Lookup Columns: Simplifying access to scientific data

Lookup Columns: Simplifying access to scientific data

Lookup Columns is a no-code query builder on Benchling’s life sciences R&D platform, built to let scientists trace complex data relationships without admin support. Launched in 2021, it now powers 14 million lookups a year and serves as the foundation for data tracing across Benchling. I led the design with a PM and four engineers, taking it from concept to launch.

The problem

The data was there, just out of reach

Notebook is the foundation of Benchling, where scientists capture experimental work and generate data that informs R&D decisions. It integrates tightly with the rest of the platform, allowing scientists to create containers or register entities without leaving their work. But while Notebook pushed data out effectively, it offered limited ways to pull data back in. Getting the right information in context was slow and technical, and even a simple question like “Is this protein’s source cell line still viable?” could require digging through layers of samples, containers, and results.

Notebook is the foundation of Benchling, where scientists capture experimental work and generate data that informs R&D decisions. It integrates tightly with the rest of the platform, allowing scientists to create containers or register entities without leaving their work. But while Notebook pushed data out effectively, it offered limited ways to pull data back in. Getting the right information in context was slow and technical, and even a simple question like “Is this protein’s source cell line still viable?” could require digging through layers of samples, containers, and results.

The only tracing tool, Lookup Fields, fell short. It didn’t cover inventory, couldn’t work across table types, and relied on formula syntax that required schema expertise. Without a better option, scientists had to jump between records, constantly losing context and wasting time. In interviews, they were clear: they needed a faster, more intuitive way to bring the right data into their tables.

The only tracing tool, Lookup Fields, fell short. It didn’t cover inventory, couldn’t work across table types, and relied on formula syntax that required schema expertise. Without a better option, scientists had to jump between records, constantly losing context and wasting time. In interviews, they were clear: they needed a faster, more intuitive way to bring the right data into their tables.

Lookup Fields

Screenshot from the Benchling Help Center showing Lookup Fields, a formula-based tool that was hard to read and limited in scope.

Entity lineage map: Purified protein

Example of the complex, multi-layered data relationships scientists navigate. This map centers on purified protein, with each linked entity branching into its own network.

The solution

Lookup Columns: scientists’ data companion

The breakthrough came from how scientists themselves described “looking up” data. I translated their language into steps and designed a no-code query builder that lets them pull multi-layered, linked data directly into their tables. With plain language, a step-by-step builder, and helpful prompts, it keeps their focus on science instead of technical details.

The breakthrough came from how scientists themselves described “looking up” data. I translated their language into steps and designed a no-code query builder that lets them pull multi-layered, linked data directly into their tables. With plain language, a step-by-step builder, and helpful prompts, it keeps their focus on science instead of technical details.

That usability also unlocked something bigger: it made complex data lineage accessible at scale. As Benchling expanded upmarket into enterprise and downstream into development and manufacturing, data traceability became essential, and Lookup Columns met that need by surfacing the right data in context while keeping the experience approachable.

That usability also unlocked something bigger: it made complex data lineage accessible at scale. As Benchling expanded upmarket into enterprise and downstream into development and manufacturing, data traceability became essential, and Lookup Columns met that need by surfacing the right data in context while keeping the experience approachable.

To make it extensible, I partnered with PMs, engineers, and the Lab Automation designer to create LookupConfig, the flexible component behind Lookup Columns. By abstracting the configuration logic, we set the foundation for bringing the same no-code query experience to other parts of the platform.

To make it extensible, I partnered with PMs, engineers, and the Lab Automation designer to create LookupConfig, the flexible component behind Lookup Columns. By abstracting the configuration logic, we set the foundation for bringing the same no-code query experience to other parts of the platform.

Configure lookup

Lookup steps

Trace data lineage step by step, with options updating by linked item type.
Trace data lineage step by step, with options updating by linked item type.

Lookup filters

Filter lookup results, such as barcodes from containers with more than 20 mL.
Filter lookup results, such as barcodes from containers with more than 20 mL.

Lookup Tables

Lookup Tables bring reference data into entries and account for 21% of structured tables.
Lookup Tables bring reference data into entries and account for 21% of structured tables.

Extensible framework

Designed for reuse, LookupConfig now powers features across Benchling, such as Run Schemas (shown here).
Designed for reuse, LookupConfig now powers features across Benchling, such as Run Schemas (shown here).

The impact

Adopted widely, integrated deeply

Since launch, Lookup Columns have seen rapid adoption and strong feedback. In the past year alone, scientists created 14 million Lookup Columns and 1.5 million Lookup Tables across 7 million structured tables. The approachable, step-by-step design made it easy for scientists to configure queries themselves, driving quick adoption. Beyond usage, LookupConfig has become a core building block for data tracing across Benchling, now powering Notebook, Bioprocess, Workflows, Lab Auto, and Computed Fields.

Since launch, Lookup Columns have seen rapid adoption and strong feedback. In the past year alone, scientists created 14 million Lookup Columns and 1.5 million Lookup Tables across 7 million structured tables. The approachable, step-by-step design made it easy for scientists to configure queries themselves, driving quick adoption. Beyond usage, LookupConfig has become a core building block for data tracing across Benchling, now powering Notebook, Bioprocess, Workflows, Lab Auto, and Computed Fields.

This is the next level of Benchling implementation. Very intuitive and focused. I believe our scientists will be able to configure Lookup Columns themselves.

Enterprise customer

I really like the idea of using simple lookup steps, like looking up results of an entity, to get to the data I need. I didn’t know what to expect at first, but once I tried it, the design made a lot of sense.

Benchling Customer Success

With Lookup Columns, I can see the freeze dates of my VPC containers right in my table, so I know right away if they’re still good for my experiments without having to dig through inventory records.

Enterprise customer

Yi-Ying Lin, 2025

Yi-Ying Lin, 2025

Yi-Ying Lin, 2025