dataesr/scientific-paragraphs-categorization huggingface dataset:
5f, g ) or the selection of big trees (Supplementary Fig. 6 ). The finding of consistently longer lifespans for slow-growing trees may seem in direct contradiction to repeated observations of a greater mortality risk for slow-growing trees that are suppressed or have undergone some other form of damage that did not initially kill the trees  [22] [23] [24] [25]. These apparently contradicting results can be reconciled, however, by differences in the analyses performed and datasets used. We only used rigorously selected species for which we had a very large number of big trees. Thus, our analysis will include with high probability the longest-living trees for a wide range of early growth categories, and permits us to perform 95 th quantile regressions to estimate tree lifespans. Unlike previous studies  [22] [23] [24] [25]  , our approach is relatively insensitive to early and intermediate stage mortality processes, which are governed by pre-death growth declines.
We argue, however, that this is not the case with two lines of evidence. First, our in-depth analyses of the dataset for Picea mariana from Quebec shows that, while lifespan and growth covary with various environmental variables, including temperature (Fig. 2c, d ), soil type, and crown cover (Supplementary Fig. 8 ), not one single factor fully explains the variation in growth rate and lifespan. Similarly, we observed comparable trade-offs in species growing in ecosystems with intrinsically different growth limitations, including boreal forests which are primarily controlled by temperature (Fig. 2a ), tropical floodplains with strong edaphic controls  26  , and closed-canopy forests with high levels of competition for light  20, 27. Secondly, growth-lifespan trade-offs remained strong even after controlling for possible environmental drivers.
4 ). Relative early growth and relative lifespan were calculated as the ratio of the early growth rate or age of each tree relative to the maximum growth or age for each species. d Histogram of the exponential decay constant of relative early growth rate vs. relative lifespan relationships for 82 species with sufficiently large datasets. (~300 mm), but do so at notably different ages (Supplementary Fig. 3 ). As we show in the next section, indeed, simple simulations with observed tree-ring data and mortality rates increasing with size, approaching a potential absolute maximum, result in a very similar growth-lifespan relationship as observed (Fig. 3b ). In contrast, age-dependent mortality functions do not reproduce the growth-lifespan trade-off (Supplementary Fig. 9a, b ). Thus, the existence of a maximum potential tree size provides a plausible explanation for at least part of the observed trade-off.
Methods Tree-ring data. We used tree-ring records from over 210,000 trees of 110 species, distributed globally in habitats ranging from the tropics to the Arctic region over more than 70,000 sites (Supplementary Fig. 1 , Supplementary Table  1 ). The largest publicly available data source from which we used data is the International Tree-Ring Data Bank (ITRDB, https://www. ncdc. noaa. gov/data-access/paleoclimatologydata/datasets/tree-ring). These were complemented with other datasets to maximize the number of records for each species and to fill in spatial gaps. A particularly large tree-ring dataset used in our analyses is the National Forestry Inventory data from the Ministère des Forêts de la Faune et des Parcs from Quebec, Canada  52, 53  (hereafter NFI-Quebec). This data consists of a complete set of ring-width data from 156,711 trees from 79.
Part of these dead tree data were obtained from the ITRDB by selecting tree-ring records of which the last measured ring width was dated to before AD1900. We assumed that most of these trees must have been dead at the time of sampling as no records were collected before 1900. In addition, we compiled published dead tree data from refs. 21, 56 , and used subfossil tree-ring data from refs. 57, 58. Supplementary Table  1  provides an overview of the datasets, and full details of each dataset are available online as supplemental info. Various data controls and selection procedures were used to assure high confidence in our dataset. Where possible we tried to identify duplicate records, i. e. , multiple cores taken from the same individual tree. This is only a problem for ITRDB, but not for the NFI-Quebec dataset where only one core per tree existed, or for datasets from co-authors.
Voelker  59. In this subset of data, each ring width series was manually re-aligned by cambial age (i. e. , ring number from pith), providing more reliable estimates of tree ages. For the datasets that were not acquired from the ITRDB, we used slightly different criteria. From NFI-Quebec, we used all available sites, excluding those that were classified with evidence of recent management (commercial thinning or clear-cutting) and where fire or insect disturbances destroyed more than 25% of the forest cover. For estimates of species-level early growth rates and lifespans (cf. Fig. 1a ), we included only species with a minimum of 30 records, as lower sample size is unlikely to provide good approximations of tree life spans. This resulted in the inclusion of 110 species, with a median sample size of 305 trees and 12 sites per species.
4% of the cases in maximum age estimates larger than the 95th percentile of the original dataset, and in 67. 2% of the cases in ages larger than the 99 th percentile original age (Supplementary Fig. 2a, b ). As more than 70% of the species had at least 100 trees we thus assumed that for most species, the estimates of their lifespan were close to true lifespans. We used this same approach to assess how sample size affected the estimate of the trade-offs (i. e. , estimation of the negative exponential decay constant; see next section for details). This analysis showed that sample size of 300 trees (corresponding to median sample sizes for trade-off analysis), leads to mean errors in the estimated slope of 12% (Supplementary Fig. 2c ). Thus, for most species we achieve relatively accurate estimates of the trade-off strength.
Low sample sizes for some species will nevertheless result in small errors of the mean slope, but we expect that positive and negative errors will cancel out against each other. Indeed, we do not observe a specific bias towards over-or under-estimation for low sample sizes, as the mean exponential decay constant for a simulated sample size of 150 trees is very similar to that observed (i. e. , -0. 409 versus -0. 399). Trade-off estimates and assessment of possible artefacts. The strength of the trade-offs between growth and tree lifespan was assessed for each species using a 95 th quantile regression between mean early growth rate and the natural logarithm of age using the QUANTREG package in R  60  , as logðA ð95th quantileÞ Þ ¼ a þ b Á RW or Lifespan % A 95th quantile ð Þ ¼ exp a þ b Á RW À Á ð1Þ where A is age of the tree, RW is the mean ring width over the first 10 years.
(1) Use of living trees: our analysis includes mostly trees that were sampled when still alive, and may thus not be representative for the true lifespan trees may achieve. To assess to which degree use of living trees may affect our results, we analyse and compare the trade-off strengths of trees that died before 1900 to living trees for 12 species with sufficient data availability (minimum of 150 dead and 150 living trees). As the slopes of dead and living trees do not differ significantly (Supplementary Fig. 5f , g, paired t-test exponential decay coefficient, t = -0. 1095, p = 0. 915, n = 12), we conclude that 95th quantile regressions on living trees can be used to approximate tree lifespan. (2) Effect of recent growth increases: recent growth stimulation of trees due e. g. to CO 2 fertilisation, warming in higher latitudes, and/or nitrogen deposition, may result in observation of a trade-off.
Thus early growth in these samples will underestimate true growth rates and would most likely weaken the observed trade-off, rather than strengthening it. A comparison of species present in both the NFI-Quebec and the ITRDB datasets confirms this. The NFI-Quebec dataset was less affected by pith offset problems, as the trees were carefully screened and trees with substantial differences between cumulative ring widths and field diameters were excluded. Yet, we find that slopes were more negative for NFI-Quebec compared to ITRDB (mean b of -0. 25 for Quebec vs. -0. 10 for ITRDB, two-sided paired t. test, t = 2. 49, p = 0. 047, n = 7 species) and pith offsets thus do not explain the relationship. This comparison also shows that estimates of the strength of the trade-offs between early growth and longevity inferred from ITRDB data are probably conservative, as the Quebec data can be considered to be of higher quality, and were collected according to standard protocols.
In contrast, data from the ITRDB may contain incomplete series and were collected for unknown purposes, and these issues probably weaken tradeoffs in the ITRDB. (  4 ) Sampling biases: one potential bias in our dataset may arise due to the tendency of tree-ring studies to sample predominantly large trees in the field (i. e. , big tree selection bias  [62] [63] [64]  ). This may result in a negative relationship between early growth and tree age, as young slow-growing trees tend to be underrepresented in the tree-ring sample (i. e. , have not reached the field minimum size threshold yet), compared to fast-growing young trees that are much larger, and therefore more likely to be sampled. This effect would reduce the number of trees with slow early growth and young ages in the tree-ring sample (i.
e. , trees in the lower left-hand corner of the early growthlifespan graphs, cf. Supplementary Fig. 6a ), and results in overestimation of the 95 th percentile age estimates for slow-growing trees. Our approach to estimate to which degree this bias affected our estimates of growth-lifespan trade-offs was as follows. We first used the tree-ring NFI-Quebec data of Picea mariana, combined with plot data from Quebec to reconstruct a new artificial tree-ring dataset with a size frequency distribution identical to the population size frequency distribution for this species in Quebec (Supplementary Fig. 6b ). For each tree of Picea mariana sampled for their tree-rings we know the early growth rate and age, and also the complete diameter-and age-trajectory up to the year of sampling. From these data, we resampled for each size class (in bin widths of 2 cm) the same number of trees as that observed in the field.
By doing this we filled in the lacking data of trees smaller than 91 mm, and created a new artificial tree-ring dataset that had an identical size structure to that observed in the field. We know the mean growth rate over the first ten years and the age at which each individual tree reached the diameter of their respective size class, and could thus reconstruct the early growth rate versus tree age graphs for the full population, including the smaller size classes which were missing from our original tree-ring sample. We then compared the early growth -lifespan relationship for the complete population to that of the trees larger than 91 mm, mimicking the NFI-Quebec field collection protocol. This shows that the exponential decrease is marginally larger (b = -0. 505 compared to -0. 470 for trees >91 mm) and that the intercept is lower (159 years compared to 220 for trees >91 mm) when sampling all trees compared to only trees with diameters >91 mm (Supplementary Fig.
6 ). Hence, the use of a minimum size threshold (91 mm) in the NFI from Quebec results in a slight underestimation of the trade-off (by ~7%) for the Picea mariana dataset. We also resampled from this artificial dataset the 10% largest trees, to mimic a hypothetical standard tree-ring sampling scenario that only samples the largest trees. Such a sampling scenario resulted in a decay constant of -0. 432, thus again causing a small underestimation of the true trade-off. This simulation proves that the trade-off is not a result of a sampling bias. Possible environmental drivers of the trade-offs. We evaluated whether the observed trade-off between early growth and tree lifespans could be caused by covariance of growth and lifespan with climate, soil or competition. Temperature variation for example reduces tree growth and lifespan in various species (cf.
We calculated the observed temperature trend for Quebec over the past 100 years from CRUTEMP data  69  , and simulated growth increases from year 300 to year 350 in response to observed warming rate, estimated to be 0. 0221 °C year -1. We used a space-for-time substitution approach on the full dataset of Quebec  43  to estimate the ring width response of Picea mariana to temperature. We conducted these simulations in age-bands of 10 years (0-10, 10-20, …. 140-150, >150 yrs) as younger trees are more sensitive to temperature increases than older trees (Supplementary Fig. 9d ), and use an exponential model of the form RW A; T ð Þ¼a Á exp λ A ð Þ Á δT ð Þ ; ð8Þ where a is a constant, λ(A) is the exponential increase rate for age class [A, A+ δA] and δT = (T -0) (°C). We then used the exponential growth increase with temperature for each age band, λ(A), to estimate the relationship between tree age and λ(A) (Supplementary Fig.
Acknowledgements We acknowledge the contributors to International Tree-Ring Data Bank for making available raw tree-ring data that were used in this study, and thank the staff at the  Direction des Inventaires Forestiers of the Ministère des Forêts, de la Faune et des Parcs du Québec  for sharing tree-ring and sample plot data from the forest inventory program in Quebec,  Canada. R. J. W. B. was supported by  NERC  grant  NE/S008659/1 , E. G. was supported by  NERC  grant  NE/N012542/1 , and G. L. and G. C. were supported by  FAPESP  grants  12/50457-4  and  17/5008-3 , J. B. was supported by the  Centre National de la Recherche Scientifique (CNRS) of France  through the presidential program Make Our Planet Great Again. S. H. was supported by the  Academy of Finland , and A. D. F. was supported by  National Geographic Society  grant  WW-136R-17.
Study Design and Participants This paper presents preplanned interim analysis of data from a cross-sectional epidemiological study, administered through an online survey. The study was launched on 17 March 2020, 17 days after the first case of COVID-19 was diagnosed in the Participants were recruited through social media and through national media outlets (BBC, March 26th 2020) and by distributing an invitation to participate through existing researcher networks. Adults aged 18 years and over, currently residing in the UK and self isolating/social-distancing due to COVID-19, were eligible to participate. Participants were directed to a data encrypted website where they indicated their consent to participate after reading an information sheet. Before completing the survey, participants were asked if they were currently self-isolating and over 18 years of age, if the participants' response was affirmative to both questions then the participant was asked to complete the survey and if the response was no to either of those questions then the participant was asked to not complete the survey.
Independent Variables Demographic data were collected, including sex (male or female), age (in 10-year age bands), marital status (single/separated/divorced/ widowed or married/in a domestic partnership), employment, and annual household income (<£15,000, £15,000e<£25,000, £25,000e<£40,000, £40,000e<£60,000, £60,000). Participants were also asked to indicate which of the 4 main UK countries they lived in (England, Northern Ireland, Scotland, Wales). Measures of health status included whether respondents were a current smoker and consumer of alcohol (yes/no). Chronic physical diseases included obesity, hypertension, myocardial infarction, angina pectoris and other coronary diseases, other cardiac diseases, varicose veins of lower extremities, osteoarthritis, chronic neck pain, chronic low back pain, chronic allergy (excluding allergic asthma), asthma (including allergic asthma), chronic bronchitis, emphysema or chronic obstructive pulmonary disease (COPD), type 1 diabetes, type 2 diabetes, diabetic retinopathy, cataract, peptic ulcer disease, urinary incontinence or urine control problems, hypercholesterolemia, chronic skin disease, chronic constipation, liver cirrhosis and other hepatic disorders, stroke, chronic migraine and other frequent chronic headaches, hemorrhoids, cancer, osteoporosis, thyroid disease, renal disease, and injury.
org/10. 5281/zenodo. 3583135). Furthermore, a slight increase of strain occurs at the beginning of unloading, indicating some minor creep contribution. However, despite the high Schmid factor in several grains, no evidence of intragranular plasticity involving dislocation motion was detected in all the analyzed movies. This is in agreement with the observation of the absence of glide of the two dislocations D1 and D2 in grain 12 of Figure  2b  when comparing the specimen before deformation (Figure  2a ) and after fracture (Figure  2c ). These results point toward a pivotal role of intergranular plasticity mechanisms that will be confirmed in the following sections. Â Ã 101 ð Þ) (Amiguet.
Journal of Geophysical Research: Solid Earth Indeed, changes of diffraction contrast (from dark to white) along this GB can be clearly observed in Figure  3c  during Cycle 5 at 225 MPa. This phenomenon initiates at the tip of the nanocrack connected to the same GB and indicated by white arrowhead in Figure  3c  (see Movie AntCU65-01 at https://doi. org/10. 5281/ zenodo. 3583135). Such a behavior was difficult to detect in the Ant624-10-01 specimen since the GB where fracture occurred was located in a region out of Bragg condition (Figure  2 ). It is also worth noting that in Figure  3f , Cycle 5 (in which GB sliding can be clearly observed) exhibits the highest permanent deformation compared to all the other cycles in the same figure. Again, the strain increases at the early stage of unloading due to activation of some creep mechanisms in Figure  3f , suggesting the activation of GB processes.
This observation agrees with the GB sliding mechanism proposed in the previous sections as the main permanent deformation mechanism. The following TEM observations add extra evidences to this scenario. Figure  5  shows TEM snapshots selected at the positions a-f along the stress-strain response of Figure  4a. It demonstrates intense GB sliding along the GBs indicated by white arrowheads in the same figure (see also Movie Ant624-10-02 at https://doi. org/10. 5281/zenodo. 3583135). Figure  6  exhibits the evolution with increasing deformation of the transmitted beam intensity across the grain boundary GB F where failure ultimately occurred, as shown in Figure  5. It clearly shows a gradual increase of intensity at GB F from Figures 5a to 5f, which can be explained by the GB sliding mechanism illustrated in the right inset of the graph of Figure  6.
The Young's modulus can be deduced from those using the classical elasticity relation E = 9KG/(3K + G). This leads to values significantly larger than ours (2 to 3 times). We compare, however,   2011 ) performed density functional theory calculations of static compression. Our experiments are the only ones where the modulus is measured from uniaxial tensile tests on specimens with very small sizes and containing a very small number of grains with a limited number of orientations. Hence, our values should be compared to single crystal data rather than to polycrystal averages. Depending on the tensile direction, the Young's modulus of antigorite single crystals ranges from 44 to 185 GPa (calculations performed from the single crystal elastic constants of  Bezacier et al. , 2010). Still our measured values appear significantly lower which suggests that what may appear as an elastic portion of the stress-strain curves does not correspond to a purely elastic behavior.
20 manned research flights were conducted over the tropical Atlantic, off the coast of Barbados (13 • 30 N, 58 • 30 W), to characterize the trade-wind clouds generated by shallow convection. These flights were conducted as part of the international EUREC 4 A (Elucidating the role of cloud-circulation coupling in climate) field campaign. One of the objectives of these flights was to characterize the trade-wind cumuli at their base for a range of meteorological conditions, convective mesoscale organizations and times of the day, with the help of sidewards-staring remote sensing instruments (lidar and radar). This paper presents the datasets associated with horizontal lidar measurements. The lidar sampled clouds from a lateral window of the aircraft over a range of about 8 km, with a horizontal resolution of 15 m, over a rectangle pattern of 20 km by 130 km.
, 2008). Therefore, during EUREC 4 A the horizontal lidar measurements made from the ATR-42 were also used to characterize the marine boundary layer and long-range transport of aerosols within the free troposphere. The goal of this paper is to present the flight strategy, measurements, data processing, and cloud and aerosol products derived from the horizontal lidar measurements made during the EUREC 4 A campaign. Section 2 presents the ALiAS lidar characteristics and Sect. 3 the implementation of the lidar in the ATR-42 aircraft. The flight plan and its decomposition into different phases are presented in Sect. 4. Section 5 describes the different levels of data processing and the cloud and aerosol products that constitute the final dataset. The conclusion is presented in Sect. 6 as well as how to access the data.
g. depending on the presence of a stratiform cloud layer near the trade inversion level. The details on the ATR-42 flight blocks (rectangles, L-shaped legs, surface legs) are given in Table  2. It should be noted that prior to the beginning of Phase 2, a best-guess estimate of the CBH, assessed from multiple sources of informa-   1 ) and calibrated data (Level 1. 5) to products (Level 2 and Level 3). The grey cells summarize the actions to be implemented for the data processing. The green colour refers to data level in the preprocessing phase. Level 2 and Level 3 are subdivided into cloud (blue) or aerosol (orange) products. tion (radiosoundings launched from BCO (Barbados Cloud Observatory) and research vessels, dropsondes released from other aircraft), was provided to the ATR-42 scientists via the on-board chat capability.
Data type The data are presented from their raw form to the analytical products. They are classified into Level 1 to Level 3 as defined in Table  3. Up to Level 2 (included), lidar profiles are processed on an individual basis. The statistics performed on Level 2 data are gathered in the Level 3 data. Statistics are computed for all flights for the aerosol Level 3 products and for Phase 2 of the flights for the cloud products. The step from Level 1 data to the final products of Level 2 and Level 3 is schematized in Fig. 4. This section presents the steps taken to derive data products. The data recording format is detailed in Sect. 6. It should be noted that for Level 1 to Level 2, the location and attitude of the aircraft are also reported for each horizontal lidar profile. https://doi. org/10.