SciVal® Strata is a flexible benchmarking tool that provides quantitative analyses of team or researcher performance, complementing the peer-review approach. Giving you access to the global researcher base within Scopus® – the largest abstract and citation database of peer-reviewed literature – SciVal Strata helps you make informed decisions regarding recruitment, retention and promotion through flexible scenario modeling and collaborative network simulations.
See the talent you choose by the benchmarks that matter
SciVal Strata's intuitive interface makes it easy to compare team and individual performance within a context you can adjust to your needs.
- Assemble teams, real or speculative, to measure productivity or potential
- SciVal Strata graphically reveals performance relative to Reference Fields you can modify, along time periods you can adjust
- Assess any researcher or a selection of their documents captured within Scopus
Measure impact through multiple perspectives
Users can evaluate teams not just by raw productivity, but by their impact on other researchers and consumers of scientific information.
- Visualize influence through numerous customizable variables: document output, citations received, cited/uncited documents, geographical collaboration networks and author networks
- Exportable tables are available on all analyses and provide detailed insights into indicators such as global percentile levels, h-index, g-index and m-index
- Based on the entire Scopus database, SciVal Strata’s dataset reaches from 1996 and extends through regular updates right up to the current week
Customize settings to your needs
You can personalize your SciVal Strata experience to include or exclude self-citations, customize Reference Fields, researchers and clusters, set defaults for the time span on which they view charts, and much more.
- Assembling teams is as simple as dragging and dropping researchers, or teams you have already created, into new groupings
- Share your teams and Reference Fields with your institutional colleagues or the world of SciVal Strata users, so they too can run analyses using your benchmarks and researchers
- Use or modify pre-existing subject categories as benchmarks, or create your own using journals of your choice
Import ready-made groups of researchers
As an additional service, you can choose to have pre-populated groups of researchers - called Ready-made Clusters - added to your institution’s SciVal Strata implementation.
These quality assured clusters provide a common starting point and allow you to evaluate teams or researchers immediately.
- Ready-made Clusters can be selected and imported by any user in the institution at any time
- Combine researchers or clusters for scenario modelling and further analysis
How it works
Users can simply select, then drag and drop research teams, individual researchers, or even team hierarchies into tables and graphs that demonstrate relative levels of performance by a variety of measures.
Assemble dream teams
Teams can be composed of researchers from any affiliation, giving you the ability to model scenarios and highlight some otherwise hidden areas for investigation to help answer questions such as:
- What potential effect could there be to our team if we recruit researcher A instead of B?
- What could happen if researcher C leaves?
- If I reorganize my department, what affect could that have?
You can also choose a selection of papers written by a researcher, rather than their complete body of work. This is particularly useful for example, in assessment exercises when a specific number of papers are required, or when you want to assess the papers written when a researcher was connected to a specific institution.
SciVal Strata comes preloaded with a fully customizable and exhaustive list of subject categories and an array of options for each analysis that allow tailoring by users to meet their exact needs. Choose, amongst other things, to include or exclude self-citations, and exclude various document types in each analysis.
With SciVal Strata, decision-makers can make objective assessments of team and individual performance through both classical metrics such as productivity, citation impact and h-index which are well-suited to established researchers, and novel indicators such as citability, and collaboration activity which are also useful for Early Career Researchers (ECRs).
Institutions can set rules for allowing their users to share data within their organization or the broader SciVal Strata community, alongside full exporting options.