cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22008/fk2/zqggp7/vl6kxp
title: Figure_SiteII.JPG

description: Example of the figure produced by the Site II Code.
DOI: 10.22008/fk2/zqggp7
title: Vertical ice-sheet temperature profiles at three sites in Northwest Greenland

description: This folder contains the source code for solving 1-D vertical ice-sheet temperature profiles at three sites in Northwest Greenland (Camp Century, NEEM and Site II). Our numerical model solves the vertical ice temperature profile based on vertical heat diffusion, vertical heat advection, and heat production associated with internal deformation. For each of the three sites, we provide three files: (1) numerical code, (2) observed ice temperature data, and (3) graphic summary of model output. The code is provided as an annotated .m file, which can be run in either MATLAB (close source) or Octave (open source) software.
DOI: 10.22008/fk2/zqggp7/7hbtdg
title: Quellard_final_report.pdf

description: Master in Atmosphere, Ocean and Climate Science report by Léa Quellard.
DOI: 10.22008/fk2/zqggp7/daoygp
title: Figure_CampCentury.JPG

description: Example of the figure produced by the Camp Century Code.
DOI: 10.22008/fk2/zqggp7/irlf2a
title: plasma.m

description: Open source code for perceptually uniform colorbar downloaded from www.mathworks.com.
DOI: 10.22008/fk2/zqggp7/stkqql
title: Code_CampCentury.m

description: Vertical ice temperature code parameterized for best fit at Camp Century.
DOI: 10.22008/fk2/zqggp7/vubwzn
title: Figure_NEEM.JPG

description: Example of the figure produced by the NEEM Code.
DOI: 10.6084/m9.figshare.20254569.v1
title: demographics and clinicsal scores

description: the file contains the demographic and clinical scores to provide further information to the uploaded data set
DOI: 10.57745/s72cql/s2qay2
title: Dataset mesocosms macrodurée.tab

description: :unav
DOI: 10.34740/kaggle/dsv/4162378
title: Workout Routes
DOI: 10.6084/m9.figshare.20929372
title: data.rar

description: AOD and land cover types of study areas
DOI: 10.6084/m9.figshare.20929372.v1
title: data.rar

description: AOD and land cover types of study areas
DOI: 10.5517/ccdc.csd.cc29xtkm
title: CCDC 2142861: Experimental Crystal Structure Determination

description: Related Article: Jyoti Yadav, R.P. Chaudhary|2022|J.Mol.Struct.|1271|133976|doi:10.1016/j.molstruc.2022.133976
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: 10.5281/zenodo.7049722
title: Influence of Inner Surface Roughness on the SIP Response - a Numerical Study

description: Supporting data for publication This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript. Comsol models are those with the <em>.mph</em> extension and were created with Comsol Multiphysics v 5.5. All numerical data used in the manuscript is available without running the Comsol simulation.
DOI: 10.5281/zenodo.7049721
title: Influence of Inner Surface Roughness on the SIP Response - a Numerical Study

description: Supporting data for publication This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript. Comsol models are those with the <em>.mph</em> extension and were created with Comsol Multiphysics v 5.5. All numerical data used in the manuscript is available without running the Comsol simulation.
DOI: 10.6084/m9.figshare.20929474
title: Source data for graphs in "Chromosome-level genome and population genomics reveal evolutionary characteristics and conservation status of Chinese indigenous geese"

description: Source data for graphs in “Chromosome-level genome and population genomics reveal evolutionary characteristics and conservation status of Chinese indigenous geese”
DOI: 10.6084/m9.figshare.20929474.v1
title: Source data for graphs in "Chromosome-level genome and population genomics reveal evolutionary characteristics and conservation status of Chinese indigenous geese"

description: Source data for graphs in “Chromosome-level genome and population genomics reveal evolutionary characteristics and conservation status of Chinese indigenous geese”
DOI: 10.5281/zenodo.5216012
title: seNorge/TM: daily mean temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily mean temperature (TM) fields for the 65-year time period 1957-2021. TM is the mean temperature between 00:00 UTC and 24:00 UTC of the day in the timestamp. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.6965895
title: seNorge/TM: daily mean temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily mean temperature (TM) fields for the 65-year time period 1957-2021. TM is the mean temperature between 00:00 UTC and 24:00 UTC of the day in the timestamp. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5216630
title: seNorge/TNb: daily minimum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily minimum temperature (TNb) fields for the 65-year time period 1957-2021. TN is the minimum temperature consistent with TM/TX (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5216631
title: seNorge/TNb: daily minimum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily minimum temperature (TNb) fields for the 64-year time period 1957-2020. TN is the minimum temperature consistent with TM/TX (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5552978
title: seNorge/TNb: daily minimum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily minimum temperature (TNb) fields for the 64-year time period 1957-2020. TN is the minimum temperature consistent with TM/TX (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.6965913
title: seNorge/TNb: daily minimum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily minimum temperature (TNb) fields for the 65-year time period 1957-2021. TN is the minimum temperature consistent with TM/TX (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.7049775
title: Informações das IFES do Brasil para ajuste de modelo RLM

description: Planilha desenvolvida para elaboração de pesquisa que consiste em avaliar o Custo da Manutenção Predial das IFES no Brasil, tendo como base informações administrativas ou financeiras das IFES. A planilha apresenta as informações observadas e os resultados do ajuste do modelo RLM.
DOI: 10.5281/zenodo.7049774
title: Informações das IFES do Brasil para ajuste de modelo RLM

description: Planilha desenvolvida para elaboração de pesquisa que consiste em avaliar o Custo da Manutenção Predial das IFES no Brasil, tendo como base informações administrativas ou financeiras das IFES. A planilha apresenta as informações observadas e os resultados do ajuste do modelo RLM.
DOI: 10.6084/m9.figshare.20930611
title: Paper_DATA.zip

description: All the data in this paper are in this file
DOI: 10.6084/m9.figshare.20930611.v1
title: Paper_DATA.zip

description: All the data in this paper are in this file
DOI: 10.6084/m9.figshare.20254569
title: demographics and clinicsal scores

description: the file contains the demographic and clinical scores to provide further information to the uploaded data set
DOI: 10.6084/m9.figshare.20254569.v2
title: demographics and clinicsal scores

description: the file contains the demographic and clinical scores to provide further information to the uploaded data set
DOI: 10.6084/m9.figshare.20431191
title: Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential

description: Azo dyes are broadly used in different industries through their chemical stability and ease of synthesis. However, these dyes are usually identified as critical environmental pollutants. Hence, a mathematical model for the adsorption affinity of azo dyes can be applied for solving tasks of medicine and ecology. Quantitative structure-property relationships for the adsorption affinity of azo dyes to a substrate (DAF, kJ/mol) were established using the Monte Carlo method by generating optimal SMILES-based descriptors. The index of ideality of correlation (<i>IIC</i>) and the correlation intensity index (<i>CII</i>) improved the model’s predictive potential, especially when they were used simultaneously. The statistical quality of the best model on the validation set was characterized by <i>n</i> = 18, <i>r</i><sup>2</sup> = 0.9468, and RMSE = 1.26 kJ/mol.
DOI: 10.6084/m9.figshare.20431191.v1
title: Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential

description: Azo dyes are broadly used in different industries through their chemical stability and ease of synthesis. However, these dyes are usually identified as critical environmental pollutants. Hence, a mathematical model for the adsorption affinity of azo dyes can be applied for solving tasks of medicine and ecology. Quantitative structure-property relationships for the adsorption affinity of azo dyes to a substrate (DAF, kJ/mol) were established using the Monte Carlo method by generating optimal SMILES-based descriptors. The index of ideality of correlation (<i>IIC</i>) and the correlation intensity index (<i>CII</i>) improved the model’s predictive potential, especially when they were used simultaneously. The statistical quality of the best model on the validation set was characterized by <i>n</i> = 18, <i>r</i><sup>2</sup> = 0.9468, and RMSE = 1.26 kJ/mol.
DOI: 10.6084/m9.figshare.20431194.v1
title: Study for the binding affinity of thyroid hormone receptors based on machine learning algorithm

description: Long-term exposure of exogenous compounds to thyroid hormone receptors (TRs) may lead to thyroid dysfunction. Quantitative structure-activity relationship (QSAR) is expected to predicting the binding affinity of compounds to TR. In this work, two comprehensive and large datasets for TRα and TRβ were collected and investigated. Five machine learning models were established to predict the pIC50 of compounds. Meanwhile, the reliability of the models was ensured by a variety of evaluation parameters. The results showed that the support vector regression model exhibited the best robustness and external prediction ability (<i>r</i><sup>2</sup><sub>train</sub> = 0.77, <i>r</i><sup>2</sup><sub>test</sub> = 0.78 for TRα, <i>r</i><sup>2</sup><sub>train</sub> = 0.78, <i>r</i><sup>2</sup><sub>test</sub> = 0.80 for TRβ). We have proposed an appropriate mechanism for explaining the TR binding affinity of a compound. The molecular volume, mass, and aromaticity affected the activity of TRα. Molecular weight, electrical properties and molecular hydrophilicity played a significant role in the binding affinity of compounds to TRβ. We also characterized the application domain of the model. Finally, the obtained models were utilized to predict the TR binding affinities of 109 compounds from the list of endocrine disruptors. Therefore, this model is expected to be an effective tool for alerting the effects of exogenous compounds on the thyroid system.
DOI: 10.6084/m9.figshare.20931331
title: An attention mechanism-based LSTM network for cancer kinase activity prediction

description: Despite the endeavours and achievements made in treating cancers during the past decades, resistance to available kinase drugs continues to be a major problem in cancer therapies. Thus, it is highly desirable to develop computational models that can predict the bioactivity of a compound against cancer kinases. Here, we present a Long Short-Term Memory (LSTM) framework for predicting the activities of lead molecules against seven different kinases. A total of 14,907 compounds from the ChEMBL database were selected for model building. Two different molecular representations, namely, 2D descriptors and MACCS fingerprints were subjected to the LSTM method for the training process. We also successfully integrated an attention mechanism into our model, which helped us to interpret the contribution of chemical features on kinase activity. The attention mechanism extracted the significant chemical moieties more effectively by taking them into consideration during the activity prediction. The recorded accuracies in the test sets for both 2D descriptors and MACCS fingerprints-based models were 0.81 and 0.78, respectively. The receiver operating characteristic curve (ROC)-area under the curve (AUC) score for both models was in the range of 0.8–0.99. The proposed framework can be a good starting point for the development of new cancer kinase drugs.
DOI: 10.6084/m9.figshare.20931331.v1
title: An attention mechanism-based LSTM network for cancer kinase activity prediction

description: Despite the endeavours and achievements made in treating cancers during the past decades, resistance to available kinase drugs continues to be a major problem in cancer therapies. Thus, it is highly desirable to develop computational models that can predict the bioactivity of a compound against cancer kinases. Here, we present a Long Short-Term Memory (LSTM) framework for predicting the activities of lead molecules against seven different kinases. A total of 14,907 compounds from the ChEMBL database were selected for model building. Two different molecular representations, namely, 2D descriptors and MACCS fingerprints were subjected to the LSTM method for the training process. We also successfully integrated an attention mechanism into our model, which helped us to interpret the contribution of chemical features on kinase activity. The attention mechanism extracted the significant chemical moieties more effectively by taking them into consideration during the activity prediction. The recorded accuracies in the test sets for both 2D descriptors and MACCS fingerprints-based models were 0.81 and 0.78, respectively. The receiver operating characteristic curve (ROC)-area under the curve (AUC) score for both models was in the range of 0.8–0.99. The proposed framework can be a good starting point for the development of new cancer kinase drugs.
DOI: 10.6084/m9.figshare.20431194
title: Study for the binding affinity of thyroid hormone receptors based on machine learning algorithm

description: Long-term exposure of exogenous compounds to thyroid hormone receptors (TRs) may lead to thyroid dysfunction. Quantitative structure-activity relationship (QSAR) is expected to predicting the binding affinity of compounds to TR. In this work, two comprehensive and large datasets for TRα and TRβ were collected and investigated. Five machine learning models were established to predict the pIC50 of compounds. Meanwhile, the reliability of the models was ensured by a variety of evaluation parameters. The results showed that the support vector regression model exhibited the best robustness and external prediction ability (<i>r</i><sup>2</sup><sub>train</sub> = 0.77, <i>r</i><sup>2</sup><sub>test</sub> = 0.78 for TRα, <i>r</i><sup>2</sup><sub>train</sub> = 0.78, <i>r</i><sup>2</sup><sub>test</sub> = 0.80 for TRβ). We have proposed an appropriate mechanism for explaining the TR binding affinity of a compound. The molecular volume, mass, and aromaticity affected the activity of TRα. Molecular weight, electrical properties and molecular hydrophilicity played a significant role in the binding affinity of compounds to TRβ. We also characterized the application domain of the model. Finally, the obtained models were utilized to predict the TR binding affinities of 109 compounds from the list of endocrine disruptors. Therefore, this model is expected to be an effective tool for alerting the effects of exogenous compounds on the thyroid system.
DOI: 10.5281/zenodo.5216393
title: seNorge/TXb: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXb) fields for the 64-year time period 1957-2020. TXb is the maximum temperature consistent with TM/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5552971
title: seNorge/TXb: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXb) fields for the 64-year time period 1957-2020. TXb is the maximum temperature consistent with TM/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5216392
title: seNorge/TXb: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXb) fields for the 65-year time period 1957-2021. TXb is the maximum temperature consistent with TM/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting