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12 results for “material retrieval”
Supplementary Material: Knobs and dials of retrieving JWST transmission spectra. I. The importance of p-T profile complexity
<p>This is supplementary material to <a title="Schleich et al. (2024)" href="https://www.aanda.org/articles/aa/abs/2024/10/aa51845-24/aa51845-24.html" target="_blank" rel="noopener">Schleich et al. (2024)</a>. The content of the provided data repository (also described in the file "content.txt") is as follows:</p> <p> </p> <h2>ADDITIONAL ANALYSIS</h2> <p>This folder contains a collection of ancillary data products for the evaluation of the retrievals performed in this work.</p> <ul> <li>'bayes-factor' contains the data tables for evaluating the Bayes' factor for each separate collection of models(*)</li> <li>'corner-plots' contains a collection of all corner plots associated with the individual input cases</li> <li>'fit-residuals' contains all fit residuals for the individual atmospheric retrievals performed in this work (used to make Fig. C.1)</li> <li>'resampled-pt-profiles' contains resampled p-T profiles to generate Figs. 6 and F.1</li> <li>'retrieval-accuracy' contains additional plots related to the accuracy of each retrieval (used to make Fig. 5, as well as Figs. E.1 - E.5)</li> </ul> <p><br>(*) SIDE NOTE:<br>Table headers in the "bayes-factor" data tables reference evidence reported from MultiNest (variable "Z"), and calculated Bayes factor (variable R). The case with log(R) = 0 is necessarily the reference case, and outliers are marked in a binary table with 1 (|log(R)| > 5) or 0 (|log(R)| < 5). In all cases, "log" refers to the natural logarithm.</p> <ul> <li>If someone actually reads this, I'm sorry. I also spent way too much time trying to track down if the values reported in MultiNest are natural or base-10 logarithm. I have now been convinced that it is worth it, always, to either specify "ln" for the base-e logarithm, or give the base of your logarithm if your write it down (i.e. log_10(X)) -Simon.</li> </ul> <h1> </h1> <h2>RETRIEVAL RESULTS</h2> <p>This folder contains the data products associated with the retrieval runs for each synthetic spectrum. The sub-directories are aranged by the following keys:</p> <ul> <li>'drs' and 'pandexo' refere to the two noise cases considered</li> <li>'inv-t' and 'norm-t' refere to the two underlying p-T profiles used to make the synthetic spectra</li> <li>'hpc', 'mpc', and 'lpc' refere two the three cloud-top pressure cases considerd</li> </ul> <p>Each individual folder contains (1) the TauREx parameter files for running retrievals using the selection of p-T profiles, (2) a folder called 'results', which containts the associated data products, and (3) a folder called 'chains', which stores the ancillary data products associated with the MultiNest sampling runs of each retrieval.</p> <p> We note that for the "drs_inv-t_mpc" case, the chains for the isothermal, 2-point, and 4-point runs have been lost</p> <p> </p> <h2>SYNTHETIC SPECTRA</h2> <p>This folder contains data products associated with the sample of synthetic transmission spectra.</p> <ul> <li>'pt-profile_*.csv' are csv-files containing the p-T points used to make Figure 1 , and to generate the synthetic transmissions spectra</li> <li>'forward-models' contains TauREx parameter files and forward models for the sample of synthetic transmission spectra. Each of the sub-directories also contains a faux-spectrum representing the wavelength-map of NIRSpec PRISM <ul> <li>'no-clouds' contains contains the above for generating Figure 3.</li> <li>'inv-t' contains forward models using the "inverse" p-T profile</li> <li>'norm-t' contains forwrad models using the "monotonic" p-T profile</li> </ul> </li> </ul>
Supplementary material for "Phase Retrieval from overexposed PSF" article
<p>Supplementary material for "Phase Retrieval from overexposed PSF" article, submitted to Optics Communications (http://dx.doi.org/10.2139/ssrn.4267855).</p> <p>The pdf files present the results of the phase retrieval task as described in Sections 5 and 6 of the article and provide an easy-to-navigate tool for browsing through the parameter space of the simulation and of the algorithm. Click on the parameter values in the right-hand side of the browser page to navigate to the corresponding result.</p>
Fig. 3 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 3. Resulting order after multiple sorting according to the columns typeStatus, country, state, city and locality, in that order. This sequence of column names corresponds to the variable Sorting Order in the Gridit software.
Fig. 2 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 2. Columns swapped according to the desired order for the information in the final text. The order of the column names (scientificName, typeStatus, sex, country, etc.) corresponds to the variable Display Order in the Gridit software.
Fig. 1 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 1. Spreadsheet data from some of the specimens of Distictus tibialis (Brullé, 1846) cited in Supeleto et al. (2019). Mandatory columns and column names in the Gridit software marked in red.
Fig. 5 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 5. Data from the spreadsheet in Fig. 4, excluding header and the column scientificName, copied and pasted into a text editor; resulting tabs (cells) replaced with comma. Sequences of "ib" and "?" in each row were grouped together with a preceding number (e.g., "11ib" in row 2) that indicates the total of subsequent repeats. Each row represents a unique collecting event. The final text generated from this file is shown in Table 2.
Fig. 4 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 4. Repetitions in each column identified with the ib code for all columns (strict usage of the gridsetting technique).
Supplementary Material: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
<p>Supplementary material for "The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra" DOI: <a href="https://ui.adsabs.harvard.edu/link_gateway/2024arXiv240307801F/doi:10.48550/arXiv.2403.07801" target="_blank" rel="noreferrer noopener">10.48550/arXiv.2403.07801</a></p> <p>Contents of this record:</p> <ul> <li>The retrieved atmospheric parameters for the population (see supplementary_material.pdf).</li> <li>The retrieval statistics per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns (see supplementary_material.pdf).</li> <li>Planet specific retrieved spectra for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> <li>Retrieved parameter cornerplots per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> </ul> <p>(NOTE: the retrieval model and priors for the results displayed in this record are specified in tables 2 and 3 of the paper.)</p>
Supplementary material for 'The MAP metric in Information Retrieval Fault Localization'
<pre># map_bench4bl This is the supplementary material, data, and evaluation source code for the paper "The MAP metric in Information Retrieval Fault Localization" by Thomas Hirsch and Birgit Hofer. ## Preliminaries ### Python environment - Python 3.8 - pandas - numpy - matplotlib ## Datasets The [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) dataset has been used in this evaluation, with the addition of intermediate files taken from the [SABL](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>) experiment performed on this Bench4BL dataset. All data used in our evaluation is included in this repository. However, if the data is to be re-imported directly from these benchmark and datasets they have to be downloaded first and their local paths have to be set in [paths.py](<em>paths.py</em>). ### Bench4BL The Bench4BL dataset was published with the paper "Bench4BL: Reproducibility study on the performance of IR-based bug localization" by Lee, J., Kim, D., Bissyandé, T.F., Jung, W. and Le Traon, Y.. The dataset can be obtained [here](<em>https://github.com/exatoa/Bench4BL</em>). Follow the steps described in the corresponding [README](<em>https://github.com/exatoa/Bench4BL/blob/master/README.md</em>) to set up the dataset. The Bench4BL dataset contains the _old subjects_ subdataset, containing 558 bugs from AspectJ, JDT, PDE, SWT, and ZXing that have been widely used in older IRFL studies. This _old subjects_ subdataset was used in answering our RQ1, as discussed below, the corresponding scripts use _old subjects_ in their name to highlight this. #### SABL The SABL dataset is the online appendix of the paper "An Extensive Study of Smell-Aware Bug Localization" by TTakahashi, A., Sae-Lim, N., Hayashi, S. and Saeki, M.. The dataset can be downloaded [here](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>). The experiments in this dataset build on top of Bench4BL and intermediate files are provided in the datapackage. #### Rankings Rankings for BLIA, BRTracer, and BugLocator were produced by running these tools on Bench4BL locally. Rankings for AmaLgam and BLUiR were taken from the SABL experiment dataset. ## Structure ### Folders Bench4BL ground truths: - bench4bl_old_subjects_summary - bench4bl_summary Localization results of the included tools in Bench4BL: - bench4bl_localization_results - bench4bl_localization_results_sabl Target projects size metrics: - cloc_results - cloc_results_old_subjects Utility functions: - utils Output folders containing results, generated figures and tables: - results - results_old_subjects ### Scripts Scripts for re-importing data from Bench4BL and SABL datasets: - data_preparation_step_1_cloc_bench4bl.py - data_preparation_step_1_cloc_old_subjects_bench4bl.py - data_preparation_step_2_import_ground_truth_from_bench4bl.py - data_preparation_step_2_import_ground_truth_from_old_subjects_bench4bl.py - data_preparation_step_3_import_bench4bl_ranking_results.py - data_preparation_step_3_import_sabl_ranking_results.py Utilities: - paths.py - utils/bench4bl_utils.py - utils/Logger.py ### Evaluation scripts for the corresponding research questions: **Dataset analysis:** - rq_0_dataset_analysis_bench4bl_issues.py **RQ1: How big is the average ground truth in Bench4BL datasets, and what proportion of bugs have a ground truth containing multiple files?** - rq_1_bench4bl_ground_truth_size.py - rq_1_old_subjects_bench4bl_ground_truth_size.py RQ2: Do the IRFL tools included in Bench4BL truncate their results? - rq_2_ranking_lengths.py **RQ3: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results on the Bench4BL dataset? RQ3a: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results when considering the bloated ground truth issue found in Bench4BL?** - rq_3_truncating_BugLocator_rankings_bench4bl.py **RQ3b: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results when undefined $AP$ values are simply ignored?** - rq_3b_undefined_ap_BugLocator_rankings_bench4bl.py ## Licence All code and results are licensed under [CCA v4](<em>https://creativecommons.org/licenses/by/4.0/</em>), according to LICENSE file. Other licences may apply for some tools and datasets contained in this repo: [cloc-1.92.pl](<em>https://github.com/AlDanial/cloc</em>) under GPL v2, [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) and [SABL](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>) under CCA 4.0.</pre>
Nightside Contamination of Hot Jupiter Transmission Retrievals - Supplementary Material
<p>Supplementary material for '<em>From the Shadows: The Impact of Nightside Thermal Emission on Ultra-hot Jupiter Transmission Spectra Retrievals</em>' - ApJ (2024).</p> <p>This repository contains the full posterior probability distributions from the retrieval simulations in Kappelmeier, MacDonald, & Lewis (2024). The file names for the 12 corner plots correspond to the model assumptions and data used as follows:</p> <ul> <li><strong>TransOnly</strong> / <strong>TransPlusNightside</strong>: The former transmission models neglect nightside thermal emission (i.e. these correspond to 'biased' retrievals) while the latter include nightside emission.</li> <li><strong>N_trans_X</strong>: 'X' corresponds to the number of transits per instrument mode. This is either 1, 2, or 3 transits per mode.</li> <li><strong>NIRISS_SOSS / NIRSpec_G395H / MIRI_LRS / All_Instruments</strong>: The simulated JWST dataset used for the retrieval. The specific names are for single-instrument retrievals, while 'All_Instruments' corresponds to the combination of NIRISS SOSS, NIRSpec G395H, and MIRI LRS.</li> </ul> <p>Each corner plot shows the correlations between pairs of retrieved parameters and marginalized histograms for each parameter. The green lines are the true parameter values used to generate the simulated JWST datasets. The solid blue lines are the median retrieved value for each parameter, while the blue dashed lines are the ±1σ confidence intervals. The shaded purple contours in the correlation plots are, from darkest to lightest, the 1σ, 2σ, and 3σ confidence levels.</p> <p>For any questions, please contact: jak485@cornell.edu</p>
Supplemental Material for Predictive Reranking using Code Smells for Information Retrieval Fault Localization
<pre># Predictive Reranking using Code Smells for Information Retrieval Fault Localization This repository constitutes the supplementary material, data, and source code for the paper "Predictive Reranking using Code Smells for Information Retrieval Fault Localization", by Thomas Hirsch and Birgit Hofer, 2023. Source code and results are also made available on GitHub: https://github.com/AmadeusBugProject/PredictiveRerankingUsingCodeSmellsForIRFL </pre> <pre>## Preliminaries ### Python environment - python=3.8 - pandas - numpy - joblib - scikit-learn==1.0.2 - keras - tensorflow - nltk - sentence-transformers - matplotlib - seaborn Conda files are located in the root directory of the repository, [conda_from_history.yml](<em>conda_from_history.yml</em>). ### Datasets The [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) dataset was used in our evaluation. All data necessary for our machine learning and localization experiments is included in this repository. However, if the data is to be re-imported and recalculated directly from Bench4BL: Bench4BL has to be downloaded and paths to the benchmark root set accordingly in [paths.py](<em>paths.py</em>). BugLocator, BRTracer, and BLIA have to be run on the Bench4BL dataset using the scripting provided by the benchmark. PMD has to be installed in version 6.45.0 and path to PMD set accordingly in [paths.py](<em>paths.py</em>). # Structure of this repository ## General utility functions - [constants.py](<em>constants.py</em>) Contains parameters for the NN model, and other parameters. - [paths.py](<em>paths.py</em>) Contains paths to external datasources and tools, e.g. Bench4BL and PMD. - [utils/bench4bl_utils.py](<em>utils/bench4bl_utils.py</em>) Helper functions for resolving paths to Bench4BL benchmark. - [utils/dataset_utils.py](<em>utils/dataset_utils.py</em>) Helper functions for loading datasets and performing dataset splits. - [utils/Logger.py](<em>utils/Logger.py</em>) Logging. - [utils/nn_classifier.py](<em>utils/nn_classifier.py</em>) NN classifier model. - [utils/scoring_utils.py](<em>utils/scoring_utils.py</em>) Metrics. - [utils/stats_utils.py](<em>utils/stats_utils.py</em>) Wrapper methods for statistical tests. ## Experiment ### Dataset setup and preparation The following scripts are responsible to create, import, and set up data that is used in our experiments. The produced data is already part of this repository, the execution of these scripts is therefore only necessary when data is to be re-imported from the Bench4BL repository. - [a00_pmd_bench4bl.py](<em>a00_pmd_bench4bl.py</em>) Runs PMD on all projects and versions contained in the Bench4BL dataset. The utilized ruleset is defined in [all_java_ruleset.xml](<em>all_java_ruleset.xml</em>). Results are stored in [pmd_results](<em>pmd_results</em>). - [a01_vectorize_pmd.py](<em>a01_vectorize_pmd.py</em>) Creates csv vectors from PMD outputs. - [a02_cloc_bench4bl.py](<em>a02_cloc_bench4bl.py</em>) Runs cloc on all projects and versions contained in the Bench4BL dataset. Only Java files are considered. Results are stored in [cloc_results](<em>cloc_results</em>). - [a02_pmd_usage_in_bench4bl_projects.py](<em>a02_pmd_usage_in_bench4bl_projects.py</em>) Searches for occurrence of PMD in the build files of all projects and versions contained in the Bench4BL dataset. Results are stored in [pmd_usage_in_bench4bl_projects](<em>pmd_usage_in_bench4bl_projects</em>). - [a03_import_bugs_from_bench4bl.py](<em>a03_import_bugs_from_bench4bl.py</em>) Imports textual bug reports and corresponding fixed files ground truth from Bench4BL. Results are stored in [bench4bl_summary](<em>bench4bl_summary</em>). - [a04_normalize_smells_by_loc.py](<em>a04_normalize_smells_by_loc.py</em>) Normalizes the smell vectors for each file with its LOC count. Results are stored in [pmd_results](<em>pmd_results</em>). - [a05_bench4bl_file_features.py](<em>a05_bench4bl_file_features.py</em>) Creates feature vectors for each bug report from PMD smells. Results are stored in [bug_smell_vectors](<em>bug_smell_vectors</em>). - [a05_bench4bl_ranking_results.py](<em>a05_bench4bl_ranking_results.py</em>) Imports the results of BugLocator, BRTracer, and BLIA from the Bench4BL benchmark. Results are stored in [bench4bl_localization_results](<em>bench4bl_localization_results</em>). - [a09_bench4bl_stackoverflow_mpnet.py](<em>a09_bench4bl_stackoverflow_mpnet.py</em>) Creates document embeddings for all textual bug reports using the [stackoverflow_mpnet-base](<em>https://huggingface.co/flax-sentence-embeddings/stackoverflow_mpnet-base</em>) model. Results are stored in [stackoverflow_mpnet_embeddings](<em>stackoverflow_mpnet_embeddings</em>). ### Preliminary experiments and dataset splitting The following scripts perform data set splitting, and the preliminary experiments used for feature selection as discussed in Section VI of the paper. - [b00_analyze_most_promising_smells.py](<em>b00_analyze_most_promising_smells.py</em>) Assumes a perfect smell oracle (by using the known smells of the ground truth files) and applies it to rerank the IRFL tools outputs on the Classification Training Set (the older half of versions in the dataset). Then evaluates the achievable localization performance increase for each smell group. Results are stored in [h_analyze_most_promising_smells](<em>h_analyze_most_promising_smells</em>). - [c00_make_dataset_splits_bootstrap.py](<em>c00_make_dataset_splits_bootstrap.py</em>) Performs dataset splitting. Splits are performed on a temporal ordering of versions of each contained software project. Data is greedily split into 50/25/25, resulting in a Classification training set (used for NN model training), a ranking training set (used to estimate weights for linear combination of smell distance and IRFL suspicousness scores), and a test set (used for evaluating the localization performance achievable by our pipeline). Bootstrapping is applied by resampling fractions of 0.8, 20 times, resulting in 20 sets of the three splits to be used in the following eperiments. Datasets are stored in [p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap](<em>p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap</em>) for the full Bench4BL dataset, for the single project experiments please refer to p_FINAL_CAMEL, p_FINAL_HBASE, and p_FINAL_ROO accordingly. - [c01_full_dataset_stats.py](<em>c01_full_dataset_stats.py</em>) Calculates various statistics on the created datasets. Results are stored in [px_summary_dataset](<em>px_summary_dataset</em>). - [d00_model_for_smell_classification_performance_all_groups_bootstrap.py](<em>d00_model_for_smell_classification_performance_all_groups_bootstrap.py</em>) Trains a NN model on the Classification data set and evaluates its classification performance on the Ranking training set. This is performed for all 20 bootstrap iterations, the resulting data is stored in [p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap](<em>p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap</em>) for the full Bench4BL dataset, for the single project experiments please refer to p_FINAL_CAMEL, p_FINAL_HBASE, and p_FINAL_ROO accordingly. - [d01_classification_performance_evaluation_for_feature_selection_preliminariy_all_smell_groups.py](<em>d01_classification_performance_evaluation_for_feature_selection_preliminariy_all_smell_groups.py</em>) Creates summary and bootstrap statistics from the previous step. Results are stored in [p_FINAL_Bench4BL/p_summary_classification](<em>p_FINAL_Bench4BL/p_summary_classification</em>) for the full Bench4BL dataset. ### Localization experiments The following scripts perform our actual localization experiments. These scripts are applied to bootstrapped dataset splits. Results are stored in [p_FINAL_Bench4BL](<em>p_FINAL_Bench4BL</em>) for the full Bench4BL dataset, for the single project experiments please refer to p_FINAL_CAMEL, p_FINAL_HBASE, and p_FINAL_ROO accordingly. For a detailed experiment setup we refer to our paper. - [e01_model_for_localization_bootstrap.py](<em>e01_model_for_localization_bootstrap.py</em>) Trains NN models for smell classification and performs predictions on the corresponding test sets. Results are stored in [p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap](<em>p_FINAL_Bench4BL/p_model_for_smell_classification_bootstrap</em>). - [e02_ranking_training_bootstrap.py](<em>e02_ranking_training_bootstrap.py</em>) Performs reranking of the Ranking training set based on predicted smells by the model created in the previous step. Score combination is performed by linear combination of IRFL tools suspicousness scores and smell distances calculated based on our predictions. Results are stored in [p_FINAL_Bench4BL/p_ranking_training_bootstrap](<em>p_FINAL_Bench4BL/p_ranking_training_bootstrap</em>). - [e03_get_best_weigths_per_project_bootstrap.py](<em>e03_get_best_weigths_per_project_bootstrap.py</em>) Evaluates the outputs of the previous steps in order to pick the best weights for each project and IRFL tool. Results are stored in [p_FINAL_Bench4BL/p_ranking_training_bootstrap](<em>p_FINAL_Bench4BL/p_ranking_training_bootstrap</em>). - [e04_ranking_test_project_wise_bootstrap.py](<em>e04_ranking_test_project_wise_bootstrap.py</em>) Uses predictions of the final NN model and the weights obtained from the previous step to perform rerankings on the Test set. Results are stored in [p_FINAL_Bench4BL/p_ranking_test_proejct_wise_bootstrap](<em>p_FINAL_Bench4BL/p_ranking_test_proejct_wise_bootstrap</em>). ### Result collection and evaluation The following scripts calculate final scores and statistics from the 20 bootstrap iterations of the previous block of scripts. - [f00_bootstrap_summary_compare_map_and_ttest.py](<em>f00_bootstrap_summary_compare_map_and_ttest.py</em>) Calculates localization performance using the MAP metric and performs statistical tests. Results are stored in [p_FINAL_Bench4BL/p_summary_bootstrap](<em>p_FINAL_Bench4BL/p_summary_bootstrap</em>). - [f01_boostrap_summary_compare_classification_performance.py](<em>f01_boostrap_summary_compare_classification_performance.py</em>) Calculates classifier performance of our final model. Results are stored in [p_FINAL_Bench4BL/p_summary_bootstrap](<em>p_FINAL_Bench4BL/p_summary_bootstrap</em>). - [f02_bootstrap_summary_model_classification_performance_eval_test_set_for_all_projects.py](<em>f02_bootstrap_summary_model_classification_performance_eval_test_set_for_all_projects.py</em>) Calculates classifier performance and project wise classifier performance of our final model. Results are stored in [p_FINAL_Bench4BL/p_summary_classification_test_set](<em>p_FINAL_Bench4BL/p_summary_classification_test_set</em>). ### Further analysis The following scripts collect statistics and results to create latex tables and additional analysis used in our paper. - [g00_project_wise_perf_stats.py](<em>g00_project_wise_perf_stats.py</em>) Creates overview latex table comparing the Bench4BL and single project trained pipelines. Results are stored in [px_summary_performance](<em>px_summary_performance</em>). - [g01_project_multiple_file_smell_distances.py](<em>g01_project_multiple_file_smell_distances.py</em>) Analyses smell distances within each bug's ground truth files. Results are stored in [px_summary_dataset](<em>px_summary_dataset</em>). - [g02_performance_correlation_analysis.py](<em>g02_performance_correlation_analysis.py</em>) Performs correlation analysis of our pipeline's MAP localization performance, classification performance, and smell distance measures from previous step. Results are stored in [p_FINAL_Bench4BL/p_summary_correlations](<em>p_FINAL_Bench4BL/p_summary_correlations</em>) for the full Bench4BL dataset. ## Results - [pmd_catalogue/all_smells.json](<em>pmd_catalogue/all_smells.json</em>) lists all PMD smells and associated groups that occur in the dataset. - [px_summary_dataset](<em>px_summary_dataset</em>) contains statistics and information about the utilized dataset. Results for preliminary experiments for feature selection: - [h_analyze_most_promising_smells](<em>h_analyze_most_promising_smells</em>) contains the results of our preliminary experiment into each smell group's information content towards localization. - [p_FINAL_Bench4BL/p_summary_classification](<em>p_FINAL_Bench4BL/p_summary_classification</em>) contains the results of our preliminary experiments into the classifiability of smell groups from textual bug reports. Results of our localization eperiments: - [p_FINAL_Bench4BL/p_summary_bootstrap](<em>p_FINAL_Bench4BL/p_summary_bootstrap</em>) contains the results of our localization experiments, project wise MAP performance summary can be found in [p_FINAL_Bench4BL/p_summary_bootstrap/project_scores_tool_wise_others.tex](<em>p_FINAL_Bench4BL/p_summary_bootstrap/project_scores_tool_wise_others.tex</em>). - [p_FINAL_Bench4BL/p_summary_classification_test_set](<em>p_FINAL_Bench4BL/p_summary_classification_test_set</em>) contains the results of our classification performance analysis of the predictions used in localization. A project wise classification performance summary can be found in [p_FINAL_Bench4BL/p_summary_classification_test_set/macro_average_classification_performances_per_projectother.tex](<em>p_FINAL_Bench4BL/p_summary_classification_test_set/macro_average_classification_performances_per_projectother.tex</em>) ## Licence All code and results are licensed under [AGPL v3](<em>https://www.gnu.org/licenses/agpl-3.0.html.en</em>), according to LICENSE file. Other licences may apply for some tools and datasets contained in this repo: [cloc-1.92.pl](<em>https://github.com/AlDanial/cloc</em>) under [GPL v2](<em>https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html</em>), and data originating from [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) under [CCA 4.0](<em>https://creativecommons.org/licenses/by/4.0/</em>). </pre>
Effects of Propranolol on the Encoding and Retrieval of Emotional Material After Single Dose Administration in Healthy Young Subjects
ClinicalTrials.gov study NCT00889096. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.