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IDEAS Policy Framework

Research Guidelines

The University of Michigan-Flint’s Institute of Data Engineering, Analytics and Science is committed to conducting ethical, transparent and socially responsible data science research. Our comprehensive research guidelines outline seven core principles from research integrity and open science to data privacy, inclusive collaboration and community engagement that govern all data science work at UM-Flint.

Whether you are a faculty researcher, student or community partner, these policies ensure compliance with federal regulations such as HIPAA and FERPA, promote reproducibility through FAIR data principles and open-access practices and prioritize the public good, particularly in service to the Flint community.

Explore our full framework below to learn how IDEAS upholds the highest standards of responsible research conduct.

Responsible data science

Our policies emphasize open science, ethical conduct, data management and compliance with regulations. We model transparency, reproducibility and public-good orientation in everything we do.

Integrity

Highest standards of research conduct

Openness

Transparent methods and accessible results

Ethics

Responsibility at the core of every project

Seven Core Guidelines

Our comprehensive framework for responsible research conduct at UM-Flint.

01

Research Integrity & Transparency

No fabrication, falsification, or plagiarism. All data science work must be transparently documented and open to scrutiny.

02

Open Science & Data Sharing

Research outputs should be made openly available to maximize impact and reproducibility. Share code via GitHub and deposit datasets in open repositories.

03

Reproducibility & Data Management

All research code under version control. Follow FAIR principles – making data Findable, Accessible, Interoperable and Reusable.

04

Ethical & Responsible Data Use

IRB approval required for human data. Proactively consider fairness and bias in algorithms. Ethical thinking infused at every stage.

05

Data Privacy & Compliance

Strict compliance with HIPAA, FERPA and all data regulations. No sensitive data on unapproved systems. Violation results in immediate action.

06

Inclusive Collaboration

Respect and professionalism required. Harassment strictly prohibited. Credit shared fairly. Diverse teams produce better research.

07

Community Engagement

Data science for public good. Serve local Flint community needs. Research should strive for positive societal impact.

Based on Best Practices

Open Access Guidelines

All scholarly work must be openly and freely accessible. Research should be legally available to everybody, free of charge.

Ethics Framework

Ethics training required for all data science students. Research on social implications of data science is mandatory.

Code of Conduct

Welcoming and supportive environment for all. No harassment or bullying. Respectful, inclusive communication required.

Reproducible Research

Version control, thorough documentation, GitHub, R Markdown. Share code and data in public repositories when possible.

“Responsibility should be the pillar of data science. Ethical thinking and social contexts must be front and center in all data science research that can impact individuals or communities.”

— Ten Simple Rules for Starting a Data Science Initiative

Detailed Requirements

Data Management Plans

Required Practices:

  • Version control (Git) for all research code
  • README files and thorough documentation
  • Jupyter/R Markdown notebooks
  • Public repositories for code and data

FAIR Principles:

  • Findable – Easy to locate by others
  • Accessible – Retrievable by identifiers
  • Interoperable – Works with other data
  • Reusable – Can be used in future research

Compliance & Security

Prohibited Data on Unapproved Systems:

HIPAA, FERPA, FISMA, ITAR/EAR protected data. Violation results in immediate account closure and data removal.

IRB Approval

Required for all human subjects research

Data Use Agreements

Must follow all data sharing terms

Secure Storage

Encryption and access control required

Required Training

All researchers must complete Responsible Conduct of Research training specific to data science, covering:

  • Human subjects protections
  • Algorithmic bias and fairness
  • Data privacy and security
  • Broader impacts of data technologies

Questions about our policies?

We’re here to help you understand and comply with all research conduct guidelines.
Contact us at flint.ideas@umich.edu