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282 results for “data engineering”
Data for the article "Elastic strain engineering for ultralow mechanical dissipation"
<p>Device fabrication masks, raw experimental data and data processing scripts</p>
Datasets and scripts related to the paper: "*Can Generative AI Help us in Open Coding of Software Engineering Data?*"
<p>This replication package contains datasets and scripts related to the paper: "<em>Can Generative AI Help us in Open Coding of Software Engineering Data?</em>"</p> <p>The replication package is organized into two directories:</p> <ul> <li> <p><code>manual_analysis</code>: This directory contains all sheets used to perform the manual analysis for RQ1, RQ2, and RQ3.</p> </li> <li> <p><code>stats</code>: This directory contains all datasets, scripts, and results metrics used for the quantitative analyses of RQ1 and RQ2.</p> </li> </ul> <p>In the following, we describe the content of each directory:</p> <h2>manual_analysis</h2> <ul> <li> <p><code>manual_analysis_rq1</code>: This directory contains all sheets used to perform manual analysis for RQ1 (independent and incremental coding).</p> <ul> <li> <p>The sub-directory <code>incremental_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_incremental.csv</code>, <code>DL_Faults_ISSUE_incremental.csv</code>, <code>DL_Fault_SO_incremental.csv</code>, <code>DRL_Challenges_incremental.csv</code> and <code>Functional_incremental.csv</code>). All these .csv files contain the following columns:</p> <ul> <li><em>Link</em>: The link to the instances</li> <li><em>Prompt</em>: Prompt used as input to GPT-4-Turbo</li> <li><em>ID</em>: Instance ID</li> <li><em>FinalTag</em>: Tag assigned by the human in the original paper</li> <li><em>Chatgpt_output_memory</em>: Output of GPT-4-Turbo with incremental coding</li> <li><em>Chatgpt_output_memory_clean</em>: (only for the DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li><em>Author1</em>: Label assigned by the first author</li> <li><em>Author2</em>: Label assigned by the second author</li> <li><em>FinalOutput</em>: Label assigned after the resolution of the conflicts</li> </ul> </li> <li> <p>The sub-directory <code>independent_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_independent.csv</code>, <code>DL_Faults_ISSUE_ independent.csv</code>, <code>DL_Fault_SO_ independent.csv</code>, <code>DRL_Challenges_ independent.csv</code> and <code>Functional_ independent.csv</code>), containing the following columns:</p> <ul> <li><em>Link</em>: The link to the instances</li> <li><em>Prompt</em>: Prompt used as input to GPT-4-Turbo</li> <li><em>ID</em>: Specific ID for the instance</li> <li><em>FinalTag</em>: Tag assigned by the human in the original paper</li> <li><em>Chatgpt_output</em>: Output of GPT-4-Turbo with independent coding</li> <li><em>Chatgpt_output_clean</em>: (only for DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li><em>Author1</em>: Label assigned by the first author</li> <li><em>Author2</em>: Label assigned by the second author</li> <li><em>FinalOutput</em>: Label assigned after the resolution of the conflicts.</li> </ul> </li> <li> <p>Also, the sub-directory contains sheets with inconsistencies after resolving conflicts. The directory <code>inconsistency_incremental_coding</code> contains .csv files with the following columns:</p> <ul> <li><em>Dataset</em>: The dataset considered</li> <li><em>Human</em>: The label assigned by the human in the original paper</li> <li><em>Machine</em>: The label assigned by GPT-4-Turbo</li> <li><em>Classification</em>: The final label assigned by the authors after resolving the conflicts. Multiple classifications for a single instance are separated by a comma “,”</li> <li><em>Final</em>: final label assigned after the resolution of the incompatibilities</li> </ul> </li> <li> <p>Similarly, the sub-directory <code>inconsistency_independent_coding</code> contains a .csv file with the same columns as before, but this is for the case of independent coding.</p> </li> </ul> </li> <li> <p><code>manual_analysis_rq2</code>: This directory contains .csv files for all datasets (<code>DL_Faults_redundant_tag.csv</code>, <code>DRL_Challenges_redundant_tag.csv</code>, <code>Functional_redundant_tag.csv</code>) to perform manual analysis for RQ2.</p> <ul> <li> <p>The <code>DL_Faults_redundant_tag.csv</code> file contains the following columns:</p> <ul> <li><em>Tags Redundant</em>: tags identified as redundant by GPT-4-Turbo</li> <li><em>Matched</em>: inspection by the authors to see if the tags are redundant matching or not</li> <li><em>FinalTag</em>: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> <p>The <code>Functional_redundant_tag.csv</code> file contains the same columns as before</p> </li> <li> <p>The <code>DRL_Challenges_redundant_tag.csv</code> file is organized as follows:</p> <ul> <li><em>Tags Suggested</em>: The final tag suggested by GPT-4-Turbo</li> <li><em>Tags Redundant</em>: tags identified as redundant by GPT-4-Turbo</li> <li><em>Matched</em>: inspection by the authors to see if the tags redundant matching or not with the tags suggested</li> <li><em>FinalTag</em>: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> <p>The sub-directory <code>code_consolidation_mapping_overview</code> contains .csv files (<code>DL_Faults_rq2_overview.csv</code>, <code>DRL_Challenges_rq2_overview.csv</code>, <code>Functional_rq2_overview.csv</code>) organized as follows:</p> <ul> <li><em>Initial_Tags</em>: list of the unique initial tags assigned by GPT-4-Turbo for each dataset</li> <li><em>Mapped_tags</em>: list of tags mapped by GPT-4-Turbo</li> <li><em>Unmatched_tags</em>: list of unmatched tags by GPT-4-Turbo</li> <li><em>Aggregating_tags</em>: list of consolidated tags</li> <li><em>Final_tags</em>: list of final tags after the consolidation task</li> </ul> </li> </ul> </li> <li> <p><code>prompt_for_each_rq</code>: This directory contains: - (i) the history of prompts used in each dataset (<code>prompts_history.txt</code>) -(ii) all final prompt used for the analysis of each dataset, prompt used for incremental coding, prompt used in rq2 to consolidate redundant codes, prompt used in rq3 to create taxonomy (<code>generic_prompt.txt</code>) -(iii) all .csv files in which there are indicate, for each dataset, the link and the prompt used (<code>prompt_DL_Faults_COMMIT.csv</code>, <code>prompt_DL_Faults_ISSUE.csv</code>, <code>prompt_DL_Faults_SO.csv</code>, <code>prompt_DRL_Challenges.csv</code>). For the Functional Dataset .csv file contains, instead, Question, Answer and Prompt used (<code>prompt_Functional.csv</code>)</p> </li> <li> <p><code>rq3</code>: This directory contains the taxonomies obtained from GPT-4-Turbo for the DL Faults and for the DRL Challenges (<code>taxonomy_DL_Faults.txt</code>,<code>taxonomy_DRL_Challenges.txt</code>)</p> </li> </ul> <h2>stats</h2> <ul> <li> <p><code>RQ1</code>: contains script and datasets used to perform metrics for RQ1. The analysis calculates all possible combinations between Matched, More Abstract, More Specific, and Unmatched.</p> <ul> <li><code>RQ1_Stats.ipynb</code> is a Python Jupyter nooteook to compute the RQ1 metrics. To use it, as explained in the notebook, it is necessary to change the values of variables contained in the first code block.</li> <li><code>independent-prompting</code>: Contains the datasets related to the independent prompting. Each line contains the following fields: <ul> <li><em>Link</em>: Link to the artifact being tagged</li> <li><em>Prompt</em>: Prompt sent to GPT-4-Turbo</li> <li><em>FinalTag</em>: Artifact coding from the replicated study</li> <li><em>chatgpt_output_text</em>: GPT-4-Turbo output</li> <li><em>chatgpt_output</em>: Codes parsed from the GPT-4-Turbo output</li> <li><em>Author1</em>: Annotator 1 evaluation of the coding</li> <li><em>Author2</em>: Annotator 2 evaluation of the coding</li> <li><em>FinalOutput</em>: Consolidated evaluation</li> </ul> </li> <li><code>incremental-prompting</code>: Contains the datasets related to the incremental prompting (same format as independent prompting)</li> <li><code>results</code>: contains files for the RQ1 quantitative results. The files are named <code>RQ1\_<<Dataset>>\_<<Prompt method>>\_<<ExcludingNegative>>\_<<MetricAggregation>>.csv</code>, where <em>Dataset</em> is the dataset name, <em>Prompt method</em> indicates whether results are for independent or incremental prompting, <em>Excluding Negatives</em> (for datasets where this applies) whether results have been obtained by excluding negative instances, and <em>MetricAggregation</em> (where it applies) how metrics have been aggregated (macro or weighted average). The files report columns indicating the <em>Dataset</em>, the <em>Matching type</em>, the <em>Accuracy</em>, <em>Precision</em>, <em>Recall</em>, <em>F1 Score</em>, and <em>Cohen's Kappa</em>.</li> </ul> </li> <li> <p><code>RQ2</code>: contains the script used to perform metrics for RQ2, the datasets it uses, and its output.</p> <ul> <li><code>RQ2_SetStats.ipynb</code> is the Python Jupyter notebook to perform the analyses. The scripts takes as input the following types of files, contained in the directory contains the script used to perform the metrics for RQ2. The script takes in input:</li> <li>RQ1 Data Files (<code>RQ1_DLFaults_Issues.csv</code>, <code>RQ1_DLFaults_Commits.csv</code>, and <code>RQ1_DLFaults_SO.csv</code>, joined in a single .csv <code>RQ1_DLFaults.csv</code>). These are the same files used in RQ1.</li> <li>Mapping Files (<code>RQ2_Mappings_DRL.csv</code>, <code>RQ2_Mappings_Functional.csv</code>, <code>RQ2_Mappings_DLFaults.csv</code>). These contain the mappings between human tags (<em>HumanTags</em>), GPT-4-Turbo tags (<em>Final Tags</em>), with indicated the type of matching (<em>MatchType</em>).</li> <li>Additional codes creating during the consolidation (<code>RQ2_newCodes_DRL.csv</code>, <code>RQ2_newCodes_Functional.csv</code>, <code>RQ2_newCodes_DLFaults.csv</code>), annotated with the matching: <em>new code</em>,<em>old code</em>,<em>human code</em>,<em>match type</em></li> <li>Set files (<code>RQ2_Sets_DRL.csv</code>, <code>RQ2_Sets_Functional.csv</code>, <code>RQ2_Sets_DLFaults.csv</code>). Each file contains the following columns: <ul> <li><em>HumanTags</em>: List of tags from the original dataset</li> <li><em>InitialTags</em>: Set of tags from RQ1,</li> <li><em>ConsolidatedTags</em>: Tags that have been consolidated,</li> <li><em>FinalTags</em>: Final set of tags (results of RQ2, used in RQ3)</li> <li><em>NewTags</em>: New tags created during consolidation</li> </ul> </li> <li><code>RQ2_Set_Metrics.csv</code>: Reports the RQ2 output metrics (Precision, Recall, F1-Score, Jaccard).</li> </ul> </li> </ul>
A green solvent enables precursor phase engineering of stable formamidinium lead triiodide perovskite solar cells - Data Availability
<p>A green solvent enables precursor phase engineering of stable formamidinium lead triiodide perovskite solar cells - Data Availability</p>
Supplementary Data for "Exploring structure-function relationships in engineered receptor performance using computational structure prediction"
<p>These data are supplementary data for the manuscript "<strong>Exploring structure-function relationships in engineered receptor performance using computational structure prediction</strong>", which has been submitted for consideration for publication. These data include protein structure predictions used in this study.</p>
Replication Package for "A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineer"
<p><strong>Content Overview</strong></p> <p>This replication package contains the following materials:</p> <p>- Data: RAW Focus Group Datasets and Aggregated Results</p> <p>For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!</p>
Data from: Soil erodibility differs according to heritable trait variation and nutrient-induced plasticity in the salt marsh engineer Spartina alterniflora
Use of landform engineers for habitat restoration has often resulted in unanticipated outcomes. It is possible that departures from expectation arise because applications do not adequately account for the influence of heritable and non-heritable phenotypic variation on ecosystem attributes. In this study, we performed a common garden greenhouse experiment to determine whether soil shear strength—a characteristic linked to erosion resistance—varies according to heritable and plastic trait expression in Spartina alterniflora grown under contrasting nutrient regimes. We detected heritable variation across a broad spectrum of functional traits, including nutrient uptake. We also found that S. alterniflora exhibited trait-specific differences in nutrient-induced phenotypic plasticity. Heritable trait differences and plasticity together explained approximately 70% of the observed variation in soil shear strength. Soil shear strength increased when plants received more nutrients, but the influence of heritable variation on soil shear strength was equal to or larger than that of nutrient-induced plasticity. These findings illustrate that heritable and non-heritable trait expression can potentially govern the fate of marsh ecosystems, which suggests that consideration should be given to both factors when deploying landform engineers for coastal restoration.
Data from: A global database and 'state of the field' review of research into ecosystem engineering by land animals.
1. Ecosystem engineers have been widely studied for terrestrial systems, but global trends in research encompassing the range of taxa and functions have not previously been synthesised. 2. We synthesised contemporary understanding of engineer fauna in terrestrial habitats and assessed the methods used to document patterns and processes, asking: 1.Which species act as ecosystem engineers and with whom do they interact? 2. What are the impacts of ecosystem engineers in terrestrial habitats and how are they distributed? 3. What are the primary methods used to examine engineer effects and how have these developed over time? We considered the strengths, weaknesses and gaps in knowledge related to each of these questions, and suggested a conceptual framework to delineate 'significant impacts' of engineering interactions for all terrestrial animals. 3. We collected peer-reviewed publications examining ecosystem engineer impacts and created a database of engineer species to assess experimental approaches and any additional covariates that influenced the magnitude of engineer impacts. 4. One hundred and twenty-two species from twenty-eight orders were identified as ecosystem engineers, performing five ecological functions. Burrowing mammals were the most researched group (27%). Half of all studies occurred in dry/arid habitats. Mensurative studies comparing sites with and without engineers (80%) were more common than manipulative studies (20%). These provided a broad framework for predicting engineer impacts upon abundance and species diversity. However, the roles of confounding factors, processes driving these patterns, and the consequences of experimentally adjusting variables, such as engineer density, have been neglected. True spatial and temporal replication have also been limited, particularly for emerging studies of engineer reintroductions. 5. Climate change and habitat modification will challenge the roles that engineers play in regulating ecosystems, and these will become important avenues for future research. We recommend future studies include simulation of engineer effects and experimental manipulation of engineer densities to determine the potential for ecological cascades through trophic and engineering pathways due to functional decline. We also recommend improving knowledge of long-term engineering effects and replication of engineer reintroductions across landscapes to better understand how large-scale ecological gradients alter the magnitude of engineering impacts.
Dynamic loading of human engineered heart tissue enhances contractile function and drives a desmosome-linked disease phenotype (TEM data)
<p>This is the TEM imaging data for the desmosome analysis as reported in the manuscript titled "Dynamic loading of human engineered heart tissue enhances contractile function and drives a desmosome-linked disease phenotype."</p>
Data for Mathematical Expressions in Software Engineering Artifacts
<p>Data for the experiments in the paper, Mathematical Expressions in Software Engineering Artifacts.</p> <p>The dataset contains the following sub-directories:</p> <ol> <li>Bug_data: Bug reports from 10 open-sourced projects.</li> <li>MathyB_A: Bug reports annotated by MEDSEA.</li> <li>MathyB_H: Bug reports annotated manually by humans.</li> <li>NNGen_modified_data: Modified log messages of <a href="https://github.com/Tbabm/nngen">NNGen</a> dataset (<a href="https://dl.acm.org/doi/10.1145/3238147.3238190">Neural-machine-translation-based commit message generation: how far are we?</a>) based on the annotations made by MEDSEA.</li> </ol>
Data from: Nucleation of jet engine oil vapours is a large source of aviation-related ultrafine particles
<p>Data of Figures 1-3</p>
Open data for "Programmable frequency-bin quantum states in a nano-engineered silicon device"
<p>The folder includes includes the raw data that were used for generation of all Figures in the paper "Programmable frequency-bin quantum states in a nano-engineered silicon device".</p>
Supporting data for Absorbing stress via molecular crumple zones: Strain engineering flexibility into the rigid UiO-66 material
<p>Supplementary data containing relevant structural information for the manuscript 'Absorbing stress via molecular crumple zones: Strain engineering flexibility into the rigid UiO-66 material'.</p> <p>Please also refer to https://github.com/SvenRogge/supporting-info for additional input scripts to reproduce these results and a detailed description of the different files.</p>
Supporting Data for: "Additivity of atomic strain fields as a tool to strain-engineering phase-stabilized CsPbI$_3$ perovskites"
<p>This dataset contains every training and validation datasets ase well as config YAML files that were used to train the NequIP MLPs in the paper titles as "Additivity of atomic strain fields as a tool to strain-engineering phase-stabilized CsPbI$_3$ perovskites".</p> <p>Also python scripts for MLP-MD and strain field analysis are supplied.</p>
Data set for 'All-microwave Lamb shift engineering for a fixed frequency multi-level superconducting qubit'
<p>Solurce data for the paper 'All-microwave Lamb shift engineering for a fixed frequency multi-level superconducting qubit' (https://www.nature.com/articles/s42005-024-01841-0).</p>
Treating the End of the Data Life Cycle as a First-Class Citizen in Data Engineering - Datasets
<p>Additional data for paper "Treating the End of the Data Life Cycle as a First-Class Citizen in Data Engineering".</p>
Data for: Engineering tRNA abundances for synthetic cellular systems
<p>Data for publication:<strong> Engineering tRNA abundances for synthetic cellular systems</strong></p> <p><strong>Abstract</strong></p> <p>Routinizing the engineering of synthetic cells requires specifying determining beforehand how many of each molecule are needed. First-principles tools for specifying molecular abundances enabling whole-cell synthetic biology are missing. We use a colloidal dynamics simulator to make predictions for how tRNA abundances impact protein synthesis rates. We use rational design and direct RNA synthesis to make 21 synthetic tRNA surrogates from scratch. We use evolutionary algorithms within a computer aided design framework to design engineer translation systems predicted to work faster or slower depending on tRNA abundance differences. We build and test the so-specified synthetic systems and find that qualitative agreement between expected and observed systems performance matchqualitatively match. First-principles modeling combined with bottom-up experiments can help molecular-to-cellular scale synthetic biology realize “design, build, work” frameworks that transcend tinker-and-test.</p> <p><strong>Data description</strong></p> <p>The data here consists of (1) All Colloidal Smoldyn & CD-CAD simulation input parameter and output files & (2) experimental data used for the associated publication. Simulation data was produced using Colloidal Dynamics modeling and Colloidal Dyamics-CAD (CD-CAD) as described in the associated manuscript. Data folders should be used directly with modeling and analysis code provided on Github: https://github.com/EndyLab/tRNACAD.</p>
Supplementary data for "Engineering PD-1-targeted small protein variants for in vitro diagnostics and in vivo PET imaging"
<p>Supplementary data for "Engineering PD-1-targeted small protein variants for in vitro diagnostics and in vivo PET imaging". The docking.zip file contains ClusPro protein/protein docking results for all binding protein variants using either murine PD-1 structure (3bikB) or human PD-1 AF2 model as receptors. The pymol session contains data and scenes used to generate the Figures in the paper.</p>
Data for "Phase engineering of anomalous Josephson effect derived from Andreev molecules"
<p>An Igor file stores source data for the figures in the main manuscript and the supplementary figures. The raw data contain the I-V traces to evaluate the switching currents. The waves in the Igor file are obtained from the raw data with the same names as the waves.</p>
Linkage of hospital records and death certificates by a search engine and machine learning: training and test set data
Open the record for dataset details and reuse information.
Data from: Are Cecropia trees ecosystem engineers? The effect of decomposing Cecropia leaves on arthropod communities
Open the record for dataset details and reuse information.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.