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In our work to disseminate and support machine learning methods in matter research of Helmholtz, we often stumble on challenges of reproducibility both of data and software. This workshop strives to prototype an educational offer for scientists in the field who create data or who create software.
The central question this workshop tries to answer: What methods or tools do I need to use in order to make my scientific work reproducible? The central hope is that any reproduction should lead to the same conclusions and not invalidate my scientific output.
The workshop is split into two parts and will be conducted completely online:
Participation is free of charge. Seats in the course will be handed out on a first-come-first-serve bases. Please inform us in case the event is booked out and you'd like to see more training events like this.
Interested participants can register for each workshop session separately.
Registering for both events is encouraged. The registration will close on Mar 15, 2022, at 11:59 am CET.
We facilitate this meeting under the Dresden Code of Conduct. Please be mindful of your peers and supportive in your communication!
Please contact the organizer Peter Steinbach in case you feel uncomfortable.
Susann Auer is a plant biologist working on plant-pathogen interactions at TU Dresden. Since 2019 she gave several workshops and talks on reproducibility in her own field and together with Reproducibility for Everyone has reached over 2000 researchers in various disciplines worldwide so far. Susann is an editor for Reproducibility at the Journal for Tropical Plant Pathology and has helped several authors to make their manuscripts more reproducible. Additionally, she is actively engaged in the Open Plant Pathology group and the German Reproducibility Network (GRN) where she recently co-organized a virtual brainstorming event on reproducibility education in Germany.
Heidi Seibold is an independent researcher at IGDORE and research and education ambassador at Johner Institute. Her work is at the intersection of Data Science, Reproducibility and Medicine. She studied statistics and did her PhD in Biostatistics with a focus on Machine Learning.
April Clyburne-Sherin is co-founder and Executive Director of Reproducibility for Everyone (R4E), a community-led reproducibility education project for researchers. She is an epidemiologist and expert in open research tools, methods, training, and community stewardship. Since 2014, she has focused on creating curriculum and running workshops for scientists in open and reproducible research methods. She has developed and run short duration reproducibility training for many groups including the Center for Open Science, Sense About Science, and Code Ocean as well as consulting for SPARC, Code for Science & Society, Open Scholarship Knowledge Base, and CUNY.
Tobias Schlauch is working for the institute for software technology at the German Aerospace Center (DLR) since 2005. He contributed to different research projects as software engineer with regard to workflow and data management and supported them in context of software quality assurance. Since 2009, he serves as the representative of the DLR software engineering initiative. In this position, he leads the DLR software engineering network and coordinates the DLR-wide application of state-of-the-art software engineering methods, processes and tools. In addition, he is member of the task group “Research Software” initiated by the Helmholtz Open Science initiative, member of the de-RSE, and works as part of the HIFIS team which supports Helmholtz researchers with regard to software development.