cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2mk98s
title: CCDC 2429695: Experimental Crystal Structure Determination

description: Related Article: Shuhai Qiu, Kai Chen, Ziqi Deng, Xuan Jin, Zuoyu Li, Guogang Liu, Li Zhang, Wei Jiang, Teng‐Teng Chen, Junzhi Liu, Zhaohui Wang|2025|Angew.Chem.,Int.Ed.|64|e202516881|doi:10.1002/anie.202516881
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.cc2mk99t
title: CCDC 2429696: Experimental Crystal Structure Determination

description: Related Article: Shuhai Qiu, Kai Chen, Ziqi Deng, Xuan Jin, Zuoyu Li, Guogang Liu, Li Zhang, Wei Jiang, Teng‐Teng Chen, Junzhi Liu, Zhaohui Wang|2025|Angew.Chem.,Int.Ed.|64|e202516881|doi:10.1002/anie.202516881
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.cc2mktxy
title: CCDC 2430211: Experimental Crystal Structure Determination

description: Related Article: V. A. Postnikov, G. A. Yurasik, A. A. Kulishov, N. I. Sorokina, T. A. Sorokin, A. S. Stepko, P. V. Lebedev-Stepanov|2025|Crystallogr.Rep.|70|738|doi:10.1134/S1063774525601194
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.cc2mnz4f
title: CCDC 2433225: Experimental Crystal Structure Determination

description: Related Article: Rahul Kumar Singh, Dibya Yadav, Ashu Singh, Shilpi Misra, Amrendra K. Singh|2026|J.Organomet.Chem.|1044||doi:10.1016/j.jorganchem.2025.123939
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.cc2mppqr
title: CCDC 2433925: Experimental Crystal Structure Determination

description: Related Article: Junhua Li, Chunyin Li, Jing Li, Zhanwei Wang, Fengyin Wang, Yian Shi|2025|Org.Chem.Front.|12|6582|doi:10.1039/d5qo00634a
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.cc2mr56s
title: CCDC 2435335: Experimental Crystal Structure Determination

description: Related Article: Jun Sun, Guan-Xin Hu, Yu-Lei Yuan, Shi-Chun Jiang, Yong-Gui Liu, Deng-Yue Liu, Jing-Song Lv, Ying-Guo Liu, Bing Zeng|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c04114
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.cc2mr57t
title: CCDC 2435336: Experimental Crystal Structure Determination

description: Related Article: Jun Sun, Guan-Xin Hu, Yu-Lei Yuan, Shi-Chun Jiang, Yong-Gui Liu, Deng-Yue Liu, Jing-Song Lv, Ying-Guo Liu, Bing Zeng|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c04114
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.cc2mz3g6
title: CCDC 2442008: Experimental Crystal Structure Determination

description: Related Article: Dorra Kanzari-Mnallah, Sirine Salhi, Sonia Ben Hamouda, Michael Knorr, Jan-Lukas Kirchhoff, Carsten Strohmann, Khadija Essafi-Benkhadir, Mohamed Lotfi Efrit, Azaiez Ben Akacha|2025|J.Mol.Struct.|1353|144765|doi:10.1016/j.molstruc.2025.144765
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.cc2n09yy
title: CCDC 2443170: Experimental Crystal Structure Determination

description: Related Article: Vasile Lozovan, Victor Ch. Kravtsov, Marina S. Fonari, Laura E. Mureşan, Ioana Perhaiţa, Graziella Liana Turdean, Mihaela Mic, Daniele Marinotto, Daniele Malpicci, Alessandra Forni|2026|Dyes Pigm.|246||doi:10.1016/j.dyepig.2025.113410
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.cc2n0cz1
title: CCDC 2443233: Experimental Crystal Structure Determination

description: Related Article: Christin N. Gilchrist, Matthias Zeller, John J. Kiernicki|2025|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04839
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.cc2n0d03
title: CCDC 2443234: Experimental Crystal Structure Determination

description: Related Article: Christin N. Gilchrist, Matthias Zeller, John J. Kiernicki|2025|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04839
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.cc2n12rk
title: CCDC 2443908: Experimental Crystal Structure Determination

description: Related Article: Na Eun Kim, Seung Jun Lee, So Won Youn|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c04286
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.cc2n12ph
title: CCDC 2443906: Experimental Crystal Structure Determination

description: Related Article: Na Eun Kim, Seung Jun Lee, So Won Youn|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c04286
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.cc2n12vn
title: CCDC 2443911: Experimental Crystal Structure Determination

description: Related Article: Na Eun Kim, Seung Jun Lee, So Won Youn|2025|Org.Lett.|||doi:10.1021/acs.orglett.5c04286
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.cc2p3162
title: CCDC 2475573: Experimental Crystal Structure Determination

description: Related Article: Lidia García, Patricia García-Atienza, Sergio Armenta, José Manuel Herrero-Martínez, Mario Prejanò, Tiziana Marino, Donatella Armentano, Thais Grancha, Jesús Ferrando-Soria, Emilio Pardo|2025|J.Mater.Chem.A|13|42204|doi:10.1039/d5ta07539d
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.cc2pk54l
title: CCDC 2489149: Experimental Crystal Structure Determination

description: Related Article: Shicheng Han, Fangcheng Liu, Gang Fu, Ji Guo, Jiaheng Lin, Hongyu Wu, Chengyi Mu, Xikui Fang|2025|Chem.Commun.|61|19509|doi:10.1039/d5cc05382j
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.cc2pkrxy
title: CCDC 2489731: Experimental Crystal Structure Determination

description: Related Article: Suman Mahala, Mayank Shekhawat, Sohan Singh, Nattamai Bhuvanesh, Anuj K. Sharma, Hemant Joshi|2025|Organometallics|||doi:10.1021/acs.organomet.5c00370
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.cc2ppm01
title: CCDC 2493423: Experimental Crystal Structure Determination

description: Related Article: Sourav Kar, Koushik Saha, Vailappully Binilkrishna, Ivo Krummenacher, Rian D. Dewhurst, Holger Braunschweig|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c17540
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.cc2ppm12
title: CCDC 2493424: Experimental Crystal Structure Determination

description: Related Article: Sourav Kar, Koushik Saha, Vailappully Binilkrishna, Ivo Krummenacher, Rian D. Dewhurst, Holger Braunschweig|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c17540
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.cc2ppm23
title: CCDC 2493425: Experimental Crystal Structure Determination

description: Related Article: Sourav Kar, Koushik Saha, Vailappully Binilkrishna, Ivo Krummenacher, Rian D. Dewhurst, Holger Braunschweig|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c17540
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.cc2p56fj
title: CCDC 2477657: Experimental Crystal Structure Determination

description: Related Article: Lorenzo Marchi, Luca Terraneo, Gabriele Vaccari, Markus Steiner, Ritik Singhal, Laura Pigani, Carlo Castellano, Francesco Demartin, Philipp Pascal Nievergelt, André Alker, Frank Stowasser, Serena Maria Fantasia, Luca Rigamonti|2025|Organometallics|||doi:10.1021/acs.organomet.5c00412
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.cc2ppm34
title: CCDC 2493426: Experimental Crystal Structure Determination

description: Related Article: Sourav Kar, Koushik Saha, Vailappully Binilkrishna, Ivo Krummenacher, Rian D. Dewhurst, Holger Braunschweig|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c17540
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: 10.15468/dl.mfraaw
title: Occurrence Download

description: A dataset containing 2047 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Material citation, Specimen)",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pinguicula"
 ]
}

 The dataset includes 2047 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053931-251120083545085/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.30878060
title: Energy Management System

description: The availability of solar, wind, and load data is crucial to the operation of advanced Energy Management Systems (EMSs). Solar and wind data enable forecasting of renewable generation, while load data reflect consumption variability. Together, these datasets support forecasting supply and demand, optimizing scheduling and dispatch, managing uncertainty through probabilistic or robust methods, and enabling real-time control to balance deviations between predicted and actual values. In this way, data forms the foundation for improving EMS decision-making, reducing costs and emissions, and enhancing reliability across scales from microgrids to national systems.
DOI: 10.6084/m9.figshare.30878060.v1
title: Energy Management System

description: The availability of solar, wind, and load data is crucial to the operation of advanced Energy Management Systems (EMSs). Solar and wind data enable forecasting of renewable generation, while load data reflect consumption variability. Together, these datasets support forecasting supply and demand, optimizing scheduling and dispatch, managing uncertainty through probabilistic or robust methods, and enabling real-time control to balance deviations between predicted and actual values. In this way, data forms the foundation for improving EMS decision-making, reducing costs and emissions, and enhancing reliability across scales from microgrids to national systems.
DOI: 10.15468/dl.t6j8qr
title: Occurrence Download

description: A dataset containing 944 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is one of (Congo, Democratic Republic of the, Cameroon)",
 "TaxonKey is Oreochromis niloticus"
 ]
}

 The dataset includes 944 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053933-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14167680
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.30754130.v1
title: Data-Driven Feeder-Bus Network Design with Multiobjective Optimization

description: Here you can find detailed results about the article "Data-Driven Feeder-Bus Network Design with Multiobjective Optimization".Abstract: Transportation systems increasingly rely on data-driven decision support tools to optimize multimodal transit operations. This paper presents an integrated framework for feeder-bus network design that combines machine learning-based demand synthesis with multiobjective evolutionary optimization. The methodology addresses a critical challenge in metropolitan transit planning: designing feeder routes that efficiently connect peripheral areas to high-capacity rail systems while balancing competing objectives of service coverage and operational efficiency. Using publicly available data (census indicators, socioeconomic indices, origin--destination surveys, and transit infrastructure) we develop two neural network models to predict trip destinations and public transport usage. These models power a Monte Carlo demand generator that produces temporally resolved origin--destination matrices at metropolitan scale. A tailored NSGA-II algorithm with station-preserving encoding and capacity-aware operators then optimizes feeder routes under vehicle capacity and duty-time constraints. Applied to the Monterrey Metropolitan Area (Mexico), the framework generates feeder networks that increase passengers served by approximately 28\% and reduce total travel time by 17\% compared to the current configuration, using the same fleet size. The approach demonstrates how accessible data sources and intelligent optimization techniques can substantially improve transit connectivity and accessibility in growing metropolitan areas with limited real-time data infrastructure.<br>Git Hub with NSGA-II code: https://github.com/Sam126RV/Optimization-of-Metro-Feeder-Lines-NSGAii.git
DOI: 10.6084/m9.figshare.30754130
title: Data-Driven Feeder-Bus Network Design with Multiobjective Optimization

description: Here you can find detailed results about the article "Data-Driven Feeder-Bus Network Design with Multiobjective Optimization".Abstract: Transportation systems increasingly rely on data-driven decision support tools to optimize multimodal transit operations. This paper presents an integrated framework for feeder-bus network design that combines machine learning-based demand synthesis with multiobjective evolutionary optimization. The methodology addresses a critical challenge in metropolitan transit planning: designing feeder routes that efficiently connect peripheral areas to high-capacity rail systems while balancing competing objectives of service coverage and operational efficiency. Using publicly available data (census indicators, socioeconomic indices, origin--destination surveys, and transit infrastructure) we develop two neural network models to predict trip destinations and public transport usage. These models power a Monte Carlo demand generator that produces temporally resolved origin--destination matrices at metropolitan scale. A tailored NSGA-II algorithm with station-preserving encoding and capacity-aware operators then optimizes feeder routes under vehicle capacity and duty-time constraints. Applied to the Monterrey Metropolitan Area (Mexico), the framework generates feeder networks that increase passengers served by approximately 28\% and reduce total travel time by 17\% compared to the current configuration, using the same fleet size. The approach demonstrates how accessible data sources and intelligent optimization techniques can substantially improve transit connectivity and accessibility in growing metropolitan areas with limited real-time data infrastructure.<br>Git Hub with NSGA-II code: https://github.com/Sam126RV/Optimization-of-Metro-Feeder-Lines-NSGAii.git
DOI: 10.6084/m9.figshare.30885887
title: Training Data: Assessing a structure agnostic machine learning framework with experimental screening of magnetic materials

description: Dataset in CSV format supporting this article in Physical Review Materials: https://doi.org/10.1103/kjrr-ypd9The set consists of 3826 rows, each corresponding to a crystalline inorganic material. Each row contains the material's composition, identifier in the Magnetic Materials Database (https://doi.org/10.1103/PhysRevMaterials.4.114408), DFT-computed saturation polarization value, and fold identifier used during leave-one-cluster-out cross validation in the present work.
DOI: 10.6084/m9.figshare.30885887.v1
title: Training Data: Assessing a structure agnostic machine learning framework with experimental screening of magnetic materials

description: Dataset in CSV format supporting this article in Physical Review Materials: https://doi.org/10.1103/kjrr-ypd9The set consists of 3826 rows, each corresponding to a crystalline inorganic material. Each row contains the material's composition, identifier in the Magnetic Materials Database (https://doi.org/10.1103/PhysRevMaterials.4.114408), DFT-computed saturation polarization value, and fold identifier used during leave-one-cluster-out cross validation in the present work.
DOI: 10.15468/dl.zvcckf
title: Occurrence Download

description: A dataset containing 7403 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vincetoxicum rossicum"
 ]
}

 The dataset includes 7403 records from 97 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053934-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48331/scielodata.3bqgsr/3tciwp
title: vigilancia_alimentar_nutricional.dat

description: Banco de dados primários sobre a realização da vigilância alimentar e nutricional em unidades básicas de saúde do estado de Pernambuco, Brasil, 2021. Os dados apresentam informações demográficas e de atuação profissional dos participantes da pesquisa, bem como informações sobre as unidades básicas de saúde e processos de trabalho realizados em tais unidades. As variáveis utilizadas na análise são do tipo categórica. O nome dos participantes e a identificação nominal das unidades foram removidos do arquivo.
DOI: 10.48331/scielodata.3bqgsr/tgv7l1
title: README.txt

description: Informações descrevendo o banco de dados do arquivo "vigilancia_alimentar_nutricional".