RSVP reading of book chapter in MEG

Subjects read Chapter 9 of Harry Potter and the Sorcerer's Stone one word at a time while their activity was recorded using an Elekta MEG scanner. Words were presented for 0.5 seconds each. The Carnegie Mellon University and the University of Pittsburgh Institutional Review Boards have approved and overseen this study. 
This study was performed by Tom Mitchell's lab at Carnegie Mellon University. 
To access this data, please fill the following form, we will contact you shortly with more information:
https://forms.gle/9pjRk6B7aw79w2xs6

This data was used in the following publications:
Wehbe, L., Vaswani, A., Knight, K., & Mitchell, T. (2014, October). Aligning context-based statistical models of language with brain activity during reading. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 233-243).
Toneva, M., & Wehbe, L. (2019). Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). Advances in Neural Information Processing Systems, 32. 
Schwartz, D., Toneva, M., & Wehbe, L. (2019). Inducing brain-relevant bias in natural language processing models. Advances in neural information processing systems, 32.
Toneva, M., Mitchell, T. M., & Wehbe, L. (2022). Combining computational controls with natural text reveals new aspects of meaning composition. BioRxiv, 2020-09.  
The files shared here include a time array indicating the timing of each column of data, and a label array indicating the order of each word in the stimulus text. To access the full MEG data, please fill the form above.

受试者逐词阅读《哈利·波特与魔法石》第九章，同时其活动通过使用Elekta MEG扫描仪进行记录。每个单词的呈现时间为0.5秒。该研究已获得卡内基梅隆大学和匹兹堡大学机构审查委员会的批准与监管。该研究由卡内基梅隆大学汤姆·米切尔实验室执行。欲获取此数据，请填写以下表格，我们将尽快与您联系以提供更多信息：https://forms.gle/9pjRk6B7aw79w2xs6。该数据被以下出版物引用：Wehbe, L., Vaswani, A., Knight, K., & Mitchell, T. (2014, October). Aligning context-based statistical models of language with brain activity during reading. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 233-243). Toneva, M., & Wehbe, L. (2019). Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). Advances in Neural Information Processing Systems, 32. Schwartz, D., Toneva, M., & Wehbe, L. (2019). Inducing brain-relevant bias in natural language processing models. Advances in neural information processing systems, 32. Toneva, M., Mitchell, T. M., & Wehbe, L. (2022). Combining computational controls with natural text reveals new aspects of meaning composition. BioRxiv, 2020-09. 分享的文件包括表示数据每列时间戳的时间数组，以及表示刺激文本中每个单词顺序的标签数组。欲获取完整的MEG数据，请填写上述表格。