cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fb4xv
title: CCDC 2244087: Experimental Crystal Structure Determination

description: Related Article: Debiao Huo|2023|CSD Communication|||
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.cc2g0kwy
title: CCDC 2264670: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Phil Baran|2023|CSD Communication|||
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.23309888
title: Fig_S8a_OscillatoryRheology_SourceData

description: Temperature sweep of 1% (w/v) <strong>P5</strong> hydrogels crosslinked by different CCLs. The samples were measured at 10% strain and 1.6Hz in an oscillatory rheometer.
DOI: 10.6084/m9.figshare.23309888.v1
title: Fig_S8a_OscillatoryRheology_SourceData

description: Temperature sweep of 1% (w/v) <strong>P5</strong> hydrogels crosslinked by different CCLs. The samples were measured at 10% strain and 1.6Hz in an oscillatory rheometer.
DOI: 10.14459/2023mp1688868
title: Collection of slope deformation data until failure with a timely resolution of daily or better supplying the papaer: "An approach for prospective forecasting of rock slope failure time" by Leinauer et al

description: This data set is a collection of slope deformation data until failure with a timely resolution of daily or better. This data collection belongs to the article: Leinauer J., Weber S., Cicoira A., Beutel J. and Krautblatter M. (2023): An approach for prospective forecasting of rock slope failure time; Commun. Earth Environ. <br> The original raw data have not been measured by the authors. The source of each data set within this collection is outlined in the first 7 lines of each data file together with some meta information. More information can be found in the article. <br> The collection includes the following 14 sites: Agoyama, Arvigo, Galterengraben, Grabengufer, Hogarth, Kegemori, La Saxe, Nevis Bluff, Ohto, Preonzo, Ryan and Call slope 2, Vaiont, Veslemannen, Weissmiess
DOI: 10.34740/kaggle/dsv/5865748
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/5865766
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.7273664
title: Supplementary data: "Physics-informed machine learning for power grid frequency modelling"

description: This repository contains result files for the paper "Physics-informed machine learning for power grid frequency modelling" (Preprint). The code for producing the processed data and the results is available at github. <strong>Results</strong> The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files: <em>tuning </em>: Results of hyper-parameter tuning. <em>best_model </em>: Weights of the trained model with best hyper-parameters. <em>best_model_&lt;scaling-variation&gt; </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling. <em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised. <em>shap_values_&lt;parameter&gt;_long.h5</em> : SHAP values for the prediction of the system parameters.
DOI: 10.5281/zenodo.7273665
title: Supplementary data: "Physics-inspired machine learning for power grid frequency modelling"

description: This repository contains processed data and result files for the paper Physics-inspired machine learning for power grid frequency modelling . The code for producing the processed data and the results is available at github. <strong>Data</strong> The data folder contains the feature data used to train the machine learning model. The target data, i.e., the grid frequency measurements, were taken from this repository and are not available in here. The data for Continental Europe comprises the following folders and files: <em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features. <em>inputs_forecast.h5 </em><em>:</em> The day-ahead available input features including the engineered features. <em>inputs_actual.h5 </em><em>:</em> Actual (ex-post available) input features including the engineered features. <em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation. The data for input features (<em>raw_input_data.h5</em> and <em>input_&lt;type&gt;.h5</em>) is derived from ENTSO-E Transparency Platform data. <strong>Results</strong> The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files: <em>tuning </em>: Results of hyper-parameter tuning. <em>best_model </em>: Weights of the trained model with best hyper-parameters. <em>best_model_&lt;scaling-variation&gt; </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling. <em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised. <em>shap_values_&lt;parameter&gt;.h5</em> : SHAP values for the prediction of the system parameters. <strong>Disclaimer</strong> The data might be subject to copyright or related rights. Please consult the primary data owner.
DOI: 10.5281/zenodo.8014065
title: Supplementary data: "Physics-informed machine learning for power grid frequency modelling"

description: This repository contains result files for the paper "Physics-informed machine learning for power grid frequency modelling" (Preprint). The code for producing the processed data and the results is available at github. <strong>Results</strong> The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files: <em>tuning </em>: Results of hyper-parameter tuning. <em>best_model </em>: Weights of the trained model with best hyper-parameters. <em>best_model_&lt;scaling-variation&gt; </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling. <em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised. <em>shap_values_&lt;parameter&gt;_long.h5</em> : SHAP values for the prediction of the system parameters.
DOI: 10.6084/m9.figshare.23310005
title: Fig_S8b_Nupack_melt_curve_SourceData

description: NUPACK calculation for predicting the melting temperature (Tm) of different splint concentrations corresponding to the actual concentration of distinct splint pairs in CCL-1 (35 μM) and CCL-256 (0.137 μM) at [Na+] = 0.15M.
DOI: 10.6084/m9.figshare.23310005.v1
title: Fig_S8b_Nupack_melt_curve_SourceData

description: NUPACK calculation for predicting the melting temperature (Tm) of different splint concentrations corresponding to the actual concentration of distinct splint pairs in CCL-1 (35 μM) and CCL-256 (0.137 μM) at [Na+] = 0.15M.
DOI: 10.6084/m9.figshare.23310014
title: Fig_S9_OscillatoryRheology_SourceData

description: Rapid self-healing tests of 1% (w/v) <strong>P5 </strong>hydrogels crosslinked by different CCLs. The samples were sheared at 1000% (disruption) and 10% (recovery) strain at 1.6Hz, 37°C for 5 repetitive cycles.
DOI: 10.5281/zenodo.8014109
title: Data set and analytic codes supporting "Direct observation to assess the effects of habitat structure and complexity on resource-use behaviour of butterflies: a study case in smallholding oil palm plantations in Peninsular Malaysia"

description: This deposit contains data set and analytic codes (with a meta data) supporting "Direct observation to assess the effects of habitat structure and complexity on resource-use behaviour of butterflies: a study case in smallholding oil palm plantations in Peninsular Malaysia". We investigated how habitat structure and complexity within smallholding oil palm plantations affected resource-use behaviours of two common butterfly species in the study areas (oil palm plantations in Banting, Selangor, Malaysia): Leptosia nina (Pieridae) and Ypthima spp. (Nymphalidae). Using direct-observation methods we developed, we followed seven and nine individuals of each species respectively, for three minutes in smallholder-owned immature monoculture and polyculture oil palm, and mature monoculture oil palm plantations. We recorded distance travelled by each individual from both straight line and the sums of distances between all perching points, and the position and characteristics of each perch location, where the individual landed. We compared our observations to control runs, generated by pairing observed distances travelled, but selecting the direction for each movement at random. By comparing the distance travelled and characteristics of locations used by butterflies and paired control points across habitats, we assessed how individuals in the two species used the local environment and whether this differed with habitat structure and complexity. Funding and research permission: Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund funded MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) funded the establishment of the plots and surveys of environmental parameters. Research permission was granted by the Economic Planning Unit (EPU) of Malaysia’s Prime Minister’s Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).
DOI: 10.5281/zenodo.8014108
title: Data set and analytic codes supporting "Direct observation to assess the effects of habitat structure and complexity on resource-use behaviour of butterflies: a study case in smallholding oil palm plantations in Peninsular Malaysia"

description: This deposit contains data set and analytic codes (with a meta data) supporting "Direct observation to assess the effects of habitat structure and complexity on resource-use behaviour of butterflies: a study case in smallholding oil palm plantations in Peninsular Malaysia". We investigated how habitat structure and complexity within smallholding oil palm plantations affected resource-use behaviours of two common butterfly species in the study areas (oil palm plantations in Banting, Selangor, Malaysia): Leptosia nina (Pieridae) and Ypthima spp. (Nymphalidae). Using direct-observation methods we developed, we followed seven and nine individuals of each species respectively, for three minutes in smallholder-owned immature monoculture and polyculture oil palm, and mature monoculture oil palm plantations. We recorded distance travelled by each individual from both straight line and the sums of distances between all perching points, and the position and characteristics of each perch location, where the individual landed. We compared our observations to control runs, generated by pairing observed distances travelled, but selecting the direction for each movement at random. By comparing the distance travelled and characteristics of locations used by butterflies and paired control points across habitats, we assessed how individuals in the two species used the local environment and whether this differed with habitat structure and complexity. Funding and research permission: Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund funded MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) funded the establishment of the plots and surveys of environmental parameters. Research permission was granted by the Economic Planning Unit (EPU) of Malaysia’s Prime Minister’s Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).
DOI: 10.25716/gude.0rta-4m4j
title: A j_eff 12 Kitaev material on the triangular lattice: The case of NaRuO2

description: Motivated by recent reports of a quantum disordered ground state in the triangular lattice compound NaRuO$_2$, we derive a $j_{\rm eff}=1/2$ magnetic model for this system by means of first-principles calculations. The pseudospin Hamiltonian is dominated by bond-dependent off-diagonal $\Gamma$ interactions, complemented by a ferromagnetic Heisenberg exchange and a notably \emph{antiferromagnetic} Kitaev term. In addition to bilinear interactions, we find a sizable four-spin ring exchange contribution with a \emph{strongly anisotropic} character, which has been so far overlooked when modeling Kitaev materials. The analysis of the magnetic model, based on the minimization of the classical energy and exact diagonalization of the quantum Hamiltonian, points toward the existence of a rather robust easy-plane ferromagnetic order, which cannot be easily destabilized by physically relevant perturbations.
description: All computation details (input, output, source codes, DFT, ProjED, classical minimization, TEMA) and figures are included. In each folder the computation details with detailed README File are included.
DOI: 10.24400/527896/a01-2022.004
title: Along-track SGDR+ Ice Sheet product for SARAL/AltiKa mission

description: SGDR+ Ice Seet products are available on one year (Oct 2018 to Nov 2019) for SARAL/AltiKa mission. It contains additional variables such as ice sheet elevation, a dedicated slope correction, using a relocation method of the altimetry measurement in order to deliver a product dedicated to ice sheet surfaces. SARAL orbit covers high latitudes up to +/-81.5deg, providing valuable information of the Earth’s polar cryosphere
DOI: 10.5281/zenodo.7968315
title: RipSetCocoaCNCH12 Dataset

description: Fruit counting and ripeness detection are computer vision applications that have gained strength<strong> </strong>in recent years due to the advancement of new algorithms, especially those based on artificial neural networks (ANN), better known as Deep Learning. In agriculture, those algorithms capable of fruit counting, including information about their ripeness are mainly applied to make production forecasts or plan different activities such as fertilization or crop harvest. This work presents the RipSetCocoaCNCH12 dataset of cocoa pods labeled at four different ripeness stages: stage 1 (0-2 months), stage 2 (2-4 months), stage 3 (4-6 months), harvest stage (&gt;6 months). An additional class was also included for pods aborted by plants in the early stage of development. 4116 images were labeled to train algorithms that mainly perform semantic and instance segmentation. The labeling was carried out with CVAT (Computer Vision Annotation Tool). The dataset, therefore, includes labeling in two formats: COCO 1.0 and Segmentation Mask 1.1. The images were taken with different mobile devices (smartphones), in field conditions, during the harvest season at different times of the day, which can allow the algorithms to be trained with data that includes many variables of lighting, colors, textures, and sizes of the cocoa pods. As far as we know, this is the first openly available dataset for cocoa pod detection with semantic segmentation for five classes, 4116 images, and 7917 instances, comprising RGB images and two different formats for labels. With the publication of this dataset, we expect that researchers in smart farming, especially in cocoa cultivation, can benefit from the quantity and variety of images it contains.
DOI: 10.5281/zenodo.7968314
title: RipSetCocoaCNCH12 Dataset

description: Fruit counting and ripeness detection are computer vision applications that have gained strength<strong> </strong>in recent years due to the advancement of new algorithms, especially those based on artificial neural networks (ANN), better known as Deep Learning. In agriculture, those algorithms capable of fruit counting, including information about their ripeness are mainly applied to make production forecasts or plan different activities such as fertilization or crop harvest. This work presents the RipSetCocoaCNCH12 dataset of cocoa pods labeled at four different ripeness stages: stage 1 (0-2 months), stage 2 (2-4 months), stage 3 (4-6 months), harvest stage (&gt;6 months). An additional class was also included for pods aborted by plants in the early stage of development. 4116 images were labeled to train algorithms that mainly perform semantic and instance segmentation. The labeling was carried out with CVAT (Computer Vision Annotation Tool). The dataset, therefore, includes labeling in two formats: COCO 1.0 and Segmentation Mask 1.1. The images were taken with different mobile devices (smartphones), in field conditions, during the harvest season at different times of the day, which can allow the algorithms to be trained with data that includes many variables of lighting, colors, textures, and sizes of the cocoa pods. As far as we know, this is the first openly available dataset for cocoa pod detection with semantic segmentation for five classes, 4116 images, and 7917 instances, comprising RGB images and two different formats for labels. With the publication of this dataset, we expect that researchers in smart farming, especially in cocoa cultivation, can benefit from the quantity and variety of images it contains.
DOI: 10.34740/kaggle/dsv/5864426
title: Impact Moon Craters (LU3M6TGT)
DOI: 10.5517/ccdc.csd.cc2f0d3z
title: CCDC 2234700: Experimental Crystal Structure Determination

description: Related Article: Zhengshan Luo|2023|CSD Communication|||
description: Related Article: Zhengshan Luo, Zequn Xing, Rui Gao, Yufang Han, Jun Ren, Zhongwen Wang|2023|Org.Biomol.Chem.|21|4637|doi:10.1039/D3OB00527E
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.cc2f233r
title: CCDC 2236343: Experimental Crystal Structure Determination

description: Related Article: Zhengshan Luo|2023|CSD Communication|||
description: Related Article: Zhengshan Luo, Zequn Xing, Rui Gao, Yufang Han, Jun Ren, Zhongwen Wang|2023|Org.Biomol.Chem.|21|4637|doi:10.1039/D3OB00527E
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.cc2f234s
title: CCDC 2236344: Experimental Crystal Structure Determination

description: Related Article: Zhengshan Luo|2023|CSD Communication|||
description: Related Article: Zhengshan Luo, Zequn Xing, Rui Gao, Yufang Han, Jun Ren, Zhongwen Wang|2023|Org.Biomol.Chem.|21|4637|doi:10.1039/D3OB00527E
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.cc2f28wp
title: CCDC 2236522: Experimental Crystal Structure Determination

description: Related Article: Zhengshan Luo|2023|CSD Communication|||
description: Related Article: Zhengshan Luo, Zequn Xing, Rui Gao, Yufang Han, Jun Ren, Zhongwen Wang|2023|Org.Biomol.Chem.|21|4637|doi:10.1039/D3OB00527E
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: 10.23729/1d6902b6-e591-4bb3-be71-89dc73f3ec36
title: Dataset: Computational NMR of the iron pyrazolylborate complexes [Tp2Fe]+ and Tp2Fe including solvation and spin-crossover effects

description: Quantum-chemical input and output files for iron pyrazolylborate complexes: -hyperfine coupling tensors -g- and zero-field splitting tensors -orbital shielding tensors -orbital shielding tensors for reference compounds -final optimised geometries -calculations of entropy for Tp2Fe -calculations of energy for Tp2Fe -calculations for the excited state of Tp2Fe+
DOI: 10.6084/m9.figshare.23310020
title: Fig_S11_Blood_test

description: Innate immune response and hemocompatibility tests. 1% (w/v) <strong>P5</strong> and <strong>P5RGD</strong> hydrogels crosslinked with CCL-64 were incubated in whole human blood.
DOI: 10.6084/m9.figshare.23310020.v1
title: Fig_S11_Blood_test

description: Innate immune response and hemocompatibility tests. 1% (w/v) <strong>P5</strong> and <strong>P5RGD</strong> hydrogels crosslinked with CCL-64 were incubated in whole human blood.
DOI: 10.5281/zenodo.8014160
title: Data supporting 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'

description: This data set contains the data produced for the paper 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)' The data set contains output from LADDIE simulations, including basal melt rates. The main simulations used in the man text are: - Crosson-Dotson: CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc <br> - Filchner-Ronne: FRIS_1.0_linear_S134.8_T1-2.3_720.nc <br> The additional simulations included in the Appendix are: - 3D forcing of Crosson-Dotson: CrossDots_0.5_mitgcm_2003_2008_100.nc <br> - Tuning of Crosson-Dotson: XX_YY_ZZ.nc, where XX is the resolution in km, YY is the value for Cd,top, and ZZ is the value for Dmin<br> - Pine Island Ice Shelf: PIG.nc <br>
DOI: 10.5281/zenodo.8014159
title: Data supporting 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'

description: This data set contains the data produced for the paper 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)' The data set contains output from LADDIE simulations, including basal melt rates. The main simulations used in the man text are: - Crosson-Dotson: CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc <br> - Filchner-Ronne: FRIS_1.0_linear_S134.8_T1-2.3_720.nc <br> The additional simulations included in the Appendix are: - 3D forcing of Crosson-Dotson: CrossDots_0.5_mitgcm_2003_2008_100.nc <br> - Tuning of Crosson-Dotson: XX_YY_ZZ.nc, where XX is the resolution in km, YY is the value for Cd,top, and ZZ is the value for Dmin<br> - Pine Island Ice Shelf: PIG.nc <br>
DOI: 10.5517/ccdc.csd.cc27982x
title: CCDC 2064478: Experimental Crystal Structure Determination

description: Related Article: Camila O. Santos, Saulo T. A. Passos, Jenny E. P. Sorto, Daniel F. S. Machado, Jose R. Correa, Eufrânio N. da Silva Júnior, Marcelo O. Rodrigues, Brenno A. D. Neto|2023|Org.Biomol.Chem.|21|4606|doi:10.1039/D3OB00007A
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.