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545 results for “decision support”
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.
Fig. 7 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 7. The average Eldana saccharina larval infestation with the passage of time simulated for the SIT/IS pilot site near the Eston area of KwaZulu-Natal, South Africa with weekly releases commencing only in fields of age at most 6 mo at the start of the release. Time, t, is measured in days. The density, e/100s, is the number of borers, e, per 100 stalks of sugarcane. The graph shows that the average infestation level in the second yr of the control program is substantially reduced.
Fig. 5 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 5. The discretization of the spatial domain. On the lef a typical sugarcane field layout is illustrated with different colors representing crop age, and on the right the discretized domain corresponding to the area within the red square. This was done by transforming the spatial information obtained from the shapefiles to a matrix data structure in Matlab containing the entries '0', '1' and '2' denoting non-sugarcane patches, patches inside a field and edge patches, respectively. In the right half of the figure these data are represented by white, blue and green, respectively.
Fig. 4. A in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 4. A model representing sugarcane dynamics as currently implemented in the simulation tool for the field application of a SIT/IS strategy against Eldana saccharina in sugarcane.
Fig. 3 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 3. The Eldana saccharina module developed in this study. The module describes the dynamics of all E. saccharina life stages under the influence of the SIT/IS technique. Other control measures may also be included, and are currently under investigation.
Fig. 6 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 6. The graphical user interface designed for the SIT/IS simulation tool for the field application of a SIT/IS strategy against Eldana saccharina in sugarcane. The initial infestation, e/100s, is the number of borers per 100 stalks of sugarcane.
Fig. 2 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 2. The pest species subsystem which may include all the important pest species in South African sugarcane.The total damage caused by the various pest species may be estimated by such a system. Currently, only the Eldana saccharina module has been developed.
Fig. 9. A in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 9. A spatial overview of the Eldana saccharina larval infestation at the end of a 24 mo simulation of a SIT/IS program at the pilot site near the Eston area of KwaZulu-Natal, South Africa. The colors indicate infestation levels measured in e/100 stalks, i.e., number of borers per 100 stalks of sugarcane. The fields colored in dark blue in the top right corner are aged 0, 1 and 2 mo, and they were harvested just before the end of the simulation; therefore the infestation levels are still low and this is before the commencement of releases of irradiated adult moths.
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>
BEE-STEWARD: a research and decision support software for effective land management to promote bumblebee populations
<p><span><span>The demand for agent-based models to explore the effects of environmental change on pollinator population dynamics is growing. However, models need a simple yet flexible interface to enable adoption by a wide range of stakeholders. </span></span><span><span>We introduce BEE-STEWARD: a research and decision-support software tool, enabling researchers, policy-makers, land management advisors, and practitioners to predict and compare the effects of bee-friendly management interventions on bumblebee populations over several years. </span></span><span><span>BEE-STEWARD integrates the BEESCOUT and <i>Bumble</i>-BEEHAVE agent-based models of bumblebee behaviour, colony growth and landscape exploration into a user-friendly interface, with reconstructed code, and expanded functionality. Bespoke automatic reports can be created to illustrate how different land management interventions can affect the densities of bumblebees and their colonies over time. </span></span><span><span>BEE-STEWARD could be an important virtual test-bed for scientists exploring the impacts of different stressors on bumblebees and used by those with little or no modelling experience, enabling a shared methodology between research, policy, and practice.</span></span></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>
Distinct value computations support rapid sequential decisions
<p>This behavioral and modeling data was used and described in the following paper:Mah, A., Schiereck, S.S., Bossio, V., Constantinople, C.M. (2023). Distinct value computations support rapid sequential decisions. Nature Communications.The dataset comprises </p><p>1) Behavioral data for the value-based decision making task in rats, and </p><p>2) computational models fit to the rat behavioral data. </p><p>All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Mah et al., 2023 is available at {https://github.com/constantinoplelab/published/tree/main/rat_behavior. Data was analyzed using Matlab 2023a with the following additional toolboxes:<br>Curve Fitter<br>Optimization<br>Signal Analyzer</p><p> </p><p>Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH111926), an Alfred P. Sloan Fellowship, a Klingenstein-Simons Fellowship in Neuroscience, an NIH Director's New Innovator Award (DP2MH126376), an NSF CAREER Award, R01MH125571, and a McKnight Scholars Award to C.M.C. A.M. was supported by 5T90DA043219 and F31MH130121. A.M. and S.S.S. were supported by 5T32MH019524.</p>
Comparative Effectiveness of Decision Support Strategies for Joint Replacement Surgery
ClinicalTrials.gov study NCT02729831. IPD Sharing: YES. Countries: 1. Publications: 2.
Patient-Centered Reproductive Decision Support Tool for Women Veterans
ClinicalTrials.gov study NCT04584294. IPD Sharing: YES. Countries: 1. Publications: 1.
Social Risk Score, Clinical Decision Support Tool and Closed Loop Referral for Social Risk Screen and Referral
ClinicalTrials.gov study NCT05574699. IPD Sharing: NO. Countries: 1. Publications: 2.
Supporting Oral Pre-exposure Prophylaxis Decision Making Among Pregnant Women in Lilongwe, Malawi
ClinicalTrials.gov study NCT06394323. IPD Sharing: YES. Countries: 1. Publications: 1.
BEE-STEWARD: a research and decision support software for effective land management to promote bumblebee populations
Open the record for dataset details and reuse information.
Poseidon 2.0 - Decision Support Tool for Water Reuse (Microsoft Excel) and Handbook
<p>Poseidon 2.0 is a user-oriented, simple and fast Excel-Tool which aims to compare different wastewater treatment techniques based on their pollutant removal efficiencies, their costs and additional assessment criteria. Poseidon can be applied for pre-feasibility studies in order to assess possible water reuse options and can show decision makers and other stakeholders that implementable solutions are available to comply with local requirements. This upload consists in:</p> <ul> <li>Poseidon 2.0 Excel File that can be used with Microsoft Excel - XLSM</li> <li>Handbook presenting main features of the decision support tool - PDF</li> </ul> <p>This dataset is linked to following additional open access resources:</p> <ol> <li>: Oertlé E, Hugi C, Wintgens T, Karavitis C, Oertlé E, Hugi C, Wintgens T, Karavitis CA. 2019. <strong>Poseidon—Decision Support Tool for Water Reuse</strong>. Water. 11(1):153. doi:10.3390/w11010153. [accessed 2019 Jan 22]. <a href="http://www.mdpi.com/2073-4441/11/1/153">http://www.mdpi.com/2073-4441/11/1/153</a> .</li> <li>Externally hosted supplementary file 1, Oertlé, Emmanuel. (2018, December 5). <strong>Poseidon - Decision Support Tool for Water Reuse (Microsoft Excel) and Handbook (Version 1.1.1)</strong>. Zenodo. <a href="http://doi.org/10.5281/zenodo.3341573">http://doi.org/10.5281/zenodo.3341573</a></li> <li>Externally hosted supplementary file 2, Oertlé, Emmanuel. (2018). <strong>Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations (Version 1.0.0)</strong> [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1247434">http://doi.org/10.5281/zenodo.1247434</a></li> <li>Externally hosted supplementary file 3, Oertlé, Emmanuel. (2018). <strong>Treatment Trains for Water Reclamation (Dataset) (Version 1.0.0)</strong> [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1972627">http://doi.org/10.5281/zenodo.1972627</a></li> <li>Externally hosted supplementary file 4, Oertlé, Emmanuel. (2018). <strong>Water Quality Classes - Recommended Water Quality Based on Guideline and Typical Wastewater Qualities (Version 1.0.2)</strong> [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3341570">http://doi.org/10.5281/zenodo.3341570</a> </li> </ol>
Anonymized source data files for figures in: Recurrent processes support a cascade of hierarchical decisions
<p>Perception depends on a complex interplay between feedforward and recurrent processing. Yet, while the former has been extensively characterized, the computational organization of the latter remains largely unknown. Here, we use magneto-encephalography to localize, track and decode the feedforward and recurrent processes of reading, as elicited by letters and digits whose level of ambiguity was parametrically manipulated. We first confirm that a feedforward response propagates through the ventral and dorsal pathways within the first 200 ms. The subsequent activity is distributed across temporal, parietal and prefrontal cortices, which sequentially generate five levels of representations culminating in action-specific motor signals. Our decoding analyses reveal that both the content and the timing of these brain responses are best explained by a hierarchy of recurrent neural assemblies, which both maintain and broadcast increasingly rich representations. Together, these results show how recurrent processes generate, over extended time periods, a cascade of decisions that ultimately accounts for subjects' perceptual reports and reaction times.</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.