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Research

Lab Notebook Examples and Template

A good lab notebook entry lets someone else repeat your work and see what you saw: what you meant to do, what you actually did, the raw numbers, the calculations, and what you will do next. Below are five complete entries from different kinds of labs and the template they share.

Updated 2026-10-27

Copy and adapt

The template

Lab notebook entryTemplate

[Entry ID]: [Short title], {{date}}

Project: [[Project name]] Author: {{author}} Reviewer or witness: [name and date, if your lab requires one] Continues from: [[Previous entry]]

Objective

[One sentence: what this session should find out or produce.]

Expected result

[What you expect and why. Write it before you start, not after.]

Materials and equipment

  • [Material or reagent], [supplier, catalog number, lot number], [amount or concentration]
  • [Instrument], [ID or location], [key settings]

Procedure

Protocol: [[Protocol name and version]]

  1. [Step as you actually did it, with amounts, times and temperatures]
  2. [...]

Deviations from protocol: [What changed and why, or "none"]

Observations and raw data

  • [Time] [What you saw or measured, including anything unexpected]
  • Data files: [instrument, folder, file name]

Calculations

[Formula, values substituted, result with units]

Results

[What the data shows, in plain sentences. Failed runs count.]

Conclusion and next steps

  • [What this means for the objective]
  • [Next experiment or follow-up] @due(YYYY-MM-DD)

Corrections

  • [Date, initials: what was wrong, what is correct, why]

5 examples

Examples for different situations

The same structure, filled in for the situations people actually bring to it. Copy the one closest to yours.

1.Wet lab: colony PCR screen

When it fits: Use this when you are a graduate student screening clones, half the colonies will be empty vector, and in a month you need to know exactly which tube came from which colony.

Wet lab: colony PCR screenExample 1

JH-2026-112: Colony PCR screen of pJH14 transformants, 2026-10-20

Project: [[mCherry reporter construct]] Author: Ji-woo Han Reviewer or witness: Dr. Okonjo, initialed 2026-10-22 Continues from: [[JH-2026-111 Ligation and transformation]]

Objective

Find colonies carrying the 750 bp mCherry-NLS insert in the pJH14 backbone.

Expected result

With vector-flanking primers VF/VR, insert-positive colonies give about 1,060 bp; empty vector gives 310 bp (sizes from [[pJH14 plasmid map]]).

Materials and equipment

  • 2x Taq master mix, lot 24C-0918, freezer box B3
  • Primers VF and VR, 10 µM working stocks made 2026-09-30
  • LB-kanamycin plate from JH-2026-111, 23 colonies after overnight at 37 °C
  • Thermocycler 2 (bay 4), gel box A, 1 kb ladder lot 0731

Procedure

Protocol: [[Colony PCR v3]]

  1. Picked 8 colonies (C1 to C8) with sterile tips, touched each to a numbered patch plate, then swirled into 25 µL reactions: 12.5 µL master mix, 1 µL VF, 1 µL VR, 10.5 µL water.
  2. Negative control: no colony (NTC). Positive control: 10 ng pJH9 (known insert).
  3. Cycling: 95 °C 5 min; 30 cycles of 95 °C 30 s, 55 °C 30 s, 72 °C 75 s; 72 °C 5 min.
  4. 5 µL of each on a 1% agarose gel, 100 V, 40 min.

Deviations from protocol: Annealing at 55 °C instead of 58 °C. Thermocycler 1 was booked and block 2 runs about 2 °C hot by the last calibration, so I set it lower.

Observations and raw data

  • 14:10 Patch plate labeled P-1 to P-8, at 37 °C.
  • 16:45 Gel imaged. Bands near 1,050 bp in C2, C5, C7. Bands near 300 bp in C1, C3, C4, C8. No band in C6. NTC clean. Positive control at expected size.
  • Data files: GelDoc, /HanJ/2026-10/JH112_colonyPCR.tif

Results

3 of 8 colonies (C2, C5, C7) carry an insert of the right size. C6 failed to amplify, likely too little template.

Conclusion and next steps

  • 37.5% positive, in line with the last two ligations at this vector:insert ratio.
  • Miniprep C2 and C5 from patch plate, send for sequencing with VF @due(2026-10-21)
  • Keep C7 patch at 4 °C as backup @due(2026-10-21)

Corrections

  • 2026-10-21, JH: first line of Observations said "P-1 to P-9." There were 8 patches. Typo, not a ninth colony.

2.Chemistry teaching lab: aspirin synthesis

When it fits: Use this when you are an undergraduate in an organic lab course, the TA grades the notebook, and the numbers tell a story you did not expect (here a good yield with a bad melting point).

Chemistry teaching lab: aspirin synthesisExample 2

Experiment 6: Synthesis of acetylsalicylic acid, 2026-10-14

Project: [[CHEM 2310 Organic Lab]] Author: Nadia Rahman (partner: Theo Brandt) Reviewer or witness: TA signature at end of session Continues from: [[Experiment 5 Recrystallization of benzoic acid]]

Objective

Make aspirin from salicylic acid and acetic anhydride, then measure yield and check purity by melting point and the ferric chloride test.

Expected result

Percent yield of 60 to 85% based on past sections. Melting point near the literature value of 135 °C if the product is pure and dry.

Materials and equipment

  • Salicylic acid, 2.00 g (138.12 g/mol), bottle lot SA-2209
  • Acetic anhydride, 5.0 mL (1.08 g/mL, 102.09 g/mol), dispensed in the fume hood
  • 85% phosphoric acid, 5 drops (catalyst)
  • Hot water bath at 75 °C, Büchner funnel, Mel-Temp apparatus 3

Procedure

Protocol: lab manual pp. 61 to 64

  1. Combined salicylic acid, acetic anhydride and phosphoric acid in a 125 mL flask. Heated 15 min at 75 °C with swirling.
  2. Added 2 mL water dropwise in the hood to destroy excess anhydride, then 20 mL cold water. Cooled in ice 10 min until crystals formed.
  3. Vacuum filtered, rinsed with 2 x 5 mL ice water, air dried on the funnel 20 min.

Deviations from protocol: Manual says dry overnight. Lab period ended, so I weighed after 20 min. Product still felt damp.

Observations and raw data

  • Solid dissolved fully after about 4 min of heating.
  • White needles formed after scratching the flask.
  • Mass of product: 2.04 g
  • Melting range: 128 to 132 °C
  • Ferric chloride test: faint purple (salicylic acid control: deep purple; commercial aspirin: no color)

Calculations

  • Moles salicylic acid = 2.00 g / 138.12 g/mol = 0.01448 mol (limiting; anhydride is 5.40 g = 0.0529 mol)
  • Theoretical yield = 0.01448 mol x 180.16 g/mol = 2.609 g
  • Percent yield = 2.04 g / 2.609 g x 100 = 78.2%

Results

78.2% yield, but the low and broad melting range and the faint purple test both say the product is impure: some unreacted salicylic acid, and probably water.

Conclusion and next steps

  • The yield is overstated. Water adds mass and lowers the melting point.
  • Dry product in desiccator until next session, reweigh @due(2026-10-21)
  • Recrystallize half from ethanol and water, retake melting point @due(2026-10-21)

3.Engineering design notebook: robotics drivetrain decision

When it fits: Use this when a student robotics team has to record a design decision for judges and next year's members: options, test data, the choice and the reason, not just what got built.

Engineering design notebook: robotics drivetrain decisionExample 3

Entry 23: Drivetrain gear ratio, 2026-10-08

Project: [[Kestrel robot 2026-27 season]] Author: Mateo Ruiz (with Sofia Lindqvist, Eli Brooks) Reviewer or witness: Coach Harmon, 2026-10-09 Continues from: [[Entry 21 First scrimmage review]]

Objective

Decide whether to change the drivetrain from 36:60 to 36:48 gearing before the Oct 24 qualifier.

Problem

At the scrimmage we lost three of five races to the center goals. Drivers said the robot feels slow but never gets pushed.

Options considered

  1. Keep 36:60 (current).
  2. Change to 36:48. Faster, less torque.
  3. Keep the ratio, cut 1.5 lb of weight from the intake.

Calculations

Motors: 600 rpm cartridges. Wheels: 4 in omni, measured 4.125 in.

  • 36:60 wheel speed = 600 x 36/60 = 360 rpm. Top speed = 360 x 3.1416 x 4.125 in / 60 s / 12 = 6.48 ft/s
  • 36:48 wheel speed = 600 x 36/48 = 450 rpm. Top speed = 8.10 ft/s

Test

12 ft straight sprint, full battery, 3 runs each. Then a pushing test against last season's robot.

RatioRun 1Run 2Run 3MeanPush test
36:602.44 s2.39 s2.41 s2.41 s (4.97 ft/s)held position
36:482.13 s2.10 s2.14 s2.12 s (5.65 ft/s)pushed back 18 in

Decision

Option 2, 36:48, with a driver rule: do not push, go around. Reason: 0.29 s faster per 12 ft, and the races we lost were to the goal, not shoving matches. Option 3 rejected because the intake weight is in the rollers we just fixed.

Sketch of the new gear stack attached as gearbox-v3.jpg.

Next steps

  • Swap gears on both sides (Sofia) @due(2026-10-10)
  • Motor temperature check after 3 back-to-back matches (Eli) @due(2026-10-13)
  • Driver practice on avoiding pushes (Mateo) @due(2026-10-20)

4.Materials test log: 3D-printed PLA tensile specimens

When it fits: Use this when you run the same test on many specimens, the data is a table, and the interesting part is a weak specimen you are tempted to drop.

Materials test log: 3D-printed PLA tensile specimensExample 4

MT-0419: Tensile strength of PLA by print orientation, 2026-10-27

Project: [[Printed bracket qualification]] Author: Grace Achterberg Reviewer or witness: Hugo Lambert, reviewed 2026-10-28 Continues from: [[MT-0418 Specimen printing and conditioning]]

Objective

Measure tensile strength of PLA printed flat (XY) and upright (Z), following ASTM D638 Type I.

Expected result

Z specimens well below XY because they load across the layer lines. Datasheet XY strength is 50 MPa.

Materials and equipment

  • PLA filament, lot PLA-0825-B, printed 0.2 mm layers, 100% infill, 210 °C nozzle, 60 °C bed
  • 10 specimens (XY-1 to XY-5, Z-1 to Z-5), conditioned 40 h at 23 °C and 50% RH
  • Universal testing machine UTM-2, 10 kN load cell, crosshead 5 mm/min
  • Digital calipers CAL-07 (calibrated 2026-08)

Procedure

Protocol: [[Tensile test SOP rev C]]. Measured width and thickness of each gauge section at three points, used the mean. Tested to break.

Deviations from protocol: none.

Observations and raw data

SpecimenWidth (mm)Thickness (mm)Area (mm²)Peak load (N)Strength (MPa)
XY-113.023.2141.8210450.3
XY-212.983.1941.4205149.5
XY-313.053.2242.0213950.9
XY-413.013.2041.6198747.7
XY-512.973.1841.2207650.3
Z-113.033.2242.0101224.1
Z-212.993.2041.6112427.0
Z-313.043.2141.986820.7
Z-413.003.1941.5106925.8
Z-513.023.2342.1104724.9
  • All Z specimens broke cleanly along a layer line. Z-3 shows a visible void about 1 mm wide at the fracture face (photo MT0419_Z3.jpg).
  • XY specimens necked slightly before breaking.
  • Raw load curves: /materials/MT-0419/

Calculations

Strength = peak load / (width x thickness).

  • XY mean 49.8 MPa, standard deviation 1.2 MPa
  • Z mean 24.5 MPa, standard deviation 2.4 MPa
  • Z / XY = 0.49

Results

Upright specimens reach about half the flat strength. Z-3 is the lowest and has a print defect. It stays in the data set; excluding it would raise the Z mean to 25.5 MPa, and the defect is the kind real parts will have too.

Conclusion and next steps

  • Brackets must be printed so the main load runs along the layers, not across them.
  • Rerun 5 Z specimens from a fresh spool to see if voids repeat @due(2026-11-06)

5.Software experiment log: JSON serializer and API latency

When it fits: Use this when the lab is a staging server, the experiment is a performance change, and the record has to pin down the commit, the load and the noise so the result holds up in code review.

Software experiment log: JSON serializer and API latencyExample 5

EXP-031: orjson vs stdlib json on the orders endpoint, 2026-10-23

Project: [[Orders API latency]] Author: Arjun Mehta Reviewer or witness: reviewed in PR 2214 Continues from: [[EXP-030 Profiling the orders endpoint]]

Objective

Find out whether replacing the standard library json encoder with orjson lowers p95 latency of GET /orders.

Expected result

EXP-030 showed 22% of request time in serialization. Expect p95 to drop 10 to 20%.

Materials and equipment

  • Baseline: commit 4f2a9c1. Candidate: commit 8d71e03 (only change: encoder)
  • Staging, 2 app instances, 4 vCPU each; database snapshot from 2026-10-20
  • k6 load test, 50 virtual users, 5 min per run, script load/orders.js

Procedure

  1. Deployed baseline, warmed up 2 min, ran k6 three times with 3 min between runs.
  2. Deployed candidate, same steps.
  3. Diffed 200 responses from each build.

Deviations from protocol: Baseline run 2 overlapped a staging database backup. Reran it at 15:40 and kept both results, marked below.

Observations and raw data

BuildRunp50 (ms)p95 (ms)Requests/s
baseline171184412
baseline2 (backup running, excluded)88236371
baseline2 rerun70179418
baseline372183409
orjson161152455
orjson260149461
orjson362153452
  • k6 summaries in /perf/EXP-031/
  • Response diff: same keys and values, but created_at changed from "2026-10-20 14:03:11" to "2026-10-20T14:03:11".

Calculations

Mean p95: baseline 182.0 ms, orjson 151.3 ms. Reduction = 30.7 / 182.0 = 16.9%.

Results

p95 down about 17%, within the expected range. The datetime format change is a contract change for clients.

Conclusion and next steps

  • Ship orjson, but not until the datetime format is decided.
  • Ask the mobile team whether their parser accepts the T separator @due(2026-10-27)
  • If not, add a serializer option to keep the old format and rerun one k6 pass @due(2026-10-30)

What every lab notebook entry needs

The lab notebook examples that universities and research institutes publish share a short list of requirements, whatever the field. Each section in the template exists because of a question someone will ask later.

  • ID, date and author. Which run was it, and who did it? Number entries so you can cite them in a report or a tube label.
  • Objective and expected result. What were you trying to find out, and what did you think would happen? Writing the prediction first is what lets you notice a surprise.
  • Materials with identifiers. Lot numbers, specimen IDs, instrument IDs and calibration dates. When a result will not repeat, this is the first place to look.
  • Procedure as performed. Link the protocol, then record what you did differently. "Followed protocol" with no deviations line is the most common reason a result cannot be reproduced.
  • Raw observations. Numbers, colors, times and oddities, written before you interpret them. Include the failed reaction and the empty lane.
  • Calculations. Formula, substituted values, result with units. A reader should be able to check your arithmetic without a calculator file.
  • Next steps. What this session sends you to do next, with a date.

Lab notebook vs engineering notebook

A lab notebook answers "what happened?" An engineering notebook also answers "why did we build it this way?" The engineering notebook template is the same skeleton with three additions, all visible in the robotics example: the problem in the team's own words, the options considered, and a decision with its reason. Sketches and CAD screenshots go in as attachments. Competition judges and design reviewers read these notebooks for the reasoning, so a page of photos with no rejected options scores poorly however good the robot is.

Translating paper rules to a digital notebook

Most notebook guides were written for bound paper notebooks. The rules still make sense on a screen; they just take a different form.

  1. No erasing becomes a Corrections section. Leave the original text. Add a dated, initialed line below with what was wrong.
  2. Numbered pages become entry IDs and links. "See page 14" becomes a link to the protocol note or the previous entry.
  3. Voiding blank space becomes one entry per session. Do not reopen last week's entry to add today's work; start a new one that continues from it.
  4. Taped-in printouts become attachments. Attach the gel image or instrument printout and write the file location of the original data.
  5. Table of contents becomes search and backlinks. You still benefit from consistent titles: ID, short description, date.

If your lab is regulated

Follow your institution's record-keeping rules first. GLP studies, clinical work, and many industry and patent-sensitive labs require a specific system, witness signatures on a schedule, and retention periods set by policy. In that setting, the official record lives where your rules say it lives. A notes app can still hold your planning, analysis and the questions you want to ask the data, if your lab permits it.

Common mistakes

  • Writing up at the end of the week. Memory fills gaps with what should have happened.
  • Results without raw data. "Worked" is not a result. The gel lane, the reading, the time.
  • Dropping the inconvenient data point. Keep it, flag it, explain it. The materials example shows how.
  • Inconsistent names. "Colony PCR," "cPCR" and "screen" for the same protocol make searching useless. Pick one.
  • No next step. An entry that ends without one is usually the start of a two-week gap.

When to use a different template

For notes on papers and ideas rather than experiments, use the research notes template. A protocol that others will follow step by step belongs in an SOP template, linked from your entries. For a running record of non-experimental work across a day, the work log template is lighter.

Customization tips

  • Write the expected result before you start

    Thirty seconds of prediction turns a session into a test. In the aspirin example, the expected melting point is what made a 78% yield look suspicious instead of good.

  • Record what you did, not what the protocol says

    Link the protocol and then write only the deviations. A separate Deviations line is where the explanation for next month's odd result usually sits, like the hot thermocycler block in the PCR entry.

  • Never delete, correct

    Paper notebooks forbid erasing; the digital version of that rule is a Corrections section. Leave the original line, add a dated line with your initials saying what was wrong and what is right.

  • Name the file, not just the result

    Instrument output, gel images and load curves live elsewhere. Write the instrument, folder and file name in every entry so the number in your notebook can be traced to raw data.

  • For engineering notebooks, record the options you rejected

    Judges, reviewers and next year's team want to know why you chose the design, which only makes sense next to the alternatives and the test that separated them.

  • One entry per session, linked in a chain

    A Continues from line on every entry makes the notebook readable as a sequence, the digital version of "continued on page 14."

In the app

How to do this in Scribelet

  1. Open Desk Settings, then Templates, tap Add Template, paste the template above and set the category to Research. Keep {{date}} and {{author}} in the header so every entry is dated and signed when you create it.

  2. Write each protocol once as its own note and link it from entries with [[Colony PCR v3]]. The protocol's backlinks panel then lists every experiment that used it, which is the quickest way to find out which runs used the old version.

  3. Attach the gel image, the instrument printout or a photo of your paper notebook page. Scribelet extracts the text from photos and PDFs so readings and labels become searchable.

  4. Turn next steps into tasks with - [ ] Miniprep C2 @due(2026-10-21). Open follow-ups appear in the Tasks view and on today's daily note until you tick them.

  5. Ask the AI chat "which runs this month used thermocycler 2, and did any fail?" It answers from your entries with citations to the specific notes.

Templates, template variables and AI generation are documented in Note templates.

Frequently asked questions

What does a good lab notebook look like?

Someone else in your field could repeat the work from it and see the same raw data you saw. That means dated entries, materials with lot or specimen IDs, the procedure as actually performed, raw observations before interpretation, calculations with units, and failures recorded alongside successes. The five examples above all follow that shape.

Can I keep my lab notebook digitally?

For coursework, personal research and most engineering teams, yes, if your instructor or lab allows it. Regulated labs are different: GLP studies, clinical manufacturing and many industry labs must follow their institution's record-keeping rules, which can require a bound paper notebook or a validated electronic lab notebook system that meets FDA 21 CFR Part 11. A general notes app does not replace that record; use it for planning, analysis and searching if your rules allow.

What is the difference between a lab notebook and a lab report?

The notebook is the raw, chronological record written during the work. A lab report is written afterwards for a reader and usually has five parts: introduction, methods, results, discussion and conclusion (often with an abstract and references). You write the report from the notebook, never the other way around.

What is an engineering notebook?

A design record rather than an experiment record. Alongside tests and data, it captures requirements, sketches, options considered, calculations and the reason each decision was made, so a reviewer or competition judge can follow how the design evolved. The robotics example above shows a typical entry.

Do lab notebooks still matter for patents?

Less for priority than they used to. Since March 2013 the US awards patents to the first inventor to file, not the first to invent. Dated, witnessed records still help show who invented what and when, for example in derivation disputes, which is why industry labs keep their witnessing rules.

Every run linked to its protocol, and every follow-up tracked

Save this lab notebook template in Scribelet, link each entry to the protocol and the previous run, and attach the gel or printout. Next month, ask your notes which runs used the old protocol. Encrypted, free to start.

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Research

Research Notes Template

Research notes go wrong in two ways: you cannot tell where a claim came from, or you cannot tell whether it is still true. This template ties every finding to a source and a confidence level, keeps observation apart from interpretation, and records when you last checked.

Work

Work Log Template

A work log records what you did, what it produced, and what got in the way, written on the day while the details are still there. Copy the template, keep the hours line if you bill time, and pick the work log example closest to your job.

Journals

Daily Log Template

A daily log is a timestamped record of one day: what you planned, what happened, what came up, and where tomorrow starts. Copy the template below, or start from the daily log example closest to your day.

Work

SOP Template (Standard Operating Procedure)

An SOP is only useful if a person who has never done the task can follow it and get the same result. This template puts the trigger, the checks and the escalation path next to the steps, and gives every procedure an owner and a review date so it does not quietly go stale.

Work

Decision Log Template

A decision log is a running record of what was decided, why, by whom, and what you expected to happen. This template adds the two fields most logs leave out: who disagreed, and a date to check whether the decision worked. Copy it for a team, or use the decision journal example for your own choices.

Guide

Note Taking App for Researchers and PhD Students: Notes You Can Still Trust in Year Three

A research project outlives the notes most people take for it: by year three you cannot tell where a claim came from or why you made a decision in year one. This page says which tool fits which researcher (and what should stay in your reference manager), then gives a setup for literature notes, a research log and a weekly routine that keeps both usable.

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