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Preprint
English
2024

Turn your raw data into income: How to prepare and upload your own data

Turn your raw data into income: How to prepare and upload your own data – dataset preview

4 Datasets

5 Files

$0 Value

English
2024
Raw Data Library
Vol 1 (1)
Indexed: BASE
DOI: 10.71008/rdl.preprint.2025.002

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Abstract

This study considers 4 experimental raw data and a survery of dataset. Stress-strain relationships of A1 and A2, R1 python file for distribution radar chart of Taguchi as a different variable data, and moment-curvature relationship of A1 were obtained. For the current study the fifth data of S1 was taken from a survey dataset of 500 responses. It is explained how the experimentally obtained raw data, code file of python and survery dataset are uploaded to the RDL platform.

How to cite this publication

Raw Data Library (2024). Turn your raw data into income: How to prepare and upload your own data. Raw Data Library, 1(1), pp. 1-4, DOI: https://doi.org/10.71008/rdl.preprint.2025.002.

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Research Datasets

4 datasets containing 5 research files

Dataset 1: Stress-strain relationship of specimen
2 files
$0

Raw Data Library. Turn your raw data into income: How to prepare and upload your own data. Raw data set for Stress-strain relationship of specimen .

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A1 preview
A1
XLSX
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A2 preview
A2
XLSX
Dataset 2: Radar chart distribution
1 files
$0

Raw Data Library. Turn your raw data into income: How to prepare and upload your own data. Raw data set for Radar chart distribution .

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R1 preview
R1
XLSX
Dataset 3: Moment-curvature relationship
1 files
$0

Raw Data Library. Turn your raw data into income: How to prepare and upload your own data. Raw data set for Moment-curvature relationship .

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A1 preview
A1
XLSX
Dataset 4: Responses for survey dataset
1 files
$0

Raw Data Library. Turn your raw data into income: How to prepare and upload your own data. Raw data set for Responses for survey dataset .

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S1 preview
S1
XLSX
Publication Details

Type

Preprint

Year

2024

Authors

1

Datasets

4

Total Files

5

Language

English

Journal

Raw Data Library

DOI

https://doi.org/10.71008/rdl.preprint.2025.002

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