cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7844044
title: PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK

description: The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.
description: This dataset contains one of the predictor variables (rainfall, inches) and the target (recession rate, feet/hour).
DOI: 10.5281/zenodo.7844043
title: PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK

description: The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.
description: This dataset contains one of the predictor variables (rainfall, inches) and the target (recession rate, feet/hour).
DOI: 10.5281/zenodo.7839889
title: Trained nnUnet models for total lymph node segmentation

description: The trained nnUnet models for Whole Regional Lymph Node Area Delineation with Deep Learning Model for Total Marrow and Lymphoid Irradiation. We are submitting the article, and we will update the information. <strong>******** Abstract </strong><strong>********</strong> <strong><em>Purpose</em></strong> Total body irradiation (TBI) has been performed for conditioning before hematopoietic stem cell transplantation. However, TBI can be related to diverse adverse events including radiation pneumonitis and cataract. Efforts to reduce these events include the total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI). Compared to TMI, TMLI requires more target delineations with lymph nodes which can be labor-intensive and time-consuming. However, with the TMI plans, the coverage to lymph node might be lower than TMLI and its clinical significance is unknown. In the current study, we aimed to develop a deep learning model for automatic delineation of whole regional lymph nodes and assess the dose coverage of lymph nodes with TMI plans using the model. <strong><em>Materials and Methods</em></strong> Whole regional lymph nodes (cervical, axillary, mediastinal, para-aortic, common iliac, external iliac, internal iliac, obturator, presacral, inguinal lymph nodes) were manually contoured and confirmed by 3 radiation oncologists in 26 patients having whole body computed tomography (CT) images. Twenty patients were designated as the training/validation set and 6 patients as the testing set, and model was developed using the 'nnUNET' framework. The trained model was evaluated with dice similiary coefficient (DSC), precision, recall and Hausdorff distance 95 (HD95). In addition, dose coverage of the manually delineated lymph nodes in TMI plans was calculated. <strong><em>Results</em></strong> The median value of DCS, precision, recall and HD95 of the trained model was 0.79 (IQR, 0.70-0.84), 0.83 (IQR, 0.75-0.89), 0.76 (IQR, 0.68-0.85), and 2.63 (IQR, 2.00-4.58) respectively. Dose parameters for manually delineated lymph nodes in previously treated TMI plans showed the median value of V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% to be 46.0%, 62.1%, and 73.5%, respectively. The highest V100% was observed in presacral (71.1%), axillary (61.2%), obturator (60.3%), and internal iliac lymph nodes (84.67%). In contrast, the lowest V100% was identified in inguinal (13.3%), external iliac (21.8%), and cervical lymph nodes (42.1%). <strong><em>Conclusion</em></strong> Automatic delineation of lymph node using deep learning showed the potential to reduce the labor-intensive process of TMLI. When treated with TMI, the coverage of inguinal, external iliac, cervical lymph nodes was lower than expected.
DOI: 10.5281/zenodo.7839888
title: Trained nnUnet models for total lymph node segmentation

description: The trained nnUnet models for Whole Regional Lymph Node Area Delineation with Deep Learning Model for Total Marrow and Lymphoid Irradiation. We are submitting the article, and we will update the information. <strong>******** Abstract </strong><strong>********</strong> <strong><em>Purpose</em></strong> Total body irradiation (TBI) has been performed for conditioning before hematopoietic stem cell transplantation. However, TBI can be related to diverse adverse events including radiation pneumonitis and cataract. Efforts to reduce these events include the total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI). Compared to TMI, TMLI requires more target delineations with lymph nodes which can be labor-intensive and time-consuming. However, with the TMI plans, the coverage to lymph node might be lower than TMLI and its clinical significance is unknown. In the current study, we aimed to develop a deep learning model for automatic delineation of whole regional lymph nodes and assess the dose coverage of lymph nodes with TMI plans using the model. <strong><em>Materials and Methods</em></strong> Whole regional lymph nodes (cervical, axillary, mediastinal, para-aortic, common iliac, external iliac, internal iliac, obturator, presacral, inguinal lymph nodes) were manually contoured and confirmed by 3 radiation oncologists in 26 patients having whole body computed tomography (CT) images. Twenty patients were designated as the training/validation set and 6 patients as the testing set, and model was developed using the 'nnUNET' framework. The trained model was evaluated with dice similiary coefficient (DSC), precision, recall and Hausdorff distance 95 (HD95). In addition, dose coverage of the manually delineated lymph nodes in TMI plans was calculated. <strong><em>Results</em></strong> The median value of DCS, precision, recall and HD95 of the trained model was 0.79 (IQR, 0.70-0.84), 0.83 (IQR, 0.75-0.89), 0.76 (IQR, 0.68-0.85), and 2.63 (IQR, 2.00-4.58) respectively. Dose parameters for manually delineated lymph nodes in previously treated TMI plans showed the median value of V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% to be 46.0%, 62.1%, and 73.5%, respectively. The highest V100% was observed in presacral (71.1%), axillary (61.2%), obturator (60.3%), and internal iliac lymph nodes (84.67%). In contrast, the lowest V100% was identified in inguinal (13.3%), external iliac (21.8%), and cervical lymph nodes (42.1%). <strong><em>Conclusion</em></strong> Automatic delineation of lymph node using deep learning showed the potential to reduce the labor-intensive process of TMLI. When treated with TMI, the coverage of inguinal, external iliac, cervical lymph nodes was lower than expected.
DOI: 10.26179/0e8g-ch72
title: Glaciology 1987 "Traverse 2" (Law Dome, Wilkes Land) Records and Measurements
DOI: 10.5281/zenodo.7819857
title: Accurate real space iterative reconstruction (RESIRE) algorithm for tomography

description: This is the code and dataset for the paper: Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Three dataset are provided in this deposit: 1. the simulation of vesicle model 2. the biomineral data 3. Amorphous Ta thinfilm data The algorithm are written in matlab and cuda code. Users can either use the cpu or gpu versions. 1. The cpu code can only do tomography reconstructions with single tilt axis (say y-axis) 2. The gpu code can do with multiple tilt axes, say ZYX<br> To use the gpu cude, you need to download and install CUDA toolkit from the NVIDA website https://developer.nvidia.com/cuda-downloads. Then, open Matlab and compile cuda functions, for example:<br> mexcuda -R2018a RT3_1GPU.cu<br> The mexw64 cuda files attached in this folder are precompiled for windows, cuda version = 11.x If you use the code or the data, please cite our paper:<br> Pham, M., Yuan, Y., Rana, A. et al. Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Sci Rep 13, 5624 (2023). https://doi.org/10.1038/s41598-023-31124-7 If you have question, feel free to email us at minhrose@ucla.edu
DOI: 10.5281/zenodo.7273313
title: Accurate real space iterative reconstruction (RESIRE) algorithm for tomography

description: This is the code and dataset for the paper: Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Three dataset are provided in this deposit: 1. the simulation of vesicle model 2. the biomineral data 3. Amorphous Ta thinfilm data The algorithm are written in matlab and cuda code. Users can either use the cpu or gpu versions. 1. The cpu code can only do tomography reconstructions with single tilt axis (say y-axis) 2. The gpu code can do with multiple tilt axes, say ZYX<br> To use the gpu cude, you need to download and install CUDA toolkit from the NVIDA website https://developer.nvidia.com/cuda-downloads. Then, open Matlab and compile cuda functions, for example:<br> mexcuda -R2018a RT3_1GPU.cu<br> The mexw64 cuda files attached in this folder are precompiled for windows, cuda version = 11.x If you use the code or the data, please cite our paper:<br> Pham, M., Yuan, Y., Rana, A. et al. Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Sci Rep 13, 5624 (2023). https://doi.org/10.1038/s41598-023-31124-7 If you have question, feel free to email us at minhrose@ucla.edu
DOI: 10.34740/kaggle/ds/1934565
title: Earthquakes in Indonesia
DOI: 10.58078/c24w2k
title: The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset
DOI: 10.5281/zenodo.7273314
title: RESIRE: Accurate tomography with real space iterative reconstruction

description: These are data used in the paper "RESIRE: Accurate tomography with real space iterative reconstruction" 1. the simulation of vesicle model 2. the biomineral data 3. The Amorphous film data Each zip file contains a set of projections and corresponding tilt angles <strong>PLEASE LOOK AT THE 2ND VERSION FOR THE FULL CODE AND DATA: https://zenodo.org/record/7819857</strong>
DOI: 10.15139/s3/eus8oi
title: Vision Zero Implementation Milestone Checklist (2020)

description: This Vision Zero Implementation Milestones Checklist, rooted in the science of program implementation, offers a series of phases and milestones toward fully implementing Vision Zero in your community. Consider using this checklist to determine the implementation phase your community is in and to gather ideas on what you could be working on next toward realizing a Vision Zero future.
DOI: 10.34740/kaggle/dsv/2827431
title: Indonesian Glossary
DOI: 10.6084/m9.figshare.22654795
title: Li et al_Supplementary Tables.xlsx

description: The dataset that supports the findings of the Li et al. study titled "A global synthesis of climate vulnerability assessments on marine fisheries: methods, scales and knowledge co-production".
DOI: 10.6084/m9.figshare.22654795.v1
title: Li et al_Supplementary Tables.xlsx

description: The dataset that supports the findings of the Li et al. study titled "A global synthesis of climate vulnerability assessments on marine fisheries: methods, scales and knowledge co-production".
DOI: 10.34740/kaggle/dsv/5451233
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.34740/kaggle/dsv/5415797
title: Earthquakes in Indonesia
DOI: 10.5281/zenodo.7834332
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2013

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2013. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2012</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2014</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2013 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7834331
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2013

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2013. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2012</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2014</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2013 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.22647766
title: data_export.csv

description: In this study, a online-questionnaire was conducted using the Fear of Covid-19 Scale, Psychological Capital Scale, Maslach Burnout Inventory Human Services Survey for Medical Professionals Scale, and SF-12 among 445 nurses (average age = 32.89 years, SD = 6.76 years) to investigate the correlation between fear of covid-19 and mental health in nurses, as well as the role of psychological capital and burnout in this relationship.
DOI: 10.6084/m9.figshare.22647766.v1
title: data_export.csv

description: In this study, a online-questionnaire was conducted using the Fear of Covid-19 Scale, Psychological Capital Scale, Maslach Burnout Inventory Human Services Survey for Medical Professionals Scale, and SF-12 among 445 nurses (average age = 32.89 years, SD = 6.76 years) to investigate the correlation between fear of covid-19 and mental health in nurses, as well as the role of psychological capital and burnout in this relationship.
DOI: 10.25901/q2pr-pf96
title: Warra Site, 5-minute, Point-Count, Bird Survey Data, Southern Forests Experimental Forest Landscape, 2010-2011
DOI: 10.6084/m9.figshare.22085948
title: default tar gz for MOFreinforce

description: default.tar.gz for MOFreinforce. <br> It contains data/ and model/
DOI: 10.6084/m9.figshare.22085948.v4
title: default tar gz for MOFreinforce

description: default.tar.gz for MOFreinforce. <br> It contains data/ and model/
DOI: 10.6084/m9.figshare.22085948.v3
title: default tar gz for MOFreinforce

description: default.tar.gz for MOFreinforce. <br> It contains data/ and model/
DOI: 10.6084/m9.figshare.22654954
title: The abundance of gut microbiota in Japanese patients with solid tumors

description: Using the composition of each individual's gut microbiota, this raw data was used to study the association between gut microbiota and anti-PD-1 antibody efficacy and immune-related adverse events (irAEs) in Japanese patients with solid tumors.
DOI: 10.57841/casdc.0000272
title: 小胶质细胞迁移中发现的"run and tumble"动力学和力学趋向性
DOI: 10.57841/casdc.0000276
title: 小胶质细胞迁移中发现的"run and tumble"动力学和力学趋向性
DOI: 10.57841/casdc.0000278
title: DNA纳米材料调控细胞群体行为
DOI: 10.5281/zenodo.7844187
title: Novel Use of a Gas Transfer Membrane Contactor Inhibits Legionella pneumophila Growth in Hot Water Systems

description: Large Scale Hot Water Model System Measurement Data Summary
DOI: 10.5281/zenodo.7844186
title: Novel Use of a Gas Transfer Membrane Contactor Inhibits Legionella pneumophila Growth in Hot Water Systems

description: Large Scale Hot Water Model System Measurement Data Summary
DOI: 10.57841/casdc.0000281
title: 巨噬细胞通过动态重构的胶原蛋白纤维中传递的机械信号靶向追踪乳腺癌细胞
DOI: 10.57841/casdc.0000282
title: 原位电子全息技术用于表征电极和固态电解质界面中的锂离子积累
DOI: 10.26182/vfjt-st92
title: Changing processes flooding a salt marsh in a microtidal estuary with a drying climate
DOI: 10.57841/casdc.0000284
title: 钙钛矿-钙铁石异质结构中的SrCoO3-δ微结构
DOI: 10.18141/1890934
title: TASK3 Laboratory Experiments Data

description: Laboratory Experiments Data of TASK3 of DOE project DE- FE0031686. Each signal has its own excel file. 150 signals are sent through the rock sample per each load configuration. There were approximately 50 to 150 load combinations tested during the experiments. Experiments were repeated after changing the polarity direction of the transducers. Signals of these repeats are in separate folders called "Reverse". There are three formations tested from the Michigan Core and four formations tested from the FutureGen site. The "Code" folder contains the Python code developed to analyze the signals. Large Dataset, please contact EDXSupport@netl.doe.gov
DOI: 10.25582/data.2022-04.2496055/1930775
title: Data for EMSL Project 51508 from April 2022
DOI: 10.25582/data.2022-04.2496901/1931161
title: Data for EMSL Project 51216 from April 2022
DOI: 10.25582/data.2022-04.2496873/1931175
title: Data for EMSL Project 51295 from April 2022
DOI: 10.25582/data.2022-04.2498088/1932997
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2497982/1932998
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2499411/1932999
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2499152/1933000
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2499346/1933001
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498542/1933006
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498402/1933007
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498738/1933010
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2497802/1933017
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2497867/1933024
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498825/1933031
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2499173/1933033
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2497937/1933034
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498597/1933053
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498007/1933054
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2499103/1933056
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498117/1933058
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498034/1933060
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498641/1933064
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2497764/1933065
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498024/1933066
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2499427/1933069
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2498823/1933070
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2496736/1933109
title: Data for EMSL Project 51361 from April 2022
DOI: 10.25582/data.2022-04.2498791/1933960
title: Data for EMSL Project 51132 from April 2022
DOI: 10.25582/data.2022-04.2497845/1933962
title: Data for EMSL Project 51132 from April 2022