79 research outputs found
Anonymous Daily Features Calculated from GPS Data (Beiwe)
No full textGPS data recorded by participants' smartphones and obtained via the Beiwe mobile app. To conserve participant privacy, this dataset excludes raw GPS traces, but contains two aggregated files for each participant instead. The first file contains a daily GPS feature table, including two daily features computed for each day the participant produced GPS data: (1) loc.var, square root of the variance in GPS coordinates recorded on a day; (2) ent.pls, normalized Shannon entropy of the time spent at different significant places. The other file contains an hourly missing status table, indicating whether the participant's smartphone produced GPS data in each hour during the study period
Participants Key File
No full textParticipant's gender, age, and the year of participation (2018 or 2019) in the UT1000 stud
Replication Data for: Forecasting Civil Unrest Using Social Media and Protest Participation Theory
No full textData used for IEEE Transactions on Computational Social Systems publication "Forecasting Civil Unrest Using Social Media and Protest Participation Theory
Daily Smartphone Sensing Features and Self-reported Sadness Scores, Averaged by the Participant
No full textAveraged daily smartphone sensing features / self-reported mental health outcomes by the participant, used to calculate the correlation/scatter plot matrix in the paper. Smartphone sensing data are available on a larger range of days than those on which self-reports were submitted; only the days on which both smartphone sensing and self-report data are available were used to compute these values. The last column in the data file, num.entries, indicates the number of days on which both data are available.
1) acc.mag.rmssd: Root Mean Square of the Successive Differences in acceleration magnitude (unit: gravity)
2) loc.var: square root of variance in raw GPS coordinates
3) ent.pls: normalized Shannon entropy of the time spent at different significant places, determined by an established temporal coordinate clustering algorithm (Kang et al. 2005 "Extracting places from traces of locations")
4) unlocked.dur: number of minutes the participant's smartphone stays unlocked
5) mean.sadness: mean value of the daily end-of-day self-report of perceived sadness of the day, evaluated on a 4-point ordinal scale (0-3)
6) sd.sadness: standard deviation of the daily end-of-day self-report of perceived sadness of the day, evaluated on a 4-point ordinal scale (0-3
Accelerometer Data (Beiwe)
No full textAccelerometer data obtained by the Beiwe mobile app. For each participant who produced accelerometer data on their smartphone, a file is generated containing daily accelerometer magnitude features for each day during the study. Magnitude is defined by the square root of the sum of squared x, y, and z-axis acceleration values. The unit of acceleration is gravity, or 9.8 meters per squared second. Raw data is available upon request however extremely large. The features computed are:
1) acc.mag.mean/sd/min/max/rmssd: mean, standard deviation, minimum, maximum, and root mean square of the successive differences of the raw acceleration magnitude recorded during a 24-hour day;
2) acc.still: the proportion of raw magnitude values that are between 0.995 and 1.005, indicating a lack of movement (hence "still") of the smartphone;
3) acc.complete: the percentage of the day accelerometer data is produced; when this value is low (e.g. <0.2), the features computed are not representative of the whole day and should not be treated as reliable
Ecological Momentary Assessment Data (Beiwe)
No full textMood, sleep, and activity survey answers recorded by the Beiwe mobile app. Each participant who provided has two files, one containing daily questions and the other containing momentary questions (up to five times a day at 9am, 12, 3, 6, and 9pm). Daily questions include a morning 9am survey of sleep quality and duration of the previous night, and an evening 9pm survey of the overall mood and energy level of the day. Momentary questions include location, with whom, doing what, and interacting in what way, in addition to mood and energy level questions. Please refer to the EMA questions code book for question text and options
Smartphone Screen Unlock Events Data (Beiwe)
No full textSmartphone screen unlock events data, obtained by the Beiwe mobile app. Each file belongs to one participant, and contains the timestamp of each unlock/lock event during the study period and the concurrent battery level
Fitbit and BEVO Beacon Data
No full textFitbit and BEVO Beacon data. All participants do not have both Fitbit and BEVO Beacon data available
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Correlates and Digital Phenotypes of College Student Loneliness: Evidence from the UT1000 Project
No full textOffice of the VP for Researc
CT slices of three Protoceratopsian skulls and example slices of other Gobi Desert vertebrates
No full textThis is a image dataset for deep learning studies. The main dataset comprises labeled CT slices from 3 protoceratopsian dinosaur skulls discovered from the Gobi Desert, Mongolia. The fossil specimens are now in the Division of Paleontology, American Museum of Natural History, New York, NY, USA. Inside the folders named after specimen number (i.e. IGM100-1021), there are six sub-folders comprising labeled or unlabled CT slice images of the specimen from three directions: axial, coronal, and sagittal. Each direction should contain all the information to reconstruct the entire specimen.
The other folder named Gobi Vert comprise CT slices, but not enough to reconstruct the original specimens, of multiple vertebrate fossils also discovered from the Gobi Desert, Mongolia. The specimen number and taxa are noted in the image names.The dataset comprises three rarely preserved embryonic protoceratopsian dinosaurs skulls, thus the morphological information embedded are also important for paleontological studies. The slices included here are enough to reconstruct the entire morphology of these embryonic skulls, therefore, if anyone would like to study the morphology of these fossils, please ask the consent from the correspondence author Congyu Yu [email protected] CT scans were done in either the Department of Earth & Planetary Sciences, Yale University, USA or the American Museum of Natural History. The segmentation was done by authors, then labelled images are exported as the groundtruth for deep learning training. The detail of the dataset is following:
Specimen number
Taxa
Dimension
Voxel size (μm)
Selected slices (training+testing)
IGM 100/3654
Protoceratops
1228*1902*1042
21.43
2059+885
IGM 100/3655
Protoceratops
1362*1731*1193
21.44
3047+1239
IGM 100/1021
Protoceratopsia
768*1784*1533
22.74
2880+1205
However, users are free to divide training, testing, and validating dataset as they need
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