cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4603938
title: miRNA target finder training datasets

description: Datasets needed to run the Galaxy training: miRNA target finder in <em>Arabidopsis thaliana</em>
description: {"references": ["Park, S. Y., Choi, J. H., Oh, D. H., Johnson, J. C., Dassanayake, M., Jeong, D. H., & Oh, M. H. (2020). Genome-wide analysis of brassinosteroid responsive small RNAs in Arabidopsis thaliana. Genes & Genomics, 42(8), 957-969.", "Oh, Eunkyoo, et al. \"Cell elongation is regulated through a central circuit of interacting transcription factors in the Arabidopsis hypocotyl.\" elife 3 (2014): e03031.", "Zhang, R., Calixto, C.P.G., Marquez, Y., Venhuizen, P., Tzioutziou, N.A., Guo, W., Spensley, M., Frei dit Frey, N., Hirt, H., James, A.B., Nimmo, H.G., Barta, A., Kalyna, M., Brown, J.W.S (2016) AtRTD2: A Reference Transcript Dataset for accurate quantification of alternative splicing and expression changes in Arabidopsis thaliana RNA-seq data. bioRxiv doi: http://dx.doi.org/10.1101/051938", "PmiREN: a comprehensive encyclopedia of plant miRNAs. Z Guo, Z Kuang, Y Wang, Y Zhao, Y Tao, C Cheng, J Yang, X Lu, C Hao, T Wang, X Cao, J Wei, L Li, and X Yang. Nucleic Acids Res. 2019. doi: 10.1093/nar/gkz894.", "Lamesch, P., Berardini, T. Z., Li, D., Swarbreck, D., Wilks, C., Sasidharan, R., ... & Huala, E. (2012). The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic acids research, 40(D1), D1202-D1210."]}
DOI: 10.5281/zenodo.4604034
title: ToolFactory training data sets for demonstration workflow

description: Small sample data sets for the ToolFactory workflow
DOI: 10.5281/zenodo.4574153
title: Pre-processing scRNA-seq data using Alevin in Galaxy

description: These datasets, from either 10.3389/fimmu.2018.02523 or Ensembl's reference datasets, are required for users to following through the Pre-processing with Alevin tutorial.
DOI: 10.5281/zenodo.4567084
title: Object Tracking with Cell Profiler

description: This tutorial will reproduce the example 'Object Tracking' in the Cell Profiler website (https://cellprofiler.org/examples). The tutorial is available in the Galaxy Training Network: https://training.galaxyproject.org/training-material/topics/imaging/
DOI: 10.5281/zenodo.4542837
title: Galaxy history for the ToolFactory in Planemo.

description: Import this history into the planemo tool_factory Galaxy It contains a variety of simple demonstrations of ToolFactory generated tools
DOI: 10.5281/zenodo.4574152
title: Pre-processing scRNA-seq data using Alevin in Galaxy

description: These datasets, from either 10.3389/fimmu.2018.02523 or Ensembl's reference datasets, are required for users to following through the Pre-processing with Alevin tutorial.
DOI: 10.5281/zenodo.4567083
title: Object Tracking with Cell Profiler

description: This tutorial will reproduce the example 'Object Tracking' in the Cell Profiler website (https://cellprofiler.org/examples). The tutorial is available in the Galaxy Training Network: https://training.galaxyproject.org/training-material/topics/imaging/
DOI: 10.5281/zenodo.4264936
title: Compute and analyze Essential Biodiversity Variables with PAMPA toolsuite

description: In this tutorial, we'll be working on data extracted from the data portal DATRAS (Database of Trawls Surveys) of the <br> International Council for the Exploration of the Sea (ICES). After pre-processing to fit the input format of the tools,<br> we'll see how to calculate Essential Biodiversity Variables and construct properly, easily and with good practice, <br> several GLMMs to test the effect of ```year``` and ```site``` on the species richness of each survey studied and <br> on the abundance of each species. https://training.galaxyproject.org/training-material/topics/ecology/tutorials/PAMPA-toolsuite-tutorial
DOI: 10.5281/zenodo.4264935
title: Compute and analyze Essential Biodiversity Variables with PAMPA toolsuite

description: In this tutorial, we'll be working on data extracted from the data portal DATRAS (Database of Trawls Surveys) of the <br> International Council for the Exploration of the Sea (ICES). After pre-processing to fit the input format of the tools,<br> we'll see how to calculate Essential Biodiversity Variables and construct properly, easily and with good practice, <br> several GLMMs to test the effect of ```year``` and ```site``` on the species richness of each survey studied and <br> on the abundance of each species. https://training.galaxyproject.org/training-material/topics/ecology/tutorials/PAMPA-toolsuite-tutorial
DOI: 10.5281/zenodo.4126404
title: Restart dataset for a single location in Norway ALP1 (61.0243N,8.12343E) for CTSM/FATES EMERALD Galaxy tutorial

description: Restart files for CLM-FATES version 2.0.1 for CLM-FATES EMERALD version 2.0.1. CTSM_FATES-EMERALD_on_inputdata_version2.0.0_ALP1.tar_(restart_info):<br> - ALP1_refcase.datm.r.2300-01-01-00000.nc <br> - ALP1_refcase.datm.rs1.2300-01-01-00000.bin<br> - ALP1_refcase.cpl.r.2300-01-01-00000.nc <br> - ALP1_refcase.clm2.r.2300-01-01-00000.nc This dataset is being used in the Galaxy Training tutorial on CLM-FATES. This work has been done in in collaboration with Galaxy Europe and EOSC-Life:<br> - Within the 1st EOSC-Life Training Open Call, two out of four proposals have been awarded to the European Galaxy team to develop climate science e-learning material and mentoring and training opportunities for our communities. CLM-FATES documentation can be found here.
description: The EOSC-Nordic project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652.
DOI: 10.5281/zenodo.4126403
title: Restart dataset for a single location in Norway ALP1 (61.0243N,8.12343E) for CTSM/FATES EMERALD Galaxy tutorial

description: Restart files for CLM-FATES version 2.0.1 for CLM-FATES EMERALD version 2.0.1. CTSM_FATES-EMERALD_on_inputdata_version2.0.0_ALP1.tar_(restart_info):<br> - ALP1_refcase.datm.r.2300-01-01-00000.nc <br> - ALP1_refcase.datm.rs1.2300-01-01-00000.bin<br> - ALP1_refcase.cpl.r.2300-01-01-00000.nc <br> - ALP1_refcase.clm2.r.2300-01-01-00000.nc This dataset is being used in the Galaxy Training tutorial on CLM-FATES. This work has been done in in collaboration with Galaxy Europe and EOSC-Life:<br> - Within the 1st EOSC-Life Training Open Call, two out of four proposals have been awarded to the European Galaxy team to develop climate science e-learning material and mentoring and training opportunities for our communities. CLM-FATES documentation can be found here.
description: The EOSC-Nordic project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652.
DOI: 10.5281/zenodo.4104428
title: Training material for Galaxy 101 tutorial

description: The data provided here are part of a Galaxy tutorial "Galaxy 101". The files contain BED-formatted 1) coding exons on chromosome 22 of human genome version hg38, 2) SNPs from dbSNP version 153 located in whole genes, and 3) repeats from hg38 chr22.
DOI: 10.5281/zenodo.4034887
title: Single cell dataset - annotated matrix

description: This is an annotated dataset of scRNA-seq, used as the input for a Galaxy tutorial for filtering, normalisation and batch correction.
DOI: 10.5281/zenodo.4104427
title: Training material for Galaxy 101 tutorial

description: The data provided here are part of a Galaxy tutorial "Galaxy 101". The files contain BED-formatted 1) coding exons on chromosome 22 of human genome version hg38, 2) SNPs from dbSNP version 153 located in whole genes, and 3) repeats from hg38 chr22.
DOI: 10.5281/zenodo.4034886
title: Single cell dataset - annotated matrix

description: This is an annotated dataset of scRNA-seq, used as the input for a Galaxy tutorial for filtering, normalisation and batch correction.
DOI: 10.5281/zenodo.4004755
title: Using Galaxy to Perform Large-Scale Interactive Data Analyses - An update

description: Datasets and workflows supporting the protocols described in the article "Using Galaxy to Perform Large‐Scale Interactive Data Analyses - An update".
DOI: 10.5281/zenodo.4004754
title: Using Galaxy to Perform Large-Scale Interactive Data Analyses - An update

description: Datasets and workflows supporting the protocols described in the article "Using Galaxy to Perform Large‐Scale Interactive Data Analyses - An update".
DOI: 10.5281/zenodo.3957887
title: Training data for "Apply modeling on population or community data to see effect of year, habitat or site on species abundance"

description: Datasets for the "Apply modeling on population or community data to see effect of year, habitat or site on species abundance" Galaxy for ecology tutorial
DOI: 10.5281/zenodo.3957886
title: Training data for "Apply modeling on population or community data to see effect of year, habitat or site on species abundance"

description: Datasets for the "Apply modeling on population or community data to see effect of year, habitat or site on species abundance" Galaxy for ecology tutorial
DOI: 10.5522/04/14217044
title: Repeatability and reproducibility of hydrophone measurements of medical ultrasound fields

description: This data was collected in order to study the repeatability and reproducibility of hydrophone measurements of ultrasound fields. Sets of independent measurements were made with two probe (0.2 mm, 40 µm) and two membrane hydrophones (0.4 mm, 0.2 mm differential) (all from Precision Acoustics) to examine the repeatability of measurements. The pressures measured by these hydrophones in three different ultrasound fields, with both linear and nonlinear, pulsed and steady state driving conditions, were acquired to assess the reproducibility of measurements between hydrophones. Repeatability measurements: Sets of five independent measurements were made with each hydrophone of the field generated by a single element focusing bowl transducer (Sonic Concepts H151) driven at a frequency of 1.1 MHz, with both a 4 cycle burst and under quasi steady state conditions. Axial and lateral line scans passing through the focus were acquired at a drive level which generated a weakly nonlinear field. Reproducibility measurements: Two single element focusing bowl transducers (H151 at 1.1 MHz, and H101 at 3.3 MHz, Sonic Concepts) and one diagnostic linear array (L14-5 at 5 MHz, Ultrasonix) sources were used. For the single element transducers, axial and lateral line scans passing through the focus were acquired with each hydrophone at two drive levels to generate both a linear and a weakly nonlinear field, with both a 4 cycle burst and under quasi steady state conditions. For the diagnostic linear array, lateral line scans were acquired passing through the beam axis at an axial distance of 40 mm. The transducer was driven with a 4 cycle burst at a power level that generated harmonics up to 30 MHz.<br>All measurements were acquired using an automated scanning tank filled with degassed, deionised water. The transducers mounted in a fixed xyz position with automated tilt, rotate adjustment. Hydrophones were mounted on an automated xyz stage, with manual tilt, rotate adjustment. <br>In total this study contains 12 datasets, the corresponding figure or table in the paper is given in brackets: 1-4: Repeatability and reproducibility - H151 x 4 hydrophones (Figs 1-4, Table 3) Each dataset contains axial and lateral line scans at 2 drive levels, with a 4 cycle and a 40 cycle burst, with 5 sets of scans at the high drive level and one set of scans at the low drive level 5-8: Reproducibility – H101 x 4 hydrophones (Figs 4-5, Table 3) Each dataset contains a single set of axial and lateral line scan at each of 2 drive levels, with a 4 cycle and a 120 cycle burst. 9-12: Reproducibility – L14-5 x 4 hydrophones (Fig 6, Table 3) Each dataset contains lateral scans at 1 power level.
DOI: 10.5522/04/14217044.v1
title: Repeatability and reproducibility of hydrophone measurements of medical ultrasound fields

description: This data was collected in order to study the repeatability and reproducibility of hydrophone measurements of ultrasound fields. Sets of independent measurements were made with two probe (0.2 mm, 40 µm) and two membrane hydrophones (0.4 mm, 0.2 mm differential) (all from Precision Acoustics) to examine the repeatability of measurements. The pressures measured by these hydrophones in three different ultrasound fields, with both linear and nonlinear, pulsed and steady state driving conditions, were acquired to assess the reproducibility of measurements between hydrophones. Repeatability measurements: Sets of five independent measurements were made with each hydrophone of the field generated by a single element focusing bowl transducer (Sonic Concepts H151) driven at a frequency of 1.1 MHz, with both a 4 cycle burst and under quasi steady state conditions. Axial and lateral line scans passing through the focus were acquired at a drive level which generated a weakly nonlinear field. Reproducibility measurements: Two single element focusing bowl transducers (H151 at 1.1 MHz, and H101 at 3.3 MHz, Sonic Concepts) and one diagnostic linear array (L14-5 at 5 MHz, Ultrasonix) sources were used. For the single element transducers, axial and lateral line scans passing through the focus were acquired with each hydrophone at two drive levels to generate both a linear and a weakly nonlinear field, with both a 4 cycle burst and under quasi steady state conditions. For the diagnostic linear array, lateral line scans were acquired passing through the beam axis at an axial distance of 40 mm. The transducer was driven with a 4 cycle burst at a power level that generated harmonics up to 30 MHz.<br>All measurements were acquired using an automated scanning tank filled with degassed, deionised water. The transducers mounted in a fixed xyz position with automated tilt, rotate adjustment. Hydrophones were mounted on an automated xyz stage, with manual tilt, rotate adjustment. <br>In total this study contains 12 datasets, the corresponding figure or table in the paper is given in brackets: 1-4: Repeatability and reproducibility - H151 x 4 hydrophones (Figs 1-4, Table 3) Each dataset contains axial and lateral line scans at 2 drive levels, with a 4 cycle and a 40 cycle burst, with 5 sets of scans at the high drive level and one set of scans at the low drive level 5-8: Reproducibility – H101 x 4 hydrophones (Figs 4-5, Table 3) Each dataset contains a single set of axial and lateral line scan at each of 2 drive levels, with a 4 cycle and a 120 cycle burst. 9-12: Reproducibility – L14-5 x 4 hydrophones (Fig 6, Table 3) Each dataset contains lateral scans at 1 power level.
DOI: 10.5522/04/14216717
title: Experimental Validation of k-Wave: Nonlinear Wave Propagation in Layered, Absorbing Fluid Media

description: The data was collected for characterisation of the source, a single element spherically focusing ultrasound transducer driven with a 4 cycle burst at 1.1 MHz (H151, Sonic Concepts), and validation of simulation of the source propagating into water at a number of different drive levels. Measurements were also made for experimental validation of simulation of a nonlinear ultrasound field propagating through planar and wedge shaped glycerol filled phantoms. All measurements were made with a PVDF needle hydrophone in an automated scanning tank. The study data contains 3 files: 1 containing measurements made in water, 1 with a planar glycerol filled phantom and 1 with a wedge shaped glycerol filled phantom. File 1, medium: water only: Axial scans cover 30 to 200 mm, lateral scans cover -20 to 20 mm. 1: XY planar scan for source characterisation at low drive level, measured at z = 40 mm over a 52 by 52 mm plane. 2 - 7: Axial scans from 30 to 200 mm, at the lowest drive level (as used in 1), 6 repeats. (Fig 2,3) 8: lateral scan, level 1 (Fig 2,3) 9-11: Axial scans, level 2 (Fig 3) 12-13: lateral scans level 2 (Fig 3) 14-16: axial scans level 3 (Fig 3) 17-18: lateral scans level 3 (Fig 3) 19-21: axial scans level 4 (Fig 3) 22-23: lateral scans level 4 (Fig 3) 24-26: Axial scans level 5 (Fig 3) 27-28: lateral scans level 5 (Fig 3) 29-33: axial scans level 6 (Figs 3, 4) 34-38: lateral scans level 6 (Figs 3, 4) File 2: medium: water background with planar glycerol phantom 1: XZ planar scan from x = -20 mm to 20 mm, z = 60 to 200 mm (beyond phantom) (Figs 5-7) File 3: medium: water background with wedge shaped glycerol phantom 1: XZ planar scan from x = -20 to 20 mm, z = 70 to 200 mm (beyond phantom) (Figs 8-10)
DOI: 10.5281/zenodo.4616825
title: Compendium for Longitudinally Resolved Spectral Retrieval (ReSpect) of WASP-43b

description: This repository contains the input and output data used in: "<strong>Longitudinally Resolved Spectral Retrieval (ReSpect) of WASP-43b</strong>" by Patricio E. Cubillos et al. (2021), submitted to The Astrophysical Journal.
DOI: 10.21227/nn4q-f037
title: Day-Ahead Forecasting using LSTM with rolling windows

description: The project involves day-ahead forecasting which takes a model fit into historical data and predicts future demand of electricity. Time series forecasting methods are used as the dataset contains an explicit order dependence between observations, i.e. a time dimension which makes the problem difficult to handle but makes the prediction more accurate. Various machine learning and deep learning algorithms were used to accomplish this work. Results obtained by autoregressive integrated moving average(ARIMA), autoregressive moving average with explanatory variable(ARIMAX), seasonal autoregressive moving average(SARIMA), seasonal autoregressive moving average(SARIMAX) and long short term memory(LSTM) recurrent neural networks with rolling windows were compared and the one with least error, i.e. LSTM with rolling windows was used for time series prediction.
DOI: 10.5281/zenodo.4616298
title: Tomographic data acquired by a combination of a grid scan and half-acquisition scans: Part 12

description: Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4616297
title: Tomographic data acquired by a combination of a grid scan and half-acquisition scans: Part 12

description: Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4616296
title: Tomographic data acquired by a combination of a grid scan and half-acquisition scans: Part 11

description: Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4616295
title: Tomographic data acquired by a combination of a grid scan and half-acquisition scans: Part 11

description: Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4616294
title: Tomographic data acquired by a combination of a grid scan and half-acquisition scans: Part 10

description: Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4616293
title: Tomographic data acquired by a combination of a grid scan and half-acquisition scans: Part 10

description: Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4616290
title: Tomographic data acquired by a combination of a grid scan and half-acquisition scans: Part 09

description: Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"