cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1204087.09fu
title: SH1204087.09FU

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/sh1204088.09fu
title: SH1204088.09FU

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/sh1204089.09fu
title: SH1204089.09FU

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/sh1204090.09fu
title: SH1204090.09FU

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: 10.6084/m9.figshare.20445945.v1
title: Additional file 3 of Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach

description: Additional file 3. Table S3. Results of different feature sets based on SNPs genotype data with wGRS as the target variable. Performance metrics are based on MAE and RMSE scores.
DOI: 10.6084/m9.figshare.20445948
title: Additional file 4 of Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach

description: Additional file 4. Table S4. Results of different feature sets based on SNPs genotype data with wGRS+GF as the target variable. Performance metrics are based on MAE and RMSE scores.
DOI: 10.6084/m9.figshare.20445948.v1
title: Additional file 4 of Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach

description: Additional file 4. Table S4. Results of different feature sets based on SNPs genotype data with wGRS+GF as the target variable. Performance metrics are based on MAE and RMSE scores.
DOI: 10.6084/m9.figshare.20445978
title: Additional file 1 of The association between early formula and reduced risk of cow’s milk allergy during the first three year of life: a Japanese cohort study

description: Additional file 1: Table S1. Baseline characteristics of children with early eczema vs. those without
DOI: 10.6084/m9.figshare.20445978.v1
title: Additional file 1 of The association between early formula and reduced risk of cow’s milk allergy during the first three year of life: a Japanese cohort study

description: Additional file 1: Table S1. Baseline characteristics of children with early eczema vs. those without
DOI: 10.6084/m9.figshare.20445987
title: Additional file 1 of Integrated metabolomic and transcriptomic analyses of the parasitic plant Cuscuta japonica Choisy on host and non-host plants

description: Additional file 1: Figure S1. Comparison of metabolite abundance in Cuscuta japonica attached to the host and non-host plants at 24 h (A) and 72 h (B). Figure S2. Number of differentially clustered metabolites in the HTD (A) and MTD (B) groups. The metabolite abundance trends of the differentially clustered profiles in the HTD (C) and MTD (D) groups are shown. Figure S3. Number of differentially clustered genes in the HTD (A) and MTD (B) groups. The gene expression trends of the differentially clustered profiles in the HTD (C) and MTD (D) groups are shown. Table S1. Metabolome profiles in Cuscuta japonica in the HTD and MTD groups. Table S2. Metabolite differences in Cuscuta japonica in MTD vs. _HTD at 24 haa. Table S3. Metabolite differences of C. japonica in MTD vs. _HTD at 72 haa. Table S4. Statistic of RNA-seq data. Table S5. DEGs of MTD vs HTD at 24 haa. Table S6. DEGs of MTD vs HTD at 72 haa.
DOI: 10.6084/m9.figshare.20445987.v1
title: Additional file 1 of Integrated metabolomic and transcriptomic analyses of the parasitic plant Cuscuta japonica Choisy on host and non-host plants

description: Additional file 1: Figure S1. Comparison of metabolite abundance in Cuscuta japonica attached to the host and non-host plants at 24 h (A) and 72 h (B). Figure S2. Number of differentially clustered metabolites in the HTD (A) and MTD (B) groups. The metabolite abundance trends of the differentially clustered profiles in the HTD (C) and MTD (D) groups are shown. Figure S3. Number of differentially clustered genes in the HTD (A) and MTD (B) groups. The gene expression trends of the differentially clustered profiles in the HTD (C) and MTD (D) groups are shown. Table S1. Metabolome profiles in Cuscuta japonica in the HTD and MTD groups. Table S2. Metabolite differences in Cuscuta japonica in MTD vs. _HTD at 24 haa. Table S3. Metabolite differences of C. japonica in MTD vs. _HTD at 72 haa. Table S4. Statistic of RNA-seq data. Table S5. DEGs of MTD vs HTD at 24 haa. Table S6. DEGs of MTD vs HTD at 72 haa.
DOI: 10.25919/c2f1-pj71
title: Parkes observations for project P1139 semester 2022APRS_04

description: We have made the first direct detection of an ultra-long period magnetar, using a low-frequency sky survey performed by the Murchison Widefield Array (MWA). Its radio emission is highly polarised and periodic on a timescale of ~20 minutes. Its dynamic spectrum shows high-fluence narrow-timescale “spikes” which are unresolved by our data, with fluence approaching the Fast Radio Bursts generated by the Galactic centre magnetar. The source was visible in Q1 2018 but has since entered a quiescent state. In the event that it switches back on, we requre follow-up of this source to determine an accurate position, a wide-band spectral index, a precise dispersion measure, determination of its polarisation properties at high frequencies, and to better understand the emission on short timescales. Therefore, we are requesting triggered observations with ATCA and Parkes based on MWA monitoring. This semester we request to extend the triggering criterion to any similar source detected in an upcoming monitoring campaign of the Galactic plane using the MWA.
DOI: 10.34740/kaggle/dsv/4042286
title: Russian Invasion of Ukraine
DOI: 10.6084/m9.figshare.20446017
title: ECE-2022-03-00421-data

description: This data represents the diameter at breast height, specific location, and plot number of two Pinus species in the 25-ha (500 m×500 m) Qinling Huangguan Forest Dynamics Plot in China. Topographic factors, soil basic chemical physicochemical properties of related quadrats.These data were used for point pattern analysis and studies affecting the distribution of tree species and coexistence of congeneric species.
DOI: 10.6084/m9.figshare.20446017.v1
title: ECE-2022-03-00421-data

description: This data represents the diameter at breast height, specific location, and plot number of two Pinus species in the 25-ha (500 m×500 m) Qinling Huangguan Forest Dynamics Plot in China. Topographic factors, soil basic chemical physicochemical properties of related quadrats.These data were used for point pattern analysis and studies affecting the distribution of tree species and coexistence of congeneric species.
DOI: 10.34740/kaggle/dsv/4042344
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.6972733
title: Supporting datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS"

description: This archive contains datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS".
DOI: 10.5281/zenodo.6972732
title: Supporting datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS"

description: This archive contains datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS".
DOI: 10.6084/m9.figshare.20446029
title: Questionnaire for Face Mask Use

description: These data contain items used in the research with the title: <strong>Predictors of face mask use during the COVID-19 pandemic in Indonesia: Application of the health belief model, psychological distress and health motivation</strong>
DOI: 10.6084/m9.figshare.20446374
title: Supplementary Table 1.xlsx

description: Supplementary Table 1:109 huamn Campylobacter coli isolates from 2018 to 2021.
DOI: 10.6084/m9.figshare.20446374.v1
title: Supplementary Table 1.xlsx

description: Supplementary Table 1:109 huamn Campylobacter coli isolates from 2018 to 2021.
DOI: 10.6084/m9.figshare.20446524
title: Supplementary Table 2

description: 200 strains of Campylobacter (2018: n=50, 2019: n=50, 2020: n=50, 2021: n=50) antibiotic susceptibility test results for 11 antibiotics
DOI: 10.6084/m9.figshare.20446524.v1
title: Supplementary Table 2

description: 200 strains of Campylobacter (2018: n=50, 2019: n=50, 2020: n=50, 2021: n=50) antibiotic susceptibility test results for 11 antibiotics
DOI: 10.26183/m9k1-zr06
title: EMNIST: an extension of MNIST to handwritten letters
DOI: 10.5281/zenodo.6972744
title: Application-Oriented Performance Benchmarks for Quantum Computing

description: Complete dataset and Jupyter Notebook used to produce image files for the paper at https://arxiv.org/abs/2110.03137. To execute the notebook, copy the .ipynb file and the _data directory to the top level of the repository at: https://github.com/SRI-International/QC-App-Oriented-Benchmarks
DOI: 10.5281/zenodo.6972743
title: Application-Oriented Performance Benchmarks for Quantum Computing

description: Complete dataset and Jupyter Notebook used to produce image files for the paper at https://arxiv.org/abs/2110.03137. To execute the notebook, copy the .ipynb file and the _data directory to the top level of the repository at: https://github.com/SRI-International/QC-App-Oriented-Benchmarks
DOI: 10.6084/m9.figshare.20446572
title: Supplementary Table 3.xlsx

description: Distribution of drug resistance genes of strains in different years
DOI: 10.6084/m9.figshare.20446572.v1
title: Supplementary Table 3.xlsx

description: Distribution of drug resistance genes of strains in different years
DOI: 10.6084/m9.figshare.20446590
title: Supplementary Table 4.xlsx

description: Drug resistance gene correlation matrix
DOI: 10.6084/m9.figshare.20446590.v1
title: Supplementary Table 4.xlsx

description: Drug resistance gene correlation matrix
DOI: 10.6084/m9.figshare.20446629
title: Supplementary Table 5.xlsx

description: Statistics of ST type and related housekeeping genes of 120 strains of Campylobacter
DOI: 10.6084/m9.figshare.20446629.v1
title: Supplementary Table 5.xlsx

description: Statistics of ST type and related housekeeping genes of 120 strains of Campylobacter
DOI: 10.6084/m9.figshare.20446641
title: Supplementary Table 6.xlsx

description: Prediction statistics of virulence genes of 120 strains of Campylobacter.
DOI: 10.6084/m9.figshare.20446641.v1
title: Supplementary Table 6.xlsx

description: Prediction statistics of virulence genes of 120 strains of Campylobacter.
DOI: 10.5281/zenodo.6940749
title: Dataset for "Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements"

description: The final dataset used in manuscript "Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements" by Kohonen et al. (2022). The dataset contains carbonyl sulfide (COS) and carbon dioxide (CO2) eddy covariance flux data and in-situ meteorological data measured at Hyytiälä forest in Juupajoki, Southern Finland, as well as GPP estimates derived from COS and CO2 flux measurements as described in the manuscript from January 2013 to December 2017. Raw data are available upon request from the author.
DOI: 10.6084/m9.figshare.20446824
title: Untitled Item

description: planilha de codificação
DOI: 10.6084/m9.figshare.20446824.v1
title: Untitled Item

description: planilha de codificação
DOI: 10.34740/kaggle/dsv/4042487
title: WallStreetBets 2022
DOI: 10.5281/zenodo.6972774
title: The global land fAOD dataset (2001–2020) using DLFE-Satellite

description: The fAOD is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Global fAOD can be obtained from point-scale Aerosol Robotic Network (AERONET) measurements, but the spatial coverage is limited. To obtain spatially-continuous global fAODs, we applied the DLFE-Satellite method with Moderate Resolution Imaging Spectroradiometer (MODIS) data, ERA5 climate reanalyses, and AERONET data from 2001 to 2020 as inputs. For independent validation, we used fAOD retrievals obtained from six Surface Radiation budget network (SURFRAD) sites in the USA. Note that SURFRAD sites are in the vicinity of AERONET sites and were not used to train the model. Data from these sites could thus be used for the independent validation of fAOD estimates. The linear relation between DLFE-Satellite and AERONET retrievals had an R value of 0.93 and RMSE of 0.09 μg/m<sup>3 </sup>; for validation with SURFRAD retrievals , R = 0.77 and RMSE = 0.14 μg/m<sup>3</sup>.
DOI: 10.5281/zenodo.6972773
title: The global land fAOD dataset (2001–2020) using DLFE-Satellite

description: The fAOD is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Global fAOD can be obtained from point-scale Aerosol Robotic Network (AERONET) measurements, but the spatial coverage is limited. To obtain spatially-continuous global fAODs, we applied the DLFE-Satellite method with Moderate Resolution Imaging Spectroradiometer (MODIS) data, ERA5 climate reanalyses, and AERONET data from 2001 to 2020 as inputs. For independent validation, we used fAOD retrievals obtained from six Surface Radiation budget network (SURFRAD) sites in the USA. Note that SURFRAD sites are in the vicinity of AERONET sites and were not used to train the model. Data from these sites could thus be used for the independent validation of fAOD estimates. The linear relation between DLFE-Satellite and AERONET retrievals had an R value of 0.93 and RMSE of 0.09 μg/m<sup>3 </sup>; for validation with SURFRAD retrievals , R = 0.77 and RMSE = 0.14 μg/m<sup>3</sup>.
DOI: 10.25439/rmt.15001230.v1
title: The 2018 Australian National Liveability dataset: spatial urban liveability indicators for 21 cities

description: The 2018 Australian National Liveability Study dataset comprises a suite of policy relevant spatial indicators of local neighbourhood liveability and amenity access estimated for residential address points across Australia's 21 largest cities. The indicators and measures included encompass topics including community and health services, employment, food, housing, public open space, transportation, walkability and overall liveability. The address level dataset was produced through analysis of built environment and social data from multiple sources including OpenStreetMap and the Australian Bureau of Statistics, and is provided in geopackage format under an Open Data Commons Open Database licence. The 2018 Australian National Liveability data will be of interest to planners, population health and urban researchers with an interest in the spatial distribution of built environment exposures and outcomes for data linkage, modelling and mapping purposes. Area level summaries for the data were used to create the indicators for the Australian Urban Observatory at its launch in 2020. <br> The spatial data were developed by the Healthy Liveable Cities Lab, Centre for Urban Research with funding support provided from the Australian Prevention Partnership Centre #9100003, NESP Clean Air and Urban Landscapes Hub, NHMRC Centre of Research Excellence in Healthy, Liveable Communities #1061404 and an NHMRC Senior Principal Research Fellowship GNT1107672; with interactive spatial indicator maps accessible via the Australian Urban Observatory. Any publications utilising the data are not necessarily the view of or endorsed by RMIT University or the Centre of Urban Research. RMIT excludes all liability for any reliance on the data.<br>
DOI: 10.6084/m9.figshare.20446962
title: EVIDENCE-BASED ONLINE MODEL PROCTORED EXAMINATION-Google Feedback Form

description: The document contains the questions used in the questionnaire as feedback from students.
DOI: 10.17608/k6.auckland.19618014.v1
title: Raw Data Concept Maps Tutorial Self-Efficacy

description: raw data
DOI: 10.5281/zenodo.6973159
title: Similarity matrix between the core genome sequences

description: Data table.
DOI: 10.5281/zenodo.6973158
title: Similarity matrix between the core genome sequences

description: Data table.
DOI: 10.5281/zenodo.6973165
title: Results of mining Gordonia reference genomes by the antiSMASH tool

description: Data table.
DOI: 10.5281/zenodo.6973164
title: Results of mining Gordonia reference genomes by the antiSMASH tool

description: Data table.
DOI: 10.5281/zenodo.6973171
title: Results of mining G. polyisoprenivorans, G. rubripertincta, and G. terrae genomes by the antiSMASH tool

description: Data table.
DOI: 10.5281/zenodo.6973170
title: Results of mining G. polyisoprenivorans, G. rubripertincta, and G. terrae genomes by the antiSMASH tool

description: Data table.
DOI: 10.5281/zenodo.6973149
title: Roary presence/absence table

description: Data table.
DOI: 10.5281/zenodo.6973148
title: Roary presence/absence table

description: Data table.
DOI: 10.34740/kaggle/dsv/4042676
title: 6 ovarian cancer datasets
DOI: 10.34740/kaggle/dsv/4042703
title: 6 ovarian cancer datasets
DOI: 10.5281/zenodo.6957842
title: Data Cleaning, Translation & Split of the Dataset for the Automatic Classification of Documents for the Classification System for the Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft

description: Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer. Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset. ger_train.csv – The German training set as CSV file. ger_validation.csv – The German validation set as CSV file. en_test.csv – The English test set as CSV file. en_train.csv – The English training set as CSV file. en_validation.csv – The English validation set as CSV file. splitting.py – The python code for splitting a dataset into train, test and validation set. DataSetTrans_de.csv – The final German dataset as a CSV file. DataSetTrans_en.csv – The final English dataset as a CSV file. translation.py – The python code for translating the cleaned dataset.
DOI: 10.5281/zenodo.6957841
title: Data Cleaning, Translation & Split of the Dataset for the Automatic Classification of Documents for the Classification System for the Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft

description: Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer. Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset. ger_train.csv – The German training set as CSV file. ger_validation.csv – The German validation set as CSV file. en_test.csv – The English test set as CSV file. en_train.csv – The English training set as CSV file. en_validation.csv – The English validation set as CSV file. splitting.py – The python code for splitting a dataset into train, test and validation set. DataSetTrans_de.csv – The final German dataset as a CSV file. DataSetTrans_en.csv – The final English dataset as a CSV file. translation.py – The python code for translating the cleaned dataset.
DOI: 10.14459/2021mp1636761.002
title: TUM-FAÇADE

description: The latest, second version of the dataset, provides a clear partition to 'training' and 'test' datasets. Previously non-annotated buildings (ID...58 and ID...59), are now labelled and serve as the 'test' data. Thus, our dataset is enriched with another 3 annotated facades, which translates to extra 138 mln annotated points. The updated statistics presents as follows: 17 annotated facades and 12 non-annotated for further benchmark extension or testing , 256 mln annotated points, in local and global coordinate reference system, settings file for adding your own data. To see allversions of the dataset, check out "Versions" in the bottom of the page
DOI: 10.34740/kaggle/dsv/4042776
title: 6 ovarian cancer datasets