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What you'll learn


Introduction to Data Management

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What you'll learn


Introduction to Data Management

Equips learners with an understanding of the different types of data management. Provides learners with the tools and knowledge to manage data effectively, covering the strategies for organizing research data. Supports learners in sharing their data securely and effectively.

This course will help learners to:

  • Recognize different types of research data, describe the importance of documenting their research and evaluate why and when to use metadata

  • Identify the benefits of data management and the sources of support, applying the best practices for successful data management 

  • Determine responsibilities for data management in a research team and share data applying the FAIR principles  

  • Formulate a data management plan and engage with data management planning tools, support and guidance

  • Employ the options available to them to safely store their data and recognize the importance of data backups

  • Describe what is meant by data integrity, and explain its importance

  • Anonymize, encrypt, and destroy sensitive data when required for ethical reasons and describe what is meant by data integrity

  • Identify the perks of data sharing, both for personal growth and for advancing ideas and knowledge and protect their data once published and shared, by complying with data protection legislation

  • Recognize the difference between copyright, licence and open access and how to control restrictions on their data

Language: English

Time to complete: 4 hours

Level: Beginner

Instructor: Dr. Alessandra Vigilante

How to access: Sage Campus is a digital library product. If you are a librarian, find out how to get Sage Campus for your university. If you are faculty, a researcher, or a student, recommend Sage Campus to your library.

Course modules


Course modules


 There are 5 modules in this course:

 

1. Understanding the different types of research data

Before we leap into defining what we mean by data management, it’s important to establish what it is we will be managing, by defining different types of research data. This module will also walk you through the key steps of the data life cycle, with an emphasis on the use of different data types and data documentation, so that you understand how everything fits together.

2. Data management principles and best practice

This module will give an overview of the wider advantages of good data management and the importance of creating a data management plan. It will also explore real life examples which highlight the problems that can arise from poor data management, and the application of the FAIR principles.

3. Planning a data management strategy

This module will show you some common data management planning tools that will allow you to start your project with a detailed plan that takes into consideration important issues like storage, backups and ethics. It will be discussed the importance of being organized when handling data and the relevance of data integrity as a foundation of good science.

4. Ensuring data integrity and storing data effectively

This module will look at the different strategies you may use to back up your data safely and efficiently, and evaluate where the best place is to store your data, and how to do so ethically. It will also be discussed how to appropriately protect your data and what it means to preserve data integrity.

5. The benefits of sharing data

This module will explore the value of sharing your data from an institutional, personal and legal perspective. It will share some insights into the benefits of open access data, and provide guidance and tips on sharing your data safely.

Who it's for


Who it's for


Who it’s for

This course is aimed at all social science students or digital humanities students dealing with volumes of data, primarily working at postgraduate level.

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Dr. Alessandra Vigilante
Dr. Alessandra Vigilante

Dr. Alessandra Vigilante is a Senior Lecturer in Bioinformatics at the Center for Stem Cells and Regenerative Medicine with a focus on genotype-phenotype interactions and data integration. Alessandra obtained her PhD in Bioinformatics in Naples (2008-2011) before moving to the UK to join the Nicholas Luscombe group first at the EMBL-European Bioinformatics Institute as a visiting student (2011-2012) and then as a postdoctoral fellow at UCL (2012-2017).

Alessandra Vigilante’s group has significant expertise and experience in the analysis and integration of large scale genomic, epigenomic and transcriptomic data (i.e. single-cell RNA-seq and ATAC-seq datasets, ChIP-seq etc…), and in the implementation of novel computational methods for various bespoke analyses to gain biological insights.She is actively involved in a great network of collaborations to develop multidisciplinary approaches to research efforts, working with faculty members within King’s and other research institutes.

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