cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7764656
title: Dataset for "Species diversity and molecular characterization of Alternaria section Alternaria isolates collected mainly from cereal crops in Canada" by Jeremy R. Dettman, Quinn Eggertson, and Natalie E. Kim

description: Dataset consists of three files, each containing aligned nucleotide sequences from 559 Alternaria strains. The three sequenced loci are ASA-10, ASA-19, and rpb2. Strain names are stated in the header of each sequence.
DOI: 10.48567/qskd-hy88
title:  Year of Polar Prediction in the South Hemisphere: AEMET-γSREPS model output, 2022.
DOI: 10.5281/zenodo.7464182
title: Dataset for "A shift in plant growth strategy is caused by increased nutrient requirements due to polyploidy in Heuchera cylindrica".

description: Dataset for "A shift in plant growth strategy is caused by increased nutrient requirements due to polyploidy in <em>Heuchera cylindrica</em>".
DOI: 10.5281/zenodo.7464183
title: Dataset for "A shift in plant growth strategy is caused by increased nutrient requirements due to polyploidy in Heuchera cylindrica".

description: Dataset for "A shift in plant growth strategy is caused by increased nutrient requirements due to polyploidy in <em>Heuchera cylindrica</em>".
DOI: 10.5281/zenodo.7041377
title: supplementary data for Bemelmans et al., 2023, High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia.

description: This repository contains the supplementary materials for the paper "High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia." to be published in JGR: Solid Earth. The dataset contains files assiciated with the StaMPS time series processing. Each dataset has its own folder containing: *_data.csv : data file containing latitude, longitude, incidence angle, heading and LOS displacement for each acquistion (date is listed in the column name in the format yyyymmdd). parms.mat : parameter file used for StaMPS processing of that dataset The dataset also contains input and results from the GBIS modelling (/GBIS_results/). the *.inp files are the input files for each model inversion where the letter ('M','T','P','Y', or 'D') refer to the Mogi (point), McTigue (sphere), penny-shaped crack, Yang (ellipsoid), and dyke (also sill) model used for that run. folders with the same name as the *.inp file contain the inversion results (invert_*.mat), a summary table (summary_*.txt) and several figures showing the distribution and convergence of the model inversion. The input for the GBIS inversions is stored in /GBIS_results/INSAR_input/ the file matlab_functions.zip contains matlab functions used for data processing, visualisation, storage and conversion. the file GBISv1_1_MJWB.zip contains GBIS code adapted by the author to perform statistical analysis of the model inversion, perform region-of-interest based subsampling and store modeled results as shapefiles for further processing.
DOI: 10.5281/zenodo.7041376
title: supplementary data for Bemelmans et al., 2023, High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia.

description: This repository contains the supplementary materials for the paper "High-resolution InSAR reveals localised pre-eruptive deformation inside the crater of Agung volcano, Indonesia." to be published in JGR: Solid Earth. The dataset contains files assiciated with the StaMPS time series processing. Each dataset has its own folder containing: *_data.csv : data file containing latitude, longitude, incidence angle, heading and LOS displacement for each acquistion (date is listed in the column name in the format yyyymmdd). parms.mat : parameter file used for StaMPS processing of that dataset The dataset also contains input and results from the GBIS modelling (/GBIS_results/). the *.inp files are the input files for each model inversion where the letter ('M','T','P','Y', or 'D') refer to the Mogi (point), McTigue (sphere), penny-shaped crack, Yang (ellipsoid), and dyke (also sill) model used for that run. folders with the same name as the *.inp file contain the inversion results (invert_*.mat), a summary table (summary_*.txt) and several figures showing the distribution and convergence of the model inversion. The input for the GBIS inversions is stored in /GBIS_results/INSAR_input/ the file matlab_functions.zip contains matlab functions used for data processing, visualisation, storage and conversion. the file GBISv1_1_MJWB.zip contains GBIS code adapted by the author to perform statistical analysis of the model inversion, perform region-of-interest based subsampling and store modeled results as shapefiles for further processing.
DOI: 10.6084/m9.figshare.22320847
title: Fig 3C - Abundances

description: Data plotted in Figure 3C of Hesketh-Best and Bosco-Santos et al. 2023.
DOI: 10.6084/m9.figshare.22320847.v1
title: Fig 3C - Abundances

description: Data plotted in Figure 3C of Hesketh-Best and Bosco-Santos et al. 2023.
DOI: 10.6084/m9.figshare.22320841
title: Fig 3A - Auxiliary Metabolic Genes

description: Data plotted in Figure 3A of Hesketh-Best and Bosco-Santos et al. 2023.
DOI: 10.6084/m9.figshare.22320844
title: Fig 3B - gene annotations

description: Data plotted in Figure 3B of Hesketh-Best and Bosco-Santos et al. 2023.
DOI: 10.6084/m9.figshare.22320841.v1
title: Fig 3A - Auxiliary Metabolic Genes

description: Data plotted in Figure 3A of Hesketh-Best and Bosco-Santos et al. 2023.
DOI: 10.5281/zenodo.7749514
title: Real world networks for network classification method evaluation

description: This research presents a set of network databases based on real-world data, aimed at evaluating the effectiveness of network classification methods. The first database, called the "Social database," consists of networks from the Stanford Network Analysis Project (SNAP) platform, including two classes, namely, Google+ and Twitter, we have made some preprocessment and also reduced the original dataset from the SNAP platform such that each class contains 50 networks. The other databases presented in this study are collectively known as the "Metabolic database," as they are constructed using the substrate-product network model and are based on biochemical reactions of organisms obtained from the Kyoto Encyclopedia of Genes and Genomes database (KEGG). The networks were generated using a model that considers metabolites as vertices and chemical reactions as edges. The Metabolic database comprises six classification schemes, which include the "kingdom-database" with 160 network samples, where each of the four classes contains 40 networks representing animal, plant, fungi, and protist kingdoms. The remaining databases in the Metabolic database are the "Animal-database," "Fungi-database," "Plant-database," "Firmicutes-Bacilis-database," and "Actinobacteria-database," each containing a varying number of network samples.<br> <br> To ensure consistency and comparability of our results, we implemented a standardization procedure whereby all networks were converted to adjacency list format. This allowed us to more efficiently process and analyze the data.
DOI: 10.6084/m9.figshare.22320844.v1
title: Fig 3B - gene annotations

description: Data plotted in Figure 3B of Hesketh-Best and Bosco-Santos et al. 2023.
DOI: 10.6084/m9.figshare.22320838
title: Fig1C and D - bacterial taxonomic annotations

description: Data used for plotting Figures 1C and 1D in Hesketh-Best and Bosco-Santos et al. 2023.
DOI: 10.6084/m9.figshare.22320838.v1
title: Fig1C and D - bacterial taxonomic annotations

description: Data used for plotting Figures 1C and 1D in Hesketh-Best and Bosco-Santos et al. 2023.
DOI: 10.5281/zenodo.7749513
title: Real world networks for network classification method evaluation

description: This research presents a set of network databases based on real-world data, aimed at evaluating the effectiveness of network classification methods. The first database, called the "Social database," consists of networks from the Stanford Network Analysis Project (SNAP) platform, including two classes, namely, Google+ and Twitter, we have made some preprocessment and also reduced the original dataset from the SNAP platform such that each class contains 50 networks. The other databases presented in this study are collectively known as the "Metabolic database," as they are constructed using the substrate-product network model and are based on biochemical reactions of organisms obtained from the Kyoto Encyclopedia of Genes and Genomes database (KEGG). The networks were generated using a model that considers metabolites as vertices and chemical reactions as edges. The Metabolic database comprises six classification schemes, which include the "kingdom-database" with 160 network samples, where each of the four classes contains 40 networks representing animal, plant, fungi, and protist kingdoms. The remaining databases in the Metabolic database are the "Animal-database," "Fungi-database," "Plant-database," "Firmicutes-Bacilis-database," and "Actinobacteria-database," each containing a varying number of network samples.<br> <br> To ensure consistency and comparability of our results, we implemented a standardization procedure whereby all networks were converted to adjacency list format. This allowed us to more efficiently process and analyze the data.
DOI: 10.5281/zenodo.7749500
title: Synthetic networks for classification methods

description: The current study presents a collection of synthetic network databases intended for evaluating classification methods. The first database, termed "4-models," comprises synthetic networks that have been generated based on four distinct network classes, namely, Random, Small-world, Scale-free, and Geographical. This database includes 2800 networks for each class, resulting in a total of 11,200 networks. The second database, named "Scale-free," is composed of scale-free synthetic models generated using the models proposed by Barabási and Albert and by Dorogovstev and Mendes. This database includes five classes, comprising four Barabasi models based on the network's power law parameter and a Mendes model, each class consisting of 100 networks. The third database, named "Noisy," is created by modifying the topology of the networks from the 4-models and Scale-free databases via the addition or removal of edges. This database contains three sub-datasets, based on the level of randomness added to the networks, and eight different classes, each of which includes 100 networks. To ensure consistency and comparability of our results, we implemented a standardization procedure whereby all databases were converted to adjacency list format. This allowed us to more efficiently process and analyze the data.
DOI: 10.5517/ccdc.csd.cc294g0y
title: CCDC 2119439: Experimental Crystal Structure Determination

description: Related Article: Yan Mao, Wenxi Chen, Changchang Li, Lin Miao, Yanfei Lin, Fei Ling, Zhangpei Chen, Jinzhong Yao|2023|Chem.Commun.|59|3775|doi:10.1039/D3CC00017F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7749499
title: Synthetic networks for classification methods

description: The current study presents a collection of synthetic network databases intended for evaluating classification methods. The first database, termed "4-models," comprises synthetic networks that have been generated based on four distinct network classes, namely, Random, Small-world, Scale-free, and Geographical. This database includes 2800 networks for each class, resulting in a total of 11,200 networks. The second database, named "Scale-free," is composed of scale-free synthetic models generated using the models proposed by Barabási and Albert and by Dorogovstev and Mendes. This database includes five classes, comprising four Barabasi models based on the network's power law parameter and a Mendes model, each class consisting of 100 networks. The third database, named "Noisy," is created by modifying the topology of the networks from the 4-models and Scale-free databases via the addition or removal of edges. This database contains three sub-datasets, based on the level of randomness added to the networks, and eight different classes, each of which includes 100 networks. To ensure consistency and comparability of our results, we implemented a standardization procedure whereby all databases were converted to adjacency list format. This allowed us to more efficiently process and analyze the data.
DOI: 10.5517/ccdc.csd.cc299xzj
title: CCDC 2124708: Experimental Crystal Structure Determination

description: Related Article: Jinyuan Jiang, Jidan Liu, Zhenke Yang, Liyao Zheng, Zhao-Qing Liu|2022|Adv.Synth.Catal.|364|2540|doi:10.1002/adsc.202200396
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.4424865
title: Dynamic and membrane DAC ramp dataset storage, raw and azimuthal integrated as xy.files.

description: The datasets are part of a project exploring the onset of phase transitions, the development of microstrain and the lattice parameters during fast compression rates, resembling those during e.g. propagation seismic shockwaves and large body impacts. The strain rate conditions achieved in our experiments are between those reached in the common static (DAC) and shock experiments. <br> The current datasets contain a selected set of xy.files of azimuthal integrated 2D-diffraction images of either Mg0.2Fe0.8O sample or Fe (Iron) - with and without Pt (platinum) pressure marker and with and without Ne (Neon) pressure transmitting medium (PTM).<br> Diffraction images were collected at ambient temperature (in 2019 and 2020) at the ECB P02.2 Beamline, PETRA III, DESY, Germany using the piezo-driven dynamic diamond anvil cells (dDAC) in combination with fast LAMBDA GaAs 2M detectors at 25.6 keV (0.4843 Å).<br> <br> The datasets are provided as test datasets for machine learning application on spectra classification.<br> The version will be further updated once the whole work has been published and the full datasets can be made available.<br> Please find below the link towards the machine learning application. https://github.com/European-XFEL-examples/panosc-ml-spectra-classification
DOI: 10.5281/zenodo.4424866
title: Dynamic and membrane DAC ramp dataset storage, raw and azimuthal integrated as xy.files.

description: The dataset is part of a project exploring the onset of phase transitions, the development of microstrain and the lattice parameters during fast compression rates, resembling those during e.g. propagation seismic shockwaves and large body impacts. The strain rate conditions achieved in our experiments are between those reached in the common static (DAC) and shock experiments. <br> The current dataset contains a selected set of xy.files of azimuthal integrated 2D-diffraction images of an Mg0.2Fe0.8O sample.<br> Diffraction images were collected at ambient temperature (in 2019) at the ECB P02.2 Beamline, PETRA III, DESY, Germany<br> using the piezo-driven dynamic diamond anvil cells (dDAC) in combination with fast LAMBDA GaAs 2M detectors at 25.6 keV (0.4843 Å).<br> <br> The dataset is provided as a test dataset for machine learning application on spectra classification.<br> The version will be updated once the whole work has been published and the full datasets can be made available.<br> Please find below the link towards the machine learning application. https://github.com/European-XFEL-examples/panosc-ml-spectra-classification
DOI: 10.5281/zenodo.7764815
title: Dynamic and membrane DAC ramp dataset storage, raw and azimuthal integrated as xy.files.

description: The datasets are part of a project exploring the onset of phase transitions, the development of microstrain and the lattice parameters during fast compression rates, resembling those during e.g. propagation seismic shockwaves and large body impacts. The strain rate conditions achieved in our experiments are between those reached in the common static (DAC) and shock experiments. <br> The current datasets contain a selected set of xy.files of azimuthal integrated 2D-diffraction images of either Mg0.2Fe0.8O sample or Fe (Iron) - with and without Pt (platinum) pressure marker and with and without Ne (Neon) pressure transmitting medium (PTM).<br> Diffraction images were collected at ambient temperature (in 2019 and 2020) at the ECB P02.2 Beamline, PETRA III, DESY, Germany using the piezo-driven dynamic diamond anvil cells (dDAC) in combination with fast LAMBDA GaAs 2M detectors at 25.6 keV (0.4843 Å).<br> <br> The datasets are provided as test datasets for machine learning application on spectra classification.<br> The version will be further updated once the whole work has been published and the full datasets can be made available.<br> Please find below the link towards the machine learning application. https://github.com/European-XFEL-examples/panosc-ml-spectra-classification
DOI: 10.5517/ccdc.csd.cc29brs6
title: CCDC 2125508: Experimental Crystal Structure Determination

description: Related Article: Daniel Bindl, Pradeep K. Mandal, Ivan Huc|2022|Chem.-Eur.J.|28|e202200538|doi:10.1002/chem.202200538
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29bs0g
title: CCDC 2125515: Experimental Crystal Structure Determination

description: Related Article: Daniel Bindl, Pradeep K. Mandal, Ivan Huc|2022|Chem.-Eur.J.|28|e202200538|doi:10.1002/chem.202200538
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7764528
title: Air pollution datasets

description: Processed air pollution datasets originally obtained from the open data portal of the Madrid City Hall. The pollutants include: Fine particulate matter: <strong>PM<sub>2.5</sub></strong> Coarse particulate matter: <strong>PM<sub>10</sub></strong> Ozone: <strong>O<sub>3</sub></strong> Nitrogen monoxide: <strong>NO</strong> Nitrogen dioxide: <strong>NO<sub>2</sub></strong> Nitrogen oxides: <strong>NOx</strong> Sulfur dioxide: <strong>SO<sub>2</sub></strong> Carbon monoxide: <strong>CO</strong> Toluene: <strong>TOL</strong> Benzene: <strong>BEN</strong> Ethylbenzene: <strong>EBE</strong> The period covered goes from the 1<sup>st</sup> of January 2010 to the 30<sup>th</sup> of April 2022. Each pollutant is recorded by a variable number of sensors, between 6 and 24 of them (additional information here). They cover Madrid city and surroundings (see this map). Additional information about the pollutants can be found here. The specific datasets in HDF5 format are: <strong>01h_flat_raw.h5</strong>: Hourly raw data, one table per pollutant. The first column is the timestamp, which are in time-since-epoch. 108049 rows and between 7 and 25 columns. <strong>01h_35x30_norm_linear_J0.0.h5</strong>: Hourly mesh-grid data, normalized and linearly interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_norm_nearest_J0.0.h5</strong>: Hourly mesh-grid data, normalized and nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_raw_linear_J0.0.h5</strong>: Hourly mesh-grid data, linearly interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_raw_nearest_J0.0.h5</strong>: Hourly mesh-grid data, nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_stand_linear_J0.0.h5</strong>: Hourly mesh-grid data, standardized and linearly interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_stand_nearest_J0.0.h5</strong>: Hourly mesh-grid data, standardized and nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11). This datasets are prepared to work with the framework published at https://github.com/iipr/air-quality
DOI: 10.17632/tkxc2m29rj.1
title: FCPT and WTCH (Angie)

description: Data from Angie's thesis.
DOI: 10.5281/zenodo.7764527
title: Air pollution datasets

description: Processed air pollution datasets originally obtained from the open data portal of the Madrid City Hall. The pollutants include: Fine particulate matter: <strong>PM<sub>2.5</sub></strong> Coarse particulate matter: <strong>PM<sub>10</sub></strong> Ozone: <strong>O<sub>3</sub></strong> Nitrogen monoxide: <strong>NO</strong> Nitrogen dioxide: <strong>NO<sub>2</sub></strong> Nitrogen oxides: <strong>NOx</strong> Sulfur dioxide: <strong>SO<sub>2</sub></strong> Carbon monoxide: <strong>CO</strong> Toluene: <strong>TOL</strong> Benzene: <strong>BEN</strong> Ethylbenzene: <strong>EBE</strong> The period covered goes from the 1<sup>st</sup> of January 2010 to the 30<sup>th</sup> of April 2022. Each pollutant is recorded by a variable number of sensors, between 6 and 24 of them (additional information here). They cover Madrid city and surroundings (see this map). Additional information about the pollutants can be found here. The specific datasets in HDF5 format are: <strong>01h_flat_raw.h5</strong>: Hourly raw data, one table per pollutant. The first column is the timestamp, which are in time-since-epoch. 108049 rows and between 7 and 25 columns. <strong>01h_35x30_norm_linear_J0.0.h5</strong>: Hourly mesh-grid data, normalized and linearly interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_norm_nearest_J0.0.h5</strong>: Hourly mesh-grid data, normalized and nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_raw_linear_J0.0.h5</strong>: Hourly mesh-grid data, linearly interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_raw_nearest_J0.0.h5</strong>: Hourly mesh-grid data, nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_stand_linear_J0.0.h5</strong>: Hourly mesh-grid data, standardized and linearly interpolated. Single table of shape: (108049, 35, 30, 11). <strong>01h_35x30_stand_nearest_J0.0.h5</strong>: Hourly mesh-grid data, standardized and nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11). This datasets are prepared to work with the framework published at https://github.com/iipr/air-quality
DOI: 10.17632/n3ccmv7rmv.2
title: Pornography and gender normative beliefs

description: This study (N = 1,440, Mage = 23.86, SD = 4.79) adds important insights to the literature by investigating the indirect association between pornography consumption and gender normative beliefs through gendered attitudes, and the moderating role of gender typicality. Gender normative beliefs were measured via vignettes describing a school context, in which a teacher and a student violated gender norms. Findings indicated that more gendered attitudes associate with more gender normative beliefs. Furthermore, compared to women, men’s pornography consumption indirectly related to gender normative beliefs through gendered attitudes. Specific levels of gender typicality and gender atypicality form a strengthening and buffering role, respectively.