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1,034 results for “Empirical”

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zenodo32/100

Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID90

<p>Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID90</p> <p>Dynamic vs constant liquid flow calibrations down to 20 nL/min</p>

opencc-by-4.0Dec 2022View details →
zenodo32/100

Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID88

<p>Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID88</p> <p>First comparison of inline measurements of dynamic viscosity</p>

opencc-by-4.0Dec 2022View details →
zenodo32/100

Data set for EMPIR project MeDDII Report A2.3.3

<p>Data set for EMPIR project MeDDII Report A2.3.3</p> <p>Characterization of devices for the in-line measurement of the dynamic viscosity and the density of Newtonian liquids</p>

opencc-by-4.0Dec 2022View details →
zenodo32/100

An Empirical Investigation into Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

<p>Paper:&nbsp;An Empirical Investigation into&nbsp;Learning Bug-Fixing Patches in the Wild via Neural Machine Translation</p> <p>Authors:&nbsp;Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk</p> <p>Journal:&nbsp;TOSEM 2019 - ACM Transactions on Software Engineering and Methodology&nbsp;</p>

opencc-by-4.0Dec 2022View details →
zenodo32/100

Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis

<p>Word tree STEM</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis

<p>Word tree gender</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis

<p>Word tree gap</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis

<p>Word tree rights</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis

<p>Empirical conceptual map</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis

<p>Theoretical concept map</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Empirical analysis of security-related code reviews in npm packages

<p>This package contains the dataset&nbsp;used to analyze our RQs in the manuscript.</p> <p>For more information on how to understand the folder structure&nbsp;and dataset, please read the README.md</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Dataset: An empirical study on self-admitted technical debt in modern code review

<pre>This data was used in the IST paper &quot;An Empirical Study on Self-Admitted Technical Debt in Modern Code Review&quot;. The program to use this data is published in GitHub (https://github.com/Yutaro-Kashiwa/ReviewSATD_RP) When you use this data in your research, please cite the following papers: ``` @article{Kashiwa:IST:2022:SATD_Review, author = {Yutaro Kashiwa and Ryoma Nishikawa and Yasutaka Kamei and Masanari Kondo and Emad Shihab and Ryosuke Sato and Naoyasu Ubayashi}, title = {An empirical study on self-admitted technical debt in modern code review}, journal = {Information and Software Technology}, volume = {146}, pages = {106855}, year = {2022}, url = {https://doi.org/10.1016/j.infsof.2022.106855}, doi = {10.1016/j.infsof.2022.106855} } ``` </pre>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Dataset of "Poster - BugOss: Regression Bug Benchmark for Empirical Study of Regression Fuzzing Techniques"

<p>Dataset of &quot;Poster - BugOss: Regression Bug Benchmark for Empirical Study of Regression Fuzzing Techniques&quot;</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Replication package for our TOSEM paper entitled "An Empirical Study on GitHub Pull Requests' Reactions"

<p>This package contains our dataset and the source code used to collect data from the the top 10,000 most starred GitHub repositories, and the selected six repositories (i.e., Cataclysm-DDA, Julia, Laravel, Node, RPCS3 and Rust), as well as the source code to analyze the data and generate all the figures in the paper.&nbsp;</p> <p>Please carefully read the README.md file for more details.</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Dataset Strategic behaviour by wind generators: an empirical investigation- IJIO

<p>Dataset</p>

opencc-by-4.0Mar 2023View details →
zenodo32/100

Empirical Dataset of 1200 hours of Experiment: Reliability and Performance Measurements of Dynamic Routing Applications

<p>This artifact presents the empirical measurements of an experiment with 1200 hours of runtime. We repeated the experiment 200 runs, each for 6 hours. The data folder provides the empirical data, and the plots folder gives the distribution of reliability and performance measurements in 200 runs.&nbsp;</p> <p><strong>For reliability</strong>, we simulate component crashes and measure the request loss. To simulate component crashes, we generate a random number for each service and router and stop the docker container if the generated number is below a crash probability (0.5% in this study).&nbsp;</p> <p><strong>For performance</strong>, we consider the round-trip time of requests going through several services until the requests are processed.</p> <p>The experiment cases are:&nbsp;</p> <p>Four levels of call frequency <span class="math-tex">\(cf\)</span>: 10, 25, 50, 100 requests/second (r/s)</p> <p>Three levels of service: 3, 5, 10<br> the number of services <span class="math-tex">\(n_{serv}\)</span>&nbsp;in a call sequence</p> <p>Three architectures:&nbsp;<br> Central Entity (CE): one router&nbsp;<span class="math-tex">\(n_{rout} = 1\)</span><br> Dynamic Routers (DR): three&nbsp;routers&nbsp;<span class="math-tex">\(n_{rout} = 3\)</span><br> Sidecar Architecture (SA): 1 router per service <span class="math-tex">\(n_{rout} = n_{serv}\)</span></p>

opencc-by-4.0Mar 2023View details →
zenodo32/100

Data set for "Spatially explicit ecological modeling improves empirical characterization of dispersal"

<p>Data set used and created in the simulations, analysis and figures of the associated paper.</p>

opencc-by-4.0Apr 2023View details →
zenodo32/100

THE EDUCATION SYSTEM IN THE RUSSIAN EMPIRE IN THE XIX-TH CENTURY

<p>The article deals with the process of education in the Russian Empire in the XIX-th century. It is shown that the leading trend in the development of education in post-Soviet Russia was the desire to revive various forms of education characteristic of society in the pre-October period, namely, the revival of gymnasiums, lyceums, various schools, cadet corps, etc. Modern Russia is trying to focus on humanistic values, movement towards the ideals of justice, morality and intelligence of the human person. The article focuses on the fact that education should be one of the ways to awaken the social mobility of the population. Today it is necessary to overcome the false stereotypes that have prevailed for many decades and expand the boundaries of historical knowledge, which is important not only for understanding the true place and role of the country in world history, but also for solving the practical problems of renewal, determining the prospects for moving forward to qualitatively new social economic and political decisions.</p>

opencc-by-4.0Apr 2023View details →
zenodo32/100

Data from: Exploring the impact of read clustering thresholds on RADseq-based systematics: an empirical example from European amphibians.

<p>This repository contains genetic sequences obtained from Hybrid-Enrichment and RAD sequencing protocols of the amphibian genera <em>Discoglossus</em>, <em>Lissotriton</em>, <em>Rana </em>and <em>Triturus, </em>as well as phylogenetic trees inferred from the RADseq data. This data was generated for the manuscript &quot;Exploring the impact of read clustering thresholds on RADseq-based systematics: an empirical example from European amphibians.&quot;, in which we tested the influence of the clustering threshold used to assemble RADseq data on downstream phylogenetic inferences. Details on the data generation and analyses can be found in the manuscript and related supplementary materials.</p> <p>The repository is organised as follow:</p> <p>-&gt; Hybrid-Enrichment: alignments of the Hybrid-Enrichment markers in phylip/fasta format (with one subdirectory for each of the four datasets assembled: Discoglossus, Lissotriton, Rana, Triturus)</p> <p>--&gt; RADseq: Assemblies and phylogenetic trees obtained from a RADseq protocol</p> <p>&nbsp;&nbsp;&nbsp; --&gt; Assemblies: RADseq assemblies (complete loci sequences and SNP matrices, spreadsheets with assembly metrics). Divided into &quot;iCT&quot; (assemblies produced with 23 different intra-sample Clustering Threshold [iCT] and a fixed between-samples Clustering Threshold [bCT]) and &quot;bCT&quot; (assemblies produced with a fixed iCT and 23 different bCT). Both iCT and bCT are further divided in four sub-directories corresponding to the four datasets: Discoglossus, Lissotriton, Rana, Triturus)</p> <p>&nbsp;&nbsp;&nbsp; --&gt; Trees: Phylogenetic trees inferred from the aforementionned assemblies. Divided into &quot;iCT&quot; (RAxML concatenation trees inferred from the assemblies with different iCTs) and &quot;bCT&quot; (RAxML concatenation trees and Tetrad species trees inferred from the assemblies with different bCTs).</p>

opencc-by-4.0Apr 2023View details →
zenodo32/100

"Empirical study on Visual Attention Characteristics of basketball players of different levels during free-throw shooting"AOI and Heatmap

<p>&ldquo;罚球投篮中不同水平篮球运动员视觉注意力特征的实证研究&rdquo;AOI与热图</p>

opencc-by-4.0Apr 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record

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Your academic CV and NIH biosketch

Make your CV in your school's format and your NIH biosketch from it, then download both as Word files.