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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: fNIRS-QC: crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
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Identification Number: |
doi:10.21979/N9/C8VYZG |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2021-08-12 |
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Version: |
1 |
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Bibliographic Citation: |
Gabrieli, Giulio; Bizzego, Andrea; Esposito, Gianuca, 2021, "Replication Data for: fNIRS-QC: crowd-sourced creation of a dataset and machine learning model for fNIRS quality control", https://doi.org/10.21979/N9/C8VYZG, DR-NTU (Data), V1, UNF:6:vYqYHMToBUb3vKU5cL7RmA== [fileUNF] |
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Citation |
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Title: |
Replication Data for: fNIRS-QC: crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
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Identification Number: |
doi:10.21979/N9/C8VYZG |
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Authoring Entity: |
Gabrieli, Giulio (Nanyang Technological University) |
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Bizzego, Andrea (University of Trento) |
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Esposito, Gianuca (Nanyang Technological University) |
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Software used in Production: |
Python |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Esposito, Gianluca |
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Depositor: |
Gabrieli Giulio |
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Date of Deposit: |
2021-07-22 |
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Holdings Information: |
https://doi.org/10.21979/N9/C8VYZG |
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Study Scope |
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Keywords: |
Social Sciences, Social Sciences, fNIRS |
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Abstract: |
Replication Data for: fNIRS-QC: crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
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Country: |
Singapore |
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Kind of Data: |
fNIRS data, labels, ML models |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
Please contact gianluca.esposito@ntu.edu.sg to access the file. |
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Other Study Description Materials |
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File Description--f73784 |
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File: ratings.tab |
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Notes: |
UNF:6:EcnRCYcJUcoWSmexjaAaPw== |
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File Description--f73789 |
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File: users.tab |
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Notes: |
UNF:6:yOqh1wHUSdZHZHMgl4T8dA== |
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List of Variables: | |
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Variables |
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f73784 Location: |
Variable Format: character Notes: UNF:6:Odr5bipyPzgF40lCxx737w== |
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f73784 Location: |
Summary Statistics: StDev 9.012598663444814; Min. 1.0; Max. 25.0; Valid 2438.0; Mean 17.088187038556235 Variable Format: numeric Notes: UNF:6:+/rD8bfwKL5IKm4y4Aaulw== |
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f73784 Location: |
Variable Format: character Notes: UNF:6:8YiU7a0drrIGgI9wqXtDBA== |
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f73784 Location: |
Summary Statistics: Valid 2438.0; Mean 30711.16201804819; Min. 158.0; StDev 841440.0446752573; Max. 4.1273325E7; Variable Format: numeric Notes: UNF:6:8qM8/kM0V2+8VngT2U3BOg== |
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f73789 Location: |
Summary Statistics: StDev 6.487166818676188; Min. 1.0; Max. 25.0; Mean 15.916666666666668; Valid 12.0; Variable Format: numeric Notes: UNF:6:OMMgyzTA9TEBTJtlikqfpQ== |
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f73789 Location: |
Variable Format: character Notes: UNF:6:5OXa0yt5DC8ebeVPuQcINA== |
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Label: |
01_process_ratings.py |
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Text: |
Script to process the labels |
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Notes: |
text/x-python |
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Label: |
02_maketraintest.py |
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Text: |
Train/Test partition splitter |
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Notes: |
text/x-python |
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Label: |
03_training.py |
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Text: |
Model training |
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Notes: |
text/x-python |
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Label: |
04_predict.py |
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Text: |
Model testing |
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Notes: |
text/x-python |
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Label: |
dataset.py |
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Text: |
file to handle the dataset |
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Notes: |
text/x-python |
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Label: |
labels.csv |
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Text: |
Labels used for the model |
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Notes: |
text/csv |
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Label: |
model.pth |
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Text: |
Trained mdoel |
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Notes: |
application/octet-stream |
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Label: |
nets.py |
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Text: |
Network architecture |
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Notes: |
text/x-python |
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Label: |
Signals.zip |
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Text: |
Signals used for the study |
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Notes: |
application/zip |
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Label: |
testlabels.csv |
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Text: |
test labels |
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Notes: |
text/csv |
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Label: |
trainlabels.csv |
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Text: |
train labels |
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Notes: |
text/csv |