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1,855 results for “Framework”
Fig. 12 in Morphology and genetics of grasshopper mice revisited in a paleontological framework: reinstatement of Onychomyini (Rodentia, Cricetidae)
Fig. 12.—Selected dental, mandibular, and cranial characters of Onychomys: (A) oblique occlusal view of m1 showing tubercular hypsodonty of procingulid and primary cusps that taper to sharp apices; (B) lateral view of sword or scimitar-like, elongated coronoid process of mandible that extends well posterior of incisor capsule; (C) lateral view of mandible showing dorsoventrally narrowed masseteric scar that terminates anteriorly under anterior root of m1 and dorsal of mental foramen; (D) ventral view of palate showing positions of posterior borders of incisive foramina relative anterior border of M1 and anterior border of posterior nares relative to posterior border of M3; (E) dorsal view of skull showing posteriorly tapered nasals with wedgeshaped termination, and anterior inflation of frontals (arrows). (A–C) O leucogaster, MACN 13433. (D–E) O. torridus, CNP 6482.
Fig. 9 in Morphology and genetics of grasshopper mice revisited in a paleontological framework: reinstatement of Onychomyini (Rodentia, Cricetidae)
Fig. 9.—Majority-rule consensus tree obtained from Bayesian inference analysis of combined morphological and molecular data sets continued. Part of the consensus tree corresponding to Neotominae and non-neotomine outgroup taxa. Posterior probabilities values are indicated in grayscale circles for each node. Terminal in bold indicates fossil species (†, extinct).
Database of simulated impulse responses and a scene constructor framework for the Audiovisual Immersion Lab (AVIL) at DTU
<p>This package contains a set of simulated impulse responses (IRs) intended to represent low, mid, and high reverberation environments, originally developed for the Cognitive Control of a Hearing Aid (<a href="https://cocoha.org" target="_blank" rel="noopener">COCOHA</a>) project, and rendered to be reproduced in the Audiovisual Immersion Lab (AVIL) at the Technical University of Denmark (DTU). </p> <p>A MATLAB-based scene constructor framework is also provided, which allows the generation of acoustic scenes based on the supplied IR databases with any input signal, as well as providing a means to generate binaural output signals.</p> <p>The impulse responses are provided as <a href="https://www.sofaconventions.org/mediawiki/index.php/SOFA_(Spatially_Oriented_Format_for_Acoustics)" target="_blank" rel="noopener">SOFA</a> (Spatially Oriented Format for Acoustics) files, including metadata about source and receiver positions, and require installation of the <a href="https://sourceforge.net/projects/sofacoustics/" target="_blank" rel="noopener">MATLAB SOFA API.</a></p>
Phytohormone profiling in an evolutionary framework
<p>This repository contains raw LC/MS-acquired data for the study 'Phytohormone profiling in an evolutionary framework'.</p> <p>For sample codes see an attached excel file ('_LCMS-sample-codes.xlsx') at the end of the list.</p> <p>Phytohormone analysis was performed with a LC/MS system consisting of UHPLC 1290 Infinity II (Agilent, Santa Clara, CA, USA) coupled to 6495 Triple Quadrupole Mass Spectrometer (Agilent, Santa Clara, CA, USA), operating in MRM mode, with quantification by the isotope dilution method. Data acquisition and processing was performed with Mass Hunter software B.08 (Agilent, Santa Clara, CA, USA).</p>
Open Data Package: Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science.
<p>Open Data Package for the paper: Stefanie Betz, Birgit Penzenstadler, Leticia Duboc, Ruzanna Chitchyan, Sedef Akinli Kocak, Ian Brooks, Shola Oyedeji, Jari Porras, Norbert Seyff, and Colin C. Venters. 2024. Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science. ACM Trans. Softw. Eng. Methodol. 24 00, JA, Article 00 (March 2024), 39 pages. https://doi.org/10.1145/3649597 25</p>
Model data for: Analysis of the global atmospheric background sulfur budget in a multi-model framework
<p>The present dataset contains all model data used in the model intercomparison in ACP. All data is provided as monthly means. For more data, please contact the first author. V2 addresses inconsistencies in the time axes, vertical coordinates, and variable names between models.</p>
A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals
<p>This database relies on the article entitled "A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals " to be published in Earth's Future. The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang. The file named "Supplementary Data" contains the necessary Water Bulletin and Statistical Yearbook data.</p>
TIdeS: a comprehensive framework for accurate open reading frame identification and classification in eukaryotic transcriptomes
<p>Includes:</p> <ul> <li>ORF predictions for 28 diverse eukaryotic taxa, employing common tools/approaches</li> <li>Raw transcriptome assemblies used for ORF predictions</li> <li>Phylogenetic trees used as the basis for contamination identification, as well as validating TIdeS's efficacy</li> <li>Various TIdeS outputs (ORF prediction/classification)</li> <li>Copy of current TIdeS source code</li> </ul>
Processed KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
<p>The original public dataset is published in https://zenodo.org/records/10439422, we processed the KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework.</p>
Data from: A statistical mechanics framework for constructing non-equilibrium thermodynamic models
<p><span>Far-from-equilibrium phenomena are critical to all natural and engi</span><span>neered systems, and essential to biological processes responsible </span><span>for life. For over a century and a half, since Carnot, Clausius, Maxwell, </span><span>Boltzmann, and Gibbs, among many others, laid the foundation for </span><span>our understanding of equilibrium processes, scientists and engineers </span><span>have dreamed of an analogous treatment of non-equilibrium systems. </span><span>But despite tremendous efforts, a universal theory of non-equilibrium </span><span>behavior akin to equilibrium statistical mechanics and thermodynam</span><span>ics has evaded description. Several methodologies have proved their </span><span>ability to accurately describe complex non-equilibrium systems at </span><span>the macroscopic scale, but their accuracy and predictive capacity is </span><span>predicated on either phenomenological kinetic equations fit to mi</span><span>croscopic data, or on running concurrent simulations at the particle </span><span>level. Instead, we provide a framework for deriving stand-alone macro</span><span>scopic thermodynamics models directly from microscopic physics </span><span>without fitting in overdamped Langevin systems.</span> <span>The only neces</span><span>sary ingredient is a functional form for a parameterized, approximate </span><span>density of states, in analogy to the assumption of a uniform density </span><span>of states in the equilibrium microcanonical ensemble. We highlight </span><span>this framework's effectiveness by deriving analytical approximations </span><span>for evolving mechanical and thermodynamic quantities in a model of </span><span>coiled-coil proteins and double stranded DNA, thus producing, to the </span><span>authors' knowledge, the first derivation of the governing equations for </span><span>a phase propagating system under general loading conditions without </span><span>appeal to phenomenology. The generality of our treatment allows </span><span>for application to any system described by Langevin dynamics with </span><span>arbitrary interaction energies and external driving, including colloidal </span><span>macromolecules, hydrogels, and biopolymers.</span></p>
Solid-state NMR data for the publication "Bonding of Polyethylenimine in Covalent Organic Frameworks for CO2 Capture from Air"
<p>This upload contains the solid-state NMR of COF-709 series obtained by using Bruker Avance IV NEO 400 MHz NMR instrument. The instrument used in this work was supported by the National Science Foundation under Grant No. 2018784. Per NSF requirements, the data will be publicly available at the time of publication. This dataset can be opened and processed by using MestreNova NMR or similar softwares.</p>
Supply Chain 5.0: technology-based and resilience-oriented framework - datasets
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Replication Package of the Paper "How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow"
<p>This replication package contains datasets and scripts related to the paper: "<em>How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow</em>"</p> <ul> <li> <p><code>Contributor_Classification.csv</code>: contains the assignment of each contributor to a specific classification. The file contains the following columns:</p> <ul> <li><em>Date</em> : contains the date of each comment</li> <li><em>Type</em>: describe the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC)</li> <li><em>ID</em> : specific ID of the comment</li> <li><em>Body</em> : contains the body of the comment analyzed</li> <li><em>Url</em> : link at each comments</li> <li><em>NumberPR</em> : number of a PR</li> <li><em>Name_Contributor</em>: contains the name of a contributor for each comment</li> <li><em>Contributor_classification</em>: contains the assignment of a specific classification (academic, bot, ML expert, software engineer, unknown), obtained after manual analysis, for each contributor of a comment</li> </ul> </li> <li> <p><code>Contributors_ManualAnalysis.csv</code>: contains the manual analysis performed by two authors to assign a classification for each contributor. The file contains the following columns:</p> <ul> <li><em>Contributors</em>: contains the name of the contributor for each comment</li> <li><em>Link GitHub</em>: contains the link to the GitHub page for each contributor</li> <li><em># PR</em>: contains the number of PRs in which a specific contributor is involved</li> <li><em>Annotator1</em>: manual classification of the first annotator</li> <li><em>Annotator2</em>: manual classification of the second annotator</li> <li><em>Final Classification</em>: contains the final label (academic, bot, ML expert, software engineer, unknown)assigned for each contributor</li> <li><em>Organization</em>: contains the organization, if any (Google, Hugging Face, Microsoft, OpenAI)</li> </ul> </li> <li> <p><code>ManualAnalysis.csv</code>: contains the manual analysis performed regarding Comment Type, Nature of Comment and Artifact. The .csv contains the following columns:</p> <ul> <li><em>URL</em>: contains the link to each comment</li> <li><em>NumberPR</em> : number of pull request</li> <li><em>CommentType1</em>: contains the classification of the first annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other)</li> <li><em>CommentType2</em>: contains the classification of the second annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other)</li> <li><em>NatureComment1</em>: contains the classification of the first annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment)</li> <li><em>NatureComment2</em>: contains the classification of the second annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment)</li> <li><em>Artifact1</em>: contains the classification of the first annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other)</li> <li><em>Artifact2</em>: contains the classification of the second annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other)</li> <li><em>FINALCommentType</em>: contains the final classification of the comment type after the resolution of the conflicts</li> <li><em>FINALNatureComment</em>: contains the final classification of the nature of the comment after resolution of conflicts</li> <li><em>FINALArtifact</em>: contains the final classification of the artifact after the resolution of conflicts</li> </ul> </li> <li> <p><code>Summary_PR.csv</code>: contains the details about the composition of each PRs. The file contains the following columns:</p> <ul> <li><em>#PullRequest</em>: contains the number of all pull requests analyzed</li> <li><em>#events</em>: contains the number of all the events analyzed for each PR</li> <li><em>#comments</em>: contains the number of all comments for each PR</li> <li><em>#Commit</em>:contains the number of Commit for each PR</li> <li><em>PC</em>: contains the number of PC for each PR</li> <li><em>RC</em>: contains the number of RC for each PR</li> </ul> </li> <li> <p><code>Total_PR_Comments.csv</code>: contains information about all comments analyzed. The columns are:</p> <ul> <li><em>Date</em>: contains the date of each comment</li> <li><em>Type</em>: describes the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC)</li> <li><em>ID</em>: SHAn of the comment</li> <li><em>NumberPR</em>: PR number</li> <li><em>Name_Contributor</em>: contains the name of a contributor for each comment</li> <li><em>Body</em>: contains the body of the comment analyzed</li> <li><em>Url</em>: link at each comment</li> </ul> </li> </ul> <p>The replication also contains a directory <code>results</code> in which there are quantitative results. The directory contains:</p> <ul> <li> <p><code>Artifacts.csv</code>: This file contains the results for the artifact. The columns are:</p> <ul> <li><em>#PullRequest</em>: number of the pull request analyzed</li> <li><em>Article</em>: contains the percentage of the occurrences of the article in the specific pull request</li> <li><em>Code</em>: contains the percentage of the occurrences of the code in the specific pull request</li> <li><em>Commit</em>: contains the percentage of the occurrences of the commit in the specific pull request</li> <li><em>Issue reference</em>: contains the percentage of the occurrences of the issue reference in the specific pull request</li> <li><em>External link</em>: contains the percentage of the occurrences of the external link in the specific pull request</li> <li><em>Review</em>: contains the percentage of the occurrences of the review in the specific pull request</li> <li><em>Other</em>: contains the percentage of the occurrences of the other in the specific pull request</li> </ul> <p>ßAlso, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (Article, Code, Commit, Issue reference, External link, Review, Other)</p> </li> <li> <p><code>CommentType.csv</code>: this file contains the results for the comment type. The columns are:</p> <ul> <li><em>#PullRequest</em>:number of the pull request analyzed</li> <li><em>Conventional review</em>: contains the percentage of the occurrences of the conventional review in the specific pull request</li> <li><em>Initial implementation</em>: contains the percentage of the occurrences of the initial implementation in the specific pull request</li> <li><em>Management</em>: contains the percentage of the occurrences of the management in the specific pull request</li> <li><em>ML review</em>: contains the percentage of the occurrences of the ML review in the specific pull request</li> <li><em>Other</em>: contains the percentage of the occurrences of the Other in the specific pull request</li> </ul> <p>Also, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (Conventional review, Initial implementation, Management, ML review, Other)</p> </li> <li> <p><code>Contributor.csv</code>: this file contains the results for the contributors. The columns are:</p> <ul> <li><em>#PullRequest</em>: number of the pull requests analyzed</li> <li><em>academic</em>: contains the percentage of the occurrences of the academic in the specific pull request</li> <li><em>bot</em>: contains the percentage of the occurrences of the bot in the specific pull request</li> <li><em>ML expert</em>: contains the percentage of the occurrences of the ML expert in the specific pull request</li> <li><em>software engineer</em>: contains the percentage of the occurrences of the software engineer in the specific pull request</li> <li><em>unknown</em>: contains the percentage of the occurrences of unknown in the specific pull request</li> </ul> <p>Also, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (academic, bot, ML expert, software engineer, unknown)</p> </li> </ul>
Boosting Protein Encapsulation through Lewis-Acid-Mediated Metal–Organic Framework Mineralization: Toward Effective Intracellular Delivery
<p>Relevant data for publication with DOI:</p> <p><a title="DOI URL" href="https://doi.org/10.1021/acs.chemmater.2c01338">10.1021/acs.chemmater.2c01338</a></p>
Semiconductor Porous Hydrogen-Bonded Organic Frameworks Based on Tetrathiafulvalene Derivatives
<p>Relevant data for publication with DOI:</p> <table> <tbody> <tr> <td><a href="https://doi.org/10.1021/jacs.1c07802"><span>https://doi.org/10.1021/jacs.1c07802</span></a></td> </tr> </tbody> </table>
Adsorptive Separation of CO2 by a Hydrophobic Carborane-Based Metal−Organic Framework under Humid Conditions
<p>relevant data for publication with doi:</p> <table> <tbody> <tr> <td><a href="https://doi.org/10.1021/acsami.2c20373"><span>https://doi.org/10.1021/acsami.2c20373</span></a></td> </tr> </tbody> </table>
Source data of the MultiSTAAR manuscript "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies".
<p>This dataset serves as the source data for Figures 2-3 and Extended Data Figures 1-2 of the MultiSTAAR manuscript titled "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies". MultiSTAAR is a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies.<br><br><strong>Figure 2.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and ncRNA multi-trait analysis of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerids (TG) using TOPMed data (<em>n</em> = 61,838).<br><br><strong>Figure 3.</strong> TOPMed genetic region (2-kb sliding window) unconditional multi-trait analysis results of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) using TOPMed data (<em>n</em> = 61,838).<br><br><strong>Extended Data Figure 1.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of fasting glucose (FG) and fasting insulin (FI) using TOPMed data (<em>n</em> = 21,731).<br><br><strong>Extended Data Figure 2.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of C-reactive protein (CRP), interleukin 6 (IL-6), lipoprotein-associated phospholipase A2 (Lp-PLA2) activity, and lipoprotein-associated phospholipase A2 (Lp-PLA2) mass using TOPMed data (<em>n</em> = 9,380).</p>
ASE Database of CO2 Electro Capture on Redox-Active Metal-Organic Frameworks
<p>ASE database containing simulated structures for 1D, 2D and 3D conductive metal-organic frameworks.</p>
Heterometallic palladium–iron metal–organic framework as a highly active catalyst for cross-coupling reactions
<p>Relevant data for the publication with DOI: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D2SC05192C">10.1039/D2SC05192C</a></p>
Survey Results for: MM-LDTF: Multi-Model Legal Document Translation Framework for Vernacular Languages
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ScienceDex guides
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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.