{"id":51292143,"url":"https://github.com/mcaelanremigio/rubin_variable_star_workflow","last_synced_at":"2026-06-30T11:01:27.792Z","repository":{"id":366898848,"uuid":"1136153170","full_name":"McAelanRemigio/Rubin_Variable_Star_Workflow","owner":"McAelanRemigio","description":"Minimal, end-to-end data analysis workflow for identifying candidate variable stars. Demonstrates EDA, variability feature engineering, and ranking logic, derived from methods used in a Vera C. Rubin Observatory project.","archived":false,"fork":false,"pushed_at":"2026-06-23T18:41:55.000Z","size":392,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-06-23T20:22:23.085Z","etag":null,"topics":["adql","astronomy","astrophysics","data-science","python","time-series"],"latest_commit_sha":null,"homepage":"https://mcaelanremigio.github.io/Rubin_Variable_Star_Workflow/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/McAelanRemigio.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-01-17T06:46:27.000Z","updated_at":"2026-06-23T19:05:18.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/McAelanRemigio/Rubin_Variable_Star_Workflow","commit_stats":null,"previous_names":["mcaelanremigio/rubin_variable_star_workflow"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/McAelanRemigio/Rubin_Variable_Star_Workflow","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/McAelanRemigio%2FRubin_Variable_Star_Workflow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/McAelanRemigio%2FRubin_Variable_Star_Workflow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/McAelanRemigio%2FRubin_Variable_Star_Workflow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/McAelanRemigio%2FRubin_Variable_Star_Workflow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/McAelanRemigio","download_url":"https://codeload.github.com/McAelanRemigio/Rubin_Variable_Star_Workflow/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/McAelanRemigio%2FRubin_Variable_Star_Workflow/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34963642,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-30T02:00:05.919Z","response_time":92,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["adql","astronomy","astrophysics","data-science","python","time-series"],"created_at":"2026-06-30T11:01:24.959Z","updated_at":"2026-06-30T11:01:27.786Z","avatar_url":"https://github.com/McAelanRemigio.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Rubin Variable Star Workflow Demo\n\nThis repository presents a minimal, end-to-end data analysis workflow\nfor identifying candidate variable stars. It is a standalone, personal\ndemonstration derived from methods used in a collaborative\nVera C. Rubin Observatory project.\n\nThe goal of this repository is to demonstrate how an exploratory,\nresearch-oriented analysis can be distilled into a clear and\nreproducible workflow.\n\n---\n\n## Workflow Overview\n\nThe analysis follows a simple, linear pipeline:\n\n1. Load and inspect time-series data\n2. Perform exploratory data analysis (EDA)\n3. Compute variability metrics\n4. Rank candidate variable stars\n5. Generate diagnostic outputs\n\n---\n\n## Repository Structure\n\n```\nrubin-variable-star-workflow/\n├── run_pipeline.ipynb # End-to-end workflow notebook\n├── data/ # Input data (small sample)\n├── outputs/ # Generated tables and plots\n└── README.md\n```\n\n---\n\n## Outputs\n\n- **Ranked candidate table** (`outputs/ranked_candidates.csv`)\n- **Diagnostic plot(s)** (`outputs/diagnostics.png`)\n\nThese outputs represent the terminal artifacts of the workflow.\n\n---\n\n## Scope \u0026 Limitations\n\nThis repository is intended as a workflow demonstration rather than a\ncomplete scientific analysis. It does not include full astrophysical\nvalidation or machine learning models.\n\nIn practice, ranked candidates produced by this workflow would be\npassed to downstream classification or modeling stages.\n\n---\n\n## Attribution\n\nThis demo is informed by prior collaborative work conducted as part of\na Vera C. Rubin Observatory project. The full collaborative archive is\nmaintained separately.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmcaelanremigio%2Frubin_variable_star_workflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmcaelanremigio%2Frubin_variable_star_workflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmcaelanremigio%2Frubin_variable_star_workflow/lists"}