<p>Open Science has established itself as a movement across all scientific disciplines in recent years. It supports good practices in science and research that lead to more robust, comprehensible, and reusable results. The aim is to improve the transparency and quality of scientific results so that more trust is achieved, both in the sciences themselves and in society. Transparency requires that uncertainties and assumptions are made explicit and disclosed openly.&#160;<br>Currently, the Open Science movement is largely driven by grassroots initiatives and small scale projects. We discuss some examples that have taken on different facets of the topic:</p><ul><li>The software developed and used in the research process is playing an increasingly important role. The Research Software Engineers (RSE) communities have therefore organized themselves in national and international initiatives to increase the quality of research software.</li>
<li>Evaluating reproducibility of scientific articles as part of peer review requires proper creditation and incentives for both authors and specialised reviewers to spend extra efforts to facilitate workflow execution. The Reproducible AGILE initiative has established a reproducibility review at a major community conference in GIScience.</li>
<li>Technological advances for more reproducible scholarly communication beyond PDFs, such as containerisation, exist, but are often inaccessible to domain experts who are not programmers. Targeting geoscience and geography, the project Opening Reproducible Research (o2r) develops infrastructure to support publication of research compendia, which capture data, software (incl. execution environment), text, and interactive figures and maps.</li>
</ul><p>At the core of scientific work lie replicability and reproducibility. Even if different scientific communities use these terms differently, the recognition that these aspects need more attention is commonly shared and individual communities can learn a lot from each other. Networking is therefore of great importance. The newly founded initiative German Reproducibility Network (GRN) wants to be a platform for such networking and targets all of the above initiatives. GRN is embedded in a growing network of similar initiatives, e.g. in the UK, Switzerland and Australia. Its goals include&#160;</p><ul><li>Support of local open science groups</li>
<li>Connecting local or topic-centered initiatives for the exchange of experiences</li>
<li>Attracting facilities for the goals of Open Science&#160;</li>
<li>Cultivate contacts to funding organizations, publishers and other actors in the scientific landscape</li>
</ul><p>In particular, the GRN aims to promote the dissemination of best practices through various formats of further education, in order to sensitize particularly early career researchers to the topic. By providing a platform for networking, local and domain-specific groups should be able to learn from one another, strengthen one another, and shape policies at a local level.</p><p>We present the GRN in order to address the existing local initiatives and to win them for membership in the GRN or sibling networks in other countries.</p>
Learning from reproducing computational results: introducing three principles and the
Reproduction Package