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141 results for “decision support system”
Presentation of e-DSS: the e-SAFE Decision Support System
<p>e-DSS (e-SAFE Decision Support System) is a simple and intuitive software tool conceived to support professionals during the preliminary co-design process of an e-SAFE building renovation. The users can assess the energy performance of the building in its current state. Then, they are guided in the creation of a renovation project with the e-SAFE solutions (e-PANEL, e-EXOS, e-CLT, e-THERM): the tool facilitates the choice of the most suitable solution in terms of energy and seismic improvement, while also calculating energy savings, environmental benefit, and payback time. The tool is intended for all types of non-historical buildings and is available at <a href="https://esafe.eng.it/">https://esafe.eng.it/</a>.</p> <p>e-DSS has been created in the framework of Work Package 4 in the H2020 "e-SAFE" project. A final updated and refined version will be released at the end of the project. This video wants to show the tool with its different functions, including input data and results. </p> <p>For further information please contact: marilena.lazzaro@eng.it.</p>
Raw (main) dataset for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Raw dataset for farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
The Application of a Snowpack Runoff Decision Support System for Rain-on-Snow Events Dataset
<p>This work was funded by the State of Nevada - Department of Transportation award No. P296-22-803 and UCAR COMET Outreach Program SUBAWD004566. </p>
Samples of Rectified Transfemoral Sockets with Fuzzy-Logic-Based Decision Support System
<p>This dataset contains sample rectified transfemoral sockets as an output of the fuzzy-logic DSS.</p> <p>This work was supported by the EU Horizon2020 research and innovation project SocketSense, No 825429.</p> <p>Relevant paper DOI: <a href="https://doi.org/10.3390/s21113743">https://doi.org/10.3390/s21113743</a></p>
Curated dataset for analysis for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Dataset created from farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
Appendices for the paper "Decision Support Systems Adoption in Pesticide Management"
Open the record for dataset details and reuse information.
Figure 4 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 4: Crop sequence in rotation 3: standard sugar beets variety "Mixer"; Cereals; WOSRWWOSR; Cereals; Oil radish; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 8 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 8: SBN population dynamic under the semi-tolerant variety "Rosalinda" followed by non-host crops at The SBN initial population Pi (eggs g−1 soil) = 4, Tolerance (T) = 0.273, proportion of the population survived (s) = 0.35 and reproduction factor (Rf) = 3.8. The suggested number of waiting years by SBN-Watch to the next "Rosalinda" crop is four years (Pf <T).
Figure 2 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 2: Crop sequence in rotation 1: standard sugar beets variety "Mixer"; Cereals; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 3 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 3: Crop sequence in rotation 2: standard sugar beets variety "Mixer"; Cereals; WOSR; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 1 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 1: A screenshot of the user interface showing a selected crop rotation and the estimated final SBN population (Pf) values, sugar yield (tonnes/ha), income (SEK/ha) and the reproduction factor (Rf) values.
Figure 9 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 9: (A) relationship between initial SBN population (Pi eggs g−1 soil) and reproduction factors (Rf) of three sugar beets varieties estimated by SBNWatch; (B) relationship between initial SBN population Pi (eggs g−1 soil) and reproduction factors (Rf) of four sugar beets varieties sown in microplots in 2013–2014 (n = 4). The bars represent means of Rf ± Sd.
Figure 7 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 7: Crop sequence in rotation 6: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; Oil radish; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 5 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 5: Crop sequence in rotation 4: tolerant sugar beets variety "Julietta"; Cereals; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 6 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 6: Crop sequence in rotation 5: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Intentional Forgetting in Organizations: The Positive Effects of Decision Support Systems on Mental Resources and Well-being
<p>This dataset contains raw data collected in an experimental study at the University of Muenster, Germany. The study is part of a larger research project and examined “intentional forgetting” effects in a simulated sales planning scenario. Intentional forgetting was operationalized via computer-based decision support system that enabled users to forget decision-relevant background information. We assumed that such intentional forgetting not only enhances decision quality but also decreases strain of decision makers and releases memory capacities for additional tasks.</p>
"Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.
<p> "Decision support systems halve fungicide compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.https://doi.org/10.1038/s43247-021-00291-8 | www.nature.com/commsenv</p> <p>Dataset includes the results of 80 independent experiments reported in 22 articles and it has a dimension of 329 rows x 42 columns. Further information in "Description Supplementary Data 1.pdf" file. </p> <p>This dataset was assembled including also the data from the publication Agronomy 2020, 10(4), 560; https://doi.org/10.3390/agronomy10040560.</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
Short rotation woody crop decision support system
<p>From http://edis.ifas.ufl.edu/fr169</p> <p>Plantations of short-rotation woody crops (SRWCs) use fast-growing tree species that coppice, i.e., resprout from the stump, for repeated harvests that minimize planting costs. Under coppice management, 3–5 growth stages (coppices) can be harvested during the SWRC life (rotation or cycle), with each coppice lasting 2–10 years. SRWCs can produce wood for biomass, mulch, pulpwood, and other products, while also providing environmental services. For example, SRWC plantations can be irrigated with municipal wastewater or fertilized with treated biosolids or municipal compost, simultaneously increasing biomass production, reducing fertilizer costs, and intercepting nitrates and phosphates to reduce nutrient loading in waterways (Rosenqvist et al. 1997; Labrecque et al. 1997; Aronsson & Perttu 2001; Rockwood et al. 2004; Licht & Isebrands 2005; Langholtz et al. 2005; Mirck et al. 2005). SRWCs can also help build soil organic matter, recycle nutrients, and maintain vegetative cover to restore ecological functions of mined lands and other degraded lands (Stricker et al. 1993; Bungart & Huttl 2001; Rockwood et al. 2006). SRWCs established on agricultural lands as shelterbelts or buffer zones to protect riparian areas are likely to reduce soil erosion and runoff of agricultural inputs and improve wildlife habitat (Joslin & Schoenholtz 1997; Tolbert & Wright 1998; Thornton et al. 1998). In spite of these benefits, SRWC production is not always economically viable, and evaluating the economics of SRWC production is not easy.</p> <p>Because SRWCs can have multiple coppices per rotation, evaluating the economics of SRWCs is more complicated than that of conventional forestry. For example, in the evaluation of a pine plantation, the future value of harvested timber is discounted to the year of planting, and planting costs are subtracted to calculate the net present value (NPV) of one harvest rotation. NPV is then used to calculate land expectation value (LEV), i.e. the value of the land assuming the adoption of this forestry practice. However, in the case of SRWC systems, multiple coppices require that the value of every coppice is discounted to the beginning of the rotation. Furthermore, the costs associated with establishment of each rotation and coppice stage must be discounted differently, and determining the optimum harvest scheduling and replanting age is also more complicated than for conventional forestry. Theory behind economic evaluation and optimization of SRWCs is described by Medema & Lyon (1985), Tait (1986), and Smart & Burgess (2000). Economics of SRWC systems in Florida are evaluated by Langholtz et al.<em> </em>(2005; 2007).</p> <p> </p> <p>The Florida Institute of Phosphate Research (FIPR) has supported research in the development of SRWCs as commercial tree crops on phosphate mined lands in Florida. A product of this research is a SRWC Decision Support System (DSS) that can be used to evaluate the economic viability of SRWC systems. The DSS allows a user to input operational costs, planting densities, stumpage prices and other variables and calculate NPVs, LEV, equal annual equivalent (EAE), internal rate of return (IRR), and benefit/cost ratio of a SRWC system. The DSS is in the form of a Microsoft® Excel spreadsheet (Figure 1).</p> <p>The DSS allows users to enter variables in yellow cells in the “Inputs” section on the left side of the worksheet and view results in green cells in the “Outputs” section on the right. Input variables include stumpage price, capital cost, and costs of each start-up, rotation, coppice, and year. The user can specify what portion of total biomass is harvested, the number of coppices, and their harvest ages. Financial incentives for renewable energy or other environmental benefits can be incorporated on a per-ton basis in the stumpage price. The DSS uses growth and yield functions developed from measurements of two planting densities of <em>Eucalyptus amplifolia</em> in a field trial of SRWCs on a phosphate mine clay settling area (CSA) near Lakeland, FL. Yields for each growth stage are displayed, and can be modified by adjusting the initial planting density or by adjusting yields under the general parameters. Ranges of values used to assess SRWC production on CSAs are shown in Table 1.</p> <p>Under all possible combinations of the assumptions in Table 1, the profitability of <em>E. amplifolia</em> on CSAs varies widely, with LEVs ranging from -$909 to $6,740 acre<sup>-1</sup>. Under the base case scenario identified in Table 1, the resulting LEV is $308 acre<sup>-1</sup> assuming an interest rate of 10% and $2,633 acre<sup>-1</sup> assuming an interest rate of 4%. LEV, EAE, and IRR results of the base case scenario under a range of discount rates and stumpage prices are shown in Table 2.</p> <p>This DSS does not automatically determine optimum harvest ages or the optimum number of stages per cycle, which both require dual optimization of continuous functions. DSS users can either input probable harvest and replanting ages and “zero in” inputs to maximize economic returns, or contact the authors to arrange a customized DSS. The DSS in either Excel or MathCad format could be modified to incorporate alternative growth and yield functions that might be developed for other SRWC species or conditions. For more information see the FIPR report “Commercial Tree Crops for Phosphate Mined Lands”, Rockwood et al. (in press).</p> <p> </p> <p>From http://edis.ifas.ufl.edu/fr169</p>
ScienceDex guides
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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.