scholarly journals Beiwe: A data collection platform for high-throughput digital phenotyping

2021 ◽  
Vol 6 (68) ◽  
pp. 3417
Author(s):  
Jukka-Pekka Onnela ◽  
Caleb Dixon ◽  
Keary Griffin ◽  
Tucker Jaenicke ◽  
Leila Minowada ◽  
...  
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Digital Phenotyping

scholarly journals Correction to: Soybean iron deficiency chlorosis high throughput phenotyping using an unmanned aircraft system

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Austin A. Dobbels ◽  
Aaron J. Lorenz
Keyword(s):  
Iron Deficiency ◽  
Data Collection ◽  
Data Processing ◽  
High Throughput ◽  
Image Data ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft System ◽  
Iron Deficiency Chlorosis ◽  
High Throughput Phenotyping ◽  
Aircraft System

In the original article [1], under the subheading “Image data processing”, last paragraph, last sentence that reads as “The least …… data collection” was incorrectly published. The correct sentence should read as “Least-significant differences (P < 0.20) were calculated for all 36 trials on both ground-based and UAS-image based scores for both dates of data collection.” The original article has been corrected.

scholarly journals NMRbot: Python scripts enable high-throughput data collection on current Bruker BioSpin NMR spectrometers

Metabolomics ◽  
2013 ◽  
Vol 9 (3) ◽  
pp. 558-563 ◽  
Author(s):  
Lawrence J. Clos ◽  
M. Fransisca Jofre ◽  
James J. Ellinger ◽  
William M. Westler ◽  
John L. Markley
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
High Throughput Data

scholarly journals Robust indexing for automatic data collection

2004 ◽  
Vol 37 (3) ◽  
pp. 399-409 ◽  
Author(s):  
Nicholas K. Sauter ◽  
Ralf W. Grosse-Kunstleve ◽  
Paul D. Adams
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Visual Inspection ◽  
The Novel ◽  
Automatic Data ◽  
X Ray ◽  
Alternative Approach ◽  
Automatic Data Collection ◽  
Diffraction Patterns ◽  
Improved Methods

Improved methods for indexing diffraction patterns from macromolecular crystals are presented. The novel procedures include a more robust way to verify the position of the incident X-ray beam on the detector, an algorithm to verify that the deduced lattice basis is consistent with the observations, and an alternative approach to identify the metric symmetry of the lattice. These methods help to correct failures commonly experienced during indexing, and increase the overall success rate of the process. Rapid indexing, without the need for visual inspection, will play an important role as beamlines at synchrotron sources prepare for high-throughput automation.

scholarly journals High-throughput sample handling and data collection at synchrotrons: embedding the ESRF into the high-throughput gene-to-structure pipeline

2006 ◽  
Vol 62 (10) ◽  
pp. 1162-1169 ◽  
Author(s):  
A. Beteva ◽  
F. Cipriani ◽  
S. Cusack ◽  
S. Delageniere ◽  
J. Gabadinho ◽  
...  
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Sample Handling

scholarly journals MicroED data collection with SerialEM

2018 ◽  
Author(s):  
M. Jason de la Cruz ◽  
Michael W. Martynowycz ◽  
Johan Hattne ◽  
Tamir Gonen
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Large Scale ◽  
Large Scale Data ◽  
Data Analyses ◽  
Continuous Rotation ◽  
Scale Data

AbstractWe developed a procedure for the cryoEM method MicroED using SerialEM. With this approach, SerialEM coordinates stage rotation, microscope operation, and camera functions for automated continuous-rotation MicroED data collection. More than 300 datasets can be collected overnight in this way, facilitating high-throughput MicroED data collection for large-scale data analyses.

scholarly journals Sociodemographic Characteristics of Missing Data in Digital Phenotyping

2021 ◽  
Author(s):  
Mathew V Kiang ◽  
Jarvis T Chen ◽  
Nancy Krieger ◽  
Caroline O Buckee ◽  
Monica J Alexander ◽  
...  
Keyword(s):  
Missing Data ◽  
Data Collection ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Sensor Data ◽  
Sociodemographic Characteristics ◽  
Natural Environments ◽  
Accelerometer Data ◽  
Bayesian Hierarchical ◽  
Digital Phenotyping

AbstractThe ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphone applications for “digital phenotyping,” the collection of phone sensor and log data to study the lived experiences of subjects in their natural environments. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. Here, we show that digital phenotyping presents a viable method of data collection, over long time periods, across diverse study participants with a range of sociodemographic characteristics. We examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression. We found that iOS users had higher rates of accelerometer non-collection but lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection while Asian participants had slightly lower non-collection. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.

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