cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bqwvt
title: CCDC 2166957: Experimental Crystal Structure Determination

description: Related Article: Jindan Zhang, Chi Li, Mengqi Zhu, Junming Qiu, Yisi Yang, Lu Li, Shicheng Tang, Zhenghong Li, Ziwen Mao, Zhibing Cheng, Shengchang Xiang, Xiaoliang Zhang, Zhangjing Zhang|2023|Nano Energy|108|108217|doi:10.1016/j.nanoen.2023.108217
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.cc2g59hg
title: CCDC 2269215: Experimental Crystal Structure Determination

description: Related Article: Hai-Peng Li, Jia-Wen Wang, Quan-Guo Zhai|2023|J.Mater.Chem.A|11|21203|doi:10.1039/D3TA04732F
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.cc2g59jh
title: CCDC 2269216: Experimental Crystal Structure Determination

description: Related Article: Hai-Peng Li, Jia-Wen Wang, Quan-Guo Zhai|2023|J.Mater.Chem.A|11|21203|doi:10.1039/D3TA04732F
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: 10.34740/kaggle/dsv/6724477
title: Global Crypto Currency Price Database
DOI: 10.6084/m9.figshare.24328870
title: Machine learning results and code

description: Results for all the Four MLP-NN, RF, XGBR, and RF algorithms for each of geophysical array`s and machine learning python code is provided.
DOI: 10.5281/zenodo.10015796
title: Saviani e a Pedagogia Histórico-Crítica: Uma Abordagem Transformadora para a Educação

description: Este artigo tem como escopo a análise e investigação das valiosas contribuições do renomado educador brasileiro, Dermeval Saviani, para o campo da educação, centrando-se na abordagem pedagógica conhecida como Pedagogia Histórico-Crítica. O estudo contemplará a exposição minuciosa dos principais conceitos e fundamentos dessa perspectiva educacional, aprofundando-se em sua origem histórica e desenvolvimento teórico. Adicionalmente, será traçado um perfil da trajetória acadêmica de Saviani, elucidando as influências intelectuais que moldaram suas ideias pedagógicas, bem como suas principais obras que desempenharam papel relevante na consolidação e difusão da Pedagogia Histórico-Crítica. Ao longo da abordagem, será empreendida uma análise crítica da aplicabilidade dessa abordagem no cenário educacional contemporâneo, considerando suas potencialidades, limitações e desafios. Dessa forma, este artigo almeja contribuir para uma compreensão mais aprofundada da Pedagogia Histórico-Crítica como um arcabouço teórico-prático significativo para o aprimoramento das práticas educativas e para o desenvolvimento de uma consciência histórica crítica e emancipatória entre educadores e educandos.
DOI: 10.5281/zenodo.10015797
title: Saviani e a Pedagogia Histórico-Crítica: Uma Abordagem Transformadora para a Educação

description: Este artigo tem como escopo a análise e investigação das valiosas contribuições do renomado educador brasileiro, Dermeval Saviani, para o campo da educação, centrando-se na abordagem pedagógica conhecida como Pedagogia Histórico-Crítica. O estudo contemplará a exposição minuciosa dos principais conceitos e fundamentos dessa perspectiva educacional, aprofundando-se em sua origem histórica e desenvolvimento teórico. Adicionalmente, será traçado um perfil da trajetória acadêmica de Saviani, elucidando as influências intelectuais que moldaram suas ideias pedagógicas, bem como suas principais obras que desempenharam papel relevante na consolidação e difusão da Pedagogia Histórico-Crítica. Ao longo da abordagem, será empreendida uma análise crítica da aplicabilidade dessa abordagem no cenário educacional contemporâneo, considerando suas potencialidades, limitações e desafios. Dessa forma, este artigo almeja contribuir para uma compreensão mais aprofundada da Pedagogia Histórico-Crítica como um arcabouço teórico-prático significativo para o aprimoramento das práticas educativas e para o desenvolvimento de uma consciência histórica crítica e emancipatória entre educadores e educandos.
DOI: 10.5281/zenodo.10015780
title: Environmental effects caused by the Mw 7.7, September 19, 2022, Michoacán (Mexico)

description: This dataset includes documentation of Earthquake Environmental Effects (EEEs) triggered by the Mw 7.7, September 19, 2022, Michoacán (Mexico) earthquake. Data derive from original field surveys, published reports and papers, and from a search for EEEs posted online in social media and other websites.
The files include:

Shapefile of the sites where each EEE has been documented
Shapefile of earthquake-triggered landslides, mapped as polygons from the interpretation of satellite images
Shapefile of the area investigated for landslide mapping
Pdf document providing a description of each EEE.
Shapefiles are in coordinates WGS84 UTM Zone 13 N
 
Credits:

EEE documentation from field surveys: Velázquez Bucio M. Magdalena, Pizza Marco, Andres David Nuñez-Meneses
EEE documentation from social media: Muccignato Eliana, Velázquez Bucio M. Magdalena, Pizza Marco
Landslide mapping from interpretation of satellite images: Ferrario Francesca, Sridharan Aadityan
ESI intensity assessment: Velázquez Bucio M. Magdalena, Ferrario Francesca, Lacan Pierre, Muccignato Eliana, Pizza Marco, Porfido Sabina, Michetti Alessandro M.
Supervision: Velázquez Bucio M. Magdalena, Lacan Pierre, Michetti Alessandro M.
DOI: 10.5281/zenodo.10015781
title: Environmental effects caused by the Mw 7.7, September 19, 2022, Michoacán (Mexico)

description: This dataset includes documentation of Earthquake Environmental Effects (EEEs) triggered by the Mw 7.7, September 19, 2022, Michoacán (Mexico) earthquake. Data derive from original field surveys, published reports and papers, and from a search for EEEs posted online in social media and other websites.
The files include:

Shapefile of the sites where each EEE has been documented
Shapefile of earthquake-triggered landslides, mapped as polygons from the interpretation of satellite images
Shapefile of the area investigated for landslide mapping
Pdf document providing a description of each EEE.
Shapefiles are in coordinates WGS84 UTM Zone 13 N
 
Credits:

EEE documentation from field surveys: Velázquez Bucio M. Magdalena, Pizza Marco, Andres David Nuñez-Meneses
EEE documentation from social media: Muccignato Eliana, Velázquez Bucio M. Magdalena, Pizza Marco
Landslide mapping from interpretation of satellite images: Ferrario Francesca, Sridharan Aadityan
ESI intensity assessment: Velázquez Bucio M. Magdalena, Ferrario Francesca, Lacan Pierre, Muccignato Eliana, Pizza Marco, Porfido Sabina, Michetti Alessandro M.
Supervision: Velázquez Bucio M. Magdalena, Lacan Pierre, Michetti Alessandro M.
DOI: 10.6084/m9.figshare.24328876
title: No trade-off between growth and OsHV-1 tolerance in Pacific oysters (<i>Crassostrea gigas</i>) farmed on the West Coast, USA

description: Phenotypes (survival and weight) and genotypes of Pacific oyster families planted in Tomales Bay, California, and Willapa Bay, Washington, that were used in a study published in <i>Animal Genetics</i>.
DOI: 10.6084/m9.figshare.24328876.v1
title: No trade-off between growth and OsHV-1 tolerance in Pacific oysters (<i>Crassostrea gigas</i>) farmed on the West Coast, USA

description: Phenotypes (survival and weight) and genotypes of Pacific oyster families planted in Tomales Bay, California, and Willapa Bay, Washington, that were used in a study published in <i>Animal Genetics</i>.
DOI: 10.22002/dw66e-mbs82
title: AI-guided histopathology predicts brain metastasis in lung cancer patients

description:  Haowen Zhou, Mark Watson, Cory T. Bernadt, Steven (Siyu) Lin, Chieh-yu Lin, Jon. H. Ritter, Alexander Wein, Simon Mahler, Sid Rawal, Ramaswamy Govindan, Changhuei Yang*, and Richard J. Cote*. "AI-guided histopathology predicts brain metastasis in lung cancer patients." Journal of Pathology (In review).
DOI: 10.5281/zenodo.10014974
title: Data sets for "On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector" by Wright et al.

description: Copy of data described in 'On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector' by Wright et. al and to appear in the Journal of Space Weather and Space Climate (JSWSC), 2023.
DOI: 10.5281/zenodo.10014973
title: Data sets for "On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector" by Wright et al.

description: Copy of data described in 'On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector' by Wright et. al and to appear in the Journal of Space Weather and Space Climate (JSWSC), 2023.
DOI: 10.5281/zenodo.10011957
title: PEDS datasets and figure data

description: Datasets:

y_fisher25.csv: reaction-diffusion equation; the thermal flux corresponding to structures with 25 holes
X_fisher25.csv: reaction-diffusion equation; the side lengths of the 25 holes in the structures
y_fisher16.csv: reaction-diffusion equation; the thermal flux corresponding to structures with 16 holes
X_fisher16.csv: reaction-diffusion equation; the side lengths of the 16 holes in the structures
y_fourier25.csv: diffusion equation; the thermal flux corresponding to structures with 25 holes
X_fourier25.csv: diffusion equation; the side lengths of the 25 holes in the structures
y_fourier16.csv: diffusion equation; the thermal flux corresponding to structures with 16 holes
X_fourier16.csv: diffusion equation; the side lengths of the 16 holes in the structures
y_maxwell10.csv: Helmholtz equation; the complex transmission through the 10-layered structure
X_maxwell10.csv: Helmholtz equation; the side lengths of the 10 holes in each layer of the structure followed by a one-hot encoding of the frequency [0.5, 0.75, 1]
Figure data:

nb_trainingpoints_Fig1.csv: number of training points in the dataset–x-coordinates (Fig 1, S1, and S2)
baseline_alFig1.csv: error of the baseline ensemble using a dataset that was generated using active learning (Fig 1, S1, and S2)
baseline_noalFig1.csv: error of the baseline ensemble using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)
baseline_single_noalFig1.csv: error of the baseline (single model) using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)
PEDS_alFig1.csv: error of the PEDS ensemble using a dataset that was generated using active learning (Fig 1, S1, and S2)
PEDS_noalFig1.csv: error of the PEDS ensemble using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)
PEDS_single_noalFig1.csv: error of the PEDS (single model) using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)
SM10_ALFigS1.csv: error of the space mapping ensemble with a resolution of 10 using a dataset that was generated using active learning (Fig S1)
SM10_noALFigS1.csv: error of the space mapping ensemble with a resolution of 10 using a dataset that was sampled uniformly at random (Fig S1)
SM10_single_noALFigS1.csv: error of the space mapping (single model) with a resolution of 10 using a dataset that was sampled uniformly at random (Fig S1)
SM20_single_noalFigS2.csv: error of the space mapping (single model) with a resolution of 20 using a dataset that was sampled uniformly at random (Fig S2)
SM20_ALFigS2.csv: error of the space mapping ensemble with a resolution of 20 using a dataset that was generated using active learning (Fig S2)
SM20_noALFigS2.csv: error of the space mapping ensemble with a resolution of 20 using a dataset that was sampled uniformly at random (Fig S2)
resolutionFigS4.csv: resolution of the middle fidelity model–x-coordinate (Fig. S4)
error_midfidFigS4.csv: error of the middle fidelity model (Fig. S4)
DOI: 10.5281/zenodo.10011958
title: PEDS datasets and figure data

description: Datasets:

y_fisher25.csv: reaction-diffusion equation; the thermal flux corresponding to structures with 25 holes
X_fisher25.csv: reaction-diffusion equation; the side lengths of the 25 holes in the structures
y_fisher16.csv: reaction-diffusion equation; the thermal flux corresponding to structures with 16 holes
X_fisher16.csv: reaction-diffusion equation; the side lengths of the 16 holes in the structures
y_fourier25.csv: diffusion equation; the thermal flux corresponding to structures with 25 holes
X_fourier25.csv: diffusion equation; the side lengths of the 25 holes in the structures
y_fourier16.csv: diffusion equation; the thermal flux corresponding to structures with 16 holes
X_fourier16.csv: diffusion equation; the side lengths of the 16 holes in the structures
y_maxwell10.csv: Helmholtz equation; the complex transmission through the 10-layered structure
X_maxwell10.csv: Helmholtz equation; the side lengths of the 10 holes in each layer of the structure followed by a one-hot encoding of the frequency [0.5, 0.75, 1]
Figure data:

nb_trainingpoints_Fig1.csv: number of training points in the dataset–x-coordinates (Fig 1, S1, and S2)
baseline_alFig1.csv: error of the baseline ensemble using a dataset that was generated using active learning (Fig 1, S1, and S2)
baseline_noalFig1.csv: error of the baseline ensemble using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)
baseline_single_noalFig1.csv: error of the baseline (single model) using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)
PEDS_alFig1.csv: error of the PEDS ensemble using a dataset that was generated using active learning (Fig 1, S1, and S2)
PEDS_noalFig1.csv: error of the PEDS ensemble using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)
PEDS_single_noalFig1.csv: error of the PEDS (single model) using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)
SM10_ALFigS1.csv: error of the space mapping ensemble with a resolution of 10 using a dataset that was generated using active learning (Fig S1)
SM10_noALFigS1.csv: error of the space mapping ensemble with a resolution of 10 using a dataset that was sampled uniformly at random (Fig S1)
SM10_single_noALFigS1.csv: error of the space mapping (single model) with a resolution of 10 using a dataset that was sampled uniformly at random (Fig S1)
SM20_single_noalFigS2.csv: error of the space mapping (single model) with a resolution of 20 using a dataset that was sampled uniformly at random (Fig S2)
SM20_ALFigS2.csv: error of the space mapping ensemble with a resolution of 20 using a dataset that was generated using active learning (Fig S2)
SM20_noALFigS2.csv: error of the space mapping ensemble with a resolution of 20 using a dataset that was sampled uniformly at random (Fig S2)
resolutionFigS4.csv: resolution of the middle fidelity model–x-coordinate (Fig. S4)
error_midfidFigS4.csv: error of the middle fidelity model (Fig. S4)
DOI: 10.6084/m9.figshare.24328912
title: TT_plasma data_raw_10.17.23

description: HPLC-MS/MS data for plasma concentrations of triterpenes in 5xFAD mice administered a water extract of <i>Centella asiatica</i> (1000 mg/kg/day) for five weeks in the drinking water or in the diet
DOI: 10.6084/m9.figshare.24328912.v1
title: TT_plasma data_raw_10.17.23

description: HPLC-MS/MS data for plasma concentrations of triterpenes in 5xFAD mice administered a water extract of <i>Centella asiatica</i> (1000 mg/kg/day) for five weeks in the drinking water or in the diet
DOI: 10.6084/m9.figshare.24328852
title: CQA_plasma data_raw_10.17.23

description: HPLC-MS/MS data for plasma concentrations of caffeoylquinic acids and their metabolites in 5xFAD mice administered a water extract of <i>Centella asiatica</i> (1000 mg/kg/day) for five weeks in the drinking water or in the diet
DOI: 10.6084/m9.figshare.24328852.v1
title: CQA_plasma data_raw_10.17.23

description: HPLC-MS/MS data for plasma concentrations of caffeoylquinic acids and their metabolites in 5xFAD mice administered a water extract of <i>Centella asiatica</i> (1000 mg/kg/day) for five weeks in the drinking water or in the diet
DOI: 10.17595/20221215.001
title: 1000-member ensemble simulation output from NIES Chemistry-Climate Models for investigating Southern Hemisphere Stratospheric response to the sea surface temperature

description: The response of the southern hemisphere stratosphere to the ocean-surface conditions in 2002 and 2019, when exceptional sudden stratospheric warming (SSW) events occurred, were examined through 1010-year continuous Chemistry-Climate Model (CCM) runs. In comparison, the experiments with 2000 climate conditions and 2019 atmospheric nudging were performed. The datasets include zonal wind, temperature, and total ozone outputs with calculated EP flux data in these simulations. Data from the last 1000 years of the 1010-year calculation are stored as a 1000-member ensemble.
DOI: 10.6084/m9.figshare.24328933
title: Primer3plus parameters for qPCR primer-probe design

description: This resource was used for designing primers and probes in this paper - Kalvapalle, Prashant, et al. "Long duration environmental biosensing by recording analyte detection in DNA using recombinase memory." <i>bioRxiv</i> (2023): 2023-08. <b>doi</b>: https://doi.org/10.1101/2023.08.10.552812
DOI: 10.5281/zenodo.8035110
title: DORIS-MAE-v0

description: In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical. Existing evaluation datasets for this task are limited, primarily due to the high costs and effort required to annotate resources that effectively represent complex queries. To address this, we propose a novel task, <strong>S</strong>cientific <strong>DO</strong>cument <strong>R</strong>etrieval using <strong>M</strong>ulti-level <strong>A</strong>spect-based qu<strong>E</strong>ries (DORIS-MAE), which is designed to handle the complex nature of user queries in scientific research. The DORIS-MAE dataset is publicly available at https://github.com/Real-Doris-Mae/Doris-Mae-Dataset. This dataset is comprised of four main sub-datasets, each serving distinct purposes. The <strong>Query</strong> dataset contains 50 human-crafted complex queries spanning across five categories: ML, NLP, CV, AI, and Composite. Each category has 10 associated queries. Queries are broken down into aspects (ranging from 3 to 9 per query) and sub-aspects (from 0 to 6 per aspect, with 0 signifying no further breakdown required). For each query, a corresponding candidate pool of relevant paper abstracts, ranging from 99 to 130, is provided. The <strong>Corpus</strong> dataset is composed of 363,133 abstracts from computer science papers, published between 2011-2021, and sourced from arXiv. Each entry includes title, original abstract, URL, primary and secondary categories, as well as citation information retrieved from Semantic Scholar. A masked version of each abstract is also provided, facilitating the automated creation of queries. The <strong>Annotation</strong> dataset includes generated annotations for all 83,591 question pairs, each comprising an aspect/sub-aspect and a corresponding paper abstract from the query's candidate pool. It includes the original text generated by ChatGPT (version chatgpt-3.5-turbo-0301) explaining its decision-making process, along with a three-level relevance score (e.g., 0,1,2) representing ChatGPT's final decision. Finally, the <strong>Test Se</strong>t dataset contains human annotations for a random selection of 250 question pairs used in hypothesis testing. It includes each of the three human annotators' final decisions, recorded as a three-level relevance score (e.g., 0,1,2).
description: The DORIS-MAE dataset is contained in a paper submitted to NeurIPS 2023 Dataset Track for review. Please refer to benchmarking details in the GitHub link: https://github.com/Real-Doris-Mae/Doris-Mae-Dataset. Author information will be made available shortly.
description: {"references": ["https://github.com/Real-Doris-Mae/Doris-Mae-Dataset"]}
DOI: 10.5517/ccdc.csd.cc2fwxnx
title: CCDC 2261160: Experimental Crystal Structure Determination

description: Related Article: Iswar Chandra Mondal, Priya Rawat, Maksym Galkin, Snata Deka, Anirban Karmakar, Prosenjit Mondal, Subrata Ghosh|2023|Org.Biomol.Chem.|21|7831|doi:10.1039/D3OB01314F
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.cc2c3y4l
title: CCDC 2178529: Experimental Crystal Structure Determination

description: Related Article: E. Ganczar, A. Białońska|2023|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|79|392|doi:10.1107/S205252062300673X
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.cc2c3y7p
title: CCDC 2178532: Experimental Crystal Structure Determination

description: Related Article: E. Ganczar, A. Białońska|2023|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|79|392|doi:10.1107/S205252062300673X
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.cc2gtfd4
title: CCDC 2288556: Experimental Crystal Structure Determination

description: Related Article: Kevin Breitwieser, Fabian Dankert, Annette Grünwald, Paula R. Mayer, Frank W. Heinemann, Dominik Munz|2023|Chem.Commun.|59|12104|doi:10.1039/D3CC03964A
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.