Please direct any questions regarding the data, data collection, methods used in the administration of this survey and/or the summarization of responses provided in this document (or the corresponding report) to Julie Wernert at Indiana University, jwernert@iu.edu.
Here we present CAFE 5, a completely re-written software package with numerous performance and user-interface enhancements over previous versions. These include improved support for multithreading, the explicit modelling of rate variation among families using gamma-distributed rate categories, and command-line arguments that preclude the use of accessory scripts.
This dataset documents the outcomes of a research project where we identified, analyzed, and described a set of existing ethics-focused methods designed to support design research and practice for a range of audiences (such as technology and design researchers and practitioners, and educators). The final dataset includes 63 ethics-focused methods, describing the intended audience(s), format of guidance, interaction qualities, utilization of existing knowledge or concepts, implementation opportunities within design processes, and the "core" or "script" of the method.
The purpose of this study was to examine how IMW affects the sensory and affective components of dyspnea, exercise performance, and NIRS-derived metaboreflex effects during a cycling time to exhaustion test. Additionally, to augment the ventilatory response for better elucidation of the cardiorespiratory effects of IMW, we added hypoxia as an intervention. Using both normoxic and hypoxic conditions, our hypotheses were: 1) both sensory and affective components of dyspnea would be attenuated following IMW in each condition, 2) the extent of skeletal muscle deoxygenation (i.e., a NIRS-derived surrogate for the metaboreflex) in the leg would be reduced after IMW in each condition, and 3) participants’ time to exhaustion would be prolonged following IMW in each condition.
The purpose of this study was to examine how IMW affects the sensory and affective components of dyspnea, exercise performance, and NIRS-derived metaboreflex effects during a cycling time to exhaustion test. Additionally, to augment the ventilatory response for better elucidation of the cardiorespiratory effects of IMW, we added hypoxia as an intervention. Using both normoxic and hypoxic conditions, our hypotheses were: 1) both sensory and affective components of dyspnea would be attenuated following IMW in each condition, 2) the extent of skeletal muscle deoxygenation (i.e., a NIRS-derived surrogate for the metaboreflex) in the leg would be reduced after IMW in each condition, and 3) participants’ time to exhaustion would be prolonged following IMW in each condition.
The data set consists of movement kinematic data from 92 participants that are neurotypical or have certain neurodevelopmental disorders (Autism, Attention Deficit/Hyperactivity Disorder, and their comorbidity). The data is of roughly 100 trials of a reaching paradigm per participant. The raw data consists of the linear acceleration (a), the roll (R), pitch (P), and yaw (Y), and the rate of change of the roll, pitch, and yaw.
Description of the data and file structure.
There are three primary folders: Data, Matlab Code, and the Deep Learning Code., Data:
Each folder corresponds to one participant. Inside is their motion data (sensor_data.csv) and a file describing their diagnosis and severity (diagnosis.txt). The columns of the csv are: a in x, a in y, a in z, dR/dt, dP/dt, dY/dt, R, P, Y
The diagnosis.txt file contains two lines: the first is their diagnosis (ASD, ADHD, A^2, NT), the second is their severity (HF for high functioning, MF for mid functioning, LF for low functioning, and NA for not applicable)., Matlab Code:
Information on how to analyze participants can be found in README.txt file within the folder.
The Matlab code was written and run on MATLAB_R2023b. The signal processing toolbox is required., and Deep Learning Code:
Information on how to perform the deep learning analysis can be found in README.txt file within the folder.
The deep learning code is written in Python3 and relies on PyTorch.
K. Doctor, C. McKeever, A. Phadnis, D. Wu, J. Nurnberger Jr., M. Plawecki, & J.V. Jose. Deep learning and statistical millisecond motor assessments of neurodevelopmental disorders. Science, In Submission.
Title:
Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders
The data set consists of movement kinematic data from 92 participants that are neurotypical or have certain neurodevelopmental disorders (Autism, Attention Deficit/Hyperactivity Disorder, and their comorbidity). The data is of roughly 100 trials of a reaching paradigm per participant. The raw data consists of the linear acceleration (a), the roll (R), pitch (P), and yaw (Y), and the rate of change of the roll, pitch, and yaw.
Description of the data and file structure.
There are three primary folders: Data, Matlab Code, and the Deep Learning Code., Data:
Each folder corresponds to one participant. Inside is their motion data (sensor_data.csv) and a file describing their diagnosis and severity (diagnosis.txt). The columns of the csv are: a in x, a in y, a in z, dR/dt, dP/dt, dY/dt, R, P, Y
The diagnosis.txt file contains two lines: the first is their diagnosis (ASD, ADHD, A^2, NT), the second is their severity (HF for high functioning, MF for mid functioning, LF for low functioning, and NA for not applicable)., Matlab Code:
Information on how to analyze participants can be found in README.txt file within the folder.
The Matlab code was written and run on MATLAB_R2023b. The signal processing toolbox is required., and Deep Learning Code:
Information on how to perform the deep learning analysis can be found in README.txt file within the folder.
The deep learning code is written in Python3 and relies on PyTorch.
K. Doctor, C. McKeever, A. Phadnis, D. Wu, J. Nurnberger Jr., M. Plawecki, & J.V. Jose. Deep learning and statistical millisecond motor assessments of neurodevelopmental disorders. Science, In Submission.
Title:
Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders
Data for this research study were collected and assembled in the attached Excel file. These data were subsequently analyzed for the manuscript using GraphPad Prism version 9.0.0 (Prism, San Diego, CA) statistical software.
Data for this research study were collected and assembled in the attached Excel file. These data were subsequently analyzed for the manuscript using GraphPad Prism version 9.0.0 (Prism, San Diego, CA) statistical software.
As part of a study to make food policy recommendations to the City of Indianapolis, this component of the study was concerned with assessing the food environment of SNAP retailers across the city. This survey was carried out to understand food access in Indianapolis, especially when such access concerns marginalized residents.
Citation to related publication:
Title:
2021 Store Survey Dataset of SNAP Retailers in Indianapolis, IN
As part of a study to make food policy recommendations to the City of Indianapolis, this component of the study was concerned with assessing the food environment of SNAP retailers across the city. This survey was carried out to understand food access in Indianapolis, especially when such access concerns marginalized residents.
