Applications of AI and data-driven science have resulted in spectacular discoveries. But this new wave of (doing) science has also created a number of vexing problems concerning the production, synthesis and use of evidence: When can we trust the data generating/collection methods? How to synthesise big data with other types of evidence? How to base decisions on the output of computer models when the traditional methods are too slow to produce evidence?

While this “new wave of doing science” is gaining momentum, the old-fashioned way of doing science still has a wealth of interesting foundational problems concerning evidence in science. How can we measure (the evidential support of) evidence? Can the methodology of evidence production be used to address the demarcation problem? How does social fabric of science influence evidence production and consumption? What distinguished good from bad scientific evidence?

Appropriate topics for submission include, among others:
Evidence & computers: AI, ML, ABM
Evidence production
Evidence synthesis
Evidence use
Strategic forms of Evidence use and abuse
Statistical Evidence, stopping rules
Meta analysis
Non-evidential confirmation
Analogy
Awareness growth
Fast science
Evidence measures
Questionable research practices/fraudulent Evidence
Biased Evidence
Evidence communication
Variety of Evidence

For further information, please contact the guest editor: juergen_landes@yahoo.de
The deadline for submissions is: 31st of December 2025