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To find the data behind a neutrino result, start with the observatory’s official release page or the paper’s supplementary repository—not a general-purpose download directory. First identify the specific analysis and data release, then read its selection, coverage, processing notes, and reuse guidance. A published event is usually represented in a processed research product, not as a universal record of raw detector readout.
Start with the event, paper, or analysis you want to trace
Search using the observatory name together with the paper title, event designation, or analysis topic. If you know only the collaboration, its publication list can lead you to the relevant paper and its supplementary materials. KM3NeT, for example, maintains a publication page that includes papers related to KM3-230213A.
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Once you find a paper or candidate dataset, follow its links to the collaboration’s own release page or repository. A general portal can help you discover where data may be available, but the experiment or archive’s landing page is where you should confirm the authoritative version, metadata, and terms.
Use the observatory’s release page to choose a dataset
IceCube’s Data Releases page lists releases and links to their records. It currently features IceTracks-DR2, a track-event dataset spanning 2008–2022, released on May 21, 2026. The collaboration describes it as covering 14 years of track-like observations, adding four years to the prior release and incorporating calibration and event-processing improvements. IceCube says public data are released after the main analyses are complete and results published, noting the challenges of event reconstruction, background rejection, and systematic effects.
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Do not choose a release based on its name or date alone. Read the dataset record and documentation before downloading, and check the following:
- Analysis purpose: What kind of search or measurement was this sample designed to support?
- Selection and topology: Which events were retained, and what event types or detector configurations are represented?
- Coverage: What data-taking period does it cover?
- Contents and response: Which event fields, effective areas, and angular or energy resolution information are provided?
- Version and compatibility: Does this release replace an earlier one, and does its documentation allow combination with other samples?
- Use guidance: What software, attribution, or data-use notes accompany the release?
Why release compatibility matters: IceTracks-DR2
The IceTracks-DR2 repository record says the sample is commonly used for time-integrated neutrino point-source searches. It is intended to supersede IceTracks-DR1 and is not intended to be combined with other IceCube public releases. Treat that as a dataset-specific instruction, not a general rule for every release. Check the record for the exact products you plan to use before joining samples or comparing results.
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For a named event, follow the paper to its supplementary materials
When the goal is to inspect one published event, the paper’s supplementary repository may be more useful than a broad event catalog. KM3NeT’s KM3-230213A repository supplements the paper Observation of an Ultra-high-energy Cosmic Neutrino with KM3NeT. It separates data and single-hit information, Jupyter notebooks, scripts, and a Julia-based interactive event display.
The repository README identifies example outputs including an event display, skymap, hit distributions, flux comparison, and an expected-event table. It also describes which parts of the publication can be reproduced using the materials. Read those notes alongside the paper: a plot or table in a notebook is an analysis product with a defined scope, not a complete substitute for the publication’s methods and caveats.
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Open the KM3NeT example
- Inspect the README and linked paper. Confirm that the materials correspond to the event and result you mean to examine.
- Choose a documented environment. The repository describes a hosted Binder option and local installation with Conda or venv.
- Obtain the full data if needed. The README states: “For the full use of all notebooks, you have to retrieve the full dataset for
/datafrom the KM3NeT Open Data Center.” A partial repository checkout may not include everything required by every notebook. - Use the appropriate display workflow. The interactive event display is Julia-based; the notebooks use Jupyter.
- Check reuse terms. The repository provides a BSD 3-Clause license and a data reuse disclaimer. Follow the repository’s stated conditions and the collaboration’s guidance.
The README says, “The repository is mainly based on the use of Jupyter notebooks and provides an interactive event display.” This describes the repository’s intended workflow; it is not a guarantee that every notebook or environment will work unchanged on every system.
Know what public observatory data represents
Public datasets are often prepared research products rather than raw detector streams. KM3NeT explains that raw data require calibration, event reconstruction, and formatting before external use. Its public-data guidance also discusses simulated data, sample analysis programs, documentation, training, and questions of scrutiny, authorship, scientific responsibility, and intellectual property in external scientific use. Consult the collaboration’s current terms and project guidance for the dataset you plan to use.
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IceCube likewise describes release timing in relation to completed analyses and published results. In both cases, processing and selection are part of what makes the data usable; they also define what conclusions the files can support. Distinguish reconstructed event parameters and selected candidate samples from raw detector readout, and use the release’s documentation to understand cuts, uncertainties, and response information.
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The icecube_tools documentation describes Python functions for downloading publicly available IceCube information and loading effective areas and angular and energy resolution. It also describes simulation and likelihood workflows for reproducing standard point-source searches. This can be useful for analysis work, but it does not replace the documentation attached to the specific release you are using.
Neutrinos Open Data aggregates links to public datasets and benchmark simulations while the experiments host their own data. Its examples span MicroBooNE, MINERvA, Daya Bay, IceCube, NOvA, and T2K; the portal marks DUNE as future or in construction and its open-data policy as under development. Use a directory like this to discover a destination, then confirm the dataset version, metadata, and terms at the original experiment or archive.
Compare datasets before combining or interpreting them
Two samples bearing the same observatory name—or nearby data years—are not automatically compatible. Before combining datasets or making a comparison, establish how they differ across these dimensions:
- Observatory and detector configuration
- Event selection and topology
- Data-taking period
- Analysis purpose
- Available event fields and detector response
- Version, supersession, and combination policy
- Software requirements and data-use terms
Follow explicit compatibility statements in each release record. If the documentation does not establish that two products can be combined, do not infer compatibility from overlapping dates or similar filenames.
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