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Dataset results
142 results for “user evaluation”
Understanding Evaluation of Patient Information Sheets by User Testing Method
ClinicalTrials.gov study NCT03155334. IPD Sharing: YES. Countries: 2. Publications: 4.
A Study to Evaluate the Effect of Ovaleap® on the Pregnancy Rate and Clinical Effects as Well as the User-friendliness of the Ovaleap®-Pen.
ClinicalTrials.gov study NCT02809989. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluate Misoprostol for the Healing of Small Bowel Ulcers in Low-dose Aspirin Users Complicated by Small Bowel Bleeding
ClinicalTrials.gov study NCT01998776. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Evaluation of a Smartphone-delivered Peer-led Physical Activity Counselling Program for Manual Wheelchair Users
ClinicalTrials.gov study NCT02826707. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study to Evaluate the Oral Abuse Potential of PF614 in Non-Dependent Recreational Opioid Users (PF614-104)
ClinicalTrials.gov study NCT05571345. IPD Sharing: NO. Countries: 1. Publications: 2.
Evaluating Consumer m-Health Services for User Engagement and Health Promotion: An Organizational Field Experiment
ClinicalTrials.gov study NCT02206893. IPD Sharing: Not stated. Countries: 1. Publications: 1.
User Site Testing Study to Evaluate Usability of the Q300™ Device Under "Real-life Conditions" in a Reproductive Laboratory Environment Use
ClinicalTrials.gov study NCT06232720. IPD Sharing: NO. Countries: 1. Publications: 2.
IBDoc® Canadian User Performance Evaluation
ClinicalTrials.gov study NCT03408249. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: User-friendly and evidence-based tool to evaluate probability of eradication of aquatic non-indigenous species
1. The gap between practitioners and conservation or environmental management science is difficult to bridge. Managers sometimes use limited scientific information in their decision-making process, mainly because they have little time to review primary literature before making a decision. Making data readily available to managers is expected to improve the overall efficiency of management interventions. Here we present an approach to develop user-friendly applications for evidence-based management and illustrate the concept by presenting a simple computer program designed to evaluate the probability of eradication of aquatic non-indigenous species. 2. We conducted a review of case studies that attempted to control aquatic non-indigenous species and used a statistical model to relate the outcome (eradication or non-eradication) to characteristics of the populations and interventions conducted. Based on a few key variables, the model returned accurate probabilities of eradication as evaluated with a Receiver Operating Characteristic curve and jackknife and cross-validation procedures. 3. We packaged the statistical model in a user-friendly computer program that can be used by managers to 1) rapidly calculate the probability of success of a planned intervention with associated uncertainty, 2) compare the success probabilities of different possible interventions, and 3) prioritize what information should be collected to increase the reliability of estimates. 4. Synthesis and applications. Our decision-support tool is easy to implement, statistically flexible, and could be used for any type of conservation or management intervention, given a sufficient number of case studies available in the literature. We recommend that scientists develop such tools whenever they conduct reviews of effectiveness of intervention. This is likely to result in greater use of data by practitioners, increased reliability of cost–benefit analyses, and an overall increase in efficiency in conservation and environmental management.
Dataset for "Development and evaluation of a novel user-operated slider based audiometry method"
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Utah TIC consent builder user evaluation 1
<p>This dataset accompanies the manuscript titled "Consent Builder - An Innovative Tool for Creating Research Informed Consent Documents" submitted for publication in JAMIA Open July 2022 (Manuscript ID JAMIO-2021-0170). The data are a usability study conducted to evaluate a software tool for building consent documents for multi-center trials. The sample was project managers who had used to tool to design consent forms for trials.</p>
Trust and Believe – Should We? Evaluating the Trustworthiness of Twitter Users
<p><strong>Trust and Believe – Should We? Evaluating the Trustworthiness of Twitter Users</strong></p> <p>This model is used to analyze the Twitter users and assigns a score calculated based on their social profiles, the credibility of his tweets, the h-indexing score of the tweets. Users with a higher score are not only considered as more influential but also their tweets are considered to have greater credibility. The model is based on both the user level and content level features of a Twitter user. The details for feature extraction and calculating the Influence score is given in the paper.</p> <p><strong>Description</strong><br> To extract the features from Twitter and generate the dataset we used Python. A modAL framework is used to randomly selects ambiguous data points from the unlabeled data pool using three different sampling techniques and the human manually annotates the selected data. We generate a dataset for 50000 Twitter users and then used different classifiers to classify the Twitter user either as Trusted or Untrusted.</p> <p><strong>Organization</strong><br> The project consists of the following files:</p> <p><strong>Dataset.csv</strong><br> The dataset consists of different features of 50000 Twitter users (Politicians) without labels.</p> <p><strong>Manually_labeled-Dataset.csv</strong><br> This CSV file contains all those Twitter users classified manually as Trusted or Untrusted</p> <p><strong>feature_extraction.py</strong><br> This python script is used to calculate the Influence score of a Twitter user and further used to generate a dataset. The Influence score is based on:</p> <p>- Social reputation of the user<br> - Content score of the tweets<br> - Tweets credibility <br> - Index score for the number of re-tweets and likes</p> <p><strong>Activelearner.ipynb</strong><br> To classify a large pool of unlabeled data, we used an active learning model (ModAL Framework). A semi-supervised learning algorithm ideal for a situation in which the unlabeled data is abundant but manual labeling is expensive. The active learner randomly selects ambiguous data points from the unlabeled data pool using three different sampling techniques and the human manually annotates the selected data. Further, we use four different classifiers (Support Vector Machine, Logistic Regression, Multilayer Perceptron and Random Forest) to classify the Twitter user as either Trusted Or Untrusted.</p> <p><strong>twitter_reputation.ipynb</strong><br> We used different regression models to test its performance on our generated dataset (It is only for testing, now no more part of our work). We train and evaluate our models using different regression models.<br> Training and testing three regression models:<br> 1. Multilayer perceptron<br> 2. Deep neural network<br> 3. Linear regression</p> <p><strong>twitter_credentials.py</strong><br> In order to extract the features of Twitter users first, one need to authenticate by providing the credentials given in this file.</p> <p><strong>Screen names (Screen_name_1.txt, Screen_name_2.txt, Screen_name_3.txt)</strong><br> These text files consist of all the Twitter user screen_names. All of them are politicians. We remove the names of all those politicians whose accounts are private. In addition, all those politicians who have no followers/followings are not on the list are also removed. The text of the tweets are not saved. Furthermore, we also remove duplicate names.</p> <p><strong>References</strong><br> [1] https://stackoverflow.com/questions/38881314/twitter-data-to-csv-getting-error-when-trying-to-add-to-csv-file</p> <p>[2] https://stackoverflow.com/questions/48157259/python-tweepy-api-user-timeline-for-list-of-multiple-users-error</p> <p>[3] https://gallery.azure.ai/Notebook/Computing-Influence-Score-for-Twitter-Users-1</p> <p>[4] https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html</p> <p>[5] https://towardsdatascience.com/deep-neural-networks-for-regression-problems-81321897ca33</p>
Raw data used in Akshay Madhav Deshmukh and Ricardo Chalmeta 2025 DEVELOPMENT AND VALIDATION OF A USER EXPERIENCE EVALUATION QUESTIONNAIRE FOR VOICE USER INTERFACES
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Evaluation of User Experience for Dashboard Project Management Information System within the Project Operation Unit of a Telecommunication Company
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Evaluating a Noise Reduction Algorithm With Cochlear Implant Users
ClinicalTrials.gov study NCT05805384. IPD Sharing: YES. Countries: 1. Publications: 0.
Mobile Service Robot for Task-Oriented Stroke Therapy: User Evaluations
ClinicalTrials.gov study NCT02779517. IPD Sharing: NO. Countries: 1. Publications: 0.
User Evaluation of the MiniMed 620G and 640G Insulin Pumps
ClinicalTrials.gov study NCT01726621. IPD Sharing: Not stated. Countries: 4. Publications: 0.
Study Evaluating the Abuse Potential of NEURONTIN® in Healthy Non-drug Dependent, Recreational Opioid Users
ClinicalTrials.gov study NCT05319756. IPD Sharing: NO. Countries: 1. Publications: 0.
User Evaluation of the MiniMed 640G Insulin Pump
ClinicalTrials.gov study NCT01991548. IPD Sharing: NO. Countries: 2. Publications: 0.
Study Evaluating Abuse Potential of Lyrica® in Healthy Non-Drug Dependent Recreational Opioid Users
ClinicalTrials.gov study NCT05053126. IPD Sharing: NO. Countries: 1. Publications: 0.
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.