White Paper: 2025 UAP Workshop - Narrative Data, Infrastructures, and Analysis

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particularly among pilots, undermines data timeliness and completeness, while the lack of
standardized reporting formats across agencies and organizations further fragments the
landscape. Time sensitivity and weak retention policies have led to the loss of critical records, as
in the well-known Nimitz case. Technical issues are also substantial. Older data can be difficult
to digitize, cursive writing resists Optical Character Recognition (OCR) systems, and
crowdsourced transcription projects suffer from low-quality outputs, recently worsened by
misuse of generative AI. Finally, the field must grapple with fake data and disinformation,
including AI-generated photos or videos, which pose risks for both public trust and analytic
integrity.
Metadata and context for usability and analysis
Effective use of UAP data requires rich contextual metadata. Every report should ideally contain
time, date, and location, preferably with geospatial precision. Distinguishing between descriptive
metadata (objective characteristics like morphology or frequency band) and interpretive metadata
(subjective effects or experiential meaning) is key. Metadata should also capture event-specific
details, such as behaviors, sensor positions, and witness background, and must extend to
technical parameters for structured data. Provenance (the chain of custody and source of the
data) is essential for ensuring interpretability and trust. For visual evidence, metadata such as
device type and embedded geotags allow validation against reported facts. Participants also
emphasized flexible and well-designed reporting forms, for example including “refuse to
answer” options to prevent fabricated entries when respondents lack knowledge.
Linking data sources and developing a unified approach
Given the fragmented nature of UAP data, participants argued for modest, pilot-scale integration
projects as a starting point. Establishing common terminology and data dictionaries is important
to harmonize datasets across agencies and disciplines. Modular and extensible metadata
standards could lead toward a composable ecosystem, potentially implemented through
standardized templates, Interface Control Documents (ICDs), or APIs. Some form of established
governance is needed to facilitate data management and access and engagement for researchers
while alleviating inter-agency silos. Transparency was highlighted as both a goal and a
challenge, as unclassified data should be made available to academia, while sensitive material
must remain protected. Lessons from other fields, such as genetics and astronomy, were cited as
models for developing interoperable metadata standards and ontology-driven approaches.
Assessing credibility and quality of reports
Participants highlighted the importance of sensor reliability, noting that human perception is
fallible. Establishing gold standard exemplars of high-quality reports could help guide future
collection and analysis. Semi-automated triage, assisted by AI, offers promise for sifting through
massive datasets to identify cases with likely conventional explanations as well as cases of
potential interest, though human oversight remains indispensable. Furthermore, credibility is
enhanced when reports are corroborated by multiple witnesses or independent data streams, such