Research Administrator
Support research data management and sharing compliance
What You Do Today
Help researchers comply with data management plan requirements, federal data sharing mandates, and institutional data governance policies. Navigate the increasingly complex landscape of research data regulations.
AI That Applies
AI generates data management plan templates aligned to specific sponsor requirements, monitors data sharing compliance deadlines, and identifies appropriate data repositories for different data types.
Technologies
How It Works
The system ingests data sharing compliance deadlines as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — data management plan templates aligned to specific sponsor requirements — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
DMP creation becomes template-driven and sponsor-specific. Compliance with new data sharing mandates becomes more manageable.
What Stays
Helping researchers navigate the tension between data sharing mandates and intellectual property protection requires understanding both the regulations and the research context.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for support research data management and sharing compliance, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long support research data management and sharing compliance takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“How much of support research data management and sharing compliance follows repeatable rules vs. requires genuine judgment — and can we quantify that?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“If support research data management and sharing compliance were fully AI-assisted, which exceptions would still need a human — and are those the high-value parts?”
They understand the workflow dependencies that AI tools need to respect
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.