University of South Dakota Showcase

This dataset supports the study “Eco-efficiency of Room Air Conditioner Units in a Dengue-Prone Climate: A Comprehensive Life Cycle Analysis Integrating Epidemiological Perspectives.” It contains the inputs, calculations, and model outputs used to investigate the relationship between indoor thermal conditions, room air-conditioner energy consumption, environmental impacts, household costs, and dengue transmission risk. The dataset covers eight dengue-endemic cities in South and Southeast Asia: Bangkok, Cebu, Chennai, Colombo, Dhaka, Ho Chi Minh City, Jakarta, and Yangon. It includes climate and dengue observations used for model development, physics-informed machine-learning predictions of the indoor basic reproduction number, estimated hospitalized secondary dengue cases per household, Variable Cooling Degree Day energy calculations, life-cycle assessment results, environmental externality and consumer-cost estimates, and integrated eco-efficiency indicators. Scenarios span air-conditioned and non-air-conditioned living spaces, indoor temperatures of approximately 18–30 °C, different mosquito densities, vector-control interventions, and national-grid and decentralized-solar electricity supplies. Uncertainty ranges generated through Monte Carlo analysis are also provided where applicable. These data enable reproduction and further analysis of the climate–cooling–dengue nexus and can support research on climate-resilient cooling, household energy policy, environmental sustainability, and vector-borne disease prevention in tropical regions.

This dataset contains the experimental, analytical, and bioinformatic data supporting the study "Living commensal biofilms for corrosion prevention." It provides a comprehensive, multimodal evaluation of microbiome engineering as a sustainable strategy to mitigate microbiologically influenced corrosion (MIC) on copper surfaces. Specifically, the data demonstrates the efficacy of the commensal bacterium Citrobacter sp. strain MICI21 in outcompeting the highly corrosive sulfate-reducing bacterium (SRB) Oleidesulfovibrio alaskensis G20. The repository encompasses the four-part ecological framework of microbioclaim, including competitive surface occupation, biological passivation, and commensal antagonism. Data contained in this repository includes: Electrochemical Data: Open Circuit Potential (OCP), Electrochemical Impedance Spectroscopy (EIS) Nyquist/Bode plots, and potentiodynamic polarization curves demonstrating 3- to 15-fold higher corrosion resistance. Surface & Structural Characterization: Scanning Electron Microscopy (SEM) micrographs, Confocal Laser Scanning Microscopy (CLSM) surface coverage profiles, nanoindentation metrics (stiffness/elasticity), and pit-depth quantifications. Physicochemical Analysis: Gravimetric (weight-loss) assay results, Extracellular Polymeric Substance (EPS) characterization, Energy-Dispersive Spectroscopy (EDS), X-ray Diffraction (XRD), and reactor H₂S quantification. Genomic & Bioinformatic Context: 16S rRNA analysis data and outputs from comparative genomic analysis (STRING-DB protein-protein interaction networks) of publicly available Citrobacter strains highlighting putative Type VI secretion system (T6SS) antagonism features. (Note: KBase Narratives #182783 and #182791 are hosted externally). Keywords: Microbiologically Influenced Corrosion (MIC), Biofilm Engineering, Living Coatings, Citrobacter, Sulfate-Reducing Bacteria (SRB), Electrochemical Impedance Spectroscopy (EIS), Biological Passivation, Type VI Secretion System (T6SS), Copper Corrosion

This dataset supports the manuscript **“Post-diffusion cooling effects on Hall-derived active lithium donor profiles in high-purity germanium.”** It contains the measured cooling histories, the raw and processed 77 K Hall-profile data, and the Jupyter notebooks used for the reported profile fits and figures. Eight HPGe coupons were studied: four nominal Li-diffusion temperatures (240, 260, 280, and 310 °C) and two post-diffusion cooling protocols (short and long cooling), with one coupon per condition (`n = 1`). Every diffusion hold was 30 min. Figures 1 and 2 in the manuscript are photographs/schematics of the samples and apparatus and therefore have no numerical data file in this dataset.

A collection to store datasets that are used for DSRS workshops

Data: The dataset contains employee sales performance. Each record represents an individual employee and their Division, Level of Education, Trainig Level, Work Experience, Salary, Sales. Research Hypothesis: Employees with higher levels of education, more training, and greater work experience tend to achieve higher sales performance and earn higher salaries than employees with lower education, training, or experience levels. Potential Analyses: This dataset supports several statistical analyses, including: descriptive statistics, correlation analysis, regressions analysis, ANOVA. Limitation: Small sample dataset, the source could not be independently verified.

Research hypothesis: Vehicles with larger engines (higher displacement and horsepower) and greater weight tend to have lower fuel efficiency (mpg). Dataset: The dataset contains information on several automobile models. Each row represents a unique vehicle, and the dataset includes key technical and performance variables such as: name, mpg, cylinders, displacement, horsepower, weight, acceleration, model_year, and origin. Use the data for: Exploratory Data Analysis (EDA), regression modeling, teaching, etc. To inteprete the dataset: Compare variables, look for patterns, trends, etc. Limitation: Small sample dataset, the source could not be independently verified.

Urban wastewater microbiomes are complex and temporally dynamic, offering valuable insight into community-scale microbial ecology and potential public health trends. However, existing wastewater-based studies often remain descriptive, lacking tools for predictive modeling. In this study, we introduce a digital twin framework that forecasts microbial abundance trajectories in urban wastewater using an interpretable generative model, Q-net. Trained on a 30-week longitudinal metagenomic dataset from seven wastewater treatment plants, the model captures temporal microbial dynamics with high fidelity (R² > 0.97 for key taxa; R² = 0.998 at the final timepoint). Beyond accurate forecasting, Q-net provides transparent model structure through conditional inference trees and enables simulation of realistic microbial trends under hypothetical scenarios. This work demonstrates the potential of digital twins to move wastewater microbiome studies from static snapshots to dynamic, predictive systems, with broad implications for environmental monitoring and microbial ecosystem modeling. More detail in https://www.biorxiv.org/content/10.1101/2025.07.21.666059v1

A wide range of chemicals have been found to cause negative chemotaxis in Escherichia coli. Here, we report Penicillin-Streptomycin (Pen-Strep) as a repellent that can repel different strains of E. coli. E. coli DH5α grown in LB broth. These bacterial solutions were exposed to 4% agar loaded with PenStrep as a drug releasing system in 12-well plates. Penicillin at 2500 units/ml (ug/ml for Streptomycin) concentrations (25%) of the PenStrep-Agar loading system were prepared for chemotaxis evaluation. Repelling of the E. coli was clearly observed via microscopy within 30 mins of the exposure. Moreover, continuous video acquisition and analysis by real-time recording over 8 hours showed a distinct region with a high density of cells in the region away from the repellant and vice-versa for the region close to the repellant. The optical density measurements indicated a considerable reduction in cell aggregation near the repellent, while a majority of the cells were observed to be distributed farther away from the repellent area. Stimulated single cell trajectory tracking showed movement away from the repellant in the y-direction at a velocity of 0.017 µm/s when exposed to 25% PenStrep chemical gradient. Unstimulated cells showed normal motility with repeated run-and-tumble movement in random direction in the control groups. This study provides a proof of concept for high-throughput analysis of the selective chemotaxis behavior of the microbial community by microbiome engineering to achieve the goal of beneficial biofilm formation to inhibit biofilm formation by corrosive bacteria.

The challenges faced with bioscience lab protocols encourage the development of automation systems to come alongside technicians. Today’s experiments involve complex protocols with precise actions usually performed manually. Even simpler biologicals can be tedious and error-prone, as was seen during the COVID-19 pandemic and society’s demand for high-volume, rapid analysis. Moreover, reproducibility suffers from excessive variability and insufficient data, while low-income labs remain slow to adopt automation. To approach these challenges and more, we leveraged aspects of the Stanford Biodesign process to develop a modular lab automation system with open-source components. Our system’s modularity minimizes the need for multiple inflexible systems, instead allowing for removing individual components as needed. Modularizing the software architecture enables us to insert artificial intelligence and machine learning models, bringing greater value to our data. Soon, using open-source chatbots such as ChatGPT will assist in converting manual protocol steps to automated functions at a low cost. Combining all of these available, low-cost tools can ultimately bring more democratization and widespread adoption of bioscience automation.

Experimental studies exploring the effects of intranasal oxytocin are typically underpowered due to small samples. Open access to experimental data and procedures and the use of previously employed measures is critical to building more robust and replicable findings, especially in less studied areas of oxytocin research. In this paper, data is provided from a double-blind placebo-controlled crossover study exploring the effects of intranasal oxytocin (IN-OT: 24 IU) on social preference to romantic partners, parents, peers, and strangers. Young adults (N=44; 91% female) in committed dating relationships completed three phases of data collection including a screening survey followed by two laboratory visits. In addition to romantic partner-, and stranger attraction ratings, the data is the first to provide comparisons between attachment and social preference ratings to parents, close friends, and romantic partners under placebo and IN-OT conditions. The data also include differences by situational and life history factors known to moderate oxytocin effects. The detailed protocol, and dataflow can be accessed to verify the analysis and findings or to conduct a replication study. The standardized experimental design and common IN-OT protocol add to the capacity for a meta-analysis exploring oxytocin effects on partner preference, and may also be directly ported to existing or future studies with related questions to increase sample size and power.