cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2021.579
title: Implement tuned reinforcement learning in selecting different machine learning models prediction for peak electricity consumption

description: Electricity is one of the most impactful energy resources to a country's economy. Forecasting and planning for electricity is the key to success in both industrial and social aspects, especially in developing countries like Thailand. Accuracy in powerforecasting is essential to support growth in electricity demand, provide adequate power planning, and prevent power system outages. To support rising energy demand, offer enough power planning, and avoid power system disruptions, accurate powerforecasting is crucial. However, the qualities of the data and the nature of the issue have a significant impact on how accurate the various forecasting techniques are. Since more and more methods have been implemented to address the need for higher accuracy prediction, a sufficient number of models are currently available and there are rooms for developing ensemble solutions. This research implemented a modern approach with Double Deep Q-network (Double DQN), a Reinforcement Learning framework, as a model selector to assign the forecasting model with the lowest prediction error to each data point. The monthly peak electricity consumptions from 2003 to 2017 in Thailand recorded by the Electricity Generating Authority of Thailand (EGAT) were used to train and validate the proposed model. In addition, the hyperparameter tuning method based on Fractional Factorial Design is employed to reduce the tuning time. As a result, the Double DQN framework has generated the expected outcomes that beats all that of the individual models in terms of accuracy measured by mean squared error (MSE) by leveraging the strength of each forecasting model.
DOI: 10.14457/tu.the.2021.580
title: The omnichannel implementation: case study of the top home appliance retailor in Thailand

description: This research aims to analyze the efficiency factors of implementation the Omnichannel strategy on the retail company by aggregate the factors and components of the successful case study omnichannel implementation to be the standard criteria of factor evaluation. The factors will be evaluated the significant level by use Analytic Hierarchy Process (AHP). The target company is the forefront company in the Home appliance retail industry. Since 2019, The company totally transform into the Multi-channel strategy. By the end of 2021, the company aim to integrate the direction of strategy to be the Omni-channel. The main purpose of the company is to integrate the service level to handle with the concentrated competitive situation in this era. The objective of this research is to identify the factors that significant for implement the Omni-channel Strategy that suitable for the current competitive situation.
DOI: 10.14457/tu.the.2021.581
title: Direction of Thailand cricket farming software development

description: This research aims to analyze the efficiency factors of management cricket farming in Thailand by aggregate the factors and components which significant to cricket farming to be the standard criteria of factor evaluation. The factors will be evaluated the significant level by use Analytic Hierarchy Process (AHP). The target farming is Thailand cricket farm where would like to certified GAP standard or farmer who would like to bring technology to improve their farm both modern farm and traditionals farm The objective of this research is find which module should develop in to the management cricket farming software. The result of first level of AHP, that help to decision about module which should to have and the result of second level of AHP can told me what detail need a lot of concern in that module.
DOI: 10.6084/m9.figshare.20102699
title: Supplementary_table_2.xlsx

description: Supplementary Table 2. miRNAs in the 8q24 region detected in the tumor tissue and normal adjacent mucosa of colorectal patients in TCGA-COAD and TCGA-READ: Tumor tissue vs normal adjacent mucosa comparison
DOI: 10.6084/m9.figshare.20102699.v2
title: Supplementary_table_2.xlsx

description: Supplementary Table 2. miRNAs in the 8q24 region detected in the tumor tissue and normal adjacent mucosa of colorectal patients in TCGA-COAD and TCGA-READ: Tumor tissue vs normal adjacent mucosa comparison
DOI: 10.6084/m9.figshare.20102699.v1
title: Supplementary_table_2.xlsx

description: Supplementary Table 2. miRNAs in the 8q24 region detected in the tumor tissue and normal adjacent mucosa of colorectal patients in TCGA-COAD and TCGA-READ: Tumor tissue vs normal adjacent mucosa comparison
DOI: 10.6084/m9.figshare.21082177
title: Supplementary_table_4

description: Supplementary table 4. A) Amplification status of MYC gene in Study I as resulting from TSO500 HT analyses. B) Results of the differential analysis without samples carrying an amplification of MYC
DOI: 10.6084/m9.figshare.21082177.v3
title: Supplementary_table_4

description: Supplementary table 4. A) Amplification status of MYC gene in Study I as resulting from TSO500 HT analyses. B) Results of the differential analysis without samples carrying an amplification of MYC
DOI: 10.6084/m9.figshare.21082177.v2
title: Supplementary_table_4_revised

description: Supplementary table 4. A) Amplification status of MYC gene in Study I as resulting from TSO500 HT analyses. B) Results of the differential analysis without samples carrying an amplification of MYC
DOI: 10.5281/zenodo.7075436
title: New Strategy for the Development of Targeted Drug Delivery Systems in Cancer Therapy

description: Rare dataset of the experiments performed during the investigation of our newly developed SREKA pentapeptide, and its use for liposome preparation.
DOI: 10.5281/zenodo.7075435
title: New Strategy for the Development of Targeted Drug Delivery Systems in Cancer Therapy

description: Rare dataset of the experiments performed during the investigation of our newly developed SREKA pentapeptide, and its use for liposome preparation.
DOI: 10.6084/m9.figshare.20806420.v1
title: Supplementary Information.csv

description: Data table showing the locations and three-way cluster probabilities for each labelled cone presented in this work
DOI: 10.6084/m9.figshare.21091057.v2
title: Supplementary_table_4.xlsx

description: Supplementary table 4. A) Amplification status of MYC gene in Study I as resulting from TSO500 HT analyses. B) Results of the differential analysis without samples carrying an amplification of MYC.
DOI: 10.6084/m9.figshare.21091057
title: Supplementary_table_4.xlsx

description: Supplementary table 4. A) Amplification status of MYC gene in Study I as resulting from TSO500 HT analyses. B) Results of the differential analysis without samples carrying an amplification of MYC.
DOI: 10.6084/m9.figshare.21091057.v1
title: Supplementary_table_4.xlsx

description: Supplementary table 4. A) Amplification status of MYC gene in Study I as resulting from TSO500 HT analyses. B) Results of the differential analysis without samples carrying an amplification of MYC.
DOI: 10.17632/fb2xt2j8pt.1
title: LINF_320025300

description: DnaJ domain-containing protein | JDP7 | J7
Leishmania infantum (strain JPCM5)
DOI: 10.17632/hpsfsxph2g.1
title: LINF_320025200

description: protein of unknown function - conserved
Leishmania infantum (strain JPCM5)
DOI: 10.17632/zs82gfkkr9.1
title: Acoustic and deep features extracted from acoustic emission signals for the characterization of biocomposites and glass fiber epoxy composites

description: INTRODUCTION
The dataset contains 12 Excel files with extracted features for the characterization of loaded materials and classification of the source material as biocomposite and glass fiber epoxy composite. Each file contains features for 22323 samples, extracted from acoustic emission measurements on loaded composites. The dataset was collected to examine various configurations of convolutional autoencoders for deep feature extraction, and different machine learning methods for the classification of the source material.

INPUTS
The inputs of this dataset are composed of two types of extracted features: 
1.	Standard features
2.	Deep features 

Standard features are extracted from acoustic emission signals as follows:
c1:	Peak amplitude [nm] – burst signal linear peak amplitude,
c2:	Burst signal rise-time [µs] – elapsed time after the first threshold crossing and until the burst signal maximum amplitude,
c3:	Burst signal duration [µs] – elapsed time after the first and until the last threshold crossing of a burst signal,
c4:	Burst signal energy [au],
c5:	RMS Background noise [µV],
c6:	Counts – Number of positive threshold crossings [/],
c7:	Spectral centroid [Hz],
c8:	Frequency of the max. amplitude of Fourier transform spectrum [Hz],
c9:	Frequency of the max. amplitude of continuous wavelet transformation (using the complex Morlet wavelet) [Hz],
c10:	Partial power of Fourier spectrum between 0 and 75 kHz [/], 
c11:	Partial power of Fourier spectrum between 75 and 150 kHz [/],
c12:	Partial power of Fourier spectrum between 150 and 300 kHz [/],
c13:	Partial power of Fourier spectrum between 300 and 475 kHz [/].

Deep features are extracted by using the convolutional autoencoders (CAE) as described in the accompanying paper. 
Deep features are extracted automatically in an unsupervised manner and therefore don’t have physical meaning but are designed to minimize information loss of the input-output mapping of the CAE. Deep features are denoted as: d1, d2, d3, etc.

OUTPUT
The outputs in this dataset are collected in the last column of each file and denote the class (labels_bio-0_cfe-1) as:
Class = 0:  biocomposite
Class = 1:  fiber epoxy composite

FILE NAME DESCRIPTION
File names are composed of CAE1 or CA2 prefixes denoting two types of used CAEs, and the remaining part of the file name defines the CAE hyperparameters as “s1-s2-s3-s4-s5-s6-s7”, which stand for:
s1:	Number of kernels used in 1st convolutional layers,
s2:	Number of kernels used in 2nd convolutional, and 1st transposed convolutional layers,
s3:	Number of kernels used in convolutional and transposed convolutional layers of the latent section,
s4:	Number of neurons in 2nd and 4th FC layer,
s5:	Number of neurons in the bottleneck (3rd FC) layer. This number also represents the number of extracted deep features per input image.
s6:	Number of training epochs,
s7:	The batch size of training samples.
DOI: 10.17632/zs82gfkkr9.2
title: Acoustic and deep features extracted from acoustic emission signals for the characterization of biocomposites and glass fiber epoxy composites

description: INTRODUCTION
The dataset contains 12 Excel files with extracted features for the characterization of loaded materials and classification of the source material as biocomposite and glass fiber epoxy composite. Each file contains features for 22323 samples, extracted from acoustic emission measurements on loaded composites. The dataset was collected to examine various configurations of convolutional autoencoders for deep feature extraction, and different machine learning methods for the classification of the source material.

INPUTS
The inputs of this dataset are composed of two types of extracted features: 
1.	Standard features
2.	Deep features 

Standard features are extracted from acoustic emission signals as follows:
c1:	Peak amplitude [nm] – burst signal linear peak amplitude,
c2:	Burst signal rise-time [µs] – elapsed time after the first threshold crossing and until the burst signal maximum amplitude,
c3:	Burst signal duration [µs] – elapsed time after the first and until the last threshold crossing of a burst signal,
c4:	Burst signal energy [au],
c5:	RMS Background noise [µV],
c6:	Counts – Number of positive threshold crossings [/],
c7:	Spectral centroid [Hz],
c8:	Frequency of the max. amplitude of Fourier transform spectrum [Hz],
c9:	Frequency of the max. amplitude of continuous wavelet transformation (using the complex Morlet wavelet) [Hz],
c10:	Partial power of Fourier spectrum between 0 and 75 kHz [/], 
c11:	Partial power of Fourier spectrum between 75 and 150 kHz [/],
c12:	Partial power of Fourier spectrum between 150 and 300 kHz [/],
c13:	Partial power of Fourier spectrum between 300 and 475 kHz [/].

Deep features are extracted by using the convolutional autoencoders (CAE) as described in the accompanying paper. 
Deep features are extracted automatically in an unsupervised manner and therefore don’t have physical meaning but are designed to minimize information loss of the input-output mapping of the CAE. Deep features are denoted as: d1, d2, d3, etc.

OUTPUT
The outputs in this dataset are collected in the last column of each file and denote the class (labels_bio-0_cfe-1) as:
Class = 0:  biocomposite
Class = 1:  fiber epoxy composite

FILE NAME DESCRIPTION
File names are composed of CAE1 or CA2 prefixes denoting two types of used CAEs, and the remaining part of the file name defines the CAE hyperparameters as “s1-s2-s3-s4-s5-s6-s7”, which stand for:
s1:	Number of kernels used in 1st convolutional layers,
s2:	Number of kernels used in 2nd convolutional, and 1st transposed convolutional layers,
s3:	Number of kernels used in convolutional and transposed convolutional layers of the latent section,
s4:	Number of neurons in 2nd and 4th FC layer,
s5:	Number of neurons in the bottleneck (3rd FC) layer. This number also represents the number of extracted deep features per input image.
s6:	Number of training epochs,
s7:	The batch size of training samples.
DOI: 10.6084/m9.figshare.20102687
title: Supplementary_table_5.xlsx

description: Supplementary table 5. A) Table reporting the validated target genes of up-regulated miRNAs, B) Top 20 enriched terms
DOI: 10.6084/m9.figshare.20102687.v2
title: Supplementary_table_5.xlsx

description: Supplementary table 5. A) Table reporting the validated target genes of up-regulated miRNAs, B) Top 20 enriched terms
DOI: 10.6084/m9.figshare.20102687.v1
title: Supplementary_table_4.xlsx

description: Supplementary table 4. A) Table reporting the validated target genes of up-regulated miRNAs, B) Top 20 enriched terms
DOI: 10.6084/m9.figshare.20102738.v1
title: Supplementary_table_5.xlsx

description: Supplementary table 5. A) Table reporting the validated target genes of down-regulated miRNAs, B) Top 20 enriched terms
DOI: 10.6084/m9.figshare.21091075
title: Biomass and Composition 2022.csv

description: All fishes data with coordinates
DOI: 10.6084/m9.figshare.20102738.v2
title: Supplementary_table_5.xlsx

description: Supplementary table 5. A) Table reporting the validated target genes of down-regulated miRNAs, B) Top 20 enriched terms
DOI: 10.6084/m9.figshare.20102693
title: Supplementary_table_9.xlsx

description: Supplementary Table 9. miRNAs in the 8q24 region detected in the tumor tissue and normal adjacent mucosa of bladder cancer patients in TCGA-BLCA: Tumor tissue vs adjacent mucosa comparison
DOI: 10.6084/m9.figshare.20102693.v2
title: Supplementary_table_9.xlsx

description: Supplementary Table 9. miRNAs in the 8q24 region detected in the tumor tissue and normal adjacent mucosa of bladder cancer patients in TCGA-BLCA: Tumor tissue vs adjacent mucosa comparison
DOI: 10.6084/m9.figshare.20102693.v1
title: Supplementary_table_8.xlsx

description: Supplementary Table 8. miRNAs in the 8q24 region detected in the tumor tissue and normal adjacent mucosa of bladder cancer patients in TCGA-BLCA: Tumor tissue vs adjacent mucosa comparison
DOI: 10.6084/m9.figshare.20102738
title: Supplementary_table_7.xlsx

description: Supplementary table 7. A) Table reporting the validated target genes of down-regulated miRNAs, B) Top 20 enriched terms
DOI: 10.6084/m9.figshare.20102738.v4
title: Supplementary_table_7.xlsx

description: Supplementary table 7. A) Table reporting the validated target genes of down-regulated miRNAs, B) Top 20 enriched terms
DOI: 10.6084/m9.figshare.20102738.v3
title: Supplementary_table_6.xlsx

description: Supplementary table 5. A) Table reporting the validated target genes of down-regulated miRNAs, B) Top 20 enriched terms
DOI: 10.6084/m9.figshare.20102726
title: Supplementary_table_8.xlsx

description: Supplementary table 8. miRNAs in the 8q24 region detected in the plasma of colorectal cancer patients and controls: A) Case vs control comparison; B) Stage vs controls comparison; C) Grade vs controls comparison; D) Localization (colon or rectum) vs controls
DOI: 10.6084/m9.figshare.20102726.v2
title: Supplementary_table_8.xlsx

description: Supplementary table 8. miRNAs in the 8q24 region detected in the plasma of colorectal cancer patients and controls: A) Case vs control comparison; B) Stage vs controls comparison; C) Grade vs controls comparison; D) Localization (colon or rectum) vs controls
DOI: 10.6084/m9.figshare.20102726.v1
title: Supplementary_table_7.xlsx

description: Supplementary table 7. miRNAs in the 8q24 region detected in the plasma of colorectal cancer patients and controls: A) Case vs control comparison; B) Stage vs controls comparison; C) Grade vs controls comparison; D) Localization (colon or rectum) vs controls
DOI: 10.6084/m9.figshare.20102705
title: Supplementary_table_10.xlsx

description: Supplementary table 10. miRNAs in the 8q24 region detected in the urine of bladder cancer patients and healthy controls
DOI: 10.6084/m9.figshare.20102705.v2
title: Supplementary_table_10.xlsx

description: Supplementary table 10. miRNAs in the 8q24 region detected in the urine of bladder cancer patients and healthy controls
DOI: 10.6084/m9.figshare.20102705.v1
title: Supplementary_table_9.xlsx

description: Supplementary table 9. miRNAs in the 8q24 region detected in the urine of bladder cancer patients and healthy controls
DOI: 10.6084/m9.figshare.20102732
title: Supplementary_table_11.xlsx

description: Supplementary table 11. miRNAs in the 8q24 region detected in the plasma EVs of bladder cancer patients and controls
DOI: 10.6084/m9.figshare.20102732.v2
title: Supplementary_table_11.xlsx

description: Supplementary table 11. miRNAs in the 8q24 region detected in the plasma EVs of bladder cancer patients and controls
DOI: 10.6084/m9.figshare.20102732.v1
title: Supplementary_table_10.xlsx

description: Supplementary table 10. miRNAs in the 8q24 region detected in the plasma EVs of bladder cancer patients and controls
DOI: 10.15472/vzde3s
title: Lista de flora acuática continental de Colombia

description: El listado de referencia de la vegetación acuática continental de Colombia incluye plantas acuáticas, semiacuáticas y ribereñas. Además, contiene información sobre la distribución por regiones, departamentos, el biotipo de las especies, su potencial invasivo y bibliografía básica. Este listado contiene 634 especies (incluidas seis subespecies y 49 sinonimias) distribuidas en 285 géneros, 89 familias, 40 órdenes, 8 clases y 3 phylum.
description: En relación con el biotipo se definieron 7 categorías: Errantes flotantes (FL), Errantes sumergidas (SU), Enraizadas en el sedimento, con todas o casi todas las hojas sumergidas (ES), Enraizadas en el sedimento, con hojas flotantes sobre la superficie (EF), Enraizadas en el sedimento, con hojas emergidas (EE), Enraizadas en zona húmeda – en zonas de transición- (EH) y Fijas sobre piedras (FC).
description: 13 especies fureron catalogadas como endémicas y 76 especies fueron catalogadas como potencialmente invasoras, según Cárdenas et al., (2017) y Díaz-Espinosa et al., (2012).
DOI: 10.5281/zenodo.6560267
title: Supplementary Material for Creativity and Design Thinking as Facilitators in Requirements Elicitation

description: Supplementary Material for the paper <em>Creativity and Design Thinking as Facilitators in Requirements Elicitation.</em> The <em>survey_questions</em>.<em>pdf</em> file contains the form questions used to conduct the survey, the <em>survey_responses.csv</em> file contains the responses to this form, and the <em>table_techniques.pdf </em>file contains a supplementary table with creativity techniques and design thinking techniques.
DOI: 10.5281/zenodo.6560268
title: Supplementary Material for Design Thinking and Creativity as Facilitators in Requirements Elicitation

description: Supplementary Material for the paper <em>Design Thinking and Creativity as Facilitators in Requirements Elicitation.</em> The <em>survey_questions</em>.<em>pdf</em> file contains the form questions used to conduct the survey and the <em>table_techniques.pdf </em>file contains a supplementary table with creativity techniques and design thinking techniques.
DOI: 10.5281/zenodo.6783068
title: Supplementary Material for Creativity and Design Thinking as Facilitators in Requirements Elicitation

description: Supplementary Material for the paper <em>Creativity and Design Thinking as Facilitators in Requirements Elicitation.</em> The <em>survey_questions</em>.<em>pdf</em> file contains the form questions used to conduct the survey, the <em>survey_responses.csv</em> file contains the responses to this form, and the <em>table_techniques.pdf </em>file contains a supplementary table with creativity techniques and design thinking techniques.
DOI: 10.5281/zenodo.7058212
title: Supplementary Material for Creativity and Design Thinking as Facilitators in Requirements Elicitation

description: Supplementary Material for the paper <em>Creativity and Design Thinking as Facilitators in Requirements Elicitation.</em> The <em>survey_questions</em>.<em>pdf</em> file contains the form questions used to conduct the survey, the <em>survey_responses.csv</em> file contains the responses to this form, and the <em>table_techniques.pdf </em>file contains a supplementary table with creativity techniques and design thinking techniques.
DOI: 10.6084/m9.figshare.21091177
title: papilio_gigon_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21091177.v1
title: papilio_gigon_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21091168
title: ornithoptera_priamus_zanfona_1.fasta.gz

description: ........
DOI: 10.6084/m9.figshare.21091168.v1
title: ornithoptera_priamus_zanfona_1.fasta.gz

description: ........
DOI: 10.34740/kaggle/dsv/4198991
title: Elon Musk Tweets
DOI: 10.18112/openneuro.ds004264.v1.0.0
title: Steer the Ship