Collaborative research is common practice in modern life sciences. For most projects several researchers from multiple universities collaborate on a specific topic. Frequently, these research projects produce a wealth of data that requires central and secure storage, which should also allow for easy sharing among project participants. Only under best circumstances, this comes with minimal technical overhead for the researchers. Moreover, the need for data to be analyzed in a reproducible way often poses a challenge for researchers without a data science background and thus represents an overly time-consuming process. Here, we report on the integration of CyVerse Austria (CAT), a new cyberinfrastructure for a local community of life science researchers, and provide two examples how it can be used to facilitate FAIR data management and reproducible analytics for teaching and research. In particular, we describe in detail how CAT can be used (i) as a teaching platform with a defined software environment and data management/sharing possibilities, and (ii) to build a data analysis pipeline using the Docker technology tailored to the needs and interests of the researcher.

合作研究是现代生命科学中的常见做法。对于大多数项目，来自多所大学的多名研究人员就特定主题进行合作。通常，这些研究项目会产生大量需要集中和安全存储的数据，这也应该允许项目参与者之间轻松共享。只有在最佳情况下，这才能为研究人员带来最少的技术开销。此外，以可重现的方式分析数据的需求通常对没有数据科学背景的研究人员构成挑战，因此代表了一个过于耗时的过程。在这里，我们报告了 CyVerse Austria (CAT) 的整合情况，这是一个面向当地生命科学研究人员社区的新网络基础设施，并提供两个示例，说明如何使用它来促进 FAIR 数据管理和可重复的教学和研究分析。特别是，我们详细描述了如何使用 CAT (i) 作为具有定义的软件环境和数据管理/共享可能性的教学平台，以及 (ii) 使用根据需求量身定制的 Docker 技术构建数据分析管道和研究者的兴趣。