MaSS (Multilingual corpus of Sentence-aligned Spoken utterances) is an extension of the CMU Wilderness Multilingual Speech Dataset, a speech dataset based on recorded readings of the New Testament. MaSS extends it by providing a large and clean dataset of 8,130 parallel spoken utterances across 8 languages (56 language pairs). The covered languages are: Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish. Source: MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible Image Source: https://arxiv.org/pdf/1907.12895v3.pdf
The Montreal Archive of Sleep Studies (MASS) is an open-access and collaborative database of laboratory-based polysomnography (PSG) recordings O’Reilly, C., et al. (2014) J Seep Res, 23(6):628-635. Its goal is to provide a standard and easily accessible source of data for benchmarking the various systems developed to help the automation of sleep analysis. It also provides a readily available source of data for fast validation of experimental results and for exploratory analyses. Finally, it is a shared resource that can be used to foster large-scale collaborations in sleep studies. MASS is composed of cohorts themselves comprising subsets. Recordings within subsets is kept as homogeneous as possible, whereas it is more heterogeneous between subsets. To allow inter-study comparisons, researchers validating their results on MASS are encouraged to specify which portion of the database they used in their assessment (e.g., MASS-C1 for the whole cohort 1, MASS-C1/SS1-SS3 for subsets 1, 2 and 3 of cohort 1). Currently, the first MASS cohort available is described in O’Reilly, C., et al. (2014) J Seep Res, 23(6):628-635. This cohort comprises polysomnograms of 200 complete nights recorded in 97 men and 103 women of age varying between 18 and 76 years (mean: 38.3 years, SD: 18.9 years). It has been split into five different subsets. Source: Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research
MODA is a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects) that was produced by the Massive Online Data Annotation project. Sleep spindles were detected as a consensus of a number of human-expert scorers. With a median number of 5 experts scoring every EEG segment, MODA offers sleep spindle annotations of a quality unseen in previous datasets. The dataset was described and introduced in the following publication: Lacourse, K., Yetton, B., Mednick, S. et al. Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data. Sci Data 7, 190 (2020). https://doi.org/10.1038/s41597-020-0533-4