Samuel Jackson , Saiful Khan , Nathan Cummings , James Hodson , Shaun de Witt , Stanislas Pamela , Rob Akers , Jeyan Thiyagalingam , The MAST Team
{"title":"FAIR-MAST：融合设备数据管理系统","authors":"Samuel Jackson , Saiful Khan , Nathan Cummings , James Hodson , Shaun de Witt , Stanislas Pamela , Rob Akers , Jeyan Thiyagalingam , The MAST Team","doi":"10.1016/j.softx.2024.101869","DOIUrl":null,"url":null,"abstract":"<div><p>We are introducing FAIR-MAST, a data management system designed for historical diagnostic data from the Mega Ampere Spherical Tokamak (MAST) fusion experiments. Following the FAIR (findability, accessibility, interoperability, and re-usability) principles, our system aims to address current accessibility issues with data that supports artificial intelligence and machine learning (AI/ML) and advanced data analysis. The system features public APIs with a searchable metadata index and object storage for remote data access. The API integrates a high-performance data analysis stack for scalable data analysis and AI/ML application development. Performance analysis demonstrates a tenfold improvement in data access speed compared to the legacy system, enabling more efficient and comprehensive data exploration. Additionally, our system is designed to be adaptable to other tokamak facilities, such as MAST-Upgrade (MAST-U) and the Joint European Torus (JET), to expedite fusion energy research and promote collaboration.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"27 ","pages":"Article 101869"},"PeriodicalIF":2.4000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024002395/pdfft?md5=b97ccfaee99b49131d0376b806ae8f90&pid=1-s2.0-S2352711024002395-main.pdf","citationCount":"0","resultStr":"{\"title\":\"FAIR-MAST: A fusion device data management system\",\"authors\":\"Samuel Jackson , Saiful Khan , Nathan Cummings , James Hodson , Shaun de Witt , Stanislas Pamela , Rob Akers , Jeyan Thiyagalingam , The MAST Team\",\"doi\":\"10.1016/j.softx.2024.101869\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We are introducing FAIR-MAST, a data management system designed for historical diagnostic data from the Mega Ampere Spherical Tokamak (MAST) fusion experiments. Following the FAIR (findability, accessibility, interoperability, and re-usability) principles, our system aims to address current accessibility issues with data that supports artificial intelligence and machine learning (AI/ML) and advanced data analysis. The system features public APIs with a searchable metadata index and object storage for remote data access. The API integrates a high-performance data analysis stack for scalable data analysis and AI/ML application development. Performance analysis demonstrates a tenfold improvement in data access speed compared to the legacy system, enabling more efficient and comprehensive data exploration. Additionally, our system is designed to be adaptable to other tokamak facilities, such as MAST-Upgrade (MAST-U) and the Joint European Torus (JET), to expedite fusion energy research and promote collaboration.</p></div>\",\"PeriodicalId\":21905,\"journal\":{\"name\":\"SoftwareX\",\"volume\":\"27 \",\"pages\":\"Article 101869\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2352711024002395/pdfft?md5=b97ccfaee99b49131d0376b806ae8f90&pid=1-s2.0-S2352711024002395-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SoftwareX\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352711024002395\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711024002395","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
We are introducing FAIR-MAST, a data management system designed for historical diagnostic data from the Mega Ampere Spherical Tokamak (MAST) fusion experiments. Following the FAIR (findability, accessibility, interoperability, and re-usability) principles, our system aims to address current accessibility issues with data that supports artificial intelligence and machine learning (AI/ML) and advanced data analysis. The system features public APIs with a searchable metadata index and object storage for remote data access. The API integrates a high-performance data analysis stack for scalable data analysis and AI/ML application development. Performance analysis demonstrates a tenfold improvement in data access speed compared to the legacy system, enabling more efficient and comprehensive data exploration. Additionally, our system is designed to be adaptable to other tokamak facilities, such as MAST-Upgrade (MAST-U) and the Joint European Torus (JET), to expedite fusion energy research and promote collaboration.
期刊介绍：
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
