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31 results for “Reproducible Research”
Multi-stakeholder research data management training as a tool to improve the quality, integrity, reliability and reproducibility of research: Quantitative data of the post-course surveys
<p>Data contains doctoral students' and postdoc researchers' (n=168) self-ratings of their RDM competencies before and after the 3 ECTS credits "Basics of Research Data Management" (BRDM) trainings held 2019-2021 in the University of Turku and Åbo Akademi University, Finland. Moreover, data contains respondents' self-reported further learning needs.</p>
Publishing Reproducible Research Outputs - Interviewees and interview questions
<p>The table '<strong>Interview questions</strong>' shows the focus of our investigation and stakeholder engagement activities. It should be noted that not all interview questions were asked to all stakeholder groups based on appropriateness and time available. Some questions in the table may appear to be repeated: this is because slightly different phrasing was used based on the stakeholder interviewed.</p> <p>Legend:</p> <ul> <li>Research Funding Organisations: RFO</li> <li>Research Performing Organisations: RPO</li> <li>Infrastructure Providers: IP </li> <li>Academic Publishers: AP</li> <li>Researchers and research groups: RRG</li> </ul> <p>The table '<strong>List of interviewees</strong>' includes all stakeholders engaged in the context of this research.</p>
Publishing Reproducible Research Outputs - Thematic coding of interview findings
<p>The spreadsheet in the present dataset (CSV format) includes the anonymised thematic coding that has been applied to our interview findings. A list of interviewees and interview questions is available <a href="https://doi.org/10.5281/zenodo.5141665">here</a>.</p> <p>The thematic coding has been applied by using <a href="https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home">NVivo</a>, a professional qualitative analysis software, and then exported in spreadsheet form for public sharing. The findings of this analysis have been used to inform our final report, which is available in our <a href="https://zenodo.org/communities/ke-prro/?page=1&size=20">Zenodo project Community</a>.</p>
Reproduction package for paper "How far are we from reproducible research on code smell detection? A systematic literature review"
<p>Checklist and data extracted from publications analyzed for "How far are we from reproducible research on code smell detection? A systematic literature review" paper, together with processing scripts and calculations of Cohen's Kappa.</p> <p>Paper that describes details of the data is available here: https://doi.org/10.1016/j.infsof.2021.106783</p>
Including Data Management in Research Culture Increases the Reproducibility of Scientific Results
<p><strong>General Information:</strong></p> <p>This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. </p> <p>The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30</p> <p>This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.</p> <p><strong>Run the code:</strong></p> <p>To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". </p> <p>Alternatively, you can import the .ipyab file in the colab [2] and run it. </p> <p><br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br> </p>
Roadmap for Developing a Dynamic and Reproducible Research Article with ARTE workflow
<p>The figures illustrates a roadmap for developing a dynamic and reproducible research article using <strong>ARTE (Article Reproducibility Template & Environment) </strong>workflow. The process is categorized into three levels of reproducibility: <strong>Minimal, Proper, and Full</strong>. Each level integrates specific tools and practices to enhance the reproducibility of the research.</p> <p>This proposal is published in the following <strong>OSF project</strong>: <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a><br>Shared in the following <strong>GitHub repository</strong>: <a title="GitHub" href="https://github.com/phdpablo/article-template" target="_blank" rel="noopener">https://github.com/phdpablo/article-template</a><br>Exemplified in the following <strong>URL address</strong>: <a title="Article Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <h1>Minimal Reproducibility</h1> <p><strong>1. Use this template</strong>: Start by utilizing the provided template, which is pre-configured with the <strong>TIER Protocol 4.0</strong>. This protocol helps organize research projects in a systematic manner.</p> <p><strong>2. Edit READMEs</strong>: Customize the README files to reflect the details and conclusions of your research. These README files help document the project structure and contents.</p> <p><strong>3. Share on OSF</strong>: Share the project on the <strong>Open Science Framework (OSF)</strong> to ensure accessibility and transparency. This can be done at the beginning, during, or at the end of the research process.</p> <h1>Proper Reproducibility</h1> <p>In addition to the steps mentioned above, the following steps are added:</p> <p><strong>4. Quarto settings:</strong> Adjust the Quarto configuration to fit the needs of your project. This includes modifying the <em>_quarto.yml</em> file for different themes and output formats.</p> <p><strong>5. Develop your narrative</strong>: Write the research narrative using <em>Quarto’s .qmd files</em> within RStudio. This narrative forms the main body of your article and integrates text, code, and outputs seamlessly.</p> <p><strong>6. Environment control:</strong> Implement environment control using the <em>renv package</em>. This ensures that the R environment is consistent and reproducible. The <em>renv.lock</em> file captures the exact versions of R packages used in the project.</p> <p><strong>7. Share dynamic article:</strong> Render and share the dynamic document via GitHub Pages. The Quarto-generated HTML files (docs folders) are hosted on GitHub Pages, making the research accessible and interactive.</p> <h1>Full Reproducibility</h1> <p>Building on the proper reproducibility steps, full reproducibility adds:</p> <p><strong>8. Use Docker:</strong> Employ Docker for operating system-level environment control. A Docker container encapsulates the entire project environment, ensuring that the research can be replicated exactly, regardless of the local machine setup.</p> <h2>Tools Utilized</h2> <ul> <li><strong>TIER Protocol 4.0</strong>: Provides a framework for organizing and documenting research projects.</li> <li><strong>OSF:</strong> A platform for sharing research outputs and ensuring open science practices.</li> <li><strong>Quarto:</strong> A tool for creating dynamic documents that integrate text, code, and outputs.</li> <li><strong>RStudio:</strong> An integrated development environment (IDE) for R, facilitating data analysis and reproducible research.</li> <li><strong>Git/GitHub:</strong> Version control systems that track changes and manage project versions.</li> <li><strong>renv: </strong>An R package for managing and reproducing consistent R environments.</li> <li><strong>GitHub Pages:</strong> A service for hosting static websites directly from a GitHub repository.</li> <li><strong>Docker:</strong> A platform for containerizing applications to ensure consistent environments across different systems.</li> </ul> <h2>Summary</h2> <p>This template guides researchers through creating a reproducible and dynamic article using ARTE (Article Reproducibility Template & Environment) workflow. It starts with basic project setup and documentation, progresses through developing the research narrative with environment control, and culminates in full reproducibility with Docker. This structured approach ensures that research is well-documented, versioned, and easily shareable, promoting open science practices.</p>
Data to reproduce the results presented in Sehgal et al. 2022. Water Resources Research, https://doi.org/10.1029/2021WR030624 ("Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer")
<p>This repository consists data to reproduce results as presented in: "Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer", Water Resorces Research. Kindly refer to the readme.text file to navigate through the dataset.</p> <p> </p> <p> </p>
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
Reproducibility Package for "Reproducible research and GIScience: an evaluation using AGILE conference papers"
<p>Data and code for analysis and plots used in the manuscript "Reproducible research and GIScience: an evaluation using AGILE conference papers": <a href="https://doi.org/10.7287/peerj.preprints.26561v1">https://doi.org/10.7287/peerj.preprints.26561v1</a></p> <p>The deposited archived includes a <a href="https://en.wikipedia.org/wiki/Docker_(software)">Dockerfile</a> and an <a href="http://rmarkdown.rstudio.com/">R Markdown</a> document suitable for use with <a href="http://mybinder.org/">Binder</a>: <a href="https://mybinder.org/v2/gh/nuest/reproducible-research-and-giscience/6">https://mybinder.org/v2/gh/nuest/reproducible-research-and-giscience/6</a></p> <p>The version tag of this repository matches the <a href="https://git-scm.com/book/en/v2/Git-Basics-Tagging">git tag</a> on the code repository at <a href="https://github.com/nuest/reproducible-research-and-giscience">https://github.com/nuest/reproducible-research-and-giscience</a>, except version <code>6-fixed</code> which matches the tag <code>6</code>.</p> <p> </p>
reproducible_research_data
<p>Data for the final project on reproducible research course at Unicamp (2020/1st)</p>
Data and code - Reproducibility improves exponentially over 63 years of research - Minocher et al. 2020
<p>Data and code to reproduce analyses in the publication - Minocher, et al. "Reproducibility improves exponentially over 63 years of social learning research". </p> <p>This repository is maintained at github https://github.com/rianaminocher/reproducibility-analysis.</p>
Reproducible Research in Archaeology
<p>This is a recording of a presentation given at the workshop on 'Reproducible research in Archaeology' at Durham University on 15th October 2021. The workshop included an introduction to what reproducibility is, why it is important for archaeological research and how you can make your research workflow reproducible. It also included some case studies demonstrating reproducible workflows used in archaeological research. This workshop was organised by Software Sustainability Institute Fellows - Alison Clarke and Emma Karoune. The slides from the presentation are available at <a href="https://doi.org/10.5281/zenodo.5564648">https://doi.org/10.5281/zenodo.5564648</a>.</p>
Code and dataset used for the paper "The Economics of Research Reproducibility"
<p>This deposit includes the numerical data (code and dataset) used to generate the results displayed in the article "<a href="https://zenodo.org/record/5211312#.YRuv_I4zaUk">The Economics of Research Reproducibility</a>".</p>
Dataset about Reproducibility in Software Engineering Research: A Systematic Mapping Study
<p>This artifact contains the results collected in one Systematic Mapping Study (SMS), about Reproducibility in Software Engineering Research. The results are associated with the selected studies and with the research questions considered.</p>
TerraSenseTK - Towards Reproducible Machine-Learning and Remote Sensing Research
<p>Dataset used in TerraSenseTK - Towards Reproducible Machine-Learning and<br> Remote Sensing Research.</p> <p>Nutrient Estimation in Common wheat Case Study available in the notebook</p> <p>Documentation is available in <a href="https://terrasensetk.readthedocs.io/en/latest/">here</a>.</p>
Results of the ISMRM 2020 joint Reproducible Research & Quantitative MR study groups reproducibility challenge on phantom and human brain T<sub>1</sub> mapping
<p>This dataset includes both raw data and processed T1 maps obtained from the 2020 challenge on inversion recovery T1 mapping organized by the International Society in Magnetic Resonance in Medicine (ISMRM) Reproducible Research Study Group (RRSG). For a comprehensive overview of the data distribution submitted for this challenge, please visit <a href="https://rrsg2020.db.neurolibre.org/">https://rrsg2020.db.neurolibre.org</a>. It's important to note that this dataset exclusively comprises ISMRM-NIST system phantom data.</p> <p>Dataset provided for NeuroLibre preprint. Author repo: https://github.com/rrsg2020/paper NeuroLibre fork:https://github.com/roboneurolibre/paper</p> <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/14">NeuroLibre technical screening.</a></p> <p><strong><a href="https://neurolibre.org">https://neurolibre.org</a></strong></p>
GHTraffic: A Dataset for Reproducible Research in Service-Oriented Computing
<p>This is the latest version of the GHTraffic project. The main aim is to model a variety of transaction sequences to reflect more complex service behaviour.</p> <p>It has two editions: Small (S) and Large (L) where the records were created by selecting the same repositories as the original Small and Large datasets. The newest S dataset contains records from <a href="https://github.com/google/guava">google/guava</a> repository. The L dataset contains records from eight repositories (i.e., <a href="https://github.com/twbs/bootstrap">twbs/bootstrap</a>, <a href="https://github.com/symfony/symfony">symfony/symfony</a>, <a href="https://github.com/docker/docker">docker/docker</a>, <a href="https://github.com/Homebrew/homebrew">Homebrew/homebrew</a>, <a href="https://github.com/rust-lang/rust">rust-lang/rust</a>, <a href="https://github.com/kubernetes/kubernetes">kubernetes/kubernetes</a>, <a href="https://github.com/rails/rails">rails/rails</a>, and <a href="https://github.com/angular/angular.js">angular/angular.js</a>). </p> <p>The entire data generation process is quite similar to the original GHTraffic design. But it incorporates minor changes to the process of synthetic data generation where it uses a random date after successfully posting a resource to make up the request and response for all of the HTTP methods. It also adds yet another subset of unsuccessful transactions by stipulating requests before resource creation is successful.</p> <p>This results in a far more dynamic series of transactions to named resources.</p> <p>Scripts used for datasets construction are accessible from the <a href="https://bitbucket.org/tbhagya/ghtraffic-version-2.0.0">repository</a>.</p>
ISMRM Reproducible Research Study Group: Data for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge"
<p>Challange data (brain/heart) and supplementary data (cardiac/rawdata_sprial) for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge".</p>
Characterisation of core histone sequences and nuclear mobility using a reproducible research approach
<p>Dataset required to build the PhD thesis. Includes the public sequences that would otherwise be downloaded anew.</p>
Dataset to reproduce the figures in "Revisiting AMOC Transport Estimates from Observations and Models" in Geophysical Research Letters
<p>Reference level assumptions used to calculate the Atlantic meridional overturning circulation transports at the RAPID and MOVE observing arrays are revisited in an eddying ocean model. Observational transport calculation methods are complemented by several alternative approaches. At RAPID, the model transports from the observational method and the model truth (based on the actual model velocities) agree well in their mean and variability. There are substantial differences among the transport estimates obtained with various methods at the MOVE site. These differences result from relatively large and time-varying reference velocities at depth in the model, not supporting a level-of-no-motion. The methods that account for these reference velocities properly at MOVE produce transports that are in good agreement with the model truth. In contrast with the observational estimates, the model transport trends at MOVE and RAPID largely agree with each other on pentadal to multi-decadal time scales. The datasets listed here are output fields from the Meridional ovErTurning ciRculation diagnostIC (METRIC) package which enables consistent calculations of AMOC estimates at the MOVE and RAPID sections from observations and models. The METRIC package is available on GitHub at https://github.com/NCAR/metric. Citation for the code: Castruccio F. S., 2021: NCAR/metric: metric v0.1. doi/10.5281/zenodo.4708277 Citation for the method: Danabasoglu et al. (2021). Revisiting AMOC Transport Estimates from Observations and Models. Geophysical Research Letters.</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.