Open science offers a vision of how scholarly communication might function — collaborative, equitable, transparent, reproducible, and trusted. When it comes to practical application, though, the way forward isn’t always clear. How do we bridge from the broad goals and policies around open science to the actions we expect from individual researchers for a particular piece of research?
DataSeer’s AI solutions span the gap between policy and practice, bringing open science monitoring, researcher support, and enforcement within practical reach for organizations of all sizes – we dive a little deeper into these solutions below.
The implementation challenges of transparent research requirements
A single study may contain many datasets, code objects, and protocols. Sharing expectations and reporting guidelines vary by discipline and by methodological approach. Effective open science requires researchers to develop a whole new reproducibility skill set. And everyone — from researchers to publishers to administrators — is under-resourced and pressed for time.
The new standards developed by publishers, funders, and universities cannot realistically be incorporated into existing workflows — at least not using the current manual approach. Journal offices can’t check every link in every Data Availability Statement to ensure that the data are there, or dissect the methods section to ensure that every dataset has been deposited. University grant offices cannot track whether every faculty member has complied with specific transparency requirements associated with each of their grants. To do so would require an investment in time and staff hours that even the wealthiest organizations cannot sustain.
Systematizing transparency at scale
Between open science policy and compliance is an implementation gap. A chasm filled with questions — what to share, when and where to share it, what metadata to include, how to sustain access over time, and whether researchers have met their obligations.
Using natural language processing and AI, DataSeer provides affordable, scalable solutions for identifying transparent research practices and coaching researchers to successfully meet best-practice standards. Whether your organization is looking to quickly triage manuscripts, perform detailed compliance checks, or glean business intelligence from the published literature, DataSeer has an accessible option to fit your needs.
- SnapShot – A light-touch, instantaneous, and scalable integration that lets journals screen submitted manuscripts for indicators of research integrity and then address any issues.
- Open Science Metrics – An aggregate summation of open science practices across a large number of published articles. Used to benchmark, set goals, and measure progress over time.
- Compliance Checks – A detailed, article-specific analysis that produces an actionable researcher-facing report tailored to meet specific requirements.
“Standardized norms and expectations are vital to creating a more transparent system of scholarly research,” said DataSeer’s founder and CEO Tim Vines, “but without visibility into researcher practice, we can’t know if those standards are being met, or where outreach, training, and support may be needed to help improve.”
Get in touch with us to find out how DataSeer can be incorporated into your workflow.
