dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 1). Then, we will identify and characterize NPF events (Sect. 3. 2). Following that, we will quantify and analyze relevant parameters that describe NPF events (Sect. 3. 3) and use those parameters, together with meteorological variables, to understand why and when NPF occurs at our site (Sect. 3. 4). We further present a regression and a classification analysis (Sect. 3. 5). All the data in this article are presented in local time (UTC+3 from 25 March to 28 October 2018 and UTC+2 during the rest of the campaign). Unless otherwise indicated, we mainly focus on daytime data having global radiation > 50 W m -2 as this is the time period relevant for most NPF events, but we also briefly mention some nighttime events. For reference, the monthly range of day hours having global radiation > 50 W m -2 is presented in Fig.
Acknowledgements. The authors thank  Hanna Manninen ,  Kaspar Dällenbach ,  Jenni Kontkanen ,  Runlong Cai , and  Dominik Stolzenburg  for fruitful scientific discussions. Frans Korhonen ,  Pekka Rantala ,  Pasi Aalto ,  Erki Siivola ,  Sander Mirme ,  Joonas Vanhanen , and  Aki Halonen  are kindly acknowledged for their continuous and indispensable support. Financial support. This publication has been produced within the framework of the  EMME-CARE  project which received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme , under grant agreement no. 856612  and from the  Cyprus Government. This work has received additional funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research And Innovation Programme  ( ERC , project no.
742206  " ATM-GTP "). The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein. Additional support was received from the  Academy of Finland  (grant agreement nos. 307331 ,  337549 ,  302958 , and  316114 ), the  European Regional Development Fund , and the  Republic of Cyprus  through the  Research and Innovation Foundation  (project:  INTEGRATED/0916/0016 ). JK acknowledges support from the  Academy of Finland  (project  1325656 ) and the  University of Helsinki  3-year grant  75284132. TJ acknowledges support from the  Academy of Finland  (project  334514 ). Open-access funding was provided by the  Helsinki University Library. Review statement. This paper was edited by  Fangqun Yu  and reviewed by two anonymous referees.
2c ; Extended Data Fig. 4 ). We perform a 'leave-one-out' analysis of the Indian-Pacific dataset which shows that no single record is contributing more than 5% of the total reconstruction and indicates that Indian-Pacific ∆LatSST reconstruction primarily reflects a midlatitude signal (Methods; Extended Data Fig. 4 ). By contrast, we find a slight warming anomaly (relative to the global-mean LGM SST change) at all latitudes in the western Atlantic (Fig. 1 ), possibly a signal of a weakened Atlantic Meridional Overturning Circulation (AMOC)  29, 30  , and no significant change in the SST front latitude within the Atlantic sector over deglaciation (Extended Data Fig. 4 ). This result is consistent with strong bathymetric steering of currents in the Atlantic sector  28  , and the lack of a modelled relationship between the SST front latitude and wind latitude in this sector, particularly in the eastern Atlantic where the vast majority of the mid-latitude cores are located (Fig.
3c ). Extending our analysis to span a longer time interval (22 to 6. 5 ka) indicates an early Holocene extremum in the poleward position of the westerlies, followed by a ~1. 5° equatorward shift in the winds over 10-6. 5 ka (Extended Data Fig. 6 ). Despite the larger uncertainties in the early Holocene reconstruction, it agrees well with the analysis of the full dataset (i. e. , 10-20 ka) in the overlapping sections (Extended Data Fig. 6 ). Our results thus indicate a 4. 7° (2. 9-6. 9° 95% CI) equatorward shift of the westerlies during the LGM (20 ka)     relative to the mid Holocene (6. 5 ka). Assuming no substantial shift in the westerlies from the mid-to late-Holocene (Methods), the magnitude of the reconstructed wind shift is substantially greater than that predicted by any of the models within the PMIP3/4 ensemble between preindustrial and LGM states (Extended Data Fig.
Reconstructed change in SST front latitude (∆LatSST) within the regional subsets. (d) Reconstructed zonal-mean wind latitude (∆Latwind) calculated from the regional ∆LatSST reconstruction (c) and the relationships between regional SST front and zonal-mean wind latitude given in (b). based on radiocarbon dating, and further details of the age models can be found in ref  52  for core MD07-3119 and ref  53  for core MD12-3396CQ. Reservoir age changes for the Kerguelen area followed recent results by ref  53  to establish the age model of core MD12-3401CQ. The new data are provided in Table  S1  and are available on Pangaea (DOI pending).
08 ‰ (2s) to all planktic foraminiferal d 18 O records. The R code and data used to perform the analysis is provided on Github (https://github. com/willyrgray/SOd18O). We repeat the analysis over a longer time period (22-6. 5 ka) which necessitates using a smaller subsection of cores and thus results in larger uncertainties (Extended Data Fig. 6 ). Still, the results show excellent agreement with the complete set of cores (i. e. spanning 10-20 ka) (Extended Data Fig. 6 ), with a ratio of 0. 95 during the overlapping interval. We apply this correction to the longer reconstruction, though the correction makes a negligible difference and does not impact our conclusions (Extended Data Fig. 6 ). Performing the analysis further into the Holocene (22-2. 5 ka) results in substantially higher uncertainties due to the lack of cores spanning this interval, but the results suggest the westerlies are relatively stable through the late Holocene (Extended Data Fig.
Leave-one-out analysis We perform a jackknife resampling of the dataset to determine the contribution of each planktic foraminiferal d 18 O record to the Indian-Pacific ∆LatSST reconstruction. We sequentially remove each record from the dataset, recompute ∆LatSST, and determine the contribution of that record as the time-integrated absolute difference from ∆LatSST computed using the entire dataset (expressed as a percentage of the absolute cumulative change in ∆LatSST over deglaciation; Extended Data Fig. 4 ). This analysis shows that no single record is contributing more than 5% of the total signal, and that the cores with the highest weighting are all located in the midlatitudes. Thus, we conclude that our reconstruction is primarily tracking a mid-latitude signal.
Acknowledgements Some of the collaborations in this study were initiated through the  PAGES QUIGS  workshop 2020. EM received financial support from  French-Swedish  project on  SO VR-349-2012-6278  and  CNRS-INSU  project  INDIEN-SUD. RCJW was funded by  NSF  Grant  AGS-1929775. LM acknowledges funding from  Australian Research Council  grant  FT180100606. PS was supported by the  Australian Research Council  grant  FT190100413  and the  Australian National Computing Infrastructure. MH acknowledges funding from  Australian Research Council  grant  DP210101650. We acknowledge the  World Climate Research Programme's   Working Group on Coupled Modelling  for the  coordination of CMIP  and thank the climate  modelling groups  for producing and making available their model output.
Code availability The R code and data used to perform the ∆LatSST analysis is provided on Github (https://github. com/willyrgray/SOd18O). Extended Data 7. LGM-PI change in the PMIP3/4 ensemble. LGM-PI change in position of maximum zonal-mean zonal wind stress in the PIMP3 (circles) and PMIP4 (triangles) ensemble, compared to the reconstructed change in ∆Latwind between 20-6. 5 ka. Extended Data 8. Deglacial changes in wind latitude, wind strength, wind stress at 60°S and northward Ekman transport at 60°S. Reconstructed changes in (a) the wind latitude (∆Latwind) (b) the wind strength (max tu) (c) zonal-mean wind stress at 60°S (60S tu) (d) northward Ekman transport at 60°S. (b) and (c) are calculated using the reconstructed changes in wind latitude (a) and the relationships between wind latitude and these parameters in the model ensemble (Extended Data Fig.
We compiled existing records of planktic foraminiferal d 18  O from near-surface dwelling species from core sites across the Southern Ocean and generated new data from the Kerguelen plateau and southeast Pacific, resulting in a dataset of 64 planktic foraminiferal d 18  O records spanning the last deglaciation (Fig. 1a; Extended Data Figs. 2 and 3; Methods). Meridional shifts in the SST front (∆LatSST) are calculated by finding the latitudinal shift that minimises the difference between the Holocene d 18 O profile and the profile at each time step within a 10° latitudinal window that includes the steepest part of the meridional d 18 O profile 19.
Materials and Methods The International COVID-19 Sleep Study (ICOSS) is a collaboration that assesses a variety of sleep aspects to evaluate different types of sleep-wake disorders and other symptoms affected by prolonged social confinement. The detailed protocol has previously been described. 18 This research was conducted according to the Declaration of Helsinki, and all countries obtained ethical approval or exemptions in keeping with national research governance and regulations (see more details in Table  S1 ). Data collection occurred between 01st May 2020 and 07th September 2020 in several places across the world (ie Austria, Canada, China (Jilin and Hong Kong, in the northeastern and southeastern regions of China, respectively), Finland, France, Germany, Italy, Japan, Norway, Poland, Sweden, United Kingdom, and United States of America) with the survey being translated according to each region's main language.
For the present analysis, we assessed sleep timing reports to calculate SJL as the difference in sleep midpoint (ie, the clock time between sleep onset and waking up) on work vs free days, before and during the pandemic. Differences between SJL before and during were also computed and were used to categorize our cohort into three groups: "Unchanged" (respondents with no changes in SJL), "Increased" (respondents with positive changes in SJL), and "Reduced" (respondents with negative changes in SJL). Data were first filtered to exclude respondents who did not answer any item of the evaluated questions (eg, sleep time and sleep-related problems), extreme sleep durations (threshold defined according to the interquartile rule with constant equal 2 -respondents that sleep less than 3 hours or more than 13 hours), and erroneous responses (eg, bedtime occurred only a few hours later than wake-up time).
Significance was set at P<0. 05 and reported as two-sided P-values unless specified otherwise. Pearson's chi-squared test and adjusted residuals were computed to evaluate if sex or social restriction proportion differences did not arise by chance. A locally weighted regression (ie, non-parametric, locally estimated scatterplot smoothing -LOESS -regression) was also implemented to understand and reveal trends between SJL parameters before and during the pandemic. Neighborhood size was controlled and adjusted to 95% of the entire data for the locally weighted regression. All statistical analyses, variable computation, and graphs were done using the R software (v 4. 0. 3), with the packages survey  27  (v 4. 0), DescTools (v 0. 99. 42) and the ggplot2 28 (v 3. 3. 3). Group comparison results are presented as median and interquartile range (ie, median [1st, 3rd interquartile]), or as mean and standard deviation as detailed for each section.
In this sense, despite canonically considering a reduction in SJL as a marker of improved sleep and well-being,  12  the psychological burden associated with the COVID-19 pandemic, could in sum explain why even reduced levels of SJL -at least as herein early in the pandemic -were associated with worse symptoms of insomnia and sleep quality. Our data corroborate these previous findings, since we find that those with changes in the extent of social jetlag reported higher levels of psychological distress. Another factor that could contribute to this type of sleep irregularity and impaired sleep quality is an increase in the use of digital media, a parameter which we did not assess. Others have however noted that during the pandemic, use of digital media near bedtime has increased across all age groups.
Acknowledgments We like to thank  Dr Fang Han  from  Peking University People's Hospital  for the survey data collection in the Jilin region. We would like to thank all the anonymous participants for responding to our questions. The study was supported by the  Swedish Society for Medical Research (SSMF) , the  Swedish Research Council  (VR;  2019-01051 ), and the following foundations:  Diabetesfonden  ( 2019-489 ),  Diabetes Wellness Sverige  ( 25-2231 ), Selander,  Göran Gustafsson ,  Magnus Bergvalls  ( 2018-02975 ),  NovoNordisk  ( NNF19OC0056694 ), the  Swedish Brain  ( FO2019-0086 ;  FO2019-0274 ), and  The Swedish Cancer foundation. None of the study sponsors/funders were involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; and they did not impose any restrictions regarding the publication of the report.
We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018-2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due.
This study focuses on investigating the GPP:SIF relations at three short timescales: half-hourly, daily, and weekly, testing the predictability of inter-daily GPP variations within the growing season. We further study the effects of water stress, light saturation, and site/ecosystem characteristics on the GPP:SIF relations. We use data from 31 eddy covariance (EC) sites in the northern hemisphere. SIF data are taken from the recent TROPOMI measurements, taking advantage of its high spatial and temporal resolution, and measuring SIF near noontime, which leads to a better assessment of water stress and light saturation. Data are restricted to the 2018-2019 growing seasons of each site, as we focus on evaluating the intra-seasonal effects of physiological connections between SIF and GPP, and aim to avoid the longer timescales, where GPP variation is driven primarily by seasonal phenology.
Study Sites Eddy covariance data were obtained through the AmeriFlux database 1  , and the European Fluxes Database Cluster (EFDC) 2. This study was focused on temperate forest ecosystems located between 35 • N and 65 • N and considers the following IGBP land cover classifications: Evergreen Needleleaf Forests (ENF), Evergreen Broadleaf Forests (EBF), Deciduous Needleleaf Forests (DNF), Deciduous Broadleaf Forests (DBF), Mixed Forests (MF), and Woody Savannas (WSA). The study period comprised of growing seasons 2018 and 2019, depending on data availability from each site. We selected all sites that reported carbon dioxide fluxes for at least 50% of the year in 2018 and/or 2019. After filtering, 31 sites with 47 site-year growing seasons' data were available for analysis: BE-Vie, CH-Lae, CZ-BK1, CZ-Lnz, CZ-RAJ, CZ-Stn, DE-Hai, DE-HoH, DE-Hzd, DE-Obe, DE-Tha, FR-Fon, IT-SR2, RU-Fyo, SE-Nor, US-Me2, US-Me6, US-MMS, US-NC3, US-NR1, US-PFa, US-Rpf, US-SRM, US-Syv, US-UMB, US-UMd, US-Vcm, US-WCr, YS-xBR, US-xDL, and US-xRM.
Solar-Induced Chlorophyll Fluorescence Data Solar-Induced Chlorophyll Fluorescence data were calculated from spectral observations by the TROPOspheric Monitoring Instrument (TROPOMI) satellite, launched on October 13th, 2017. TROPOMI spectral range met the 743-758 nm range for detecting SIF  (Köhler et al. , 2018) , and was provided at a spatial resolution of 7 × 3. 5 km 2 with global coverage. SIF emissions were detected daily at approximately same solar time (∼13:30 pm) at the equator. Swath time correction was applied at all sites in order to determine the local solar time for each [µmolPhoton m -2 s -1 ] b Fitted coefficient for Hyperbolic GPP:SIF Model [mW m -2 sr -1 nm -1 ] C CO2 Carbon Dioxide Molar Fraction [µmol mol -1 ] Cp Specific Heat of Dry Air [J g -1 K -1 ] EF Evaporative Fraction - FC Carbon Dioxide Flux [µmolCO 2 m -2 s -1 ] fPAR Fraction of Absorbed Photosynthetically Active Radiation - gc Stomatal Conductance [m s -1 ] GPP Gross Primary Production [µmolCO 2 m -2 s -1 ] GPP gap-filled Gap-filled Gross Primary Production [µmolCO 2 m -2 s -1 ] GPPmax Fitted coefficient for Hyperbolic GPP:SIF Model [µmolCO 2 m -2 s -1 ] H Sensible Heat Flux [W m -2 ] H 0 Step Function to zero negative cos(θ) - LAI Leaf Area Index - LE Latent Heat Flux [W m -2 ] NEE Net Ecosystem Exchange [µmolCO 2 m -2 s -1 ] NIR Near-Infrared Reflectance - NPQ Non-Photochemical Quenching - PAR Photosynthetically Photosynthetic Active Radiation [µmolPhoton m -2 s -1 ] PRI Photochemical Reflectance Index - R 11 Reflectance at band 11 (526-536 nm) -R 12 Reflectance at band 12 (546-556 nm) - r ah Aerodynamic Resistance [s m -1 ] Reco Ecosystem Respiration [µmolCO 2 m -2 s -1 ] RH Relative Humidity - Rn Net Radiation [W m -2 ] SC Storage Term of Carbon Dioxide Flux [µmolCO 2 m -2 s -1 ] SIF Solar Induced Fluorescence [mW m -2 sr -1 nm -1 ] SIF Daily Upscaled Daily SIF Average [mW m -2 sr -1 nm -1 ] SIF Inst SIF Value at Time of Measurement [mW m -2 sr -1 nm -1 ] SM Soil Moisture - SWC Soil Water Content - TA Air Temperature [K] tm SIF Time of Measurement - u* Friction Velocity [m s -1 ] VPD Vapor Pressure Deficit [Pa] WS Wind Speed [m s -1 ] α Linear Mixed Effect (LME) Model Slope [µmolCO 2 s -1 mW -1 sr nm] β Site Site Level Intercept of LME Model (Random Intercept) [µmolCO 2 m -2 s -1 ] γ Psychometric Constant [Pa K -1 ] γ i Response slope of additional variable in LME Depends on the units of variable i in LME Slope of the saturated vapor pressure curve [Pa K -1 ] θ Solar Zenith Angle Degree ρ Air density [kg m -3 ] ρa Molar Density of Air [mol m -3 ] observation.
Overall SIF satellite measurements fell between 10 am and 2 pm local time. Observations obstructed by cloudcover conditions were filtered out. SIF data were reported in milliwatt per meter squared, per steradian, per nanometer [mW m -2 sr -1 nm -1 ]. In order to get equivalent SIF values at the daily timescale, we used the scaling approach proposed by  Frankenberg et al. (2011)  to convert instantaneous SIF to daily average SIF. This method accounts for the variations in overpass time (including due to swath), length of day, and solar zenith angle: SIF Daily = SIF Inst × 1 cos(θ (t m )) ∫ t=t m +12hours t=t m -12hours cos (θ (t)) ×H 0 (cos (θ (t))) dt (1) where SIF Daily is the upscaled daily SIF average, SIF Inst is the SIF value at time of measurement t m , θ(t m ) is the corresponding solar zenith angle, and H 0 is a step function where H 0 (cos (θ (t))) equals zero when cos (θ (t)) is negative and equals one when cos (θ (t)) is positive  (Köhler et al.
Gross Primary Production Data All sites reported turbulent net carbon dioxide fluxes (column FC, in the EC data), but Net Ecosystem Exchange (NEE) and GPP were not provided by all sites. For consistency, a unified modeling approach for estimating GPP was followed across all sites. NEE was calculated as the sum of the turbulent Flux of CO 2 (FC) and Storage of CO 2 (SC): NEE = FC + SC (2) Some of the sites did not provide SC, thus SC was approximated using the following equation: SC = ρ a × h ∫ 0 dC CO 2 dt dz (3) where ρ a is the molar density of the air [mol m -3 ], and C CO 2 is Carbon Dioxide molar fraction [µmolmol -1 ]. Integration includes all available measurements for carbon dioxide concentration from the ground to the CO 2 flux measurement height, h.
It was assumed that during the night there was no photosynthetic activity, therefore, nighttime NEE was equal to R eco. Thus, the ANN was trained with nighttime data. The resulting model was used to gap fill nighttime R eco and NEE, and to simulate daytime R eco. We assumed no nighttime GPP. The daytime was determined based on the Greenwich Mean Time (GMT) offset of each site and its latitude  (Tramontana et al. , 2020). During daytime, GPP was: GPP = NEE -R eco (4) Where, during the daytime period of observations, GPP was a negative quantity and R eco positive. This approach resulted in an observation gap whenever NEE observations were not available. We used these GPP data (corresponding with time when observed NEE values were available from EC and not gapfilled) for the half-hourly analysis.
Ecosystem State Variables Several environmental variables were used in the analysis along with SIF and GPP: canopy conductance (g c ), evaporative fraction (EF), APAR, photochemical reflectance index (PRI), PAR, soil moisture (SM), VPD, RH, TA, LE, fPAR, LAI, and NIR. All these variable data were provided using EC and remote sensing measurements or derived from data from EC measurements. Full descriptions for the source and calculation of each variable are listed below. MODIS data product (MCD15A2H v006) was used for leaf area index (LAI) and fraction of absorbed PAR (fPAR), at 8day time resolution and 500 m pixel size for each site using the Earthdata open-source repository 3. APAR [µmolPhoton m -2 s -1 ] was calculated using the following equation: APAR = fPAR × PAR (5) where PAR [µmolPhoton m -2 s -1 ] observations were measured at the EC flux sites.
r ah was approximated using site observations of u * and wind speed (ū) based on the empirical approach by  Monteith and Unsworth (1990) : r ah = ū u * 2 + 6. 2 u * 2/3 (7) Evaporative Fraction was also used as an indicator for water stress conditions, where it is the ratio of latent heat flux to the 3 https://search. earthdata. nasa. gov/ GPP gap-filled [µmolCO 2 m -2 s -1 ] NEE-R eco N/A N/A N/A N/A The table includes goodness-of-fit statistics for ANN models used to gap fill fluxes. Values represent averages ± standard deviation of R 2 and root mean square error (RMSE) across all sites and years. sum of latent heat flux and sensible heat flux, H [W m -2 ]: EF = LE H + LE (8) Surface soil water content (SWC) observations were provided by ground-based measurements (half hourly) at the EC sites, and from the spaceborne SMAP dataset 4  at 6 am and 6 pm local time at daily basis.
Both data sets were used separately in the analysis. Photochemical Reflectance Index (PRI) is sensitive to changes in carotenoid pigments and used as an inverse proxy for NPQ at short timescales. MODIS data product (MYDOCGA, v006) is used to get reflectance at bands 11 and 12 at daily resolution and calculate a proxy of PRI at each site as described in  Wang et al. (2020) : PRI = R 11 -R 12 R 11 + R 12 (9) where R 11 and R 12 are the reflectance at bands 11 (526-536 nm) and 12 (546-556 nm), respectively. MODIS data product (MOD13Q1 v006) was used for getting NIR. Table  1  above includes all the acronyms used in this study.
Data Analysis Data analysis involves three timescales: half-hourly, daily, and weekly. Instantaneous SIF measurements are used for half-hourly timescale, upscaled daily average SIF values (Eq. 1) are used for the daily timescale, while weekly averages of SIF measurements are used for the weekly timescale. As for GPP, the half-hourly window during which a SIF observation was available is used for the half-hourly timescale, while daily and weekly averaged GPP gap-filled values are used for the daily and weekly timescales, respectively. For all statistical inferences, a 0. 05 significance level is considered. The GPP:SIF relations was evaluated at multiple spatial scales and at the three temporal scales: half-hourly, daily, and weekly for each spatial scale. First, it was tested at the site level, where both a linear and a hyperbolic fit models were tested at each site.