Connecting Scientometrics: Dimensions as a Route to Broadening Context for Analyses.

Modern cloud-based data infrastructures open new vistas for the deployment of scientometric data into the hands of practitioners. These infrastructures lower barriers to entry by making data more available and compute capacity more affordable. In addition, if data are prepared appropriately, with unique identifiers, it is possible to connect many different types of data. Bringing broader world data into the hands of practitioners (policymakers, strategists, and others) who use scientometrics as a tool can extend their capabilities. These ideas are explored through connecting Dimensions and World Bank data on Google BigQuery to study international collaboration between countries of different economic classification.

Measuring Research Information Citizenship Across ORCID Practice.

Over the past 10 years, stakeholders across the scholarly communications community have invested significantly not only to increase the adoption of ORCID adoption by researchers, but also to build the broader infrastructures that are needed both to support ORCID and to benefit from it. These parallel efforts have fostered the emergence of a "research information citizenry" between researchers, publishers, funders, and institutions. This paper takes a scientometric approach to investigating how effectively ORCID roles and responsibilities within this citizenry have been adopted. Focusing specifically on researchers, publishers, and funders, ORCID behaviors are measured against the approximated research world represented by the Dimensions dataset.

Scaling Scientometrics: Dimensions on Google BigQuery as an Infrastructure for Large-Scale Analysis.

Cloud computing has the capacity to transform many parts of the research ecosystem, from particular research areas to overall strategic decision making and policy. Scientometrics sits at the boundary between research and the decision-making, policy-making, and evaluation processes that underpin research. One of the biggest challenges in research policy and strategy is having access to data in a way that allows for analysis that can respond in an iterative way to inform decisions. Many decisions are based on "global" measures such as benchmark metrics that are hard to source and hence are often nonspecific or outdated. The use of cloud technologies may be promising in addressing this area of providing data for research strategy and policy decisions. A novel visualisation technique is introduced and used as a means to explore the potential for scaling scientometrics by democratising both access to data and compute capacity using the cloud.

Real-Time Bibliometrics: Dimensions as a Resource for Analyzing Aspects of COVID-19.

Dimensions was built as a platform to allow stakeholders in the research community, including academic bibliometricians, to more easily create and understand the context of different types of research object through the linkages between these objects. Links between objects are created via persistent identifiers and machine learning techniques, while additional context is introduced via data enhancements such as per-object categorisations and person and institution disambiguation. While these features make analytical use cases accessible for end users, the COVID-19 crisis has highlighted a different set of needs to analyze trends in scholarship as they occur: Real-time bibliometrics. The combination of full-text search, daily data updates, a broad set of scholarly objects including pre-prints and a wider set of data fields for analysis, broadens opportunities for a different style of analysis. A subset of these emerging capabilities is discussed and three basic analyses are presented as illustrations of the potential for real-time bibliometrics.