Open Source Software
Open Source Software - Related Videos
08:32
Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software
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Cited by 7 •
2011
The C. elegans embryo is a powerful system for studying cell biology and development. We present a protocol for live imaging of C. elegans embryos utilizing DIC optics or fluorescence using readily available epifluorescent microscopes and open-source software.
01:23
GIS Software, Hardware, and Sources of GIS Data
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2025
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
11:30
Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software
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Cited by 1 •
2021
We present a label-free live imaging protocol using transmitted light microscopy techniques to capture images, analyze and quantify growth kinetics of the filamentous fungus A. nidulans in both submerged cultures and solid media. This protocol can be used in conjunction with fluorescence microscopy.
09:27
Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
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2019
The size and shape of particles in activated sludge are important parameters that are measured using varying methods. Inaccuracies arise from non-representative sampling, suboptimal images, and subjective analysis parameters. To minimize these errors and ease measurement, we present a protocol specifying every step, including an open source software pipeline.
08:22
Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software
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Cited by 4 •
2018
This protocol describes a calibration-free approach for quantifying protein homo-oligomerization in vitro based on fluorescence fluctuation spectroscopy using commercial light scanning microscopy. The correct acquisition settings and analysis methods are shown.
FAQs
Shared repositories, version control, documentation, and community review create a visible development path for statistical code. Contributors can inspect changes, propose improvements, and use revision history to understand how an analysis evolved. This structure supports coordinated work on cleaning, visualization, modeling, or simulation tasks while making technical decisions easier to examine and discuss.
Licensing terms determine what users and developers may do with modified software and derived works, including how those changes can be redistributed. That legal framework complements technical collaboration: repositories and review support development, while the license sets distribution conditions. In statistical projects, clarifying those terms helps teams share tools and adaptations consistently.
Transparent code and versioned workflows make the reasoning behind a statistical analysis easier to verify. A reviewer can examine the implemented procedures and follow changes across revisions rather than relying only on reported results. This supports reproducibility, because another analyst can inspect the workflow and understand how the analysis was assembled and maintained.
R and Python packages can support several stages of statistical work, including data cleaning, visualization, statistical modeling, simulation, and reproducible analysis. Their role is not limited to one type of dataset or method; the same open tooling context can connect preparation, exploration, computation, and documented analysis.
A workflow can combine statistical code, a shared repository, version control, and documentation. Analysts use these elements to keep analysis materials organized, record revisions, and communicate how data cleaning, visualization, modeling, or simulation were performed. The resulting versioned workflow gives collaborators and reviewers a clearer basis for checking, extending, and reproducing the work.
Accessibility can lower barriers to advanced statistical methods by making relevant languages and packages more available to users. In statistics, this broader access supports work across data cleaning, visualization, modeling, simulation, and reproducible analysis. It also expands opportunities for collaboration and verification because users can engage with shared tools and workflows rather than treating them as inaccessible resources.