diff --git a/articles/gracie_at_acres_msu_nsf/info.json b/articles/gracie_at_acres_msu_nsf/info.json index 32e2885..4d9bc67 100644 --- a/articles/gracie_at_acres_msu_nsf/info.json +++ b/articles/gracie_at_acres_msu_nsf/info.json @@ -8,7 +8,6 @@ "tags": ["undergraduate"], "platforms": [ "kg", - "tardis", "dti" ], "short_description": "Gracie Tvrdik presented her research on the evolution of collaboration dynamics in Astrophysics at the annual ACRES REU (Research Experiences for Undergraduates) Symposium. Her outstanding presentation earned her the 1st Place Outstanding Talk Award.", diff --git a/articles/identify_telescope_machine_vicente/info.json b/articles/identify_telescope_machine_vicente/info.json new file mode 100644 index 0000000..ac8c5fd --- /dev/null +++ b/articles/identify_telescope_machine_vicente/info.json @@ -0,0 +1,28 @@ +{ + "title": " Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories", + "author_id": "vicente_amado", + "article_id": "identify_telescope_machine_vicente", + "display": true, + "date": "11-12-2024", + "category": "News", + "tags": ["news"], + "platforms": [ + "kg", + "dti" + ], + "short_description": "Vicente, our graduate student and collaborators, have developed a machine learning framework that automatically identifies when scientific facilities like telescopes are used in astrophysics papers, addressing a long-standing challenge in tracking research facility usage.", + "cover_image_height": "520px", + "cover_image_width": "330px", + "cover_image": "media/images/mastvsnomast.png", + "content": { + "1_para": "Vicente, our graduate student and collaborators, have developed a machine learning framework that automatically identifies when scientific facilities like telescopes are used in astrophysics papers, addressing a long-standing challenge in tracking research facility usage.", + "2_img": "media/images/mastvsnomast.png", + "3_para": "The framework analyzes scientific text using natural language processing and Support Vector Machine classification to accurately detect when missions like Kepler and TESS are used in research, achieving 92.9% accuracy. This innovative approach saves valuable research time by eliminating the need for manual classification while being adaptable for use across various scientific facilities worldwide. To see more and read the full paper: https://arxiv.org/abs/2411.00987.", + "4_img": "media/images/fig_2.png", + "5_para":"
Collaborators: