dataesr/scientific-paragraphs-categorization huggingface dataset:
Ancillary vegetation dataset Two ancillary vegetation datasets were used in this study. The MODIS-based land cover map over Africa was used to assist in soil model parameter calibration and to interpret the VOD inter-comparison results as a function of land cover types. This land cover map is produced by combining the 500 m MCD12Q1 (Collection 6) over 2017 in the International Geosphere-Biosphere Programme (IGBP) scheme (Sulla-Menashe and Friedl, 2019). In addition, the tree cover percentage (TCP) data from MOD44B Vegetation Continuous Fields (VCF) product  (DiMiceli et al. , 2015)  over 2017 were used to assist in calibrating the soil and vegetation model parameters. The TCP data were re-scaled to the 0. 25 degree grid by arithmetic averaging, and the land cover type with the maximum cover fraction in each 0.
5 m 2 ⋅m -2. Then the "bare soil" pixels were divided into two categories: pixels where σ • obs(dB) is sensitive to soil moisture (SM) (category 1) and pixels where σ • obs(dB) is in very dry conditions all the time, so that σ • obs(dB) ~ constant (category 2). More specifically, to distinguish pixels/dates corresponding to categories 1 and 2 we used the following criteria: First, we extracted the time series of σ • obs(dB) and soil moisture (SM) from, respectively, the ASCAT and ERA5 Land SM datasets for pixels/ dates corresponding to the case 1 and 2. For category 1, the standard deviation of σ • obs(dB) and SM (corresponding to the dates where LAI < X. Liu et al. 0. 5 m 2 ⋅m -2 or LAI = Nan) should be larger than 0. 5 dB and 0. 04 m 3 /m 3 , respectively, and the number of the σ • obs(dB) data corresponding to these two cases should be larger than 30% of the whole number of backscatter observations.
Step 3: soil parameters calibration for all pixels Based on the results of step 2 (where we computed the values of the C and D parameters from the pixels where these parameters could be determined), we used the corresponding soil property data (Appendix Table  1 ) and terrain data (Appendix Table  2 ) as predictors to train two random forest (RF) regression models for the C and D parameters, separately. RF regression is a machine learning method that has the advantage to be a nonlinear and nonparametric method, and the contribution of each predictor to the target that is computed by the RF model is very useful for tuning the model. We implemented the RF analysis using the python sklearn package  (Pedregosa et al. , 2011)  for each soil model parameter. The GridSearchCV function was used to find the optimal setting of the two RF parameters (n_estimators and max_features).
Evaluation of IB VOD The performance of IB VOD was evaluated in both space and time. The spatial correlation between IB VOD and AGB (Saatchi AGB, CCI AGB), TH  (Simard TH, Patapov TH)  and the temporal correlation between IB VOD and VIs (NDVI, EVI and LAI) were computed as performance metrics. In addition, three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) retrieved at the same frequency band (C-band) were also included in the inter-comparison. As VODCA LPRM V6 VOD data was only updated until the year 2018, the temporal performance of IB VOD was evaluated from 2015 to 2018. The changing patterns with the latitude of IB VOD are more consistent with those of V16 VOD which is computed from the same sensor (ASCAT) (Fig. 8(d) ). There are also some differences between IB VOD and the three other VOD.
Evaluating IB VOD against aboveground biomass and tree height When considering the spatial relationship between the four yearly average VOD and AGB (Fig. 10 ), it was found to be almost linear for the active VOD datasets (IB and V16) and quite non-linear (exponential form) for the passive ones. In terms of linear fit, highest spatial correlation values were obtained with IB VOD (R = 0. 92), followed by V16 VOD (R = 0. 83-0. 86), AMSR2 LPRM V5 VOD (R = 0. 76) and VODCA LPRM V6 VOD (R = 0. 69). The spatial relationship between the two AGB and the four VOD datasets was computed for each land cover type (Table  2 ). The highest spatial correlation (R-value) with AGB was obtained with IB VOD in most of the vegetation types, except for evergreen broadleaf forest (EBF) and cropland (CRO). For EBF, CRO and barren or sparsely vegetated (BSV), the highest R-values were obtained with ASCAT V16 and AMSR2/VODCA VOD, respectively.
In order to assess the capacity of VOD to predict AGB, two functions (linear and exponential regression) were selected to compute the bestfitted relationships for each VOD dataset. The R correlation coefficient calculated between predicted and reference AGB is used to evaluate the quality of the prediction. In terms of predicted AGB, the highest R values (R = 0. 92) were obtained by IB VOD, followed by AMSR2 LPRM V5 VOD (R ~ 0. 88-0. 90) while lower R values were obtained for VODCA LPRM V6 and ASCAT V16 VOD (R ~ 0. 83-0. 86). Fig. 11  shows the density scatter plot between two tree height (TH) datasets  (Simard and Patapov)  and the four VOD datasets. The active VOD datasets have a more pronounced linear spatial relationship with the two TH datasets than the passive VOD, similarly with the result obtained for AGB.
IB VOD presents the best spatial linear relationship with the two TH datasets (R ~ 0. 89). In contrast, VODCA LPRM V6 VOD got the lowest R-value (R = 0. 74-0. 78). Regarding the potential to predict TH, VODCA LPRM V6 VOD also showed the poorest performances as saturation happened for high VOD values. The best ability to predict TH for both the Simard and Potapov datasets was obtained by IB and AMSR2 LPRM V5 VOD (R ~ 0. 89-0. 90).
Evaluating IB VOD against MODIS VIs With respect to the spatial relationship between the VOD and MODIS VIs, Fig. 12  shows saturation for high VIs values is more obvious for the active VOD datasets (ASCAT IB and V16), while the relationship is almost linear for the passive VOD (AMSR2 LPRM V5 and VODCA LPRM V6). Saturation starts when the values of NDVI (EVI, LAI) exceeds ~0. 7 (0. 4, 2). The correlation coefficients obtained with the active VOD dataset are lower than 0. 75. Higher spatial correlations (R ~ 0. 90-0. 93) were obtained with passive VOD, but saturation can also be noted for AMSR2 LPRM V5 for higher NDVI and EVI values and VODCA LPRM V6 for higher LAI values. In order to evaluate the ability of IB VOD to monitor the vegetation dynamics, the temporal correlations between 16-day average VOD and MODIS VIs (NDVI, EVI and LAI) were computed for each pixel from Negative correlation values (R ~ -0.
7) can be noted in some arid and semi-arid regions, such as the south of Ethiopia and western Namibia. Compared with the results of the three other VOD datasets, we found that the spatial distribution of pixels with a positive correlation obtained with IB VOD is similar to that obtained with AMSR2 LPRM V5 and VODCA LPRM V6 VOD. Although ASCAT V16 VOD shows generally different spatial patterns (Fig. 13(b), (f ) and (g)), similar negative correlation values were found in South Africa and the Sahara Desert. Interestingly, for some pixels in the north of Africa, the temporal correlation between each VI and passive VOD is opposite to that obtained with the active VOD. A more detailed analysis of these results as a function of land cover classes is given in Supplementary. To get an easier overview of the performance of each VOD dataset in terms of temporal correlation, a map showing which VOD products showed the highest temporal correlation with MODIS VIs over Africa is given in Fig.
Therefore, IGBP-based or pixel-based calibration of A could be considered in future works. Moreover, the daily A value was calibrated over VDV in Africa and then extended to the whole of Africa. When we will extend our method to a global scale, the calibration of the A value should thus be re-evaluated. In addition,  Shamambo et al. (2019)  found that the correlation between the observed backscatter and WCM simulated backscatter is small or negative in karstic areas. This information should also be considered in future analyses. Our retrieval algorithm used the ERA5-Land SM as a known SM input of the retrieval algorithm. Therefore, the IB VOD retrievals made in this study may be sensitive to the quality of the ERA5-Land SM dataset. A simple sensitivity analysis was made to assess the effect of the uncertainty in SM on the retrieved VOD values.
Conclusion and outlook An alternative ASCAT-IB VOD product was retrieved in this study during 2015-2019 over Africa by using the water cloud model coupled with the Ulaby linear model. The idea of using the soil moisture as input was adopted in the retrievals of VOD. Two Random Forest models were trained to map the soil parameters (C and D) of the Ulaby linear model, and the trained model showed good performance (R 2 = 0. 85 for C and R 2 = 0. 61 for D). For the vegetation parameter (A) of WCM, a temporally dynamic value calibrated from observations over the very dense vegetated area was used. IB VOD and the three other VOD products were evaluated against several vegetation datasets (AGB, tree height and MODIS VIs). Comparison with other VOD products suggested IB VOD has advantages in terms of both spatial and temporal performances.
Especially, IB VOD presents a very good linear relationship with AGB and tree height data (R ~ 0. 89-0. 92) showing the considerable potential of IB VOD to study global AGB and tree height changes. Moreover, the temporal correlation between IB VOD and NDVI or EVI showed obvious improvements (>0. 1) in savanna and woody savanna compared to the three other VOD products considered in the present study. The encouraging results found in Africa suggest that we can extend the proposed method to produce a long term (from 2007-present) and global IB VOD product. In addition,  Steele-Dunne et al. (2012)  and  Frolking et al. (2011)  found that variations in canopy water content could account for the backscatter variations between the ascending and descending orbits. Therefore, IB VOD retrieved from different orbits can be explored in the future to analyse daily changes in the vegetation water content.
Moreover, when soil moisture datasets at a finer spatial resolution downscaled from several sensors will be available  (Fan et al. , 2015)  and swarms of SAR cubesats will be available in a decade, the method used in the present study could be extended to retrieve a highresolution active VOD product (e. g. from Sentinel-1). More importantly, IB VOD is independent of passive microwave observations, and as such, it could be used in inter-comparison of VOD products based on the triple collocation (TC) or TC-related methods  (Li et al. , 2021; Ma et al. , 2019). Moreover, two independently (passive & active) retrieved VOD products could be used in a physics-based VOD model  (Jackson and Schmugge, 1991)  to decouple the effects of the vegetation moisture content/structure/biomass on the microwave observations.
Acknowledgments This work is conducted under the support of  CNES, France  ( Centre X. Liu et al. National d'Etudes Spatiales ) within the  TOSCA (Terre Ocean Surface Continentales Atmosphère) program. The authors would like to thank  EUMETSAT  for providing the ASCAT data, LAADS  DAAS  for providing the MODIS data, and ISRIC providing the SoilGird250m data. Fan acknowledges additional support from the  National Natural Science Foundation of China  (Grant No. 41801247 ) and  Natural Science Foundation of Jiangsu Province  (Grant No. BK20180806 ). Xiaojing Bai  was supported by the funding from  National Natural Science Foundation of China  (Grand No. 41801248 ). Xiangzhuo Liu was sponsored by  China Scholarship Council  ( 201906070167 ), and wished to thank one of his best friends,  Gongfan Wang.
, 2015 Saatchi AGB data 1 km Yearly 2015 validation Carreiras et al. , 2017; Saatchi et al. , 2011 CCI AGB data 100 m Yearly 2017 validation Santoro and Cartus, 2019 Simard tree height data 1 km Yearly 2005 validation Simard et al. , 2011 Potapov tree height data 30 m Yearly 2019 validation Potapov et al. , 2021 MODIS NDVI and EVI 1 km 16-day 2015-2018 validation Didan, 2015 AMSR2 LPRM V5 VOD 0. 25 • Daily 2015-2019 inter-comparison https://disc. gsfc. nasa. gov/ VODCA LPRM V6 VOD 0. 25 • Daily 2015-2018 inter-comparison https://doi. org/10. 5281/zenodo. 2575599 ASCAT V16 VOD 0. 25 • Daily 2015-2019 inter-comparison Vreugdenhil et al. , 2016 X. Liu et al.
The implementation of Electronic Health Records (EHR) in UK hospitals provides new opportunities for clinical 'big data' analysis. The representation of observations routinely recorded in clinical practice is the first step to use these data in several research tasks. Anonymised data were extracted from 11 158 first emergency admission episodes (AE) in older adults. Irregular records from 23 laboratory blood tests and vital signs were normalized and regularised into daily bins and represented as numerical multivariate time-series (MVTS). Unsupervised Hidden Markov Models (HMM) were trained to represent each day of each AE as one of 17 state spaces. The visual clinical interpretation of these states showed remarkable differences between patients who died at the end of the AE and those who were discharged.
Comprehensive real-time clinical information is recorded for every patient from admission to discharge. This generates large and detailed datasets, with thousands of data pertaining to thousands of patients collected and stored every day. Machine Learning (ML) can unlock the research potential of this 'big data' by unravelling hidden relationships within its large and complex structure. In selected settings, hospital EHRs have already been used to explore how ML could augment development of stratified medicine, automated medical image analysis, and the prediction of clinical diagnoses and outcomes  [6] [7] [8] [9]. This potential for ML to transform healthcare delivery  [10]  has not yet been realised. Ethical and legal guidance, balancing the benefits of using routinely collected healthcare data for research against potential harm through breaches in patient confidentiality, is evolving.
Data security can limit access to and sharing of data for ML, and data quality varies across organisations and other features of collection framework  [11, 12]. Data are collected with the primary purpose of supporting clinical care and different mechanisms for entering information can complicate data extraction. Additionally, datasets often contain heterogeneous entries. Not only is there unstructured, semi-structured and structured data, even amongst structured numerical variables there is variation in scale, frequency, regularity and completeness. Therefore, generation of a research ready dataset from hospital EHRs requires clinical, technical, ethical and information governance expertise. Numerical time-series data, such as commonly measured laboratory blood tests and bedside vital signs, is a sub-group of hospital EHR data that presents a rich longitudinal account of patients' metabolic and physiologic profiles throughout an admission.
However, given the irregularity and heterogeneity in these data it is not clear how data should be optimised for ML analyses. Although different ML techniques require different degrees of feature engineering  [13] , most ML methods benefit from an intermediate, regularised representation of the raw data, with this representation used as input data to train prediction models  [14] [15] [16]. A particular task in real world data is how to handle missing entries. While some ML techniques can handle missing data implicitly in their scheme, most of them require a complete dataset and, therefore, an imputation step prior to model training. The missingness, distribution and normalisation of these data in general inpatient populations is also infrequently studied, partly because there are few publicly available hospital EHR datasets.
The 'Medical Information Mart for Intensive Care' database is a notable exception, but focuses on intensive care patients only  [17]. Furthermore, some approaches ignore test values and only include the occurrence of a test, reflecting a previous reliance on code based representations of EHR data  [8]. Representations of EHR data are usually evaluated on prediction tasks and by the performance of ML models, measured by metrics such as recall and precision  [9, 18, 19]. However, clinicians often want to evaluate associations between features and outcomes to make hypotheses about causal pathways. This is hard to do from outcome based ML models, which by design have been developed to maximise prediction, and this 'black box' of ML limits the clinical translation of research findings  [14, 20, 21].
Successful representations of EHR data need to address the interpretability of ML models as well as their performance on prediction tasks. Additionally, less emphasis has been placed on simply exploring hospital EHR data, and thus the inherent variation in patient populations, which will also be of value to clinicians trying to understand relationships between health determinants and outcomes or disease clusters. Considering this current landscape, we formed a collaboration between clinicians, data scientists, research governance experts and clinical informatics specialists. Our shared aim was to extract comprehensive, anonymised, high quality data from an EHR at a tertiary care NHS hospital and analyse it for exploration of the inherent variation in the dataset and certain straightforward prediction tasks.
We focus on data from older adult inpatients admitted as an emergency under any hospital specialty. Older adults place a disproportionately high demand on emergency inpatient services  [22]  and relatively modest gains in healthcare delivery and effectiveness can have large system wide effects. We aim to develop methods of representing hospital EHR data from this heterogenous inpatient group for ML analyses, particularly exploring pre-processing approaches that work across a range of modern and classical ML methods. We also explore the interpretable representation of inpatient trajectories, derived from irregular time-series numerical data including 23 commonly measured laboratory blood test and vital sign values. Thus, our main goal is to use generative, unbiased ML techniques (Hidden Markov Models), which make only broad assumptions of the data, and to compare the outcome of these models to hold-out clinical diagnoses and outcome information for clinical interpretation.
Data Data from all patients aged 65 years or older admitted to Addenbrooke's Hospital as an emergency between January 2015 and December 2019 were retrieved by the hospital's Clinical Informatics team from the Epic EHR system introduced in October 2014. Supplementary Table  1  summarises the variables retrieved and the anonymisation process, which was developed by clinical and research governance experts. In brief, patients were characterised by five-year age groups, sex, month of admission, hospital admission and discharge service, clinical frailty scale (CFS) score  [23] , disease phenotype information at admission and discharge (top-level ICD-10 category class), laboratory blood test results (comprised of clinically relevant and commonly requested tests), bedside vital signs (indicative of a patient's physiological status and illness acuity), length of inpatient stay and clinical outcomes of the admission episode (AE) including: discharge alive (DA), inpatient mortality (IM, referred to as Inpatient Death [ID] in multiclass outcome analyses), 30-day post-discharge readmission (PDR), post-discharge readmission and 30-day post-discharge mortality (PDRM) and 30-day post-discharge mortality (PDM).
Data transfer and access A bespoke data sharing agreement was established between CUH and EMBL-EBI. Anonymised data was uploaded to a private and password protected STP site hosted by the EBI server and stored using electronic protection from UNIX security models. Data is shared through a dataset specific UNIX group and all individuals granted access to the data receive internal training and acknowledge their responsibilities before being granted access to the data. The lead technical and clinical analysts also hold honorary contracts with CUHNFT and EMBL-EBI respectively. No data is stored outside of the controlled, project storage volumes at EMBL-EBI. 2. 5. Pre-processing: data cleaning, normalization, and regularization for time-series numerical data 23 different variables denoted laboratory blood test results (17/23) and vital signs (5/23).
Data were first normalized for each variable using inverse rank normalization (IRN) to reduce the impact of outliers without having to define maximum or minimum reference levels. We defined 24-hour bins running from midnight to midnight and aimed to select a unique observation for each lab test or vital sign in each daily bin. Blood tests are not often measured more than once per day but if multiple observations were recorded on the same day, the earliest record was selected as the unique value. The time the blood sample was taken from the patient was defined as the 'reference time' for each daily bin for each patient and the closest vital sign measurements to this reference time were selected, since vital signs are usually measured several times per day. Observations not recorded for a given daily bin were considered 'missing observations'.
Patient cohort Additional inclusion criteria were defined to avoid potential biases due to imputation of a large number of missing values whilst not restricting the cohort to a very closely monitored sub-sample. AEs were included if the first and last days were 'rich-information' days and if 'rich-information' days accounted for at least 2/3 of the length of the time series. The minimum number of recorded laboratory test results (≥ 14 out of 18) used to define a 'richinformation' day was chosen in an iterative process as the largest number leading to the desired sample size (around 10,000 patients). The final dataset was randomly divided into a 'training and test' set (80% AE) for exploratory analyses and a 'hold-out validation' set (20% AE) for confirmatory analyses.
Imputation of missing values Imputation was a two-step process: multiple imputation (MI) of 'rich-information' days and linear interpolation (LI) imputation of 'poor-information' days. MI is the method of choice for complex incomplete data, and is commonly used in clinical research  [24]. MI was implemented with Predictive Mean Matching (PMM) and conducted for each time interval (i. e. , it does not specifically include information on the time series) using information from the same and the other patients in the cohort. Explanatory variables were defined for each predicted variable excluding 1) variables with the largest fractions of missingness in the dataset (glucose, alkaline phosphatase [alp], alanine transaminase [alt], bilirubin, urea and respiratory rate); 2) variables with highly correlated missingness patterns (usually variables requested as a batch, i.
e. , alt and alp); and 3) highly correlated variables (e. g. , neutrophils and white blood cell counts [WBC]). Age, sex, discharge specialty and primary diagnosis at admission were also included as predictors. The predictors matrix is shown in Supplementary Figure  1D. The dataset was imputed ten times. LI imputation assumes a linear relationship between data points in the time series and relies on non-missing values from adjacent observations to compute the missing value  [25]. LI imputation was conducted at the patient and variable level meaning the imputed value is independent of other variables and other patients. The R packages mice and imputeTS were used for MI and LI imputation, respectively. Imputation was evaluated by comparing the distribution of original and imputed values and analysing the convergence plots for MI.