eduKateSG · Why Science?
Turn a proteomics study into a discoverable, reusable public dataset—while preserving files, metadata, repository context, versions and responsible access
Choose the closest route, then return to the full index whenever you need the wider evidence chain.
Science learning becomes powerful when students can tell a measured pattern from a model, a statistical decision from a mechanism, and a useful lead from a finished conclusion. ProteomeXchange is a consortium that coordinates standard data-submission and dissemination pipelines across major proteomics repositories and promotes open-data policies. Its official site directs users to public PXD datasets through ProteomeCentral and identifies current member repositories including PRIDE, PeptideAtlas, MassIVE, jPOST, iProX and Panorama Public. The network supports MS/MS proteomics, SRM and other workflows through repository-specific routes. A PXD accession makes a dataset discoverable; it is not proof that every file is complete, unrestricted, correctly analysed or suitable for a new purpose. This guide supports Primary Science, PSLE Science, Secondary Science, O-Level Science and STEM education as a longform evidence-reading route; it is not medical advice, a laboratory protocol, an admission promise or a career guarantee.
Related eduKate reading: Universal Spectrum Identifiers and mass spectrometry; mzML and open mass-spectrometry data; mzIdentML and proteomics identification results; mzTab and tabular proteomics results; Science Learning Hub; Education Hub; STEM Education; Careers by Subject Capability.
Primary and official sources checked on 12 October 2026: ProteomeXchange Consortium official mission, member-repository and public-data page, checked 12 October 2026; ProteomeXchange official data-submission routes, checked 12 October 2026; ProteomeCentral official public dataset portal, checked 12 October 2026; 2026 Singapore–Cambridge O-Level Biology syllabus; MOE G2/G3 Lower Secondary Science teaching and learning syllabus; 2026 MOE G2 Computing syllabus. Specifications, vocabularies, repository services and recommendations can change, so preserve dated documentation, exact source data and software versions.
A deeper reading route
Proteomics data are large, heterogeneous and expensive to create. A paper alone rarely contains the raw spectra, search outputs, metadata and processing receipts needed for independent inspection. ProteomeXchange coordinates repositories so submitters have recognised deposit routes and users can discover public datasets through a shared accession and central catalogue.
The official mission emphasises globally coordinated submission and dissemination rather than one giant storage server. Member repositories serve different communities and workflows. PRIDE and PeptideAtlas are founding members; the official site also lists MassIVE, jPOST, iProX and Panorama Public. The consortium layer improves discovery while each repository remains responsible for its services and submission interfaces.
A PXD identifier is a dataset-level accession. It helps papers, repositories and tools refer to the same deposit, but the accession alone says little about individual files, versions or analysis choices. Users should open the landing page, inspect status and repository, read metadata, enumerate files and preserve retrieval dates and checksums for anything downloaded.
Submission packages commonly include raw or open spectral data, search or quantitative results, sequence databases or identifiers, protocols and metadata. The appropriate contents depend on the repository route and workflow. The official submission page directs GUI-based PRIDE submissions, SRM/MRM data to PASSEL and shotgun workflows to MassIVE, while broader guidance should be checked before deposit.
Public does not mean context-free. A dataset may be announced after an embargo, updated after publication, distributed under conditions or accompanied by partial metadata. Personal, clinical or culturally sensitive information requires governance even when mass-spectrometry files look de-identified. Submitters and reusers must follow consent, repository policy and applicable law.
Standards improve the package. mzML can preserve spectra, mzIdentML identifications, mzQuantML quantities, mzTab summaries, SDRF-Proteomics sample-to-file metadata and USIs spectrum-level references. No single format replaces all the others. A strong deposit uses each layer for its intended role and supplies a simple manifest connecting them.
Reuse begins with a question, not a download. A new analyst should decide which samples, instruments, acquisition modes and outcomes fit the planned comparison. Files from different datasets can be technically accessible yet scientifically incomparable. Review design, reference databases, processing, missingness and licences before harmonising tables.
Integrity receipts matter because public datasets can include many large files. Keep landing-page metadata, repository file identifiers, sizes, cryptographic hashes where available, download logs and tool versions. If a file changes or a deposit is updated, the original analysis can then be reconstructed instead of being tied to an unrecorded live state.
A practical audit selects one PXD dataset and builds a map from the ProteomeCentral record to the member-repository landing page, sample metadata, raw files, results and related publication. It then retrieves one spectrum through an inspectable identifier where possible. Broken links or mismatched counts become explicit findings rather than invisible assumptions.
The hopeful impact is wider participation in science. Public proteomics data can support teaching, method comparison, meta-analysis and new biological questions without repeating every experiment. Reuse honours the original work when provenance, citation, governance and limitations travel with the downloaded bytes.
A durable citation should include the PXD accession, member repository, dataset title, submitters or consortium citation, retrieval date and relevant publication. For a derived analysis, also publish the exact subset of file identifiers and hashes. This lets readers distinguish credit for the original experiment from responsibility for the new processing and interpretation.
Automated downloads should respect repository interfaces, rate limits and authentication rules. Log requests without exposing credentials, retry transient failures safely and verify every completed object. A script that downloads quickly but cannot prove which bytes arrived is convenient rather than reproducible.
Cross-dataset studies need an explicit harmonisation table. Compare organism, tissue, phenotype, preparation, instrument, acquisition, database, search engine, quantification method and missingness policy before combining values. Some differences can be modelled; others define separate strata. A transparent non-merge can be more scientific than one enormous but incoherent matrix.
Repository records are evidence indexes, not endorsements of every conclusion in an associated paper. Independent reviewers should inspect source files, controls, processing and uncertainty. Corrections or reanalyses can strengthen the ecosystem when they cite the original deposit carefully and publish their own full receipts.
For education, one public dataset can support many lessons without exposing student data: navigate the landing page, classify files, resolve a USI, inspect an SDRF, compare a processed result with one spectrum and write a bounded conclusion. The activity shows open science as a chain of people, standards and evidence rather than a single download button.
A local snapshot should be self-describing. Alongside downloaded files, store the dataset metadata response, the chosen file list, repository URLs, timestamps, sizes, checksums and any access or licence note. Future researchers can then tell whether a missing file reflects the original selection, a failed transfer or a later repository change, and they can reproduce the exact starting point before trying a newer snapshot. Keep the snapshot manifest under version control.
| Stage | Question | Evidence | Responsible output |
|---|---|---|---|
| Deposit | Which repository route fits the workflow? | Consortium guidance, member service and submission package | A coordinated dataset submission |
| Identify | How is the study found? | PXD accession, landing page, metadata and publication links | A discoverable public record |
| Retrieve | Which exact files are used? | Repository identifiers, sizes, hashes and download receipts | A reproducible local snapshot |
| Reuse | What new claim is justified? | Design review, reanalysis, validation, governance and citation | A bounded secondary analysis |
Inside this guide
1–12 · Foundations and design
- 1. Start with the sharing question
- 2. Understand the consortium model
- 3. Use the official member list
- 4. Choose the appropriate repository
- 5. Prepare a file manifest
- 6. Preserve raw evidence
- 7. Preserve identifications
- 8. Preserve quantitative results
- 9. Preserve sample metadata
- 10. Preserve protocols
- 11. Understand PXD accessions
- 12. Read the landing page
13–24 · Models and estimation
- 13. Use ProteomeCentral for discovery
- 14. Use file-level identifiers
- 15. Use spectrum-level identifiers
- 16. Distinguish complete and partial submissions
- 17. Respect embargo and status
- 18. Track dataset updates
- 19. Verify transfer integrity
- 20. Inspect licences and citation
- 21. Review sensitive information
- 22. Assess design compatibility
- 23. Reprocess transparently
- 24. Validate against known examples
25–36 · Diagnostics and interpretation
- 25. Report unavailable evidence
- 26. Separate availability from quality
- 27. Separate reuse from ownership
- 28. Connect to Primary Science
- 29. Connect to Secondary Science
- 30. Connect to mathematics
- 31. Connect to computing
- 32. Connect to pathways
- 33. Did you know?
- 34. Write a reader-ready methods note
- 35. Build a validation ladder
- 36. Finish with a reproducible bundle
Section 1 of 36
1. Start with the sharing question
Decide whether the goal is publication support, open reuse, controlled access, method benchmarking or teaching.
Working checkpoint — ProteomeXchange: Write the audience, data types and governance constraints. Keep PXD accession, repository record, file manifest, metadata, methods and retrieval receipts together.
For a classroom model, audit a fictional repository manifest.
Section 2 of 36
2. Understand the consortium model
ProteomeXchange coordinates submission and dissemination across member repositories rather than replacing them.
Working checkpoint — PXD accession: Name both the PXD accession and hosting repository. Read every reused result beside its sample design, source file, pipeline and dataset status.
For future study, connect biology to repositories and computing while checking official pathways separately.
Section 3 of 36
3. Use the official member list
The official page currently lists PRIDE, PeptideAtlas, MassIVE, jPOST, iProX and Panorama Public.
Working checkpoint — public repository: Check the live list before choosing a route. Preserve the original deposit and your downloaded snapshot; a landing page can change.
For scientific writing, cite PXD, repository, retrieval date, files, workflow and limits.
Section 4 of 36
4. Choose the appropriate repository
Repository services have different workflow scopes, interfaces and submission requirements.
Working checkpoint — proteomics dataset: Follow current official guidance for the experiment type. Separate public availability from data quality, reuse fitness and biological meaning.
For students, ask how a public dataset points back to real files and methods.
Section 5 of 36
5. Prepare a file manifest
A manifest connects every filename to its type, sample, analysis role, size and checksum.
Working checkpoint — data submission: Create it before upload and verify it after deposit. Treat file identifiers, versions and hashes as scientific inputs: one wrong download can invalidate the analysis.
For parents, ask whether openness also keeps consent, credit and limitations visible.
Section 6 of 36
6. Preserve raw evidence
Raw vendor files or open spectra enable independent reprocessing and instrument-level inspection.
Working checkpoint — data reuse: Keep acquisition context and immutable hashes. Keep PXD accession, repository record, file manifest, metadata, methods and retrieval receipts together.
For a classroom model, audit a fictional repository manifest.
Section 7 of 36
7. Preserve identifications
Identification results need spectra links, sequence database releases, scores, thresholds and software provenance.
Working checkpoint — ProteomeXchange: Use open result formats where practical. Read every reused result beside its sample design, source file, pipeline and dataset status.
For future study, connect biology to repositories and computing while checking official pathways separately.
Section 8 of 36
8. Preserve quantitative results
Abundance matrices need units, normalisation, study-variable definitions and missing-value policy.
Working checkpoint — PXD accession: Link summary values back to assay evidence. Preserve the original deposit and your downloaded snapshot; a landing page can change.
For scientific writing, cite PXD, repository, retrieval date, files, workflow and limits.
Section 9 of 36
9. Preserve sample metadata
SDRF-Proteomics can map biological characteristics and design factors to data files.
Working checkpoint — public repository: Validate every sample-to-file relationship. Separate public availability from data quality, reuse fitness and biological meaning.
For students, ask how a public dataset points back to real files and methods.
Section 10 of 36
10. Preserve protocols
Preparation, instrument and analysis protocols explain how files became results.
Working checkpoint — proteomics dataset: Include enough detail to reproduce key decisions. Treat file identifiers, versions and hashes as scientific inputs: one wrong download can invalidate the analysis.
For parents, ask whether openness also keeps consent, credit and limitations visible.
Section 11 of 36
11. Understand PXD accessions
A PXD identifier names a dataset-level record across the consortium.
Working checkpoint — data submission: Do not treat it as a file hash or spectrum identifier. Keep PXD accession, repository record, file manifest, metadata, methods and retrieval receipts together.
For a classroom model, audit a fictional repository manifest.
Section 12 of 36
12. Read the landing page
Status, repository, title, submitters, publication links and files supply context for the accession.
Working checkpoint — data reuse: Save a dated copy or metadata export. Read every reused result beside its sample design, source file, pipeline and dataset status.
For future study, connect biology to repositories and computing while checking official pathways separately.
Section 13 of 36
13. Use ProteomeCentral for discovery
The official site directs users to ProteomeCentral to browse public PXD datasets.
Working checkpoint — ProteomeXchange: Verify the authoritative repository before downloading. Preserve the original deposit and your downloaded snapshot; a landing page can change.
For scientific writing, cite PXD, repository, retrieval date, files, workflow and limits.
Section 14 of 36
14. Use file-level identifiers
Large deposits contain many raw, result and auxiliary files whose roles differ.
Working checkpoint — PXD accession: Record repository file ID, path, size and hash. Separate public availability from data quality, reuse fitness and biological meaning.
For students, ask how a public dataset points back to real files and methods.
Section 15 of 36
15. Use spectrum-level identifiers
A USI can point to a particular spectrum within a public dataset when supported.
Working checkpoint — public repository: Test resolution and retain the source file context. Treat file identifiers, versions and hashes as scientific inputs: one wrong download can invalidate the analysis.
For parents, ask whether openness also keeps consent, credit and limitations visible.
Section 16 of 36
16. Distinguish complete and partial submissions
Submission modes can provide different levels of processed-result support and reanalysis readiness.
Working checkpoint — proteomics dataset: Read current guidelines instead of inferring from accession alone. Keep PXD accession, repository record, file manifest, metadata, methods and retrieval receipts together.
For a classroom model, audit a fictional repository manifest.
Section 17 of 36
17. Respect embargo and status
A reserved or announced accession may not expose the same files as a fully public record.
Working checkpoint — data submission: Check status at the time of use. Read every reused result beside its sample design, source file, pipeline and dataset status.
For future study, connect biology to repositories and computing while checking official pathways separately.
Section 18 of 36
18. Track dataset updates
Metadata and files can be corrected or added after initial release.
Working checkpoint — data reuse: Record retrieval date and compare manifests. Preserve the original deposit and your downloaded snapshot; a landing page can change.
For scientific writing, cite PXD, repository, retrieval date, files, workflow and limits.
Section 19 of 36
19. Verify transfer integrity
Large downloads can truncate or corrupt without obvious visual signs.
Working checkpoint — ProteomeXchange: Check sizes and cryptographic hashes. Separate public availability from data quality, reuse fitness and biological meaning.
For students, ask how a public dataset points back to real files and methods.
Section 20 of 36
20. Inspect licences and citation
Open access still carries attribution, repository terms and sometimes reuse conditions.
Working checkpoint — PXD accession: Cite dataset, repository, submitters and publication. Treat file identifiers, versions and hashes as scientific inputs: one wrong download can invalidate the analysis.
For parents, ask whether openness also keeps consent, credit and limitations visible.
Section 21 of 36
21. Review sensitive information
Clinical or rare-condition metadata can remain identifying even without names.
Working checkpoint — public repository: Apply consent and governance review before reuse. Keep PXD accession, repository record, file manifest, metadata, methods and retrieval receipts together.
For a classroom model, audit a fictional repository manifest.
Section 22 of 36
22. Assess design compatibility
Different instruments, cohorts, preparations and analysis pipelines can produce incomparable values.
Working checkpoint — proteomics dataset: Define harmonisation criteria before merging. Read every reused result beside its sample design, source file, pipeline and dataset status.
For future study, connect biology to repositories and computing while checking official pathways separately.
Section 23 of 36
23. Reprocess transparently
A secondary workflow should preserve original files and record containers, databases, parameters and outputs.
Working checkpoint — data submission: Publish a reproducible manifest. Preserve the original deposit and your downloaded snapshot; a landing page can change.
For scientific writing, cite PXD, repository, retrieval date, files, workflow and limits.
Section 24 of 36
24. Validate against known examples
Small public fixtures can show whether a pipeline retrieves files and reproduces expected summaries.
Working checkpoint — data reuse: Keep successful and failed cases. Separate public availability from data quality, reuse fitness and biological meaning.
For students, ask how a public dataset points back to real files and methods.
Section 25 of 36
25. Report unavailable evidence
Missing metadata, inaccessible files or unresolved links are findings, not blanks to fill by guesswork.
Working checkpoint — ProteomeXchange: State their effect on the conclusion. Treat file identifiers, versions and hashes as scientific inputs: one wrong download can invalidate the analysis.
For parents, ask whether openness also keeps consent, credit and limitations visible.
Section 26 of 36
26. Separate availability from quality
A public dataset can still contain bias, incomplete metadata or flawed analysis.
Working checkpoint — PXD accession: Review design and evidence independently. Keep PXD accession, repository record, file manifest, metadata, methods and retrieval receipts together.
For a classroom model, audit a fictional repository manifest.
Section 27 of 36
27. Separate reuse from ownership
Public access supports new work but does not erase credit, community context or participant interests.
Working checkpoint — public repository: Use responsible citation and collaboration. Read every reused result beside its sample design, source file, pipeline and dataset status.
For future study, connect biology to repositories and computing while checking official pathways separately.
Section 28 of 36
28. Connect to Primary Science
A fictional dataset manifest lets younger learners separate an observation from the label attached to it. The goal is careful comparison, not memorising specialist jargon.
Working checkpoint — proteomics dataset: Use invented records, define every column and ask what remains unknown. Preserve the original deposit and your downloaded snapshot; a landing page can change.
For scientific writing, cite PXD, repository, retrieval date, files, workflow and limits.
Section 29 of 36
29. Connect to Secondary Science
ProteomeXchange dataset supports lessons about molecules, measurement, controls and the difference between evidence and explanation.
Working checkpoint — data submission: Trace one claim to its supporting record and state one limitation. Separate public availability from data quality, reuse fitness and biological meaning.
For students, ask how a public dataset points back to real files and methods.
Section 30 of 36
30. Connect to mathematics
Counts, coordinates, scores, ratios and uncertainty show why scientific data need units, definitions and compatible denominators.
Working checkpoint — data reuse: Recalculate one simple example and explain every symbol. Treat file identifiers, versions and hashes as scientific inputs: one wrong download can invalidate the analysis.
For parents, ask whether openness also keeps consent, credit and limitations visible.
Section 31 of 36
31. Connect to computing
Repository APIs, identifiers, manifests and checksums show how distributed services support reproducible research.
Working checkpoint — ProteomeXchange: Break one reference or field, run a check and repair it transparently. Keep PXD accession, repository record, file manifest, metadata, methods and retrieval receipts together.
For a classroom model, audit a fictional repository manifest.
Section 32 of 36
32. Connect to pathways
Public proteomics connects biology, data stewardship, statistics, repositories and software and can illuminate STEM education and career pathways.
Working checkpoint — PXD accession: Check current official education and qualification routes separately before making decisions. Read every reused result beside its sample design, source file, pipeline and dataset status.
For future study, connect biology to repositories and computing while checking official pathways separately.
Section 33 of 36
33. Did you know?
A short, readable ProteomeXchange dataset record can depend on a much longer chain of source data, identifiers, versions and validation receipts.
Working checkpoint — public repository: Follow one record backwards until every dependency is named. Preserve the original deposit and your downloaded snapshot; a landing page can change.
For scientific writing, cite PXD, repository, retrieval date, files, workflow and limits.
Section 34 of 36
34. Write a reader-ready methods note
State the specification or service, release, inputs, software, validation, uncertainty and the narrow claim the evidence supports.
Working checkpoint — proteomics dataset: Place the note beside the machine-readable material. Separate public availability from data quality, reuse fitness and biological meaning.
For students, ask how a public dataset points back to real files and methods.
Section 35 of 36
35. Build a validation ladder
Check bytes, structure, identifiers, controlled meanings, cross-references, scientific plausibility and a small known fixture in that order.
Working checkpoint — data submission: Stop at the first unresolved layer instead of polishing a later result. Treat file identifiers, versions and hashes as scientific inputs: one wrong download can invalidate the analysis.
For parents, ask whether openness also keeps consent, credit and limitations visible.
Section 36 of 36
36. Finish with a reproducible bundle
Deliver source data or stable identifiers, metadata, software versions, configuration, validator output, checksums and a plain-language limitations note.
Working checkpoint — data reuse: Make the published conclusion traceable and reviewable. Keep PXD accession, repository record, file manifest, metadata, methods and retrieval receipts together.
For a classroom model, audit a fictional repository manifest.
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