cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15121/1995528
title: DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

description: DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model
DOI: 10.18141/1995986
title: International Offshore Geologic Carbon Storage Story Map

description: The International Offshore Geologic Carbon Storage Story Map provides an overview of offshore geologic carbon storage (GCS) project maturity on a global scale. This story map reviews the history of CO2 storage in offshore sedimentary basins, basic geologic requirements, project timelines and lessons learned through defined project stage progression, areas previously studied for potential future domestic offshore GCS development, and regulatory considerations within different global regions. This resource is a distillation of information collected and recorded in the Offshore Geologic Carbon Storage Inventory Version 1.0 (https://edx.netl.doe.gov/dataset/offshore-gcs-data-inventory).
DOI: 10.21227/1j0d-s124
title: Training with a Visual-Haptic Simulator for Trocar Insertion - Data

description: This dataset is used in conjunction with the manuscript submission entitled: “Training with a Visual-Haptic Simulator for Trocar Insertion,” which is submitted for consideration as a short paper in Transactions on Haptics for presentation with the 2024 Haptics Symposium Conference. Trocar insertions have the greatest risk of injuring a patient during laparoscopic surgery, with most of the injuries being attributed to surgeon error. However, surgeon error can be reduced through proper training. A previous study performed initial testing on a Stewart platform haptic device using a simplified numerical model to simulate key characteristics of a trocar insertion. Thus, in conjunction with the prevalence of laparoscopic surgery and promising preliminary results, the submission presents an iteration of a previous Stewart platform haptic device, utilizing higher fidelity linear actuators, increasing available workspace, and providing a visualization to work alongside the device. A study was performed with eight novices to investigate if the device and visualization train users in the trocar insertion procedure. By the end of the experiment, subjects lowered their error when utilizing the device by over 30% on average. Overall, the device shows potential in the training of trocar insertion, possibly leading to fewer complications and deaths following the procedure.
DOI: 10.21227/yzef-4378
title: LTE_RFF_IDENTIFICATION_DATASET

description: This LTE_RFFI project sets up an LTE device radio frequency fingerprint identification system using deep learning techniques. The LTE uplink signals are collected from ten different LTE devices using a USRP N210 in different locations. The sampling rate of the USRP is 25 MHz. The received signal is resampled to 30.72 MHz in Matlab. Then, the signals are processed and saved in the MAT file form. More details about the datasets can be found in the README document.Please visit https://github.com/eexuanyang/LTE_RFFI to get more details. Please cite the paper ‘LED-RFF: LTE DMRS Based Channel Robust Radio Frequency Fingerprint Identification Scheme’, IEEE Trans. Inf. Forensics Secur. (Under Review), 2023.
DOI: 10.18141/2001068
title: Generic Aquifer Component Training Dataset

description: Training dataset for the Generic Aquifer Component of NRAP-Open-IAM. Consists of the results of 62,500 STOMP-CO2 simulations of brine and CO2 leakage of varying rates and salinity into aquifers of varying thickness, depth, porosity, permeability, anisotropy and initial salinity. Described in report https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-32590.pdf
DOI: 10.21947/2001447
title: Pacific Coast High Frequency Radar 6 km

description: High frequency (HF) radar systems measure the speed and direction of ocean surface currents in near real time provided by Integrated Ocean Observing System (IOOS). The HFR data along the Pacific Coast are at a spatial resolution of 6 km. These data are collected as part of an observational database developed to support the floating offshore wind energy research within the ORACLE project funded by DOE WETO.
DOI: 10.15121/2001489
title: Utah FORGE 1-2410: Development of a Smart Completion and Stimulation Solution - Workshop Presentation

description: This is a presentation on the Development of a Smart Completion & Stimulation Solution project by Welltec in collaboration with the University of Oklahoma, presented by Yosafat Esquitin, a Senior Business Development Manager at Welltec. The project's objective was to develop an annular isolation system, a stimulation isolation system, and a multi-open-close flow system for geothermal environments. These systems were developed to enable effective zonal isolation and stimulation, implement downhole Enhanced Geothermal Systems (EGS) in any location, and extend the productive life of the geothermal well. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development EGS reservoirs and resources.
DOI: 10.15121/2001498
title: Utah FORGE 2-2404: Application of Advanced Techniques for Determination of Reservoir-Scale Stress State at Utah FORGE - Workshop Presentation

description: This is a presentation on the Application of Advanced Techniques for Determination of Reservoir-Scale Stress State at Utah FORGE project by the University of Oklahoma, presented by Dr. Ahmad Ghassemi, McCasland Chair Professor. The project's objective was to develop a methodology for the determination of reservoir-scale in-situ stress and use it to estimate the stress state at Utah FORGE. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.
DOI: 10.15121/2001502
title: Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - Workshop Presentation

description: This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the U.S DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement project by Battelle [Columbus, OH], presented by Mark Kelley. The project's objective was to characterize stress in the Utah FORGE EGS reservoir using three methods: a laboratory rock-core stress estimation combined with a Machine Learning approach for estimation of in-situ stress from field sonic-log data, a field based in-situ measurement (min-frac) approach, and a modeling approach. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.
DOI: 10.7799/2002868
title: Q1 2023 U.S. Solar Photovoltaic System and Energy Storage Cost Benchmarks With Minimum Sustainable Price Analysis Data File

description: The U.S. Department of Energy's (DOE's) Solar Energy Technologies Office (SETO) aims to accelerate the advancement and deployment of solar technology in support of an equitable transition to a decarbonized economy no later than 2050, starting with a decarbonized power sector by 2035. Its approach to achieving this goal includes driving innovations in technology, hardware, and soft cost reductions to make solar affordable and accessible for all. As part of this effort, SETO must track solar cost trends so it can focus its research and development (R&D) on the highest-impact activities. The benchmarks in this report are bottom-up cost estimates of all major inputs to PV and energy storage system installations. Bottom-up costs are based on national averages and do not necessarily represent typical costs in all local markets. Like last year's report, this year's report includes two distinct sets of benchmarks: minimum sustainable price (MSP) benchmarks and modeled market price (MMP) benchmarks. MSP benchmarks can be interpreted as the minimum price a company needs to charge to remain financially solvent in the long term based on the minimum sustainable prices of all inputs including minimum sustainable profit margins. MMP benchmarks can be interpreted as the actual cash sales price a company charges in the given benchmark period. These simplified estimates are useful for tracking technological progress, but they do not reflect all experiences. In fact, no individual estimate under any approach can reflect the diversity of the PV and storage manufacturing and installation industries.
DOI: 10.15121/2002871
title: Utah FORGE 2-2446: Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity - Workshop Presentation

description: This is a presentation on the Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity project by Lawrence Livermore National Laboratory, presented by Dr. Matteo Cusini. The project's objective was to employ a combination of high-fidelity simulations and true-triaxial block fracturing tests at high temperature to explore the intricate relationship between in-situ stress and hydraulic fracture patterns and better characterize the in-situ stress at Utah FORGE. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.
DOI: 10.15121/2002877
title: Utah FORGE 3-2417: Fiber-Optic Geophysical Monitoring of Reservoir Evolution - Workshop Presentation

description: This is a presentation on the Fiber-Optic Geophysical Monitoring of Reservoir Evolution at the Utah FORGE Milford Site project by Rice University, presented by Prof. Jonathan Ajo-Franklin. The project's objective was to develop an end-to-end fiber-optic sensing approach for EGS to track the initial zone of fracture creation, zones of connected mechanically compliant fractures, and zones of flowing fractures linking the injection/production well pair. This approach would also be used to integrate data into an improved thermo-hydro-mechanical model (THM). This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.
DOI: 10.15121/2002899
title: Utah FORGE 3-2418: Wellbore Fracture Imaging Using Inflow Detection Measurements - Workshop Presentation

description: This is a presentation on the Wellbore Fracture Imaging Using Inflow Detection Measurements project by Stanford University, presented by Dr. Roland N. Horne. The project's objective was to make measurements in the Utah FORGE wells, both before and after fracturing experiments, to map the fractures and estimate their flow magnitude. The approach utilizes a ion-specific electrode that will measure chloride in the well. The idea is that water flowing into the well from the fractures will have a different concentration of chloride than the surrounding water. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.
DOI: 10.5281/zenodo.7709534
title: Technical Debt Prioritization Using Machine Learning

description: Technical debt (TD) identification tools can find thousands of technical debt items (TDIs) in a software project. Remedying all of them would take months or even years, so prioritization and decision-making are needed to make this process efficient. On the other hand, advances in machine learning over the last few decades have allowed researchers to apply methods to cluster behaviors and identify patterns in software engineering data. In this study, we aim to develop machine learning methods to decide whether and when a given TDI should be paid off in \st{real} software projects. We performed a survey to collect data from Java open-source software projects hosted on GitHub. From the 2,616 survey responses, we created a dataset using three different labeling strategies - "pay or not", 3-classes, and priority. We applied nine well-known machine learning methods over 27 source code metrics to build models to predict if and when a TDI should be paid off. The best methods for determining whether an item should be paid off achieved a mean accuracy of 0.86 and an F1-score of 0.85. For when to make the payment, we applied four approaches. Their performance achieved an accuracy of 0.59 using traditional analysis and 0.83 with tuned analysis for the most flexible method.
DOI: 10.5281/zenodo.7709535
title: Technical Debt Prioritization Using Machine Learning

description: Technical debt (TD) identification tools can find thousands of technical debt items (TDIs) in a software project. Remedying all of them would take months or even years, so prioritization and decision-making is needed to make this process efficient. On the other hand, advances in machine learning over the last few decades have allowed researchers to apply methods to cluster behaviors and identify patterns in software engineering data. In this study, we aim to develop machine learning methods to decide whether and when a given TDI should be paid off in real software projects. We performed a survey to collect data from Java open-source software projects hosted on GitHub. From the 2616 survey responses, we created a dataset using three different labeling strategies - "pay or not", 3-classes, and priority. We applied nine well-known machine learning methods over 27 source code metrics to build models to predict if and when a TDI should be paid off. The best methods for determining if an item should be paid off achieved accuracy, precision, and recall of about 0.86. For when to make the payment, we applied four approaches. Their performance achieved 0.81 using traditional analysis and 0.92 with tuned analysis.
DOI: 10.5281/zenodo.8378312
title: Technical Debt Prioritization Using Machine Learning

description: Technical debt (TD) identification tools can find thousands of technical debt items (TDIs) in a software project. Remedying all of them would take months or even years, so prioritization and decision-making are needed to make this process efficient. On the other hand, advances in machine learning over the last few decades have allowed researchers to apply methods to cluster behaviors and identify patterns in software engineering data. In this study, we aim to develop machine learning methods to decide whether and when a given TDI should be paid off in \st{real} software projects. We performed a survey to collect data from Java open-source software projects hosted on GitHub. From the 2,616 survey responses, we created a dataset using three different labeling strategies - "pay or not", 3-classes, and priority. We applied nine well-known machine learning methods over 27 source code metrics to build models to predict if and when a TDI should be paid off. The best methods for determining whether an item should be paid off achieved a mean accuracy of 0.86 and an F1-score of 0.85. For when to make the payment, we applied four approaches. Their performance achieved an accuracy of 0.59 using traditional analysis and 0.83 with tuned analysis for the most flexible method.
DOI: 10.57841/casdc.0002527
title: 双渐开线齿轮传动的油膜刚度研究
DOI: 10.5281/zenodo.8378314
title: Dados de rastreamento de autores e fontes

description: Planilhas com os dados do rastreamento de autores e referências do artigo: <strong>Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, Yupeng Wu (2023). How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection. Disponível em: https://arxiv.org/abs/2301.07597. Acesso em: 20/09/2023.</strong> Esta tarefa é parte do trabalho 1 da disciplina Inteligência Artificial, da prof.<strong> Ana Cristina Bicharra Garcia</strong>, no semestre 2023/2 do PPGI/UNIRIO.
DOI: 10.5281/zenodo.8378313
title: Dados de rastreamento de autores e fontes

description: Planilhas com os dados do rastreamento de autores e referências do artigo: <strong>Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, Yupeng Wu (2023). How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection. Disponível em: https://arxiv.org/abs/2301.07597. Acesso em: 20/09/2023.</strong> Esta tarefa é parte do trabalho 1 da disciplina Inteligência Artificial, da prof.<strong> Ana Cristina Bicharra Garcia</strong>, no semestre 2023/2 do PPGI/UNIRIO.
DOI: 10.57841/casdc.0002528
title: MWCNTs复合物纳米流体的摩擦学性能
DOI: 10.17909/h26w-zh06
title: JWST data for PEARLSDG
DOI: 10.6084/m9.figshare.20752642
title: Supplementary_Material_HADEG

description: Contains the Supplementary Tables of the article of HADEG database
DOI: 10.6084/m9.figshare.20752642.v2
title: Supplementary_Material_HADEG

description: Contains the Supplementary Tables of the article of HADEG database
DOI: 10.25910/zt4v-2n59
title: The effects of sport or physical recreation for adults with physical or intellectual disabilities: a systematic review with meta-analysis

description: This is the Dataset, Supplementary Table, and Supplementary File for the article: Hassett L, McKay MJ, Cole J, Moseley AM, Chagpar S, Geerts M, Kwok WS, Jensen C, Sherrington C, Shields N. The effects of sport or physical recreation for adults with physical or intellectual disabilities: a systematic review with meta-analysis. British Journal of Sports Medicine 2023
DOI: 10.25910/mhrf-k640
title: The effects of sport or physical recreation for adults with physical or intellectual disabilities: a systematic review with meta-analysis
DOI: 10.34740/kaggle/dsv/6542356
title: Our World in Data - COVID-19
DOI: 10.5281/zenodo.8378302
title: Extreme Weather Event database over Aotearoa New Zealand

description: <strong>The Aotearoa New Zealand (ANZ) Extreme Weather Events (EWE) database </strong>(EWE_database_V1.0.0.xlsx)<strong> is a comprehensive record of extreme weather events in ANZ. The events listed in this database have been carefully assessed and categorized based on their meteorological significance, considering their rarity and whether they broke records or triggered official weather warnings. Some of the metrics used to classify each event rely on subjective judgment and expert opinions. The database captures meteorologically significant events, including those that have caused substantial damage to properties or led to casualties, and, in some cases, includes supplementary information about their socioeconomic impacts. The information in the EWE database is primarily sourced from the Meteorological Service of New Zealand Ltd (MetService) and the National Institute of Water and Atmospheric Research (NIWA). Additional impact data have been added from various media sources, with insured loss data for some events sourced from the Insurance Council of New Zealand (ICNZ).</strong> Note - For more information about the database and the other additional files, please look into the Metadata (Metadata_EWE_V.1.0.0.docx) and the supplementary document (Supplementary document on EWE_V.1.0.0.docx).
description: The database was created as part of a five-year multidisciplinary research programme funded by the New Zealand Ministry of Business, Innovation, and Employment (MBIE) called Whakahura - Extreme Events and the Emergence of Climate Change (Grant number: RTVU1906) administered through Victoria University of Wellington - Te Herenga Waka, New Zealand.
DOI: 10.57841/casdc.0002529
title: 椭圆形织构摩擦片在核电站安全制动器中作用机理的数值研究
DOI: 10.5281/zenodo.8378361
title: Resultados - Avaliação Heurística

description: Resultados - Avaliação Heurística.
DOI: 10.5281/zenodo.8378360
title: Resultados - Avaliação Heurística

description: Resultados - Avaliação Heurística.
DOI: 10.5281/zenodo.8378356
title: User Reviews - Mobile Apps

description: User reviews extracted from google play mobile apps.
DOI: 10.5281/zenodo.8378355
title: User Reviews - Mobile Apps

description: User reviews extracted from google play mobile apps.
DOI: 10.57841/casdc.0002530
title: 列车车轮滚动接触疲劳裂纹评价研究
DOI: 10.5281/zenodo.8371838
title: Factors associated with work engagement of nurses during the fifth wave of the COVID-19 pandemic in Japan: Web-based cross-sectional study

description: A cross-sectional survey using Google Form was conducted in a target population of 400 nurses working in acute care and psychiatric institutions in Chiba and Tokyo prefectures, Japan. A total of 209 nurses participated in the study. Of these participants, 25 participants with missing survey items were excluded, resulting in a final sample of 184 participants (valid response rate: 46.0%). The survey was conducted from August 8 to September 30, 2021, during the “fifth wave” of the COVID-19 pandemic, when the number of positive cases was increasing.