example). Therefore, the reports do not alone constitute data that can support a repeatable, reproducible analysis, and the hypothesis that what was witnessed was a manifestation of known natural or technological phenomena cannot be falsified.
Collecting New Data
The instrumental characteristics of the equipment that can potentially capture UAP data are important information that should be available for researchers studying the observations. This is essential for a data- driven study of UAP.
These characteristics may include lab-measured (rather than field-reported) error rates of sensors that are routinely used by both civilian and military aircraft; modeling of optical "ghosting" in the images due to scattering of solar and lunar glints within the camera system; solar or bright star glints from oceans' surfaces; and noise sources intrinsic to the sensors themselves.
Multisensor platforms are important for providing a complete picture of a UAP event. An object's motion should be recorded, as well as its shape (imaging data), color (multispectra or hyperspectral data) and any sounds and other characteristics. Crowd-sourced observations that are standardized can also offer important metadata information that can be used to filter and classify events.
The panel sees an advantage to augmenting potential data collection efforts using modern crowd-sourcing techniques, including open-source smartphone-based apps. Using open-source software is consistent with NASA's commitment to transparency. From multiple near-simultaneous observations with smartphones, imaging and sound data could be collated, and metadata used to triangulate an object's location and estimate its velocity and size.
Such a database could be developed through a partnership involving AARO, NASA, and commercial partners. The collected data would need to meet the standards described above, so platform developers would need to focus on constructing a data architecture that would support such collection. NASA can use its experience in citizen science projects to help minimize data noise, systematic errors, and cognitive biases related to human observed events (as opposed to sensors).
Once an anomalous signal is identified, new discovery infrastructure may be needed to characterize it in full. Collecting additional data on a rapidly evolving phenomenon of interest has become a common practice in astrophysics, but collection of what in astrophysics is referred to as "follow-up data" requires a high level of automation in the collection, reduction, (real time) analysis of the discovery data, and robotization of follow-up facilities. While NASA has historically paved the way for this mode of observing by developing and supporting the General Coordinates Network (GCN) that enables rapid coordination of observations from ground and space assets, consideration of developing such an infrastructure should follow after careful planning of the discovery data as outlined above as such a plan is significantly resource-intensive. If systematic studies of these events continue to reveal anomalies, then future studies may consider optimizing such a system of follow-up observations.
Data Curation and Integration
There is no standardized Federal system for making civilian UAP reports. While the DoD is establishing a systematic mechanism for military UAP reports, current FAA guidelines instruct persons wanting to report UAP to contact local law enforcement or a non-governmental organization such as the National UFO Reporting Center[1]. This results in inhomogeneously collected, processed, and curated data.
Integrating NASA's open, civilian dataset with DoD's more focused, restricted information would take some effort. Additionally, data integration opportunities exist with NOAA. Assets such as the NEXRAD Doppler radar network (160 weather radars jointly operated by the FAA, U.S. Air Force, and National Weather Service) or the Geostationary Operational Environmental Satellites may be very useful for distinguishing interesting objects from airborne (windborne) clutter.
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