Building your list

There are two ways to start, and you can mix them freely.

Most people arrive with a spreadsheet: one tab per university, a row per professor, columns of links collected over weeks. That's exactly what Import takes.

But you don't need one. If you can name a university, that's enough — §2.0.

2.0 Add a university by name#

Go to Universities → Add university, type the name, and add it. Everything else is optional: its departments, programs, labs and faculty are what the research finds.

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This is the ordinary way to start when you don't already have a list. "I should look at Illinois" is a complete thought; you should not have to assemble a spreadsheet of people who work there before you can act on it.

While you type, the app checks what you already have:

• An exact match is refused. Abbreviations count — typing UIUC when you already have University of Illinois Urbana-Champaign is caught, not duplicated.
• A similar name is flagged, not blocked. Adding "Illinois State University" shows you the five Illinois schools you already have, with links, and lets you continue. Both campuses of a state system are a legitimate thing to track; a workspace holding two records for the same school is not, and it splits your professors between them in a way nobody notices for weeks.

Once the record exists, ask your assistant to research it — see chapter 10.

2.1 Prepare your file#

Import handles one university per file. Export each tab of your spreadsheet as its own CSV, then bring them in one at a time. It's a few more clicks, but it means a bad file only ever affects one university.

Recognised columns (names are matched case-insensitively, and extra whitespace is fine):

Anything you leave blank is left blank. Any column the app doesn't recognise is reported back to you rather than silently dropped, so a typo in a header is visible instead of mysterious.

Your row order is kept. If your sheet is ordered by priority, that priority survives the import and shows up in the research queue.

2.2 Run a dry run first#

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1. Go to Import.
2. Paste the URL of the sheet you exported from into Source sheet URL. This is only recorded so you can trace a row back to where it came from later.
3. Put the university's name in University / tab name — "Georgia Institute of Technology", not "Sheet3". If you pick the CSV file first, the filename is used as a starting point.
4. Choose your CSV file.
5. Click Dry run.

A dry run changes nothing. It tells you exactly what a real import would do:

Read the Issues list at the bottom. Each entry names the row number and the problem, so you can fix your spreadsheet and re-run the dry run until it's clean.

"Universities created: 2" for one file almost always means the name is spelled two different ways in your sheet, or you typed a different name than the one already in your workspace. Fix the name and dry-run again.

2.3 Commit the import#

When the dry run reads the way you expect, click Commit import. The same report appears, this time describing what actually happened, and your new rows show up under Universities and Professors immediately.

Repeat for each remaining file.

Anything flagged waits for you#

Rows the import wasn't confident about — a name that closely matches someone already on your list, a malformed link — are kept and flagged rather than dropped or merged. They collect under Review in the sidebar, where you decide whether each one is a new person or the same person you already had. Nothing is thrown away while it waits.

2.4 What your list looks like afterwards#

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Universities gives you a row per institution: how many faculty you have, how many have been researched, how many came back as strong matches, how many you've contacted, and how many applications you've opened there. The Researched column is the one to watch early on — it's your progress bar.

Click a university for its detail page. It has five tabs, and the counts in the tab labels tell you what is there before you open anything:

• Overview — what you imported and where it came from.
• Departments — the schools and departments recorded for it.
• Professors — its faculty, in research-queue order.
• Programs — the degree programmes, with their deadlines.
• Labs — research groups, with their leads.

Departments, programs and labs start empty after an import, because a spreadsheet of professors does not describe the institution around them. They fill in when you research the university rather than its individual faculty — see chapter 3.

Going a level deeper#

Each of those is a page in its own right, and the hierarchy is worth knowing because it is where "why this department" comes from:

• A department page repeats the same shape one level down: Overview, Professors, Programs and Labs scoped to that department. This is the level most applications actually ask about — you apply to a department's programme, not to a university.
• A program page carries the deadline, degree types and requirements, and links to any application you have opened against it.
• A lab page carries the group's focus and its members. A professor's lab is often the most concrete thing you can point at in an email, more so than the department.

You can reach all of them by clicking through from the university, and every one of them is editable by hand — anything you type is marked Yours and research will not overwrite it.

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Professors is the same idea across every institution at once. Freshly imported rows look sparse, which is correct — nothing has been researched yet. The columns fill in as you work:

The filter chips above the table are the fastest way to find work: Waiting on you, Researched, High match, Needs draft, Draft to review, Stale research. Click a chip to apply it, click it again to clear. Search matches on name.

Waiting on you comes first because it is the only one of the six that describes something blocked rather than something to start.

The Stage column#

One badge per professor, answering "where is this one up to". Sorting by it groups your list into what is done, what is moving, and what has not started.

Waiting on you outranks everything else. A professor who already has a match and a draft but new findings sitting in Review still shows Waiting on you, because that is the thing stopping them — showing Draft ready would hide it.

Rank is not the same as appointment#

Three separate facts hide inside a job title, and the list keeps them apart:

• Appointment — tenure-track, adjunct, emeritus, postdoc. What kind of post it is.
• Rank — assistant, associate, professor, distinguished chair. How senior.
• Administrative post — dean, department chair, director.

Someone can be all three at once, and commonly is. An associate dean is an administrative post usually held by a full professor — not an associate professor, which is why the two are recorded separately rather than read off the title. Holding an administrative post is never a reason to skip somebody.

Both Rank and the appointment stay blank on imported rows. Spreadsheets record the appointment type, not job titles, so there is nothing to read them from until research looks at a faculty page.

Changing something on a row#

Row values are read-only where they sit. To change one, open the row's menu (the ⋯ at the end) and choose Edit status — recruiting and research settings open together in a panel that has room to explain what each value does.

They used to be editable in the cell itself, which sounds convenient and went wrong in one specific way: with eleven thousand near-identical rows, a click one row off target changed the wrong professor and nothing told you.

The Data column#

This is the honesty column, and it's worth learning:

Never write to a professor off an Imported or Unchecked record. Getting a lab name or a paper wrong in a cold email is worse than not sending it.

What's next#

You have a list, and none of it is researched yet. That's the next step: researching professors →