cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6512468
title: Generalized Deception Dataset

description: We took labeled datasets from five different deception-detection tasks with no licensing issues and converted them to a standard format. We inspected each dataset for quality and generated new cleaned versions. Task # Deceptive # Truthful Product Reviews 10493 10481 Phishing 6134 9202 Job Scams 608 13735 Political Statements 5669 7167 Fake News 27486 34615 Our data is structured as five jsonlines files (one for each task) with a text to classify and a Boolean is_deceptive label. Sample data point: <pre><code>{ "text":"the Annies List political group supports third-trimester abortions on demand.", "is_deceptive":true } </code></pre> <br>
description: This work was completed in part with resources provided by the Research Computing Data Core at the University of Houston and supported in part by NSF grants DGE 1433817, CCF 1950297, ARO award W911NF-20-1-0254, and ONR award N00014-19-S-F009.
description: {"references": ["Pawan Kumar Verma, Prateek Agrawal, and Radu Prodan. 2021. WELFake dataset for fake news detection in text data. https://doi.org/10.5281/zenodo.4561253", "Sokratis Vidros, Constantinos Kolias, Georgios Kambourakis, and Leman Akoglu. 2017. Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset. Future Internet 9, 1 (2017).", "Rakesh M Verma, Victor Zeng, and Houtan Faridi. 2019. Data quality for security challenges: Case studies of phishing, malware, and intrusion detection datasets. In Proc. ACM SIGSAC Conf. on Computer and Communications Security. 2605\u20132607.", "Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018. Where is your Evidence: Improving Fact-checking by Justification Modeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER). 85\u201390"]}
DOI: 10.5281/zenodo.8371762
title: Generalized Deception Dataset

description: We took labeled datasets from five different deception-detection tasks with no licensing issues and converted them to a standard format. We inspected each dataset for quality and generated new cleaned versions. Task # Deceptive # Truthful Product Reviews 10493 10481 Phishing 6134 9202 Job Scams 608 13735 Political Statements 5669 7167 Fake News 27486 34615 Our data is structured as five jsonlines files (one for each task) with a text to classify and a Boolean is_deceptive label. Sample data point: <pre><code>{ "text":"the Annies List political group supports third-trimester abortions on demand.", "is_deceptive":true } </code></pre> Changelog <strong>1.1</strong> Fixed flipped labels in the job scams dataset.
description: This work was completed in part with resources provided by the Research Computing Data Core at the University of Houston and supported in part by NSF grants DGE 1433817, CCF 1950297, ARO award W911NF-20-1-0254, and ONR award N00014-19-S-F009.
description: {"references": ["Pawan Kumar Verma, Prateek Agrawal, and Radu Prodan. 2021. WELFake dataset for fake news detection in text data. https://doi.org/10.5281/zenodo.4561253", "Sokratis Vidros, Constantinos Kolias, Georgios Kambourakis, and Leman Akoglu. 2017. Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset. Future Internet 9, 1 (2017).", "Rakesh M Verma, Victor Zeng, and Houtan Faridi. 2019. Data quality for security challenges: Case studies of phishing, malware, and intrusion detection datasets. In Proc. ACM SIGSAC Conf. on Computer and Communications Security. 2605\u20132607.", "Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018. Where is your Evidence: Improving Fact-checking by Justification Modeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER). 85\u201390"]}
DOI: 10.5281/zenodo.8361548
title: Dataset used in the paper 'Virtual reflection height of nighttime equatorial ionosphere estimated with low-frequency magnetic sferics measured in Malacca'

description: The dataset here includes GLD360 lightning data and LF data analyzed in the paper "Virtual reflection height of nighttime equatorial ionosphere estimated with low-frequency magnetic sferics measured in Malacca".
DOI: 10.5281/zenodo.8361547
title: Dataset used in the paper 'Virtual reflection height of nighttime equatorial ionosphere estimated with low-frequency magnetic sferics measured in Malacca'

description: The dataset here includes GLD360 lightning data and LF data analyzed in the paper "Virtual reflection height of nighttime equatorial ionosphere estimated with low-frequency magnetic sferics measured in Malacca".
DOI: 10.5281/zenodo.5598405
title: Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks

description: Abstract Catchment water storage capacity (CWSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the CWSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the CWSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global CWSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. CWSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global CWSC parameter dataset as a benchmark for geoscientific modelling by users. A global terrestrial CWSC dataset with 0.5 spatial resolution is now available. All input factors and the global CWSC data are publicly available as NetCDF files or download from smsc_data.zip at Zenodo. Python codes are available to calculate the basin average CWSC value from grid values in any interested basin on a global scale. The Fortran codes for parameter calibration of semi distributed global monthly water balance models are available at https://github.com/xiekangwhu/CWSC_monthly_water_balance_models. The Python codes of deep residual network we developed for the global reconstruction map of CWSC are available at https://github.com/xiekangwhu/CWSC_deep_residual_network. Major code contributor: Kang Xie (PhD Student, Wuhan University), Liting Zhou (PhD Student, Wuhan University), Shujie Cheng (PhD Student, Wuhan University), and Shuanghong Shen (PhD Student, University of Science and Technology of China) Citations If you find our code to be useful, please cite the following papers: Xie, K. et al. Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy - ScienceDirect. Journal of Hydrology 598, doi:10.1016/j.jhydrol.2020.125772 (2020). Xie, K. et al. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology 603, doi:10.1016/j.jhydrol.2021.127043 (2021). Xie, K. et al. Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models. Journal of Hydrologic Engineering 25, doi:10.1061/(asce)he.1943-5584.0001887 (2020).
DOI: 10.5281/zenodo.5595939
title: Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks

description: Abstract Catchment water storage capacity (CWSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the CWSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the CWSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global CWSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. CWSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global CWSC parameter dataset as a benchmark for geoscientific modelling by users. A global terrestrial CWSC dataset with 0.5 spatial resolution is now available. All input factors and the global CWSC data are publicly available as NetCDF files or download from smsc_data.zip at Zenodo. Python codes are available to calculate the basin average CWSC value from grid values in any interested basin on a global scale. The Fortran codes for parameter calibration of semi distributed global monthly water balance models are available at https://github.com/xiekangwhu/CWSC_monthly_water_balance_models. The Python codes of deep residual network we developed for the global reconstruction map of CWSC are available at https://github.com/xiekangwhu/CWSC_deep_residual_network. Major code contributor: Kang Xie (PhD Student, Wuhan University), Liting Zhou (PhD Student, Wuhan University), Shujie Cheng (PhD Student, Wuhan University), and Shuanghong Shen (PhD Student, University of Science and Technology of China) Citations If you find our code to be useful, please cite the following papers: Xie, K. et al. Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy - ScienceDirect. Journal of Hydrology 598, doi:10.1016/j.jhydrol.2020.125772 (2020). Xie, K. et al. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology 603, doi:10.1016/j.jhydrol.2021.127043 (2021). Xie, K. et al. Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models. Journal of Hydrologic Engineering 25, doi:10.1061/(asce)he.1943-5584.0001887 (2020).
DOI: 10.6084/m9.figshare.24186594
title: Data for the three experiments.

description: Data for the three behavioral experiments.
DOI: 10.6084/m9.figshare.24186594.v1
title: Data for the three experiments.

description: Data for the three behavioral experiments.
DOI: 10.6084/m9.figshare.24182406
title: Raw data.xlsx

description: The reproduction performance of Monochamus alternatus after extreme high temperatures treatments.
DOI: 10.6084/m9.figshare.24182406.v1
title: Raw data.xlsx

description: The reproduction performance of Monochamus alternatus after extreme high temperatures treatments.
DOI: 10.6084/m9.figshare.24161289
title: Spectroscopy of a mesospheric ghost reveals iron emissions

description: Dataset and Python code to generate figures 3 and 4 in article titled "Spectroscopy of a mesospheric ghost reveals iron emissions".
DOI: 10.34740/kaggle/dsv/6523739
title: OnlineFree
DOI: 10.34740/kaggle/dsv/6523738
title: AutoXGB
DOI: 10.6084/m9.figshare.24186783
title: Supplementary Material for: Associations between Antiepileptic Use and Hypogammaglobulinaemia: Findings from a Population-Based Case-Control Study Using Data Linkage

description: <b><i>Background and Objectives:</i></b> Increased mortality in epilepsy due to infections (other than pneumonia) has been demonstrated. Small case series of people on antiepileptic drugs (AEDs) have described hypogammaglobulinaemia, which may predispose to infections. It is unclear whether hypogammaglobulinaemia is more frequent in people on AEDs, what AEDs it is associated with, or what clinical impact it has. In this population-based study, we aimed to determine whether AEDs were associated with hypogammaglobulinaemia, which AEDs were associated, and whether the associations may be causal. <b><i>Methods:</i></b> We conducted an unmatched case-control study using data linkage of routinely collected biochemistry, prescribing, and morbidity datasets in North-East Scotland from 2009–2021. Cases were participants with immunoglobulin levels less than the reference range. Controls were those with normal/high immunoglobulin levels. Logistic regression was used to investigate associations between AED exposure and any hypogammaglobulinaemia, adjusting for age, sex, and comorbidity. We also analysed low IgA, IgM, and IgG separately. We analysed “any AED” exposure and common individual drugs separately. Cumulative exposure data were used to determine whether an exposure-response relationship was present. <b><i>Results:</i></b> 18,666 cases and 127,157 controls were identified. Use of any AED was associated with increased risk of hypogammaglobulinaemia (adjusted odds ratio [aOR] 1.20 [95% CI: 1.15–1.25]). Phenytoin use was strongly associated with low IgA (aOR 5.90 [95% CI: 3.04, 10.43]). Carbamazepine and lamotrigine were also associated with low IgA. Apart from topiramate, which was associated with a non-significant decrease in odds of hypogammaglobulinaemia, there was a consistent increase in odds of hypogammaglobulinaemia across most AEDs studied. Panhypogammaglobulinaemia was associated with any AED use, carbamazepine, lamotrigine, gabapentin, and multiple AED use. There was evidence of an exposure-response relationship between any AED use and any hypogammaglobulinaemia, low IgA, and low IgG. Carbamazepine and probably lamotrigine also had an exposure-response relationship with any hypogammaglobulinaemia. <b><i>Discussion:</i></b> AEDs may increase hypogammaglobulinaemia risk. Specific classes of immunoglobulins are differentially affected, and the exposure-response analysis suggests this may be causal. Further work should investigate the clinical impact of these findings. Clinicians should check immunoglobulin levels if unusual or recurrent infections occur in patients treated with AEDs.
DOI: 10.6084/m9.figshare.24186783.v1
title: Supplementary Material for: Associations between Antiepileptic Use and Hypogammaglobulinaemia: Findings from a Population-Based Case-Control Study Using Data Linkage

description: <b><i>Background and Objectives:</i></b> Increased mortality in epilepsy due to infections (other than pneumonia) has been demonstrated. Small case series of people on antiepileptic drugs (AEDs) have described hypogammaglobulinaemia, which may predispose to infections. It is unclear whether hypogammaglobulinaemia is more frequent in people on AEDs, what AEDs it is associated with, or what clinical impact it has. In this population-based study, we aimed to determine whether AEDs were associated with hypogammaglobulinaemia, which AEDs were associated, and whether the associations may be causal. <b><i>Methods:</i></b> We conducted an unmatched case-control study using data linkage of routinely collected biochemistry, prescribing, and morbidity datasets in North-East Scotland from 2009–2021. Cases were participants with immunoglobulin levels less than the reference range. Controls were those with normal/high immunoglobulin levels. Logistic regression was used to investigate associations between AED exposure and any hypogammaglobulinaemia, adjusting for age, sex, and comorbidity. We also analysed low IgA, IgM, and IgG separately. We analysed “any AED” exposure and common individual drugs separately. Cumulative exposure data were used to determine whether an exposure-response relationship was present. <b><i>Results:</i></b> 18,666 cases and 127,157 controls were identified. Use of any AED was associated with increased risk of hypogammaglobulinaemia (adjusted odds ratio [aOR] 1.20 [95% CI: 1.15–1.25]). Phenytoin use was strongly associated with low IgA (aOR 5.90 [95% CI: 3.04, 10.43]). Carbamazepine and lamotrigine were also associated with low IgA. Apart from topiramate, which was associated with a non-significant decrease in odds of hypogammaglobulinaemia, there was a consistent increase in odds of hypogammaglobulinaemia across most AEDs studied. Panhypogammaglobulinaemia was associated with any AED use, carbamazepine, lamotrigine, gabapentin, and multiple AED use. There was evidence of an exposure-response relationship between any AED use and any hypogammaglobulinaemia, low IgA, and low IgG. Carbamazepine and probably lamotrigine also had an exposure-response relationship with any hypogammaglobulinaemia. <b><i>Discussion:</i></b> AEDs may increase hypogammaglobulinaemia risk. Specific classes of immunoglobulins are differentially affected, and the exposure-response analysis suggests this may be causal. Further work should investigate the clinical impact of these findings. Clinicians should check immunoglobulin levels if unusual or recurrent infections occur in patients treated with AEDs.
DOI: 10.6084/m9.figshare.24186786.v1
title: Supplementary Material for: Associations between Antiepileptic Use and Hypogammaglobulinaemia: Findings from a Population-Based Case-Control Study Using Data Linkage

description: <b><i>Background and Objectives:</i></b> Increased mortality in epilepsy due to infections (other than pneumonia) has been demonstrated. Small case series of people on antiepileptic drugs (AEDs) have described hypogammaglobulinaemia, which may predispose to infections. It is unclear whether hypogammaglobulinaemia is more frequent in people on AEDs, what AEDs it is associated with, or what clinical impact it has. In this population-based study, we aimed to determine whether AEDs were associated with hypogammaglobulinaemia, which AEDs were associated, and whether the associations may be causal. <b><i>Methods:</i></b> We conducted an unmatched case-control study using data linkage of routinely collected biochemistry, prescribing, and morbidity datasets in North-East Scotland from 2009–2021. Cases were participants with immunoglobulin levels less than the reference range. Controls were those with normal/high immunoglobulin levels. Logistic regression was used to investigate associations between AED exposure and any hypogammaglobulinaemia, adjusting for age, sex, and comorbidity. We also analysed low IgA, IgM, and IgG separately. We analysed “any AED” exposure and common individual drugs separately. Cumulative exposure data were used to determine whether an exposure-response relationship was present. <b><i>Results:</i></b> 18,666 cases and 127,157 controls were identified. Use of any AED was associated with increased risk of hypogammaglobulinaemia (adjusted odds ratio [aOR] 1.20 [95% CI: 1.15–1.25]). Phenytoin use was strongly associated with low IgA (aOR 5.90 [95% CI: 3.04, 10.43]). Carbamazepine and lamotrigine were also associated with low IgA. Apart from topiramate, which was associated with a non-significant decrease in odds of hypogammaglobulinaemia, there was a consistent increase in odds of hypogammaglobulinaemia across most AEDs studied. Panhypogammaglobulinaemia was associated with any AED use, carbamazepine, lamotrigine, gabapentin, and multiple AED use. There was evidence of an exposure-response relationship between any AED use and any hypogammaglobulinaemia, low IgA, and low IgG. Carbamazepine and probably lamotrigine also had an exposure-response relationship with any hypogammaglobulinaemia. <b><i>Discussion:</i></b> AEDs may increase hypogammaglobulinaemia risk. Specific classes of immunoglobulins are differentially affected, and the exposure-response analysis suggests this may be causal. Further work should investigate the clinical impact of these findings. Clinicians should check immunoglobulin levels if unusual or recurrent infections occur in patients treated with AEDs.
DOI: 10.5281/zenodo.4009400
title: European maritime region definition

description: With the increasing share of the installed Renewable Sources (RES) capacity, evaluating the effect of renewable energy on the energy supply is a very important issue which is addressed in several climate services projects such as C3S-Energy, Clim2power or C3S-ECEM. Prospective analysis are generally made at regional level (e.g. TIMES model) and it is becoming a standard practice to aggregate gridded RES power generation data into aggregated values at NUTS1 or NUTS2 level. This approach raises an issue for the consideration of the offshore wind energy as well as other Marine Renewable Energies (MRE) since there is to the best of our knowledge no commonly accepted region definition corresponding to the NUTS boundaries for the maritime area.
DOI: 10.1594/wdcc/dphase_prevah
title: dphase_prevah: hydrological model PREVAH run by IAC_ETH and WSL for the MAP D-PHASE project

description: Project: D-PHASE, Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region - The Mesoscale Alpine Programme (MAP, the first WWRP Research and Development Project (RDP)) is an international research initiative devoted to the study of atmospheric and hydrological processes over mountainous terrain. It aims towards expanding our knowledge of weather and climate over complex topography, and thereby to improve current forecasting capabilities. A large-scale field phase in the Alpine region took place from 7 September to 15 November 1999. D-PHASE (Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region) is a WWRP Forecast Demonstration Project (FDP) and aims at demonstrating some of the many achievements of MAP, in particular the ability of forecasting heavy precipitation and related flooding events in the Alpine region. The MAP FDP will address the entire forecasting chain ranging from limited-area ensemble forecasting, high-resolution atmospheric modelling(km-scale), hydrological modelling, and nowcasting to decision making by the end users, i.e., it is foreseen to set up an end-to-end forecasting system. The demonstration period of MAP D-PHASE will be 1 June to 30 November 2007. See also official homepage: http://www.map.meteoswiss.ch/map-doc/dphase/dphase_info.htm Please be aware of the common COPS/GOP/D-PHASE data policy, which you please find at http://cops.wdc-climate.de/ Summary: dphase_prevah The hydrological model PREVAH is adopted for (ensemble) runoff forecasts for several basins in Switzerland and North Italy. Runoff nowcasting is driven by observed meteorology consisting of data from meteorological stations and operational radar precipitation data. Forecasts are computed with three deterministic NWP models and with one atmospheric ensemble predictions system (EPS). Principal investigators are the Institute for Atmospheric and Climate Sciences of the ETH (IAC_ETH) and the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Data providers: Observed meteorology: MeteoSwiss, WSL, IST-SUPSI Rainfall Radar: MeteoSwiss CLEPS, COSMOCH2 and COSMOCH7: MeteoSwiss MM5_15: FZK, IMK-IFU Observed discharge: Swiss Federal Office for Environment (FOEN), CONTICINO Basins: Verzasca_at_Lavertezzo, Ticino_at_Bellinzona, Maggia_at_Solduno, Tresa_at_Rocchetta, Toce_at_Candoglia, Ticino_at_Miorina, Thur_at_Andelfingen, Linth_at_Mollis Sub-Experiments: Runoff nowcasting, CLEPS, COSMOCH2 and COSMOCH7 at all basins MM5_15 and Rainradar only at Verzasca_at_Lavertezzo, Thur_at_Andelfingen, Linth_at_Mollis
DOI: 10.5517/cc13r5lm
title: CCDC 1035170: Experimental Crystal Structure Determination

description: Related Article: Ronald J. Voll, Frank R. Fronczek, Ezzat S. Younathan|2014|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.