dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The project was conceived, designed, and conducted during the 2019 fall course HST. 953 Collaborative Data Science in Medicine at the  Harvard-MIT Division of Health Science and Technology. Yueh-Hsu ,  Runyu Hong ,  Shari B. Brosnahan ,  John S. Munger ,  David A. Kaufman , and  Kimiko Huang  supported this research during the 2019 NYU Health Datathon. LAC is funded by the  National Institutes of Health  through  R01 EB017205. DAK is funded by the  National Institutes of Health  through  1UG3HL141722-01A1  and  R01HL140362.
Dataset (number of patient encounters) Inclusion criteria: sepsis patients Exclusion criteria In-hospital death population Fluid balance exclusion 36-84 h criteria fluid balance population eICU (200,859 patient 41,071 Diagnosis of bleeding or 2825 3246 Missing 36-84 h Fluid 957 2206 encounters) receiving blood products Balance 83 Excess Fluid Expulsion RRT Patients 3616 840 Patient Mortality before 84 h Patients Under 16 and 6 Erroneous Demographic Data Unreliable Fluid Data 9186 Missing VLI Equation- 21,352 related data patient encounters) MIMIC III (n = 53,423 receiving blood products 18,764 Diagnosis of bleeding or 2690 4056 Balance Missing 36-84 h Fluid 144 391 3521 Excess Fluid Expulsion RRT Patients 251 1777 Patient Mortality before 84 h Patients Under 16 and 820 Erroneous Demographic Data Missing VLI Equation- 9170 related data AmsterdamUMC 4363 Diagnosis of bleeding or 1295 1617 Missing 36-84 h Fluid 237 1297 (n = 23,172 patient encounters) receiving blood products RRT Patients Patients Under 16 and 318 0 Balance Patient Mortality before 84 h 83 Erroneous Demographic Data Missing VLI Equation- 1133 related data SNUH (n = 16,082 patient encounters) 794 RRT Patients Data Erroneous Demographic Patients Under 16 and 541 6 146 Missing 36-84 h Fluid Balance 84 h Patient Mortality before 0 17 129 Missing VLI Equation- 101 related data.
NO data Tropospheric NO 2 columns measured by two ground-based Système d'Analyse par Observation Zénithale (SAOZ) instruments were analyzed to trace and intercompare the evolution of NO 2 in the urban and suburban regions of Île-de-France. The analysis was supplemented by a study of NO 2 column satellite measurements using the TROPOMI instrument. In addition, the in situ measurements of NO 2 surface concentrations from the Airparif air quality network were also considered. In this work, the 10-year period of 2011-2020, with the first year corresponding to the start of the SAOZ measurements at the suburban site of Guyancourt, was considered. Table  1  shows the ground-based stations, type of instrument and geographical coordinates, and Fig. 1  shows the location of each station in the Île-de-France region.
TROPOMI data Tropospheric NO 2 columns retrieved by TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel 5 Precursor (S5P) satellite  (Veefkind et al. , 2012)  launched in October 2017 were also used to discriminate air masses above SAOZ instruments benefiting from the high spatial resolution of this instrument (3. 5 × 7 km 2 and 3. 5 × 5. 5 km 2 since August 2019). TROPOMI is a passive-sensing hyperspectral nadir-viewing imager, aboard a near-polar sun synchronous orbit satellite at an altitude of 817 km, with an overpass at 13:30 local time and practically daily global coverage. Retrieval applied on TROPOMI data allows the distinction between tropospheric, stratospheric and total NO 2 columns. The algorithm was adapted from the DOMINO/TEMIS (Dutch OMI NO 2 /Tropospheric Emission Monitoring Internet Service) approach for the ozone monitoring instrument (OMI;  Boersma et al.
, 2007 Boersma et al. , , 2011)) , based on the DOAS method to obtain slant column densities (SCDs) of NO 2 that are assimilated to the TM5-MP chemical transport model (CTM) to separate the SCD. The CTM runs using 0-12 h forecast meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) corresponding to the offline product. Finally, each slant column is converted to vertical column using the precalculated air mass factor (AMF) look-up tables. A detailed description can be found at the TROPOMI web page (http://www. tropomi. eu/ data-products/nitrogen-dioxide, last access: January 2021). Van Geffen et al. (2020)  analyzed the uncertainties of the SCD of TROPOMI and compared them to OMI-QA4ECV data  (Boersma et al. , 2018). They show a very good agreement over a remote Pacific Ocean sector, with a correlation of 0.
In this study, two Airparif sites near the SAOZ of Paris were used, with one being considered as a traffic site (Quai de Célestins) and the other as urban (Paris 13th). Airparif data of Versailles, the nearest station to the SAOZ of Guyancourt, were used to represent the suburban site. Finally, two more stations at the base (Paris 7) and at the top of the Eiffel Tower were considered to compare the evolution of the NO 2 concentration at different altitudes in the boundary layer. Data were obtained from Airparif web page (https:// data-airparif-asso. opendata. arcgis. com/, last access: 22 January 2021). Daily average data between 06:00 and 18:00 UT are used in this study as for the SAOZ instrument.
Methodology The evaluation of the lockdown effects on atmospheric NO 2 amounts is performed by selecting air masses moving from the Parisian agglomeration to the suburban region. The objective is to consider only the days on which air masses for In this work, the sampling filter of the air masses coming particularly from the Parisian agglomeration was determined with the purpose of evaluating the decrease in human activities linked to the lockdown in Paris at both sites. The downwind direction from Paris to Guyancourt is privileged to filter out air masses originating from the western sector, which are mainly of oceanic origin and have not yet encountered many European emissions. Combined wind speed and direction are considered in this study to identify such days. This procedure aims at selecting data sets with similar meteorological conditions for different years, thus reducing the impact of interannual weather variability.
The evolution of NO 2 concentrations and tropospheric columns at Airparif and SAOZ stations (Table  1 ) are considered. The data of NO 2 concentration measurements by in situ instruments and NO 2 tropospheric column measurements by SAOZ were averaged daily between 06:00 and 18:00 UT. The measurement data are filtered using the wind speed and direction of ERA5 analysis at noon to select the weather conditions in which the Guyancourt site receives air masses that have passed the Paris agglomeration. Equation (  1 ) represents the estimated residential time, t, of air masses coming from the center of Paris to Guyancourt. t = cos abs dir g -θ era5 × π/180 × D/ (ν era5 ) , (1) where ν era5 and θ era5 correspond to the speed and direction of the wind at 12:00 UT and 950 hPa (altitude level in the middle of the convective boundary layer), dir g is the direction between Guyancourt and Paris (290 • ), and D is the approximate diameter of agglomeration (9.
g. ,  Ding et al. , 2020; Prunet et al. , 2020; Siddiqui et al. , 2020; Koukouli et al. , 2021). SAOZ measurements between 11:00 and 14:00 UT were averaged to match overpass time of TROPOMI above the stations. TROPOMI data were previously filtered for the QA > 0. 5 (see Sect. 2. 1. 2) and a radius of 5 km around SAOZ stations. Figure  5  shows the evolution of the monthly mean and two standard errors (2σ ) of tropospheric NO 2 columns above Paris and Guyancourt stations since January 2019, as observed by SAOZ and TROPOMI (left panels). The standard error corresponds to the standard deviation of the mean divided by the root number of considered days. Similar intermonthly evolution is observed by both instruments, with a generally good agreement within ±2σ and a correlation of 0. 80 at Paris and 0.
Some restrictions, such as travel of <1 km maximum, are relaxed at the end of November. an even larger decrease in NO 2 values, varying from 52 % to 86 %, during the lockdown in a 120 km region around Paris using yearly 2019-2020 TROPOMI data and the city-scale NO 2 plume mass method. It should be noted that the SAOZ data sets have shown a long-term negative trend since 2011. Font et al. (2019)  have used in situ data to study the impact of policy initiatives in different megacities. They have shown a mean NO 2 decrease in roadside (background) sites of -2. 9 (1. 7) % yr -1 in Île-de-France for the 2010-2016 period, linked to the introduction of the Euro V regulations for heavy-duty vehicles in October 2009; other policies were implemented thereafter (e. g. , Euro VI regulations in 2014). The trend of tropospheric NO 2 amounts needs to be considered to better quantify the effects of lockdown on air pollution, which cannot rely on the comparison with a single reference year as was done in many other studies (e.
7 ). Moreover, choosing earlier years as a reference would pose the problem of NO 2 variability factors associated with both the lockdown and the long-term NO 2 reductions. This confirms the advantage of our method that calculates the reference from a decadal database and corrects for the long-term trend. It should be noted that the data filtering procedure based on meteorological conditions (wind speed and direction) significantly changes the result of the NO 2 reduction estimate in Guyancourt, making it statistically insignificant (9. 7±41. 6 %) if filtering is not applied; at the same time, the estimate for Paris has not changed much (58. 3±20. 9 %). Table  3  presents a summary of the NO 2 reductions in 2020, using different data sets described previously in the text. This indicates that the results at the Paris site located in the center of the agglomeration are not dependent, in 2020, on meteorological conditions.
The magnitude of the NO 2 supplementary reduction in 2020, which we calculate to be around 50 %, is consistent with the reduction in emissions associated with the lockdown in France, as suggested in a recent modeling study  (Menut, 2020). Data availability. The data used in this study are publicly available. Tropospheric NO 2 data can be accessed from SAOZ instruments at http://saoz. obs. uvsq. fr (SAOZ, 2021) and from the TROPOMI satellite instrument at https://s5phub. copernicus. eu (European Space Agency, 2021). Data under the ODbL license and NO 2 concentration data are available at https://data-airparif-asso. opendata. arcgis. com/ (Airparif, 2021). Wind speed and direction data from ERA5 can be found at https://confluence. ecmwf. int/ display/CKB/ERA5  (ECMWF, 2021). The data of Airparif can be obtained directly by searching for the year (yyyy) and station name in the first column of Table  1  (https://data-airparif-asso.
opendata. arcgis. com/datasets/YYYY-station/explore). An example for Quai des Célestins or CELES (Table  1 ) and the year 2020 is available at https://data-airparif-asso. opendata. arcgis. com/datasets/ 2020-celes/explore. Author contributions. AP, FG and MP contributed to the processing, analysis and availability of the SAOZ data. AB and DI processed the TROPOMI data. AH provided ERA5 data for the area above Paris. MB developed the filter method to account for meteorological conditions. AP and SGB performed the statistical analysis. AP wrote the paper, with the assistance from all authors. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank the Copernicus Services Data Hub for providing the TROPOMI/S5P data, Airparif for the in situ observations and ECMWF for ERA5 wind data. The authors thank the two anonymous referees for their constructive reviews. Financial support. This research has been supported by the Centre National d'Etudes Spatiales (grant no. ValS5PSAOZ). Review statement. This paper was edited by Eduardo Landulfo and reviewed by two anonymous referees.
| Sample preparation in the laboratory and chemical analyses All collected leaves were individually scanned with the ImageJ software at 1,600 d. p. i. on the day of the field sampling to calculate their one-sided LA surface  (Schindelin et al. , 2012). Then, leaves were oven-dried at 60°C for 72 hr and individually weighed with a precision scale to obtain their dry weight. The SLA was calculated as LA per unit of dry weight expressed in cm 2 /g. All soil samples were sieved (<2-mm mesh size) to remove stones, roots and undecomposed leaf litter. Samples were oven-dried at 80°C for 48 hr before chemical analyses. A subset of samples was.
(4) iWUE = A g s = c a -c i 1. 6 = c a 1. 6 Δ 13 C l b -a , to assess differences between recent and long-established forests along the altitudinal gradient for iWUE. There was no multicollinearity between fixed factors, as indicated by R values ≤0. 7 in the correlation matrix of fixed effects. The model included the different plots within each forest type as a random effect. Marginal (only fixed factors) and conditional (fixed + random factors) proportions of the explained variance for each dependent variable (R 2 m and R 2 c respectively) were also calculated (package mumIn,  Bartoń, 2017). For each dependent variable, we first started with the simple LM and then increased the complexity (i. e. the random effect to account for variability between plots within each forest type). A final predictive model for each variable was selected based on the criteria of minimizing the Akaike information criterion (AIC), as well as on the quality of fitness assessed using residual distribution plots and quantile-quantile (Q-Q) plots  (Zurr et al.
, 2009). In the case of the %N s and the difference δ 15 N lδ 15 N s , we found that including the random factor did not improve the model, that is, marginal and conditional R 2 were identical and AIC values were not different between LMM and LM. Hence, the results from the LM analyses were used for these variables. Finally, a log-transformation of the data was necessary to improve the model fitting for SLA and %N s. A description of the final models is given in Table  S1. To test for differences between the two forest types in resourceuse strategies (Hypothesis 2), we assessed the strength and directionality of correlations between parameters associated with physiology, tree productivity and N availability in recently established versus long-established forests. Spearman rank correlation matrices (package ggcorrplot,  Kassambara, 2019)  were applied to account for non-normality and heteroscedasticity in some of the variables (i.
(a) the full dataset (540 observations) including leaf traits as well as wood density; (b) the subset of trees for which tree productivity data, evaluated as BAI from 2016 (384 observations) and 2017 (334 observations), were available (from Alfaro-Sánchez et al. , 2019) and (c) the subset of trees for which δ 15 N s data were available; in this latter case, we also included data on ECM fungal species richness taken from Correia et al. (2021) (54 observations). All variables included in the analyses are shown in Table.
The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB > 250 Mg ha -1 , where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426-571 Pg).
Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates.
, 2012; Thurner et al. , 2014; Liu et al. , 2015; Avitabile et al. , 2016; Hu et al. , 2016)  sometimes presents remarkable differences  (Mitchard et al. , 2013; Ometto et al. , 2014; Schepaschenko et al. , 2015; Rodríguez-Veiga et al. , 2017) , implying a strong variability in the global biomass pool estimate (Table  S1  in the Supplement). Global datasets of forest AGB from remote sensing observations represent the stocks for a snapshot ranging between 2000 and 2010, and their coarse spatial resolution (≥ 500 m) hinders description of the fine-scale spatial variability in biomass. This aspect is of major importance when trying to capture changes in land use, natural disturbances, and growth patterns  (Houghton et al. , 2009)  or monitor management practices  (Erb et al. , 2018). Here, we assembled a wide set of publicly available radar, lidar, and optical satellite observations suited to estimate forest variables with the objective of generating a high-resolution global map of spatially explicit estimates of forest live AGB (unit: megagrams of dry mass per hectare) so as to provide more recent, more detailed, and possibly more accurate information on the spatial distribution of global AGB with respect to existing datasets.
Our forest AGB map has a pixel size of 1 ha and is based on satellite remote sensing observations from around the year 2010. Here, we present the dataset together with an assessment of its validity using an extensive database of plot-level measurements of AGB. The significance of our map estimates is demonstrated in the context of biomass stock assessments by benchmarking with respect to the FAO FRA country statistics. In addition, we compare our estimates and other published estimates of forest AGB derived from remote sensing observations with AGB measurements from inventory plots to illustrate the reliability of our estimates.
Satellite data The spatially explicit estimates of AGB were based on the radar backscattered intensity recorded by the Phased Array-type L-band Synthetic Aperture Radar (PALSAR) instrument, on board the Advanced Land Observing Satellite (ALOS) satellite, and the Advanced Synthetic Aperture Radar (ASAR) instrument operating at C-band, on board the Environmental Satellite (Envisat) (Sect. S1 in the Supplement). In addition, lidar-based metrics and surface reflectances were used throughout the process of biomass estimation. ALOS PALSAR was an active microwave sensor operating at L-band (wavelength of 23 cm). Between 2006 and 2011, PALSAR acquired images in the Fine Beam Dual (FBD) polarization mode with 20 m spatial resolution. Image acquisition followed a predefined observation scenario with the aim of achieving spatially and temporally consistent large-scale observational datasets  (Rosenqvist et al.
g. frozen vs. unfrozen conditions), were manually replaced with the corresponding image tile from the mosaic of 2009. This replacement ensured homogeneity of the Lband backscatter dataset across all landscapes. Envisat ASAR was an active microwave sensor acquiring images at C-band (wavelength of 6 cm) between 2002 and 2012. ASAR operated in four different modes over land, with a spatial resolution of 30 m (Image Mode, IM, and Alternating Polarization Mode, APM), 150 m (Wide Swath Mode, WSM), and 1000 m (Global Monitoring Mode, GMM). Approximately 80 % of the total number of observations consisted of GM observations. We processed the entire dataset of ASAR images of the synthetic aperture radar (SAR) backscatter made available by the European Space Agency (ESA) through the Grid Processing on Demand platform to stacks of terrain-geocoded, pixel-area-normalized, and speckle-filtered images  (Santoro et al.
, 2015b). Images acquired with the IM and WSM were geocoded to a pixel size of 0. 0013888 • in latitude and longitude, corresponding to an area on the ground of roughly 150 m × 150 m at the Equator. Images acquired with the GMM were geocoded to a pixel size of 0. 01 • in latitude and longitude, corresponding to an area on the ground of approximately 1000 m × 1000 m at the Equator. To obtain global full coverage, the IM and WSM images were further averaged and resampled to the pixel size of the GMM dataset to form a single 1 km dataset of C-band backscatter observations. Each image was divided into tiles of 2 • × 2 • in latitude and longitude. For this study, we used all Envisat ASAR images acquired in 2010 and 2011. The density of the observations in time (Fig. S1  in the Supplement) decreased from the polar latitudes, with several observations per day, to the tropical latitudes, with approximately 200 observations on average over 2 years.
Because GLAS observations consisted of approximately 65 m large footprints acquired every 170 m along-track with a distance between tracks of the order of 60 km, the GLA14 dataset was not dense enough to allow direct spatially explicit estimates of biomass. Here, we used the entire archive of GLA14 data products, provided by the National Snow and Ice Data Centre (NSIDC), to estimate forest height after filtering for footprints affected by topography and various noise sources in the waveforms  (Los et al. , 2012; Simard et al. , 2011). In addition, we computed an estimate of the canopy density for each footprint as the ratio of energy received from the canopy (i. e. returns from above the ground peak) to the total energy received. Our database of GLAS-based metrics consisted of 26. 5 million footprints homogeneously distributed over all vegetated surfaces.
While the SAR observations were used as predictors in the retrieval model (Sect. 3), the lidar observations supported the estimation of parameters of the retrieval model that are time-invariant such as forest transmissivity (Sect. 3). Herewith, the time difference between the SAR imagery (2010) and the lidar observations  (2003) (2004) (2005) (2006) (2007) (2008) (2009)  did not impact the retrieval. Global reflectances of Landsat 7 images (bands 3, 4, 5, and 7) acquired in 2010 were used to rescale biomass estimates from ASAR to the pixel size of the ALOS PALSAR dataset. The dataset was available in the form of a mosaic from the USGS website  (Hansen et al. , 2013). The square pixels of the mosaic had a spacing of 0. 00027 • , i. e. roughly 30 m at the Equator. The dataset was downloaded from Google Earth Engine and resampled with the nearest neighbour interpolation technique to the geometry of the ALOS PALSAR dataset.
The AGB retrieval algorithm is outlined in the flowchart in Fig. 1  showing the interdependencies of datasets and retrieval models. We applied a model-based approach known as BIOMASAR  (Santoro et al. , 2011; Cartus et al. , 2012b)  separately to the ALOS PALSAR  (Cartus et al. , 2012b)  and the Envisat ASAR radar backscatter datasets  (Santoro et al. , 2011 (Santoro et al. , , 2015a ) to obtain two independent, spatially explicit estimates of GSV. BIOMASAR inverts the Water Cloud Model  (Pulliainen et al. , 1994; Santoro et al. , 2002). σ 0 for = σ 0 gr e -βV + σ 0 veg 1 -e -βV , (1) where σ 0 for represents the forest backscatter, i. e. the SAR backscatter observation from an ALOS PALSAR or an Envisat ASAR image (Sect. 2. 1), and σ 0 gr and σ 0 veg represent the backscattering coefficients of the ground and vegetation layer, respectively.