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  1. Docs/
  2. Guides/
  3. Datasets Workflow
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Guide: Datasets Workflow

Understand the signed-in datasets surfaces across feature builds, contract validation, lineage, and research dataset coverage.

Signed-in workflow

What this guide covers

Use this guide when you need to move from a watchlist or research idea into a reproducible dataset artifact with contracts, lineage, and coverage context preserved.

  • Use /datasets for feature builds, contract validation, artifact downloads, lineage review, and agent-context fetches tied to the current workspace.
  • Use /research/datasets for PIT-style research dataset builds and per-dataset coverage inspection.
  • Treat contracts, lineage, and coverage as part of the same reproducibility loop instead of separate cleanup steps.

Routes and surfaces

These are the current signed-in dataset surfaces that shape the build and validation workflow.

/datasets

Datasets home

Build feature datasets, review past runs, and manage downloadable artifacts from the main datasets page.

/datasets · contracts & validation

Contracts and validation

Select dataset contracts, validate requested columns, and confirm schema expectations before or after a build.

/datasets · artifacts & lineage

Artifacts and lineage

Inspect produced artifacts and lineage edges so the resulting dataset can be traced back through its inputs.

/datasets · agent context

Agent context

Fetch agent-ready context from dataset-linked instruments when the build will feed downstream automation or research.

/research/datasets

Research datasets

Build research-specific universe datasets with asset-class, date, and interval controls.

/research/datasets · coverage rows

Coverage review

Inspect coverage rows, expected bars, actual bars, and coverage percentage once a research dataset exists.

Recommended dataset loop

Use this sequence to keep dataset generation reproducible and reviewable.

01

Define the dataset shape

Start by choosing the watchlist or research scope, date window, interval, and feature or universe intent.

02

Validate against contracts

Check the selected contract and expected columns before treating the requested artifact as valid.

03

Build and review artifacts

Run the feature or research dataset build, then inspect the produced runs and downloadable outputs.

04

Confirm lineage and coverage

Review lineage edges and coverage rows so downstream consumers know what the dataset actually contains.

Build and validation controls

The dataset form is where reproducibility starts, not where it ends.

dataset scope

Watchlist, instrument, interval, and date inputs

Set the watchlist, instrument, interval, and time window first so repeated builds stay comparable.

feature spec

Feature-list definition

The feature-list input is part of the contract. Treat changes to it as a dataset-shape change, not just a convenience edit.

schema gate

Dataset contract selection

Pick the active contract before building so the requested columns and schema expectations are explicit.

build actions

Build, validate, and fetch-agent-context actions

The three action buttons do different jobs: build produces artifacts, validate checks schema shape, and fetch agent context packages the dataset for downstream consumers.

Artifact, lineage, and context controls

After a build finishes, the evidence panels determine whether the dataset is safe to reuse.

validation result

Contract status and violations

Use the contract status panel to distinguish between a reusable artifact and one that still has schema violations.

job history

Feature runs and job status

Review feature-run history so new builds are compared with prior attempts instead of treated as isolated jobs.

artifact list

Dataset artifacts, download, and lineage selection

The dataset list is where you choose which artifact to download and which one to inspect for lineage. Keep selection intentional.

provenance context

Lineage edges and agent-context preview

Use lineage edges and agent-context preview together when the dataset will feed research, backtests, or agent workflows.

Example dataset playbooks

Use these scenarios when a build needs a concrete reproducibility pattern instead of only the generic dataset loop.

feature build

Build a factor panel for one watchlist review

Define the watchlist, date range, and feature list up front, validate against the intended contract, then compare the new run with prior feature-run history before downloading anything.

Expected outcome

The dataset artifact is reusable because its shape, contract, and run history all agree.

schema change

Validate a contract revision before rerunning research

Select the next contract version, run validation before a full build, and inspect contract-status or violation output before you commit a downstream scan or label workflow to the new shape.

Expected outcome

You know whether the new schema is a safe upgrade or a breaking change before expensive rebuilds begin.

provenance trace

Prepare lineage evidence for an agent or backtest handoff

After the build succeeds, open lineage edges, choose the artifact intentionally, and fetch agent context only once the coverage rows and artifact selection match the downstream consumer.

Expected outcome

The handoff carries provenance, coverage, and the correct artifact instead of an unverified download.

Contracts and reproducibility reminders

  • Do not treat a dataset as reusable until the contract and selected columns agree with the intended workflow.
  • Use lineage and coverage review before you hand an artifact into research, backtests, or agents.
  • Keep watchlist or research-scope assumptions explicit so repeated builds stay comparable.
  • Use agent-context fetches and research datasets deliberately, since they package the dataset for different downstream consumers.

Next steps

Open Datasets

Jump into the signed-in datasets surface and review builds, contracts, artifacts, and lineage.

Research guide

Use the research workflow when the dataset is part of scan, label, or workbench iteration.

API Reference

Cross-check the generated contracts and endpoint surface that back dataset artifacts and workflows.

Last updated

Mar 24, 2026

Feedback

Report unclear guidance, stale contracts, missing coverage, or broken docs UI on this page.

Open feedback issue
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News Workflow
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Global Language Selection
On this page

Jump to the section you need without losing your place.

  • What this guide covers
  • Routes and surfaces
  • Recommended dataset loop
  • Build and validation controls
  • Artifact, lineage, and context controls
  • Example dataset playbooks
  • Contracts and reproducibility reminders
  • Next steps
Last updated

Mar 24, 2026

Feedback

Report unclear guidance, stale contracts, missing coverage, or broken docs UI on this page.

Open feedback issue