cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e151181
title: Code for: Childhood Housing and Adult Outcomes: A Between-Siblings Analysis of Housing Vouchers and Public Housing

description: We create a national-level longitudinal dataset to analyze how children’s participation in public and voucher-assisted housing affects age 26 earnings and adult incarceration. Naïve OLS estimates suggest that returns to subsidized housing participation are negative, but that relationship is driven by household selection into assisted housing. Household fixed-effects estimates indicate that additional years of public housing increase earnings by 6.2% for females and 6.1% for males, while voucher-assisted housing increases earnings by 4.8% for females and 2.7% for males. Childhood participation in assisted housing also reduces the likelihood of adult incarceration for all household race/ethnicity groups.
DOI: 10.3886/e151181v1
title: Code for: Childhood Housing and Adult Outcomes: A Between-Siblings Analysis of Housing Vouchers and Public Housing

description: We create a national-level longitudinal dataset to analyze how children’s participation in public and voucher-assisted housing affects age 26 earnings and adult incarceration. Naïve OLS estimates suggest that returns to subsidized housing participation are negative, but that relationship is driven by household selection into assisted housing. Household fixed-effects estimates indicate that additional years of public housing increase earnings by 6.2% for females and 6.1% for males, while voucher-assisted housing increases earnings by 4.8% for females and 2.7% for males. Childhood participation in assisted housing also reduces the likelihood of adult incarceration for all household race/ethnicity groups.
DOI: 10.5281/zenodo.4686545
title: Field campaign data Dry Chaco, July-August 2019

description: KU Leuven, Belgium Field data collected in July-August 2019 in the central part of the Dry Chaco in Argentina within 27 EASEv2 satellite pixels as described in Vincent et al., (2021): 1) Surface (0-5 cm) soil data, covering 27 large-scale pixels: Dielectric_properties.csv: Dielectric properties measured with TDR, HydraGO, Hanna, YSI proDDS pH.csv: pH measured with YSI proDDS Soil_moisture.csv: Soil moisture measured with HydraGO, ThetaProbe, TDR SoilMoisture_Gravimetric.csv: Soil moisture gravimetrically derived from soil samples Temperature.csv: Temperature measured with HydraGO 2) Surface and deeper soil data across some transects: Transects.csv: Soil moisture, temperature, dielectric constant (real, imaginary part, 0.05 GHz), bulk EC, porewater EC, saturated paste EC, pH, measured with various devices For details, please refer to Vincent, F., Maertens, M., Bechtold, M., Jobaggy, E., Reichle, R., Vanacker, V., Vrugt, J., Wigneron, J.-P., De Lannoy, G. (2022). L-Band Microwave Satellite Data and Model Simulations over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation and Soil Salinity. IEEE Journal of Selected Topics in applied Earth Observations and Remote Sensing, 10.1109/JSTARS.2022.3193636. Vincent, F., De Lannoy, G., Maertens, M., Jobbagy, E., Reichle, R., Vanacker, V., Bechtold, M. (2021). Forward and inverse L-band radiative transfer modeling over the Dry Chaco, using SMOS observations, land surface modeling and in situ data, IGARSS proceeding, 12-16 July, Brussels, Belgium. Or contact: gabrielle [dot] delannoy [at] kuleuven [dot] be
description: Vincent, F., Maertens, M., Bechtold, M., Jobaggy, E., Reichle, R., Vanacker, V., Vrugt, J., Wigneron, J.-P., De Lannoy, G. (2022). L-Band Microwave Satellite Data and Model Simulations over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation and Soil Salinity. IEEE Journal of Selected Topics in applied Earth Observations and Remote Sensing, 10.1109/JSTARS.2022.3193636.
DOI: 10.5281/zenodo.4686544
title: Field campaign data Dry Chaco, July-August 2019

description: KU Leuven, Belgium Field data collected in July-August 2019 in the central part of the Dry Chaco in Argentina within 27 EASEv2 satellite pixels as described in Vincent et al., (2021): 1) Surface (0-5 cm) soil data, covering 27 large-scale pixels: Dielectric_properties.csv: Dielectric properties measured with TDR, HydraGO, Hanna, YSI proDDS pH.csv: pH measured with YSI proDDS Soil_moisture.csv: Soil moisture measured with HydraGO, ThetaProbe, TDR SoilMoisture_Gravimetric.csv: Soil moisture gravimetrically derived from soil samples Temperature.csv: Temperature measured with HydraGO 2) Surface and deeper soil data across some transects: Transects.csv: Soil moisture, temperature, dielectric constant (real, imaginary part, 0.05 GHz), bulk EC, porewater EC, saturated paste EC, pH, measured with various devices For details, please refer to Vincent, F., Maertens, M., Bechtold, M., Jobaggy, E., Reichle, R., Vanacker, V., Vrugt, J., Wigneron, J.-P., De Lannoy, G. (2022). L-Band Microwave Satellite Data and Model Simulations over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation and Soil Salinity. IEEE Journal of Selected Topics in applied Earth Observations and Remote Sensing, 10.1109/JSTARS.2022.3193636. Vincent, F., De Lannoy, G., Maertens, M., Jobbagy, E., Reichle, R., Vanacker, V., Bechtold, M. (2021). Forward and inverse L-band radiative transfer modeling over the Dry Chaco, using SMOS observations, land surface modeling and in situ data, IGARSS proceeding, 12-16 July, Brussels, Belgium. Or contact: gabrielle [dot] delannoy [at] kuleuven [dot] be
description: Vincent, F., Maertens, M., Bechtold, M., Jobaggy, E., Reichle, R., Vanacker, V., Vrugt, J., Wigneron, J.-P., De Lannoy, G. (2022). L-Band Microwave Satellite Data and Model Simulations over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation and Soil Salinity. IEEE Journal of Selected Topics in applied Earth Observations and Remote Sensing, 10.1109/JSTARS.2022.3193636.
DOI: 10.34740/kaggle/dsv/4024429
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.6957805
title: Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonality

description: Codes for the analysis and figure generation for drifter data
DOI: 10.6084/m9.figshare.20422185.v1
title: MSL - OKR (Objective and Key Results)

description: This file describes a mapping study related to OKR (Objective and Key Results) and show relevant findings about the topic.
DOI: 10.6084/m9.figshare.20422185
title: MSL - OKR (Objetivos e Resultados-Chave)

description: This file describes a mapping study related to OKR (Objective and Key Results) and show relevant findings about the topic.
DOI: 10.6084/m9.figshare.20422185.v3
title: MSL - OKR (Objetivos e Resultados-Chave)

description: This file describes a mapping study related to OKR (Objective and Key Results) and show relevant findings about the topic.
DOI: 10.21227/kwpw-4n84
title: Power profile for student campus

description: This data set is for a student building in Romania, for power profiles.
DOI: 10.5281/zenodo.6957815
title: Geological Map of the Derain (H10) Quadrangle of Mercury (3 crater class version)

description: Geological (morphostratigraphic) map recognising 3 crater degradation classes. We also have a 5 crater class version that is otherwise identical. This version is slightly revised after review for publication in J Maps (3 Aug 2022).
DOI: 10.5281/zenodo.6957814
title: Geological Map of the Derain (H10) Quadrangle of Mercury (3 crater class version)

description: Geological (morphostratigraphic) map recognising 3 crater degradation classes. We also have a 5 crater class version that is otherwise identical. This version is slightly revised after review for publication in J Maps (3 Aug 2022).
DOI: 10.5066/p98kzu8z
title: qpcr amplification data for tissue and water samples collected from glacier national park in 2020 amplified with a lednia tumana specific assay

description: Tabular amplification data for individual technical replicates of tissue and water samples collected from Glacier National Park in 2020 and amplified with a Lednia tumana species-specific qPCR assay. Reported results include fractional detection rates, mean quantification cycles, and estimated gene copies per reaction.
DOI: 10.6084/m9.figshare.19653849.v1
title: Data and R Script: Female reproduction and viral infection in a long-lived mammal

description: R script and data used for preparation of the manuscript entitled, "Female reproduction and viral infection in a long-lived mammal".
DOI: 10.5281/zenodo.6825004
title: Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"

description: Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates" Please find below an explanation for the <strong>files </strong>in this repository: <br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong> Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset" <strong>02_CNN_PhenotypeClassif.7z</strong> CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder. <strong>03_ExampleMeasurement.zip</strong> One measurement file and a corresponding scatterplot <strong>04_Dataset_load.zip</strong> The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv". <strong>05_RF_training</strong> Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv"). <strong>07_pytranskit</strong> Scripts for training and evaluating CDT-PLDA classifier
DOI: 10.5281/zenodo.6825003
title: Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"

description: Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates" Please find below an explanation for the <strong>files </strong>in this repository: <br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong> Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset" <strong>02_CNN_PhenotypeClassif.7z</strong> CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder. <strong>03_ExampleMeasurement.zip</strong> One measurement file and a corresponding scatterplot <strong>04_Dataset_load.zip</strong> The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv". <strong>05_RF_training</strong> Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv"). <strong>07_pytranskit</strong> Scripts for training and evaluating CDT-PLDA classifier
DOI: 10.34740/kaggle/dsv/4024481
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.19849590.v1
title: Nationwide database analysis of insomnia, depression, and sleeping pill prescriptions in hepatocellular carcinoma patients

description: To clarify the status of insomnia and depression and the prescription of sleeping pills in hepatocellular carcinoma (HCC) patients before and after HCC diagnosis and treatment. Patients’ data from a Japanese health insurance claims database were analyzed retrospectively to determine the incidence of insomnia and depression and their association with sleeping pill prescriptions during the 6 months before and after HCC diagnosis and treatment. A total of 9,109 HCC patients (median age at diagnosis = 71.5 years, 68.1% male) were analyzed. The incidences of insomnia and depression increased significantly after HCC diagnosis. Insomnia was reported in 15.0% of patients before diagnosis, and it increased to 27.6% after diagnosis. Similarly, depression was reported in 6.3% and 11.3% before and after diagnosis, respectively. The incidences of insomnia and depression before diagnosis were higher in patients with concomitant liver diseases including hepatitis, cirrhosis, and hepatic encephalopathy. However, the rate of sleeping pill prescription was significantly lower in patients with concomitant liver diseases after diagnosis. The incidence of fracture was higher in insomnia or depression patients than others and in patients treated with sleeping pills than without before and after diagnosis. HCC patients had increased risks of insomnia and depression after diagnosis. The high risk of fracture in HCC patients with insomnia and depression and treated with sleeping pills suggests that it is difficult to optimize the management of HCC patients, especially those with concomitant liver diseases.
DOI: 10.6084/m9.figshare.19849590
title: Nationwide database analysis of insomnia, depression, and sleeping pill prescriptions in hepatocellular carcinoma patients

description: To clarify the status of insomnia and depression and the prescription of sleeping pills in hepatocellular carcinoma (HCC) patients before and after HCC diagnosis and treatment. Patients’ data from a Japanese health insurance claims database were analyzed retrospectively to determine the incidence of insomnia and depression and their association with sleeping pill prescriptions during the 6 months before and after HCC diagnosis and treatment. A total of 9,109 HCC patients (median age at diagnosis = 71.5 years, 68.1% male) were analyzed. The incidences of insomnia and depression increased significantly after HCC diagnosis. Insomnia was reported in 15.0% of patients before diagnosis, and it increased to 27.6% after diagnosis. Similarly, depression was reported in 6.3% and 11.3% before and after diagnosis, respectively. The incidences of insomnia and depression before diagnosis were higher in patients with concomitant liver diseases including hepatitis, cirrhosis, and hepatic encephalopathy. However, the rate of sleeping pill prescription was significantly lower in patients with concomitant liver diseases after diagnosis. The incidence of fracture was higher in insomnia or depression patients than others and in patients treated with sleeping pills than without before and after diagnosis. HCC patients had increased risks of insomnia and depression after diagnosis. The high risk of fracture in HCC patients with insomnia and depression and treated with sleeping pills suggests that it is difficult to optimize the management of HCC patients, especially those with concomitant liver diseases.
DOI: 10.6084/m9.figshare.20194269
title: Assessing the correlation between second progression-free survival (PFS2) and overall survival (OS) in advanced prostate cancer patients using medical data vision (MDV) claims database in Japan

description: To assess the correlation between PFS2 and OS among patients with advanced prostate cancer (PC) in a real-world setting for Japan. This was a retrospective analysis using the Japanese MDV database. Patients with nmCRPC (non-metastatic Castration-Resistant PC), mCRPC (metastatic Castration-Resistant PC), and mCNPC (metastatic Castration-Naïve PC) were identified and their medical records were investigated for PFS2 and death. Association between PFS2 and OS was determined using the Pearson’s, Spearman’s, Kendall's Tau, and Fleischers’ correlation coefficients. A total of 386,484 patients with PC were identified from the database, of which, 1,783 patients with nmCRPC, 630 with mCRPC, and 454 with mCNPC met the predefined eligibility criteria. Significant correlation between PFS2 and OS was observed in patients with nmCRPC (Pearson’s <i>r</i> = 0.873; 95% CI: 0.849−0.897, Spearman’s <i>r</i> = 0.909; 95% CI: 0.893−0.925; Kendall’s Tau <i>r</i> = 0.831; 95% CI: 0.812−0.850, Fleischers’ <i>r</i> = 0.682; 95% CI: 0.601−0.764), mCRPC (Pearson’s <i>r</i> = 0.812; 95% CI: 0.758−0.865, Spearman’s <i>r</i> = 0.895; 95% CI: 0.868−0.923, Kendall’s Tau <i>r</i> = 0.789; 95% CI: 0.755−0.823, Fleischers’ <i>r</i>= 0.439; 95% CI: 0.334−0.544), and mCNPC (Pearson’s <i>r</i> = 0.931; 95% CI: 0.899−0.964, Spearman’s <i>r</i> = 0.943; 95% CI: 0.922−0.964, Kendall’s Tau <i>r</i> = 0.866; 95% CI: 0.836−0.896, Fleischers’ <i>r</i> = 0.756; 95% CI: 0.624−0.888). The results of this study indicate a significant correlation between PFS2 and OS, which adds additional evidence to the existing literature of using PFS2 as a surrogate endpoint for OS in patients with PC.
DOI: 10.6084/m9.figshare.20194269.v2
title: Assessing the correlation between second progression-free survival (PFS2) and overall survival (OS) in advanced prostate cancer patients using medical data vision (MDV) claims database in Japan

description: To assess the correlation between PFS2 and OS among patients with advanced prostate cancer (PC) in a real-world setting for Japan. This was a retrospective analysis using the Japanese MDV database. Patients with nmCRPC (non-metastatic Castration-Resistant PC), mCRPC (metastatic Castration-Resistant PC), and mCNPC (metastatic Castration-Naïve PC) were identified and their medical records were investigated for PFS2 and death. Association between PFS2 and OS was determined using the Pearson’s, Spearman’s, Kendall's Tau, and Fleischers’ correlation coefficients. A total of 386,484 patients with PC were identified from the database, of which, 1,783 patients with nmCRPC, 630 with mCRPC, and 454 with mCNPC met the predefined eligibility criteria. Significant correlation between PFS2 and OS was observed in patients with nmCRPC (Pearson’s <i>r</i> = 0.873; 95% CI: 0.849−0.897, Spearman’s <i>r</i> = 0.909; 95% CI: 0.893−0.925; Kendall’s Tau <i>r</i> = 0.831; 95% CI: 0.812−0.850, Fleischers’ <i>r</i> = 0.682; 95% CI: 0.601−0.764), mCRPC (Pearson’s <i>r</i> = 0.812; 95% CI: 0.758−0.865, Spearman’s <i>r</i> = 0.895; 95% CI: 0.868−0.923, Kendall’s Tau <i>r</i> = 0.789; 95% CI: 0.755−0.823, Fleischers’ <i>r</i>= 0.439; 95% CI: 0.334−0.544), and mCNPC (Pearson’s <i>r</i> = 0.931; 95% CI: 0.899−0.964, Spearman’s <i>r</i> = 0.943; 95% CI: 0.922−0.964, Kendall’s Tau <i>r</i> = 0.866; 95% CI: 0.836−0.896, Fleischers’ <i>r</i> = 0.756; 95% CI: 0.624−0.888). The results of this study indicate a significant correlation between PFS2 and OS, which adds additional evidence to the existing literature of using PFS2 as a surrogate endpoint for OS in patients with PC.
DOI: 10.5517/ccdc.csd.cc28pvzt
title: CCDC 2106387: Experimental Crystal Structure Determination

description: Related Article: Qi Li, Qian Zhang, Zhen-Zhen Xue, Ji-Xiang Hu, Guo-Ming Wang|2022|Inorg.Chem.|61|10792|doi:10.1021/acs.inorgchem.2c01019
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.cc28pw0w
title: CCDC 2106388: Experimental Crystal Structure Determination

description: Related Article: Qi Li, Qian Zhang, Zhen-Zhen Xue, Ji-Xiang Hu, Guo-Ming Wang|2022|Inorg.Chem.|61|10792|doi:10.1021/acs.inorgchem.2c01019
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.cc28qxfc
title: CCDC 2107393: Experimental Crystal Structure Determination

description: Related Article: Tamara A. Bazhenova, Vyacheslav A. Kopotkov, Denis V. Korchagin, Yuriy V. Manakin, Leokadiya V. Zorina, Sergey V. Simonov, Ilya A. Yakushev, Vladimir S. Mironov, Alexander N. Vasiliev, Olga V. Maximova, Eduard B. Yagubskii|2021|Molecules|26|6908|doi:10.3390/molecules26226908
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.cc28qxgd
title: CCDC 2107394: Experimental Crystal Structure Determination

description: Related Article: Tamara A. Bazhenova, Vyacheslav A. Kopotkov, Denis V. Korchagin, Yuriy V. Manakin, Leokadiya V. Zorina, Sergey V. Simonov, Ilya A. Yakushev, Vladimir S. Mironov, Alexander N. Vasiliev, Olga V. Maximova, Eduard B. Yagubskii|2021|Molecules|26|6908|doi:10.3390/molecules26226908
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.cc28rzhj
title: CCDC 2108418: Experimental Crystal Structure Determination

description: Related Article: Yasir Iqbal, Muhammad Haroon, Tashfeen Akhtar, Muhammad Ashfaq, Muhammad Nawaz Tahir, Lubna Rasheed, Muhammad Yousuf, Muhammad Abid Zia|2022|J.Mol.Struct.|1267|133620|doi:10.1016/j.molstruc.2022.133620
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.cc28trmh
title: CCDC 2110127: Experimental Crystal Structure Determination

description: Related Article: Li Liu, Wenjia Hao, Qi Huang, Bo Jin, Rufang Peng|2022|J.Solid State Chem.|314|123375|doi:10.1016/j.jssc.2022.123375
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.