1 Define data quality and interoperability

Data quality refers to the degree to which the data in a digital library meets the expectations and requirements of the users and stakeholders. It involves aspects such as completeness, accuracy, timeliness, relevance, and usability. Interoperability refers to the ability of different digital library systems and standards to communicate and exchange data and services with each other. It involves aspects such as compatibility, alignment, integration, and coordination.

2 Adopt common standards and formats

One of the main ways to ensure data quality and interoperability is to adopt common standards and formats for the data and metadata in a digital library. Standards and formats provide guidelines and specifications for how the data and metadata should be structured, encoded, described, and exchanged. They also facilitate the discovery, retrieval, and reuse of the data and metadata by different systems and users. Some examples of common standards and formats for digital libraries are Dublin Core, MARC, MODS, METS, OAI-PMH, and RDF.

3 Implement quality control and assurance processes

Another way to ensure data quality and interoperability is to implement quality control and assurance processes throughout the data lifecycle. Quality control and assurance processes involve checking, verifying, validating, correcting, and improving the data and metadata in a digital library. They also involve monitoring, evaluating, and reporting on the data quality and interoperability performance and issues. Some examples of quality control and assurance processes for digital libraries are data cleaning, data auditing, data mapping, data testing, and data feedback.

4 Use tools and techniques for data quality and interoperability

A third way to ensure data quality and interoperability is to use tools and techniques that support and enhance the data and metadata in a digital library. Tools and techniques are software applications or methods that help to create, manage, manipulate, transform, analyze, or visualize the data and metadata in a digital library. They also help to facilitate the data and metadata exchange and integration between different systems and standards. Some examples of tools and techniques for data quality and interoperability are data conversion, data normalization, data enrichment, data linking, and data visualization.

5 Follow best practices and principles for data quality and interoperability

A fourth way to ensure data quality and interoperability is to follow best practices and principles that guide the design, development, and maintenance of digital library systems and standards. Best practices and principles are recommendations or rules that reflect the current knowledge and experience of the digital library community. They also reflect the values and goals of the digital library users and stakeholders. Some examples of best practices and principles for data quality and interoperability are FAIR principles, Open Data principles, Linked Data principles, and Trustworthy Digital Repository principles.

6 Learn from examples and case studies of data quality and interoperability

A fifth way to ensure data quality and interoperability is to learn from examples and case studies of digital library systems and standards that have successfully implemented or improved data quality and interoperability. Examples and case studies are real-world scenarios or projects that illustrate how data quality and interoperability can be achieved or enhanced in different domains, contexts, or challenges. They also provide lessons learned, best practices, or tips for data quality and interoperability. Some examples of examples and case studies of data quality and interoperability are Europeana, DPLA, HathiTrust, and WorldCat.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?