Have the following discussions explicitly, in writing, before the work begins:
Authorship
In many fields, authorship order carries specific meaning — first author is typically the primary contributor, last author the senior investigator. In mathematics and some other disciplines, authors are listed alphabetically and order implies nothing. Whatever the convention in your field, agree in advance on:
- Who is first author, and under what conditions that might change
- Whether the project might produce multiple papers, and how authorship rotates across them
- What contribution level justifies authorship versus acknowledgment
For biomedical fields, the ICMJE criteria are a commonly used reference point: substantial contribution to conception, design, acquisition, analysis, or interpretation of the work; drafting or critically revising it; final approval; and accountability for the work's integrity. Other fields have their own norms, but the underlying question is the same — what level of contribution earns authorship rather than acknowledgment?
Data and Intellectual Property
Determine who owns the data, who can use it for future projects, and what happens to derived datasets. This is especially important for collaborations crossing institutional lines, where university IP policies may conflict.
Communication Cadence
Agree on how often you'll have synchronous check-ins and what the expected response time is for asynchronous messages. Different researchers have very different norms about this. Mismatched expectations are a persistent source of friction that compounds over time.
What Makes Collaborations Last
The partnerships that survive beyond a single paper share a few characteristics:
Each person continues to need the other. A collaboration sustained only by goodwill eventually fades. One sustained by genuine mutual dependency — each person's work is better because of the other — tends to persist. This often means deliberately keeping your research questions linked rather than letting them drift apart after a paper is published.
Credit is unambiguous. Collaborations that produce visible, equitable credit for both parties are easier to sustain. When one collaborator feels underrecognized — on a paper, in a grant acknowledgment, in a departmental presentation — it erodes the relationship quietly and quickly.
Conflict has a resolution path. Disagreements about method, interpretation, or direction are normal. Partnerships without a clear way to resolve them tend to dissolve at the first serious dispute. Knowing in advance that you'll defer to the domain expert on methodological calls, or that disagreements go to a third colleague for input, reduces the cost of conflict considerably.
The collaboration evolves. The most durable research partnerships adapt as both researchers' interests develop. A collaboration that locks both parties into a fixed problem eventually becomes a constraint rather than an asset. The best collaborations have enough flexibility to follow interesting threads as they emerge.
Finding Your First Collaborator
If you're starting from scratch — new to a field, at a new institution, or deliberately trying to expand outside your existing network — the most direct path is to build visibility before you ask for anything.
Writing and sharing work publicly (preprints, conference presentations, public talks), commenting substantively on others' work, and participating in field-specific online communities puts you on researchers' radar before you reach out. A cold email is much warmer when the recipient has already seen your name.
Tools that make the discovery step faster: Streamlined AI indexes faculty profiles and publication histories, letting you filter by research area and institution to find researchers whose work complements yours. It's particularly useful when you're trying to identify expertise outside your home department or discipline.
The collaboration itself, though, still has to be built in the same way it always has — with a clear question, honest communication, and a willingness to invest time before the returns are visible.
Amos Oppong is an entrepreneur leveraging AI to solve everyday problems in academia. He is the founder of Streamlined AI.