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 addresses the broader concept of Research Data Management through the biomedical research data lifecycle. Participants will be guided through current concepts, working procedures and tooling on data management with examples for different types of data (clinical, OMICS, imaging). 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 a basic and an optional module. Before the start of the basic module, an e-learning module needs to be completed.
The basic module (e-learning and day 1) is mandatory for all participants. The optional module focusses on clinical data (days 2 and 3). You can optionally subscribe individually to the basic module (including e-learning) only, or the basic module ánd the optional (clinical) module.
- October 7 thru 11, 2019. (full)
- January 20 thru 24, 2020. (full)
- May 25 thru 29, 2020. (full)
- Foreknowledge: Basic module: e-learning (available 3 weeks before session dates)
- Day 1, 9.00 – 17.00h: Basic module: sessions 1 and 2..
- Day 2, 9.00 – 12.30h: Module 1: Clinical data, session 1.
- Day 3, 9.00 – 12.30h: Module 1: Clinical data, session 2.
- What is research data management, FAIR principles.
- AMC procedures on research data management.
- The Research data lifecycle.
- Study preparation: privacy aspects, reuse of data, data capture systems, data storage
- Data collection: data validation, change control
- Processing & analysis: version control, statistical analysis plan
- Writing & publishing: linking data to a publication
- Archiving & open data: tooling, open/restricted access
- Summary of Research data lifecycle, questions and answers.
- Demos of finding existing data and samples collections.
- Privacy aspects.
- Practice: FAIR data management.
- Practice: write your own Data management plan.
- Practice: write a Statistical Analysis Plan for a case study.
Module 1: Clinical data
- Practice on data validation and management in the statistical environment.
- Practice on preparing the database for statistical analysis.
The basic course addresses topics of interest to any researcher that deals with data, while module 1 targets clinical data. We advise to take the course in the beginning of your PhD trajectory.
To qualify for the certificate, a participant must complete the e-learning and attend all lectures of the selected module(s). Attendance will be registered.
2-3 hours for the e-learning. 7 hours for the basic module (including homework) and 7 additional hours for module 1.
Number of participants:
Maximum 30 participants for the basic module, 25 participants for module 1.
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.
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