cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc28fz8z
title: CCDC 2098801: Experimental Crystal Structure Determination

description: Related Article: Shuanggang Ma, Rubing Wang, Rongmei Gao, Xiaojing Wang, Yunbao Liu, Yong Li, Li Li, Yuhuan Li, Jing Qu, Shishan Yu|2022|Chin.Chem.Lett.|33|4248|doi:10.1016/j.cclet.2022.02.050
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.cc28fx4s
title: CCDC 2098735: Experimental Crystal Structure Determination

description: Related Article: Sourav Mukherjee, Nagarjuna Amarnath, Bimlesh Lochab|2021|Macromolecules|54|10001|doi:10.1021/acs.macromol.1c01582
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.cc28fx9y
title: CCDC 2098740: Experimental Crystal Structure Determination

description: Related Article: Aarón Terán, Miguel Cortijo, Ángel Gutiérrez, Ana E. Sánchez-Peláez, Santiago Herrero, Reyes Jiménez-Aparicio|2021|Ultrason.Sonochem.|80|105828|doi:10.1016/j.ultsonch.2021.105828
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.cc28fz0q
title: CCDC 2098793: Experimental Crystal Structure Determination

description: Related Article: Antônio S. N. Aguiar, Jaqueline E. Queiroz, Pollyana P. Firmino, Wesley F. Vaz, Ademir J. Camargo, Gilberto L. B. de Aquino, Hamilton B. Napolitano, Solemar S. Oliveira|2020|J.Mol.Model.|26|243|doi:10.1007/s00894-020-04506-1
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.cc28fxbz
title: CCDC 2098741: Experimental Crystal Structure Determination

description: Related Article: Aarón Terán, Miguel Cortijo, Ángel Gutiérrez, Ana E. Sánchez-Peláez, Santiago Herrero, Reyes Jiménez-Aparicio|2021|Ultrason.Sonochem.|80|105828|doi:10.1016/j.ultsonch.2021.105828
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.cc28fypc
title: CCDC 2098783: Experimental Crystal Structure Determination

description: Related Article: Andrei A. Ivashchenko, Oleg D. Mitkin, Jeremy C. Jones, Alexander V. Nikitin, Angela G. Koryakova, Alexey Ryakhovskiy, Ruben N. Karapetian, Dmitry V. Kravchenko, Vladimir Aladinskiy, Irina A. Leneva, Irina N. Falynskova, Ekaterina A. Glubokova, Elena A. Govorkova, Alexandre V. Ivachtchenko|2020|J.Med.Chem.|63|9403|doi:10.1021/acs.jmedchem.0c00565
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.30445157.v1
title: Supporting dataset for multi-scale water use simulation across 341 Chinese prefectures

description: This dataset provides supporting materials for the paper <i>“A multi-scale water use simulation framework integrating Cellular Automata and GLUE uncertainty analysis”</i> submitted to <i>Hydrology and Earth System Sciences (HESS)</i>. It includes (1) AIC values and probability distribution fitting results for each prefecture and spatial scale; (2) the appropriate spatial scale (in km) for each prefecture derived from the deep learning-based scale selection model; and (3) full simulation results of total water use (1998–2013).
DOI: 10.6084/m9.figshare.30445157
title: Supporting dataset for multi-scale water use simulation across 341 Chinese prefectures

description: This dataset provides supporting materials for the paper <i>“A multi-scale water use simulation framework integrating Cellular Automata and GLUE uncertainty analysis”</i> submitted to <i>Hydrology and Earth System Sciences (HESS)</i>. It includes (1) AIC values and probability distribution fitting results for each prefecture and spatial scale; (2) the appropriate spatial scale (in km) for each prefecture derived from the deep learning-based scale selection model; and (3) full simulation results of total water use (1998–2013).
DOI: 10.15468/dl.dxc2v3
title: Occurrence Download

description: A dataset containing 106100 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Fish (NSMK-PI)"
 ]
}

 The dataset includes 106100 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056679-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xqq4na
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Barbus euboicus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-26.542322 25.637500, 53.987701 25.637500, 53.987701 73.184014, -26.542322 73.184014, -26.542322 25.637500))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056686-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17442492
title: Data & Code for "Dark-sector imprints in compact geometries"

description: Dark-sector imprints in compact geometries: analysis code & data

 1. Cloud-optimized dark matter scanner scripts (Python) 2. Simulated positron spectroscopy data (3000 events) 3. Analysis results with 6.14σ WIMP detection at 211 GeV 4. Material sensitivity hierarchy (Re > Fe > C > H) 5. Visualization outputs and statistical reports The scanner implements τ-spectroscopy analysis with Bayesian optimization, reproducing the multi-messenger dark matter signatures reported in the main publication.

This archive contains Python scripts, raw τ-spectroscopy time series, MCMC chains, and compiled HZ-pattern response libraries (no source disclosure) supporting the detection of three new bosonic forces mediated by dark-matter particles.Scripts: MIT licenceRaw data: CC-BY 4.0
DOI: 10.6084/m9.figshare.30445295
title: Exploring students’ understanding of evolutionary biology through large-scale national university entrance examinations

description: Evolutionary biology is essential in science education, yet students often struggle with basic principles due to misconceptions and sociocultural influences. This study investigates Brazilian high school students’ understanding of evolutionary biology using big data from the National High School Examination (<i>ENEM</i>). We aimed to identify changes in comprehension over time, detect prevalent misconceptions, and analyse the association of socioeconomic and cultural variables. We examined over 150 million responses to 66 questions administered across 26 years, employing descriptive and inferential statistical methods. Results revealed very low comprehension (27.9%) and frequent reliance on intuitive reasoning, especially Lamarckian and progressivist thinking. School type was significantly associated with performance, highlighting the role of educational quality and access to resources. Despite this, even private school students showed limited understanding. Religiosity and scientific reading habit were also associated with scores, with less religious students and scientific readers scoring higher. Religious affiliation also appeared important, given substantial differences between some groups. However, multivariate logistic regression showed these variables had only modest discriminative power, indicating other contributing factors. These findings emphasise the urgent need for curricular reforms, improved teacher training, continuing education, and more engaging, conceptually grounded instructional strategies to correct misconceptions and enhance students’ understanding of evolution.
DOI: 10.6084/m9.figshare.30445295.v1
title: Exploring students’ understanding of evolutionary biology through large-scale national university entrance examinations

description: Evolutionary biology is essential in science education, yet students often struggle with basic principles due to misconceptions and sociocultural influences. This study investigates Brazilian high school students’ understanding of evolutionary biology using big data from the National High School Examination (<i>ENEM</i>). We aimed to identify changes in comprehension over time, detect prevalent misconceptions, and analyse the association of socioeconomic and cultural variables. We examined over 150 million responses to 66 questions administered across 26 years, employing descriptive and inferential statistical methods. Results revealed very low comprehension (27.9%) and frequent reliance on intuitive reasoning, especially Lamarckian and progressivist thinking. School type was significantly associated with performance, highlighting the role of educational quality and access to resources. Despite this, even private school students showed limited understanding. Religiosity and scientific reading habit were also associated with scores, with less religious students and scientific readers scoring higher. Religious affiliation also appeared important, given substantial differences between some groups. However, multivariate logistic regression showed these variables had only modest discriminative power, indicating other contributing factors. These findings emphasise the urgent need for curricular reforms, improved teacher training, continuing education, and more engaging, conceptually grounded instructional strategies to correct misconceptions and enhance students’ understanding of evolution.
DOI: 10.34740/kaggle/dsv/13498141
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.23maj3
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Barbus peloponnesius",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-26.542322 25.637500, 53.987701 25.637500, 53.987701 73.184014, -26.542322 73.184014, -26.542322 25.637500))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 14 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056687-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5mphdn
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Fish (MNHM-PI)"
 ]
}

 The dataset includes 76 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056688-251009101135966/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.30445259
title: <b>ELECTROENCEPHALOGRAM ABNORMALITIES IN ELDERLY PATIENTS</b>

description: Background and aims: Electroencephalography (EEG) abnormalities are common in the elderly due to a higher risk of vascular diseases, cognitive decline, and other systemic disorders. However, electroencephalogram abnormalities in the elderly have not been thoroughly investigated in Southeast Asia. We aimed to elucidate the prevalence and associated factors of EEG abnormalities in elderly patients. Materials and methods: This cross-sectional study was conducted between October 2024 and October 2025 at the National University of Malaysia. Demographic and clinical data of the participants were collected from patients aged 65 years or older who underwent EEG. Patients with agitation, acute psychosis, and non-epileptic seizures were excluded. EEG findings were classified as normal or abnormal. The outcome of the patients was evaluated using the Glasgow Outcome Scale. Results: A total of 288 participants were recruited for this study with a median age of 74.0 years (IQR: 69.0 – 80.0), and 50.0% were male. Common comorbidities include hypertension (76.0%), stroke (62.2%), and diabetes mellitus (56.6%). EEG abnormalities were present in 73.3% of patients, consisting of generalized slowing (54.9%), focal slowing (10.8%), and epileptic changes (7.6%). In multivariate analysis, significant risk factors for abnormal EEG included seizures (aOR = 6.53, p &lt; 0.001), hyperlipidaemia (aOR = 2.14, p = 0.039), infection (aOR = 3.56, p = 0.043), structural causes (aOR = 2.98, p = 0.019), stroke (aOR = 5.65, p = 0.012), infarct (aOR = 0.23, p = 0.044), creatinine (aOR = 1.01, p = 0.011), albumin (aOR = 0.88, p &lt; 0.001), and length of hospitalisation (aOR = 1.07, p = 0.014). Conclusion: EEG abnormalities are commonly observed in elderly patients and are associated with multiple vascular, metabolic, and structural factors. The incorporation of EEG into geriatric care can improve diagnostic accuracy and guide management strategies.
DOI: 10.6084/m9.figshare.30445259.v1
title: <b>ELECTROENCEPHALOGRAM ABNORMALITIES IN ELDERLY PATIENTS</b>

description: Background and aims: Electroencephalography (EEG) abnormalities are common in the elderly due to a higher risk of vascular diseases, cognitive decline, and other systemic disorders. However, electroencephalogram abnormalities in the elderly have not been thoroughly investigated in Southeast Asia. We aimed to elucidate the prevalence and associated factors of EEG abnormalities in elderly patients. Materials and methods: This cross-sectional study was conducted between October 2024 and October 2025 at the National University of Malaysia. Demographic and clinical data of the participants were collected from patients aged 65 years or older who underwent EEG. Patients with agitation, acute psychosis, and non-epileptic seizures were excluded. EEG findings were classified as normal or abnormal. The outcome of the patients was evaluated using the Glasgow Outcome Scale. Results: A total of 288 participants were recruited for this study with a median age of 74.0 years (IQR: 69.0 – 80.0), and 50.0% were male. Common comorbidities include hypertension (76.0%), stroke (62.2%), and diabetes mellitus (56.6%). EEG abnormalities were present in 73.3% of patients, consisting of generalized slowing (54.9%), focal slowing (10.8%), and epileptic changes (7.6%). In multivariate analysis, significant risk factors for abnormal EEG included seizures (aOR = 6.53, p &lt; 0.001), hyperlipidaemia (aOR = 2.14, p = 0.039), infection (aOR = 3.56, p = 0.043), structural causes (aOR = 2.98, p = 0.019), stroke (aOR = 5.65, p = 0.012), infarct (aOR = 0.23, p = 0.044), creatinine (aOR = 1.01, p = 0.011), albumin (aOR = 0.88, p &lt; 0.001), and length of hospitalisation (aOR = 1.07, p = 0.014). Conclusion: EEG abnormalities are commonly observed in elderly patients and are associated with multiple vascular, metabolic, and structural factors. The incorporation of EEG into geriatric care can improve diagnostic accuracy and guide management strategies.
DOI: 10.5281/zenodo.17442753
title: Open Data - Pengaruh AI-Driven Analytics terhadap Perceived Security, Service Value Perception, dan Dampaknya pada Brand Engagement dengan Customer Trust sebagai Moderator pada Perbankan Digital
DOI: 10.5281/zenodo.17442754
title: Open Data - Pengaruh AI-Driven Analytics terhadap Perceived Security, Service Value Perception, dan Dampaknya pada Brand Engagement dengan Customer Trust sebagai Moderator pada Perbankan Digital
DOI: 10.6084/m9.figshare.30432619
title: Dataset

description: Dataset of interviews
DOI: 10.6084/m9.figshare.30432619.v1
title: Dataset

description: Dataset of interviews
DOI: 10.6084/m9.figshare.30432619.v2
title: Dataset

description: Dataset of interviews
DOI: 10.5517/ccdc.csd.cc28fyqd
title: CCDC 2098784: Experimental Crystal Structure Determination

description: Related Article: Andrei A. Ivashchenko, Oleg D. Mitkin, Jeremy C. Jones, Alexander V. Nikitin, Angela G. Koryakova, Alexey Ryakhovskiy, Ruben N. Karapetian, Dmitry V. Kravchenko, Vladimir Aladinskiy, Irina A. Leneva, Irina N. Falynskova, Ekaterina A. Glubokova, Elena A. Govorkova, Alexandre V. Ivachtchenko|2020|J.Med.Chem.|63|9403|doi:10.1021/acs.jmedchem.0c00565
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.cc28fxf2
title: CCDC 2098744: Experimental Crystal Structure Determination

description: Related Article: Mustapha Haddach, Jerome Michaux, Michael K. Schwaebe, Fabrice Pierre, Sean E. O’Brien, Cosmin Borsan, Joe Tran, Nicholas Raffaele, Suchitra Ravula, Denis Drygin, Adam Siddiqui-Jain, Levan Darjania, Ryan Stansfield, Chris Proffitt, Diwata Macalino, Nicole Streiner, Joshua Bliesath, May Omori, Jeffrey P. Whitten, Kenna Anderes, William G. Rice, David M. Ryckman|2012|ACS.Med.Chem.Lett.|3|135|doi:10.1021/ml200259q
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.cc28fyrf
title: CCDC 2098785: Experimental Crystal Structure Determination

description: Related Article: Andrei A. Ivashchenko, Oleg D. Mitkin, Jeremy C. Jones, Alexander V. Nikitin, Angela G. Koryakova, Alexey Ryakhovskiy, Ruben N. Karapetian, Dmitry V. Kravchenko, Vladimir Aladinskiy, Irina A. Leneva, Irina N. Falynskova, Ekaterina A. Glubokova, Elena A. Govorkova, Alexandre V. Ivachtchenko|2020|J.Med.Chem.|63|9403|doi:10.1021/acs.jmedchem.0c00565
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.cc28fxg3
title: CCDC 2098745: Experimental Crystal Structure Determination

description: Related Article: Zakariyya Ishtaiwi, Deeb Taher, Marcus Korb, Wissam Helal, Hassan K. Juwhari, Afnan Al-Hunaiti, Hazem Amarne, Khaleel Assaf, Lubna Alrawashdeh, Mohammad W Amer, Yaser A YouSef, Heinrich Lang|2023|J.Mol.Struct.|1288|135747|doi:10.1016/j.molstruc.2023.135747
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.cc28fxh4
title: CCDC 2098746: Experimental Crystal Structure Determination

description: Related Article: Zakariyya Ishtaiwi, Deeb Taher, Marcus Korb, Wissam Helal, Hassan K. Juwhari, Afnan Al-Hunaiti, Hazem Amarne, Khaleel Assaf, Lubna Alrawashdeh, Mohammad W Amer, Yaser A YouSef, Heinrich Lang|2023|J.Mol.Struct.|1288|135747|doi:10.1016/j.molstruc.2023.135747
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.cc28fz1r
title: CCDC 2098794: Experimental Crystal Structure Determination

description: Related Article: Aliakbar Dehno Khalaji|2020|J. Med. Chem. Sci.|3|176|doi:10.26655/JMCHEMSCI.2020.2.9
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.cc28fz2s
title: CCDC 2098795: Experimental Crystal Structure Determination

description: Related Article: Aliakbar Dehno Khalaji|2020|J. Med. Chem. Sci.|3|176|doi:10.26655/JMCHEMSCI.2020.2.9
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.cc28fz3t
title: CCDC 2098796: Experimental Crystal Structure Determination

description: Related Article: Ahmed S. M. Al‐Janabi, Osama'a A. Y. Al‐Samrai, Tarek A. Yousef|2020|Appl.Organomet.Chem.|34|e5967|doi:10.1002/aoc.5967
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.cc28fyth
title: CCDC 2098787: Experimental Crystal Structure Determination

description: Related Article: Ahmad Khajeh Ebrahimi, Iran Sheikhshoaie, Mahdieh Mehran|2017|J.Mol.Liq.|240|803|doi:10.1016/j.molliq.2017.06.097
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.