Why Openness Wins: Five Research Habits That Give Molecular Biology Labs an Edge

For years, open science was pitched primarily as a moral argument: taxpayers fund research, so the public should be able to read it. That argument remains sound, but at McKnight Lab we have come to believe the pragmatic case is even stronger. Labs that share their work openly tend to collaborate more, cite and get cited more, and adapt faster to new findings. In a competitive US funding environment, openness is not charity — it is strategy. Here are five habits we recommend every molecular biology lab adopt in 2025.

1. Preprint Early and Often

The traditional publication cycle can stretch a year or more between submission and appearance. Services like bioRxiv have changed that calculus entirely. Posting a preprint timestamps your discovery, establishes priority, and lets the community engage with your work while the journal review proceeds.

Critics once worried that preprinting would invite being scooped. In practice, the opposite tends to occur: preprints attract collaborators, and early feedback frequently strengthens the final manuscript. Several NIH funding mechanisms and many tenure committees now explicitly recognize preprints as legitimate research outputs. For early-career scientists especially, a preprint record can make the difference in a competitive job market.

2. Share Raw Data, Not Just Figures

A published figure is an interpretation. The raw data behind it — the reads, the traces, the raw image files — are the actual evidence. Depositing datasets in repositories such as SRA, Zenodo, or Figshare with proper DOIs costs relatively little time and pays substantial dividends.

Journals increasingly require data availability statements, and funders including the NIH now expect data management and sharing plans. But beyond compliance, shared data multiplies the value of your work. Secondary analyses, meta-analyses, and methods validation studies all depend on accessible raw data, and datasets that get reused also get cited.

3. Embrace Registered Reports

Registered reports flip the traditional review model: authors submit their study design and analysis plan before collecting data, and peer reviewers evaluate the plan. If the design is sound, the journal commits to publishing the results regardless of outcome.

For hypothesis-driven molecular biology, this structure combats the subtle biases that creep into exploratory work — selective reporting, unplanned analyses, and the temptation to keep experimenting until a p-value cooperates. Labs that adopt preregistration, even in a lightweight form through the Open Science Framework, often report clearer hypotheses and cleaner analyses. Negative results, once buried, become publishable contributions that steer the field away from dead ends.

4. Publish Open Protocols

How many times has your lab spent weeks optimizing a protocol that another group had already perfected? Open protocol sharing through platforms like protocols.io or even a well-maintained lab wiki turns private know-how into community infrastructure.

We now version-control our core protocols and link them directly from our methods sections. The response has been striking: collaborators can reproduce our assays without lengthy email exchanges, and researchers who would never have found our papers discover our work through protocol searches. Detailed, open methods also serve as an honest quality signal — labs confident in their technique have nothing to hide.

5. Put Your Code in a Public Repository

Computational analysis is now inseparable from molecular biology, from single-cell pipelines to image quantification. Code that lives on a graduate student's laptop is both fragile and invisible. Code deposited in GitHub, with releases archived to Zenodo and DOIs cited in papers, is durable, discoverable, and reusable.

Public repositories also improve scientific rigor. When analyses are visible, errors surface faster and methods spread more widely. Reviewers increasingly appreciate reproducible code, and prospective trainees often evaluate labs by the quality of their public software contributions.

Openness as a Competitive Advantage

None of these practices requires heroism — they require habit. Start with one: post your next manuscript to bioRxiv, or archive the dataset behind your next paper. The cumulative effect is a lab whose work travels further, attracts stronger collaborators, and withstands scrutiny better.

The old model treated openness as a cost borne for the common good. The evidence increasingly suggests something different: in modern molecular biology, the labs that share are the labs that win.