cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2p30pj
title: CCDC 2475557: Experimental Crystal Structure Determination

description: Related Article: Daniel Werner, Zhifang Guo, Victoria L. Blair, Glen B. Deacon, Peter C. Junk|2025|New J.Chem.|49|18725|doi:10.1039/d5nj03383g
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.cc2p30qk
title: CCDC 2475558: Experimental Crystal Structure Determination

description: Related Article: Daniel Werner, Zhifang Guo, Victoria L. Blair, Glen B. Deacon, Peter C. Junk|2025|New J.Chem.|49|18725|doi:10.1039/d5nj03383g
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.cc2p30rl
title: CCDC 2475559: Experimental Crystal Structure Determination

description: Related Article: Daniel Werner, Zhifang Guo, Victoria L. Blair, Glen B. Deacon, Peter C. Junk|2025|New J.Chem.|49|18725|doi:10.1039/d5nj03383g
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.cc2p30sm
title: CCDC 2475560: Experimental Crystal Structure Determination

description: Related Article: Daniel Werner, Zhifang Guo, Victoria L. Blair, Glen B. Deacon, Peter C. Junk|2025|New J.Chem.|49|18725|doi:10.1039/d5nj03383g
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.cc2p4w0r
title: CCDC 2477334: Experimental Crystal Structure Determination

description: Related Article: Nafiseh Karimi, Farnaz Jafarpour|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c03207
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.cc2p8v72
title: CCDC 2481154: Experimental Crystal Structure Determination

description: Related Article: Mike Jörges, Daniel Knyszek, Manoj Kumar, Varre S. V. S. N. Swamy, Viktoria H. Gessner|2025|Chemical Science|16|21087|doi:10.1039/d5sc06549f
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.cc2p8v83
title: CCDC 2481155: Experimental Crystal Structure Determination

description: Related Article: Mike Jörges, Daniel Knyszek, Manoj Kumar, Varre S. V. S. N. Swamy, Viktoria H. Gessner|2025|Chemical Science|16|21087|doi:10.1039/d5sc06549f
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.cc2p8v50
title: CCDC 2481152: Experimental Crystal Structure Determination

description: Related Article: Mike Jörges, Daniel Knyszek, Manoj Kumar, Varre S. V. S. N. Swamy, Viktoria H. Gessner|2025|Chemical Science|16|21087|doi:10.1039/d5sc06549f
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.cc2p937c
title: CCDC 2481402: Experimental Crystal Structure Determination

description: Related Article: Vaishnavi Atreya, Anirban Sil, Debangsu Sil, Subrata Chakraborty|2025|Org.Biomol.Chem.|23|10118|doi:10.1039/d5ob01409c
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.cc2pbzpq
title: CCDC 2483214: Experimental Crystal Structure Determination

description: Related Article: Longxie Wei, Guigui Yan, Changzhuan Li, Zhichao Yang, Zhixu Zhou, Chunshen Zhao, Chun Ji |2026|J.Mol.Struct.|1350|144165|doi:10.1016/j.molstruc.2025.144165
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.cc2pd4js
title: CCDC 2484325: Experimental Crystal Structure Determination

description: Related Article: Congcong Jin, Yang Li, Chong-An Chen, Jihyun Lee, Chanhee Ko, Sooyeon Lee, Kang Min Ok
|2025|J. Am. Ceram. Soc.|||doi:10.1021/jacs.5c15741
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.cc2pd4kt
title: CCDC 2484326: Experimental Crystal Structure Determination

description: Related Article: Congcong Jin, Yang Li, Chong-An Chen, Jihyun Lee, Chanhee Ko, Sooyeon Lee, Kang Min Ok
|2025|J. Am. Ceram. Soc.|||doi:10.1021/jacs.5c15741
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.cc2pd4lv
title: CCDC 2484327: Experimental Crystal Structure Determination

description: Related Article: Congcong Jin, Yang Li, Chong-An Chen, Jihyun Lee, Chanhee Ko, Sooyeon Lee, Kang Min Ok
|2025|J. Am. Ceram. Soc.|||doi:10.1021/jacs.5c15741
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.cc2l5vtg
title: CCDC 2388916: Experimental Crystal Structure Determination

description: Related Article: Yi Shen, Laura Abella, Yang-Rong Yao, Qingyu Meng, Rongrong Luo, Bei Li, Zixin Wu, Josep M. Poblet, Antonio Rodríguez-Fortea, Ning Chen|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c12573
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.cc2pd4mw
title: CCDC 2484328: Experimental Crystal Structure Determination

description: Related Article: Congcong Jin, Yang Li, Chong-An Chen, Jihyun Lee, Chanhee Ko, Sooyeon Lee, Kang Min Ok
|2025|J. Am. Ceram. Soc.|||doi:10.1021/jacs.5c15741
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: 10.57967/hf/7003
title: kabr-worked-examples
DOI: 10.6084/m9.figshare.29937104
title: Multi-Task Learning in Analyzing the Working capacity of MOFs

description: The screening and rational design of materials with optimal methane capture performance remain key challenges for environmental and energy applications. Metal–organic frameworks (MOFs) are considered among the most promising candidates for methane capture. However, existing studies often emphasize single-point adsorption capacity, overlooking the dynamic mechanisms associated with pressure swing adsorption (PSA) required for practical applications. The construction of this dataset aims to address this challenge and provide insights into PSA-related mechanisms in MOFs.This dataset comprises geometric and chemical descriptors, as well as methane adsorption capacities under six different pressures, calculated for chemically reasonable MOF structures screened from eight publicly available datasets. In addition, we provide our in-house multitask learning (MTL) algorithm for predicting the gravimetric and volumetric working capacities of MOFs.<b>CIF files</b>: CIF files for 252,352 MOFs;<b>Geometric descriptors</b>: 14 geometric descriptors;<b>Chemical descriptors</b>: 176 chemical descriptors;<b>Methane_v, Methane_g</b>: Volumetric and gravimetric working capacities for methane adsorption, including methane adsorption data under six pressures across three application scenarios (landfill gas treatment, methane purification, and methane storage);<b>MTL4MOFsWC</b>: Python code for training the MTL models to predict the working capacity of methane adsorption in MOFs;<b>best_model_v_full, best_model_v_sim, best_model_g_full, best_model_g_sim</b>: Pre-trained MTL models.<br>
DOI: 10.6084/m9.figshare.29937104.v1
title: Multi-Task Learning in Analyzing the Working capacity of MOFs

description: The screening and rational design of materials with optimal methane capture performance remain key challenges for environmental and energy applications. Metal–organic frameworks (MOFs) are considered among the most promising candidates for methane capture. However, existing studies often emphasize single-point adsorption capacity, overlooking the dynamic mechanisms associated with pressure swing adsorption (PSA) required for practical applications. The construction of this dataset aims to address this challenge and provide insights into PSA-related mechanisms in MOFs.This dataset comprises geometric and chemical descriptors, as well as methane adsorption capacities under six different pressures, calculated for chemically reasonable MOF structures screened from eight publicly available datasets. In addition, we provide our in-house multitask learning (MTL) algorithm for predicting the gravimetric and volumetric working capacities of MOFs.<b>CIF files</b>: CIF files for 252,352 MOFs;<b>Geometric descriptors</b>: 14 geometric descriptors;<b>Chemical descriptors</b>: 176 chemical descriptors;<b>Methane_v, Methane_g</b>: Volumetric and gravimetric working capacities for methane adsorption;<b>MTL4MOFsWC</b>: Python code for training the MTL models to predict the working capacity of methane adsorption in MOFs;<b>best_model_v_full, best_model_v_sim, best_model_g_full, best_model_g_sim</b>: Pre-trained MTL models.
DOI: 10.6084/m9.figshare.29937104.v2
title: Multi-Task Learning in Analyzing the Working capacity of MOFs

description: The screening and rational design of materials with optimal methane capture performance remain key challenges for environmental and energy applications. Metal–organic frameworks (MOFs) are considered among the most promising candidates for methane capture. However, existing studies often emphasize single-point adsorption capacity, overlooking the dynamic mechanisms associated with pressure swing adsorption (PSA) required for practical applications. The construction of this dataset aims to address this challenge and provide insights into PSA-related mechanisms in MOFs.This dataset comprises geometric and chemical descriptors, as well as methane adsorption capacities under six different pressures, calculated for chemically reasonable MOF structures screened from eight publicly available datasets. In addition, we provide our in-house multitask learning (MTL) algorithm for predicting the gravimetric and volumetric working capacities of MOFs.<b>CIF files</b>: CIF files for 252,352 MOFs;<b>Geometric descriptors</b>: 14 geometric descriptors;<b>Chemical descriptors</b>: 176 chemical descriptors;<b>Methane_v, Methane_g</b>: Volumetric and gravimetric working capacities for methane adsorption, including methane adsorption data under six pressures across three application scenarios (landfill gas treatment, methane purification, and methane storage);<b>MTL4MOFsWC</b>: Python code for training the MTL models to predict the working capacity of methane adsorption in MOFs;<b>best_model_v_full, best_model_v_sim, best_model_g_full, best_model_g_sim</b>: Pre-trained MTL models.<br>
DOI: 10.17909/280v-ry85
title: Data For Paper UGC2369S
DOI: 10.15468/dl.8bw94c
title: Occurrence Download

description: A dataset containing 1076 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Canis rufus, Canis simensis)"
 ]
}

 The dataset includes 1076 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033937-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5bzy89brkv.3
title: A Real-World Hibiscus and Tea Leaf Image Dataset for Classification

description: The Combined Hibiscus and Tea Leaf Image Dataset is a comprehensive and well-balanced collection comprising 1,413 original high-quality leaf images captured under natural outdoor conditions across various regions of Bangladesh using a SONY α7 II DSLR camera and a OnePlus 7T smartphone. The dataset includes two major plant species—Hibiscus and Tea—and aims to facilitate research in plant disease detection, agricultural image analysis, and computer vision–based crop health monitoring. The Hibiscus subset consists of 1,165 images categorized into eight distinct classes: Healthy (473 images), Mild Edge Damage (226 images), Citruspot (150 images), Slightly Diseased (109 images), Early Mild Spotting (83 images), Wrinkled (56 images), Senescent (40 images), and Fungal Infected (28 images). The Tea subset contains 248 images divided into five disease categories: Algal Leaf Spot (54 images), Brown Blight (48 images), Grey Blight (53 images), Healthy (49 images), and Red Leaf Spot (44 images). These class distributions capture a diverse range of leaf conditions, disease severities, and environmental variations, providing a realistic foundation for machine learning and deep learning applications. To overcome class imbalance and enrich the dataset, extensive image augmentation was performed using both PIL and OpenCV techniques, including brightness and contrast adjustment, color enhancement, rotation, flipping, scaling, cropping, shifting, zooming, and Gaussian noise addition. Through this process, each class was expanded to 1,000 images, resulting in a total of 13,000 augmented images evenly distributed across the 13 classes. All images are stored in .JPG format and organized into separate folders per class, maintaining a consistent structure and naming convention. Overall, this dataset offers a rich and diverse resource for developing robust models for leaf disease classification, precision agriculture, and automated plant health monitoring, making it a valuable contribution to the fields of computer vision and agricultural research.
DOI: 10.17632/3w4mgfzbhp
title: Volumetric Behaviour Analysis (VBA) of Hong Kong Public Housing Flats - Interview Case 13PHOC1R
DOI: 10.17632/3w4mgfzbhp.1
title: Volumetric Behaviour Analysis (VBA) of Hong Kong Public Housing Flats - Interview Case 13PHOC1R
DOI: 10.17632/7b2ty3jp2b
title: Income, Place, and Perceptions of Fiscal Fairness: Evidence from Australian Federalism

description: This repository provides the data and R scripts necessary to replicate the tables, figures, and empirical analyses reported in the article “Income, Place, and Perceptions of Fiscal Fairness: Evidence from Australian Federalism” by Tracy Fenwick and Thiago N. Silva, published in Territory, Politics, Governance.
DOI: 10.17632/7b2ty3jp2b.2
title: Income, Place, and Perceptions of Fiscal Fairness: Evidence from Australian Federalism

description: This repository provides the data and R scripts necessary to replicate the tables, figures, and empirical analyses reported in the article “Income, Place, and Perceptions of Fiscal Fairness: Evidence from Australian Federalism” by Tracy Fenwick and Thiago N. Silva, published in Territory, Politics, Governance.
DOI: 10.17632/f675cbz84s
title: MCA: A Multicellular analysis Calcium Imaging toolbox for ImageJ

description: Functional imaging using genetically encoded indicators, such as GCaMP, has become a foundational tool for in vivo experiments and allows for the analysis of cellular dynamics, sensory processing, and cellular communication. However, large scale or complex functional imaging experiments pose analytical challenges. Many programs have worked to create pipelines to address these challenges, however, most platforms require proprietary software, impose operational restrictions, offer limited outputs, or require significant knowledge of various programming languages, which collectively can limit utility. To address this, we designed MCA (a Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software which has been the standard image analysis platform for the last 30 years. We developed MCA to be visually intuitive, utilizing ImageJ’s platform to generate new images based on completed tasks so users can visually see each step in the analysis pipeline. In addition, MCA implements a user-friendly GUI providing a simple interface which resembles

other native ImageJ plugins. We incorporated functionality for rigid registration to correct motion artifacts, algorithms for cell body prediction, and methods for annotating cells and exporting data. For cell prediction, we trained a custom model in Cellpose 2.0 for segmentation of nuclei expressing pan-neuronal nuclear localized GCaMP in zebrafish. We validated the accuracy of MCA output to previously published zebrafish calcium imaging data which elicited visually evoked neuronal responses. To show the versatility of MCA, we also show that our software can be utilized for multiple sensory modalities, brain regions, and multiple model organisms including Drosophila and mouse. Together these data show that MCA is viable for extracting calcium dynamics in a user-friendly environment for multiple forms of functional imaging.
DOI: 10.17632/f675cbz84s.2
title: MCA: A Multicellular analysis Calcium Imaging toolbox for ImageJ

description: Functional imaging using genetically encoded indicators, such as GCaMP, has become a foundational tool for in vivo experiments and allows for the analysis of cellular dynamics, sensory processing, and cellular communication. However, large scale or complex functional imaging experiments pose analytical challenges. Many programs have worked to create pipelines to address these challenges, however, most platforms require proprietary software, impose operational restrictions, offer limited outputs, or require significant knowledge of various programming languages, which collectively can limit utility. To address this, we designed MCA (a Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software which has been the standard image analysis platform for the last 30 years. We developed MCA to be visually intuitive, utilizing ImageJ’s platform to generate new images based on completed tasks so users can visually see each step in the analysis pipeline. In addition, MCA implements a user-friendly GUI providing a simple interface which resembles

other native ImageJ plugins. We incorporated functionality for rigid registration to correct motion artifacts, algorithms for cell body prediction, and methods for annotating cells and exporting data. For cell prediction, we trained a custom model in Cellpose 2.0 for segmentation of nuclei expressing pan-neuronal nuclear localized GCaMP in zebrafish. We validated the accuracy of MCA output to previously published zebrafish calcium imaging data which elicited visually evoked neuronal responses. To show the versatility of MCA, we also show that our software can be utilized for multiple sensory modalities, brain regions, and multiple model organisms including Drosophila and mouse. Together these data show that MCA is viable for extracting calcium dynamics in a user-friendly environment for multiple forms of functional imaging.
DOI: 10.17632/w292z59t7n
title: Postgraduate students awareness of the Gender Pay gap

description: Postgraduate students awareness of the gender pay gap
DOI: 10.17632/w292z59t7n.1
title: Postgraduate students awareness of the Gender Pay gap

description: Postgraduate students awareness of the gender pay gap
DOI: 10.18126/tc7d-1e15
title: Dataset for "Softening of shear-thickening in suspensions by the addition of large particles" A. Pelosse
DOI: 10.17632/xwfwgngxvf
title: Supplementary Materials - Serum proteomics of insulin resistance disorders distinguish MASLD from lipodystrophy and insulin receptor defects

description: Supplementary Materials for 
Mironova et al, Serum proteomics of insulin resistance disorders distinguish MASLD from lipodystrophy and insulin receptor defects
Journal of Clinical Endocrinology & Metabolism, 2025
https://doi.org/10.1210/clinem/dgaf516
DOI: 10.17632/xwfwgngxvf.1
title: Supplementary Materials - Serum proteomics of insulin resistance disorders distinguish MASLD from lipodystrophy and insulin receptor defects

description: Supplementary Materials for 
Mironova et al, Serum proteomics of insulin resistance disorders distinguish MASLD from lipodystrophy and insulin receptor defects
Journal of Clinical Endocrinology & Metabolism, 2025
https://doi.org/10.1210/clinem/dgaf516
DOI: 10.17632/b2r7xw6wgm
title: AIR_QUALITY_HIGH_ALTITUDE_ROOMS

description: The shared dataset compiles measured air-changes-per-hour values in a high-altitude university in Quito, presented as an alternative mitigation strategy to maintaining 2 m of social distancing in classroom environments. These data enable meaningful comparison with findings from other regions, thereby supporting informed decision-making aimed at reducing infection risk.