Empirical research increasingly runs on web data — prices, listings, reviews, ratings, availability, public filings. But researchers face a distinct set of needs that commercial data buyers don't: consistency across time, documented completeness for peer review, reproducibility, budget constraints, and the ability to validate a dataset before committing grant funds. This guide is for PhD students, faculty, research labs and think tanks who need web data they can defend in a paper.
The deliverable is evidence, not data. A pile of scraped rows isn't research-ready. A structured panel with a data dictionary and a documented completeness report is — that's the difference between something a reviewer questions and something they trust.
| Type | Examples |
|---|---|
| Price/market panels | Retail or e-commerce prices over time; grocery/inflation studies |
| Platform activity | Restaurant, hotel, marketplace or gig-platform activity & ratings |
| Review/text corpora | Reviews for NLP, sentiment and consumer-behaviour research |
| Public filings | Regulatory, real-estate (RERA), tender or company disclosures |
| Historical reconstructions | Point-in-time snapshots assembled into multi-year panels |
A graduate team needed a multi-year, restaurant-day historical panel around a 2020 policy shock for a difference-in-differences study. Actowiz assembled the history, structured it as a consistent panel, and delivered it with a completeness report documenting coverage and gaps — plus a validation sample and academic pricing. (Full story in our academic research case study.)
"The completeness documentation is what got the dataset past review. Without it, we'd have had a spreadsheet; with it, we had evidence."
— Graduate Researcher, leading university (name withheld)
Tell us your variables, units, time window and design. We'll scope a research-ready dataset with a pre-purchase sample, completeness documentation and academic pricing.
Talk to Us About Research DataActowiz collects only publicly available, non-personal information and delivers it with provenance, so your work stays on sound ethical and IRB footing. We're glad to discuss scope with your department or ethics board. Collection follows our responsible-scraping framework.
Yes — academic-appropriate pricing and a pre-purchase validation sample are standard for research projects.
Where point-in-time sources allow, yes — we reconstruct consistent historical panels, and document coverage and gaps.
Yes — data is structured to your design (e.g., unit-day panels) with consistent identifiers, plus a data dictionary.
That's the goal — completeness documentation, field definitions and provenance are provided so the dataset can be described transparently and defended.
Yes — public, non-personal data with provenance, suitable for IRB-sensitive work; we'll discuss scope with your ethics board.
Custom extracts, historical panels, completeness documentation and academic pricing.
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