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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With recent technological advances, large-scale experimental facilities generate huge datasets, into the petabyte range, every year, thereby creating the Big Data deluge effect. Data management, including the collection, management, and curation of these large datasets, is a significantly intensive precursor step in relation to the data analysis that underpins scientific investigations. The rise of artificial intelligence (AI), machine learning (ML), and robotic automation has changed the landscape for experimental facilities, producing a paradigm shift in how different datasets are leveraged for improved intelligence, operation, and data analysis. Therefore, such facilities, known as superfacilities, which fully enable user science while addressing the challenges of the Big Data deluge, are critical for the scientific community. In this work, we discuss the process of setting up the Big Data Science Center within the Shanghai Synchrotron Radiation Facility (SSRF), China\u2019s first superfacility. We provide details of our initiatives for enabling user science at SSRF, with particular consideration given to recent developments in AI, ML, and robotic automation.<\/jats:p>","DOI":"10.1088\/2632-2153\/abe193","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T09:02:25Z","timestamp":1619168545000},"page":"035003","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Deploying the Big Data Science Center at the Shanghai Synchrotron Radiation Facility: the first superfacility platform in China"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7026-7726","authenticated-orcid":false,"given":"Chunpeng","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9502-3277","authenticated-orcid":false,"given":"Feng","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0897-1149","authenticated-orcid":false,"given":"Yiyang","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1976-6180","authenticated-orcid":false,"given":"Xiaoyun","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6954-6413","authenticated-orcid":false,"given":"Jige","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2167-1343","authenticated-orcid":false,"given":"Jeyan","family":"Thiyagalingam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2320-9398","authenticated-orcid":false,"given":"Alessandro","family":"Sepe","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2021,4,22]]},"reference":[{"key":"mlstabe193bib1","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1126\/science.1170411","volume":"323","author":"Bell","year":"2009","journal-title":"Science"},{"key":"mlstabe193bib2","doi-asserted-by":"publisher","first-page":"117","DOI":"10.3233\/978-1-61499-583-8-117","volume":"26","author":"Foster","year":"2015","journal-title":"Big Data High Perform. 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