From f34db5e13e5a62ecf635e6ec7d9a3d5895455ffb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Llu=C3=ADs=20Revilla?= Date: Thu, 23 Jul 2026 14:51:17 +0200 Subject: [PATCH 1/5] Add onboarding guide as md --- adoption_guide.md | 339 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 339 insertions(+) create mode 100644 adoption_guide.md diff --git a/adoption_guide.md b/adoption_guide.md new file mode 100644 index 0000000..aa5073d --- /dev/null +++ b/adoption_guide.md @@ -0,0 +1,339 @@ +# **Validated Repository** **Early Adoption Guide** 🚀 + +## Welcome & Context {#welcome-&-context} + +Thank you for joining the **R Validation Hub** as an early adopter! The R Validation Hub is a community-driven effort to bring transparency and confidence in the use of open-source R packages in regulated environments through solutions for the validation of R packages. + +As an early adopter, you are part of a small group of organizations shaping how the life sciences community evaluates and manages risk in R-based analytics and applications. + +Your participation helps refine tools, test workflows in real-world environments, and contribute to shared best practices that ultimately make open-source safer, more trusted, and more accessible across our industry. + +Early adopters play a dual role: + +- as *Implementers* by piloting the tools within their environment. +- as *Contributors* by offering insights and feedback. + +This guide will help you plan and scale your adoption, while ensuring alignment with both your internal compliance requirements and the R Validation Hub working group goals. + +## Who Typically Implements This Internally {#who-typically-implements-this-internally} + +In many organizations, the people implementing validation workflows are not necessarily expert R developers. Often they are: + +- Platform engineers +- Statisticians or analysts +- Data scientists responsible for analytics infrastructure +- IT teams supporting regulated systems + +This guide is written to support practical implementation, even for teams that are still building experience with R package infrastructure. While some familiarity with R and package installation is helpful, the R Validation Hub tools are designed to support structured and automated workflows that reduce the need for manual package validation. Deep expertise in R package development is not required. + +## Terminology {#terminology} + +| Term | Definition | +|------------------------------------|------------------------------------| +| **Baseline Image** | A base container image containing a validated version of R and a minimal set of foundational packages upon which additional validated packages may be added. | +| **CI/CD Pipeline** | Automated workflows (e.g., GitHub Actions) used to compute metrics, generate reports, update repositories, and build validated environments. | +| **Closed Repository** | A restricted package repository where only packages that meet validation criteria are available for installation. | +| **Early Adopter** | An organization piloting the R Validation Hub tooling to help evaluate workflows, provide feedback, and contribute to community best practices. | +| **Filtering Criteria** | Logical rules applied to package metrics to determine whether a package satisfies validation requirements. | +| **Internal Repository** | A private package repository maintained by an organization that hosts approved or validated packages for internal use. | +| **Package Governance** | Organizational processes used to manage the lifecycle of open-source packages, including evaluation, approval, monitoring, and retirement. | +| **Package Validation** | The process of assessing an open-source package to determine whether it meets defined quality, reliability, and governance requirements. | +| **Qualified Environment** | A computing environment (e.g., container or system installation) containing a predefined set of validated packages and configuration settings approved for use in regulated workflows. | +| **Qualified Package** | An R package that has passed a defined validation policy and is approved for use within an organization. | +| **Reproducible Environment** | A computing environment that can be recreated reliably, ensuring that analyses and applications behave consistently across systems and time. | +| **Risk Assessment** | Evaluation of potential risks associated with using a package, typically informed by metrics such as maintenance activity, documentation completeness, and testing coverage. | +| **Validated Image / Container** | A containerized environment (e.g., Docker image) that includes an approved set of validated packages built according to an organization’s validation process. | +| **Validated Repository** | A curated repository of R packages where each package has undergone a documented assessment based on defined validation metrics and organizational policies. | +| **Validation Dashboard** | An interactive interface (e.g., Shiny app) used to explore package metrics, review validation status, and discuss approval decisions. | +| **Validation Metrics** | Quantitative measures used to evaluate characteristics of an R package (e.g., presence of tests, documentation, maintenance activity, reverse dependencies). | +| **Validation Policy** | A set of rules or criteria used by an organization to determine whether an R package is acceptable for use in production or regulated environments. | +| **Validation Report** | A structured report generated for each package summarizing validation metrics, evidence, and the resulting validation decision. | + +## Why Early adoption matters {#why-early-adoption-matters} + +Early adoption allows your team to: + +- Help develop the standards for R package validation in pharma, biotech, finance, agriculture and beyond. +- Accelerate internal readiness for R in validated workflows. +- Gain visibility as a thought leader in open-source validation and regulatory compliance. +- Collaborate directly with the R Validation Hub. + +## Early Adopter Expectations {#early-adopter-expectations} + +As an early adopter of the R Validation Hub solutions, you’re joining a collaborative effort to strengthen how the life sciences industry uses and validates open-source R in regulated environments. Your participation is essential to ensure the tools, documentation, and workflows reflect real-world needs and constraints. + +Here’s what we expect from early adopters — and what you can expect in return. + +## What We Ask of Early Adopters {#what-we-ask-of-early-adopters} + +- **Pilot with Purpose**: Select a clear use case, for example, assessing a small group of R packages already used in production or evaluating packages planned for use in submissions. Focus on learning and feedback rather than full organizational deployment.\ +- **Share Honest, Actionable Feedback**: + - We need your perspective on: + - Usability of the solutions. + - Which metrics and reports are most meaningful for your teams. + - What gaps, pain points, or documentation needs arise during adoption.\ + - Your feedback directly informs future updates and community guidance.\ +- **Participate in Knowledge Sharing**: attend the R Validation Hub Repository meetings or community meetings to exchange lessons learned.\ +- (Optional but encouraged) Share anonymized workflows or process templates that could benefit other organizations.\ +- **Document Your Journey**: Document + - Packages assessed. + - Metrics used. + - Key findings or challenges.\ +- **Respect the [R Consortium’s Code of Conduct](https://r-consortium.org/codeofconduct.html)** + +## What You Can Expect in Return {#what-you-can-expect-in-return} + +- **Priority Support and Collaboration**: Early adopters receive direct communication channels with the R Validation Hub maintainers, including feedback sessions and early access to updates.\ +- **Recognition and Visibility**: Your organization’s role as an early adopter will be acknowledged in community reports, presentations, and case studies (if you choose to be public).\ +- **Influence on Future Direction**: Your feedback will shape the roadmap for the R Validation Hub tools.\ +- **Accelerated Internal Readiness**: Through guided testing and collaboration, your organization will leave the pilot with: + - A practical R package validation framework. + - Internal awareness and documentation. + - A scalable foundation for open-source governance + +## Tools {#tools} + +The R Validation Hub Repository group maintains these open tools for all companies. The tools are designed to work together as a cohesive pipeline. As shown in the diagram below, val.meter and val.repo generate the foundational metrics, which feed into reporting (val.report), filtering (val.criterion), and user-friendly review (val.dashboard). + +- [{val.meter}](https://github.com/pharmaR/val.meter) – Easily measure characteristics of packages, built to support the process of *validation* required in regulated industries, but generally useful to anyone who wants to survey packages. Successor of [riskmetrics](https://github.com/pharmaR/riskmetric): it provides objectives metrics and lets the user decide which risk are acceptable for your organization\ +- [val.repo](https://github.com/pharmaR/val.repo) – is a curated repository of R packages that is similarly to CRAN but with additional metadata: It hosts validation reports on its associated website, published via GitHub Pages.\ +- [{val.report}](https://github.com/pharmaR/val.report) – R package for generating reports: provides a template used on val.repo.\ +- [{val.criterion}](https://github.com/pharmaR/val.criterion) – R package for distributing sensible default filters, specifying bespoke filters and visualizing filters as decision trees.\ +- val.dashboard – Shiny app for reviewing metrics in repository, visually applying filtering criteria, risk review and discussion. Founded on the experience of [riskassessment](https://github.com/pharmaR/riskassessment)\ +- {val.setup} – Repository bringing together the R Validation Hub's tools under a single Github Actions workflow to produce the outputs necessary to qualify R packages for a GxP system. + +## Getting Started {#getting-started} + +The R Validation Hub toolchain supports multiple implementation workflows depending on an organization’s governance model and maturity level. + +Organizations may begin with lightweight workflows that empower analysts to make informed package decisions and gradually evolve toward centralized governance and automated validation pipelines. + +Before implementing these workflows, teams are encouraged to review the **R Validation Hub** [white paper](https://pharmar.org/white-paper/)., which describes the conceptual framework for assessing R package risk and validation considerations in regulated environments. The workflows described below operationalize the principles outlined in that document. + +Additionally, we encourage teams to explore the public validated repository at val.repo: + +## Choose a starting workflow + +Use the workflow descriptions below to identify the best starting point for your organization. + +| Workflow | Best for | Main tools | What you produce | Choose this when... | +|---------------|---------------|---------------|---------------|---------------| +| **A. Empower analysts to make validation decisions** | Individual analysts, statisticians, developers | `val.repo`, `{val.meter}` | Package assessments informed by validation metrics | You want users to make better package choices without central enforcement | +| **B. Curate packages using a validation policy** | Project leads, validation teams, platform teams | `{val.meter}`, `{val.criterion}` | A curated list of packages that meet organizational criteria | You want a shared approval process and a reusable package list | +| **C. Impose a validation policy for an enterprise system** | IT teams, platform engineers, analytics infrastructure teams | `{val.criterion}`, repository configuration | System-level package filtering and controlled installation behavior | You need centralized governance across a managed environment | +| **D. Generate an internal validated repository or image** | Platform teams, QA teams, data science infrastructure teams | `{val.meter}`, `{val.criterion}`, `{val.report}`, `{val.setup}` | An internal validated repository and/or image, with reports and repeatable automation | You want a fully governed, organization-specific workflow with CI/CD support | + +## Workflow A: Empower analysts to make validation decisions + +### **Who this is for** + +Analysts, statisticians, Shiny developers, and data scientists who need to evaluate packages before using them in analyses, applications, or regulated workflows. + +### **When to use it** + +Use this workflow when individual users need a practical way to review package characteristics, compare packages against internal expectations, and document their package choices. + +### **What you need** + +- Access to the public validated repository +- Internal guidance on what kinds of package characteristics matter in your organization +- A basic understanding of how package metrics will inform package-use decisions + +### **What you do** + +1. Explore package reports and metrics in the public validated repository maintained by the R Validation Hub.\ +2. Review whether the available metrics align with your organization’s current package review needs.\ +3. Retrieve repository metadata for the packages under consideration.\ +4. Use the available metrics to support package selection, comparison, and review. + +Example: + +``` r +options(repos = c( + Validated = "https://raw.githubusercontent.com/pharmaR/val.repo/refs/heads/main/", + CRAN = "https://cran.r-project.org" +)) + +ap <- available.packages() +View(ap) +``` + +The repository metadata provides information such as documentation, testing status, and other validation-related metrics. + +### **What you get** + +Analysts can make more informed package decisions while maintaining alignment with organizational policies and validation frameworks. + +### **Read next** + +- Use [Workflow B](#6.3-workflow-b:-curate-packages-using-a-validation-policy) when your organization is ready to define shared criteria rather than rely on individual review alone.\ +- See [the metrics of the val.repo repository](https://raw.githubusercontent.com/pharmaR/val.repo/refs/heads/main/src/contrib/PACKAGES) + +## Workflow B: Curate packages using a validation policy {#6.3-workflow-b:-curate-packages-using-a-validation-policy} + +### **Who this is for** + +Project leads, analytics platform teams, and validation teams that want to define shared package acceptance criteria. + +### **When to use it** + +Use this workflow when your organization wants to move from ad hoc package review to a repeatable policy-based curation process. + +### **What you need** + +- Package metrics, generated or retrieved from a repository\ +- Agreed validation criteria or filtering rules\ +- A process for reviewing and maintaining the resulting curated package list + +### **What you do** + +1. Generate package metrics using `{val.meter}`, or consume metrics already available from a validated repository.\ +2. Define validation criteria using `{val.criterion}`.\ +3. Apply those criteria to identify packages that meet organizational requirements.\ +4. Store and maintain the resulting curated package list for internal or project-specific use. + +Example: + +``` r +options(repos = c( + Validated = "https://raw.githubusercontent.com/pharmaR/val.repo/refs/heads/main/", + CRAN = "https://cran.r-project.org" +)) + +ap <- available.packages() +subset(ap, has_examples == 1 && has_vignettes == 1) +``` + +This example illustrates the general pattern of filtering packages based on validation-relevant characteristics. In practice, organizations will usually define a broader and more explicit set of criteria. + +### **What you get** + +A controlled list of packages aligned with internal validation policies, which teams can reuse across projects and review over time. + +### **Read next** + +- Use [Workflow C](#6.4-workflow-c:-impose-a-validation-policy-for-an-enterprise-system) when package policy needs to be enforced centrally rather than applied manually.\ +- Use [Workflow D](#6.5-workflow-d:-generate-an-internal-validated-repository-or-image) when the curated package list needs to become part of an internal repository or automated delivery process.\ +- Placeholder: add link to `{val.criterion}` examples or criteria reference + +## Workflow C: Impose a validation policy for an enterprise system {#workflow-c:-impose-a-validation-policy-for-an-enterprise-system} + +### **Who this is for** + +Platform engineers, IT teams, and analytics infrastructure teams managing shared R environments. + +### **When to use it** + +Use this workflow when your organization wants to enforce package policy across a managed enterprise system so that users can only install packages that meet defined criteria. + +### **What you need** + +- A validated or curated package source +- System-level access to configure repository behavior and filtering +- Agreed package installation criteria + +### **What you do** + +1. Install `{val.criterion}` and configure the validation filters your organization wants to enforce.\ +2. Add a validated repository to the repositories used by the managed R environment.\ +3. Configure system-wide filtering policies so that only packages meeting the allowed criteria are available for installation. + +For example, `R.home()/etc/repositories` may include: + +``` +Validated "Validated R-Hub" https://raw.githubusercontent.com/pharmaR/val.repo/refs/heads/main/ FALSE FALSE FALSE +``` + +A system-wide filter can then be configured in `R.home()/etc/Rprofile.site`: + +``` r +options(available_packages_filters = {}) +``` + +### **What you get** + +All users in the enterprise environment install packages that comply with organizational validation policies. This supports centralized governance while preserving a consistent user experience. + +### **Read next** + +- Use [Workflow D](#6.5-workflow-d:-generate-an-internal-validated-repository-or-image) when you also need organization-specific reports, internal packages, automated updates, or internal hosting.\ +- **\[Placeholder: add a short explanation of common filter patterns or link to filter documentation\]**\ +- **\[Placeholder: add supported deployment patterns, such as managed workstation, server, or containerized runtime\]** + +## Workflow D: Generate an internal validated repository or image {#6.5-workflow-d:-generate-an-internal-validated-repository-or-image} + +### **Who this is for** + +Platform teams, QA teams, and data science infrastructure teams that need a governed, repeatable internal package distribution process. + +### **When to use it** + +Use this workflow when your organization wants to apply its own validation criteria, include internal packages, produce validation reports, and distribute packages through an internal repository or qualified image. + +### **What you need** + +- A defined package scope or seed list\ +- Organizational validation criteria\ +- A process for generating metrics and reports\ +- An internal hosting or delivery target, such as a repository endpoint, container image, or both\ +- A CI/CD environment for repeatable execution (optional) + +### **What you do** + +1. Identify the set of packages used or approved within the organization.\ +2. Customize validation metrics.\ +3. Generate validation metrics using `{val.meter}`.\ +4. Apply organizational criteria using `{val.criterion}`.\ +5. Generate reports using `{val.report}`.\ +6. Build and publish the outputs required for internal use, such as: + - an internal validated repository,\ + - validation reports,\ + - a dashboard for review and discussion, and/or\ + - a container or image containing approved packages.\ +7. Automate the workflow through CI/CD where appropriate. + +Tool support in this area is evolving: + +- `{val.setup}` is intended to bring the R Validation Hub tools together under a single GitHub Actions workflow to produce the outputs needed to qualify packages for a GxP system.\ +- `val.dashboard` is intended to support visual review of metrics, filtering criteria, risk review, and discussion. + +**\[Placeholder: clearly label which parts of this workflow are available now versus planned\]** + +### **What you get** + +An internal, organization-specific package distribution workflow aligned with governance policies, supported by repeatable metrics, reports, and deployment artifacts. + +### **Read next** + +- Continue to **Section 7** for practical walkthroughs, including an end-to-end example of building an internal repository or image through CI.\ +- **\[Placeholder: add links to `{val.meter}`, `{val.report}`, and `{val.setup}` vignettes\]** + +# Key Use Cases {#key-use-cases} + +A repository using the tools for generating a closed repository for Arcus: [val.pipeline](https://github.com/pharmaR/val.pipeline)\ +Add a new package to a closed image:\ +Validate an internal validated package: it won’t have (external) download metrics but it might still be validated. + +# Roadmap & What’s Coming {#roadmap-&-what’s-coming} + +Phase 1: Kick off Early Adoption community\ +Reflections and iteration based on Phase 1\ +Phase 2: Health Authority Submission Pilot 4 or 5\ +Reflections and iteration based on Phase 2\ +Phase 3: Large audience kick off\ +Reflections and iteration based on Phase 3 + +# Support & Community {#9.-support-&-community} + +R Validation Hub [website](https://pharmar.org/contribute/)\ +R Validation Slack (link)\ +R Consortium [website](https://r-consortium.org/)\ +R Consortium [Code of conduct](https://r-consortium.org/codeofconduct.html) + +# Feedback Loop {#10.-feedback-loop} + +If it is a technical doubt, bug or question open issues on the repositories. + +# Next Steps for You, early adopter {#11.-next-steps-for-you,-early-adopter} + +Other steps From 5a78a013684cf6ec22bf09fe3947bd40fd767e17 Mon Sep 17 00:00:00 2001 From: Zhenglei Gao Date: Fri, 7 Aug 2026 19:19:50 +0200 Subject: [PATCH 2/5] Wire early adoption guide into Hugo content and navigation. Move the guide under content/, add a Validation Initiatives menu entry, and link it from the homepage Our Approach to Validation carousel slide so it is discoverable on the site. Co-authored-by: Cursor --- config.toml | 6 ++++++ adoption_guide.md => content/adoption_guide.md | 4 ++++ data/carousel/solutions.yaml | 2 ++ 3 files changed, 12 insertions(+) rename adoption_guide.md => content/adoption_guide.md (99%) diff --git a/config.toml b/config.toml index 4b732b6..4473aea 100644 --- a/config.toml +++ b/config.toml @@ -131,6 +131,12 @@ ignoreFiles = [ url = "/repository/" weight = 26 parent = "section.initiatives" + +[[menu.main]] + name = "Early Adoption Guide" + url = "/adoption_guide/" + weight = 27 + parent = "section.initiatives" [[menu.main]] identifier = "resources" diff --git a/adoption_guide.md b/content/adoption_guide.md similarity index 99% rename from adoption_guide.md rename to content/adoption_guide.md index aa5073d..f64bc3b 100644 --- a/adoption_guide.md +++ b/content/adoption_guide.md @@ -1,3 +1,7 @@ +--- +title: "Validated Repository Early Adoption Guide" +--- + # **Validated Repository** **Early Adoption Guide** 🚀 ## Welcome & Context {#welcome-&-context} diff --git a/data/carousel/solutions.yaml b/data/carousel/solutions.yaml index 3a75fa8..1a8972f 100644 --- a/data/carousel/solutions.yaml +++ b/data/carousel/solutions.yaml @@ -5,5 +5,7 @@ description: >
  • Check out our {riskmetric} package and our {riskassessment} app!
    • +
  • + Read our early adoption guide?
    • image: "img/carousel/riskmetric_and_riskassessment.png" From 5b84f40bfdca58238e025ab04f831b94fa12806f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Llu=C3=ADs?= Date: Thu, 13 Aug 2026 11:35:25 +0200 Subject: [PATCH 3/5] Change section and title --- config.toml | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/config.toml b/config.toml index 4473aea..1f97ee9 100644 --- a/config.toml +++ b/config.toml @@ -31,13 +31,13 @@ ignoreFiles = [ url = "/origins-and-mission/" weight = 1 parent = "section.rvalhub" - + [[menu.main]] name = "Partner Initiatives" url = "/partner-initiatives/" weight = 13 parent = "section.rvalhub" - + [[menu.main]] name = "Our Team" identifier = "section.team" @@ -51,7 +51,7 @@ ignoreFiles = [ url = "/members-and-workstreams/" weight = 21 parent = "section.team" - + [[menu.main]] name = "Validation" identifier = "menu.validation" @@ -95,25 +95,25 @@ ignoreFiles = [ url = "/regulations/" weight = 1 parent = "section.why" - + [[menu.main]] name = "The {riskmetric} Package" url = "/riskmetric/" weight = 21 parent = "section.products" - + [[menu.main]] name = "The {riskassessment} App" url = "/riskassessment/" weight = 22 parent = "section.products" - + [[menu.main]] name = "The {riskscore} Package" url = "/riskscore/" weight = 23 parent = "section.products" - + [[menu.main]] name = "Our White Paper" url = "/white-paper/" @@ -133,11 +133,11 @@ ignoreFiles = [ parent = "section.initiatives" [[menu.main]] - name = "Early Adoption Guide" + name = "Regulatory Repository" url = "/adoption_guide/" weight = 27 - parent = "section.initiatives" - + parent = "section.products" + [[menu.main]] identifier = "resources" name = "Resources" @@ -161,37 +161,37 @@ ignoreFiles = [ name = "Case Studies" url = "/categories/case-studies/" weight = 63 - + [[menu.main]] parent = "resources" name = "Minutes" url = "/minutes/" weight = 64 - + [[menu.main]] parent = "resources" name = "Glossary" url = "/glossary/" weight = 65 - + [[menu.main]] identifier = "involved" name = "Get Involved" url = "/involved/" weight = 7 - + [[menu.main]] parent = "involved" name = "Community Meetings" url = "/community-meetings/" weight = 71 - + [[menu.main]] parent = "involved" name = "Contribute" url = "/contribute/" weight = 72 - + [[menu.main]] parent = "involved" name = "Contact Us" From be23fb96aff70be0b24b7623016018bb46988272 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Llu=C3=ADs?= Date: Thu, 13 Aug 2026 11:35:47 +0200 Subject: [PATCH 4/5] Clean up quarto context --- content/adoption_guide.md | 44 +++++++++++++++++++-------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/adoption_guide.md b/content/adoption_guide.md index f64bc3b..4e1bfe2 100644 --- a/content/adoption_guide.md +++ b/content/adoption_guide.md @@ -1,10 +1,10 @@ --- -title: "Validated Repository Early Adoption Guide" +title: "Regulatory Repository: Early Adoption Guide" --- # **Validated Repository** **Early Adoption Guide** 🚀 -## Welcome & Context {#welcome-&-context} +## Welcome & Context Thank you for joining the **R Validation Hub** as an early adopter! The R Validation Hub is a community-driven effort to bring transparency and confidence in the use of open-source R packages in regulated environments through solutions for the validation of R packages. @@ -19,7 +19,7 @@ Early adopters play a dual role: This guide will help you plan and scale your adoption, while ensuring alignment with both your internal compliance requirements and the R Validation Hub working group goals. -## Who Typically Implements This Internally {#who-typically-implements-this-internally} +## Who Typically Implements This Internally In many organizations, the people implementing validation workflows are not necessarily expert R developers. Often they are: @@ -30,7 +30,7 @@ In many organizations, the people implementing validation workflows are not nece This guide is written to support practical implementation, even for teams that are still building experience with R package infrastructure. While some familiarity with R and package installation is helpful, the R Validation Hub tools are designed to support structured and automated workflows that reduce the need for manual package validation. Deep expertise in R package development is not required. -## Terminology {#terminology} +## Terminology | Term | Definition | |------------------------------------|------------------------------------| @@ -53,7 +53,7 @@ This guide is written to support practical implementation, even for teams that a | **Validation Policy** | A set of rules or criteria used by an organization to determine whether an R package is acceptable for use in production or regulated environments. | | **Validation Report** | A structured report generated for each package summarizing validation metrics, evidence, and the resulting validation decision. | -## Why Early adoption matters {#why-early-adoption-matters} +## Why Early adoption matters Early adoption allows your team to: @@ -62,13 +62,13 @@ Early adoption allows your team to: - Gain visibility as a thought leader in open-source validation and regulatory compliance. - Collaborate directly with the R Validation Hub. -## Early Adopter Expectations {#early-adopter-expectations} +## Early Adopter Expectations As an early adopter of the R Validation Hub solutions, you’re joining a collaborative effort to strengthen how the life sciences industry uses and validates open-source R in regulated environments. Your participation is essential to ensure the tools, documentation, and workflows reflect real-world needs and constraints. Here’s what we expect from early adopters — and what you can expect in return. -## What We Ask of Early Adopters {#what-we-ask-of-early-adopters} +## What We Ask of Early Adopters - **Pilot with Purpose**: Select a clear use case, for example, assessing a small group of R packages already used in production or evaluating packages planned for use in submissions. Focus on learning and feedback rather than full organizational deployment.\ - **Share Honest, Actionable Feedback**: @@ -85,7 +85,7 @@ Here’s what we expect from early adopters — and what you can expect in retur - Key findings or challenges.\ - **Respect the [R Consortium’s Code of Conduct](https://r-consortium.org/codeofconduct.html)** -## What You Can Expect in Return {#what-you-can-expect-in-return} +## What You Can Expect in Return - **Priority Support and Collaboration**: Early adopters receive direct communication channels with the R Validation Hub maintainers, including feedback sessions and early access to updates.\ - **Recognition and Visibility**: Your organization’s role as an early adopter will be acknowledged in community reports, presentations, and case studies (if you choose to be public).\ @@ -95,7 +95,7 @@ Here’s what we expect from early adopters — and what you can expect in retur - Internal awareness and documentation. - A scalable foundation for open-source governance -## Tools {#tools} +## Tools The R Validation Hub Repository group maintains these open tools for all companies. The tools are designed to work together as a cohesive pipeline. As shown in the diagram below, val.meter and val.repo generate the foundational metrics, which feed into reporting (val.report), filtering (val.criterion), and user-friendly review (val.dashboard). @@ -106,7 +106,7 @@ The R Validation Hub Repository group maintains these open tools for all compani - val.dashboard – Shiny app for reviewing metrics in repository, visually applying filtering criteria, risk review and discussion. Founded on the experience of [riskassessment](https://github.com/pharmaR/riskassessment)\ - {val.setup} – Repository bringing together the R Validation Hub's tools under a single Github Actions workflow to produce the outputs necessary to qualify R packages for a GxP system. -## Getting Started {#getting-started} +## Getting Started The R Validation Hub toolchain supports multiple implementation workflows depending on an organization’s governance model and maturity level. @@ -170,10 +170,10 @@ Analysts can make more informed package decisions while maintaining alignment wi ### **Read next** -- Use [Workflow B](#6.3-workflow-b:-curate-packages-using-a-validation-policy) when your organization is ready to define shared criteria rather than rely on individual review alone.\ +- Use Workflow B, when your organization is ready to define shared criteria rather than rely on individual review alone.\ - See [the metrics of the val.repo repository](https://raw.githubusercontent.com/pharmaR/val.repo/refs/heads/main/src/contrib/PACKAGES) -## Workflow B: Curate packages using a validation policy {#6.3-workflow-b:-curate-packages-using-a-validation-policy} +## Workflow B: Curate packages using a validation policy ### **Who this is for** @@ -216,11 +216,11 @@ A controlled list of packages aligned with internal validation policies, which t ### **Read next** -- Use [Workflow C](#6.4-workflow-c:-impose-a-validation-policy-for-an-enterprise-system) when package policy needs to be enforced centrally rather than applied manually.\ -- Use [Workflow D](#6.5-workflow-d:-generate-an-internal-validated-repository-or-image) when the curated package list needs to become part of an internal repository or automated delivery process.\ +- Use Workflow C when package policy needs to be enforced centrally rather than applied manually.\ +- Use Workflow D when the curated package list needs to become part of an internal repository or automated delivery process.\ - Placeholder: add link to `{val.criterion}` examples or criteria reference -## Workflow C: Impose a validation policy for an enterprise system {#workflow-c:-impose-a-validation-policy-for-an-enterprise-system} +## Workflow C: Impose a validation policy for an enterprise system ### **Who this is for** @@ -260,11 +260,11 @@ All users in the enterprise environment install packages that comply with organi ### **Read next** -- Use [Workflow D](#6.5-workflow-d:-generate-an-internal-validated-repository-or-image) when you also need organization-specific reports, internal packages, automated updates, or internal hosting.\ +- Use Workflow D when you also need organization-specific reports, internal packages, automated updates, or internal hosting.\ - **\[Placeholder: add a short explanation of common filter patterns or link to filter documentation\]**\ - **\[Placeholder: add supported deployment patterns, such as managed workstation, server, or containerized runtime\]** -## Workflow D: Generate an internal validated repository or image {#6.5-workflow-d:-generate-an-internal-validated-repository-or-image} +## Workflow D: Generate an internal validated repository or image ### **Who this is for** @@ -312,13 +312,13 @@ An internal, organization-specific package distribution workflow aligned with go - Continue to **Section 7** for practical walkthroughs, including an end-to-end example of building an internal repository or image through CI.\ - **\[Placeholder: add links to `{val.meter}`, `{val.report}`, and `{val.setup}` vignettes\]** -# Key Use Cases {#key-use-cases} +# Key Use Cases A repository using the tools for generating a closed repository for Arcus: [val.pipeline](https://github.com/pharmaR/val.pipeline)\ Add a new package to a closed image:\ Validate an internal validated package: it won’t have (external) download metrics but it might still be validated. -# Roadmap & What’s Coming {#roadmap-&-what’s-coming} +# Roadmap & What’s Coming Phase 1: Kick off Early Adoption community\ Reflections and iteration based on Phase 1\ @@ -327,17 +327,17 @@ Reflections and iteration based on Phase 2\ Phase 3: Large audience kick off\ Reflections and iteration based on Phase 3 -# Support & Community {#9.-support-&-community} +# Support & Community R Validation Hub [website](https://pharmar.org/contribute/)\ R Validation Slack (link)\ R Consortium [website](https://r-consortium.org/)\ R Consortium [Code of conduct](https://r-consortium.org/codeofconduct.html) -# Feedback Loop {#10.-feedback-loop} +# Feedback Loop If it is a technical doubt, bug or question open issues on the repositories. -# Next Steps for You, early adopter {#11.-next-steps-for-you,-early-adopter} +# Next Steps for You, early adopter Other steps From b9fb75b5c75ad66b192e07a7b759cc03fe01329c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Llu=C3=ADs?= Date: Thu, 13 Aug 2026 11:37:07 +0200 Subject: [PATCH 5/5] Remove title and use the section title --- content/adoption_guide.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/content/adoption_guide.md b/content/adoption_guide.md index 4e1bfe2..a643ced 100644 --- a/content/adoption_guide.md +++ b/content/adoption_guide.md @@ -2,8 +2,6 @@ title: "Regulatory Repository: Early Adoption Guide" --- -# **Validated Repository** **Early Adoption Guide** 🚀 - ## Welcome & Context Thank you for joining the **R Validation Hub** as an early adopter! The R Validation Hub is a community-driven effort to bring transparency and confidence in the use of open-source R packages in regulated environments through solutions for the validation of R packages.