{"id":22853013,"url":"https://github.com/vgherard/hepscrape","last_synced_at":"2025-03-31T07:12:58.947Z","repository":{"id":159807755,"uuid":"387204160","full_name":"vgherard/hepscrape","owner":"vgherard","description":"arXiv:hep-ph scraper","archived":false,"fork":false,"pushed_at":"2022-02-19T19:57:51.000Z","size":3983489,"stargazers_count":0,"open_issues_count":3,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-06T11:53:23.758Z","etag":null,"topics":["natural-language-processing","particle-physics","physics","text-mining"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vgherard.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","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}},"created_at":"2021-07-18T15:09:14.000Z","updated_at":"2022-01-10T00:26:15.000Z","dependencies_parsed_at":"2023-06-28T21:09:54.087Z","dependency_job_id":null,"html_url":"https://github.com/vgherard/hepscrape","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vgherard%2Fhepscrape","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vgherard%2Fhepscrape/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vgherard%2Fhepscrape/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vgherard%2Fhepscrape/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vgherard","download_url":"https://codeload.github.com/vgherard/hepscrape/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246429493,"owners_count":20775808,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["natural-language-processing","particle-physics","physics","text-mining"],"created_at":"2024-12-13T06:10:18.146Z","updated_at":"2025-03-31T07:12:58.942Z","avatar_url":"https://github.com/vgherard.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\"\n)\n```\n\n# hepscrape\n\n\u003c!-- badges: start --\u003e\n![example workflow](https://github.com/vgherard/hepscrape/actions/workflows/update_hep_arxiv.yml/badge.svg)\n\u003c!-- badges: end --\u003e\n\nThis repository automatically scrapes [arXiv](https://arxiv.org/) on a daily basis, for new articles in the hep-ph category (also crossposted). \n\nThe resulting dataset is stored in R serialized data format (.rds) in `data/hep_arxiv.rds`, and is a dataframe with the following fields:\n\n```\n- id: arXiv unique identifier\n- submitted: date of submission\n- authors\n- title\n- abstract\n```\n\nThis dataset is kept up-to-date with the full [arXiv Metadata OAI Snapshot](https://www.kaggle.com/Cornell-University/arxiv), and it contains all arXiv:hep-ph records over the last 30 years.\n\nMore info coming soon.\n\n![hep-ph word cloud](https://raw.githubusercontent.com/vgherard/hepscrape/master/img/cloud.png)\n\nFigure: Word cloud from hep-ph abstracts. Words' character sizes are proportional to their Term-Frequency - Inverse-Document-Frequency, whereas color gradients are proportional to Term-Frequency. The `idf` weight is given by `w = ln (1 / df) ^ 1.5`. Term-frequencies are averaged over the last 100 arXiv submissions, while Inverse Document Frequencies are computed from the whole arXiv Metadata OAI Snapshot corpus.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvgherard%2Fhepscrape","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvgherard%2Fhepscrape","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvgherard%2Fhepscrape/lists"}