Chapter 15: Keep the researchers close¶

In this chapter you will learn to:

Describe the roles in the research ecosystem and what each needs from a registry.
Search for researchers by mechanism and neighboring disease.
Keep a data request log that teaches you about your data.

Case study: A Room That Hung on Every Word¶

Many registry founders come to research through a diagnosis, and the researchers they know first are clinician-scientists: physicians who also see patients, whom families meet through their own care, second opinions, webinars, and trial announcements. I was no different. Even as a data analyst at a major academic medical center, I did not understand what basic scientists did, or that many of them work across diseases, following a mechanism or a drug target rather than a single gene. A mechanism under study in Parkinson's or Alzheimer's disease may also be at play in ALS. Clinical research is easier to picture, because many of us have filled out a study survey or taken part in a trial, and even there it takes effort to understand what a drug is acting on. It is tempting to tune out what is hard to follow. In many conditions, though, basic science paired with clinical data is what will make the difference.

My own interests have widened the same way. Age is a leading risk factor for many neurodegenerative diseases, so I follow research on aging, resilience, and survival, and on genes related to aging that may shape how a person experiences a neurodegenerative disease. Researchers who work on Alzheimer's disease are often interested in ALS as well. A patient advocacy group that searches only within its own monogenic community will not find them, and an AI summary of the literature will not find them either unless someone asks it to look across mechanisms and neighboring diseases.

I recently attended a conference of genetics experts working across pediatric neurodevelopmental conditions, not only Charlie's. When I described Charlie's phenotype, the room hung on every word, and I could see them working out how it fit with what they knew. Basic scientists who are not also clinicians do not see patients, and many of them want very much to learn from people with lived experience.

I listen the same way when people describe their journeys through the health system. A family tells me their child did get a wheelchair, but through a separate supplier, which is why the wheelchair never appears in the health record in any detail. I would not know that from the data. I know it because someone told me, and because I have been the parent ordering the wheelchair. Exchanges like that change how both sides understand the data.

Lessons for registry leaders¶

The researchers a family meets first are rarely the only ones who can help. Basic scientists working on a shared mechanism, and researchers in neighboring diseases, often want what a registry has, both its data and the experience of its community, and a registry leader who seeks them out turns a clinical data set into translational research.

Do not assume they know¶

Spell out what usually goes unsaid, in both directions. Do not assume participants know you are grateful, that their participation is helping, or that a published paper could not have happened without their data; tell them, in the consent form and every time after. Do not assume a basic scientist working on Alzheimer's disease understands what living with ALS or multiple sclerosis is like, and do not assume it would not interest them. In my experience it does, and you are among the few people who can tell them.

The rules¶

Learn the roles in the research ecosystem: clinician-scientists who see patients, clinical researchers, basic scientists, translational researchers, and the people who work with the U.S. Food and Drug Administration (FDA). Each needs something different from a registry.
Seek out basic scientists, not only clinicians. Many follow a mechanism or a drug target across diseases rather than one gene, so search by mechanism and by neighboring disease, not only by your condition's name.
Look beyond your own condition: neuroscience broadly, related diseases, and fields such as aging and resilience research, where shared mechanisms and risk factors bring in researchers who may not know your community exists.
Find researchers where they present and publish. Watch who speaks at conferences, including online sessions, and search PubMed regularly for who is publishing in your area. If you use AI to survey the field, ask it to look across mechanisms and neighboring diseases, and then talk to the people it finds.
Keep a list of the researchers interested in your condition or its mechanisms, across basic, clinical, and translational work, and stay in touch with each on a schedule.
Ask them what they would want from the registry and what would make them trust it, and build the answers into the plan.
Bring lived experience to them. Invite researchers to office hours or a standing call, describe phenotypes and patient journeys, and share what the data can and cannot answer.
Learn enough of the science to follow the conversation, even when it is hard, and ask researchers to explain.
Learn what a researcher needs in order to use the data: the dictionary, provenance, the access process, the citation. Have those ready.
Route every data request through a defined process: review by a data access committee, IRB approval, and a data use agreement. My Data Sharing in a Box playbook has sample language for each step. Review times range with the request and the committee, so tell researchers what to expect at the start.
Keep a record of every data request, its outcome, and what it taught you about the data.
Spell out what goes unsaid: tell participants their participation helps and credit them when their data contribute to a publication, and tell researchers what living with the condition is like.

What AI can do, and what only a person can do¶

What AI can do¶

Search the literature across mechanisms and neighboring diseases when you ask it to; summarize a researcher's publications before a call; prepare descriptive tables for review.

What only a person can do¶

Describe a phenotype in a way that starts a scientist thinking; hold the conversation; understand what the researcher is really asking; decide what to share.

Take-home¶

The researchers who can help most are often outside your disease and outside the clinic; seek out basic and translational scientists, bring them your community's experience, and tell everyone, participants included, what their part made possible.

Practice¶

Exercise¶

Open the researcher map and data request log.

Search PubMed for researchers working on your condition. Then search again by the mechanism or pathway involved, and by one neighboring disease that shares it.
Place each researcher on the map as basic, clinical, translational, or working with regulators, and note what they might want from your registry.
Record one data request, real or imagined, in the log: who asked, what for, how it moved through review by the data access committee, IRB approval, and the data use agreement, what you shared, and what the request taught you about your data. The Data Sharing in a Box playbook has sample language for each step of the review.

Check your work

A strong map includes researchers outside your own condition who work on the same mechanism, at least one basic scientist, and a note on what lived experience you could offer each of them. A strong log entry records each step of the review and who decided it, and what the request revealed, such as a field that was harder to explain than expected.

Your workbook page¶

The Researcher map and data request log page of the Slow Registry Workbook applies this chapter to your own registry. Fill it in online or print it and work by hand. The AI prompt library has a prompt for this chapter's topic, paired with the step only a person can do.

Check your understanding¶

Why search by mechanism as well as by condition?

Many basic scientists follow a mechanism or a drug target across diseases. A search limited to your condition misses researchers in neighboring diseases who could use your data.

What should participants be told when their data contribute to a publication?

That their participation helped, that you are grateful, and that the paper could not have happened without them. Do not assume they know.

Up next: Some of the conversations are with regulators and companies, and those have their own rules. Continue to Chapter 16.