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.
284
datasets available to search
ShareScore release 0.9.0
Dataset results
284 results for “reasoner”
Efficient Validation of SHACL Shapes with Reasoning
<p>This dataset comprises the experimental data used in our paper, "Efficient Validation of SHACL Shapes with Reasoning".</p> <p>By making these datasets publicly available, we aim to facilitate other researchers in reproducing our experimental results or conducting further research based on our work.</p>
Children' and adolescents' reasoning about exclusion in novel groups
<p>This study examined children’s and adolescents’ reasoning about the exclusion of others in peer and school contexts. Participants (80 8-year-olds, 85 11-year-olds, 74 14-year-olds, and 73 20-year-olds) were asked to judge and reason about the acceptability of exclusion from novel groups by children and school principals. Three contexts for exclusion between two groups were systematically varied: unequal economic status, geographical location, or a control (no reason provided for group differences). Regardless of condition, participants believed that exclusion was less acceptable in peer than school contexts, and when children excluded rather than principals. Participants also used more moral and less social conventional reasoning for peer than school contexts. In terms of condition, whereas 8-year-olds rated exclusion based on unequal economic status as less acceptable than when based on geographical location or no reason when enacted by a principal, 14-year-olds rated the unequal economic condition as more acceptable than the other two contexts. Eleven- and 20-year-olds did not distinguish economic status differences. The findings suggest that children and adolescents are sensitive to context and take multiple variables into account, including the type of group difference (socioeconomic status or other reasons), authority status of the perpetrator of exclusion, and setting (school or peer). Patterns may have differed from past research because of the socio-cultural context in which exclusion was embedded and the contexts of group differences.</p>
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions
<p>This is the math_sync dataset that used in the MathChat paper.</p>
Data From: ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial
<p>Dataset Info</p> <p><strong>1) Immediate Test</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants’ iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants. (0: No, 1: Yes)<br>- Column E to H represent the scores in four different uncomplicated urinary tract infection (UTI) Key-Features Questions Items separately. <br>- Column I represents the total scores in uncomplicated UTI Key-Features Questions Items. <br>- Column J to M represent the scores in four different complicated UTI Key-Features Questions Items separately. <br>- Column N represents the total scores in complicated UTI Key-Features Questions Items. <br>- Column O to R represent the scores in four different pyelonephritis Key-Features Questions Items separately. <br>- Column S represents the total scores in pyelonephritis Key-Features Questions Items. <br>- Column T represents the total scores in immediate test. </p> <p><strong>2) Delayed Test</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants. (0: No, 1: Yes)<br>- Column E to H represent the scores in four different uncomplicated urinary tract infection (UTI) Key-Features Questions Items separately. <br>- Column I represents the total scores in uncomplicated UTI Key-Features Questions Items. <br>- Column J to M represent the scores in four different complicated UTI Key-Features Questions Items separately. <br>- Column N represents the total scores in complicated UTI Key-Features Questions Items. <br>- Column O to R represent the scores in four different pyelonephritis Key-Features Questions Items separately. <br>- Column S represents the total scores in pyelonephritis Key-Features Questions Items. <br>- Column T represents the total scores in delayed test. </p> <p><strong>3) Pre-Intervention Survey on Critical Approach to AI</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants (0: No, 1: Yes).<br>- Column E to J represent the responses of the participants to survey questions before the intervention. Each column is evaluated on a scale from 1 to 7. As it progresses from 1 to 7, the agreement status of participants to survey questions increases. (1: No agreement at all, 7: completely agree)</p> <p><strong>4) Post-Intervention Survey on Critical Approach to AI</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants (0: No, 1: Yes).<br>- Column E to J represent the evaluation of the participants to survey questions after intervention. Each column is evaluated on a scale from 1 to 7. As it progresses from 1 to 7, the agreement status of participants to survey questions increases. (1: No agreement at all, 7: completely agree)</p>
Towards an Analogical Reasoning with LLMs in Software Engineering Education
<p>Data collected from professors and students using our approach to teach/understand SE concepts </p>
Semantic competency as a marker of clinical reasoning performance
<p>Purpose: This study sought to explore the relationship between semantic competence (or dyscompetence) displayed during think alouds performed by resident and attending physicians and clinical reasoning performance.</p> <p>Methods: Internal medicine resident physicians and practicing internists participated in "think-alouds" performed after watching videos of typical presentations of common diseases in internal medicine. The think-alouds were evaluated for the presence of semantic competence and dyscompetence and these results were correlated with clinical reasoning performance. </p> <p>Results: We found that the length of think-aloud was negatively correlated with clinical reasoning performance. Beyond this finding, however, we did not find any other significant correlations between semantic competence or dyscompetence and clinical reasoning performance.</p> <p>Conclusion: While this study did not produce the previously hypothesized findings of correlation between semantic competence and clinical reasoning performance, we discuss the possible implications and areas of future study regarding the relationship between semantic competency and clinical reasoning performance.</p>
Roadmap to Reasoning in Microservice systems: A Rapid Review Dataset
<p>It contains data retrieved from Scopus based on the following query:</p> <p>TITLE-ABS-KEY ( ( microservices OR microservice OR micro-service ) AND architecture AND ( ( reconstruction OR "Intermediate Representation" ) OR reasoning OR ( evolution AND changes ) ) AND ( technique OR solution OR model ) ) AND PUBYEAR > 1959 AND PUBYEAR < 2023 AND PUBYEAR > 1959 AND PUBYEAR < 2023 </p> <p> </p> <p>The filter process is applied by the authors to build a microservice systems reasoning roadmap.</p>
Materials for "Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning"
<p>This repository contains materials including source code, datasets and results for "Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning"</p>
TraVLR: Now You See It, Now You Don't! A Bimodal Dataset for Evaluating Visio-Linguisic Reasoning
<p>TraVLR dataset for evaluation of visio-linguistic reasoning and cross-modal transfer. Four reasoning tasks are supported: spatiality, cardinality, numerical comparison and quantifiers. For some of the datasets, we provide two versions which differ only in their training sets: one with 8k training examples, and another with 32k training examples. The validation sets, in-distribution and out-of-distribution test sets are identical. </p>
Housework Reallocation Between Genders and Generations during China's COVID-19 Lockdowns: Patterns & Reasons
<p>Coding summary of interview participants</p>
EnviroStream: A Stream Reasoning Benchmark for Climate and Ambient Monitoring
<p>Stream Reasoning (SR) focuses on developing advanced approaches for applying inference to dynamic data streams; it has become increasingly relevant in various application scenarios such as IoT, Smart Cities, Emergency Management, and Healthcare, despite being a relatively new field of research.</p> <p>The current lack of standardized formalisms and benchmarks has been hindering the comparison between different SR approaches. <br> We propose a new benchmark, called <em>EnviroStream</em>, for evaluating SR systems on weather and environmental data from two European cities. </p> <p>The benchmark includes queries and datasets of different sizes. We adopt <em>I-DLV-sr</em>, a recently released SR system based on Answer Set Programming, as a baseline experiment. We illustrate how the queries can be modeled via <em>I-DLV-sr</em> input language and report evaluation times. We also assess continuous online reasoning via a web application.</p> <p>############################################################################################</p> <ul> <li>Data can and queries can be also downloaded via the GitHub repository: <a href="https://github.com/DeMaCS-UNICAL/EnviroStream">https://github.com/DeMaCS-UNICAL/EnviroStream</a></li> <li>Real-time data can be visualized via the following link: <a href="https://experiments.demacs.unical.it/">https://experiments.demacs.unical.it/</a></li> </ul>
Combining Proofs for Description Logic and Concrete Domain Reasoning - RuleML+RR23 - Resources
<p><strong>experiment-RuleML+RR-2023.zip</strong> contains the materials used in the experiment described in the paper, and a README file with instructions on how to rerun the experiment.</p>
Reflection and Feedback in Clinical Reasoning
ClinicalTrials.gov study NCT03472001. IPD Sharing: NO. Countries: 1. Publications: 3.
Serious Game Versus Traditional Teaching to Improve Clinical Reasoning Skills
ClinicalTrials.gov study NCT03428269. IPD Sharing: NO. Countries: 1. Publications: 2.
Physician Reasoning on Diagnostic Cases With Large Language Models
ClinicalTrials.gov study NCT06157944. IPD Sharing: NO. Countries: 1. Publications: 1.
Are Perception of Pain and Psychological Distress Before Knee Arthroplasty Associated With Reason for Revision?
ClinicalTrials.gov study NCT05105646. IPD Sharing: NO. Countries: 1. Publications: 17.
Reasons for Prehospital Delay in Patients With Acute Ischemic Stroke
ClinicalTrials.gov study NCT02798770. IPD Sharing: YES. Countries: 1. Publications: 1.
Eyes That Do Not Meet the Eligibility Criteria of Clinical Trials on Age-Related Macular Degeneration: Proportion of the Real-World Patient Population and Reasons for Exclusion
ClinicalTrials.gov study NCT04836143. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Ablation of Supraventricular Arrhythmias With As Low As Reasonably Achievable X-Ray Exposure
ClinicalTrials.gov study NCT04716270. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Diagnostic Reasoning With Customized GPT-4 Model
ClinicalTrials.gov study NCT06911645. 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.