cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25573/data.21422193.v8
title: BHL Optical Character Recognition (OCR) - Full Text Export (new)

description: The dataset contains a full export of the 60+ million pages of OCR content in the Biodiversity Heritage Library, for items hosted by BHL. For contextual information and key definitions about this dataset see the <strong>Biodiversity Heritage Library Open Data Collection</strong> and the data dictionary below. <strong>Data Dictionary: </strong>s.si.edu/bhlocrtxt <strong>Release Date: </strong>the 17th of each month <strong>Frequency: </strong>Monthly <strong>bureauCode:</strong> 452:11 <strong>Access Level:</strong> public
DOI: 10.34740/kaggle/dsv/5729102
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.7950024
title: A new non-linear instability for scalar fields

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "A new non-linear instability for scalar fields" (https://arxiv.org/abs/2107.14215). <br> This paper has also been published as a <em>letter </em>in Physical Review D and can be found in Volume 105, Issue 2, article id.L021304, which can be accessed at this link: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.L021304. Directories <strong>Codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution and Latfield2. <strong>Simulations_data</strong>: This directory includes the data for the scalar field snapshots, information about the average of the scalar field, and power spectra. <strong>Figure_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>Figure_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "codes" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "data" directory to access the simulation data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com
DOI: 10.5281/zenodo.7950023
title: A new non-linear instability for scalar fields

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "A new non-linear instability for scalar fields" (https://arxiv.org/abs/2107.14215). <br> This paper has also been published as a <em>letter </em>in Physical Review D and can be found in Volume 105, Issue 2, article id.L021304, which can be accessed at this link: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.L021304. Directories <strong>Codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution and Latfield2. <strong>Simulations_data</strong>: This directory includes the data for the scalar field snapshots, information about the average of the scalar field, and power spectra. <strong>Figure_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>Figure_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "codes" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "data" directory to access the simulation data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com
DOI: 10.34740/kaggle/dsv/5729195
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.7950670
title: The detection of relativistic corrections in cosmological N-body simulations

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "The detection of relativistic corrections in cosmological N-body simulations" (https://arxiv.org/abs/1909.04652).<br> <br> This paper has also been published in Celestial Mechanics and Dynamical Astronomy and can be found in volume 132, Article number: 2 (2020) which can be accessed at this link: https://link.springer.com/article/10.1007/s10569-019-9943-z . Directories <strong>codes</strong>: This directory contains different codes used to generate and post-process the simulation data. <strong>data</strong>: This directory contains the data generated as part of the project. <strong>notebooks</strong>: This directory includes Jupyter notebooks and perl files to reproduce the figures presented in the paper. <strong>supplementary</strong>: This directory contains the supplementary materials associated with the project. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "<strong>data</strong>" directory to access the data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com or Jean-Pierre.Eckmann@unige.ch
DOI: 10.5281/zenodo.7950669
title: The detection of relativistic corrections in cosmological N-body simulations

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "The detection of relativistic corrections in cosmological N-body simulations" (https://arxiv.org/abs/1909.04652).<br> <br> This paper has also been published in Celestial Mechanics and Dynamical Astronomy and can be found in volume 132, Article number: 2 (2020) which can be accessed at this link: https://link.springer.com/article/10.1007/s10569-019-9943-z . Directories <strong>codes</strong>: This directory contains different codes used to generate and post-process the simulation data. <strong>data</strong>: This directory contains the data generated as part of the project. <strong>notebooks</strong>: This directory includes Jupyter notebooks and perl files to reproduce the figures presented in the paper. <strong>supplementary</strong>: This directory contains the supplementary materials associated with the project. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "<strong>data</strong>" directory to access the data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com or Jean-Pierre.Eckmann@unige.ch
DOI: 10.5281/zenodo.7953069
title: Microsatellite data

description: Scored alleles of 15 microsatellite markers used in a population genetics study of <em>Harmonia axyridis. </em>Includes global samples, but with a strong focus on South Africa.
DOI: 10.5281/zenodo.7953068
title: Microsatellite data

description: Scored alleles of 15 microsatellite markers used in a population genetics study of <em>Harmonia axyridis. </em>Includes global samples, but with a strong focus on South Africa.
DOI: 10.34740/kaggle/dsv/5729454
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.22965200
title: QDM magnetic microscopy dataset of a speleothem from Morocco

description: The dataset was measured on a Quantum Diamond Microscope (QDM) at Harvard University, using a stalagmite from Wintimdouine cave (Morroco). <br> We performed a magnetic map over a sample section of approximately 1410 μm × 2256 μm with a grid spacing of 2.35 μm and a sensor-sample distance of approximately 5 μm, totaling 576,000 data points. The data were taken in projected magnetic microscopy (PMM) mode and converted to the vertical component of magnetic field (𝑏𝑧). <br> In this sample, the magnetic remanence is carried by magnetite and hematite. We apply two IRM pulse fields parallel to the sample surface, 2.7 T and 0.3 T, respectively, toward the +Y and -Y directions. The high coercivity grains (hematite) would point towards +Y and the low coercivity ones (magnetite) would align in the -Y direction.
DOI: 10.34740/kaggle/dsv/5729408
title: Inner Speech Recognition
DOI: 10.20375/0000-000f-ef00-4
title: converted-data.xlsx
DOI: 10.20375/0000-000f-ef01-3
title: DocuDataCollection.pdf
DOI: 10.20375/0000-000f-ef02-2
title: original-data.sav
DOI: 10.20375/0000-000f-ef03-1
title: participation-rates.pdf
DOI: 10.20375/0000-000f-ef04-0
title: Volkov+Kolesnikov2022-excerpt.pdf
DOI: 10.20375/0000-000f-ef05-f
title: Готовность участвовать в опросах - результаты эксперимента.pdf
DOI: 10.48320/7d74bb3a-fda9-4fc1-9876-ae1d4551719d
title: The War in Ukraine in the Perception of the Russian Population
DOI: 10.5281/zenodo.7953094
title: Environmental DNA detection range for Hydrilla

description: Dataset associated with publication of the same name including qPCR results, measured variables, and other supplemental data.
DOI: 10.5281/zenodo.7953093
title: Environmental DNA detection range for Hydrilla

description: Dataset associated with publication of the same name including qPCR results, measured variables, and other supplemental data.
DOI: 10.5281/zenodo.6950236
title: Suppl. Data for ZSF1rat sGC

description: Suppl. Data for ZSF1rat sGC
DOI: 10.5281/zenodo.6950235
title: Suppl. Data for ZSF1rat sGC

description: Suppl. Data for ZSF1rat sGC
DOI: 10.34740/kaggle/dsv/5729510
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.7950666
title: Parametrising non-linear dark energy perturbations

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Parametrising non-linear dark energy perturbations" (https://arxiv.org/abs/1910.01105). <br> This paper has also been published JCAP and can be found in Volume 2020, April 2020, which can be accessed at this link: https://iopscience.iop.org/article/10.1088/1475-7516/2020/04/039. Directories <strong>codes</strong>: This directory contains k-evolution code used to generate and post-process the simulation data. <strong>notebooks_data</strong>: This directory includes the data and jupyter notebooks to reproduce the figures presented in the paper. <strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>notebooks_data</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "<strong>notebooks_data</strong>" directory to access the simulation data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com
DOI: 10.5281/zenodo.7950665
title: Parametrising non-linear dark energy perturbations

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Parametrising non-linear dark energy perturbations" (https://arxiv.org/abs/1910.01105). <br> This paper has also been published JCAP and can be found in Volume 2020, April 2020, which can be accessed at this link: https://iopscience.iop.org/article/10.1088/1475-7516/2020/04/039. Directories <strong>codes</strong>: This directory contains k-evolution code used to generate and post-process the simulation data. <strong>notebooks_data</strong>: This directory includes the data and jupyter notebooks to reproduce the figures presented in the paper. <strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>notebooks_data</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "<strong>notebooks_data</strong>" directory to access the simulation data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com
DOI: 10.5281/zenodo.7799288
title: Available Community Energy Transition Plan Data

description: This is the extracted data for 186 communities that have made a pledge to be 100% renewable. A Systematic online search was conducted to find current plans and assess the gaps in community energy transitions. Please cite and reference the manuscript "<strong>Ready or not, here it comes: Assessing the gaps in community plans for renewable energy transitions</strong><strong> within the United States" </strong>published in Energy Research and Social Science
DOI: 10.5281/zenodo.7799287
title: Available Community Energy Transition Plan Data

description: This is the extracted data for 186 communities that have made a pledge to be 100% renewable. A Systematic online search was conducted to find current plans and assess the gaps in community energy transitions. Please cite and reference the manuscript "<strong>Ready or not, here it comes: Assessing the gaps in community plans for renewable energy transitions</strong><strong> within the United States" </strong>published in Energy Research and Social Science
DOI: 10.34740/kaggle/dsv/5729735
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7950184
title: Clustering dark energy imprints on cosmological observables of the gravitational field

description: This file contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clustering dark energy imprints on cosmological observables of the gravitational field" (https://arxiv.org/abs/2007.04968). <br> This paper has also been published in MNRAS which can be accessed at this link: https://doi.org/10.1093/mnras/staa3589. Directories <strong>codes</strong>: This directory includes the codes to generate and post-process the simulation data. <strong>data</strong>: This directory contains the data generated as part of the project. <strong>jupyter_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper. <strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>jupyter_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "<strong>data</strong>" directory to access the simulation data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com
DOI: 10.5281/zenodo.7950183
title: Clustering dark energy imprints on cosmological observables of the gravitational field

description: This file contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clustering dark energy imprints on cosmological observables of the gravitational field" (https://arxiv.org/abs/2007.04968). <br> This paper has also been published in MNRAS which can be accessed at this link: https://doi.org/10.1093/mnras/staa3589. Directories <strong>codes</strong>: This directory includes the codes to generate and post-process the simulation data. <strong>data</strong>: This directory contains the data generated as part of the project. <strong>jupyter_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper. <strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>jupyter_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "<strong>data</strong>" directory to access the simulation data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com
DOI: 10.5281/zenodo.7953189
title: k-evolution: a relativistic N-body code for clustering dark energy

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "k-evolution: a relativistic N-body code for clustering dark energy" (https://arxiv.org/abs/1910.01104). <br> This paper has also been published in JCAP which can be accessed at this link: https://iopscience.iop.org/article/10.1088/1475-7516/2019/12/011. Directories <strong>Analysis_notebooks_data</strong>: This directory includes the data and jupyter notebooks to reproduce the figures presented in the paper. <strong>codes</strong>: This directory contains the data generated as part of the project. <strong>supplementary</strong>: This directory contains the supplementary materials associated with the project. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>Analysis_notebooks_data</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "<strong>Analysis_notebooks_data</strong>" directory to access the simulation data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com
DOI: 10.5281/zenodo.7953188
title: k-evolution: a relativistic N-body code for clustering dark energy

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "k-evolution: a relativistic N-body code for clustering dark energy" (https://arxiv.org/abs/1910.01104). <br> This paper has also been published in JCAP which can be accessed at this link: https://iopscience.iop.org/article/10.1088/1475-7516/2019/12/011. Directories <strong>Analysis_notebooks_data</strong>: This directory includes the data and jupyter notebooks to reproduce the figures presented in the paper. <strong>codes</strong>: This directory contains the data generated as part of the project. <strong>supplementary</strong>: This directory contains the supplementary materials associated with the project. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>Analysis_notebooks_data</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Navigate to the "<strong>Analysis_notebooks_data</strong>" directory to access the simulation data. <br> If you have any feedback or request feel free to email farbod.hassani@gmail.com
DOI: 10.5281/zenodo.7831054
title: Disconnected vector loop g-2 HISQ coarse physical ensemble

description: Disconnecected vector loop operators for the g-2 project. The symmetric and isospin broken correlators can be computed from this data set. They were generated on HPC resources at Cambridge and Durham using a modified version of the MILC code. The is coarse physical ensemble. l4864f211b600m001907m05252m6382 The file Coarse_g-2ttim.pdf contains some information about the conventions WARNING The second set of loops for the noise sources 128 to 256 were the same as 1 to 128 were identical for streams e and f.
DOI: 10.5281/zenodo.7831053
title: Disconnected vector loop g-2 HISQ coarse physical ensemble

description: Disconnecected vector loop operators for the g-2 project. The symmetric and isospin broken correlators can be computed from this data set. They were generated on HPC resources at Cambridge and Durham using a modified version of the MILC code. The is coarse physical ensemble. l4864f211b600m001907m05252m6382 The file Coarse_g-2ttim.pdf contains some information about the conventions WARNING The second set of loops for the noise sources 128 to 256 were the same as 1 to 128 were identical for streams e and f.
DOI: 10.17909/9ymc-em60
title: Didymos NIRSpec and MIRI Observations

description: Creating DOI for citation use in publication.
DOI: 10.5281/zenodo.7953183
title: Individual .ttl files of the PF 1,600 plant extracts

description: Each file contains the ENPKG data (.ttl) of a given plant extract.
DOI: 10.5281/zenodo.7953182
title: Individual .ttl files of the PF 1,600 plant extracts

description: Each file contains the ENPKG data (.ttl) of a given plant extract.
DOI: 10.5281/zenodo.7953246
title: Mikrostruktura materiálů Ampcoloy 940 a Albromet W130

description: <strong>Metalografické preparáty</strong> byly připraveny běžnými technikami – broušením za mokra a leštěním diamantovými pastami pomocí automatického preparačního systému Tegramin 30 firmy Struers. Topografie takto připraveného povrchu dosahovala Ra=5 nm, což je dostatečné pro následné materiálové analýzy. Pro zviditelnění mikrostruktury byl preparát leptán leptadlem obsahujícím 50 ml H<sub>2</sub>O a 50 ml HNO<sub>3</sub>. Materiál AMPCOLOY 940 byl takto leptán po dobu 8 s a materiál ALBROMET W130 po dobu 30 s. <strong>Analýza mikrostruktury</strong> byla provedena u obou materiálů, přičemž byla provedena jak použitím světelné mikroskopie (LM) na přístroji značky Zeiss tak i užitím elektronové mikroskopie (SEM) na mikroskopu značky Tescan pomocí detektoru zpětně odražených elektronů (BSE). Při použití světelné mikroskopie bylo zvětšení vždy 20x a následně 50x. Při užití elektronové mikroskopie bylo zvětšení 1000x.
description: TA ČR, projekt FW06010039, název: Výzkum nahraditelnosti materiálu AMPCOLOY 940 novým materiálem ALBROMET W130 pro tvarové díly forem se speciální texturou vyrobené pomocí elektroerozivního drátového řezání