cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26188/22097036
title: Errey collection featuring high resolution TIFF image files

description: The Errey Collection consists of photographs taken in Melbourne, Australia, from the 1968 to 1970 as part of Sophia Errey's PhD thesis at The University of Melbourne. <br> This dataset consists of high-resolution versions of images in the Errey Collection that are already freely available online in lower-resolution forms. <br> The high-resolution images are available on request. <br> A CSV file describing the photos has been included in this dataset for the sake of completness.
DOI: 10.26188/22097036.v1
title: Errey collection featuring high resolution TIFF image files

description: The Errey Collection consists of photographs taken in Melbourne, Australia, from the 1968 to 1970 as part of Sophia Errey's PhD thesis at The University of Melbourne. <br> This dataset consists of high-resolution versions of images in the Errey Collection that are already freely available online in lower-resolution forms. <br> The high-resolution images are available on request. <br> A CSV file describing the photos has been included in this dataset for the sake of completness.
DOI: 10.5281/zenodo.7514255
title: Semi-implicit barotropic mode solver using ForTrilinos in MPAS-O and its test data

description: To run the ForTrilinos-enabled MPAS-O, Download all files Install Trilinos Unzip: tar -xzvf Trilinos.tar.gz cd Trilinos ; mkdir build ; cd build ; cp ../do-configure_gnu ./ Check install directories and options in 'do-configure_gnu' Run 'do-configure_gnu' Trilinos information & installation refer to https://trilinos.github.io/ Install ForTrilinos (inside Trilinos) cd Trilinos/ForTrilinos ; mkdir build ; cd build ; cp ../do-configure_gnu ./ Check Trilinos and ForTrilinos install directories and options in 'do-configure_gnu' Run 'do-configure_gnu' ForTrilinos information & installation refer to https://fortrilinos.readthedocs.io/en/latest/ Install ForTrilinos-enabled MPAS-O Unzip: tar xzvf MPAS-Model_fortrilinos.tar.gz cd MPAS-Model_fortrilinos Check ForTrilinos directories at line 478 (FORTRILINOS_ROOT) in 'Makefile' Install PIO (refer to https://ncar.github.io/ParallelIO/) MAPS-O information & installation refer to https://mpas-dev.github.io/ocean/ocean.html For GNU compiler: make gnu-nersc USE_PIO2=true FORTRILINOS=true Run test cases Example cd MPAS-O_Initial_data/baroclinicEddies/strong_scaling Link a MPAS-O compiled executable to a test case directory: ln -fs MPAS-Model_fortrilinos/ocean_model MPAS-O_Initial_data/baroclinicEddies/strong_scaling/ Link a XML deck (solver configurations) for Trilinos to a test case directory ln -fs xml_decks/no_precond/stratimikos.xml_SCG MPAS-O_Initial_data/baroclinicEddies/strong_scaling/stratimikos.xml Run mpirun -n $N ocean_model If the simulation was successful, you will see: <pre><code>tail -n 1 log.ocean.0000.out Logging complete. Closing file at ...</code></pre> For the global test case, please download here: MPAS-O_V6.0_RRS30to10.tar. Please see http://mpas-dev.github.io for User's Guide, github release page, description of each test case, and more.
DOI: 10.5281/zenodo.7514256
title: Semi-implicit barotropic mode solver using ForTrilinos in MPAS-O and its test data

description: To run the ForTrilinos-enabled MPAS-O, Download all files Install Trilinos Unzip: tar -xzvf Trilinos.tar.gz cd Trilinos ; mkdir build ; cd build ; cp ../do-configure_gnu ./ Check install directories and options in 'do-configure_gnu' Run 'do-configure_gnu' Trilinos information & installation refer to https://trilinos.github.io/ Install ForTrilinos (inside Trilinos) cd Trilinos/ForTrilinos ; mkdir build ; cd build ; cp ../do-configure_gnu ./ Check Trilinos and ForTrilinos install directories and options in 'do-configure_gnu' Run 'do-configure_gnu' ForTrilinos information & installation refer to https://fortrilinos.readthedocs.io/en/latest/ Install ForTrilinos-enabled MPAS-O Unzip: tar xzvf MPAS-Model_fortrilinos.tar.gz cd MPAS-Model_fortrilinos Check ForTrilinos directories at line 478 (FORTRILINOS_ROOT) in 'Makefile' Install PIO (refer to https://ncar.github.io/ParallelIO/) MAPS-O information & installation refer to https://mpas-dev.github.io/ocean/ocean.html For GNU compiler: make gnu-nersc USE_PIO2=true FORTRILINOS=true Run test cases Example cd MPAS-O_Initial_data/baroclinicEddies/strong_scaling Link a MPAS-O compiled executable to a test case directory: ln -fs MPAS-Model_fortrilinos/ocean_model MPAS-O_Initial_data/baroclinicEddies/strong_scaling/ Link a XML deck (solver configurations) for Trilinos to a test case directory ln -fs xml_decks/no_precond/stratimikos.xml_SCG MPAS-O_Initial_data/baroclinicEddies/strong_scaling/stratimikos.xml Run mpirun -n $N ocean_model If the simulation was successful, you will see: <pre><code>tail -n 1 log.ocean.0000.out Logging complete. Closing file at ...</code></pre> For the global test case, please download here: MPAS-O_V6.0_RRS30to10.tar. Please see http://mpas-dev.github.io for User's Guide, github release page, description of each test case, and more.
DOI: 10.5281/zenodo.7084789
title: Dataset for Barotropic tides in MPAS-Ocean (E3SM V2): impact of ice shelf cavities (GMD, 2023)

description: This is the data set containing initial condition and parameter files for the results in our paper.
DOI: 10.6084/m9.figshare.22041212
title: MDDSymptoms

description: All summary statistics have the following columns: CHR: chromosome SNP: rsID of SNP POS: base-pair coordinate A1: effect allele A2: other allele MAF: minor allele frequency OR: odds ratio of effect allele SE: standard error of log(OR) P: P value of association N: sample size (which may differ between different SNPs due to missing genotypes at SNPs) <br> GWAS summary statistics are available for the following phenotypes: 1. PHQ9A1-A9: MDD symptoms in UK Biobank, based on PHQ9. 2. WorstEpisodeA1-A9 (no A5): WorstEpisode symptoms in UK Biobank, based on CIDI-SF. 3. dsPHQ9A3, A4, A6, A7, A8: PHQ9 symptoms down sampled to the same effective sample size as corresponding WorstEpisode. 4. dsWorstEpisodeA1, A2, A9: WorstEpisode symptoms down sampled to the same effective sample size as corresponding PHQ9 symptoms. 5. PHQ9SkipA3-A9: PHQ9 symptoms with a skip structure. LifetimeMDD: lifetime MDD phenotype in 67K individuals in UK Biobank, as described in Cai et al 2020 Nature Genetics.
DOI: 10.6084/m9.figshare.22041212.v1
title: MDDSymptoms

description: All summary statistics have the following columns: CHR: chromosome SNP: rsID of SNP POS: base-pair coordinate A1: effect allele A2: other allele MAF: minor allele frequency OR: odds ratio of effect allele SE: standard error of log(OR) P: P value of association N: sample size (which may differ between different SNPs due to missing genotypes at SNPs) <br> GWAS summary statistics are available for the following phenotypes: 1. PHQ9A1-A9: MDD symptoms in UK Biobank, based on PHQ9. 2. WorstEpisodeA1-A9 (no A5): WorstEpisode symptoms in UK Biobank, based on CIDI-SF. 3. dsPHQ9A3, A4, A6, A7, A8: PHQ9 symptoms down sampled to the same effective sample size as corresponding WorstEpisode. 4. dsWorstEpisodeA1, A2, A9: WorstEpisode symptoms down sampled to the same effective sample size as corresponding PHQ9 symptoms. 5. PHQ9SkipA3-A9: PHQ9 symptoms with a skip structure. LifetimeMDD: lifetime MDD phenotype in 67K individuals in UK Biobank, as described in Cai et al 2020 Nature Genetics.
DOI: 10.5281/zenodo.7641894
title: A comparison and coupling of two novel sensors for intertidal bed-level dynamics observation

description: Raw datasets for paper "A comparison and coupling of two novel sensors for intertidal bed-level dynamics observation" published on Limnology and Oceanography: Methods
DOI: 10.5281/zenodo.7641893
title: A comparison and coupling of two novel sensors for intertidal bed-level dynamics observation

description: Raw datasets for paper "A comparison and coupling of two novel sensors for intertidal bed-level dynamics observation" published on Limnology and Oceanography: Methods
DOI: 10.5281/zenodo.7641950
title: Solidity Smart Contract

description: Dataset2: manual labeled smart contracts with Reentrancy, Unchecked Call, and TxOrigin vulnerabilities.<br> Dataset3: 361 real-world smart contracts with Reentrancy, Unchecked Call, and TxOrigin vulnerabilities.
DOI: 10.5281/zenodo.7641949
title: Solidity Smart Contract

description: Dataset2: manual labeled smart contracts with Reentrancy, Unchecked Call, and TxOrigin vulnerabilities.<br> Dataset3: 361 real-world smart contracts with Reentrancy, Unchecked Call, and TxOrigin vulnerabilities.
DOI: 10.5281/zenodo.7622682
title: Dataset for paper Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda "Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model" submitted to JAMES.

description: The dataset consists of the directory tree of .zarr archives. See Github repository for the description of the dataset. The directory tree is: <pre><code>├── eddy │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires ├── jet │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires</code></pre> Every individual dataset is a <code>.zarr</code> archive <code>eddy/jet</code> - configuration of the pyqg; eddy is default; See Ross2022 for description <code>hires.zarr</code> - high-resolution simulation at 256x256 grid <code>48/64/96</code> - resolution of the coarse models <code>lores.zarr</code> - low-resolution simulation <code>gauss.zarr</code>, <code>sharp.zarr</code> - training datasets for prediction of subgrid forcing obtained with Gaussian or Sharp filters <code>hires-gauss.zarr</code>, <code>hires-sharp.zarr</code> - high-resolution simulation projected onto coarse grid with Gaussian or Sharp filters The directory tree is split into small tar.gz files each representing a separate .zarr archive. Download any required parts of the dataset and unpack with: <strong>tar -xf *.tar.gz </strong> <strong>The directory tree will be restored automatically!</strong>
DOI: 10.5281/zenodo.7638125
title: High-Resolution Daily Precipitation Data over the Philippines (Alcantara and Ahn, 2023)

description: The high-resolution nationwide 20-year (2001-2020) daily precipitation data over the Philippines generated by Alcantara and Ahn (2023) is a dataset that provides daily precipitation data for the entire country with a spatial resolution of 0.1° x 0.1°. This dataset is created using the Quadruple Collocation (QC) approach, which combines four parent datasets - ERA5, PERSIANN, CHIRPS, and GPM - to produce a more accurate dataset than any of the individual parent datasets. The dataset is available in NetCDF format (version 4), which is a standardized data format commonly used for the exchange of large data files. This format can be read using major programming languages such as R, Python, C++, and others, making it easily accessible for use in various research applications. With its high spatial and temporal resolution, the dataset can be a valuable resource for climate research, hydrological modeling, and other related fields. In the future, data from 2021 up to present will also be added.
description: {"references": ["Alcantara, A. L., & Ahn, K. H. (2023). Improving Daily Precipitation Estimates over the Philippines through the Quadruple Collocation-Based Error Estimation and Data Merging Approach. Journal of Hydrology. Manuscript submitted for publication."]}
DOI: 10.5281/zenodo.7638124
title: High-Resolution Daily Precipitation Data over the Philippines (Alcantara and Ahn, 2023)

description: The high-resolution nationwide 20-year (2001-2020) daily precipitation data over the Philippines generated by Alcantara and Ahn (2023) is a dataset that provides daily precipitation data for the entire country with a spatial resolution of 0.1° x 0.1°. This dataset is created using the Quadruple Collocation (QC) approach, which combines four parent datasets - ERA5, PERSIANN, CHIRPS, and GPM - to produce a more accurate dataset than any of the individual parent datasets. The dataset is available in NetCDF format (version 4), which is a standardized data format commonly used for the exchange of large data files. This format can be read using major programming languages such as R, Python, C++, and others, making it easily accessible for use in various research applications. With its high spatial and temporal resolution, the dataset can be a valuable resource for climate research, hydrological modeling, and other related fields. In the future, data from 2021 up to present will also be added.
description: {"references": ["Alcantara, A. L., & Ahn, K. H. (2023). Improving Daily Precipitation Estimates over the Philippines through the Quadruple Collocation-Based Error Estimation and Data Merging Approach. Journal of Hydrology. Manuscript submitted for publication."]}
DOI: 10.6084/m9.figshare.22098961
title: Experimental-trajectories-high-flow-rate.zip from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: Raw data of the simulation trajectories in the high flow rate regime
DOI: 10.6084/m9.figshare.22098961.v1
title: Experimental-trajectories-high-flow-rate.zip from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: Raw data of the simulation trajectories in the high flow rate regime
DOI: 10.6084/m9.figshare.22098967
title: Bioreactor_for_waterbath_plus_sampling_1ml_one.stl from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: STL file for 3D printing of the mini-bioreactor
DOI: 10.6084/m9.figshare.22098967.v1
title: Bioreactor_for_waterbath_plus_sampling_1ml_one.stl from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: STL file for 3D printing of the mini-bioreactor
DOI: 10.6084/m9.figshare.22098976
title: colicin_model_CE.jl from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: Julia codes of the individual-based simulation model
DOI: 10.6084/m9.figshare.22098976.v1
title: colicin_model_CE.jl from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: Julia codes of the individual-based simulation model
DOI: 10.6084/m9.figshare.22098979
title: Experimental-trajectories-low-flow-rate.zip from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: Raw data of the simulation trajectories in the low flow rate regime
DOI: 10.6084/m9.figshare.22098979.v1
title: Experimental-trajectories-low-flow-rate.zip from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: Raw data of the simulation trajectories in the low flow rate regime
DOI: 10.6084/m9.figshare.22098988
title: colicin_model.jl from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: Julia codes for plotting the simulation results presented in the paper
DOI: 10.6084/m9.figshare.22098988.v1
title: colicin_model.jl from Nutrients and flow shape the cyclic dominance games between <i>Escherichia coli</i> strains

description: Julia codes for plotting the simulation results presented in the paper
DOI: 10.6084/m9.figshare.22099021
title: Mathematica code for the simulation model from Hermaphroditic origins of anisogamy

description: Anisogamy—the size dimorphism of gametes—is the defining difference between the male and female sexual strategies. Game-theoretic thinking led to the first convincing explanation for the evolutionary origins of anisogamy in the 1970s. Since then, formal game-theoretic models have continued to refine our understanding of when and why anisogamy should evolve. Such models typically presume that the earliest anisogamous organisms had separate sexes. However, in most taxa, there is no empirical evidence to support this assumption. Here, we present a model of the coevolution of gamete size and sex allocation, which allows for anisogamy to emerge alongside either hermaphroditism or separate sexes. We show that hermaphroditic anisogamy can evolve directly from isogamous ancestors when the average size of spawning groups is small and fertilization is relatively efficient. Sex allocation under hermaphroditism becomes increasingly female-biased as group size decreases and the degree of anisogamy increases. When spawning groups are very small, our model also predicts the existence of complex isogamous organisms in which individuals allocate resources equally to two large gamete types. We discuss common, but potentially unwarranted, assumptions in the literature that could be relaxed in future models.This article is part of the theme issue ‘Half a century of evolutionary games: a synthesis of theory, application and future directions’.
DOI: 10.6084/m9.figshare.22099021.v1
title: Mathematica code for the simulation model from Hermaphroditic origins of anisogamy

description: Anisogamy—the size dimorphism of gametes—is the defining difference between the male and female sexual strategies. Game-theoretic thinking led to the first convincing explanation for the evolutionary origins of anisogamy in the 1970s. Since then, formal game-theoretic models have continued to refine our understanding of when and why anisogamy should evolve. Such models typically presume that the earliest anisogamous organisms had separate sexes. However, in most taxa, there is no empirical evidence to support this assumption. Here, we present a model of the coevolution of gamete size and sex allocation, which allows for anisogamy to emerge alongside either hermaphroditism or separate sexes. We show that hermaphroditic anisogamy can evolve directly from isogamous ancestors when the average size of spawning groups is small and fertilization is relatively efficient. Sex allocation under hermaphroditism becomes increasingly female-biased as group size decreases and the degree of anisogamy increases. When spawning groups are very small, our model also predicts the existence of complex isogamous organisms in which individuals allocate resources equally to two large gamete types. We discuss common, but potentially unwarranted, assumptions in the literature that could be relaxed in future models.This article is part of the theme issue ‘Half a century of evolutionary games: a synthesis of theory, application and future directions’.
DOI: 10.34740/kaggle/dsv/5001464
title: Ubaya COVID-19 Voice Dataset
DOI: 10.5519/qd.zapuoe2v
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10 records)
DOI: 10.26186/147683
title: Tasmanian Tiers Airborne Magnetic, Radiometric and Digital Elevation Survey, TAS, 2021, (P5003), TMI RTP , first vertical derivative grid

description: Total magnetic intensity (TMI) data measures variations in the intensity of the Earth's magnetic field caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. The data are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose. . This Tasmanian Tiers Airborne Magnetic, Radiometric and Digital Elevation Survey, TAS, 2021, (P5003), TMI RTP , first vertical derivative grid is the first vertical derivative of the TMI RTP grid of the Tasmanian Tiers Magnetic and Radiometric Survey, 2021 survey. This grid has a cell size of degrees (approximately 40m) , and given in units of nT per metre (nT/m). The data used to produce the TMI grid was acquired in 2021 by the TAS Government, and consisted of 32951 line-kilometres of data at 200m line spacing and 80m terrain clearance. The data has had a variable reduction to the pole applied to centre the magnetic anomaly over the magnetised body. The VRTP processing followed a differential reduction to pole calculation up to 5th order polynomial. Magnetic inclination and declination were derived from the IGRF-11 geomagnetic reference model using a data representative date and elevation representative of the survey. A first vertical derivative was calculated by applying a fast Fourier transform (FFT) process to the TMI RTP grid of the Tasmanian Tiers Magnetic and Radiometric Survey, 2021 survey to produce this grid. This grid was calculated using an algorithm from the INTREPID Geophysics software package. This grid shows the magnetic response of subsurface features with contrasting magnetic susceptibilities. The grid can also be used to locate structural features such as dykes.
DOI: 10.6084/m9.figshare.22099252
title: Landmarks partitioning for distance matrix approach from Whole-body variational modularity in the zebrafish: an inside-out story of a model species

description: A text file named "partition_data" containing the partitioning of the landmarks for the distance matrix analyses required in the R script lines 361 and 366.
DOI: 10.6084/m9.figshare.22099252.v1
title: Landmarks partitioning for distance matrix approach from Whole-body variational modularity in the zebrafish: an inside-out story of a model species

description: A text file named "partition_data" containing the partitioning of the landmarks for the distance matrix analyses required in the R script lines 361 and 366.
DOI: 10.6084/m9.figshare.22099261
title: Data for network representations from Whole-body variational modularity in the zebrafish: an inside-out story of a model species

description: Zip folder containing data for network representations of hypotheses 3 and 5
DOI: 10.6084/m9.figshare.22099261.v1
title: Data for network representations from Whole-body variational modularity in the zebrafish: an inside-out story of a model species

description: Zip folder containing data for network representations of hypotheses 3 and 5
DOI: 10.6084/m9.figshare.22099264
title: R Code from Whole-body variational modularity in the zebrafish: an inside-out story of a model species

description: R script
DOI: 10.6084/m9.figshare.22099264.v1
title: R Code from Whole-body variational modularity in the zebrafish: an inside-out story of a model species

description: R script
DOI: 10.6084/m9.figshare.22094861
title: VISA_NIBMG.zip

description: Analysis of gene expression data of <em>S. aureus </em>in response to stress and with vancomycin-intermediate resistance (VISA). Step 1: Download the file and save it in your system. Unzip and extract all the files in the desired location. Step 2: Start R (we used version 4.1.0), and Change the directory to this location. Step 3: Run the script <em>analyse.R</em> The folder <strong>VISA_NIBMG</strong> contains files with the GSE IDs of all datasets analyzed, which consists of a <em>getData.R</em> file, which is used to make the expression set for each dataset. Another folder named <strong>Rcode</strong> consists of a folder called <strong>funcDefs</strong>, which has all the function definitions used in the main <em>analyse.R</em> file. All files in <strong>Rcode</strong> folder are called by the <em>analyse.R</em> file. <strong>Metadata</strong> folder contains all the additional information needed to run the <em>analyse.R</em> file