The data consists of litterfall production in a fertilised old growth forest in Central Amazon. Data was collected in a full factorial nutrient addition experiment (nitrogen, phosphorus and cation treatments). Within each plot we have installed five litter traps of 50 cm x 50 cm, 1 m above ground, occupying an area of 1.25 m2 per plot, and ensuring litter reaching the trap was produced within the experimental plot area. The study was funded by NERC, BDFFP (logistical support) and the Brazilian government (students scholarship). Full details about this dataset can be found at https://doi.org/10.5285/c0294ec9-45d6-464c-b543-ce9ece9fd968

The data consists of leaf nutrients from litterfall in a fertilised old-growth forest in the Central Amazon. Data was collected in a full factorial nutrient addition experiment (nitrogen, phosphorus, and cation treatments). The dataset includes realized nutrient concentration carbon, nitrogen, phosphorus, potassium, magnesium, calcium in grams and manganese, zinc in miligrams. Also hemicellulose, cellulose and lignin as a percentage. The fieldwork was completed in August of 2017, 2018, and 2019. The study was funded by NERC, BDFFP (logistical support), and the Brazilian government (students scholarship). Full details about this dataset can be found at https://doi.org/10.5285/c2eaade3-5395-4c62-9733-d9dbed1546af

The data consists of carbon, micro and macro nutrient concentrations in fine roots (<2mm diameter) in old growth forests in Central Amazon. Fine roots younger than three months were sampled using the ingrowth core technique in a large-scale nutrient fertilisation experiment. Carbon and nutrient concentrations refer to year one of fertilisation, bulking samples from November 2017 to August 2018. Concentrations are given as a mean of the community per plot in year 1, where five points inside each plot were sampled in the 0-10 cm and 10-30 cm soil layers. The dataset depicts the concentrations of iron, zinc, manganese, calcium, magnesium, potassium, phosphorus, nitrogen and carbon in fine roots in two different soil depths. Samples were collected at the AFEX project area in Manaus, Brazil at the Biological Dynamics of Forest Fragments Project (BDFFP/ INPA). The study was funded by NERC, BDFFP (logistical support) and Brazilian government (student scholarship). Full details about this dataset can be found at https://doi.org/10.5285/ba190356-1731-4558-be7a-3fedc570663a

The Brazil-UK Network for Investigation of Amazonian Atmospheric Composition and Impacts on Climate (BUNIAACIC) collaboration was a NERC (Natural Environment Research Council) funded project (NE/I030178/1) This project aimed to develop a coherent strategy for UK studies of atmospheric composition and impacts in the Amazon. This dataset contains measurements from the Manchester UV-LIF spectrometer data (fluorine and chlorine number concentration and particle size distribution) processed with "MUTANT data processing toolkit (MAN-WIBS3M)

The Brazil-UK Network for Investigation of Amazonian Atmospheric Composition and Impacts on Climate (BUNIAACIC) collaboration was a NERC (Natural Environment Research Council) funded project (NE/I030178/1) This project aimed to develop a coherent strategy for UK studies of atmospheric composition and impacts in the Amazon. This dataset contains cloud condensation nuclei measurements.

The Brazil-UK Network for Investigation of Amazonian Atmospheric Composition and Impacts on Climate (BUNIAACIC) collaboration was a NERC (Natural Environment Research Council) funded project (NE/I030178/1) This project aimed to develop a coherent strategy for UK studies of atmospheric composition and impacts in the Amazon. This dataset contains humidity and aerosol measurements from the Manchester Hygroscopicity Tandem Differential Mobility Analyser (man-htdma)

The Brazil-UK Network for Investigation of Amazonian Atmospheric Composition and Impacts on Climate (BUNIAACIC) collaboration was a NERC (Natural Environment Research Council) funded project (NE/I030178/1) This project aimed to develop a coherent strategy for UK studies of atmospheric composition and impacts in the Amazon. This dataset contains black carbon measurements by the Manchester Multiangle Absorption Photometer (MAN-MAAP-BC)

The Brazil-UK Network for Investigation of Amazonian Atmospheric Composition and Impacts on Climate (BUNIAACIC) collaboration was a NERC (Natural Environment Research Council) funded project (NE/I030178/1) This project aimed to develop a coherent strategy for UK studies of atmospheric composition and impacts in the Amazon. This dataset contains measurements from the Aerodyne Aerosol Chemical Speciation Monitor operated by Universidade de Sao Paulo (USP-ACSM)

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations. Plot VCR-02 is located in Nova Xavantina, Brazil, is part of the Global Ecosystem Monitoring (GEM) network and is managed by UNEMAT The TLS data were collected on a 10 m x 10 m grid where at each position the scanner captured data in an upright and tilted position. The scanner was set to an angular step of 0.04 degrees for all scans. In between each scan position, a set of retro-reflective targets were positioned to be used as tie-points between scans. For more information on TLS acquisition refer to Wilkes et al. (2017). Scan data were coregistered using RiSCAN Pro, the 4x4 rotation transformation matrices to transform the point cloud data into a common reference coordinate system can be found in the "matrix" directory.

This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations. Plot NXV-01 is located in Nova Xavantina, Brazil, is part of the Global Ecosystem Monitoring (GEM) network and is managed by UNEMAT The TLS data were collected on a 10 m x 10 m grid where at each position the scanner captured data in an upright and tilted position. The scanner was set to an angular step of 0.04 degrees for all scans. In between each scan position, a set of retro-reflective targets were positioned to be used as tie-points between scans. For more information on TLS acquisition refer to Wilkes et al. (2017). Scan data were coregistered using RiSCAN Pro, the 4x4 rotation transformation matrices to transform the point cloud data into a common reference coordinate system can be found in the "matrix" directory.