{"title":"Keeling curve","code":"# /// script\n# requires-python = \">=3.11\"\n# dependencies = [\"marimo>=0.25.1\", \"numpy\", \"pandas\", \"matplotlib\"]\n# ///\n\nimport marimo\n\n__generated_with = \"0.25.1\"\napp = marimo.App(width=\"medium\", app_title=\"The Keeling curve\")\n\nwith app.setup:\n import numpy as np\n import pandas as pd\n\n DATA = \"data/co2_mm_mlo.csv\"\n\n\n@app.function\ndef load_co2(path: str = DATA) -> pd.DataFrame:\n \"\"\"Monthly mean CO₂ in the air at Mauna Loa, Hawaii, in ppm, from March 1958.\n\n Columns: year, month, decimal_date, average, deseasonalized, ndays, sdev,\n unc. NOAA fills months without measurements by interpolation and marks\n them with a negative ndays.\n \"\"\"\n co2 = pd.read_csv(path, comment=\"#\")\n return co2.rename(columns={\"decimal date\": \"decimal_date\"})\n\n\n@app.function\ndef yearly_mean(path: str = DATA) -> pd.Series:\n \"\"\"The deseasonalized concentration averaged over each complete year, in ppm, by year.\"\"\"\n co2 = load_co2(path)\n complete = co2.groupby(\"year\").filter(lambda year: len(year) == 12)\n return complete.groupby(\"year\")[\"deseasonalized\"].mean()\n\n\n@app.function\ndef growth_rate(start_year: int, end_year: int, path: str = DATA) -> float:\n \"\"\"Average growth of CO₂ between two complete years, in ppm a year.\n\n >>> round(growth_rate(1960, 1970), 2)\n 0.88\n \"\"\"\n yearly = yearly_mean(path)\n if start_year not in yearly.index or end_year not in yearly.index:\n raise ValueError(f\"Both years need twelve months of data; there are {yearly.index.min()} to {yearly.index.max()}.\")\n return float((yearly[end_year] - yearly[start_year]) / (end_year - start_year))\n\n\n@app.function\ndef seasonal_cycle(path: str = DATA) -> pd.Series:\n \"\"\"Each month's mean departure from the deseasonalized trend, in ppm: the breath of the northern forests.\"\"\"\n co2 = load_co2(path)\n return (co2[\"average\"] - co2[\"deseasonalized\"]).groupby(co2[\"month\"]).mean()\n\n\n@app.cell\ndef _():\n import marimo as mo\n import matplotlib.pyplot as plt\n\n return mo, plt\n\n\n@app.cell(hide_code=True)\ndef _(mo):\n mo.md(r\"\"\"\n # The Keeling curve\n\n In March 1958 Charles David Keeling began measuring carbon dioxide in the\n air on the slope of Mauna Loa, Hawaii, far from cities and forests. The\n record has run almost without a break since; NOAA's Global Monitoring\n Laboratory continues it today. It shows two things at once: a seasonal\n rhythm as the land plants of the northern hemisphere grow and decay, and a\n rise that has not paused in any year.\n\n This project is the data and the notebook together. The file\n `data/co2_mm_mlo.csv` is NOAA's monthly record as published; the functions\n below read it, and anyone can call them from Python with\n `sciencehub.load(\"sciencehub-team/keeling-curve\")`.\n \"\"\")\n return\n\n\n@app.cell\ndef _():\n co2 = load_co2()\n first_year, last_year = int(co2[\"year\"].min()), int(co2[\"year\"].max())\n return co2, first_year, last_year\n\n\n@app.cell\ndef _(first_year, last_year, mo):\n years = mo.ui.range_slider(\n start=first_year,\n stop=last_year,\n step=1,\n value=[first_year, last_year],\n label=\"Years\",\n full_width=True,\n )\n years\n return (years,)\n\n\n@app.cell\ndef _(co2, mo, plt, years):\n _start, _end = years.value\n _shown = co2[(co2[\"year\"] >= _start) & (co2[\"year\"] <= _end)]\n fig, ax = plt.subplots(figsize=(7, 3.8))\n ax.plot(_shown[\"decimal_date\"], _shown[\"average\"], color=\"#0969da\", lw=1, label=\"monthly mean\")\n ax.plot(_shown[\"decimal_date\"], _shown[\"deseasonalized\"], color=\"#1f2328\", lw=2, label=\"without the seasons\")\n ax.set_xlabel(\"year\")\n ax.set_ylabel(\"CO₂ (ppm)\")\n ax.legend(frameon=False, loc=\"upper left\")\n for _side in (\"top\", \"right\"):\n ax.spines[_side].set_visible(False)\n fig.tight_layout()\n\n _rise = _shown[\"deseasonalized\"].iloc[-1] - _shown[\"deseasonalized\"].iloc[0]\n mo.vstack(\n [\n fig,\n mo.md(\n f\"From {_start} to {_end} the concentration rose by **{_rise:.1f} ppm**, \"\n f\"from {_shown['deseasonalized'].iloc[0]:.1f} to {_shown['deseasonalized'].iloc[-1]:.1f} ppm.\"\n ),\n ]\n )\n return\n\n\n@app.cell(hide_code=True)\ndef _(mo):\n mo.md(r\"\"\"\n ## The rise is speeding up\n\n The growth rate is the average yearly rise over a decade. In the 1960s the\n concentration grew by under one ppm a year; it now grows by more than two.\n \"\"\")\n return\n\n\n@app.cell\ndef _(mo, plt):\n _yearly = yearly_mean()\n _decades = list(range(int(_yearly.index.min()) // 10 * 10 + 10, int(_yearly.index.max()), 10))\n _rates = [growth_rate(_d - 10, _d) if _d - 10 in _yearly.index else np.nan for _d in _decades]\n fig2, ax2 = plt.subplots(figsize=(7, 3))\n ax2.bar([f\"{_d - 10}s\" for _d in _decades], _rates, color=\"#1f2328\")\n ax2.set_ylabel(\"ppm a year\")\n for _side in (\"top\", \"right\"):\n ax2.spines[_side].set_visible(False)\n fig2.tight_layout()\n mo.vstack([fig2, mo.md(f\"Over the last complete decade: **{_rates[-1]:.2f} ppm a year**.\")])\n return\n\n\n@app.cell(hide_code=True)\ndef _(mo):\n mo.md(r\"\"\"\n ## The seasons\n\n Every year the concentration falls from May to October, while northern\n forests and crops take up carbon, and rises again through winter. The\n swing at Mauna Loa is about six ppm.\n \"\"\")\n return\n\n\n@app.cell\ndef _(plt):\n _cycle = seasonal_cycle()\n fig3, ax3 = plt.subplots(figsize=(7, 2.8))\n ax3.bar([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"], _cycle.values, color=[\"#9a6700\" if _v > 0 else \"#0969da\" for _v in _cycle.values])\n ax3.axhline(0, color=\"#1f2328\", lw=0.8)\n ax3.set_ylabel(\"ppm from the trend\")\n for _side in (\"top\", \"right\"):\n ax3.spines[_side].set_visible(False)\n fig3.tight_layout()\n fig3\n return\n\n\n@app.cell(hide_code=True)\ndef _(mo):\n mo.md(r\"\"\"\n ## Data, limits and references\n\n **Data.** `data/co2_mm_mlo.csv`, the monthly means from NOAA's Global\n Monitoring Laboratory as published on 9 October 2026, unchanged. Values\n before May 1974 are from the Scripps Institution of Oceanography. Months\n without measurements are interpolated by NOAA (negative `ndays`). From\n December 2022 to July 2023, after the eruption of Mauna Loa, the\n measurements come from Maunakea, 21 miles to the north.\n\n **Limits.** One site measures the air over the central Pacific, which\n represents the well-mixed atmosphere well but not every region. The\n growth rates here are differences of yearly means, not the smoothed rates\n NOAA publishes.\n\n **Cite the data.** Lan, X., Tans, P. and Thoning, K. W.: Trends in\n globally-averaged CO₂ determined from NOAA Global Monitoring Laboratory\n measurements, https://gml.noaa.gov/ccgg/trends/; Keeling, C. D. et al.,\n Scripps Institution of Oceanography, https://scrippsco2.ucsd.edu/. NOAA\n makes the data freely available and asks for this credit.\n\n Keeling, C. D. (1960). The concentration and isotopic abundances of carbon\n dioxide in the atmosphere. *Tellus* 12(2), 200–203.\n \"\"\")\n return\n\n\nif __name__ == \"__main__\":\n app.run()\n","revision":"2026-10-13T16:58:19.539Z","appConfig":{"width":"medium","app_title":"The Keeling curve"},"firstPaint":{"notebook":{"cells":[{"code":"import numpy as np\nimport pandas as pd\n\nDATA = \"data/co2_mm_mlo.csv\"","code_hash":"ba04aa4a93dd65d9f6dba0cafca611a3","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"setup","name":"setup"},{"code":"def load_co2(path: str = DATA) -> pd.DataFrame:\n \"\"\"Monthly mean CO₂ in the air at Mauna Loa, Hawaii, in ppm, from March 1958.\n\n Columns: year, month, decimal_date, average, deseasonalized, ndays, sdev,\n unc. NOAA fills months without measurements by interpolation and marks\n them with a negative ndays.\n \"\"\"\n co2 = pd.read_csv(path, comment=\"#\")\n return co2.rename(columns={\"decimal date\": \"decimal_date\"})","code_hash":"49ea8d76784030fdfba7754a51d993da","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"Hbol","name":"*load_co2"},{"code":"def yearly_mean(path: str = DATA) -> pd.Series:\n \"\"\"The deseasonalized concentration averaged over each complete year, in ppm, by year.\"\"\"\n co2 = load_co2(path)\n complete = co2.groupby(\"year\").filter(lambda year: len(year) == 12)\n return complete.groupby(\"year\")[\"deseasonalized\"].mean()","code_hash":"38bbaa158a590d63d22591e26ae340ba","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"MJUe","name":"*yearly_mean"},{"code":"def growth_rate(start_year: int, end_year: int, path: str = DATA) -> float:\n \"\"\"Average growth of CO₂ between two complete years, in ppm a year.\n\n >>> round(growth_rate(1960, 1970), 2)\n 0.88\n \"\"\"\n yearly = yearly_mean(path)\n if start_year not in yearly.index or end_year not in yearly.index:\n raise ValueError(f\"Both years need twelve months of data; there are {yearly.index.min()} to {yearly.index.max()}.\")\n return float((yearly[end_year] - yearly[start_year]) / (end_year - start_year))","code_hash":"fb99f0d5dd9276948276d081bf5f233e","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"vblA","name":"*growth_rate"},{"code":"def seasonal_cycle(path: str = DATA) -> pd.Series:\n \"\"\"Each month's mean departure from the deseasonalized trend, in ppm: the breath of the northern forests.\"\"\"\n co2 = load_co2(path)\n return (co2[\"average\"] - co2[\"deseasonalized\"]).groupby(co2[\"month\"]).mean()","code_hash":"278b70f52b0cb187e25c3392b51c447c","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"bkHC","name":"*seasonal_cycle"},{"code":"import marimo as mo\nimport matplotlib.pyplot as plt","code_hash":"7802eb41d8b52c22105f8bbe08d60476","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"lEQa","name":"_"},{"code":"mo.md(r\"\"\"\n# The Keeling curve\n\nIn March 1958 Charles David Keeling began measuring carbon dioxide in the\nair on the slope of Mauna Loa, Hawaii, far from cities and forests. The\nrecord has run almost without a break since; NOAA's Global Monitoring\nLaboratory continues it today. It shows two things at once: a seasonal\nrhythm as the land plants of the northern hemisphere grow and decay, and a\nrise that has not paused in any year.\n\nThis project is the data and the notebook together. The file\n`data/co2_mm_mlo.csv` is NOAA's monthly record as published; the functions\nbelow read it, and anyone can call them from Python with\n`sciencehub.load(\"sciencehub-team/keeling-curve\")`.\n\"\"\")","code_hash":"f42efa386a1f33b843ac64844a81e0dc","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":true},"id":"PKri","name":"_"},{"code":"co2 = load_co2()\nfirst_year, last_year = int(co2[\"year\"].min()), int(co2[\"year\"].max())","code_hash":"d86bb772855d43b7a027501305c25695","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"Xref","name":"_"},{"code":"years = mo.ui.range_slider(\n start=first_year,\n stop=last_year,\n step=1,\n value=[first_year, last_year],\n label=\"Years\",\n full_width=True,\n)\nyears","code_hash":"91e69bf275c1111446d14463e6f884f3","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"SFPL","name":"_"},{"code":"_start, _end = years.value\n_shown = co2[(co2[\"year\"] >= _start) & (co2[\"year\"] <= _end)]\nfig, ax = plt.subplots(figsize=(7, 3.8))\nax.plot(_shown[\"decimal_date\"], _shown[\"average\"], color=\"#0969da\", lw=1, label=\"monthly mean\")\nax.plot(_shown[\"decimal_date\"], _shown[\"deseasonalized\"], color=\"#1f2328\", lw=2, label=\"without the seasons\")\nax.set_xlabel(\"year\")\nax.set_ylabel(\"CO₂ (ppm)\")\nax.legend(frameon=False, loc=\"upper left\")\nfor _side in (\"top\", \"right\"):\n ax.spines[_side].set_visible(False)\nfig.tight_layout()\n\n_rise = _shown[\"deseasonalized\"].iloc[-1] - _shown[\"deseasonalized\"].iloc[0]\nmo.vstack(\n [\n fig,\n mo.md(\n f\"From {_start} to {_end} the concentration rose by **{_rise:.1f} ppm**, \"\n f\"from {_shown['deseasonalized'].iloc[0]:.1f} to {_shown['deseasonalized'].iloc[-1]:.1f} ppm.\"\n ),\n ]\n)","code_hash":"743ed684cbb36751f7b8e54c6a4cc528","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"BYtC","name":"_"},{"code":"mo.md(r\"\"\"\n## The rise is speeding up\n\nThe growth rate is the average yearly rise over a decade. In the 1960s the\nconcentration grew by under one ppm a year; it now grows by more than two.\n\"\"\")","code_hash":"6737f4f7a82226c9c2ff84ed75c14e65","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":true},"id":"RGSE","name":"_"},{"code":"_yearly = yearly_mean()\n_decades = list(range(int(_yearly.index.min()) // 10 * 10 + 10, int(_yearly.index.max()), 10))\n_rates = [growth_rate(_d - 10, _d) if _d - 10 in _yearly.index else np.nan for _d in _decades]\nfig2, ax2 = plt.subplots(figsize=(7, 3))\nax2.bar([f\"{_d - 10}s\" for _d in _decades], _rates, color=\"#1f2328\")\nax2.set_ylabel(\"ppm a year\")\nfor _side in (\"top\", \"right\"):\n ax2.spines[_side].set_visible(False)\nfig2.tight_layout()\nmo.vstack([fig2, mo.md(f\"Over the last complete decade: **{_rates[-1]:.2f} ppm a year**.\")])","code_hash":"04c44292d64fbe6a1ee58a5232567e42","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"Kclp","name":"_"},{"code":"mo.md(r\"\"\"\n## The seasons\n\nEvery year the concentration falls from May to October, while northern\nforests and crops take up carbon, and rises again through winter. The\nswing at Mauna Loa is about six ppm.\n\"\"\")","code_hash":"355efef2d4e9c0a2b7965f5f6fcc65b8","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":true},"id":"emfo","name":"_"},{"code":"_cycle = seasonal_cycle()\nfig3, ax3 = plt.subplots(figsize=(7, 2.8))\nax3.bar([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"], _cycle.values, color=[\"#9a6700\" if _v > 0 else \"#0969da\" for _v in _cycle.values])\nax3.axhline(0, color=\"#1f2328\", lw=0.8)\nax3.set_ylabel(\"ppm from the trend\")\nfor _side in (\"top\", \"right\"):\n ax3.spines[_side].set_visible(False)\nfig3.tight_layout()\nfig3","code_hash":"db3848da89a075fb10a0b17c5dd2d3a1","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":false},"id":"Hstk","name":"_"},{"code":"mo.md(r\"\"\"\n## Data, limits and references\n\n**Data.** `data/co2_mm_mlo.csv`, the monthly means from NOAA's Global\nMonitoring Laboratory as published on 9 October 2026, unchanged. Values\nbefore May 1974 are from the Scripps Institution of Oceanography. Months\nwithout measurements are interpolated by NOAA (negative `ndays`). From\nDecember 2022 to July 2023, after the eruption of Mauna Loa, the\nmeasurements come from Maunakea, 21 miles to the north.\n\n**Limits.** One site measures the air over the central Pacific, which\nrepresents the well-mixed atmosphere well but not every region. The\ngrowth rates here are differences of yearly means, not the smoothed rates\nNOAA publishes.\n\n**Cite the data.** Lan, X., Tans, P. and Thoning, K. W.: Trends in\nglobally-averaged CO₂ determined from NOAA Global Monitoring Laboratory\nmeasurements, https://gml.noaa.gov/ccgg/trends/; Keeling, C. D. et al.,\nScripps Institution of Oceanography, https://scrippsco2.ucsd.edu/. NOAA\nmakes the data freely available and asks for this credit.\n\nKeeling, C. D. (1960). The concentration and isotopic abundances of carbon\ndioxide in the atmosphere. *Tellus* 12(2), 200–203.\n\"\"\")","code_hash":"a4db8747c2557d84a01a715934963556","config":{"column":null,"disabled":false,"expand_output":false,"hide_code":true},"id":"nWHF","name":"_"}],"metadata":{"marimo_version":"0.25.1"},"version":"1"},"session":{"cells":[{"code_hash":"ba04aa4a93dd65d9f6dba0cafca611a3","console":[],"id":"setup","outputs":[{"data":{"text/plain":""},"type":"data"}]},{"code_hash":"49ea8d76784030fdfba7754a51d993da","console":[],"id":"Hbol","outputs":[{"data":{"text/plain":""},"type":"data"}]},{"code_hash":"38bbaa158a590d63d22591e26ae340ba","console":[],"id":"MJUe","outputs":[{"data":{"text/plain":""},"type":"data"}]},{"code_hash":"fb99f0d5dd9276948276d081bf5f233e","console":[],"id":"vblA","outputs":[{"data":{"text/plain":""},"type":"data"}]},{"code_hash":"278b70f52b0cb187e25c3392b51c447c","console":[],"id":"bkHC","outputs":[{"data":{"text/plain":""},"type":"data"}]},{"code_hash":"7802eb41d8b52c22105f8bbe08d60476","console":[],"id":"lEQa","outputs":[{"data":{"text/plain":""},"type":"data"}]},{"code_hash":"f42efa386a1f33b843ac64844a81e0dc","console":[],"id":"PKri","outputs":[{"data":{"text/markdown":"

The Keeling curve

\nIn March 1958 Charles David Keeling began measuring carbon dioxide in the\nair on the slope of Mauna Loa, Hawaii, far from cities and forests. The\nrecord has run almost without a break since; NOAA's Global Monitoring\nLaboratory continues it today. It shows two things at once: a seasonal\nrhythm as the land plants of the northern hemisphere grow and decay, and a\nrise that has not paused in any year.\nThis project is the data and the notebook together. The file\ndata/co2_mm_mlo.csv is NOAA's monthly record as published; the functions\nbelow read it, and anyone can call them from Python with\nsciencehub.load(\"sciencehub-team/keeling-curve\")."},"type":"data"}]},{"code_hash":"d86bb772855d43b7a027501305c25695","console":[],"id":"Xref","outputs":[{"data":{"text/plain":""},"type":"data"}]},{"code_hash":"91e69bf275c1111446d14463e6f884f3","console":[],"id":"SFPL","outputs":[{"data":{"text/html":""},"type":"data"}]},{"code_hash":"743ed684cbb36751f7b8e54c6a4cc528","console":[],"id":"BYtC","outputs":[{"data":{"text/html":"
From 1958 to 2026 the concentration rose by 115.1 ppm, from 314.4 to 429.6 ppm.
"},"type":"data"}]},{"code_hash":"6737f4f7a82226c9c2ff84ed75c14e65","console":[],"id":"RGSE","outputs":[{"data":{"text/markdown":"

The rise is speeding up

\nThe growth rate is the average yearly rise over a decade. In the 1960s the\nconcentration grew by under one ppm a year; it now grows by more than two."},"type":"data"}]},{"code_hash":"04c44292d64fbe6a1ee58a5232567e42","console":[],"id":"Kclp","outputs":[{"data":{"text/html":"
Over the last complete decade: 2.41 ppm a year.
"},"type":"data"}]},{"code_hash":"355efef2d4e9c0a2b7965f5f6fcc65b8","console":[],"id":"emfo","outputs":[{"data":{"text/markdown":"

The seasons

\nEvery year the concentration falls from May to October, while northern\nforests and crops take up carbon, and rises again through winter. The\nswing at Mauna Loa is about six ppm."},"type":"data"}]},{"code_hash":"db3848da89a075fb10a0b17c5dd2d3a1","console":[],"id":"Hstk","outputs":[{"data":{"application/vnd.marimo+mimebundle":"{\"image/png\":\"https://plvp7twgybmoyw2svb6yojv4vv.notebooks.sciencehubusercontent.com/_outputs/eyJuYW1lc3BhY2VJZCI6Im5zLXdicjZya2tkOWZqMG1neGUiLCJwcm9qZWN0SWQiOiJwcm9qLXRzNGI2ejBwYjFmeDR2M2MiLCJub3RlYm9va0lkIjoibmItdmhmM2hxNjdtYjJtdjB3bSIsImhhc2giOiJhMDYzNzMzNjgyMDE4ZTU3MjNhZTY1NjcxYzIxYmU0OSJ9.0.2eVggfMnjCQjQ_pLXeoDMMRZG_5mVsyj-t8wYoQVwZI\",\"__metadata__\":{\"image/png\":{\"width\":690,\"height\":270}}}"},"type":"data"}]},{"code_hash":"a4db8747c2557d84a01a715934963556","console":[],"id":"nWHF","outputs":[{"data":{"text/markdown":"

Data, limits and references

\nData. data/co2_mm_mlo.csv, the monthly means from NOAA's Global\nMonitoring Laboratory as published on 9 October 2026, unchanged. Values\nbefore May 1974 are from the Scripps Institution of Oceanography. Months\nwithout measurements are interpolated by NOAA (negative ndays). From\nDecember 2022 to July 2023, after the eruption of Mauna Loa, the\nmeasurements come from Maunakea, 21 miles to the north.\nLimits. One site measures the air over the central Pacific, which\nrepresents the well-mixed atmosphere well but not every region. The\ngrowth rates here are differences of yearly means, not the smoothed rates\nNOAA publishes.\nCite the data. Lan, X., Tans, P. and Thoning, K. W.: Trends in\nglobally-averaged CO₂ determined from NOAA Global Monitoring Laboratory\nmeasurements, https://gml.noaa.gov/ccgg/trends/; Keeling, C. D. et al.,\nScripps Institution of Oceanography, https://scrippsco2.ucsd.edu/. NOAA\nmakes the data freely available and asks for this credit.\nKeeling, C. D. (1960). The concentration and isotopic abundances of carbon\ndioxide in the atmosphere. Tellus 12(2), 200–203."},"type":"data"}]}],"metadata":{"marimo_version":"0.25.1","script_metadata_hash":null},"version":"1"},"appConfig":{"app_title":"The Keeling curve","sql_output":"auto","width":"medium"},"layout":null,"renderedOn":"browser"},"files":[{"path":"data/co2_mm_mlo.csv","size":38776,"url":"/sciencehub-team/keeling-curve/raw/data/co2_mm_mlo.csv"},{"path":"README.md","size":1158,"url":"/sciencehub-team/keeling-curve/raw/README.md"}]}