cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2p91dg
title: CCDC 2481345: Experimental Crystal Structure Determination

description: Related Article: Julia Gasol-Cardona, Martin R. Ward, Davide Comboni, Micheal Hanfland, Rebecca Scatena, Mark R. Warren, Andrew G. P. Maloney, Iain D. H. Oswald|2025|CrystEngComm|27|7662|doi:10.1039/d5ce00855g
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.cc2p91fh
title: CCDC 2481346: Experimental Crystal Structure Determination

description: Related Article: Julia Gasol-Cardona, Martin R. Ward, Davide Comboni, Micheal Hanfland, Rebecca Scatena, Mark R. Warren, Andrew G. P. Maloney, Iain D. H. Oswald|2025|CrystEngComm|27|7662|doi:10.1039/d5ce00855g
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.cc2p91gj
title: CCDC 2481347: Experimental Crystal Structure Determination

description: Related Article: Julia Gasol-Cardona, Martin R. Ward, Davide Comboni, Micheal Hanfland, Rebecca Scatena, Mark R. Warren, Andrew G. P. Maloney, Iain D. H. Oswald|2025|CrystEngComm|27|7662|doi:10.1039/d5ce00855g
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.cc2p91hk
title: CCDC 2481348: Experimental Crystal Structure Determination

description: Related Article: Julia Gasol-Cardona, Martin R. Ward, Davide Comboni, Micheal Hanfland, Rebecca Scatena, Mark R. Warren, Andrew G. P. Maloney, Iain D. H. Oswald|2025|CrystEngComm|27|7662|doi:10.1039/d5ce00855g
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.cc2pdg9w
title: CCDC 2484628: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgbx
title: CCDC 2484629: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgcy
title: CCDC 2484630: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgdz
title: CCDC 2484631: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgf0
title: CCDC 2484632: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgg1
title: CCDC 2484633: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgh2
title: CCDC 2484634: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgj3
title: CCDC 2484635: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgk4
title: CCDC 2484636: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2pdgl5
title: CCDC 2484637: Experimental Crystal Structure Determination

description: Related Article: Matija Čulig, Vinko Nemec, Nikola Bregović, Dominik Cinčić|2025|CrystEngComm|27|7638|doi:10.1039/d5ce00870k
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.cc2kb165
title: CCDC 2363136: Experimental Crystal Structure Determination

description: Related Article: Kazuhiro Uemura, Shota Kanamori|2025|CrystEngComm|27|7633|doi:10.1039/d5ce00818b
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.cc2nfcj0
title: CCDC 2455712: Experimental Crystal Structure Determination

description: Related Article: Masatoshi Kawahata, Hayahide Makino, Tadashi Hyodo, Masahide Tominaga, Kentaro Yamaguchi|2025|CrystEngComm|27|7655|doi:10.1039/d5ce00655d
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.cc2kb154
title: CCDC 2363135: Experimental Crystal Structure Determination

description: Related Article: Kazuhiro Uemura, Shota Kanamori|2025|CrystEngComm|27|7633|doi:10.1039/d5ce00818b
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: 10.25318/3310109401-eng
title: Future outlook over the next 12 months, fourth quarter of 2025

description: Future outlook over the next 12 months, fourth quarter of 2025, by percentage ranges, North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2025.
DOI: 10.5281/zenodo.17778062
title: Supplementary Material - The microwave bacteriome: biodiversity of domestic and laboratory microwave ovens

description: Supplementary Material from "The microwave bacteriome: biodiversity of domestic and laboratory microwave ovens", Iglesias et al., 2024, Frontiers in Microbiology. https://doi.org/10.3389/fmicb.2024.1395751
DOI: 10.5281/zenodo.17778063
title: Supplementary Material - The microwave bacteriome: biodiversity of domestic and laboratory microwave ovens

description: Supplementary Material from "The microwave bacteriome: biodiversity of domestic and laboratory microwave ovens", Iglesias et al., 2024, Frontiers in Microbiology. https://doi.org/10.3389/fmicb.2024.1395751
DOI: 10.5281/zenodo.17777906
title: ChriSmA2 Dataset – Evaluation Criteria and Analysis of IoT Platforms (Scalability, Replicability & Real-world Applications)

description: This dataset accompanies the paper “ChriSmA2: A Comprehensive Handbook and Review for IoT Platforms: Scalability, Replicability, and Real-world Applications”.The Excel file contains the full evaluation results of 18 IoT platforms based on the criteria defined in the methodology (community support, documentation quality, security, deployment options, scalability, replicability, use-cases, and limitations).It supports transparency, reproducibility, and reusability of the analysis presented in the paper.
DOI: 10.5281/zenodo.17777905
title: ChriSmA2 Dataset – Evaluation Criteria and Analysis of IoT Platforms (Scalability, Replicability & Real-world Applications)

description: This dataset accompanies the paper “ChriSmA2: A Comprehensive Handbook and Review for IoT Platforms: Scalability, Replicability, and Real-world Applications”.The Excel file contains the full evaluation results of 18 IoT platforms based on the criteria defined in the methodology (community support, documentation quality, security, deployment options, scalability, replicability, use-cases, and limitations).It supports transparency, reproducibility, and reusability of the analysis presented in the paper.
DOI: 10.17026/ar/ebr1la
title: PAN-00173366 - wire brooch with angular bow variant A1

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00173366&lt;/p&gt;
description: Modified: 2025-04-29T11:06:12+02:00
DOI: 10.15468/dl.q62jau
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is one of (Capriolo Capriolo (Linnaeus, 1758), Capriolo Capriolo Capriolo, Cervus Capriolo Linnaeus, 1758)",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Occurrence evidence, Material sample, Living Specimen)",
 "Country is one of (Italy)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020350-251120083545085/datasets/export for details.
DOI: 10.15468/dl.g6nbaq
title: Occurrence Download

description: A dataset containing 626 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Episyrphus balteatus",
 "HasCoordinate is true",
 "Country is Netherlands",
 "Year is 2020,2022",
 "CoordinateUncertaintyInMeters is less than 5000"
 ]
}

 The dataset includes 626 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020343-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ct89y
title: Occurrence Download

description: A dataset containing 22598 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tyto alba",
 "HasCoordinate is true",
 "Country is Netherlands",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 2020",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 22598 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020346-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r3uyrs
title: Occurrence Download

description: A dataset containing 1066 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Austria",
 "TaxonKey is Streptopus amplexifolius"
 ]
}

 The dataset includes 1066 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020348-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29924065
title: Complex interaction of δ-globin variant HbA<sub>2</sub>-Famagusta combined with β-globin variant Hb Monroe in <i>cis</i> with the HBB:c.-92C&gt;G mutation: hematological characteristics and molecular diagnostic insights

description: Hemoglobin (Hb) variants can cause clinical conditions like thalassemia. Understanding their molecular phenotypes and clinical profiles is essential for accurate diagnosis and genetic counseling. Blood samples from a 67-year-old Thai female and four relatives were analyzed using HPLC and capillary electrophoresis. PCR techniques and sequencing were used to identify globin gene mutations and β-globin haplotypes. Pathogenicity and transcription factor-binding sites were predicted. A rapid diagnostic method was developed and validated using 1,414 samples to assess the geographic distribution of the variants. Hb analysis revealed HbA, HbA<sub>2</sub>, and a slower-migrating Hb fraction. Mutations identified included HBB:c.92 G &gt; C(HbMonroe) in <i>cis</i> with HBB:c.-92C &gt; G, and HBD:c.60C &gt; A(HbA<sub>2</sub>-Famagusta). This novel combination exhibited normal HbA<sub>2</sub> levels, potentially masking β-thalassemia. Combining HbMonroe with HbE leads to severe clinical symptoms. HbA<sub>2</sub>-Famagusta was predicted to be benign, while HbMonroe was deleterious. The geographic distribution of HbMonroe was found to be 0.09% with an allele frequency of 0.00042 and 1.30% with an allele frequency of 0.00649 in β-thalassemia carriers and EF syndrome, respectively. HbMonroe is a β<sup>0</sup>-thalassemic variant affecting splicing and structure, undetectable by standard Hb analysis. Including HbA<sub>2</sub> variants in total HbA<sub>2</sub> quantification is essential for accurate diagnosis and improved genetic counseling in thalassemia-endemic areas.
DOI: 10.6084/m9.figshare.29924065.v2
title: Complex interaction of δ-globin variant HbA<sub>2</sub>-Famagusta combined with β-globin variant Hb Monroe in <i>cis</i> with the HBB:c.-92C&gt;G mutation: hematological characteristics and molecular diagnostic insights

description: Hemoglobin (Hb) variants can cause clinical conditions like thalassemia. Understanding their molecular phenotypes and clinical profiles is essential for accurate diagnosis and genetic counseling. Blood samples from a 67-year-old Thai female and four relatives were analyzed using HPLC and capillary electrophoresis. PCR techniques and sequencing were used to identify globin gene mutations and β-globin haplotypes. Pathogenicity and transcription factor-binding sites were predicted. A rapid diagnostic method was developed and validated using 1,414 samples to assess the geographic distribution of the variants. Hb analysis revealed HbA, HbA<sub>2</sub>, and a slower-migrating Hb fraction. Mutations identified included HBB:c.92 G &gt; C(HbMonroe) in <i>cis</i> with HBB:c.-92C &gt; G, and HBD:c.60C &gt; A(HbA<sub>2</sub>-Famagusta). This novel combination exhibited normal HbA<sub>2</sub> levels, potentially masking β-thalassemia. Combining HbMonroe with HbE leads to severe clinical symptoms. HbA<sub>2</sub>-Famagusta was predicted to be benign, while HbMonroe was deleterious. The geographic distribution of HbMonroe was found to be 0.09% with an allele frequency of 0.00042 and 1.30% with an allele frequency of 0.00649 in β-thalassemia carriers and EF syndrome, respectively. HbMonroe is a β<sup>0</sup>-thalassemic variant affecting splicing and structure, undetectable by standard Hb analysis. Including HbA<sub>2</sub> variants in total HbA<sub>2</sub> quantification is essential for accurate diagnosis and improved genetic counseling in thalassemia-endemic areas.
DOI: 10.6084/m9.figshare.29564639
title: Exploring vegetation-driven microclimatic effects on soil temperature dynamics in tropical climates through machine learning and explainable AI

description: Soil temperature (ST) is a crucial parameter in tropical environments, influencing microbial activity, nutrient cycling, and root growth. Accurate and cost-effective prediction of ST is essential for understanding soil health and supporting resilient ecosystems. This study investigates ST dynamics across four different urban tropical microclimates within a 150-square-meter area over a four-month period, utilizing a dataset comprising 5,856 observations collected in a tropical setting. Advanced machine learning modelling, including Random Forest Regressor (RFR), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Multilayer Perceptron (MLP), coupled with Explainable Artificial Intelligence (XAI) was employed. Results reveal that the interplay of solar azimuth and vegetation cover governs ST variations. The XGBoost outperformed all other machine learning models, exhibiting the most accurate predictions and resulting root mean square error (RMSE) values of 0.298 ± 0.008ºC for modelling ST at 10 cm depth, 0.117 ± 0.006ºC at 30 cm and 0.064 ± 0.002ºC at 50 cm. XAI analysis highlighted air temperature as the dominant predictor of ST at 10 cm, while deeper layers were influenced by temperature of the overlaying soil layer, followed by solar radiation and soil water content. These findings emphasize the potential of integrating machine learning (ML) and XAI for explicit and reliable ST prediction and advancing plant growth.
DOI: 10.6084/m9.figshare.29564639.v1
title: Exploring vegetation-driven microclimatic effects on soil temperature dynamics in tropical climates through machine learning and explainable AI

description: Soil temperature (ST) is a crucial parameter in tropical environments, influencing microbial activity, nutrient cycling, and root growth. Accurate and cost-effective prediction of ST is essential for understanding soil health and supporting resilient ecosystems. This study investigates ST dynamics across four different urban tropical microclimates within a 150-square-meter area over a four-month period, utilizing a dataset comprising 5,856 observations collected in a tropical setting. Advanced machine learning modelling, including Random Forest Regressor (RFR), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Multilayer Perceptron (MLP), coupled with Explainable Artificial Intelligence (XAI) was employed. Results reveal that the interplay of solar azimuth and vegetation cover governs ST variations. The XGBoost outperformed all other machine learning models, exhibiting the most accurate predictions and resulting root mean square error (RMSE) values of 0.298 ± 0.008ºC for modelling ST at 10 cm depth, 0.117 ± 0.006ºC at 30 cm and 0.064 ± 0.002ºC at 50 cm. XAI analysis highlighted air temperature as the dominant predictor of ST at 10 cm, while deeper layers were influenced by temperature of the overlaying soil layer, followed by solar radiation and soil water content. These findings emphasize the potential of integrating machine learning (ML) and XAI for explicit and reliable ST prediction and advancing plant growth.