Making Drone Data FAIR Through a Community-Developed Information Framework

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.5334/dsj-2023-001. This is version 2 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Andrea Thomer , Lindsay Barbieri , Jane Wyngaard , Sarah Swanz

Abstract

Small Uncrewed Aircraft Systems (sUAS) are an increasingly common tool for data collection in many scientific fields. However, there are few standards or best practices guiding the collection, sharing, or publication of data collected with these tools. This makes collaboration, data quality control, and reproducibility challenging. To that end, we have used iterative rounds of data modeling and user engagement to develop a Minimum Information Framework (MIF) to guide sUAS users in collecting the metadata necessary to ensure that their data is trust-worthy, shareable and reusable. This paper briefly outlines our methods and the MIF itself, which includes 74 metadata terms in four classes that sUAS users should consider collecting for any given study. The MIF provides a foundation which can be used for developing standards and best practices.

DOI

https://doi.org/10.31223/X5Z338

Subjects

Computer and Systems Architecture, Library and Information Science

Keywords

sUAS, drones, RPAs, data standard

Dates

Published: 2021-08-02 18:30

Last Updated: 2023-05-10 09:41

Older Versions
License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
N/A