Improved discovery through recommendations and tagging of Atmospheric Radiation Measurement (ARM) data

The ARM Data Center (https://www.arm.gov/data) handles processing, submission, archiving, and distribution of data collected from instruments, value-added products, and field campaigns to the scientific community. However, our archive of 3 petabytes of data, spread across multiple global sites and diverse instruments, can be difficult to navigate for new users. In this work, we will discuss the key practices for data publication, and recent improvements in the redesigned ARM Data Discovery Tool (https://adc.arm.gov/discovery) that help scientists find and access datasets and ensure FAIR data practices. We will discuss the workflow to showcase data that are recommended, and provide information about a recent initiative to add keyword tagging for selected datasets on top of existing measurement tags. For example, recent database additions for rationale statements provide context to recommendations, as well as external links and tags of potential data limitations. Data tagging is also coupled with a new initiative that enhances discoverability of certain characteristics (e.g., by land type or data epoch), and uses community standards to allow interoperability (e.g., machine readable for further processing). We will discuss challenges to implement tagging and strategies to overcome these challenges.