cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0993899.09fu
title: SH0993899.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0993900.09fu
title: SH0993900.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0993901.09fu
title: SH0993901.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0993902.09fu
title: SH0993902.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0993903.09fu
title: SH0993903.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.21227/7dqk-2z16
title: Methods for reducing the amount of data transmitted and stored in IoT systems

description: In this paper presented method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can apply cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach is focused on on-the-sensor data processing. Correlation and autocorrelation methods for detecting events, depending on shape of a wave, were described in details and proposed endpoint architecture implementation. Novelty and another feature of this approach is the use of not full shape of waves, but their components and on-device-processing. This greatly reduces the number of operations and implementation complexity. Other methods are oriented to Cloud Computing paradigm. Simulation results show that when the data rate from the sensor ~ 10 MSamples/s the proposed method makes it possible to transfer and store 280 bytes in 70 minutes instead of 157 GB with the bypass method. Reducing the requirements for the volume of transmitted and stored data will allow simplifying and reducing the cost of IoT systems, increase performance, and apply additional precision sensors to get more accurate object data.Current solutions are focused on low-powered and FPGA/ASIC implementations.
DOI: 10.21227/zw4c-sk57
title: Student Acceptance of Blended Learning - primary science curriculum - China

description: Blended Learning has been widely used in current basic education as a new teaching model, and how to improve the acceptance of students in Blended Learning is a hot issue that needs to be solved in the practice of teaching.
DOI: 10.6084/m9.figshare.12838259
title: Going Viral Data (HIJ 2020)

description: We used Media Cloud to compile and characterize mainstream and social media content published between January 2015 and September 2019 on overdose risk from casual fentanyl exposure.
DOI: 10.6084/m9.figshare.12838259.v1
title: Going Viral Data (HIJ 2020)

description: We used Media Cloud to compile and characterize mainstream and social media content published between January 2015 and September 2019 on overdose risk from casual fentanyl exposure.
DOI: 10.17632/knsz9r4vby.3
title: Data for: Ocimum americanum and Lippia alba essential oils as anaesthetics for Nile tilapia: Induction, recovery of apparent unconsciousness and sensory analysis of fillets

description: Data from Ocimum americanum and Lippia alba essential oils as anaesthetics for Nile tilapia: Induction, recovery of apparent unconsciousness and sensory analysis of fillets
DOI: 10.21227/ev95-mz75
title: Path Outlines

description: We provide the data corresponding to the studies of our paper Path Outlines- Study 1: - data collected:study1.csv - analysis:analyseStudy1.R - tasks:tasksStudy1.rtf- Study 2: - RDF data to run the study:nobel_persee.zip - data collected: study2.csv - analysis: analyseStudy2.R - tasks:tasksStudy2.txt
DOI: 10.6084/m9.figshare.12675374
title: Supplementary data for coral Acropora digitifera microbiome artificial light pollution at night study

description: 16S rRNA sequences from coral Acropora digitifera together with other supplementary data.
DOI: 10.6084/m9.figshare.12675374.v2
title: Supplementary data for coral Acropora digitifera microbiome artificial light pollution at night study

description: 16S rRNA sequences from coral Acropora digitifera together with other supplementary data.
DOI: 10.6084/m9.figshare.12675374.v1
title: Supplementary data for coral Acropora digitifera microbiome artificial light pollution at night study

description: 16S rRNA sequences from coral Acropora digitifera together with other supplementary data.
DOI: 10.21227/8004-v980
title: Path Outlines

description: We provide the data corresponding to the studies of our paper Path Outlines- Study 1: - data collected:study1.csv - analysis:analyseStudy1.R - tasks:tasksStudy1.rtf- Study 2: - RDF data to run the study:nobel_persee.zip - data collected: study2.csv - analysis: analyseStudy2.R - tasks:tasksStudy2.txt
DOI: 10.5281/zenodo.4015955
title: Dataset Reuse Indicators Datasets

description: This dataset contains two files. 1) A python pickle file (github_dataset.zip) that contains Github repositories with datasets. Specifically, using Google’s public dataset copy of Github and the BigQuery service to build a list of repositories that have a CSV or XLSX or XLS file. We then used the GitHub API to collect nformation about each repository in this list. The resulting dataset consists of 87936 repositories that contain at least a CSV, XLSX or XLS file, alongside with information about their features (e.g. number of open and closed issues and license) from GitHub. This corpus had more than two million data files. We then excluded those files withless then ten rows, which was the case for 65537 repositories with a total of 1,467,240 data files. 2) A python pickle file (processed_dataset.zip) containing the feature information necessary to train a machine learning model to predict reuse on these Github datasets Source code can be found at: https://github.com/laurakoesten/Dataset-Reuse-Indicators For a full description of the content see: Koesten, Laura and Vougiouklis, Pavlos and Simperl, Elena and Groth, Paul, Dataset Reuse: Translating Principles to Practice. Available at SSRN: https://ssrn.com/abstract=3589836 or http://dx.doi.org/10.2139/ssrn.3589836
DOI: 10.5281/zenodo.4015954
title: Dataset Reuse Indicators Datasets

description: This dataset contains two files. 1) A python pickle file (github_dataset.zip) that contains Github repositories with datasets. Specifically, using Google’s public dataset copy of Github and the BigQuery service to build a list of repositories that have a CSV or XLSX or XLS file. We then used the GitHub API to collect nformation about each repository in this list. The resulting dataset consists of 87936 repositories that contain at least a CSV, XLSX or XLS file, alongside with information about their features (e.g. number of open and closed issues and license) from GitHub. This corpus had more than two million data files. We then excluded those files withless then ten rows, which was the case for 65537 repositories with a total of 1,467,240 data files. 2) A python pickle file (processed_dataset.zip) containing the feature information necessary to train a machine learning model to predict reuse on these Github datasets Source code can be found at: https://github.com/laurakoesten/Dataset-Reuse-Indicators For a full description of the content see: Koesten, Laura and Vougiouklis, Pavlos and Simperl, Elena and Groth, Paul, Dataset Reuse: Translating Principles to Practice. Available at SSRN: https://ssrn.com/abstract=3589836 or http://dx.doi.org/10.2139/ssrn.3589836
DOI: 10.5281/zenodo.4016459
title: Contaminación atmosférica durante el COVID-19

description: La enfermedad respiratoria aguda severa SARS-CoV-2 o simplemente COVID-19, tiene impactos negativos directos sobre la salud de las personas a nivel mundial. Pero a su vez, ha generado impactos positivos sobre el medio ambiente, debido a la cuarentena y la paralización de las diferentes actividades. Considerando que ha repercutido principalmente sobre la atmósfera, muchos de los investigadores se han centrado en estudiar los impactos producidos por esta situación. De esta manera, nuestro aporte consiste en recopilar los impactos informados por la literatura científica en el entorno internacional, los cuales se compilaron del 04 de julio al 05 de setiembre del 2020, mediante la selección y búsqueda en las diferentes bases de datos reconocidas, encontrando 297 trabajos publicados desde el 01 de enero del 2020. Los temas de búsqueda fueron: “environmental impact coronavirus”, “COVID-19 impact”, “air pollution COVID-19”. Todas las investigaciones que contenían información sobre un impacto o respuesta a la pandemia de COVID-19 relacionados con la contaminación del aire se consideraron relevantes para la elaboración de la base de datos. Para cada publicación, se extrajo información:
DOI: 10.5281/zenodo.4016458
title: Contaminación atmosférica durante el COVID-19

description: La enfermedad respiratoria aguda severa SARS-CoV-2 o simplemente COVID-19, tiene impactos negativos directos sobre la salud de las personas a nivel mundial. Pero a su vez, ha generado impactos positivos sobre el medio ambiente, debido a la cuarentena y la paralización de las diferentes actividades. Considerando que ha repercutido principalmente sobre la atmósfera, muchos de los investigadores se han centrado en estudiar los impactos producidos por esta situación. De esta manera, nuestro aporte consiste en recopilar los impactos informados por la literatura científica en el entorno internacional, los cuales se compilaron del 04 de julio al 05 de setiembre del 2020, mediante la selección y búsqueda en las diferentes bases de datos reconocidas, encontrando 297 trabajos publicados desde el 01 de enero del 2020. Los temas de búsqueda fueron: “environmental impact coronavirus”, “COVID-19 impact”, “air pollution COVID-19”. Todas las investigaciones que contenían información sobre un impacto o respuesta a la pandemia de COVID-19 relacionados con la contaminación del aire se consideraron relevantes para la elaboración de la base de datos. Para cada publicación, se extrajo información:
DOI: 10.6084/m9.figshare.12319787.v1
title: Artificial single-cell datasets with cell clusters used for singleCellHaystack manuscript

description: An archive containing 100 artificial single-cell datasets. The data of each dataset is an R data file (.rda). The file names have the following format: splatter_<i>thousands_of_cells</i>kCells_groups_set_<i>set_id</i>.rda'For example: "splatter_1kCells_groups_set_9.rda" represents the 9th set containing 1000 cells made using the "groups" option of splatSimulate. You can import the data into R using the load() function. Each dataset includes the following R objects:1) counts : the number of reads or UMIs for each gene in each cell2) gene.data : a summary of the data generated by Splatter3) params : the parameters used to generate the dataset
DOI: 10.6084/m9.figshare.12319787
title: Artificial single-cell datasets with cell clusters used for singleCellHaystack manuscript

description: An archive containing 100 artificial single-cell datasets. The data of each dataset is an R data file (.rda). The file names have the following format: splatter_<i>thousands_of_cells</i>kCells_groups_set_<i>set_id</i>.rda'For example: "splatter_1kCells_groups_set_9.rda" represents the 9th set containing 1000 cells made using the "groups" option of splatSimulate. You can import the data into R using the load() function. Each dataset includes the following R objects:1) counts : the number of reads or UMIs for each gene in each cell2) gene.data : a summary of the data generated by Splatter3) params : the parameters used to generate the dataset
DOI: 10.6084/m9.figshare.12319247
title: Artificial single-cell datasets with paths between cell clusters used for singleCellHaystack manuscript

description: An archive containing 100 artificial single-cell datasets. The data of each dataset is an R data file (.rda). The file names have the following format: splatter_<i>thousands_of_cells</i>kCells_paths_set_<i>set_id</i>.rda' For example: "splatter_1kCells_paths_set_9.rda" represents the 9th set containing 1000 cells made using the "paths" option of splatSimulate. You can import the data into R using the load() function. Each dataset includes the following R objects: 1) counts : the number of reads or UMIs for each gene in each cell2) gene.data : a summary of the data generated by Splatter3) params : the parameters used to generate the dataset
DOI: 10.6084/m9.figshare.12319247.v1
title: Artificial single-cell datasets with paths between cell clusters used for singleCellHaystack manuscript

description: An archive containing 100 artificial single-cell datasets. The data of each dataset is an R data file (.rda). The file names have the following format: splatter_<i>thousands_of_cells</i>kCells_paths_set_<i>set_id</i>.rda' For example: "splatter_1kCells_paths_set_9.rda" represents the 9th set containing 1000 cells made using the "paths" option of splatSimulate. You can import the data into R using the load() function. Each dataset includes the following R objects: 1) counts : the number of reads or UMIs for each gene in each cell2) gene.data : a summary of the data generated by Splatter3) params : the parameters used to generate the dataset
DOI: 10.17632/knsz9r4vby.4
title: Data for: Ocimum americanum and Lippia alba essential oils as anaesthetics for Nile tilapia: Induction, recovery of apparent unconsciousness and sensory analysis of fillets

description: Data from Ocimum americanum and Lippia alba essential oils as anaesthetics for Nile tilapia: Induction, recovery of apparent unconsciousness and sensory analysis of fillets
DOI: 10.6084/m9.figshare.12923189
title: R0195_20171016a_videos

description: Rat 0195, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.15161/oar.it/30683
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.6084/m9.figshare.12783980
title: DATA: Accurate arterial path length estimation for pulse wave velocity calculation in growing children and adolescents

description: DATASHEETS
DOI: 10.6084/m9.figshare.12783980.v2
title: DATA: Accurate arterial path length estimation for pulse wave velocity calculation in growing children and adolescents

description: DATASHEETS
DOI: 10.6084/m9.figshare.12783980.v1
title: DATA: Accurate arterial path length estimation for pulse wave velocity calculation in growing children and adolescents

description: DATASHEETS
DOI: 10.17632/xydxtwxyw6.1
title: Data for: Mineralogy, geochemistry, and Sr–Pb and in situ S isotopic compositions of hydrothermal precipitates from the Tangyin hydrothermal field, southern Okinawa Trough: evaluation of the contribution of magmatic fluids and sediments to hydrothermal sy

description: The EPMA data of sulfide minerals
DOI: 10.5281/zenodo.3960552
title: Claustrum Segmentations of HCP Young Adult Dataset

description: The dataset consists segmentations of the left and right claustrum, for 1068 from the Human Connectome Project Young Adults dataset. The segmentations were generated automativally. The dataset also includes mannual segmentation of the left and right claustrum for 10 subjects. The segmentations are available as Nifti files. The name of each file states the HCP ID of that subjects. The segmentations in the same space and resolution of each subject's T1w_acpc_dc_restore file.
DOI: 10.5281/zenodo.3960551
title: Claustrum Segmentations of HCP Young Adult Dataset

description: The dataset consists segmentations of the left and right claustrum, for 1068 from the Human Connectome Project Young Adults dataset. The segmentations were generated automativally. The dataset also includes mannual segmentation of the left and right claustrum for 10 subjects. The segmentations are available as Nifti files. The name of each file states the HCP ID of that subjects. The segmentations in the same space and resolution of each subject's T1w_acpc_dc_restore file.
DOI: 10.6084/m9.figshare.12646238.v3
title: Quantification of T cell of 40 human samples

description: The raw counts of four featrues including gene expression by HTSeq, isoform expression by RSEM, splicing junction by STAR, and gene expression which only exonic reads was quantified (ExonOnly) by HTSeq. using the Gencode version 19 annotation. And the other file includes the value of highly variable genes from ExonOnly after rlog transformation and batch correction using ComBat.
DOI: 10.21227/6143-9922
title: Defect Dataset

description: Three groups of defective sample images and the ground-truth images were artificially generated through some algorithms, including five types of defects, namely, broken end, hole, netting multiple, thin bar and thick bar.
DOI: 10.21227/zwen-za45
title: Defect Dataset

description: Three groups of defective sample images and the ground-truth images were artificially generated through some algorithms, including five types of defects, namely, broken end, hole, netting multiple, thin bar and thick bar.
DOI: 10.5281/zenodo.4016613
title: RCMIP Phase 1 Data

description: RCMIP phase 1 dataset accompanying the initial submission of <em>Reduced complexity model intercomparison project phase 1: Protocol, results and initial observations</em> to <em>Geoscientific Model Development. </em>The full author list can be found in the paper, see https://doi.org/10.5194/gmd-2019-375.
DOI: 10.5281/zenodo.3593569
title: RCMIP Phase 1 Data

description: RCMIP phase 1 dataset accompanying the initial submission of <em>Reduced complexity model intercomparison project phase 1: Protocol, results and initial observations</em> to <em>Geoscientific Model Development. </em>The full author list can be found in the paper, see https://doi.org/10.5194/gmd-2019-375.
DOI: 10.6084/m9.figshare.12923186
title: R0193_20171018a_videos

description: Rat 0193, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.12923186.v1
title: R0193_20171018a_videos

description: Rat 0193, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.5519/qd.6z486thv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4601860 records)