cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2g4hhm
title: CCDC 2268440: Experimental Crystal Structure Determination

description: Related Article: Hui-Lin Mao, Yu-Xin Wang, Xue Wang, Hai-Ying Wang, Wen-Juan Hao, Bo Jiang|2023|Org.Lett.|25|5963|doi:10.1021/acs.orglett.3c02051
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.cc2g6j5d
title: CCDC 2270383: Experimental Crystal Structure Determination

description: Related Article: Maximilian Benz, Sabrina Dörrich, Thomas M. Klapötke, Tobias Lenz, Mohamed Mouzayek, Jörg Stierstorfer|2023|Org.Lett.|25|5974|doi:10.1021/acs.orglett.3c02079
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.cc2g6j6f
title: CCDC 2270384: Experimental Crystal Structure Determination

description: Related Article: Maximilian Benz, Sabrina Dörrich, Thomas M. Klapötke, Tobias Lenz, Mohamed Mouzayek, Jörg Stierstorfer|2023|Org.Lett.|25|5974|doi:10.1021/acs.orglett.3c02079
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.cc2g6j7g
title: CCDC 2270385: Experimental Crystal Structure Determination

description: Related Article: Maximilian Benz, Sabrina Dörrich, Thomas M. Klapötke, Tobias Lenz, Mohamed Mouzayek, Jörg Stierstorfer|2023|Org.Lett.|25|5974|doi:10.1021/acs.orglett.3c02079
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.cc2g6j8h
title: CCDC 2270386: Experimental Crystal Structure Determination

description: Related Article: Maximilian Benz, Sabrina Dörrich, Thomas M. Klapötke, Tobias Lenz, Mohamed Mouzayek, Jörg Stierstorfer|2023|Org.Lett.|25|5974|doi:10.1021/acs.orglett.3c02079
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.cc2g6j9j
title: CCDC 2270387: Experimental Crystal Structure Determination

description: Related Article: Maximilian Benz, Sabrina Dörrich, Thomas M. Klapötke, Tobias Lenz, Mohamed Mouzayek, Jörg Stierstorfer|2023|Org.Lett.|25|5974|doi:10.1021/acs.orglett.3c02079
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.cc2g6z1q
title: CCDC 2270813: Experimental Crystal Structure Determination

description: Related Article: Yi-Hang Deng, Wen-Li Xu, Lei Wang, Cheng-Yang Tang, Ji-Ya Fu, Chuan-Bao Zhang|2023|Org.Biomol.Chem.|21|6681|doi:10.1039/D3OB00982C
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.8286126
title: Common Beans Imagery Dataset for Early Detection of Crop Diseases

description: The annotated dataset consists of common beans leaf imagery for early diseases detection. The common beans crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 20<sup>th</sup> October 2022 and 10<sup>th</sup> April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Bean anthracnose, and (ii) Bean rust diseases in common beans. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones. All images are in the <strong>.zip files</strong>; “anthra.zip” has 13,531 images, “healthy.zip” has 24,973 images, and “rust.zip” has 20,568 images. A total of 59,072 image files are labelled. This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.
DOI: 10.5281/zenodo.8286125
title: Common Beans Imagery Dataset for Early Detection of Crop Diseases

description: The annotated dataset consists of common beans leaf imagery for early diseases detection. The common beans crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 20<sup>th</sup> October 2022 and 10<sup>th</sup> April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Bean anthracnose, and (ii) Bean rust diseases in common beans. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones. All images are in the <strong>.zip files</strong>; “anthra.zip” has 13,531 images, “healthy.zip” has 24,973 images, and “rust.zip” has 20,568 images. A total of 59,072 image files are labelled. This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.
DOI: 10.25810/fsdf-t849
title: Distinct regions of the kinesin-5 C-terminal tail are essential for mitotic spindle midzone localization and sliding force

description: This dataset contains spinning disk confocal microscopy using live cell preparation from the 2023 JCS paper titled " Distinct regions of the kinesin-5 C-terminal tail are essential for mitotic spindle midzone localization and sliding force." The two general categories of microscopy images cut7 tail modifications in either pkl1 and klp2 wild type and pkl1 and klp2 deletion strain backgrounds.
DOI: 10.5281/zenodo.8290814
title: Dataset on digital rural banking products

description: The dataset consists of the attitudes of rural customers towards digital banking products. It explains how rural customers perceive the digital products of banking in the post-pandemic situation. It also explains how digital products create a greater impact during and after the COVID-19 pandemic among customers especially those who reside in rural areas.
DOI: 10.5281/zenodo.8290813
title: Dataset on digital rural banking products

description: The dataset consists of the attitudes of rural customers towards digital banking products. It explains how rural customers perceive the digital products of banking in the post-pandemic situation. It also explains how digital products create a greater impact during and after the COVID-19 pandemic among customers especially those who reside in rural areas.
DOI: 10.26207/h9kc-k012
title: Evaluating the Potential for Citizen Science to Increase Consumer Awareness of and Address EDCs in Consumer Products and Waterways
DOI: 10.5281/zenodo.8286529
title: Irish Potato Imagery Dataset for Early Detection of Crop Diseases

description: The annotated dataset consists of irish potatoes leaf imagery for early diseases detection. The irish potato crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 22<sup>nd</sup> November 2022 and 08<sup>th</sup> April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Early blight, and (ii) Late blight diseases in irish potatoes. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones. All images are in the <strong>.zip files</strong>; “lateblt.zip” has 20,499 images, “healthy.zip” has 20,438 images, and “earlyblt.zip” has 17,772 images. A total of 58,709 image files are labelled. This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.
DOI: 10.5281/zenodo.8286528
title: Irish Potato Imagery Dataset for Early Detection of Crop Diseases

description: The annotated dataset consists of irish potatoes leaf imagery for early diseases detection. The irish potato crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 22<sup>nd</sup> November 2022 and 08<sup>th</sup> April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Early blight, and (ii) Late blight diseases in irish potatoes. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones. All images are in the <strong>.zip files</strong>; “lateblt.zip” has 20,499 images, “healthy.zip” has 20,438 images, and “earlyblt.zip” has 17,772 images. A total of 58,709 image files are labelled. This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.
DOI: 10.5066/p94v65t8
title: Vegetation and soil cover from 134 reclaimed oil and gas well pads and 583 AIM reference plots in the Southwestern United States

description: These data were compiled to assess the recovery of vegetation on reclaimed oil and gas sites. Objective(s) of our study were to assess patterns in reclamation outcomes relative to 1) soil attributes, climate, and time since 39 reclamation and 2) plant and soil reference benchmarks. These data represent observations of vegetation and soil cover from 134 reclaimed oil and gas well pads and 583 AIM reference plots. These data were collected on lands impacted by oil and gas development on the Colorado Plateau as well as Arizona and New Mexico Plateau of New Mexico, Colorado, and Utah. Data was collected from July- September of 2020 and May-September of 2021. These data were collected by Assessment Inventory and Monitoring (AIM) certified field crews using field observations and AIM methods. These data can be used to estimate plant community recovery on reclaimed oil and gas pads.
DOI: 10.5281/zenodo.7415215
title: Variable Scintillation Arcs in Millisecond Pulsars observed with the Large European Array for Pulsars

description: Dynamic spectra used in "Variable Scintillation Arcs in Millisecond Pulsars observed with the Large European Array for Pulsars". The dynamic spectra are simple ascii (the same output produced by psrflux), produced as described in the manuscript. A function to read them in python is included on github: https://github.com/ramain/scintillation/blob/master/scintillation/dynspectools/dynspectools.py, and they should all be compatible with the scintools package.
DOI: 10.5281/zenodo.7415214
title: Variable Scintillation Arcs in Millisecond Pulsars observed with the Large European Array for Pulsars

description: Dynamic spectra used in "Variable Scintillation Arcs in Millisecond Pulsars observed with the Large European Array for Pulsars". The dynamic spectra are simple ascii (the same output produced by psrflux), produced as described in the manuscript. A function to read them in python is included on github: https://github.com/ramain/scintillation/blob/master/scintillation/dynspectools/dynspectools.py, and they should all be compatible with the scintools package.
DOI: 10.6084/m9.figshare.24043521
title: Use of mind genomics to public health and wellbeing. Lessons from COVID-19 pandemic data set

description: Sample size (n=100) was determined based on the minimum requirements of the mind genomics engine when mapping the mindset of a population living in a certain area. The survey-based study had a cross-sectional design on a representative group of the UAE population (citizens and residents). The experiment construct was created using the tenets of mind genomics, which are derived from established fields including statistics, market research, and experimental psychology. This emerging science examines how a typical person evaluates different aspects of a situation to reach a particular decision using a mind genomics mapping. Participants evaluated a set of 24 combinations (also called vignettes) related to four aspects of physical distancing: 1) perceived risk, 2) specific action to be taken, 3) compliance policy, and 4) communicator of the information, which were randomly mixed by the program. The rating of each combination represented the extent to which it described the respondent’s perception and mindset related to COVID-19 safety rules. Each vignette was rated on a 5-point scale (1=Not me at all; 5=Perfectly me). Response time of participants was also recorded to assess the degree to which each message or element engaged participants’ attention. Questions and answers were entered into a computer program (www.BimiLeap.com), which allowed a rapid processing of data to acquire the expected conclusions (Bellissimo et al., 2020). <br>data are generated by the BimiLeap website and downloaded from there
DOI: 10.6084/m9.figshare.24043521.v1
title: Use of mind genomics to public health and wellbeing. Lessons from COVID-19 pandemic data set

description: Sample size (n=100) was determined based on the minimum requirements of the mind genomics engine when mapping the mindset of a population living in a certain area. The survey-based study had a cross-sectional design on a representative group of the UAE population (citizens and residents). The experiment construct was created using the tenets of mind genomics, which are derived from established fields including statistics, market research, and experimental psychology. This emerging science examines how a typical person evaluates different aspects of a situation to reach a particular decision using a mind genomics mapping. Participants evaluated a set of 24 combinations (also called vignettes) related to four aspects of physical distancing: 1) perceived risk, 2) specific action to be taken, 3) compliance policy, and 4) communicator of the information, which were randomly mixed by the program. The rating of each combination represented the extent to which it described the respondent’s perception and mindset related to COVID-19 safety rules. Each vignette was rated on a 5-point scale (1=Not me at all; 5=Perfectly me). Response time of participants was also recorded to assess the degree to which each message or element engaged participants’ attention. Questions and answers were entered into a computer program (www.BimiLeap.com), which allowed a rapid processing of data to acquire the expected conclusions (Bellissimo et al., 2020). <br>data are generated by the BimiLeap website and downloaded from there
DOI: 10.34740/kaggle/dsv/6379812
title: Question-based Assessment: Human vs. ChatGPT
DOI: 10.5281/zenodo.8289186
title: Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"

description: Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere". Authors: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023 Figure 1 has been plotted from two data files named Bx_By_Bz_prs_t_4783_C0.vtk and Bx_By_Bz_prs_t_4783_C1.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb" and the associated data files have been uploaded alongwith. Figure 3 has been plotted using the data file named "visit_prs_Bx_By_Bz_t_4964s.vtk" and the visualisation toolkit VisIt. Figure 4 has been plotted using the associated data files and the visualisation toolkit VisIt. Figures 5 and 6 has been plotted using the ipython notebook named "new_fig_5_and_6.ipynb" and the associated data files have been uploaded alongwith. Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb" and the associated data files have been uploaded alongwith. Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb" and the associated data files have been uploaded alongwith. Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb" and the associated data files have been uploaded alongwith. An example swarm CSV data file is added for the python script. The original SWARM data can be downloaded from https://swarm-diss.eo.esa.int/ and the FAC data from two spacecrafts, Swarm A and C, named SW_OPER_FAC_TMS_2F has been used. This data was first published in Dong et.al. 2023 [https://doi.org/10.1029/2022GL102460].
DOI: 10.5281/zenodo.8291132
title: Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"

description: Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere". Authors: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023 Figure 1 has been plotted from two data files named Bx_By_Bz_prs_t_4783_C0.vtk and Bx_By_Bz_prs_t_4783_C1.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb" and the associated data files have been uploaded alongwith. Figure 3 has been plotted using the data file named "visit_prs_Bx_By_Bz_t_4964s.vtk" and the visualisation toolkit VisIt. Figure 4 has been plotted using the associated data files and the visualisation toolkit VisIt. Figures 5 and 6 has been plotted using the ipython notebook named "new_fig_5_and_6.ipynb" and the associated data files have been uploaded alongwith. Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb" and the associated data files have been uploaded alongwith. Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb" and the associated data files have been uploaded alongwith. Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb" and the associated data files have been uploaded alongwith. An example swarm CSV data file is added for the python script. The original SWARM data can be downloaded from https://swarm-diss.eo.esa.int/ and the FAC data from two spacecrafts, Swarm A and C, named SW_OPER_FAC_TMS_2F has been used. This data was first published in Dong et.al. 2023 [https://doi.org/10.1029/2022GL102460].
DOI: 10.5066/p9cvg5al
title: Rain measurements in and near the Dolan Fire Area, Los Padres National Forest, California, 2022 to 2023

description: Rainfall measurements were collected in and near the Dolan Fire burn area, Los Padres National Forest, California. The CZU Fire ignited in Los Padres National Forest, California, on August 18, 2020. By the time of full containment on December 31, 2020, the fire had burned 518 km2 (128,050 acres) in Monterey County. The U.S. Geological Survey (USGS) installed seven rain gages in and near the Dolan Fire burn area in October 2021 to measure rainfall during two post-fire wet seasons. This data release contains rain records from the Mill Creek and upper Nacimiento River watersheds collected during water year 2023 (October 1, 2022, through spring 2023). Previous datasets from this region included an additional rain gage (Dolan1), but due to vandalism its data are not usable over winter 2022-2023 and are not reported. Two other gages (Dolan4 and Dolan6) ran out of battery power and so did not record rain over part of the winter of 2022-2023; extreme rain conditions closed access to Los Padres National Forest so that batteries could not be replaced. This affected the following gages after the following dates: Dolan4 did not record data after 2022-11-03 00:54:39, and Dolan6 did not record data after 2022-11-10 16:07:27. Gages Dolan2, Dolan3, Dolan5, and Dolan7 collected complete rain records throughout the wet season. All seven rain gages were removed on May 25, 2023.
DOI: 10.6084/m9.figshare.19431548
title: Sustainable cocoa farming practices

description: Contains sustainable farming practices for two cocoa producer samples in Ecuador and Uganda as well as farmer, farm, and supply chain factors.
DOI: 10.6084/m9.figshare.19431548.v1
title: Sustainable cocoa farming practices

description: Contains sustainable farming practices for two cocoa producer samples in Ecuador and Uganda as well as farmer, farm, and supply chain factors.
DOI: 10.5281/zenodo.4127055
title: PUDL Raw EPA CEMS Hourly

description: Raw US EPA hourly Continuous Emissions Monitoring System (CEMS) data, archived from ftp://newftp.epa.gov/dmdnload/emissions/hourly/monthly This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources: The PUDL Repository on GitHub PUDL Documentation Other Catalyst Cooperative data archives
DOI: 10.5281/zenodo.4127054
title: PUDL Raw EPA CEMS Hourly

description: Raw US EPA hourly Continuous Emissions Monitoring System (CEMS) data, archived from ftp://newftp.epa.gov/dmdnload/emissions/hourly/monthly This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources: The PUDL Repository on GitHub PUDL Documentation Other Catalyst Cooperative data archives