cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7649201
title: Data from: high-resolution bioclimatic surfaces for southern Peru: an approach to climate reality for biological conservation

description: Climatic and bioclimatic surfaces were elaborated for southern Peru (Arequipa, Moquegua and Tacna). For the interpolations, meteorological information from in-situ stations, as well as orographic and geographic covariates were used. Statistical evaluations gave good results, showing some differences with other models also performed for the area. These data will contribute to a better understanding of the ecoclimatic requirements of the species in terms of ENMs and SDMs.
description: The following files are the result of the following article: High resolution bioclimatic surfaces for southern Peru: an approximation to the climatic reality for biological conservation. Here are 12 surfaces for maximum temperature, 12 for minimum temperature, 12 for average temperature, 12 for precipitation and 19 bioclimatic surfaces for the study area. Details of some of the published figures are also included. Where: Bios asc = 19 bioclimatic surfaces in .asc format. Bios tif = 19 bioclimatic surfaces in .tif format. Prec = 12 monthly precipitation surface in .tif format. Tmax = 12 monthly maximum temperature surfaces in .tif format. Tmin = 12 monthly minimum temperature surfaces in .tif format. The names of the bioclimatic surfaces correspond to the following: bio1=average annual mean temperature (°C), bio2=average diurnal range (°C), bio3=isothermality, bio4=temperature seasonality (standard deviation), bio5=maximum temperature of the warmest month (°C), bio6=minimum temperature of the coldest month (°C), bio7=annual temperature range (°C), bio8=average temperature of the wettest quarter (°C), bio9=average temperature of the driest quarter (°C), bio10=average temperature of the warmest quarter (°C), bio11=average temperature of the coldest quarter (°C), bio12=annual precipitation (mm/year), bio13=precipitation of the rainiest month (mm/month), bio14=precipitation of the driest month (mm/month), bio15=precipitation seasonality (coefficient of variation), bio16=precipitation of the wettest quarter (mm/quarter), bio17=precipitation of the driest quarter (mm/quarter), bio18=precipitation of the warmest quarter (mm/quarter), bio19=precipitation of the coldest quarter (mm/quarter). The names of each climatic surface correspond to the following: jan = January, feb = February, mar = March, apr = April, may = May, jun = June, jul = July, aug = August, sep = September, oct = October, nov = November, dic = December.
DOI: 10.5281/zenodo.7538101
title: DQA: An Open-Domain Dataset of Deep Questions Answering

description: DQA, the first open-domain question answering dataset consisting of comprehensive questions with verbose answers. The dataset comprises of 4 question types which are classified as Level 1 questions according to Bloom’s Taxonomy i.e understanding type Q/A.
DOI: 10.5281/zenodo.7538102
title: DQA: An Open-Domain Dataset of Deep Questions

description: DQA, the first open-domain question answering dataset consisting of comprehensive questions with verbose answers. The dataset comprises of 4 question types which are classified as Level 1 questions according to Bloom’s Taxonomy i.e understanding type Q/A.
DOI: 10.5281/zenodo.7538113
title: DQA: An Open-Domain Dataset of Deep Questions Answering

description: DQA, the first open-domain question answering dataset consisting of comprehensive questions with verbose answers. The dataset comprises of 4 question types which are classified as Level 1 questions according to Bloom’s Taxonomy i.e understanding type Q/A.
DOI: 10.34740/kaggle/dsv/5020959
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7652858
title: simalia_tracyae_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7652857
title: simalia_tracyae_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22044470
title: Icono 14 base de datos de empleabilidad

description: Acceso a la base de datos de las 13 887 publicaciones enTikTok del 1 de noviembre de 2021 al 30 de septiembre de 2022 con contenido de empleabilidad inclusiva. <br> El fichero Excel pone a disposición de los interesados, para corroborar o indagar en la base de datos empleadas en el artículo científico en las publicaciones que contengan las palabra educación junto con inclusión o discapacidad ligado a una serie de palabras de empleabilidad que son: trabajo, empleo, empleabilidad (ES), work job y employability. <br> Los datos son de libre acceso, la difusión científica del uso de estos requiere un acuerdo de difusión con el grupo de investigación XXXX.
DOI: 10.6084/m9.figshare.22044470.v1
title: Icono 14 base de datos de empleabilidad

description: Acceso a la base de datos de las 13 887 publicaciones enTikTok del 1 de noviembre de 2021 al 30 de septiembre de 2022 con contenido de empleabilidad inclusiva. <br> El fichero Excel pone a disposición de los interesados, para corroborar o indagar en la base de datos empleadas en el artículo científico en las publicaciones que contengan las palabra educación junto con inclusión o discapacidad ligado a una serie de palabras de empleabilidad que son: trabajo, empleo, empleabilidad (ES), work job y employability. <br> Los datos son de libre acceso, la difusión científica del uso de estos requiere un acuerdo de difusión con el grupo de investigación XXXX.
DOI: 10.5281/zenodo.7652889
title: Results of the crowd-mapping action within the project TeRRIFICA [Dataset No. 1 dated 2022-09-19]

description: The dataset includes the results of the crowd-mapping action within the project "Territorial RRI fostering innovative climate action" - TeRRIFICA (Horizon 2020 under GA 824489) dated 2022-09-19. The data are points added to the map by the users (volunteers) and represent locations where climate change-related issues occur regarding air temperature, air quality, water, soil, and wind (SPOTS). The second part of the dataset is related to the crowd-mapping users and their anonymized characteristics (USERS). More details are available at https://terrifica.eu/.
description: Results of the project TeRRIFICA - Territorial RRI fostering innovative climate action. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 824489.
DOI: 10.5281/zenodo.7652888
title: Results of the crowd-mapping action within the project TeRRIFICA [Dataset No. 1 dated 2022-09-19]

description: The dataset includes the results of the crowd-mapping action within the project "Territorial RRI fostering innovative climate action" - TeRRIFICA (Horizon 2020 under GA 824489) dated 2022-09-19. The data are points added to the map by the users (volunteers) and represent locations where climate change-related issues occur regarding air temperature, air quality, water, soil, and wind (SPOTS). The second part of the dataset is related to the crowd-mapping users and their anonymized characteristics (USERS). More details are available at https://terrifica.eu/.
description: Results of the project TeRRIFICA - Territorial RRI fostering innovative climate action. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 824489.
DOI: 10.5281/zenodo.7652911
title: Results of the crowd-mapping action within the project TeRRIFICA [Dataset No. 2 dated 2023-01-19]

description: The dataset includes the results of the crowd-mapping action within the project "Territorial RRI fostering innovative climate action" - TeRRIFICA (Horizon 2020 under GA 824489) dated 2023-01-19. The data are points added to the map by the users (volunteers) and represent locations where climate change-related issues occur regarding air temperature, air quality, water, soil, and wind (SPOTS). The second part of the dataset is related to the crowd-mapping users and their anonymized characteristics (USERS). More details are available at https://terrifica.eu/.
description: Results of the project TeRRIFICA - Territorial RRI fostering innovative climate action. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 824489.
DOI: 10.5281/zenodo.7652918
title: Data of Atmospheric Turbulent Characteristics under Summer Shamal in Coastal Qatar

description: Here are the data for the figures in the paper 'Atmospheric Turbulent Characteristics under Summer Shamal in Coastal Qatar'. The data are in '.mat' which can be easily processed through MATLAB.
DOI: 10.5281/zenodo.7652917
title: Data of Atmospheric Turbulent Characteristics under Summer Shamal in Coastal Qatar

description: Here are the data for the figures in the paper 'Atmospheric Turbulent Characteristics under Summer Shamal in Coastal Qatar'. The data are in '.mat' which can be easily processed through MATLAB.
DOI: 10.6084/m9.figshare.22121504.v1
title: Complex refractive index of GaAs.xlsx

description: A dataset of complex refractive index of GaAs.
DOI: 10.5281/zenodo.7633472
title: OSH Automated Documentation Data

description: CAD files that are used as examples to show how to generate assembly manuals with OSH Automated Documentation.
description: This research was funded by the European Regional Development Fund (ERDF) in the context of the INTERFACER Project.
DOI: 10.5281/zenodo.7633471
title: OSH Automated Documentation Data

description: CAD files that are used as examples to show how to generate assembly manuals with OSH Automated Documentation.
description: This research was funded by the European Regional Development Fund (ERDF) in the context of the INTERFACER Project.
DOI: 10.6084/m9.figshare.21671201
title: C4Evol Brassicaceae genomes

description: This project intends to sequence the genome and transcriptome of several Brassicaceae species, including C3 and C3-4 intermediates, and thereafter to pinpoint genetic differences between groups with different photosynthesis kinds. By cross-referencing genetic data with detailed phenotype evaluation, we aim to identify new genetic connections to the development of C3-4 intermediacy. Less...
DOI: 10.6084/m9.figshare.22121606
title: A novel nomogram combined with radiomics features and clinical features to predict pathological grading of bladder cancer

description: <strong>Background</strong>：Based on multi-parameter thin-slice enhanced CT texture features and related clinical indicators, a preoperative pathological grade prediction model of bladder urothelial carcinoma (GUC) was established. <strong>Method</strong>：The CT images and clinical data of 372 patients with urothelial carcinoma in our hospital from January 2015 to October 2020 were collected. 372 patients were divided into two groups: HGUC(n=190) and LGUC(n=182). All patients were divided into 10 groups on average, of which 7 were used as training group (n=259) and the remaining 3 as verification group (n=113). Then, by using 3D-Slicer software from all enhanced CT images split out the interested area (ROI), we separately extracted 1223 texture feature from the tumor image data based on first, second, high order and filtering characteristics. The correlation coefficient (ICC &gt; 0.75) was used between classes and least absolute shrinkage selection operator (LASSO) regression feature selection. Secondly, the clinical predictive model was obtained by logistic regression analysis. Finally, the selected clinical key indicators and imaging features were mapped. In order to verify the predictive ability of the nomogram, conformance index (C-index), calibration curve, Receiver operator characteristic (ROC) curve and clinical decision curve (DCA) were used to test the nomogram. <strong>Result</strong>：11 radiomics features were significantly correlated with the pathological grade of bladder cancer. After comparing the four models, logistic regression model was proved to be the best prediction ability (AUC=0.858). The results of multivariate analysis showed that age and proteinuria were independent risk factors. A comprehensive model for predicting the pathological grade of bladder cancer was constructed by combining clinical features with 11 imaging features. Compared with clinical feature model and imaging model, it was found that the predictive performance of imaging comprehensive model combined with clinical factors was the best (AUC=0.864). <strong>Conclusion</strong>：The radiomics model has the best ability to predict high-or low-grade BCA (AUC=0.864).
DOI: 10.6084/m9.figshare.22121606.v1
title: A novel nomogram combined with radiomics features and clinical features to predict pathological grading of bladder cancer

description: <strong>Background</strong>：Based on multi-parameter thin-slice enhanced CT texture features and related clinical indicators, a preoperative pathological grade prediction model of bladder urothelial carcinoma (GUC) was established. <strong>Method</strong>：The CT images and clinical data of 372 patients with urothelial carcinoma in our hospital from January 2015 to October 2020 were collected. 372 patients were divided into two groups: HGUC(n=190) and LGUC(n=182). All patients were divided into 10 groups on average, of which 7 were used as training group (n=259) and the remaining 3 as verification group (n=113). Then, by using 3D-Slicer software from all enhanced CT images split out the interested area (ROI), we separately extracted 1223 texture feature from the tumor image data based on first, second, high order and filtering characteristics. The correlation coefficient (ICC &gt; 0.75) was used between classes and least absolute shrinkage selection operator (LASSO) regression feature selection. Secondly, the clinical predictive model was obtained by logistic regression analysis. Finally, the selected clinical key indicators and imaging features were mapped. In order to verify the predictive ability of the nomogram, conformance index (C-index), calibration curve, Receiver operator characteristic (ROC) curve and clinical decision curve (DCA) were used to test the nomogram. <strong>Result</strong>：11 radiomics features were significantly correlated with the pathological grade of bladder cancer. After comparing the four models, logistic regression model was proved to be the best prediction ability (AUC=0.858). The results of multivariate analysis showed that age and proteinuria were independent risk factors. A comprehensive model for predicting the pathological grade of bladder cancer was constructed by combining clinical features with 11 imaging features. Compared with clinical feature model and imaging model, it was found that the predictive performance of imaging comprehensive model combined with clinical factors was the best (AUC=0.864). <strong>Conclusion</strong>：The radiomics model has the best ability to predict high-or low-grade BCA (AUC=0.864).
DOI: 10.5281/zenodo.7652970
title: brotogeris_chrysoptera_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7652969
title: brotogeris_chrysoptera_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.21996254.v1
title: SST from FIO-CPS v2.0, FIO-ESM v2.0 and FIO-CPS-HY

description: This data set contains predicted SST from short climate prediction system FIO-CPS v2.0 in 01/1982-11/2022, SST from earth system model FIO-ESM v2.0 in 01/1854-10/2022 and corrected SST from the combination of FIO-CPS v2.0 and XGBoost algorithm, named FIO-CPS-HY, in 01/1982-11/2022 .
DOI: 10.5281/zenodo.7643403
title: The global 30-m forest canopy height map for 2020

description: The global forest canopy height map with a resolution of 30 m for 2020 (GlobeFCH_2020_30m_v1) was generated by integrating the new-generation space-borne LiDAR (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and Land Elevation Satellite-2, ICESat-2), Sentinel-1 SAR images, Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeFCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the forest canopy height value is centimeter. The GlobeFCH_2020_30m_v1 was divided into 305 files, and the range of each file is 10°×10°.
DOI: 10.5281/zenodo.7643402
title: The global 30-m forest canopy height map for 2020

description: The global forest canopy height map with a resolution of 30 m for 2020 (GlobeFCH_2020_30m_v1) was generated by integrating the new-generation space-borne LiDAR (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and Land Elevation Satellite-2, ICESat-2), Sentinel-1 SAR images, Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeFCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the forest canopy height value is centimeter. The GlobeFCH_2020_30m_v1 was divided into 305 files, and the range of each file is 10°×10°.
DOI: 10.6084/m9.figshare.21996254
title: SST and Nino3.4 index from FIO-CPS v2.0, FIO-ESM v2.0 and FIO-CPS-HY

description: This data set contains predicted Nino3.4 index from short climate prediction system FIO-CPS v2.0, prediction from 01/1982 to 01/2023, SST from earth system model FIO-ESM v2.0 in 01/1854-01/2023 and corrected Nino3.4 index from the combination of FIO-CPS v2.0 and XGBoost algorithm, named FIO-CPS-HY, prediction from 01/1982 to 01/2023.
DOI: 10.6084/m9.figshare.21996254.v3
title: SST and Nino3.4 index from FIO-CPS v2.0, FIO-ESM v2.0 and FIO-CPS-HY

description: This data set contains predicted Nino3.4 index from short climate prediction system FIO-CPS v2.0, prediction from 01/1982 to 01/2023, SST from earth system model FIO-ESM v2.0 in 01/1854-01/2023 and corrected Nino3.4 index from the combination of FIO-CPS v2.0 and XGBoost algorithm, named FIO-CPS-HY, prediction from 01/1982 to 01/2023.
DOI: 10.6084/m9.figshare.21996254.v2
title: SST from FIO-CPS v2.0, FIO-ESM v2.0 and FIO-CPS-HY

description: This data set contains predicted SST from short climate prediction system FIO-CPS v2.0, prediction from 01/1982 to 01/2023, SST from earth system model FIO-ESM v2.0 in 01/1854-01/2023 and corrected SST from the combination of FIO-CPS v2.0 and XGBoost algorithm, named FIO-CPS-HY, prediction from 01/1982 to 01/2023.
DOI: 10.5281/zenodo.5978049
title: Pultruded carbon fiber profiles - 3D x-ray tomography data-sets for two different pultruded profiles

description: 3D X-ray scan on Zeiss Xradia 520 A1 A2 A2S B1 B1S B2 B3 Scanning Voxel size [μm] 1.9767 1.97 1.9731 1.9751 1.9753 1.9771 1.9752 FoV: (x; y; z) [mm] 2.0x2.0 2.0x2.0 2.0x2.0 2.0x2.0 2.0x2.0 2.0x2.0 2.0x2.0 Accelerating Voltage [kV] 30 30 30 30 30 30 30 Power [W] 2 2 2 2 2 2 2 Filter Air LE1 LE1 LE1 LE1 Air LE1 Optical magnification 4x 4x 4x 4x 4x 4x 4x Detector to sample distance [mm] 25.5 25.52 26.82 25.4 26.75 25.5 25.8 Source to sample distance [mm] 10.6 10.61 11.11 10.5 11.1 10.6 10.7 Exposure time [s] 15 18 20 18 18 15 18 No. of projections 5201 5201 5201 5201 5201 5201 5201 Rotation 360 360 360 360 360 360 360 Binning 2 2 2 2 2 2 2 Total scanning time [h] 26 33 33 32 32 26 29 File-size [GB] 2 2 2 2 2 2 2
DOI: 10.5281/zenodo.5898529
title: Pultruded carbon fiber profiles - 3D x-ray tomography data-sets for two different pultruded profiles

description: 3D X-ray scan on Zeiss Xradia 520 A1 A2 A2S B1 B1S B2 B3 Scanning Voxel size [μm] 1.9767 1.97 1.9731 1.9751 1.9753 1.9771 1.9752 FoV: (x; y; z) [mm] 2.0x2.0 2.0x2.0 2.0x2.0 2.0x2.0 2.0x2.0 2.0x2.0 2.0x2.0 Accelerating Voltage [kV] 30 30 30 30 30 30 30 Power [W] 2 2 2 2 2 2 2 Filter Air LE1 LE1 LE1 LE1 Air LE1 Optical magnification 4x 4x 4x 4x 4x 4x 4x Detector to sample distance [mm] 25.5 25.52 26.82 25.4 26.75 25.5 25.8 Source to sample distance [mm] 10.6 10.61 11.11 10.5 11.1 10.6 10.7 Exposure time [s] 15 18 20 18 18 15 18 No. of projections 5201 5201 5201 5201 5201 5201 5201 Rotation 360 360 360 360 360 360 360 Binning 2 2 2 2 2 2 2 Total scanning time [h] 26 33 33 32 32 26 29 File-size [GB] 2 2 2 2 2 2 2
DOI: 10.5281/zenodo.7653018
title: cacatua_sanguinea_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7653017
title: cacatua_sanguinea_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22121492
title: Supporting Information Minimal Data set.docx

description: Expert feedback on Delphi Survey Round 1, 2 and 3.
DOI: 10.5281/zenodo.7653035
title: balanus_nubilus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7653034
title: balanus_nubilus_zanfona_1.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/5021561
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.7653066
title: balanus_glandula_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7653065
title: balanus_glandula_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7646885
title: Comparing zero-parameter theories for the WCA and harmonic-repulsive melting lines (data)

description: This dataset contains data related to the paper titled "Comparing zero-parameter theories for the WCA and harmonic-repulsive melting lines" by Jeppe C. Dyre and Ulf R. Pedersen, with the abstract: The melting line of the Weeks-Chandler-Andersen (WCA) system was recently determined accurately and compared to the predictions of four analytical hard-sphere approximations [Attia \textit{et al.}, J. Chem. Phys. \textbf{157}, 034502 (2022)]. Here, we study an alternative zero-parameter prediction based on the isomorph theory, the input of which relate to properties at a single reference state point on the melting line. The two central assumptions made are that the harmonic-repulsive potential approximates the WCA potential and that pair collisions are uncorrelated. The new approach gives excellent predictions at high temperatures, while the hard-sphere-theory based predictions are better at lower temperatures. Supplementing the WCA investigation, the face-centered-crystal to fluid coexistence line is determined for a system of harmonic-repulsive particles and compared to the zero-parameter theories. The results indicate that the excellent isomorph-theory predictions for the WCA potential at higher temperatures may be partly due to a cancellation of errors between the two above-mentioned assumptions.
DOI: 10.5281/zenodo.7646884
title: Comparing zero-parameter theories for the WCA and harmonic-repulsive melting lines (data)

description: This dataset contains data related to the paper titled "Comparing zero-parameter theories for the WCA and harmonic-repulsive melting lines" by Jeppe C. Dyre and Ulf R. Pedersen, with the abstract: The melting line of the Weeks-Chandler-Andersen (WCA) system was recently determined accurately and compared to the predictions of four analytical hard-sphere approximations [Attia \textit{et al.}, J. Chem. Phys. \textbf{157}, 034502 (2022)]. Here, we study an alternative zero-parameter prediction based on the isomorph theory, the input of which relate to properties at a single reference state point on the melting line. The two central assumptions made are that the harmonic-repulsive potential approximates the WCA potential and that pair collisions are uncorrelated. The new approach gives excellent predictions at high temperatures, while the hard-sphere-theory based predictions are better at lower temperatures. Supplementing the WCA investigation, the face-centered-crystal to fluid coexistence line is determined for a system of harmonic-repulsive particles and compared to the zero-parameter theories. The results indicate that the excellent isomorph-theory predictions for the WCA potential at higher temperatures may be partly due to a cancellation of errors between the two above-mentioned assumptions.
DOI: 10.34740/kaggle/dsv/5021623
title: Egypt Gold Prices | Daily Updated
DOI: 10.6084/m9.figshare.20103743
title: Data for nematode diversity estimation in a paddy–upland rotation system

description: Community composition of soil nematodes under five fertilization treatments in a paddy rice-upland wheat rotation system. CK: unfertilized control; CF: chemical fertilizer; MCF: pig manure plus chemical fertilizer; SCF: straw plus chemical fertilizer; MSCF: pig manure plus straw plus chemical fertilizer.