diff --git a/platform_versioned_docs/version-23.4/getting-started/deployment-options.mdx b/platform_versioned_docs/version-23.4/getting-started/deployment-options.mdx
index e75eaa5f8..bfe1681fb 100644
--- a/platform_versioned_docs/version-23.4/getting-started/deployment-options.mdx
+++ b/platform_versioned_docs/version-23.4/getting-started/deployment-options.mdx
@@ -1,5 +1,5 @@
---
-title: "Deploy Seqera Platform"
+title: "Deploy Platform"
description: "An overview of deployment versions and ways to run Seqera Platform."
date: "21 Apr 2023"
tags: [deployment]
@@ -7,15 +7,15 @@ tags: [deployment]
Seqera Platform is available in two deployment editions and can be accessed via the web-based user interface (UI), [API](../api/overview.mdx), [CLI](../cli/cli.mdx), or in Nextflow directly using a flag.
-## Seqera Platform editions
+## Platform editions
-### Seqera Platform Cloud
+### Seqera Cloud
-The hosted Seqera Cloud edition is recommended for users who are new to Seqera. It's an ideal choice for individuals and organizations looking to set up quickly. Seqera Cloud has a limit of five concurrent workflow runs per user. It's available free of charge at [cloud.seqera.io](https://cloud.seqera.io).
+The hosted Seqera Cloud edition is recommended for users who are new to Platform. It's an ideal choice for individuals and organizations looking to set up quickly. [Seqera Cloud](https://cloud.seqera.io) has a free tier with a limit of five concurrent workflow runs per user. Seqera Cloud Pro offers unlimited runs, dedicated support, and more — [contact us](https://cloud.seqera.io/demo/) for a demo to discuss your requirements.
-### Seqera Platform Enterprise
+### Seqera Enterprise
-[Seqera Platform Enterprise](../enterprise/index.mdx) is installed in an organization's own cloud or on-premises infrastructure. It includes:
+[Seqera Enterprise](../enterprise/index.mdx) is installed in an organization's own cloud or on-premises infrastructure. It includes:
- Monitoring, logging, and observability
- Pipeline execution Launchpad
@@ -26,15 +26,15 @@ The hosted Seqera Cloud edition is recommended for users who are new to Seqera.
- Full-featured API
- Dedicated support for Nextflow and Seqera Platform
-To install Seqera Platform in your organization's infrastructure, [contact us](https://cloud.seqera.io/demo/) for a demo to discuss your requirements.
+To install Platform in your organization's infrastructure, [contact us](https://cloud.seqera.io/demo/) for a demo to discuss your requirements.
-## How to use Seqera Platform
+## How to use Platform
You can access your Seqera instance through the UI, the [API](../api/overview.mdx), the [CLI](../cli/cli.mdx), or in Nextflow directly using the `-with-tower` option.
-### Seqera Platform web-based UI
+### Platform web-based UI
-1. Create an account and log in to Seqera at [cloud.seqera.io](https://cloud.seqera.io).
+1. Create an account and log in to Seqera Cloud at [cloud.seqera.io](https://cloud.seqera.io).
:::note
Platform login sessions remain active as long as the application browser window remains open and active. When the browser window is terminated, automatic logout occurs within 6 hours by default.
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/add-data.mdx b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/add-data.mdx
new file mode 100644
index 000000000..fb24d079e
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/add-data.mdx
@@ -0,0 +1,112 @@
+---
+title: "Add data"
+description: "An introduction to adding pipeline input data in Seqera Platform"
+date: "21 Jul 2024"
+tags: [platform, data, data explorer, datasets]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Most bioinformatics pipelines require an input of some sort. This is typically a samplesheet where each row consists of a sample, the location of files for that sample (such as FASTQ files), and other sample details. Reliable shared access to pipeline input data is crucial to simplify data management, minimize user data-input errors, and facilitate reproducible workflows.
+
+In Platform, samplesheets and other data can be made easily accessible in one of two ways:
+- Use **Data Explorer** to browse and interact with remote data from AWS S3, Azure Blob Storage, and Google Cloud Storage repositories, directly in your organization workspace.
+- Use **Datasets** to upload structured data to your workspace in CSV (Comma-Separated Values) or TSV (Tab-Separated Values) format.
+
+## Data Explorer
+
+For pipeline runs in the cloud, users typically need access to buckets or blob storage to upload files (such as samplesheets and reference data) for analysis and to view pipeline results. Managing credentials and permissions for multiple users and training users to navigate cloud consoles and CLIs can be complicated. Data Explorer provides the simplified alternative of viewing your data directly in Platform.
+
+### Add a cloud bucket
+
+Private cloud storage buckets accessible by the [credentials](../../credentials/overview.mdx) in your workspace are added to Data Explorer automatically by default. However, you can also add custom directory paths within buckets to your workspace to simplify direct access.
+
+To add individual buckets (or directory paths within buckets):
+
+1. From the **Data Explorer** tab, select **Add cloud bucket**.
+1. Specify the bucket details:
+ - The cloud **Provider**.
+ - An existing cloud **Bucket path**.
+ - A unique **Name** for the bucket.
+ - The **Credentials** used to access the bucket. For public cloud buckets, select **Public** from the dropdown menu.
+ - An optional bucket **Description**.
+1. Select **Add**.
+
+ 
+
+You can now use this data in your analysis without the need to interact with cloud consoles or CLI tools.
+
+#### Public data sources
+
+Select **Public** from the credentials dropdown menu to add public cloud storage buckets from resources such as:
+
+- [The Cancer Genome Atlas (TCGA)](https://registry.opendata.aws/tcga/)
+- [1000 Genomes Project](https://registry.opendata.aws/1000-genomes/)
+- [NCBI SRA](https://registry.opendata.aws/ncbi-sra/)
+- [Genome in a Bottle Consortium](https://docs.opendata.aws/giab/readme.html)
+- [MSSNG Database](https://cloud.google.com/life-sciences/docs/resources/public-datasets/mssng)
+- [Genome Aggregation Database (gnomAD)](https://cloud.google.com/life-sciences/docs/resources/public-datasets/gnomad)
+
+### View pipeline outputs
+
+In Data Explorer, you can:
+
+ - **View bucket details**:
+ Select the information icon next to a bucket in the list to view the cloud provider, bucket address, and credentials.
+
+ 
+
+ - **View bucket contents**:
+ Select a bucket name from the list to view the bucket contents. The file type, size, and path of objects are displayed in columns next to the object name. For example, view the outputs of your [nf-core/rnaseq](./comm-showcase.mdx#launch-the-nf-corernaseq-pipeline) run:
+
+ 
+
+ - **Preview files**:
+ Select a file to open a preview window that includes a **Download** button. For example, view the resultant gene counts of the salmon quantification step of your [nf-core/rnaseq](./comm-showcase.mdx#launch-the-nf-corernaseq-pipeline) run:
+
+ 
+
+## Datasets
+
+Datasets in Platform are CSV (comma-separated values) and TSV (tab-separated values) files stored in a workspace. You can select stored datasets as input data when launching a pipeline.
+
+
+ **Example: nf-core/rnaseq test samplesheet**
+
+ The [nf-core/rnaseq](https://github.com/nf-core/rnaseq) pipeline works with input datasets (samplesheets) containing sample names, FASTQ file locations, and indications of strandedness. The Seqera Community Showcase sample dataset for nf-core/rnaseq specifies the paths to 7 small sub-sampled FASTQ files from a yeast RNAseq dataset:
+
+ **Example nf-core/rnaseq dataset**
+
+ | sample | fastq_1 | fastq_2 | strandedness |
+ | ------------------- | ------------------------------------ | ------------------------------------ | ------------ |
+ | WT_REP1 | s3://nf-core-awsmegatests/rnaseq/... | s3://nf-core-awsmegatests/rnaseq/... | reverse |
+ | WT_REP1 | s3://nf-core-awsmegatests/rnaseq/... | s3://nf-core-awsmegatests/rnaseq/... | reverse |
+ | WT_REP2 | s3://nf-core-awsmegatests/rnaseq/... | s3://nf-core-awsmegatests/rnaseq/... | reverse |
+ | RAP1_UNINDUCED_REP1 | s3://nf-core-awsmegatests/rnaseq/... | | reverse |
+ | RAP1_UNINDUCED_REP2 | s3://nf-core-awsmegatests/rnaseq/... | | reverse |
+ | RAP1_UNINDUCED_REP2 | s3://nf-core-awsmegatests/rnaseq/... | | reverse |
+ | RAP1_IAA_30M_REP1 | s3://nf-core-awsmegatests/rnaseq/... | s3://nf-core-awsmegatests/rnaseq/... | reverse |
+
+
+
+Download the nf-core/rnaseq [samplesheet_test.csv](samplesheet_test.csv).
+
+### Add a dataset
+
+From the **Datasets** tab, select **Add Dataset**.
+
+
+
+Specify the following dataset details:
+
+- A **Name** for the dataset, such as `nf-core-rnaseq-test-dataset`.
+- A **Description** for the dataset.
+- Select the **First row as header** option to prevent Platform from parsing the header row of the samplesheet as sample data.
+- Select **Upload file** and browse to your CSV or TSV file in local storage, or simply drag and drop it into the box.
+
+Notice the location of the files in the nf-core/rnaseq example dataset point to a path on S3. This could also be a path to a shared filesystem, if using an HPC compute environment. Nextflow will use these paths to stage the files into the task working directory.
+
+:::info
+Platform does not store the data used for analysis in pipelines. The datasets must provide the locations of data that is stored on your own infrastructure.
+:::
\ No newline at end of file
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/add-pipelines.mdx b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/add-pipelines.mdx
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--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/add-pipelines.mdx
@@ -0,0 +1,81 @@
+---
+title: "Add pipelines"
+description: "An introduction to adding pipelines to Seqera Platform workspaces"
+date: "12 Jul 2024"
+tags: [platform, launch, pipelines, launchpad]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The Launchpad lists the preconfigured Nextflow pipelines that can be executed on the [compute environments](../../compute-envs/overview.mdx) in your workspace.
+
+Platform offers two methods to import pipelines to your workspace Launchpad — directly from Seqera Pipelines, or manually via **Add pipeline** in Platform. This page illustrates each method, using [nf-core/rnaseq](https://github.com/nf-core/rnaseq) as an example.
+
+### Import nf-core/rnaseq from Seqera Pipelines
+
+[Seqera Pipelines](https://seqera.io/pipelines) is an open-source resource that provides a curated collection of high-quality, open-source pipelines. Each pipeline includes a curated test dataset to use in a test run in just a few steps.
+
+
+
+To import the `nf-core/rnaseq` pipeline:
+1. Select **Launch** next to the pipeline name in the list. In the **Add pipeline** tab, select **Cloud** or **Enterprise** depending on your Platform account type, then provide the information needed for Seqera Pipelines to access your Platform instance:
+ - **Seqera Cloud**: Paste your Platform **Access token** and select **Next**.
+ - **Seqera Enterprise**: Specify the **Seqera Platform URL** (hostname) and **Base API URL** for your Enterprise instance, then paste your Platform **Access token** and select **Next**.
+ :::note
+ If you do not have a Platform access token, select **Get your access token from Seqera Platform** to open the Access tokens page in a new browser window.
+ :::
+1. Select the Platform **Organization**, **Workspace**, and **Compute environment** for the imported pipeline.
+1. (Optional) Customize the **Pipeline Name** and **Pipeline Description**.
+1. Select **Add Pipeline**.
+
+
+
+:::tip
+To launch pipelines directly with CLI tools, select the **Launch Pipeline** tab to grab commands for Nextflow, [Seqera Platform CLI](./automation.mdx), and [nf-core/tools](https://nf-co.re/docs/nf-core-tools):
+
+
+:::
+
+### Add nf-core/rnaseq from the Launchpad
+
+
+
+From your workspace Launchpad, select **Add Pipeline** and specify the following pipeline details:
+
+- **Name**: `nf-core/rnaseq`, or a custom name of your choice.
+- (*Optional*) **Description**: A summary of the pipeline or any information that may be useful to workspace participants when selecting a pipeline to launch.
+- (*Optional*) **Labels**: Categorize the pipeline according to arbitrary criteria (such as reference genome version) that may help workspace participants to select the appropriate pipeline for their analysis.
+- **Compute environment**: Select an existing workspace [compute environment](../../compute-envs/overview.mdx).
+- **Pipeline to launch**: `https://github.com/nf-core/rnaseq`
+ - Platform allows you to select any public or private Git repository that contains Nextflow source code.
+- **Revision number**: Platform will search all of the available tags and branches in the provided pipeline repository and render a dropdown to select the appropriate version.
+ :::tip
+ Selecting a specific pipeline version is important for reproducibility as this ensures that each run with the same input data will generate the same results.
+ :::
+- (*Optional*) **Config profiles**: `test`
+ - Select a predefined profile for the Nextflow pipeline.
+ :::info
+ All nf-core pipelines include a `test` profile that is associated with a minimal test dataset. This profile runs the pipeline with heavily sub-sampled input data for the purposes of [CI/CD](https://resources.github.com/devops/ci-cd/) and to quickly confirm that the pipeline runs on your infrastructure.
+ :::
+- (*Optional*) **Pipeline parameters**:
+ - Set any custom pipeline parameters that will be prepopulated when users launch the pipeline from the Launchpad. For example, set the path to local reference genomes so users don't have to worry about locating these files when launching the pipeline.
+ 
+- (*Optional*) **Pre-run script**:
+ - Define Bash code that executes before the pipeline launches in the same environment where Nextflow runs.
+ :::info
+ Pre-run scripts are useful for defining executor settings, troubleshooting, and defining a specific version of Nextflow with the `NXF_VER` environment variable.
+ :::
+ 
+
+:::note
+Pre-filled pipeline settings (such as compute environment, config profiles, and pipeline parameters) can be overridden during pipeline launch by workspace participants with the necessary [permissions](../../orgs-and-teams/roles.mdx).
+:::
+
+After you have populated the appropriate fields, select **Add**. `nf-core/rnaseq` is now available for workspace participants to launch in the preconfigured compute environment.
+
+
+
+
+
+
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+---
+title: "Automation"
+description: "An introduction to automation with APIs and CLI tools in Seqera Platform"
+date: "21 Jul 2024"
+tags: [platform, automation, api, cli, seqerakit]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Seqera Platform provides multiple methods of programmatic interaction to automate the execution of pipelines, chain pipelines together, and integrate Platform with third-party services.
+
+### Platform API
+
+The Seqera Platform public API is the lowest-level method of programmatic interaction. All operations available in the user interface can be achieved through the API.
+
+The API can be used to trigger the launch of pipelines based on a file event (such as the upload of a file to a bucket) or completion of a previous run.
+
+The API can be accessed from `https://api.cloud.seqera.io`.
+
+The full list of endpoints is available in Seqera's [OpenAPI schema](https://cloud.seqera.io/openapi/index.html). The API requires an authentication token to be specified in every API request. This can be created in your user menu under **Your tokens**.
+
+
+
+The token is only displayed once. Store your token in a secure place. Use this token to authenticate requests to the API.
+
+
+ **Example pipeline launch API request**
+ ```
+ curl -X POST "https://api.cloud.seqera.io/workflow/launch?workspaceId=38659136604200" \
+ -H "Accept: application/json" \
+ -H "Authorization: Bearer " \
+ -H "Content-Type: application/json" \
+ -H "Accept-Version:1" \
+ -d '{
+ "launch": {
+ "computeEnvId": "hjE97A8TvD9PklUb0hwEJ",
+ "runName": "first-time-pipeline-api-byname",
+ "pipeline": "first-time-pipeline",
+ "workDir": "s3://nf-ireland",
+ "revision": "master"
+ }
+ }'
+ ```
+
+
+
+
+### Platform CLI
+
+For bioinformaticians and scientists more comfortable with the CLI, Platform uses a command line utility called `tw` to manage resources.
+
+The CLI provides an interface to launch pipelines, manage compute environments, retrieve run metadata, and monitor runs on Platform. It provides a Nextflow-like experience for bioinformaticians who prefer the CLI and allows you store Seqera resource configuration (pipelines, compute environments, etc.) as code. The CLI is built on top of the [Seqera Platform API](#platform-api) but is simpler to use. For example, you can refer to resources by name instead of their unique identifier.
+
+
+
+See [CLI](../../cli/cli.mdx) for installation and usage details.
+
+:::info
+**Example pipeline launch CLI command**
+
+```console
+tw launch hello --workspace community/showcase
+```
+:::
+
+### seqerakit
+
+`seqerakit` is a Python wrapper for the Platform CLI which can be leveraged to automate the creation of all Platform entities via a YAML format configuration file. It can be used to automate the creation of entities, from organizations and workspaces to pipelines and compute environments, and the execution of workflows with one YAML file.
+
+The key features are:
+
+- **Simple configuration**: All of the command-line options available in the Platform CLI can be defined in simple YAML format.
+- **Infrastructure as Code**: Enable users to manage and provision their infrastructure specifications.
+- **Automation**: End-to-end creation of entities within Platform, from adding an organization to launching pipeline(s) within that organization.
+
+See the [seqerakit GitHub repository](https://github.com/seqeralabs/seqera-kit/) for installation and usage details.
+
+
+ **Example pipeline launch seqerakit configuration and command**
+
+ Create a YAML file called `hello.yaml`:
+
+ ```yaml
+ launch:
+ - name: "hello-world"
+ url: "https://github.com/nextflow-io/hello"
+ workspace: "seqeralabs/showcase"
+ ```
+
+ Then run seqerakit:
+
+ ```console
+ $ seqerakit hello.yaml
+ ```
+
+
+
+## Resources
+Common use cases for the automation methods above include automatically executing a pipeline as data arrives from a sequencer, or integrating Platform into a broader user-facing application. For a step-by-step guide to set up these automation methods, see [Workflow automation for Nextflow pipelines](https://seqera.io/blog/workflow-automation/).
+
+For examples of how to use automation methods, see [Automating pipeline execution with Nextflow and Tower](https://seqera.io/blog/automating-workflows-with-nextflow-and-tower/).
\ No newline at end of file
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/comm-showcase.mdx b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/comm-showcase.mdx
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+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/comm-showcase.mdx
@@ -0,0 +1,357 @@
+---
+title: "Explore Platform Cloud"
+description: "Seqera Platform Cloud demonstration walkthrough"
+date: "8 Jul 2024"
+tags: [platform, launch, pipelines, launchpad, showcase tutorial]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+:::info
+This demo tutorial provides an introduction to Seqera Platform, including instructions to:
+- Launch, monitor, and optimize the [nf-core/rnaseq](https://github.com/nf-core/rnaseq) pipeline
+- Select pipeline input data with [Data Explorer](../../data/data-explorer.mdx) and Platform [datasets](../../data/datasets.mdx)
+- Perform tertiary analysis of pipeline results with [Data Studios](../../data/data-studios.mdx)
+
+The Platform Community Showcase is a Seqera-managed demonstration workspace with all the resources needed to follow along with this tutorial. All [Seqera Cloud](https://cloud.seqera.io) users have access to this example workspace by default.
+:::
+
+The Launchpad in every Platform workspace allows users to easily create and share Nextflow pipelines that can be executed on any supported infrastructure, including all public clouds and most HPC schedulers. A Launchpad pipeline consists of a pre-configured workflow repository, [compute environment](../../compute-envs/overview.mdx), and launch parameters.
+
+The Community Showcase contains 15 preconfigured pipelines, including [nf-core/rnaseq](https://github.com/nf-core/rnaseq), a bioinformatics pipeline used to analyze RNA sequencing data.
+
+The workspace also includes three preconfigured AWS Batch compute environments to run Showcase pipelines, and various Platform datasets and public data sources (accessed via Data Explorer) to use as pipeline input.
+
+:::note
+To skip this Community Showcase demo and start running pipelines on your own infrastructure:
+1. Set up an [organization workspace](../workspace-setup.mdx).
+1. Create a workspace [compute environment](../../compute-envs/overview.mdx) for your cloud or HPC compute infrastructure.
+1. [Add pipelines](./add-pipelines.mdx) to your workspace.
+:::
+
+## Launch the nf-core/rnaseq pipeline
+
+:::note
+This guide is based on version 3.14.0 of the nf-core/rnaseq pipeline. Launch form parameters may differ in other versions.
+:::
+
+Navigate to the Launchpad in the `community/showcase` workspace and select **Launch** next to the `nf-core-rnaseq` pipeline to open the launch form.
+
+ 
+
+
+ Nextflow parameter schema
+
+ The launch form lets you configure the pipeline execution. The pipeline parameters in this form are rendered from a [pipeline schema](../../pipeline-schema/overview.mdx) file in the root of the pipeline Git repository. `nextflow_schema.json` is a simple JSON-based schema describing pipeline parameters for pipeline developers to easily adapt their in-house Nextflow pipelines to be executed in Platform.
+
+ :::tip
+ See [Best Practices for Deploying Pipelines with the Seqera Platform](https://seqera.io/blog/best-practices-for-deploying-pipelines-with-seqera-platform/) to learn how to build the parameter schema for any Nextflow pipeline automatically with tooling maintained by the nf-core community.
+ :::
+
+
+
+### Parameter selection
+
+Adjust the following Platform-specific options:
+
+- **Workflow run name**: A unique identifier for the run, pre-filled with a random name. This can be customized.
+- **Labels**: Assign new or existing labels to the run. For example, a project ID or genome version.
+
+`nf-core/rnaseq` requires a set of parameters to run:
+
+#### input
+
+Most nf-core pipelines use the `input` parameter in a standardized way to specify an input samplesheet that contains paths to input files (such as FASTQ files) and any additional metadata needed to run the pipeline. Use **Browse** to select either a file path in cloud storage via **Data Explorer**, or a pre-loaded **Dataset**:
+
+
+
+ **Data Explorer**
+
+ In the **Data Explorer** tab, select the `nf-tower-data` bucket, then search for and select the `rnaseq_sample_data.csv` file.
+
+
+
+
+ **Datasets**
+
+ In the **Datasets** tab, search for and select `rnaseq_sample_data`.
+
+
+
+
+
+:::tip
+- See [Add data](./add-data.mdx) to learn how to add datasets and Data Explorer cloud buckets to your own workspaces.
+:::
+
+#### output
+
+Most nf-core pipelines use the `outdir` parameter in a standardized way to specify where the final results created by the pipeline are published. `outdir` must be unique for each pipeline run. Otherwise, your results will be overwritten.
+
+For this tutorial test run, keep the default `outdir` value (`./results`).
+
+:::tip
+For the `outdir` parameter in pipeline runs in your own workspace, select **Browse** to specify a cloud storage directory using Data Explorer, or enter a cloud storage directory path to publish pipeline results to manually.
+
+
+:::
+
+#### Pipeline-specific parameters
+
+Modify other parameters to customize the pipeline execution through the parameters form. For example, under **Read trimming options**, change the `trimmer` to select `fastp` in the dropdown menu instead of `trimgalore`.
+
+
+
+Select **Launch** to start the run and be directed to the **Runs** tab with your run in a **submitted** status at the top of the list.
+
+## View run information
+
+### Run details page
+
+As the pipeline runs, run details will populate with parameters, logs, and other important execution details:
+
+
+ View run details
+
+ - **Command-line**: The Nextflow command invocation used to run the pipeline. This contains details about the pipeline version (`-r 3.14.0` flag) and profile, if specified (`-profile test` flag).
+ - **Parameters**: The exact set of parameters used in the execution. This is helpful for reproducing the results of a previous run.
+ - **Resolved Nextflow configuration**: The full Nextflow configuration settings used for the run. This includes parameters, but also settings specific to task execution (such as memory, CPUs, and output directory).
+ - **Execution Log**: A summarized Nextflow log providing information about the pipeline and the status of the run.
+ - **Datasets**: Link to datasets, if any were used in the run.
+ - **Reports**: View pipeline outputs directly in the Platform.
+
+ 
+
+
+
+### View reports
+
+Most Nextflow pipelines generate reports or output files which are useful to inspect at the end of the pipeline execution. Reports can contain quality control (QC) metrics that are important to assess the integrity of the results.
+
+
+ View run reports
+
+
+ 
+
+ For example, for the nf-core/rnaseq pipeline, view the [MultiQC](https://docs.seqera.io/multiqc) report generated. MultiQC is a helpful reporting tool to generate aggregate statistics and summaries from bioinformatics tools.
+
+ 
+
+ The paths to report files point to a location in cloud storage (in the `outdir` directory specified during launch), but you can view the contents directly and download each file without navigating to the cloud or a remote filesystem.
+
+ #### Specify outputs in reports
+
+ To customize and instruct Platform where to find reports generated by the pipeline, a [tower.yml](https://github.com/nf-core/rnaseq/blob/master/tower.yml) file that contains the locations of the generated reports must be included in the pipeline repository.
+
+ In the nf-core/rnaseq pipeline, the MULTIQC process step generates a MultiQC report file in HTML format:
+
+ ```yaml
+ reports:
+ multiqc_report.html:
+ display: "MultiQC HTML report"
+ ```
+
+
+
+:::note
+See [Reports](../../reports/overview.mdx) to configure reports for pipeline runs in your own workspace.
+:::
+
+### View general information
+
+The run details page includes general information about who executed the run and when, the Git hash and tag used, and additional details about the compute environment and Nextflow version used.
+
+
+ View general run information
+
+ 
+
+ The **General** panel displays top-level information about a pipeline run:
+
+ - Unique workflow run ID
+ - Workflow run name
+ - Timestamp of pipeline start
+ - Pipeline version and Git commit ID
+ - Nextflow session ID
+ - Username of the launcher
+ - Work directory path
+
+
+
+### View process and task details
+
+Scroll down the page to view:
+
+- The progress of individual pipeline **Processes**
+- **Aggregated stats** for the run (total walltime, CPU hours)
+- **Workflow metrics** (CPU efficiency, memory efficiency)
+- A **Task details** table for every task in the workflow
+
+The task details table provides further information on every step in the pipeline, including task statuses and metrics:
+
+
+ View task details
+
+ Select a task in the task table to open the **Task details** dialog. The dialog has three tabs: **About**, **Execution log**, and **Data Explorer**.
+
+ #### About
+
+ The **About** tab includes:
+
+ 1. **Name**: Process name and tag
+ 2. **Command**: Task script, defined in the pipeline process
+ 3. **Status**: Exit code, task status, and number of attempts
+ 4. **Work directory**: Directory where the task was executed
+ 5. **Environment**: Environment variables that were supplied to the task
+ 6. **Execution time**: Metrics for task submission, start, and completion time
+ 7. **Resources requested**: Metrics for the resources requested by the task
+ 8. **Resources used**: Metrics for the resources used by the task
+
+ 
+
+ #### Execution log
+
+ The **Execution log** tab provides a real-time log of the selected task's execution. Task execution and other logs (such as stdout and stderr) are available for download from here, if still available in your compute environment.
+
+
+
+### Task work directory in Data Explorer
+
+If a task fails, a good place to begin troubleshooting is the task's work directory. Nextflow hash-addresses each task of the pipeline and creates unique directories based on these hashes.
+
+
+ View task log and output files
+
+ Instead of navigating through a bucket on the cloud console or filesystem, use the **Data Explorer** tab in the Task window to view the work directory.
+
+ Data Explorer allows you to view the log files and output files generated for each task, directly within Platform. You can view, download, and retrieve the link for these intermediate files to simplify troubleshooting.
+
+ 
+
+
+
+## Tertiary analysis
+
+Tertiary analysis of pipeline results is often performed in platforms like Jupyter Notebook or RStudio. Setting up the infrastructure for these platforms, including accessing pipeline data and the necessary bioinformatics packages, can be complex and time-consuming.
+
+**Data Studios** streamlines the process of creating interactive analysis environments for Platform users. With built-in templates, creating a data studio is as simple as adding and sharing pipelines or datasets.
+
+### Analyze RNAseq data in Data Studios
+
+In the **Data Studios** tab, you can monitor and see the details of the data studios in the Community Showcase workspace.
+
+Data Studios is used to perform bespoke analysis on the results of upstream workflows. For example, in the Community Showcase workspace we have run the **nf-core/rnaseq** pipeline to quantify gene expression, followed by **nf-core/differentialabundance** to derive differential expression statistics. The workspace contains a data studio with these results from cloud storage mounted into the studio to perform further analysis. One of these outputs is an RShiny application, which can be deployed for interactive analysis.
+
+#### Connect to the RNAseq analysis studio
+
+Select the `rnaseq_to_differentialabundance` data studio. This studio consists of an RStudio environment that uses an existing compute environment available in the showcase workspace. The studio also contains mounted data generated from the nf-core/rnaseq and subsequent nf-core/differentialabundance pipeline runs, directly from AWS S3.
+
+
+
+Select **Connect** to view the running RStudio environment. The `rnaseq_to_differentialabundance` studio includes the necessary R packages for deploying an RShiny application to visualize the RNAseq data.
+
+Deploy the RShiny app in the data studio by selecting the green play button on the last chunk of the R script:
+
+
+
+:::note
+Data Studios allows you to specify the resources each studio will use. When [creating your own data studios](../../data/data-studios.mdx) with shared compute environment resources, you must allocate sufficient resources to the compute environment to prevent data studio or pipeline run interruptions.
+:::
+
+### Explore results
+
+The RShiny app will deploy in a separate browser window, providing a data interface. Here you can view information about your sample data, perform QC or exploratory analysis, and view the results of differential expression analyses.
+
+
+
+
+ Sample clustering with PCA plots
+
+ In the **QC/Exploratory** tab, select the PCA (Principal Component Analysis) plot to visualize how the samples group together based on their gene expression profiles.
+
+ In this example, we used RNA sequencing data from the publicly-available ENCODE project, which includes samples from four different cell lines:
+
+ - **GM12878** — a lymphoblastoid cell line
+ - **K562** — a chronic myelogenous leukemia cell line
+ - **MCF-7** — a breast cancer cell line
+ - **H1-hESC** — human embryonic stem cells
+
+ What to look for in the PCA plot:
+
+ - **Replicate clustering**: Ideally, biological replicates of the same cell type should cluster closely together. For example, replicates of MCF-7 (breast cancer cell line) group together. This indicates consistent gene expression profiles among biological replicates.
+ - **Cell type separation**: Different cell types should form distinct clusters. For instance, GM12878, K562, MCF-7, and H1-hESC samples should each form their own separate clusters, reflecting their unique gene expression patterns.
+
+ From this PCA plot, you can gain insights into the consistency and quality of your sequencing data, identify any potential issues, and understand the major sources of variation among your samples - all directly in Platform.
+
+ 
+
+
+
+
+ Gene expression changes with Volcano plots
+
+ In the **Differential** tab, select **Volcano plots** to compare genes with significant changes in expression between two samples. For example, filter for `Type: H1 vs MCF-7` to view the differences in expression between these two cell lines.
+
+ 1. **Identify upregulated and downregulated genes**: The x-axis of the volcano plot represents the log2 fold change in gene expression between the H1 and MCF-7 samples, while the y-axis represents the statistical significance of the changes.
+
+ - **Upregulated genes in MCF-7**: Genes on the left side of the plot (negative fold change) are upregulated in the MCF-7 samples compared to H1. For example, the SHH gene, which is known to be upregulated in cancer cell lines, prominently appears here.
+
+ 2. **Filtering for specific genes**: If you are interested in specific genes, use the filter function. For example, filter for the SHH gene in the table below the plot. This allows you to quickly locate and examine this gene in more detail.
+
+ 3. **Gene expression bar plot**: After filtering for the SHH gene, select it to navigate to a gene expression bar plot. This plot will show you the expression levels of SHH across all samples, allowing you to see in which samples it is most highly expressed.
+
+ - Here, SHH is most highly expressed in MCF-7, which aligns with its known role in cancer cell proliferation.
+
+ Using the volcano plot, you can effectively identify and explore the genes with the most significant changes in expression between your samples, providing a deeper understanding of the molecular differences.
+
+ 
+
+
+
+### Collaborate in the data studio
+
+To share the results of your RNAseq analysis or allow colleagues to perform exploratory analysis, share a link to the data studio by selecting the options menu for the data studio you want to share, then select **Copy data studio URL**. With this link, other authenticated users with the **Connect** [role](../../orgs-and-teams/roles.mdx) (or greater) can access the session directly.
+
+:::note
+See [Data Studios](../../data/data-studios.mdx) to learn how to create data studios in your own workspace.
+:::
+
+## Pipeline optimization
+
+Seqera Platform's task-level resource usage metrics allow you to determine the resources requested for a task and what was actually used. This information helps you fine-tune your configuration more accurately.
+
+However, manually adjusting resources for every task in your pipeline is impractical. Instead, you can leverage the pipeline optimization feature available on the Launchpad.
+
+Pipeline optimization analyzes resource usage data from previous runs to optimize the resource allocation for future runs. After a successful run, optimization becomes available, indicated by the lightbulb icon next to the pipeline turning black.
+
+
+ Optimize nf-core/rnaseq
+
+ Navigate back to the Launchpad and select the lightbulb icon next to the nf-core/rnaseq pipeline to view the optimized profile. You have the flexibility to tailor the optimization's target settings and incorporate a retry strategy as needed.
+
+ #### View optimized configuration
+
+ When you select the lightbulb, you can access an optimized configuration profile in the second tab of the **Customize optimization profile** window.
+
+ This profile consists of Nextflow configuration settings for each process and each resource directive (where applicable): **cpus**, **memory**, and **time**. The optimized setting for a given process and resource directive is based on the maximum use of that resource across all tasks in that process.
+
+ Once optimization is selected, subsequent runs of that pipeline will inherit the optimized configuration profile, indicated by the black lightbulb icon with a checkmark.
+
+ :::note
+ Optimization profiles are generated from one run at a time, defaulting to the most recent run, and _not_ an aggregation of previous runs.
+ :::
+
+ 
+
+ Verify the optimized configuration of a given run by inspecting the resource usage plots for that run and these fields in the run's task table:
+
+ | Description | Key |
+ | ------------ | ---------------------- |
+ | CPU usage | `pcpu` |
+ | Memory usage | `peakRss` |
+ | Runtime | `start` and `complete` |
+
+
+
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/data-studios.mdx b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/data-studios.mdx
new file mode 100644
index 000000000..348630789
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/data-studios.mdx
@@ -0,0 +1,96 @@
+---
+title: "Data Studios"
+description: "An introduction to Data Studios in Seqera Platform"
+date: "8 Jul 2024"
+tags: [platform, data, data studios]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+:::info
+This guide provides an introduction to Data Studios using a demo studio in the Community Showcase workspace. See [Data Studios](../../data/data-studios.mdx) to learn how to create data studios in your own workspace.
+:::
+
+Tertiary analysis of pipeline results is often performed in platforms like Jupyter Notebook or RStudio. Setting up the infrastructure for these platforms, including accessing pipeline data and the necessary bioinformatics packages, can be complex and time-consuming.
+
+Data Studios streamlines the process of creating interactive analysis environments for Platform users. With built-in templates, creating a data studio is as simple as adding and sharing pipelines or datasets. Platform manages all the details, enabling you to easily select your preferred interactive tool and analyze your data.
+
+In the **Data Studios** tab, you can monitor and see the details of the data studios in the Community Showcase workspace.
+
+
+
+Select the options menu next to a data studio to:
+- See data studio details
+- Start or stop the studio, and connect to a running studio
+- Copy the studio URL to share it with collaborators
+
+### Analyze RNAseq data in Data Studios
+
+Data Studios is used to perform bespoke analysis on the results of upstream workflows. For example, in the Community Showcase workspace we have run the nf-core/rnaseq workflow to quantify gene expression, followed by nf-core/differentialabundance to derive differential expression statistics. The workspace contains a data studio with these results from cloud storage mounted into the studio to perform further analysis. One of these outputs is an RShiny application, which can be deployed for interactive analysis.
+
+### Open the RNAseq analysis studio
+
+Select the `rnaseq_to_differentialabundance` data studio. This studio consists of an RStudio environment that uses an existing compute environment available in the showcase workspace. The studio also contains mounted data generated from the nf-core/rnaseq and subsequent nf-core/differentialabundance pipeline runs, directly from AWS S3.
+
+
+
+:::info
+Data Studios allows you to specify the resources each studio will use. When [creating your own data studios](../../data/data-studios.mdx) with shared compute environment resources, you must allocate sufficient resources to the compute environment to prevent data studio or pipeline run interruptions.
+:::
+
+### Connect to the data studio
+
+This data studio will start an RStudio environment which already contains the necessary R packages for deploying an RShiny application to interact with various visualizations of the RNAseq data. The studio also contains an R Markdown document with the commands in place to generate the application.
+
+Deploy the RShiny app in the data studio by selecting the green play button on the last chunk of the R script:
+
+
+
+### Explore results in the RShiny app
+
+The RShiny app will deploy in a separate browser window, providing a data interface. Here you can view information about your sample data, perform QC or exploratory analysis, and view the differential expression analyses.
+
+
+
+#### Sample clustering with PCA plots
+
+In the **QC/Exploratory** tab, select the PCA (Principal Component Analysis) plot to visualize how the samples group together based on their gene expression profiles.
+
+In this example, we used RNA sequencing data from the publicly-available ENCODE project, which includes samples from four different cell lines:
+
+- **GM12878** — a lymphoblastoid cell line
+- **K562** — a chronic myelogenous leukemia cell line
+- **MCF-7** — a breast cancer cell line
+- **H1-hESC** — a human embryonic stem cell line
+
+What to look for in the PCA plot:
+
+- **Replicate clustering**: Ideally, replicates of the same cell type should cluster closely together. For example, replicates of the MCF-7 cells group together. This indicates consistent gene expression profiles among replicates.
+- **Cell type separation**: Different cell types should form distinct clusters. For instance, GM12878, K562, MCF-7, and H1-hESC cells should each form their own separate clusters, reflecting their unique gene expression patterns.
+
+From this PCA plot, you can gain insights into the consistency and quality of your sequencing data, identify any potential issues, and understand the major sources of variation among your samples - all directly in Platform.
+
+
+
+#### Gene expression changes with Volcano plots
+
+In the **Differential** tab, select **Volcano plots** to compare genes with significant changes in expression between two samples. For example, filter for `Type: H1 vs MCF-7` to view the differences in expression between these two cell lines.
+
+1. **Identify upregulated and downregulated genes**: The x-axis of the volcano plot represents the log2 fold change in gene expression between the H1 and MCF-7 cell lines, while the y-axis represents the statistical significance of the changes.
+
+ - **Upregulated genes in MCF-7**: Genes on the left side of the plot (negative fold change) are upregulated in the MCF-7 samples compared to H1. For example, the _SHH_ gene, which is known to be upregulated in cancer cell lines, prominently appears here.
+
+2. **Filtering for specific genes**: If you are interested in specific genes, use the filter function. For example, filter for the _SHH_ gene in the table below the plot. This allows you to quickly locate and examine this gene in more detail.
+
+3. **Gene expression bar plot**: After filtering for the _SHH_ gene, select it to navigate to a gene expression bar plot. This plot will show you the expression levels of _SHH_ across all samples, allowing you to see in which samples it is most highly expressed.
+
+ - Here, _SHH_ is most highly expressed in MCF-7, which aligns with its known role in cancer cell proliferation.
+
+Using the volcano plot, you can effectively identify and explore the genes with the most significant changes in expression between your samples, providing a deeper understanding of the molecular differences.
+
+
+
+### Collaborate in the data studio
+
+To share the results of your RNAseq analysis or allow colleagues to perform exploratory analysis, share a link to the data studio by selecting the options menu for the data studio you want to share, then select **Copy data studio URL**. With this link, other authenticated users with the **Connect** [role](../../orgs-and-teams/roles.mdx) (or greater) can access the session directly.
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/launch-pipelines.mdx b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/launch-pipelines.mdx
new file mode 100644
index 000000000..79d4ed749
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/launch-pipelines.mdx
@@ -0,0 +1,100 @@
+---
+title: "Launch pipelines"
+description: "An introduction to launching nf-core/rnaseq in the community/showcase workspace"
+date: "8 Jul 2024"
+tags: [platform, launch, pipelines, launchpad, showcase tutorial]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+:::info
+This tutorial provides an introduction to Seqera Platform, including instructions to:
+- Launch, monitor, and optimize the [nf-core/rnaseq](https://github.com/nf-core/rnaseq) pipeline
+- Select pipeline input data with [Data Explorer](../../data/data-explorer.mdx) and Platform [datasets](../../data/datasets.mdx)
+- Perform tertiary analysis of pipeline results with [Data Studios](../../data/data-studios.mdx)
+
+The Platform Community Showcase workspace contains all the resources needed to follow along with this tutorial. All [Seqera Cloud](https://cloud.seqera.io) users have access to this example workspace by default.
+:::
+
+The Launchpad in every Platform workspace allows users to easily create and share Nextflow pipelines that can be executed on any supported infrastructure, including all public clouds and most HPC schedulers. A Launchpad pipeline consists of a pre-configured workflow repository, [compute environment](../../compute-envs/overview.mdx), and launch parameters.
+
+The Community Showcase contains 15 preconfigured pipelines, including [nf-core/rnaseq](https://github.com/nf-core/rnaseq), a bioinformatics pipeline used to analyze RNA sequencing data.
+
+The workspace also includes three preconfigured AWS Batch compute environments to run Showcase pipelines, and various Platform datasets and public data sources (accessed via Data Explorer) to use as pipeline input.
+
+:::info
+To skip this Community Showcase tutorial and start running pipelines on your own infrastructure:
+1. Create an [organization](../../orgs-and-teams/organizations.mdx) and add members.
+1. Create a [workspace](../../orgs-and-teams/workspace-management.mdx) and add workspace participants.
+1. Create a workspace [compute environment](../../compute-envs/overview.mdx) for your cloud or HPC compute infrastructure.
+1. [Add pipelines](./add-pipelines.mdx) to your workspace.
+:::
+
+## Launch the nf-core/rnaseq pipeline
+
+Navigate to the Launchpad in the `community/showcase` workspace and select **Launch** next to the `nf-core-rnaseq` pipeline to open the launch form.
+
+ 
+
+### Nextflow parameter schema
+
+The launch form lets you configure the pipeline execution. The pipeline parameters are rendered from a [pipeline schema](../../pipeline-schema/overview.mdx) file in the root of the pipeline Git repository. `nextflow_schema.json` is a simple JSON-based schema describing pipeline parameters for pipeline developers to easily adapt their in-house Nextflow pipelines to be executed in Platform.
+
+:::tip
+See [Best Practices for Deploying Pipelines with the Seqera Platform](https://seqera.io/blog/best-practices-for-deploying-pipelines-with-seqera-platform/) to learn how to build the parameter schema for any Nextflow pipeline automatically with tooling maintained by the nf-core community.
+:::
+
+### Parameter selection
+
+Adjust the following Platform-specific options:
+
+- `Workflow run name`: A unique identifier for the run, pre-filled with a random name. This can be customized.
+- `Labels`: Assign new or existing labels to the run. For example, a project ID or genome version.
+
+Each pipeline requires a set of parameters to run:
+
+#### input
+
+Most nf-core pipelines use the `input` parameter in a standardized way to specify an input samplesheet that contains paths to input files (such as FASTQ files) and any additional metadata needed to run the pipeline. Use **Browse** to select either a file path in cloud storage via **Data Explorer**, or a pre-loaded **Dataset**:
+
+
+
+ **Data Explorer**
+
+ In the **Data Explorer** tab, select the `nf-tower-data` bucket, then search for and select the `rnaseq_sample_data.csv` file.
+
+
+
+
+ **Datasets**
+
+ In the **Datasets** tab, search for and select `rnaseq_sample_data`.
+
+
+
+
+
+:::tip
+- See [Add data](./add-data.mdx) to upload your own samplesheet datasets, and add private and public cloud storage buckets to your workspace.
+:::
+
+#### output
+
+Most nf-core pipelines use the `outdir` parameter in a standardized way to specify where the final results created by the pipeline are published. `outdir` must be unique for each pipeline run. Otherwise, your results will be overwritten.
+
+For this tutorial test run, keep the default `outdir` value (`./results`).
+
+:::info
+For the `outdir` parameter in pipeline runs in your own workspace, select **Browse** to specify a cloud storage directory using Data Explorer, or enter a cloud storage directory path to publish pipeline results to manually.
+
+
+:::
+
+#### Pipeline-specific parameters
+
+Modify other parameters to customize the pipeline execution through the parameters form. For example, under **Read trimming options**, change the `trimmer` to select `fastp` in the dropdown menu instead of `trimgalore`.
+
+
+
+Select **Launch** to start the run and be directed to the **Runs** tab with your run in a **submitted** status at the top of the list.
\ No newline at end of file
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/logo.svg b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/logo.svg
new file mode 100644
index 000000000..ac470e9eb
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/logo.svg
@@ -0,0 +1,17 @@
+
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/monitor-runs.mdx b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/monitor-runs.mdx
new file mode 100644
index 000000000..55148ef18
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/monitor-runs.mdx
@@ -0,0 +1,46 @@
+---
+title: "Monitor runs"
+description: "An introduction to monitoring runs in Seqera Platform"
+date: "8 Jul 2024"
+tags: [platform, monitoring]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+There are several ways to monitor pipeline runs in Seqera Platform:
+
+### Workspace view
+
+Access a full history of all runs in a given workspace via the **Runs** tab.
+
+
+
+### All runs view
+
+Access the **All runs** page from the user menu. This page provides a comprehensive overview of the runs across the entire Platform instance. The default view includes all organizations and workspaces accessible to the user. However, you can select visible workspaces from the dropdown next to **View**, and filter for a particular set of runs using any of the following fields:
+
+- `status`
+- `label`
+- `workflowId`
+- `runName`
+- `username`
+- `projectName`
+- `after: YYYY-MM-DD`
+- `before: YYYY-MM-DD`
+- `sessionId`
+- `is:starred`
+
+For example:
+
+```
+rnaseq username:johndoe status:succeeded after:2024-01-01
+```
+
+
+
+### Dashboard view
+
+Access the **Dashboard** from the user menu. This page provides an overview of the total runs across the Platform instance, grouped by run status. The default view includes all organizations and workspaces accessible to the user. Select visible workspaces from the dropdown next to **View** and filter by time, including a custom date range up to 12 months. Select **Export data** to download a CSV file with the available export data.
+
+
\ No newline at end of file
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/pipeline-optimization.mdx b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/pipeline-optimization.mdx
new file mode 100644
index 000000000..cd67be592
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/pipeline-optimization.mdx
@@ -0,0 +1,41 @@
+---
+title: "Pipeline optimization"
+description: "An introduction to pipeline optimization in Seqera Platform"
+date: "8 Jul 2024"
+tags: [platform, runs, pipeline optimization]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Seqera Platform's task-level resource usage metrics allow you to determine the resources requested for a task and what was actually used. This information helps you fine-tune your configuration more accurately.
+
+However, manually adjusting resources for every task in your pipeline is impractical. Instead, you can leverage the pipeline optimization feature available on the Launchpad.
+
+Pipeline optimization analyzes resource usage data from previous runs to optimize the resource allocation for future runs. After a successful run, optimization becomes available, indicated by the lightbulb icon next to the pipeline turning black.
+
+### Optimize nf-core/rnaseq
+
+Navigate back to the Launchpad and select the lightbulb icon next to the nf-core/rnaseq pipeline to view the optimized profile. You have the flexibility to tailor the optimization's target settings and incorporate a retry strategy as needed.
+
+### View optimized configuration
+
+When you select the lightbulb, you can access an optimized configuration profile in the second tab of the **Customize optimization profile** window.
+
+This profile consists of Nextflow configuration settings for each process and each resource directive (where applicable): **cpus**, **memory**, and **time**. The optimized setting for a given process and resource directive is based on the maximum use of that resource across all tasks in that process.
+
+Once optimization is selected, subsequent runs of that pipeline will inherit the optimized configuration profile, indicated by the black lightbulb icon with a checkmark.
+
+:::note
+Optimization profiles are generated from one run at a time, defaulting to the most recent run, and _not_ an aggregation of previous runs.
+:::
+
+
+
+Verify the optimized configuration of a given run by inspecting the resource usage plots for that run and these fields in the run's task table:
+
+| Description | Key |
+| ------------ | ---------------------- |
+| CPU usage | `pcpu` |
+| Memory usage | `peakRss` |
+| Runtime | `start` and `complete` |
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/requirements.txt b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/requirements.txt
new file mode 100644
index 000000000..0f35d1166
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/requirements.txt
@@ -0,0 +1,9 @@
+markdown~=3.5
+mkdocs~=1.5.3
+mkdocs-material~=9.5.6
+mkdocs-material-extensions>=1.0
+pygments~=2.16
+pymdown-extensions~=10.2
+jinja2>=2.11.1
+pillow
+cairosvg
\ No newline at end of file
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/samplesheet_test.csv b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/samplesheet_test.csv
new file mode 100644
index 000000000..b183db0a4
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/samplesheet_test.csv
@@ -0,0 +1,8 @@
+sample,fastq_1,fastq_2,strandedness
+WT_REP1,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357070_1.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357070_2.fastq.gz,auto
+WT_REP1,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357071_1.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357071_2.fastq.gz,auto
+WT_REP2,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357072_1.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357072_2.fastq.gz,reverse
+RAP1_UNINDUCED_REP1,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357073_1.fastq.gz,,reverse
+RAP1_UNINDUCED_REP2,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357074_1.fastq.gz,,reverse
+RAP1_UNINDUCED_REP2,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357075_1.fastq.gz,,reverse
+RAP1_IAA_30M_REP1,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357076_1.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357076_2.fastq.gz,reverse
diff --git a/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/view-run-information.mdx b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/view-run-information.mdx
new file mode 100644
index 000000000..214e3f636
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/quickstart-demo/view-run-information.mdx
@@ -0,0 +1,141 @@
+---
+title: "View run information"
+description: "View pipeline run details in Seqera Platform"
+date: "8 Jul 2024"
+tags: [platform, runs, pipelines, monitoring, showcase tutorial]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+When you launch a pipeline, you are directed to the **Runs** tab which contains all executed workflows, with your submitted run at the top of the list.
+
+Each new or resumed run is given a random name, which can be customized prior to launch. Each row corresponds to a specific run. As a job executes, it can transition through the following states:
+
+- **submitted**: Pending execution
+- **running**: Running
+- **succeeded**: Completed successfully
+- **failed**: Successfully executed, where at least one task failed with a terminate error strategy
+- **cancelled**: Stopped forceably during execution
+- **unknown**: Indeterminate status
+
+
+
+### View run details for nf-core/rnaseq
+
+The pipeline launched [previously](./launch-pipelines.mdx) is listed on the **Runs** tab. Select it from the list to view the run details.
+
+#### Run details page
+
+As the pipeline runs, run details will populate with the following tabs:
+
+- **Command-line**: The Nextflow command invocation used to run the pipeline. This contains details about the pipeline version (`-r 3.14.0` flag) and profile, if specified (`-profile test` flag).
+- **Parameters**: The exact set of parameters used in the execution. This is helpful for reproducing the results of a previous run.
+- **Configuration**: The full Nextflow configuration settings used for the run. This includes parameters, but also settings specific to task execution (such as memory, CPUs, and output directory).
+- **Datasets**: Link to datasets, if any were used in the run.
+- **Execution Log**: A summarized Nextflow log providing information about the pipeline and the status of the run.
+- **Reports**: View pipeline outputs directly in the Platform.
+
+
+
+### View reports
+
+Most Nextflow pipelines generate reports or output files which are useful to inspect at the end of the pipeline execution. Reports can contain quality control (QC) metrics that are important to assess the integrity of the results.
+
+
+
+For example, for the nf-core/rnaseq pipeline, view the [MultiQC](https://docs.seqera.io/multiqc) report generated. MultiQC is a helpful reporting tool to generate aggregate statistics and summaries from bioinformatics tools.
+
+
+
+The paths to report files point to a location in cloud storage (in the `outdir` directory specified during launch), but you can view the contents directly and download each file without navigating to the cloud or a remote filesystem.
+
+#### Specify outputs in reports
+
+To customize and instruct Platform where to find reports generated by the pipeline, a [tower.yml](https://github.com/nf-core/rnaseq/blob/master/tower.yml) file that contains the locations of the generated reports must be included in the pipeline repository.
+
+In the nf-core/rnaseq pipeline, the MULTIQC process step generates a MultiQC report file in HTML format:
+
+```yaml
+reports:
+ multiqc_report.html:
+ display: "MultiQC HTML report"
+```
+
+:::info
+See [Reports](../../reports/overview.mdx) to configure reports for pipeline runs in your own workspace.
+:::
+
+### View general information
+
+The run details page includes general information about who executed the run and when, the Git hash and tag used, and additional details about the compute environment and Nextflow version used.
+
+
+
+The **General** panel displays top-level information about a pipeline run:
+
+- Unique workflow run ID
+- Workflow run name
+- Timestamp of pipeline start
+- Pipeline version and Git commit ID
+- Nextflow session ID
+- Username of the launcher
+- Work directory path
+
+### View details for a task
+
+Scroll down the page to view:
+
+- The progress of individual pipeline **Processes**
+- **Aggregated stats** for the run (total walltime, CPU hours)
+- A **Task details** table for every task in the workflow
+- **Workflow metrics** (CPU efficiency, memory efficiency)
+
+The task details table provides further information on every step in the pipeline, including task statuses and metrics.
+
+### Task details
+
+Select a task in the task table to open the **Task details** dialog. The dialog has three tabs: **About**, **Execution log**, and **Data Explorer**.
+
+#### About
+
+The **About** tab includes:
+
+1. **Name**: Process name and tag
+2. **Command**: Task script, defined in the pipeline process
+3. **Status**: Exit code, task status, and number of attempts
+4. **Work directory**: Directory where the task was executed
+5. **Environment**: Environment variables that were supplied to the task
+6. **Execution time**: Metrics for task submission, start, and completion time
+7. **Resources requested**: Metrics for the resources requested by the task
+8. **Resources used**: Metrics for the resources used by the task
+
+
+
+#### Execution log
+
+The **Execution log** tab provides a real-time log of the selected task's execution. Task execution and other logs (such as stdout and stderr) are available for download from here, if still available in your compute environment.
+
+### Task work directory in Data Explorer
+
+If a task fails, a good place to begin troubleshooting is the task's work directory.
+
+Nextflow hash-addresses each task of the pipeline and creates unique directories based on these hashes. Instead of navigating through a bucket on the cloud console or filesystem to find the contents of this directory, use the **Data Explorer** tab in the Task window to view the work directory.
+
+Data Explorer allows you to view the log files and output files generated for each task in its working directory, directly within Platform. You can view, download, and retrieve the link for these intermediate files in cloud storage from the **Data Explorer** tab to simplify troubleshooting.
+
+
+
+### Resume a pipeline
+
+Platform uses [Nextflow resume](../../launch/cache-resume.mdx) to resume a failed or cancelled workflow run with the same parameters, using the cached results of previously completed tasks and only executing failed and pending tasks.
+
+
+
+:::info
+To resume a run in your own workspace:
+
+- Select **Resume** from the options menu next to the run.
+- Edit the parameters before launch, if needed.
+- If you have the appropriate [permissions](../../orgs-and-teams/roles.mdx), you may edit the compute environment if needed.
+:::
diff --git a/platform_versioned_docs/version-23.4/getting-started/start-collaborating.mdx b/platform_versioned_docs/version-23.4/getting-started/start-collaborating.mdx
deleted file mode 100644
index fcbce9952..000000000
--- a/platform_versioned_docs/version-23.4/getting-started/start-collaborating.mdx
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: "Start collaborating"
-description: "Start collaborating with your organization and peers."
-date: "15 April 2024"
-tags: [collaborate, workspaces, users]
----
-
-Before you get started with Seqera Platform, you must set up an organization. You can then add members, create teams, and invite external peers for collaboration within your organization.
-
-## Organizations
-
-Organizations are the top-level structure and contain workspaces, members, and teams. You can also add external collaborators to an organization. See [Organization management](../orgs-and-teams/organizations.mdx).
-
-## User workspaces
-
-Each user has a unique personal workspace to manage resources such as pipelines, compute environments, and credentials. See [Workspace management](../orgs-and-teams/workspace-management.mdx).
-
-## Shared workspaces
-
-You can create multiple workspaces within an organization context and associate each of these workspaces with dedicated teams of users, providing fine-grained access control for each team. See [Workspace management](../orgs-and-teams/workspace-management#create-a-shared-workspace).
\ No newline at end of file
diff --git a/platform_versioned_docs/version-23.4/getting-started/workspace-setup.mdx b/platform_versioned_docs/version-23.4/getting-started/workspace-setup.mdx
new file mode 100644
index 000000000..74ea66516
--- /dev/null
+++ b/platform_versioned_docs/version-23.4/getting-started/workspace-setup.mdx
@@ -0,0 +1,52 @@
+---
+title: "Set up your workspace"
+description: "Instructions to create an organization workspace and add participants in Seqera Platform."
+date: "15 April 2024"
+tags: [platform, organizations, workspaces, users]
+---
+
+Workspaces in Seqera Platform contain the resources to run your analyses and manage your computing infrastructure. Workspace members are granted various access roles to interact with the pipelines, compute environments, and data in a workspace. While each Platform user has a personal workspace, resource sharing and access management happens in an organization workspace context.
+
+To create an organization workspace and begin adding participants, first create your organization:
+
+### Create an organization
+
+Organizations are the top-level structure and contain workspaces, members, and teams. You can also add external collaborators to an organization. See [Organization management](../orgs-and-teams/organizations.mdx) for more information.
+
+1. Expand the **Organization | Workspace** dropdown and select **Add organization**.
+1. Complete the organization details fields:
+ - The **Name** to be associated with the organization in Platform.
+ - The **Full name** of the organization.
+ - A **Description** of the organization to provide contextual information that may be helpful to other organization members.
+ - The organization's **Location**.
+ - The organization's **Wesbite URL**.
+ - Drag and drop or upload an image to be used as the organization's **Logo** in Platform.
+1. Select **Add**.
+
+You are the first **Owner** of the organizations that you create. Add other organization owners and members as needed from the organization's **Members** tab.
+
+### Create a workspace
+
+1. From the organization's **Workspaces** tab, select **Add Workspace**.
+1. Complete the workspace details fields:
+ - The **Name** to be displayed for the workspace in Platform.
+ - The **Full name** of the workspace.
+ - A **Description** of the workspace to provide contextual information that may be helpful to other workspace participants.
+ - **Visibility**: Choose whether the workspace's pipelines must be **Shared** to all organization members, or only visible to workspace participants (**Private**).
+1. Select **Add**. You are redirected to your organization's **Workspaces** tab with your new workspace listed.
+1. Select your new workspace, then select the **Participants** tab to **Add Participants**.
+1. Enter the names of existing organization members or teams and select **Add**.
+1. Update a participant's access **Role** from the dropdown, if needed.
+
+### Simplify workspace access with teams
+
+Teams simplify workspace role-based access control (RBAC) for groups of organization members. Per-workspace access roles assigned to teams are inherited by all team members.
+
+Create a new team, add team members, and add the team to workspaces from the **Teams** tab on your organization page:
+
+1. Select **Add Team**, enter the team's details and an optional team avatar image, then select **Add**.
+1. Select **Edit** next to the team name in the list, then select the **Members of team** tab to add new members by name or email.
+ :::note
+ Team members must be existing organization members.
+ :::
+1. From the team edit screen's **Workspaces** tab, add workspaces by name and select an access **Role** from the dropdown next to each workspace in the list. All team members inherit the workspace access role for the team.
diff --git a/platform_versioned_docs/version-23.4/index.mdx b/platform_versioned_docs/version-23.4/index.mdx
index e563dd41c..c7a709738 100644
--- a/platform_versioned_docs/version-23.4/index.mdx
+++ b/platform_versioned_docs/version-23.4/index.mdx
@@ -4,18 +4,44 @@ description: "Introduction to Seqera Platform."
date: "24 Apr 2023"
---
-Seqera Platform is the centralized command post for the management of [Nextflow](https://www.nextflow.io/) data pipelines. It offers monitoring, logging, and observability for distributed workflows and simplifies the deployment of pipelines on any cloud, cluster, or laptop.
-Users can launch pre-configured pipelines with ease, while the flexible API provides programmatic integration to meet the needs of organizations building on Seqera Platform. Workflow developers can publish pipelines to shared workspaces and administrators can set up and manage the infrastructure required to run data analysis at scale.
+Seqera Platform is an intuitive, centralized command post designed to make scientific analysis accessible at any scale.
+
+Platform acts as a pane of glass to effortlessly launch, manage, monitor, and collaborate on scalable [Nextflow](https://www.nextflow.io) data analysis using your own computing resources and infrastructure. Researchers can focus on the science that matters, rather than worrying about infrastructure engineering.
+
+Seqera helps organizations:
+
+- Launch, manage, and monitor portable Nextflow pipelines from anywhere in real-time
+- Enable non-technical users to run pipelines via the intuitive Launchpad interface
+- Easily provision and leverage cloud-based and HPC compute environments
+- Share pipelines and data and collaborate securely between local and remote teams
+- Access a [curated library](https://seqera.io/pipelines/) of production-proven Nextflow community pipelines from [nf-core](https://nf-co.re/) and others
+- [Automate](./getting-started/quickstart-demo/automation.mdx) complex tasks as part of broader enterprise processes
+
+### Deployment methods
+
+Seqera offers two deployment methods:
+
+- **Seqera Cloud**: A version of the application available as a SaaS solution, hosted on Seqera's infrastructure.
+- **Seqera Enterprise**: A lightweight, deployable version of the application hosted on your own infrastructure.
:::tip
-[**Sign up**](https://tower.nf "Seqera Platform") to try Seqera for free, or request a [**demo**](https://cloud.tower.nf/demo/ "Seqera Platform Demo") for deployments in your own on-premises or cloud environment.
+[**Sign up**](https://cloud.seqera.io "Seqera Platform") to try Seqera for free, or request a [**demo**](https://seqera.io/demo "Seqera Platform Demo") for deployments in your own on-premises or cloud environment.
:::
-**What is Nextflow?**
+### Access Platform
+
+Log in to [Seqera Cloud](https://cloud.seqera.io/login) with your GitHub or Google account, or by providing an email address. If you are signing in for the first time, Seqera Cloud will send an authentication link to the email address to enable login.
+
+Upon your first login, you arrive in `community/showcase`, a workspace pre-filled with resources to launch your first pipeline with public data.
+
+- To begin launching Showcase pipelines, see [Launch pipelines](./getting-started/quickstart-demo/launch-pipelines.mdx).
+- To skip the Showcase and begin adding your own pipelines and resources, see [Set up your workspace](./getting-started/workspace-setup.mdx) to first create your own organizations and workspaces.
+
+### What is Nextflow?
-Nextflow is a framework for the development of data workflows. It enables engineers and data scientists to create and securely deploy custom, parallel data applications to the cloud or traditional on-premises infrastructure. Nextflow is characterized by its powerful dataflow programming paradigm and execution engines that allow for transparent deployment.
+[Nextflow](https://www.nextflow.io) is a framework for the development of data workflows. It enables engineers and data scientists to create and securely deploy custom, parallel data applications to the cloud or traditional on-premises infrastructure. Nextflow is characterized by its powerful dataflow programming paradigm and execution engines that allow for transparent deployment.
-Nextflow is both a programming workflow language and an execution runtime that supports a wide range of execution platforms, including popular traditional grid scheduling systems such as Slurm and IBM LSF, and cloud services such as AWS Batch and Google Cloud Life Sciences.
+Nextflow is both a programming workflow language and an execution runtime that supports a wide range of execution platforms, including popular traditional grid scheduling systems such as Slurm and IBM LSF, and cloud services such as AWS, Azure, and Google Cloud Batch.
-New to Nextflow? [Get started quickly](https://www.nextflow.io/#:~:text=Features-,Quick%20start,-Learn%20Nextflow) or see the [reference documentation](https://www.nextflow.io/docs/latest/getstarted.html).
+See the [Nextflow documentation](https://www.nextflow.io/docs/latest/) to learn more.
diff --git a/platform_versioned_docs/version-24.1/data/datasets.mdx b/platform_versioned_docs/version-24.1/data/datasets.mdx
index f32bc4af0..2177a071f 100644
--- a/platform_versioned_docs/version-24.1/data/datasets.mdx
+++ b/platform_versioned_docs/version-24.1/data/datasets.mdx
@@ -11,7 +11,7 @@ This feature is only available in organization workspaces.
Datasets in Seqera Platform are CSV (comma-separated values) and TSV (tab-separated values) files stored in a workspace. They are used as inputs to pipelines to simplify data management, minimize user data-input errors, and facilitate reproducible workflows.
-The most commonly used datasets for Nextflow pipelines are samplesheets, where each row consists of a sample, the location of files for that sample (such as fastq files), and other sample details. For example, [nf-core/rnaseq](https://github.com/nf-core/rnaseq) works with input datasets (samplesheets) containing sample names, fastq file locations, and indications of strandedness. The Seqera Community Showcase sample dataset for _nf-core/rnaseq_ looks like this:
+The most commonly used datasets for Nextflow pipelines are samplesheets, where each row consists of a sample, the location of files for that sample (such as FASTQ files), and other sample details. For example, [nf-core/rnaseq](https://github.com/nf-core/rnaseq) works with input datasets (samplesheets) containing sample names, FASTQ file locations, and indications of strandedness. The Seqera Community Showcase sample dataset for _nf-core/rnaseq_ looks like this:
**Example rnaseq dataset**
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diff --git a/platform_versioned_docs/version-24.1/getting-started/deployment-options.mdx b/platform_versioned_docs/version-24.1/getting-started/deployment-options.mdx
index e75eaa5f8..bfe1681fb 100644
--- a/platform_versioned_docs/version-24.1/getting-started/deployment-options.mdx
+++ b/platform_versioned_docs/version-24.1/getting-started/deployment-options.mdx
@@ -1,5 +1,5 @@
---
-title: "Deploy Seqera Platform"
+title: "Deploy Platform"
description: "An overview of deployment versions and ways to run Seqera Platform."
date: "21 Apr 2023"
tags: [deployment]
@@ -7,15 +7,15 @@ tags: [deployment]
Seqera Platform is available in two deployment editions and can be accessed via the web-based user interface (UI), [API](../api/overview.mdx), [CLI](../cli/cli.mdx), or in Nextflow directly using a flag.
-## Seqera Platform editions
+## Platform editions
-### Seqera Platform Cloud
+### Seqera Cloud
-The hosted Seqera Cloud edition is recommended for users who are new to Seqera. It's an ideal choice for individuals and organizations looking to set up quickly. Seqera Cloud has a limit of five concurrent workflow runs per user. It's available free of charge at [cloud.seqera.io](https://cloud.seqera.io).
+The hosted Seqera Cloud edition is recommended for users who are new to Platform. It's an ideal choice for individuals and organizations looking to set up quickly. [Seqera Cloud](https://cloud.seqera.io) has a free tier with a limit of five concurrent workflow runs per user. Seqera Cloud Pro offers unlimited runs, dedicated support, and more — [contact us](https://cloud.seqera.io/demo/) for a demo to discuss your requirements.
-### Seqera Platform Enterprise
+### Seqera Enterprise
-[Seqera Platform Enterprise](../enterprise/index.mdx) is installed in an organization's own cloud or on-premises infrastructure. It includes:
+[Seqera Enterprise](../enterprise/index.mdx) is installed in an organization's own cloud or on-premises infrastructure. It includes:
- Monitoring, logging, and observability
- Pipeline execution Launchpad
@@ -26,15 +26,15 @@ The hosted Seqera Cloud edition is recommended for users who are new to Seqera.
- Full-featured API
- Dedicated support for Nextflow and Seqera Platform
-To install Seqera Platform in your organization's infrastructure, [contact us](https://cloud.seqera.io/demo/) for a demo to discuss your requirements.
+To install Platform in your organization's infrastructure, [contact us](https://cloud.seqera.io/demo/) for a demo to discuss your requirements.
-## How to use Seqera Platform
+## How to use Platform
You can access your Seqera instance through the UI, the [API](../api/overview.mdx), the [CLI](../cli/cli.mdx), or in Nextflow directly using the `-with-tower` option.
-### Seqera Platform web-based UI
+### Platform web-based UI
-1. Create an account and log in to Seqera at [cloud.seqera.io](https://cloud.seqera.io).
+1. Create an account and log in to Seqera Cloud at [cloud.seqera.io](https://cloud.seqera.io).
:::note
Platform login sessions remain active as long as the application browser window remains open and active. When the browser window is terminated, automatic logout occurs within 6 hours by default.
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/add-data.mdx b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/add-data.mdx
new file mode 100644
index 000000000..fb24d079e
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/add-data.mdx
@@ -0,0 +1,112 @@
+---
+title: "Add data"
+description: "An introduction to adding pipeline input data in Seqera Platform"
+date: "21 Jul 2024"
+tags: [platform, data, data explorer, datasets]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Most bioinformatics pipelines require an input of some sort. This is typically a samplesheet where each row consists of a sample, the location of files for that sample (such as FASTQ files), and other sample details. Reliable shared access to pipeline input data is crucial to simplify data management, minimize user data-input errors, and facilitate reproducible workflows.
+
+In Platform, samplesheets and other data can be made easily accessible in one of two ways:
+- Use **Data Explorer** to browse and interact with remote data from AWS S3, Azure Blob Storage, and Google Cloud Storage repositories, directly in your organization workspace.
+- Use **Datasets** to upload structured data to your workspace in CSV (Comma-Separated Values) or TSV (Tab-Separated Values) format.
+
+## Data Explorer
+
+For pipeline runs in the cloud, users typically need access to buckets or blob storage to upload files (such as samplesheets and reference data) for analysis and to view pipeline results. Managing credentials and permissions for multiple users and training users to navigate cloud consoles and CLIs can be complicated. Data Explorer provides the simplified alternative of viewing your data directly in Platform.
+
+### Add a cloud bucket
+
+Private cloud storage buckets accessible by the [credentials](../../credentials/overview.mdx) in your workspace are added to Data Explorer automatically by default. However, you can also add custom directory paths within buckets to your workspace to simplify direct access.
+
+To add individual buckets (or directory paths within buckets):
+
+1. From the **Data Explorer** tab, select **Add cloud bucket**.
+1. Specify the bucket details:
+ - The cloud **Provider**.
+ - An existing cloud **Bucket path**.
+ - A unique **Name** for the bucket.
+ - The **Credentials** used to access the bucket. For public cloud buckets, select **Public** from the dropdown menu.
+ - An optional bucket **Description**.
+1. Select **Add**.
+
+ 
+
+You can now use this data in your analysis without the need to interact with cloud consoles or CLI tools.
+
+#### Public data sources
+
+Select **Public** from the credentials dropdown menu to add public cloud storage buckets from resources such as:
+
+- [The Cancer Genome Atlas (TCGA)](https://registry.opendata.aws/tcga/)
+- [1000 Genomes Project](https://registry.opendata.aws/1000-genomes/)
+- [NCBI SRA](https://registry.opendata.aws/ncbi-sra/)
+- [Genome in a Bottle Consortium](https://docs.opendata.aws/giab/readme.html)
+- [MSSNG Database](https://cloud.google.com/life-sciences/docs/resources/public-datasets/mssng)
+- [Genome Aggregation Database (gnomAD)](https://cloud.google.com/life-sciences/docs/resources/public-datasets/gnomad)
+
+### View pipeline outputs
+
+In Data Explorer, you can:
+
+ - **View bucket details**:
+ Select the information icon next to a bucket in the list to view the cloud provider, bucket address, and credentials.
+
+ 
+
+ - **View bucket contents**:
+ Select a bucket name from the list to view the bucket contents. The file type, size, and path of objects are displayed in columns next to the object name. For example, view the outputs of your [nf-core/rnaseq](./comm-showcase.mdx#launch-the-nf-corernaseq-pipeline) run:
+
+ 
+
+ - **Preview files**:
+ Select a file to open a preview window that includes a **Download** button. For example, view the resultant gene counts of the salmon quantification step of your [nf-core/rnaseq](./comm-showcase.mdx#launch-the-nf-corernaseq-pipeline) run:
+
+ 
+
+## Datasets
+
+Datasets in Platform are CSV (comma-separated values) and TSV (tab-separated values) files stored in a workspace. You can select stored datasets as input data when launching a pipeline.
+
+
+ **Example: nf-core/rnaseq test samplesheet**
+
+ The [nf-core/rnaseq](https://github.com/nf-core/rnaseq) pipeline works with input datasets (samplesheets) containing sample names, FASTQ file locations, and indications of strandedness. The Seqera Community Showcase sample dataset for nf-core/rnaseq specifies the paths to 7 small sub-sampled FASTQ files from a yeast RNAseq dataset:
+
+ **Example nf-core/rnaseq dataset**
+
+ | sample | fastq_1 | fastq_2 | strandedness |
+ | ------------------- | ------------------------------------ | ------------------------------------ | ------------ |
+ | WT_REP1 | s3://nf-core-awsmegatests/rnaseq/... | s3://nf-core-awsmegatests/rnaseq/... | reverse |
+ | WT_REP1 | s3://nf-core-awsmegatests/rnaseq/... | s3://nf-core-awsmegatests/rnaseq/... | reverse |
+ | WT_REP2 | s3://nf-core-awsmegatests/rnaseq/... | s3://nf-core-awsmegatests/rnaseq/... | reverse |
+ | RAP1_UNINDUCED_REP1 | s3://nf-core-awsmegatests/rnaseq/... | | reverse |
+ | RAP1_UNINDUCED_REP2 | s3://nf-core-awsmegatests/rnaseq/... | | reverse |
+ | RAP1_UNINDUCED_REP2 | s3://nf-core-awsmegatests/rnaseq/... | | reverse |
+ | RAP1_IAA_30M_REP1 | s3://nf-core-awsmegatests/rnaseq/... | s3://nf-core-awsmegatests/rnaseq/... | reverse |
+
+
+
+Download the nf-core/rnaseq [samplesheet_test.csv](samplesheet_test.csv).
+
+### Add a dataset
+
+From the **Datasets** tab, select **Add Dataset**.
+
+
+
+Specify the following dataset details:
+
+- A **Name** for the dataset, such as `nf-core-rnaseq-test-dataset`.
+- A **Description** for the dataset.
+- Select the **First row as header** option to prevent Platform from parsing the header row of the samplesheet as sample data.
+- Select **Upload file** and browse to your CSV or TSV file in local storage, or simply drag and drop it into the box.
+
+Notice the location of the files in the nf-core/rnaseq example dataset point to a path on S3. This could also be a path to a shared filesystem, if using an HPC compute environment. Nextflow will use these paths to stage the files into the task working directory.
+
+:::info
+Platform does not store the data used for analysis in pipelines. The datasets must provide the locations of data that is stored on your own infrastructure.
+:::
\ No newline at end of file
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/add-pipelines.mdx b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/add-pipelines.mdx
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+---
+title: "Add pipelines"
+description: "An introduction to adding pipelines to Seqera Platform workspaces"
+date: "12 Jul 2024"
+tags: [platform, launch, pipelines, launchpad]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The Launchpad lists the preconfigured Nextflow pipelines that can be executed on the [compute environments](../../compute-envs/overview.mdx) in your workspace.
+
+Platform offers two methods to import pipelines to your workspace Launchpad — directly from Seqera Pipelines, or manually via **Add pipeline** in Platform. This page illustrates each method, using [nf-core/rnaseq](https://github.com/nf-core/rnaseq) as an example.
+
+### Import nf-core/rnaseq from Seqera Pipelines
+
+[Seqera Pipelines](https://seqera.io/pipelines) is an open-source resource that provides a curated collection of high-quality, open-source pipelines. Each pipeline includes a curated test dataset to use in a test run in just a few steps.
+
+
+
+To import the `nf-core/rnaseq` pipeline:
+1. Select **Launch** next to the pipeline name in the list. In the **Add pipeline** tab, select **Cloud** or **Enterprise** depending on your Platform account type, then provide the information needed for Seqera Pipelines to access your Platform instance:
+ - **Seqera Cloud**: Paste your Platform **Access token** and select **Next**.
+ - **Seqera Enterprise**: Specify the **Seqera Platform URL** (hostname) and **Base API URL** for your Enterprise instance, then paste your Platform **Access token** and select **Next**.
+ :::note
+ If you do not have a Platform access token, select **Get your access token from Seqera Platform** to open the Access tokens page in a new browser window.
+ :::
+1. Select the Platform **Organization**, **Workspace**, and **Compute environment** for the imported pipeline.
+1. (Optional) Customize the **Pipeline Name** and **Pipeline Description**.
+1. Select **Add Pipeline**.
+
+
+
+:::tip
+To launch pipelines directly with CLI tools, select the **Launch Pipeline** tab to grab commands for Nextflow, [Seqera Platform CLI](./automation.mdx), and [nf-core/tools](https://nf-co.re/docs/nf-core-tools):
+
+
+:::
+
+### Add nf-core/rnaseq from the Launchpad
+
+
+
+From your workspace Launchpad, select **Add Pipeline** and specify the following pipeline details:
+
+- **Name**: `nf-core/rnaseq`, or a custom name of your choice.
+- (*Optional*) **Description**: A summary of the pipeline or any information that may be useful to workspace participants when selecting a pipeline to launch.
+- (*Optional*) **Labels**: Categorize the pipeline according to arbitrary criteria (such as reference genome version) that may help workspace participants to select the appropriate pipeline for their analysis.
+- **Compute environment**: Select an existing workspace [compute environment](../../compute-envs/overview.mdx).
+- **Pipeline to launch**: `https://github.com/nf-core/rnaseq`
+ - Platform allows you to select any public or private Git repository that contains Nextflow source code.
+- **Revision number**: Platform will search all of the available tags and branches in the provided pipeline repository and render a dropdown to select the appropriate version.
+ :::tip
+ Selecting a specific pipeline version is important for reproducibility as this ensures that each run with the same input data will generate the same results.
+ :::
+- (*Optional*) **Config profiles**: `test`
+ - Select a predefined profile for the Nextflow pipeline.
+ :::info
+ All nf-core pipelines include a `test` profile that is associated with a minimal test dataset. This profile runs the pipeline with heavily sub-sampled input data for the purposes of [CI/CD](https://resources.github.com/devops/ci-cd/) and to quickly confirm that the pipeline runs on your infrastructure.
+ :::
+- (*Optional*) **Pipeline parameters**:
+ - Set any custom pipeline parameters that will be prepopulated when users launch the pipeline from the Launchpad. For example, set the path to local reference genomes so users don't have to worry about locating these files when launching the pipeline.
+ 
+- (*Optional*) **Pre-run script**:
+ - Define Bash code that executes before the pipeline launches in the same environment where Nextflow runs.
+ :::info
+ Pre-run scripts are useful for defining executor settings, troubleshooting, and defining a specific version of Nextflow with the `NXF_VER` environment variable.
+ :::
+ 
+
+:::note
+Pre-filled pipeline settings (such as compute environment, config profiles, and pipeline parameters) can be overridden during pipeline launch by workspace participants with the necessary [permissions](../../orgs-and-teams/roles.mdx).
+:::
+
+After you have populated the appropriate fields, select **Add**. `nf-core/rnaseq` is now available for workspace participants to launch in the preconfigured compute environment.
+
+
+
+
+
+
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+---
+title: "Automation"
+description: "An introduction to automation with APIs and CLI tools in Seqera Platform"
+date: "21 Jul 2024"
+tags: [platform, automation, api, cli, seqerakit]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Seqera Platform provides multiple methods of programmatic interaction to automate the execution of pipelines, chain pipelines together, and integrate Platform with third-party services.
+
+### Platform API
+
+The Seqera Platform public API is the lowest-level method of programmatic interaction. All operations available in the user interface can be achieved through the API.
+
+The API can be used to trigger the launch of pipelines based on a file event (such as the upload of a file to a bucket) or completion of a previous run.
+
+The API can be accessed from `https://api.cloud.seqera.io`.
+
+The full list of endpoints is available in Seqera's [OpenAPI schema](https://cloud.seqera.io/openapi/index.html). The API requires an authentication token to be specified in every API request. This can be created in your user menu under **Your tokens**.
+
+
+
+The token is only displayed once. Store your token in a secure place. Use this token to authenticate requests to the API.
+
+
+ **Example pipeline launch API request**
+ ```
+ curl -X POST "https://api.cloud.seqera.io/workflow/launch?workspaceId=38659136604200" \
+ -H "Accept: application/json" \
+ -H "Authorization: Bearer " \
+ -H "Content-Type: application/json" \
+ -H "Accept-Version:1" \
+ -d '{
+ "launch": {
+ "computeEnvId": "hjE97A8TvD9PklUb0hwEJ",
+ "runName": "first-time-pipeline-api-byname",
+ "pipeline": "first-time-pipeline",
+ "workDir": "s3://nf-ireland",
+ "revision": "master"
+ }
+ }'
+ ```
+
+
+
+
+### Platform CLI
+
+For bioinformaticians and scientists more comfortable with the CLI, Platform uses a command line utility called `tw` to manage resources.
+
+The CLI provides an interface to launch pipelines, manage compute environments, retrieve run metadata, and monitor runs on Platform. It provides a Nextflow-like experience for bioinformaticians who prefer the CLI and allows you store Seqera resource configuration (pipelines, compute environments, etc.) as code. The CLI is built on top of the [Seqera Platform API](#platform-api) but is simpler to use. For example, you can refer to resources by name instead of their unique identifier.
+
+
+
+See [CLI](../../cli/cli.mdx) for installation and usage details.
+
+:::info
+**Example pipeline launch CLI command**
+
+```console
+tw launch hello --workspace community/showcase
+```
+:::
+
+### seqerakit
+
+`seqerakit` is a Python wrapper for the Platform CLI which can be leveraged to automate the creation of all Platform entities via a YAML format configuration file. It can be used to automate the creation of entities, from organizations and workspaces to pipelines and compute environments, and the execution of workflows with one YAML file.
+
+The key features are:
+
+- **Simple configuration**: All of the command-line options available in the Platform CLI can be defined in simple YAML format.
+- **Infrastructure as Code**: Enable users to manage and provision their infrastructure specifications.
+- **Automation**: End-to-end creation of entities within Platform, from adding an organization to launching pipeline(s) within that organization.
+
+See the [seqerakit GitHub repository](https://github.com/seqeralabs/seqera-kit/) for installation and usage details.
+
+
+ **Example pipeline launch seqerakit configuration and command**
+
+ Create a YAML file called `hello.yaml`:
+
+ ```yaml
+ launch:
+ - name: "hello-world"
+ url: "https://github.com/nextflow-io/hello"
+ workspace: "seqeralabs/showcase"
+ ```
+
+ Then run seqerakit:
+
+ ```console
+ $ seqerakit hello.yaml
+ ```
+
+
+
+## Resources
+Common use cases for the automation methods above include automatically executing a pipeline as data arrives from a sequencer, or integrating Platform into a broader user-facing application. For a step-by-step guide to set up these automation methods, see [Workflow automation for Nextflow pipelines](https://seqera.io/blog/workflow-automation/).
+
+For examples of how to use automation methods, see [Automating pipeline execution with Nextflow and Tower](https://seqera.io/blog/automating-workflows-with-nextflow-and-tower/).
\ No newline at end of file
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/comm-showcase.mdx b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/comm-showcase.mdx
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+---
+title: "Explore Platform Cloud"
+description: "Seqera Platform Cloud demonstration walkthrough"
+date: "8 Jul 2024"
+tags: [platform, launch, pipelines, launchpad, showcase tutorial]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+:::info
+This demo tutorial provides an introduction to Seqera Platform, including instructions to:
+- Launch, monitor, and optimize the [nf-core/rnaseq](https://github.com/nf-core/rnaseq) pipeline
+- Select pipeline input data with [Data Explorer](../../data/data-explorer.mdx) and Platform [datasets](../../data/datasets.mdx)
+- Perform tertiary analysis of pipeline results with [Data Studios](../../data/data-studios.mdx)
+
+The Platform Community Showcase is a Seqera-managed demonstration workspace with all the resources needed to follow along with this tutorial. All [Seqera Cloud](https://cloud.seqera.io) users have access to this example workspace by default.
+:::
+
+The Launchpad in every Platform workspace allows users to easily create and share Nextflow pipelines that can be executed on any supported infrastructure, including all public clouds and most HPC schedulers. A Launchpad pipeline consists of a pre-configured workflow repository, [compute environment](../../compute-envs/overview.mdx), and launch parameters.
+
+The Community Showcase contains 15 preconfigured pipelines, including [nf-core/rnaseq](https://github.com/nf-core/rnaseq), a bioinformatics pipeline used to analyze RNA sequencing data.
+
+The workspace also includes three preconfigured AWS Batch compute environments to run Showcase pipelines, and various Platform datasets and public data sources (accessed via Data Explorer) to use as pipeline input.
+
+:::note
+To skip this Community Showcase demo and start running pipelines on your own infrastructure:
+1. Set up an [organization workspace](../workspace-setup.mdx).
+1. Create a workspace [compute environment](../../compute-envs/overview.mdx) for your cloud or HPC compute infrastructure.
+1. [Add pipelines](./add-pipelines.mdx) to your workspace.
+:::
+
+## Launch the nf-core/rnaseq pipeline
+
+:::note
+This guide is based on version 3.14.0 of the nf-core/rnaseq pipeline. Launch form parameters may differ in other versions.
+:::
+
+Navigate to the Launchpad in the `community/showcase` workspace and select **Launch** next to the `nf-core-rnaseq` pipeline to open the launch form.
+
+ 
+
+
+ Nextflow parameter schema
+
+ The launch form lets you configure the pipeline execution. The pipeline parameters in this form are rendered from a [pipeline schema](../../pipeline-schema/overview.mdx) file in the root of the pipeline Git repository. `nextflow_schema.json` is a simple JSON-based schema describing pipeline parameters for pipeline developers to easily adapt their in-house Nextflow pipelines to be executed in Platform.
+
+ :::tip
+ See [Best Practices for Deploying Pipelines with the Seqera Platform](https://seqera.io/blog/best-practices-for-deploying-pipelines-with-seqera-platform/) to learn how to build the parameter schema for any Nextflow pipeline automatically with tooling maintained by the nf-core community.
+ :::
+
+
+
+### Parameter selection
+
+Adjust the following Platform-specific options:
+
+- **Workflow run name**: A unique identifier for the run, pre-filled with a random name. This can be customized.
+- **Labels**: Assign new or existing labels to the run. For example, a project ID or genome version.
+
+`nf-core/rnaseq` requires a set of parameters to run:
+
+#### input
+
+Most nf-core pipelines use the `input` parameter in a standardized way to specify an input samplesheet that contains paths to input files (such as FASTQ files) and any additional metadata needed to run the pipeline. Use **Browse** to select either a file path in cloud storage via **Data Explorer**, or a pre-loaded **Dataset**:
+
+
+
+ **Data Explorer**
+
+ In the **Data Explorer** tab, select the `nf-tower-data` bucket, then search for and select the `rnaseq_sample_data.csv` file.
+
+
+
+
+ **Datasets**
+
+ In the **Datasets** tab, search for and select `rnaseq_sample_data`.
+
+
+
+
+
+:::tip
+- See [Add data](./add-data.mdx) to learn how to add datasets and Data Explorer cloud buckets to your own workspaces.
+:::
+
+#### output
+
+Most nf-core pipelines use the `outdir` parameter in a standardized way to specify where the final results created by the pipeline are published. `outdir` must be unique for each pipeline run. Otherwise, your results will be overwritten.
+
+For this tutorial test run, keep the default `outdir` value (`./results`).
+
+:::tip
+For the `outdir` parameter in pipeline runs in your own workspace, select **Browse** to specify a cloud storage directory using Data Explorer, or enter a cloud storage directory path to publish pipeline results to manually.
+
+
+:::
+
+#### Pipeline-specific parameters
+
+Modify other parameters to customize the pipeline execution through the parameters form. For example, under **Read trimming options**, change the `trimmer` to select `fastp` in the dropdown menu instead of `trimgalore`.
+
+
+
+Select **Launch** to start the run and be directed to the **Runs** tab with your run in a **submitted** status at the top of the list.
+
+## View run information
+
+### Run details page
+
+As the pipeline runs, run details will populate with parameters, logs, and other important execution details:
+
+
+ View run details
+
+ - **Command-line**: The Nextflow command invocation used to run the pipeline. This contains details about the pipeline version (`-r 3.14.0` flag) and profile, if specified (`-profile test` flag).
+ - **Parameters**: The exact set of parameters used in the execution. This is helpful for reproducing the results of a previous run.
+ - **Resolved Nextflow configuration**: The full Nextflow configuration settings used for the run. This includes parameters, but also settings specific to task execution (such as memory, CPUs, and output directory).
+ - **Execution Log**: A summarized Nextflow log providing information about the pipeline and the status of the run.
+ - **Datasets**: Link to datasets, if any were used in the run.
+ - **Reports**: View pipeline outputs directly in the Platform.
+
+ 
+
+
+
+### View reports
+
+Most Nextflow pipelines generate reports or output files which are useful to inspect at the end of the pipeline execution. Reports can contain quality control (QC) metrics that are important to assess the integrity of the results.
+
+
+ View run reports
+
+
+ 
+
+ For example, for the nf-core/rnaseq pipeline, view the [MultiQC](https://docs.seqera.io/multiqc) report generated. MultiQC is a helpful reporting tool to generate aggregate statistics and summaries from bioinformatics tools.
+
+ 
+
+ The paths to report files point to a location in cloud storage (in the `outdir` directory specified during launch), but you can view the contents directly and download each file without navigating to the cloud or a remote filesystem.
+
+ #### Specify outputs in reports
+
+ To customize and instruct Platform where to find reports generated by the pipeline, a [tower.yml](https://github.com/nf-core/rnaseq/blob/master/tower.yml) file that contains the locations of the generated reports must be included in the pipeline repository.
+
+ In the nf-core/rnaseq pipeline, the MULTIQC process step generates a MultiQC report file in HTML format:
+
+ ```yaml
+ reports:
+ multiqc_report.html:
+ display: "MultiQC HTML report"
+ ```
+
+
+
+:::note
+See [Reports](../../reports/overview.mdx) to configure reports for pipeline runs in your own workspace.
+:::
+
+### View general information
+
+The run details page includes general information about who executed the run and when, the Git hash and tag used, and additional details about the compute environment and Nextflow version used.
+
+
+ View general run information
+
+ 
+
+ The **General** panel displays top-level information about a pipeline run:
+
+ - Unique workflow run ID
+ - Workflow run name
+ - Timestamp of pipeline start
+ - Pipeline version and Git commit ID
+ - Nextflow session ID
+ - Username of the launcher
+ - Work directory path
+
+
+
+### View process and task details
+
+Scroll down the page to view:
+
+- The progress of individual pipeline **Processes**
+- **Aggregated stats** for the run (total walltime, CPU hours)
+- **Workflow metrics** (CPU efficiency, memory efficiency)
+- A **Task details** table for every task in the workflow
+
+The task details table provides further information on every step in the pipeline, including task statuses and metrics:
+
+
+ View task details
+
+ Select a task in the task table to open the **Task details** dialog. The dialog has three tabs: **About**, **Execution log**, and **Data Explorer**.
+
+ #### About
+
+ The **About** tab includes:
+
+ 1. **Name**: Process name and tag
+ 2. **Command**: Task script, defined in the pipeline process
+ 3. **Status**: Exit code, task status, and number of attempts
+ 4. **Work directory**: Directory where the task was executed
+ 5. **Environment**: Environment variables that were supplied to the task
+ 6. **Execution time**: Metrics for task submission, start, and completion time
+ 7. **Resources requested**: Metrics for the resources requested by the task
+ 8. **Resources used**: Metrics for the resources used by the task
+
+ 
+
+ #### Execution log
+
+ The **Execution log** tab provides a real-time log of the selected task's execution. Task execution and other logs (such as stdout and stderr) are available for download from here, if still available in your compute environment.
+
+
+
+### Task work directory in Data Explorer
+
+If a task fails, a good place to begin troubleshooting is the task's work directory. Nextflow hash-addresses each task of the pipeline and creates unique directories based on these hashes.
+
+
+ View task log and output files
+
+ Instead of navigating through a bucket on the cloud console or filesystem, use the **Data Explorer** tab in the Task window to view the work directory.
+
+ Data Explorer allows you to view the log files and output files generated for each task, directly within Platform. You can view, download, and retrieve the link for these intermediate files to simplify troubleshooting.
+
+ 
+
+
+
+## Tertiary analysis
+
+Tertiary analysis of pipeline results is often performed in platforms like Jupyter Notebook or RStudio. Setting up the infrastructure for these platforms, including accessing pipeline data and the necessary bioinformatics packages, can be complex and time-consuming.
+
+**Data Studios** streamlines the process of creating interactive analysis environments for Platform users. With built-in templates, creating a data studio is as simple as adding and sharing pipelines or datasets.
+
+### Analyze RNAseq data in Data Studios
+
+In the **Data Studios** tab, you can monitor and see the details of the data studios in the Community Showcase workspace.
+
+Data Studios is used to perform bespoke analysis on the results of upstream workflows. For example, in the Community Showcase workspace we have run the **nf-core/rnaseq** pipeline to quantify gene expression, followed by **nf-core/differentialabundance** to derive differential expression statistics. The workspace contains a data studio with these results from cloud storage mounted into the studio to perform further analysis. One of these outputs is an RShiny application, which can be deployed for interactive analysis.
+
+#### Connect to the RNAseq analysis studio
+
+Select the `rnaseq_to_differentialabundance` data studio. This studio consists of an RStudio environment that uses an existing compute environment available in the showcase workspace. The studio also contains mounted data generated from the nf-core/rnaseq and subsequent nf-core/differentialabundance pipeline runs, directly from AWS S3.
+
+
+
+Select **Connect** to view the running RStudio environment. The `rnaseq_to_differentialabundance` studio includes the necessary R packages for deploying an RShiny application to visualize the RNAseq data.
+
+Deploy the RShiny app in the data studio by selecting the green play button on the last chunk of the R script:
+
+
+
+:::note
+Data Studios allows you to specify the resources each studio will use. When [creating your own data studios](../../data/data-studios.mdx) with shared compute environment resources, you must allocate sufficient resources to the compute environment to prevent data studio or pipeline run interruptions.
+:::
+
+### Explore results
+
+The RShiny app will deploy in a separate browser window, providing a data interface. Here you can view information about your sample data, perform QC or exploratory analysis, and view the results of differential expression analyses.
+
+
+
+
+ Sample clustering with PCA plots
+
+ In the **QC/Exploratory** tab, select the PCA (Principal Component Analysis) plot to visualize how the samples group together based on their gene expression profiles.
+
+ In this example, we used RNA sequencing data from the publicly-available ENCODE project, which includes samples from four different cell lines:
+
+ - **GM12878** — a lymphoblastoid cell line
+ - **K562** — a chronic myelogenous leukemia cell line
+ - **MCF-7** — a breast cancer cell line
+ - **H1-hESC** — human embryonic stem cells
+
+ What to look for in the PCA plot:
+
+ - **Replicate clustering**: Ideally, biological replicates of the same cell type should cluster closely together. For example, replicates of MCF-7 (breast cancer cell line) group together. This indicates consistent gene expression profiles among biological replicates.
+ - **Cell type separation**: Different cell types should form distinct clusters. For instance, GM12878, K562, MCF-7, and H1-hESC samples should each form their own separate clusters, reflecting their unique gene expression patterns.
+
+ From this PCA plot, you can gain insights into the consistency and quality of your sequencing data, identify any potential issues, and understand the major sources of variation among your samples - all directly in Platform.
+
+ 
+
+
+
+
+ Gene expression changes with Volcano plots
+
+ In the **Differential** tab, select **Volcano plots** to compare genes with significant changes in expression between two samples. For example, filter for `Type: H1 vs MCF-7` to view the differences in expression between these two cell lines.
+
+ 1. **Identify upregulated and downregulated genes**: The x-axis of the volcano plot represents the log2 fold change in gene expression between the H1 and MCF-7 samples, while the y-axis represents the statistical significance of the changes.
+
+ - **Upregulated genes in MCF-7**: Genes on the left side of the plot (negative fold change) are upregulated in the MCF-7 samples compared to H1. For example, the SHH gene, which is known to be upregulated in cancer cell lines, prominently appears here.
+
+ 2. **Filtering for specific genes**: If you are interested in specific genes, use the filter function. For example, filter for the SHH gene in the table below the plot. This allows you to quickly locate and examine this gene in more detail.
+
+ 3. **Gene expression bar plot**: After filtering for the SHH gene, select it to navigate to a gene expression bar plot. This plot will show you the expression levels of SHH across all samples, allowing you to see in which samples it is most highly expressed.
+
+ - Here, SHH is most highly expressed in MCF-7, which aligns with its known role in cancer cell proliferation.
+
+ Using the volcano plot, you can effectively identify and explore the genes with the most significant changes in expression between your samples, providing a deeper understanding of the molecular differences.
+
+ 
+
+
+
+### Collaborate in the data studio
+
+To share the results of your RNAseq analysis or allow colleagues to perform exploratory analysis, share a link to the data studio by selecting the options menu for the data studio you want to share, then select **Copy data studio URL**. With this link, other authenticated users with the **Connect** [role](../../orgs-and-teams/roles.mdx) (or greater) can access the session directly.
+
+:::note
+See [Data Studios](../../data/data-studios.mdx) to learn how to create data studios in your own workspace.
+:::
+
+## Pipeline optimization
+
+Seqera Platform's task-level resource usage metrics allow you to determine the resources requested for a task and what was actually used. This information helps you fine-tune your configuration more accurately.
+
+However, manually adjusting resources for every task in your pipeline is impractical. Instead, you can leverage the pipeline optimization feature available on the Launchpad.
+
+Pipeline optimization analyzes resource usage data from previous runs to optimize the resource allocation for future runs. After a successful run, optimization becomes available, indicated by the lightbulb icon next to the pipeline turning black.
+
+
+ Optimize nf-core/rnaseq
+
+ Navigate back to the Launchpad and select the lightbulb icon next to the nf-core/rnaseq pipeline to view the optimized profile. You have the flexibility to tailor the optimization's target settings and incorporate a retry strategy as needed.
+
+ #### View optimized configuration
+
+ When you select the lightbulb, you can access an optimized configuration profile in the second tab of the **Customize optimization profile** window.
+
+ This profile consists of Nextflow configuration settings for each process and each resource directive (where applicable): **cpus**, **memory**, and **time**. The optimized setting for a given process and resource directive is based on the maximum use of that resource across all tasks in that process.
+
+ Once optimization is selected, subsequent runs of that pipeline will inherit the optimized configuration profile, indicated by the black lightbulb icon with a checkmark.
+
+ :::note
+ Optimization profiles are generated from one run at a time, defaulting to the most recent run, and _not_ an aggregation of previous runs.
+ :::
+
+ 
+
+ Verify the optimized configuration of a given run by inspecting the resource usage plots for that run and these fields in the run's task table:
+
+ | Description | Key |
+ | ------------ | ---------------------- |
+ | CPU usage | `pcpu` |
+ | Memory usage | `peakRss` |
+ | Runtime | `start` and `complete` |
+
+
+
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/data-studios.mdx b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/data-studios.mdx
new file mode 100644
index 000000000..348630789
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/data-studios.mdx
@@ -0,0 +1,96 @@
+---
+title: "Data Studios"
+description: "An introduction to Data Studios in Seqera Platform"
+date: "8 Jul 2024"
+tags: [platform, data, data studios]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+:::info
+This guide provides an introduction to Data Studios using a demo studio in the Community Showcase workspace. See [Data Studios](../../data/data-studios.mdx) to learn how to create data studios in your own workspace.
+:::
+
+Tertiary analysis of pipeline results is often performed in platforms like Jupyter Notebook or RStudio. Setting up the infrastructure for these platforms, including accessing pipeline data and the necessary bioinformatics packages, can be complex and time-consuming.
+
+Data Studios streamlines the process of creating interactive analysis environments for Platform users. With built-in templates, creating a data studio is as simple as adding and sharing pipelines or datasets. Platform manages all the details, enabling you to easily select your preferred interactive tool and analyze your data.
+
+In the **Data Studios** tab, you can monitor and see the details of the data studios in the Community Showcase workspace.
+
+
+
+Select the options menu next to a data studio to:
+- See data studio details
+- Start or stop the studio, and connect to a running studio
+- Copy the studio URL to share it with collaborators
+
+### Analyze RNAseq data in Data Studios
+
+Data Studios is used to perform bespoke analysis on the results of upstream workflows. For example, in the Community Showcase workspace we have run the nf-core/rnaseq workflow to quantify gene expression, followed by nf-core/differentialabundance to derive differential expression statistics. The workspace contains a data studio with these results from cloud storage mounted into the studio to perform further analysis. One of these outputs is an RShiny application, which can be deployed for interactive analysis.
+
+### Open the RNAseq analysis studio
+
+Select the `rnaseq_to_differentialabundance` data studio. This studio consists of an RStudio environment that uses an existing compute environment available in the showcase workspace. The studio also contains mounted data generated from the nf-core/rnaseq and subsequent nf-core/differentialabundance pipeline runs, directly from AWS S3.
+
+
+
+:::info
+Data Studios allows you to specify the resources each studio will use. When [creating your own data studios](../../data/data-studios.mdx) with shared compute environment resources, you must allocate sufficient resources to the compute environment to prevent data studio or pipeline run interruptions.
+:::
+
+### Connect to the data studio
+
+This data studio will start an RStudio environment which already contains the necessary R packages for deploying an RShiny application to interact with various visualizations of the RNAseq data. The studio also contains an R Markdown document with the commands in place to generate the application.
+
+Deploy the RShiny app in the data studio by selecting the green play button on the last chunk of the R script:
+
+
+
+### Explore results in the RShiny app
+
+The RShiny app will deploy in a separate browser window, providing a data interface. Here you can view information about your sample data, perform QC or exploratory analysis, and view the differential expression analyses.
+
+
+
+#### Sample clustering with PCA plots
+
+In the **QC/Exploratory** tab, select the PCA (Principal Component Analysis) plot to visualize how the samples group together based on their gene expression profiles.
+
+In this example, we used RNA sequencing data from the publicly-available ENCODE project, which includes samples from four different cell lines:
+
+- **GM12878** — a lymphoblastoid cell line
+- **K562** — a chronic myelogenous leukemia cell line
+- **MCF-7** — a breast cancer cell line
+- **H1-hESC** — a human embryonic stem cell line
+
+What to look for in the PCA plot:
+
+- **Replicate clustering**: Ideally, replicates of the same cell type should cluster closely together. For example, replicates of the MCF-7 cells group together. This indicates consistent gene expression profiles among replicates.
+- **Cell type separation**: Different cell types should form distinct clusters. For instance, GM12878, K562, MCF-7, and H1-hESC cells should each form their own separate clusters, reflecting their unique gene expression patterns.
+
+From this PCA plot, you can gain insights into the consistency and quality of your sequencing data, identify any potential issues, and understand the major sources of variation among your samples - all directly in Platform.
+
+
+
+#### Gene expression changes with Volcano plots
+
+In the **Differential** tab, select **Volcano plots** to compare genes with significant changes in expression between two samples. For example, filter for `Type: H1 vs MCF-7` to view the differences in expression between these two cell lines.
+
+1. **Identify upregulated and downregulated genes**: The x-axis of the volcano plot represents the log2 fold change in gene expression between the H1 and MCF-7 cell lines, while the y-axis represents the statistical significance of the changes.
+
+ - **Upregulated genes in MCF-7**: Genes on the left side of the plot (negative fold change) are upregulated in the MCF-7 samples compared to H1. For example, the _SHH_ gene, which is known to be upregulated in cancer cell lines, prominently appears here.
+
+2. **Filtering for specific genes**: If you are interested in specific genes, use the filter function. For example, filter for the _SHH_ gene in the table below the plot. This allows you to quickly locate and examine this gene in more detail.
+
+3. **Gene expression bar plot**: After filtering for the _SHH_ gene, select it to navigate to a gene expression bar plot. This plot will show you the expression levels of _SHH_ across all samples, allowing you to see in which samples it is most highly expressed.
+
+ - Here, _SHH_ is most highly expressed in MCF-7, which aligns with its known role in cancer cell proliferation.
+
+Using the volcano plot, you can effectively identify and explore the genes with the most significant changes in expression between your samples, providing a deeper understanding of the molecular differences.
+
+
+
+### Collaborate in the data studio
+
+To share the results of your RNAseq analysis or allow colleagues to perform exploratory analysis, share a link to the data studio by selecting the options menu for the data studio you want to share, then select **Copy data studio URL**. With this link, other authenticated users with the **Connect** [role](../../orgs-and-teams/roles.mdx) (or greater) can access the session directly.
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/launch-pipelines.mdx b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/launch-pipelines.mdx
new file mode 100644
index 000000000..79d4ed749
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/launch-pipelines.mdx
@@ -0,0 +1,100 @@
+---
+title: "Launch pipelines"
+description: "An introduction to launching nf-core/rnaseq in the community/showcase workspace"
+date: "8 Jul 2024"
+tags: [platform, launch, pipelines, launchpad, showcase tutorial]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+:::info
+This tutorial provides an introduction to Seqera Platform, including instructions to:
+- Launch, monitor, and optimize the [nf-core/rnaseq](https://github.com/nf-core/rnaseq) pipeline
+- Select pipeline input data with [Data Explorer](../../data/data-explorer.mdx) and Platform [datasets](../../data/datasets.mdx)
+- Perform tertiary analysis of pipeline results with [Data Studios](../../data/data-studios.mdx)
+
+The Platform Community Showcase workspace contains all the resources needed to follow along with this tutorial. All [Seqera Cloud](https://cloud.seqera.io) users have access to this example workspace by default.
+:::
+
+The Launchpad in every Platform workspace allows users to easily create and share Nextflow pipelines that can be executed on any supported infrastructure, including all public clouds and most HPC schedulers. A Launchpad pipeline consists of a pre-configured workflow repository, [compute environment](../../compute-envs/overview.mdx), and launch parameters.
+
+The Community Showcase contains 15 preconfigured pipelines, including [nf-core/rnaseq](https://github.com/nf-core/rnaseq), a bioinformatics pipeline used to analyze RNA sequencing data.
+
+The workspace also includes three preconfigured AWS Batch compute environments to run Showcase pipelines, and various Platform datasets and public data sources (accessed via Data Explorer) to use as pipeline input.
+
+:::info
+To skip this Community Showcase tutorial and start running pipelines on your own infrastructure:
+1. Create an [organization](../../orgs-and-teams/organizations.mdx) and add members.
+1. Create a [workspace](../../orgs-and-teams/workspace-management.mdx) and add workspace participants.
+1. Create a workspace [compute environment](../../compute-envs/overview.mdx) for your cloud or HPC compute infrastructure.
+1. [Add pipelines](./add-pipelines.mdx) to your workspace.
+:::
+
+## Launch the nf-core/rnaseq pipeline
+
+Navigate to the Launchpad in the `community/showcase` workspace and select **Launch** next to the `nf-core-rnaseq` pipeline to open the launch form.
+
+ 
+
+### Nextflow parameter schema
+
+The launch form lets you configure the pipeline execution. The pipeline parameters are rendered from a [pipeline schema](../../pipeline-schema/overview.mdx) file in the root of the pipeline Git repository. `nextflow_schema.json` is a simple JSON-based schema describing pipeline parameters for pipeline developers to easily adapt their in-house Nextflow pipelines to be executed in Platform.
+
+:::tip
+See [Best Practices for Deploying Pipelines with the Seqera Platform](https://seqera.io/blog/best-practices-for-deploying-pipelines-with-seqera-platform/) to learn how to build the parameter schema for any Nextflow pipeline automatically with tooling maintained by the nf-core community.
+:::
+
+### Parameter selection
+
+Adjust the following Platform-specific options:
+
+- `Workflow run name`: A unique identifier for the run, pre-filled with a random name. This can be customized.
+- `Labels`: Assign new or existing labels to the run. For example, a project ID or genome version.
+
+Each pipeline requires a set of parameters to run:
+
+#### input
+
+Most nf-core pipelines use the `input` parameter in a standardized way to specify an input samplesheet that contains paths to input files (such as FASTQ files) and any additional metadata needed to run the pipeline. Use **Browse** to select either a file path in cloud storage via **Data Explorer**, or a pre-loaded **Dataset**:
+
+
+
+ **Data Explorer**
+
+ In the **Data Explorer** tab, select the `nf-tower-data` bucket, then search for and select the `rnaseq_sample_data.csv` file.
+
+
+
+
+ **Datasets**
+
+ In the **Datasets** tab, search for and select `rnaseq_sample_data`.
+
+
+
+
+
+:::tip
+- See [Add data](./add-data.mdx) to upload your own samplesheet datasets, and add private and public cloud storage buckets to your workspace.
+:::
+
+#### output
+
+Most nf-core pipelines use the `outdir` parameter in a standardized way to specify where the final results created by the pipeline are published. `outdir` must be unique for each pipeline run. Otherwise, your results will be overwritten.
+
+For this tutorial test run, keep the default `outdir` value (`./results`).
+
+:::info
+For the `outdir` parameter in pipeline runs in your own workspace, select **Browse** to specify a cloud storage directory using Data Explorer, or enter a cloud storage directory path to publish pipeline results to manually.
+
+
+:::
+
+#### Pipeline-specific parameters
+
+Modify other parameters to customize the pipeline execution through the parameters form. For example, under **Read trimming options**, change the `trimmer` to select `fastp` in the dropdown menu instead of `trimgalore`.
+
+
+
+Select **Launch** to start the run and be directed to the **Runs** tab with your run in a **submitted** status at the top of the list.
\ No newline at end of file
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/logo.svg b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/logo.svg
new file mode 100644
index 000000000..ac470e9eb
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/logo.svg
@@ -0,0 +1,17 @@
+
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/monitor-runs.mdx b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/monitor-runs.mdx
new file mode 100644
index 000000000..55148ef18
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/monitor-runs.mdx
@@ -0,0 +1,46 @@
+---
+title: "Monitor runs"
+description: "An introduction to monitoring runs in Seqera Platform"
+date: "8 Jul 2024"
+tags: [platform, monitoring]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+There are several ways to monitor pipeline runs in Seqera Platform:
+
+### Workspace view
+
+Access a full history of all runs in a given workspace via the **Runs** tab.
+
+
+
+### All runs view
+
+Access the **All runs** page from the user menu. This page provides a comprehensive overview of the runs across the entire Platform instance. The default view includes all organizations and workspaces accessible to the user. However, you can select visible workspaces from the dropdown next to **View**, and filter for a particular set of runs using any of the following fields:
+
+- `status`
+- `label`
+- `workflowId`
+- `runName`
+- `username`
+- `projectName`
+- `after: YYYY-MM-DD`
+- `before: YYYY-MM-DD`
+- `sessionId`
+- `is:starred`
+
+For example:
+
+```
+rnaseq username:johndoe status:succeeded after:2024-01-01
+```
+
+
+
+### Dashboard view
+
+Access the **Dashboard** from the user menu. This page provides an overview of the total runs across the Platform instance, grouped by run status. The default view includes all organizations and workspaces accessible to the user. Select visible workspaces from the dropdown next to **View** and filter by time, including a custom date range up to 12 months. Select **Export data** to download a CSV file with the available export data.
+
+
\ No newline at end of file
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/pipeline-optimization.mdx b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/pipeline-optimization.mdx
new file mode 100644
index 000000000..cd67be592
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/pipeline-optimization.mdx
@@ -0,0 +1,41 @@
+---
+title: "Pipeline optimization"
+description: "An introduction to pipeline optimization in Seqera Platform"
+date: "8 Jul 2024"
+tags: [platform, runs, pipeline optimization]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Seqera Platform's task-level resource usage metrics allow you to determine the resources requested for a task and what was actually used. This information helps you fine-tune your configuration more accurately.
+
+However, manually adjusting resources for every task in your pipeline is impractical. Instead, you can leverage the pipeline optimization feature available on the Launchpad.
+
+Pipeline optimization analyzes resource usage data from previous runs to optimize the resource allocation for future runs. After a successful run, optimization becomes available, indicated by the lightbulb icon next to the pipeline turning black.
+
+### Optimize nf-core/rnaseq
+
+Navigate back to the Launchpad and select the lightbulb icon next to the nf-core/rnaseq pipeline to view the optimized profile. You have the flexibility to tailor the optimization's target settings and incorporate a retry strategy as needed.
+
+### View optimized configuration
+
+When you select the lightbulb, you can access an optimized configuration profile in the second tab of the **Customize optimization profile** window.
+
+This profile consists of Nextflow configuration settings for each process and each resource directive (where applicable): **cpus**, **memory**, and **time**. The optimized setting for a given process and resource directive is based on the maximum use of that resource across all tasks in that process.
+
+Once optimization is selected, subsequent runs of that pipeline will inherit the optimized configuration profile, indicated by the black lightbulb icon with a checkmark.
+
+:::note
+Optimization profiles are generated from one run at a time, defaulting to the most recent run, and _not_ an aggregation of previous runs.
+:::
+
+
+
+Verify the optimized configuration of a given run by inspecting the resource usage plots for that run and these fields in the run's task table:
+
+| Description | Key |
+| ------------ | ---------------------- |
+| CPU usage | `pcpu` |
+| Memory usage | `peakRss` |
+| Runtime | `start` and `complete` |
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/requirements.txt b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/requirements.txt
new file mode 100644
index 000000000..0f35d1166
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/requirements.txt
@@ -0,0 +1,9 @@
+markdown~=3.5
+mkdocs~=1.5.3
+mkdocs-material~=9.5.6
+mkdocs-material-extensions>=1.0
+pygments~=2.16
+pymdown-extensions~=10.2
+jinja2>=2.11.1
+pillow
+cairosvg
\ No newline at end of file
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/samplesheet_test.csv b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/samplesheet_test.csv
new file mode 100644
index 000000000..b183db0a4
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/samplesheet_test.csv
@@ -0,0 +1,8 @@
+sample,fastq_1,fastq_2,strandedness
+WT_REP1,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357070_1.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357070_2.fastq.gz,auto
+WT_REP1,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357071_1.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357071_2.fastq.gz,auto
+WT_REP2,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357072_1.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357072_2.fastq.gz,reverse
+RAP1_UNINDUCED_REP1,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357073_1.fastq.gz,,reverse
+RAP1_UNINDUCED_REP2,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357074_1.fastq.gz,,reverse
+RAP1_UNINDUCED_REP2,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357075_1.fastq.gz,,reverse
+RAP1_IAA_30M_REP1,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357076_1.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/testdata/GSE110004/SRR6357076_2.fastq.gz,reverse
diff --git a/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/view-run-information.mdx b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/view-run-information.mdx
new file mode 100644
index 000000000..214e3f636
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/quickstart-demo/view-run-information.mdx
@@ -0,0 +1,141 @@
+---
+title: "View run information"
+description: "View pipeline run details in Seqera Platform"
+date: "8 Jul 2024"
+tags: [platform, runs, pipelines, monitoring, showcase tutorial]
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+When you launch a pipeline, you are directed to the **Runs** tab which contains all executed workflows, with your submitted run at the top of the list.
+
+Each new or resumed run is given a random name, which can be customized prior to launch. Each row corresponds to a specific run. As a job executes, it can transition through the following states:
+
+- **submitted**: Pending execution
+- **running**: Running
+- **succeeded**: Completed successfully
+- **failed**: Successfully executed, where at least one task failed with a terminate error strategy
+- **cancelled**: Stopped forceably during execution
+- **unknown**: Indeterminate status
+
+
+
+### View run details for nf-core/rnaseq
+
+The pipeline launched [previously](./launch-pipelines.mdx) is listed on the **Runs** tab. Select it from the list to view the run details.
+
+#### Run details page
+
+As the pipeline runs, run details will populate with the following tabs:
+
+- **Command-line**: The Nextflow command invocation used to run the pipeline. This contains details about the pipeline version (`-r 3.14.0` flag) and profile, if specified (`-profile test` flag).
+- **Parameters**: The exact set of parameters used in the execution. This is helpful for reproducing the results of a previous run.
+- **Configuration**: The full Nextflow configuration settings used for the run. This includes parameters, but also settings specific to task execution (such as memory, CPUs, and output directory).
+- **Datasets**: Link to datasets, if any were used in the run.
+- **Execution Log**: A summarized Nextflow log providing information about the pipeline and the status of the run.
+- **Reports**: View pipeline outputs directly in the Platform.
+
+
+
+### View reports
+
+Most Nextflow pipelines generate reports or output files which are useful to inspect at the end of the pipeline execution. Reports can contain quality control (QC) metrics that are important to assess the integrity of the results.
+
+
+
+For example, for the nf-core/rnaseq pipeline, view the [MultiQC](https://docs.seqera.io/multiqc) report generated. MultiQC is a helpful reporting tool to generate aggregate statistics and summaries from bioinformatics tools.
+
+
+
+The paths to report files point to a location in cloud storage (in the `outdir` directory specified during launch), but you can view the contents directly and download each file without navigating to the cloud or a remote filesystem.
+
+#### Specify outputs in reports
+
+To customize and instruct Platform where to find reports generated by the pipeline, a [tower.yml](https://github.com/nf-core/rnaseq/blob/master/tower.yml) file that contains the locations of the generated reports must be included in the pipeline repository.
+
+In the nf-core/rnaseq pipeline, the MULTIQC process step generates a MultiQC report file in HTML format:
+
+```yaml
+reports:
+ multiqc_report.html:
+ display: "MultiQC HTML report"
+```
+
+:::info
+See [Reports](../../reports/overview.mdx) to configure reports for pipeline runs in your own workspace.
+:::
+
+### View general information
+
+The run details page includes general information about who executed the run and when, the Git hash and tag used, and additional details about the compute environment and Nextflow version used.
+
+
+
+The **General** panel displays top-level information about a pipeline run:
+
+- Unique workflow run ID
+- Workflow run name
+- Timestamp of pipeline start
+- Pipeline version and Git commit ID
+- Nextflow session ID
+- Username of the launcher
+- Work directory path
+
+### View details for a task
+
+Scroll down the page to view:
+
+- The progress of individual pipeline **Processes**
+- **Aggregated stats** for the run (total walltime, CPU hours)
+- A **Task details** table for every task in the workflow
+- **Workflow metrics** (CPU efficiency, memory efficiency)
+
+The task details table provides further information on every step in the pipeline, including task statuses and metrics.
+
+### Task details
+
+Select a task in the task table to open the **Task details** dialog. The dialog has three tabs: **About**, **Execution log**, and **Data Explorer**.
+
+#### About
+
+The **About** tab includes:
+
+1. **Name**: Process name and tag
+2. **Command**: Task script, defined in the pipeline process
+3. **Status**: Exit code, task status, and number of attempts
+4. **Work directory**: Directory where the task was executed
+5. **Environment**: Environment variables that were supplied to the task
+6. **Execution time**: Metrics for task submission, start, and completion time
+7. **Resources requested**: Metrics for the resources requested by the task
+8. **Resources used**: Metrics for the resources used by the task
+
+
+
+#### Execution log
+
+The **Execution log** tab provides a real-time log of the selected task's execution. Task execution and other logs (such as stdout and stderr) are available for download from here, if still available in your compute environment.
+
+### Task work directory in Data Explorer
+
+If a task fails, a good place to begin troubleshooting is the task's work directory.
+
+Nextflow hash-addresses each task of the pipeline and creates unique directories based on these hashes. Instead of navigating through a bucket on the cloud console or filesystem to find the contents of this directory, use the **Data Explorer** tab in the Task window to view the work directory.
+
+Data Explorer allows you to view the log files and output files generated for each task in its working directory, directly within Platform. You can view, download, and retrieve the link for these intermediate files in cloud storage from the **Data Explorer** tab to simplify troubleshooting.
+
+
+
+### Resume a pipeline
+
+Platform uses [Nextflow resume](../../launch/cache-resume.mdx) to resume a failed or cancelled workflow run with the same parameters, using the cached results of previously completed tasks and only executing failed and pending tasks.
+
+
+
+:::info
+To resume a run in your own workspace:
+
+- Select **Resume** from the options menu next to the run.
+- Edit the parameters before launch, if needed.
+- If you have the appropriate [permissions](../../orgs-and-teams/roles.mdx), you may edit the compute environment if needed.
+:::
diff --git a/platform_versioned_docs/version-24.1/getting-started/start-collaborating.mdx b/platform_versioned_docs/version-24.1/getting-started/start-collaborating.mdx
deleted file mode 100644
index fcbce9952..000000000
--- a/platform_versioned_docs/version-24.1/getting-started/start-collaborating.mdx
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: "Start collaborating"
-description: "Start collaborating with your organization and peers."
-date: "15 April 2024"
-tags: [collaborate, workspaces, users]
----
-
-Before you get started with Seqera Platform, you must set up an organization. You can then add members, create teams, and invite external peers for collaboration within your organization.
-
-## Organizations
-
-Organizations are the top-level structure and contain workspaces, members, and teams. You can also add external collaborators to an organization. See [Organization management](../orgs-and-teams/organizations.mdx).
-
-## User workspaces
-
-Each user has a unique personal workspace to manage resources such as pipelines, compute environments, and credentials. See [Workspace management](../orgs-and-teams/workspace-management.mdx).
-
-## Shared workspaces
-
-You can create multiple workspaces within an organization context and associate each of these workspaces with dedicated teams of users, providing fine-grained access control for each team. See [Workspace management](../orgs-and-teams/workspace-management#create-a-shared-workspace).
\ No newline at end of file
diff --git a/platform_versioned_docs/version-24.1/getting-started/workspace-setup.mdx b/platform_versioned_docs/version-24.1/getting-started/workspace-setup.mdx
new file mode 100644
index 000000000..74ea66516
--- /dev/null
+++ b/platform_versioned_docs/version-24.1/getting-started/workspace-setup.mdx
@@ -0,0 +1,52 @@
+---
+title: "Set up your workspace"
+description: "Instructions to create an organization workspace and add participants in Seqera Platform."
+date: "15 April 2024"
+tags: [platform, organizations, workspaces, users]
+---
+
+Workspaces in Seqera Platform contain the resources to run your analyses and manage your computing infrastructure. Workspace members are granted various access roles to interact with the pipelines, compute environments, and data in a workspace. While each Platform user has a personal workspace, resource sharing and access management happens in an organization workspace context.
+
+To create an organization workspace and begin adding participants, first create your organization:
+
+### Create an organization
+
+Organizations are the top-level structure and contain workspaces, members, and teams. You can also add external collaborators to an organization. See [Organization management](../orgs-and-teams/organizations.mdx) for more information.
+
+1. Expand the **Organization | Workspace** dropdown and select **Add organization**.
+1. Complete the organization details fields:
+ - The **Name** to be associated with the organization in Platform.
+ - The **Full name** of the organization.
+ - A **Description** of the organization to provide contextual information that may be helpful to other organization members.
+ - The organization's **Location**.
+ - The organization's **Wesbite URL**.
+ - Drag and drop or upload an image to be used as the organization's **Logo** in Platform.
+1. Select **Add**.
+
+You are the first **Owner** of the organizations that you create. Add other organization owners and members as needed from the organization's **Members** tab.
+
+### Create a workspace
+
+1. From the organization's **Workspaces** tab, select **Add Workspace**.
+1. Complete the workspace details fields:
+ - The **Name** to be displayed for the workspace in Platform.
+ - The **Full name** of the workspace.
+ - A **Description** of the workspace to provide contextual information that may be helpful to other workspace participants.
+ - **Visibility**: Choose whether the workspace's pipelines must be **Shared** to all organization members, or only visible to workspace participants (**Private**).
+1. Select **Add**. You are redirected to your organization's **Workspaces** tab with your new workspace listed.
+1. Select your new workspace, then select the **Participants** tab to **Add Participants**.
+1. Enter the names of existing organization members or teams and select **Add**.
+1. Update a participant's access **Role** from the dropdown, if needed.
+
+### Simplify workspace access with teams
+
+Teams simplify workspace role-based access control (RBAC) for groups of organization members. Per-workspace access roles assigned to teams are inherited by all team members.
+
+Create a new team, add team members, and add the team to workspaces from the **Teams** tab on your organization page:
+
+1. Select **Add Team**, enter the team's details and an optional team avatar image, then select **Add**.
+1. Select **Edit** next to the team name in the list, then select the **Members of team** tab to add new members by name or email.
+ :::note
+ Team members must be existing organization members.
+ :::
+1. From the team edit screen's **Workspaces** tab, add workspaces by name and select an access **Role** from the dropdown next to each workspace in the list. All team members inherit the workspace access role for the team.
diff --git a/platform_versioned_docs/version-24.1/index.mdx b/platform_versioned_docs/version-24.1/index.mdx
index e563dd41c..c7a709738 100644
--- a/platform_versioned_docs/version-24.1/index.mdx
+++ b/platform_versioned_docs/version-24.1/index.mdx
@@ -4,18 +4,44 @@ description: "Introduction to Seqera Platform."
date: "24 Apr 2023"
---
-Seqera Platform is the centralized command post for the management of [Nextflow](https://www.nextflow.io/) data pipelines. It offers monitoring, logging, and observability for distributed workflows and simplifies the deployment of pipelines on any cloud, cluster, or laptop.
-Users can launch pre-configured pipelines with ease, while the flexible API provides programmatic integration to meet the needs of organizations building on Seqera Platform. Workflow developers can publish pipelines to shared workspaces and administrators can set up and manage the infrastructure required to run data analysis at scale.
+Seqera Platform is an intuitive, centralized command post designed to make scientific analysis accessible at any scale.
+
+Platform acts as a pane of glass to effortlessly launch, manage, monitor, and collaborate on scalable [Nextflow](https://www.nextflow.io) data analysis using your own computing resources and infrastructure. Researchers can focus on the science that matters, rather than worrying about infrastructure engineering.
+
+Seqera helps organizations:
+
+- Launch, manage, and monitor portable Nextflow pipelines from anywhere in real-time
+- Enable non-technical users to run pipelines via the intuitive Launchpad interface
+- Easily provision and leverage cloud-based and HPC compute environments
+- Share pipelines and data and collaborate securely between local and remote teams
+- Access a [curated library](https://seqera.io/pipelines/) of production-proven Nextflow community pipelines from [nf-core](https://nf-co.re/) and others
+- [Automate](./getting-started/quickstart-demo/automation.mdx) complex tasks as part of broader enterprise processes
+
+### Deployment methods
+
+Seqera offers two deployment methods:
+
+- **Seqera Cloud**: A version of the application available as a SaaS solution, hosted on Seqera's infrastructure.
+- **Seqera Enterprise**: A lightweight, deployable version of the application hosted on your own infrastructure.
:::tip
-[**Sign up**](https://tower.nf "Seqera Platform") to try Seqera for free, or request a [**demo**](https://cloud.tower.nf/demo/ "Seqera Platform Demo") for deployments in your own on-premises or cloud environment.
+[**Sign up**](https://cloud.seqera.io "Seqera Platform") to try Seqera for free, or request a [**demo**](https://seqera.io/demo "Seqera Platform Demo") for deployments in your own on-premises or cloud environment.
:::
-**What is Nextflow?**
+### Access Platform
+
+Log in to [Seqera Cloud](https://cloud.seqera.io/login) with your GitHub or Google account, or by providing an email address. If you are signing in for the first time, Seqera Cloud will send an authentication link to the email address to enable login.
+
+Upon your first login, you arrive in `community/showcase`, a workspace pre-filled with resources to launch your first pipeline with public data.
+
+- To begin launching Showcase pipelines, see [Launch pipelines](./getting-started/quickstart-demo/launch-pipelines.mdx).
+- To skip the Showcase and begin adding your own pipelines and resources, see [Set up your workspace](./getting-started/workspace-setup.mdx) to first create your own organizations and workspaces.
+
+### What is Nextflow?
-Nextflow is a framework for the development of data workflows. It enables engineers and data scientists to create and securely deploy custom, parallel data applications to the cloud or traditional on-premises infrastructure. Nextflow is characterized by its powerful dataflow programming paradigm and execution engines that allow for transparent deployment.
+[Nextflow](https://www.nextflow.io) is a framework for the development of data workflows. It enables engineers and data scientists to create and securely deploy custom, parallel data applications to the cloud or traditional on-premises infrastructure. Nextflow is characterized by its powerful dataflow programming paradigm and execution engines that allow for transparent deployment.
-Nextflow is both a programming workflow language and an execution runtime that supports a wide range of execution platforms, including popular traditional grid scheduling systems such as Slurm and IBM LSF, and cloud services such as AWS Batch and Google Cloud Life Sciences.
+Nextflow is both a programming workflow language and an execution runtime that supports a wide range of execution platforms, including popular traditional grid scheduling systems such as Slurm and IBM LSF, and cloud services such as AWS, Azure, and Google Cloud Batch.
-New to Nextflow? [Get started quickly](https://www.nextflow.io/#:~:text=Features-,Quick%20start,-Learn%20Nextflow) or see the [reference documentation](https://www.nextflow.io/docs/latest/getstarted.html).
+See the [Nextflow documentation](https://www.nextflow.io/docs/latest/) to learn more.
diff --git a/platform_versioned_sidebars/version-23.4-sidebars.json b/platform_versioned_sidebars/version-23.4-sidebars.json
index 282ba1bf8..dd2fb8018 100644
--- a/platform_versioned_sidebars/version-23.4-sidebars.json
+++ b/platform_versioned_sidebars/version-23.4-sidebars.json
@@ -6,9 +6,13 @@
"label": "Get started",
"collapsed": false,
"items": [
- "getting-started/overview",
- "getting-started/deployment-options",
- "getting-started/start-collaborating"
+ "getting-started/quickstart-demo/comm-showcase",
+ "getting-started/deployment-options",
+ "getting-started/workspace-setup",
+ "getting-started/quickstart-demo/add-pipelines",
+ "getting-started/quickstart-demo/add-data",
+ "getting-started/quickstart-demo/monitor-runs",
+ "getting-started/quickstart-demo/automation"
]
},
{
diff --git a/platform_versioned_sidebars/version-24.1-sidebars.json b/platform_versioned_sidebars/version-24.1-sidebars.json
index f3fa856ae..06ff881a3 100644
--- a/platform_versioned_sidebars/version-24.1-sidebars.json
+++ b/platform_versioned_sidebars/version-24.1-sidebars.json
@@ -6,9 +6,13 @@
"label": "Get started",
"collapsed": false,
"items": [
- "getting-started/overview",
- "getting-started/deployment-options",
- "getting-started/start-collaborating"
+ "getting-started/quickstart-demo/comm-showcase",
+ "getting-started/deployment-options",
+ "getting-started/workspace-setup",
+ "getting-started/quickstart-demo/add-pipelines",
+ "getting-started/quickstart-demo/add-data",
+ "getting-started/quickstart-demo/monitor-runs",
+ "getting-started/quickstart-demo/automation"
]
},
{