5.2Theme A — Unity and diversity

Tool 2: Technology — IB Biology SL Revision Notes & Topical Questions

5 min read 12 parts Lesson 3 of 11 Unit quiz

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5.2 Tool 2: Technology — IB BIOLOGY SL notes: Use sensors, data loggers, spreadsheets, databases and simulations to collect and process biological data, and evaluate their limitations.Sensor (probe) = an input device that detects a physical or chemical change and converts it into…

Advantages
Temperature probe
Physiological monitors
Calibration
Sampling rate (Hz)
Rate from a graph

Part 1 of 10Sensors and data loggers

Sensor (probe)

Sensor (probe) an input device that detects a physical or chemical change and converts it into an electrical signal

Common IB sensors measure temperature, pH, light intensity, O₂, CO₂, pressure, heart rate and conductivity.

Data logger

Data logger a device that records sensor readings automatically at set intervals over a set period

It lets data be sent to a computer and formatted into a table.
The computer then calculates averages, plots graphs and finds gradients faster and more accurately than a person.

Advantages

Advantages frequent, precise, continuous data with less human error
A logger detects rapid or long-term changes a human could not record, such as overnight CO₂ levels in a leaf chamber.

Part 2 of 10Examples of sensors

pH meter

pH meter measures acidity or alkalinity as a pH value
pH is a measure of the concentration of hydrogen ions (H⁺).
It is used when investigating the effect of pH on enzyme activity.

Temperature probe

Temperature probe measures the temperature of a system or reaction and can replace a thermometer

Physiological monitors

Physiological monitors a spirometer for lung volumes and a heart-rate monitor during exercise

Other technologies

GPS and remote sensing for field mapping.
Camera traps for animal surveys.
Digital microscopes and image analysis software, such as ImageJ, for counting cells or measuring areas.
Software analysis of videos, such as joints in motion.

Part 3 of 10Using a sensor and data logger

Choose a suitable sensor, such as a dissolved O₂ probe, and calibrate it against known standards.

Connect the sensor to the data logger and set the sampling rate and total duration.

Set up the experiment, keep the controlled variables constant and start recording.

Change the independent variable, such as light intensity, and allow equilibration time before each recording period.

Export the data to a spreadsheet and calculate rates from gradients.

Plot graphs, calculate means and standard deviations, and apply statistical tests.

Calibration

Calibration a pH probe is calibrated in buffers of pH 4 and 7, and an O₂ probe in air (about 20.9%)

Sampling rate (Hz)

Sampling rate (Hz) number of readings divided by time in seconds

The interval between readings is 1 divided by the sampling rate.
It must be high enough to capture rapid changes.

Rate from a graph

Rate from a graph the gradient, change in y divided by change in time, for example % s^-1

Part 4 of 10Limitations and comparison

Limitations
- cost, the need for calibration, drift over time, probe response time and interference
- A CO₂ sensor can be affected by humidity.

Feature	Manual	Sensor and data logger
Frequency of readings	Limited	Very high
Human error	Reaction time and reading errors	Reduced, but calibration is needed
Duration	Only while the observer is present	Overnight or for days
Cost	Low	Higher

Part 5 of 10Spreadsheets and graphs

Spreadsheets and graphs

Spreadsheet

Spreadsheet software that organises data in rows and columns and uses formulae to process it

Raw data are entered, categorised and organised into columns and rows.
Formulae calculate mean, median, SD, % change and rate and reduce arithmetic mistakes.
Check that the cell ranges are correct.

Useful functions

=AVERAGE(range), =STDEV(range) and =T.TEST
- (range1, range2, 2, type)

Graphs

Graphs built-in functions draw graphs and charts to show trends, patterns and correlations
A continuous independent variable gives a line or scatter graph.
A categorical variable gives a bar chart.
Error bars show SD or the range of repeats.

Large datasets

Large datasets population data with large ranges are easier to handle on a computer

Part 6 of 10Colorimetry and calibration curves

Absorbance

Absorbance measured by a colorimeter and proportional to concentration within a range

Calibration curve

Calibration curve made from standards of known concentration so absorbance can be converted to concentration

Part 7 of 10Databases and bioinformatics

Database

Database a structured collection of data that can be searched, sorted, filtered and analysed quickly

Nucleotide databases such as GenBank and protein databases such as UniProt and PDB are freely accessible.
They are used to compare genes, find homologues and study evolution.
GBIF records where species occur.

BLAST

BLAST searches a database with a query sequence and ranks the hits by similarity
It is used, for example, to identify a species from DNA barcoding.

Percentage similarity

Percentage similarity identical positions divided by total aligned positions, × 100

Multiple sequence alignment

Multiple sequence alignment software such as Clustal lines up homologous sequences
The number of differences is used to build a cladogram.
This assumes mutations accumulate at a roughly constant rate, as in the molecular clock.

Part 8 of 10Using BLAST

Obtain the DNA sequence, for example from a barcoding gene, in FASTA format.

Open the NCBI BLAST website and choose nucleotide BLAST (blastn).

Paste in the sequence, select the database and run the search.

Examine the hits ranked by percentage identity and E-value, where a lower E-value is more significant.

Download the top sequences and align them to build a cladogram with tree-building software.

Part 9 of 10Models, simulations and molecular visualisation

Simulation

Simulation a computer model that represents a system with mathematical rules so variables can be changed and outcomes predicted
Examples are population growth, genetic drift, Hardy-Weinberg, epidemic spread and predator-prey cycles.
They allow many repeats quickly and let variables be changed that cannot be changed in real life.
They are only as good as their assumptions, so outputs should be checked against real data.

Mesocosm

Mesocosm a small enclosed model ecosystem in which environmental variables can be changed under control
It is used to predict what may happen in real ecosystems.

Molecular visualisation

Molecular visualisation software such as JSmol displays 3D structures from PDB files
It shows active sites, enzyme-substrate fit and levels of protein structure.
Colours can show α-helices, β-pleated sheets and active sites.

Part 10 of 10More from the specification

Digital simulation models in science
- computerized models simulate ecological population dynamics and genetic drift under variable environmental selection pressures.

Key terms

Term	Meaning
Sensor (probe)	A device that detects a physical or chemical quantity and converts it into an electrical signal.
Data logger	A device that records sensor readings automatically at set time intervals.
Sampling rate	The number of readings a data logger takes per unit time.
Database	An organised, searchable store of data such as GenBank, UniProt or PDB.
BLAST	Software that compares a query sequence with a database and scores the similarity of hits.
Sequence alignment	Arranging sequences so that homologous positions line up, with gaps where needed.
Simulation	A computer model of a system with rules, used to predict outcomes when variables change.
Mesocosm	A small enclosed model ecosystem used to study the effect of changing variables.

Avoid these mistakes

Assuming sensor readings are always accurate — sensors need calibration and can drift

Treating a simulation output as real data — it depends on its assumptions and must be validated

Choosing a low sampling rate for a rapid change — the logger would miss the change

Using a high BLAST E-value as a good match — a lower E-value means a more significant match

Last updated: 26 October 2026