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1,032 results for “Decision making”
Decision-making of ctDNA in Patients With mCRC After Failure of First-line Treatment Containing Cetuximab - a Single-center, Phase II Clinical Study
ClinicalTrials.gov study NCT04831528. IPD Sharing: Not stated. Countries: 0. Publications: 0.
MPFC Theta Burst Stimulation as a Tool to Affect Rational Decision Making
ClinicalTrials.gov study NCT05917002. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Exploration of Risks in Autonomous Decision-Making Applied to Aeronautics
Prior research into metrics and design for autonomy were presented. At this time, the prospect of adding significant autonomous decision-making on a piloted aircraft is viewed with some degree of concern for the ability of the system to add value without adding risk.
Autonomous Decision Making for Planetary Rovers Using Diagnostic and Prognostic Information
Rover missions typically involve visiting a set of predetermined waypoints to perform science functions, such as sample collection. Given the communication delay between Earth and the rover, and the possible occurrence of faults, an autonomous decision making system is essential to ensure that the rover maximizes the scientific operations performed without damaging itself further or stalling. This paper presents a modular software architecture for autonomous decision making for rover operations that uses diagnostic and prognostic information to influence mission planning and decision making to maximize the completion of mission objectives. The decision making system consists of separate modules that perform the functions of control, diagnosis, prognosis, and decision making.We demonstrate our implementation of this architecture on a simulated rover testbed.
A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning
The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in PDM is finding a sufficiently expressive yet compact mathematical representation of the system for use with decision optimization algorithms. In this paper we describe a general modeling approach for a class of PDM problems with non-linear system degradation processes and uncertainties in state estimation, action effects, and future operating conditions. The approach is based on continuous Partially Observable Markov Decision Processes (POMDPs) used in conjunction with 'black box' system simulations. The proposed modeling framework can be cast into simpler representations, depending on which sources of uncertainty are being included. The approach is illustrated with a mission planning case study for an unmanned aerial vehicle (UAV). In the case study a PDM system is tasked with optimizing the vehicle route after an in-flight component fault is detected. A stochastic algorithm (based on particle filtering) is used for decision optimization, with a second, deterministic algorithm providing a performance evaluation baseline. Both algorithms utilize a UAV physics simulator for generating predictions of future vehicle states. Performance benchmarking is done on a set of mission scenarios of increasing complexity.
A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making
The ability to utilize prognostic system health information in operational decision making, especially when fused with information about future operational, environmental, and mission requirements, is becoming desirable for both manned and unmanned aerospace vehicles. A vehicle capable of evaluating its own health state and making (or assisting the crew in making) decisions with respect to its system health evolution over time will be able to go further and accomplish more mission objectives than a vehicle fully dependent on human control. This paper describes the development of a hardware testbed for integration and testing of prognostics-enabled decision making technologies. Although the testbed is based on a planetary rover platform (K11), the algorithms being developed on it are expected to be applicable to a variety of aerospace vehicle types, from unmanned aerial vehicles and deep space probes to manned aircraft and spacecraft. A variety of injectable fault modes is being investigated for electrical, mechanical, and power subsystems of the testbed. A software simulator of the K11 has been developed, for both nominal and off-nominal operating modes, which allows prototyping and validation of algorithms prior to their deployment on hardware. The simulator can also aid in the decision-making process. The testbed is designed to have interfaces that allow reasoning software to be integrated and tested quickly, making it possible to evaluate and compare algorithms of various types and from different sources. Currently, algorithms developed (or being developed) at NASA Ames - a diagnostic system, a prognostic system, a decision-making module, a planner, and an executive - are being used to complete the software architecture and validate design of the testbed.
Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms
As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.
An Approach to Prognostic Decision Making in the Aerospace Domain
The field of Prognostic Health Management (PHM) has been undergoing rapid growth in recent years, with development of increasingly sophisticated techniques for diagnosing faults in system components and estimating fault progression tra- jectories. Research efforts on how to utilize prognostic health information (e.g. for extending the remaining useful life of the system, increasing safety, or maximizing operational ef- fectiveness) are mostly in their early stages, however. This process of using prognostic information to determine a sys- tem’s actions or its configuration is beginning to be referred to as Prognostic Decision Making (PDM). In this paper we, first, propose a formulation of the PDM problem with the at- tributes of the aerospace domain in mind, outline some of the key requirements on PDM methods, and explore techniques that can be used as a foundation of PDM development. The problem of Pareto set viability, i.e. satisfaction of perfor- mance goals set for objective functions, is discussed next, followed by ideas for possible solutions. The ideas, termed Dynamic Constraint Redesign (DCR), have roots in the fields of Multidisciplinary Design Optimization and Game Theory. Prototype PDM and DCR algorithms are also described and results of their testing are presented.
microRNA signature to support clinical-decision making in patients with suspected Obstructive Sleep Apnea
GEO Series GSE229362. Homo sapiens. 53 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Effect of Presentation Format on Decision-Making in the Sharing Economy
<p>The coding rules and interpretations for each variable are provided in the comments attached to their respective column names. </p>
LabSCoC Moral Decision-Making - data
<p>This repository contains the raw data, preprocessed data, script and jamovi files of the <a href="https://zenodo.org/records/14070419"><em>LabSCoC Moral Decision-Making - project (https://zenodo.org/records/14070419)</em></a></p>
Exploring the Influence of GenAI-based Tools on Student's Planning and Decision Making in Software Modeling
<p>The file describes our sample modelling problem used for the controlled experiment with students. Also, the file contains two sets of initial questions for students to use to interact with their respective product owners and domain experts. </p>
Supporting students in making informed data sharing decisions: from comprehension to consenting
<p>Whether deciding to participate in research or gain access to online applications, studies on consent suggest that individuals often do not read or fail to understand the text outlining the conditions they agree to when giving their consent. Limited comprehension related to the risks of big data and habituation may contribute to consent being given in a manner that counters the best interests of individuals (i.e. a privacy paradox). Are those asking students to use new technologies and share their data ethically responsible for helping students learn to make informed decisions regarding the sharing of their data? As educational technology researchers, we conducted a quasi-experimental study involving 127 undergraduate students in which we explored the effects on study enrollment of supporting students in making informed data sharing decisions. Prior to reading the consent form for a study involving the evaluation of a collaborative learning application, one group of participants was primed to consider data sharing risks whereas the other group was not. The primed group scored lower on a consent form comprehension quiz yet had higher enrollment rates and were more likely to cite trust as reasoning for enrolling in the study.</p> <p>The data set is composed by:</p> <ul> <li>‘Classes’ spreadsheet: including the relation of flows, group of students, number of students, instructor id, day and time</li> <li>‘Participants’ spreadsheet: including the relation of ID (of each participant), if s/he consented, group class, flow and language</li> <li>‘Prior knowledge Survey’ spreadsheet: participant’s ID and ID, relevant prior knowledge questionnaire data (questions included).</li> <li>‘Quiz data’ spreadsheet: participant’s ID, quiz data and survey data (questions included)</li> <li>PDF including: <ul> <li>Table S1. Summary of participating classes </li> <li>Table S2. Consent form subsections and summaries </li> <li>Table S3. Consent form quiz questions, question types, and assigned points </li> <li>Table S4. Guiding questions in the prior knowledge questionnaire </li> <li>Table S5. Comparison of Flow A and Flow B quiz scores per question</li> </ul> </li> </ul>
Coded interviews - dataset - project: From dialog to deliberation. Non-public actors as (non)present participants in local decision-making
<p>Coded interviews - dataset. The interviews were conducted as part of the project <em>From dialog to deliberation. Non-public actors as (non)present participants in local decision-making, </em>financed within the framework of the Programme DIALOG by the Ministry of Science and Higher Education of the Republic of Poland, in the years 2018-2020 (project no. DIALOG 0163/2017). </p>
Decision-making, interactions and complexity across three cancer multidisciplinary teams
<p>Observational data on team decision-making, interactions, size and disciplinary composition, and case complexity. </p>
ESI_Electrifying Freight-Modeling the Decision Making Process for Battery Electric Truck Procurement
Open the record for dataset details and reuse information.
ESI_Electrifying Freight-Modeling the Decision Making Process for BET Procurement
Open the record for dataset details and reuse information.
The Transformative Impact of Digital Technologies on Entrepreneurial Decision-Making and Strategic Adaptation
Open the record for dataset details and reuse information.
Dataset related to article: "Comparative Accuracy of Bowel Ultrasound Versus Magnetic Resonance Enterography in Combination With Colonoscopy in Assessing Crohn's Disease and Guiding Clinical Decision-making"
<p>This record contains raw data related to article: "This record contains raw data related to article "Comparative Accuracy of Bowel Ultrasound Versus Magnetic Resonance Enterography in Combination With Colonoscopy in Assessing Crohn's Disease and Guiding Clinical Decision-making"</p> <p>The comparative accuracy of bowel ultrasound [US] versus magnetic resonance enterography [MRE] in combination with colonoscopy [CS] in assessing Crohn's disease [CD] and influencing the decision-making process is unknown.</p> <p>Methods:</p> <p>Consecutive ileo-colonic CD patients seen in a tertiary referral centre were prospectively assessed by MRE, CS, and bowel US, within 1 week. Sensitivity, specificity, accuracy, positive predictive value [PPV], and negative predictive value [NPV] of bowel US in assessing localisation, enhancement [presence of vascularisation at Power Doppler], active disease [presence of ulcers at colonoscopy], strictures, fistulas, and abscesses were calculated using CS + MRE findings together as a reference standard. Two blinded inflammatory bowel disease [IBD] specialists reviewed MRE and bowel US findings and were asked to decide the therapeutic strategy [continue versus change therapy]. Kappa agreement with clinical decision was calculated.</p> <p>Results:</p> <p>Sixty CD patients [36 with endoscopic disease activity, 28 with complications] were enrolled. For localisation, sensitivity, specificity, accuracy, PPV, and NPV of bowel US were 88%, 96%, 91%, 96%, and 85%, respectively; for enhancement, 87%, 92%, 89%, 93%, and 86%; for activity, 92%, 100%, 96%, 100%, and 94%; for strictures, 75%, 86%, 81%, 78%, and 83%; for fistulas, 100%, 98%, 98%, 66%, and 100%; for abscesses, 100%, 96%, 96%, 33%, and 100%. The concordance of management of CD patients based on bowel US or MRE findings, alone, compared with clinical decision, was 0.768 and 0.767, respectively [p <0.001]. The concordance between bowel US and MRE on management of CD patients was 0.800 [p <0.001].</p> <p>Conclusions:</p> <p>Bowel US is very accurate in assessing CD and is a non-invasive, easy-to-use tool to manage CD patients in clinical practice.</p>
Enhancing consent forms to support participant decision making in multimodal learning research
<p>Advances in the field of multimodal learning research is often accomplished by actively exploring new technologies and techniques related to the collection and analysis of data. However, the exploration of the ethical principles and procedures for governing the usage of the new technologies and techniques is not as eagerly pursued. As multimodal learning data grows in complexity, and to an extent invasiveness, a need is arising to scrutinize the ethical aspects of multimodal learning research. The process of informed consent in multimodal learning research may be an appropriate starting point as the results of bioethical studies suggest that many informed consent processes do not adequately support participant comprehension of the studies they consent to. Studies have shown that enhancing consent forms can improve participant comprehension and contribute to validating the consent received. The following data set include two types of enhanced consent forms: one form is written from a researcher perspective and the other from a participant perspective. Results of the study involving first year undergraduate students suggest that the overall level of comprehension did not differ between conditions. Yet, the participant-oriented consent form had significantly lower rates of enrollment. </p> <p>The data set is composed by:</p> <p>Anonymized Data Sheet: participant responses to the Informed Consent Test for Educational Technology and survey questions</p> <p>Enhanced Consent Forms: Enhanced consent forms for educational technology research for each condition in the study and in both English and Spanish. Form A is written from a research perspective (researcher-oriented) – which is how most forms are written. Form B is written from a participant perspective (participant-oriented).</p> <p> </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.