Enabling reproducibility in data science - learn why it matters and how you can do it

Contribution List

6. Welcome

HIDA, Helmholtz Open Science Office

09/06/2022, 09:30

5. Impulse lecture: Open science & reproducibility

Helmholtz Open Science Office

09/06/2022, 09:40

Research is reproducible when it is possible to (independently) recreate the same results from the same data and same code/analysis as used by the original researcher or team of researchers. Reproducibility enhances collaboration and transparency in science and supports reusability of scientific products. This closely links with the open science endeavour towards the cultural change in science...

4. Impulse lecture: Reproducibility in data science and machine learning

Peter Steinbach (HZDR)

09/06/2022, 10:00

Machine Learning is becoming ubiquitous in many scientific domains. However, practitioners struggle to apply every new addition to the Machine Learning market on their data with comparable effects than published. In this talk, I'd like to present recent observations on reproducibility of Machine Learning results and how the community strives to tackle related challenges.

3. Foundations of research software publication

Tobias Schlauch (DLR)

09/06/2022, 10:30

Workshop session

We will provide you with actionable advice about how to prepare your research code before publishing it or submitting it alongside a research publication.

This talk will cover the following topics:
• Code repository structuring
• Minimum coding practices
• Documentation
• Open source licensing
• Minimum software release practices
• Software citation

We will discuss these topics at...

1. Practical steps towards reproducible research

Heidi Seibold

09/06/2022, 10:30

Workshop session

In this workshop we will look at four key steps to get started with reproducible research:

(1) Get organized
(2) Use Open Source Software
(3) Use Version Control
(4) Make your work available online.

Although reproducible research practices can first seem intimidating, we will find a way to get started that works for you.

2. Scientific metadata: Fundamentals of structured and standardized research data annotation

Annika Strupp, Silke Gerlich (HMC)

09/06/2022, 10:30

Workshop session

This course is limited to 25 participants

Did you ever feel lost in incomprehensible research data documentation? This session introduces the basics of machine-readable research data annotation with domain-specific metadata schemas and standards. Learn, why accurate and harmonized description of research data is key for scientific exchange and how to find a suitable metadata framework...

7. Wrap-up

Annika Strupp, Heidi Seibold, Silke Gerlich (HMC), Tobias Schlauch (DLR)

09/06/2022, 12:30