05-BMG-GI1 Research Data Management and Analysis

Fundamentals of research data management, bringing order into data collection, documentation, storage and use, including basic concepts of metadata description.
Finding and accessing research data from multidisciplinary data sources.
Use of scientific data portals, metadata-supported search. Introduction into domain specific scientific data formats, standards and terminologies (e.g. ontologies).

Reuse of research data with Python: loading data into data frames, getting an overview on the data, data cleaning, exploration and preparation.
Basic and advanced statistics with Python using PANGAEA data. Distribution analysis, missing data treatment, outlier detection. Applied data analytics, regression analysis, trends, smoothing. Basic plotting of data using Python.

Students are acquainted to the data life-cycle and the FAIR data principles.
Students are introduced to methods to manage, submit and archive research data in relevant information systems.
Students will learn how to understand and select appropriate ontologies and community standards.
Students are introduced to methods for data handling, data exploration, data analysis and statistics with Python.

Computer Course: Python

Course Type 1: Lecture (L) 1.0 SWS ( 14.0 h)

Course Type 2: Exercise (E) 2.0 SWS ( 28.0 h)

Course Type 3: Seminar (S) 1.0 SWS ( 14.0 h)

Tutorial(s): -

56.0 h presence time
64.0 h self-study
60.0 h exam workload

180 h total workload

module exam

exam elements: 1
SL: 0

100 % written exam

Literature list will be provided in the course