From First Email to First Dataset: A 90‑Day Research Onboarding Plan

It’s Day 0. You’ve just decided you want research on your CV before applications go out—maybe you’re premed targeting MD‑PhD programs, or an M1 eyeing academic residencies later. You have no project, no mentor, and no data. Just the vague sense that “I need research.”

The goal over the next 90 days: move from your very first outreach email to having your hands on a real dataset and a concrete role in a project.

Below is your step‑by‑step, time‑bound onboarding map.

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Days 0–7: Clarify Your Targets and Prepare to Email

At this point you should not send a single email yet. You’re laying the groundwork so that when you do reach out, you sound focused and useful—not like “I’ll do anything.”

Day 0–1: Define your research “lane”

Spend 1–2 focused hours answering:

1. What fields genuinely interest you?
   • Examples:
     • Premed interested in pediatrics and public health → child obesity, vaccination uptake
     • M1 liking cardiology → heart failure outcomes, imaging, EKG AI
2. What kind of research is realistic in 90 days?
   • Fast‑moving options:
     • Retrospective chart reviews
     • Survey studies
     • Secondary analysis of existing databases
     • Case reports/series
   • Slower to start (often >90 days):
     • Wet lab bench work
     • Prospective clinical trials

Write down 1–2 primary areas and 1 backup.

Example:

• Primary: Emergency medicine outcomes research
• Secondary: Medical education in EM

Day 2–3: Build a shortlist of potential mentors

At this point you should be identifying people, not projects.

1. Use your institution’s website / PubMed

   • Search: “[your school] cardiology outcomes,” “[hospital name] emergency medicine resident research.”
   • Look for:
     • Assistant/associate professors (often more available than full professors)
     • Recent publications (last 2–3 years)
     • Evidence of working with students (students or residents as co‑authors)

2. Create a simple tracking sheet (Google Sheet or Excel) Columns:

   • Name
   • Department
   • Email
   • Recent project topics
   • Student co‑authors? (Y/N)
   • Priority (High/Med/Low)
   • Date emailed
   • Response
   • Follow‑up date

Aim for:

• 10–15 potential mentors at a large academic center
• 5–8 at a smaller institution

Day 4–5: Draft your research CV and email templates

At this point you should have something you can attach and something you can send quickly.

1. Streamlined 1‑page research CV Sections:

   • Education
   • Relevant coursework (statistics, epidemiology, data science, lab courses)
   • Skills:
     • “Basic R for data analysis (intro course, small projects)”
     • “Excel: pivot tables, basic formulas”
     • “SPSS user: univariate/multivariate analysis (coursework‑level)”
   • Any previous research, posters, or class projects (even if not medical)

2. Core cold email template (customizable) Keep it short; 3–5 sentences. Example:

   Subject: Medical student interested in [field] research

   Dear Dr. [Name],
   I’m a [premed/M1] at [School] with a strong interest in [specific area, e.g., cardiology outcomes and quality improvement]. I’ve read your recent work on [1 specific paper or project] and would love to get involved with your team if you are working on any projects suitable for a motivated student. I have experience with [1–2 concrete skills—Excel, basic R, literature reviews] and can commit [X hours/week] over the next several months. Would you have 15–20 minutes for a brief meeting to discuss potential opportunities?

   Best regards,
   [Name]
   [Year, School]
   [Phone]
   [Attached: 1‑page CV]

Prepare a second, even shorter follow‑up email (“Just checking in on my prior message…”).

Day 6–7: Final targeting and ready‑to‑send list

At this point you should have:

• 1 sheet with 10–15 names
• 1 polished CV
• 1–2 email templates

Check:

• Have you included at least a few faculty with obvious student co‑authors?
• Do you have at least 2 different specialties in case one group is non‑responsive?

Tomorrow you’ll start sending.

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Days 8–21: First Emails, First Meetings, First Commitments

The goal for this two‑week window: secure 1–2 active mentors and a specific project you can plug into by around Day 21.

Day 8–9: Send the first wave of emails

At this point you should send in batches, not all at once.

• Email 5–7 highest‑priority mentors first
• Customize 1–2 sentences for each:
  • Mention a specific paper: “your 2023 JAMA Cardiology article on post‑MI readmissions”
  • Or a project on their lab webpage

Track:

• Date sent
• Auto‑replies/vacation messages
• Bouncebacks

Day 10–13: Handle responses and schedule meetings

Typical patterns:

• Some respond within 24–48 hours
• Others never respond
• A few delegate you to a fellow or resident

At this point you should:

1. Reply immediately to any positive response

   • Offer 3–4 time slots for a 20–30 minute Zoom/in‑person chat
   • Confirm if they prefer video or phone

2. If no responses by Day 12

   • Send a polite follow‑up to the first batch
   • Send new emails to the next 5–7 names on your list

Day 14–17: First mentor meetings

Your goal in each meeting: leave with either

• a clear project you can join, or
• a timeline for them to get back to you with options.

Prepare a 3‑point script:

1. 30‑second intro
   • Year, school, interests (very specific)
   • Time you can commit weekly
2. Show you’ve done homework
   • “I read your paper on [X], I was especially interested in [brief point].”
3. Ask targeted questions
   • “Do you have ongoing retrospective or survey projects where a student could help with data collection or analysis in the next few months?”
   • “Do your students typically work on one project at a time or join a lab group?”

Take notes immediately after each meeting:

• Projects mentioned
• Who actually supervises day‑to‑day (resident, fellow, post‑doc)
• Expected time frame and tasks

Day 18–21: Choose your lane and commit

By now you may have:

• 0 offers → expand outreach (see below)
• 1 “maybe later”
• 1–2 “we have something now”

At this point you should:

1. Prioritize projects that:

   • Already have IRB approval or are close
   • Have clear roles for students
   • Use existing data or simple data collection

2. Clarify expectations in writing Send a summary email like:

   • “From our meeting, my understanding is that I’ll help with [tasks] on the [project name] project, working primarily with [resident/fellow]. I can commit ~5–7 hours/week. Our initial goal is to [e.g., clean dataset by X date, submit abstract by conference deadline].”

3. If still mentor‑less by Day 21

   • Expand to:
     • Department research coordinators
     • Program directors
     • Senior residents with known projects
   • Example email opener:
     • “I’m an M1 very interested in [field] research and was wondering if there are any ongoing resident or faculty projects that might be a good fit for a student.”

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Days 22–45: Onboarding, Approvals, and Skill Boot‑Up

The next 3 weeks are about becoming “actually useful” on your chosen project and getting through institutional steps.

Day 22–25: Formal onboarding and access

At this point you should be knocking out all bureaucratic tasks fast.

Checklist:

• IRB inclusion:
  • Are you being added to an existing IRB protocol?
  • Do you need to complete human subjects training (CITI, etc.)?
• Data access:
  • EMR access level required?
  • REDCap or other database logins?
• Research team introduction:
  • Group email or meeting where you’re introduced?
  • Clear point person for day‑to‑day questions?

Ask directly:

• “What training modules do I need to complete to work with this dataset?”
• “Who should I ask when I get stuck with data or workflow questions?”

Day 26–30: Understand the project and data structure

Before touching data, you must understand the “story” of the project.

At this point you should schedule a 30–45 minute dedicated session with your point person to cover:

1. Project overview

   • Research question
   • Primary outcome(s)
   • Main hypothesis

2. Study design basics

   • Retrospective vs prospective?
   • Inclusion/exclusion criteria?
   • Key variables (age, diagnosis, intervention, outcome, etc.)

3. Data roadmap

   • Where is the data stored? (REDCap, Excel, EMR reports)
   • Who originally collected it?
   • Is there a data dictionary?

Ask for:

• A copy of the IRB protocol (even if you just skim)
• A recent abstract/manuscript from the same group to see style and expectations

Day 31–35: Skill tune‑up (targeted, not generic)

At this point you should do just‑in‑time learning tied to your project.

Common scenarios:

1. Chart review project

   • Skills:
     • Navigating EMR efficiently
     • Applying inclusion/exclusion consistently
     • Abstracting variables reliably
   • Ask for:
     • Sample of 5–10 charts with “gold standard” abstraction
     • A data abstraction manual or checklist

2. Dataset already available

   • Skills:
     • Cleaning in Excel/R/SPSS
     • Identifying missing or inconsistent data
   • Do:
     • 1–2 hours of focused practice on:
       • Filters, pivot tables (Excel)
       • Importing CSV and simple summaries (R or Python)

3. Survey study

   • Skills:
     • Using survey platforms (Qualtrics/REDCap)
     • Basic descriptive statistics
     • Response rate tracking

Ask your mentor:

• “What specific tools should I learn for this project—Excel, R, SPSS, something else?”
• “Is there a previous student’s codebook or sheet I can model?”

Day 36–45: Start your first real task

By now you should be past theory and into action.

Typical first tasks:

• Extract data from 20–30 charts
• Clean a subset of variables in an existing dataset
• Pilot test a survey with a small internal group
• Build a preliminary data dictionary (if none exists)

Structure your work:

• 2–3 short working blocks per week (60–90 minutes)
• Weekly micro‑update to your point person:
  • “This week: reviewed 25 charts, abstracted 20; questions about [X]. Next week: aim for 15 more charts.”

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Days 46–75: Scaling Up to a Real Dataset

Now you move from “trying things” to substantial, consistent progress. The aim: by Day 75 you should have contributed to a meaningful portion of the dataset or data collection.

Day 46–50: Quality check and correction loop

At this point you should not just keep cranking out data without feedback.

Ask for a formal spot‑check:

• Your mentor or resident reviews:
  • 10–15 of your charts
  • Or a slice of your cleaned dataset
• Compare your abstraction with theirs:
  • Where did you differ?
  • Are your operational definitions correct?

Update:

• Data abstraction guide
• Variable definitions
• Any codebooks or notes

Document examples of ambiguous cases (e.g., “borderline sepsis,” “uncertain diagnosis date”) so you can handle them consistently.

Day 51–60: Move into steady‑state data collection/cleaning

At this point you should know:

• How many records exist
• Your personal weekly capacity
• The team’s target timeline

Set a concrete goal with your supervisor:

• “I will abstract 20–30 charts per week”
• Or “I will clean and verify 200 rows per week”

Build a simple progress tracker:

• Columns: Date, number of records completed, cumulative total
• Use it in your weekly check‑ins

Day 61–68: Begin basic descriptive analysis (if allowed)

If your dataset is far enough along, this is when you transition from mere data entry to understanding the data.

At this point you should:

• Generate simple summaries (with guidance):
  • Mean/median age
  • Gender distribution
  • Frequencies of key diagnoses or interventions
• Make basic tables:
  • Table of baseline characteristics
  • Simple bar charts or histograms (if you’re comfortable)

Discuss findings with your mentor:

• “Anything surprising?”
• “Do these patterns match what you expected clinically?”

Ask:

• “What’s the planned analysis plan for this project?”
• “Are there any secondary questions we might explore given this data?”

Day 69–75: Clarify your role in next steps (abstracts, manuscripts)

By now you’re more than just “extra hands.” You’re becoming part of the intellectual process.

At this point you should have a direct conversation about:

1. Authorship expectations

   • “Given my contributions so far and planned work, what authorship position is realistic if this becomes an abstract/manuscript?”
   • Get a clear, honest answer; many teams have standard expectations.

2. Upcoming deadlines

   • Conference submission windows (e.g., ACEP, AHA, local research day)
   • Internal milestones:
     • “Complete dataset by [date]”
     • “First draft of abstract by [date]”

3. Your continued role

   • Will you help with:
     • Abstract writing?
     • Figure/table preparation?
     • Manuscript drafting of background or methods?

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Days 76–90: From “Helper” to Emerging Researcher

The last two weeks are where your work converts into something tangible: a near‑final dataset, clear progress toward a paper or presentation, and a sustainable relationship with your mentor.

Day 76–80: Lock down the dataset segment you own

At this point you should aim for closure on a defined segment of the data—something you can point to as “my contribution.”

Examples:

• “I completed abstraction for 150 of the 400 total patients.”
• “I cleaned and verified all lab values and outcomes fields.”
• “I managed all survey responses and data export through REDCap.”

Tasks:

• Final pass for missing data or obvious errors
• Document:
  • How you handled outliers
  • Any assumptions made
  • Any issues that remain unresolved

Share a brief summary to your supervisor:

• What’s done
• What’s pending
• What questions you still have

Day 81–85: Participate in writing or figure creation

Even if the manuscript will take longer, you can start contributing now.

At this point you should:

1. Offer to draft a small piece

   • One paragraph of Background
   • A draft of the Methods section describing:
     • Inclusion/exclusion criteria
     • Data collection process (especially what you did)
   • A basic Table 1 (baseline characteristics)

2. Get feedback early

   • Expect heavy edits—that’s normal
   • Watch for:
     • How they phrase research questions
     • What details matter in Methods
     • How they report statistics

This is where you start learning the “language” of academic writing in your field.

Day 86–90: Secure continuity and next steps

You’ve gone from first email to first dataset. Now you prevent this from becoming a one‑off experience.

At this point you should actively plan the next 3–6 months:

1. Follow‑up meeting with mentor Agenda:

   • What you completed
   • What’s next for the project
   • Clear expectations for your ongoing role and time commitment

2. Discuss expansion

   • Is there a related secondary analysis you could own?
   • Could this become:
     • A poster at a regional conference?
     • A short communication or letter to the editor?
   • Any chance to present at:
     • Departmental research day
     • Student research symposium

3. Ask for broader integration

   • “Would it be possible to join your regular lab or research meetings?”
   • “If another project starts in this area, could I be considered for involvement?”

Finally, update your CV:

• Add:
  • “Research assistant, [Department], [Institution], [Dates]”
  • Brief description: “Assisted with data collection and cleaning for retrospective study on [topic]; contributed to initial data analysis and drafting of [section].”

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Quick 90‑Day Checklist Summary

By Day 30 you should:

• Have a committed research mentor
• Be onboarded to a specific project
• Understand the study’s aims and data structure

By Day 60 you should:

• Be actively collecting or cleaning data each week
• Have had your work quality‑checked
• Know the project’s basic analysis plan

By Day 90 you should:

• Have contributed to a concrete portion of a real dataset
• Understand your likely authorship role
• Have a clear plan for the project’s next steps and your involvement

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FAQ (Exactly 3 Questions)

1. What if I do not get any responses to my first round of emails?
Wait 5–7 days, send a single brief follow‑up to each person, then widen your net. Contact department research coordinators, program directors, and senior residents known to publish. You can also look at recent student posters from your school’s research day, identify faculty mentors from those, and email them specifically mentioning their student’s work.

2. I’m a premed with no stats or programming background. Can I still be useful in 90 days?
Yes. Many teams need help with literature reviews, chart abstraction using structured forms, survey management, and manual cleaning of existing spreadsheets. Learn just enough Excel and basic research methods to handle your specific tasks; you can pick up more advanced skills over time as you’re exposed to real projects.

3. How many hours per week should I realistically commit during this 90‑day period?
For most premeds and early medical students, 3–7 hours per week is sustainable. Early weeks (emailing and meetings) might be lighter, while active data collection or cleaning phases may hit the upper end of that range. The key is to state your time honestly at the start and maintain consistent, predictable effort rather than short bursts followed by long gaps.

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Open your tracking sheet—or start one if you have not—and list three faculty you’ll email this week. Then draft and send the first email within the next 20 minutes while their names are still on your screen.