cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c45mb
title: CCDC 2178761: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45nc
title: CCDC 2178762: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45pd
title: CCDC 2178763: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45qf
title: CCDC 2178764: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45rg
title: CCDC 2178765: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45sh
title: CCDC 2178766: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45tj
title: CCDC 2178767: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45vk
title: CCDC 2178768: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45wl
title: CCDC 2178769: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45xm
title: CCDC 2178770: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45yn
title: CCDC 2178771: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c45zp
title: CCDC 2178772: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c460r
title: CCDC 2178773: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2c461s
title: CCDC 2178774: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Shunya Masuda, Jee Young Kim, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2022|Inorg.Chem.|61|15182|doi:10.1021/acs.inorgchem.2c02426
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.cc2bw51g
title: CCDC 2171055: Experimental Crystal Structure Determination

description: Related Article: Jingting Yang, Yunqiang Sun, Zuowei Xie|2022|Organometallics|41|2580|doi:10.1021/acs.organomet.2c00299
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.cc29dh18
title: CCDC 2127159: Experimental Crystal Structure Determination

description: Related Article: Zi-You Zhang, Guo-Xu Qin, Xiao-Min Li, Hong-Liang Dong, Shun Wan, Yong-Hong Ni, Jiang Liu, Zhi-Qiang Chen, Zhi Su|2022|Inorg.Chem.|61|15166|doi:10.1021/acs.inorgchem.2c02357
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: 10.6084/m9.figshare.21303261
title: Data from " Chemical Origin of Temperature-Dependent Phase Transition and Conductivity in Hydroxyapatite"

description: This dataset contains data relates to the publication " Chemical Origin of Temperature-Dependent Phase Transition and Conductivity in Hydroxyapatite", by Wang et al., 2022. In this work, we use deep potential molecular dynamics simulation to study Temperature-Dependent Phase Transition and Conductivity in Hydroxyapatite. The deep neural network potential is trained on DFT calculations. This dataset contains the deep potential models and the DFT training data reported in the publication, the input files of vasp, lammps, deepmd-kit, dpgen, and the structures of hydroxyapatite.
DOI: 10.6084/m9.figshare.21303261.v1
title: Data from " Chemical Origin of Temperature-Dependent Phase Transition and Conductivity in Hydroxyapatite"

description: This dataset contains data relates to the publication " Chemical Origin of Temperature-Dependent Phase Transition and Conductivity in Hydroxyapatite", by Wang et al., 2022. In this work, we use deep potential molecular dynamics simulation to study Temperature-Dependent Phase Transition and Conductivity in Hydroxyapatite. The deep neural network potential is trained on DFT calculations. This dataset contains the deep potential models and the DFT training data reported in the publication, the input files of vasp, lammps, deepmd-kit, dpgen, and the structures of hydroxyapatite.
DOI: 10.5281/zenodo.7179866
title: Research Software Funders Global South

description: The Research Software Alliance's (ReSA) mission is to bring research software communities together to collaborate on the advancement of research software. Given the ReSA mission, it is important to understand the landscape of communities involved with research software. In 2020, ReSA completed an initial exercise to scope the international research software community landscape. This work was reported by ReSA's Software Landscape Analysis task force via a blog post. The majority of the communities in the previous analysis represented the global north. To improve the extent of this landscape analysis, ReSA announced a paid opportunity for short-term contractors located in the global south to collect data on communities and funders in their region in early 2022. This document describes how the work was undertaken, a summary of findings, the gaps and opportunities perceived by the data collectors and some highlights. This work identified 126 organisations and communities and 62 funder bodies that support research software in the global south. Their main activities are connecting people, training, and networking, and support through research grants. To add to this funders list please fill in the following form: https://forms.gle/CJWo24MUCjhWKh9U8
description: This work was supported by the Wellcome Trust grant [222436/Z/21/Z]
DOI: 10.5281/zenodo.7179806
title: Research Software Communities Global South

description: The Research Software Alliance's (ReSA) mission is to bring research software communities together to collaborate on the advancement of research software. Given the ReSA mission, it is important to understand the landscape of communities involved with research software. In 2020, ReSA completed an initial exercise to scope the international research software community landscape. This work was reported by ReSA's Software Landscape Analysis task force via a blog post. The majority of the communities in the previous analysis represented the global north. To improve the extent of this landscape analysis, ReSA announced a paid opportunity for short-term contractors located in the global south to collect data on communities and funders in their region in early 2022. This document describes how the work was undertaken, a summary of findings, the gaps and opportunities perceived by the data collectors and some highlights. This work identified 126 organisations and communities and 62 funder bodies that support research software in the global south. Their main activities are connecting people, training, and networking, and support through research grants. To add to this communities list please fill in the following form https://forms.gle/KJE9vkBnM6vhh7cEA
description: This work was supported by the Wellcome Trust grant [222436/Z/21/Z]
DOI: 10.5281/zenodo.7179807
title: Research Software Communities Global South

description: The Research Software Alliance's (ReSA) mission is to bring research software communities together to collaborate on the advancement of research software. Given the ReSA mission, it is important to understand the landscape of communities involved with research software. In 2020, ReSA completed an initial exercise to scope the international research software community landscape. This work was reported by ReSA's Software Landscape Analysis task force via a blog post. The majority of the communities in the previous analysis represented the global north. To improve the extent of this landscape analysis, ReSA announced a paid opportunity for short-term contractors located in the global south to collect data on communities and funders in their region in early 2022. This document describes how the work was undertaken, a summary of findings, the gaps and opportunities perceived by the data collectors and some highlights. This work identified 126 organisations and communities and 62 funder bodies that support research software in the global south. Their main activities are connecting people, training, and networking, and support through research grants. To add to this communities list please fill in the following form https://forms.gle/KJE9vkBnM6vhh7cEA
description: This work was supported by the Wellcome Trust grant [222436/Z/21/Z]
DOI: 10.6084/m9.figshare.17057615
title: Bryophyte presence-absence and their environment data

description: They are datasets on moss and liverwort presence-absence and their environment data
DOI: 10.6084/m9.figshare.17057615.v7
title: Bryophyte presence-absence and their environment data

description: They are datasets on moss and liverwort presence-absence and their environment data
DOI: 10.6084/m9.figshare.19771180
title: Understanding regional streamflow trend magnitudes in the Southern Murray-Darling Basin, Australia

description: Understanding long-term trends in streamflow is important for water resource management. In this study, we investigate the long-term streamflow trends at 47 gauging sites within the southern Murray-Darling Basin (MDB), Australia. This study aims to estimate regional streamflow trends while understanding the impact of catchment characteristics on the spatial variation in these trends. To achieve this, we applied a Bayesian hierarchical model (BHM) to make the best use of available streamflow records from multiple sites and catchment characteristics such as climate, terrain, geology, land use and vegetation. The results show that streamflow trends from tested sites are consistently negative, with magnitudes of up to 2.7% per year relative to the annual average flow. We also find that spatial variability in trends can be best linked to differences in average climatic and terrain conditions. This finding can be used to inform future water planning for consumptive and environmental uses in the MDB.
DOI: 10.6084/m9.figshare.19771180.v2
title: Understanding regional streamflow trend magnitudes in the Southern Murray-Darling Basin, Australia

description: Understanding long-term trends in streamflow is important for water resource management. In this study, we investigate the long-term streamflow trends at 47 gauging sites within the southern Murray-Darling Basin (MDB), Australia. This study aims to estimate regional streamflow trends while understanding the impact of catchment characteristics on the spatial variation in these trends. To achieve this, we applied a Bayesian hierarchical model (BHM) to make the best use of available streamflow records from multiple sites and catchment characteristics such as climate, terrain, geology, land use and vegetation. The results show that streamflow trends from tested sites are consistently negative, with magnitudes of up to 2.7% per year relative to the annual average flow. We also find that spatial variability in trends can be best linked to differences in average climatic and terrain conditions. This finding can be used to inform future water planning for consumptive and environmental uses in the MDB.
DOI: 10.13012/b2idb-5689586_v1
title: UAV remote sensing imagery - Miscanthus trials 2020 - Energy Farm - UIUC

description: Aerial imagery utilized as input in the manuscript "Deep convolutional neural networks exploit high spatial and temporal resolution aerial imagery to predict key traits in miscanthus" . Data was collected over M. Sacchariflorus and Sinensis breeding trials at the Energy Farm, UIUC in 2020. Flights were performed using a DJI M600 mounted with a Micasense Rededge multispectral sensor at 20 m altitude around solar noon. Imagery is available as tif file by field trial and date (10). The post-processing of raw images into orthophoto was performed in Agisoft Metashape software. Each crop surface model and multispectral orthophoto was stacked into an unique raster stack by date and uploaded here. Each raster stack includes 6 layers in the following order: Layer 1 = crop surface model, Layer 2 = Blue, Layer 3 = Green, Layer 4 = Red, Layer 5 = Rededge, and Layer 6 = NIR multispectral bands. Msa raster stacks were resampled to 1.67 cm spatial resolution and Msi raster stacks were resampled to 1.41 cm spatial resolution to ease their integration into further analysis. 'MMDDYYYY' is the date of data collection, 'MSA' is M. Sacchariflorus trial, 'MSI' is Miscanthus Sinensis trial, 'CSM' is crop surface model layer, and 'MULTSP' are the five multispectral bands.
DOI: 10.6084/m9.figshare.19771180.v1
title: Understanding regional streamflow trend magnitudes in the Southern Murray–Darling basin, Australia

description: Understanding long-term trends in streamflow is important for water resource management. In this study, we investigate the long-term streamflow trends at 47 gauging sites within the southern Murray–Darling basin (MDB), Australia. This study aims to estimate regional streamflow trends while understanding the impact of catchment characteristics on the spatial variation in these trends. To achieve this, we applied a Bayesian hierarchical model (BHM) to make the best use of available streamflow records from multiple sites and catchment characteristics such as climate, terrain, geology, land use and vegetation. The results show that streamflow trends from tested sites are consistently negative, with magnitudes of up to 2.7% per year relative to the annual average flow. We also find that spatial variability in trends can be best linked to differences in average climatic and terrain conditions. This finding can be used to inform future water planning for consumptive and environmental uses in the MDB.
DOI: 10.6084/m9.figshare.16587311.v16
title: AREAdata updating output files

description: AREAdata output files for automatically updating climate averages. These are daily climate estimates based on the CDS ERA-5 reanalysis, see pearselab.github.io/areadata/.<br>File names begin with the climate variable given:temp = Temperaturespechumid = Specific Humidityrelhumid = Relative Humidityprecip = Precipiationuv = Ultraviolet<br>File names contain the administrative division level:<br>countries - highest administrative division<br>GID1 - lower administrative division, equivalent to states<br>GID2 - lower administrative division, equivalent to counties<br>Files downloadable as RDS files for direct use in the R statistical programming environment (appended .RDS), or as zip-compressed tab delimited files for other applications (appended .zip).<br>
DOI: 10.34740/kaggle/dsv/4306473
title: AutoXGB
DOI: 10.6084/m9.figshare.21218378
title: Factors Contributing to Low Antiretroviral Therapy Enrolment of Children in the Simiyu Region: A Cross-Sectional Creswell’s Mixed-Methods Sequential Explanatory Design

description: <br> The dataset includes children living with HIV aged two to fourteen years whose caregivers were willing to share information regarding their children's medical history. A questionnaire was given to caregivers, and the corresponding answers were recorded. This included the age of the child, marital status of the caregiver, distance to the health facility, education level of the caregiver, source of income, relationship to the child, date of birth, birthplace, time since HIV diagnosis, beliefs about discrimination, history of HIV testing of the child (date tested, ART status, ART start date), and experiences of stigma. For all quantitative data analyses, we utilised Stata™ software. The University of Dodoma and the National Health Research Ethics Sub-Committee (NatHREC) approved the data collection under the National Institute for Medical Research (NIMR). Participation was voluntary and undertaken with written consent. The data were collected cross-sectionally using a mixed-methods sequential explanatory design described by Creswell, 2006.