cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds2-qsz0-aj58
title: Database of Tension Lap Splices and Embedded Bars

description: This database compiles worldwide data of reinforced concrete beam specimens tested to evaluate the bond strength of straight reinforcing bars in tension. The test data were collected from 72 references published between 1955 and 2024. The specimen selection criteria included: (1) normal-weight concrete with compressive strength obtained from tests of cylinders; (2) bottom-cast, uncoated, deformed steel reinforcing bars; (3) contact lap splices or bars embedded (developed) from a critical section; (4) transverse reinforcement (if present) in the form of stirrups or ties; and (5) statically applied load. The specimens were classified into six types depending on the loading, geometry, and location of the critical section. In addition to the bar stress reported by the study authors, the stress at the critical section was calculated using moment-curvature analysis. For each specimen, the database reports up to 40 parameters to describe the following data categories: reference citation, specimen identification, beam dimension, longitudinal reinforcement, concrete cover, transverse reinforcement, material property, loading parameter, beam moment, bar stress, and additional information (test type and special notes). The maximum bar stress at failure reported in the database was 155,000 psi (1,070 MPa), and concrete compressive strengths ranged from 1,820 to 16,100 psi (12.5 to 111 MPa). The maximum lap length was 120 bar diameters, and the bar diameters ranged from 0.375 to 2.25 in. (9.5 to 57 mm). 
The number of specimens in this database far exceeds those contained in other databases populated with comparable criteria. This database is intended to facilitate the development of more reliable and accurate equations for bond strength. The database spreadsheet includes readme instructions as a separate worksheet.
DOI: 10.17603/ds2-ypvx-4817
title: Non-skew bridge with uniform scour

description: This project is about the seismic demands of a non-skewed bridge with uniform scour depth. The nonlinear response history analysis were done by using OpenSees. Superstructure displacement, curvature demands of bridge column, shear force demands of bridge column and shear force demands of pile elements were engineering demand parameters for this project.
DOI: 10.25378/janelia.13108343
title: Automatic segmentation of organelles in isotropic electron microscopy data of wild-type, interphase HeLa cell (jrc_hela-2)

description: The cell interior contains hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demand. Obtaining a complete understanding of this organization is challenging and requires nanometer-level, three-dimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To overcome this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing all known cellular organelles from entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from ER to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures and their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. The data, computer code, and trained models are all shared through an open data and open source web platform OpenOrganelle, enabling scientists everywhere to query and further reconstruct the datasets.Sample: Wild-type, interphase HeLa cell (ATCC CCL-2).Dataset ID: jrc_hela-2EM Data DOI: 10.25378/janelia.13114211EM voxel size (nm): 4.0 x 4.0 x 4.0 (X, Y, Z)Segmentation voxel size (nm): 4.0 x 4.0 x 4.0 (X, Y, Z)Dataset URL: S3://janelia-cosem-datasets/jrc_hela-2/jrc_hela-2.n5/labelsVisualization Website: https://openorganelle.janelia.org/datasets/jrc_hela-2Publication: Heinrich et el., 2020Segmented Organelles: Centrosome (A975k), Centrosome D App (A900k), Centrosome SD App (A1075k), Chromatin (A600k), ECS (M625k), Endo (M925k), Endo mem (M1025k), ER (M625k), ER mem (M625k), ERES (A925k), Golgi (F650k), Golgi mem (F650k), N-H Chrom (A1200k), LD (A1175k), LD mem (A625k), Lyso (A900k), Lyso mem (A900k), MT (F1400k), MT out (F1325k), Mito (F575k), Mito mem (A900k), NE (A750k), NE mem (A1150k), NP (A1075k), NP out (A975k), Nucleolus (A950k), Nucleus (A600k), PM (M650k), Ribo (F525k), Vesicle (F675k), Vesicle mem (A1075k)Included in Dataset: manual annotations, predictions, segmentations
DOI: 10.25378/janelia.13108343.v1
title: Automatic segmentation of organelles in isotropic electron microscopy data of wild-type, interphase HeLa cell (jrc_hela-2)

description: The cell interior contains hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demand. Obtaining a complete understanding of this organization is challenging and requires nanometer-level, three-dimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To overcome this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing all known cellular organelles from entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from ER to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures and their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. The data, computer code, and trained models are all shared through an open data and open source web platform OpenOrganelle, enabling scientists everywhere to query and further reconstruct the datasets.Sample: Wild-type, interphase HeLa cell (ATCC CCL-2).Dataset ID: jrc_hela-2EM Data DOI: 10.25378/janelia.13114211EM voxel size (nm): 4.0 x 4.0 x 4.0 (X, Y, Z)Segmentation voxel size (nm): 4.0 x 4.0 x 4.0 (X, Y, Z)Dataset URL: S3://janelia-cosem-datasets/jrc_hela-2/jrc_hela-2.n5/labelsVisualization Website: https://openorganelle.janelia.org/datasets/jrc_hela-2Publication: Heinrich et el., 2020Segmented Organelles: Centrosome (A975k), Centrosome D App (A900k), Centrosome SD App (A1075k), Chromatin (A600k), ECS (M625k), Endo (M925k), Endo mem (M1025k), ER (M625k), ER mem (M625k), ERES (A925k), Golgi (F650k), Golgi mem (F650k), N-H Chrom (A1200k), LD (A1175k), LD mem (A625k), Lyso (A900k), Lyso mem (A900k), MT (F1400k), MT out (F1325k), Mito (F575k), Mito mem (A900k), NE (A750k), NE mem (A1150k), NP (A1075k), NP out (A975k), Nucleolus (A950k), Nucleus (A600k), PM (M650k), Ribo (F525k), Vesicle (F675k), Vesicle mem (A1075k)Included in Dataset: manual annotations, predictions, segmentations
DOI: 10.17603/ds2-09yf-w512
title: 2022 ShakeAlert Earthquake Early Warning and Schools Survey
title: in ShakeAlert Earthquake Early Warning and Schools in the United States

description: In March and April of 2022, we conducted an online survey as the second phase of our study of earthquake early warning in schools. The survey was emailed via a Qualtrics link to all school district superintendents in Alaska, California, Oregon, and Washington. The survey instrument was designed using the ready, willing, and able theoretical framework, and asked questions to understand more about the potential to adopt ShakeAlert at the school district level. The survey, which was developed by our research team and pilot tested by academics, emergency managers, and school officials, takes  approximately 15 minutes to complete. Specific survey topics include: (a) past earthquake experience, (b) current earthquake drills and warnings in schools, (c) knowledge and current use of ShakeAlert, (d) perceived opportunities of ShakeAlert in schools, (e) perceived barriers of ShakeAlert in schools, (f) ShakeAlert delivery channels, (g) preferences for alert messaging and tolerance for false or missed alerts, (h) ShakeAlert funding, and (i) respondent and school district characteristics.
description: California, Oregon, and Washington are the first states in the United States to activate ShakeAlert, a regional Earthquake Early Warning (EEW) system that can send warnings of significant earthquakes to areas outside the immediate epicenter before serious shaking occurs. The vital seconds provided by ShakeAlert can allow automatic shutdowns of industrial and technological systems and prompt people to take recommended protective actions—such as drop, cover, and hold on. This research project is designed to fill a major gap in the literature by assessing the readiness, willingness, and ability of K-12 schools in Alaska, California, Oregon, and Washington to adapt their school emergency plans to include EEW. Phase 1 of this project involves qualitative research in Anchorage and the Matanuska-Susitna Borough, Alaska, and in Ridgecrest and Trona, California. These regions were affected by earthquake activity in November 2018 and July 2019, respectively. Our research team interviewed school district administrators; school principals; teachers, students, and parents; emergency management personnel; and engineers about their earthquake experiences, damage to the built environment, disruption to the social environment, and receptivity to using EEW. During Phase 2 of this project, the research team will distribute an online survey to school district officials in Alaska, California, Oregon, and Washington regarding their perceptions and/or adoption of EEW systems in their schools. This project includes the research instruments, IRB protocols, and other materials that will be of use to other researchers interested in the study of earthquake early warning, protective actions, earthquake impacts, and schools.
DOI: 10.17603/ds2-232w-3869
title: Numerical implementation and simulation.
title: in Comparative Study of Numerical Models for Dynamic Analysis of Seismically Isolated Structures with High Damping Rubber Bearings

description: This investigation addresses the effects of different HDRB models by evaluating their accuracy in reproducing isolator experimental responses subject to different load patterns, and propagating that uncertainty into the seismic responses of isolated structures.
description: Seismic isolation has gained popularity due to its effectiveness in protecting buildings and their contents during earthquakes. The mechanical filtering produced by the isolation system controls the earthquake demand on the superstructure and its frequency content, and hence, the numerical models used to characterize the force-deformation constitutive behavior of isolation devices are critical for accurately estimating seismic responses. High Damping Rubber Bearings (HDRBs) are isolators widely used in practice, but some aspects of their force-deformation behavior are highly nonlinear and challenging to model. As a result, several numerical models have been developed for simulating the behavior of these devices, each based on different assumptions, offering distinct advantages and limitations. As of today, the decision to use one particular model depends on professional judgment and availability in commercial structural software, but the impact of the model selection on the structural responses is not fully understood. Consequently, this study evaluates different HDRB models by assessing their accuracy in reproducing experimental responses of a benchmark device under different load patterns and propagating this epistemic uncertainty into the seismic responses of isolated structures. Eight numerical models with different sophistication levels are evaluated, including rate-dependent and independent models, as well as axial-shear coupling effects. To minimize any eventual bias in parameter calibration, all model parameters were estimated by matching experimental results using an optimization algorithm. Results show significant variations between models in predicting shear forces of the isolation system and superstructure responses. While most models estimate similar displacements for the isolation layer, more elaborate models predict larger demands on other response parameters, potentially influencing the fragility estimates of the structure. These results highlight the importance of model selection in seismic performance assessment and risk evaluation of isolated buildings.
DOI: 10.17603/ds2-3187-xr32
title: Hurricane Ivan 2004
title: in FCMP Ground Level Hurricane Wind Data (1999 - 2008)

description: The Florida Coastal Monitoring Program (FCMP) is a collaborative experimental effort to measure ground level hurricane surface winds. This publication provides data files of ground level wind speed and direction measured along the east coast of Florida during landfall of Hurricanes: Irene 1999,  Gordon 2000, Lili 2002, Charley 2004, Ivan 2004, Jeanne 2004, Dennis 2005, Katrina 2005, Rita 2005, Wilma 2005, Gustav 2008, Hanna 2008 and, Ike 2008. Urbanists, social scientists, structural engineers, and meteorologists, among others, can use these real wind speed measurements and directions in myriad disaster research and planning projects.
description: Portable weather stations were deployed along the coast and/or inland to collect wind observations. This publication provides data files of ground level wind speed and direction measured along the coast during landfall of Hurricane Ivan 2004.
DOI: 10.17603/ds2-7e6k-ca26
title: Relationship between fines content (FC) and soil behavior type index (Ic)
title: in Next Generation Liquefaction - Supported Modeling Team

description: The purpose of this study is to gather pairs of fines content (FC) and soil behavior type index (Ic) values from collocated boring logs and cone penetration tests. Samples from the boring logs were tested in the laboratory to measure the fines content, and in some cases plasticity characteristics including the plasticity index (PI). Values of Ic were computed from the cone penetration test collocated with the boring log. Collocation was assigned to boring logs and cone penetration tests within 10m of each other. Data come from California, Turkey, Mexico, Japan, New Zealand, Taiwan, and China.
description: The Next Generation Liquefaction (NGL) Project is advancing the state of the art in liquefaction research and working toward providing end users with a consensus approach to assess liquefaction potential within a probabilistic and risk-informed framework. Specifically, NGL’s goal is to first collect and organize liquefaction information in a common and comprehensive database to provide all researchers with a substantially larger, more consistent, and more reliable source of liquefaction data than existed previously. That database can be found at https://www.nextgenerationliquefaction.org (Ulmer et al. 2023). Based on this database, the supported modeling team is developing probabilistic models that provide hazard- and risk-consistent bases for assessing liquefaction susceptibility, the potential for liquefaction to be triggered in susceptible soils, and the likely consequences. NGL is committed to an open and objective evaluation and integration of data, models and methods, as recommended in a National Academies report. The evaluation and integration of the data into new models and methods will be clear and transparent. Following these principles will ensure that the resulting liquefaction susceptibility, triggering, and consequence models are reliable, robust and vetted by the scientific community, providing a solid foundation for designing, constructing and overseeing critical infrastructure projects.

National Academies of Sciences, Engineering, and Medicine. 2021. State of the Art and Practice in the Assessment of Earthquake-Induced Soil Liquefaction and Its Consequences. Washington, DC: The National Academies Press. https://doi.org/10.17226/23474.

Ulmer K.J., Zimmaro P., Brandenberg S.J., Stewart J.P., Hudson K.S., Stuedlein A.W., Jana A., Dadashiserej A., Kramer S.L., Cetin K.O., Can G., Ilgac M., Franke K.W., Moss R.E.S., Bartlett S.F., Hosseinali M., Dacayanan H., Kwak D.Y., Stamatakos J., Mukherjee J., Salman U., Ybarra S., Weaver T. (2023). Next-Generation Liquefaction Database, Version 2. Next-Generation Liquefaction Consortium. DOI: 10.21222/C23P70.
DOI: 10.17603/ds2-a2vx-sx21
title: Interconnected Hazards Data
title: in Natural Hazards Research Summit 2024: Interconnected Hazards: Retrospective Data Analysis for Climate Resilience in the United States

description: Climate change is exacerbating extreme weather events. While most research typically focuses on individual extremes, such as heatwaves or floods or forest fires, our study aims to explore how different extremes interact and compound, impacting vulnerable communities. We examine the cascading and compounding effects of these events occurring simultaneously in terms of space and time. Specifically, we investigate how factors like temperature, rainfall, and other extreme weather conditions coincide in the United States (US) due to climate change. Using data and forecasts from climate models, we analyze both past and future scenarios. We assess the effects of various extreme events, such as heatwaves, floods, and droughts, on different geographical regions. 

Our objective is to identify regions at risk of experiencing multiple extreme events simultaneously and to evaluate the vulnerability of the populations residing there. This enables us to pinpoint the areas in the US most threatened by climate change. Understanding the likelihood of multiple extreme events occurring in areas with vulnerable populations is crucial for future planning and disaster risk reduction efforts. By employing machine learning and data assimilation techniques, this study develops a framework to predict likelihood and impacts of associated hazards and assess vulnerability. With a proper understanding of strength and connections, policymakers can make informed decisions to bolster community resilience. Our study serves as a model for comprehending climate risks not only within the US but also globally.
description: We examine the cascading and compounding effects of several extreme events occurring simultaneously in terms of space and time. Specifically, we investigate how factors like temperature, rainfall, and other extreme weather conditions coincide in the United States (US).
DOI: 10.17603/ds2-fdr3-5963
title: Natural Hazards Research Summit 2024: Design and component testing of the long-stroke and rate-independent pressurized sand damper

description: Motivated by the recent failures and displacement limitations of existing energy dissipation devices, this projects proposes an innovative, reliable, long-stroke, low-cost energy dissipation device in which a steel sphere is moving within a cylindrical tube filled with sand that is under pressure. 
One of the novel aspects of the proposed pressurized sand damper is that it dissipates energy through the hysteretic behavior of sand that can be enhanced by controlling the externally exerted pressure on the sand.

The experimental results, design, or code that support the findings of this project can be provided by the authors upon reasonable request.
DOI: 10.17603/ds2-jzcv-he68
title: Scenario Hurricane Risk Analysis
title: in Scenario-based Hurricane Risk Analysis (SHRA) Framework

description: Scenario Hurricane Risk Analysis
description: This project is a publication of a python-based "Scenario-based Hurricane Risk Analysis (SHRA)" tool to perform scenario-based hurricane-induced winds risk analysis at a community scale. This tool executes a novel framework developed to study community-level social and physical impacts of hurricane-induced winds through synthetic scenario analysis. This tool can easily be executable on Jupyter Notebook. The analysis is illustrated in an example Jupyter Notebook.
DOI: 10.17603/ds2-mba9-3366
title: ISROC Dataset for: Boulder transport by the 2011 Great East Japan tsunami: Comprehensive field observations and whither model predictions?
title: in Inundation Signatures on Rocky Coastlines (ISROC) Global Coastal Boulder Deposits Datasets Version 1

description: This document contains standardized data as an Excel and NC file and explanatory notes for "Boulder transport by the 2011 Great East Japan tsunami: Comprehensive field observations and whither model predictions." The project focused on 14 coastal boulder deposits for the Great East Japan tsunami in Iwate and Miyagi Prefecture. These boulders are used because they offer the necessary information to validate numerical modeling for modern and historical wave events.
description: This project is a collection of datasets on studies about coastal boulder deposits worldwide. Each document is a unique dataset that contains data from a published study. The document's name reflects the name of the associated paper, and all authors of papers are given credit in the respective document. Each dataset's data is standardized into an ISROC-formatted Excel and NC File. Also, every dataset has explanatory notes, which contain a data dictionary explaining data parameters and appropriate units, Some datasets have the raw data given to ISROC by the paper's authors. By creating a database in the public domain, ISROC hopes to promote academic collaboration in the field. ISROC, an NSF-funded research network, funds this project.
DOI: 10.17603/ds2-p9jf-xe52
title: Flash Flood BERT Text Classification Model: Dataset

description: This published dataset is a source for researchers and practitioners to improve upon the performances of the (Flash Flood) FF-BERT model. FF-BERT is a multi-label text classification model that classifies the paragraphs of FF event related webpages into one of more of the seven categories: (1) Damage and Economic Impact (DI), (2) Fatalities, Injuries, and Rescue (FIR), (3) Hydrometeorology (HM), (4) Warning and Emergency (WE), (5) Response and Recovery (RR), (6) Public Health (PH), and (7) Mitigation (MG). To develop FF-BERT, we labeled 21,180 paragraphs from FF-related webpages and performed experiments with multiple model architectures based on the widely used Bidirectional Encoder Representation from Transformers (BERT). The FF-related webpages are the ones labeled 1 in the FF-IR ML dataset (https://doi.org/10.17603/ds2-rwg3-v337). Our final model outperforms the baseline by 11.83%, as measured by the micro-F1 score. In addition, FF-BERT significantly improves the prediction of minority labels (RR-32.1%, PH-260.4%, and MG-138.6%). The study is currently under publication. We have designed FF-BERT to be jointly used with FF-IR published here: https://doi.org/10.1016/j.envsoft.2023.105734. Researchers and practitioners can use FF-IR jointly with FF-BERT to uncover information about past flash flood events from the web and enhance the information contained in existing databases. This dataset contains two csv files, ‘FF_BERT_Train.csv’ and ‘FF_BERT_Test.csv’ which contain 80% and 20% of the 21,180 paragraphs, respectively, manually labeled for developing FF-BERT. We used the train file to train multiple BERT-based models and the test file to compare the performances of the trained models using unseen test data. Details regarding the dataset are available in the readme file.