Open Science Metrics
Measure adoption, identify trends, and track impact
Open Science Metrics delivers accurate, detailed stats on open science adoption across your publishing portfolio and beyond.
Using AI and full-text Natural Language Processing, Open Science Metrics uncover open science behaviors that are invisible to traditional, citation-based tracking methods.
The data you need
Receive an intuitive, graphics rich summary report, and raw tabular data files with an array of data points for each article in your corpus.
Grow your business
Armed with reliable business intelligence, your journal team can make data-driven strategy and policy decisions, identify opportunities, and track progress over time.
Operationalize your policy with DataSeer SnapShot
SnapShot integrates into your journal submission platform, where it scans the full text of submitted manuscripts against your journal’s specific editorial policies in seconds. You’ll get actionable results, automated manuscript tirage, and author-ready sendbacks that make upholding journal policy simple.
Specs
What do Open Science Metrics measure?
- Data generation and sharing
- Sources of re-used data
- Code generation and sharing
- Preprint posting
- Protocol sharing
- Study Registration
- Presence of identifiers (ROR, RRID, ORCID)
Break down results by...
- Publication date
- Publisher & Journal
- Open Access licenses
- Funder & Institution
- County
- Research discipline
- And more…
NEW: Track FAIRness
- Pragmatic assessment of FAIRness
- Capture ‘FAIR zero’ – where data are generated but not shared
Measure impact
- Catalog all newly generated data for your corpus
- Capture downstream re-use of those data in the broader literature
Open Science Metrics in practice
How could Open Science Metrics support your organization?
DataSeer delivers a graphics-rich shareable report and raw tabular data files with an array of data points offering detailed insight into most widely-used open practices for each study in your corpus.
Get the results you need on your schedule. Here’s an example of just one possible workflow.
Select your sample
Start by establishing your baseline with a core dataset—for example, 5,000 articles from one publisher.
Compare and contrast
Consider including a comparator dataset to see how your publications stack up against similar articles in the same time period.
Get your data
Receive both an aggregate summary and article-level breakdown of Open Science practices across your entire sample.
Make data-driven decisions
Armed with data, establish meaningful goals, and implement policies and processes to meet them. (DataSeer solutions can help!)
Measure change
Schedule regular updates to track trends, showcase the impact of new workflows, and policies, and hold editors accountable.
see it it action
PLOS (Public Library of Science) uses DataSeer’s Open Science Metrics to measure open science across their twelve journals. The PLOS dataset currently includes more than six years worth of publication data and counting, along with a comparator dataset 20% of the size.
