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Manuscript Submission Deadline: April 30, 2023

Recent changes in data science are transforming cybersecurity in a computing context. Applied science is the process of applying scientific methods, machine learning techniques, processes, and systems to data. While Cybersecurity Data Science (CSDS) enables more actionable and intelligent computing in the domain of cybersecurity as compared to traditional methods. It encompasses the rapidly growing practice of applying data science to prevent, detect, and remediate cybersecurity threats.

Cybersecurity data science is a fast-developing field that uses data science techniques to address cybersecurity issues. Data-driven, statistical, and analytical methodologies are increasingly used to close security holes. It examines the healthcare, transportation, surveillance, social media, and law enforcement sectors, in order to evaluate the specific issues they pose and how they can be addressed.

Cybersecurity data science is the focus of this special issue, with analytics supporting the most recent trends to optimize security solutions. The data is acquired from reliable cybersecurity sources. Using machine learning, the problem also aims to develop a multi-layered cybersecurity modeling framework. Data-driven intelligent decision-making can help defend systems against cyberattacks as we address cybersecurity data science and pertinent methodologies.

• Potential topics include, but are not limited to:
• Cloud-based cybersecurity analytics
• Real-time IoT/endpoint-based detection
• Deep learning and reinforcement learning
• Human-in-the-loop cyclical machine learning
• Adversarial attacks on machine learning systems
• AI-driven fake news and disinformation campaigns
• Cybercrime analysis, intelligence, and security
• Big crime data science algorithms and open-source situational awareness
• Misinformation and hate speech detection and mitigation
• Data-driven cyber knowledge base development
• Data Science to demonstrate cyber weakness
• Robustness and interpretability in ML for security tasks

Special Collection Editors:

Zhenfeng Liu, Shanghai Maritime University

Xiaogang Ma, University of Idaho

Anwar Vahed, Data Intensive Research Initiative of South Africa

The deadline for the call for papers for the Special Collection on Data Management Planning across Disciplines and Infrastructures (Date Science Journal) has been extended until December 15th 2022 (previously November 15th 2022). For further information on the call, see the announcement below.

The Data Science Journal (https://datascience.codata.org/) is seeking papers for a special collection devoted to “Data Management Planning across Disciplines and Infrastructures”. Papers should focus on work related to Data Management Plans and their implementation, not data management per se.

Deadline for submissions: November 15th 2022 (UPDATE: deadline is now December 15th 2022

Details:
Data Management Plans (DMPs) are evolving. Several communities address challenges in developing DMP templates, e.g., to provide proper guidance across disciplines, used collaboratively along the data life cycle, or in the development of machine-actionable tools to facilitate efficient data management and sharing as well as fostering the reuse of (pieces) of already existing DMPs in new research context. Thereby, DMP templates do not only have to provide tailored guidance, but they should also ensure interoperability across research disciplines to foster multidisciplinary research. Although DMPs support the management of research data, DMP tools are often not integrated into researchers’ project workflows yet, but serve as an additional component next to project management software, electronic lab notebooks, metadata tools, etc..

The international Data Science Journal seeks papers describing practical experiences, concepts, and future directions on the design and deployment of effective data management. Journal’s topics cover hypothesis and investigation planning, data management planning, organization and operation, reproducibility planning, provenance recording, and data maintenance and publication are considered in their entirety.

For the collection, we welcome papers from researchers, data professionals, data managers and curators, data providers, founders, IT specialists, software developers, and others, who are using, developing, or experimenting with the effective use of data management planning. Submissions from lab researchers and practitioners are particularly encouraged, as contributions from open-source movements. Papers might, e.g., focus core activities of experimentation, data processing and analysis, long-term preservation or data sharing, or consider the integration of data management planning into existing or future infrastructures. This could reflect innovative research into new directions for DMPs. Moreover, papers detailing practical examples on supporting researchers are welcomed.

Submissions can be made in one or several of the following categories:

1. implementation of DMPs in project management strategies, e.g. combining DMP update cycles with (agile) project management, development of practicable implementing strategies in projects, e.g., by data stewards or data managers, or automatic triggers between linked tools / systems, e.g., CRIS system updating DMP once funding has been awarded
2. data management across disciplines, examining interoperability and cross-disciplinary (re-)use of Data Management Plans, as, e.g., intended by the FAIR Data Principles, e.g., in the context of templates and guidance
3. discipline-specific data management planning, providing examples and/or use cases on how discipline-specific support can foster data management and processing of data following the FAIR Data Principles, e.g., in the context of Science Europe’s domain data protocols,
4. data management planning integrating infrastructures, presenting DMP tools, machine-actionable DMPs, concepts of mapping and harvesting, connecting DMP tools to interfaces of other RDM tools such as Current Research Information Systems (CRIS) and Electronic Lab Notebooks (ELN), FAIR Digital Objects in the context of DMPs, etc.
5. future directions in the development of Data Management Plans, e.g., in terms of innovative approaches or technical solutions to support to data management planning

For further information and submission details see guidance of the Data Science Journal:  https://datascience.codata.org/about/submissions.

Published articles will be charged with a fee of £650, covering all publication costs (editorial processes; web hosting; indexing; marketing; archiving; DOI registration; etc.) and ensuring full (golden) open access, under a Creative Commons Attribution License.

Collection editors (in alphabetical order):

Ivonne Anders, Kevin Ashley, Daniela Hausen, Christin Henzen, Sarah Jones, Tomasz Miksa, Sebastian Netscher, Maria Praetzellis, Chris Wiley