cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/gzrx-1t37
title: Gaussian Blobs of Varying numbers of samples, centers and features for Testing Clustering Algorithms

description: The dataset has Gaussian Blobs of varying samples, centers and features. The datasets can be used for testing clustering algorithms for their effectiveness. This dataset is related to a paper which is under review.
DOI: 10.21227/knpa-c384
title: Gaussian Blobs of Varying numbers of samples, centers and features for Testing Clustering Algorithms

description: The dataset has Gaussian Blobs of varying samples, centers and features. The datasets can be used for testing clustering algorithms for their effectiveness. This dataset is related to a paper which is under review.
DOI: 10.6084/m9.figshare.13027979.v3
title: Raw data for: Humeral diaphysis structure across mammals

description: File S1. Archive with raw data acquisition for both extant and extinct species, as well as timetree used for phylogenetically informed tests.<br><br>Table S1. Data table comprising the raw data file names for each extant species (LabelHum), species name, lifestyle, body mass (BM), catalogue number (CNum), mean total cross-sectional area (MeanTotArea), p-value associated to the slope of the Cg profile regression (slopeP), as well as its slope and R2. <br><br>Figure S1. Phenogram of compactness (Cg) profiles slopes (see simplified version in main text, figure 2c).<br><br>
DOI: 10.17037/data.00001884
title: Data supporting "Anthelminthic treatment receipt and its predictors in Lake Victoria fishing communities, Uganda: intervention coverage results from the LaVIISWA cluster randomised trial"

description: This dataset comprises data from the LaVIISWA cluster randomised trial. It contains village-level summaries of praziquantel and albendazole treatment receipt over the 4-year trial period, together with village-level summaries of the proportion infected with Schistosoma mansoni and Trichuris trichiura at baseline and after 4 years.
DOI: 10.5281/zenodo.3739472
title: Tour de France data for the improvement of energy consumption in devices powered by limited energy sources

description: We propose a set of data that were collected as part of a "tour de France" with electrical wheelchair. Part of these data are allowed to propose a mathematical model based on an experimental methodology on the energy consumed in smartphones. The objective is to make accessible the data related to the publications in several fields of research (computer science, telecommunication, meteorological science, artificial intelligence, statistics ...)
DOI: 10.5281/zenodo.3739471
title: Tour de France data for the improvement of energy consumption in devices powered by limited energy sources

description: We propose a set of data that were collected as part of a "tour de France" with electrical wheelchair. Part of these data are allowed to propose a mathematical model based on an experimental methodology on the energy consumed in smartphones. The objective is to make accessible the data related to the publications in several fields of research (computer science, telecommunication, meteorological science, artificial intelligence, statistics ...)
DOI: 10.6084/m9.figshare.13055804
title: IPNI plant names linked to bibliographic identifiers

description: A mapping between plant names in the International Plant Names Index (https://www.ipni.org) and bibliographic identifiers (such as DOIs, Handles, JSTOR, BHL, etc.). This is a subset of the complete IPNI dataset where the Id for a plant name, e.g., 77104049-1 (<i>Begonia rubiteae</i>) is linked to the DOI for article in which the species is named DOI:10.1017/S0960428609990266.<br>Some of these links between names and identifiers are in the original IPNI data (e.g., as "remarks"), most I have added (see https://github.com/rdmpage/ipni-names for details). This dataset is a snapshot, is incomplete, and almost certainly contains errors.<br>The CC0 license applies to the mapping I have made between the IPNI Id and the bibliographic identifiers. The IPNI data included here (Id, name, and bibliographic metadata) is a subset of the complete IPNI dataset, which has a CC-BY license.<br>
DOI: 10.5281/zenodo.4067163
title: The COUGHVID crowdsourcing dataset: Private testing dataset

description: The COUGHVID crowdsourcing dataset provides over 20,000 crowdsourced cough recordings gathered throughout the COVID-19 pandemic. In order to ensure the reproducibility of the experimental results that use the COUGHVID dataset, a private test set of 500 recordings has been kept out from publishing. Recordings in this private set have been randomly selected from those having at least labels from one expert.
DOI: 10.5281/zenodo.4067162
title: The COUGHVID crowdsourcing dataset: Private testing dataset

description: The COUGHVID crowdsourcing dataset provides over 20,000 crowdsourced cough recordings gathered throughout the COVID-19 pandemic. In order to ensure the reproducibility of the experimental results that use the COUGHVID dataset, a private test set of 500 recordings has been kept out from publishing. Recordings in this private set have been randomly selected from those having at least labels from one expert.
DOI: 10.6084/m9.figshare.13054187.v1
title: R0235_20181110a_videos

description: Rat 0235, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.21227/88f4-r479
title: Cost of resources in cloud with reserved, spot and on-demand instances

description: It contains 1 pdf file and 3 excel sheets. The pdf file contains the result graphs while the excel sheets contain the result data.
DOI: 10.21227/v1f3-g388
title: Cost of resources in cloud with reserved, spot and on-demand instances

description: It contains 1 pdf file and 3 excel sheets. The pdf file contains the result graphs while the excel sheets contain the result data.
DOI: 10.5281/zenodo.4068121
title: Novel Coronavirus (COVID-19) Cases in The Netherlands

description: On 27 February 2020, the first case of COVID-19 disease was confirmed in The Netherlands by RIVM (National Institute for Public Health and the Environment). In the weeks after, thousands of people were diagnosed with the infectious disease. Data on COVID-19 case counts are important for research and applications on various topics like epidemiology and statistics. This dataset contains reported case counts derived from official sources like RIVM (National Institute for Public Health and the Environment), LCPS (National Coordination Center for Patient Distribution), and NICE (National Intensive Care Evaluation). Data from these sources are collected, standardized, and published in various formats on a daily basis. The README document in this repository provides an overview of the available datasets, their file location(s), and codebooks. Copies of the original data are stored in the folder named 'raw_data'. Scripts to process the raw data into standardized files can be found in the folder workflows.
DOI: 10.21227/v4f9-q921
title: Dataset for binary classification of digital sensor signals

description: The dataset is composed of digital signals obtained from a capacitive sensor electrodes that are immersed in water or in oil. Each signal, stored in one row, is composed of 10 consecutive intensity values and a label in the last column. The label is +1 for a water-immersed sensor electrode and -1 for an oil-immersed sensor electrode. This dataset should be used to train a classifier to infer the type of material in which an electrode is immersed in (water or oil), given a sample signal composed of 10 consecutive values.
DOI: 10.6084/m9.figshare.12688109
title: Heterotrimetallic Na/Zn2 and K/Zn2 complexes based on Trost ProPhenol ligand: DFT geometry optimisations of dialkyl and dialkoxide complexes and coordination of L-Lactide

description: Data to support article:<br><br> <b>Combining Alkali Metals and Zinc to Harness Heterometallic Cooperativity in Cyclic Ester Ring-Opening Polymerisation <br></b><b><br></b> Journal: Chemical Science, 2020, DOI: 10.1039/d0sc04705h<br><br><i>Authors:</i><i> Weronika Gruszka,<sup>a</sup> Anna Lykkeberg,<sup>a </sup> Gary S. Nichol,<sup>a</sup> Michael P. Shaver,<sup>b,c</sup> Antoine Buchard,<sup>d</sup> Jennifer A. Garden*<sup>a</sup></i> <br> <i>For DFT calculation, contact <br></i><i>Dr Antoine Buchard<sup>d</sup>; a.buchard@bath.ac.uk<br></i><i><sup>d</sup></i>: Centre for Sustainable Chemical Technologies, Department of Chemistry, University of Bath, Bath BA2 7AY, United Kingdom<br>Study: - DFT optimised geometries of complexes 3', 4', 5' and 6', investigating the configuration of the ligand and the number of THF molecules coordinated to the alkali metals<br>- Thermochemistry of reaction between 3'/4' and one/two equivalents of benzyl alcohol<br> - Thermochemistry of coordination of L-Lactide to 5' and<br><br>Protocol: rwb97XD/6-31++g(d)/6-31g(d)/cpcm=tetrahydrofuran<br> <br>Content: - Gaussian09 rev D.02 output files- Computational_full details_results.pdf<br><br><br>
DOI: 10.6084/m9.figshare.12688109.v1
title: Heterotrimetallic Na/Zn2 and K/Zn2 complexes based on Trost ProPhenol ligand: DFT geometry optimisations of dialkyl and dialkoxide complexes and coordination of L-Lactide

description: Data to support article:<br><br> <b>Combining Alkali Metals and Zinc to Harness Heterometallic Cooperativity in Cyclic Ester Ring-Opening Polymerisation <br></b><b><br></b> Journal: Chemical Science, 2020, DOI: 10.1039/d0sc04705h<br><br><i>Authors:</i><i> Weronika Gruszka,<sup>a</sup> Anna Lykkeberg,<sup>a </sup> Gary S. Nichol,<sup>a</sup> Michael P. Shaver,<sup>b,c</sup> Antoine Buchard,<sup>d</sup> Jennifer A. Garden*<sup>a</sup></i> <br> <i>For DFT calculation, contact <br></i><i>Dr Antoine Buchard<sup>d</sup>; a.buchard@bath.ac.uk<br></i><i><sup>d</sup></i>: Centre for Sustainable Chemical Technologies, Department of Chemistry, University of Bath, Bath BA2 7AY, United Kingdom<br>Study: - DFT optimised geometries of complexes 3', 4', 5' and 6', investigating the configuration of the ligand and the number of THF molecules coordinated to the alkali metals<br>- Thermochemistry of reaction between 3'/4' and one/two equivalents of benzyl alcohol<br> - Thermochemistry of coordination of L-Lactide to 5' and<br><br>Protocol: rwb97XD/6-31++g(d)/6-31g(d)/cpcm=tetrahydrofuran<br> <br>Content: - Gaussian09 rev D.02 output files- Computational_full details_results.pdf<br><br><br>
DOI: 10.25345/c5cx88
title: MassIVE MSV000086240 - Protein profile of spinal cord lysates from EV71 infected WT and TLR7KO mice
title: Analysis of protein profile of spinal cord lysates from EV71-infected WT and TLR7KO mice at the recovery phase
DOI: 10.5281/zenodo.4067927
title: Understanding the role and function of maternal touch in children with neurodevelopmental disabilities

description: Raw data of the article " Understanding the role and function of maternal touch in children with neurodevelopmental disabilities". The study investigated the functions of maternal touch in dyads of mothers and children with neurodevelopmental disability , compared to dyads of children with typical development. This study was supported by Italian Ministry of Health (Ricerca Finalizzata RF-2016-02361884 to Dr. Rosario Montirosso).
DOI: 10.5281/zenodo.4067926
title: Understanding the role and function of maternal touch in children with neurodevelopmental disabilities

description: Raw data of the article " Understanding the role and function of maternal touch in children with neurodevelopmental disabilities". The study investigated the functions of maternal touch in dyads of mothers and children with neurodevelopmental disability , compared to dyads of children with typical development. This study was supported by Italian Ministry of Health (Ricerca Finalizzata RF-2016-02361884 to Dr. Rosario Montirosso).
DOI: 10.5281/zenodo.3600132
title: S083

description: A fully annotated subset of the SO242/1_83-1_AUV10 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S155: https://doi.org/10.5281/zenodo.3603803 S171: https://doi.org/10.5281/zenodo.3603809 S233: https://doi.org/10.5281/zenodo.3603815 This dataset contains the following files: <code>annotations/test.csv</code>: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. <code>annotations/train.csv</code>: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. <code>images/</code>: Directory that contains all the original image files. <code>dataset.json</code>: JSON file that contains information about the dataset. <code>name</code>: The name of the dataset. <code>images_dir</code>: Name of the directory that contains the original image files. <code>metadata_file</code>: Path to the CSV file that contains image metadata. <code>test_annotations_file</code>: Path to the CSV file that contains the test annotations. <code>train_annotations_file</code>: Path to the CSV file that contains the train annotations. <code>annotation_patches_dir</code>: Name of the directory that should contain the scale- and style-transferred annotation patches. <code>crop_dimension</code>: Edge length of an annotation or style patch in pixels. <code>metadata.csv</code>: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
DOI: 10.5281/zenodo.3600131
title: S083

description: A fully annotated subset of the SO242/1_83-1_AUV10 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S155: https://doi.org/10.5281/zenodo.3603803 S171: https://doi.org/10.5281/zenodo.3603809 S233: https://doi.org/10.5281/zenodo.3603815 This dataset contains the following files: <code>annotations/test.csv</code>: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. <code>annotations/train.csv</code>: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. <code>images/</code>: Directory that contains all the original image files. <code>dataset.json</code>: JSON file that contains information about the dataset. <code>name</code>: The name of the dataset. <code>images_dir</code>: Name of the directory that contains the original image files. <code>metadata_file</code>: Path to the CSV file that contains image metadata. <code>test_annotations_file</code>: Path to the CSV file that contains the test annotations. <code>train_annotations_file</code>: Path to the CSV file that contains the train annotations. <code>annotation_patches_dir</code>: Name of the directory that should contain the scale- and style-transferred annotation patches. <code>crop_dimension</code>: Edge length of an annotation or style patch in pixels. <code>metadata.csv</code>: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
DOI: 10.5281/zenodo.3603803
title: S155

description: A fully annotated subset of the SO242/2_155-1 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S083: https://doi.org/10.5281/zenodo.3600132 S171: https://doi.org/10.5281/zenodo.3603809 S233: https://doi.org/10.5281/zenodo.3603815 This dataset contains the following files: <code>annotations/test.csv</code>: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. <code>annotations/train.csv</code>: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. <code>images/</code>: Directory that contains all the original image files. <code>dataset.json</code>: JSON file that contains information about the dataset. <code>name</code>: The name of the dataset. <code>images_dir</code>: Name of the directory that contains the original image files. <code>metadata_file</code>: Path to the CSV file that contains image metadata. <code>test_annotations_file</code>: Path to the CSV file that contains the test annotations. <code>train_annotations_file</code>: Path to the CSV file that contains the train annotations. <code>annotation_patches_dir</code>: Name of the directory that should contain the scale- and style-transferred annotation patches. <code>crop_dimension</code>: Edge length of an annotation or style patch in pixels. <code>metadata.csv</code>: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
DOI: 10.5281/zenodo.3603802
title: S155

description: A fully annotated subset of the SO242/2_155-1 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S083: https://doi.org/10.5281/zenodo.3600132 S171: https://doi.org/10.5281/zenodo.3603809 S233: https://doi.org/10.5281/zenodo.3603815 This dataset contains the following files: <code>annotations/test.csv</code>: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. <code>annotations/train.csv</code>: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. <code>images/</code>: Directory that contains all the original image files. <code>dataset.json</code>: JSON file that contains information about the dataset. <code>name</code>: The name of the dataset. <code>images_dir</code>: Name of the directory that contains the original image files. <code>metadata_file</code>: Path to the CSV file that contains image metadata. <code>test_annotations_file</code>: Path to the CSV file that contains the test annotations. <code>train_annotations_file</code>: Path to the CSV file that contains the train annotations. <code>annotation_patches_dir</code>: Name of the directory that should contain the scale- and style-transferred annotation patches. <code>crop_dimension</code>: Edge length of an annotation or style patch in pixels. <code>metadata.csv</code>: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
DOI: 10.5281/zenodo.3603809
title: S171

description: A fully annotated subset of the SO242/2_171-1 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S083: https://doi.org/10.5281/zenodo.3600132 S155: https://doi.org/10.5281/zenodo.3603803 S233: https://doi.org/10.5281/zenodo.3603815 This dataset contains the following files: <code>annotations/test.csv</code>: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. <code>annotations/train.csv</code>: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. <code>images/</code>: Directory that contains all the original image files. <code>dataset.json</code>: JSON file that contains information about the dataset. <code>name</code>: The name of the dataset. <code>images_dir</code>: Name of the directory that contains the original image files. <code>metadata_file</code>: Path to the CSV file that contains image metadata. <code>test_annotations_file</code>: Path to the CSV file that contains the test annotations. <code>train_annotations_file</code>: Path to the CSV file that contains the train annotations. <code>annotation_patches_dir</code>: Name of the directory that should contain the scale- and style-transferred annotation patches. <code>crop_dimension</code>: Edge length of an annotation or style patch in pixels. <code>metadata.csv</code>: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
DOI: 10.5281/zenodo.3603808
title: S171

description: A fully annotated subset of the SO242/2_171-1 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S083: https://doi.org/10.5281/zenodo.3600132 S155: https://doi.org/10.5281/zenodo.3603803 S233: https://doi.org/10.5281/zenodo.3603815 This dataset contains the following files: <code>annotations/test.csv</code>: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. <code>annotations/train.csv</code>: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. <code>images/</code>: Directory that contains all the original image files. <code>dataset.json</code>: JSON file that contains information about the dataset. <code>name</code>: The name of the dataset. <code>images_dir</code>: Name of the directory that contains the original image files. <code>metadata_file</code>: Path to the CSV file that contains image metadata. <code>test_annotations_file</code>: Path to the CSV file that contains the test annotations. <code>train_annotations_file</code>: Path to the CSV file that contains the train annotations. <code>annotation_patches_dir</code>: Name of the directory that should contain the scale- and style-transferred annotation patches. <code>crop_dimension</code>: Edge length of an annotation or style patch in pixels. <code>metadata.csv</code>: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.