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@@ -36,7 +36,7 @@ The **DTE Trust Dashboard** kicks off our open-source journey, visualizing AI go
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Find it in `/tools/data-trust-dashboard/DTE_Trust_Dashboard.html`. More artifacts—like data quality patterns and trust evals—are on the way!
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Explore [UseCases.md](UseCases.md) to see how to apply DTE principles with open-source code.
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Explore [USE_CASES.md](/docs/patterns/USE_CASES.md) to see how to apply DTE principles with open-source code.
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## Getting Started
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-`/Manifesto.md`: The *Data Trust Engineering Manifesto*.
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-`/tools`: Artifacts like `/tools/data-trust-dashboard`.
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-`/docs`: Case studies, patterns (e.g., `/docs/patterns/data-quality-guide.md`).
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-`/docs`: Case studies, patterns (e.g., `/docs/patterns/USE_CASES.md`).
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-`/community`: Contribution guidelines, code of conduct.
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-`/LICENSE.md`: MIT License.
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-**Artifacts**: Data quality scripts, lineage trackers, or AI tools to `/tools`.
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-**Manifesto Updates**: Refine principles in `/Manifesto.md`.
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-**Case Studies**: Share success stories in `/docs/case-studies`.
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-**Use Cases**: Explore [USE_CASES.md](/docs/patterns/USE_CASES.md) to see how to apply DTE principles with open-source code.
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Start with “good first issues” (e.g., typo fixes) or discuss ideas in GitHub Discussions. See [CONTRIBUTING.md](/community/CONTRIBUTING.md) and [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md).
- For general inquiries, use [Slack](https://join.slack.com/t/datatrustengineering/shared_invite/zt-3br05le6v-pxGSBeJGLpVgOsNM9ejGuw) or a [Contact issue](https://github.com/datatrustengineering/DataTrustEngineering/issues/new?template=contact.yml).
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- Visit [datatrustmanifesto.org](https://datatrustmanifesto.org) for more.
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# Support for Data Trust Engineering (DTE)
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Welcome to **Data Trust Engineering (DTE)**! We’re here to help you engage with our community, contribute to artifacts like the [DTE Trust Dashboard](/tools/data-trust-dashboard/DTE_Trust_Dashboard), or learn about our mission to certify data systems by use case, risk, and value. Here’s how to get help:
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Welcome to **Data Trust Engineering (DTE)**! We’re here to help you engage with our community, contribute to artifacts like the [DTE Trust Dashboard](/tools/data-trust-dashboard/DTE_Trust_Dashboard.html), or learn about our mission to certify data systems by use case, risk, and value. Here’s how to get help:
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## How to Get Support
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-**Community Questions**:
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- Report bugs via a [Bug Report issue](https://github.com/askbrdatatrustengineering/DataTrustEngineering/issues/new?template=bug_report.md).
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- Suggest features via a [Feature Request issue](https://github.com/askbrdatatrustengineering/DataTrustEngineering/issues/new?template=feature_request).
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-**Security or Code of Conduct Issues**:
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- Report privately via our [security form](https://forms.gle/BkMJNgfbMuavkUKc8). See [SECURITY.md](/SECURITY).
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- Report privately via our [security form](https://forms.gle/BkMJNgfbMuavkUKc8). See [SECURITY.md](/SECURITY.md).
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## Guidelines
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- Follow our [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT) for respectful interactions.
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- Follow our [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md) for respectful interactions.
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- Check [CONTRIBUTING.md](/community/CONTRIBUTING) for contribution details.
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- Visit [datatrustmanifesto.org](https://datatrustmanifesto.org) for more resources.
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title: Contributing to Data Trust Engineering (DTE)
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---
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Welcome to the **Data Trust Engineering (DTE)** community! We're building a movement to replace traditional data governance’s 70-80% failure rate [Gartner, 2025] with engineering-driven trust, certification, and AI-readiness. Inspired by the [DataTrustManifesto.org](https://datatrustmanifesto.org)’s agility and collaboration, DTE invites data professionals, enterprises, SMBs, and hyperscalers to contribute to our open-source ecosystem. Whether you're enhancing the [DTE Trust Dashboard](/tools/data-trust-dashboard/DTE_Trust_Dashboard), proposing new artifacts, or sharing case studies, your contributions shape the #DTERevolution.
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Welcome to the **Data Trust Engineering (DTE)** community! We're building a movement to replace traditional data governance’s 70-80% failure rate [Gartner, 2025] with engineering-driven trust, certification, and AI-readiness. Inspired by the [DataTrustManifesto.org](https://datatrustmanifesto.org)’s agility and collaboration, DTE invites data professionals, enterprises, SMBs, and hyperscalers to contribute to our open-source ecosystem. Whether you're enhancing the [DTE Trust Dashboard](/tools/data-trust-dashboard/DTE_Trust_Dashboard.html), proposing new artifacts, or sharing case studies, your contributions shape the #DTERevolution.
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This guide explains how to contribute to the `DataTrustEngineering` repository, ensuring a productive, inclusive community. Join us at [datatrustmanifesto.org](https://datatrustmanifesto.org) or our [Slack org]([Slack Invite Link]).
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## Code of Conduct
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All contributors must follow our [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT), based on the Contributor Covenant. Be respectful, constructive, and focused on DTE’s mission to certify data systems by use case, risk, and value. Harassment, off-topic rants, or disruptive behavior will be addressed by maintainers to keep our community welcoming.
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All contributors must follow our [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md), based on the Contributor Covenant. Be respectful, constructive, and focused on DTE’s mission to certify data systems by use case, risk, and value. Harassment, off-topic rants, or disruptive behavior will be addressed by maintainers to keep our community welcoming.
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## What Can You Contribute?
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DTE is a community-driven movement, and we welcome contributions that align with our [Manifesto](/Manifesto) and advance trust, engineering rigor, and AI-readiness. Examples include:
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DTE is a community-driven movement, and we welcome contributions that align with our [Manifesto](/Manifesto.md) and advance trust, engineering rigor, and AI-readiness. Examples include:
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-**Artifacts**: Tools and scripts for data quality (e.g., Great Expectations configs), lineage (e.g., Apache Atlas scripts), or AI governance (e.g., Fairlearn integrations). See `/tools`.
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-**Manifesto Updates**: Refine or propose principles in `/Manifesto.md` to strengthen DTE’s vision.
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-**Case Studies**: Share real-world DTE applications (e.g., “Reduced AI bias by 15%”) in `/docs/case-studies`. Use our [template](/docs/case-studies/template).
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-**Patterns and Guides**: Document DTE best practices (e.g., data quality patterns) in `/docs/patterns`.
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-**Trust Dashboard Enhancements**: Add metrics, APIs (e.g., Evidently AI, MLflow), or UI improvements to `/tools/data-trust-dashboard`.
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-**Documentation**: Improve clarity, fix typos, or add tutorials in `/docs`.
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-** Contribute Code**: Explore [USE_CASES.md](/docs/patterns/USE_CASES.md) to see how to apply DTE principles with open-source code.
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**Good First Issues**: New to DTE? Start with issues tagged “good first issue” (e.g., typo fixes, adding a case study). These are great for building skills and joining the #DTERevolution!
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## How to Contribute
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1.**Explore the Repo**:
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- Read the [README.md](/README) and [Manifesto.md](/Manifesto) to understand DTE’s goals.
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- Read the [README.md](/README.md) and [Manifesto.md](/Manifesto.md) to understand DTE’s goals.
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- Check `/tools` and `/docs` for existing artifacts and inspiration.
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2.**Discuss Your Idea**:
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2.**Review Process**:
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- Maintainers will review within 5-7 days, checking for:
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- Alignment with the [DTE Manifesto](/Manifesto).
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- Alignment with the [DTE Manifesto](/Manifesto.md).
- Relevance to DTE’s goals (e.g., supports certification, cloud-native architectures).
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- Expect feedback (e.g., “Can you add an example for SMBs?”). Revise as needed.
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-**Stay On-Topic**: Contributions should support DTE’s focus on certifying data systems for trust, AI-readiness, and cloud scalability. Off-topic ideas (e.g., unrelated tech policies) may be redirected.
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-**Be Collaborative**: Engage in GitHub Discussions or Slack (#contributions) before submitting large PRs to align with community goals.
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-**Respect Feedback**: Maintainers may request changes to ensure quality. Respond constructively to keep the #DTERevolution moving forward.
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-**Avoid Chaos**: Follow the [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT). Disruptive behavior (e.g., spamming issues) may lead to warnings or blocks.
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-**Avoid Chaos**: Follow the [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md). Disruptive behavior (e.g., spamming issues) may lead to warnings or blocks.
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-**Transparency**: Decisions are made openly, with clear rationale shared in GitHub Issues, Discussions, or Slack.
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-**Collaboration**: Contributions from data professionals, enterprises, SMBs, and hyperscalers shape DTE’s evolution.
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-**Alignment**: All decisions support DTE’s mission to certify data systems by use case, risk, and value, ensuring trust and AI-readiness.
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-**Inclusivity**: We welcome diverse perspectives while maintaining focus, as enforced by our [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT).
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-**Inclusivity**: We welcome diverse perspectives while maintaining focus, as enforced by our [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md).
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-**Agility**: Inspired by DataOps, we prioritize rapid feedback and iterative improvements.
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## Roles and Responsibilities
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-**Maintainers**:
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-**Who**: Initially, Brian Brewer (bbrewer@infolibcorp.com) and future trusted contributors elevated based on active participation.
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-**Responsibilities**:
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- Review and merge pull requests (PRs) for alignment with the [Manifesto](/Manifesto).
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- Moderate discussions, issues, and PRs per [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT).
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- Review and merge pull requests (PRs) for alignment with the [Manifesto](/Manifesto.md).
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- Moderate discussions, issues, and PRs per [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md).
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- Make final decisions on contributions and conflicts, seeking community input where possible.
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- Update repo structure, roadmap, and governance as DTE grows.
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-**Selection**: New maintainers are nominated by existing maintainers based on consistent, high-quality contributions (e.g., multiple merged PRs) and community trust. Nominations are discussed in GitHub Discussions and confirmed by consensus.
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-**Contributors**:
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-**Who**: Anyone who submits PRs, issues, or participates in Discussions/Slack, from beginners to experts.
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-**Responsibilities**:
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- Follow [CONTRIBUTING.md](/community/CONTRIBUTING) and [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT).
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- Follow [CONTRIBUTING.md](/community/CONTRIBUTING.md) and [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md).
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- Propose artifacts (e.g., data quality scripts), case studies, or manifesto updates that advance DTE’s mission.
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- Engage respectfully and respond to feedback during PR reviews.
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- Maintainers moderate to keep discussions on-topic and respectful.
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3.**Decision**:
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- For minor changes (e.g., typo fixes, small PRs), maintainers approve or reject based on alignment with the [Manifesto](/Manifesto).
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- For minor changes (e.g., typo fixes, small PRs), maintainers approve or reject based on alignment with the [Manifesto](/Manifesto.md).
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- For major changes (e.g., new principles, governance updates), maintainers seek community input for 5-7 days via Discussions or Slack. If consensus isn’t reached, maintainers make the final call, explaining the rationale publicly.
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- Example: Adding a new manifesto principle requires 2+ maintainer approvals and community feedback.
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## Pull Request (PR) Review and Approval
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PRs are the primary way to contribute to DTE (e.g., artifacts, case studies, manifesto updates). The process follows [CONTRIBUTING.md](/community/CONTRIBUTING):
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PRs are the primary way to contribute to DTE (e.g., artifacts, case studies, manifesto updates). The process follows [CONTRIBUTING.md](/community/CONTRIBUTING.md):
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1.**Submission**:
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- Contributors fork the repo, create a branch, and submit a PR with a clear description (e.g., “Add Great Expectations script for data quality”).
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- Rejected PRs receive polite feedback (e.g., “This is great but outside DTE’s focus on technical trust”).
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5.**Merge**:
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- Approved PRs are merged into the main branch, with contributors credited in [CONTRIBUTORS.md](/CONTRIBUTORS).
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- Approved PRs are merged into the main branch, with contributors credited in [CONTRIBUTORS.md](/CONTRIBUTORS.md).
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## Conflict Resolution
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Conflicts may arise over PRs, principles, or community interactions. We resolve them transparently:
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-**Step 1: Discussion**:
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- Address conflicts in GitHub Issues, Discussions, or Slack (#general or #contributions).
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- Maintainers moderate to ensure respectful dialogue per [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT).
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- Maintainers moderate to ensure respectful dialogue per [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md).
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-**Step 2: Mediation**:
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- If consensus isn’t reached within 5 days, maintainers propose a solution (e.g., trial period for a new principle).
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- Community input is sought via Discussions or Slack.
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-**Step 3: Final Decision**:
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- Maintainers make the final call, documenting the rationale in the relevant Issue or Discussion.
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- Example: If two contributors disagree on a PR’s wording, maintainers may merge a compromise version after discussion.
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-**Step 4: Enforcement**:
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- Violations of the Code of Conduct (e.g., harassment) are addressed with warnings, content removal, or bans, as outlined in [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT).
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- Violations of the Code of Conduct (e.g., harassment) are addressed with warnings, content removal, or bans, as outlined in [CODE_OF_CONDUCT.md](/community/CODE_OF_CONDUCT.md).
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## Community Engagement
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-**Slack**: Join [datatrustengineering]([Slack Invite Link]) for real-time collaboration (#contributions, #trust-dashboard).
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-**X**: Share your contributions with #DTERevolution or tag @bbrewer_infolib.
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-**Good First Issues**: Start with tasks tagged “good first issue” (e.g., typo fixes, case studies) to build skills and join the community.
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-**Recognition**: Contributors are listed in [CONTRIBUTORS.md](/CONTRIBUTORS) and highlighted in Slack or X for impactful work.
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-**Recognition**: Contributors are listed in [CONTRIBUTORS.md](/CONTRIBUTORS.md) and highlighted in Slack or X for impactful work.
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## Data Trust Engineering (DTE) Use Cases and Trust Dashboard
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Welcome to the **Data Trust Engineering (DTE) Use Cases** and **Trust Dashboard**, flagship artifacts of the Data Trust Engineering movement. These open-source tools and examples embody DTE’s principles of trust, engineering rigor, and AI-readiness as outlined in the [Data Trust Manifesto](../Manifesto).
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Welcome to the **Data Trust Engineering (DTE) Use Cases** and **Trust Dashboard**, flagship artifacts of the Data Trust Engineering movement. These open-source tools and examples embody DTE’s principles of trust, engineering rigor, and AI-readiness as outlined in the [Data Trust Manifesto](../Manifesto.md).
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