Search Constraints

Search Results

  • Supplementary materials for "Sympathetic Controls Fate and Function of Adult Sensory Neurons"

    Type:
    Dataset
    Description/Abstract:
    This file includes: Figs. S1 to S3, Tables S1 to S2.
    Creator/Author:
    Zhang, Jun-Ming
    Submitter:
    Jun-Ming Zhang
    Date Uploaded:
    02/12/2026
    Date Modified:
    02/12/2026
    Date Created:
    2022-2026
    License:
    All rights reserved

  • Gas streetlights in Boston, Massachusetts

    Type:
    Document
    Description/Abstract:
    This historic document indicates the location of natural gas streetlights in the city of Boston, Massachusetts.
    Creator/Author:
    Townsend-Small, Amy
    Submitter:
    Amy Townsend-Small
    Date Uploaded:
    01/26/2026
    Date Modified:
    01/26/2026
    Date Created:
    1985-08-27
    License:
    Public Domain Mark 1.0

  • Boston gas streetlights

    User Collection
    Type:
    Collection
    Description/Abstract:
    Methane emissions and other environmental and historical information about natural gas streetlights in Boston, Massachusetts
    Creator/Author:
    Townsend-Small, Amy
    Submitter:
    Amy Townsend-Small
    License:
    Open Data Commons Public Domain Dedication and License (PDDL)
    0Collections
    0Works

  • Artificial Intelligence Presentation University of Cincinnati

    Type:
    Generic Work
    Description/Abstract:
    This is the slide deck for the DTS Hands On Training.
    Creator/Author:
    Scherz, Thomas
    Submitter:
    Thomas Scherz
    Date Uploaded:
    01/13/2026
    Date Modified:
    01/13/2026
    Date Created:
    January 12, 2026
    License:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches

    Type:
    Article
    Description/Abstract:
    Histopathology image analysis plays a pivotal role in disease diagnosis and treatment planning, relying heavily on accurate nuclei segmentation for extracting vital cellular information. In recent years, artificial intelligence (AI) and in particular deep learning models have been applied successfully in solving computational pathology image analysis tasks. The You Only Look Once (YOLO) object detection framework, which is based on a convolutional neural network (CNN) architecture has gained traction across various domains for its real-time processing capabilities. This systematic review aims to comprehensively explore and evaluate the advancements, challenges, and applications of YOLO-based methodologies in nuclei segmentation within the domain of histopathological images. The review encompasses a structured analysis of recent literature, focusing on the utilization of YOLO variants for nuclei segmentation. Key methodologies, training strategies, dataset specifics, and performance metrics are evaluated to elucidate the strengths and limitations of YOLO in this context. Additionally, the review highlights the unique characteristics of YOLO that enable efficient object detection and delineation of nuclei structures, offering a comparative analysis against traditional segmentation approaches. This systematic review underscores the promising outcomes achieved through YOLO-based architectures, emphasizing their potential for accurate and rapid nuclei segmentation. Furthermore, it identifies persistent challenges such as handling variances in nuclei appearances, optimizing model architectures for histopathological images, and improving generalization across diverse datasets. Insights derived from this review can provide a foundation for future research directions and enhancements in nuclei segmentation methodologies using YOLO within histopathology, fostering advancements in disease diagnosis and biomedical research.
    Creator/Author:
    Debsarkar, Shyam
    Submitter:
    Shyam Debsarkar
    Date Uploaded:
    12/28/2025
    Date Modified:
    12/28/2025
    Date Created:
    March 25, 2025
    License:
    All rights reserved

  • Helpful Online Resources + Websites

    Type:
    Document
    Description/Abstract:
    Additional free resources for personnel working in the research enterprise.
    Creator/Author:
    Green-Schwartz, Clair
    Submitter:
    Clair Green-Schwartz
    Date Uploaded:
    12/22/2025
    Date Modified:
    12/22/2025
    License:
    Public Domain Mark 1.0

  • National Science Foundation Graduate Student and Postdoctoral Mentoring Plan Template

    Type:
    Document
    Description/Abstract:
    This document is a template for creating the National Science Foundation's Graduate Student and Postdoctoral Mentoring Plan. Not all NSF grant proposals require this document. Be sure to check grant-specific requirements or instructions for more details.
    Creator/Author:
    Green-Schwartz, Clair and DenBleyker, Emma
    Submitter:
    Clair Green-Schwartz
    Date Uploaded:
    12/22/2025
    Date Modified:
    12/22/2025
    License:
    Public Domain Mark 1.0

  • Timeline Example 2

    Type:
    Document
    Description/Abstract:
    This document is a template for creating professional grant proposal timelines. The template can be adopted for a variety of funders. Be sure to check grant-specific requirements or instructions for more details.
    Creator/Author:
    Green-Schwartz, Clair
    Submitter:
    Clair Green-Schwartz
    Date Uploaded:
    12/22/2025
    Date Modified:
    12/22/2025
    License:
    Public Domain Mark 1.0

  • Timeline Example 1

    Type:
    Document
    Description/Abstract:
    This document is a template for creating professional grant proposal timelines. The template can be adopted for a variety of funders. Be sure to check grant-specific requirements or instructions for more details.
    Creator/Author:
    Green-Schwartz, Clair and Foster, Daniel J.
    Submitter:
    Clair Green-Schwartz
    Date Uploaded:
    12/22/2025
    Date Modified:
    12/22/2025
    License:
    Public Domain Mark 1.0

  • One-Pager Template

    Type:
    Document
    Description/Abstract:
    This document is a template for creating one or two page project summaries. The template can be adopted for a variety of disciplines. Be sure to check grant- or funder-specific requirements or instructions for more details.
    Creator/Author:
    Green-Schwartz, Clair and DenBleyker, Emma
    Submitter:
    Clair Green-Schwartz
    Date Uploaded:
    12/22/2025
    Date Modified:
    12/22/2025
    License:
    Public Domain Mark 1.0

  • Letter of Support Template

    Type:
    Document
    Description/Abstract:
    This document is a template for creating letters of support. The template can be adopted for a variety of funders. Be sure to check grant-specific requirements or instructions for more details
    Creator/Author:
    Green-Schwartz, Clair
    Submitter:
    Clair Green-Schwartz
    Date Uploaded:
    12/22/2025
    Date Modified:
    12/22/2025
    License:
    Public Domain Mark 1.0

  • Leveling the Grant Proposal Playing Field: Increasing Research Capacity for Social Sciences with Free Resources

    Type:
    Media
    Description/Abstract:
    This presentation was created to share the preliminary results of this study, NSF #2324585, at the 2024 National Council of University Research Administrators Annual Meeting.
    Creator/Author:
    Green-Schwartz, Clair and Kennedy, Erica
    Submitter:
    Clair Green-Schwartz
    Date Uploaded:
    12/15/2025
    Date Modified:
    12/15/2025
    Date Created:
    2024-08-04
    License:
    Public Domain Mark 1.0

  • Collaborative Convenings: Increasing Capacity in the Social Science Research Enterprise with HBCUs, PUIs, and More

    User Collection
    Type:
    Collection
    Description/Abstract:
    This collection includes a variety of resources compiled as a result of a study (#2324585) funded by the National Science Foundation. The study convened research-oriented and research enterprise professionals from social science fields, in a variety of ways, to discuss barriers, challenges, and solutions to increasing research funding at their institutions and beyond. Please see the "Helpful Online Resources" document for additional free resources. The organic repository will be reviewed and updated, if needed, annually. To suggest additional resources, please email  ImpactAccelerator@uc.edu. This material is based upon work supported by the National Science Foundation under Grant Number 2324585. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
    Creator/Author:
    Green-Schwartz, Clair
    Submitter:
    Clair Green-Schwartz
    License:
    Public Domain Mark 1.0
    0Collections
    7Works

  • Test after netapp

    Type:
    Image
    Description/Abstract:
    test
    Creator/Author:
    Scherz, Thomas
    Submitter:
    Thomas Scherz
    Date Uploaded:
    09/29/2025
    Date Modified:
    09/29/2025
    License:
    Attribution 4.0 International

  • Q2000 Deep Learning Model Package

    Type:
    Dataset
    Description/Abstract:
    Q2000 Deep Learning Model Package This Technical Resource Bundle provides the Q200 Deep Learning model for open access download and use. The Q2000 DL model is built to detect Maya structures in Lidar data visualized at one meter per pixel. Currently, this repository contains the ESRI ArcGIS compatible DL model in the format .dlpk. We expect to convert the model in its current form into Pytorch (.pt) and TensorFlow (.h5) formats to incude them also here for user access. The Q2000.dlpk file was created in 2025 at University of Cincinnati by Benjamin Britton, using ArcGIS Pro v.3.3 with data from the NASA Ames G-LiHT. It is intended as an experiment to evaluate the practicability of creating a broadscale deep learning model that can be used effectively to identify Maya structures in Lidar data across the length and breadth of the Yucatan Peninsula. The Q2000 model is the subject of an article, a draft of which is also included in this dataset, called "Evaluating Broadscale Deep Learning for Maya Settlement Detection in G-LiHT Lidar" which examines the process and rationale of the model development in detail. The article has been accepted for publication and this site is included in that article with a link to this permanent (DOI) publication site. To use the model with ArcGIS Pro, use a Lidar dataset converted to a 1m/pixel DEM file and visualized as a 3-channel RGB Hillshade or other customized visualization as source input. -Using the ArcGIS Pro Spatial Analyst Extension, the geoprocessing tool called "Detect Object Using Deep Learning" may be invoked. -For Input Raster, add your Lidar visualization (a Hillshade visualization might be easiest for most users). -For Output Detected Objects, specify a new Layer name, and this will be the layer on which the detections will be recorded and displayed. -For Model Definition, use Q2000.dlpk. -Unless you want to run "arguments", you can leave the Arguments boxes as their Default. -I suggest checking the box (On) for Non Max suppression because it will reduce the amount of overlapping detections if target objects are located very close to each other, and I suggest a Non-Maximum Suppression (NMS) ratio of 0.5. This will tend to suppress detections that overlap by more than 50 percent. -I suggest you use Pixel Space unchecked (Off), since it is for an unrelated sort of object detection. -Before you click run, open the "Environments" tab (at the top of the window, next to the "Parameters" tab). Leave all the settings at their defaults there - except scroll down to the bottom of Parameters tab to the section called "Processor Type", pull down the Processor type pull-down, and choose GPU (it is set to CPU by default). -Then click Run and it will generate a new layer showing its detections as bounding boxes around target objects. You can see details for each detection by opening the Attribute Table on the new layer. You can see a screen capture of such a configuration in the image called Q200DemoScreenCap.jpg, included in this site's dataset, showing a detection on G-LiHT transect Yucatan_South_GLAS_395 near Pixoyal, with a detection of a Maya staircase highlighted on the display, and its corresponding information highlighted in the Attribute Table for it.
    Creator/Author:
    Britton, Benjamin
    Submitter:
    Benjamin Britton
    Date Uploaded:
    08/23/2025
    Date Modified:
    11/13/2025
    Date Created:
    March 15, 2025
    License:
    Public Domain Mark 1.0

  • BUILD TO ADAPT: HOW E.D.E.N. REIMAGINES CONTINUOUS IMPROVEMENT FOR NONSTOP OPERATIONS

    Type:
    Student Work
    Description/Abstract:
    This capstone report presents E.D.E.N. (Every Day, Every Night) — an original continuous improvement framework designed for nonstop, high-dependency operations in industries such as logistics, aviation, digital infrastructure, and healthcare. Drawing on principles from Lean, Six Sigma, high-reliability organizations (HROs), and data-driven decision science, the paper introduces four interlocking pillars: Engaged & Empowered Teams, Data-Driven Continuous Feedback, End-to-End Alignment, and Nonstop Adaptive Resilience. Through detailed analysis of recent global disruptions — including the 2025 CrowdStrike outage, Boeing’s manufacturing failures, the Red Sea shipping crisis, and Taiwan’s semiconductor challenges — the work demonstrates how organizations can embed real-time adaptability, resilience, and continuous improvement directly into their operations. The E.D.E.N. framework is proposed as a new model for achieving operational excellence and resilience in an era where downtime is no longer an option.
    Creator/Author:
    Agoba, EJ
    Submitter:
    EJ Agoba
    Date Uploaded:
    07/16/2025
    Date Modified:
    07/16/2025
    Date Created:
    2025-04-10
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • A versatile near-infrared fluorescent probe for fast assessment of lysosomal status via a large multimodal model

    Type:
    Image
    Description/Abstract:
    These are original images for a new paper. This paper will be published soon.
    Creator/Author:
    Chen, Rui
    Submitter:
    Rui Chen
    Date Uploaded:
    06/23/2025
    Date Modified:
    06/23/2025
    License:
    Open Data Commons Open Database License (ODbL)

  • Test Work

    Type:
    Generic Work
    Description/Abstract:
    etst
    Creator/Author:
    Scherz, Thomas
    Submitter:
    Thomas Scherz
    Date Uploaded:
    05/28/2025
    Date Modified:
    05/28/2025
    License:
    All rights reserved

  • Die Extruder Modifications

    Type:
    Student Work
    Description/Abstract:
    Senior design capstone report.
    Creator/Author:
    Rosenfeld, Ryan
    Submitter:
    CEAS Library Staff
    Date Uploaded:
    05/27/2025
    Date Modified:
    05/27/2025
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • Scramble - Combat Robot

    Type:
    Student Work
    Description/Abstract:
    Senior design capstone report.
    Creator/Author:
    Kampman, Calvin; Frye, Parker, and Steckner, Andrew
    Submitter:
    CEAS Library Staff
    Date Uploaded:
    05/27/2025
    Date Modified:
    05/27/2025
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International