ABC 2022, 9(4):416-427

Animal Behavior and Cognition DOI: https://doi.org/10.26451/abc.09.04.05.2022

©Attribution 3.0 Unported (CC BY 3.0)

Department of Biological Sciences, University of Cincinnati

Citation – Hobson, E. A. (2022). A low-cost solution for documenting, tracking, and verifying cage-level animal

husbandry tasks using wireless QR scanners and cloud-based spreadsheets. Animal Behavior and Cognition, 9(4),

416-427. https://doi.org/10.26451/abc.09.04.05.2022

Abstract – Animal care is a critical component underlying successful behavioral and cognition research.

Technological solutions for documentation and verification of care can aid in monitoring that activities are completed

to setting up our system: (1) purchase a QR scanner, (2) generate and print QR codes, (3) set up the central hub for

data input, (4) input data from QR scans, (5) filter and collate the raw data, and (6) summarize filtered data to verify

and track care. Paired with simple scripts in a cloud-based spreadsheet, scanned QR code data can then be easily

summarized in real time to provide verification of care. The flexibility of the system allows it to be customized to a

large range of species.

Keywords – Animal care, paperless documentation, verification, QR code

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Behavioral and cognition experiments are frequently conducted with animals in controlled

experiments. Most traditional concern around animal welfare has focused on mitigating any negative

impacts on animal health and wellbeing. However, there is growing awareness that “positive welfare”

(managing animal welfare beyond what is needed for immediate survival and basic mitigation of alleviating

suffering) should be the new standard of care (Fife-Cook & Franks, 2019). This focus on welfare is

particularly relevant to behavioral and cognitive studies because recent work has argued that animal welfare

conditions can affect cognitive capacity and cognitive function (Franks, 2018). In behavioral or cognition

studies, variation in welfare could then be an important factor that affects cognitive performance, and

variability in care that affects welfare could then negatively affect our ability to cleanly evaluate cognitive

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and behaviorally focused hypotheses. Properly managing the health and welfare of animals in behavioral

required to document care compliance as well as being a method for validating that care has been received

for each animal on the correct schedule. Quick response (QR) codes are widely used in many fields in

biology. QR codes can encode URLs as quick links to open websites, can link to online data entry forms,

or can contain simple text. Pairing QR codes with smartphone/smart device technology has become a

popular method to document animal care (e.g., Green et al., 2017), track specimens (e.g., Diazgranados &

Funk, 2013), or facilitate data entry (e.g., Oteyo & Toili, 2020).

Although using technology to document animal care more easily can save time spent reporting

basic care, the quality and consistency of care also need to be monitored and tracked. Technological

solutions can also help track and verify that care has been provided to each animal on the proper schedule.

Verification would help double-check that no animals have been accidentally overlooked and that care

(especially tasks that do not occur on a daily basis) are following the intended schedule. A verification

working with a new system in the laboratory and now have about 45 adult Northern bobwhite quail (Colinus

University of Cincinnati IACUC (protocol 21-02-23-01). The population is cared for by several laboratory

members and has tasks that need to be completed on different time intervals. For example, on a daily

schedule, lab members change each cage’s food and water and check the health of all the birds, on a weekly

schedule we clean the cage trays, and at least once every two weeks we provide enrichment. The need for

a method track all of these activities, verify that everything has been completed according to the schedule,

and to manage care across multiple people were the drivers behind the design of our system.

My system is a low-cost, flexible, and easy to use method that facilitates documentation of our

animal husbandry activities, enables real-time and remote-enabled verification that critical daily tasks have

been completed for every cage, and helps us monitor our longer-term tasks to make sure that our care team

the internet (to access Google Sheets) but does not require wireless internet access to use. A formatted and

publicly accessible example of both spreadsheets, containing all scripts used to automatically add the date

and time, filter, collate, summarize, and conditionally format the data, and the summary table is available

at https://bit.ly/3yycw1b. This file can be copied as a template and then modified for each laboratory’s

individual requirements as necessary.

There are six main steps to setting up the system, which are described below: (1) purchase a QR

scanner, (2) generate and print QR codes, (3) set up the central hub for data input, (4) input data from QR

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scans (Figure 1a), (5) filter and collate the raw data (Figure 1b), and (6) summarize filtered data to verify

Note. (a) The format of the data input sheet. (b) How data from the spreadsheet in (a) are filtered by category (here, into separate

sheets for health checks and food/water replacements). (c) The daily summary sheet, updated by date of last scan per cage and per

Step 1: Purchase QR Scanner Device

Data are inputted into the system by scanning QR codes into a spreadsheet using a handheld QR

scanner. These scanners are wireless and transmit over Bluetooth to a USB receiver plugged into a computer

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which acts as a central hub (see Step 3). Wireless QR scanners are available for between $30-100 USD. I

room, despite concrete walls and two closed doors between the scanner and the receiver (each QR scanner

model will have range specifications which should be checked to pick a model that will work well in the

desired scanning environment).

Step 2: Generate and Print QR Codes

Custom QR codes can be freely generated using the R package “qrcode” (Teh & Onkelinx, 2021).

QR codes can also be generated using several free websites for those not comfortable with R. In our system,

each code contains the cage ID and the category or task that is being scanned, separated by a space (for

example, “Cage01 good.health”). See Box 1 for the R script I used to generate tags for 20 cages, each with

seven different categories of tasks or activities that we scan and track. In my laboratory, we use the

one row of codes per cage. I used Microsoft Word to compile the tables: I found that this approach, while

added a colored background to each QR code that corresponded to each code’s category to help users more

quickly differentiate categories and reduce the chances of scanning the wrong code (see Figure 2). We

generally have good success scanning QR codes that are sized at 0.75 in.

Once printed, these tags are cut out, with each cage’s tags in a single strip and laminated for long-

term durability. I used an inexpensive thermal laminator to protect the tags. Tag strips are laminated with 5

mil laminator pouches. Laminators are widely available for $20-60 USD (e.g., Apache AL13 Thermal

Laminator). An optional paper trimmer greatly facilitates this step (e.g., Worklion Paper Cutter with

Security Blade). I used zip ties to secure the tags to the front of each cage. To avoid mis-scanning adjacent

codes, we notched out a paper card with a 1 in opening and use it to cover all QR codes except the desired

one when scanning.

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Step 3: Set Up Central Hub for Data Input

Data from the QR scanner are transmitted to a central computer via a USB-connected Bluetooth

receiver. Many kinds of computers can be used as the central hub: at a minimum, the computer needs a

USB port, an internet connection, and the ability to access Google Sheets. We use a basic desktop computer

on which we do other kinds of data entry. Any computer that can connect to the internet (either via wired

connection or via Wi-Fi to access Google Sheets) and which has a USB port (for the QR scanner’s Bluetooth

receiver) can be used as the hub. For example, small single-board computers like the Raspberry Pi could be

a very low-cost option.

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modifications to use prior to data entry, even if the cage numbers and/or the care categories have been

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Note. This is an example showing how “health” data are filtered. We want to have the “good health,” “problem,” and “recovering”

scans all compiled in this sheet so that we can double-check that health was assessed every day. In cell A2 of the “HEALTH”

intermediary sheet (highlighted below in yellow), the following code is entered, which will filter and import all relevant data from

the recent data, which is already filtered into the sheet “last.30.days.” The category-only data are in column E of the sheet

“last.30.days.”

For laboratories that want different animal care categories, these sheets by care type will require

change the name of the sheet to match each new category of care (i.e. for a new category called “dose” that

replaces “good.health”, change “HEALTH” to “DOSE” in Box 4), then change the filter code in cell A2 to

pull the correct data from the input data sheet (i.e., change “good.health” to “dose” and delete the code

mentioning the other health types).

Step 6: Summarize Filtered Data to Verify and Track Care

The final type of sheet is the summary and verification sheet, shown in Figure 1c. This sheet uses

the cage IDs to summarize the date of the last scan by cage and category. We use this summary sheet as a

since the last scan and the interval at which each category of care needs to be completed (Figure 1c). To set

or change the conditional formatting rules, select “Format” then “Conditional formatting” in the Google

Sheet to view the rule menu. In “Apply to range” specify the cells which the rule applies to (here,

conditional formatting is applied consistently across all cages within each care category). In “Format rule,”

choose “Custom formula is,” then add the conditional formatting desired and choose the response color to

use when the rule is satisfied. For an example of our two rules for color-coding to identify whether a task

has or has not been completed each day, see Box 6. In our system, we set each cage’s cell to green when

each care category has been completed within the time limit set by the conditional formatting (Box 6a)

while tasks which still need to be completed are set to yellow (Box 6b). For example, Box 5a shows that

all the health checks and changes of food and water have been completed “today” for all cages and are

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shaded green; if a cage has not been checked or had these changed on the current date, the cell color turns

As an additional indicator of whether tasks have been completed on schedule, we also use

conditional formatting rules to control and automatically update the color of the cells based on the time

since the last scan and the interval at which each category of care needs to be completed (Figure 1c). To set

or change the conditional formatting rules, select “Format” then “Conditional formatting” in the Google

Sheet to view the rule menu. In “Apply to range” specify the cells which the rule applies to (here,

conditional formatting is applied consistently across all cages within each care category). In “Format rule,”