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38 results for “temporal coding”
Code from: The relative influence of climate extremes and species richness on the temporal variability of bird communities
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Codes and data: Community size predicts temporal β-diversity at local but not regional scales
<p>UPDATED VERSION 2025-08-30 (models were updated)</p> <p>This zip file contains the codes demonstrating how I analyzed and selected publicly available and globally extensive data on fish composition and environmental variables to test the hypothesis that random fluctuations caused by demographic stochasticity in small populations might extend to communities and metacommunities, potentially affecting stability propagation across biological levels and spatial scales. The READ_ME file contains additional details about the steps I took to develop this analysis.</p> <p>This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) - Finance Code 001.</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Temporal validity of software datasets for code metrics: an empirical assessment of sampling strategies
<p>This is the repository for the scripts and data of the study "Building and updating software datasets: an empirical assessment".</p> <h2>Data collected</h2> <p>The data generated for the study it can be downloaded as a zip file. Each folder inside the file corresponds to one of the datasets of projects employed in the study (qualitas, currentSample and qualitasUpdated). Every dataset comprised three files "class.csv", "method.csv" and "sample.csv", with class metrics, method metrics and repository metadata of the projects respectively. Here is a description of the datasets:</p> <ul> <li>qualitas: includes code metrics and repository metrics from the projects in the release 20130901r of the Qualitas Corpus.</li> <li>currentSample: includes code metrics and repository metrics from a recent sample collected with our sampling procedure.</li> <li>qualitasUpdated: includes code metrics and repository metrics from an updated version of the Qualitas Corpus applying our maintenance procedure.</li> </ul> <h2>Plot graphics</h2> <p>To plot the results and graphics in the article there is a Jupyter Notebook "Experiment.ipynb". It is initially configured to use the data in "datasets" folder.</p> <h2>Replication Kit</h2> <p>For replication purposes, the datasets containing recent projects from Github can be re-generated. To do so, the virtual environment must have installed the dependencies in "requirements.txt" file, add Github's tokens in "./token" file, re-define or leave as is the paths declared in the constants (variables written in caps) in the main method, and finally run "main.py" script. The portable versions of the source code scanner <a href="https://sourcemeter.com/" target="_blank" rel="noopener">Sourcemeter</a> are located as zip files in "./Sourcemeter/tool" directory. To install Sourcemeter the appropriate zip file must be decompressed excluding the root folder "SourceMeter-10.2.0-x64-<OS>".</p> <p>The script comprise 5 steps:</p> <ol> <li>Project retrieval from Github: at first the sampling frame with projects complying with a specific quality criteria are retrieved from Github's API.</li> <li>Create samples: with the sampling frame retrieved, the current samples are selected (currentSample and qualitasUpdated). In the case of qualitasUpdated, it is important to have first the "sample.csv" file inside the qualitas folder of the dataset originally created for the study. This file contains the metadata of the projects in Qualitas Corpus.</li> <li>Project download and analysis: when all the samples are selected from the sampling frame (currentSample and qualitasUpdated), the repositories are downloaded and scanned with SourceMeter. In the cases in which the analysis is not possible, the projects are replaced with another one with similar size.</li> <li>Outlier detection: once the datasets are collected, it is necessary to manually look for possible outliers in the code metrics under study. In the notebook "Experiment.ipynb" there are specific sections dedicated for it ("Outlier detection (Section 4.2.2)").</li> <li>Outlier replacement: when the outliers are detected, in the same notebook there is also a section for outlier replacement ("Replace Outliers") where the outliers' url have to be listed to find the appropriate replacement.</li> </ol> <ul> <li>If it is required, the metrics from the Qualitas Corpus can also be re-generated. First, it is necessary to download the release 20130901r from its <a href="http://www.qualitascorpus.com/download/" target="_blank" rel="noopener">official webpage</a>. Second, decompress the .tar files downloaded. Third, make sure that the compressed files with source code from the projects (.java files) are placed in the "compressed" folder, in some cases it is necessary to read the "QC_README" file in the project's folder. Finally, run the original main script "Generate metrics for the Qualitas Corpus (QC) dataset" part of the code. </li> </ul>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF) and Convolutional Neural Network (CNN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <div>[1] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</div> <div>[2] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
The Temporality of Technical Debt Introduction on New Code and Confounding Factors
<p>Code Technical Debt (TD) is intentionally or unintentionally created when developers introduce inefficiencies in the codebase. This can be attributed to various reasons such as heavy workload, tight delivery schedule, or developers' lack of experience. Since a software system grows mostly through the addition of new code, it is interesting to study how TD fluctuates along this process. Specifically, in this paper we investigate: (a) the temporality of code TD introduction in new code, i.e., whether the introduction of TD is stable across the lifespan of the project, or if its evolution presents spikes; and (b) the relation of TD introduction to the development team’s workload in a given period, as well as to the experience of the development team. To answer these questions, we have performed a case study on 47 open source projects from two well-known ecosystems (Apache and Eclipse) as well as additional isolated projects from GitHub (not selected from a specific ecosystem) and inspected the number of TD issues introduced in 6-month sliding temporal windows. The results of the study suggested that: (a) overall, the number of TD issues introduced through new code is a stable measure, although it presents spikes; and (b) the number of commits performed, as well as developers' experience are not strongly correlated to the number of introduced TD issues.</p>
Codes and data regarding "Temporal dissipative structures in optical Kerr resonators with transient loss fluctuation"
<p>Here we upload codes and figure data relate to the article "Temporal dissipative structures in optical Kerr resonators with transient loss fluctuation" that was published on Optics Express (<a href="https://doi.org/10.1364/OE.439212">https://doi.org/10.1364/OE.439212</a>)</p> <p>Please note that the codes have been tested using Matlab in the version of 2019a.</p> <p>The ".opj" and ".opju" files in the folder "Figure_data" can be opened via <a href="https://www.originlab.com/viewer/">the Origin viewer</a> (a free app published by Originlab)</p>
Codes and datasets for: Quantifying the impact of SARS-CoV-2 temporal vaccination trends and disparities on disease control
<p>Codes and datasets to reproduce the main figures of the article titled: "Quantifying the impact of SARS-CoV-2 temporal vaccination trends and disparities on disease control".</p>
Data from: The peripheral olfactory code in Drosophila larvae contains temporal information and is robust over multiple timescales
We studied the electrophysiological activity of two classes of Drosophila melanogaster larval olfactory sensory neurons (OSNs), Or24a and Or74a, in response to 1 s stimulation with butanol, octanol, 2-heptanone, and propyl acetate. Each odour/OSN combination produced unique responses in terms of spike count and temporal profile. We used a classifier algorithm to explore the information content of OSN activity, and showed that as well as spike count, the activity of these OSNs included temporal information that enabled the classifier to accurately identify odours. The responses of OSNs during continuous odour exposure (5 and 20 min) showed that both types of neuron continued to respond, with no complete adaptation, and with no change to their ability to encode temporal information. Finally, we exposed larvae to octanol for 3 days and found only minor quantitative changes in OSN response to odours, indicating that the larval peripheral code is robust when faced with long-term exposure to odours, such as would be found in a natural context.
Data & Code of "A new function of offset response in the primate auditory cortex: marker of temporal integration"
Open the record for dataset details and reuse information.
Data from: The peripheral olfactory code in Drosophila larvae contains temporal information and is robust over multiple timescales
Open the record for dataset details and reuse information.
Sub-compartment specific small non-coding RNA expression deregulation and altered nucleoli in mesial temporal lobe epilepsy
GEO Series GSE269625. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
A daily temporal program for rhythmic expression of protein-coding and non-coding genes in response to light and dark in Synechocystis sp. PCC 6803
GEO Series GSE47482. Synechocystis sp. PCC 6803. 62 samples. Type: Expression profiling by array.
Based on the Spatio-temporal Coding Characteristics of Frontotemporal Network, This Paper Explores the Mechanism of Five-tone Speech Training in Reshaping Language Fluency Function.
ClinicalTrials.gov study NCT07329751. IPD Sharing: NO. Countries: 0. Publications: 0.
Analysis of the coding and non-coding RNA temporal transcriptome of developing seeds in sunflower [ncRNA-seq]
GEO Series GSE151552. Helianthus annuus. 9 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Analysis of the coding and non-coding RNA temporal transcriptome of developing seeds in sunflower
GEO Series GSE151779. Helianthus annuus. 18 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Analysis of the coding and non-coding RNA temporal transcriptome of developing seeds in sunflower [RNA-seq]
GEO Series GSE151778. Helianthus annuus. 9 samples. Type: Expression profiling by high throughput sequencing.
fMRI data: Shape coding in occipito-temporal cortex relies on object silhouette, curvature and medial-axis
<p>This is the fMRI dataset described in "Shape coding in occipito-temporal cortex relies on object silhouette, curvature and medial-axis", bioRxiv (2019).</p> <p>Note that the data consists of already pre-processed volumes, warped into the MNI152 template (see the paper for details) along with the stimulus set. Due to privacy concerns, we cannot provide raw data and access is public but restricted (see 'Conditions' below).</p> <p><strong>Conditions</strong>:</p> <p>In accordance with EU privacy norms, access to the data is granted after submission of a Data Use Agreement (DUA). You can download the DUA <a href="https://zenodo.org/record/4003861">here</a>. Please, sign it and send it to the email adress found at the bottom of the DUA.</p> <p>A description of the aims of your project is appreciated but not required.</p> <p> </p>
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.