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3,363 results for “Replication”
Replication Package for Maintainability Challenges in ML: A Systematic Literature Reveiw
<p>In order to ensure transparency and reproducibility, we have made all study artefacts publicly here. Replication package for <strong>Maintainability Challenges in ML : A Systematic Literature Review</strong>. This package contains the data used for this Systematic Literature Review process and additional data synthesised from this study.</p> <ul> <li>Coding Summary Report Generated from Nvivo project. (Coding Summary By Code Report.pdf)</li> <li>Detailed List of Selected papers for Literature Review papers(Literature Review Papers.xlsx)</li> <li>Papers relating to implications for developers and Researchers(Implication for Developer and Researchers.pdf)</li> <li>Search query from Different Database.pdf</li> <li>No of papers used in to answer RQ using the Literature review paper by categories (No of Literature review papers.pdf)</li> <li>Maintainability challenges in ML workflow from SLR.pdf</li> </ul>
Replication data for: Enhancing user awareness on inferences obtained from fitness trackers data
<p>Survey results and fitness trackers datasets used for the evaluation of PrivacyEnhAction application.</p>
Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?
<p>Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?"</p>
Replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits"
<p>This repository contains replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits". All non-confidential data inputs are included, as well as intermediate data outputs, final data outputs, and final tables and figures for all main text and supplementary tables and figures. Some input data are confidential (e.g., mortality records in some countries); therefore, intermediate regression results files are included in the upload to ensure all later stages of the analysis are fully replicable. The full data output files resulting from Monte Carlo simulations of future climate change impacts on mortality far exceed Zenodo file size limits; therefore, key aggregates of the raw output files are included here, which allow for replication of all tables and figures in the paper.</p> <ul> <li><strong>data.zip </strong>contains raw, intermediate, and final datasets</li> <li><strong>outputs.zip </strong>contains output tables and figures </li> </ul> <p>All replication code for the paper is available on a public Github repository, accessible <a href="https://github.com/ClimateImpactLab/carleton_mortality_2022">here</a>.<br><br>The manuscript and supplementary information are available at the QJE, <a href="https://doi.org/10.1093/qje/qjac020">here</a>.</p>
Raw data of compounds extracted by GC-MS from each population replicate's of I. uriae ticks from Iceland.
<p>Raw data representing all the compounds extracted by GC-MS from each population replicate’s of <em>I. uriae</em> ticks from three sites in Iceland. Each replicate contain a pool of 10 living flat female ticks.</p> <p>Site: name of the site where ticks were collected.</p> <p>Host: name of the host bird.</p> <p>Replicate: number of the replicate (1 to 4).</p> <p>Peak: number of the detected peaks correponding to extracted compounds.</p> <p>Retention Time: time elapsed between sample introduction and the maximum signal of the given compound.</p> <p>Area: area under the curves of each detected coumpounds on the chromatogram.</p>
Replication package of `Can We Automatically Generate Class Comments in Pharo?`
<pre><code class="language-markdown"># RP Automatic-comment-generation This folder contains all the material needed to replicate the experiments. ## Content - [RP Automatic-comment-generation](#automatic-comment-generation) - Appendix.pdf - [Content](#content) - [Dataset/](#dataset/) - [Online evaluation/](#online-evaluation/) - [Results/](#results/) - [SI-Approach/](#SI-Approach/) ## Dataset/ This contains the data for the online evaluation - #### Online evaluation/ Contains set of questions used in online evaluation, results of these questions and lists of classes used for each online evaluation - [Class_understanding_questions.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Class_understanding_questions.xlsx) Questions used to determin if a participant understood the functionality of a class. - [Q_and_A_class_comment_characteristics.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_class_comment_characteristics) Questions and possible answers used for the evaluation of the characteristics adequacy, conciseness and comprehensibility of a generated class comment. - [Q_and_A_what_participants_write_and_look_for.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_what_participants_write_and_look_for.xlsx) Questions and possible answers used to determine what participants look for and write in class comments. - [Classes_used_in_evaluation1.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation1.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 1. - [Classes_used_in_evaluation2.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation2.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 2. - [Classes_used_in_evaluation3.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation3.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 3. - [Classes_used_in_evaluation4.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation4.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 4. - [How_often_participants_write_comments.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\How_often_participants_write_comments.xlsx) Results for distribution of how often participants claim to write comments. - [How_participants_follow_class_comment_template.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\How_participants_follow_class_comment_template.xlsx) Results for distribution of how participants follow the class comment template. - [What_participants_look_for_in_comments.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_look_for_in_comments.xlsx) Results of evaluation for what participants look for in class comments. - [What_participants_write_in_comments.xlsx](\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_write_in_comments.xlsx) Results of evaluation for what participants write in class comments. ## Results/ - #### SI-Approach/ Contains all the data for the results related to SI-Approach and the scripts used to generate said data. - [Class_stereotype_distribution.xlsx](RP-Automatic-comment-generation\Results\SI-Approach\Class_stereotype_distribution.xlsx) Extracted class stereotypes for 350 random classes in the Pharo base image. Evaluated in 10 classes per step. - [Class_stereotype_script.txt](RP-Automatic-comment-generation\Results\SI-Approach\Class_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many classes get assigned the specific class stereotypes. Returns numbers for the class stereotypes in alphabetical order. Boundary => Small - [Method_stereotype_dstribution.xlsx](RP-Automatic-comment-generation\Results\SI-Approach\Method_stereotype_distribution.xlsx) Extracted method stereotypes for 500 random classes in the Pharo base image. Evaluated in 20 classes per step. - [Method_stereotype_script.txt](RP-Automatic-comment-generation\Results\SI-Approach\Method_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many methods get assigned the specific method stereotypes. Returns numbers for the method stereotypes in the order Accessors, Getters, Mutators, Setters, Collaborators, Controllers, Factories and Degenerate. </code></pre> <p>Replication package of 'Can We Automatically Generate Class Comments in Pharo?'</p> <pre><code class="language-markdown"># RP Automatic-comment-generation This folder contains all the material needed to replicate the experiments. ## Content - [RP Automatic-comment-generation](#automatic-comment-generation) - [Content](#content) - [Dataset/](#dataset/) - [Online evaluation/](#online-evaluation/) - [Results/](#results/) - [SI-Approach/](#SI-Approach/) ## Dataset/ This contains the data for the online evaluation - #### Online evaluation/ Contains set of questions used in online evaluation, results of these questions and lists of classes used for each online evaluation - [Class_understanding_questions.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Class_understanding_questions.xlsx) Questions used to determin if a participant understood the functionality of a class. - [Q_and_A_class_comment_characteristics.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_class_comment_characteristics) Questions and possible answers used for the evaluation of the characteristics adequacy, conciseness and comprehensibility of a generated class comment. - [Q_and_A_what_participants_write_and_look_for.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_what_participants_write_and_look_for.xlsx) Questions and possible answers used to determine what participants look for and write in class comments. - [Classes_used_in_evaluation1.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation1.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 1. - [Classes_used_in_evaluation2.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation2.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 2. - [Classes_used_in_evaluation3.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation3.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 3. - [Classes_used_in_evaluation4.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation4.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 4. - [How_often_participants_write_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\How_often_participants_write_comments.xlsx) Results for distribution of how often participants claim to write comments. - [How_participants_follow_class_comment_template.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\How_participants_follow_class_comment_template.xlsx) Results for distribution of how participants follow the class comment template. - [What_participants_look_for_in_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_look_for_in_comments.xlsx) Results of evaluation for what participants look for in class comments. - [What_participants_write_in_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_write_in_comments.xlsx) Results of evaluation for what participants write in class comments. ## Results/ - #### SI-Approach/ Contains all the data for the results related to SI-Approach and the scripts used to generate said data. - [Class_stereotype_distribution.xlsx](thesis\RP-Automatic-comment-generation\Results\RQ2\Class_stereotype_distribution.xlsx) Extracted class stereotypes for 350 random classes in the Pharo base image. Evaluated in 10 classes per step. - [Class_stereotype_script.txt](thesis\RP-Automatic-comment-generation\Results\RQ2\Class_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many classes get assigned the specific class stereotypes. Returns numbers for the class stereotypes in alphabetical order. Boundary => Small - [Method_stereotype_dstribution.xlsx](thesis\RP-Automatic-comment-generation\Results\RQ2\Method_stereotype_distribution.xlsx) Extracted method stereotypes for 500 random classes in the Pharo base image. Evaluated in 20 classes per step. - [Method_stereotype_script.txt](thesis\RP-Automatic-comment-generation\Results\RQ2\Method_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many methods get assigned the specific method stereotypes. Returns numbers for the method stereotypes in the order Accessors, Getters, Mutators, Setters, Collaborators, Controllers, Factories and Degenerate.</code></pre> <p> </p>
Replication Package for "Evaluating the layout quality of UML class diagrams using machine learning"
<p>Open Science material including dataset and replication instructions accompanying the article "Evaluating the layout quality of UML class diagrams using machine learning."</p>
Salivary steroid collection in children under conditions replicating home sampling
<p>This is supplementary materials for the article "Salivary steroid collection in children under conditions replicating home sampling"</p>
Replication Package for "TriggerBench: A Performance Benchmark for Serverless Function Triggers"
<p>This replication package contains the code (`aws-triggers` and `azure-trigger`), data analysis scripts (`data-analysis`), and dataset (`data`) of the TriggerBench cross-provider serverless benchmark.</p> <p>It also bundles a customized extension of the `serverless-benchmarker` tool to automate and analyze serverless performance experiments.</p> <p><strong>TriggerBench</strong></p> <p>The Github repository <a href="https://github.com/joe4dev/trigger-bench/">joe4dev/trigger-bench</a> contains the last version of TriggerBench. This replication package describes the version for the paper "TriggerBench: A Performance Benchmark for Serverless Function Triggers".</p> <p>TriggerBench currently supports three triggers on AWS and eight triggers on Microsoft Azure.</p> <p><strong>Dataset</strong></p> <p>The `data/aws` and `data/azure` directories contain data from benchmark executions from April 2022.</p> <p>Each execution is a separate directory with a timestamp in the format `yyyy-mm-dd-HH-MM-SS` (e.g., `2022-04-15_21-58-52`) and contains the following files:</p> <ul> <li>`k6_metrics.csv`: Load generator HTTP client logs in CSV format (see [K6 docs](https://k6.io/docs/results-visualization/csv/))</li> <li>`sb_config.yml`: serverless benchmarker execution configuration including experiment label.</li> <li>`trigger.csv`: analyzer output CSV per trace. <ul> <li>`root_trace_id`: The trace id created by k6 and adopted by the invoker function</li> <li>`child_trace_id`: The trace id newly created by the receiver function if trace propagation is not supported (this is the case for most asynchronous triggers)</li> <li>`t1`-`t4`: Timestamps following the trace model (see paper)</li> <li>`t5`-`t9`: Additional timestamps for measuring timestamping overhead</li> <li>`coldstart_f1=True|False`: coldstart status for invoker (f1) and receiver (f2) functions</li> <li>`trace_ids.txt`: text file with each pair of `root_trace_id` and `child_trace_id` on a new line.</li> </ul> </li> <li>`traces.json`: raw trace JSON representation as retrieved from the provider tracing service. For AWS, see [X-Ray segment docs](https://docs.aws.amazon.com/xray/latest/devguide/xray-api-segmentdocuments.html). For Azure, see [Application Insights telemetry data model](https://docs.microsoft.com/en-us/azure/azure-monitor/app/data-model).</li> <li>`workload_options.json`: [K6 load scenario](https://k6.io/docs/using-k6/scenarios/) configuration.</li> </ul> <p><strong>Replicate Data Analysis</strong></p> <p><strong>Installation</strong></p> <p>1. Install [Python](https://www.python.org/downloads/) 3.10+</p> <p>2. Install Python dependencies `pip install -r requirements.txt`</p> <p><strong>Create Plots</strong></p> <p>1. Run `python plots.py` generates the plots and the statistical summaries presented in the paper.</p> <p>By default, the plots will be saved into a `plots` sub-directory.<br> An alternative output directory can be configured through the environment variable `PLOTS_PATH`.</p> <p>> Hint: For interactive development, we recommend the VSCode [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) in [interactive mode](https://youtu.be/lwN4-W1WR84?t=107).</p> <p> </p> <p><strong>Replicate Cloud Experiments</strong></p> <p>The following experiment plan automates benchmarking experiments with different types workloads (constant and bursty).</p> <p>This generates a new dataset in the same format as described above.</p> <p> </p> <ol> <li> Set up a load generator as vantage point following the description in [LOADGENERATOR](./serverless-benchmarker/docs/LOADGENERATOR.md).</li> <li> Choose the `PROVIDER` (aws or azure) in the [constant.py](./experiment-plans/constant.py) experiment plan</li> <li> Run the [constant.py](./experiment-plans/constant.py) experiment plan <ol> <li> Open tmux</li> <li> Activate virtualenv `source sb-env/bin/activate`</li> <li> Run `./constant.py 2>&1 | tee -a constant.log`</li> </ol> </li> </ol> <p><strong>Contributors</strong></p> <p>The initial trigger implementations for AWS and Azure are based on two master thesis projects at Chalmers University of Technology in Sweden supervised by Joel:</p> <ul> <li>AWS + Azure: <a href="https://odr.chalmers.se/handle/20.500.12380/302822">Performance Comparison of Function-as- a-Service Triggers: A Cross-Platform Performance Study of Function Triggers in Function-as-a-Service</a> by Marcus Bertilsson and Oskar Grönqvist, 2021.</li> <li>Azure Extension: <a href="https://odr.chalmers.se/handle/20.500.12380/305138">Serverless Function Triggers in Azure: An Analysis of Latency and Reliability</a> by Henrik Lagergren and Henrik Tao, 2022.</li> </ul> <p>Joel contributed many improvements to their original source code as documented in the import commits <a href="https://github.com/joe4dev/trigger-bench/commit/a00b67a1dd8476ca77d026e59adf2674c7807e68">a00b67a</a> and <a href="https://github.com/joe4dev/trigger-bench/commit/6d2f5ef8bda0596b3f295cb6c6cbeba212c6ef43">6d2f5ef</a> and developed TriggerBench as an integrated benchmark suite (see commit history for detailed changelog).</p>
Replication package for "Teams and Individuals in Standard Auction Formats: Decision and Emotions"
<p>This is the replication package for the paper "Teams and Individuals in Standard Auction Formats: Decision and Emotions" by Maria Karmeliuk, Martin Kocher and Georg Schmidt, forthcoming in <em>Experimental Economics</em>.</p> <p>The package contains instructions and zTree code for running the laboratory experiment, as well as raw data from the experiment including the data from face recognition software FaceReader. It also contains Stata and R code for data cleaning, cleaned datasets and Stata code for replicating data analysis and statistics reported in the paper.</p>
Replication material for "Competition with Indivisibilities and Few Traders"
<p>The replication material for the paper <em>Competition with Indivisibilities and Few Traders</em>. </p>
Replication package for "An Exploratory Study on the Predominant Programming Paradigms in Python Code"
<p>This dataset includes scripts and data files used to generate all analysis and results from the paper. A README.md file is included for details on using the scripts - though all of the data the scripts generate should already be cached and none of the scripts actually need run.</p> <p>It also includes a spreadsheet containing the human judgements from Table 4 of the paper.</p> <p>Always current source for the scripts is available on GitHub: <a href="https://github.com/psybers/python-paradigms">https://github.com/psybers/python-paradigms</a></p>
Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"
<p><strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong></p> <p><strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> </p> <p><em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis</p> <p> - "main-program-grid-search.py" file: Main program executing all experiments for the grid search analysis</p> <p><em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.</p>
Data set for the article 'Robust replication initiation from coupled homeostatic mechanisms'
<p>This data set contains the data of the submitted article "Robust replication initiation from coupled homeostatic mechanisms". The data was generated using simulations in python that are linked below. Experiments indicate that E. coli controls replication initiation via titration and activation of the initiator protein DnaA. We study by mathematical modelling how these two mechanisms interact to generate robust replication-initiation cycles. </p>
Replication Data for "Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades"
<p>In this research (publication included in the package), we have used author-assigned keywords as a quantitative data source for understanding the connections between keywords and research topics in software testing research, based on a large sample of studies from Scopus.</p> <p>We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics, examine how topics connect, and examine how the field is changing. This package contains the map and network files used to perform our analyses, as well as the publication sample.</p>
Replication data for measurement report: Evolution and distribution of NH3 over Mexico City from ground-based and satellite infrared spectroscopic measurements
<p>This dataset of atmospheric ammonia (NH3) has been generated from solar absorption spectra measured in central Mexico using ground-based Fourier-Transform Infrared (FTIR) spectrometers. The FTIR experiments have been operated by the “Spectroscopy and Remote Sensing” Research Group of the ICAyCC-UNAM (Instituto de Ciencias de la Atmósfera y Cambio Climático of the Universidad Nacional Autónoma de México, http://www.epr.atmosfera.unam.mx/)</p> <p>Related Publication:<br> Herrera, B., Bezanilla, A., Blumenstock, T., Dammers, E., Hase, F., Clarisse, L., Magaldi, A., Rivera, C., Stremme, W., Strong, K., Viatte, C., Van Damme, M., and Grutter, M.: Measurement report: Evolution and distribution of NH3 over Mexico City from ground-based and satellite infrared spectroscopic measurements, Atmos. Chem. Phys. https://doi.org/10.5194/acp-2022-217, Accepted, 2022.</p> <p>Abstract:<br> Ammonia (NH3) is the most abundant alkaline compound in the atmosphere, with consequences for the environment, human health, and radiative forcing. In urban environments, it is known to play a key role in the formation of secondary aerosols through its reactions with nitric and sulphuric acids. However, there are only a few studies about NH3 in Mexico City. In this work, atmospheric NH3 was measured over Mexico City between 2012 and 2020 by means of ground-based solar absorption spectroscopy using Fourier transform infrared (FTIR) spectrometers at two sites (urban and remote). Total columns of NH3 were retrieved from the FTIR spectra and compared with data obtained from the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument. The diurnal variability of NH3 differs between the two FTIR stations and is strongly influenced by the urban sources. Most of the NH3 measured at the urban station is from local sources, while the NH3 observed at the remote site is most likely transported from the city and surrounding areas. The evolution of the boundary layer and the temperature play a significant role in the recorded seasonal and diurnal patterns of NH3. Although the vertical columns of NH3 are much larger at the urban station, the observed annual cycles are similar for both stations, with the largest values in the warm months, such as April and May. The IASI measurements underestimate the FTIR NH3 total columns by an average of 32.2 ± 27.5 % but exhibit similar temporal variability. The NH3 spatial distribution from IASI shows the largest columns in the northeast part of the city. In general, NH3 total columns over Mexico City exhibited an average annual increase of 92 ± 3.9 x 1013 molecules/cm2 yr (urban) and 8.4 ± 1.4 x 1013 molecules/cm2 yr (remote) was observed in Mexico City at both FTIR stations and a decadal increase of 62 % with IASI data.</p> <p>Description UNAM_FTIRdata.csv:<br> Atmospheric composition measurements made at the Universidad Nacional Autónoma de Mexico Observatory on the rooftop of the Instituto de Ciencias de la Atmósfera y Cambio Climático (UNAM, 19.33°N, 99.18°W, 2280 m.a.s.l.) located at the south of Mexico City. <br> These are retrieved from Fourier Transfor InfraRed (FTIR) solar absorption spectra recorded with a Vertex 80 spectrometer from April 2012 to October 2019. <br> The dataset contains the local time (YYYY-MM-DD hh:mm:ss AM/PM), the total columns (molecules/cm2), total error (molecules/cm2), systematic error (molecules/cm2), random error (molecules/cm2), and Degrees of Freddom (DOF).</p>
PCEDNet - Replicability material
<p>Material required to replicate the paper:</p> <blockquote> <p>Chems-Eddine Himeur, Thibault Lejemble, Thomas Pellegrini, Mathias Paulin, Loic Barthe, and Nicolas Mellado. 2021. <br> PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds. <br> ACM Trans. Graph. 41, 1, Article 10 (February 2022), 21 pages. <br> DOI:https://doi.org/10.1145/3481804</p> </blockquote> <p>Includes the following files:</p> <ul> <li>networks.zip: pre-trained networks,</li> <li>default.zip: dataset provided by the authors, including Ground Truth labels (see paper for more details)</li> <li>abc.zip: dataset generated from the ABC dataset, including Ground Truth labels (see paper for more details)</li> <li>point-clouds.zip: point-clouds without Ground Truth (see paper for more details) </li> </ul> <p> </p>
# Replication code and data for: Global projections of heat exposure of older adults
<p># Replication code and data for: Global projections of heat exposure of older adults<br>By Giacomo Falchetta, Enrica De Cian, Ian Sue Wing and Deborah Carr<br>Nature Communications. DOI: 10.1038/s41467-024-47197-5</p> <p>An output data file, containing grid-cell level counts of people by age group, of climate hazard indicators, and of heat exposure metrics for both the historical climate and current demography and for future scenarios and projections is contained in the repository ("aging_climate_output_data.csv"). </p> <p>Software requirements:<br>- R v4.3+: https://cran.r-project.org/bin/windows/base/<br>- RStudio: v2023.06.0+: https://posit.co/download/rstudio-desktop/<br>- Package dependencies: raster, sf, tidyverse, rasterVis, rgdal, maptools, pbapply, terra, knitr, kableExtra, modelsummary, openxlsx, xtable, ggforce, maptools, weights, spatstat, rworldmap, scales, patchwork, stars, viridis, devtools, stargazer, readxl, nominatimlite, urbnmapr</p> <p>To replicate the analysis:<br>- Clone the replication code repository from https://github.com/giacfalk/aging_climate<br>- Download input data from this Zenodo data repository<br>- Download all the 1km age and gender-stratified global population counts rasters from the following WorldPop page https://hub.worldpop.org/geodata/summary?id=24798 and put them in a subdirectory of the working directory called "AGEPOP"<br>- Run the "project_pop.R" script to generate gridded age-stratified population data for each SSP scenario<br>- Run the "compare_pop_projections.R" file to compare the generated gridded age-stratified population data with an array of pre-existing sources from different countries and produce a summary comparison table (NOTE: before running the script, decompress the "new_comparison_data.zip" folder into the working directory)<br>- Run "projections_exposure_m.R" to quantify heat exposure and generate the figures and tables reported in the paper</p> <p>To process the data and run succesfully, the script requires a computer with at least 32GB RAM. The running time varies based on CPU characteristics, but a runtime of at least 2 hours should be expected to generate all the output data, figures, and tables. All output files are saved in the working directory.</p> <p>___</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p> </p> <p> </p> </div> </div> </div> </div> </div>
Replicated functional evolution in cichlid adaptive radiations
<p>Adaptive radiations highlight the mechanisms by which species and traits diversify and the extent to which these patterns are predictable. We used 1,110 high-speed videos of suction feeding to study functional and morphological diversification in 300 cichlid species from three African Great Lake radiations of varying ages (Victoria, Malawi and Tanganyika) and an older, spatially dispersed continental radiation in the Neotropics. Among African radiations, standing diversity was reflective of time. Morphological and functional variance in Lake Victoria, the youngest radiation, was a subset of that within Lake Malawi, which itself was nested within the older Tanganyikan radiation. However, functional diversity in Neotropical cichlids was often lower than in Lake Tanganyika, despite being much older. These two radiations broadly overlapped, but each diversified into novel trait spaces not found in the youngest lake radiations. Evolutionary rates across radiations were inversely related to age, suggesting extremely rapid trait evolution at early stages, particularly in lake radiations. Despite this support for early bursts, other patterns of trait diversity were inconsistent with expectations of adaptive radiations. This work suggests that cichlid functional evolution has played out in strikingly similar fashion in different radiations, with contingencies eventually resulting in lineage-specific novelties.</p>
Dataset and Replication Package for the View-Based Retriever Approach To Reverse Engineering Software Architecture Models
<div> <div><span>Dataset and replication package for the view-based Retriever approach to reverse engineering software architecture models. Each Dataset project is structured as follows:</span></div> <ul> <li><span>The .ruleengine.yml file contains the configuration for running the Retriever approach.</span> <ul> <li><span>The repository value is the ID of a GitHub repository.</span></li> <li><span>The current_version value is the latest version of the retriever approach used to build the architectural models.</span></li> <li><span>The rules values are the rules used to build the architectural models.</span></li> </ul> </li> <li><span>The model_re folder contains the architectural model of the system automatically generated by the Retriever approach.</span> <ul> <li><span>The pcm folder contains the Palladio Component Model (PCM) of the system.</span></li> <li><span>The uml folder contains the PlantUML model.</span></li> </ul> </li> <li><span>The model_gs folder contains our manual gold standards for the system.</span></li> </ul> <div><span>The easiest way to use our approach is to use the CLI application with the given parameters: ./eclipse -i /path/to/input/directory -o /path/to/output/directory -r supported_rules</span></div> </div>
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.