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41 results for “plan quality”
Assessing Quality Variations in Early Career Researchers' Data Management Plans: Quantitative Data of the Content Analysis
<p>The data includes the numerical results of the ranking of the data management plans created during the Basics of Research Data Management (BRDM) courses worth 3 ECTS credits in the years 2020 - 2022. The ranking was made using the Finnish DMP Evaluation Guidance (https://doi.org/10.5281/zenodo.4729831). Additionally, the data contains the results of the analysis of the best RDM practices included in the DMPs.</p> <p>Note 1: The comma-separated coded CSV version 1 (5.2.2024) may not open correctly on MacOS. You can use the comma-delimited CSV file version 2 or 3 (31.5.2024).</p> <p>Note 2: Versions 1 (Quality_variations_in_ECRs_DMPs_data) and 3 (Quality_variations_in_ECRs_DMPs_data_ver_3) contain evaluations of DMPs, best practices for data management, as well as methods for data sharing, storage, and preservation. In version 2 (Quality_variations_in_ECRs_DMPs_data_ver_2), the methods for data sharing, storage, and preservation are missing.</p> <p>Data is related to the research article https://doi.org/10.2218/ijdc.v18i1.873.</p>
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.
FISHPASS ASSESSMENT PLAN LONG-TERM MONITORING OF HYDROLOGIC AND WATER QUALITY DATA
The Great Lakes Fishery Commissions’ (GLFC) FishPass project seeks to reconnect the waterscape for only desired species (i.e., selective passage) by integrating a multitude of existing and novel passage techniques and technologies. The probability of a fish passing through a sorting system is dependent on environmental conditions and a fish’s motivation ─ its internal state in relation to environmental stimuli. While fish decision making abilities introduce complexity to the sorting operations, they also provide an opportunity to exploit behavioral tendencies and abilities to achieve selective sorting. The FishPass Assessment Plan details a monitoring program aimed at quantifying fish movement and sorting capabilities associated with both individual mechanisms and integrated sorting systems. The results of the monitoring program will be used to inform future adjustments to the selection of techniques and technologies and their configuration to optimize passage of desirable species while blocking and/or removing undesirable species. A key component to the Assessment Plan is the long-term monitoring of abiotic variables in and around FishPass. This data set contains the hydrologic (e.g., river discharge, water level) and water quality data (e.g., temperature, specific conductivity, conductivity, and turbidity) collected at mostly static stations throughout the Boardman/Ottaway River. The dataset is updated annually. These data are collected until the initiation and/or substantial completion of the FishPass structure. Collection of this type of data are expected to continue after FishPass construction completion but modifications to the extent and location of monitoring stations are anticipated. As a result, a new dataset will be updated in the future containing all long term hydrologic and water quality monitoring post construction. R. Swanson, GLFC Assessment Biologist, is primarily responsible for maintaining the monitoring equipment, data retrieval, quality assuran
Dataset from publication "Predicting context-sensitive urban green space quality to support urban green infrastructure planning"
<p><strong>Data Description:</strong></p><p>This dataset presents the spatial outcome of an analysis modelling perceived green space quality across the city of Espoo, Finland. The analysis relies on data gathered through the My Espoo on the Map survey (<i>Mun Espoo kartalla</i>) in the autumn of 2020 as part of the NordForsk-funded research project NORDGREEN. A comprehensive account of the analytical process and potential applications of the dataset is available in the associated publication, "<i>Predicting context-sensitive urban green space quality to support urban green infrastructure planning</i>" (open access: <a href="https://doi.org/10.1016/j.landurbplan.2023.104952">https://doi.org/10.1016/j.landurbplan.2023.104952</a>).</p><p><strong>Data Processing:</strong></p><p>This dataset results from an analysis that integrates both primary and secondary sources of geospatial data. The primary data were collected with an online public participation GIS (PPGIS) survey directed for the adult inhabitants of Espoo. The data collection took place in September-October 2020 and was executed in collaboration with Aalto University and the City of Espoo. For a detailed overview of the data collection process, please refer to the related publication.</p><p><strong>Data characteristics:</strong></p><p>Format: Shapefile (50m x 50m grid)</p><p>Geographical area: Espoo, Finland</p><p>Spatial reference: EUREF FIN TM35FIN</p><p>Note: Only grid cells intersecting with greenspace have been included in the dataset. For the employed definition of green areas, please consult the related publication.</p><p><strong>Data attributes and their descriptions:</strong></p><p><strong>"</strong><i>P_PROB"</i>: Probability (P), positive perceived quality</p><p><i>"N_PROB"</i>: Probability (P), negative perceived quality</p><p><strong>Funding: </strong></p><p>This research was funded by NordForsk, Sustainable Urban Development and Smart Cities Programme, Project Smart Planning for Healthy and Green and Nordic Cities – NORDGREEN, under Grant Number: 95322.</p>
Exemplary landscape planning results using different qualities of LU/LC data to derive implications for practical planning.
<p>This Dataset consist of two GIS Shape files showing examples of common landscape planning activities, namely the an assessment of the climate protection function and planning towards locating environmental protection measures using different data basis. Using overlay operation it is possible to use descriptive statistics to assess uncertainties and inconsistencies between different land use / land cover data sets and highlight their implications for practical planning.</p> <p>Once published, the results of such analysis can be seen in the related publication "Uncertainties in land use data may have substantial effects on environmental planning recommendations: a plea for careful consideration. Submitted to PLOS ONE."</p>
Data related to Mapping user-related comfort conditions to identify urban planning issues for improved quality of places in cities: The case of Ljubljana, Slovenia
<p>The data consists of parameters including air temperature, noise levels, humidity, and air quality (PM 2.5), measured using a portable ICT device while cycling through the city of Ljubljana between August 1 and August 30, 2022. The data is available as raw CSV files, with each measurement session stored in a separate file. An additional QGIS Project file (qgz) is included, along with maps (in jpg format in ppt) that visually represent the results presented in the article.</p>
AAPM TG 236 Report 236: Recommendations on volume-image-based treatment planning, dosimetry, and quality management for HDR intracavitary brachytherapy: Part I breast
<p>This is a zipped file for Appendix A.1 DVH validation using reference DCIOM datasets for AAPM TG report 236: Part I. Intracavitary breast brachytherapy. One can download and unzip the file. These are CT images and a structure file in DICOM format to validate the DVH information in your HDR brachytherapy treatment planning system either Varian BrachyVision or Elekta Oncentra. One can import all the DICOM files into your brachytherapy TPS and compute the dose. One can follow the instruction described in detail in Appendix A.1 of the AAPM TG 236: Part I report.</p>
Development of an Osteoarthritis (OA) Care Plan to Improve Process and Quality of OA Treatment Decisions
ClinicalTrials.gov study NCT03102580. IPD Sharing: NO. Countries: 1. Publications: 10.
Investigation of the Effect of Sexuality-Based Family Planning Education Given to Women Via Podcast on Contraceptive Method Selection and Sexual Life Quality
ClinicalTrials.gov study NCT06330428. IPD Sharing: NO. Countries: 1. Publications: 3.
Anesthesia Quality Improvement and Patients With Planned ICU Admission
ClinicalTrials.gov study NCT05626153. IPD Sharing: NO. Countries: 1. Publications: 14.
Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models (Acronym: MIKAPOCo, Multi-Institutional Knowledge-based Approach in Plan Optimization for the Co
ClinicalTrials.gov study NCT06317948. IPD Sharing: NO. Countries: 1. Publications: 5.
The "Hand-in-Hand Study": Improvement of Quality of Life in Palliative Cancer Patients Through Collaborative Advance Care Planning
ClinicalTrials.gov study NCT03387436. IPD Sharing: UNDECIDED. Countries: 1. Publications: 17.
Reducing Structural Inequities in Heart Failure Management: An Approach to Improve the Quality of Heart Failure Care on the General Medicine Service: Longitudinal Equity Action Plan (LEAP)
ClinicalTrials.gov study NCT03942978. IPD Sharing: NO. Countries: 1. Publications: 10.
Mindfulness to Enhance Quality of Life and Support Advance Care Planning
ClinicalTrials.gov study NCT03257007. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Evaluation of a person-centered quality improvement intervention for maternal health and family planning in Kenya
Open the record for dataset details and reuse information.
Generating Adaptation Plans Based on Quality Models for Cloud Platforms
<p>Apresentação referente ao artigo "Generating Adaptation Plans Based on Quality Models for Cloud Platforms". SBES 2020 - Trilha de Ideias Inovadoras e Resultados Emergentes.</p>
Generating Trustworthiness Adaptation Plans Based on Quality Models for Cloud Platforms
<p>Apresentação referente ao artigo "Generating Trustworthiness Adaptation Plans Based on Quality Models for Cloud Platforms". SBCARS 2020 - Artigos Completos</p>
Using Personalized Letters and Emails to Increase Health Insurance Take-up and Improve Plan Choice Quality
ClinicalTrials.gov study NCT05216458. IPD Sharing: YES. Countries: 1. Publications: 0.
Dual-Energy CT on Plan Quality, Dose-delivery Accuracy, and Simulated Outcomes of Patients Treated With Proton or Photon Therapy
ClinicalTrials.gov study NCT03403361. IPD Sharing: NO. Countries: 1. Publications: 0.
Function and Quality of Life After Emergency vs Planned Creation of a Stoma
ClinicalTrials.gov study NCT05102981. IPD Sharing: NO. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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