cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6800359
title: Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review

description: This is the replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review Submitted to Transaction on Software Engineering Journal. Yalemisew Abgaz, Andrew McCarren, Peter Elger, David Solan, Neil Lapuz, Marin Bivol, Glenn Jackson, Murat Yilmaz, Jim Buckley, and Paul Clarke This package contains the following folders and files that provide additional insight to replicate the SLR.<br> - Literature_Search_Strategy.pdf : A detailed description of the literature search outlining the steps and the results of the steps.<br> - Literature_Search_Data: A folder containing the different stages of search results and refinements. It contains four files listing studies included from the 1st round to the 4th round of filtering. A description of each file is included in the Literature_Search_Strategy document.<br> - Snowballing_Data: A folder containing the list of studies included in the snowballing process.<br> - Data_Extraction.xlsx: The data extraction template and the summary of the memoing result. If you have futher questions regarding the survey, feel free to contact us via e-mail. Yalemisewm.abgaz@dcu.ie
DOI: 10.5281/zenodo.7108083
title: Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review

description: This is the replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review Submitted to Transaction on Software Engineering Journal. Yalemisew Abgaz, Andrew McCarren, Peter Elger, David Solan, Neil Lapuz, Marin Bivol, Glenn Jackson, Murat Yilmaz, Jim Buckley, and Paul Clarke This package contains the following folders and files that provide additional insight to replicate the SLR.<br> - Literature_Search_Strategy.pdf : A detailed description of the literature search outlining the steps and the results of the steps.<br> - Literature_Search_Data: A folder containing the different stages of search results and refinements. It contains four files listing studies included from the 1st round to the 4th round of filtering. A description of each file is included in the Literature_Search_Strategy document.<br> - Snowballing_Data: A folder containing the list of studies included in the snowballing process.<br> - Data_Extraction.xlsx: The data extraction template and the summary of the memoing result. If you have futher questions regarding the survey, feel free to contact us via e-mail. Yalemisewm.abgaz@dcu.ie
DOI: 10.5281/zenodo.7506763
title: Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review

description: <strong>Replication Package</strong> <strong>Title:</strong> Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review. <strong>Authors:</strong> Yalemisew Abgaz, Andrew McCarren, Peter Elger, David Solan, Neil Lapuz, Marin Bivol, Glenn Jackson, Murat Yilmaz, Jim Buckley, and Paul Clarke <strong>Year</strong> This replication package was initially generated in 2022 and following feedback from reviewers revised in 2023. This package contains two files and three folders that provide additional insight for researchers who wish to replicate our work or who would like to expand the review in the future. <strong>Files</strong> The file contains a detailed description of the literature search outlining the steps and the results obtained. Readme.md: A readme file (this file). <strong>Folders</strong> A folder containing the search results and the refinement steps. It contains four files listing studies included in the refinement process (Refinement_Step_1 to Refinement_Step_4) and a master file combining all the steps in one file. The master file contains detailed information about how the refinement steps are executed and all the intermediate results following each refinement step. Users may explore by expanding the filters in Refinement Step 2 (J), Refinement Step 3 (M) and Refinement Step 4 § columns in the master sheet. Readers can also directly go to the sheets that contain the selected studies in any of the refinement stages. A description of each file is also included in the Literature_Search_Strategy.pdf file. This folder contains the list of studies included in the snowballing process, including the last two refinement steps (Refinement_Step_5 and Refinement_Step_6). The snowballing master sheet contains studies extracted using the snowballing process and the data cleaning and filtering criteria used. The different sheets also contain the selected studies at each stage of the snowballing process. This folder contains the data extracted from the selected literature by employing the Systematic Review and Ground Theory. The two files included in this folder contain the data extraction template and the data extracted from the 35 selected studies including some intermediate notes. If you have further questions regarding the survey, feel free to contact us via e-mail. Yalemisewm.abgaz@dcu.ie.
DOI: 10.5281/zenodo.7506835
title: Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review

description: <strong>Replication Package</strong> <strong>Title:</strong> Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review. <strong>Authors:</strong> Yalemisew Abgaz, Andrew McCarren, Peter Elger, David Solan, Neil Lapuz, Marin Bivol, Glenn Jackson, Murat Yilmaz, Jim Buckley, and Paul Clarke <strong>Year</strong> This replication package was initially generated in 2022 and following feedback from reviewers revised in 2023. This package contains two files and three folders that provide additional insight for researchers who wish to replicate our work or who would like to expand the review in the future. <strong>Files</strong> The file contains a detailed description of the literature search outlining the steps and the results obtained. Readme.md: A readme file (this file). <strong>Folders</strong> A folder containing the search results and the refinement steps. It contains four files listing studies included in the refinement process (Refinement_Step_1 to Refinement_Step_4) and a master file combining all the steps in one file. The master file contains detailed information about how the refinement steps are executed and all the intermediate results following each refinement step. Users may explore by expanding the filters in Refinement Step 2 (J), Refinement Step 3 (M) and Refinement Step 4 § columns in the master sheet. Readers can also directly go to the sheets that contain the selected studies in any of the refinement stages. A description of each file is also included in the Literature_Search_Strategy.pdf file. This folder contains the list of studies included in the snowballing process, including the last two refinement steps (Refinement_Step_5 and Refinement_Step_6). The snowballing master sheet contains studies extracted using the snowballing process and the data cleaning and filtering criteria used. The different sheets also contain the selected studies at each stage of the snowballing process. This folder contains the data extracted from the selected literature by employing the Systematic Review and Ground Theory. The two files included in this folder contain the data extraction template and the data extracted from the 35 selected studies including some intermediate notes. If you have further questions regarding the survey, feel free to contact us via e-mail. Yalemisewm.abgaz@dcu.ie.
DOI: 10.5281/zenodo.7594531
title: Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review

description: <strong>Replication Package</strong> <strong>Title:</strong> Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review. <strong>Authors:</strong> Yalemisew Abgaz, Andrew McCarren, Peter Elger, David Solan, Neil Lapuz, Marin Bivol, Glenn Jackson, Murat Yilmaz, Jim Buckley, and Paul Clarke <strong>Year</strong> This replication package was initially generated in 2022 and following feedback from reviewers, it is revised in 2023. This package contains two files and three folders that provide additional insight for researchers who wish to replicate our work or who would like to expand the review in the future. <strong>Files</strong> The file contains a detailed description of the literature search outlining the steps and the results obtained. Readme.md: A readme file (this file). <strong>Folders</strong> A folder containing the search results and the refinement steps. It contains four files listing studies included in the refinement process (Refinement_Step_1 to Refinement_Step_4) and a master file combining all the steps in one file. The master file contains detailed information about how the refinement steps are executed and all the intermediate results following each refinement step. Users may explore by expanding the filters in Refinement Step 2 (J), Refinement Step 3 (M) and Refinement Step 4 § columns in the master sheet. Readers can also directly go to the sheets that contain the selected studies in any of the refinement stages. A description of each file is also included in the Literature_Search_Strategy.pdf file. This folder contains the list of studies included in the snowballing process, including the last two refinement steps (Refinement_Step_5 and Refinement_Step_6). The snowballing master sheet contains studies extracted using the snowballing process and the data cleaning and filtering criteria used. The different sheets also contain the selected studies at each stage of the snowballing process. This folder contains the data extracted from the selected literature by employing the Systematic Review and Ground Theory. The two files included in this folder contain the data extraction template and the data extracted from the 35 selected studies including some intermediate notes. If you have further questions regarding the survey, feel free to contact us via e-mail. Yalemisewm.abgaz@dcu.ie.
DOI: 10.5281/zenodo.7899996
title: Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review

description: <strong>Replication Package</strong> <strong>Title:</strong> Replication package for Decomposition of Monolithic Applications into Microservices Architectures: A Systematic Review. <strong>Authors:</strong> Yalemisew Abgaz, Andrew McCarren, Peter Elger, David Solan, Neil Lapuz, Marin Bivol, Glenn Jackson, Murat Yilmaz, Jim Buckley, and Paul Clarke <strong>Year</strong> This replication package was initially generated in 2022 and following feedback from reviewers, it is revised in 2023. This package contains two files and three folders that provide additional insight for researchers who wish to replicate our work or who would like to expand the review in the future. <strong>Files</strong> The file contains a detailed description of the literature search outlining the steps and the results obtained. Readme.md: A readme file (this file). <strong>Folders</strong> A folder containing the search results and the refinement steps. It contains four files listing studies included in the refinement process (Refinement_Step_1 to Refinement_Step_4) and a master file combining all the steps in one file. The master file contains detailed information about how the refinement steps are executed and all the intermediate results following each refinement step. Users may explore by expanding the filters in Refinement Step 2 (J), Refinement Step 3 (M) and Refinement Step 4 § columns in the master sheet. Readers can also directly go to the sheets that contain the selected studies in any of the refinement stages. A description of each file is also included in the Literature_Search_Strategy.pdf file. This folder contains the list of studies included in the snowballing process, including the last two refinement steps (Refinement_Step_5 and Refinement_Step_6). The snowballing master sheet contains studies extracted using the snowballing process and the data cleaning and filtering criteria used. The different sheets also contain the selected studies at each stage of the snowballing process. This folder contains the data extracted from the selected literature by employing the Systematic Review and Ground Theory. The two files included in this folder contain the data extraction template and the data extracted from the 35 selected studies including some intermediate notes. If you have further questions regarding the survey, feel free to contact us via e-mail. Yalemisewm.abgaz@dcu.ie.
DOI: 10.5281/zenodo.7899655
title: SlumberNet: Deep learning classification of sleep stages using residual neural networks

description: Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of accuracy (~98%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~50x faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet demonstrates the power of deep learning in sleep research, and looking forward, SlumberNet could be adapted to human EEG analysis and sleep stage classification. Thus, SlumberNet could be a valuable tool in understanding both sleep physiology and disorders in mammals.
DOI: 10.5281/zenodo.7899654
title: SlumberNet: Deep learning classification of sleep stages using residual neural networks

description: Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of accuracy (~98%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~50x faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet demonstrates the power of deep learning in sleep research, and looking forward, SlumberNet could be adapted to human EEG analysis and sleep stage classification. Thus, SlumberNet could be a valuable tool in understanding both sleep physiology and disorders in mammals.
DOI: 10.35078/yf4lgg
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Diamantina | Município: Inhaúma

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.57745/pqb0pu
title: rpg_complete_2021_d80.zip

description: :unav
DOI: 10.57745/kk4rkr
title: rpg_complete_2021_d62.zip

description: :unav
DOI: 10.57745/ukhm0q
title: rpg_complete_2021_d02.zip

description: :unav
DOI: 10.57745/7lhniy
title: rpg_complete_2021_d60.zip

description: :unav
DOI: 10.57745/donjkc
title: rpg_complete_2021_d59.zip

description: :unav
DOI: 10.57745/nywo6x
title: Notice_RPG_complété_2021.pdf

description: :unav
DOI: 10.34740/kaggle/dsv/5609402
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.22770083
title: Scabrous is distributed via signaling filopodia to modulate Notch response during bristle patterning in Drosophila

description: Confocal microscopy imaging data of <strong>FIGURE 1:</strong> Scabrous immunostaining in w1118 tissues (2022-02-23) <br> <strong>FIGURE 2:</strong> Cytochalasin B experimental data 60 min, DMSO (nd123-129.nd2 files) 60 min, cytB (nd130-145.nd2 files) 30 min, DMSO (DMSO_30min.lif) 30 min, cytB (CYTB_30min.lif) 5 min, DMSO (DMSO_5min.lif, 23C28_5minDMSO) 5 min, cytB (CYTB_5min.lif, 23C28_5minCYTB) <br> <strong>FIGURE 3:</strong> Notch transcriptional reporter in a pannier-GAL4 &gt; UAS-Scabrous GFP background (23b24, 23b28, 23c02) Notch transcriptional reporter in a pannier-GAL4 &gt; UAS-Scabrous RNAi background (22c04, 2022-03-02) Notch transcriptional reporter in a pannier-GAL4 &gt; UAS-White RNAi background (2021-11-10, <br> <br>
DOI: 10.6084/m9.figshare.22770083.v2
title: Scabrous is distributed via signaling filopodia to modulate Notch response during bristle patterning in Drosophila

description: Confocal microscopy imaging data of <strong>FIGURE 1:</strong> Scabrous immunostaining in w1118 tissues (2022-02-23) <br> <strong>FIGURE 2:</strong> Cytochalasin B experimental data 60 min, DMSO (nd123-129.nd2 files) 60 min, cytB (nd130-145.nd2 files) 30 min, DMSO (DMSO_30min.lif) 30 min, cytB (CYTB_30min.lif) 5 min, DMSO (DMSO_5min.lif, 23C28_5minDMSO) 5 min, cytB (CYTB_5min.lif, 23C28_5minCYTB) <br> <strong>FIGURE 3:</strong> Notch transcriptional reporter in a pannier-GAL4 &gt; UAS-Scabrous GFP background (23b24, 23b28, 23c02) Notch transcriptional reporter in a pannier-GAL4 &gt; UAS-Scabrous RNAi background (22c04, 2022-03-02) Notch transcriptional reporter in a pannier-GAL4 &gt; UAS-White RNAi background (2021-11-10, <br> <br>
DOI: 10.6084/m9.figshare.22770083.v1
title: NsfGFP with Scabrous-GFP overexpressed

description: Confocal microscopy imaging data of Notch transcriptional reporter in a pannier-GAL4 &gt; UAS-Scabrous GFP background.
DOI: 10.3886/e190921v1
title: Volume III and Issue V of the Journal of Language Teaching is a Mirror

description: AbstractThis literature review examines how virtual reality (VR) language learning simulations could assist traditional language classes, those with access constraints, and the global economy. Many areas could benefit from a qualitative study, including but not limited to foreign language anxiety, the Common European Framework of Reference for Languages (CEFR), self-evaluation, goal setting, progress monitoring, gamification, teacher perceptions, student-developed templates, metaphors, cultural sensitivity, visual aids, and the validity of the CEFR.Keywords: English language teaching, language teaching, language journal, journal education, Innovation, teacher teaching
DOI: 10.5519/qd.i2lvu904
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.6084/m9.figshare.22770191.v1
title: DLH & resoiratory function

description: dataset in this study
DOI: 10.6084/m9.figshare.22770191
title: DLH & resoiratory function

description: dataset in this study
DOI: 10.34740/kaggle/dsv/5609483
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.57771/89h3-as60
title: Environmental Noises
title: Environmental Noises

description: <p>A dataset of short-duration audio files capturing typical environmental sounds is available, including airport, baby crying, city, construction, kids, rain, stadium, subway, and traffic sounds. This dataset is highly useful for audio augmentation purposes, such as in machine learning and sound design.</p>
DOI: 10.57771/g08g-5x86
title: Environmental Noises
title: Environmental Noises

description: <p>A dataset of short-duration audio files capturing typical environmental sounds is available, including airport, baby crying, city, construction, kids, rain, stadium, subway, and traffic sounds. This dataset is highly useful for audio augmentation purposes, such as in machine learning and sound design.</p>
DOI: 10.57771/vs0a-8429
title: Speech and Transcription Corpus with Augmented Audio Data in the Asturian Language
title: Audio data augmentation of Asturian Language

description: <p>A corpus of audio data augmentation applied to the Asturian language dataset is available to download. The <code>ADA/</code> folder contains 169,162 audio files, which is 20 times the original dataset. </p>
DOI: 10.57771/yq5t-wv40
title: Speech and Transcription Corpus with Augmented Audio Data in the Asturian Language
title: Audio data augmentation of Asturian Language

description: <p>A corpus of audio data augmentation applied to the Asturian language dataset is available to download. The <code>ADA/</code> folder contains 169,162 audio files, which is 20 times the original dataset. </p>