Industry Context — Common BS Fingerprints in Science, Research & Laboratories
SciPy
(https://scipy.org) 📸 Data Snapshot: May 24, 2026Analyze the raw signals below. How would a machine score this business’s credibility?
Here are the exact signals captured from up to six pages of the site — the same raw inputs the evaluation engine analyzed. They are grouped by signal type so you can weigh each the way the machine does.
🏗️ Semantic Structure — heading hierarchy & page identity (Info Density · Commodity Fingerprint)
HOMEPAGE SciPy (https://scipy.org)
SciPy
Why SciPy? Fundamental algorithms. Broadly applicable. Foundational. Interoperable. Performant. Open source.
NAV_REPEATED_FOOTER SciPy – About Us (https://scipy.org/about/)
SciPy – About Us
Why SciPy? Fundamental algorithms. Broadly applicable. Foundational. Interoperable. Performant. Open source.
NAV_REPEATED_BODY_FOOTER SciPy – Installation (https://scipy.org/install/)
SciPy – Installation
Why SciPy? Fundamental algorithms. Broadly applicable. Foundational. Interoperable. Performant. Open source.
NAV_REPEATED_BODY_FOOTER SciPy – Community (https://scipy.org/community/)
SciPy – Community
Why SciPy? Fundamental algorithms. Broadly applicable. Foundational. Interoperable. Performant. Open source.
📝 The Narrative — clean text per page (Info Density · Semantic Coherence)
HOMEPAGE (https://scipy.org) SciPy
SciPy [IMG: SciPy logo. A blue circle with a snake in the shape of the letter] Fundamental algorithms for scientific computing in Python Get started SciPy 1.17.1 released! 2026-02-22 Fundamental algorithms SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems. Broadly applicable The algorithms and data structures provided by SciPy are broadly applicable across domains. Foundational Extends NumPy providing additional tools for array computing and provides specialized data structures, such as sparse matrices and k-dimensional trees. Performant SciPy wraps highly-optimized implementations written in low-level languages like Fortran, C, and C++. Enjoy the flexibility of Python with the speed of compiled code. Easy to use SciPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. Open source Distributed under a liberal BSD license, SciPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.
SUB-PAGE (https://scipy.org/about/) SciPy – About Us
[H1] About Us SciPy is developed in the open on GitHub, through the consensus of the SciPy and wider scientific Python community. For more information on our governance approach, please see our Governance Document. [H2] Steering Council# The role of the SciPy Steering Council is to ensure, through working with and serving the broader SciPy community, the long-term well-being of the project, both technically and as a community. The SciPy Steering Council currently consists of the following members (in alphabetical order): Andrew Nelson Charles Harris Christoph Baumgarten CJ Carey Eric Larson Evgeni Burovski İlhan Polat Jake Bowhay Josef Perktold Lucas Colley Matt Haberland Matthew Brett Nikolay Mayorov Pauli Virtanen (BDFL) Ralf Gommers (Chair) Tyler Reddy (Release manager) Warren Weckesser Emeritus: Anne Archibald Eric Jones (co-creator of SciPy) Eric Moore Eric Quintero Jaime Fernández del Río Jarrod Millman Josh Wilson Paul van Mulbregt Pearu Peterson (co-creator of SciPy) Robert Kern Stéfan van der Walt Travis Oliphant (co-creator of SciPy) [H2] Teams# The SciPy project is growing; we have teams for code website triage See the Teams page for individual team members. [H2] Sponsors# SciPy receives direct funding from the following sources: [IMG: Logo of the Chan Zuckerberg Initiative] [IMG: Logo of Tidelift] [H2] Institutional Partners# Institutional Partners are organizations that support the project by employing people that contribute to SciPy as part of their job. Current Institutional Partners include: Quansight (Ralf Gommers, Peter Bell, Melissa Weber Mendonça, Evgeni Burovski, Albert Steppi) [IMG: Logo of Quansight] Los Alamos National Laboratory (Tyler Reddy) [H2] Donate# SciPy will always be 100% open source software, free for all to use and released under the liberal terms of the modified BSD license. While we have a large number of contributors who volunteer their time to improve SciPy, financial resources are needed to run the project and accelerate its development. If you have found SciPy useful in your work, research, or company, please consider making a donation to the project commensurate with your resources. Any amount helps! Donations are managed by the NumFOCUS foundation, which passes your contribution to the SciPy project, and provides the SciPy development team with basic administrative and legal services. NumFOCUS is a 501(c)3 non-profit foundation, so if you are subject to the US Tax law, your contributions are tax-deductible. [H2] Acknowledgements# The SciPy development team would like to thank the following companies and organizations for providing financial support, services, or development infrastructure: JetBrains: licenses of all their products for all active maintainers Tidelift: financial support for SciPy through the Tidelift open source subscription CircleCI: continuous integration credit TravisCI: continuous integration credit Appveyor: continuous integration credit Azure: continuous integration credit Enthought: scipy.org and mailing lists hosting, holding the SciPy trademark NumFOCUS: several small development grants, and a hosted Mac Mini build machine Google: support for many Google Summer of Code students Intel: Intel MKL licenses BYU: employed Travis Oliphant while working on SciPy Mayo Clinic: employed Travis Oliphant while working on SciPy This list is ordered by time (most recent contributions first) and was last updated in January 2022. [H2] Social Media# @scipy on Mastodon @SciPy_team on X
SUB-PAGE (https://scipy.org/install/) SciPy – Installation
[H1] Installation Tip This page assumes that you are comfortable with using a terminal and happy to learn how to use a package manager. If you are a beginner and just want to get started with SciPy as quickly as possible, check out the beginner installation guide! The recommended method of installing SciPy depends on your preferred workflow. The common workflows can roughly be broken down into the following categories: Project-based (e.g. uv, pixi) (recommended for new users) Environment-based (e.g. pip, conda) (the traditional workflow) System package managers (not recommended) Building from source (for debugging and development) To install SciPy with static type stubs, see Installing with type stubs. Tip Installing type stubs may be required for Integrated Development Environments (IDEs) to provide accurate type hints. Project Based Environment Based Package Manager Building from Source [H3] Installing with uv# Here is a step-by-step guide to setting up a project to use SciPy, with uv, a Python package manager. Install uv following, the instructions in the uv documentation. Create a new project in a new subdirectory, by executing the following in a terminal: uv init try-scipy cd try-scipy Hint The second command changes directory into the directory of your project. Add SciPy to your project: uv add scipy Note This will automatically install Python if you don’t already have it installed! Tip You can install other Python libraries in the same way, e.g. uv add matplotlib Try out SciPy! uv run python This will launch a Python interpreter session, from which you can import scipy. See next steps in the SciPy user guide. Note After rebooting your computer, you’ll want to navigate to your try-scipy project directory and execute uv run python to drop back into a Python interpreter with SciPy importable. To execute a Python script, you can use uv run myscript.py. Read more at the uv guide to working on projects. [H3] Installing with pixi# If you work with non-Python packages, you may prefer to install SciPy as a Conda package, so that you can use the same workflow for packages which are not available on PyPI, the Python Package Index. Conda can manage packages in any language, so you can use it to install Python itself, compilers, and other languages. The steps to install SciPy from conda-forge using the package management tool pixi are very similar to the steps for uv: Install pixi, following the instructions in the pixi documentation. Create a new project in a new subdirectory: pixi init try-scipy cd try-scipy Add SciPy to your project: pixi add scipy Try out SciPy! pixi run python In project-based workflows, a project is a directory containing a manifest file describing the project, a lock-file describing the exact dependencies of the project, and the project’s (potentially multiple) environments. In contrast, in environment-based workflows you install packages into an environment, which you can activate and deactivate from any directory. These workflows are well-established, but lack some reproducibility benefits of project-based workflows. [H3] Installing with pip# Install Python. Create and activate a virtual environment with venv. Hint See the tutorial in the Python Packaging User Guide. Install SciPy, using pip: python -m pip install scipy [H3] Installing with conda# Miniforge is the recommended way to install conda and mamba, two Conda-based environment managers. After creating an environment, you can install SciPy from conda-forge as follows: conda install scipy # or mamba install scipy [H2] Installing system-wide via a system package manager# System package managers can install the most common Python packages. They install packages for the entire computer, often use older versions, and don’t have as many available versions. They are not the recommended installation method. [H3] Ubuntu and Debian# Using apt-get: sudo apt-get install python3-scipy [H3] Fedora# Using dnf: sudo dnf install python3-scipy [H3] macOS# macOS doesn’t have a preinstalled package manager, but you can install Homebrew and use it to install SciPy (and Python itself): brew install scipy A word of warning: building SciPy from source can be a nontrivial exercise. We recommend using binaries instead if those are available for your platform via one of the above methods. For details on how to build from source, see the building from source guide in the SciPy docs. See next steps in the SciPy user guide. [H2] Installing with Type Stubs# Static type stubs are available via a separate package, scipy-stubs, on PyPI and conda-forge. You can also install SciPy and scipy-stubs as a single package, via the scipy-stubs[scipy] extra on PyPI, or the scipy-typed package on conda-forge. To get a specific version x.y.z of SciPy (such as 1.14.1), you should install version x.y.z.*, for example: uv add "scipy-stubs[scipy]==1.14.1.*" # or pixi add "scipy-typed=1.15.0.*" # or python -m pip install "scipy-stubs[scipy]" # or conda install "scipy-typed>=1.14" Please direct questions about static typing support to the scipy-stubs GitHub repository.
SUB-PAGE (https://scipy.org/community/) SciPy – Community
[H1] Community SciPy is a community-driven open source project developed by a diverse group of contributors. The SciPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the SciPy Code of Conduct for guidance on how to interact with others in a way that makes the community thrive. We offer several communication channels to learn, share your knowledge and connect with others within the SciPy community. [H2] Participate online# The following are ways to engage directly with the SciPy project and community. Please note that we encourage users and community members to support each other for usage questions. Search for an answer first, because someone may already have found a solution to your problem. Lastly, maintainers are mostly monitoring the forum and GitHub. [H3] SciPy community meetings# SciPy community meetings are ideal to anyone wanting to contribute to SciPy or just know how current development is going. You can follow our community calendar from your preferred calendar manager, or look out for the announcements on our development forum. [H3] SciPy new contributor meetings# Once a month we have special meetings for folks who want to start contributing or have just started. All are welcome! Check our community calendar for details, or look out for the announcements on our development forum. [H3] SciPy development forum# This space is the main forum for longer-form discussions, like adding new features to SciPy, making changes to the SciPy Roadmap, and all kinds of project-wide decision making. Announcements about SciPy, such as for releases, developer meetings, sprints or conference talks are also made on this forum. A searchable archive of the old mailing list is available here. [H3] SciPy Slack space# The SciPy team also has a Slack space that you can join. This is not a user support forum, but you can ask questions about contributing and getting involved in the community. To join, please follow this invite link. [H3] Scientific Python Discord# You can also join the #scipy channel on the Scientific Python discord. To join, please follow this invite link. [H3] StackOverflow# You can ask questions with the scipy tag on StackOverflow. [H3] GitHub issue tracker# For bug reports (e.g. “np.arange(3).shape returns (5,), when it should return (3,)”); documentation issues (e.g. “I found this section unclear”); and feature requests (e.g. “I would like to have a new statistical test in scipy.stats”). Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in SciPy, please report it here. [H2] Study Groups and Meetups# If you would like to find a local meetup or study group to learn more about SciPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the PyData meetups (150+ meetups, 100,000+ members). SciPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the forum. [H2] Conferences# The SciPy project doesn’t organize its own conferences. The conferences that have traditionally been most popular with SciPy maintainers, contributors and users are the SciPy and PyData conference series: SciPy US EuroSciPy SciPy Latin America SciPy India SciPyData (Japan) PyData conferences (15-20 events a year spread over many countries) Many of these conferences include tutorial days that cover SciPy and/or sprints where you can learn how to contribute to SciPy or related open source projects. [H2] Join the SciPy community# To thrive, the SciPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to SciPy. If you are interested in becoming a SciPy contributor (yay!) we recommend checking out our Contribute page.
🛡️ Trust Signals — reviews, proof links, trust-theatre flag (Trust & Proof)
| Page | Reviews | Proof links |
|---|---|---|
| / (home) | 0 | 0 |
| /about/ | 0 | 0 |
| /install/ | 0 | 0 |
| /community/ | 0 | 0 |
🔗 Identity & Technical Layer — schema JSON-LD: identity chains, entity gaps (Identity & Authority)
Your Diagnosis
Before revealing the machine’s verdict, predict the BS score for each signal. Higher = more BS (more fluff, less verifiable substance). Drag each slider, then submit to compare your judgment against the engine.
Stuck? Reveal the heuristic lens — how the deterministic page-auditor reads each signal (no AI, pure pattern rules)
These are the structural rules a local, deterministic auditor applies — the same lens you can use to judge each signal. They describe what to look for, not this company’s result.
Classify each sentence as substantive or hollow. Grounding markers — numbers, currencies, dates, technical units, named entities — outweigh marketing adjectives. When fluff sits right next to hard evidence, the fluff is forgiven.
Pull the main entities out of the H1, then check whether they actually recur through the body. A page that announces one thing and then talks about another drifts. Headings with no real sentences underneath read as pseudo-substance.
Count trust words (review, testimonial, rating, verified) against real outbound proof links (Google, Trustpilot, Clutch, G2, Yelp). Lots of trust language with zero verification links is trust theatre. Unlinked logo galleries count against it.
Look at how much sentence length varies. Natural writing varies its rhythm; templated or mass-produced copy is statistically uniform. Very low variation reads as commodity content — unless unique named entities break the pattern.
Inspect the JSON-LD. Is there an Organization or Person schema, and does it carry sameAs links to real external profiles (LinkedIn, socials)? Missing schema or no identity declaration signals an anonymous entity.
Want to apply this lens yourself? The free BS Indicator Chrome extension runs these heuristic checks live on any page. Bear in mind it is a single-page, deterministic tool — it relies only on pattern rules for the page in front of it and does not perform the cross-page semantic correlation this audit uses, so its readout is a starting lens, not the full verdict.
Based on 126 businesses audited.
SciPy has 26.3 points less BS than the average for Science, Research & Laboratories.
Science, Research & Laboratories BS: SciPy (scipy.org)
This is a rare case of a zero-fluff, substance-first technical repository. The site functions as a utility for the scientific community, delivering extreme specificity with absolutely no marketing theater or semantic drift. The few points deducted are for technical metadata omissions and stale dates in secondary acknowledgement sections.
Implement Organization and SoftwareSourceCode schema to bridge the technical SEO gap and formally declare the steering council members as Person entities in structured data. Update the Acknowledgements section to reflect contributions between 2022 and 2026 to ensure all proof of support is current. Include a ‘Powered by SciPy’ or ‘Research Citations’ section that links to a curated list of peer-reviewed papers on Google Scholar or PubMed to provide external performance validation. Replace the [H2] ‘Performant’ with a more descriptive heading like ‘Low-Level Language Integration for Speed’ to further reduce power-word saturation.
The site perfectly matches the Science, Research & Laboratories category, specifically as it pertains to the foundational software infrastructure required for these fields. The content is deeply technical, focusing on algorithms for optimization, integration, and statistics, which are core to scientific research pipelines.
“The score of 8 reflects a nearly perfect score, driven primarily by the high information density and absolute semantic coherence. The minor points are purely forensic, resulting from missing JSON-LD schema and a four-year gap in the last-updated timestamp for the Acknowledgements list. This site serves as a benchmark for minimal bullshit in the scientific software industry.”
This training module utilizes a snapshot of public data from SciPy, captured on May 24, 2026, to demonstrate how machine logic evaluates different types of business narratives.
Purpose: This data is presented under “Fair Use” / “Educational Exception” for the purpose of forensic semantic analysis, allowing users to compare human intuition against machine-generated evaluations.
Notice to SciPy: This analysis is part of a non-adversarial audit conducted by 1 Euro SEO. The results provided by 1EuroSEO are intended as professional feedback to help improve any website’s machine-readability and authority signals. The 1EuroSEO BS Detection Tool is a free tool, and anyone can test any company to see how their content is interpreted by AI models.
Any company can use the insights for free and improve its voice by comparing it to industry clichés or competitors. When a company has updated its content, it can always submit a new audit request, which will be reflected in a new current score.
To all users: You are encouraged to visit the live site at https://scipy.org to view the most current version of its content and learn from the source what this company is about and what it offers.