{"id":34662295,"url":"https://github.com/psolymos/point-count-data-analysis","last_synced_at":"2026-05-28T05:31:48.081Z","repository":{"id":310762936,"uuid":"1039367678","full_name":"psolymos/point-count-data-analysis","owner":"psolymos","description":"Analysis of avian point-count data in the presence of detection error","archived":false,"fork":false,"pushed_at":"2025-11-13T06:12:55.000Z","size":37961,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-01-24T22:47:43.321Z","etag":null,"topics":["birds","detectability","workshop"],"latest_commit_sha":null,"homepage":"https://peter.solymos.org/point-count-data-analysis/","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-sa-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/psolymos.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":"2025-08-17T04:15:31.000Z","updated_at":"2025-11-13T06:12:59.000Z","dependencies_parsed_at":"2025-08-20T04:47:12.949Z","dependency_job_id":"67f12eb9-8a9b-4754-92ab-5243d11f9196","html_url":"https://github.com/psolymos/point-count-data-analysis","commit_stats":null,"previous_names":["psolymos/point-count-data-analysis-workshop-2025","psolymos/point-count-data-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/psolymos/point-count-data-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/psolymos%2Fpoint-count-data-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/psolymos%2Fpoint-count-data-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/psolymos%2Fpoint-count-data-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/psolymos%2Fpoint-count-data-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/psolymos","download_url":"https://codeload.github.com/psolymos/point-count-data-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/psolymos%2Fpoint-count-data-analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33596316,"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":"2026-05-28T02:00:06.440Z","response_time":99,"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":["birds","detectability","workshop"],"created_at":"2025-12-24T18:54:25.784Z","updated_at":"2026-05-28T05:31:48.076Z","avatar_url":"https://github.com/psolymos.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Analysis of avian point-count data in the presence of detection error\n\n## Instructor\n\nDr. Péter Sólymos is an ecologist and R programmer. He has worked with continental scale data sets and\ndeveloped statistical techniques for estimating population density from messy data sets. He is\nthe author of numerous well-known R packages, including detect, dclone, vegan, and\nResourceSelection. He works currently as a data scientist helping utility companies\nimproving their outage and impact prevention practices, and is an adjunct professor at the\nUniversity of Alberta in Edmonton, Canada.\n\n## Course overview\n\nThis course is aimed towards researchers analysing field observations, who are often faced by\ndata heterogeneities due to field sampling protocols changing from one project to another, or\nthrough time over the lifespan of projects, or trying to combine legacy data sets with new\ndata collected by recording units.\n\nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead\nto information loss when filtered and standardized to common standards. Accounting for\nthese issues is important for better inference regarding status and trend of species and\ncommunities.\n\nAnalysts of such \"messy\" data sets need to feel comfortable with manipulating the data, need a\nfull understanding the mechanics of the models being used (i.e. critically interpreting the\nresults and acknowledging assumptions and limitations), and should be able to make\ninformed choices when faced with methodological challenges.\n\nThe course emphasizes critical thinking and active learning through hands on programming\nexercises. We will use publicly available data sets to demonstrate the data manipulation and\nanalysis. We will use freely available and open-source R packages.\n\nThe expected outcome of the course is a solid foundation for further professional\ndevelopment via increased confidence in applying these methods for field observations.\n\nBy the end of the course, participants should:\n\n- Understand basic statistical concepts related to detection error\n- Work with field collected data and data from automated recording units (ARU)\n- Know packages such as unmarked, detect, bSims\n- Critically evaluate modelling options and assumptions using simulations\n- Fit N-mixture, distance sampling, and time-removal models to data\n\n## Intended Audience\n\n- Academics and post-graduate students working on projects related to avian data\n- Applied researchers and analysts in public, private or third-sector organizations who\nneed the reproducibility, speed and flexibility of a programming language such as R\nfor analysing point count data arising from avian field surveys\n\n## Course details\n\n- Venue: Delivered remotely\n- Time zone: UK (GMT)\n- Duration: 3 days\n- Contact hours: Approx. 12 hours\n- ECT’s: Equal to 3 ECT’s\n- Language: English\n\n## Course outline\n\n- [Day 0: Getting ready](./day-00/README.md)\n- [Day 1: Laying the groundwork](./day-01/README.md)\n- [Day 2: Understanding mechanisms](./day-02/README.md)\n- [Day 3: Advanced topics](./day-03/README.md)\n\n## Teaching Format\n\nIntroductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures\ninterspersed with hands-on mini practicals and longer projects. Data sets for computer\npracticals will be provided by the instructors, but participants are welcome to bring their own\ndata.\n\n## Prerequisites\n\n### Assumed quantitative knowledge\n\nA basic understanding of statistical, mathematical and physical concepts. Specifically,\ngeneralised linear regression models, including mixed models; basic knowledge of calculus.\n\n### Assumed computer background\n\nFamiliarity with R, ability to import/export data, manipulate data frames, fit basic statistical\nmodels (up to GLM) and generate simple exploratory and diagnostic plots.\n\n## References\n\nSee publications listed in the [`papers`](./papers/) folder.\n\n## License\n\n© 2025 Péter Sólymos\n\nThis work is licensed under \u003ca href=\"https://creativecommons.org/licenses/by-sa/4.0/\"\u003eCC BY-SA 4.0\u003c/a\u003e\n\n\u003cimg src=\"https://mirrors.creativecommons.org/presskit/icons/cc.svg\" alt=\"\" style=\"max-width: 1em;max-height:1em;margin-left: .2em;\"\u003e\u003cimg src=\"https://mirrors.creativecommons.org/presskit/icons/by.svg\" alt=\"\" style=\"max-width: 1em;max-height:1em;margin-left: .2em;\"\u003e\u003cimg src=\"https://mirrors.creativecommons.org/presskit/icons/sa.svg\" alt=\"\" style=\"max-width: 1em;max-height:1em;margin-left: .2em;\"\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpsolymos%2Fpoint-count-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpsolymos%2Fpoint-count-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpsolymos%2Fpoint-count-data-analysis/lists"}