cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/76jx-4966
title: Incomplete big datasets for parallel fractional hot-deck imputation

description: Parallel fractional hot-deck imputation (P-FHDI) is a general-purpose, assumption-free tool for handling item nonresponse in big incomplete data by combining the theory of FHDI and parallel computing. FHDI cures multivariate missing data by filling each missing unit with multiple observed values (thus, hot-deck) without resorting to distributional assumptions. P-FHDI can tackle big incomplete data with millions of instances (big-n) or 10, 000 variables (big-p). However, handling ultra incomplete data (i.e., concurrently big-n and big-p) with tremendous instances and high dimensionality has posed challenges to P-FHDI due to excessive memory requirement and execution time. We developed the ultra data-oriented P-FHDI (named as UP-FHDI) capable of curing ultra incomplete data. In addition to the parallel Jackknife method, UP-FHDI enables a computationally efficient ultra data-oriented variance estimation by using parallel linearization techniques. The object of this repository is to illustrate the use of UP-FHDI with different types of big incomplete data.
DOI: 10.6084/m9.figshare.14903259
title: File S4

description: File S4 is contains tables of all QTL identified in the study, their locations, and their effect sizes estimates from the bi-allelic, ancestral haplotype, and founder models.
DOI: 10.6084/m9.figshare.14903259.v1
title: File S4

description: File S4 is contains tables of all QTL identified in the study, their locations, and their effect sizes estimates from the bi-allelic, ancestral haplotype, and founder models.
DOI: 10.6084/m9.figshare.14903202
title: removed

description: HTA dataset
DOI: 10.6084/m9.figshare.14903202.v3
title: removed

description: HTA dataset
DOI: 10.6084/m9.figshare.14903202.v2
title: GRDM-TABELLA-SUPPLEMENTARE.pdf

description: HTA dataset
DOI: 10.5281/zenodo.1410651
title: THE HIGH CADENCE TRANSIENT SURVEY (HITS): Source, light-curve and classification catalogs

description: The High Cadence Transient Survey (HiTS) aims to discover and study transient objects with characteristic timescales between hours and days, such as pulsating, eclipsing and exploding stars. This survey represents a unique laboratory to explore large <em>etendue</em> observations from cadences of about 0.1 days and to test new computational tools for the analysis of large data. This work follows a fully <em>Data Science</em> approach: from the raw data to the analysis and classification of variable sources. We compile a catalog of ~15 million object detections and a catalog of ~2.5 million light-curves classified by variability. The typical depth of the survey is 24.2, 24.3, 24.1 and 23.8 in <em>u</em>, <em>g</em>, <em> </em>r, and <em>i</em> bands, respectively. We classified all point-like non-moving sources by first extracting features from their light--curves and then applying a Random Forest classifier. For the classification, we used a training set constructed using a combination of cross-matched catalogs, visual inspection, transfer/active learning, and data augmentation. The classification model consists of several Random Forest classifiers organized in a hierarchical scheme. The classifier accuracy estimated on a test set is approximately 97%. In the unlabeled data, 3,485 sources were classified as variables, of which 1,321 were classified as periodic. Among the periodic classes we discovered with high confidence, 1 \(\delta\) scuti, 39 eclipsing binaries, 48 rotational variables, and 90 RR-Lyrae. For the non-periodic classes we discovered 1 cataclysmic variables, 630 QSO, and 1 supernova candidate.
description: This is the data release 1 of HiTS, which contains: i) the source catalogs for 2013, 2014 and 2015 observational campaigns; ii) light curves for sources detected in 2014 and 2015; iii) the labeled and training set used during the automatic classification along with the predictions.
DOI: 10.5281/zenodo.1410650
title: THE HIGH CADENCE TRANSIENT SURVEY (HITS): Source, light-curve and classification catalogs

description: The High Cadence Transient Survey (HiTS) aims to discover and study transient objects with characteristic timescales between hours and days, such as pulsating, eclipsing and exploding stars. This survey represents a unique laboratory to explore large <em>etendue</em> observations from cadences of about 0.1 days and to test new computational tools for the analysis of large data. This work follows a fully <em>Data Science</em> approach: from the raw data to the analysis and classification of variable sources. We compile a catalog of ~15 million object detections and a catalog of ~2.5 million light-curves classified by variability. The typical depth of the survey is 24.2, 24.3, 24.1 and 23.8 in <em>u</em>, <em>g</em>, <em> </em>r, and <em>i</em> bands, respectively. We classified all point-like non-moving sources by first extracting features from their light--curves and then applying a Random Forest classifier. For the classification, we used a training set constructed using a combination of cross-matched catalogs, visual inspection, transfer/active learning, and data augmentation. The classification model consists of several Random Forest classifiers organized in a hierarchical scheme. The classifier accuracy estimated on a test set is approximately 97%. In the unlabeled data, 3,485 sources were classified as variables, of which 1,321 were classified as periodic. Among the periodic classes we discovered with high confidence, 1 \(\delta\) scuti, 39 eclipsing binaries, 48 rotational variables, and 90 RR-Lyrae. For the non-periodic classes we discovered 1 cataclysmic variables, 630 QSO, and 1 supernova candidate.
description: This is the data release 1 of HiTS, which contains: i) the source catalogs for 2013, 2014 and 2015 observational campaigns; ii) light curves for sources detected in 2014 and 2015; iii) the labeled and training set used during the automatic classification along with the predictions.
DOI: 10.5281/zenodo.3820679
title: Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"

description: This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (OGLE), while ancillary information was obtained from the Gaia Data Release 2 (GAIA DR2). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference). This dataset is realized in tandem with the corresponding GitHub and article.
DOI: 10.5281/zenodo.3820678
title: Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"

description: This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (OGLE), while ancillary information was obtained from the Gaia Data Release 2 (GAIA DR2). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference). This dataset is realized in tandem with the corresponding GitHub and article.
DOI: 10.6084/m9.figshare.14900763.v1
title: Prevention and Management of Running-Related Injuries in Ultrarunners - Theme Table

description: Three overarching themes emerged from the data which reflected strategies used by participants to prevent and manage injuries during their ultrarunning participation: <i>Listen to Your Body</i>, <i>Back on Track</i>, and <i>Mental Toughness</i>. These three theme tables describe the over-arching themes, core categories, and sub-categories and provide sample quotations for findings from the qualitative data analysis. The first over-arching theme is <i>Listen to Your Body</i>, corresponds with RRI prevention strategies and resources, and the second over-arching theme is <i>Back on Track</i> and corresponds with RRI management strategies and resources. The third theme is <i>Mental Toughness</i>, which corresponds with the behavioral and psychological aspects of ultrarunning participation.
DOI: 10.6084/m9.figshare.14900763
title: Prevention and Management of Running-Related Injuries in Ultrarunners - Theme Table

description: Three overarching themes emerged from the data which reflected strategies used by participants to prevent and manage injuries during their ultrarunning participation: <i>Listen to Your Body</i>, <i>Back on Track</i>, and <i>Mental Toughness</i>. These three theme tables describe the over-arching themes, core categories, and sub-categories and provide sample quotations for findings from the qualitative data analysis. The first over-arching theme is <i>Listen to Your Body</i>, corresponds with RRI prevention strategies and resources, and the second over-arching theme is <i>Back on Track</i> and corresponds with RRI management strategies and resources. The third theme is <i>Mental Toughness</i>, which corresponds with the behavioral and psychological aspects of ultrarunning participation.
DOI: 10.6084/m9.figshare.14900763.v3
title: Prevention and Management of Running-Related Injuries in Ultrarunners - Theme Table

description: Three overarching themes emerged from the data which reflected strategies used by participants to prevent and manage injuries during their ultrarunning participation: <i>Listen to Your Body</i>, <i>Back on Track</i>, and <i>Mental Toughness</i>. These three theme tables describe the over-arching themes, core categories, and sub-categories and provide sample quotations for findings from the qualitative data analysis. The first over-arching theme is <i>Listen to Your Body</i>, corresponds with RRI prevention strategies and resources, and the second over-arching theme is <i>Back on Track</i> and corresponds with RRI management strategies and resources. The third theme is <i>Mental Toughness</i>, which corresponds with the behavioral and psychological aspects of ultrarunning participation.
DOI: 10.6084/m9.figshare.14900763.v2
title: Prevention and Management of Running-Related Injuries in Ultrarunners - Theme Table

description: Three overarching themes emerged from the data which reflected strategies used by participants to prevent and manage injuries during their ultrarunning participation: <i>Listen to Your Body</i>, <i>Back on Track</i>, and <i>Mental Toughness</i>. These three theme tables describe the over-arching themes, core categories, and sub-categories and provide sample quotations for findings from the qualitative data analysis. The first over-arching theme is <i>Listen to Your Body</i>, corresponds with RRI prevention strategies and resources, and the second over-arching theme is <i>Back on Track</i> and corresponds with RRI management strategies and resources. The third theme is <i>Mental Toughness</i>, which corresponds with the behavioral and psychological aspects of ultrarunning participation.
DOI: 10.5281/zenodo.5059899
title: Metadata: A validation standard for Area of Habitat maps for terrestrial birds and mammals

description: The files summarize the validation analysis done for Area of Habitat (AOH) for terrestrial birds and mammals. Please refer to the "column index file" to find description about each column.
DOI: 10.5281/zenodo.5059159
title: Biblical Allusions Test Set

description: This is a benchmark test set to allow evaluation of algorithms for automatic identification of Biblical allusions within a Hebrew text. See: Avi Shmidman, "Automatic Identification of Biblical Citations and Allusions in Hebrew Texts", in: #DHJewish - Jewish Studies in the Digital Age, ed. Michelle Chesner, et al., Berlin: DeGruyter, 2022 (in press)
DOI: 10.5281/zenodo.5059158
title: Biblical Allusions Test Set

description: This is a benchmark test set to allow evaluation of algorithms for automatic identification of Biblical allusions within a Hebrew text. See: Avi Shmidman, "Automatic Identification of Biblical Citations and Allusions in Hebrew Texts", in: #DHJewish - Jewish Studies in the Digital Age, ed. Michelle Chesner, et al., Berlin: DeGruyter, 2022 (in press)
DOI: 10.5281/zenodo.5045674
title: Questionnaire used in FAIR Workbench user evaluation

description: A pdf copy of the Questionnaire filled out by users at the end of the FAIR Workbench user evaluation study. The questions regarded the evaluation notebook, published here: http://doi.org/10.5281/zenodo.5045448
DOI: 10.5281/zenodo.5045447
title: Evaluation notebook and files for FAIR Workbench user evaluation

description: This archive contains the Jupyter notebook and associated (image) files used in the June 2021 evaluation of the FAIR Workbench.
DOI: 10.5281/zenodo.5044940
title: Dump truck object detection with manual annotations

description: Doing manual annotations can sometimes be resource heavy, depending on the amount of data. This dataset was designed to created to use in conjunction with a semi-automatic annotation method based on linear interpolation. The dataset contains 799 images, where 679 lies in the trainingset, and the rest lies in the validationset. The images are taken from 6 different video streams, where a remote controlled wheel loader approaches a miniature dump truck at different angles. 4 of the videos are used in the trainingset. The labels can contain up to 5 classes which are: 0 - front wheel <br> 1 - middle wheel<br> 2 - back wheel<br> 3 - tipping body<br> 4 - cap This dataset was used to train a YOLOv3 model, hence the labels will be written in the YOLO labeling format.
DOI: 10.5281/zenodo.5044939
title: Dump truck object detection with manual annotations

description: Doing manual annotations can sometimes be resource heavy, depending on the amount of data. This dataset was designed to created to use in conjunction with a semi-automatic annotation method based on linear interpolation. The dataset contains 799 images, where 679 lies in the trainingset, and the rest lies in the validationset. The images are taken from 6 different video streams, where a remote controlled wheel loader approaches a miniature dump truck at different angles. 4 of the videos are used in the trainingset. The labels can contain up to 5 classes which are: 0 - front wheel <br> 1 - middle wheel<br> 2 - back wheel<br> 3 - tipping body<br> 4 - cap This dataset was used to train a YOLOv3 model, hence the labels will be written in the YOLO labeling format.
DOI: 10.18112/openneuro.ds003711.v1.0.0
title: The Contributionsof Eye Gaze Fixations and Target-Lure Similarity to Behavioral and fMRI Indices of Pattern Separation and Pattern Completion
DOI: 10.5281/zenodo.5044894
title: Additional climate information for research paper «Ecological and Geographical Analysis of Distribution of Heracleum persicum, H. mantegazzianum and H. sosnowskyi on The Northern Limit of Its Invaded Range in Europe» submitted to Russian Journal of Biological Invasions

description: <strong>Additional climate information for research paper «Ecological and Geographical Analysis of Distribution of Heracleum persicum, H. mantegazzianum and H. sosnowskyi on The Northern Limit of Its Invaded Range in Europe» submitted to Russian Journal of Biological Invasions </strong>
DOI: 10.5281/zenodo.5044893
title: Additional climate information for research paper «Ecological and Geographical Analysis of Distribution of Heracleum persicum, H. mantegazzianum and H. sosnowskyi on The Northern Limit of Its Invaded Range in Europe» submitted to Russian Journal of Biological Invasions

description: <strong>Additional climate information for research paper «Ecological and Geographical Analysis of Distribution of Heracleum persicum, H. mantegazzianum and H. sosnowskyi on The Northern Limit of Its Invaded Range in Europe» submitted to Russian Journal of Biological Invasions </strong>
DOI: 10.5281/zenodo.5044533
title: Tutorial output for Tourmaline amplicon sequence processing workflow

description: Tutorial output for the Tourmaline amplicon sequence processing workflow. Tourmaline was run on the test data provided in the directory 00-data, which were downloaded along with the rest of the repository using this command: <pre><code>git clone https://github.com/aomlomics/tourmaline</code></pre> Reference data were downloaded and symlinked using these commands: <pre><code>cd tourmaline/01-imported wget https://data.qiime2.org/2021.2/common/silva-138-99-seqs-515-806.qza wget https://data.qiime2.org/2021.2/common/silva-138-99-tax-515-806.qza ln -s silva-138-99-seqs-515-806.qza refseqs.qza ln -s silva-138-99-tax-515-806.qza reftax.qza</code></pre> Paths in 00-data/manifest_pe.csv and 00-data/manifest_se.csv were edited to match the local paths. Output for all modes of the workflow were then generated in series: <pre><code>conda activate qiime2-2021.2 snakemake dada2_pe_report_unfiltered snakemake dada2_pe_report_filtered snakemake dada2_se_report_unfiltered snakemake dada2_se_report_filtered snakemake deblur_se_report_unfiltered snakemake deblur_se_report_filtered</code></pre>
DOI: 10.5281/zenodo.5041324
title: Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

description: <strong>CreateBASiCS.R</strong><br> Create BASiCS chain objects, which include mean and noise data, from the raw count matrix. This step takes 10~20 hours. <strong>Main.R</strong> <br> Generate figures in the main results. <strong>FunctionSetting.R,</strong> <strong>DifferenceTestAll.R </strong>and <strong>DifferenceTestClustest.R</strong> are executed in this script. <strong>STP12_detail_analysis.Rmd</strong><br> Output results mentioned in the discussion. Code is placed within the chunk.<br> Please make sure to execute this after Main.R . <strong>SupFig1Generator.R</strong><br> Generate supplement figure 1 <strong>YeastGeneNameList.csv</strong><br> Data that convert ORF id to the common name<br> This data includes 3 columns, Name, ORF, and SGD.<br> - Name: Common name (e.g., TSC3)<br> - ORF: ORF id with Yeastract format (e.g., YBR058C.A)<br> - SGD: ORF id with SGD format (e.g., YBR058C-A) <br> <strong>GeneticInteraction(ORF).csv</strong><br> Genetic interaction data downloaded from Yeastract. This data includes two-column, V1 and V2.<br> The interaction is described as two matrices such as V1 output to V2. <strong>NoiseChangeGeneList</strong><br> This folder contains data required for generating tables 1 and S2.
DOI: 10.5281/zenodo.5063011
title: GRDM

description: GRDM
DOI: 10.17632/tv23z95r5g.1
title: Data for: Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies

description: This dataset is the original data of the field observation used in the paper “Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies” and “Diurnal and seasonal variation in bulk stomatal conductance of the rice canopy and its dependence on developmental stage” by Maruyama. The dataset includes meteorological data, flux data, vegetation data, and soil data for rice paddies in three locations (Saito, Saga, and Aso) in Kyushu, Japan. The dataset contains one year of data for Saito and Saga, and two years of data for Aso. The total duration of the data is 4 years (4 rice cultivation years). The data stored in each file is as follows. 1. MetFlux_60min.csv: Meteorological and flux data of paddy fields at 60-minute intervals 2. MetFlux_1day.csv: Meteorological and flux data of paddy fields at 1-day intervals 3. Vegetation.csv: Vegetation data of paddy fields 4. Soil.csv: Soil property data of paddy fields 5. DataDetails.txt: A text file with details of the data
DOI: 10.5281/zenodo.5063027
title: GRDM

description: GRDM
DOI: 10.5281/zenodo.5063026
title: GRDM

description: GRDM
DOI: 10.6084/m9.figshare.14903325
title: Prism files - data + stats + figures.pzfx

description: Data are presented in two files. Figure 1: all data is presented in the first spreadsheet, and the following spreadsheets were used to create the figures. Each spreadsheet is linked to the respective statistic and graph.Figure 2+3: data for figures 2 and 3 are in separated spreadsheets linked to respective statistics and graphs.
DOI: 10.6084/m9.figshare.14903325.v1
title: Prism files - data + stats + figures.pzfx

description: Data are presented in two files. Figure 1: all data is presented in the first spreadsheet, and the following spreadsheets were used to create the figures. Each spreadsheet is linked to the respective statistic and graph.Figure 2+3: data for figures 2 and 3 are in separated spreadsheets linked to respective statistics and graphs.
DOI: 10.5281/zenodo.5063039
title: GRDM

description: GRDM
DOI: 10.5281/zenodo.5063010
title: GRDM

description: GRDM
DOI: 10.5281/zenodo.5063049
title: GRDM - SCHEDA HANAROSTENT

description: GRDM - SCHEDA HANAROSTENT
DOI: 10.5281/zenodo.5063048
title: GRDM - SCHEDA HANAROSTENT

description: GRDM - SCHEDA HANAROSTENT
DOI: 10.6084/m9.figshare.14044220
title: Pediatric EEG Data & Analyses in High Gamma Band from Boston Children's Hospital

description: Analyses on 10 pediatric patients with intractable seizures based on data collected at the Boston Children's hospital using signal processing techniques to create ERP images via EEGLAB, a MATLAB toolbox. Fifteen seizures per patient were used: 5 preictal, 5 interictal and 5 ictal (for time-stamping).<br><br>Data was extracted to Excel.
DOI: 10.6084/m9.figshare.14044220.v2
title: Pediatric EEG Data & Analyses in High Gamma Band from Boston Children's Hospital

description: Analyses on 10 pediatric patients with intractable seizures based on data collected at the Boston Children's hospital using signal processing techniques to create ERP images via EEGLAB, a MATLAB toolbox. Data was extracted to Excel.
DOI: 10.5281/zenodo.5063250
title: GRDM SCHEDA BUNDLE

description: GRDM SCHEDA BUNDLE
DOI: 10.5281/zenodo.5063249
title: GRDM SCHEDA BUNDLE

description: GRDM SCHEDA BUNDLE
DOI: 10.5281/zenodo.5063254
title: SCHEDA HTA WAVELINQ

description: SCHEDA HTA WAVELINQ
DOI: 10.5281/zenodo.5063253
title: SCHEDA HTA WAVELINQ

description: SCHEDA HTA WAVELINQ
DOI: 10.5281/zenodo.5063258
title: GRDM - DOCUMENTO CRIOABLAZIONE

description: GRDM - DOCUMENTO CRIOABLAZIONE
DOI: 10.5281/zenodo.5063257
title: GRDM - DOCUMENTO CRIOABLAZIONE

description: GRDM - DOCUMENTO CRIOABLAZIONE
DOI: 10.5281/zenodo.5063262
title: SCHEDA HTA INTELLANAV

description: SCHEDA HTA INTELLANAV
DOI: 10.5281/zenodo.5063261
title: SCHEDA HTA INTELLANAV

description: SCHEDA HTA INTELLANAV
DOI: 10.5281/zenodo.5063265
title: Supplemental Material for Leite et al: Duodenal microbiome changes in postmenopausal women: effects of hormone therapy and implications for cardiovascular risk

description: Supplemental Material for Leite et al: Duodenal microbiome changes in postmenopausal women: effects of hormone therapy and implications for cardiovascular risk
DOI: 10.5281/zenodo.4723797
title: Supplemental Material for Leite et al: Duodenal microbiome changes in postmenopausal women: effects of hormone therapy and implications for cardiovascular risk

description: Supplemental Material for Leite et al: Duodenal microbiome changes in postmenopausal women: effects of hormone therapy and implications for cardiovascular risk
DOI: 10.5281/zenodo.5063268
title: Alatina alata SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5063267
title: Alatina alata SPAdes preassembly

description: .
DOI: 10.5517/ccdc.csd.cc24bqdm
title: CCDC 1976510: Experimental Crystal Structure Determination

description: Related Article: Ji-Gang Gao, Xu-Kai Guan, Dong-Yang Sun, Heng Zhang, Wen-Hua Mi, Xiang-Shuo Qin, Guang-Liang Zhang, Suoqin Zhang|2021|Org.Lett.|23|4876|doi:10.1021/acs.orglett.1c01600