scientific-task-formulation

name	scientific-task-formulation
description	Use when you are starting a new mass spectrometry analysis task or feature request where the problem scope is unclear, the tool chain (e.g., OpenMS + Python + KNIME integration) is not yet selected, or acceptance criteria (code quality, test coverage, documentation) have not been established.
license	CC-BY-4.0
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evidence_spans	[]
claims	[]
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schema_version	0.2.0

scientific_task_formulation

Summary

Formulate a well-scoped research question and methodological workflow for mass spectrometry data analysis by defining clear objectives, selecting appropriate tools (e.g., OpenMS, KNIME), and establishing acceptance criteria (unit tests, functional tests, documentation) before implementation.

When to use

You are starting a new mass spectrometry analysis task or feature request where the problem scope is unclear, the tool chain (e.g., OpenMS + Python + KNIME integration) is not yet selected, or acceptance criteria (code quality, test coverage, documentation) have not been established. Particularly relevant when contributing new analysis algorithms or workflows to a large scientific software project.

When NOT to use

• The task scope is already fully defined and validated (e.g., existing algorithm with clear parameters and test suite)
• You are implementing a routine analysis on data with a known, proven workflow (no novel methodology formulation needed)
• The input data or output expectations are so ambiguous that formulation itself cannot proceed without external domain expertise or stakeholder input

Inputs

• Research question or analysis objective (text)
• Input data format specification (e.g., mzML, mzXML, mzIdentXML)
• Available computational resources and target platforms (Windows/macOS/Linux)

Outputs

• Formulated task specification document
• Selected tool and library components (OpenMS, Python, KNIME, etc.)
• Workflow step sequence (ordered list)
• Acceptance criteria checklist (unit tests, functional tests, documentation, coding conventions, Python bindings if applicable)

How to apply

Begin by formulating a clear research question aligned with your scientific objective (e.g., 'quantify peptides using label-free or SILAC protocols'). Identify the required input data formats (mzML, mzXML, mzIdentXML, etc.) and desired output (quantitation tables, identifications). Select tool components from the OpenMS ecosystem (C++ library, TOPPTools, pyOpenMS Python bindings, KNIME nodes) that cover your workflow. Document the required workflow steps (e.g., spectrum preprocessing, peptide identification, quantitation) and establish acceptance criteria: unit tests and functional tests for correctness, Python bindings if algorithm exposure is needed, and proper documentation (inline code comments, method descriptions). Before opening a pull request, verify adherence to coding conventions and completion of all test and documentation requirements.

Related tools

• OpenMS (Core C++ library providing LC-MS data management, analysis algorithms, and infrastructure for rapid tool development) — https://github.com/OpenMS/OpenMS
• TOPPTools (Pre-built ready-to-use analysis tools for proteomics and metabolomics data processing and mining) — https://github.com/OpenMS/OpenMS
• pyOpenMS (Python bindings to OpenMS C++ API enabling rapid algorithm development and integration into Python workflows) — https://github.com/OpenMS/OpenMS
• KNIME (Workflow engine for integrating and orchestrating OpenMS TOPPTools and analyses)
• TOPPView (1D, 2D, and 3D visualization tool for LC-MS data exploration and validation) — https://github.com/OpenMS/OpenMS

Evaluation signals

• Research question is explicit, measurable, and aligned with supported MS workflows (label-free, SILAC, iTRAQ, TMT, SRM, SWATH, DIA, or targeted protocols)
• Input/output data formats are specified and supported by OpenMS (mzML, mzXML, mzIdentXML, pepXML, mzTab, etc.)
• All workflow steps are documented, each with a designated tool component (OpenMS C++, TOPPTool, pyOpenMS, or KNIME node)
• Acceptance criteria include passing unit tests, functional tests, and adherence to OpenMS coding conventions; Python bindings are documented if algorithm exposure is needed
• Task specification can be traced to a corresponding issue or pull request in https://github.com/OpenMS/OpenMS with evidence of maintainer assignment and severity classification

Limitations

• The formulation process relies on internal OpenMS maintainer expertise to assign severity and assess blockers; no automated heuristic was detected to validate finding strength in the provided source materials
• No changelog or version compatibility constraints were mentioned in the source material, so long-term maintenance and backward-compatibility implications must be clarified separately
• Task formulation in OpenMS assumes familiarity with mass spectrometry concepts, file formats (mzML, mzXML, etc.), and quantitation protocols; domain expertise is not generated by this skill

Evidence

• [methods] Before you open the pull request, make sure you adhere to our coding conventions and have unit tests and functional tests as well as proper documentation and Python bindings: "Before you open the pull request, make sure you adhere to [our coding conventions] and have [unit tests and functional tests] as well as [proper documentation] and have Python bindings — nanobind"
• [readme] OpenMS is an open-source software C++ library for LC-MS data management and analyses offering infrastructure for rapid tool development: "OpenMS is an open-source software C++ library for LC-MS data management and analyses. It offers an infrastructure for rapid development of mass spectrometry-related software."
• [readme] OpenMS offers analyses for various quantitation protocols including label-free, SILAC, iTRAQ, TMT, SRM, SWATH: "OpenMS offers analyses for various quantitation protocols, including label-free quantitation, SILAC, iTRAQ, TMT, SRM, SWATH, etc."
• [readme] OpenMS supports easy integration into workflow engines like KNIME via the TOPPTools concept and unified parameter handling: "It supports easy integration of OpenMS built tools into workflow engines like nextflow, KNIME, Galaxy, and TOPPAS via the TOPPTools concept and a unified parameter handling via a 'common tool"
• [other] The severity is usually assigned by OpenMS maintainers and used internally to indicate if a bug is a blocker for a new release: "The severity is usually assigned by OpenMS maintainers and used internally to e.g. indicate if a bug is a blocker for a new release."