Information about the course Research Data Management.
Adequate research data management is at the basis of research quality, integrity and reproducibility. A researcher should be in control of his or her data through the entire research data life cycle: from study preparation and data collection, data processing and data analysis, to publishing and archiving. Both AMC and funding agencies request a data management plan and publishers increasingly require data to be available. But, what is actually a data management plan? How to enhance the quality of data collected during research? And how to share a dataset?
This course replaces the former Clinical Data Management course and addresses the broader concept of Research Data Management through the biomedical research data lifecycle. Participants will be guided current concepts, working procedures and tooling on data management. They will learn how to define, collect and process your data in order to deliver a reliable data set for data analysis and future reuse.
The course is composed of five sessions within one week. The basic course (two sessions) is mandatory for all participants. Two additional modules focus on clinical data (module 1, two sessions) and on imaging / OMICS data (module 2, one session).
All participants follow the complete course.
- October 7 thru 11, 2019. (full)
- January 20 thru 24, 2020. (full)
- May 25 thru 29, 2020.
- Day 1, 9.00 – 14.00h: Basic course: session 1.
- Day 2, 9.00 – 14.00h: Module 1: Clinical data, session 1.
- Day 3, 9.00 – 14.00h: Module 1: Clinical data, session 2.
- Day 4, 9.00 – 14.30h: Module 2: Imaging and OMICS data, session 1.
- Day 5, 9.00 – 14.00h: Basic course, session 2.
- What is research data management, FAIR principles.
- Initiatives for data management plans and data sharing.
- AMC procedures on research data management.
The Research data lifecycle:
- Study preparation: data sources, privacy aspects.
- Data collection & preparation: tooling, reuse of data, prepare for analysis.
- Processing & analysis: statistical analysis plan.
- Writing & publishing: linking data to a publication.
- Archiving & open data: tooling, open access.
Data management plans:
- Practice: Data management plan for a case study.
- Presentations and discussion.
Module 1: Clinical data
- Practice: From CRF to data set.
- Practice: Data validation and management in the statistical environment: Introduction to statistical packages./Data validation in the statistical environment.
- Practice: Preparting the database for statistical analysis.
Module 2: Imaging and OMICS data.
- Introduction to TraIT: tools for biomedical data management.
- Introduction to XNAT: management of imaging data.
- Introduction to biological databases.
- Introduction to software management tools.
AMC PhD candidates that will be handling data, including clinical, imaging and OMICS.
To qualify for the certificate, a participant must attend all lectures of the selected module(s). Attendance wille be registered.
10 hours for the Basic course sessions, 10 additional hours for Module 1, 5 additional hours for the Module 2.
Number of participants:
Maximum 25 per course.
No charge for registered AMC PhD candidates.
Dr. R.A. Scholte / Clinical Research Unit / firstname.lastname@example.org / tel. +31 (0)20 - 566 7649.
Dr. S Delgado Olabarriaga / Dept of Clin. Epidemiology, Biostatistics and Bioinformatics (KEBB) / email@example.com / tel. +31 (0)20 - 566 4660.
From the course coordinator or AMC Graduate School / firstname.lastname@example.org / E2-172 / tel. +31 (0)20 - 566 4618.
AMC Graduate School
Tel: +31 (0)20 - 566 3108