Data Train–Training in Research Data Management and Data Science
Kick-off Event am 3.3.2021 um 15-17 Uhr (online)
Programm (in Englisch)
1. Data Train – Towards a cross-disciplinary education program on research data management and data science
Prof. Dr. Iris Pigeot & Dr. Tanja Hörner
Data Train intends to strengthen competencies in data science, research data management and data literacy. Doctoral candidates from the member institutions of the U Bremen Research Alliance are cordially invited to join our pilot-phase starting in March 2021. In their talk, Prof. Dr. Iris Pigeot (Leibniz Institute for Prevention Research and Epidemiology) and Dr. Tanja Hörner (U Bremen Research Alliance) will provide information about Data Train, its scope and concept. Further, the audience will get insights into the preliminary curriculum planned for 2021.
After this informative introduction, we will listen to two inspiring talks about data science and applied data management: Dr. Lena Steinmann (Data Science Center, University of Bremen) elucidates why we should think about research data management before we intend to do data science. And Prof. Dr. Frank Oliver Glöckner (Alfred Wegener Institute - Helmholtz-Centre for Marine and Polar Research) takes us through the data life cycle of the unprecedented MOSAiC expedition:
2. The intersection of Data Science and Data Stewardship
Dr. Lena Steinmann & Prof. Dr. Rolf Drechsler
As a result of digitalization, huge amounts of heterogeneous data are produced across all scientific fields, economic sectors, and even in our daily lives, which can no longer be handled with conventional analytics tools. The emerging field of data science provides the methods and technologies to harness the full potential of big data and transform it into knowledge. Hence, data science is considered as key discipline for the modern digital society. However, in order to be able to apply data science methods such as machine learning, the data must be FAIR (Findable, Accessible, Interoperable, Re-usable). Accordingly, sustainable data stewardship is a basic prerequisite for data science. Thus, the two topics are inextricably linked and of fundamental importance for digital transformation.
3. Data Management of the MOSAiC expedition
Prof. Dr. Frank Oliver Glöckner
The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) is the first year-round expedition into the central Arctic exploring the Arctic climate system. The focus of MOSAiC lies on direct in-situ observations of the climate processes that couple the atmosphere, ocean, sea ice, biogeochemistry and ecosystem. Data volumes of more than 700 TB are expected which must be handled in a harmonized and standardized procedure. Data management of the MOSAiC expedition is defined by the MOSAiC data policy which ultimately adopts the FAIR (Findable, Accessible, Interoperable, Re-usable) data principles and sets clear rules for data usage, release dates, publication practices and authorships. Technically, research data management for MOSAiC is based on the integrated Observation to Archives (O2A) framework developed by AWI‘s Computing and Data Centre. O2A consists of a series of modular components and services designed to support scientists in their data workflows. All MOSAiC data will become publicly accessible latest on January 1st 2023.