How Do We Measure Value in Data Reuse? Nuancing Ethical Data Sharing and Attribution for the Social Sciences and Indigenous Communities
As a result of the ‘open data’ movement, an increased focus on how data and information should be attributed and cited has become increasingly important. As data becomes reused in scientific analyses or decision-making, efforts have turned to crediting the data creator, such as data citation and metrics of reuse to ensure appropriate attribution to the original data author. The increased focused on metrics and citation, however, need to be carefully considered when it comes to social science data, local observations, and Indigenous Knowledge held by Indigenous communities. These diverse and sometimes sensitive data/information/knowledge sets often require deep nuance, thought, and compromise within the ‘open data’ framework, in order to consider issues of the confidentiality of research subject and the ownership of data and information, often in a colonial context. These data and knowledge sets have been used to further specific causes that may not be beneficial to those who created and hold the knowledge. Furthermore, these datasets are often highly valuable to one or two villages, saving lives and retaining culture within. In these cases, quantitative metrics of “data reuse” and citation do not adequately measure a dataset’s ‘value.’ In this talk, I will provide examples of datasets that are highly valuable to small communities from my research in the Arctic and US Southwest, as well as critically approach issues of data ownership and sovereignty when it comes to data sharing and reuse. Many of these datasets are not highly cited or have impressive quantitative metrics (e.g., number of downloads) but have been incredibly valuable to the community where the data/information/knowledge are held. These case studies include atlases of placenames held by elders in small Arctic communities, as well as databases of local observations of wildlife and sea ice in Alaska that are essential for sharing knowledge across multiple villages. These examples suggest that a more nuanced approach to understanding how data should be accredited is needed when working with social science data and Indigenous Knowledge.