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1,855 results for “Framework”
Thesis Framework and Dataset
<p>A copy of the UnQover framework with the data I used in my bachelor's thesis. Steps to reproduce results can be found in the README, which is edited from the original README provided by the original framework, which can be found here: https://github.com/allenai/unqover</p>
Data for: A general framework for modelling thermal and hydric constraints on eggs developing in soil
<p>Data files to reproduce all the figures and appendices of "A general framework for modelling thermal and hydric constraints on eggs developing in soil".</p>
Met4DX: A mass spectrum-oriented computational framework for ion mobility-resolved untargeted metabolomics
<p><strong>Raw LC-IM-MS data</strong> to run Met4DX and <strong>RT recalibration table</strong> for multi-dimensional match</p> <p>Each dataset was archieved into a zip, containing raw LC-IM-MS data (.d format) and corresponding RT recalibration table (.csv format).</p>
The KISS principle in Software-Defined Networking: a framework for secure communications - dkreutz data
<p>Scripts, summarized data, plots</p> <p>type of data: raw data and processed data</p>
Datasets for "A skill assessment framework for the Fisheries and Marine Ecosystem Model Intercomparison Project"
<p>CMIP6-forced EcoOcean data for "A skill assessment framework for the Fisheries and Marine Ecosystem Model Intercomparison Project"</p> <p> </p>
Data for "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating, interpreting, and extending the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet or Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning
<p>It contains reference training and test set configurations (and corresponding energy, forces, and virial stress values) for ZIF-8 and MOF-5.</p>
CO2 and NOy aircraft measurement data obtained within the framework of the ECLIF3 campaign
<p>Based on airborne in-situ NOy and CO2 measurements from six flights in 2021 we estimate the NOx emission index of a long-range Airbus aircraft. Measurement flights were conducted with the DLR Falcon within the joint ECLIF3 (Emission and CLimate Impact of alternative Fuels 3) project. </p>
AiiDA archive of the study of electronic properties of HexP-Monolayer on Au(111) using Quantum ESPRESSO framework
Open the record for dataset details and reuse information.
A Unified Framework to Analyze Transposable Element Insertion Polymorphisms using Graph Genomes
<p>GraffiTE_analyses_scripts_and_data_052924 <br>**********************************************</p> <p>Contact/info: goubert.clement@gmail.com </p> <p>Figures: <br>^^^^^^<br>This directory contains all the files and R script necessary to reproduce the data figures of the manuscript. </p> <p>GraffiTE pMEs datasets:<br>^^^^^^^^^^^^^^^^^^<br>This directory contains new pMEs datasets produced by GraffiTE for the different models presented in the manuscript. All datasets have been filtered according to the descriptions given in the Methods and Supplementary Methods sections of the manuscript. These data, along with more intermediate file, metadata and analysis script used to produce the figure are also available in the "Figures" directory.</p> <p> </p>
Framework for Assessing Changes To Sea-level (FACTS) Module Data - EBM3 FAIR2
<p>Input module data sets from the Framework for Assessing Changes To Sea-level (FACTS). These files should be installed in the modules-data/ directory. See the FACTS <a href="https://github.com/radical-collaboration/facts">github</a> for more information. </p> <p>NOTE: This is an additional database for FACTS versions 1.2 and above and does not include all of the modules data files needed to run FACTS. The other files can be found <a href="../records/7478192">here.</a></p> <p> </p> <p><strong>FAIR 2.0 DATA</strong></p> <ul> <li>Data included in fair2_climate_project_data.tgz are the constrained data sets as outlined in the FAIR github repo <a href="https://github.com/OMS-NetZero/FAIR/blob/master/examples/calibrated_constrained_ensemble.ipynb">here</a>. It includes the following: <ul> <li>Calibrated Constrained Parameters</li> <li>FAIR AR6 Climate Parameters</li> <li>Solar Forcing</li> <li>Volcanic Forcing</li> <li>Species Configurations</li> <li>RCMIP Concentration Annual Means</li> <li>RCMIP Emissions Annual Means</li> </ul> </li> </ul> <p><strong>EMB3 DATA</strong></p> <ul> <li>Data included in ebm3_thermal_expansion_data.tgz are: <ul> <li>Calibrated Constrained Parameters</li> <li>Expansion Coeffecient files</li> </ul> </li> </ul> <p> </p> <p> </p>
Pollution Tracks in Lagrangian Framework Dataset
<p>This dataset contains hand-logged positions of pollution tracks containing within MODIS granule at six locations (Come By Chance Oil Refinery, Norilsk Nickel Refinery, and effusive volcanoes at Kilauea, Ambrym, Yasur, and Mt. Michael) from 2015 -2017. Also included are PartMC input data for simualtions of Kilauea on day 6/13/2015 which can be obtained from <a title="Original URL: https://github.com/compdyn/partmc. Click or tap if you trust this link." href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fcompdyn%2Fpartmc&data=05%7C02%7Cmatt.christensen%40pnnl.gov%7C4eba9070676b4d1db19b08dc4edc0dc5%7Cd6faa5f90ae240338c0130048a38deeb%7C0%7C0%7C638471954798843224%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=NQfVqCm887UHkqB5t7Ou3MYoF3NVNdE52iGuZuiM9r0%3D&reserved=0" target="_blank" rel="noopener noreferrer">https://github.com/compdyn/partmc.</a></p>
Replication data for the paper "Leveraging Large Language Models for Comprehensive Psychological Analysis: Insights from Four Theoretical Frameworks"
<p>This is a replication data for the paper titled "Leveraging Large Language Models for Comprehensive Psychological Analysis: Insights from Four Theoretical Frameworks" submitted for a blind review.</p> <p>Abstract</p> <p>The rapid advancement of generative Artificial Intelligence (AI) has significantly transformed various research domains. This paper introduces a novel, fully automated methodology for applying Large Language Models (LLMs) to psychological text analysis. The approach includes prompt design for zero-shot and few-shot learning, model internal consistency analysis, autonomous machine evaluation, and additional human validation. Applied to four psychological theories—Self-Determination Theory, the Big Five Personality Traits, Psychological Well-being, and Cognitive Behavioral Therapy—this methodology is tested on a dataset of 25,780 emails written by a senior executive (called Person X) over 16 years. The analysis involves extracting psychological characteristics from the emails and regressing these characteristics against personal, professional, and environmental factors. The results demonstrate that the methodology provides unique insights into the examined psychological theories, offering a detailed understanding of how various factors influence psychological states and traits over time. This research highlights the potential of LLMs in capturing and analyzing complex psychological patterns in large text corpora, contributing a robust framework for future studies and practical applications in psychological assessment and intervention. The findings underscore the transformative impact of generative AI in psychological research, opening new avenues for understanding human behavior through advanced language models.</p> <p>The zipped file contains five csv files:</p> <ol> <li>Email_classification-csv: LLM (GPT-3.5 Turbo) classification of 25,780 emails for four psychological theories: SDT, Big Five, PWB and CBT.</li> <li>SDT_regression_data.csv</li> <li>Big_Five_regression_data.csv</li> <li>PWB_regression_data.csv</li> <li>CBT_regression_data.csv</li> </ol> <p>For 2-5 files the dependent variable is monthy percentage share of emails the were assigned a given value for categories of one of the four psychological theories analyzed. </p> <p>Linear regression model has been applied, where dependent variable is the percentage of emails in a specified category that assigned a specific value in this category. For example in Big Five Traits Model, for the Openness category, for each month we calculated percentage of emails that exhibit <em>High</em> or <em>Low</em> openness, or <em>None</em> if the content of the email does not provide enough information to assess whether the specific need is relevant. Two dependent variables were created: <em>Openness-high</em> and <em>Openness-low</em> and regressed on all independent variables. Regressions were not run for the <em>None</em> values.</p> <p>Descriptions of independent variables:</p> <p>- <em>income_index</em>: Person X salary income and consulting fees in a given month, normalized to [0,1].</p> <p>- <em>card_spending</em>: Person X credit card expenditures in a given month, normalized to [0,1].</p> <p>- <em>abroad_far</em>: dummy variable set to 1 for months when Person X worked in Central Asia</p> <p>- <em>abroad_near</em>: dummy variable set to 1 when Person X worked in other EU country</p> <p>- <em>death_1_war</em>: variable set to 1 in a month when Person X’ farther in law passed away. In the same month Russia invaded Ukraine. The variable was set to .75 in the following month, and to .5 in the month after that.</p> <p>- <em>death_2</em>: variable set to 1 in a month when Person X’ mother passed away. The variable was set to .75 in the following month, and to .5 in the month after that.</p> <p>- <em>court_case</em>: dummy variable set to 1 for months with the emotionally engaging inheritance court case involving other family members.</p> <p>- <em>BIG4_partner</em>: dummy variable set to 1 for months when Person X worked as a partner in BIG4 accounting firm, which resulted in adopting a professional activity sharply different from the usual Person X habits.</p> <p>- <em>AI_company</em>: dummy variable set to 1 for months when Person X worked as C-level executive at a company specializing in artificial intelligence.</p> <p>- <em>elections</em>: dummy variable set to 1 for months when Person X unsuccessfully run in parliamentary elections</p> <p>- <em>covid_lockdown</em>: dummy variable set to 1 for month where Polish government imposed tough measures during two covid lockdowns.</p> <p>- <em>no_receive</em>: number of different email recipients each month, normalized to [0,1].</p> <p>- <em>avg_length</em>: average number of words in emails sent each month, normalized to [0,1].</p> <p> While the email data was collected for January 2008 – March 2014 period, financial data was available from October 2009. There were some months where no emails with more than 10 words were sent, yielding 166 monthly observations used for regressions, before removing outliers.</p> <p> Independent variables were tested for multicollinearity, outlier months were removed, regressions were estimated with robust standard errors, and a range of standard tests were conducted for normality and autocorrelation of residuals, confirming good statistical properties of estimated models.</p> <p>Due to privacy concerns, the email texts cannot be publicly shared. However, the classifications of psychological categories derived from the email texts, along with all other relevant data, are made publicly available in this open access repository, with the consent of email author.</p> <p> </p>
Data from: Ecosystem engineers shape ecological network structure and stability: a framework and literature review
<p>Ecosystem engineering is a ubiquitous process where species influence the physical environment and thereby structure ecological communities. However, there has been little effort to synthesise or predict how ecosystem engineering may impact the structure and stability of interaction networks. To assess the current scientific understanding of ecosystem engineering impacts via habitat forming, habitat modification, and bioturbation on interaction networks/food webs, we reviewed the literature covering marine, freshwater, and terrestrial food webs, plant-pollinator networks, and theory. We provide a conceptual framework and identify three major pathways of engineering impact on networks through changes in resource availability and energy flow, habitat heterogeneity, and environmental filtering. These three processes often work in concert and most studies report that engineering increases species richness. This is particularly marked for engineers that increase habitat heterogeneity and thereby the number of available niches. The response of network structure to ecosystem engineering varies, however some patterns emerge from this review. Engineered habitat heterogeneity leads to a higher number of links between species in the networks and increases link density. Connectance can be negatively or positively affected by ecosystem engineer impact, depending on the engineering pathway and the engineer impact of species richness. We discuss how ecosystem engineers can stabilize or destabilize communities through the changes in niche space, diversity, network structure, and the dependency on the engineering impact. Theory and empirical evidence need to inform each other to better integrate ecosystem engineering and ecological networks. A mechanistic understanding how ecosystem engineering traits shape interactions networks and their stability will be important to predict species extinctions and can provide crucial information for conservation and ecosystem restoration.</p>
Data of Rosenbrock method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"
<p>This deposit contains the datasets generated by the Rosenbrock method for the paper:</p> <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> <p>Download the individual files and store them inside the directory <code>data/rosenbrock/</code>.</p> <p>The file names are structured as follows.</p> <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution at the previous time step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=200</code>: maximum number of iterations</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>nsteps=45|150</code>: number of Rosenbrock steps</li> <li><code>tspan=(4500.0, 0.0)</code>: global time span of DRE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> <p>Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries:</p> <ul> <li><code>file["rosenbrock_metrics"]</code>: data frame containing execution metrics of Rosenbrock iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Rosenbrock iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed (always <code>false</code>) </li> </ul> <p>All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>.</p> <p>Generating this dataset took ~22h and consumed ~2.72kWh of electricity.</p>
Data of Newton method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"
<p>This deposit contains the datasets generated by the Newton method for the paper:</p> <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> <p>Download the individual files and store them inside the directory <code>data/newton-adi/</code>.</p> <p>The file names are structured as follows.</p> <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution of the previous Newton step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=1000</code>: maximum number of iterations</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>newton_kwargs=...</code>: keyword arguments passed to Newton method <ul> <li><code>inexact=true|false</code>: whether to use inexact Newton method</li> <li><code>inexact_hybrid=true|false</code>: whether to switch back to classical Newton method in later iterations (only present if <code>inexact=true</code>)</li> <li><code>linesearch=true|false</code>: whether to employ line search</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> </ul> </li> <li><code>β=1000</code>: scaling of the quadratic term in the ARE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> <p>Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries:</p> <ul> <li><code>file["newton_metrics"]</code>: data frame containing execution metrics of Newton iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Newton iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed</li> </ul> <p>All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>.</p> <p>Generating this dataset took ~3h and consumed ~0.25kWh of electricity.</p>
A Unified Theoretical Framework for the Synergistic Integration of Transformers and Diffusion Models
<p>This paper introduces a novel, comprehensive theoretical framework for the synergistic integration of Transformer and Diffusion models, two paradigms that have independently revolutionized machine learning. We establish a fundamental correspondence between these models through a unified representation and a generalized dynamics equation, bridging the gap between their seemingly disparate architectures. Our key contributions include:</p> <p>(1) A unified mathematical formulation that encapsulates both Transformer and Diffusion processes;</p> <p>(2) A novel Diffusion-Enhanced Attention mechanism that incorporates Diffusion dynamics into Transformer attention;</p> <p>(3) Rigorous theoretical analyses including convergence guarantees, generalization bounds, and sample efficiency proofs for the integrated model.</p> <p>We provide detailed mathematical derivations and empirical validations across various tasks, demonstrating significant improvements over standalone models and existing hybrid approaches. This work lays the foundation for a new class of AI models that leverage the strengths of both paradigms, potentially leading to more powerful, efficient, and versatile AI systems. Our framework opens up new avenues for research in areas such as enhanced language modeling, advanced image generation, and multi-modal learning, paving the way for the next generation of AI technologies.</p>
An evolutionary framework of Acanthaceae based on transcriptomes and genome skims
<p>Acanthaceae is a family of tropical flowering plants with approximately 4000 species. Despite remarkable variation in morphological traits, research on patterns of character evolution has been limited by uncertain relationships among some of the major lineages. We sampled from these major lineages to estimate a phylogenomic framework using a combination of newly sequenced shotgun genome skims plus new and publicly available transcriptomes. We used OrthoFinder2 to infer a species tree with strong branch support. Except for the placement of Crabbea, our results corroborate the most recent chloroplast and nrITS sequence-based topology. Of 587 single copy loci, 10 were recovered for all 16 species; a RAxML tree estimated from these 10 loci resulted in the same topology as other datasets assembled in this study, with the exception of relationships among three sampled species of Barleria; however, branch support was lower compared to the tree reconstructed using more data. ABBA-BABA tests were conducted to investigate patterns of introgression involving Crabbea; few nucleotides supported alternative topologies. SplitsTree networks of the 587 loci and 6,136 trees revealed conflict among the branches leading to Andrographideae, Whitfieldieae, and Neuracanthus. A principal components analysis in treespace found no distinct clusters of trees. Our results strongly corroborate the previously published chloroplast and nr-ITS-based phylogeny of Acanthaceae with increased resolution among Barlerieae, Andrographideae, Whitfieldieae, and Neuracanthus. We propose that the tree presented here is the best estimate to date of Acanthaceae phylogeny. This advance in our knowledge of relationships will allow us to investigate character evolution and other phenomena within this diverse group of plants.</p>
A Critical View on the OQuaRE Ontology Quality Framework
<p>The dataset to the corresponding publication "A Critical View on the OQuaRE Ontology Quality Framework"</p>
Optimized structures of imine-linked donor-acceptor covalent organic frameworks
<p>Structures of covalent organic frameworks (COFs) optimized using PBE+D3. Includes the pure COFs, protonated versions (with ascorbate as the anion), and intermediates of the oxygen evolution and hydrogen evolution reactions. The COFs were experimentally described by Yang et al., Nature Commun. 13, 6317, 2022 (DOI: <a title="Externen Link im neuen Fenster öffnen" href="http://doi.org/10.1038/s41467-022-33875-9" target="_top">10.1038/s41467-022-33875-9</a>).</p> <p>This set of files is part of a manuscript published in Phys. Chem. Chem. Phys: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CP02019G">https://doi.org/10.1039/D4CP02019G</a></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.