Digital Commons Network^™

Ox Creek Nutrients, Geochemistry And Streamflow Raw Data, Md Helal Ahmmed, Tyson L. Jeannotte, Taufique H. Mahmood

Datasets

This dataset contains hydrologic, nutrient, and hydrogeochemical observations collected from the Ox Creek Watershed, a cold-region agricultural watershed in North Dakota, from 2024 to 2026. The dataset includes laboratory-measured nutrient concentrations and major hydrogeochemical constituents from collected water samples and in situ water-level measurements. The observations span contrasting hydrologic conditions, including spring snowmelt and summer rainfall-runoff events. The dataset supports investigation of how changes in runoff regimes, hydrologic pathways, and hydrogeochemical conditions influence nutrient mobilization and transport in cold-region agricultural watersheds.

Novel Suspensory Prey Capture By A Green Heron (Butorides Virescens) In A Florida Mangrove Habitat, Deby L. Cassill

Florida Field Naturalist

No abstract provided.

Fos Special Publications, Florida Field Naturalist

Florida Field Naturalist

No abstract provided.

Notes On The Juvenile Plumage, Nest, And Eggs Of The Carolina Parakeet (Conuropsis Carolinensis), Benjamin E. Leese

Florida Field Naturalist

No abstract provided.

Friends Of Ffn, Florida Field Naturalist

Florida Field Naturalist

No abstract provided.

Repeated Occurrence And Apparent Site Fidelity Of Brown Booby (Sula Leucogaster) At Lake Okeechobee, Florida, Jennifer E. Chastant, Anna M. Swigris, Michael J. Baranski, Daniel A. Marchio

Florida Field Naturalist

No abstract provided.

First Report Of A Chigger From A Common Yellowthroat (Geothlypis Trichas) In Florida, Lawrence J. Hribar

Florida Field Naturalist

No abstract provided.

Ecu-Malnett V2, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone

Research Datasets

ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …

Exnovation And Digital Open Innovation: A Capability Pathway From Digital Dependency To Digital Sovereignty, Areeba Abid, Balqees Almessabi, Ajmal Waheed, Noor Ul Hadi

All Works

Digital transformation in emerging economies increasingly unfolds under conditions of dependence on externally controlled platforms, infrastructures, standards, and data ecosystems. Although digital sovereignty has become a prominent policy objective, the institutional processes through which public organizations move from digital dependency toward greater governance autonomy remain insufficiently theorized. To address this gap, this study develops a conceptual framework through a concept-driven hermeneutic narrative review to explain how digital open innovation can support the development of digital sovereignty. The framework conceptualizes digital sovereignty as an institutional governance capability that emerges through a sequential process rather than as a static policy objective. Specifically, …

Preliminary Data: Old Trails Fire--Field Reconnaissance (Rpd1195), Rapid, University Of Washington

Spokane Wildfire Academic Consortium Repository

The .ZIP file contains preliminary field reconnaissance findings from the Old Trails Fire in Spokane, Washington, including geospatial datasets and the source code used to support the project's interactive web map. The data and observations reflect work completed through September 4 and should be considered preliminary. As additional analysis and information become available, updates will be published as new records in the Wildfire Consortium Repository.

Access the interactive map: https://designsafe-ci.github.io/rapid-fire-field-reconnaissance/

Access the DesignSafe-CI rapid-fire-field-reconnaissance GitHub repository: https://github.com/DesignSafe-CI/rapid-fire-field-reconnaissance/

Habitat Suitability For Breeding Gymnorhinus Cyanocephalus (Pinyon Jay) Depends On Tree Cover Heterogeneity Across Spatial Scales, Emily D. Macklin, Amy Seglund, Amanda E. Cheeseman

NRM Departmental Data Sets

These data were collected across Colorado between 2020 and 2024. The response variable represents Pinyon Jay colony presence locations and stratified random background locations evaluated at a 656-m radius spatial scale. 
See Macklin (2025) for more information: https://openprairie.sdstate.edu/etd2/1864/.

File types: .csv and .txt
File size: 64 KB

Primary file: Macklin 2025 Pinyon Jay Habitat Suitability Dataset
Supplemental file: Macklin 2025 Pinyon Jay Habitat Suitability Dataset Description

Efficacy Of Point Counts And Acoustic Surveys For The Declining Pinyon Jay, Micah E. Hunter, Emily D. Macklin, Amanda E. Cheeseman

NRM Departmental Data Sets

These data were collected on lands managed by the Bureau of Land Management Royal Gorge Field Office in 2023–2024. The detection/non-detection response variables represent detections of Pinyon Jays within a 300-m radius of point count locations and detections from corresponding acoustic recording units (ARUs).

File types: .csv and .txt
File size: 46 KB

Primary file: Pinyon Jay Multi-Method Occupancy Dataset
Supplemental file: Pinyon Jay Multi-Method Occupancy Dataset Description

Badlands Bombing Range 2024-2025 Surveys, Amanda E. Cheeseman

NRM Departmental Data Sets

This dataset contains terrestrial small mammal survey data collected at the Badlands Bombing Range, South Dakota, during summer 2024 and 2025. Surveys were conducted across mixed-grass prairie, sandy tableland, prairie dog colonies, and White River floodplain habitats using Sherman live traps, pitfall traps, and remotely triggered cameras. Data include trapping effort, species detections and captures, capture locations and associated capture data, and camera detections and were collected to document mammal diversity, evaluate habitat use, and support natural-resource management of the installation.

File type: csv
File size: 132 KB

Assessing Effects Of Woodland Treatment And Other Potential Drivers On Gymnorhinus Cyanocephalus (Pinyon Jay) Habitat Use During The Breeding Season, Emily D. Macklin, Amanda E. Cheeseman

NRM Departmental Data Sets

These data were collected on lands managed by Bureau of Land Management - Royal Gorge Field Office in 2023-2024. The response variable represents detections of Pinyon Jays within a 300-m radius of point count site locations. 
See Macklin (2025) for more information: https://openprairie.sdstate.edu/etd2/1864/.

File types: .csv and .txt
File size: 266 KB

Primary file: Macklin 2025 Pinyon Jay Spatial Occupancy Dataset
Supplemental file: Macklin 2025 Pinyon Jay Spatial Occupancy Dataset Description

Tolkien Fanzine Dataset, 1967-2017 [Bulk 1969-1979], Robinson Ensz, Gabriel Marotto, Aidan Oakey, Ivy Stanich, Ethan Thomas

Tolkien Resources

This dataset reveals the heretofore hidden contents of fan magazines (fanzines) related to the works of J.R.R. Tolkien that were created from 1969 through the publication of Tolkien’s The Silmarillion in 1977. It supplements the earlier Tolkien Fandom Review by S. Gary Hunnewell that discussed fanzines created through 1968. The dataset provides an item level index of the fanzines’ content and is intended to aid researchers in Fan Studies. It is accompanied by an inventory document that lists the fanzine titles and provides brief notes about some of them. Data was drawn from Marquette’s large holding of Tolkien fanzines, including …

Humboldt Wetland Restoration & Mitigation Geodatabase, Sylvia Van Royen

Spatial Data

This geodatabase brings together restoration and mitigation project data for wetlands and eelgrass in and around Humboldt Bay, alongside referential layers, such as historic saltmarsh extent, to support future restoration planning. The scope of the database is limited to projects that have restored tidal influence, created or restored tidal slough channels or wetland channels, restored wetland contours, or created off-channel ponds. Projects were excluded that only performed invasive plant removal, vegetation management, or fish passage barrier removal.

Project data was sourced from Coastal Development Permits and a collection of restoration project documents called the Low Tide Archive. All of the …

Historic Atlas Of Humboldt Bay And Eel River Delta (Updated), Aldaron Laird

Spatial Data

This dataset was developed as part of the Humboldt Bay / Eel River Delta Historic Atlas project. It is intended to give a general representation of spatial salt marsh changes over time. Historic maps through 1940 were digitized, these and aerial photos were utilized to identify active channels, centerlines, salt marsh, shoreline, reclaimed saltmarsh, riparian soils and active channels for each decade of available imagery.

The original 2007 data was merged and cleaned for ease of display and data transfer, within the “vector_merged” dataset of the geodatabase. The original individual feature classes can be found in the “vector_original” dataset - …

Toward Equitable Energy Futures: The Influence Of Digital Government And Financial Development On Clean Energy Justice, Martin Spraggon, Muhammad Hafeez, Sana Ullah, Chanyanan Somthawinpongsai, Zurul Aisya Osman, Sidra Sohail

All Works

Fossil fuel energy is the largest source of carbon emissions and the main driver of climate change and global warming. Therefore, the world is switching towards clean and green energy sources as an alternative to fossil fuel-based energy sources. Nevertheless, the equitable distribution of clean energy resources in urban and rural areas among every segment of society has become a concern for policymakers. Thus, urban-rural clean energy justice is an investigation topic that needs our attention. In this analysis, our main focus is on investigating the influence of financial development and digital government on clean energy justice using an advanced …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

All Works

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Uncovering Customer Sentiment And Brand Perception By Leveraging Large Language Models: A Case Study In The Automotive Industry, Sujith Samuel Mathew, Kadhim Hayawi, Faseela Abdullakutty, Neethu Venugopal, May El Barachi

All Works

Analyzing customer perceptions has become increasingly important in the automotive industry as it provides actionable insights into consumer satisfaction, preferences, and areas requiring improvement. This study proposes a novel perception analysis framework for automotive reviews using advanced Large Language Models (LLMs), including BERT, FLAN-XXL, and Mistral 7B, leveraging zero-shot learning to categorize reviews without task-specific training data. The framework follows a two-stage evaluation process, beginning with zero-shot perception classification and followed by a detailed topic-wise perception analysis. Model performance was evaluated using accuracy, precision, recall, and F1-score across reviews from five major automotive brands—Toyota, Kia, Honda, Nissan, and Hyundai. While …

Dustmambanet: A Hybrid Inceptionv3–State-Space Network For Robust Solar Panel Dust Detection, Kadhim Hayawi, Sakib Shahriar

All Works

To develop a robust, scalable vision-based model for automatic detection and quantification of dust accumulation on solar photovoltaic panels, overcoming limitations of existing convolutional and attention-based methods and supporting proactive maintenance. We propose DustMambaNet, a hybrid model that consists of a pretrained InceptionV3 convolutional neural network as a feature extractor and two selective state space sequence modules. The state space modules use gated depthwise convolutions to represent long-range spatial dependencies that are of linear complexity, after rearranging spatial features to form sequences. The network provides a binary classification of dust with a severity index (DSI) and a continuous one. All …

Deployment-Oriented Evaluation Of Temporal And Spatial Forecasting Approaches For Electric Vehicle Charging Demand In Energy Systems, Maher Alaraj, Eyob Solomon Getachew

All Works

Accurate short-term electric vehicle (EV) charging demand forecasting is important for charging infrastructure operation, grid management, and energy-system planning. This study presents a deployment-oriented and reproducible evaluation of temporal, spatial, and unified spatio-temporal forecasting approaches for day-ahead EV charging demand prediction. Using publicly available charging-session data aggregated at hourly resolution across ZIP-code regions, we compare persistence and ARIMA baselines, XGBoost, Long Short-Term Memory (LSTM) networks, Graph Convolutional Networks (GCNs), and a unified GCN+LSTM architecture under a consistent preprocessing pipeline, leakage-free validation protocol, and rolling-origin evaluation framework. For the Boulder ZIP-code dataset considered in this study, temporal information provided the dominant …

Light-Wdef: Lightweight Deepfake Evaluation Framework For Efficient And Reliable Visual Detection, Muhammad Yousaf, Sarwar Shah Khan, Babar Shah, Muhammad Shehzad Khan, Muhammad Awais Bacha, Mian Saeed Akbar, Ijaz Ali, Fernando Moreira

All Works

Deepfake technology synthesizes and manipulates digital content to produce artificial videos in which an individual’s identity, facial appearance, or actions are convincingly altered. These forgeries can be highly realistic, posing serious risks to trust in digital media and undermining personal reputation, dignity, and the integrity of public discourse. This paper presents the Lightweight Deepfake Evaluation Framework (Light-wDEF)– a resource-efficient, reproducible framework tailored for large-scale evaluation of visual deepfake detectors on modest hardware. Light-wDEF combines deterministic frame sampling, batched MTCNN face extraction, streaming append-only storage with lazy offset indexing, online Albumentations augmentations, stratified splits with class-aware sampling, and robust training (AdamW, …

The Impact Of Partner Support On Vaginismus Treatment Outcomes: A Mixed Methods Study, Anna R. Ames, Alisha H. Redelfs, Nana Mensah

ScholarsArchive Data

A mixed-methods design was used to gain a nuanced understanding of how partner support affects the experiences of people with vaginismus.

Study aim: To guide partners of people with vaginismus in understanding how they can best help their partners with their vaginismus experience. 

• Quantitative Data: cross-sectional survey to reveal correlations in the relationship between vaginismus treatment outcomes and partner support [Available]
• Qualitative Data: Individual interviews to explore specific ways that people with vaginismus receive, or want to receive, support from their partners and the significance that they place on partner support  [Not Available]

Survey Data
Survey participants were recruited in …

Dataset For Effects Of Demineralization Protocols On Hydrogen And Oxygen Isotope Ratios In Fossil Bone Collagen, Linda M. Reynard

Geosciences Research Data

This dataset accompanies a peer-reviewed publication. The effect of three commonly used preparation protocols on bone collagen d2H and d18O values was investigated, using six generally well-preserved faunal bones (cattle, bison, horse) ranging in age from the Holocene to Late Pleistocene. The treatments tested were ethylene diamine tetraacetic acid demineralization without gelatinization and hydrochloric acid demineralization with and without gelatinization. There were small d2H changes noted by method; however, given the expected water isotopic variation among labs this should be an insignificant source of inter-lab variation.

Data For Effect Of Simple Motor Repetition On Inverted Pendulum Control: Exploring Age-Related Variations, Kimia Kiani, Qiushi Fu

Research Data and Datasets

This dataset contains data from a human motor-control experiment investigating how prior motor repetition influences subsequent control and learning of a virtual inverted-pendulum task in young and older adults. Participants performed the inverted-pendulum task using a robotic interface and completed either force-based or movement-based repetition training between task blocks. The dataset includes measures of pendulum control performance, task progression, training condition, hand, and age group. These data support the study “Effect of Simple Motor Repetition on Inverted Pendulum Control: Exploring Age-Related Variations.”

Replication Stata File For The Article "Charlie Kirk's Assassination And Cross-Partisan Dialogue", Andrew Scott Bledsoe, Susan Turner Haynes

Faculty Works

No abstract provided.

Droplet Clustering Signatures Of Entrainment In Clouds, Suryadev Pratap Singh, Michael Larsen, Jesse Anderson, Hamed F. Sadi, Jae Min Yeom, Will Cantrell, Raymond A. Shaw

Michigan Tech Research Data

The spatial distribution of cloud droplets is relevant for various microphysical processes, such as condensational growth, collision-coalescence, riming and aggregation, and radiative transfer. Cloud droplets and other hydrometeors exhibit clustering at dissipation scales and above due to turbulence-induced preferential concentration. Here, we investigate the role of entrainment in modulating the scale-dependent clustering of cloud droplets and explore how this mechanism differs from inertial clustering. We have performed experiments in the Pi Convection-Cloud Chamber, and imaged 2-D droplet fields in the cloud-top region where dry-air entrained through an open cylindrical flange leads to an entrainment-mixing process. Captured images are processed to …

Theological Truths That Divide Us And Engaging In Disagreement Well, David M. Westfall

Faculty Work Comprehensive List

""Only when we locate our differences within the shared context of God’s will to unite all things in his Son can we “get with the program” and start to relate in love, allowing our objective identity in him to exert continual pressure on our relationship.""

Posting about ­­­­­­­­how pursuing unity is part of Christian transformation from In All Things, an online hub that offers insight into maintaining and faithful and orthodox Reformed Christian worldview while fearlessly engaging in every aspect of contemporary life – until all is made new.

Dataset For 'Variability Of Howitzer Artillery Infrasound Recorded In Little Cottonwood Canyon, Utah', Owen A. Walsh, Jeffrey B. Johnson

Boise State University Infrasound Data Repository

These data are a supplement to the MS thesis, “Variability of Howitzer Artillery Infrasound Recorded in Little Cottonwood Canyon, Utah”, by Department of Geosciences student, Owen Walsh. Data provided are for howitzer artillery infrasound signals recorded by six infrasound microphones (infraBSU v2) from March 22 through April 5, 2023 in Little Cottonwood Canyon, Utah, USA. This dataset consists of 142, 20-second signals sampled at 100 Hz. Data provided have been filtered using a 0.5 Hz (2-second) highpass, acausal, 2-pole butterworth filter.