowledge and how it is understood and managed. Indigenous knowledge has long been undervalued, misrepresented, and exploited in science, and the Indigenous contributors often remain invisible in science. 

The DSJ is honored to have published The CARE Principles for Indigenous Data Governance and seeks to live up to those principles of Collective benefit, Authority to control, Responsibility, and Ethics. We are also inspired by the recent position statement from a consortium of rural health journals who will publish “nothing about Indigenous peoples, without Indigenous peoples”.

DSJ will adopt the same principles. How this will be precisely applied will vary depending on the people and knowledge in question, but in essence, we will reject submitted papers that concern Indigenous communities but do not provide evidence of the care taken towards engagement with Indigenous communities including appropriate attribution, appropriate access, and ideally Indigenous authorship. We encourage authors to include details of their perspective and background in the author description.

DSJ will continue to explore how we can foster inclusive principles of data science in our publications and practice.

The Data Science Journal is excited to be celebrating 20 years of publication!

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 or intellectual) 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.

So…

What have we learned?

Where are we now?

What’s next?

The journal plans several activities to celebrate the anniversary and explore these questions. We welcome your contributions and ideas.

Special collection: The evolution and future directions of data science.

Throughout 2022, DSJ encourages submissions related to the evolution and future directions of data science (broadly defined), including recent advances, retrospective analyses, and community inspirations and provocations. We encourage a broad range of contributions. Please contribute.

Events at International Data Week

We plan several events at IDW including a research session, community discussion, and convivial reception. We’ll provide more information as the program develops.

CODATA is pleased to welcome Matthew Mayernik as Joint Editor-in-Chief of the Data Science Journal, joining Mark Parsons who has been Editor- in-Chief since 2019. Beginning 1 September, Matt and Mark will work as co-equals to collaborate around the duties of the Editor-in-Chief.

Mark writes: 
I am thrilled that Matt will be joining me as Joint Editor-in-Chief. I have known Matt for a decade or so, and have always admired his holistic yet precise and insightful views of the “science of data”. Matt has been a long-time, dedicated member of the DSJ editorial board. He is responsible and productive, but more importantly, Matt is thoughtful and considered in his work. It will be fun to work with him.

Matt writes:
I am very excited to join Mark Parsons as co-Editor in Chief of the Data Science Journal. I have long felt that the DSJ has a unique role in the data science publication landscape, with its focus on the "data" part of data science. The journal has been instrumental in advancing the science of data broadly, including data system design and implementation, analytics and visualization, metadata and standards, and data policies. This broad view of data science is critical as data continues to grow in importance in all sectors of our societies. I look forward to working with Mark, the DSJ editorial board and staff, and CODATA to move the journal forward.

BIO
Matthew Mayernik is a Project Scientist and Research Data Services Specialist at the National Center for Atmospheric Research (NCAR), based in Boulder, CO, USA. His work is focused on research and service development related to research data curation. His professional interests include metadata practices and standards, data curation education, data citation and identity, and social and institutional aspects of research data. He is also a member of the Committee on Open Environmental Information Services (COEIS) within the American Meteorological Society. He received his Master's in Library and Information Science and Ph.D. in Information Studies from the University of California, Los Angeles (UCLA).