Research Data Management and Sharing
This course from The University of North Carolina at Chapel Hill and The University of Edinburgh provides learners with an introduction to research data management and sharing. Upon completion, learners will have a better understanding of the importance of data management and sharing in research. ▼
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Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
Learners can learn about the diversity of data and their management needs across the research data lifecycle, including components of good data management plans, best practices for working with data, and the importance of archiving and sharing data. They can also gain knowledge and skills to support the long-term preservation, access, and reuse of data, as well as how to assess the trustworthiness of repositories. Additionally, learners can understand how to optimize research outputs, increase the impact of research, and support open scientific inquiry through effective data management. Finally, they can learn how to manage data throughout the entire research data lifecycle from project planning to the end of the project when data ideally are shared and made available within a trustworthy repository.
[Applications]
After completing this course, learners will be better equipped to apply research data management and sharing best practices throughout the research data lifecycle. This includes being able to identify the components of good data management plans, understanding the importance of archiving and sharing data, and being familiar with best practices for working with data including the organization, documentation, and storage and security of data. Additionally, learners will be able to assess the trustworthiness of repositories and be able to optimize research outputs, increase the impact of research, and support open scientific inquiry.
[Career Paths]
1. Data Management Analyst: Data Management Analysts are responsible for the organization, storage, and security of data. They work with data producers to develop data management plans and ensure that data is properly documented and stored. They also work with data repositories to ensure that data is properly archived and shared. As data becomes increasingly important to research, the demand for Data Management Analysts is expected to grow.
2. Data Curator: Data Curators are responsible for the long-term preservation, access, and reuse of data. They work with data producers to assess the trustworthiness of repositories and ensure that data is properly archived and shared. They also work with data repositories to ensure that data is properly documented and stored. As data becomes increasingly important to research, the demand for Data Curators is expected to grow.
3. Data Scientist: Data Scientists are responsible for analyzing data and extracting insights from it. They work with data producers to develop data models and algorithms to analyze data and extract insights. They also work with data repositories to ensure that data is properly documented and stored. As data becomes increasingly important to research, the demand for Data Scientists is expected to grow.
4. Data Engineer: Data Engineers are responsible for the design, development, and maintenance of data systems. They work with data producers to develop data systems and ensure that data is properly documented and stored. They also work with data repositories to ensure that data is properly archived and shared. As data becomes increasingly important to research, the demand for Data Engineers is expected to grow.
[Education Paths]
1. Master of Science in Data Science: This degree program focuses on the development of skills and knowledge related to the collection, analysis, and interpretation of data. Students learn to use data-driven methods to solve problems and develop strategies for data-driven decision-making. This degree is becoming increasingly popular as businesses and organizations rely more heavily on data-driven insights.
2. Master of Science in Data Analytics: This degree program focuses on the application of data analytics to solve real-world problems. Students learn to use data-driven methods to identify patterns, trends, and correlations in data. They also learn to develop strategies for data-driven decision-making and to use data-driven insights to inform business decisions.
3. Master of Science in Data Management: This degree program focuses on the management of data and the development of data-driven strategies. Students learn to use data-driven methods to organize, store, and secure data. They also learn to develop strategies for data-driven decision-making and to use data-driven insights to inform business decisions.
4. Master of Science in Data Visualization: This degree program focuses on the development of skills and knowledge related to the visualization of data. Students learn to use data-driven methods to create visual representations of data. They also learn to develop strategies for data-driven decision-making and to use data-driven insights to inform business decisions.
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1. What is the purpose of the CRADLE Project?
2. What is the purpose of the Institute of Museum and Library Services?
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4. What is the hashtag for the Research Data Management and Sharing MOOC?
Correct Answer: #RDMSmooc
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