Services that offer reliable and scalable determination and disambiguation of research identities are essential services that data repositories and digital libraries need to provide. Such services enable distributed grouping, linking, aggregation, and retrieval of scholarship; evaluation of the research productivity and impact of individuals, groups, and institutions; and the identification of expertise [1–3]. Publishers, libraries, universities, search engines, and content aggregators use many different research identity management systems, often referred to as research information management systems (RIMSs) or current research information systems (CRIS), with different data models, levels of coverage, and levels of quality (e.g., Florida ExpertNet, Google Scholar, ORCID, REACH NC, ResearchGate). These databases use different approaches to and mechanisms for curating research identity information: manual curation by information professionals or users, including the subjects of identity data; automated data mining and curation scripts (aka bots); or some combination thereof. With universities engaging in the curation of digital scholarship produced by their faculty, staff, and students through institutional repositories (IR), some of these universities and IRs try to manage the research identity profiles of their contributors locally (e.g., Stanford Profiles). Some large academic libraries use the VIVO ontology  to make their data, including researcher identity information, discoverable and linkable for cross-institutional retrieval, processing, and analysis by both human and computational agents. The use of ontologies and Semantic Web technologies can make data machine-processable and “understandable,” and hence may reduce the cost of data aggregation and analysis. Ultimately, however, the completeness and accuracy of the data are what make RIMSs reliable. Although knowledge curation by professionals usually produces the highest quality results, it is costly and may not be scalable . Libraries and IRs may not have sufficient resources and expertise to control the quality of large-scale uncontrolled metadata, often batch harvested and ingested from faculty-authored websites and journal databases . The effective aggregation of data may require knowing community, discipline-based, and cultural differences in data and metadata quality requirements, rules, norms, and reference sources. They may need the participation of subject specialists, librarians, and especially the researchers themselves in data curation activities to ensure the quality and reliability of research identity data [3, 6–8].