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208 results for “Research System”
Research Data for System test results for the R290 systems
<p>This dataset provides the research data system test results of R-290 (propane) systems. In the TRI-HP project, a new heat pump system with R-290 was integrated into multiple renewable energy sources. There were two experimental campaigns. After analysing the results of the first experimental campaign, the prototype was further improved. The second experimental campaign was conducted with the improved prototype.</p> <p>The detailed information can be found in ZENODO:</p> <p><a href="https://zenodo.org/record/7324246">Refined heat pump design and results of final testing</a></p> <p><a href="https://zenodo.org/record/7285499">Critical review of heat pump prototype operation and required modications</a></p> <p><a href="https://zenodo.org/record/5937065">Experimental results of dual source heat exchanger</a></p> <p>The background of this dataset:</p> <p>During the first experimental campaign, the heat pump prototypes were tested in the laboratories of the project<br> partners in static conditions. These conditions are defined in standards like, for example, EN 14511 [1], providing<br> specific temperatures on the evaporator and condenser sides. These tests enable us to characterize the COP and<br> EER in the nominal conditions of the equipment. However, when such heat pumps are installed in a real building<br> in a specific climate, they will be subject to varying conditions, depending on the load, the weather along<br> the seasons, intermediate storage vessels etc. For this reason, it is important also to carry out</p> <p>dynamic tests.<br> The dynamic tests presented in this deliverable were performed in two laboratories, following the principles of the<br> hardware-in-the-loop where part of the systems is installed in the lab (e.g. the heat pump), and part of the systems<br> is simulated and emulated to provide to real installed systems the response of the virtual element (e.g. building,<br> ground source, weather, solar thermal and PV). The full methodology and setups are described in detail in the<br> deliverable D7.1 [2].<br> The solar-ice system was tested in SPF-OST facilities in Rapperswil, Switzerland. The applied methodology was<br> the Concise Cycle Test (CCT), where 7 individual days along the year for the given climate of Bern were selected.<br> The complete system includes solar thermal, ice storage and water storage. The system was assembled in<br> the laboratory, and the actual setup is described in deliverable D7.3 [3]. The electrical part, including<br> household electricity, PV and the battery, will be tested for the CO2 slurry system due to time constraints since this<br> part does not affect the thermal behaviour of the installed system. The full efficiency of the propane solar-ice<br> slurry system, including the electrical part, will be provided in D7.9, "Demonstrated energetic performance and cost<br> competitiveness of systems".<br> The dual-source system was tested in the IREC laboratory SEILAB in Tarragona, Spain. The methodology is slightly<br> different since the selected periods are not individual days but a series of four consecutive days in<br> the summer and winter seasons. The overall system includes storage tanks, a geothermal loop as well as<br> Photovoltaics (PV) and an electrical battery. The assembled system and the lab setup were described in <br> deliverable D7.2 [4].<br> The present deliverable compiles the results of these two series of dynamic experiments with the propane heat<br> pumps. The results of the solar ice system are first analyzed in section 2, including a summary of the laboratory<br> setup and the control strategies used. The same structure is followed for the dual source system in section 3.<br> 2 Deliverable D7.4</p>
Research data supporting: "Machine learning of microscopic structure-dynamics relationships in complex molecular systems"
<p>This repository contains the set of data and the code to reproduce the results shown in "Machine learning of microscopic structure-dynamics relationships in complex molecular systems" published on Machine Learning: Science and Technology (DOI: 10.1088/2632-2153/ad0fa5).</p>
Autonomous millimeter scale high throughput battery research system
<p>In this study, we present data from high-throughput cyclic voltammetry (CV) test, derived by our digital workflow, Auto-MISCHBARES, complemented by XPS analysis. This research is part of the pre-print publication named "Autonomous millimeter scale high throughput battery research system", showcasing Cathode Electrolyte Interphase (CEI) investigation. The electrolyte used for this experiment is 1M of LiPF6 solution in an Ethylene Carbonate (EC): Ethyl Methyl Carbonate (EMC) mixture with a 3:7 weight ratio, along with LFP as our electrode material. The CV tests were conducted through a high-throughput sequential process for two cycles, each with varying stop potentials. After the experimentation phase, XPS analysis was applied to characterize the synthesized CEI.</p>
Data and code from: Unoccupied aerial systems adoption in agricultural research
<div> </div> <p>This repository contains data and code supporting the findings of the study on the adoption of Unoccupied Aerial Systems (UAS) in agricultural research as reported by Lachowiec et al (2024) in The Plant Phenome Journal.</p> <p>We collected data through an online survey as well as through in person interviews.</p> <div> <h2>Description of Repository Contents</h2> </div> <div> <h3>Data</h3> </div> <p>Data are in the <code>/data</code> directory:</p> <ul> <li><code>Ag_Drones_Codebook_14Jun2023.pdf</code>: Codebook providing detailed descriptions of survey questions and coding schemes. This contains detailed descriptions of the content of the two CSV files listed below.</li> <li><code>Results_Ag_Drones_2021_Survey.csv</code>: This is raw survey data collected from agricultural researchers regarding their use of UAS technology.</li> <li><code>countries_code.csv</code>: Country codes used in the survey data for respondent location.</li> <li><code>interviews/</code>: A directory containing interview transcripts and summary provided as both Microsoft Word and plain text (Markdown) formats, specifically: <ul> <li>Notes from nine one on one interviews named <code><interviewee last name>)UAS_Interview.[md|docx]</code></li> <li>A summary document, <code>Feldman_AG2PI_InterviewSummary_2022-08-10.docx</code>.</li> </ul> </li> </ul> <div> <h3>Code</h3> </div> <p>Code used to process data and generate the manuscript's analysis and figures.</p> <ul> <li><code>data_code.R</code>: R Script for preprocessing and cleaning the survey data.</li> <li><code>dataAnalysis.R</code>: R script for statistical analysis and visualization of survey results.</li> </ul> <div> <h2>Citing this work</h2> </div> <p>This repository contains data and code to support the manuscript:</p> <blockquote> <p>Lachowiec, J., Feldman, M.J., Matias, F.I., LeBauer, D., Gregory, A. (2024). Unoccupied aerial systems adoption in agricultural research. Zenodo. The Plant Phenome Journal Volume(Issue), pages 00. doi:DOI</p> </blockquote> <p>If you use the data or code from this repository, please also cite:</p> <blockquote> <p>Lachowiec, J., Feldman, M.J., Matias, F.I., LeBauer, D., Gregory, A. (2024). Data and code from: Unoccupied aerial systems adoption in agricultural research. Zenodo. doi:10.5281/zenodo.10573428</p> </blockquote> <p>And consider contributing cleaned data and code to this repository.</p> <div> <h2>Acknowlegements and Support</h2> </div> <p><strong>Acknowledgments</strong></p> <p>We thank all survey respondents for their participation. We acknowledge the Montana State University HELPS lab for aiding in the development and implementation of the survey.</p> <p><strong>Funding</strong></p> <p>This research was supported by the intramural research program of the U.S. Department of Agriculture, National Institute of Food and Agriculture, Agricultural Genome to Phenome Initiative (2020-70412-32615 and 2021-70412-35233). The findings and conclusions in this preliminary presentation have not been formally disseminated by the U. S. Department of Agriculture and should not be construed to represent any agency determination or policy.</p>
Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"
<p>Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019</p>
Search results for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)
<p>This datasets is the result of the search for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Topics, approaches and contributions of research about CRIS
<p>The size of the diamonds indicates the amount of articles belonging to each category.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Number of articles included during the search and qualitative evaluation process of the study
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Topics covered in the analyzed articles
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Process of systematic mapping
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications per country
<p>Publications per country. Map based on longitude and latitude. The circle’s colors show each country, while their size indicates the number of articles.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications by year and research areas
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Contributions of the studies
<p>The size of the rectangles shows the amount of articles in each category.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Research approaches
<p>The size of the diamonds shows the number of articles that belong to each category.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"
<p><strong>Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em></strong></p> <p>Dear reader,</p> <p>reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em>. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below.</p> <p>The research data covers the following files:</p> <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> <p>Meta-data files are provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep).</p> <p>Literature:</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p>Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737<br><br><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Figure 2. Some screenshots from the software system-Design and Development of a Software System for Swarm Intelligence Based Research Studies
<p>All of the mentioned operations can be performed easily by using the provided controls over<br> the related interfaces – windows of each algorithm. It is also important that each algorithm interface<br> is supported by visual controls to view obtained results with typical iteration-based graphics or<br> problem oriented visual elements. For instance, resulting graph structures are automatically shown<br> by the algorithm interfaces after solving some specific, popular problems like Travelling Salesman<br> Problem (TSP), Vehicle Routing Problem (VCP)…etc. Visually improved using features and<br> functions of the software system are critical aspects to provide more effective and useful platform to<br> perform SI based research studies better.<br> Related to the designed and developed software system, some screenshots from the software<br> system [interfaces of two algorithms (IWDs and ABC)] are represented in Fig. 2.</p>
Supplementary material for research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history"
<p>This public repository contains mainly datasets generated and analyzed during the current study, closely linked to the research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history". Furthermore, the repository contains additional figures and deep dives on the methodological background the research article was built on.</p>
Data sets for "MELISSA: System description and spectral features of pre‐ and post‐midnight F‐region echoes. Journal of Geophysical Research: Space Physics" by Rodrigues et al.
<p>Observations used in the study "Rodrigues, F. S., Zhan, W., Milla, M. A., Fejer, B. G., de Paula, E. R., Neto, A. C., et al ( 2019). MELISSA: System description and spectral features of pre‐ and post‐midnight <em>F</em>‐region echoes. <em>Journal of Geophysical Research: Space Physics</em>, 124. <a href="https://doi.org/10.1029/2019JA027445">https://doi.org/10.1029/2019JA027445</a>."</p> <p>The uploaded files include the RTI maps measured by the MELISSA radar system between 2014 and 2018 (.tif files). They also include values of SNR versus local time and height and the spectra presented in the manuscript (.mat files).</p> <p>Please, see README.txt for additional details.</p>
FIGURE 1 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 1. Simplification of the center of head movement as a joint in extinct Temnospondyli amphibian when biting. Elaborated from the original image (en.wikipedia.org/wiki/File:Jammerbergia_formops.jpg). Under license: CC BY-SA 3.0 (creativecommons.org/licenses/by-sa/3.0/).
FIGURE 4 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 4. Von Mises stress distribution in the skull for the Static Analysis in FEA in cases 1A, 2A, 3A, 1B, 2B and 3B.
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International Brain Laboratory public data
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OpenNeuro
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