{"title":"Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing.","authors":"Nurfaten Hamzah, Nurul Hashimah Ahamed Hassain Malim, Jafri Malin Abdullah, Putra Sumari, Ariffin Marzuki Mokhtar, Siti Nur Syamila Rosli, Sharifah Aida Shekh Ibrahim, Zamzuri Idris","doi":"10.1007/s12021-023-09637-3","DOIUrl":null,"url":null,"abstract":"<p><p>The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"589-600"},"PeriodicalIF":2.7000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12021-023-09637-3","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.
在过去几年中,开放获取的神经成像数据的共享显著增加。共享神经成像数据对于加速科学进步至关重要,特别是在神经科学领域。目前正在开发一些将增加可用神经成像数据量的重大举措。大大脑数据倡议项目是由马来西亚理科大学发起的,是马来西亚第一个以数据共享为目的的神经成像数据存储平台。为了确保本项目神经成像数据的可访问性、可用性和安全性,并为用户提供可持续访问的高质量数据,我们首先提出了良好的数据管理实践。然后,我们开发了MyneuroDB,一个用于数据共享的在线存储数据库系统。在这里,我们描述了Big Brain Data Initiative和MyneuroDB, MyneuroDB是一个数据存储库,它提供了公开共享神经成像数据的能力,目前包括磁共振成像(MRI)、脑电图(EEG)和脑磁图(MEG),遵循FAIR数据共享原则。
期刊介绍:
Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.