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1,034 results for “Empirical”
Aerosol Optical Depth (AOD) over the Nepalese cryosphere derived from an empirical model
<p>In this manuscript, we demonstrate the spatial and temporal variability of AOD over the Nepalese cryosphere may be reconstructed through empirical relationships developed from numerical weather model analysis fields and geographic variables. Our manuscript is novel, providing:<br> * a capability to extrapolate AOD over the cryospheric portion of Nepalese Himalayas<br> * an understanding of the relationships between ECMWF Reanalysis with observed AOD<br> * and a cross-validation of an empirical model using MODIS derived AOD and AERONET data.</p> <p>The empirical model that we present in this study, could potentially be applied to other mountains regions. We believe that the findings presented in our manuscript provide a novel approach to address the challenge of paucity of observations for aerosols in the cryospheric portion in Himalaya region. </p> <p> </p> <p> </p>
Manuscript dataset - Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia
<p>This is a dataset associated with submission of the manuscript "Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia"</p>
Supporting model output for 'Empirical stream thermal sensitivities may underestimate stream temperature response to climate warming'.
<p>Model output used to generate figures in the manuscript 'Empirical stream thermal sensitivities may underestimate stream temperature response to climate warming'.</p>
Empirical Predictions for the Period Distribution of Planets to be Discovered by the Transiting Exoplanet Survey Satellite
<p>This repository contains the supplement materials for our paper :</p> <p>Jonathan H. Jiang, Xuan Ji, Nicolas Cowan, Renyu Hu, Zonghong Zhu, Empirical Predictions for the Period Distribution of Planets to be Discovered by the Transiting Exoplanet Survey Satellite, Submitted to AJ.</p> <p>Supplemental materials include:</p> <p>1.Data behind the Figures</p> <p>2.Simulation Results to estimate the error range</p> <p> </p>
Raw Coding Data for "Insights for Improving Diagram Editing Gained from an Empirical Study"
<p>This MaxQDA Exchange file contains the raw coding data for "Insights for Improving Diagram Editing Gained from an Empirical Study"</p>
Data of the Paper "How to Communicate when Submitting Patches: an Empirical Study of the Linux Kernel"
<p>This repository includes the data of the paper "How to Communicate when Submitting Patches: an Empirical Study of the Linux Kernel".</p> <p>It contains three parts:</p> <ol> <li>Related online documents.</li> <li>Original questionnaire of the survey.</li> <li>Survey results.</li> </ol> <p> </p>
Magnetospheric `penetration' of IMF By viewed through the lens of an empirical RBF modeling
<p>(1) The model sample source code (fortran), named B_calculation_sample_source_code.for</p> <p>(2) Two data files for IMF Bz>0 and Bz<0 with self-explanatory filenames Bz_gt_0_All_By.dat and Bz_lt_0_All_By.dat </p> <p>(3) A text file, named Data_format.txt, with a description of the data format of the above data files. <br> (4) A file Kurihara_3D_grid.dat containing coordinates of the RBF grid nodes (required as input to the model source code)</p> <p>(5) Two model input parameter files, named Parameters_Negative_IMF_Bz.par and Parameters_Positive_IMF_Bz.par</p> <p>(6) A file <a href="https://zenodo.org/api/files/9e2ade95-5805-4a8f-9e22-4a61031927f5/TA15_Model_description.pdf?versionId=c46c8fe5-4fd5-4bf5-921b-2da142226051">TA15_Model_description.pdf</a> with details on the interplanetary N- and B-indices, included in the data files</p>
On the Continuous Code Quality Outcomes of Continuous Integration: An Empirical Study
<p>Here we provide all datasets that we rely on in our research questions and all violin plots for RQ3</p>
An Empirical Study of Word Embedding Dimensionality Reduction
<p>In order to analyze the impact on model quality while reducing the number of dimensions, strictly controlled trainings of word embedding are performed on Wikipedia corpora of 170 languages. The specially designed word embedding training tool makes use of processed corpus and intermediate results to accelerate the training, while keeping the consistency of negative sampling.</p> <p>Tests of semantic relatedness show that, except for some corpora of poor scale, the margin gain from extra dimensions significantly decreases above 200.</p>
Figure 2 in Development of empirical standard weight equation for Pursak chub Squalius pursakensis, an endemic cyprinid species of Northwest Anatolia
Figure 2. Area of distribution of Squalius pursakensis (dark gray area).
Figure 1 in Development of empirical standard weight equation for Pursak chub Squalius pursakensis, an endemic cyprinid species of Northwest Anatolia
Figure 1. Squalius pursakensis from Seydi Suyu in Sakarya Basin (445 mm TL).
Dataset for "Property-based Testing within ML Projects: an Empirical Study" (ICSME NIER 2024)
<p>The dataset for the ICSME NIER 2024 paper "Property-based Testing within ML Projects: an Empirical Study". Descriptions of each column can be found in the readme.md file.</p>
Online Appendix of "Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance"
<div> <div><strong>Abstract</strong></div> <div>This is the dataset of the paper entitled "Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance", presented at SCAM 2024. It contains information related to the target commits and their attributes, as well as simulation results pertaining to the research questions in the paper.</div> <div> </div> <div>The target projects and commits in the dataset are based on a prior study: Nugroho, et al.: "How different are different diff algorithms in Git?: Use --histogram for code changes", Empirical Software Engineering, 2020, https://doi.org/10.1007/s10664-019-09772-z</div> <div> </div> </div> <div> <div><strong>Survey Overview</strong></div> <div> <div><em>1. Filtration</em></div> <div> <div>We collected attributes related to the changes for filtering and RQ purposes.</div> <ul> <li>Number of lines</li> <li>Number of changed lines</li> <li>Similarity distance</li> <li>Number of mismatch diff area</li> </ul> <div><em>2. RQ1</em></div> <div> <div>We investigate the minimum number of feedback actions needed to correct the initial diffs to the target diffs. Regarding the simulation, two types of heuristic functions (non-admissible and admissible functions) have been used to reduce costs. When the search with the non-admissible heuristic function of the initial state does not match the ideal optimal result, i.e., when there is room for improvement in the number of feedback actions, we applied another A* search with the admissible heuristic function.</div> <div> </div> <div> <div>We obtained the following results through search:</div> <ul> <li>Number of feedback actions (A* search with non-admissible heuristic)</li> <li>Number of feedback actions (A* search with admissible heuristic)</li> </ul> <div> </div> <div><em>3. RQ2</em></div> <div> <div> <div>We investigated the various effects that feedbacks have on the diffs by examining the diffs at depth 1 of the search tree. The dataset records the maximum, minimum, median, mean, and standard deviation for each search problem.</div> <br> <div>The study yielded the following results:</div> <ul> <li>Similarity distance</li> <li>Number of mismatch diff area</li> </ul> </div> </div> </div> </div> </div> </div> <div> <div> </div> <div> <div><strong>Dataset Columns</strong></div> <div>The following are the contents represented by the columns in the CSV file and their descriptions.</div> <div> </div> <div> <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>project_name</td> <td>Name of the project associated with the data.</td> </tr> <tr> <td>filename</td> <td>Name of the file being analyzed.</td> </tr> <tr> <td>filepath</td> <td>Path to the file within the project.</td> </tr> <tr> <td>commit_id</td> <td>Commit hash representing the new version of the file.</td> </tr> <tr> <td>parent_commit</td> <td>Commit hash representing the old version of the file.</td> </tr> <tr> <td>error_commit</td> <td>An error occurred when retrieving the commit from the repository.</td> </tr> <tr> <td>error_setup</td> <td>Any error when generating the new and old versions of the file.</td> </tr> <tr> <td>error_analyze</td> <td>An error when collecting information for filtering.</td> </tr> <tr> <td>new_loc</td> <td>Lines of the new version of the source code.</td> </tr> <tr> <td>old_loc</td> <td>Lines of the old version of the source code.</td> </tr> <tr> <td>histogram_len</td> <td>Path length of the diff when using the Histogram algorithm.</td> </tr> <tr> <td>myers_len</td> <td>Path length of the diff when using the Myers algorithm.</td> </tr> <tr> <td>histogram-myers#edge</td> <td>Number of difference edges between Histogram and Myers diff.</td> </tr> <tr> <td>histogram-dp#edge</td> <td>Number of difference edges between Histogram and initial diff.</td> </tr> <tr> <td>myers-dp#edge</td> <td>Number of difference edges between Myers and initial diff.</td> </tr> <tr> <td>histogram-myers#area</td> <td>Number of mismatch diff areas between Histogram and Myers diff.</td> </tr> <tr> <td>histogram-dp#area</td> <td>Number of mismatch diff areas between Histogram and initial diff.</td> </tr> <tr> <td>myers-dp#area</td> <td>Number of mismatch diff areas between Myers and initial diff.</td> </tr> <tr> <td>dp#candidate</td> <td>Number of feedback candidates of initial diff (similarity distance).</td> </tr> <tr> <td>#insert</td> <td>Number of lines added in the change.</td> </tr> <tr> <td>#delete</td> <td>Number of lines deleted in the change.</td> </tr> <tr> <td>#change</td> <td>#insert + #delete.</td> </tr> <tr> <td>error_Asearch</td> <td>An error occurring during A* search with a non-admissible heuristic.</td> </tr> <tr> <td>#feedback_A</td> <td>Number of feedback actions for A* search with a non-admissible heuristic.</td> </tr> <tr> <td>time_A</td> <td>Time taken for A* search with a non-admissible heuristic (ms).</td> </tr> <tr> <td>RQ1_error_iteration</td> <td>An error when iterations exceed the limit (10,000,000) during A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1_error_timeout</td> <td>An error when the search time exceeds the limit (1,800 seconds).</td> </tr> <tr> <td>RQ1_error_other</td> <td>Other errors encountered during A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1#feedback</td> <td>Number of feedback actions for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1#iter</td> <td>Number of iterations for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1_time</td> <td>Time taken for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ2_error_exceed</td> <td>An error due to exceeding time or iteration limits in RQ2.</td> </tr> <tr> <td>RQ2_error_other</td> <td>Other errors encountered during RQ2.</td> </tr> <tr> <td>RQ2#children</td> <td>Number of children nodes of the initial state (= similarity distance).</td> </tr> <tr> <td>RQ2#candidate_min</td> <td>Minimum similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_max</td> <td>Maximum similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_ave</td> <td>Average of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_median</td> <td>Median of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_sd</td> <td>Standard deviation of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#area_min</td> <td>Minimum number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_max</td> <td>Maximum number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_ave</td> <td>Average number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_median</td> <td>Median number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_sd</td> <td>Standard deviation of number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>is_used</td> <td>Indicates whether this data is used in the results of RQ1 and RQ2.</td> </tr> </tbody> </table> </div> </div> </div> </div>
Data Science Tasks used in "AI Support for Data Scientists: An Empirical Study on Workflow and Alternative Code Recommendation"
<p>This entry contains the supplementary files for a scientific article. </p> <p>The dataset contains the necessary files for the two data science tasks used in the experiment study from scientific article "AI Support for Data Scientists: An Empirical Study on Workflow and Alternative Code Recommendation"</p> <p> </p>
An Empirical Model of Soft Bellows Actuator
<p>The dataset includes:</p> <p>(1) bellows CAD files</p> <p>(2) experimental videos of output force estimation of gripper 1</p> <p>(3) experimental videos of handling products</p> <p> </p>
ResNet and Asymmetric ResNet Weights, for "The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof"
<p>Trained checkpoints of tens of thousands of neural networks, for use in our paper "The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof" (https://arxiv.org/abs/2405.20231).</p>
The dataset of "Evaluation of Digital Supply Chain Technology's Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
<p>This dataset involves the data from the questionnaire, which come from the project "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain". It comprises three dimensions questions, technology, sustainability and supply chain dynamism. The datas come from two Chinese energy firms, <span>China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd.</span></p>
Empirical Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<h3>Description of the data and file structure</h3> <p>The files contain the source data for Fig. 1 and Fig. 3A,B of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <h4>Files and variables</h4> <h5>File: Fountain_Fish_coordinates.zip</h5> <p><strong>Description:</strong> </p> <p>The zip file contains 30 folders, each containing information on one predator attack and respective prey evasion. Each folder is named according to the drone ID used for the filming (e.g., DJI _1, DJI _2, DJI _3) and the respective frame number (e.g., f930) from the video recording. </p> <h5>File naming</h5> <p>Each folder contains JPG and CSV files. </p> <p>The JPG files show the image from the footage at the corresponding frame indicated in the files' name (e.g., frame_001_im).</p> <p>There are 2 types of CSV files. Files with the name 'polygon.csv' contain coordinates (in pixels) of points (x, y) defining the polygon outlining the prey school at the particular frame as indicated in the files' name (e.g., frame001) and corresponding to the image in the JPG file with the same frame number. Files with the name 'sardines_and_marlin.csv' contain coordinates (in pixels) of points (x, y), defining the head (columns 1 and 2) and the dorsal fin (columns 3 and 4) of single sardine individuals (by rows), and of the respective attacking marlin: marlin's head (columns 5 and 6), marlin's dorsal fin (columns 7 and 8), and marlin's tip of the bill (columns 9 and 10). These coordinates correspond to the respective image with the same frame number.</p>
Replication package for "An Empirical Study of Q&A Websites for Game Developers"
<p><strong>Replication package for the paper "An Empirical Study of Q&A Websites for Game Developers"</strong></p> <p>This repository contains the datasets and scripts used to replicate the results from the paper "An Empirical Study of Q&A Websites for Game Developers".</p> <p>This is an exact copy of the repository on GitHub: https://github.com/asgaardlab/done-21-arthur-gamedev_qa_websites-code</p> <p><strong>Replication data</strong></p> <p>The datasets used to replicate the results for the paper can be found in the data directory (data/). These are the datasets we obtained after running all of the notebooks in this repository.</p> <p>Two of the studied websites are owned by companies (Epic and Unity) and we are not legally allowed to share the textual contents of the questions and answers as they are considered intellectual property. Therefore, instead of sharing the content of those posts, we included the URLs to all of the pages where the information used in the paper can be found, so that they can be crawled by future researchers.</p> <p>This is not an issue for Stack Overflow and the Game Development Stack Exchange, since that data is provided by Stack Exchange in the Stack Exchange Data Dump (https://archive.org/details/stackexchange).</p> <p><strong>Survey data:</strong> Unfortunately, our University's ethics board only allows us to share the survey responses in aggregated format, which is done in the paper. In this repository, we added the list of communities in which we shared the survey (data/surveyed_communities.csv).</p> <p><strong>Using this repository</strong></p> <p>If you are using the datasets provided in this repository, you just need to run the analysis notebook (code/analysis/paper_results.ipynb) to obtain the results as shown in the paper.</p> <p>Otherwise, if you want to run the whole pipeline from scratch, follow these steps:</p> <p>1. Download the data from Unity Answers and the UE4 AnswerHub from their websites (you can use the URLs provided in our datasets). Parse the HTML pages and extract the required information.</p> <p>2. Download the data from Stack Overflow and the Game Development Stack Exchange from the Stack Exchange Data Dump (https://archive.org/details/stackexchange). Run the notebooks to process the XML files from the Stack Exchange data dump (code/process_xml). For Stack Overflow, run the select_gamedev_posts.ipynb (code/process_xml/stackoverflow/select_gamedev_posts.ipynb) first.</p> <p>3. Run the text processing notebook (code/text_processing.ipynb).</p> <p>4. Run the topic modelling notebook (code/topic_modelling.ipynb).</p> <p>5. Run the topic comparisons notebook (code/topic_comparisons.ipynb).</p> <p>6. Finally, run the analysis notebook (code/analysis/paper_results.ipynb) to get the results as shown on the paper.</p>
Dataset for ESE submission "Pull Request Latency Explained: An Empirical Overview"
<p>This is the dataset for ESE submission "Pull Request Latency Explained: An Empirical Overview".</p> <p>For research purpose, if you need `pull request id`, please request <a href="https://zenodo.org/record/7299639#.Y2mQ-HpBwUE">the column</a>.</p>
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.