Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
150
datasets available to search
ShareScore release 0.9.0
Dataset results
150 results for “system design”
Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository
<p>The data for a multivocal literature review on design patterns for AI-based systems.</p> <ul> <li>mlr-search-and-selection.xlsx: the results from the queried databases and search engines, the inclusion/exclusion process, and the backward and forward snowballing results</li> <li>mlr-results.xlsx: the final set of selected resources, the patterns extracted from them, and some analysis</li> <li>query-strings-google-and-google-scholar.txt: the individual terms of the search query (broken up for Google Scholar and Google Search)</li> </ul>
Data for Process Design and Energy Assessment of an Onboard Carbon Capture System with Boilers or Heat Pumps for Additional Steam Generation
<p>1. Supporting file includes main stream information used in Aspen HYSYS model, process simulations of boiler and heat pump for model construction.<br> 2. Supporting file also includes main information used in ProMAX model, process simulations of carbon capture process for model construction.</p>
Evaluation of Fresh and Hardened Properties of 3D-Printed Engineered Cementitious Composites (ECC) Designed for Sustainable and Resilient Infrastructure Systems
<p>3D concrete printing is a cutting-edge construction technique that has the potential to revolutionize the construction industry due to cost-saving in terms of labor and formwork costs, efficiency in construction, lower safety-related risks, and a higher degree of automation. However, several issues still make its adoption relatively slower on a large scale. Engineered cementitious composites (ECC), a class of ultra-high-strength concrete, can be a potential solution to some problems, such as reinforcement and durability. The preliminary phase of a Tran SET project focused on the design of 3D printable ECC, considering the concrete mix design proportions and their potential effects on fresh and hardened properties to achieve an optimized printable ECC mix. The type and content of various concrete ingredients such as cement, admixtures, aggregates, and fibers have considerable influence on the several properties in the fresh and hardened state. The replacement levels for cement were 0%, 50%, and 75% for mineral admixture, 10% for silica fume, and 0.4% for nano-clay by cement mass. Locally available fine aggregates were also used at 25% and 40% by mass of binder. In addition, the influence of fiber types and contents was also investigated. Two types of fibers, polyethylene (PE) and polyvinyl alcohol (PVA) fibers, were used at different levels, such as 0%, 1%, 1.5%, and 2% of the total volume of the mix. Moreover, four mixes, including FA50-MC, FA40SF10-MC, S50-MC, and FA40MK10-MC, were evaluated in terms of mechanical performance (flexural strength and direct tensile strength). The flowability of the ECC mixes was reduced with the incorporation of slag, metakaolin, higher aggregate content, and PE fibers. The 2% of both PVA and PE fibers had a reduction in compressive strength as compared to the low fiber content. Moreover, the PE-ECC exhibited superior tensile ductility as compared to the PVA-ECC due to the more fiber bridging by the PE fibers at the crack interface.</p>
Design Of A Condition Monitoring System (CMS) For A Gas Compressor Using Shell Bonny Terminal As A Case Study
<p><strong>Subcontracted</strong> <strong>Researcher</strong> <strong>&</strong> <strong>Field</strong> <strong>Electrical</strong> <strong>and</strong> <strong>Electronics</strong> <strong>Engineer</strong>, <em>Shell</em> <em>Bonny</em> <em>Terminal, Nigeria</em>— 2013</p> <p>I served as a Hooks Electric subcontracted researcher and field electrical and electronics engineer at Shell Bonny Terminal, spearheading a groundbreaking project to design and construct a state-of-the-art software condition monitoring system (CMS) for a gas compressor. In addition to my technical responsibilities, I also took charge of integrating this innovative design into the existing SCADA (Supervisory Control and Data Acquisition) infrastructure of the plant.</p> <p>Working collaboratively with a team of four talented engineers, I successfully managed and coordinated their efforts throughout the project. Together, we leveraged our collective expertise to develop and implement a fully functional software condition monitoring system. This collaborative approach ensured that our project benefitted from diverse perspectives and skill sets, leading to a comprehensive and robust solution.</p> <p>Utilizing my proficiency in LabVIEW, C#, native C, Java and ActiveX library, I led the development of a cutting-edge data acquisition (DAQ) device for CM system. This device seamlessly captured sensor readings from the live gas compressor in real-time, while the systemic algorithm I designed skillfully tracked false alarms using fallback sensors. By monitoring critical parameters such as ambient and self-temperature, axial speed, vibration, inflow pressure, and outflow pressure, we were able to enhance the system's reliability and performance.</p> <p>Furthermore, I effectively integrated the software condition monitoring system into the plant's existing SCADA infrastructure, ensuring seamless communication and compatibility with the overall plant monitoring and control system. This integration facilitated centralized data management and provided comprehensive insights into the gas compressor's operation, enabling proactive maintenance and efficient decision-making.</p> <p>By successfully managing the team and overseeing the integration process, I ensured that our project aligned seamlessly with the plant's existing infrastructure, resulting in an optimized and harmonized system.</p> <p>Through our collective efforts, we delivered a highly sophisticated and fully integrated software condition monitoring system that significantly improved the management and performance of the gas compressor at Shell Bonny Terminal.</p> <p>I have curated all the resource files that will enable anyone replicate our work. This is shared under the CC-by-4 license.</p> <p>The LabVIEW codes are documented in Work.zip file and all manuals and equipment specifications have been uploaded. I have created a test bench to help anyone quickly test the LabVIEW software in simulation mode. </p> <p>I have also provided a slide presentation that summarizes our work, including methodologies and results.</p>
Developing Deep Learning Approaches to Find and Classify Architectural Design Decisions in Issue Tracking Systems
<p>This upload contains three files:</p> <ol> <li>mongodump-JiraRepos_2023-03-07-16 00.archive: Archive containing the issue data pulled from the Jira API.</li> <li>mongodump-MiningDesignDecisions.archive: Archive containing the data of our deep learning models and the labelled issues.</li> <li>mongodump-MiningDesignDecisions-lite.archive: Similar to the archive above, except this one only contains the best trained model (BERT). Also, it does not contain any embeddings or other files.</li> </ol> <p>This archive contains the data of our deep learning models and the labelled issues (MiningDesignDecisions archive).</p>
Microelectromechanical-System-Based Design of a High-Finesse Fiber Cavity Integrated with an Ion Trap
<p>We present a numerical study of a microelectromechanical-system-based design of a fiber cavity integrated with an ion-trap system. Each fiber mirror is supported by a microactuator that controls the mirror’s position in three dimensions. The mechanical stability is investigated by a feasibility analysis, which shows that the actuator offers stable support of the fiber. The actuators move the fibers’ positions continuously with a stroke of more than 10μm, with mechanical resonance frequencies on the order of kilohertz. A calculation of the trapping potential shows that a separation between the ion and the fiber consistent with strong ion-cavity coupling is feasible. Our miniaturized ion-photon interface constitutes a viable approach to integrated hardware for quantum information.</p>
Data set - The design and evaluation of an integrated training load and injury/illness surveillance system in competitive swimming.
<p>Dataset for paper as per title above. </p>
Part design aiming at dataset generation for training a vision based quality monitoring system of a circular economy cell
<p>Part design aiming at dataset generation</p> <p> </p> <p>Related to the publication: Stavropoulos, P., Papacharalampopoulos, A., Athanasopoulou, L., Kampouris, K., & Lagios, P. (2022). Designing a digitalized cell for remanufacturing of automotive frames. <em>Procedia CIRP</em>, <em>109</em>, 513-519.</p> <p> </p> <p>Contents of the ZIP file: Different configurations as CATPart files</p> <p> </p> <p>It can open with the help of CATIA software</p>
Equipment Design for a Small Binary System Power Plant Using Geothermal Heating Fluid A Case Study
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Data for "Borealis: An Advanced Digital Hardware and Software Design for SuperDARN Radar Systems"
<p>Clyde River SuperDARN data for 2022-02-07, 18:00-20:00UT.</p> <p>Included are the RAWACF and FITACF files, the latter of which was used to produce Figure 6 in the partner paper: "Borealis: An Advanced Digital Hardware and Software Design for SuperDARN Radar Systems".</p> <p>Data can be processed further using the Radar Software Toolkit (RST: https://github.com/SuperDARN/rst), or plotted using pyDARN (https://github.com/SuperDARN/pydarn).</p>
dateset for "Design and Evaluation of an Efficient High-Precision Ocean Surface Wave Model with a Multiscale Grid System (MSG_Wav1.0)"
<p>Here is the dataset for the paper named "Design and Evaluation of an Efficient High-Precision Ocean Surface Wave Model with a Multiscale Grid System (MSG_Wav1.0)".</p>
Understanding Explainability during System Design for Safety Critical AI Systems
<p>Our research aims to explore the perspective of internal stakeholders on explainability</p>
SESMG Model Definitions: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the files is one SESMG model definition used for the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". Further information can be found in this publication. The file names indicate to which sensitivity analysis of the study the individual model definition belongs to. Used acronyms: "ng" = natural gas.</p>
SESMG Model Results: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the folders contains SESMG results for a sensitivity analysis of the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". More information can be found in this publication. Each folder contains two subfolders. The "cost-minimum" subfolder contains the results for financially optimized systems, and the "emission-minimum" subfolder contains the results for GHG emission-optimized systems. Within these subfolders, the results for different gradations of the respective sensitivity parameters are stored in separate sub-subfolders. The 01_reference_total_ghg_emissions folder has a slightly different structure. Since the results are not separated into financially and emissions-optimized scenarios, the results of different gradations are stored directly in the main folder of this sensitivity analysis.</p>
Dataset for Correct-by-Design Control of Parametric Stochastic Systems
Open the record for dataset details and reuse information.
Design and Clinical Evaluation of a Smartphone-based Low Vision Enhancement System
ClinicalTrials.gov study NCT03728660. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
The Impact of a Modified Probe Tip Design on Patient Perception of Discomfort on Probing and Clinical Parameters Using the Florida Probe® System
ClinicalTrials.gov study NCT03690479. IPD Sharing: NO. Countries: 1. Publications: 9.
Hospice and End-of-life Symptom Monitoring & Support Using an Automated System Designed for Family Caregivers
ClinicalTrials.gov study NCT02112461. IPD Sharing: NO. Countries: 1. Publications: 1.
Design Of WELL Being Monitoring Systems, Application in Autism
ClinicalTrials.gov study NCT02275455. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Investigation of the Effect of Deep Margin Elevation Technique on the Clinical Performance of Endocrown Restorations Produced by Computer-Aided Design and Computer-Aided Manufacturing (CAD/CAM) System
ClinicalTrials.gov study NCT06222918. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.