Scientific honesty and the observance of the principles of good scientific practice are essential in all scientific work which seeks to expand our knowledge and which is intended to earn respect from the public. The principles of good scientific practice can be violated in many ways – from a lack of care in the application of scientific methods or in documenting data, to serious scientific misconduct through deliberate falsification or deceit. Every Ph.D. student should have a professional understanding of these topics.
In public, "good scientific practice" is often connected with cases of plagiarism when it comes to dissertations. However, this important topic covers a substantially wider spectrum of scientific conduct: dealing with data (including checking, recording, ownership and storage), the publishing process and authorship, responsible supervision, academic cooperation, conflicts of interest and dealing with conflicts. Inappropriate academic behaviour includes inventing or faking data, violating intellectual property (theft of ideas or plagiarism), and sabotaging the research of others. More subtle topics, such as scepticism, critical thinking, reproducibility, handling creativity, the danger of axiomatic assumptions and confirmation bias represent the “heart of good scientific practice”.
This Responsible Conduct in Research workshop provides an introduction to these important topics with an experienced professional trainer, combined with subject-specific input from senior IMPRS-IS and CLS researchers. Participants should attend all sessions.
For IMPRS-IS scholars: this workshop in combination with the 2021 boot camp will earn 3 credit points.
Date: Monday, November 22, 2021
Time: 9:00 a.m. - 17:00 p.m. CET
Location: Remote via Zoom
Trainer: Dr. Alexander Schiller, Schiller & Mertens
Scientific ombudspersons at MPI-IS: Dr. Lijuan Wang and Dr. Michael Mühlebach
Panellists: Katherine J. Kuchenbecker, Ph.D. and Prof. Dr. Robert C. Williamson.
Workshop Content: In the first part of the workshop (9:00 – 15:00), Alexander Schiller will guide participants through these important topics:
During the remainder of the workshop (15:30 – 17:00), participants will receive information about local arrangements supporting integrity and good scientific practice, and will meet the scientific ombudspersons at MPI-IS, Lijuan Wang and Michael Mühlebach.
This will lead into a panel discussion on examples of good scientific practice in research on intelligent systems, featuring panellists Katherine J. Kuchenbecker and Robert C. Williamson.
About the trainer:
Alexander Schiller was a DFG Heisenberg fellow and junior professor for inorganic chemistry at the University of Jena. In 2011 he started the project “Schiller & Mertens”. Since 2016 he is a full-time trainer, certified coach and facilitator: teaching advanced research skills, such as communication in science, team building and leading competences and didactics and methodology in university teaching. Since 2020 he is a member of the Coaching Pool of the Max Planck Academy. As researcher and group leader Dr. Schiller knows the challenges of scientists and addresses them interactively in an innovative setting.
About the panellists:
Katherine J. Kuchenbecker directs the Haptic Intelligence Department at the Max Planck Institute for Intelligent Systems and is speaker of the IMPRS-IS. She was previously an Associate Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania, where she held the Class of 1940 Bicentennial Endowed Term Chair and a secondary appointment in Computer and Information Science. Her research centers on haptic interfaces, which enable a user to touch virtual and distant objects as though they were real and within reach, as well as haptic sensing systems, which allow robots to physically interact with objects and people.
Robert C. Williamson is Professor for "Foundations of Machine Learning Systems" at the Eberhard Karls University Tübingen. He is interested in understanding and designing machine learning systems as a whole. To that end he is pursuing theoretical questions regarding machine learning problems and how they relate to each other, including information theoretic limits of performance; the connections between information theory and societal challenges in machine learning (such as fairness); as well as developing new approaches to the overall architecture of machine learning systems that support trustworthiness and reliability in their use.
This is a free workshop to members of our community. Please register at your earliest convenience via the registration button below. Deadline to register: Monday 15 November.
All registrants will receive a confirmation email shortly after the deadline with details of how to join the event.
If you are unable to register, please contact Sara Sorce (email@example.com) for assistance.