We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Metrics in Context: Towards A Data Specification For Scholarly Metrics

Formal Metadata

Title
Metrics in Context: Towards A Data Specification For Scholarly Metrics
Alternative Title
Metrics in Context: A Data Specification For Scholarly Metrics
Title of Series
Number of Parts
637
Author
License
CC Attribution 2.0 Belgium:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
Google Scholar, Web of Science, Scopus, Dimensions, Crossref, Scite.ai, ... What used to be the home turf of for-profit publishers has become a buzzing field of technological innovation. Scholarly metrics, not only limited to citations and altmetrics, come from a host of data providers using an even wider range of technologies to capture and disseminate their data. Citations come as closed or open data, using traditional text processing or AI methods by private corporations, research projects or NGOs. What is missing is a language and standard to talk about the provenance of scholarly metrics. In this lightning talk, I will present an argument why we need to pay more attention to the processes of tracing and patterning that go into the creation of the precious data that determine our academic profiles, influence hiring and promotion decitions, and even national funding strategies. Furthmermore, I present an early prototype of Metrics in Context, a data specification for scholarly metrics implemented in Frictionless Data. Additionally, the benefits and application of Metrics in Context is presented using both traditional citation data and a selection of common altmetrics such as the number of Tweets or FB shares.