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- Type:
- Dataset
- Description/Abstract:
- This dataset contains an original article, "Maya Pottery Red: Hue as a Perceptual Prior for Object Detection in Remote Sensing for Drone-based Areal Survey of Maya Settlements" and supplementary material for the article, including results and source data.
- Creator/Author:
- Britton, Benjamin; Dunning, Nicholas, and McLellan, Alec
- Submitter:
- Benjamin Britton
- Date Uploaded:
- 03/22/2026
- Date Modified:
- 06/01/2026
- Date Created:
- 2026-03-31
- License:
- Open Data Commons Attribution License (ODC-By)
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- Type:
- Article
- Description/Abstract:
- As I have previously shown, Alexandre Brongniart established a coherent science of ceramics. By the mid-nineteenth century, Brongniart had popularised the term "la céramique" as a widely-applicable name for the field of pottery and porcelain making, and other related arts. In the Twentieth Century, ceramic manufacturing became increasingly technical. The inclusive field of artisans and industrialists that Brongniart had once envisioned was fracturing. Voices called for the separation of pottery making from experimental, industrial ceramics and the meaning of the term “ceramics” was hotly debated. Numerous etymologies were traced, but, as the predominant language of science transferred from French to English, none of the twentieth-century authors recognized Brongniart’s key role in the invention of the term. Critically, this language debate coincided with and reflected the global politics, nationalism, and warfare of the first half of the Twentieth Century.
- Creator/Author:
- Carr-Trebelhorn, Julia
- Submitter:
- Julia Carr-Trebelhorn
- Date Uploaded:
- 03/09/2026
- Date Modified:
- 03/09/2026
- Date Created:
- 2026-03-09
- License:
- Attribution-NonCommercial-NoDerivs 4.0 International
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- Type:
- Article
- Description/Abstract:
- Taking on the task of ordering the sciences related to pottery and clay-based objects, natural historian and porcelainier Alexandre Brongniart sought a new way of describing the ancient practice. Early in his forty-seven-year career as director of the Sèvres Porcelain Manufactory, Brongniart developed a research center for the advanced study of pottery and porcelain making. Brongniart recognized that an inclusive and distinct term for the field was necessary, but it had to be introduced carefully, so that it was welcomed rather than rejected as presumptuous. Through close reading of Brongniart’s writings, as well as contemporary periodicals and the texts of other authors, the development of the word “ceramic” – originally introduced by Brongniart and his associates in French as “la céramique” – can be traced closely. I show that this was a deliberate, methodical, and years-long effort to create a durable, comprehensive term.
- Creator/Author:
- Carr-Trebelhorn, Julia
- Submitter:
- Julia Carr-Trebelhorn
- Date Uploaded:
- 03/09/2026
- Date Modified:
- 03/09/2026
- Date Created:
- 2026-03-09
- License:
- Attribution-NonCommercial-NoDerivs 4.0 International
-
- 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
-
- 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
-
- 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
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- 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
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- Type:
- Dataset
- Description/Abstract:
- 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.
- Creator/Author:
- Kilroy, Mary
- Submitter:
- Mary Kilroy
- Date Uploaded:
- 05/24/2025
- Date Modified:
- 05/24/2025
- License:
- Attribution 4.0 International
-
- Type:
- Dataset
- Description/Abstract:
- 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.
- Creator/Author:
- Matter, Stephen F.
- Submitter:
- Stephen F. Matter
- Date Uploaded:
- 05/21/2025
- Date Modified:
- 06/16/2025
- Date Created:
- 2024
- License:
- Open Data Commons Attribution License (ODC-By)
-
- Type:
- Dataset
- Description/Abstract:
- 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 version (1.3) 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 and Dataset 1.3 along with a NSS Dictionary 1.2&1.3 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.
- Creator/Author:
- Millard, Matthew; Kovac, Igor, and Ivanov, Ivan Dinev
- Submitter:
- Ivan Ivanov
- Date Uploaded:
- 05/12/2025
- Date Modified:
- 06/04/2026
- Date Created:
- 2025-04-18
- License:
- All rights reserved