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1,719 results for “Making”
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>
Experiments of Multiple Decision Making in CDCL SAT Solvers
<p>With these experiments (submission), we show that Multiple Decision Making (MDM) has a positive impact on CDCL, for many different SAT application problems when alternating between different decision queues and frequently running the WalkSAT strategy. Overall, MDM allowed the CDCL search to solve extra 10 formulas in the SAT competition 2020 benchmark set, and achieves a minimum PAR-2 score of 3,473 compared to the state of the art solver Kissat.</p>
What Influences Algorithmic Decision-Making? A Systematic Literature Review on Algorithm Aversion
<p>Abstract</p> <p> </p> <p>With the continuing application of artificial intelligence (AI) technologies into decision-making, algorithmic decision-making is becoming more efficient, even often outperforming human counterpart. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of this literature, we systematically review 80 empirical studies identified through searching in seven academic databases and performing citation chaining. We map the emergent themes following grounded theory and categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little effort has been given to explore the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Implications for research and practitioners about the findings of the study are discussed.</p>
SLR on Algorithmic Decision Making
<p>Abstract</p> <p> </p> <p>With the continuing application of artificial intelligence (AI) technologies into decision-making, algorithmic decision-making is becoming more efficient, even often outperforming human counterpart. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of this literature, we systematically review 80 empirical studies identified through searching in seven academic databases and performing citation chaining. We map the emergent themes following grounded theory and categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little effort has been given to explore the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Implications for research and practitioners about the findings of the study are discussed.</p> <p> </p> <p> </p>
Data: Promoting informed decision making about Maternal Pertussis Vaccination in the Netherlands
<p>We used Intervention Mapping to develop and test two interventions promoting informed decision making about Maternal Pertussis Vaccination. Anonymised focus-group/interview data, trial data in which one of the interventions is tested, and data from an experiment testing strategies to reduce negative affect in the decision making process about MPV are included. Data descriptions can be found in the accompanied files. Results are yet to be published by the authors.</p>
Making Predictions using Large Scale Gaussian Processes
One of the key problems that arises in many areas is to estimate a potentially nonlinear function [tex] G(x, \theta)[/tex] given input and output samples [tex] ( X,y ) [/tex] so that [tex]y approx G(x, \theta)[/tex]. There are many approaches to addressing this regression problem. Neural networks, regression trees, and many other methods have been developed to estimate [tex]$G$[/tex] given the input output pair [tex] ( X,y ) [/tex]. One method that I have worked with is called Gaussian process regression. There many good texts and papers on the subject. For more technical information on the method and its applications see: http://www.gaussianprocess.org/ A key problem that arises in developing these models on very large data sets is that it ends up requiring an [tex]O(N^3)[/tex] computation where N is the number of data points and the training sample. Obviously this becomes very problematic when N is large. I discussed this problem with Leslie Foster, a mathematics professor at San Jose State University. He, along with some of his students, developed a method to address this problem based on Cholesky decomposition and pivoting. He also shows that this leads to a numerically stable result. If ou're interested in some light reading, I’d suggest you take a look at his [recent paper]( ) (which was accepted in the Journal of Machine Learning Research) posted on dashlink. We've also posted code for you to try it out. Let us know how it goes. If you are interested in applications of this method in the area of prognostics, check out our [new paper](/dashlink/resources/51/) on the subject which was published in IEEE Transactions on Systems, Man, and Cybernetics.
Genotyping Porcine circovirus 3 (PCV-3) nowadays: does it make sense?
<p>Supplementary data 1. List of the accession numbers of the sequences used in the present study. Relevant metadata are also reported.<br> Supplementary figure 1. Maximum likelihood phylogenetic tree based on the ORF2 sequences. The tree nodes have been colour coded based on the respective bootstrap support.</p>
Raw data for Peters et al., ENeuro (2020): Dopamine and risky decision-making in gambling disorder
<p>Raw behavioral data for Peters et al. (2020), eNeuro: Dopamine and risky decision-making in gambling disorder. The data structure (Matlab matrix) is explained in the Readme.txt</p>
Dopaminergic modulation of the exploration/exploitation trade-off in human decision-making
<p>raw choice data of the four-armed bandit task</p> <p>1. fMRI study (31 participants, 3 drug conditions (placebo, L-dopa, haloperidol) )</p> <p>2. pilot study (16 participants)</p>
Data set from Varrica A, Caldaroni F, Saitto G, Satriano A, Lo Rito M, Chiarello C, Ranucci M, Frigiola A, Giamberti A. Outcomes and Quality of Life After Ross Reintervention: Would You Make the Same Choice Again? Ann Thorac Surg. 2020 Jul;110(1):214-220. doi: 10.1016/j.athoracsur.2019.10.007. Epub 2019 Nov 23. PMID: 31770502.
<p>Data set from Varrica A, Caldaroni F, Saitto G, Satriano A, Lo Rito M, Chiarello C, Ranucci M, Frigiola A, Giamberti A. Outcomes and Quality of Life After Ross Reintervention: Would You Make the Same Choice Again? Ann Thorac Surg. 2020 Jul;110(1):214-220. doi: 10.1016/j.athoracsur.2019.10.007. Epub 2019 Nov 23. PMID: 31770502</p> <p> </p> <p>This is the abstract:</p> <p><strong>Background: </strong>The Ross procedure was introduced as a long-term if not definitive solution for aortic pathology. However, the rate of reoperation is not negligible.</p> <p><strong>Methods: </strong>This single-center prospective study assessed the general outcome of Ross reoperation and patients' perceived quality of life compared with 2 control groups (Ross non-reoperation and mechanical aortic valve replacement). Patient's preference regarding the choice between mechanical aortic valve and Ross procedure was investigated in a subgroup that could theoretically have been directed to either of the 2 procedures.</p> <p><strong>Results: </strong>Between 2005 and 2017, 64 consecutive patients underwent reoperation after Ross. Median age was 31 years. Median freedom from reoperation after the Ross procedure was 136 months. An autograft reoperation was required in 49, and 25 had homograft failure. No in-hospital death was recorded. Mean follow-up was 77 months (range, 6-164 months). Quality of life was assessed with the 36-Item Short Form Health Survey questionnaire. The Ross reoperation group showed a lower score involving psychological concerns compared with the other groups. In the reoperated-on patients group, 52 had adequate aortic annulus dimensions to receive a prosthetic valve instead of a Ross procedure. When asked whether they would make the same choice, only 31% confirmed the preference.</p> <p><strong>Conclusions: </strong>Reoperations after Ross procedure have low mortality and morbidity. Long-term follow-up showed a high quality of life, even after reoperations. However, owing to psychological concerns after the redo operation, when choosing a Ross procedure, it is our duty to thoroughly explain to patients that a high level of disillusion is predictable in case of reoperations.</p>
Decision-making across four cancer multidisciplinary teams
<p>Observational data of four cancer teams measuring quality of decision-making processes.</p>
Data and code for project "What makes users click: the effect of news values negativity and surprise in news headlines on the CTR"
<p>This repository includes the following files and corresponding Python codes saved in Jupiter Notebooks:</p> <p><strong>upworthy-archive-confirmatory-packages-03.12.2020.csv:</strong> Original dataset downloaded from The Upworthy Research Archive (https://osf.io/jd64p/)</p> <p><strong>cleaned_data.csv</strong>: <strong>upworthy-archive-confirmatory-packages-03.12.2020.csv</strong> cleaned with <strong>notebook_clean_data.ipynb</strong></p> <ul> <li>Data sorted by <em>clickability_test_id</em></li> <li>Headlines with 0 clicks deleted</li> <li>Variables <em>ctr</em>, <em>mean_ctr</em>, and <em>lift</em> created</li> <li>Variable <em>winner</em> converted to dummy variable</li> <li>Only kept first occurrence of headlines that occur multiple times in the dataset per test</li> <li>Deleted tests with only one headline</li> </ul> <p><strong>sample_manual_annotations.csv</strong>: sample of 300 headlines of <strong>cleaned_data.csv </strong>retrieved with <strong>notebook_sample_manual_annotations.ipynb</strong></p> <p><strong>inter-annotator_sentiment.csv</strong>: input for <strong>notebook_inter-annotator_scores_confusion_matrix.ipynb</strong> to calculate inter-annotator agreement scores with regard to annotating sentiment</p> <p><strong>inter-annotator_emotion.csv</strong>: input for <strong>notebook_inter-annotator_scores_confusion_matrix.ipynb</strong> to calculate inter-annotator agreement scores with regard to annotating emotion</p> <p><strong>input_sentiment.tsv</strong>: input for <strong>notebook_sentiment_emotion_test_set.ipynb </strong>for sentiment analysis on sample of 300 headlines in <strong>sample_manual_annotations.csv</strong></p> <p><strong>input_emotion.tsv</strong>: input for <strong>notebook_sentiment_emotion_test_set.ipynb </strong>for emotion analysis on sample of 300 headlines in <strong>sample_manual_annotations.csv</strong></p> <p><strong>input_all_headlines.tsv</strong>: input for <strong>notebook_sentiment_emotion_final_annotation.ipynb</strong> to annotate all headlines in <strong>cleaned_data.csv</strong></p> <p><strong>final_dataset.csv</strong>: output from <strong>notebook_merge_dataframes.ipynb</strong> that merges all csv output files from <strong>notebook_sentiment_emotion_final_annotation.ipynb </strong>with <strong>cleaned_data.csv</strong></p> <p><strong>notebook_descriptive_statistics.ipynb</strong>: Python code to retrieve descriptive statistics of various variables in <strong>cleaned_data.csv</strong></p> <p><strong>NRC-Emotion-Lexicon-Wordlevel-v0.92.txt:</strong> NRC Emotion Lexicon used in <strong>notebook_sentiment_emotion_test_set.ipynb </strong>and<strong> notebook_sentiment_emotion_final_annotation.ipynb</strong></p>
Does childhood maltreatment make us more morally disengaged? The indirect effect of expressive suppression
<p>Data set for </p> <p><strong>Does childhood maltreatment make us more morally disengaged? The indirect effect of expressive suppression</strong></p> Enable Ginger<em>Cannot connect to Ginger</em> Check your internet connection<br> or reload the browserDisable in this text fieldRephraseRephrase current sentence<span class="ginger-floatingG-bar-tool-mistakes-count">2</span>Log in to edit with Ginger×
COAWST and WRF-only simulated tracks and SST for TCs making landfall in South China
<p>This dataset is associated with the following papers:</p> <ul> <li>Lok et al. (2021): Importance of Air-Sea Coupling in Simulating Tropical Cyclone Intensity at Landfall,<em> accepted by Advances in Atmospheric Sciences</em></li> <li>Lok and Chan (2018): Simulating seasonal tropical cyclone intensities at landfall along the South China coast,<br> <em>Climate Dynamics</em>, <strong>50(1-2)</strong>: 2661–2672</li> </ul> <p>Details of the files available in this dataset:</p> <ul> <li>sst_cfsr-wrf.zip - Sea surface temperature from CFSR as seen by WRF in Lok and Chan (2018)</li> <li>sst_cwst.zip - Sea surface temperature simulated by COAWST in Lok et al. (2021)</li> <li>tc_track_wrf.zip - Simulated tropical cyclone tracks of Lok and Chan (2018)</li> <li>tc_track_cwst.zip - Simulated tropical cyclone tracks of Lok et al. (2021)</li> </ul>
EEG-based approach for predicting varied human cognitive decision-making in driving
<p>These data are part of the data sample of the paper "EEG-based approach for predicting varied human cognitive decision-making in driving". For use by editors and reviewers. This includes one compressed files: 'RAW Data.zip'. 'RAW Data.zip' includes EEG Data and driving behavior data as well as code for extracting features and training individual models. A detailed description can be found in 'Read_Me.txt'. </p>
EEG-based approach for predicting varied human cognitive decision-making in driving
<p>These data are part of the data sample of the paper "EEG-based approach for predicting varied human cognitive decision-making in driving". For use by Nature Communications editors. This includes one compressed files: 'RAW Data.zip'. 'RAW Data.zip' includes EEG Data and driving behavior data as well as code for extracting features and training individual models. A detailed description can be found in 'Read_Me.txt'. </p>
Investor Decision making and Founder Social Capital
<p>Data set with analysis of choice models that best describe investor decisions based on founder human and social capital </p>
Moral decision making in healthcare and medical professions during the COVID-19 pandemic
<p>The Data supports the findings of the article with the same name.</p>
Worst Case for high stakes surgical decision making conversations
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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.