{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:06:41Z","timestamp":1773778001855,"version":"3.50.1"},"reference-count":5,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T00:00:00Z","timestamp":1618531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC02-07CH11359"],"award-info":[{"award-number":["DE-AC02-07CH11359"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab\u2019s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster\u2019s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.<\/jats:p>","DOI":"10.3390\/data6040042","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T10:20:11Z","timestamp":1618568411000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["BOOSTR: A Dataset for Accelerator Control Systems"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1716-463X","authenticated-orcid":false,"given":"Diana","family":"Kafkes","sequence":"first","affiliation":[{"name":"Fermi National Accelerator Laboratory, Batavia, IL 60510, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8110-4108","authenticated-orcid":false,"given":"Jason","family":"St. John","sequence":"additional","affiliation":[{"name":"Fermi National Accelerator Laboratory, Batavia, IL 60510, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"key":"ref_1","unstructured":"John, J.S., Herwig, C., Kafkes, D., Pellico, W.A., Perdue, G.N., Quintero-Parra, A., Schupbach, B.A., Seiya, K., Tran, N., and Duarte, J.M. (2020). Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster. arXiv."},{"key":"ref_2","first-page":"106","article-title":"The Fermilab Accelerator Control System","volume":"47","author":"Cahill","year":"2008","journal-title":"ICFA Beam Dyn. Newsl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kafkes, D., and St. John, J. (2021, April 15). BOOSTR: A Dataset for Accelerator Control Systems (Full Release 2020). Available online: https:\/\/doi.org\/10.5281\/zenodo.4382663.","DOI":"10.3390\/data6040042"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kafkes, D., and St. John, J. (2021, April 15). BOOSTR: A Dataset for Accelerator Control Systems (Partial Release 2020). Available online: https:\/\/doi.org\/10.5281\/zenodo.4088982.","DOI":"10.3390\/data6040042"},{"key":"ref_5","unstructured":"Hazelwood, K.J. (2021, April 15). Fermilab Electronic Logbook, Available online: www-bd.fnal.gov\/Elog."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/6\/4\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:48:47Z","timestamp":1760161727000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/6\/4\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,16]]},"references-count":5,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["data6040042"],"URL":"https:\/\/doi.org\/10.3390\/data6040042","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,16]]}}}