Explorations in data reuse and repurposing are crucial, given the growth in digital assets accessible in a wide variety of domains. Arguably, the best-documented data are in scientific publishing, motivated by international data sharing requirements, open data initiatives, and software sustainability programs. A severe limitation is that research on data reuse is shallow, primarily based upon simple citation counts and impact measures. Scientific endeavors can be significantly transformed by reporting data reuse via rich ontological associations (equivalence, derivative, sequential, etc.) in order to more meaningfully demonstrate adaptation during the lifecycle. An ontology tracking high-impact cases of data and algorithm adaptation will provide a more accurate view of reuse and enable radical, new adaptation combinations to be learned and pursued. The objectives of the proposed work are to:
1) Develop an ontology for tracking data and algorithm adaptation in multiple domains, such as the biological and earth sciences, astronomy, health data domain, and an engineering sub-domain, drawing from structured and unstructured data sources
2) Develop an algorithm that incorporates the ontology and more accurately tracks data and algorithm adaptation
3) Enable data and algorithm producers, owners, and publishers to transform data adaptation services and support better science.