Restoring the Record

We're an Indigenous-led independent project team creating a metadata infrastructure initiative

Restoring the Record

An archival tool that restores credit and consent for Indigenous contributors, advancing sovereignty in cultural heritage collections.

We are based in Minneapolis, MN and Washington, DC.

United States

Not registered as any organization

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Indigenous contributors to cultural heritage repositories have historically been anonymized, omitted, misrepresented, or deprioritized in institutional metadata. Without consent built into institutional processes, mass digitization and the promulgation of non-Indigenous scholarly research on Indigenous collections and histories has scaled this erasure. Today, as institutions rush to unify legacy systems and meet newly instituted and pressing Native American Graves Protection and Repatriation Act (NAGPRA) requirements, known systemic inadequacies create real risk for contemporary Indigenous priorities like language preservation, cultural survival, repatriation, and the agency to govern themselves as sovereign political entities. Current standards like the International Documentation Committee Conceptual Reference Model (CIDOC CRM) and ubiquitous Collections Information Systems (CIS) like Axiell and TMS, are optimized for object-oriented recordkeeping and institutional control. They offer no standard for prioritizing Indigenous attribution in particular, or narrative consent/refusal. While acknowledging the role that tools such as Traditional Knowledge (TK) labels can play, these are often applied retroactively, disconnected from metadata generation and governance, and risk being employed as a stand-in for actual Indigenous engagement. Such reactive applications are a bandaid for the larger issue of data disenfranchisement that perpetuates miscredit, mistrust, and structural barriers to meaningful intellectual sovereignty in rarified digital cultural heritage practice.

Indigenous contributors to cultural heritage repositories have long been misrepresented, omitted, or anonymized in institutional metadata. As institutions unify legacy systems and address new NAGPRA rules, structural gaps in attribution and consent present real risks to language, cultural survival, and sovereignty. Our solution, Community Profile(s), is a modular metadata infrastructure built ethically from research, public sources, and partner data, that embeds Indigenous authorship, credit, and refusal into the descriptive record. It can be deployed alongside legacy tools like Axiell and TMS, without requiring full system overhaul. Contributors will be able to: • Be credited by self-determined name, role, and affiliation • Flag records for refusal or redaction • Inform attribution protocols and context • Identify relevant collections for review • Contribute to templated outreach and rights documentation We also dream of exploring experimental functionalities with MIT collaborators, including: • ML-assisted metadata triage for repository audits • ML-assisted relevance and relationality mapping between described collections and discretely defined source communities based on semantic markup, chronological features, and geolocation data points. • Protocol-aware export and ingestion formats This builds on a test-case developed by our co-lead, indexing over 550 Tribal Nations and multiple repositories. Our vision is grounded in relationality and restoration—bringing community governance into the heart of metadata.

Our primary audience is Indigenous communities, cultural workers, knowledge bearers and contributors seeking agency over the names, stories, and knowledge representation of their people. Secondarily, repositories like museums, archives, journalistic and scholarly institutions, libraries, and funders who want to create more ethical infrastructures around attribution, repatriation, knowledge-generation, advocacy and trust-building will benefit from this work. Short term impacts: • Public-facing registry and outreach templates • Metadata audit tools to identify under-credited contributions • Demonstration of consent-based metadata integration Long term impacts: • Field-wide adoption of relational and community-owned metadata standards • Scalable model for Indigenous-centered AI/ML metadata repair • A shift from extractive to reciprocal data systems