How to Connect Semantic Scholar to Excel (4 Methods Compared)

The Problem With Getting Workbook Data In and Out of Semantic Scholar

You have an Excel workbook full of research inputs — DOIs exported from a citation database, faculty author IDs from your department roster, topic keywords from last quarter's grant proposal. You need those lists run through Semantic Scholar and the results written back, without rebuilding the process every time.

Semantic Scholar is good at academic search and citation intelligence across hundreds of millions of papers. But routing structured inputs through it and back into your workbook is more work than it should be. The default flow is: export your list to CSV, paste into the search interface one item at a time, copy results, reformat, import back — and hope your column structure survived.

Below are the four common ways research teams handle this. Only the last one scales.

Method 1: Manual Copy-Paste (and CSV Export)

The default for Excel users. Export your input list to CSV, run searches manually in Semantic Scholar, copy the results rows you want, paste them into the workbook, and reformat the columns to match.

For a one-off lookup that's tolerable. For a systematic review spanning 30 foundational papers and their full reference lists, you're pasting and reformatting for the better part of a day. Every time a colleague updates the paper list, you start over.

The specific failure point for Excel users: the CSV round-trip introduces encoding issues, date formats shift, and citation counts arrive as text strings that break your downstream formulas until you convert them.

Method 2: Power Automate

Power Automate can call Semantic Scholar's API and write results back into an Excel workbook stored in OneDrive or SharePoint. You build a flow that triggers on a new row, calls the search or batch endpoint, maps the fields, and writes the output.

Quick check before going further: are you comfortable with HTTP connectors, JSON parsing expressions, and handling paginated API responses in Power Automate? If any of those feel unfamiliar, skip ahead to Method 3 or 4.

For those who stayed: the flow structure works. You authenticate via the API, call the right endpoint for your query type, parse the JSON response, and map each output field to its target column. It runs unattended once it's built.

The catch is what it takes to build it right.

A row-by-row trigger means 120 author lookups fire 120 separate HTTP calls.

If Semantic Scholar rate-limits call 47, Power Automate drops the result and moves on. You have no clean way to rerun only the failed rows without rebuilding the filtering logic.

You probably just need the h-index and affiliation columns filled in. You probably have no idea how to write a retry policy in Power Automate — and nobody hired you to learn it. So you put in a ticket to IT, and now you're waiting on a backlog queue while the grant proposal deadline holds.

And once you need to join across two workbook sheets — author IDs plus affiliation lookup — you've left what a single-trigger flow can handle.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best repeatable option was a category of add-ons that let you configure mappings, save an endpoint template, and re-run it. You picked your query type, tagged your parameters, mapped your output columns, and ran it on demand.

That was a genuine improvement over manual export-and-paste. Configs were reusable, the team could hand them off, formatting was consistent run to run.

But you still owned every configuration decision: which endpoint, which fields, how to handle pagination, how to manage the parameter for field-of-study filtering. The add-on handled the transport. The logic of what to ask and how was still on you. And when Semantic Scholar changed a response field name, your config silently broke until someone noticed the blank column and dug in.

This is the previous generation. It reduced the manual effort without removing the manual thinking.

The Easy Way: Using SheetXAI in Excel

There is a different approach. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands what you're looking at, and through its built-in Semantic Scholar integration it can run batch searches, enrich author rosters, pull citation networks, and write everything back — for you. No endpoint templates, no pagination handling, no formula cleanup. You just ask.

Example 1: Enrich a roster of researchers with author metrics

Search Semantic Scholar for each author name in column A and fill in their affiliation, h-index, and total citation count in columns B, C, and D

SheetXAI works down the list in column A, resolves each name to its Semantic Scholar author record, and fills the three adjacent columns without a single manual lookup.

Example 2: Pull ranked papers for a list of keywords

Search Semantic Scholar for each keyword in column A filtered to the Computer Science field of study, pull the top 10 results by relevance, and paste all results into a single flat table in Excel with a Keyword column so I can filter by topic

The result is a clean flat table — one row per paper, a Keyword column for pivot filtering, and citation counts as numbers not text.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with paper IDs, author names, or research topics. The Semantic Scholar integration is included in every SheetXAI plan.