cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24452758.v1
title: Real-time flash flood forecasting approach for development of early warning systems: integrated hydrological and meteorological application

description: This study proposes an integrated hydrometeorological modelling framework approach and methodology for flash flood Early Warning Systems in the Chugoku region of Japan. Unprecedented rainfall-induced hydrometeorological disasters and flash floods are increasingly occurring worldwide. Comprehensive efforts are conducted to simultaneously combine multiple disciplines into integrated modelling framework approaches to reduce disaster resilience. This enables more accurate hindcasts, reanalyses, real-time forecasts or nowcasts for flash floods. This study integrates proposed hydrological calibration approach with meteorological input. Two real-time rainfall forecasts by the Weather Research and Forecasting model forced by the Atmospheric Reanalysis v5 (ERA5) and the Japanese 55-year Reanalysis (JRA55) were used as input data to the hydrological model ensemble parameterized previously. This approach was applied to seven major rivers to evaluate river discharges real-time forecasts accuracy during the Heavy Rainfall Event of July 2018. Long lead-times of up to 29 h with a satisfactory reproducible range of Nash-Sutcliffe Efficiency were obtained using both meteorological forecast for all rivers cumulatively. This indicates that the proposed integrated hydrometeorological approach enables accurate flash flood real-time forecasting for this event. Similarly, the joint hydrometeorological approach enables framework for development of real-time flash food forecasting application in Japan and presumably worldwide. Development of integrated hydrometeorological river discharge forecasts in real time.This study integrates hydrological ensemble calibration and meteorological forecasts.Weather Research and Forecasting (WRF) outputs with ensemble hydrological parameters.Long lead-times of 29 h with satisfactory accurate reproducibility were obtained.Our integrated hydrometeorological approach enables accurate river discharge nowcasts. Development of integrated hydrometeorological river discharge forecasts in real time. This study integrates hydrological ensemble calibration and meteorological forecasts. Weather Research and Forecasting (WRF) outputs with ensemble hydrological parameters. Long lead-times of 29 h with satisfactory accurate reproducibility were obtained. Our integrated hydrometeorological approach enables accurate river discharge nowcasts.
DOI: 10.6084/m9.figshare.24452758
title: Real-time flash flood forecasting approach for development of early warning systems: integrated hydrological and meteorological application

description: This study proposes an integrated hydrometeorological modelling framework approach and methodology for flash flood Early Warning Systems in the Chugoku region of Japan. Unprecedented rainfall-induced hydrometeorological disasters and flash floods are increasingly occurring worldwide. Comprehensive efforts are conducted to simultaneously combine multiple disciplines into integrated modelling framework approaches to reduce disaster resilience. This enables more accurate hindcasts, reanalyses, real-time forecasts or nowcasts for flash floods. This study integrates proposed hydrological calibration approach with meteorological input. Two real-time rainfall forecasts by the Weather Research and Forecasting model forced by the Atmospheric Reanalysis v5 (ERA5) and the Japanese 55-year Reanalysis (JRA55) were used as input data to the hydrological model ensemble parameterized previously. This approach was applied to seven major rivers to evaluate river discharges real-time forecasts accuracy during the Heavy Rainfall Event of July 2018. Long lead-times of up to 29 h with a satisfactory reproducible range of Nash-Sutcliffe Efficiency were obtained using both meteorological forecast for all rivers cumulatively. This indicates that the proposed integrated hydrometeorological approach enables accurate flash flood real-time forecasting for this event. Similarly, the joint hydrometeorological approach enables framework for development of real-time flash food forecasting application in Japan and presumably worldwide. Development of integrated hydrometeorological river discharge forecasts in real time.This study integrates hydrological ensemble calibration and meteorological forecasts.Weather Research and Forecasting (WRF) outputs with ensemble hydrological parameters.Long lead-times of 29 h with satisfactory accurate reproducibility were obtained.Our integrated hydrometeorological approach enables accurate river discharge nowcasts. Development of integrated hydrometeorological river discharge forecasts in real time. This study integrates hydrological ensemble calibration and meteorological forecasts. Weather Research and Forecasting (WRF) outputs with ensemble hydrological parameters. Long lead-times of 29 h with satisfactory accurate reproducibility were obtained. Our integrated hydrometeorological approach enables accurate river discharge nowcasts.
DOI: 10.6084/m9.figshare.24647690.v1
title: Estimating the period of probable landslide event using advanced D-InSAR technique for time-series deformation study of Kotrupi region

description: Himalayan region is prone to landslides and every year many landslides occur. Some of these events are reported while, many of these landslides are not reported due to their remote location of occurrence, or nature of impact, and its date of occurrence is also unknown. This study tries to find a method to estimate the probable dates of landslide occurrences using time-series deformation analysis applying advanced DInSAR approach by adopting a short temporal baseline graph. We used 118 Sentinel 1-A SLC images to identify the nature, pattern, and trends of landslide deformation. Based on the time series cumulative deformation values, we found a ‘V’-shaped kink that matches with the landslide events. Such kinks were observed around 12 June 2015, 25 May 2016, 05 August 2016, 23 June 2017, 15 September 2019, 27 May 2018, 08 June 2018, and 15 July 2020. The majority of these kinks are visible during monsoon season which is expected. High-resolution Planet satellite data around these time ranges were used to verify the existence of new landslides. Moreover, the performance is evaluated using AUC-ROC, precision, recall; and F-Score. This approach has good prediction capabilities with the precision of 87%, AUC-ROC of 77% and F-score of 74%.
DOI: 10.6084/m9.figshare.24647690
title: Estimating the period of probable landslide event using advanced D-InSAR technique for time-series deformation study of Kotrupi region

description: Himalayan region is prone to landslides and every year many landslides occur. Some of these events are reported while, many of these landslides are not reported due to their remote location of occurrence, or nature of impact, and its date of occurrence is also unknown. This study tries to find a method to estimate the probable dates of landslide occurrences using time-series deformation analysis applying advanced DInSAR approach by adopting a short temporal baseline graph. We used 118 Sentinel 1-A SLC images to identify the nature, pattern, and trends of landslide deformation. Based on the time series cumulative deformation values, we found a ‘V’-shaped kink that matches with the landslide events. Such kinks were observed around 12 June 2015, 25 May 2016, 05 August 2016, 23 June 2017, 15 September 2019, 27 May 2018, 08 June 2018, and 15 July 2020. The majority of these kinks are visible during monsoon season which is expected. High-resolution Planet satellite data around these time ranges were used to verify the existence of new landslides. Moreover, the performance is evaluated using AUC-ROC, precision, recall; and F-Score. This approach has good prediction capabilities with the precision of 87%, AUC-ROC of 77% and F-score of 74%.
DOI: 10.6084/m9.figshare.23648021
title: Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya

description: Landslide susceptibility mapping plays an imperative role in mitigating hazards and determining the future direction of developmental activities in mountainous regions. Here, we used 518 landslide occurrences and nine landslide-conditioning parameters to build landslide vulnerability models in the Kailash Sacred Landscape (KSL), India. Four multivariate statistical models were applied, namely the generalized linear model (GLM), maximum entropy (MaxEnt), Mahalanobis <i>D<sup>2</sup></i> (MD), and support vector machine (SVM), to calibrate and compare four maps of landslide susceptibility. The results demonstrated the outperformance of Mahalanobis <i>D<sup>2</sup></i> for predictability compared to other models obtained from the area under the receiver operating characteristic curve (ROC). The ensemble model data shows that 10.5% of the landscape has susceptible conditions for future landslides, whereas 89.50% of the landscape falls under the safe zone. The occurrence of landslides in the KSL is linked to the middle elevations, vicinity to water bodies, and the motorable roads. Furthermore, the observed patterns and the resulting models exhibit the major variables that cause landslides and their respective significance. The current modelling approach could provide baseline data at the regional scale to improve the developmental planning in the KSL.
DOI: 10.6084/m9.figshare.23648021.v1
title: Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya

description: Landslide susceptibility mapping plays an imperative role in mitigating hazards and determining the future direction of developmental activities in mountainous regions. Here, we used 518 landslide occurrences and nine landslide-conditioning parameters to build landslide vulnerability models in the Kailash Sacred Landscape (KSL), India. Four multivariate statistical models were applied, namely the generalized linear model (GLM), maximum entropy (MaxEnt), Mahalanobis <i>D<sup>2</sup></i> (MD), and support vector machine (SVM), to calibrate and compare four maps of landslide susceptibility. The results demonstrated the outperformance of Mahalanobis <i>D<sup>2</sup></i> for predictability compared to other models obtained from the area under the receiver operating characteristic curve (ROC). The ensemble model data shows that 10.5% of the landscape has susceptible conditions for future landslides, whereas 89.50% of the landscape falls under the safe zone. The occurrence of landslides in the KSL is linked to the middle elevations, vicinity to water bodies, and the motorable roads. Furthermore, the observed patterns and the resulting models exhibit the major variables that cause landslides and their respective significance. The current modelling approach could provide baseline data at the regional scale to improve the developmental planning in the KSL.
DOI: 10.5281/zenodo.10394253
title: ПЕНСИЯ ТЎЛОВЛАРИНИ ИНДЕКСАЦИЯЛАШНИНГ ХОРИЖ ТАЖРИБАСИ
DOI: 10.5281/zenodo.10394254
title: ПЕНСИЯ ТЎЛОВЛАРИНИ ИНДЕКСАЦИЯЛАШНИНГ ХОРИЖ ТАЖРИБАСИ
DOI: 10.34740/kaggle/ds/4174717
title: Baldur's Gate 3 Steam Reviews
DOI: 10.6084/m9.figshare.24845130
title: Raw data for Tan et al

description: Raw data for Tan et al., forest stream DOC
DOI: 10.6084/m9.figshare.24845130.v1
title: Raw data for Tan et al

description: Raw data for Tan et al., forest stream DOC
DOI: 10.6084/m9.figshare.24845151
title: Supplementary Data.xlsx

description: These data including: the oxygen isotope-element data sets of olivine and the corresponding reference materials；the Whole-rock major and trace elements of komatiitesthe element data of serpentine
DOI: 10.5281/zenodo.10394317
title: TECHNOLOGY FOR THE DEVELOPMENT OF HYGIENIC CULTURE IN STUDENTS
DOI: 10.5281/zenodo.10394318
title: TECHNOLOGY FOR THE DEVELOPMENT OF HYGIENIC CULTURE IN STUDENTS
DOI: 10.21227/76ky-qc65
title: ITM-HDR-VQA DATASET

description: The ITM-HDR-VQA dataset is a video quality assessment dataset for inversely tone-mapped videos. It contain 200 HDR10 videos with their MOS.We capture videos of 20 typical HDR scenes including daylight scenes containing both sunlit areas and deep shadows and night scenes lit by artificial lights. The contents of these scenes can be roughly divided into two categories, man-made architecture and natural scenery.A SONY A7S3 camera capable of recording a luminance range of up to 15 stops was used. The camera configurations are as follows: conversion curve = S-Log3, color management space = S-Gamut3, resolution = 3840×2160, and frame rate = 50 fps. After obtaining SDR videos, we map them to ITM-HDR-Videos through ten representative ITM algorithms, including traditional and deep learning-based methods.
DOI: 10.5281/zenodo.10394400
title: METHODOLOGY FOR PREPARING PRIMARY CLASS STUDENTS FOR THE PIRLS INTERNATIONAL ASSESSMENT CONTROL
DOI: 10.5281/zenodo.10394401
title: METHODOLOGY FOR PREPARING PRIMARY CLASS STUDENTS FOR THE PIRLS INTERNATIONAL ASSESSMENT CONTROL
DOI: 10.5281/zenodo.10394412
title: O'ZBЕK MILLIY CHОLG'U SOZLARINING YOSH AVLOD TARBIYASIDAGI O'RNI.
DOI: 10.5281/zenodo.10394413
title: O'ZBЕK MILLIY CHОLG'U SOZLARINING YOSH AVLOD TARBIYASIDAGI O'RNI.
DOI: 10.5281/zenodo.10394418
title: TURIZM YO'NALISHIDA ZAMONAVIY AXBOROT TEXNOLOGIYALARIDAN FOYDALANISH VA MULOQOT QOBILIYATI
DOI: 10.5281/zenodo.10394419
title: TURIZM YO'NALISHIDA ZAMONAVIY AXBOROT TEXNOLOGIYALARIDAN FOYDALANISH VA MULOQOT QOBILIYATI
DOI: 10.5281/zenodo.10394443
title: TOPONIM VARIANTLARINING SHE'RIY NUTQDA QO'LLANISHI
DOI: 10.5281/zenodo.10394444
title: TOPONIM VARIANTLARINING SHE'RIY NUTQDA QO'LLANISHI
DOI: 10.25835/9u32rwwm
title: Interactions of manganese oxides with natural dissolved organic matter

description: Manuscript by Brüggenwirth et al. Interactions of manganese oxides with natural dissolved organic matter: Implications for soil organic carbon cycling
DOI: 10.20375/0000-0011-4997-1
title: Figures

description: a collection of figures illustrating research history at the three sites of Buca della Iena, Grotta del Capriolo and Grotta del Cervo
DOI: 10.20375/0000-0011-49b3-1
title: SI_Gennai_Piano di Mommio.pdf

description: Transcription and Translation (English) of relevant sources to understand research history at the Buca della iena, Grotta del Capriolo and Grotta del Cervo sites
DOI: 10.20375/0000-0011-49b4-0
title: bollettino gruppo di ricerche archeologiche Blanc_1_1967.pdf

description: Information Bulletin Gruppo di Ricerche Preistoriche ed Archeologiche "Alberto Carlo Blanc" Issue 1, 1967
DOI: 10.20375/0000-0011-49b5-f
title: bollettino gruppo di ricerche archeologiche Blanc_5_1968.pdf

description: Information Bulletin Gruppo di Ricerche Preistoriche ed Archeologiche "Alberto Carlo Blanc" Issue 5, 1968
DOI: 10.20375/0000-0011-49b6-e
title: bollettino gruppo di ricerche archeologiche Blanc_9_1972.pdf

description: Information Bulletin Gruppo di Ricerche Preistoriche ed Archeologiche "Alberto Carlo Blanc", Issue 9, 1972
DOI: 10.20375/0000-0011-49b7-d
title: bollettino gruppo di ricerche archeologiche Blanc_11_1973 .pdf

description: Information Bulletin Gruppo di Ricerche Preistoriche ed Archeologiche "Alberto Carlo Blanc" Issue 11, 1973
DOI: 10.20375/0000-0011-49b8-c
title: bollettino gruppo di ricerche archeologiche Blanc_13_1975.pdf

description: Information Bulletin Gruppo di Ricerche Preistoriche ed Archeologiche "Alberto Carlo Blanc" Issue 13, 1975
DOI: 10.20375/0000-0011-49b9-b
title: Buca della Iena 1966-1973 settori A e B.pdf

description: Activities Diary Gruppo di Ricerche Preistoriche ed Archeologiche "Alberto Carlo Blanc", selected pages mentioning Buca della Iena and Grotta del Capriolo
DOI: 10.20375/0000-0011-49ba-a
title: Grotta del Capriolo_diario di scavo Tozzi 1970.pdf

description: Fieldwork notes Grotta del Capriolo 1970 Carlo Tozzi excavation
DOI: 10.20375/0000-0011-49bb-9
title: Grotta del Capriolo_inventario manufatti Fornaciari-Tozzi.pdf

description: List of artefacts found at Grotta del Capriolo during 1968 and 1970 excavations
DOI: 10.20375/0000-0011-49bc-8
title: Grotta del Cervo_1989_Cocchi.pdf

description: Unpublished report about 1989 excavation at Grotta del Cervo
DOI: 10.20375/0000-0011-49bd-7
title: Buca Iena_Diario Fornaciari trascritto.pdf

description: Fieldwork notes Buca della Iena excavation in 1966 with transcription
DOI: 10.20375/0000-0011-49bf-5
title: Siti Musteriano Toscana.csv

description: a list (.csv) of locations in Tuscany with Mousterian finds with WGS 84 coordinates
DOI: 10.20375/0000-0011-49c0-2
title: Siti Musteriano Toscana.xlsx

description: a list (.xlsx) of locations in Tuscany with Mousterian finds with WGS 84 coordinates
DOI: 10.20375/0000-0011-49be-6
title: Coordinates of Mousterian sites in Tuscany

description: The list of locations with Mousterian finds in Tuscany (Italy)
DOI: 10.5281/zenodo.10380473
title: Estimated numbers of incident cases of pandemic influenza in Prussian army camps around Arnsberg (Germany) 1918/19

description: Hiroshi Nishiura estimated the numbers of (incident) cases of influenza in Prussian army camps in the region of Arnsberg (Germany) from reported death cases during September 1918 and January 1919. The estimated numbers are presented as dashed line in Figure 1 of [1] ("Onset"). We extracted the estimated numbers of incident cases from Figure 1. For our purpose, the accuracy of the extracted numbers of incident cases is sufficient. In case of need for higher accuracy, we recommend to refer to the original paper [1]. Note that the numbers of incident cases are estimates (as detailed in [1]), the reported original data are the number of fatalities presented as solid line in Figure 1 ("Death") [1]. The numbers of fatalities are kindly given as an electronic supplement of [1] ("Additional File 1").

The variable "Day" is counted from September 9, 1918, onwards.

Reference:1. Nishiura H: Time variations in the transmissibility of pandemic influenza in Prussia, Germany, from 1918–19. Theor Biol Med Model 4, 20, 2007. DOI 10.1186/1742-4682-4-20
DOI: 10.5281/zenodo.10380474
title: Estimated numbers of incident cases of pandemic influenza in Prussian army camps around Arnsberg (Germany) 1918/19

description: Hiroshi Nishiura estimated the numbers of (incident) cases of influenza in Prussian army camps in the region of Arnsberg (Germany) from reported death cases during September 1918 and January 1919. The estimated numbers are presented as dashed line in Figure 1 of [1] ("Onset"). We extracted the estimated numbers of incident cases from Figure 1. For our purpose, the accuracy of the extracted numbers of incident cases is sufficient. In case of need for higher accuracy, we recommend to refer to the original paper [1]. Note that the numbers of incident cases are estimates (as detailed in [1]), the reported original data are the number of fatalities presented as solid line in Figure 1 ("Death") [1]. The numbers of fatalities are kindly given as an electronic supplement of [1] ("Additional File 1").

The variable "Day" is counted from September 9, 1918, onwards.

Reference:1. Nishiura H: Time variations in the transmissibility of pandemic influenza in Prussia, Germany, from 1918–19. Theor Biol Med Model 4, 20, 2007. DOI 10.1186/1742-4682-4-20
DOI: 10.6084/m9.figshare.24845145
title: uploaded fastQ and analysis

description: FastQ and analysis for paper of "<b>High-throughput, low-cost, and rapid DNA sequencing with quasi-Couette flow microfluidics</b>"
DOI: 10.6084/m9.figshare.24845145.v1
title: uploaded fastQ and analysis

description: FastQ and analysis for paper of "<b>High-throughput, low-cost, and rapid DNA sequencing with quasi-Couette flow microfluidics</b>"
DOI: 10.5281/zenodo.10393885
title: Artifacts of the paper under review by ISSTA

description: Artifacts of the paper under review by ISSTA

Welcome to the online repository of the ISSTA 2024 Submission 1305. We release the source code, dataset, and outputs of our study.

Getting Started

Requirements

To run the project, please ensure that you have the following dependencies installed:

 

    pytorch=2.0.0;

    torchvision=0.15.1;

    torchaudio;

    datasets==1.16.1;

    transformers==4.21.1;

    tensorboard==2.12.2;

    tree-sitter==0.19.1;

    nltk=3.8.1;

    scipy=1.10.1;

You can either install these requirements manually or execute the following script:

bash scripts/setup.sh

Dataset

We perform our experiments on following datasets:

    * CodeSearchNet from [here](https://github.com/github/CodeSearchNet).

    * BFP dataset from Tufano et al.'s TOSEM'19 paper.

    * Defects4J dataset from René et al.'s ISSTA'14 paper.

    * VulRD from Fu et al.'s FSE'22 paper.

Download the pre-training dataset:

python Dataset\pre-training\download.py

Pretrain the model

bash scripts/pre-train.sh -g [GPU_ID]

Task 1: bug repair

bash scripts/finetune_d4j.sh -g [GPU_ID]

If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.

To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path.

 

Task 2: vulnerability repair

bash scripts/finetune_vul.sh -g [GPU_ID]

If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.

To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path.

Outputs

The outputs of our tool are stored in the `outputs` folder.
DOI: 10.5281/zenodo.10393886
title: Artifacts of the paper under review by ISSTA

description: Artifacts of the paper under review by ISSTA

Welcome to the online repository of the ISSTA 2024 Submission 1305. We release the source code, dataset, and outputs of our study.

Getting Started

Requirements

To run the project, please ensure that you have the following dependencies installed:

 

    pytorch=2.0.0;

    torchvision=0.15.1;

    torchaudio;

    datasets==1.16.1;

    transformers==4.21.1;

    tensorboard==2.12.2;

    tree-sitter==0.19.1;

    nltk=3.8.1;

    scipy=1.10.1;

You can either install these requirements manually or execute the following script:

bash scripts/setup.sh

Dataset

We perform our experiments on following datasets:

    * CodeSearchNet from [here](https://github.com/github/CodeSearchNet).

    * BFP dataset from Tufano et al.'s TOSEM'19 paper.

    * Defects4J dataset from René et al.'s ISSTA'14 paper.

    * VulRD from Fu et al.'s FSE'22 paper.

Download the pre-training dataset:

python Dataset\pre-training\download.py

Pretrain the model

bash scripts/pre-train.sh -g [GPU_ID]

Task 1: bug repair

bash scripts/finetune_d4j.sh -g [GPU_ID]

If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.

To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path.

 

Task 2: vulnerability repair

bash scripts/finetune_vul.sh -g [GPU_ID]

If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.

To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path.

Outputs

The outputs of our tool are stored in the `outputs` folder.
DOI: 10.6084/m9.figshare.24637646
title: Tables S2 and S3 from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Statistical tables for females and males
DOI: 10.6084/m9.figshare.24637646.v3
title: Tables S2 and S3 from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Statistical tables for females and males