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.
260
datasets available to search
ShareScore release 0.9.0
Dataset results
260 results for “logic”
Dataset: Cirrus Logic, Inc. (CRUS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Lightwave Logic, Inc. (LWLG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Computational Artifacts for the Paper "Are Noise-resilient Logical Timers useful for Performance Analysis?"
<p>This repository contains computational artifacts for the paper "Are Noise-resilient Logical Timers useful for Performance Analysis?" to be submitted to <a href="https://sc-protools-workshop.github.io/protools24/">ProTools@SC24.</a></p> <p>See also the <a href="https://sc24.supercomputing.org/program/papers/reproducibility-initiative/">SC24 reproducibility initiative.</a></p> <p> </p> <p>Contains</p> <ul> <li>Source code of <a href="https://doi.org/10.5281/zenodo.10822140">Score-P </a>, including implementation of the logical clock algorithm from the paper</li> <li>Software to post-process the Cube files generated by measurements</li> <li>Benchmarks <ul> <li>Source code</li> <li>Configuration skripts</li> <li>Measurement results, including output logs, Cube files</li> <li>Post-processing skripts and results</li> </ul> </li> </ul> <p> </p>
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 3. The logical data model of tables and views
<p>The five tables, named TAB, TABT, AREA, ARET and FLD, are combined within three views (TABV, AREV and FLDV) which build a cluster view, TAFC (figure 3). </p>
MINBLoG Benchmarks: Boolean Logic Functions for Logic Minimization
<h3>Introduction:</h3> <p>The dataset contains synthetically generated boolean functions in PLA file format that can be used for testing runtime and QoR of logic minimization approaches (e.g Espresso, BOOM, MinBLoG etc.) The size of the functions ranges from 100 to 400 variables and 200 to 1000 cubes. The dataset is divided into three different types of boolean functions:</p> <blockquote> <p>F-Type: Functions specified using their ON-Set. Contained in synth_bench_f.zip. </p> <p>FR-Type: Functions specified using their ON-Set and OFF-Set. Contained in synth_bench_fr.zip. </p> <p>FD-Type: Functions specified using their ON-Set and DontCare-Set (DC-Set). Contained in synth_bench_fd.zip.</p> </blockquote> <h3>File Naming Convention:</h3> <p>The filenames of the PLA files indicate the size of the function it contain. </p> <p>Example:</p> <blockquote> <p>fd_200_400-0.pla = Contains an FD type function with 200 variables and 400 cubes. The suffix 0 at the end indicates it is the first of the 5 random functions generated with 200 variables and 400 cubes.</p> </blockquote> <h3>Reference:</h3> <p>Please cite the work below when using this benchmark. The dataset was generated for testing the boolean logic minimization tool MinBLoG published in this work. </p> <p>Prianka Sengupta, Aakash Tyagi, Jiang Hu, Vivek K Rajan, Hesham Mostafa, and Somdeb Majumdar. 2024. MinBLoG: Minimization of Boolean Logic Functions using Graph Attention Network. In 2024 ACM/IEEE International Symposium on Machine Learning for CAD (MLCAD ’24), September 9–11, 2024, Salt Lake City, UT, USA. ACM, New York, NY, USA, 8 pages. https: //doi.org/10.1145/3670474.3685962</p> <p>For the most up-to-date version of the dataset and accompanying tools, please check the GitHub repository below:</p> <p><a href="https://github.com/puprianka/minblog" target="_blank" rel="noopener">https://github.com/puprianka/minblog</a></p>
FVLLMONTI Application examples: Single reconfigurable VNWFET TCAD simulation and Reference logic cell DTCO flow
<p>This data set presents two application examples collecting and highlighting some of the key components to the TCAD, compact modelling and DTCO approaches carried out as part of the FVLLMONTI project in relation to Deliverable D3.2.</p> <p>First we present an example that demonstrates the functionality of the U-shaped reconfigurable transistor, switching the device behaviour between NMOS and PMOS using the Program Gate and giving the transistor characteristics of those devices.</p> <p>Secondly, this data set gives an introduction into the FVLLMONTI DTCO approach. The provided simulation project shows a reference DTCO flow from layout to library characteristics for previously delivered physical logic cell designs. The DTCO flow provides insight into logic cell parasitics, netlist for 3rd party SPICE analysis, and KPI determination and characterization.</p> <p>Both simulation projects can be seen as template for similar setups employed to overcome the challenges and answer the detailed questions faced in investigating a newly developed semiconductor technology such as the FVLLMONTI stack.</p>
Logic Mini Conference ANU 1980
<p><strong>Logic Mini-Conference at the Australian National University, 1980.</strong></p> <p>Logic Group, Department of Philosophy, Research School of Social Sciences.</p> <p>People in the photo L to R back row: Errol Martin, Paul Thistlewaite, Michael McRobbie, Adrian Abraham, Ross Brady, John Slaney, Chris Mortensen, Graham Priest</p> <p>Seated: Richard Routley, Bob Meyer</p>
Dataset of the study: "Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard"
<p>This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.</p>
LOGiC - Lapatinib Optimization Study in ErbB2 (HER2) Positive Gastric Cancer: A Phase III Global, Blinded Study Designed to Evaluate Clinical Endpoints and Safety of Chemotherapy Plus Lapatinib
ClinicalTrials.gov study NCT00680901. IPD Sharing: YES. Countries: 23. Publications: 2.
Data from: 3D printed digital pneumatic logic for the control of soft robotic actuators
Open the record for dataset details and reuse information.
Supplementary material and supplementary data files for: Handling logical character dependency in phylogenetic inference: Extensive performance testing of assumptions and solutions using simulated and empirical data
Open the record for dataset details and reuse information.
Logical model for mutually exclusive and co-occurring genetic alterations in bladder tumorigenesis
<p>Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modelling to explain patterns of genetic alterations seen in 178 patients with bladder tumours (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signalling, cell cycle and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publically. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signalling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3 activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favour invasiveness. Further, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness.</p> <p> </p> <p>GINsim archive (zginml) with the model, its annotations and simulation parameters; the SBML file can be imported using any tool supporting the SBML qual format</p> <p>Warning: the zginml archive should be open using a recent GINsim version (>2.8)</p>
Superposition for Lambda-Free Higher-Order Logic — Supplementary Material for the Journal Article
<p>We provide the following supplementary material for our <a href="http://matryoshka-project.github.io/pubs/lfhosup_article.pdf">article</a>.</p> <p><strong>Zipperposition</strong></p> <p>Compilation instructions for Zipperposition, in particular instructions for compilation for <a href="https://www.starexec.org/">StarExec</a>, can also be found in the <a href="https://github.com/sneeuwballen/zipperposition#starexec">Zipperposition readme</a>. We used OCaml 4.07.0, branch <code>lmcs2020</code>, commit <a href="https://github.com/sneeuwballen/zipperposition/tree/2031e216c1941acd76187882a073e8f1e53383f2">2031e216c1941acd76187882a073e8f1e53383f2</a></p> <p><strong>Problems</strong></p> <p>We used the following first-order (TFF) and the higher-order (THF) <a href="http://www.cs.miami.edu/~tptp/">TPTP (v7.3.0) problems</a> for the evaluation: <a href="https://zenodo.org/record/3992618/files/list_TFF.txt">TFF problem list</a> <a href="https://zenodo.org/record/3992618/files/list_THF.txt"> THF problem list</a>. These lists were obtained by excluding all problems that contain arithmetic, the symbols <code>(@@+)</code>, <code>(@@-)</code>, <code>(@+)</code>, <code>(@-)</code>, <code>(&)</code>, or tuples, as well as the <code>SYN000</code> problems, which are only intended to test the parser, and problems whose clausal normal form takes longer than 15s to compute or falls outside the lambda-free fragment. The following archive contains instructions on how the benchmarks were selected: <a href="https://zenodo.org/record/3992618/files/benchmark_selection.zip">Benchmark selection</a></p> <p>Note that Zipperposition is not aware that our calculi are complete for this fragment and it will always report "GaveUp" instead of "CounterSatisfiable" if the calculus saturates.</p> <p>The selection of TPTP problems and the problems generated by Isabelle/Sledgehammer can be downloaded here: <a href="https://zenodo.org/record/3992618/files/benchmarks.zip">Benchmarks</a></p> <p><strong>Run scripts</strong></p> <p>We used the following run scripts on StarExec. This archive also contains the Zipperposition binary, compiled for StarExec: <a href="https://zenodo.org/record/3992618/files/run_scripts.zip">StarExec run scripts</a></p> <p>The scripts use the following command-line options for Zipperposition</p> <ul> <li>First-order mode:<br> <code>./zipperposition.exe --mode=fo-complete-basic</code></li> <li>Applicative encoding mode (intensional):<br> <code>./zipperposition.exe --mode=fo-complete-basic --app-encode=intensional</code></li> <li>Applicative encoding mode (extensional):<br> <code>./zipperposition.exe --mode=fo-complete-basic --app-encode=extensional</code></li> <li>Nonpurifying intensional calculus:<br> <code>./zipperposition.exe --mode=lambda-free-intensional</code></li> <li>Nonpurifying extensional calculus:<br> <code>./zipperposition.exe --mode=lambda-free-extensional</code></li> <li>Purifying intensional calculus:<br> <code>./zipperposition.exe --mode=lambda-free-purify-intensional</code></li> <li>Purifying extensional calculus:<br> <code>./zipperposition.exe --mode=lambda-free-purify-extensional</code></li> </ul> <p>As additional command line arguments, we provided the problem's filename, the order (<code>--ord=lambdafree_rpo</code> or <code>--ord lambdafree_kbo</code> or <code>--ord epo</code>), and the following parameters for heuristics that were obtained by optimizing the first-order mode in preliminary experiments:</p> <pre>--kbo-weight-fun=modarity \ -q "7|prefer-sos|pnrefined(2,1,1,1,2,2,2)" \ -q "4|prefer-short-trail|pnrefined(1,1,1,2,2,2,0.5)" \ -q "1|prefer-processed|fifo" \ -q "7|prefer-ground|conjecture-relative-var(1,l,f)" \ -q "6|prefer-goals|conjecture-relative-var(1,s,f)" \ --select=e-selection7 </pre> <p>On Starexec, we chose a wallclock timeout of 360 s, a CPU timeout of 180 s, and a memory limit of 128 GB. StarExec's machine specifications are:</p> <pre>Intel(R) Xeon(R) CPU E5-2609 0 @ 2.40GHz (2393 MHZ) 10240 KB Cache 263932744 kB main memory OS: CentOS Linux release 7.7.1908 (Core) kernel: 3.10.0-1062.4.3.el7.x86_64 </pre> <p><strong>Results</strong></p> <p>Download the raw output of the evaluation and the .csv files created by StarExec here:</p> <ul> <li><a href="https://zenodo.org/record/3992618/files/results_tff.zip">Raw evaluation output TFF</a></li> <li><a href="https://zenodo.org/record/3992618/files/results_sh256.zip">Raw evaluation output SH256</a></li> <li><a href="https://zenodo.org/record/3992618/files/results_sh16.zip">Raw evaluation output SH16</a></li> <li><a href="https://zenodo.org/record/3992618/files/results_thf.zip">Raw evaluation output THF</a></li> <li><a href="https://zenodo.org/record/3992618/files/results_csv.zip">Evaluation results as CSV file + script to compile the statistics</a></li> </ul> <p><strong>Examples</strong></p> <p>We tested the examples given in our paper in Zipperposition. Here are the problem files we used. Some are in TPTP format (.p) and some are in Zipperposition format (.zf).</p> <ul> <li><a href="https://zenodo.org/record/3992618/files/example_varcond.zf">Example 3.3</a></li> <li><a href="https://zenodo.org/record/3992618/files/example_posext.zf">Example 3.4</a></li> <li><a href="https://zenodo.org/record/3992618/files/example_supatvars_rpo.zf">Example 3.5</a></li> <li><a href="https://zenodo.org/record/3992618/files/example_negext.zf">Example 3.6</a></li> <li><a href="https://zenodo.org/record/3992618/files/example_add_equiv_defs.p">Example 3.7</a></li> </ul>
Logical inferences from visual and auditory information in ruffed lemurs and sifakas
<p>Inference by exclusion, or the ability to select a correct course of action by systematically excluding other potential alternatives, is a form of logical inference that allow individuals to solve problems without complete information. Current comparative research shows that several bird, mammal, and primate species can find hidden food through inference by exclusion. Yet there is also wide variation in how successful different species are, as well kinds of sensory information they can use to do so. An important question is therefore why some species are better at engaging in logical inference than others. Here, we investigate the evolution of logical reasoning abilities by comparing two strepsirrhine primate species that vary in dietary ecology: frugivorous ruffed lemur (<em>Varecia</em> spp.) and folivorous Coquerel's sifakas (<em>Propithecus coquereli</em>). Across two studies, we examined their abilities to locate food using direct information versus inference from exclusion and using both visual and auditory information. In Study 1, we assessed whether these lemurs could make inferences when full visual and auditory information about the two potential locations of food were provided. In Study 2, we then compared their ability to make direct inferences versus inferences by exclusion in both the visual and auditory domains. We found that both lemur species can use visual information to find food, but that only ruffed lemurs were also able to use auditory cues, mirroring differences in the complexity of their wild ecology. We further found that, unlike many anthropoid species tested to date, both strepsirrhine species failed to make inferences by exclusion. These results highlight the importance of natural history in understanding the evolution of logical inference, and help reconstruct the deeper phylogeny of primate cognition.</p>
Empirical Investigation of Subsumption Test Hardness in Description Logic Classification
<p>This is the dataset supporting the analysis for the submission "Empirical Investigation of Subsumption Test Hardness in Description Logic Classification" to CADE, 2015. Data in its very raw form is not included, but the dataset containing non aggregated subsumption test data (labelled subsumptiontest_data) and the overall module data (labelled module_data) contain all the records corresponding to the raw data (albeit amended by some additional measurements and ontology metadata). </p> <p>The r_scripts directory contains all the<strong> r files needed to run the analysis</strong>. However, the code is written ad hoc and is very slow, and should only be considered as a reference. Running the code does not work out of the box, as a series of directory paths need to be set manually.</p> <p>The r_data directory contains all the <strong>datasets used in the analysis.</strong>.</p> <p>-- map_subsumptiontest_data_non_aggregated contains the non aggregated subsumption test data from which the first experiment, the subsumption test hardness survey, is conducted<br /> -- map_module_data_non_aggregated contains the non aggregated module data from which the first experiment, the subsumption test hardness survey, is conducted</p> <p>-- module_data_non_aggregated.RData contains the non aggregated module data from which the intra-module analysis is conducted<br /> -- subsumptiontest_data_non_aggregated.RData contains the non aggregated subsumption test data from which the intra-module analysis is conducted</p> <p>-- inter_module_data.RData contains the results of the inter-module analysis from the perspective of the modules<br /> -- intra_module_data.RData contains the results of the intra-module analysis from the perspective of the modules<br /> -- inter_subsumptiontest_data.RData contains the results of the inter-module analysis from the perspective of the subsumption tests<br /> -- intra_subsumptiontest_data.RData contains the results of the intra-module analysis from the perspective of the subsumption tests</p> <p>The csv_data directory contains the same data as the r_data directory in CSV form.</p>
Module Data for the Empirical Investigation of Subsumption Test Hardness in Description Logic Classification
<p>This dataset contains 224 modules of 14 distinct ontologies for the submission "Empirical Investigation of Subsumption Test Hardness in Description Logic Classification" to CADE, 2015. The submitted version with the dataset description can be found here:</p> <p>http://owl.cs.manchester.ac.uk/publications/supporting-material/subtest-hardness-in-classification/</p> <p>The ontologies in this set are BioPortal ontologies from the snapshot provided at http://dx.doi.org/10.5281/zenodo.15667. </p> <p>The following ontologies are in the set: </p> <p>MFOEM - Emotion Ontology<br /> BT - Biotop Ontology<br /> NEMO - Neural Electromagnetic Ontology<br /> NTDO - Neglected Tropical Disease Ontology<br /> OBI - Ontology for Biomedical Investigations<br /> OGSF - Ontology for Genetic Susceptibility Factor<br /> ONL-MSA - Mental State Assemssment<br /> VSO - Vital Sign Ontology<br /> CAO - Clusters of Orthologous Groups Cog Analysis Ontology <br /> ICO - Informed Consent Ontology<br /> OMRSE - Ontology of Medically Related Social Entities<br /> STATO - Statistics Ontology<br /> NPO - Nanoparticle Ontology<br /> OBCS - Ontology of Biological and Clinical Statistics</p>
Towards the formal verification of data-intensive applications through metric temporal logic
p>The dataset consists of a set of model descriptions representingnbsp;span>Storm topologies. It is designed on purpose to show the approach presented in the paper quot;/span>span>Towards the formal verification of data-intensive applications through m/span>span>etric/span>span>nbsp;temporalnbsp;/span>span>logicquot; (F. Marconi, M.M. Bersani, M. Erascu and M. Rossi) which focuses on the analysisnbsp;/span>span>of bottleneck nodes of data intensive applications implemented with Storm./span>/p>
Universal quantum gate set for Gottesman-Kitaev-Preskill logical qubits
<p>The Excel files contain experimental data corresponding to figures 2,3,4 of the paper "Universal Quantum Gate Set for Gottesman-Kitaev-Preskill Logical Qubits", available at <a href="https://arxiv.org/abs/2409.05455">arxiv:2409.05455</a> </p> <p>The contents of the files are described in README.txt.</p>
Tomography of entangling two-qubit logic operations in exchange-coupled donor electron spin qubits
<p>Scalable quantum processors require high-fidelity universal quantum logic operations in a manufacturable physical platform. Donors in silicon provide atomic size, excellent quantum coherence and compatibility with standard semiconductor processing, but no entanglement between donor-bound electron spins has been demonstrated to date. Here we present the experimental demonstration and tomography of universal 1- and 2-qubit gates in a system of two weakly exchange-coupled electrons, bound to single phosphorus donors introduced in silicon by ion implantation. We surprisingly observe that the exchange interaction has no effect on the qubit coherence. We quantify the fidelity of the quantum operations using gate set tomography (GST), and we use the universal gate set to create entangled Bell states of the electrons spins, with fidelity ≈ 93%, and concurrence 0.91 ± 0.08. These results form the necessary basis for scaling up donor-based quantum computers.</p>
Data for "Suppressing quantum errors by scaling a surface code logical qubit"
<p>Includes the circuits that were executed, the samples that were collected, the corrections predicted by several decoders, as well as other intermediate files. See the README.txt file at the root of the ZIP archive for a more detailed overview.</p>
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.