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Data Librarian

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April 11, 2024 Updated April 14, 2025 15 minute read

Exploring a Career as a Data Librarian

A Data Librarian occupies a unique and increasingly vital role at the intersection of information science, technology, and research. At a high level, these professionals specialize in managing, curating, preserving, and providing access to research data. They serve as crucial bridges between complex datasets and the researchers, students, and institutions that rely on them, ensuring data is findable, accessible, interoperable, and reusable (FAIR).

Working as a Data Librarian can be deeply engaging. You might find excitement in collaborating directly with researchers across diverse fields, helping them navigate the complexities of data management from project inception to long-term archiving. The role also involves mastering new technologies and standards, offering continuous learning opportunities. Furthermore, contributing to open science and ensuring ethical data handling provides a strong sense of purpose.

Understanding the Role of a Data Librarian

Data Librarians focus on the lifecycle of research data. Their responsibilities extend far beyond traditional library tasks, encompassing the stewardship of digital information generated through scholarly inquiry. They are key players in making research more transparent, reproducible, and impactful by ensuring the underlying data is well-managed and accessible.

Defining the Modern Data Librarian

A Data Librarian is an information professional specializing in the acquisition, curation, management, preservation, and dissemination of datasets, particularly those generated from research. They often work within academic institutions, research centers, government agencies, or even corporations, supporting data-intensive projects and initiatives. Their work involves not just organizing data, but also understanding its context, structure, and potential uses.

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Salaries for Data Librarian

City
Median
New York
$105,000
San Francisco
$112,000
Seattle
$87,000
See all salaries
City
Median
New York
$105,000
San Francisco
$112,000
Seattle
$87,000
Austin
$104,000
Toronto
$97,000
London
£81,000
Paris
€70,000
Berlin
€72,000
Tel Aviv
₪461,000
Singapore
S$80,000
Beijing
¥422,000
Shanghai
¥80,000
Shenzhen
¥24,000
Bengalaru
₹455,000
Delhi
₹63,000
Bars indicate relevance. All salaries presented are estimates. Completion of this course does not guarantee or imply job placement or career outcomes.

Path to Data Librarian

Take the first step.
We've curated 15 courses to help you on your path to Data Librarian. Use these to develop your skills, build background knowledge, and put what you learn to practice.
Sorted from most relevant to least relevant:

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Provides a practical guide to using public datasets for data science projects. It covers topics such as data cleaning, data analysis, and data visualization.
Provides a comprehensive guide to using Apache Spark for big data analytics. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use big data for analytics purposes.
Provides a comprehensive overview of data science. It covers topics such as data mining, machine learning, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding the principles of data science.
Provides a comprehensive guide to using MapReduce for data-intensive text processing. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use MapReduce for big data analysis purposes.
Provides a practical guide to statistics for data scientists. It covers topics such as data collection, data analysis, and data interpretation. While it does not focus specifically on public datasets, it provides a good foundation for understanding the statistical principles used in data science.
Provides a detailed guide to dimensional modeling, which key technique for organizing and managing data in a data warehouse.
Provides a comprehensive guide to using Pandas for data analysis. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use Pandas for data analysis purposes.
Provides a comprehensive guide to using R for data mining. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use R for data mining purposes.
Provides a practical introduction to data visualization. It covers topics such as data visualization techniques, data visualization tools, and data visualization best practices. While it does not focus specifically on public datasets, it provides a good foundation for understanding the principles of data visualization.
Provides a basic introduction to Microsoft SQL Server 2016, which relational database management system.
Provides a basic introduction to location intelligence, which is the use of data to understand the relationship between people, places, and things.
Provides a business-oriented introduction to data science. It covers topics such as data mining, machine learning, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use data for business purposes.
Provides a comprehensive guide to using Python for data analysis. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use Python for data analysis purposes.
Provides a comprehensive reference for MySQL, which relational database management system.
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