cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2gkryr
title: CCDC 2281195: Experimental Crystal Structure Determination

description: Related Article: Benjamin W. Walusiak, Adharsh Raghavan, Christopher L. Cahill|2024|Cryst.Growth Des.|24|678|doi:10.1021/acs.cgd.3c01040
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.cc2gpg2q
title: CCDC 2284733: Experimental Crystal Structure Determination

description: Related Article: Kerstin Rickmeyer, Matthias Huber, Corinna R. Hess|2024|Chem.Commun.|60|819|doi:10.1039/D3CC04777F
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.cc2gpg3r
title: CCDC 2284734: Experimental Crystal Structure Determination

description: Related Article: Kerstin Rickmeyer, Matthias Huber, Corinna R. Hess|2024|Chem.Commun.|60|819|doi:10.1039/D3CC04777F
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.cc2grg3t
title: CCDC 2286656: Experimental Crystal Structure Determination

description: Related Article: Daichi Okumatsu, Kensuke Kiyokawa, Linh Tran Bao Nguyen, Manabu Abe, Satoshi Minakata|2024|Chemical Science|15|1068|doi:10.1039/D3SC06090J
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.cc2grg4v
title: CCDC 2286657: Experimental Crystal Structure Determination

description: Related Article: Daichi Okumatsu, Kensuke Kiyokawa, Linh Tran Bao Nguyen, Manabu Abe, Satoshi Minakata|2024|Chemical Science|15|1068|doi:10.1039/D3SC06090J
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.cc2grg5w
title: CCDC 2286658: Experimental Crystal Structure Determination

description: Related Article: Daichi Okumatsu, Kensuke Kiyokawa, Linh Tran Bao Nguyen, Manabu Abe, Satoshi Minakata|2024|Chemical Science|15|1068|doi:10.1039/D3SC06090J
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.cc2grt02
title: CCDC 2286994: Experimental Crystal Structure Determination

description: Related Article: Ya-Fan Hao, Su-Ling Xu, Jin-Jin Zhao, Jie Gao, Ning Ren, Jian-Jun Zhang|2024|J.Saudi Chem.Soc.|28|101792|doi:10.1016/j.jscs.2023.101792
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.cc2grt13
title: CCDC 2286995: Experimental Crystal Structure Determination

description: Related Article: Ya-Fan Hao, Su-Ling Xu, Jin-Jin Zhao, Jie Gao, Ning Ren, Jian-Jun Zhang|2024|J.Saudi Chem.Soc.|28|101792|doi:10.1016/j.jscs.2023.101792
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.cc2grt24
title: CCDC 2286996: Experimental Crystal Structure Determination

description: Related Article: Ya-Fan Hao, Su-Ling Xu, Jin-Jin Zhao, Jie Gao, Ning Ren, Jian-Jun Zhang|2024|J.Saudi Chem.Soc.|28|101792|doi:10.1016/j.jscs.2023.101792
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.cc2grt35
title: CCDC 2286997: Experimental Crystal Structure Determination

description: Related Article: Ya-Fan Hao, Su-Ling Xu, Jin-Jin Zhao, Jie Gao, Ning Ren, Jian-Jun Zhang|2024|J.Saudi Chem.Soc.|28|101792|doi:10.1016/j.jscs.2023.101792
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.cc2grt46
title: CCDC 2286998: Experimental Crystal Structure Determination

description: Related Article: Ya-Fan Hao, Su-Ling Xu, Jin-Jin Zhao, Jie Gao, Ning Ren, Jian-Jun Zhang|2024|J.Saudi Chem.Soc.|28|101792|doi:10.1016/j.jscs.2023.101792
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.cc2gs0w5
title: CCDC 2287207: Experimental Crystal Structure Determination

description: Related Article: Sudip Shit, Chinmayee Choudhury, Anil K. Saikia|2024|Org.Biomol.Chem.|22|568|doi:10.1039/D3OB01764H
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: 10.18434/mds2-3109
title: Smart network repository based on Neo4j native graph database
DOI: 10.3886/e152681
title: Not all disadvantages are equal

description: An analysis of institutional data to understand the outcome of obstacles faced by students from historically disadvantaged backgrounds is important in order to work towards promoting equity and inclusion. We use 10 years of institutional data at a large public research university to investigate the grades earned by students categorized on four demographic characteristics: gender, race/ethnicity, low-income status, and first-generation college student status. We find that on average across all years of study, underrepresented minority students experience a larger penalty to their mean overall and STEM GPA than even the most disadvantaged non-URM students. Moreover, the underrepresented minority students with additional disadvantages due to socioeconomic status or first-generation college status were further penalized in their average GPA. These inequitable outcomes point to systemic inequities in higher education for students with historically disadvantaged backgrounds and the need to dismantle institutional inertia to support them.
DOI: 10.3886/e152681v1-145412
title: Not all disadvantages are equal

description: An analysis of institutional data to understand the outcome of obstacles faced by students from historically disadvantaged backgrounds is important in order to work towards promoting equity and inclusion. We use 10 years of institutional data at a large public research university to investigate the grades earned by students categorized on four demographic characteristics: gender, race/ethnicity, low-income status, and first-generation college student status. We find that on average across all years of study, underrepresented minority students experience a larger penalty to their mean overall and STEM GPA than even the most disadvantaged non-URM students. Moreover, the underrepresented minority students with additional disadvantages due to socioeconomic status or first-generation college status were further penalized in their average GPA. These inequitable outcomes point to systemic inequities in higher education for students with historically disadvantaged backgrounds and the need to dismantle institutional inertia to support them.
DOI: 10.5281/zenodo.8041867
title: Supplementary Information for "Analytical electron microscopy study of the composition of BaHfO₃ nanoparticles in REBCO films: The influence of rare-earth ionic radii and REBCO composition"

description: Supplementary information containing image and spectroscopy data, and data-processing notebooks for the article "Analytical electron microscopy study of the composition of BaHfO3 nanoparticles in REBCO films: The influence of rare-earth ionic radii and REBCO composition".

Article (open access):  https://doi.org/10.1039/D3MA00447C

The data is sorted in folders for each figure, and the Notes.pdf file contains additional information about the different file formats and processing steps.

If you have questions, you can contact Lukas Grünewald at the E-Mail listed on the ORCID page: https://orcid.org/0000-0002-5898-0713
DOI: 10.5281/zenodo.8041868
title: Supplementary Information for "Analytical electron microscopy study of the composition of BaHfO₃ nanoparticles in REBCO films: The influence of rare-earth ionic radii and REBCO composition"

description: Supplementary information containing image and spectroscopy data, and data-processing notebooks for the article "Analytical electron microscopy study of the composition of BaHfO3 nanoparticles in REBCO films: The influence of rare-earth ionic radii and REBCO composition".

Article (open access):  https://doi.org/10.1039/D3MA00447C

The data is sorted in folders for each figure, and the Notes.pdf file contains additional information about the different file formats and processing steps.

If you have questions, you can contact Lukas Grünewald at the E-Mail listed on the ORCID page: https://orcid.org/0000-0002-5898-0713
DOI: 10.5281/zenodo.10433689
title: Resource Usage and Optimization Opportunities in Workflows of GitHub Actions - Artifact

description: This package contains the data, the code used for collection and the analysis notebooks used to obtain the results presented in the paper: "Resource Usage and Optimization Opportunities in Workflows of GitHub Actions" published at ICSE2024.
DOI: 10.5281/zenodo.10460584
title: Resource Usage and Optimization Opportunities in Workflows of GitHub Actions - Artifact

description: This package contains the data, the code used for collection and the analysis notebooks used to obtain the results presented in the paper: "Resource Usage and Optimization Opportunities in Workflows of GitHub Actions" published at ICSE2024.
DOI: 10.5281/zenodo.10529665
title: Resource Usage and Optimization Opportunities in Workflows of GitHub Actions - Artifact

description: This package contains the data, the code used for collection and the analysis notebooks used to obtain the results presented in the paper: "Resource Usage and Optimization Opportunities in Workflows of GitHub Actions" published at ICSE2024.
DOI: 10.5281/zenodo.8344575
title: Resource Usage and Optimization Opportunities in Workflows of GitHub Actions - Artifact

description: This package contains the data, the code used for collection and the analysis notebooks used to obtain the results presented in the paper: "Resource Usage and Optimization Opportunities in Workflows of GitHub Actions" published at ICSE2024.
DOI: 10.5281/zenodo.8344576
title: Resource Usage and Optimization Opportunities in Workflows of GitHub Actions - Artifact

description: This package contains the data, the code used for collection and the analysis notebooks used to obtain the results presented in the paper: "Resource Usage and Optimization Opportunities in Workflows of GitHub Actions" published at ICSE2024.
DOI: 10.18112/openneuro.ds004356.v1.0.1
title: Music and Speech Elicit Similar Subcortical Responses in Human Listeners
DOI: 10.5281/zenodo.10528287
title: Improving MODIS Gross Primary Productivity by Bridging Big-Leaf and Two-Leaf Light Use Efficiency Models

description: Dataset used for the analysis described in "Improving MODIS Gross Primary Productivity by Bridging Big-Leaf and Two-Leaf Light Use Efficiency Models"

This data set contains CTL - MOD17 model production 2010 every eight days of spatial resolution of 0.05 ° of GPP correction (CTL_GPP) products, as well as MOD17A2HGPP from NASA's official website (https://ladsweb.modaps.eosdis.nasa.gov/), and MOD15A2H LAL products. The accuracy difference of MODIS GPP and CTL-GPP correction was compared on a global scale, the relationship between the source of uncertainty of MOD17 products and LAI was studied, and the advantages of two-leaf (TL) theory were analyzed from the correction perspective and the accuracy of MODIS GPP products was effectively solved. In addition, we also provide code for the CTL-MOD17 model used in the study.
DOI: 10.5281/zenodo.10528288
title: Improving MODIS Gross Primary Productivity by Bridging Big-Leaf and Two-Leaf Light Use Efficiency Models

description: Dataset used for the analysis described in "Improving MODIS Gross Primary Productivity by Bridging Big-Leaf and Two-Leaf Light Use Efficiency Models"

This data set contains CTL - MOD17 model production 2010 every eight days of spatial resolution of 0.05 ° of GPP correction (CTL_GPP) products, as well as MOD17A2HGPP from NASA's official website (https://ladsweb.modaps.eosdis.nasa.gov/), and MOD15A2H LAL products. The accuracy difference of MODIS GPP and CTL-GPP correction was compared on a global scale, the relationship between the source of uncertainty of MOD17 products and LAI was studied, and the advantages of two-leaf (TL) theory were analyzed from the correction perspective and the accuracy of MODIS GPP products was effectively solved. In addition, we also provide code for the CTL-MOD17 model used in the study.
DOI: 10.18112/openneuro.ds004356.v2.0.0
title: Music and Speech Elicit Similar Subcortical Responses in Human Listeners
DOI: 10.5281/zenodo.7464756
title: JWST convolutions for a modular set of synthetic SEDs for young stellar objects (Robitaille, 2017)

description: This is a companion to the models released alongside the publication: <em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017) The models are convolved with JWST filters taken from the SVO’s filter profile service. Some models with rotationally flattened envelopes (i.e. geometries with<strong> u</strong>)<strong> </strong>not present in the original model grid have since been completed; their convolved SEDs are included here. Files unzip to {geometry}/convolved/JWST/{SVO_filtername}.fits. This is a subset of the information included in https://doi.org/10.5281/zenodo.8114592.
DOI: 10.5281/zenodo.10420762
title: Data and Plotting Tools to Reproduce Figures in Manuscript: A Parametric Study of the SASI Comparing General Relativistic and Non-Relativistic Treatments

description: This repository contains two gzipped tarballs: plottingTools.tar.gz and plottingData.tar.gz. Within plottingData.tar.gz are the data files necessary to reproduce all of the figures in the manuscript: A Parametric Study of the SASI Comparing General Relativistic and Non-Relativistic Treatments. Within plottingTools.tar.gz are the scripts necessary to reproduce the figures.

Contents of plottingTools.tar.gz:



globalVariables.py: this contains some user-defined variables such as where the data is stored and what extension to use for saved figures (pdf, png, etc.)

plotFigure*.py: these scripts generate the various figures in the manuscript

publication.sty: this sets the formatting for the figures

requirements.txt: this contains the python packages necessary to execute the scripts
DOI: 10.5281/zenodo.10529594
title: Data and Plotting Tools to Reproduce Figures in Manuscript: A Parametric Study of the SASI Comparing General Relativistic and Non-Relativistic Treatments

description: This repository contains two gzipped tarballs: plottingTools.tar.gz and plottingData.tar.gz. Within plottingData.tar.gz are the data files necessary to reproduce all of the figures in the manuscript: A Parametric Study of the SASI Comparing General Relativistic and Non-Relativistic Treatments. Within plottingTools.tar.gz are the scripts necessary to reproduce the figures.

Contents of plottingTools.tar.gz:



globalVariables.py: this contains some user-defined variables such as where the data is stored and what extension to use for saved figures (pdf, png, etc.)

plotFigure*.py: these scripts generate the various figures in the manuscript

publication.sty: this sets the formatting for the figures

requirements.txt: this contains the python packages necessary to execute the scripts
DOI: 10.5281/zenodo.7878370
title: Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

description: Short Description:

In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.

Code and More Details: https://github.com/lepotatoguy/aqi
DOI: 10.5281/zenodo.7878371
title: Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

description: Short Description:

In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.

Code and More Details: https://github.com/lepotatoguy/aqi
DOI: 10.21227/ftj1-m259
title: IEEEXtreme 11: Game of Life

description: This dataset contains a total of 2748 submissions, with 4% correct solutions and 79% partial solutions. Link to the original challenge: https://csacademy.com/ieeextreme-practice/task/d12a7784af1a3d3f6d88601c81a4bb81/ See the included PDF document for details about the original challenge.
DOI: 10.21227/2a3d-a227
title: IEEEXtreme 11: Gotta Catch 'Em All

description: This dataset contains a total of 5089 submissions, with 4% correct solutions and 78% partial solutions. Link to the original challenge: https://csacademy.com/ieeextreme-practice/task/e610aba28810ebcf2d3998692269b5a0/ See the included PDF document for details about the original challenge.
DOI: 10.17909/ecf8-2z68
title: Data from "An Independent Determination of the Hubble Constant Based on the J-region Asymptotic Giant Branch (JAGB) Method"

description: Data of 10 supernova Ia host galaxies, NGC 4258, and M74.
DOI: 10.18715/ipgp.2024.lrgjyb8a
title: Swarm Alpha ASM-V data version 050X

description: This dataset consists of version 050X 1 Hz (bandwidth 0-0.4 Hz) self-calibrated experimental vector data acquired by the ASM magnetometer on board satellite Alpha of the ESA Swarm mission. These data are produced by IPGP when the ASM instrument is set in its “vector mode”, which is the mode in which this instrument has been operated for the most part of the mission, except at times (typically during one week sessions) when the instruments are set in their alternative "scalar burst mode". The dataset is organized in annual zipped folders (one per year since 2013), each folder containing all available daily files for the year of interest. The dataset also comes with a SW-ASMV-DD-IPGP-0007_ASMV_Experimental_L1B_Product_Definition Revision 1 file, providing details of the data definition and format of the daily files.
DOI: 10.21227/cn9b-xn14
title: IEEEXtreme 11: Vangelis the Batbear and the Bubbles challenge

description: This dataset contains a total of 7455 submissions, with 17% correct solutions and 32% partial solutions. Link to the original challenge: https://csacademy.com/ieeextreme-practice/task/979a09a0cd8c4e98dd0a690f39a55bd2/ See the included PDF document for details about the original challenge.