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Creative Commons License BY 4.0.
This report is for the watershed with an outlet near 0°26'13"S, 50°44'16"W, or (-0.437, -50.738), with a drainage area of around 5,900,000 km².
The watershed is located in nine countries, as shown below in Table 1 below.
Table 1. Countries in the watershed.
| Country | Area (km²) | Percent of watershed |
|---|---|---|
| Brazil | 3,690,000 | 63% |
| Peru | 961,000 | 16% |
| Bolivia | 715,000 | 12% |
| Colombia | 340,000 | 6% |
| Ecuador | 130,000 | 2% |
| Venezuela | 53,600 | 1% |
| Guyana | 12,800 | 0% |
| Suriname | 294 | 0% |
| France | 116 | 0% |
Data on political boundaries comes from Natural Earth, https://www.naturalearthdata.com.
The watershed has an estimated population of 33,600,000 in the year 2020. Figure 1 shows how population has changed from 1990 to 2020. The population grew at an average rate of 1.6% per year over this time period.
Population data comes from GlobPop, Global Gridded Population Estimates, created by researchers at Beijing Normal University (Liu 2024).
Human population growth can affect water quality via increased pollution from households, industry, and agriculture. Further, land use and land cover change associated with population growth can have a major impact on watersheds (see the next section of this report).
The most common land cover type in the watershed is tree cover, covering 4,270,000 km² in 2020. More detailed information about land cover and how it has changed is shown in Table 3 and Figure 2 below. Pairs of bars represent the area for each land cover type in the years 2000 and 2020.
Table 3. Land cover in the watershed.
| Land Cover Type | Area in 2000, km² | Area in 2020, km² | Change |
|---|---|---|---|
| Tree cover | 4,450,000 | 4,270,000 | -3% |
| Dense short vegetation | 517,000 | 608,000 | 17% |
| Wetland + tree cover | 571,000 | 563,000 | -1% |
| Wetland + dense short vegetation | 175,000 | 173,000 | 0% |
| Open surface water | 114,000 | 121,000 | 6% |
| Cropland | 47,900 | 108,000 | 125% |
| Semi-arid | 51,400 | 51,200 | 0% |
| Built-up | 12,100 | 39,300 | 225% |
Land cover data comes from the GLAD: Global Land Cover and Land Use Change, 2000-2020 (Popatov et al. 2022). This dataset, created by researchers at the University of Maryland, is available online here. Classification is based on satellite imagery from Landsat and machine learning tools.
Land cover change can profoundly influence watershed hydrology and water quality. Urbanization and development can increase the impervious cover, causing more water to run off rather than infiltrate into the ground. This can decrease groundwater recharge and river baseflows, or the flows that occur during dry times. Deforestation and agricultural development are often accompanied by an increase in soil erosion and sediment loads. Other land use types are associated with water pollution. For example, agriculture can increase loads of pesticides and nutrients (nitrogen and phosphorus) from fertilizers. Urbanization and industrialization can cause contamination from a wide range of chemicals used in households and industry.
The average annual precipitation over the watershed is 2,157 mm/year. (Precipitation includes all forms of water, including snow and rain.) Some of this water leaves the watershed surface via evaporation and transpiration, or the loss of water from plants. Annual evapotranspiration is estimated at 1,414 mm/year. The basin climatology, or the monthly average precipitation and evapotranspiration, is shown in Figure 4.
Precipitation data comes from WorldClim, a global gridded dataset by researchers at the University of East Anglia (Harris et al. 2020). This dataset is based on downscaling and bias-correcting the CRU-TS dataset (Fick and Hijmans 2017), which is based on a large collection of station observations that span 1901–2018.
Evapotranspiration is even more difficult to estimate than precipitation. Here, we use a dataset, GLEAM v3.6B, which combines modeling and remote sensing data (Martens et al. 2017; Miralles et al. 2011).
It is difficult to estimate water cycle variables over large areas. So you should keep in mind that these estimates are uncertain (not perfect).
The GRACE satellites provide information about changes in the amount of water over different locations on the Earth. Figure 3 shows the average terrestrial water storage anomaly over the watershed.
The GRACE satellites make highly accurate measurements of the Earth's gravitational field, and provide measurements of changes in the mass of water on a monthly time scale. These measurements do not tell us how much water there is in a region, but rather, how it the amount of water has changed compared to a baseline. The measurement includes all forms of water, including water in rivers, lakes, and reservoirs, soil moisture, groundwater, glaciers, snow, and ice. For an introduction to GRACE, see the section of my PhD thesis on Remote Sensing of the Water Cycle.
The total amount of water in the watershed appears to be trending upwards at a rate of 0.7 cm per decade (P = 0.61). This P-value indicates that the observed trend is not statistically significant. In other words, there is insufficient evidence to conclude that there is a meaningful trend in the data.
GRACE Terrestrial Water Storage Anomaly data is from the Center for Space Research at the University of Texas, Austin (Save et al. 2016, 2022).
The watershed had about 7167.4 km² of land equipped for irrigation in 2005. This is about 0.1% of the watershed. Figure 5 shows the development of irrigated area from 1900 to 2005. These estimates are based on a global dataset published by an international team of researchers (Siebert et al. 2015).
Irrigation brings many benefits for growing crops. In arid regions, it enables production where it would be otherwise impossible. In humid regions, irrigation can increase crop quality and yield, and provide more certainty against unpredictable climate. Yet, the expansion of irrigation can have impacts on watersheds that need to be carefully managed.
Increased irrigation usually requires more water to be withdrawn from rivers, lakes, or groundwater sources, which can reduce streamflow and alter natural flow patterns. This reduced flow can harm aquatic ecosystems and diminish the water available downstream for other uses. Irrigation often introduces fertilizers and pesticides into the watershed, and may increase soil erosion and sediment transport. When nutrients run off into rivers and streams, they can lead to nutrient pollution, algal blooms, and eutrophication. This plus chemical pesticides and herbicides negatively impacts aquatic life and water quality for human uses.
This watershed contains 163 dams identified in the Global Dam Watch database, with a total storage capacity of 41,000 million m³.
Information on these dams is listed in Table 4. The number and size of dams in a watershed is one measure of hydromodification, or how much the natural hydrologic cycle is influenced by human activities.
Table 4. Dams in the watershed. Unknown or missing data shown with a dash.
| Dam Name | Reservoir Name | River | Main Use | Year | Dam Height (m) | Capacity (10⁶ m³) | Latitude | Longitude | URL |
|---|---|---|---|---|---|---|---|---|---|
| Cidezal | – | Juruena | Hydroelectricity | – | -13.370 | -59.013 | – | ||
| – | – | – | – | – | -2.749 | -78.414 | – | ||
| – | – | – | – | – | -7.260 | -59.632 | – | ||
| – | – | – | – | – | -10.582 | -65.491 | – | ||
| Dardanelos | – | – | Hydroelectricity | – | -10.165 | -59.448 | – | ||
| Santa Cruz de Monte Negro | – | Jamari | Hydroelectricity | – | -10.230 | -63.233 | – | ||
| Cachoeira Santo Antônio | – | Curuquetê | Hydroelectricity | – | -8.598 | -65.552 | – | ||
| – | – | – | – | – | -12.465 | -74.787 | – | ||
| – | – | – | – | – | -9.577 | -76.745 | – | ||
| – | – | – | – | – | -13.544 | -59.030 | – | ||
| – | – | – | – | – | -13.024 | -58.187 | – | ||
| Salto Corgão | – | Guapore | Hydroelectricity | – | -14.448 | -59.485 | – | ||
| Primavera | – | – | Hydroelectricity | – | -11.904 | -61.235 | – | ||
| Marcol | – | JiParaná | Hydroelectricity | – | -12.851 | -60.323 | – | ||
| Incomex | – | – | Hydroelectricity | – | -11.910 | -62.180 | – | ||
| Esperança | – | Guapore | Hydroelectricity | – | -13.778 | -59.770 | – | ||
| – | – | – | – | – | -12.484 | -74.745 | – | ||
| San Gabán I | – | San Gabán | Hydroelectricity | – | -13.720 | -70.453 | – | ||
| San Gabán IV | – | Corani Macusani | Hydroelectricity | – | -13.786 | -70.472 | – | ||
| Balbina | Balbina Reservoir | Uatuma | Hydroelectricity | Built 1987 | 33.0 | 17533.0 | -1.914 | -59.477 | – |
| Belo Monte | Calha Do Xingu Reservoir | Xingu | Hydroelectricity | Built 2019 | 36.0 | 4518.0 | -3.434 | -51.947 | link |
| Samuel | – | Jamari | – | Built 1989 | 38.0 | 3493.0 | -8.740 | -63.468 | – |
| Sinop | – | Teles Pires | Hydroelectricity | Built 2019 | 76.0 | 3070.0 | -11.274 | -55.452 | link |
| Jirau | – | Madeira | Hydroelectricity | Built 2015 | 62.0 | 2746.7 | -9.267 | -64.648 | link |
| Colider | – | Teles Pires | Hydroelectricity | Built 2019 | 40.0 | 1520.0 | -10.984 | -55.763 | link |
| Teles Pires | – | Teles Pires | Hydroelectricity | Built 2016 | 80.0 | 997.0 | -9.353 | -56.777 | link |
| – | – | – | – | Built 1985 | 899.0 | -0.865 | -59.606 | – | |
| Sao Manoel | – | Teles Pires | Hydroelectricity | Built 2017 | 577.0 | -9.190 | -57.051 | link | |
| Upamayo | Junin | Lago Junin | – | Built 1936 | 10.0 | 556.0 | -10.979 | -76.196 | link |
| Rondon II | Repressa da Rondon II | Comemoracao | Hydroelectricity | Built 2010 | 11.0 | 478.3 | -11.998 | -60.697 | link |
| Curua Una | – | Curua Una | – | Built 1977 | 472.0 | -2.817 | -54.300 | – | |
| Mazar | – | Paute | Hydroelectricity | Built 2011 | 166.0 | 410.0 | -2.599 | -78.624 | link |
| Chaglla | – | Huallaga | Hydroelectricity | Built 2016 | 202.0 | 375.0 | -9.695 | -75.836 | link |
| Misicuni | – | Misicuni River | Hydroelectricity | Built 2017 | 120.0 | 185.5 | -17.099 | -66.327 | link |
| – | – | – | – | Built 2007 | 181.3 | -13.852 | -53.256 | – | |
| – | – | – | – | Built 2006 | 175.6 | -12.794 | -56.006 | – | |
| Choclococha | – | Pampas | – | Built 1960 | 12.0 | 170.0 | -13.243 | -75.073 | – |
| – | – | – | – | Built 2003 | 155.6 | -9.630 | -54.973 | – | |
| Corani | – | Corani | – | Built 1965 | 150.0 | -17.229 | -65.892 | – | |
| Santo Antonio do Jari | – | Jari | Hydroelectricity | Built 2014 | 14.0 | 133.4 | -0.642 | -52.522 | link |
| Santo Antonio | – | Madeira | Hydroelectricity | Built 2016 | 60.0 | 130.4 | -8.802 | -63.952 | link |
| – | – | – | – | Built before 1985 | 121.0 | -13.352 | -75.086 | – | |
| Daniel Palacios | – | Paute | Hydroelectricity | Built 1982 | 120.0 | -2.592 | -78.566 | link | |
| Sibinacocha | Lake Sibinacocha | Sibinacocha | Hydroelectricity | Built 1996 | 12.0 | 110.0 | -13.901 | -71.006 | link |
| – | – | – | – | Built 1994 | 108.4 | -11.312 | -59.229 | – | |
| La Angostura | Lago del Eden | Sulti | – | Built 1945 | 22.0 | 100.0 | -17.529 | -66.086 | – |
| – | – | – | – | Built 1998 | 95.4 | -9.777 | -54.990 | – | |
| Figueira | – | JiParaná | Hydroelectricity | Built 2005 | 95.3 | -11.996 | -62.172 | – | |
| – | – | – | – | – | 75.9 | -11.405 | -76.332 | – | |
| – | – | – | – | Built 2002 | 64.6 | -15.123 | -58.960 | – | |
| – | – | – | – | Built 2010 | 64.4 | -12.533 | -57.877 | – | |
| – | – | – | – | Built 2010 | 64.0 | -13.224 | -59.028 | – | |
| Antacoto | Antacoto | Santa Eulalia | – | Built 1966 | 61.2 | -11.408 | -76.362 | – | |
| – | – | – | – | Built before 1985 | 58.7 | -0.541 | -78.225 | – | |
| – | – | – | – | Built before 1985 | 54.1 | -11.959 | -75.912 | – | |
| – | – | – | – | Built 2007 | 50.8 | -9.684 | -54.963 | – | |
| – | – | – | – | Built 2010 | 47.9 | -13.266 | -59.020 | – | |
| – | – | – | – | Built 2016 | 47.4 | -0.199 | -77.686 | – | |
| – | – | – | – | – | 44.5 | -11.719 | -76.122 | – | |
| Pias I | – | – | Hydroelectricity | – | 39.2 | -7.890 | -77.567 | – | |
| – | – | – | – | Built 1996 | 38.8 | -9.719 | -65.157 | – | |
| – | – | – | – | Built 2011 | 36.4 | -12.903 | -58.914 | – | |
| – | – | – | – | Built 1993 | 30.8 | -12.800 | -52.562 | – | |
| – | – | – | – | – | 30.5 | -0.740 | -60.081 | – | |
| – | – | – | – | Built 1999 | 28.4 | -8.348 | -51.452 | – | |
| – | – | – | – | Built 2011 | 28.4 | -17.348 | -60.284 | – | |
| – | – | – | – | Built 2010 | 28.0 | -13.199 | -58.985 | – | |
| – | – | – | – | – | 26.7 | -11.777 | -76.093 | – | |
| – | – | – | – | Built 2016 | 26.5 | -12.290 | -74.685 | – | |
| – | – | – | – | Built 2010 | 25.2 | -13.075 | -58.976 | – | |
| – | – | – | – | – | 22.8 | 2.893 | -60.989 | – | |
| – | – | – | – | Built 2003 | 18.8 | -12.194 | -55.594 | – | |
| – | – | – | – | Built 1994 | 18.8 | 0.923 | -59.331 | – | |
| – | – | – | – | Built 2001 | 17.4 | -12.990 | -55.861 | – | |
| – | – | – | – | Built 2004 | 16.8 | -0.233 | -78.158 | – | |
| – | – | – | – | Built 2000 | 13.8 | -11.879 | -56.297 | – | |
| – | – | – | – | Built 2009 | 13.5 | -9.311 | -51.420 | – | |
| – | – | – | – | Built 1995 | 12.5 | -10.121 | -53.674 | – | |
| – | – | – | – | Built 2016 | 12.0 | 0.112 | -77.962 | – | |
| – | – | – | – | Built 1990 | 10.6 | -11.102 | -54.556 | – | |
| – | – | – | – | Built 2003 | 10.3 | -17.942 | -64.572 | – | |
| – | – | – | – | – | 10.3 | -17.274 | -63.276 | – | |
| – | – | – | – | Built 1995 | 9.2 | -0.781 | -60.017 | – | |
| – | – | – | – | Built 1999 | 9.0 | -10.568 | -59.208 | – | |
| – | – | – | – | – | 8.6 | 1.462 | -75.480 | – | |
| – | – | – | – | Built before 1985 | 8.2 | 3.216 | -61.090 | – | |
| – | – | – | – | Built before 1985 | 7.1 | -11.478 | -76.272 | – | |
| – | – | – | – | Built 1989 | 7.0 | -13.526 | -52.449 | – | |
| – | – | – | – | Built 2015 | 6.8 | -13.356 | -57.615 | – | |
| – | – | – | – | Built 1992 | 6.8 | -13.144 | -52.572 | – | |
| – | – | – | – | Built 1993 | 6.7 | -10.286 | -55.750 | – | |
| – | – | – | – | Built 1988 | 4.8 | -7.107 | -55.392 | – | |
| – | – | – | – | – | 4.6 | -10.219 | -52.433 | – | |
| – | – | – | – | – | 4.6 | -2.077 | -78.204 | – | |
| – | – | – | – | Built 1989 | 4.5 | -10.394 | -56.577 | – | |
| – | – | – | – | Built 1987 | 4.4 | -11.679 | -61.825 | – | |
| – | – | – | – | Built 1999 | 4.2 | -10.646 | -68.101 | – | |
| – | – | – | – | – | 4.2 | -14.318 | -55.793 | – | |
| – | – | – | – | Built 2006 | 4.1 | -1.193 | -78.827 | – | |
| – | – | – | – | Built 1989 | 4.0 | -10.378 | -58.508 | – | |
| – | – | – | – | – | 4.0 | -0.379 | -78.158 | – | |
| – | – | – | – | – | 3.9 | -11.947 | -51.881 | – | |
| – | – | – | – | Built 1999 | 3.8 | -11.497 | -56.978 | – | |
| – | – | – | – | Built 2016 | 3.5 | -19.322 | -64.491 | – | |
| – | – | – | – | Built 1998 | 3.3 | -10.472 | -67.665 | – | |
| – | – | – | – | – | 3.2 | -11.893 | -51.914 | – | |
| – | – | – | – | – | 3.0 | -7.502 | -72.614 | – | |
| – | – | – | – | Built 1999 | 2.9 | -11.758 | -56.809 | – | |
| – | – | – | – | Built 1997 | 2.8 | -15.787 | -61.684 | – | |
| – | – | – | – | Built 1988 | 2.7 | -10.419 | -55.749 | – | |
| – | – | – | – | – | 2.5 | -10.589 | -53.864 | – | |
| – | – | – | – | Built 1990 | 2.5 | -7.335 | -73.079 | – | |
| – | – | – | – | Built 1994 | 2.4 | -10.449 | -53.794 | – | |
| – | – | – | – | – | 2.4 | -11.168 | -56.810 | – | |
| – | – | – | – | Built 1998 | 2.3 | -10.639 | -65.103 | – | |
| – | – | – | – | Built 2006 | 2.1 | -12.309 | -55.446 | – | |
| – | – | – | – | Built 2000 | 2.0 | -10.333 | -58.500 | – | |
| – | – | – | – | – | 2.0 | -10.139 | -67.679 | – | |
| – | – | – | – | – | 1.9 | -10.682 | -68.200 | – | |
| – | – | – | – | Built 2012 | 1.9 | -1.209 | -78.809 | – | |
| – | – | – | – | Built 1988 | 1.8 | -10.350 | -55.717 | – | |
| – | – | – | – | – | 1.8 | -7.173 | -78.277 | – | |
| – | – | – | – | Built 1995 | 1.7 | -11.094 | -57.096 | – | |
| – | – | – | – | Built 1986 | 1.6 | -10.041 | -67.594 | – | |
| – | – | – | – | – | 1.6 | -10.470 | -55.643 | – | |
| – | – | – | – | – | 1.6 | -1.398 | -78.383 | – | |
| – | – | – | – | – | 1.5 | -10.699 | -68.206 | – | |
| – | – | – | – | – | 1.5 | 2.912 | -60.325 | – | |
| – | – | – | – | Built 1987 | 1.5 | -10.331 | -58.536 | – | |
| – | – | – | – | Built 2000 | 1.4 | -11.165 | -68.570 | – | |
| – | – | – | – | Built 1995 | 1.0 | -10.403 | -58.510 | – | |
| – | – | – | – | Built 1995 | 0.9 | -9.453 | -64.608 | – | |
| – | – | – | – | Built 2001 | 0.9 | -7.721 | -72.791 | – | |
| – | – | – | – | – | 0.8 | -7.744 | -72.704 | – | |
| – | – | – | – | Built 1998 | 0.7 | -10.735 | -68.415 | – | |
| – | – | – | – | Built 1991 | 0.4 | -10.660 | -68.259 | – | |
| – | – | – | – | – | 0.4 | -11.077 | -68.569 | – | |
| – | – | – | – | – | 0.3 | -10.350 | -58.501 | – | |
| – | – | – | – | – | 0.3 | -7.080 | -55.412 | – | |
| – | – | – | – | Built 1987 | 0.3 | -7.264 | -73.082 | – | |
| – | – | – | – | Built before 1985 | 0.3 | -7.699 | -72.777 | – | |
| – | – | – | – | Built 2000 | 0.3 | -9.468 | -62.430 | – | |
| – | – | – | – | Built 1988 | 0.3 | -10.148 | -53.703 | – | |
| – | – | – | – | – | 0.2 | -7.719 | -72.708 | – | |
| – | – | – | – | Built 1997 | 0.2 | -7.700 | -72.825 | – | |
| – | – | – | – | – | 0.2 | -7.692 | -72.891 | – | |
| – | – | – | – | Built 1999 | 0.2 | -7.749 | -72.686 | – | |
| – | – | – | – | – | 0.2 | -7.679 | -72.771 | – | |
| – | – | – | – | Built 2000 | 0.2 | -7.225 | -73.101 | – | |
| – | – | – | – | – | 0.2 | -10.682 | -68.229 | – | |
| – | – | – | – | Built 2003 | 0.1 | -7.309 | -73.065 | – | |
| – | – | – | – | Built 2000 | 0.1 | -7.089 | -55.411 | – | |
| – | – | – | – | Built 1989 | 0.1 | -7.724 | -72.675 | – | |
| – | – | – | – | – | 0.1 | -9.383 | -62.553 | – | |
| – | – | – | – | Built 1988 | 0.1 | -7.332 | -73.065 | – | |
| – | – | – | – | – | 0.1 | -7.694 | -72.813 | – | |
| – | – | – | – | – | 0.1 | -7.727 | -72.675 | – | |
| – | – | – | – | Built 1989 | 0.1 | -7.740 | -72.692 | – | |
| – | – | – | – | – | 0.1 | -10.340 | -58.468 | – | |
| – | – | – | – | – | 0.1 | -10.311 | -58.529 | – | |
| – | – | – | – | Built 1999 | 0.1 | -10.304 | -58.560 | – | |
| – | – | – | – | – | 0.1 | -7.700 | -72.699 | – | |
| – | – | – | – | Built 1988 | 0.1 | -12.577 | -60.965 | – |
Dams serve many useful purposes including electric generation, water supply, irrigation, flood control, and recreation. However, if not carefully planned and managed, dams can have a heavy environmental impact. Today, many governments are removing dams to bring rivers back to life.
For a detailed look at the impact of dams on people and the environment, I encourage you to read the book Silenced Rivers by Patrick McCully. To help conserve free-flowing rivers, consider supporting the nonprofit International Rivers.
Data on dams comes from the Global Dam Watch database, published in July 2024. For more details, see the journal article by lead researchers at McGill University (Lehner et al. 2024).
The longest river is the watershed is 6,570 km long. It is shown highlighted in the map in Figure 5 below.
This is the longest continuous flowline in the source dataset, MERIT-Basins. It is not necessarily the mainstem of the river, or the one with the same name. When it comes to naming rivers, historical, legal, and cultural influences are also important.
Figure 6 shows the elevation profile of the longest river reach. Elevations on the plot are in meters above mean sea level. The river begins at 4,180 m, and the outlet is at 0 m.
Elevation data for the profile plot is from MERIT-DEM (Yamazaki et al. 2017). Actual distances on the plot may be underestimated somewhat. This is because river paths are based on a grid or raster data, which simplifies the meandering path of real-world rivers.
Terrain elevations in the watershed range from 0.3 m to 5143.1 m above sea level. The average, or mean, elevation is 435.0 m. Figure 7 shows a distribution of terrain elevations in the watershed.
Elevation data is provided by EarthEnv (Amatulli et al. 2021), and is based on the Global Multi-resolution Terrain Elevation Data 2010 dataset (GMTED2010). Statistics are based on gridded elevation data with pixels that are about 10 km on a side. Because of the size of the pixels, some smoothing takes place, and so the statistics reported above may not capture the true minimum and maximum elevation.
Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., & Jetz, W. (2018). A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5(1), 180040. https://doi.org/10.1038/sdata.2018.40
Fick, S.E., and R.J. Hijmans. 2017. “WorldClim 2: New 1‐km Spatial Resolution Climate Surfaces for Global Land Areas.” International Journal of Climatology 37 (12): 4302–15. https://doi.org/10.1002/joc.5086
Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3
Lehner, B., Beames, P., Mulligan, M. et al. The Global Dam Watch database of river barrier and reservoir information for large-scale applications. Scientific Data 11, 1069 (2024). https://doi.org/10.1038/s41597-024-03752-9
Liu, L., X. Cao, S. Li, and N. Jie. “A 31-Year (1990–2020) Global Gridded Population Dataset Generated by Cluster Analysis and Statistical Learning.” Scientific Data 11, no. 1 (January 24, 2024): 124. https://doi.org/10.1038/s41597-024-02913-0
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