dataesr/scientific-paragraphs-categorization huggingface dataset:
Laboratory Visit Full details of the wider project have been reported elsewhere  [26]. The data for this sub-study were collected during and after an initial visit to the laboratory, previously described as Lab Visit #1  [26]. Participants completed a Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) and were also screened for arrhythmia and hypertension, determined during resting electrocardiography and blood pressure measurements, respectively. Continuation with the study was conditional on the screening process, and physician approval was sought at this stage where required. Otherwise, participants were cleared for participation by a Certified Exercise Physiologist (Canadian Society for Exercise Physiology). Details of the maximal exercise test protocol have been reported in detail elsewhere  [26].
Data collected during the test and used for the main study (e. g. peak oxygen uptake, heart rate, rating of perceived exertion (RPE) and blood lactate) is reported for the purpose of characterizing the test and describing the sub-study participants. Borg's RPE scale  (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20)   [29]  was administered according to published instructions  [30].
Discussion The purpose of this study was to investigate self-reported incidences of PEM in people with chronic CRF. Our results provide preliminary evidence that a subset (up to 33% in this sample, a possible 6/18 participants) of people with chronic CRF experience symptoms of PEM. These findings must be replicated/extended in larger and more diverse samples of people with chronic CRF. Nevertheless, this finding has potential implications for exercise professionals working with people experiencing chronic CRF. We recommend that CRF is monitored throughout an exercise program (including asking people about their symptoms between individual exercise sessions, and after increases in, e. g. exercise intensity -with an awareness that symptom onset may vary and may be delayed) and that trials involving people with chronic CRF report this patient-level data to facilitate the understanding of PEM in this context, and therefore help mitigate the potential for harm.
Non-Linear Rheology Non-linear rheology was used to monitor the mechanical properties at high deformations above the LCST. The temperature was raised to 50 • C and an equilibration time of 60 min was applied. After that, shear start-up experiments were performed by shearing the samples at constant shear rate (. γ = 0. 1 s -1 ) and by monitoring the evolution of the shear stress (σ) as a function of strain (ε). Two replicas were conducted to ensure data reproducibility.
In Table  2  the characteristics of each analysed sample are reported, together with the name codes assigned (CCx, where CC stands for complex coacervate and x is the total SiO 2 content in the complex coacervate). Due to the presence of PNIPAM, the anionic copolymer chains can absorb onto the silica particle during the preparation stage. Since most of the polymer chains end up in the concentrated phase upon complexation, the silica nanoparticles partition preferentially in the complex coacervate phase: in every sample, more than 50% of the total silica ends up in the complex coacervate phase, with the concentration of nanoparticle, therefore, always being higher than in the dilute phase. Additionally, from the TGA data the water content of the samples is also determined. In Figure  3B , it is observed that the water content decreases linearly with the silica concentration.
Additionally, from the TGA data the water content of the samples is also determined. In Figure  3B , it is observed that the water content decreases linearly with the silica concentration. The silica can be retained in the complex coacervate phase at the expense of water since both salt and polymer content, whose values can be obtained respectively by the second and third releasing step in the TGA, remain constant (Figure  3B , inset). The lower water retention might be explained by a higher tendency to phase separation, due to the PNIPAM adsorption onto the silica particles: when not bound to silica particles, the hydrophilic PNIPAM chains tend to contrast the tendency to phase separate due to complex coacervation, with a higher water content in PNIPAM-functionalized complex coacervates than the PNIPAM-free counterpart  [25, 26].
Acknowledgments: The authors acknowledge Costantino Creton for providing feedback to the manuscript and  Claudio Dompé & Isabella Blengino  for the high-resolution pictures. J. V. D. G. acknowledges the  European Research Council  for financial support ( ERC Consolidator grant Softbreak ). T. E. K. acknowledges the  Nederlandse Wetenschappelijk Onderzoek (NWO VENI)  for financial support.
Northern Hemisphere winter (December, January, February; DJF) was longer and summer (June, July, August; JJA) correspondingly shorter from an insolation perspective than in the present day and the piControl simulation. However simulation output by CMIP6 models is restricted to modern calendars  (Juckes et al. , 2020). This is not a problem for annual or daily diagnostics, but summarising model output using only the modern calendar prohibits straightforward adjustment of the numbers of days over which the aggregation of monthly simulation output takes place. To take account of these differences in calculating monthly or seasonal variables, we use the PaleoCalAdjust software  (Bartlein and Shafer, 2019) , which interpolates from non-adjusted monthly averages to pseudo-daily values and then calculates the average values for adjusted months defined as angular fractions of the orbit.
This software has been favourably evaluated for monthly temperature and precipitation variations with both PMIP4-CMIP6 and transient simulations  (Bartlein and Shafer, 2019). Given the experimental protocol fixes the date of the vernal equinox as 21 March  (Otto-Bliesner et al. , 2017) , the largest impact of the calendar adjustment occurs in September (a key month for Arctic sea ice coverage). The PaleoCalAdjust software computes adjusted monthly variables from original monthly means, a computation which could impact the accuracy of variables that change abruptly throughout the year, rather than gradually, such as the sudden increase in precipitation in monsoon regions  (Pollard and Reusch, 2002). To explore whether potential interpolation errors from PaleoCalAdjust are justified in such situations, we analysed the averaged rain rate during the monsoon sea-son over the South American monsoon domain in the IPSL-CM6A-LR midHolocene, for which daily-resolution data are also provided on the Earth System Grid Federation.
Since the areal extent of South American monsoon domain varies slightly when using different temporal data, we make this comparison based on the grid points that always fall within the monsoon domain to provide the most robust assessment of the impact of the change in calendar. The average monsoon rain rate from the daily-resolution data is 7. 0 mm d -1 , compared to 6. 7 mm d -1 from calendar-adjusted monthly data and 7. 1 mm d -1 using monthly data without this adjustment. The average monsoon rain rate in the piControl is 7. 5 mm d -1. We have therefore not applied the calendar adjustment when analysing monsoon variables. The analysis presented here mainly uses generalised evaluation software tools derived from the Climate Variability Diagnostics Package  (Phillips et al. , 2014) , which has been adapted for palaeoclimate purposes  (Brierley and Wainer, 2018).
Palaeoclimate reconstructions and model evaluation We provide only a preliminary quantitative evaluation of the realism of the PMIP4-CMIP6 simulations, drawing attention to obvious similarities and mismatches between the simulations and observational evidence of past climates. We concentrate our evaluation on two compilations of quantitative reconstructions from a number of sources. We use temperature reconstructions from the recent Temperature 12k database  (Kaufman et al. , 2020b). We extracted anomalies for the mid-Holocene compared to the last millennium interval (from 6. 0 ± 0. 5 to 0. 6 ± 0. 5 ka) for site-level comparison with the PMIP4-CMIP6 simulations. This database has 1319 time series reconstructions of temperature (mean annual, summer, and winter temperature) based on a variety of different ecological, geochemical, and biophysical marine (209) and terrestrial (470) sites  (Kaufman et al.
There is good coverage of Northern Hemisphere terrestrial sites, although there are gaps in the coverage especially for the tropics and Southern Hemisphere  (Bartlein et al. , 2011). The  Bartlein et al. (2011)  dataset was extended with some speleothem and ice core temperature reconstructions and used to evaluate the PMIP3-CMIP5 simulations  (Harrison et al. , 2014). In this study we use the pollenonly dataset from  Bartlein et al. (2011)  and the multi-proxy dataset  (Kaufman et al. , 2020b)  to provide a measure of the uncertainties in reconstructed climates, although differences in methodology and coverage preclude direct comparison between the two datasets. We incorporate an additional dataset to facilitate comparisons of the northern African monsoon between the CMIP6-PMIP4 simulations and previous generations of simulations, namely water-balance estimates of the quantitative change in precipitation required to support the observed mid-Holocene vegetation change at each latitude compared to present  (Joussaume et al.
There is some difficulty in diagnosing biases in the piControl, because there are few spatially explicit observations for the pre-industrial Clim. Past, 16, 1847 Past, 16, -1872 Past, 16, , 2020  https://doi. org/10. 5194/cp-16-1847-2020 period, especially for precipitation. We therefore evaluate these simulations using reanalysed climatological temperatures (between 1871 and 1900 CE;  Compo et al. , 2011)  for the spatial pattern (Fig. 3 ) and zonal averages of observed temperature (Fig. 4 ) for the period 1850-1900 CE from the HadCRUT4 dataset  (Morice et al. , 2012; Ilyas et al. , 2017). We compare these with the mean difference between the pre-industrial climatology of each model (i. e. the ensemble mean bias). The PMIP4-CMIP6 models are generally cooler than the observations, most noticeably in polar regions, over land, and over the NH oceans (Fig.
4 ). The PMIP4-CMIP6 ensemble is equivocal about whether the polar regions were warmer or cooler on the annual mean. Furthermore, the PMIP4-CMIP6 models show a consistent cooling in the tropics. Tropical cooling was present, but less pronounced, in the PMIP3-CMIP5 ensemble (Fig. 4 ). Tropical cooling is not consistent with the Temperature 12k area averages  (Kaufman et al. , 2020a)  (although the  Bartlein et al. , 2011, compilation  does not discount it, the majority of their reconstructions are solely from Africa). Interestingly, for comparisons over Europe and North America, both well sampled by the  Bartlein et al. (2011)  compilation, the models appear to show too much warming in both summer and winter (Fig. S3 ). Further work is required to determine whether the discrepancies between the temperature reconstructions and PMIP4-CMIP6 simulations arise from systematic model error, sampling biases in the data compilation (e.
Code and data availability. The necessary output variables from both the midHolocene and piControl simulations are freely available from the Earth System Grid Federation at https://esgf-node. llnl. gov/search/cmip6/ (last access: 28 August 2020; please see Table  S1  for further details, including digital object identifiers, of the precise datasets used in this analysis). (HadGEM3-GC31-LL and UofT-CCSM-4 have committed to lodge their data as soon as practical). A GitHub repository is available at https://doi. org/10. 5281/zenodo. 4048333 (Brierley and Zhao, 2020)  with the code used for this analysis. The Temperature 12k database, along with latitude-zone and global temperature reconstructions using multiple statistical methods, is available through the World Data Service (NOAA) for Paleoclimatology (https://doi.
org/10. 25921/4ry2-g808, Kaufman  et al. , 2020c). The Bartlein et al. (2011)  reconstructions are downloadable as an electronic supplementary material of the article itself. The  Compo et al. (2011)  reanalysis can be found at http: //www. esrl. noaa. gov/psd/data/gridded/data. 20thC_ReanV2c. html (Compo et al. , 2020). The precipitation observations of  Adler et al. (2003)  and  Xie and Arkin (1997)  are archived at https: //www. esrl. noaa. gov/psd/data/gridded/data. cmap. html (Xie, 2020)  and https://www. esrl. noaa. gov/psd/data/gridded/data. gpcp. html (Wang, 2020)  respectively. The pre-industrial latitudinal average temperatures were created using anomalies of Ilyas et al. (2017)  from https://oasishub. co/ dataset/global-monthly-temperature-ensemble-1850-to-2016 (Ilyas, 202020)  combined with the HadCRUT4 (Morice  et al.
, 2005). Although these problems have been reduced with the advent of the GPM mission thanks to the new Dual-frequency Precipitation Radar (DPR), recent works show that there is still room for improvement  (Tan et al. , 2016; O et al. , 2017; Gebregiorgis et al. , 2018b). Model reanalysis datasets, such as the European Centre for Medium Weather Forecast (ECMWF) Interim Reanalysis (ERA-interim; extensively described in  Dee et al. , 2011)  and the new ERA5 (European Centre For Medium-Range Weather Forecasts, 2017), are the obvious alternative to ground-and satellite-based rainfall products. Although they offer good performance in simulating synoptic weather systems, they often misrepresent the variability of convective systems, mainly due to their relatively low resolution and deficiencies in the parameterization of sub-grid processes  (Roads, 2003; Ebert et al.
, 2007; Kidd et al. , 2013; Beck et al. , 2017). Although reanalysis datasets perform relatively well globally  (Massari et al. , 2017a)  and provide consistent long-term precipitation estimation (which is paramount in many research fields), they are normally released with a latency that does not suit water resource and agricultural applications. Despite these inherent limitations, SRE and reanalysis products are still the only valuable alternative to gaugebased observations within gauge-scarce regions, and the efforts to improve these datasets by merging procedures or by including other ancillary information has been significantly increasing in the last decade. For instance,  Beck et al. (2017)  released Multi-Source Weighted-Ensemble Precipitation (MSWEP), a dataset with a 3-hourly temporal resolution that covers the period 1979 to the near present.
MSWEP is a unique product, as it exploits the complementary strengths of gauge-, satellite-and reanalysis-based data to provide rainfall estimates over the entire globe. Other notable exam-ples are the CHIRPS (Climate Hazards Group Infrared Precipitation with Station data) rainfall estimates  (Funk et al. , 2015) , which are based on a combination of gauges and infrared cold cloud duration (CCD) observations. However, these datasets rely upon the availability of gauge observations, which constitute the "land" or the "bottom-up" perspective of the precipitation signal (i. e. the precipitation that effectively reaches the land surface), in contrast to satellite (and reanalysis) estimates, which are more informative about the precipitation in the atmosphere layers (i. e. by cloud and atmospheric models).
Regional rainfall datasets Different ground-based rainfall datasets were used for the four different regions to cross-validate the integrated product, namely, the Australian Water Availability Project (AWAP) in Australia, the ECA&D (European Climate Assessment & Dataset) rainfall dataset E-OBS (ENSEMBLES daily gridded observational dataset) gridded dataset in Europe, the National Centers for Environmental Prediction (NCEP) Stage IV dataset over CONUS and the India Metrological Department (IMD) rainfall gridded dataset over India. Below we describe the main features of these datasets (readers interested in more details can refer to the related publications). 1. The Australian Water Availability Project (AWAP) rainfall product is generated via spatial analyses on the quality-controlled daily rain gauge measurements from the Australian Bureau of Meteorology daily rain gauge network.
, 2001)   (Entekhabi et al. , 2010). In this study, the version 5 of the Level 3 SM retrievals was used. 4. For AMSR2, the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission for Water satellite is a radiometer operating in the microwave band. Soil moisture retrieval from AMSR2 is obtained from the C and X bands, which allow for obtaining a spatial-temporal resolution of 25 km daily  (Kim et al. , 2015). In this study, we focused on the X-band SM product obtained by the application of the Land Parameter Retrieval Model to AMSR2 brightness temperature data  (Parinussa et al. , 2015). Note that AMSR2 was inverted to obtain rainfall via SM2RAIN, but the resulting rainfall was not used in the integration, whereas it was used in the validation via TC as an auxiliary dataset.
Global rainfall datasets In addition to satellite SM products, different rainfall datasets were used in the study both for cross-comparison purposes and as a part of the integration procedure. In the following the main characteristics of each dataset are provided. 1. The First Guess Daily product provided by the Global Precipitation Climatology Center (GPCC;  Schamm et al. , 2014 ) is a ground-based rainfall dataset, which has been available since 1 January 2009 with a spatial sampling grid of 1 •. This dataset is used within the processing chain of in many gauge-corrected satellite rainfall products. Being based on gauge observations, this dataset is very accurate where the station density is relatively high like in Europe, Australia and the United States, whereas it suffers from serious interpolation errors in areas uncovered by stations.
The optimal linear combination approach The optimal linear combination (OLC) approach  (Bishop and Abramowitz, 2013; Hobeichi et al. , 2018)  provides an analytically optimal linear combination of ensemble members (rainfall estimates in this case) that minimizes the mean square error when compared to a dataset that is assumed to be accurate enough to be considered as a calibration dataset Y REF and thus accounts for both the performance differences and error covariance between the rainfall products. The optimal linear combination is therefore insensitive to the addition https://doi. org/10. 5194/hess-24-2687-2020 Hydrol. Earth Syst. Sci. , 24, 2687-2710, 2020 of redundant information. This weighting approach has two key advantages: (1) it provides an optimal solution for integrating different rainfall datasets, and (2) it accounts for the error covariance between the different datasets (caused by the fact that single datasets may share a similar information); that is, they may not provide independent estimates.
Given an ensemble of N + 1 rainfall estimates and a corresponding calibration dataset Y REF , the weighting builds a linear combination of the N + 1 ensemble members that minimizes the mean square difference with respect to Y REF such that N j =1 (Y REF -w T Y PROD ) 2 (3) is minimized, where Y PROD = [y IMERG , y SM2RAIN 1 , y SM2RAIN 2 ,. , y SM2RAIN N ] (4) represents the different SM2RAIN products plus the IMERG-ER product. The vector of coefficients w is calculated using w = A -1 1 T A -1 1 , ( 5 ) where A is the (N + 1) × (N + 1) error covariance matrix of Y PROD with respect to Y REF and 1 T = [1, 1, 1,. , 1] a vector of N + 1 elements. The integrated product is then calculated as P R+SM = w T Y PROD. ( 6 ) Note that for the OLC method to be analytically optimal, a bias correction of the ensemble members in Y PROD (i.
3. 3. 1 Step 0: soil moisture and rainfall pre-processing Global SM and rainfall products come with different resolutions and grids. Moreover, the application of the SM2RAIN algorithm to SM observations requires preliminary processing. In step 0, we resampled all the datasets to the same 0. 25 • × 0. 25 • grid over land between ±60 • by using nearestneighbour interpolation on the ASCAT grid (25 km). In particular, the IMERG products, characterized by a resolution of 0. 1 • , were upscaled to 0. 25 • using a box-shaped kernel with antialiasing, an approach that was found to outperform simple spatial averaging. Rainfall accumulations were aggregated to daily scale (from 00:00 to 23:59 UTC). As satellite SM data are not provided regularly spaced in time and contain gaps (for instance we did not include in the analysis observations characterized by frozen soils, snow presence or radio interference contamination; by using the specific flags for each product), they were linearly interpolated at 00:00 UTC to produce SM2RAIN daily rainfall from 00:00 to 23:59 UTC (see step 1).
3. 2, the application of OLC requires unbiased ensemble members. This implies matching the long-term temporal mean of Y REF with the ones of IMERG-ER, SM2RAIN-ASCAT, SM2RAIN-SMOS and SM2RAIN-SMAP by using a different (and temporally constant) multiplication factor for each member (i. e. a factor that multiplied by the mean of the member guarantees the matching with the mean of the calibration dataset). However, applying this procedure resulted in an overall reduction of the quality of the SM2RAIN members because a temporally constant multiplication factor deteriorated the quality of light rainfall (< 5 mm d -1 ) with an increase of the false alarms (due to the noise contained in the satellite SM time series). To overcome this issue, we adopted a slightly different strategy which, despite not guaranteeing a perfect matching of the long temporal means and thus not being theoretically optimal, limited the problem of the increase of false alarms.
Step 3: application of OLC For the application of OLC (i. e. integration), we proceeded by considering these three methodological aspects: 1. First off, we performed a quality check, by comparing the correlation coefficient of each SM2RAIN product with the calibration dataset (Y REF ). When the correlation was found less than 0. 4 (i. e. no correlation), the product was automatically excluded, and OLC was applied on the reminder of them. If all the SM2RAIN products correlation fall below this threshold (for example in dense forests or high mountainous regions), only IMERG-ER was retained. The value 0. 4 was set to exclude the poor performance of SM2RAIN products at such thresholds, which could potentially impact the overall quality of the integrated product. To select this value, we performed ad hoc experiments (not shown) over CONUS, Australia, Europe and India and found 0.
3. The application of OLC among the SM2RAIN products and IMERG-ER was carried only when IMERG-ER values are larger than zero, taking advantage of the enhanced rain-no-rain detection accuracy of IMERG that uses DPR  (Gebregiorgis et al. , 2018a) , whereas when IMERG-ER was zero, this value was kept in the merged product. This tactic mitigates the degradation of rainfall estimates during low-rainfall time steps as demonstrated by  Massari et al. (2019). 4. The final product is then composed of multiple rainfall datasets weighed according to Eq. (  6 ). IMERG-ER is always present, whereas the presence of the three SM2RAIN rainfall estimates derived from AS-CAT, SMOS and SMAP depends on their relative accuracy (if they satisfy the threshold) and availability in time and space. The success of the overall procedure described above is dependent upon the quality of Y REF.
3. 4. 2). TC offers a viable way to validate rainfall products in data-scarce regions by providing (theoretical) error and correlation of each product with the "unknown" truth. Note that we tested the validity of the TC validation by applying it to the same key regions where the classical validation was carried out. Then, TC was applied to Africa and South America to validate the integrated product and the other datasets that are part of the analysis. The validation with TC was carried out in 2018 (only 1 year), which is independent from the calibration period (2015-2017).