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.
- FREE ONLINE COURSE : GODAN e-learning course on Open Data & Research Data Management in Agriculture and Nutrition
- FREE ONLINE COURSE : MANTRA - how to manage digital data as part of your research project
- FREE USGS Data Management Training Modules : many of the practices are applicable to any discipline
- Essentials 4 Data Support is an introductory course for those people who (want to) support researchers in storing, managing, archiving and sharing their research data
- The Class Central website has curated a list of several data science and analysis methods MOOCs, developed by reputable source. The MOOCs listed here have been developed through Johns Hopkins University, and offered through the Coursera platform. They are part of a Data Science Specialization series of of courses, and have applicability to data management practices outside of specific analytical techniques. Each of these courses lasts 4 weeks, and are frequently offered. Some of the are:
- - The Data Scientist’s Toolbox. The 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 Data. This 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:
- These three short narrated tutorials (USGS) give overviews of the value of data management, planning, and best practices for preparing data to share
- Short guide“10 Simple Rules for the Care and Feeding of Scientific Data” offers practical advice for researchers on practices they can follow to manage their data for sharing and reuse
- The University of Washington offers a well organized, comprehensive data management guide. Most of the resources listed are publicly available
- Make FAIR your Datasets using Springer Nature RESEARCH DATA SUPPORT
- Guidelines for Data Management Plan from SNSF : learning from each other
- Implement effective Open Data & Keep it alive with Open Data TOOLKIT
- Articulating the value of DATA with the Australian National Data Service (ANDS)