ENRICH Data Equity Incubator

The ENRICH Data Equity Incubator supports Indigenous led and designed research for equitable and ethical change in the storage of Indigenous cultural heritage and data, transforming research practices and enabling Indigenous data futures. 

The focus of the Incubator is on equity within Indigenous data collection, digital infrastructures and machine learning contexts affecting responsible engagement, collection and use of Indigenous data and further development of tools making Indigenous cultural heritage data FAIR compliant.

This work is foundational to the radical reform of relationships between Indigenous communities and wider society with a sharp focus on decolonial transformation for Indigenous innovation.

Current Project

Indigenous Data Detection Algorithm

In 2019-2020 a team of data science, computer science, and museum studies faculty and students at New York University (NYU) have worked to identify the key elements and attributes that would be necessary to build an ML tool — the Indigenous Data Detection Algorithm (ID2) — that finds Indigenous data at an accelerated scale. 

The initial training dataset for the development of this tool used the largest publicly available dataset about Indigenous collections in the US — thirty years of records (14,000 Notices published in the Federal Register) produced through the US federal legislation Native American Graves Repatriation and Protection Act (1990). 

Leveraging this already existing work for the creation of ML tools that can help find Indigenous data, our four primary outcomes are: 

  1. Finding Indigenous data; 
  2. Creating Indigenous land, geospatial and place-based datasets from federal repositories that combine the FAIR Principles with the CARE Principles and; 
  3. Tagging Indigenous data with Traditional Knowledge and/or Biocultural Notices that indicate Indigenous interests as the data travels; and
  4. Developing an initial set of criteria around Indigenous metadata, Indigenous provenance, and Indigenous citation and annotation.