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97 results for “code pattern”
Code, benchmarks and experiment data for the SoCS 2022 paper "Additive Pattern Databases for Decoupled Search"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper.</p> <p>sievers-et-al-socs2022-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.0 for reproducing all experiments of the paper, under experiments/decoupled-abstractions. The scripts 2022-04-* contain configurations for running the experiments and the script paper-tables-*.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.)</p> <p>sievers-et-al-socs2022-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks).</p> <p>sievers-et-al-socs2022-autoscale-benchmarks.zip contains the Autoscale 21.11 benchmarks (from https://github.com/AI-Planning/autoscale-benchmarks).</p> <p>sievers-et-al-socs2022-lab.tar.gz contains a copy of Lab 7.0 (https://github.com/aibasel/lab).</p> <p>sievers-et-al-socs2022-raw-data.zip and sievers-et-al-socs2022-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-socs2022-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" (sievers-et-al-socs2022-processed-data.zip) ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p>
Effective Teaching through Code Reviews: Patterns and Anti-Patterns
Open the record for dataset details and reuse information.
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
Data and R Code from "PAT-GEOM: A Software Package for the Analysis of Animal Patterns" (published in Methods in Ecology and Evolution)
<p>Datasets for Figures 2 and 4 in the article "PAT-GEOM: A Software Package for the Analysis of Animal Patterns" (published in Methods in Ecology and Evolution) and the R code used to perform the analysis described in the article.</p>
Data and code for "High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra"
<p><strong>Repository structure</strong></p> <p>The zipped folder includes the following subfolders:</p> <p><em>data</em></p> <p>In-situ measurement data from the plots. Remotely-sensed data could not be included in the repository due to their large size. </p> <p><em>src</em></p> <p>R codes to reproduce the data cleaning, prosessing, and statistical analysis steps.</p> <p><em>results</em></p> <p>Model parameters, performance statistics, model files, figures, edited tables together with some summary tables produces from upscaling results.</p> <p><em>raster data and upscaled results</em></p> <p>Averaged flux, soil moisture and temperature maps for the growing season (July 1-August 2nd, 8 am - 8 pm) as well as static maps produced in this study. All the upscaled results could not be included in the repository due to their large size. </p> <p>Note that the analysis to produce the vegetation classification map are described here: https://github.com/poniitty/kilpisjarvi_vegclass</p>
Data and analysis code for "Forest recovery following extreme drought in California, USA: natural patterns and effects of pre-drought management"
Open the record for dataset details and reuse information.
Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin
GEO Series GSE63979. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing.
Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development
GEO Series GSE176588. Mus musculus; Homo sapiens. 47 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing identifies specific PIWI-interacting small non-coding RNA expression patterns in breast cancer
GEO Series GSE56134. Homo sapiens. 22 samples. Type: Non-coding RNA profiling by high throughput sequencing; Third-party reanalysis.
Empirical Study of the Relationship between Design Patterns and Code Smells
<p>Software systems are often developed in such a way that good practices in the object-oriented paradigm are not met, causing the occurrence of specific disharmonies, which are sometimes called code smells. Design patterns catalogue best practices for developing object-oriented software systems. Although code smells and design patterns are widely divergent, there might be a co-occurrence relation between them. The objective of this paper is to empirically evaluate if the presence of design patterns is related to the presence of code smells at different granularity levels. We performed an empirical replication study using 20 design patterns, and 13 code smells in ten small-size to medium-size, open-source Java-based systems. We applied statistical analysis and association rules. Results confirm that classes participating in design patterns have less smell-proneness and smell frequency than classes not participating in design patterns. We also noticed that every design pattern category act in the same way in terms of smell-proneness in the subject systems. However, we observed, based on the association rules learning and the proposed validation technique, that some patterns may be associated with certain smells in some cases. For instance, Command patterns can co-occur with God Class, Blob and External Duplication smell.</p> <p><strong>The published data set contains the following:</strong></p> <ol> <li>List of the selected systems (source code files)</li> <li>The P-MARt: the design pattern repository as XML for the selected systems. </li> <li>Data of design patterns and code smells: We processed this data by parsing the design pattern XML file and running the smell detection tool (inFusion).</li> <li>The data of the data mining analysis.</li> </ol>
Data file for turbulence driving pattern in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** this file is automatically downloaded by phantom on running the code **</p> <p>This is the default driving pattern file used in the "turbdrive" setup of the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code, which was originally written for the set of simulations shown in <a href="http://adsabs.harvard.edu/abs/2010MNRAS.406.1659P">Price & Federrath (2010)</a>. The driving pattern file was originally created by Christoph Federrath.</p> <p>For details and references on the Ornstein-Uhlenbeck stochastic turbulent driving algorithm itself, see section 2.5 of the Phantom code paper (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>).</p> <p>The same pattern file has been used in several subsequent studies, including <a href="http://adsabs.harvard.edu/abs/2016MNRAS.461.1260T">Tricco, Price & Federrath (2016)</a> and <a href="http://adsabs.harvard.edu/abs/2017MNRAS.471L..52T">Tricco, Price & Laibe (2017)</a>.</p> <p>For details on how to read this file see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/main/forcing.F90">src/main/forcing.f90</a>)</p>
Data and code for: Excitation creates a distributed pattern of cortical suppression due to varied recurrent input
<p>Data and code for research article:<br> "Excitation creates a distributed pattern of cortical suppression due to varied recurrent input"<br> <br> Unzipped archive should have the following structure:</p> <pre><code>── corticalSuppressionRepo ├── data │ ├── 2p_longStim_data.npz │ ├── 2p_shortStim_data.npz │ ├── ephys_dynamics_data.npz │ ├── ephys_spatialDistribuition_data.npz │ ├── ephys_timeseries_data.npz │ └── widefield_stim_data.npz ├── notebooks │ ├── 2p_longStim.ipynb │ ├── 2p_shortStim.ipynb │ ├── balancedStateModel_sim.ipynb │ ├── ephys_dynamics.ipynb │ ├── ephys_spatialDistribution.ipynb │ ├── ephys_timeseries.ipynb │ └── widefield_stim.ipynb ├── src │ ├── analysisFunctions.py │ └── randNetSimulation.py └── environment.yml </code></pre> <p>Build and activate environment from environment.yml:</p> <pre><code>conda env create -f environment.yml conda activate minenv</code></pre> <p>Each notebook should run using relative path locations, pulling data from the data directory and sometimes importing code from the src directory.</p>
Stage Specific Non-coding RNA Expression Patterns During in vitro Human B Cell Differentiation into Antibody Secreting Plasma Cells
GEO Series GSE194123. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Distinct patterns of histone acetyltransferase and Mediator deployment at yeast protein-coding genes
GEO Series GSE109235. Saccharomyces cerevisiae. 70 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
A combinatorial code for pattern formation in Drosophila oogenesis
GEO Series GSE12477. Drosophila melanogaster. 18 samples. Type: Expression profiling by array.
RNA Deep Sequencing Reveals Expression Patterns of Long Non-coding RNAs during Swine Ovary Development
GEO Series GSE72115. Sus scrofa. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Identification and Expression Patterns of Novel Long Non-Coding RNAs in Neural Progenitors of the Developing Mammalian Cortex
GEO Series GSE65487. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
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.