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284 results for “reasoning”
Brain Development of Deductive Reasoning
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Brain Correlates of Deductive Reasoning in Adults
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Individual Differences in Fluid Reasoning and RAPM-like Problem Solving
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Datset of automated economic reasoning problems for QE / SMT
<p>This dataset is generated by 45 economics theorems "A implies H" where A are assumptions and H a hypothesis. These are taken from textbooks and papers and chosen for their suitability for automatic solution with Quantifier Elimination (QE) or Satisfiability Modulo Theory (SMT) technology. </p> <p>For each theorem three problems are generated: checking the compatibility of the assumptions; checking for the existence of an example of the theorem; and checking for the existence of a counterexample. </p> <p>There are three files:</p> <p>1. EconomicReasoningBenchmarks-Apr18-SMT2.zip</p> <p>This zip file will uncompress into a directory with 45 files, one for each theorem stating the three existence checks within the SMT2 format. Thus these files are suitable for use with any SMT solver supporting the theory.</p> <p> </p> <p>2. EconomicReasoningBenchmarks-Apr20-Redlog.txt</p> <p>This plain text file can be run with the Redlog Package for the Computer Algebra System Reduce. It contains definitions and calls to Redlog's QE command to check for a counterexample for all 45 theorems.</p> <p> </p> <p>3. EconomicReasoningBenchmarks-Apr23-Maple.txt</p> <p>This plain text file is for use with the Maple Computer Algebra System. For each theorem it provides the polynomials used in the Tarski formula to check for a counterexample. The polynomials are given as a list of lists with the outer list representing logical OR between entries and each inner list logical AND. </p> <p> </p>
Dataset regarding the « Reasons for concern » about climate change from figures in IPCC and related publications
<p>This data corresponds to the 'burning ember' diagrams from IPCC reports and related publications (IPCC TAR, Smith et al. 2009 for AR4-related embers, AR5 and SR15). It was used to build figure 3 of Zommers et al. 2020 (<em>Burning Embers: Towards more transparent and robust climate change risk assessments</em>. Accepted for publication in Nature Reviews Earth & Environment). The data provided here is the result of extraction of information from the original figures, as presented in the related technical document <a href="https://doi.org/10.5281/zenodo.3992856">10.5281/zenodo.3992856</a>. As explained in the Supplementary Information of Zommers et al. 2020 and the technical document, this is not data from the IPCC. The provided values are approximations of the global mean temperature increase corresponding to each change in risk in the original diagrams. The rigour of the preparation process and the limitations of the dataset are explained in the technical document.</p>
Analogical reasoning sequential design fMRI
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REsolved ALMA and SMA Observations of Nearby Stars (REASONS)
<p>This is the data release of the REASONS survey, a sample of planetesimal belts around nearby stars resolved interferometrically (see journal article for full details). Every tar file corresponds to a planetary system, and contains:<br>Data:<br>1 - the calibrated continuum visibility data in CASA .ms format<br>2 - a FITS file with the non-primary-beam-corrected image of the system. <br>3 - a PDF image of the system<br>Visibility modelling results:<br>4 - an ASCII file ('*_fitresults.txt') containing the results of the MCMC visibility fitting, as reported in Table X in the article but containing extra parameters fitted (such as background sources, extra astrometry for fits of multiple datasets/pointings, weight-rescaling factors)<br>5 - an ASCII notes ('*_fitnotes.txt') file, which should always be consulted when interpreting the fit results, as it typically points out peculiarities in the posterior probability distributions.<br>6 - a PDF of the triangle ('corner') plot of the N-dimensional posterior probability distribution of the fitted parameters, which should be consulted to get a better idea of the results reported in the ASCII files.<br>7 - a PDF image (targetstar_imagecombo.pdf) showing the data, model, residuals and visibility data+model curves, to visually evaluate the goodness of the visibility fit.<br>8 - a PDF image (targetstar.pdf) showing the multiwavelength photometry for the planetary system and a star+belt modified-blackbody fit, with parameters reported in the journal article.<br>Please refer to the journal article for more details on the methods used to obtain these data and modelling results.</p>
Data for: Klein et al., Viscosity of aqueous ammonium nitrate--organic particles: Equilibrium partitioning may be a reasonable assumption for most tropospheric conditions, egusphere-2024-1459
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental and modelled data to the figures shown in the main manuscript and Appendix.</p> <p>Figure 3B AIOMFAC-VISC (AIOMFAC-VISC modelling of sucrose)</p> <p>Figure 3B Experimental (Viscosity measurements of sucrose)</p> <p>Figure 4 (Viscosity measurements of ammonium nitrate - sucrose - water mixtures)</p> <p>Figure 5 A and C (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using mixing rules)</p> <p>Figure 5 B and D (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using AIOMFAC-VISC)</p> <p>Figure 6 (Viscosity estimations of inorganic - sucrose - water mixtures using mixing rules)</p> <p>Figure 7 (Mixing times for ammonium nitrate - sucrose - water and Toluene SOA - sucrose - water aerosol particles for varies cities) </p> <p>Figure A2 (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using a mass fraction based mixing rules)</p>
Database of the assessment of two instructional design variables in verbal reasoning and mathematical reasoning courses from the perspective of a Peruvian pre-university center students
<p>These are the data obtained from 4 evaluations made to a sample of 630 students of a Peruvian pre-university center. First, two study variables were evaluated: teaching sequence compliance and the student's educational need according to the perspective of 315 students of the verbal reasoning course. Second, the same study variables were assessed in the remaining 315 students of the mathematical reasoning course. This information is being used in research to obtain an academic degree and later to make a publication of a scientific article.</p> <p>For the treatment of these data, inferential statistics was used through the software R version 3.4.4 (2018) The R Foundation for Statistical Computing.</p>
lilGym: Natural Language Visual Reasoning with Reinforcement Learning, model files
<p>Baselines models for the paper <a href="https://lil.nlp.cornell.edu/lilgym"><em>lil</em>Gym: Natural Language Visual Reasoning with Reinforcement Learning</a>.</p>
Logical reasoning study
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Spatial Span and Matrix Reasoning data from the UW-Madison Learning and Transfer Lab
<p><strong>Matrices_SpatialSpan.csv</strong> includes one row for every mouse click for every trial for each participant's spatial span performance (for similar spatial span methods see Cochrane, Simmering, & Green, 2019, PLOS One). Participant IDs, trial numbers, the presence [f] or absence [n] of feedback, and task order (spatial span first or spatial span second) are included alongside by-click accuracy. Also included are each participants' average scores on a subset of items from the UCMRT (Pahor et al., 2019, Beh. Res. Meth) and from the matrices developed at Sandia National Laboratories (Matzen et al., 2010, Beh. Res. Meth.).</p> <p><strong>robustCor.R </strong>is R code implementing a test of bivariate correlation. Univariate Yeo-Johnson transformations are applied, then bootstrapped correlations coefficients are calculated. Point estimates, CI, and Bayes Factors are each returned.</p> <p>Data were collected and code was developed as part of A. Cochrane's dissertation work at the University of Wisconsin - Madison under the supervision of C. Shawn Green.</p>
OWL Reasoner Evaluation Results
<p>Correctness, performance and energy impact evaluation results produced with <a href="http://swot.sisinflab.poliba.it/evowluator">evOWLuator</a> for the following OWL reasoners:</p><ul><li><a href="https://bitbucket.org/dtsarkov/factplusplus">Fact++</a> (version 1.6.5);</li><li><a href="http://www.hermit-reasoner.com">HermiT</a> (version 1.3.8);</li><li><a href="http://jfact.sourceforge.net">JFact</a> (version 1.2.1);</li><li><a href="https://www.derivo.de/en/products/konclude/">Konclude</a> (version 0.6.2-544);</li><li><a href="http://swot.sisinflab.poliba.it/minime">Mini-ME</a> (version 2.0);</li><li><a href="http://swot.sisinflab.poliba.it/minime-swift">Mini-ME Swift</a> (version 1.0);</li><li><a href="https://github.com/stardog-union/pellet">Pellet</a> (version 2.3.1);</li><li><a href="http://trowl.org">TrOWL</a> (version 1.5).</li></ul><p>Ontologies mentioned in the csv files are part of the <a href="https://zenodo.org/record/10791">ORE 2014 Reasoner Competition</a> and <a href="https://bioportal.bioontology.org">BioPortal</a> datasets.</p><p><strong>Publication:</strong> <a href="http://sisinflab.poliba.it/Publications/2021/SBRGL21">A multiplatform energy-aware OWL reasoner benchmarking framework</a></p>
Automated Reasoning in Temporal DL-Lite
<p><strong>Automated Reasoning in Temporal DL-Lite*</strong></p> <p>We investigate the feasibility of automated reasoning over temporal DL-Lite (TDL-Lite) knowledge bases (KBs). We translate TDL-Lite KBs into a fragment of First Order temporal logic and then into LTL, and apply off-the-shelf LTL and FO-based reasoners for checking the satisfiability. We conduct various experiments to analyse the size of the LTL translation as well as the runtime performance of different reasoners on toy scenarios and on randomly generated TDL-Lite KBs. To improve the reasoning performance when dealing with large ABoxes, our work also proposes an approach for abstracting temporal assertions in KBs. We run several experiments with this approach to assess the effectiveness of the technique by measuring the gain in terms of the size of the translation, and the number of both ABox assertions and individuals. We also measure the runtime of the solvers on such abstracted KBs. Lastly, in an effort to make the usage of TDL-Lite KBs a reality, we present a fully-fledged tool with a graphical interface to design and reason over them. Our interface is based on conceptual modeling principles and it is integrated with our translation tool and a temporal reasoner.</p> <p>(*) This work has been submitted to the Journal of Automated Reasoning</p>
Supplemental data for submission "Bridging between LegalRuleML and TPTP for Automated Normative Reasoning"
<p>These files are supplementary material to the submission<br> Bridging between LegalRuleML and TPTP for Automated Normative Reasoning<br> by<br> Alexander Steen and David Fuenmayor<br> submitted to the 6th International Joint Conference on Rules and Reasoning (RuleML+RR 2022), 2022.</p> <p>Files ex1.lrml.xml and ex2.lrml.xml are two example LegalRuleML files.<br> Files ex1.dsl.p and ex2.dsl.p are two examples from above translated to the NMF DSL.<br> The files ex1.output.X.p and ex2.output.X.p are the translations of the NMF files into the concrete logic X (X = SDL or X = cJ (Carmo Jones) or X = aqvist (system E)).</p> <p>Alexander Steen, <alexander.steen@uni-greifswald.de></p>
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning
<p>This repository contains resources developed for the paper: Gupta, V., Zhang, S., Vempala, A., He, Y., Choji, T., Srikumar V., Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning. In: Proceeding of the The Association of Computational Linguistic 2022 (ACL ’22), May 2022".</p> <p><strong>I</strong>t includes the relevant rows marking for the train set of the InfoTabS dataset (<a href="https://infotabs.github.io/">https://infotabs.github.io/</a>) Gupta et. al. 2020 [1]. </p> <p>We followed the protocol of Gupta et al. (2022) [2] which annotated the development and test sets (alpha1, alpha2, alpha3) sets: one table and three distinct hypotheses formed a HIT. We divide the tasks equally into 110 batches, each batch having 51 HITs each having three examples. In total, we collected 81,282 annotations from 90 distinct annotators. </p> <p>Overall, twenty five annotators completed over 1000 tasks, corresponding to 87.75 % of the examples, indicating a tail distribution with the annotations. Overall, 16,248 training set table-hypothesis pairs were successfully labeled with the evidence rows. On average, we obtain 89.49% F1-score with equal precision and recall for annotation agreement when compared with majority vote. It also includes an annotation template used on the mTurk platform for crowdsourcing. The cited datasets were used in this work. The cited datasets were used in this work.</p> <p>Files to access the annotation follow the below structure:</p> <ul> <li>annotation_batches</li> <li>batches_test: contain final results “.csv” files for all the development and test set batches (taken from Gupta et. al. 2022)</li> <li>batches_train: contain our annotated results “.csv” files for all the train set batches</li> <li>README.md: contain the readme for the annotation batches details</li> <li>main_template_row_relevant.html: content the annotation template used for each HIT i.e. marking the relevant row for each instance</li> <li>annotation_stats.md: Have details of the annotation statistics</li> <li>release_mturk: contain the release batches details i.e. csv for corresponding batches released</li> </ul> <p>Files to recreate the annotation statistics and pre-processed data:</p> <ul> <li>results_test: contain the pre-processed batch csv for dev and test set each batch. In the dev and test set. The integrated one computes the agreement stats for all the batches.(taken from Gupta et. al. 2022)</li> <li>results_train: similar to resutls_train expect contain the pre-processed batch csv for train set.</li> <li>scripts: contain the scripts needed to create the csv in the results_test and results_train sets. The script title denotes the function (the statistic it computes) for the scripts.</li> <li>src: the scripts use these python files to create the relevant statistics.</li> </ul> <p><strong>References:</strong></p> <p>[1] InfoTabS: Inference on Tables as Semi-structured Data, Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar, <a href="https://acl2020.org/">ACL 2020</a></p> <p>[2] Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning, Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Srivastava, Maneesh Singh, Vivek Srikumar, <a href="https://transacl.org/index.php/tacl">TACL 2022</a>, presented at <a href="https://www.2022.aclweb.org/">ACL 2022</a></p>
Text-fig. 4. Correlations of the strata in the Urema Graben, the Cheringoma Plateau and other parts of Mozambique proposed by various authors. The positions of fossiliferous units such as the Grudja and Cheringoma formations have been reasonably stable, whereas correlations of other rock units, especially the Mazamba Formation and the volcanics, have varied a great deal. The time scale is from Gradstein et al. (2020). in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 4. Correlations of the strata in the Urema Graben, the Cheringoma Plateau and other parts of Mozambique proposed by various authors. The positions of fossiliferous units such as the Grudja and Cheringoma formations have been reasonably stable, whereas correlations of other rock units, especially the Mazamba Formation and the volcanics, have varied a great deal. The time scale is from Gradstein et al. (2020).
CONSOLE_WP2_Task2.2_Data collection, selection and diagnosis of reasons for successes and failures of initiatives in Europe_second level diagnosis_2022.10.25
<p>This dataset contains data on the second level analysis of existing and highly potential contract solutions throughout Europe within the EU-H2020 project CONSOLE (CONtract Solutions for Effective and lasting delivery of agri-environmental-climate public goods by EU agriculture and forestry).</p> <p>The dataset is organized in one document (.rtf). The document represents the list of the 26 in-depth case studies, including data on case study ID, AECPGs addressed, performance description and evaluation. The dataset is related to the Deliverable 2.3.</p>
CONSOLE_WP2_Task2.2_Data collection, selection and diagnosis of reasons for successes and failures of initiatives in Europe_first level diagnosis_2022.10.25
<p>This dataset contains data on the first level analysis of existing and highly potential contract solutions throughout Europe within the EU-H2020 project CONSOLE (CONtract Solutions for Effective and lasting delivery of agri-environmental-climate public goods by EU agriculture and forestry).</p> <p>The dataset is organized in one document (.rtf) with two main chapters. The first part contains the information gained for the first level diagnosis based on Task 2.2 and Deliverable 2.1 and the second half of the document contains updated information on the case studies connected to Deliverable 2.6.The analyses performed with this dataset can be found in Deliverable 2.4.</p> <p>The first chapter represents the list on the 60 first level case studies and the second chapter contains the 61 updated case studies, including data on case study ID, NUTS location, short description, AECPGs addressed, main contractual features, a simplified SWOT.</p>
ARN: Analogical Reasoning on Narratives
<p>As a core cognitive skill that enables the transferability of information across domains, analogical reasoning has been extensively studied for both humans and computational models. However, while cognitive theories of analogy often focus on narratives and study the distinction between surface, relational, and system similarities, existing work in natural language processing has a narrower focus as far as relational analogies between word pairs. This gap brings a natural question: can state-of-the-art large language models (LLMs) detect system analogies between narratives? To gain insight into this question and extend word-based relational analogies to relational system analogies, we devise a comprehensive computational framework that operationalizes dominant theories of analogy, using narrative elements to create surface and system mappings. Leveraging the interplay between these mappings, we create a binary task and benchmark for Analogical Reasoning on Narratives (ARN), covering four categories of far (cross-domain)/near (within-domain) analogies and disanalogies. We show that while all LLMs can largely recognize near analogies, even the largest ones struggle with far analogies in a zero-shot setting, with GPT4.0 scoring below random. Guiding the models through solved examples and chain-of-thought reasoning enhances their analogical reasoning ability. Yet, since even in the few-shot setting, the best model only performs halfway between random and humans, ARN opens exciting directions for computational analogical reasoners.</p>
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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.