The two papers of this collection discuss the formalization and naming of ceramic science as well as on the practice of ceramics in the late nineteenth and twentieth centuries.
Data was collected on Acton Lake north of Cincinnati, Ohio in 2024. Sonar data was collected using a Lowrance Active Imaging 3-in-1 transducer. Data was recorded onto a micro SD card in sl3 file format for raw files. Eight passes of imagery were recorded (4 clockwise and 4 counterclockwise) parallel to the shoreline along the southern portion of the lake. Imagery was post-processed and reinjected with high-accuracy locational data using SonarWiz (Chesapeake Technologies, Inc.).
This is a dataset generated as a part of a research project studying the changing support among European Union (EU) members for the war in Ukraine. The dataset contains a number of conditions (variables) used to conduct fuzzy-set qualitative comparative analysis (fsQCA) to test five critical conditions that have shaped the change in public opinion that include economic growth, democratic rule, distance from the front lines, level of energy dependence from Russia and trust in social media. These conditions (or variables) include:
Num: Case number in the row
MEMBR: EU member state two or three-letter abbreviation
WEALTH: GDP per capita in Euro (measured in purchasing power parties) as reported by Eurostat
GROWTH: GDP growth in volume based on seasonally adjusted data by Eurostat
DEMOCR: the overall score for each EU member’s democracy index for 2022. Data have been drawn from the Economist Intelligence Unit (EIU) 2022 report
DISTAN: an average distance (in thousand kilometers) from the geographic center point of the national capital of each EU member-state to the south-western and north-eastern tips of the frontline of the war in Ukraine. I have accepted that the western tip of the frontline is Kinburnsʹka Kosa National Park (Geographic Coordinates: 46°34’37”N 31°30’44”E) and the eastern tip of the frontline is at the village of Topoli in Kharkiv Oblast (Geographic Coordinates: 49°57’52″N, 37°54′31″E).
TRADE: volume of trade with Russia per capita in thousand of US Dollars.
ENERGO: EU energy dependence on Russia as estimated by the European Commission (from 0 to 100 percent) for 2020. Source: Eurostat.
GOVTR: Net trust in national government (difference between the sum of fully trust and partially trust responses and fully distrust and partially distrust responses).
MEDIATR: Net trust in social media (difference between the sum of fully trust and partially trust responses and fully distrust and partially distrust responses).
CNG_MIL -- change of net support for the financing of the purchase and supply of military equipment to Ukraine, Spring 2022-Spring 2025
CNG_FIN -- change of net support for the financing of support for Ukraine, Spring 2022-Spring 2025
CNG_HUM -- change of net support for providing humanitarian support for the people affected by the war, Spring 2022-Spring 2025
CNG_REF -- change of net support for welcoming in the EU people fleeing the war, Spring 2022-Spring 2025
AVCHNG: Difference in average change of the military, economic, humanitarian and refugee support for Ukraine Spring 2022-Spring 2025.
WEALTH1: Calibrated score for national wealth (see paper for details)
GROWT1: Calibrated score for economic growth (see paper for details)
DEMOCR1: Calibrated score for democracy (see paper for details)
DISTAN1: Calibrated score for distance (see paper for details)
TRADE1: Calibrated score for trade (see paper for details)
RENERGO1: Calibrated score for energy dependence (see paper for details)
GOVTR1: Calibrated score for trust in governance (see paper for details)
RMEDIATR1: Calibrated score for trust in social media (see paper for details)
---------------
NB: File: "Dataset_UkrTime2yr.csv" contains data for the public support during the first two years (24 months) since the whole scale invasion of Ukraine, Spring 2022-Spring 2024
File"Dataset_UkrTime3yr.csv" contains data for the public support during the first three years (36 months) since the whole scale invasion of Ukraine, Spring 2022-Spring 2025
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.
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.
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.
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.
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.
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.
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
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.
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.
This capstone explores how a calming mobile living wall can improve the well-being and emotional health of individuals with ALS in palliative care, as well as those who care for them. This project examines a mobile green wall as an adaptable solution that introduces the benefits of green design into various spaces within a care facility. The goals of this project are (1) to better understand how mobile green walls enhance users’ senses, thus reducing anxiety and influencing mood and stress and (2) to gain insight into a mobile green wall’s overall impact in palliative care environments.
Varieties of International Cyber Strategies (VoICS): Text Analysis of National Cybersecurity Documents is a project that compares and contrasts the three main approaches to conceptualize national cybersecurity strategies (NSS): deterrence, norm-based approach (NBA) and cyber persistence engagement (CPE). Scholars and policymakers have initially conceptualized NSS in terms of deterrence or NBA. More recent academic research has demonstrated that these frameworks are inadequate for cyber space. As a result, Cyber Persistence Engagement (CPE) emerged as a third option.
The first version (1.0) of the VoICS database on National Cybersecurity Strategies focuses on nations in Europe and North America and includes a total of 77 NCS of the states in the North Atlantic Area—NATO allies, EU members and Switzerland—released from 2003 until the end of 2023. The current 1.2 version includes 83 strategies from 36 allies and partners. It consists of 27 variables, including country and strategy identifiers, EU and NATO membership, their respective accession dates, and total length of the documents. VoICS include eighteen variables representing different measures of relative and absolute weights of the three NSS types—deterrence, NBA and CPE.
The text analysis is based on official NSS documents provided by the NATO Cooperative Cyber Defence Centre of Excellence library (2024) and ENISA’s interactive map for National Cyber Security Strategies (2023). Both sources rely on voluntary submission from the member states. Unfortunately, some official documents were not available or accessible or were not listed at all. Authors have used various sources and contacts with a variety of cyber attachés in Brussels to determine if any additional strategies were released and to obtain the missing documents.
The 18 text analysis variables compare and contrast the extent to which different NCS are associated with a specific strategy. They represent different frequency scores based either on words, phrases, or words and phrases combined. These calculations are associated with either deterrence, NBA, or CPE in each strategy. The authors have generated respective vocabularies for the three strategic ideas through which each of these approaches are operationalized. We have conducted a text analysis using WordStat text analysis software by Provialis ( https://provalisresearch.com/products/content-analysis-software/). A detailed codebook for NSS Dataset 1.2 along with a NSS Dictionary 1.2 have been included in this collection/ repository.
The process of generating vocabulary associated with the three cybersecurity approaches involved several steps. First, upon reviewing the literature, the authors generated independently a list of words and phrases associated with each type of cybersecurity strategy. Second, the authors compared their lists to determine the degree of overlap in vocabulary. Those words and phrases that included in at least two different lists were reviewed and, if there was consensus, were incorporated in the dictionary. Finally, words and phrases which were identified in only one of lists were once again reviewed and, in case there was a consensus among the authors, these were also included in the dictionary. Third, the three vocabularies were updated on several instances when it was unanimously agreed that these words or phrases should be included in the analysis.
Meteorological data from an Onset tower including shielded air temperature, photosynthetically active radiation (PAR), relative humidity, wind speed and direction, and rainfall collected every 15 min.
The location is 50.9583N, -114.8809W, alt 2083m
The station is still operational and files will be updated after manual yearly downloads.
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.
Data on orientation angle for fall migratory monarch butterflies exposed to experimental magnetic conditions north of, at, and south of their overwintering sites in Mexico. Additional orientation data for butterflies consistently exposed to either fall-like or colder temperatures.
This data set contains ultrasound image, tissue ablation. and temperature data measured in the study reported in the article:
Real-time control of radiofrequency ablation using three-dimensional ultrasound echo decorrelation imaging in normal and diseased ex vivo human liver
Elmira Ghahramani, Peter D Grimm, Benjamin E Weiss, Nicholas S Schoenleb, Alexander J Knapp, Jiang Wang, Syed A Ahmad, Shimul A Shah, Ralph C Quillin III, Sameer H Patel
Physics in Medicine and Biology, 2025
https://iopscience.iop.org/article/10.1088/1361-6560/adaacb
To best understand and use this data set, please refer to this article.
Files in the data set include:
Annotated Excel spreadsheet containing summary data for all 109 trials in this study (12 preliminary trials, followed by 97 primary trials; see spreadsheet for variable listing):
ExVivoHumanLiver_RFA_Decorrelation_SummaryData.xlsx
CSV spreadsheet containing the same summary data without annotations:
ExVivoHumanLiver_RFA_Decorrelation_SummaryData.csv
MATLAB data file (version 7) containing the same summary data, within the structure variable 'summary':
ExVivoHumanLiver_RFA_Decorrelation_SummaryData.mat
Example MATLAB script demonstrating reading and some analysis of results from all trials, with PDF of code and its output:
ExVivoHumanLiver_RFA_Decorrelation_ExampleScript.m
ExVivoHumanLiver_RFA_Decorrelation_ExampleScript.pdf
MATLAB data files (version 7) containing results from each trial:
ExVivoHumanLiver_RFA_Decorrelation_RawData_Trial1.mat through ExVivoHumanLiver_RFA_Decorrelation_RawData_Trial109.mat.
Each of these files contains the structure variable "rawdata", containing the following fields for each trial:
segmented_ablation_zone:
60x60x60 logical array (step size 1 mm) of segmented liver tissue map (1=ablated, 0=unablated). Dimensions are (z,y,x) as defined in Ghahramani et al. (2025), with z being range from the transducer surface (horizontal in room coordinates), y the azimuthal array direction (horizontal in room coordinates), and x the elevational array direction (vertical in room coordinates). The RFA probe was placed with its tip approximately at the center of this volume, with its needle parallel to the x axis.
IQ_echo_data:
60x60x60x2xN complex array containing beamformed, demodulated IQ (in-phase/quadrature) echo data interpolated onto the same grid. The array of echo volumes comprises N pairs (one pair acquired every 22 s), with the two volumes from each pair separated by the inter-frame time of 50 ms.
instantaneous_decorrelation:
60x60x60xN real array containing echo decorrelation per ms (linear scale) computed for each frame pair, using the equation and methods from section 2.3 in Ghahramani et al. (2025).
thermocouple_locations:
4x3 real array (same for each trial), with each row comprising the (z,y,x) coordinates (voxel indices within the 60x60x60 grid) of a corresponding thermocouple integrated into the RFA probe.
thermocouple_temperatures:
4xM real array, with each row comprising the measured temperatures (degrees C) measured during ablation, synchronous with the last M ultrasound IQ and decorrelation frames of each trial (not available for trials 30, 78, 91, and 102-109).
decorrelation_at_thermocouples:
4xM real array, with each row comprising the measured decorrelation per ms (linear scale) at each thermocouple location, synchronous with the last M ultrasound IQ and decorrelation frames of each trial.
572918-348-R colorectal cancer organoids were treated with 300nM MRTX1133 for 0, 24, 48, and 72 hours before lysis and loading onto 7.5% SDS-PAGE gels. Gels were transferred to nitrocellulose membranes and cut at the 95kDa and just below the 52 kDa molecular weight (MW) markers. Membranes were then probed for proteins that fell within the MW and evaluated for change in comparison to the 0h control.
SNU-407 colorectal cancer cells were treated with 300nM MRTX1133 for 0, 24, 48, and 72 hours before lysis and loading onto 7.5% SDS-PAGE gels. Gels were transferred to nitrocellulose membranes and cut at the 95kDa and just below the 52 kDa molecular weight (MW) markers. Membranes were then probed for proteins that fell within the MW and evaluated for change in comparison to the 0h control.
