Bill Overview
Title: Secure Research Data Network Act
Description: This bill establishes a Secure Research Data Network (SRDN) pilot program in the National Science Foundation (NSF). The NSF shall direct the SRDN to carry out specified responsibilities, including supporting governmentwide evidence-building activities to support policymaking including implementation of agency multiyear evidence-building plans, deploying and operating a SRDN platform for authorized analysts to calculate statistics on data for evidence-building activity purposes using data assets made available by reporting entities for approved projects, and consulting with the National Artificial Intelligence Research Resource Task Force and considering how to integrate the Task Force's recommendations and road map for expanding access to critical artificial intelligence resources and educational tools into the SRDN. The NSF shall direct the SRDN to develop a plan for and operate a data quality service team who will help reporting entities evaluate their data and prepare it for use with the SRDN platform to achieve approved project goals. The NSF shall establish a SRDN Advisory Board. The Government Accountability Office shall conduct an evaluation of the SRDN pilot program.
Sponsors: Sen. Wyden, Ron [D-OR]
Target Audience
Population: Researchers, analysts, data-providing entities, and policymakers working with data on an international scale
Estimated Size: 60000
- The bill targets researchers who rely on data for evidence-building activities for policymaking.
- Government agencies will be impacted as they will be part of multiyear evidence-building plans.
- Entities providing data for these efforts will be impacted as they need support from a data quality service team.
- Integration with AI research means AI researchers may be impacted by this bill.
- The target population will primarily include researchers, data analysts, and policymakers globally who participate in projects requiring secure and authorized data analysis.
Reasoning
- The population targeted by this policy includes a diverse group within the U.S., including researchers, data analysts, policymakers, and entities that provide data for analysis.
- Given the budget constraints, especially in the initial years, the direct impact on individuals may vary greatly.
- Many impacted are likely within academia or government agencies, and their wellbeing will be influenced by professional outcomes rather than direct personal financial or social support.
- There may also be individuals within tech and data-related industries who will see impact or benefits from this program, particularly those involved with AI research.
- Some individuals will likely not notice a direct impact in the short term but may see long-term benefits as research and policy decisions improve due to better data resources.
Simulated Interviews
Government Policy Analyst (Washington, D.C.)
Age: 35 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 10.0 years
Commonness: 8/20
Statement of Opinion:
- This policy is important for improving evidence-based policy making.
- In the short term, there might be an adjustment phase as we figure out efficient ways to integrate new systems.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 8 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 8 | 7 |
| Year 10 | 9 | 8 |
| Year 20 | 9 | 8 |
AI Research Scientist (San Francisco, CA)
Age: 44 | Gender: male
Wellbeing Before Policy: 8
Duration of Impact: 5.0 years
Commonness: 6/20
Statement of Opinion:
- The integration of AI into these datasets is crucial for our work.
- Hopefully, this leads to better resource allocation for AI development.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 9 | 8 |
| Year 2 | 9 | 8 |
| Year 3 | 9 | 8 |
| Year 5 | 9 | 8 |
| Year 10 | 10 | 8 |
| Year 20 | 10 | 9 |
University Data Science Professor (New York City, NY)
Age: 50 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 8.0 years
Commonness: 7/20
Statement of Opinion:
- Access to secure data for research will enhance the quality of our projects.
- The support for data quality is much needed.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 8 | 6 |
| Year 3 | 8 | 6 |
| Year 5 | 9 | 7 |
| Year 10 | 10 | 7 |
| Year 20 | 10 | 7 |
Startup Data Analyst (Austin, TX)
Age: 29 | Gender: male
Wellbeing Before Policy: 5
Duration of Impact: 3.0 years
Commonness: 9/20
Statement of Opinion:
- I hope having more accessible data will open up opportunities for startups like ours.
- I'm concerned about how long integration might take.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 5 | 5 |
| Year 2 | 6 | 5 |
| Year 3 | 6 | 5 |
| Year 5 | 6 | 5 |
| Year 10 | 7 | 6 |
| Year 20 | 8 | 6 |
Healthcare Data Manager (Boston, MA)
Age: 39 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 6.0 years
Commonness: 7/20
Statement of Opinion:
- Improved data quality could significantly enhance healthcare analytics.
- The program may set new standards for data handling in healthcare.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 7 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 9 | 8 |
| Year 10 | 9 | 8 |
| Year 20 | 9 | 8 |
Freelance Journalist (Los Angeles, CA)
Age: 32 | Gender: other
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 5/20
Statement of Opinion:
- Making data more secure is a key part of public trust.
- The impact on journalism won't be direct, but it could improve sources' reliability.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 6 | 6 |
| Year 3 | 6 | 6 |
| Year 5 | 6 | 6 |
| Year 10 | 7 | 6 |
| Year 20 | 7 | 7 |
Government Advisor (Chicago, IL)
Age: 41 | Gender: male
Wellbeing Before Policy: 8
Duration of Impact: 5.0 years
Commonness: 8/20
Statement of Opinion:
- This could be a great step forward in using data effectively for policy decisions.
- The advisory board's structure will be critical for success.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 8 |
| Year 2 | 8 | 8 |
| Year 3 | 8 | 8 |
| Year 5 | 9 | 8 |
| Year 10 | 9 | 8 |
| Year 20 | 9 | 9 |
Corporate Data Strategist (Atlanta, GA)
Age: 48 | Gender: female
Wellbeing Before Policy: 9
Duration of Impact: 4.0 years
Commonness: 6/20
Statement of Opinion:
- Harmonization across different sectors will make compliance easier.
- Could provide a framework for responsible data sharing.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 9 | 9 |
| Year 2 | 9 | 9 |
| Year 3 | 9 | 9 |
| Year 5 | 9 | 9 |
| Year 10 | 10 | 9 |
| Year 20 | 10 | 9 |
Data Engineer (Seattle, WA)
Age: 27 | Gender: female
Wellbeing Before Policy: 8
Duration of Impact: 5.0 years
Commonness: 7/20
Statement of Opinion:
- Technological advancements in data handling can boost my field.
- Hoping this pilot can inform best practices industry-wide.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 8 |
| Year 2 | 9 | 8 |
| Year 3 | 9 | 8 |
| Year 5 | 9 | 9 |
| Year 10 | 9 | 9 |
| Year 20 | 10 | 9 |
Research Scientist (Denver, CO)
Age: 36 | Gender: male
Wellbeing Before Policy: 7
Duration of Impact: 6.0 years
Commonness: 8/20
Statement of Opinion:
- This policy might streamline the research process.
- Greater data access leads to more robust research outcomes.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 7 |
| Year 2 | 8 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 9 | 8 |
| Year 10 | 9 | 8 |
| Year 20 | 9 | 8 |
Cost Estimates
Year 1: $50000000 (Low: $45000000, High: $60000000)
Year 2: $52000000 (Low: $47000000, High: $62000000)
Year 3: $53500000 (Low: $48500000, High: $63500000)
Year 5: $56000000 (Low: $51000000, High: $66000000)
Year 10: $60000000 (Low: $55000000, High: $70000000)
Year 100: $80000000 (Low: $70000000, High: $90000000)
Key Considerations
- The need for a securely managed data environment to protect sensitive information.
- Integration with AI resources may broaden the program's scope and potential benefits.
- The scale of government and private sector participation will impact overall efficacy.
- Overhead costs due to the advisory board and evaluation activities should be tracked.