dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , 81% primary versus 19% secondary forests for the year of 2015, see Supplementary Note 4). Model validation. The ORCHIDEE model was rigorously validated against observations to ensure a correct estimate of E LUC in this study (for details see Supplementary Note 3). To properly simulate deforestation emissions, the spatial distribution of modeled aboveground biomass and deforestation area were compared with satellite observations (Supplementary Figs. 5  and 6 ). To capture secondary forest recovery, a database of forest biomass growth was constructed based on chronosequence observations. Modeled biomass-age relationships were then evaluated against this database for different forest types (Supplementary Fig. 7 , Supplementary Table  2 ). Different from bookkeeping models, ORCHIDEE DGVM can account for global environmental changes and simulated forest carbon sinks can thus be compared with regional forest inventory data (Fig.
Acknowledgements C. Y. and P. C. acknowledge funding from the  European Commission  project  LUC4C  (grant no. 603542 ) and from the  European Research Council  through Synergy grant  ERC-2013-SyG-610028  " IMBALANCE-P ". C. Y. is also supported by the  Strategic Priority Research Program of Chinese Academy of Sciences  (grant no. XDB40000000 ) and by the  National Natural Science Foundation of China  (grant no. 41971132 ). We acknowledge the help by  Junzhi Ye  in compiling the forest biomass growth database.
Data availability The Global Carbon Budget 2017 data released by the GCP is available at https://www. icos-cp. eu/global-carbon-budget-2017. The LUH2 data are available at https://luh. umd. edu/data. shtml. The Source data underlying Figs. 2 3 4 5 6 7 are provided as a Source data file. The satellite-based tree cover loss data used in Supplementary Fig. 5  are available at https://earthenginepartners. appspot. com/science-2013-global-forest/download_v1. 2. html. The availability of satellite-based biomass datasets used in Supplementary Fig. 6 is as follows: GEOCARBON: https://www. wur. nl/en/Research-Results/Chair-groups/ Environmental-Sciences/Laboratory-of-Geo-information-Science-and-Remote-Sensing/ Research/Integrated-land-monitoring/Forest_Biomass. htm; GlobBiomass: https://doi. pangaea.
de/10. 1594/PANGAEA. 894711. CCI Biomass: http://cci. esa. int/biomass; Liu et al. 46 : http://wald. anu. edu. au/data_services/data/global-above-ground-biomass-carbon-v1-0/. Raw model output data and other data underlying all figures, including Supplementary ones, are provided at Figshare (https://doi. org/10. 6084/m9. figshare. 12401615). The python scripts used to process the data are available from the corresponding author upon reasonable request.
Code availability The ORCHIDEE model code used in this study is open-source and distributed under the CeCILL (CEA CNRS INRIA Logiciel Libre) license. The code is hosted on the official ORCHIDEE svn server and tagged as revision 6545. It can be downloaded in a linux system using the svn command of "svn co --username anonymous svn://forge. ipsl. jussieu. fr/orchidee/branches/publications/ORCHIDEE_GLUC_r6545 ORCHIDEE", with both username and password as "anonymous". Guidance to install and run the model is provided at https://forge. ipsl. jussieu. fr/orchidee/wiki/Documentation/UserGuide. Access to the official run environment is restricted to registered users; requests can be sent to the corresponding author.
Acknowledging outsider role When working with groups of children, pre-existing relationships often come with already established power dynamics. The relationships between the children are further developed within but also, more importantly, outside of the design sessions with researchers. As such, researchers will always remain outsiders to these dynamics. However, the children's relationships, conflicts and communal understanding seep into design, which means these are fundamental to consider for PD researchers. Within a third group of children, Xenia, who had been diagnosed with Trisomy 21, was often excluded from playing with the other four children (two with an Autism diagnosis, one with an ADHD diagnosis). She was furthermore the only child growing up without speaking Turkish in the home.
High dimensional cytometry is an innovative tool for immune monitoring in health and disease, it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of multiparametric "big data" usually requires specialist computational knowledge. Here we describe ImmunoCluster (https://github. com/kordastilab/ImmunoCluster) an R package for immune profiling cellular heterogeneity in high dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a non-specialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs.
Often, a limiting factor to these analyses are the requirement of an in-depth knowledge of computational biology. There is an unmet need for an easy-to-use open-source computational framework, to explore high dimensional single-cell cytometry datasets such as LMC, IMC and FC for nonspecialists. Thus, we developed a framework which will provide appropriate data structures and methods to increase the utility of these techniques and make data analysis and interpretation accessible to all researchers to facilitate immune phenotyping projects, such as those associated with clinical trials. Here, we present ImmunoCluster, an open-source computational framework for the analysis of highdimensional LMC, IMC, and FC datasets. ImmunoCluster is an R package and framework designed for discovery, visualization of immune subpopulations, and analysis of changes in cell phenotypes and abundance across experimental conditions, including serial samples from a clinical trial setting.
The outputs from ImmunoCluster are created using the ggplot package which generates highresolution images that can be utilized directly into figures for reports and publications. Importantly, and unique to ImmunoCluster, the scope of functionality in this package also enables the analysis of IMC data with minimal supervision. IMC provides a means to analyze the spatial dimension of the cells in situ within tissues, which can provide important insight when ascribing functionality to cell populations. Methods for analyzing large cytometry datasets, in particular IMC datasets, in an open-source computational environment are currently limited. ImmunoCluster has been designed for use by a non-specialist, however would be useful to a range of users from wet-lab non-specialist experimentalists to experienced computational biologists with potential utility in day-to-day research through to large-scale clinical trials involving multiple longitudinal serial biopsies where samples need to be analyzed and compared.
Clinical immune monitoring is an area of increasing utilization  [6]. In the current work we will present examples of ImmunoCluster's ability to detect clinically relevant perturbations in immune cellular heterogeneity in LMC, IMC and FC datasets in human patient and/or healthy samples. ImmunoCluster is easily extensible to several arbitrary experimental designs and can be utilized to aid both discovery of novel subpopulations of cells within heterogeneous samples or as a foundation for monitoring longitudinal changes or responses to treatment. As such, ImmunoCluster provides a resource that will permit high dimensional data to be more widely adopted in immuno-biology and many other disciplines.
Methods Data file pre-processing and preparation for implementation into the ImmunoCluster framework Liquid mass cytometry data Here we describe an example of the ImmunoCluster package analyzing previously published mass cytometry data (CyTOF®) from 15 leukemia patients 30 and 90 days after bone marrow transplantation (BMT) which has been published by Hartmann et al. [7]. After BMT three of these patients suffered acute graft versus host disease (GvHD) and 12 had no evidence of GvHD (noted as 'none' in all figures). For systems-level biomarker discovery within this trial, a panel of 33 antibodies were incorporated into the immunophenotyping panel (Table  S1 ). The panel was designed to cover all major immune cell lineages and several functional subsets including; T-, B-, NK and myeloid cells and granulocytes (detailed in  [7] ).
The marker panel also included a variety of immune-regulatory proteins, such as PD-1, PD-L1, TIM3, and TCRVa24-Ja18. The publicly available dataset from the Hartmann et al. study was extracted for this study from the FlowRepository (http://flowrepository. org/id/FR-FCM-Z244)  [8]. Raw FCS files for the LMC dataset collected on the Helios™ CyTOF® system (Fluidigm, UK) machine were first normalized using the free CyTOF® 6. 7 system control software (files can also be concatenated using the same software if necessary). These files were gated using FlowJo™ (version: 10. 5. 3) (Becton, Dickinson and Company, UK) to remove beads and doublets, dead and non-CD45 + cells as well as erythrocytes (CD235αβ/CD61 + ) and neutrophils (CD16 + ) (Figure  S1 ), as described by Hartmann et al. [7] , before implementation into the ImmunoCluster framework.
Imaging mass cytometry data Two different examples of IMC datasets were implemented into the framework. One IMC dataset was from a human tissue section taken from a patient with head and neck squamous cell carcinoma (HNSCC). Consent was attained by the King Guy's & St Thomas' Research Biobank, within King's Health Partners Integrated Cancer Centre. The second IMC dataset was from a lymph node section from a patient with diffuse large B-cell lymphoma (DLBCL). Formalin-fixed paraffin-embedded (FFPE) DLBCL tumor tissue was obtained from King's College Hospital, in accordance with the Declaration of Helsinki and approved by the UK National Research Ethics Committee (reference 13/NW/0040). A detailed protocol is available for both datasets as a Supplementary File (File S1 and S2, respectively).
The raw HNSCC IMC data from 12 channels (10 markers + 2 intercalator-Ir channels (Table  S2 ), and the raw DLBCL IMC data from 20 channels (19 markers + nuclei channel) (Table  S3 ), was processed with Python scripts and CellProfiler pipelines according to Schulz et al. [11]  to segment the image data into individual cells. The mean marker intensity for each segmented cell was exported (comma-separated values (CSV) file) and divided into 6 regions of equal area (3 tumor and 3 stroma regions) (Figure  S2 ). The mean marker intensity for each segmented cell was multiplied by 65535 to recover the image intensities (Fluidigm IMC machine = 16-bit, the dynamic range of the measurements are 0 to (2 16 )-1) and asinh (co-factor 0. 8) transformed in R Studio. These CSV files were converted into FCS files and gated (Figure  S2 ) in Cytobank  [12] , a function is also available within the ImmunoCluster package to convert files into FCS format.
Accessing the open-source ImmunoCluster tool The ImmunoCluster package is accessible via GitHub (https://github. com/kordastilab/ImmunoCluster), where a detailed step-by-step walkthrough of the tool is available. A number of previous workflow pipelines influenced the development of ImmunoCluster, including CyTOF workflow  [13] , CATALYST  [14] , cytoClustR  [15] , and Diggins et al. methodology  [16]. The framework has incorporated methods for applying popular R packages (i. e. RPhenograph, Rtsne and FlowSOM), and its visualization tools were developed using scDataviz, a Bioconductor package  [17]  for visualizing single cell data. Users will need to have R downloaded onto their desktop to run the ImmunoCluster tool.
However, the SingleCellExperiment object can store any other form of arbitrary dimensionality reduction, such as principle component analysis (PCA)  [20]  or DiffusionMaps  [21] , amongst others. c. After the data has been visualized in dimensionality reduced space, clustering algorithms can be used to identify cell communities within the data, which allows the identification of phenotype and abundance of cell clusters within populations/groups being analyzed. A number of clustering algorithms are available within the framework to define cell populations of interest in an unbiased manner. Firstly, an ensemble (use of multiple algorithms) clustering method of FlowSOM  [22]  and Consensus clustering (ConsensusClusterPlus R package  [23] ), which tests the stability of the clusters, leading to better results than applying a basic hierarchical clustering algorithm.
Case studies This section aims to convey the scope of ImmunoCluster, its versatility, and applicability to complex datasets across a range of high dimensional approaches. We implemented the ImmunoCluster framework for three different types of high dimensional single cell data (LMC, IMC, and FC). We firstly used previously published and publicly available LMC data  [7]. This dataset was chosen as it is representative of an intricate immunophenotyping project and would test the ability of ImmunoCluster to reproduce published results. Secondly, we investigated two novel IMC datasets, from HNSCC and DLBCL patients with the aim of demonstrating the applicability of the ImmunoCluster tool for IMC data analysis and cell cluster identification and visualization. Finally, FC data from the BM of 7 HDs during hip surgery were used to show the ability of the framework to analyze conventional FC data and identify rare immune cell populations.
Liquid mass cytometry The chosen dataset  [7]  includes mass cytometry data from 15 patients with leukemia who all received BMT, three of these patients developed acute GvHD. Samples were taken at 30 and 90 days post-BMT. Thirty-three cell markers, both lineage and functional were analyzed using mass cytometry (Helios™ CyTOF® system). Prior to uploading into the ImmunoCluster framework the FCS files were normalized and gated as described in the methods section. An initial visualization of the data was first carried out using MDS plots, these were overlaid with metadata to identify the similarity of patients by condition and day of measurement (Figure  3A  and 3B). A heatmap was also created to give an overview of marker expression for each patient and annotated with the metadata provided (Figure  3C ).
Additionally, an elbow plot can be created alongside the clustering algorithm output to guide the decision on the best number of clusters for downstream analysis (Figure  S6 ). Over-clustering can allow for the identification of rare cell types within a dataset, at the expense of often generating several clusters of highly prevalent cell types that likely represent the same biological cell type. ImmunoCluster provides the tools to manually and reproducibly merge clusters of the same biological cell identity into one group after over-clustering. These clusters can additionally be merged into each other to use a less granular, higher-level population annotation (see Figure  S9  for an example of higher-level clustering). The abundance of all cell types across all samples was measured, and CD14 + CD16 -monocytes were identified as the most abundant population, correlating with the Hartmann et al.
data  [7]  (Figure  4E ). Individually displaying patient's cell type abundance means that we could identify variation within a group of interest, for example we can see that although there is variation within the GvHD and none, overall, they appear to follow the same trends in cell type abundance (Figure  5A ). Additionally, significant differences between memory B-cells (FDR p=4. 38 x 10 -3 ), naïve B-cells (FDR p=1. 35 x 10 -2 ), and naïve CD4 + T-cells (FDR p=3. 47 x 10 -2 ) were identified between the GvHD and none (Figure  5B  and C ). A difference in number of naïve B-cells was previously reported by Hartmann et al. [7]  in this dataset and can be seen in Figure  4A  (using Figure  4D  for cell island identification), where there is a noticeable reduction of these cells in the GvHD patients.
Imaging mass cytometry datasets We utilized two different IMC datasets to demonstrate how ImmunoCluster would handle IMC data from two different tumor microenvironments which possess, a) a biological image with clear boundaries, such as HNSCC, where clear tumor and stroma regions are evident; b) a biological image which was more heterogeneous and diffuse, such as the DLBCL lymph node section. ImmunoCluster's ability to explore IMC data adds a novel element to the framework and demonstrates the flexibility and applicability of the tool. This unique element within the framework means that researchers can explore IMC data easily, in-line with LMC and FC data. After data pre-processing (see methods and Figure  S2 ), the transformed gated FCS files were uploaded into the ImmunoCluster framework.
Discussion Here we provide an overview of the flexibility and scope for utilizing ImmunoCluster, an open-access nonspecialist framework using LMC, IMC and FC datasets. We have emphasized the ease-of-use, clinical utility, and quality of outputs which can be created. Explanations of the functions and workflow have been provided and an in-depth step-by-step protocol and tutorial are available on the GitHub site (https://github. com/kordastilab/ImmunoCluster). The purpose for creating the ImmunoCluster framework was to support researchers carrying out complex immunophenotyping experiments. The dawn of 'big data' and its ever-growing utilization has surpassed the number of experimental parameters that a researcher can feasibly analyze as a collective. As computational training is still a niche area, the ImmunoCluster framework was designed in collaboration between wet lab and computational biology scientists, with the purpose of creating a tool which was sophisticated in its analysis, yet easy to use in its application.
e. RPhenograph, Rtsne and FlowSOM), selected because they are open-source and regularly maintained with extensive documentation. All methods prioritize simple, understandable and attractive visualizations designed to be used by both dry-and wet-lab researchers/clinicians. The computational methods used within the framework are importantly readily scalable to a dataset of millions of cells across several samples and a variety of experimental or phenotypic conditions. ImmunoCluster can also be used on a local desktop with standard configuration, and depending on the number of cells, can usually be run within a day end-to-end. The limitations of this computational framework are important to take into consideration when designing an experiment. Unsupervised Clustering (Stage 2) of samples is the most important stage of the framework and its ability to accurately define populations across all samples is critical to all later stages of investigation.
The number of k clusters to generate (cluster resolution) can significantly affect the ability to identify or determine a change in a biologically relevant population. Merging two subpopulations due to low resolution of clustering may mask an important experimental observation. Determining the precise number of clusters that are relevant for a given dataset is an important step. In our step-by-step guide we provide complete documentation and clear user-friendly approaches to optimally define this. Technical inter-sample marker signal variability due to batch effects may impact the ability of unsupervised analysis to reliably detect certain populations, if significant batch effects are present. One approach commonly employed in mass cytometry to overcome this is to use sample barcoding, reducing the variability between each sample, allowing all samples to be exposed to the same antibody mixture.
The laser module and sensor head have been provided with an active thermal stabilization, allowing for a potential operation temperature range between 0 • C and 40 • C. Power consumption has been reduced to less than 500 Watt and battery operation is possible for field experiments. Improvement in ease of use and transportability has been achieved with each element weighing 40 kg or less. The enclosure for the lasers and the sensor head has been made water-and airtight. Connectors and cables are suitable for field conditions and a reduction in the number of connectors further facilitates fast and efficient field set-up. As an example, shifting the AQG#B01 in the Larzac observatory from one pillar to another takes around 5 minutes for one person. In late April 2020, additional AQG#B01 and FG5#228 measurements and tests were continued in the facilities of the laboratory Géosciences Montpellier in the cellar, about 100 kilometers southwest of the Larzac site.
Methods and experiments The experiment timeline and data availability are displayed in Figure  1. The iGrav#002 data are available continuously. Software malfunctioning or updates and seismic events caused data gaps in the AQG#B01 series. In late January a seismic event made a restart necessary and caused no damage to the instrument. Improvements to avoid loss of measurements caused by these incidents are in progress. An instrument test was conducted remotely by the developer on March 23rd. Apart from those, an offset of 100 nm. s -2 was observed to emerge in the AQG#B01 gravity time series before the second temperature test on 10/02/2020. The cause is still under investigation and the authors are in contact with the instrument developer. Additional monitoring variables registered during operation are being investigated.
Drift, accuracy and sensitivity The sensitivity is assessed by calculating the Allan deviation  (Allan (1966) ) as a measure of frequency stability over the gravity data series. The Allan deviation is calculated for the three gravimeters (FG5#228, AQG#B01, and iGrav#002) after classical post-processing: calibration and drift correction of the iGrav#002 data set, correction for solid Earth and ocean loading tides using Tsoft  (Camp and Vauterin (2005) ), atmospheric pressure and polar motion for all data sets. Polar coordinates were 5 obtained from the International Earth rotation and Reference systems Service. Site-specific combined ocean and solid Earth tidal parameters had been estimated with ETERNA  (Wenzel (1996) ) software based on long-term iGrav#002 time series  (Fores et al. (2016a) ).
Gravity residuals refer hereafter to the processed gravity data set. The influence of global, non-local hydrological and atmospheric gravity effects on the Larzac site was estimated using the EOST loading service  (Boy and Hinderer (2006) ;  Boy and Lyard (2008) ;  Boy et al. (2009) ) applying the model GLDAS/Noah 10 (v2. 1)  (Rodell et al. (2004) ). Gravity residuals obtained from AQG#B01 and iGrav#002 were related to local cumulative precipitation obtained from on-site rain gauges to assess the detectability of small hydrogeological mass changes. A 1D hydrological model using rainfall as input describes the gravity changes due to hydrological mass changes adequately  (Fores et al. (2016a)  (1) G refers to the gravitational constant, ρ to the density of water and the plate thickness H refers to the cumulative rainfall.
Tilt calibration under adjusted ambient temperatures Instrument tilt variations are monitored by a tiltmeter located at the top of the sensor head. This allows for a constant monitoring and in-line post-correction of g values according to the effective tilt experienced by the instrument. Mechanical alignment between the vertical measurement axis and the tiltmeter reference is susceptible to drifts. Uncorrected tilts lead to the measurement of a projection of g as compared to a well-aligned, vertical measurement. The tiltmeter reference offset can be assessed using gravity measurements and directly used for gravity correction in the AQG acquisition software. For this calibration, several measurements (~20) are performed for various x and y tilt values, up to one mrad from the initial position.