cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6123887
title: Galaxy Training Data for "Designing plasmids encoding predicted pathways by using the BASIC assembly method"

description: This dataset provides the data needed for the Galaxy BASIC assembly workflow training tutorial (https://galaxy-synbiocad.org). This workflow provides a pathway to design plasmids encoding predicted metabolic pathways using the BASIC assembly method (https://doi.org/10.1021/sb500356d). It generates scripts allowing the automatic construction of these plasmids using an Opentrons liquid handling robot. After downloading these scripts on a computer connected to an Opentrons (https://opentrons.com), the user can perform the automatic construction of the plasmids on the bench. The content of the dataset is as follows: an SBML file modeling a heterologous pathway producing lycopene such as those produced by the Pathway Analysis Workflow (https://galaxy-synbiocad.org). a CSV file listing the parts to be used (linkers, backbone and promoters) in the constructions. two YAML files providing two examples of settings, i.e. providing the identifiers of the laboratory equipment and the parameters of the DNA robot.
DOI: 10.5281/zenodo.6389652
title: Relative Humidity from Copernicus Essential Climate Variables for July months from 1980 to 2018

description: This dataset can be used if you have issues with the Essential Climate Variables Galaxy Tool for the Training "Getting your hands-on climate data" in the section "Essential Climate Variables". You can then upload this dataset in your Galaxy history and skip the 1st step (<strong>Copernicus Essential Climate Variables</strong>) and directly start with 2. <strong>map plot gridded (lat/lon) netCDF data.</strong>
DOI: 10.5281/zenodo.6123886
title: Galaxy Training Data for "Designing plasmids encoding predicted pathways by using the BASIC assembly method"

description: This dataset provides the data needed for the Galaxy BASIC assembly workflow training tutorial (https://galaxy-synbiocad.org). This workflow provides a pathway to design plasmids encoding predicted metabolic pathways using the BASIC assembly method (https://doi.org/10.1021/sb500356d). It generates scripts allowing the automatic construction of these plasmids using an Opentrons liquid handling robot. After downloading these scripts on a computer connected to an Opentrons (https://opentrons.com), the user can perform the automatic construction of the plasmids on the bench. The content of the dataset is as follows: an SBML file modeling a heterologous pathway producing lycopene such as those produced by the Pathway Analysis Workflow (https://galaxy-synbiocad.org). a CSV file listing the parts to be used (linkers, backbone and promoters) in the constructions. two YAML files providing two examples of settings, i.e. providing the identifiers of the laboratory equipment and the parameters of the DNA robot.
DOI: 10.5281/zenodo.5805953
title: PM2.5 4 days forecast from December, 22 2020 retrieved from Copernicus Monitoring Service

description: Dataset used in the Galaxy Pangeo tutorials on Xarray. Data is in netCDF format and is from Copernicus Air Monitoring Service and more precisely PM2.5 (Particle Matter &lt; 2.5 μm) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset. <strong>This dataset is not meant to be useful for scientific studies.</strong>
description: The RELIANCE (REsearch LIfecycle mAnagemeNt for Earth Science Communities and CopErnicus users in EOSC) project has received funding from the European Union's Horizon 2020 1.4.1.3. - Development, deployment and operation of ICT-based e-infrastructures under grant agreement No 101017501.
DOI: 10.5281/zenodo.5702408
title: Training material for flye genome assembly (Galaxy Training Network tutorial)

description: Datasets are subsets of 3 public datasets (Mucor mucedo Fresen. NRRL 3635 Standard Draft genome sequencing with PacBio technology) https://www.ncbi.nlm.nih.gov/sra/SRX5336965[accn] https://www.ncbi.nlm.nih.gov/sra/SRX5336964[accn] https://www.ncbi.nlm.nih.gov/sra/SRX5336963[accn]
DOI: 10.5281/zenodo.5702887
title: Image classification in Galaxy with fruit 360 dataset

description: Credit: 'Fruit recognition from images using deep learning' by H. Muresan and M. Oltean (https://arxiv.org/abs/1712.00580)<br> <br> Fruit 360 is a dataset with 90380 images of 131 fruits and vegetables (https://www.kaggle.com/moltean/fruits). Images are 100 pixel by 100 pixel and are RGB (color) images (3 values for each pixel). This dataset is a subset of Fruit 360 dataset, containing only 10 fruits/vegetables (Strawberry, Apple_Red_Delicious, Pepper_Green, Corn, Banana, Tomato_1, Potato_White, Pineapple, Orange, and Peach). We selected a subset of fruits/vegetables, so the dataset size is smaller and the neural network can be trained faster. The utilities used to create the dataset, along with step by step instructions, can be found here: https://github.com/kxk302/fruit_dataset_utilities<br> <br> First, we created feature vectors for each image. Each image is 100 pixel by pixel and are RGB (color) images (3 values for each pixel). Hence, each image can be represented by 30,000 values (100 X 100 X 3). Second, we selected a subset of 10 fruits/vegetables images (training and test dataset sizes go from 7 GB and 2.5 GB for 131 fruits/vegetables to 500 MB and 177 MB for 10 fruits/vegetables, respectively). Third, we created separate files for feature vectors and labels. Finally, we mapped the labels for the 10 selected fruits/vegetables to a range of 0 to 9.
description: Credit: 'Fruit recognition from images using deep learning' by H. Muresan and M. Oltean (https://arxiv.org/abs/1712.00580)
DOI: 10.5281/zenodo.5702407
title: Training material for flye genome assembly (Galaxy Training Network tutorial)

description: Datasets are subsets of 3 public datasets (Mucor mucedo Fresen. NRRL 3635 Standard Draft genome sequencing with PacBio technology) https://www.ncbi.nlm.nih.gov/sra/SRX5336965[accn] https://www.ncbi.nlm.nih.gov/sra/SRX5336964[accn] https://www.ncbi.nlm.nih.gov/sra/SRX5336963[accn]
DOI: 10.5281/zenodo.5702886
title: Image classification in Galaxy with fruit 360 dataset

description: Credit: 'Fruit recognition from images using deep learning' by H. Muresan and M. Oltean (https://arxiv.org/abs/1712.00580)<br> <br> Fruit 360 is a dataset with 90380 images of 131 fruits and vegetables (https://www.kaggle.com/moltean/fruits). Images are 100 pixel by 100 pixel and are RGB (color) images (3 values for each pixel). This dataset is a subset of Fruit 360 dataset, containing only 10 fruits/vegetables (Strawberry, Apple_Red_Delicious, Pepper_Green, Corn, Banana, Tomato_1, Potato_White, Pineapple, Orange, and Peach). We selected a subset of fruits/vegetables, so the dataset size is smaller and the neural network can be trained faster. The utilities used to create the dataset, along with step by step instructions, can be found here: https://github.com/kxk302/fruit_dataset_utilities<br> <br> First, we created feature vectors for each image. Each image is 100 pixel by pixel and are RGB (color) images (3 values for each pixel). Hence, each image can be represented by 30,000 values (100 X 100 X 3). Second, we selected a subset of 10 fruits/vegetables images (training and test dataset sizes go from 7 GB and 2.5 GB for 131 fruits/vegetables to 500 MB and 177 MB for 10 fruits/vegetables, respectively). Third, we created separate files for feature vectors and labels. Finally, we mapped the labels for the 10 selected fruits/vegetables to a range of 0 to 9.
description: Credit: 'Fruit recognition from images using deep learning' by H. Muresan and M. Oltean (https://arxiv.org/abs/1712.00580)
DOI: 10.5281/zenodo.5805952
title: PM2.5 4 days forecast from December, 22 2020 retrieved from Copernicus Monitoring Service

description: Dataset used in the Galaxy Pangeo tutorials on Xarray. Data is in netCDF format and is from Copernicus Air Monitoring Service and more precisely PM2.5 (Particle Matter &lt; 2.5 μm) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset. <strong>This dataset is not meant to be useful for scientific studies.</strong>
description: The RELIANCE (REsearch LIfecycle mAnagemeNt for Earth Science Communities and CopErnicus users in EOSC) project has received funding from the European Union's Horizon 2020 1.4.1.3. - Development, deployment and operation of ICT-based e-infrastructures under grant agreement No 101017501.
DOI: 10.5281/zenodo.5562251
title: Minified read mapping asignment dataset for use in Snakemake

description: This is a minified version of the dataset used in the Wranging Genomics Data Carpentries lesson for use in the Galaxy Training Network materials on Snakemake and other workflow development.
DOI: 10.5281/zenodo.5119008
title: Datasets for Galaxy Collection Operations Tutorial

description: This repository contains datasets using in Galaxy tutorial introducing users to dataset collection operations: https://training.galaxyproject.org/training-material/topics/galaxy-interface/tutorials/collections/tutorial.html
description: {"references": ["https://doi.org/10.1073/pnas.1409328111"]}
DOI: 10.5281/zenodo.5112538
title: Annotation of eukaryotic genomes with Maker datasets

description: Input and output datasets.
DOI: 10.5281/zenodo.5119007
title: Datasets for Galaxy Collection Operations Tutorial

description: This repository contains datasets using in Galaxy tutorial introducing users to dataset collection operations: https://training.galaxyproject.org/training-material/topics/galaxy-interface/tutorials/collections/tutorial.html
description: {"references": ["https://doi.org/10.1073/pnas.1409328111"]}
DOI: 10.5281/zenodo.5562250
title: Minified read mapping asignment dataset for use in Snakemake

description: This is a minified version of the dataset used in the Wranging Genomics Data Carpentries lesson for use in the Galaxy Training Network materials on Snakemake and other workflow development.
DOI: 10.5281/zenodo.5095188
title: Annotation of eukaryotic genomes with Maker datasets

description: Input and output datasets.
DOI: 10.5281/zenodo.5112537
title: Annotation of eukaryotic genomes with Maker datasets

description: Input and output datasets.
DOI: 10.5281/zenodo.5090049
title: Galaxy workflow from Galaxy 101 for everyone

description: Galaxy workflow from Galaxy 101 for everyone. This workflow is used in the training "How to reproduce published Galaxy analyses" to learn how to run a published Galaxy workflow.
DOI: 10.5281/zenodo.5095169
title: Annotation of eukaryotic genomes with Maker datasets

description: Input and output datasets.
DOI: 10.5281/zenodo.5095187
title: Annotation of eukaryotic genomes with Maker datasets

description: Input and output datasets.
DOI: 10.5281/zenodo.5090048
title: Galaxy workflow from Galaxy 101 for everyone

description: Galaxy workflow from Galaxy 101 for everyone. This workflow is used in the training "How to reproduce published Galaxy analyses" to learn how to run a published Galaxy workflow.
DOI: 10.5281/zenodo.4889110
title: Training data for 'Genome annotation with Apollo' tutorial (Galaxy Training Material)

description: The data provided here is part of a Galaxy Training Network tutorial for genome annotation with Apollo.
DOI: 10.5281/zenodo.5095168
title: Annotation of eukaryotic genomes with Maker datasets

description: Input and output datasets.
DOI: 10.5281/zenodo.4889109
title: Training data for 'Genome annotation with Apollo' tutorial (Galaxy Training Material)

description: The data provided here is part of a Galaxy Training Network tutorial for genome annotation with Apollo.
DOI: 10.5281/zenodo.4697906
title: Image classification in Galaxy with MNIST handwritten digits dataset

description: Credit: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998<br> <br> This is a subset of MNIST handwritten digits dataset (http://yann.lecun.com/exdb/mnist/). Training data of composed of 12,000 images of digits 0 to 9. Test data is composed of 6,000 images of digits 0 to 9 (Original dataset has 60,000 training and 10,000 testing images. We are using a subset for a Galaxy tutorial, so the training is not too computationally intensive). Images are grayscale and 28 by 28 pixels. Each pixel has a value between 0 and 255 (0 for color black, 255 for color white, and all other values for different shades of gray).
description: Credit: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998 http://yann.lecun.com/exdb/mnist/
DOI: 10.5281/zenodo.4729971
title: ToolFactory demonstration workflow and history

description: Workflow makes and installs all the demonstration tools in the GTN Docker running the ToolFactory
DOI: 10.5281/zenodo.4660497
title: Regression analysis in Galaxy with car purchase price prediction dataset

description: Source/Credit: Michael Grogan<br> https://github.com/MGCodesandStats<br> https://github.com/MGCodesandStats/datasets/blob/master/cars.csv Sample dataset for regression analysis. Given 5 attributes (age, gender, miles driven per day, debt, and income) predict how much someone will spend on purchasing a car. All 5 of the input attributes have been scaled to be in 0 to 1 range. Training set has 723 training examples. Test set has 242 test examples.<br> <br> This dataset will be used in an upcoming Galaxy Training Network tutorial (https://training.galaxyproject.org/training-material/topics/statistics/) on use of feedforward neural networks for regression analysis.
description: Source/Credit: Michael Grogan https://github.com/MGCodesandStats https://github.com/MGCodesandStats/datasets/blob/master/cars.csv
DOI: 10.5281/zenodo.4697905
title: Image classification in Galaxy with MNIST handwritten digits dataset

description: Credit: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998<br> <br> This is a subset of MNIST handwritten digits dataset (http://yann.lecun.com/exdb/mnist/). Training data of composed of 12,000 images of digits 0 to 9. Test data is composed of 6,000 images of digits 0 to 9 (Original dataset has 60,000 training and 10,000 testing images. We are using a subset for a Galaxy tutorial, so the training is not too computationally intensive). Images are grayscale and 28 by 28 pixels. Each pixel has a value between 0 and 255 (0 for color black, 255 for color white, and all other values for different shades of gray).
description: Credit: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998 http://yann.lecun.com/exdb/mnist/
DOI: 10.5281/zenodo.4729970
title: ToolFactory demonstration workflow and history

description: Workflow makes and installs all the demonstration tools in the GTN Docker running the ToolFactory
DOI: 10.5281/zenodo.4660496
title: Regression analysis in Galaxy with car purchase price prediction dataset

description: Source/Credit: Michael Grogan<br> https://github.com/MGCodesandStats<br> https://github.com/MGCodesandStats/datasets/blob/master/cars.csv Sample dataset for regression analysis. Given 5 attributes (age, gender, miles driven per day, debt, and income) predict how much someone will spend on purchasing a car. All 5 of the input attributes have been scaled to be in 0 to 1 range. Training set has 723 training examples. Test set has 242 test examples.<br> <br> This dataset will be used in an upcoming Galaxy Training Network tutorial (https://training.galaxyproject.org/training-material/topics/statistics/) on use of feedforward neural networks for regression analysis.
description: Source/Credit: Michael Grogan https://github.com/MGCodesandStats https://github.com/MGCodesandStats/datasets/blob/master/cars.csv
DOI: 10.5281/zenodo.4638516
title: Gene disorder groups

description: Dataset for Galaxy Training "Text-mining with the SimText toolset". In this dataset are 95 genes and their associated disorder category. The data is publicly available from ClinGen.
DOI: 10.5281/zenodo.4638515
title: Gene disorder groups

description: Dataset for Galaxy Training "Text-mining with the SimText toolset". In this dataset are 95 genes and their associated disorder category. The data is publicly available from ClinGen.
DOI: 10.5281/zenodo.4624461
title: Filter, Plot and Explore Single-cell RNA-seq Data

description: Dataset for the "Filter, Plot and Explore Single-cell RNA-seq Data" Galaxy Training Network tutorial
DOI: 10.5281/zenodo.4624451
title: Filter, Plot and Explore Single-cell RNA-seq Data

description: Dataset for the "Filter, Plot and Explore Single-cell RNA-seq Data" Galaxy Training Network tutorial
DOI: 10.5281/zenodo.4624460
title: Filter, Plot and Explore Single-cell RNA-seq Data

description: Dataset for the "Filter, Plot and Explore Single-cell RNA-seq Data" Galaxy Training Network tutorial
DOI: 10.5281/zenodo.4616823
title: Filter, Plot and Explore Single-cell RNA-seq Data

description: These datasets are for use in the Galaxy Training Network tutorial: Filter, Plot and Explore Single-cell RNA-seq Data
DOI: 10.5281/zenodo.4624450
title: Filter, Plot and Explore Single-cell RNA-seq Data

description: Dataset for the "Filter, Plot and Explore Single-cell RNA-seq Data" Galaxy Training Network tutorial
DOI: 10.5281/zenodo.4477881
title: Sentiment analysis in Galaxy with IMDB movie review dataset

description: IMDB movie review sentiment classification dataset (Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011)). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/<br> <br> The IMDB dataset was modified as follows to prepare it for use in a Galaxy Training Tutorial (https://training.galaxyproject.org/):<br> <br> The top 50 words are excluded (mostly stop words). Included the next 10,000 top words. Reviews are limited to 500 words max (Longer reviews trimmed and shorter reviews are padded). 25,000 reviews are used for training and testing each. Files are in tsv (tab separated value) format to be consumed by Galaxy (www.usegalaxy.org).
description: Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/
DOI: 10.5281/zenodo.4477880
title: Sentiment analysis in Galaxy with IMDB movie review dataset

description: IMDB movie review sentiment classification dataset (Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011)). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/<br> <br> The IMDB dataset was modified as follows to prepare it for use in a Galaxy Training Tutorial (https://training.galaxyproject.org/):<br> <br> The top 50 words are excluded (mostly stop words). Included the next 10,000 top words. Reviews are limited to 500 words max (Longer reviews trimmed and shorter reviews are padded). 25,000 reviews are used for training and testing each. Files are in tsv (tab separated value) format to be consumed by Galaxy (www.usegalaxy.org).
description: Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/
DOI: 10.5281/zenodo.4616822
title: Filter, Plot and Explore Single-cell RNA-seq Data

description: These datasets are for use in the Galaxy Training Network tutorial: Filter, Plot and Explore Single-cell RNA-seq Data
DOI: 10.5281/zenodo.3977236
title: FASTQE (training data)

description: Data is from https://qubeshub.org/publications/1092/2
DOI: 10.5281/zenodo.3977235
title: FASTQE (training data)

description: Data is from https://qubeshub.org/publications/1092/2
DOI: 10.5281/zenodo.3732359
title: Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material)

description: The data here is a copy of the corresponding SRR records in the NCBI SRA. The duplication serves a dual purpose: as a backup should there be problems connecting to NCBI servers, e.g., during Galaxy user trainings. to illustrate how to obtain raw sequencing data from alternative sources, and to organize the data into the same collection structure in a Galaxy history that is generated by specialized Galaxy SRA download tools.
DOI: 10.5281/zenodo.3776500
title: Dataset: timeseries of temperatures and anomalies for the city of Paris (France) for Climate 101 Galaxy training

description: Dataset is originally downloaded from https://knmi-ecad-assets-prd.s3.amazonaws.com/ensembles/data/Grid_0.1deg_reg_ensemble/tg_ens_mean_0.1deg_reg_v20.0e.nc Then 3 single locations have been extracted: Paris (France): latitude=48.85341,longitude=2.3488 Freiburg (Germany): latitude=47.996894,longitude=7.841431 Oslo (Norway): latitude=59.911491,longitude=10.75793 Climatologies and anomalies have been computed using cdo This dataset is meant to be used for teaching purposes only.
DOI: 10.5281/zenodo.3730474
title: Inputs for Galaxy tutorial on molecular docking on SARS-CoV-2 MPro

description: Inputs for Galaxy tutorial on molecular docking on SARS-CoV-2 main protease.
DOI: 10.5281/zenodo.3732358
title: Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material)

description: The data here is a copy of the corresponding SRR records in the NCBI SRA. The duplication serves a dual purpose: as a backup should there be problems connecting to NCBI servers, e.g., during Galaxy user trainings. to illustrate how to obtain raw sequencing data from alternative sources, and to organize the data into the same collection structure in a Galaxy history that is generated by specialized Galaxy SRA download tools.
DOI: 10.5281/zenodo.3776499
title: Dataset: timeseries of temperatures and anomalies for the city of Paris (France) for Climate 101 Galaxy training

description: Dataset is originally downloaded from https://knmi-ecad-assets-prd.s3.amazonaws.com/ensembles/data/Grid_0.1deg_reg_ensemble/tg_ens_mean_0.1deg_reg_v20.0e.nc Then 3 single locations have been extracted: Paris (France): latitude=48.85341,longitude=2.3488 Freiburg (Germany): latitude=47.996894,longitude=7.841431 Oslo (Norway): latitude=59.911491,longitude=10.75793 Climatologies and anomalies have been computed using cdo This dataset is meant to be used for teaching purposes only.
DOI: 10.5281/zenodo.3661237
title: Generating an expression matrix for droplet single-cell RNA-seq (dscRNA-seq) data

description: This tutorial is adapted from the 'Generating an expression matrix' training session at the EBI (https://www.ebi.ac.uk/training/events/2019/single-cell-rna-seq-analysis-questions-clusters).
DOI: 10.5281/zenodo.3730473
title: Inputs for Galaxy tutorial on molecular docking on SARS-CoV-2 MPro

description: Inputs for Galaxy tutorial on molecular docking on SARS-CoV-2 main protease.