Data Train–Training in Research Data Management and Data Science
Start des Data Train Curriculums im Januar 2022
Kick-off Veranstaltung am 12.01.2022 um 14-16 Uhr (online)
Programm (in Englisch)
In the first talk, Prof. Iris Pigeot (Leibniz Institute for Prevention Research and Epidemiology – BIPS), Prof. Frank Oliver Glöckner (Alfred Wegener Institute - Helmholtz-Centre for Marine and Polar Research, AWI) and Dr. Tanja Hörner (U Bremen Research Alliance, UBRA) will provide information on the Data Train program, its scope, concept, and its connection to the Germany-wide initiative "National Research Data Infrastructure (NFDI)". Further, you will get insights into the curriculum planned for 2022.
After this informative introduction, we will listen to two inspiring topic-related talks: Prof. Rolf Drechsler and Dr. Lena Steinmann (Data Science Center, University of Bremen) will elucidate why we should be aware of research data management when we intend to apply data science methods. And Moritz Stefaner (Designer and Truth & Beauty Operator) will tell Data Story #001. He will walk us through his recent explorations pushing the boundaries of data visualization — from interactive experiences over data sculptures to even using food for representing data. We will have enough time for discussions in between.
1. Data Train – The cross-disciplinary training program on research data management and data science
Prof. Iris Pigeot, Prof. Frank Oliver Glöckner and Dr. Tanja Hörner
Dealing with data and innovative technologies are key competencies of our time. Currently, we are facing a shortage of people with trained data competencies in labor markets worldwide. To meet the massive demand in science and economy, the cross-discipline training program “Data Train–Training in Research Data Management and Data Science” of the “U Bremen Research Alliance” teaches competencies in data literacy, research data management, and data science for doctoral researchers. The program is associated with the German National Research Data Infrastructure (NFDI) initiative.
The training is provided by our member institutions is targeted at doctoral researchers and open to all interested in strengthening their data skills (if capacities are available).
2. Data Stewardship and Data Science – Transforming Data into Knowledge
Dr. Lena Steinmann and Prof. 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 Story #001: Beyond the bar chart: How new data languages can make complexity graspable
Moritz Stefaner is constantly chasing the perfect shape for information
— how can we create expressive, intriguing, and elegant data experiences?
He will walk us through his recent explorations pushing the boundaries of data visualization — from interactive experiences over data sculptures to even using food for representing data. Please find more information here >>
Der nächste Starter Track Kurs:
Data science and big data, 26.01.2021 14-16 Uhr (in Englisch)
Nur auf Englisch verfügbar:
Parallel to the digital transformation, a novel scientific discipline has been developed – data science. Data science allows new approaches for interdisciplinary (big) data analyses through complex algorithms and artificial intelligence (machine learning, deep learning etc.). Such approaches extract information from the data sets beyond the current scientific knowledge. Therefore, data science is of interest for nearly all research as well as industry/economy fields and often termed as a novel key discipline (e.g. Society of Informatics e.V., 2019). This course provides a basic overview about data science applications.
To produce reliable data science results a profound knowledge about the data analyses methods, data management techniques and innovative technologies is required. Additionally, to assess these results and approaches an awareness of their ethical, legal, and social implications is demanded (all topics are addressed in the following courses and operator tracks).