cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25378/janelia.13114259.v2
title: Isotropic 3D electron microscopy reference data of wild-type, immortalized T-Cells (jrc_jurkat-1)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<b>Sample:</b> Wild-type Jurkats<b>Sampe description:</b> Understanding cellular architecture is essential for understanding biology. Electron microscopy (EM) uniquely visualizes cellular structure with nanometer resolution. However, traditional methods, such as thin-section EM or EM tomography, have limitations inasmuch as they only visualize a single slice or a relatively small volume of the cell, respectively. Here, we overcome these limitations by long-term imaging whole cells and tissues via the enhanced Focus Ion Beam Scanning Electron Microscopy (FIB-SEM) platform in high resolution mode with month-long acquisition duration. We use this approach to generate reference 3D image data sets at 4-nm isotropic voxels. Together with subsequent segmentation, we hope to create a reference library to explore comprehensive quantification of whole cells and all their constituents, thus addressing questions related to cell identities, cell morphologies, cell-cell interactions, as well as intracellular organelle organization and structure.<b>Protocol</b>: High pressure freezing, freeze-substitution resin embedding with 2% OsO4 0.1% UA 3% H2O in acetone; resin embedding in Eponate 12.<b>Contributions</b>: Sample provided by Huxley K. Hoffman and Schuyler B. van Engelenburg (U. Denver), prepared for imaging by Gleb Shtengel (HHMI/Janelia), with imaging and post-processing by C. Shan Xu (HHMI/Janelia)<b>Acquisition ID</b>: jrc_jurkat-1<b>Final voxel size (nm)</b>: 4.0 x 4.0 x 3.44 (X, Y, Z)<b>Dimensions (µm):</b> 40 x 12 x 29 (X, Y, Z)<b>Imaging start date:</b> 2018-08-10<b>Imaging duration (days)</b>: 3<b>Landing energy (eV)</b>: 1000<b>Imaging current (nA)</b>: .25<b>Scanning speed (MHz)</b>: .2<b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_jurkat-1/jrc_jurkat-1.zarr/recon-1/em/<b>Visualization Website:</b> https://openorganelle.janelia.org/datasets/jrc_jurkat-1<br><br><b>Publication</b>: Xu et al., 2021, Heinrich et al., 2021<br>
DOI: 10.25378/janelia.13114343
title: Isotropic 3D electron microscopy reference data of wild-type, THP-1 macrophage (jrc_macrophage-2)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<b>Sample</b>: Wild-type THP-1 macrophage<b>Sample description: </b>Understanding cellular architecture is essential for understanding biology. Electron microscopy (EM) uniquely visualizes cellular structure with nanometer resolution. However, traditional methods, such as thin-section EM or EM tomography, have limitations inasmuch as they only visualize a single slice or a relatively small volume of the cell, respectively. Here, we overcome these limitations by long-term imaging whole cells and tissues via the enhanced Focus Ion Beam Scanning Electron Microscopy (FIB-SEM) platform in high resolution mode with month-long acquisition duration. We use this approach to generate reference 3D image data sets at 4-nm isotropic voxels. Together with subsequent segmentation, we hope to create a reference library to explore comprehensive quantification of whole cells and all their constituents, thus addressing questions related to cell identities, cell morphologies, cell-cell interactions, as well as intracellular organelle organization and structure.<br><b>Protocol</b>: High pressure freezing, freeze-substitution resin embedding with 2% OsO4 0.1% UA 3% H2O in acetone; resin embedding in Eponate 12.<b>Contributions</b>: Sample provided by Huxley K. Hoffman and Schuyler B. van Engelenburg (U. Denver), prepared for imaging by Gleb Shtengel (HHMI/Janelia), with imaging and post-processing by C. Shan Xu (HHMI/Janelia).<b>Acquisition ID</b>: jrc_macrophage-1<b>Final voxel size (nm)</b>: 4.0 x 4.0 x 3.36 (X, Y, Z)<b>Dimensions (µm)</b>: 40 x 8 x 37 (X, Y, Z)<b>Imaging start date</b>: 2018-11-11<b>Imaging duration (days)</b>: 19<b>Landing energy (eV)</b>: 1200<b>Imaging current (nA)</b>: 0.25<b>Scanning speed (MHz)</b>: 0.2<b>Dataset URL:</b> s3://janelia-cosem-datasets/jrc_macrophage-2/jrc_macrophage-2.zarr/recon-1/em/<b>Visualization Website:</b> https://openorganelle.janelia.org/datasets/jrc_macrophage-2<br><br><b>Publication</b>: Xu et al., 2021; Heinrich et al., 2021<br>
DOI: 10.25378/janelia.13117745
title: Automatic segmentation of organelles in isotropic electron microscopy data of wild-type THP-1 macrophage (jrc_macrophage-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 THP-1 macrophage. THP-1 human monocyte cell line (ATCC TIB-202) treated with PMA to differentiate into macrophages.Dataset ID: jrc_macrophage-2EM Data DOI: 10.25378/janelia.13114343EM 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_macrophage-2/jrc_macrophage-2.n5/labelsVisualization Website: https://openorganelle.janelia.org/datasets/jrc_macrophage-2Publication: Heinrich et el., 2020Segmented Organelles: Centrosome (A600k), Centrosome D App (A900k), Chromatin (A825k), ECS (A725k), Endo (M650k), Endo mem (M1025k), ER (M650k), ER mem (A775k), ERES (A850k), Golgi (F650k), Golgi mem (F650k), N-H Chrom (A600k), LD (A925k), LD mem (A875k), Lyso (A725k), Lyso mem (A1125k), MT (A1100k), Mito (M1100k), Mito mem (M650k), NE (A725k), NE mem (A1150k), NP (A900k), NP out (A975k), Nucleolus (F625k), Nucleus (F650k), PM (M650k), Ribo (F525k), Vesicle (F550k), Vesicle mem (A1075k)Included in Dataset: manual annotations, predictions, segmentations
DOI: 10.25378/janelia.13117745.v1
title: Automatic segmentation of organelles in isotropic electron microscopy data of wild-type THP-1 macrophage (jrc_macrophage-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 THP-1 macrophage. THP-1 human monocyte cell line (ATCC TIB-202) treated with PMA to differentiate into macrophages.Dataset ID: jrc_macrophage-2EM Data DOI: 10.25378/janelia.13114343EM 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_macrophage-2/jrc_macrophage-2.n5/labelsVisualization Website: https://openorganelle.janelia.org/datasets/jrc_macrophage-2Publication: Heinrich et el., 2020Segmented Organelles: Centrosome (A600k), Centrosome D App (A900k), Chromatin (A825k), ECS (A725k), Endo (M650k), Endo mem (M1025k), ER (M650k), ER mem (A775k), ERES (A850k), Golgi (F650k), Golgi mem (F650k), N-H Chrom (A600k), LD (A925k), LD mem (A875k), Lyso (A725k), Lyso mem (A1125k), MT (A1100k), Mito (M1100k), Mito mem (M650k), NE (A725k), NE mem (A1150k), NP (A900k), NP out (A975k), Nucleolus (F625k), Nucleus (F650k), PM (M650k), Ribo (F525k), Vesicle (F550k), Vesicle mem (A1075k)Included in Dataset: manual annotations, predictions, segmentations
DOI: 10.17603/ds2-54ks-ef08
title: Undergraduate Research Experience (REU), NHERI 2019: Probabilistic Assessment of Earthquake Damage to a Potable Water Network in Shelby County, Tennessee

description: Simulates an earthquake on the New Madrid fault line to determine how the pipes in a clean water network will fare after an earthquake. Uses stochastic modeling, pipe fragilities, and hydraulic simulations to understand what areas of a community will be without access to water after an earthquake.
DOI: 10.17603/ds2-7eh2-0j40
title: Data Collection Tools for Interviews and Observations
title: in Hurricane Florence Food Environment Study

description: Interview guides and field observation guides for Hurricane Florence field work.
description: This study explores issues related to food environment disruption following a hurricane event from a systems perspective using a quick response disaster research methodology. Through interviews and observations during the week following Hurricane Florence (2018) and follow-up data collected at 6-weeks, 4-months, and 1-year post-event, this study describes the impact of the hurricane on farmers, retail and emergency food systems, and households. Study findings shed light on how to bolster food system resilience and reduce health disparities to create more weather ready communities.
DOI: 10.17603/ds2-mqys-n790
title: Understanding Flexibility Effects in the Interaction of Light-Frame Wood Structures with Wave Action
title: in Fluid Structure Interaction Simulations for Coastal Structures

description: Hurricane and tsunami events can generate extreme water flows in nearshore regions of the United States with catastrophic consequences to coastal structures. Field records have shown that light-frame wood structures exhibit high vulnerability to wave-induced forces due to their light construction characteristics, featuring flexible connections and slender pile foundation systems. Previous research has studied the behavior of these structures under wave action, but most studies used rigid body modeling, which is shown in this study to be insufficient for capturing the force demand on these structures. Using a fluid-structure interaction (FSI) framework, this research investigated the structural and soil flexibility effects in the interaction between elevated light-frame wood structures and water flows. Study variables included the flexibility of timber-to-timber connections, soil type, pile embedment, wavelength, and wave amplitude. The study concluded that structural and soil responses to water flows can introduce strong coupled motions. Neglecting this coupling effect may underestimate the force demand on the light-frame wood structures by more than 40%. Accounting for both structural and soil flexibilities is necessary to accurately quantify the wave force demand of these structures.
description: This project uses multi-physics simulations to study how waves interact with nearshore structures. The simulations account for the deformability of the structural members as well as soil flexibility effects that can be introduced by slender wood pile systems.
DOI: 10.17603/ds2-tses-zm70
title: NHERI REU Training Module 

description: This project is a free online training module available on CONVERGE to address the vulnerabilities of homeless populations in the context of natural hazards and disasters. It aims to enhance ethical practices in disaster management by focusing on the intersection of homelessness and social vulnerability. The data can be reused by educators, researchers, and practitioners for training or as a foundation for further studies. Its uniqueness lies in its focus on the often-overlooked ethical implications of homelessness in disaster contexts. The primary audience includes disaster researchers, emergency responders, and students in related fields.
DOI: 10.17603/ds22951
title: University of Auckland Ductile Wall Experimental Program
title: in University of Auckland High Axial Load Wall Tests

description: 4 large scale walls were tested at the University of Auckland Structural Testing Facility to assess effects of axial load and varied confinement detailing. This dataset summarizes the deformation, force and strain characteristics of four tested RC walls. Strain gauge, LVDT, portal gauge (displacement transducer), string potentiometer, inclinometer and load cell data is provided. Photos taken throughout the testing are also provided. All instrumentation data was collected autonomously in real-time throughout each test at a rate of 2-5Hz. This dataset is intended to serve as as a reference for future work, as well as an open resource to be used in future research.
description: Cyclic testing of four half-scale walls with applied axial load of 10-20% and applied moment at top of wall.
DOI: 10.17603/ds2-mr46-5g54
title: TESLA-Cascadia Total Water Levels and Chronic Hazard Proxies
title: in TESLA-Cascadia: Stochastic Total Water Level and Chronic Coastal Hazard Proxy Simulations

description: This project uses the stochastic climate emulator, TESLA (Anderson et al., 2019) to simulate probabilistic total water level drivers for the Cascadia Region (Northern Washington to Northern California), USA for 2020-2100 under three sea level rise scenarios (Sweet et al., 2022). Total water levels (TWLs) were calculated at 100m resolution on all outer coast beaches. Three proxies for hazard impacts (unsafe beach, erosion, and flooding) were calculated based on TWL elevation and lidar-extracted beach morphologic features (slope, backshore feature toe elevation, backshore feature crest elevation). Probabilistic TWLs generated from this project can be used as inputs to several coastal models (e.g., ecomorphodynamic models, flood models, land use change models). The hazard proxy data provides a first-cut assessment of climate-change induced coastal hazards. This dataset is intended for use by both academics and management specialists.
description: This project uses the stochastic climate emulator, TESLA (Anderson et al., 2019) to simulate probabilistic total water level drivers for the Cascadia Region (Northern Washington to Northern California), USA for 2020-2100 under three sea level rise scenarios (Sweet et al., 2022). Total water levels (TWLs) were calculated at 100m resolution on all outer coast beaches. Three proxies for hazard impacts (unsafe beach, erosion, and flooding) were calculated based on TWL elevation and lidar-extracted beach morphologic features (slope, backshore feature toe elevation, backshore feature crest elevation). 

Probabilistic TWLs generated from this project can be used as inputs to several coastal models (e.g., ecomorphodynamic models, flood models, and land use change models). The hazard proxy data provides a first-cut assessment of climate change-induced coastal hazards. This dataset is intended for use by both academics and management specialists.

Version 2 of this dataset implemented a simple bias correction to address negative bias in simulated wave heights, as well as a bug fix for beach width calculations which affected runup calculations at some transects and beach safety impacts at all transects.
DOI: 10.17603/ds2-cdg3-np75
title: Regional Analyses
title: in RAPID: Understanding the Interrelationships Among Floods, Building Characteristics, Mold Growth and Occupants’ Asthma Symptoms in the Aftermath of Hurricane Ida

description: Dedicated to analyses that highlight regional differences in data and impacts, as determined through statistical tests. This collection emphasizes the distinct environmental, structural, and health impacts observed in Northeast, Louisiana, and Florida.
description: Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indirect impacts of floods, which adversely affect human respiratory health, particularly among asthmatic individuals. There is currently a lack of understanding on interrelationships among flood characteristics and drivers, building and HVAC system properties (e.g., ventilation rates), human behaviors (e.g., time spent in homes) and vulnerability to mold growth (e.g., asthma symptoms) in the built environment, particularly in residential buildings. This project collects data in the aftermath of two recent catastrophic hurricane events - Ida and Ian - from affected residential buildings to study the relationships among flood characteristics, mold growth, building properties, human behavior and human respiratory health. Our interdisciplinary team uses survey questionnaires, laboratory experiments and machine learning modeling to answer the following scientific questions: (1) what flood characteristics and drivers, building and HVAC system properties and human behaviors cause higher levels of mold growth in residential buildings? and (2) how does living in submerged or water-damaged houses after floods affect asthma symptoms among the residents?

The developed empirical database and identified relationships can be used to guide building designers and occupational health scientists to establish resilient indoor environments, provide a foundation to develop flood-induced mold growth and asthma risk models, assist public health officials and emergency managers to have a better understanding of indirect health-related impacts of floods and support the development of timely strategies for disaster management in population centers.
DOI: 10.17603/ds2-ak27-4d16
title: Pseudo-static test of reinforced concrete walls
title: in Earthquake-Resistant T-Shaped Concrete Walls with High-Strength Steel Bars: Wall T5 and T6

description: For many year, ACI 318 limited the acceptable range of values for mechanical properties of reinforcing bars, including the yield strength, tensile-to-yield strength ratio, and fracture elongation. The maximum yield strength permitted for design purposes was set to prevent brittle failures of members and to attain sufficient deformation capacity. The use of high-strength reinforcement with yield strength in excess of 80 ksi (550 MPa) can help reduced congestion of reinforcing bars, improved quality of construction, reduced constructions time, smaller member sizes, and more usable space. The main scope of this study was to examine the effects of the mechanical properties of high-strength reinforcement on the seismic behavior of concrete walls. Two large-scale T-shaped structural walls were subjected to reversed cyclic loading to evaluate the influence of the uniform and fracture elongation, the yield strength and the tensile-to-yield strength ratio of high-strength reinforcement on the deformation capacity of concrete walls. The primary variables were the nominal yield strength of the reinforcement (Grades 100 and 120 [690 and 830 MPa]), and the tensile-to-yield strength ratio, nominally 1.2 or 1.3. These data were recorded as part of the following research project: Burgos, E. A., Lequesne, R. D., and Lepage, A., “Earthquake-Resistant T-Shaped Concrete Walls with High-Strength Steel Bars,” SM Report No. 142, The University of Kansas Center for Research, Inc., Lawrence, KS, August 2020, 330 pp. https://iri.ku.edu/sites/iri.ku.edu/files/files/pdf/SM142b.pdf and 
http://hdl.handle.net/1808/30768
description: The main scope of this study was to examine the effects of the mechanical properties of high-strength reinforcement on the seismic behavior of concrete walls. Two large-scale T-shaped structural walls were subjected to reversed cyclic loading to evaluate the influence of the uniform and fracture elongation, the yield strength and the tensile-to-yield strength ratio of high-strength reinforcement on the deformation capacity of concrete walls. The primary variables were the nominal yield strength of the reinforcement (Grades 100 and 120 [690 and 830 MPa]), and the tensile-to-yield strength ratio, nominally 1.2 or 1.3. The results confirmed previous finding for high-strength reinforcement. The tested walls were able to experience a deformation capacity similar to those of walls with conventional Grade 60 reinforcement, if the tensile-to-yield strength is greater or equal than 1.12, the uniform elongation is greater or equal than 6% and the fracture elongation is greater or equal to 10%.