1,721,472 research outputs found
RDM Needs assessment
No full textThe complexity and privacy issues inherent in social science research data makes research data management (RDM) an essential skill for future researchers. Limited data management training has been developed to specifically address the needs of graduate students in the social sciences. To address this gap, this study used a mixed methods design to investigate the RDM awareness, preparation, confidence, and challenges of social science graduate students. A survey measuring RDM preparedness and training needs was completed by 98 graduate students in a college of education at a research university in the southern United States. Then, interviews exploring data awareness, knowledge of RDM, and challenges related to RDM were conducted with 10 randomly selected graduate students. All participants had low confidence in using RDM, but United States citizens had higher confidence than international graduate students. Most participants were not aware of on-campus RDM services, and were not familiar with data repositories or data sharing. Training needs identified for social science graduate students included support with data documentation and organization when collaborating, using naming procedures to track versions, data analysis using open access software, and data preservation and security. These findings are significant in highlighting the topics to cover in RDM training for social science graduate students. Additionally, population differences exist in RDM confidence and preparation so being aware of the backgrounds of students taking the training will be essential for designing student-centered instruction
TXST_University_Libraries_Circulation_Data_2015-2024
No full textThe Texas State University Library plays a critical role in supporting academic success and resource accessibility for the Bobcat community. We have a rich dataset that includes: Annual Circulation Borrowing Rates (FY2015–FY2024), Circulation Borrowing by Patron Group (FY2015–FY2024), and Circulation of Library Materials by Location (FY2017–FY2024). This data provides an opportunity to uncover patterns in resource usage, identify challenges, and propose evidence-based solutions. This dataset is published for the use of the 2026 TXST Open Datathon event. https://www.library.txst.edu/research-and-publishing/research-data-management/txstopendatathon.htm
Advancing Urban Building Energy Modeling: The Role of Hybrid Energy Modeling in Enhancing Energy Consumption Predictions
Full text linkUrban building energy modeling (UBEM) is essential for understanding energy consumption and developing sustainable policies at the city scale. However, current UBEM approaches overlook spatial and temporal interactions and lack generalizability across diverse urban contexts. This study introduces a hybrid framework that integrates physics-based simulations with machine learning based residual learning to enhance prediction accuracy using real energy consumption data. The methodology incorporates GIS-supported data collection and processing. Multiple ML models were applied to predict monthly consumption and validate their performance. Meanwhile, a physics-based model is used to simulate hourly energy consumption. The best performing ML model was later used for daily residual learning to calibrate physics-based simulation outputs. The framework was tested on residential buildings connected to the District Heating Network in Turin, Italy. Results showed LGBM achieved the highest performance with a R2 of 0.883 and a MAPE below 15% in most months. Residual learning reduced daily prediction error in 80% of cases, with up to 75% improvement in extreme cases. After model calibration, 65% of buildings achieved a daily MAPE below 30%, and 55% fell below 20%, demonstrating consistent error reduction across varied building types and consumption levels. This confirms the effectiveness of the hybrid approach in enhancing accuracy and reliability at the urban scale
Recommended from our members
2024 Open Access Summer Speaker Series
No full textVideo recording of the second session of the Open Access Speaker Series. This session focuses on the organizing and hosting of Texas State University's inaugural Open Datathon. The Datathon was intended to promote Open Data, engage with students and faculty, and provide an opportunity for students to create and share original research. This session was held virtually, and it was organized as part of the Open Access Symposium
An Italian Geoportal for Renewable Energy Communities
Full text linkThe development and implementation of a national geoportal designed to optimize the planning and management of integrated Renewable Energy Communities (RECs) is presented in this study. This innovative tool facilitates the identification of optimal energy system configurations by selecting available renewable resources and technologies and determining community membership based on assigned input parameters. These parameters include electrical load profiles, energy prices, renewable resource availability, technological characteristics, socio-economic conditions, and territorial constraints. A multi-objective optimization framework was employed to address energy, economic, environmental, and social priorities simultaneously. The methodology adopts a place-based approach, enabling the application of energy management and optimization models tailored to the specific characteristics of each case study and the corresponding input data. The proposed geoportal incorporates features such as flexibility, scalability, and applicability to real-world territorial contexts, while providing decision support to regional planners and stakeholders. Scalability was achieved through the integration and management of spatial and temporal datasets across varying scales. The study evaluates five scenarios, including the maximum renewable energy potential utilizing solar, wind, and biomass renewable energy sources (RES) technologies, and two REC scenarios emphasizing photovoltaic (PV) energy sharing between sectors, residential prosumers, and consumers. Performance metrics and indexes were employed to assess the energy, economic, environmental, and social benefits of RES generation, distribution, and sharing. The findings indicate that REC scenarios featuring energy sharing achieve higher levels of self-consumption and self-sufficiency compared to isolated configurations. Future iterations of the geoportal aim to extend its application to additional territories, thereby enhancing the self-sufficiency of Territorial Energy Communities (TECs) and advancing sustainable energy practices on a broader scale
Variational Neural Network Embedded with Digital Twins for Probabilistic Structural Damage Quantification
No full textQuantifying structural damage using online monitoring data is crucial for condition-based maintenance to ensure aviation safety. However, most data-driven methods hardly use accumulated domain knowledge, making it difficult to address parameter variability across different structures due to manufacturing as well as compromising result interpretability. To address these challenges, this study proposes a physics-decoded variational neural network for structural damage quantification and model parameter calibration. The innovation of this method lies in seamlessly integrating a reduced-order digital twin containing damage states and influencing parameters as a decoder within the variational neural network and training a data-driven physical feature extraction model using the variational inference. This architecture enables the individualized, real-time structural damage quantification and parameter calibration across an entire fleet, while accounting for uncertainties. Validation on typical damaged aeronautical panels demonstrates that the proposed method accurately predicts structural damage states and quantifies associated uncertainties, thereby ensuring high interpretability and accuracy. This approach is expected to be integrated into the airframe digital twin framework to enable condition-based maintenance across a fleet
FIGURE 1 in Complete mitogenome of Calliptamus barbarus Costa (Orthoptera: Acrididae) and its phylogeny in Acridoidea
No full textFIGURE 1. Circular map of the annotated mitogenome of C. barbarus. Protein coding and ribosomal genes are shown with standard abbreviations. Transfer RNA (tRNA) genes are indicated using the IUPAC-IUB single letter amino acid codes.Published as part of Ding, Xunhuan, Fu, Yun, Zhou, Xuan, Yang, Shubing, Cao, Yunmeng, Hou, Fuxiao, Liu, Xiaoli & Sun, Tao, 2022, Complete mitogenome of Calliptamus barbarus Costa (Orthoptera: Acrididae) and its phylogeny in Acridoidea, pp. 427-440 in Zootaxa 5213 (4) on page 430, DOI: 10.11646/zootaxa.5213.4.6, http://zenodo.org/record/738150
- …
