Authors: David Wishart or Alexander Botzki
The first day (20 May 2021) is dedicated to Containers (Docker & Singularity) which are great tools for code portability and reproducibility of your analysis. You will learn how to use containers and how to build a container from scratch, share it with others and how to re-use and modify...
Keywords: Containers, Nextflow
Resource type: Training materials
This training session will provide the basics of protein structure determination and how this information is stored in databases. We will explore and search in online databases containing protein structure information. With the aid of the Yasara View program we will visualize the structure....
Operations: Visualisation
Keywords: Protein structure visualisation
Resource type: e-learning
- why preserve data
- what data should be preserved
- how share data
- where to deposit your data
- Fairsharing, re3data
- ELIXIR Core Resources
- Data Formats
- Generic archives
Scientific topics: Data management, Data submission, annotation, and curation
Keywords: data formats, ELIXIR Core resources, FAIR data submission
Resource type: Presentation
- how to find existing data (OmicsDI, pubmed, BioStudies, Google Data search, Data Management Hub)
- licenses
- datasets as first-class research products
- research software as first-class research products
- data citations
Scientific topics: Data management
Keywords: data licenses, software licenses, data journal
Resource type: Presentation
The content provided via this link was used in the training on 9 and 10 November 2020 organized by Ghent University and Elixir Belgium and VIB in collaboration with Interreg Vlaanderen-Nederland: https://training.vib.be/all-trainings/research-data-management-life-sciences.
Scientific topics: Data management
Resource type: Presentation, Vignette, Video
Writing a Data Management Plan
A good data management plan is crucial for any scientist and researcher. In this course, you will learn how to write one. You will find out how you can prepare, handle and share your research data.
Scientific topics: Data management
Resource type: e-learning
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.