Alternative Data Labby Agile Layer
Alternative Data Labby Agile Layer

Research-grade
alternative datasets,
built when APIs fall short.

We help researchers, economists, funds, and data teams collect, clean, normalize, and structure hard-to-source financial datasets — from fragmented APIs, dashboards, charts, exchanges, governance portals, and on-chain data.

alt-data-lab · pipeline
build #4127
dao_market_dataset.csv412 rows
UNI2020→complete
MKR2017→complete
AAVE2020→complete
NOUNS2021→partial
Crypto dataGovernance dataOn-chain dataHistorical financial dataResearch-ready outputsCrypto dataGovernance dataOn-chain dataHistorical financial dataResearch-ready outputs
The Problem01 / 08

The data you need
already exists.
It’s just scattered.

Valuable financial and crypto data rarely sits in one clean feed. It lives across systems that were never designed to be joined together.

  • 01APIsfragmented
  • 02Dashboardsfragmented
  • 03Chartsfragmented
  • 04PDFsfragmented
  • 05Web portalsfragmented
  • 06Block explorersfragmented
  • 07Exchange datafragmented
  • 08Governance platformsfragmented
  • 09Spreadsheetsfragmented
“Most teams do not need another scraper. They need a clean, validated, research-ready dataset.”
Services

Datasets we build,
end to end.

From governance records to deep price history — sourcing, extraction, cleaning, and delivery. Start from analysis, not plumbing.

01

DAO & Governance Datasets

Structured records of proposals, votes, treasuries, delegates, and participation across governance portals and DAO tooling.

02

Historical Token Price Data

Deep price history assembled and cross-checked across multiple financial APIs, with manual reconstruction where APIs fall short.

03

On-chain Data Extraction

Transactions, transfers, contract events, and wallet activity pulled directly from block explorers and node data.

04

Exchange & Market Data

Spot and derivatives market data — OHLCV, volume, and liquidity — normalized across centralized and decentralized venues.

05

Research Data Cleaning

Messy, multi-source data turned into validated, analysis-ready tables with consistent schemas and documented assumptions.

06

API + Manual Data Reconciliation

Gaps in automated sources filled with careful manual extraction, then reconciled against APIs for coverage and accuracy.

07

Fuzzy Matching & Entity Resolution

DAO names mapped to tickers, contracts, and entities using fuzzy matching and human review to eliminate mismatches.

08

CSV / Excel / Postgres Delivery

Final datasets delivered in the format your team works in — CSV, Excel, JSON, Google Sheets, or a Postgres-ready schema.

Case Study03 / 08

DAO Governance + Historical Market Dataset

We built a research-ready dataset for 400+ DAOs by mapping DAO names to token tickers, extracting historical market data, reconciling missing tokens, testing multiple financial APIs, normalizing prices across USD/BTC/ETH, removing duplicates, and using manual chart extraction where APIs failed.

Read the full breakdown
project readoutdelivered
DAOs processed400+
Data sources tested10+
Price historyHistorical coverage
NormalizationUSD / BTC / ETH
WorkflowAPI + manual extraction
DeliveryCSV / Excel, research-ready
Dataset Preview

What research-ready
actually looks like.

A sample of delivered structure — normalized, deduplicated, validated. Full datasets are scoped to your specific research question.

dao_market_dataset.csv
DAO NameTokenSourceDate RangePriceVolumeStatus
UniswapUNICoinGecko2020–Presentcompletecompleteprocessed
CompoundCOMPCoinMarketCap2020–Presentcompletecompleteprocessed
AaveAAVECoinGecko2020–Presentcompletecompleteprocessed
Nouns DAONOUNSCustom Extraction2021–Presentpartialavailableprocessed
MakerDAOMKRMulti-source2017–Presentcompletecompleteprocessed

// this is a sample preview. full datasets are built or delivered based on client requirements.

CSV / Excel delivery
API-ready structure
Postgres-ready schema
Documentation included
Validation notes included
How It Works

From research question
to research-ready dataset.

A repeatable process built around one goal: data you can trust, with every assumption documented.

01

Define research question

We start from the analysis you're trying to run — the entities, time range, and fields that actually matter for your work.

02

Identify available and hidden data sources

We map where the data lives: public APIs, dashboards, block explorers, governance portals, charts, and sources most vendors overlook.

03

Extract from APIs, web, charts, and on-chain sources

We pull data through automated pipelines and, where APIs fall short, careful web, dashboard, and chart extraction.

04

Clean, normalize, deduplicate, and reconcile

We standardize schemas, normalize across USD/BTC/ETH, remove duplicates, and reconcile conflicting sources into one trusted table.

05

Deliver research-ready dataset with documentation

You receive a validated dataset in your preferred format, with documentation covering sources, coverage, and known limitations.

Who We Help06 / 08

Built for people who
take data seriously.

Our clients share one thing: they need data that will stand up to scrutiny — in a paper, a memo, a model, or an investment decision.

01Economists
02Finance researchers
03Crypto funds
04Web3 analysts
05VC research teams
06PhD students
07Blockchain labs
08Data product teams
FAQ07 / 08

Questions,
answered.

Don’t see yours? Send a dataset request or book a call — we’re happy to talk specifics.

Yes. We specialize in creating custom datasets from fragmented public, API, web, and on-chain sources.

08 / 08 — get started

Have a dataset that doesn't exist yet?

Tell us the research question and where the data lives. We'll figure out how to build it — book a call or send a request and we'll reply within 24 hours.