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545 results for “decision support”
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>
[VERSION 2] Data set and analytic codes supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?"
<p>This is <strong>VERSION 2</strong> of data set and analytic codes (with a meta data [see the meta data from VERSION 1]) supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?". We investigated the impacts of replanting and alternative replanting decisions (replanting with monoculture versus polyculture oil palm plantations) on within-plantation environmental conditions and butterfly assemblages (diversity, density, and composition). We also assessed the effects of habitat structure and complexity within plantations on butterfly assemblages. Apart from "BantingButterflies_ButterflyData", other data are the same as in VERSION 1.</p><p><strong>## List of changes:</strong></p><p># 1. <i>Tirumala septentrionis </i>was not included in the analyses because it should have been <i>Ideopsis vulgaris</i> (had been corrected),</p><p># 2. PC5 and PC6 (from PCA) were considered as predictors for the GLMs,</p><p># 3. The Mantel test was added.</p><p><strong>## Other notes:</strong></p><p># 1. Older version of ggiNEXT could work with facet.var = "site", now it needs to be "Assemblage"</p><p># 2. Older version of ggiNEXT could work with facet.var = "order", now it needs to be "Order.q"</p><p># 3. "set.seed(42)" function was used before running "iNEXT", ANOSIM, and the Mantel test to get reproducible outputs (exactly the same outputs every time each function is run).</p><p><strong>Funding and research permission:</strong> Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund provided funding for MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) provided funding for the establishment of the plots and surveys of environmental parameters. Research permission was provided by the Economic Planning Unit (EPU) of Malaysia's Prime Minister's Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).</p>
Decision support for the deployment of hydrogen technologies at the scale of a territory - Raw dataset
<p>This dataset was created to run a capacity expansion model developed in the context of my thesis, 'Decision support for the deployment of hydrogen technologies at the scale of a territory'. Here are the raw data from which every parameter of the model is derived. The code of the model can be found in open source here: <a href="https://github.com/Anaelle-Mines/SPHYDERS">SPHYDERS</a>.</p> <p>The set includes:</p> <p>-The hourly electricity consumption in France for year 2019.</p> <p>-The hourly natural gas prices in France for year 2019.</p> <p>-The hourly grid electricity prices in France for year 2019.</p> <p>-The average hourly availability factors of different electricity production technologies in France for year 2019 (nuclear plants, onshore wind turbines, offshore wind turbines, solar PV, gas turbines, H2 turbines, hydraulic turbines, coal plants etc...).</p> <p>-The local availability factors of different electricity and hydrogen production technologies in PACA region for year 2019 (onshore wind turbines, offshore wind turbines, solar PV, methane reforming reactors, electrolysers).</p> <p>-The off-peak time calendar for various contracts with the grid operator.</p> <p>-The techno-economic data for all the technologies of the model (CAPEX, OPEX, life time)</p> <p> </p>
Understanding farmers' reasons behind mitigation decisions is key in supporting their coexistence with wildlife
<p>1. Coexistence between wildlife and farmers can be challenging and can endanger the lives of both, prompting the provisioning of mitigation methods by governments and non-governmental organisations (NGOs). However, provision of materials, demonstration of the effectiveness of methods or willingness to uptake a method do not predict uptake of methods.</p> <p>2. We used Ethnographic Decision Models to understand how farmers' work through the decisions of uptake or non-uptake of methods to mitigate crop consumption by elephants, and how the government and NGOs can either enable or impede the ability of farmers to protect themselves and their crops.</p> <p>3. While farmers were motivated to use methods if they received or could afford to buy materials and they believed in the effectiveness of the methods, they still did not use them if they considered a method to be dangerous, or issues with elephants not to be severe enough, or when the supply of materials or income was not sufficient. Methods were not even considered by farmers if they lacked awareness or knowledge of the method. Government departments and NGOs enabled farmers to mitigate elephant crop consumption by providing opportunities for cash income, and providing materials and knowledge. Yet, there was disparity between the materials farmers received and methods they wished to adopt.</p> <p>4. One-off inputs of materials did not result in sustainable use of mitigation methods. We see an opportunity for governmental departments or NGOs to stimulate logistics (e.g. roads and retail) to increase availability of mitigation materials since this promoted farmer autonomy. We also highlight the importance of empowering farmers by facilitating within community sharing of mitigation ideas and increasing knowledge about the effectiveness of promising wildlife conscious farming, as despite promising farmer testimonies, only a few farmers used these techniques.</p>
Eradication Decision Support Tools - Example Data
<p>Example data that can be used with the pest eradication decision support tool Shiny apps. Decision support tools have been developed for</p> <ul> <li>Assessing eradication feasibility</li> <li>Assessing eradication progress</li> <li>Assessing "Proof of Freedom"</li> </ul> <p>Shiny versions of the DST are available at</p> <p><a href="https://landcare.shinyapps.io/EradSim/">Eradication feasibility DST</a> </p> <p><a href="https://landcare.shinyapps.io/eradication_app/">Assessing eradication progress</a></p> <p><a href="https://landcare.shinyapps.io/proofofabsence/">Assessing proof of absence</a></p>
Pilot 1 Model-based decision support for testing drought-related adaptation strategies in the Aa of Weerijs river basin, the Netherlands: Hydrological model description, input data sources and model results
<p>This dataset contains: the report with the description of the model structure, the input data sources and the spatial locations within the catchment for which surface and groundwater results data are provided.</p>
Fig. 1 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 1. The system designed for simulating pest species dynamics in sugarcane.
FarFish FFDB Decision Support and visualisation tools outputs
<p>Decision Support and visualisation tools outputs as generated by the FarFish FFDB DLMTool</p>
Digital solutions and early warning system for decision support and risk management in water reuse for irrigation
<p>Video presentation for IWA World Water Congress & Exhibition, 11-15 September 2022, Copenhagen, Denmark.</p>
Data and code for "Representing storylines with causal networks to support decision making: framework and example"
<p>Data and code for the paper "Representing storylines with causal networks to support decision making: framework and example", along with the Shiny webapp code accompanying the paper. The paper has been submitted to the journal of Climate Risk Management and is currently under review.</p>
Vulnerability of sea turtle nesting sites to erosion and inundation: a decision support framework to maximize conservation
<p>Sandy beaches provide essential nesting habitat for sea turtles but are threatened globally by a rapidly changing climate. Identifying which nesting sites are at greatest risk from erosion and inundation remains an important goal of sea turtle conservation globally. Yet, efforts to identify at-risk sites have been hindered by the ability to model complex processes and incomplete information on nesting distribution and abundance. To assess the erosion and inundation risk to the reproductive success of a discrete genetic stock of flatback turtles (<em>Natator</em> <em>depressus</em>) across its nesting range in the Pilbara region of Western Australia, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Coastal Vulnerability Model. A relative exposure index was calculated for 402 nesting beaches in terms of six geophysical variables: wind and wave exposure, surge potential, relief, observed sea level rise and coastal geomorphology, and coupled with published information on the distribution and abundance of turtle tracks in the region. </p> <p>The majority of beaches (74%) had an intermediate to high exposure. In particular, 36% of beaches with a high abundance of flatback tracks (the top 25% of the frequency distribution) had a high exposure (the top 25% of the frequency distribution). This suggests that coastal exposure is a key vulnerability to the reproductive success of sea turtles that nest in this region. Promisingly, five beaches with a high abundance of turtle tracks also had a low exposure (bottom 25% of the frequency distribution) and these beaches may be critical for the long-term resilience of the stock against sea level rise and severe storms. Exposure varied across nesting sites and the approach presented here allows for a rapid and broadscale assessment of relative erosion and inundation risks at a scale most relevant to management. </p>
Clinical Decision Support for Opioid Use Disorders in Medical Settings (COMPUTE 2.0)
ClinicalTrials.gov study NCT04198428. IPD Sharing: NO. Countries: 1. Publications: 3.
Enhanced Dynamic Clinical Decision Support System Pragmatic Trial (E-DYNAMIC)
ClinicalTrials.gov study NCT03826758. IPD Sharing: YES. Countries: 1. Publications: 8.
Colorectal Polyp Clinical Decision Support Device Study
ClinicalTrials.gov study NCT04437615. IPD Sharing: YES. Countries: 1. Publications: 1.
OptimiZation Of Lipid Lowering Therapies Using a Decision Support System In Patients With Acute Coronary Syndrome.
ClinicalTrials.gov study NCT05844566. IPD Sharing: NO. Countries: 3. Publications: 25.
Artificial Intelligent Clinical Decision Support System Simulation Center Study for Technology Acceptance
ClinicalTrials.gov study NCT05816473. IPD Sharing: NO. Countries: 1. Publications: 3.
A Multicenter Trial of a Shared DECision Support Intervention for Patients Offered Implantable Cardioverter-DEfibrillators
ClinicalTrials.gov study NCT03374891. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Supporting Decisions About Health Insurance to Improve Care for the Uninsured
ClinicalTrials.gov study NCT02522624. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ACCESS (Access for Cancer Caregivers for Education and Support for Shared Decision Making)
ClinicalTrials.gov study NCT02929108. IPD Sharing: NO. Countries: 1. Publications: 3.
PCORI-1310-06998 Trial of a Decision Support Intervention for Patients and Caregivers Offered Destination Therapy Heart Assist Device
ClinicalTrials.gov study NCT02344576. IPD Sharing: NO. Countries: 1. Publications: 42.
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.