The Summer School Riding the Data Life Cycle is intended for early career scientists like doctoral students and postdocs, but for sure we also welcome interested PIs. We will accept 25 participants.
The vision is that across disciplines, any scientist has easy access to research data, is able to share it without borders, and structures for long-term archiving are established. Infrastructures for data acquisition and management are available, and specialists support researchers with all aspects of the Data Life Cycle. Publishing of research data promotes the scientific reputation and career path.
The goal is to raise awareness for the importance of proper research data management in general, but also to provide a practical toolbox for the acquisition, curation, documentation, archiving and publication of research data following the FAIR (Findable, Accessible, Interoperable and Re-usable) data principles (1). Furthermore, a practical training on assessing the data for re-use and integration are part of the learning goals.
To apply, just fill in the application form until 11 June 2018!
The participation fee is 100 Euro including taxes. Sponsered by the de.NBI, this amount includes accommodation from Sunday to Friday, lunch and coffee-breaks throughout the Summer School, and the joint dinner on 6 September 2018. Travel costs need to covered by you. Details on how to transfer the money will be given after your acceptance.
If you have any questions contact us at info[at]gfbio.org
The Data Life Cycle
The Data Life Cycle is a model that describes the cyclical character of the work done with data of all kinds in its various stages of processing and use within the process of creating scientific value. The most important stages in this cycle include data generation (e. g. measurements), data preparation, data evaluation/analysis, storage (including long-term archiving), and making the data available through publication (e. g. in databases and repositories, as publications in journals, and on online platforms). The cycle even includes data reuse in additional or new research contexts, which can also arise from academic settings. The cyclical character of the model emphasizes the fact that use and reuse of the data generate new results in the form of further research data.
Modified from: “Enhancing Research data Management: Performance Through Diversity” Council for Information Infrastructures Recommendations 2016.
The Data Life Cycle
(1) Wilkinson MD et al. (2016): The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018
More information about research data and metadata can be found at https://www.gfbio.org/data
Check the video Data Sharing and Management Snafu in 3 Short Acts https://youtu.be/N2zK3sAtr-4