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409 results for “information use”
Does Providing Information about Cleaning Increase People's Willingness to (Re)Use Bowls That Show Signs of Previous Use? (Study 2 Dataset)
<p>Dataset from Study 2 conducted by the University of Sheffield as part of the BUDDIE-PACK project</p>
ChEMBL Data for 'Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models'
Open the record for dataset details and reuse information.
Spoofing Signal Generation Algorithm using the Processing Information of a Receiver for Authentic GPS Signals
<p>Experimental results analyzation files.</p>
Mercury and Arsenic muscle concentration data as used in "Mixed model approaches can leverage database information to improve the estimation of size-adjusted contaminant concentrations in fish populations"
<p>These mercury and arsenic concentration data, as recieved from Gretchen Lescord, and downloaded from the MOE fish contaminant database, were used to create the publication Mixed model approaches can leverage database information to improve the estimation of size-adjusted contaminant concentrations in fish populations. The markdown and code used for the analysis of this data can be found on Github at https://github.com/GLFC-WET/HGAS_master.</p>
TÁBOA 1. Materiais, procesos e resultados levados a cabo na investigación. Used information, processes and results obtained in this research. in A paisaxe protexida dos Penedos de Pasarela e Traba (Costa da Morte, Galicia)
<p><b>TÁBOA 1</b>. Materiais,procesos e resultados levados a cabo na investigación. Used information,processes and results obtained in this research.</p><table><tbody><tr><th><b>Material</b></th><th><b>Fonte</b></th><th><b>Proceso</b></th><th>Escala de deseño</th><th><b>Resultado</b></th><th><b>Formato</b></th></tr></tbody><tbody><tr><th>Arquivos LAS (2010, 2015)</th><td>Xunta de Galicia e IGN</td><td>Construción de modelos dixitais de elevación e de superficie.</td><td>1:100</td><td>Deseño do mapa de fracturas. Elaboración do mapa de pendentes</td><td>Raster</td></tr><tr><th></th><td></td><td>Delimitación da</td><td></td><td>Mapa de fracturas.</td><td>Vectorial (1785 liñas)</td></tr><tr><th></th><td></td><td>rede de fracturas.</td><td>1:100</td><td>Mapa de densidade de fracturación.</td><td>Raster</td></tr><tr><th>Ortofotografías (2010 e 2020)</th><td>IGN</td><td>Delimitación de</td><td></td><td>Mapa de xeoformas</td><td>Vectorial (7038</td></tr><tr><th></th><td></td><td>xeoformas</td><td></td><td>Mapa de superficie da xeoformas</td><td>polígonos)</td></tr><tr><th></th><td></td><td>Situación de xeoformas singulares</td><td>1:100</td><td>Mapa de xeoformas singulares.</td><td>Vectorial (92 puntos)</td></tr></tbody></table>
Supporting information of Ultra-Wide Band Antipodal Vivaldi Antenna Using Metasurface Lens for Gain and Front-to-Back Ratio (FBR) Improvement
Open the record for dataset details and reuse information.
Supplementary Table S1. Primer sequence information for RT-qPCR used in this study.
Open the record for dataset details and reuse information.
Data from: Information-theoretic analysis of realistic odor plumes: what cues are useful for determining location?
Many species rely on olfaction to navigate towards food sources or mates. Olfactory navigation is a challenging task since odor environments are typically turbulent. While time-averaged odor concentration varies smoothly with the distance to the source, instaneous concentrations are intermittent and obtaining stable averages takes longer than the typical intervals between animals' navigation decisions. How to effectively sample from the odor distribution to determine sampling location is the focus on this article. To investigate which sampling strategies are most informative about the location of an odor source, we recorded three naturalistic stimuli with planar lased-induced fluorescence and used an information-theoretic approach to quantify the information that different sampling strategies provide about sampling location. Specifically, we compared multiple sampling strategies based on a fixed number of coding bits for encoding the olfactory stimulus. When the coding bits were all allocated to representing odor concentration at a single sensor, information rapidly saturated. Using the same number of coding bits in two sensors provides more information, as does coding multiple samples at different times. When accumulating multiple samples at a fixed location, the temporal sequence does not yield a large amount of information and can be averaged with minimal loss. Furthermore, we show that histogram-equalization is not the most efficient way to use coding bits when using the olfactory sample to determine location.
Figure 1 from: Lyal CHC (2016) Digitising legacy zoological taxonomic literature: Processes, products and using the output. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 189–206. https://doi.org/10.3897/zookeys.550.9702
Figure 1 - Outline workflow to acquire, put into a suitable format, retrieve and utilize legacy literature.
Supplementary material 2 from: Baskauf SJ, Girón Duque JC, Nielsen M, Cobb NS, Singer R, Seltmann KC, Kachian Z, Pérez M, Agosti D, Klompen AML (2023) Implementation Experience Report for Controlled Vocabularies Used with the Audubon Core Terms subjectPart and subjectOrientation. Biodiversity Information Science and Standards 7: e94188. https://doi.org/10.3897/biss.7.94188
Views Controlled Vocabularies Implementation Reporting Form
Supplementary material 1 from: Baskauf SJ, Girón Duque JC, Nielsen M, Cobb NS, Singer R, Seltmann KC, Kachian Z, Pérez M, Agosti D, Klompen AML (2023) Implementation Experience Report for Controlled Vocabularies Used with the Audubon Core Terms subjectPart and subjectOrientation. Biodiversity Information Science and Standards 7: e94188. https://doi.org/10.3897/biss.7.94188
Views Controlled Vocabularies testing notes
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>
Fig. 2 in Streamlined targeting of Amaryllidaceae alkaloids from the bulbs of Crinum scillifolium using spectrometric and taxonomically-informed scoring metabolite annotations
Fig. 2. Structures of compounds 1–4.
Fig. 3 in Streamlined targeting of Amaryllidaceae alkaloids from the bulbs of Crinum scillifolium using spectrometric and taxonomically-informed scoring metabolite annotations
Fig. 3. Key COSY and HMBC correlations of compounds 1–4.
Brain Use of Sensory Information to Generate Movement
ClinicalTrials.gov study NCT00055289. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Safety,Usability and Compliance of Using C-Scan System for Providing Information on Colonic Polypoid Lesions and Masses
ClinicalTrials.gov study NCT03735407. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Filter Ventilation and Ventilation Information on Product Use Behaviors in Cigarette Smokers
ClinicalTrials.gov study NCT04744285. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Drug Use Study of Febuxostat After Recent Changes in the Prescribing Information
ClinicalTrials.gov study NCT04853160. IPD Sharing: YES. Countries: 1. Publications: 0.
Use of the My HealtheVet for Health Information Sharing
ClinicalTrials.gov study NCT01955005. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study to Gather Information About the Actual Use of an Adhesive Patch Placed on the Skin to Deliver Oxytrol Through the Skin Into the Bloodstream.
ClinicalTrials.gov study NCT04534491. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.