Computational Archival Science (CAS) has been proposed as a trans-disciplinary field that combines computational and archival thinking. To provide grounded evidence, a foundational paper explored eight initial themes that constitute potential building blocks. In order for a CAS community to emerge, further studies are needed to test this framework. While the foundational paper for CAS provides a conceptual and theoretical basis of this new field, there is still a need to articulate useful guidelines and checkpoints that validate a CAS research agenda. In this position paper, we propose heuristics for assessing emerging CAS-related studies that researchers from traditional fields can use in their research design stage. The Human Face of Big Data project, a digital curation and interface design project for urban renewal data, is presented and analyzed to demonstrate the validity of the suggested heuristics. Finally, implications for CAS and future work are discussed.
The full paper is available at the external link (IEEE Big Data 2017 Proceedings pp. 2262-2270).
Lee, M., Zhang, Y., Chen, S., Spencer, E., Dela Cruz, J., Hong, H., & Marciano, R. (2017, December). Heuristics for Assessing Computational Archival Science (CAS) Research: The Case of the Human Face of Big Data Project. The Workshop on Computational Archival Science in IEEE Big Data 2017. pp. 2262-2270. Boston, U.S. https://doi.org/10.1109/BigData.2017.8258179.