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  • 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

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

    Image thumbnail: 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)

  • Data to accompany “Monarch butterflies (Danaus plexippus) only use magnetic cues for migratory directionality with orientation re-calibrated by coldness”

    Dataset thumbnail: Data to accompany “Monarch butterflies (Danaus plexippus) only use magnetic cues for migratory directionality with orientation re-calibrated by coldness”
    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)

  • Varieties of International Cyber Strategies (VoICS): Text Analysis of National Cybersecurity Documents

    Dataset thumbnail: Varieties of International Cyber Strategies (VoICS): Text Analysis of National Cybersecurity Documents
    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 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.
    Creator/Author:
    Millard, Matthew; Kovac, Igor, and Ivanov, Ivan Dinev
    Submitter:
    Ivan Ivanov
    Date Uploaded:
    05/12/2025
    Date Modified:
    08/29/2025
    Date Created:
    2025-04-18
    License:
    All rights reserved

  • Support for Ukraine among the European Union (EU) Members after the Full-Scale Russian Invasion

    Dataset thumbnail: Support for Ukraine among the European Union (EU) Members after the Full-Scale Russian Invasion
    Type:
    Dataset
    Description/Abstract:
    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
    Creator/Author:
    Ivanov, Ivan
    Submitter:
    Ivan Ivanov
    Date Uploaded:
    04/27/2025
    Date Modified:
    03/03/2026
    Date Created:
    5-01-2024
    License:
    All rights reserved

  • ecology-and-evolution-2025-data

    Dataset thumbnail: ecology-and-evolution-2025-data
    Type:
    Dataset
    Description/Abstract:
    Raw data for: Siers, S.R., Mungaray, J.-C., Kastner, M. & Jayne, B.C. (2025) Hard to swallow: scaling relationships between the size of avian prey and the overall size and maximal gape of brown treesnakes. Ecology and Evolution (in revision). (BCJ corresponding author)
    Creator/Author:
    Jayne, Bruce
    Submitter:
    Bruce Jayne
    Date Uploaded:
    04/02/2025
    Date Modified:
    04/02/2025
    Date Created:
    2025-04-02
    License:
    Open Data Commons Attribution License (ODC-By)

  • Monarch butterflies (Danaus plexippus) only use magnetic cues for migratory directionality with orientation re-calibrated by coldness - data file

    Dataset thumbnail: Monarch butterflies (Danaus plexippus) only use magnetic cues for migratory directionality with orientation re-calibrated by coldness - data file
    Type:
    Dataset
    Description/Abstract:
    Data of monarchs subjected to righting response orientation trials under different artificial magnetic fields pre- and post-overwintering cold treatment.
    Creator/Author:
    Shively-Moore, Samuel
    Submitter:
    Samuel Shively-Moore
    Date Uploaded:
    03/25/2025
    Date Modified:
    05/21/2025
    License:
    Attribution-NonCommercial 4.0 International

  • methane emissions from natural gas streetlights in Cincinnati, Ohio

    Dataset thumbnail: methane emissions from natural gas streetlights in Cincinnati, Ohio
    Type:
    Dataset
    Description/Abstract:
    Methane emissions from natural gas streetlights. Measurements were made by Dr. Amy Townsend-Small and her students.
    Creator/Author:
    Townsend-Small, Amy
    Submitter:
    Amy Townsend-Small
    Date Uploaded:
    11/20/2024
    Date Modified:
    11/20/2024
    Date Created:
    2024-11-20
    License:
    Public Domain Mark 1.0

  • locations of natural gas streetlights in Cincinnati

    Dataset thumbnail: locations of natural gas streetlights in Cincinnati
    Type:
    Dataset
    Description/Abstract:
    Locations of natural gas streetlights in Cincinnati, Ohio
    Creator/Author:
    Townsend-Small, Amy
    Submitter:
    Amy Townsend-Small
    Date Uploaded:
    11/20/2024
    Date Modified:
    11/20/2024
    Date Created:
    2024-11-20
    License:
    Public Domain Mark 1.0

  • Goff MS Thesis Data and Code Chapter 1

    Dataset thumbnail: Goff MS Thesis Data and Code Chapter 1
    Type:
    Dataset
    Description/Abstract:
    Data and code for analysis of thermal constraints on flight for butterflies
    Creator/Author:
    Goff, Jennifer and Matter, Stephen F.
    Submitter:
    Stephen F. Matter
    Date Uploaded:
    09/01/2024
    Date Modified:
    09/01/2024
    Date Created:
    2024-6
    License:
    Attribution-NonCommercial 4.0 International