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  • G1 G-LiHT Yucatan Reprocessing Project - Ground-Filtered LAZ files

    Dataset thumbnail: G1 G-LiHT Yucatan Reprocessing Project - Ground-Filtered LAZ files
    Type:
    Dataset
    Description/Abstract:
    Reprocessed Imagery from G-LiHT dataset of Yucatan Peninsula see attached info
    Creator/Author:
    Britton, Benjamin
    Submitter:
    Benjamin Britton
    Date Uploaded:
    04/03/2026
    Date Modified:
    04/04/2026
    Date Created:
    2026-04-03
    License:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • EMS LAW - LEGAL LESSONS LEARNED (3rd Edition)

    Document thumbnail: EMS LAW - LEGAL LESSONS LEARNED (3rd Edition)
    Type:
    Document
    Description/Abstract:
    Recent court decisions on EMS Law (3rd Edition).
    Creator/Author:
    Bennett, Lawrence
    Submitter:
    Lawrence Bennett
    Date Uploaded:
    03/31/2026
    Date Modified:
    07/06/2026
    Date Created:
    2026-03-31
    License:
    Public Domain Mark 1.0

  • Maya Pottery Red: Hue as a Perceptual Prior for Object Detection in Remote Sensing for Drone-based Areal Survey of Maya Settlements

    Dataset thumbnail: Maya Pottery Red: Hue as a Perceptual Prior for Object Detection in Remote Sensing for Drone-based Areal Survey of Maya Settlements
    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)

  • After Brongniart: Defining the Word “Ceramic” and the Persistence of “Burnt Stuff”

    Article thumbnail: After Brongniart: Defining the Word “Ceramic” and the Persistence of “Burnt Stuff”
    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

  • Alexandre Brongniart and the Arts of the Earth: Coining “La Céramique”

    Article thumbnail: Alexandre Brongniart and the Arts of the Earth: Coining “La Céramique”
    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

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

    Dataset thumbnail: 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

    Document thumbnail: 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

  • Q2000 Deep Learning Model Package

    Dataset thumbnail: 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

    Student Work thumbnail: 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

  • Figure 2 572918348R MRTX1133 Extended Time Point Full Length Blots

    Dataset thumbnail: Figure 2 572918348R MRTX1133 Extended Time Point Full Length Blots
    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