AI implementations continue to grow in cultural heritage settings and have deep connections to how computational archival science is conducted. This paper reviews how AI is being implemented in computational archival science. It then provides an overview of ethical issues and considerations when implementing AI for computational archival science. Our research team has developed an evidence-based ethical reflection aid to guide library and archives practitioners through responsible implementations of AI. When using the ethical reflection aid practitioners consider a specific AI implementation scenario, consider how the different values held by different stakeholders may align or come into conflict, and outline potential actions for responsible AI. We present an example of using the ethical reflection aid to examine a scenario related to computational archival science. Ultimately, this paper suggests that careful examination of stakeholder values can support a more responsible computational archival science practice.

Community detection plays a pivotal role in social network analysis by partitioning networks into cohesive groups of vertices with dense intra-group connections and sparse inter-group connections. In this paper, we utilized a scholarly social network based on researchers' topic similarity derived from their publication metadata to identify interdisciplinary research communities. As topics often form a hierarchy, we hypothesize that the constructed scholarly network will exhibit hierarchical community structures. Therefore, we explore the efficacy of two prominent community detection algorithms, Louvain and Spectral clustering, known for their capacity to detect hierarchical community structures within networks. While both algorithms demonstrate this capability, the original Louvain algorithm is susceptible to the resolution limit problem due to its reliance on the modularity measure. To address this limitation, we propose the nested hierarchical Louvain algorithm, which iteratively partitions the network based on previously identified subgraphs, and we find that the bias towards large communities is mitigated. To evaluate the hierarchy produced by each of the algorithms, we employ the Cophenetic Correlation Coefficient (CPCC), a metric commonly used in hierarchical clustering evaluations but less frequently utilized in hierarchical community analysis. We argue that CPCC can be a useful measure to identify the presence of implicit hierarchical community structure in social networks when it is not explicitly available from domain knowledge while also further mitigating the inherent bias present in using modularity as a metric. Experimental results, conducted on both synthetic networks and the scholarly social network, demonstrate that the nested hierarchical Louvain algorithm, as well as Spectral Clustering, successfully identifies more finely structured hierarchical communities, offering greater depth in the dendrogram compared to the basic Louvain algorithm.

Sharing and reusing research data present both opportunities and challenges for the individual researcher, their organizations, and the entire research community, but the promise of data sharing can only be actualized if the right data can be found.While grants and publications increasingly include data sharing requirements, locating the right data to answer a research question can still be challenging.As more data is shared more frequently, the data discovery problem becomes more apparent.There is simply more data to look through, and that data is distributed across a growing number of repositories, article supplements, websites, and other locations with different metadata, data standards, and search functionality.These 10 rules can be thought of as a mirror to "Eleven Quick Tips for Finding Research Data," providing key guidance on how to make your research data more findable in the complicated systems that share and provide access to research [1].As opposed to helping locate data for reuse, this article is meant to help you make your data and your research more discoverable.The rules below walk you through the process of making your data more discoverable, including key steps to take when publishing an article (Rules 8 and 9).As members of the Data Discovery Collaboration (DDC) [2], our work focuses on the issue of data discovery.We are a community of librarians and information professionals and are invested in helping to improve data findability and open data infrastructure more broadly.In service of these goals, we created these rules to provide guidance on how to improve the discovery of your research data by making key decisions early in the process (Rules 1 and 2), leveraging scholarly infrastructure already in place (Rules 3, 4, 5, 6, and 7), taking important steps at the point of publication (Rules 8 and 9), and tapping into the growing community of data professionals (Rule 10). Rule 1: Decide what level of access you can provideData discovery is not data access.You can make your data discoverable without making it openly available.Data discovery is the process of learning that a dataset exists, whereas data access is the ability to download or view that data.Both of these concepts are part of the FAIR