IA data bank · open data

Open datasets for your Maths IA

Nine real datasets, each with its source, its licence and how to cite it. Open one in the grapher to fit a model, or download the CSV for Desmos, GeoGebra or a spreadsheet.

Atmospheric CO₂ at Mauna Loa, yearly mean (1959–2025)

The yearly mean concentration of carbon dioxide in the air at the Mauna Loa Observatory, Hawaii: the Keeling curve without its seasonal wiggle.

Variables	Year against CO₂ concentration (ppm)
Size	67 rows · Year 1959–2025
Source	NOAA Global Monitoring Laboratory, Mauna Loa CO₂ annual mean data
Licence	Public domain (US Government work). Free to reuse. Cite NOAA GML (and Scripps Institution of Oceanography) as the source.
Cite as	NOAA Global Monitoring Laboratory and Scripps Institution of Oceanography: Mauna Loa CO₂ record.
Models to try	straight line, quadratic, exponential

A question to start from: Is CO₂ rising faster than a straight line? Fit linear, quadratic and exponential models to 1959–2000, then test which one predicts 2001–2025 best. Worked-up idea: Modelling the Keeling curve: trend plus seasons · How strongly is global temperature linked to ln(CO₂)?.

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See the data (67 rows)

Year	CO₂ concentration (ppm)
1959	315.98
1960	316.91
1961	317.64
1962	318.45
1963	318.99
1964	319.62
1965	320.04
1966	321.37
1967	322.18
1968	323.05
1969	324.62
1970	325.68
1971	326.32
1972	327.46
1973	329.68
1974	330.19
1975	331.13
1976	332.03
1977	333.84
1978	335.41
1979	336.84
1980	338.76
1981	340.12
1982	341.48
1983	343.15
1984	344.87
1985	346.35
1986	347.61
1987	349.31
1988	351.69
1989	353.2
1990	354.45
1991	355.7
1992	356.54
1993	357.21
1994	358.96
1995	360.97
1996	362.74
1997	363.88
1998	366.84
1999	368.54
2000	369.71
2001	371.32
2002	373.45
2003	375.98
2004	377.7
2005	379.98
2006	382.09
2007	384.02
2008	385.83
2009	387.64
2010	390.1
2011	391.85
2012	394.06
2013	396.74
2014	398.81
2015	401.01
2016	404.41
2017	406.76
2018	408.72
2019	411.65
2020	414.21
2021	416.41
2022	418.53
2023	421.08
2024	424.61
2025	427.35

Atmospheric CO₂ at Mauna Loa, monthly mean (2023–2024)

Monthly mean CO₂ at Mauna Loa for two years: a rising trend with a yearly cycle as Northern Hemisphere plants grow and decay.

Variables	Month (1 = January 2023) against CO₂ concentration (ppm)
Size	24 rows · Month (1 = January 2023) 1–24. Months are numbered 1–24 from January 2023 so the data fit straight into a sine model.
Source	NOAA Global Monitoring Laboratory, Mauna Loa CO₂ monthly mean data
Licence	Public domain (US Government work). Free to reuse. Cite NOAA GML (and Scripps Institution of Oceanography) as the source.
Cite as	NOAA Global Monitoring Laboratory and Scripps Institution of Oceanography: Mauna Loa CO₂ record.
Models to try	sinusoidal, straight line

A question to start from: Model the two years as a straight-line trend plus a sine wave. What period, amplitude and phase do you get, and in which month is CO₂ highest? Worked-up idea: Modelling the Keeling curve: trend plus seasons.

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See the data (24 rows)

Month (1 = January 2023)	CO₂ concentration (ppm)
1	419.48
2	420.31
3	420.99
4	423.35
5	424
6	423.68
7	421.83
8	419.68
9	418.5
10	418.82
11	420.46
12	421.86
13	422.8
14	424.55
15	425.38
16	426.51
17	426.9
18	426.91
19	425.55
20	422.99
21	422.03
22	422.38
23	423.85
24	425.4

Global surface temperature anomaly, NASA GISTEMP (1880–2025)

How much warmer or cooler each year was than the 1951–1980 average, for the whole globe (land and ocean).

Variables	Year against Temperature anomaly (°C)
Size	146 rows · Year 1880–2025. Rounded to 0.01 °C.
Source	NASA GISS Surface Temperature Analysis (GISTEMP v4)
Licence	Public domain (US Government work). Free to reuse. Cite NASA GISS (GISTEMP Team) as the source.
Cite as	GISTEMP Team: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies.
Models to try	straight line, piecewise, cubic and polynomials

A question to start from: Has warming sped up? Fit one straight line to 1880–2025, then a two-piece model with a break you choose. Compare them with the sum of squared residuals. Worked-up idea: How strongly is global temperature linked to ln(CO₂)?.

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See the data (146 rows)

Year	Temperature anomaly (°C)
1880	-0.18
1881	-0.09
1882	-0.12
1883	-0.18
1884	-0.28
1885	-0.34
1886	-0.32
1887	-0.37
1888	-0.18
1889	-0.11
1890	-0.36
1891	-0.23
1892	-0.28
1893	-0.32
1894	-0.31
1895	-0.23
1896	-0.12
1897	-0.12
1898	-0.28
1899	-0.18
1900	-0.09
1901	-0.16
1902	-0.29
1903	-0.38
1904	-0.48
1905	-0.27
1906	-0.23
1907	-0.39
1908	-0.43
1909	-0.49
1910	-0.44
1911	-0.45
1912	-0.38
1913	-0.36
1914	-0.17
1915	-0.15
1916	-0.36
1917	-0.46
1918	-0.3
1919	-0.28
1920	-0.28
1921	-0.19
1922	-0.28
1923	-0.27
1924	-0.27
1925	-0.22
1926	-0.11
1927	-0.22
1928	-0.2
1929	-0.36
1930	-0.16
1931	-0.09
1932	-0.16
1933	-0.29
1934	-0.13
1935	-0.2
1936	-0.15
1937	-0.03
1938	-0.01
1939	-0.02
1940	0.12
1941	0.18
1942	0.06
1943	0.09
1944	0.2
1945	0.1
1946	-0.07
1947	-0.03
1948	-0.11
1949	-0.11
1950	-0.17
1951	-0.07
1952	0.01
1953	0.08
1954	-0.13
1955	-0.14
1956	-0.19
1957	0.05
1958	0.06
1959	0.03
1960	-0.03
1961	0.06
1962	0.03
1963	0.05
1964	-0.2
1965	-0.11
1966	-0.06
1967	-0.02
1968	-0.08
1969	0.05
1970	0.03
1971	-0.08
1972	0.01
1973	0.16
1974	-0.07
1975	-0.01
1976	-0.1
1977	0.18
1978	0.07
1979	0.16
1980	0.26
1981	0.32
1982	0.14
1983	0.32
1984	0.15
1985	0.12
1986	0.18
1987	0.32
1988	0.39
1989	0.27
1990	0.45
1991	0.41
1992	0.22
1993	0.23
1994	0.32
1995	0.45
1996	0.33
1997	0.47
1998	0.61
1999	0.38
2000	0.39
2001	0.53
2002	0.63
2003	0.62
2004	0.53
2005	0.68
2006	0.64
2007	0.66
2008	0.54
2009	0.66
2010	0.72
2011	0.61
2012	0.65
2013	0.68
2014	0.75
2015	0.9
2016	1.01
2017	0.92
2018	0.85
2019	0.98
2020	1.01
2021	0.85
2022	0.89
2023	1.17
2024	1.29
2025	1.19

Global mean sea level change (1880–2013)

The reconstructed rise in global average sea level, relative to 1880, from tide gauges around the world.

Variables	Year against Sea level change since 1880 (mm)
Size	134 rows · Year 1880–2013. Converted from inches to millimetres (× 25.4) and rounded to the nearest mm.
Source	US EPA Climate Change Indicators: Sea Level (CSIRO reconstruction)
Licence	Public domain (US Government work). Free to reuse. Cite the US EPA (and CSIRO, the original reconstruction) as the source.
Cite as	US Environmental Protection Agency, Climate Change Indicators: Sea Level; data from CSIRO.
Models to try	straight line, quadratic, piecewise

A question to start from: Is sea level rise accelerating? Compare a linear and a quadratic model, and use each to estimate the rise by 2100. Why do the answers differ so much? Worked-up idea: When might the Arctic be nearly ice-free in September?.

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See the data (134 rows)

Year	Sea level change since 1880 (mm)
1880	0
1881	6
1882	-11
1883	-6
1884	15
1885	13
1886	11
1887	5
1888	8
1889	9
1890	11
1891	9
1892	13
1893	17
1894	8
1895	19
1896	12
1897	17
1898	26
1899	34
1900	29
1901	28
1902	33
1903	41
1904	30
1905	25
1906	32
1907	30
1908	28
1909	32
1910	32
1911	41
1912	37
1913	39
1914	46
1915	53
1916	52
1917	47
1918	45
1919	47
1920	48
1921	50
1922	50
1923	51
1924	43
1925	45
1926	52
1927	51
1928	47
1929	48
1930	52
1931	52
1932	58
1933	62
1934	57
1935	62
1936	58
1937	64
1938	67
1939	72
1940	66
1941	79
1942	79
1943	79
1944	72
1945	75
1946	83
1947	86
1948	90
1949	89
1950	91
1951	101
1952	98
1953	103
1954	100
1955	101
1956	96
1957	109
1958	110
1959	111
1960	114
1961	121
1962	115
1963	114
1964	106
1965	117
1966	112
1967	113
1968	114
1969	121
1970	119
1971	124
1972	133
1973	127
1974	139
1975	137
1976	136
1977	135
1978	141
1979	136
1980	142
1981	155
1982	149
1983	157
1984	156
1985	146
1986	147
1987	147
1988	152
1989	156
1990	159
1991	161
1992	162
1993	160
1994	165
1995	168
1996	172
1997	179
1998	169
1999	178
2000	179
2001	185
2002	187
2003	196
2004	196
2005	196
2006	200
2007	202
2008	211
2009	217
2010	224
2011	226
2012	235
2013	226

World population (1960–2025)

The World Bank's estimate of the total number of people in the world each year.

Variables	Year against World population (billion)
Size	66 rows · Year 1960–2025. Divided by 10⁹ and rounded to 0.001 billion.
Source	World Bank, World Development Indicators: Population, total (SP.POP.TOTL)
Licence	CC BY 4.0. Free to reuse with credit to the source.
Cite as	World Bank: Population, total (SP.POP.TOTL), World Development Indicators.
Models to try	straight line, exponential, logistic

A question to start from: Exponential or logistic? Fit both to 1960–2025 and use them to predict 2050 and 2100. Compare with the UN's projection and explain the gap. Worked-up idea: Exponential or logistic? Modelling my country's population.

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See the data (66 rows)

Year	World population (billion)
1960	3.022
1961	3.063
1962	3.117
1963	3.184
1964	3.251
1965	3.319
1966	3.389
1967	3.459
1968	3.531
1969	3.605
1970	3.68
1971	3.759
1972	3.834
1973	3.911
1974	3.987
1975	4.062
1976	4.136
1977	4.209
1978	4.283
1979	4.36
1980	4.437
1981	4.517
1982	4.599
1983	4.683
1984	4.766
1985	4.85
1986	4.937
1987	5.027
1988	5.117
1989	5.207
1990	5.299
1991	5.388
1992	5.477
1993	5.564
1994	5.651
1995	5.736
1996	5.822
1997	5.908
1998	5.994
1999	6.078
2000	6.162
2001	6.245
2002	6.327
2003	6.409
2004	6.492
2005	6.575
2006	6.66
2007	6.744
2008	6.83
2009	6.916
2010	7.001
2011	7.087
2012	7.176
2013	7.265
2014	7.354
2015	7.441
2016	7.528
2017	7.614
2018	7.696
2019	7.777
2020	7.854
2021	7.92
2022	7.989
2023	8.063
2024	8.141
2025	8.215

Glacier mass balance, world reference glaciers (1956–2023)

The average cumulative change in thickness of a set of reference glaciers around the world, in metres of water equivalent (1956 = 0). Negative means ice lost.

Variables	Year against Cumulative mass balance (m water equivalent)
Size	68 rows · Year 1956–2023
Source	US EPA Climate Change Indicators: Glaciers (World Glacier Monitoring Service data)
Licence	Public domain (US Government work). Free to reuse. Cite the US EPA (and WGMS, the original data) as the source.
Cite as	US Environmental Protection Agency, Climate Change Indicators: Glaciers; data from the World Glacier Monitoring Service.
Models to try	straight line, quadratic, piecewise

A question to start from: Is ice loss speeding up? Find the rate of loss in the 1960s and in the 2010s from your models, and decide whether a quadratic describes the data better than a line.

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See the data (68 rows)

Year	Cumulative mass balance (m water equivalent)
1956	0
1957	-0.094
1958	-0.963
1959	-1.431
1960	-2.008
1961	-2.445
1962	-2.648
1963	-3
1964	-2.682
1965	-2.524
1966	-2.75
1967	-2.869
1968	-2.939
1969	-3.428
1970	-3.715
1971	-3.946
1972	-4.225
1973	-4.402
1974	-4.589
1975	-4.815
1976	-4.998
1977	-5.255
1978	-5.443
1979	-5.86
1980	-5.983
1981	-6.174
1982	-6.662
1983	-6.535
1984	-6.794
1985	-7.102
1986	-7.583
1987	-7.487
1988	-7.561
1989	-7.789
1990	-8.274
1991	-8.777
1992	-8.893
1993	-9.026
1994	-9.557
1995	-10.016
1996	-10.489
1997	-11.13
1998	-11.852
1999	-12.551
2000	-12.91
2001	-13.181
2002	-13.609
2003	-14.134
2004	-14.866
2005	-15.683
2006	-16.398
2007	-16.938
2008	-17.313
2009	-17.765
2010	-18.614
2011	-19.351
2012	-20.076
2013	-20.788
2014	-21.489
2015	-22.293
2016	-23.276
2017	-23.928
2018	-24.865
2019	-25.859
2020	-26.742
2021	-27.419
2022	-28.509
2023	-29.738

UK carbon dioxide emissions (1900–2024)

The UK's yearly CO₂ emissions from fossil fuels and industry: a rise, a long plateau and a fall since the 1970s.

Variables	Year against Annual CO₂ emissions (million tonnes)
Size	125 rows · Year 1900–2024. Rounded to 0.1 million tonnes.
Source	Our World in Data, CO₂ and greenhouse gas emissions dataset (Global Carbon Budget)
Licence	CC BY 4.0. Free to reuse with credit to the source.
Cite as	Our World in Data, based on the Global Carbon Budget (Friedlingstein et al.).
Models to try	piecewise, cubic and polynomials, straight line

A question to start from: One curve or several pieces? Fit a cubic to 1900–2024 and a piecewise linear model with breaks you justify from history. Which tells the better story?

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See the data (125 rows)

Year	Annual CO₂ emissions (million tonnes)
1900	419.7
1901	410.7
1902	426.6
1903	430
1904	432
1905	437.7
1906	453.4
1907	473.6
1908	462
1909	466.1
1910	469.7
1911	481.4
1912	455.9
1913	498.3
1914	483.2
1915	489.5
1916	507.1
1917	501.2
1918	466.8
1919	456.1
1920	480.5
1921	332.2
1922	439.6
1923	466.1
1924	488.2
1925	461
1926	261.1
1927	481.3
1928	454.5
1929	478.2
1930	460.8
1931	431
1932	414.6
1933	412.3
1934	445.3
1935	451.2
1936	476.5
1937	491.7
1938	472.8
1939	474.3
1940	500.1
1941	499.8
1942	493
1943	490.2
1944	489.7
1945	453.1
1946	464.4
1947	485.2
1948	503.5
1949	507.6
1950	499.2
1951	543.8
1952	527.4
1953	539.1
1954	552
1955	576.1
1956	573.7
1957	570.2
1958	556
1959	546.4
1960	583.9
1961	588.6
1962	592.9
1963	603.4
1964	607.9
1965	622.1
1966	618.1
1967	592.1
1968	606.5
1969	628.4
1970	652.6
1971	660.4
1972	648
1973	659.6
1974	617.2
1975	603.3
1976	598.5
1977	604.4
1978	604.7
1979	644.5
1980	579
1981	560.5
1982	548.2
1983	545.5
1984	529.1
1985	559.6
1986	568.6
1987	571.6
1988	570.3
1989	581.6
1990	601.9
1991	609.4
1992	593.8
1993	579.6
1994	574
1995	566.2
1996	586.8
1997	562.7
1998	568.5
1999	561.6
2000	569
2001	578
2002	560.3
2003	571.6
2004	573.4
2005	570.3
2006	567.8
2007	559.6
2008	544.9
2009	494.1
2010	511.9
2011	469.7
2012	487.5
2013	477.6
2014	438.8
2015	422.5
2016	399.4
2017	387.4
2018	379.7
2019	364.8
2020	326.3
2021	342.4
2022	311.1
2023	307.8
2024	312.9

India carbon dioxide emissions (1950–2024)

India's yearly CO₂ emissions from fossil fuels and industry: fast, steady growth over 75 years.

Variables	Year against Annual CO₂ emissions (million tonnes)
Size	75 rows · Year 1950–2024. Rounded to 0.1 million tonnes.
Source	Our World in Data, CO₂ and greenhouse gas emissions dataset (Global Carbon Budget)
Licence	CC BY 4.0. Free to reuse with credit to the source.
Cite as	Our World in Data, based on the Global Carbon Budget (Friedlingstein et al.).
Models to try	exponential, logarithmic, logistic

A question to start from: Is the growth exponential? Plot ln(emissions) against year, fit a line, and find the doubling time. Has it changed since 1950?

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See the data (75 rows)

Year	Annual CO₂ emissions (million tonnes)
1950	60.9
1951	63.8
1952	67.2
1953	68.6
1954	72.3
1955	78.7
1956	81.6
1957	91.6
1958	95.8
1959	101.4
1960	111.3
1961	120.4
1962	132.6
1963	142.4
1964	139.5
1965	153.7
1966	159.4
1967	159.6
1968	174.1
1969	177.4
1970	181.7
1971	192
1972	203
1973	209.1
1974	215.9
1975	234.2
1976	244.8
1977	259
1978	263.1
1979	276.3
1980	291.7
1981	315
1982	325.4
1983	352.2
1984	361.6
1985	397.6
1986	426.3
1987	455.4
1988	491.7
1989	540.7
1990	578
1991	615.4
1992	655.4
1993	677.3
1994	714
1995	760.5
1996	823.6
1997	858
1998	875.8
1999	961.2
2000	987.1
2001	1001.2
2002	1032.8
2003	1068.4
2004	1134.6
2005	1195.4
2006	1293.3
2007	1393.5
2008	1490.4
2009	1613.3
2010	1678.5
2011	1765.7
2012	1927
2013	1995.3
2014	2148.1
2015	2231.8
2016	2352.5
2017	2425.7
2018	2595.2
2019	2611.2
2020	2422.7
2021	2675.8
2022	2831.1
2023	3062.8
2024	3193.5

UK typical stopping distances (The Highway Code)

The Highway Code's typical thinking, braking and total stopping distances for a car at 20–70 mph.

Variables	Speed (mph) against Total stopping distance (m); also Thinking distance (m) and Braking distance (m)
Size	6 rows · Speed 20–70
Source	The Highway Code, rule 126: Typical stopping distances (GOV.UK)
Licence	Open Government Licence v3.0. Crown copyright. Free to reuse with the attribution shown.
Cite as	Department for Transport, The Highway Code, rule 126. Contains public sector information licensed under the Open Government Licence v3.0.
Models to try	quadratic, straight line, power

A question to start from: Why is thinking distance linear but braking distance quadratic? Fit both, explain each from physics, and check the fit at 70 mph. Worked-up idea: Why do stopping distances grow so fast? Modelling the Highway Code table.

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See the data (6 rows)

Speed (mph)	Total stopping distance (m)	Thinking distance (m)	Braking distance (m)
20	12	6	6
30	23	9	14
40	36	12	24
50	53	15	38
60	73	18	55
70	96	21	75

Collecting your own data instead

Your own measurements can show personal engagement more directly. Each IA idea lists where its data comes from, and Desmos, GeoGebra and Excel explains how to get it into a graph. When you have a model, check it step by step with Model my data and log the version in your IA process journal.

Frequently asked questions

Can I use a public dataset in my Maths IA?

Yes. Many strong explorations use published data. The marks come from what you do with it: a precise question, a justified choice of model, checks of how well it fits and honest reflection on its limits. Cite the source where you use it and in your bibliography.

Does using someone else's data cost marks for personal engagement?

Not by itself. Criterion C rewards how you make the mathematics your own: your question, your decisions and your interpretation. Collecting some of your own data, or comparing the published data with your own context, can make that easier to show.

Which model should I fit to my data?

Start from the context, not the R² value: a quantity that grows by a fixed percentage suggests an exponential model, a repeating pattern suggests a sine model, a quantity with a ceiling suggests a logistic model. Then fit two or three candidates and compare them with residuals. The IA modelling guide works through each one.

Are these datasets free to use?

Yes. We only list data whose licence allows reuse: public-domain US Government data, Creative Commons Attribution (CC BY 4.0) or the UK Open Government Licence. Each card names the licence and how to credit the source.

Free: the IA checklist an examiner uses

Every check for Criteria A–E in a 4-page PDF, the mistakes that cost the most marks and a self-assessment grid. We'll email it with a short IA tip every few days, timed to your deadline if you give it. Free — no account, no payment.

Get the free checklist