IAP BOOK SERIES
New Methods in the Era of Big Data and AI
Big data is everywhere and artificial intelligence (AI) is already a part of our daily life now. Both big data and AI has greatly changed the ways how research is conducted today, and even more in the future. This book series aims to cover new paradigms and new research methods as impacted by big data and AI. It is an official publication of the Global Association for Research Methods and Data Science (RMDS). With this book series, the editors and authors hope to help shaping new research methods as aided with big data and augmented by AI. With the new books in this series, editors and authors also hope to contribute to the education of new generations of researchers, who will interact with big data as well as intelligent machines as part of their daily research practice.
Call for book proposalsThe books in this series will focus on emerging new research methods in facing the below new developments:
- Realty is becoming the data
- Many research components get automated or will get automated soon
- Almost no limit exists for data storage as well as for computing speed
Topics of Interest:
Any new research paradigms or methods in RM4Es framework of describing research components and research workflows, with the following
- Equation (models)-New mathematical or logical representations of reality in forms of models or equations
- Estimation-New computing algorithms or procedures
- Evaluation-New methods of evaluating models or equations
- Explanation/Execution-New ways of interpreting research results or implementing analytics is preferred
- Researchers in academic fields
- Data scientists Analysts in practical application fields
**We also welcome books addressing to the scientists, researchers and analysts in a specific academic field.
Interested authors are invited to contribute to the series with a volume, (monograph or a collective), edited in English, of approx 350 pages.
These proposals should have an innovative look and a website containing research material (data, algorithms, results, etc.).
For further inquiries and informally discuss potential proposals, please feel free to contact the editor:
Alex Liu alex@ResearchMethods.org