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Research Data Merger
Merge messy research files without hiding the decisions
Merge participant, survey, roster, and longitudinal study files by ID. Review mismatches, duplicates, repeated measurements, and incomplete responses before exporting a documented, analysis-ready dataset. Everything stays local.
Hi, I built Research Data Merger for a problem that looks simple until you actually have to deal with it. A study may include a participant roster, a baseline survey, Week 4 and Week 8 exports, and another file containing demographics. The IDs almost match. Some columns repeat because they are new measurements. One participant appears twice. Another only exists in one source. A normal merge can produce a file very quickly, but it can also hide a lot of those decisions. Research Data Merger is designed to keep that preparation step visible. You choose the base table, review ID mismatches and duplicates, preserve repeated measurements, and optionally create filtered, de-identified, or reshaped copies afterward. There is no research-data upload step in Research Data Merger. Your files are opened, processed, and exported locally on your Mac. They are not sent to IMPLEMON, a cloud service, or an AI model. I’m keeping the tool intentionally focused on research-data preparation rather than analysis. I’d be especially interested to see how it fits real longitudinal and survey workflows.
About Research Data Merger on Product Hunt
“Merge messy research files without hiding the decisions”
Research Data Merger was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #151 on the daily leaderboard. Merge participant, survey, roster, and longitudinal study files by ID. Review mismatches, duplicates, repeated measurements, and incomplete responses before exporting a documented, analysis-ready dataset. Everything stays local.
On the analytics side, Research Data Merger competes within Mac, Productivity and Data & Analytics — topics that collectively have 769.7k followers on Product Hunt. The dashboard above tracks how Research Data Merger performed against the three products that launched closest to it on the same day.
Who hunted Research Data Merger?
Research Data Merger was hunted by Lareine Han. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Research Data Merger including community comment highlights and product details, visit the product overview.
Lareine Han
Hi, I built Research Data Merger for a problem that looks simple until you actually have to deal with it.
A study may include a participant roster, a baseline survey, Week 4 and Week 8 exports, and another file containing demographics. The IDs almost match. Some columns repeat because they are new measurements. One participant appears twice. Another only exists in one source.
A normal merge can produce a file very quickly, but it can also hide a lot of those decisions.
Research Data Merger is designed to keep that preparation step visible. You choose the base table, review ID mismatches and duplicates, preserve repeated measurements, and optionally create filtered, de-identified, or reshaped copies afterward.
There is no research-data upload step in Research Data Merger. Your files are opened, processed, and exported locally on your Mac. They are not sent to IMPLEMON, a cloud service, or an AI model.
I’m keeping the tool intentionally focused on research-data preparation rather than analysis. I’d be especially interested to see how it fits real longitudinal and survey workflows.