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.
32
datasets available to search
ShareScore release 0.9.0
Dataset results
32 results for “control plans”
Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan
This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.
CAP1 - Planning and control of asphalt production - Planning algorithm implemented in R
<p>CAP1- Planning and Optimization of Asphalt Production</p> <p>The cognitive planning solution for asphalt production consists of a planning decision tool that, from the sensors data installed in the plant and contributing to the planning reference implementation layer of the CAP, allows to decide the right moment to start the production and the necessary adjustments to get the asphalt mix to leave the production plant to the asphalt application area in the optimal conditions (temperature mainly). It takes into account the industrial data streams coming from both the local control system located at the asphalt use case and the data coming from the new different sensors that have been connected to the local datalogger also available at the asphalt production plant and as part of this project development.</p> <p>The implementation of the cognitive system of production planning and optimization gathers all the data coming from the cognitive sensors developed in the project (as the content of bitumen or filler present in the asphalt mix) and any other sensors already installed alongside, with data coming from the laboratory if needed.</p> <p>It is needed to perform two types of calculations:</p> <ul> <li>A <strong>mass balance</strong> both at the dryer and mixing process on a daily basis and for each type of asphalt mix design (a recipe containing the proportions of each ingredient, the aggregates, bitumen and recycled asphalt).</li> <li>A <strong>thermal balance</strong> also both at the dryer (heating up the cold aggregates) and mixing of the hot aggregates, the bitumen and the cold RAP (recycled asphalt). Both processes have a temperature set point. For the drying, there is a temperature safety limit to not damage the baghouse filter. For the mixing, the temperature is set by the asphalt mix design so the final mix is transported and laid out at the job site at a minimum temperature.</li> </ul> <p>The advanced calculation of the mass balance throughout all the production chain is performed including the continuous part of it (aggregates drying process) and the batch one (mix tower). This mass balance is made up of the different calculations that can be performed using all the available data and taking into consideration both, stationary and dynamic (transitory) mass balances like mass balance of aggregates in the dryer, mass balance in the baghouse filter, mass balance in the bucket elevator to the mixing tower, mass balance in the upper sieves and in the hot aggregates hoppers and eventually the mass balance in the mixer taking into account the different additives (including RAP, bitumen, etc.). Also, the different recipes production historical data is used as a basis for the calculations of this tool.</p>
CAP1 - Planning and control of asphalt production - Thermal balance data (.xlsx)
<p>CAP1- Planning and Optimization of Asphalt Production</p> <p>The cognitive planning solution for asphalt production consists of a planning decision tool that, from the sensors data installed in the plant and contributing to the planning reference implementation layer of the CAP, allows to decide the right moment to start the production and the necessary adjustments to get the asphalt mix to leave the production plant to the asphalt application area in the optimal conditions (temperature mainly). It takes into account the industrial data streams coming from both the local control system located at the asphalt use case and the data coming from the new different sensors that have been connected to the local datalogger also available at the asphalt production plant and as part of this project development.</p> <p>The implementation of the cognitive system of production planning and optimization gathers all the data coming from the cognitive sensors developed in the project (as the content of bitumen or filler present in the asphalt mix) and any other sensors already installed alongside, with data coming from the laboratory if needed.</p> <p>It is needed to perform two types of calculations:</p> <ul> <li>A <strong>mass balance</strong> both at the dryer and mixing process on a daily basis and for each type of asphalt mix design (a recipe containing the proportions of each ingredient, the aggregates, bitumen and recycled asphalt).</li> <li>A <strong>thermal balance</strong> also both at the dryer (heating up the cold aggregates) and mixing of the hot aggregates, the bitumen and the cold RAP (recycled asphalt). Both processes have a temperature set point. For the drying, there is a temperature safety limit to not damage the baghouse filter. For the mixing, the temperature is set by the asphalt mix design so the final mix is transported and laid out at the job site at a minimum temperature.</li> </ul> <p>The advanced calculation of the mass balance throughout all the production chain is performed including the continuous part of it (aggregates drying process) and the batch one (mix tower). This mass balance is made up of the different calculations that can be performed using all the available data and taking into consideration both, stationary and dynamic (transitory) mass balances like mass balance of aggregates in the dryer, mass balance in the baghouse filter, mass balance in the bucket elevator to the mixing tower, mass balance in the upper sieves and in the hot aggregates hoppers and eventually the mass balance in the mixer taking into account the different additives (including RAP, bitumen, etc.). Also, the different recipes production historical data is used as a basis for the calculations of this tool.</p>
Substantial differences in source contributions to carbon emissions and health damage necessitate balanced synergistic control plans in China
<p>This dataset presents the ratio of gridded contributions to PM2.5 exposure-related health damage and CO2 emissions, alongside the gridded contributions to social costs from health damage, CO2-related climate change, and integrated costs. Additionally, the key code for calculating semi-normalized emission sensitivities based on CMAQ Adjoint outputs and source attribution is provided.</p>
AEROARMS Control-based local optimization methods for planning
<p>This package allows to perform a Dynamic (Re-)Planning within the Set-Based Task-Priority Inverse-Kinematics (SBTP-IK) framework. It is ROS-based and it is provided to work with the Kinova Jaco2 7DOFs but it is can be easily adapted to another robot system changing the files describing the robot kinematics such as Denvait Hartenberg parameters.</p> <p>The proposed approach is based on merging control-based local optimization methods inside the planning algorithms. For the experiments, a fixed-based 7 DOFs manipulator has been considered in two different scenarios. In the first case, a static environment has been considered. In the second one, the user places an obstacle on the manipulator’s path in real-time. Thus, a re-planning results necessary to manage this change in the environment.</p> <p> </p>
Feasibility Of Individualized Dietary Plan (Idp) On Glycemic Control And Total Cholestrol In Diabetic Patients At Diabetes Hospital And Research Centre Peshawar
ClinicalTrials.gov study NCT07030543. IPD Sharing: YES. Countries: 1. Publications: 3.
Randomized Controlled Trial of Wellness Recovery Action Planning
ClinicalTrials.gov study NCT01024569. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Esketamine Versus Crisis Response Planning Versus Optimized Treatment as Usual for Suicide Prevention: A Pragmatic Controlled Trial in Two Brazilian Cities
ClinicalTrials.gov study NCT07120477. IPD Sharing: NO. Countries: 0. Publications: 107.
A Randomized Controlled Trial of an Advanced Care Planning Video Decision Support Tool for Patients With End-Stage Liver Disease
ClinicalTrials.gov study NCT03557086. IPD Sharing: NO. Countries: 1. Publications: 1.
A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning
Open the record for dataset details and reuse information.
Randomized control trial of SMS for postpartum behaviors and family planning in Kiambu County, Kenya
<p><strong>Background </strong></p> <p>It is estimated that one third of maternal deaths in Kenya in 2014 could have been prevented by more timely care-seeking. Mobile health interventions are increasingly being recognized as tools for the delivery of health education and promotion. Many maternal deaths occur in the first few weeks after delivery and mothers who are given adequate care in the postpartum period have better health outcomes. Kiambu County, Kenya has a high level of literacy and phone ownership amongst mothers delivering in public hospitals and was chosen as a site for a postpartum short message service intervention. </p> <p><strong>Methods </strong></p> <p>Women were recruited after delivery and randomized to receive a package of mobile messages or standard of care only. Messages covered danger signs, general postpartum topics, and family planning. Endline phone surveys were conducted at 8 weeks postpartum to assess knowledge, care seeking behavior and family planning uptake. Analysis was conducted using Stata and is presented in odds ratios. </p> <p><strong>Results </strong></p> <p>Women who received the danger sign messages were 1.6 times more likely to be able to list at least 1 danger sign and 3.51 times more likely to seek treatment if they experienced postpartum danger signs. There was no significant difference in routine postpartum care seeking or care seeking behaviors concerning newborns. Women who received family planning messages were 1.85 times more likely to uptake family planning services compared to controls and 2.1 times more likely to choose a longacting method.</p> <p><strong>Conclusions </strong></p> <p>Simple, low-cost mobile interventions can support women in the early postpartum period when the information is targeted to particular points in the postpartum continuum. Additional research is needed to understand the interplay between healthcare providers and mobile health interventions. Health policy makers should consider direct mobile interventions for women as an option for supporting positive maternal health outcomes in certain populations.</p>
A Conceptual Model Framework for Analyzing, Simulating, Planning and Controlling of Supply-Demand Matching of Ecosystem Services in Agricultural Landscapes
<p>The video gives an explanation of our conceptual approach for the investigation of the adaptation of societal demands for ecosystem services in agricultural landscapes.</p>
Randomized Control Trial of Advance Care Planning in Primary Care
ClinicalTrials.gov study NCT03239639. IPD Sharing: NO. Countries: 1. Publications: 22.
Ability of Pupillometry to Reduce Sufentanil Consumption in Planned Cardiac Surgery: Randomized, Controlled, Single-center Clinical Superiority Trial
ClinicalTrials.gov study NCT03864016. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Plan A Birth Control: Randomized Controlled Trial of a Mobile Health Application for Contraception Information
ClinicalTrials.gov study NCT02234271. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Advance Care Planning & Goals of Care Randomized Controlled Trial in Primary Care
ClinicalTrials.gov study NCT03434626. IPD Sharing: NO. Countries: 1. Publications: 21.
Promotion of Family Planning Methods Through an Interactive Platform Offered in Growth and Development Control Services
ClinicalTrials.gov study NCT03810716. IPD Sharing: NO. Countries: 1. Publications: 4.
The Avoiding Diabetes Thru Action Plan Targeting Pilot Randomized Control Trial
ClinicalTrials.gov study NCT01473654. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pilot Study to Assess Pre-packaged, Portion-controlled Meal Plan on Weight Loss
ClinicalTrials.gov study NCT00593476. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Effectiveness of Nursing Care Plans Based in Nursing Diagnoses in Metabolic Control of Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT01482481. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.