23 April 2018 : Free online Course: RESEARCH DATA MANAGEMENT & SHARING (Coursera)

REGISTER for and START this RESEARCH DATA MANAGEMENT & SHARING online course on 23 April 2018.

This course will provide learners with an introduction to research data management and sharing. 

The rationale : 

Today, an increasing number of funding agencies, journals, and other stakeholders are requiring data producers to share, archive, and plan for the management of their data.

In order to respond to these requirements, researchers and information professionals will need the data management and curation knowledge and skills that support the long-term preservation, access, and reuse of data.

Effectively managing data can also help optimize research outputs, increase the impact of research, and support open scientific inquiry. 

After completing this course

learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data.

Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories.

This course was developed by the Curating Research Assets and Data Using Lifecycle Education (CRADLE) Project in collaboration with EDINA at the University of Edinburgh.

Week 1: Understanding Research Data
Week 2: Data Management Planning
Week 3: Working with Data
Week 4: Sharing Data
Week 5: Archiving Data


- - The Data Scientist’s ToolboxThe course gives an overview of the data, questions, and tools that data analysts and data scientists work with. It focuses on a practical introduction to tools, using version control, markdown, git, GitHub, R, and RStudio; 
- - Getting and Cleaning DataThis course will cover the basic ways that data can be obtained. It will also cover the basics of data cleaning and how to make data “tidy”. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data. Tools used in this course:  Github, R, RStudio;
- - Reproducible Research.

Data management practices have been described in detail in a variety of documentation and tutorials, which may focus on specific needs and resources applicable to the organization that produced them: 

Add comment

Log in or register to post comments