Setup and Configuration

Sustainable Catalyst Research Lab: Installation and Configuration

Install, configure, validate, and recover Sustainable Catalyst Research Lab safely.

ProductSustainable Catalyst Research LabVerified version0.36.1ComponentCalibration and Validation, Dataset Registry, Design of Experiments, Distributed Compute, Evidence and Provenance, Experiment Framework, External Discovery, Instrumentation, Method Review, Numerical Solvers, Offline Field Work, Parameter Studies, Persistent Queue, Projects and Workspaces, Publication Packages, Python Compute Core, Reproducible Runs, Scientific Calculators, Visualization, Workflow OrchestrationUpdatedJuly 17, 2026Reading time18 minutes
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Sustainable Catalyst Research Lab · How-to Guide

Governed scientific calculators, computation, datasets, runs, provenance, methods, experiments, calibration, queues, instruments, and publication packages.

Audience: product users, administrators, reviewers, and integrators.

Last verified version: 0.36.1

Reading time: 18–30 minutes

Status: Published support documentation.

Purpose

This guide covers a reversible installation or update of Sustainable Catalyst Research Lab, including baseline capture, package identity, dependencies, routes, permissions, service health, sample validation, rollback, and documentation handoff.

Prerequisites

  • A verified backup
  • Administrative or repository access
  • The canonical release archive and checksum
  • A documented rollback path
  • A test workspace and the synthetic sample below

Sample data

This article uses a fictional Resilient Community Retrofit Program. The files contain one demonstration row for every documented feature, so each capability can be tested without private or production data.

Download CSV feature examples · Download JSON workspace example

Preview

# Feature Sample action Expected result Classification
1 Scientific Calculators calculate a heat-transfer coefficient with units a method-labeled result synthetic-demo
2 Python Compute Core run summary statistics through the supported backend a typed compute response synthetic-demo
3 Numerical Solvers fit a simple energy model and inspect convergence solver output and diagnostics synthetic-demo
4 Visualization plot measured versus predicted energy use a figure tied to dataset and run IDs synthetic-demo
5 Projects and Workspaces create LAB-RETROFIT-001 a persistent research workspace synthetic-demo

The downloadable files include all 20 feature rows.

Procedure

1. Capture the baseline

  1. Record the installed interface, backend, package, and schema versions.
  2. Export or back up product-owned state.
  3. Record canonical routes, shortcodes, service URLs, repository branch, and environment variables.
  4. Check for incomplete migrations or uncommitted changes.

2. Install or update

  1. Verify the archive root, manifest, checksum, and version identity.
  2. Install through the product-supported WordPress, package, or service path.
  3. Run syntax, structure, contract, and product tests before replacing the working release.
  4. Apply database or schema migrations only after the backup is confirmed.
  5. Start or refresh the backend and verify its health response.

3. Configure

  1. Set documented endpoints and secret references; never store secret values in public code.
  2. Assign least-privilege roles and product ownership.
  3. Confirm product, version, component, issue, release, collection, and article-type context where supported.
  4. Clear only the caches affected by changed assets or routes.

4. Validate

  1. Import the feature-example CSV or JSON.
  2. Test every feature row in a nonproduction workspace.
  3. Compare actual results with the expected_result field.
  4. Reopen state after a clean reload.
  5. Record differences as draft support issues before enabling real use.

Expected output

  • One consistent product version across interface, service, manifest, package, and export
  • Healthy routes and dependencies
  • A complete synthetic feature test
  • A verified backup and rollback point
  • No credentials or private data in public artifacts

Verification

  • PHP, Python, R, JavaScript, or package checks pass where applicable.
  • The product loads in an incognito or clean session.
  • The sample data can be created, saved, reopened, exported, and removed.
  • Permissions prevent unauthorized administrative or private access.
  • The previous release can be restored.

Responsible use

The Lab supports research and teaching, not unsupported scientific, engineering, clinical, or regulatory claims.

Do not delete backups or the prior working release until the installed version and all sample features have been validated.

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