cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5142959
title: Data archive for paper "Machine Learning Emulation of Urban Land Surface Processes"

description: This archive contains models, data* (Overview), as well as the Singularity image to optionally rerun experiments described in "Machine Learning Emulation of Urban Land Surface Processes". <strong>Prerequisites</strong> Linux or macOS with Bash shell. Singularity (tested with version 3.6.3-1.el8) Please note that all steps require Singularity to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the Singularity documentation. <strong>Overview</strong> A general overview of the repository structure is given below. Due to licensing restrictions analysis and forcing data (*) cannot be included and need to be requested separately (see Initialization). Data derivatives (**) from either analysis or forcing, as well as intermediary data (***), are not included as they can be generated by rerunning experiments (see Usage). <pre><code>. ├── data │ ├── analysis* │ ├── forcing* │ ├── teb │ ├── utils │ ├── wps │ └── wrf ├── hpc ├── models │ ├── teb │ ├── unn │ ├── wps │ └── wrf-unn ├── notebooks ├── outputs │ ├── analysis** │ ├── benchmark*** │ ├── forcing** │ ├── kerastuner*** │ ├── notebooks │ ├── tabular │ ├── teb** │ ├── unn** │ ├── wps*** │ └── wrf ├── paper │ └── figures ├── singularity └── tools </code></pre> <strong>Initialization</strong> Forcing and analysis data need to be requested separately. The following directories should map to their respective data archives: <code>./data/analysis</code> -&gt; Grimmond et al. (2013) <code>./data/forcing</code> -&gt; Grimmond et al. (2021) <strong>Usage</strong> To rerun all experiments and reproduce results, run <code>tools/run_all.sh</code> from your command prompt. After completion, all results are saved in the <code>outputs</code> directory. Note that WRF simulations require high CPU time and may take hours or days to complete. Alternatively, if Portable Batch System (PBS) is available on your system, the following helpers may be used instead: <pre><code>qsub hpc/submit_init.pbs qsub hpc/submit_tuner.pbs qsub hpc/submit_unn.pbs qsub hpc/submit_find_median_unn.pbs qsub hpc/submit_wrf.pbs qsub hpc/submit_postprocess.pbs qsub hpc/submit_benchmark.pbs </code></pre> Note that you may need to modify PBS helper scripts to suit your specific environment. <strong>Development notes</strong> See DEVELOP.md. <strong>License</strong> The source code developed for this work is licensed under MIT (<code>LICENSE_CODE.txt</code>). For licensing information of third-party software see licenses under the <code>models</code> directory. Data files in this archive, including the initial and boundary condition data from the European Centre for Medium-Range Weather Forecasts (<code>data/wps/ungrib</code>), are licensed under CC BY-NC 4.0 (<code>LICENSE_DATA.txt</code>).
DOI: 10.26188/19351607.v1
title: ThyCyn2.0: A chromosome-scale hybrid genome assembly of the extinct Tasmanian tiger (Thylacinus cynocephalus)

description: Genome assembly and homology-based gene annotations for ThyCyn2.0 as described in Feigin et al. 2022: A chromosome-scale hybrid genome assembly of the extinct Tasmanian tiger (Thylacinus cynocephalus). <br><br>Preprint detailing the assembly, annotations and methods available at: https://www.biorxiv.org/content/10.1101/2022.03.02.482690v1<br>NCBI genome assembly submission hosted under BioProject: PRJNA354646 and BioSample: SAMN06049672
DOI: 10.6084/m9.figshare.19364771
title: Tabla 3-Relación marco-dimensión-estudio

description: Tabla 3-Relación marco-dimensión-estudio
DOI: 10.6084/m9.figshare.19364771.v1
title: Tabla 3-Relación marco-dimensión-estudio

description: Tabla 3-Relación marco-dimensión-estudio
DOI: 10.5281/zenodo.6353646
title: Mathematics and numerosity but not visuo-spatial working memory correlate with math-anxiety in adults

description: Data in "Database" are organized in subfolders. Each subfolder contains data of the corresponding task. Data from the Paper&Pencil tests are reported in the .xlsx file Numerosity Estimation Task: Each file contains a matrix called “matr”. Each row of the matrix “matr” is a trial. The columns contain the following information: 1<sup>st</sup>: Subject response on numerosity 2<sup>nd</sup>: Test numerosity 3<sup>rd</sup>: Subject response on numerosity 4<sup>th</sup>: 0 5<sup>th</sup>: Stimulus duration 6<sup>th</sup>: Response time Simple Calculation Task: Each file contains a matrix called “MATR”. Each row of the matrix “MATR” is a trial. The columns contain the following information: 1<sup>st</sup>: Number of trials 2<sup>nd</sup>: Subject response 3<sup>rd</sup>: Response time 4<sup>th</sup>: First digit 5<sup>th</sup>: mathematical symbols (x=1; +=2; –=3) 6<sup>th</sup>: Second digit 7<sup>th</sup>: Third digit 8<sup>th</sup>: Forth digit Complex Calculation Task: Each file contains a matrix called “MATR”. Each row of the matrix “MATR” is a trial. The columns contain the following information: 1<sup>st</sup>: Number of trials 2<sup>nd</sup>: Subject response 3<sup>rd</sup>: Response time 4<sup>th</sup>: First digit 5<sup>th</sup>: mathematical symbols (x=1; +=2; –=3; /=4) 6<sup>th</sup>: Second digit VSWM Task: Data from forward and backward conditions are located in two different subfolders. Each file contains a matrix called “Span”, with the corresponding VSWM span. For more details, please contact the first author.
DOI: 10.5281/zenodo.6353645
title: Mathematics and numerosity but not visuo-spatial working memory correlate with math-anxiety in adults

description: Data in "Database" are organized in subfolders. Each subfolder contains data of the corresponding task. Data from the Paper&Pencil tests are reported in the .xlsx file Numerosity Estimation Task: Each file contains a matrix called “matr”. Each row of the matrix “matr” is a trial. The columns contain the following information: 1<sup>st</sup>: Subject response on numerosity 2<sup>nd</sup>: Test numerosity 3<sup>rd</sup>: Subject response on numerosity 4<sup>th</sup>: 0 5<sup>th</sup>: Stimulus duration 6<sup>th</sup>: Response time Simple Calculation Task: Each file contains a matrix called “MATR”. Each row of the matrix “MATR” is a trial. The columns contain the following information: 1<sup>st</sup>: Number of trials 2<sup>nd</sup>: Subject response 3<sup>rd</sup>: Response time 4<sup>th</sup>: First digit 5<sup>th</sup>: mathematical symbols (x=1; +=2; –=3) 6<sup>th</sup>: Second digit 7<sup>th</sup>: Third digit 8<sup>th</sup>: Forth digit Complex Calculation Task: Each file contains a matrix called “MATR”. Each row of the matrix “MATR” is a trial. The columns contain the following information: 1<sup>st</sup>: Number of trials 2<sup>nd</sup>: Subject response 3<sup>rd</sup>: Response time 4<sup>th</sup>: First digit 5<sup>th</sup>: mathematical symbols (x=1; +=2; –=3; /=4) 6<sup>th</sup>: Second digit VSWM Task: Data from forward and backward conditions are located in two different subfolders. Each file contains a matrix called “Span”, with the corresponding VSWM span. For more details, please contact the first author.
DOI: 10.5281/zenodo.6341666
title: Vespa velutina interactions - additional records

description: Biotic interaction data of the Asian yellow-legged hornet <em>Vespa velutina</em> derived from literature for the upload in GLOBI (Global Biotic Interactions, http://globalbioticinteractions.org/). Data prepared in the Alien-CSI Hackathon of the Cost Action CA17122.
DOI: 10.5281/zenodo.6341665
title: Vespa velutina interactions - additional records

description: Biotic interaction data of the Asian yellow-legged hornet <em>Vespa velutina</em> derived from literature for the upload in GLOBI (Global Biotic Interactions, http://globalbioticinteractions.org/). Data prepared in the Alien-CSI Hackathon of the Cost Action CA17122.
DOI: 10.6084/m9.figshare.19364750
title: Dorado data barcode and sample info

description: documented here: https://hackmd.io/@astrobiomike/getting-Lee-Dorado-sequence-data
DOI: 10.6084/m9.figshare.19364750.v2
title: Dorado data barcode and sample info

description: documented here: https://hackmd.io/@astrobiomike/getting-Lee-Dorado-sequence-data
DOI: 10.6084/m9.figshare.19364750.v1
title: Dorado data barcode info

description: documented here: https://hackmd.io/@astrobiomike/getting-Lee-Dorado-sequence-data
DOI: 10.5281/zenodo.6358989
title: Effects of Mouthrinsing and Gargling to CT Values of SARS CoV-2 DATASET

description: <strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value. <strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5. <strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5. <strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value. <strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value. <strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5. <strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5. <strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value.
DOI: 10.5281/zenodo.6358988
title: Effects of Mouthrinsing and Gargling to CT Values of SARS CoV-2 DATASET

description: <strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value. <strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5. <strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5. <strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value. <strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value. <strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5. <strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5. <strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value.
DOI: 10.5281/zenodo.6355622
title: Multiple Nuclei HeLa cell ground truth images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.

description: This is a data set that contains <strong>labelled HeLa cell images</strong>, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background. Similar ground truth have been published for this data set, but in this case, multiple nuclei have been labelled, whilst previous ones only focused on the central cell (https://doi.org/10.5281/zenodo.3874949) Details of the imaging, preparation and segmentation have been published in: Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> <strong>2019</strong>, <em>5</em>(9), 75; https://doi.org/10.3390/jimaging5090075 Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, <strong>2020</strong>; https://doi.org/10.1371/journal.pone.0230605 Cefa Karabağ, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells,<em> J. Imaging</em> <strong>2021</strong>, <em>7</em>(6), 93; https://doi.org/10.3390/jimaging7060093 The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR.
description: {"references": ["Cefa Karaba\u011f, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, 2020; https://doi.org/10.1371/journal.pone.0230605", "Cefa Karaba\u011f, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. J. Imaging 2019, 5(9), 75; https://doi.org/10.3390/jimaging5090075", "Cefa Karaba\u011f, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells, J. Imaging 2021, 7(6), 93; https://doi.org/10.3390/jimaging7060093"]}
DOI: 10.5281/zenodo.6355621
title: Multiple Nuclei HeLa cell ground truth images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.

description: This is a data set that contains <strong>labelled HeLa cell images</strong>, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background. Similar ground truth have been published for this data set, but in this case, multiple nuclei have been labelled, whilst previous ones only focused on the central cell (https://doi.org/10.5281/zenodo.3874949) Details of the imaging, preparation and segmentation have been published in: Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> <strong>2019</strong>, <em>5</em>(9), 75; https://doi.org/10.3390/jimaging5090075 Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, <strong>2020</strong>; https://doi.org/10.1371/journal.pone.0230605 Cefa Karabağ, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells,<em> J. Imaging</em> <strong>2021</strong>, <em>7</em>(6), 93; https://doi.org/10.3390/jimaging7060093 The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR.
description: {"references": ["Cefa Karaba\u011f, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, 2020; https://doi.org/10.1371/journal.pone.0230605", "Cefa Karaba\u011f, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. J. Imaging 2019, 5(9), 75; https://doi.org/10.3390/jimaging5090075", "Cefa Karaba\u011f, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells, J. Imaging 2021, 7(6), 93; https://doi.org/10.3390/jimaging7060093"]}
DOI: 10.5281/zenodo.6355465
title: UK-LEX Dataset - Part of Chalkidis and Søgaard (2022)

description: The UK-LEX dataset is part of the work <em>"Ilias Chalkidis and Anders Søgaard. Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting. 2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.</em>" <strong>Details:</strong> United Kingdom (UK) legislation is publicly available as part of the United Kingdom's National Archives (https://www.legislation.gov.uk). Most of the laws have been categorized in thematic categories (e.g., health-care, finance, education, transportation, planning) that are presented in the document preamble and are used for archival indexing purposes. We release a new dataset, which comprises 36.5k UK laws (documents). The dataset is chronologically split in training (20k, 1975--2002), development (8.5k, 2002--2008), test (8.5k, 2008--2018) subsets. We manually extract and cluster the topics to supports two different label granularities, comprising 18, and 69 topics (labels), respectively.<br> <br> <strong>Data Files:</strong><br> <br> uk-lex18.jsonl: The dataset where documents are annotated with 18 different topics (labels).<br> uk-lex69.jsonl: The dataset where documents are annotated with 69 different topics (labels).
DOI: 10.5281/zenodo.6355464
title: UK-LEX Dataset - Part of Chalkidis and Søgaard (2022)

description: The UK-LEX dataset is part of the work <em>"Ilias Chalkidis and Anders Søgaard. Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting. 2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.</em>" <strong>Details:</strong> United Kingdom (UK) legislation is publicly available as part of the United Kingdom's National Archives (https://www.legislation.gov.uk). Most of the laws have been categorized in thematic categories (e.g., health-care, finance, education, transportation, planning) that are presented in the document preamble and are used for archival indexing purposes. We release a new dataset, which comprises 36.5k UK laws (documents). The dataset is chronologically split in training (20k, 1975--2002), development (8.5k, 2002--2008), test (8.5k, 2008--2018) subsets. We manually extract and cluster the topics to supports two different label granularities, comprising 18, and 69 topics (labels), respectively.<br> <br> <strong>Data Files:</strong><br> <br> uk-lex18.jsonl: The dataset where documents are annotated with 18 different topics (labels).<br> uk-lex69.jsonl: The dataset where documents are annotated with 69 different topics (labels).
DOI: 10.5281/zenodo.5150327
title: Data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators"

description: <strong>Overview</strong> This is the data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators". It contains the paper’s data archive with model outputs (see <code>results</code> folder) and the Singularity image for (optionally) re-running experiments. For the Python tool used to generate synthetic data, please refer to Synthia. <strong>Requirements</strong> Singularity &gt;= 3 Portable Batch System (PBS) job scheduler* Today's high-performance computer (e.g. ~ 32 CPUs @ 2 500 MHz with 64 GB of RAM ) *Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=72:00:00</code>). <strong>Usage</strong> To reproduce the results from the experiments described in the paper, first fit all copula models to the reduced NWP-SAF dataset with: <pre><code>qsub hpc/fit.sh</code></pre> then, to generate synthetic data, run all machine learning model configurations, and compute the relevant statistics use: <pre><code>qsub hpc/stats.sh qsub hpc/ml_control.sh qsub hpc/ml_synth.sh</code></pre> Finally, to plot all artifacts included in the paper use: <pre><code>qsub hpc/plot.sh</code></pre> <strong>Licence</strong> Code released under MIT license. Data from the reduced NWP-SAF dataset released under CC BY 4.0.