dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, some of the decisions delivered by the different CMRAs involved in an appeal from the same seafarer were also described. Decisions concerning first registrations were also listed. The following data were collected: a unique identifier for the seafarer (which was not the seafarer's registration number, thus ensuring anonymity) corresponding to their order of appearance in the CMRA files gender year of birth year in which the decision of permanent unfitness was taken by the CMRA CMRA location CMRA's conclusion total time spent at sea and total time spent sea fishing, working on merchant ships, shellfish farming or professional sailboating prior to being declared unfit (this data can be found in the administrative section of the Aesculapius® database) ICD-10 coding of the main affection or the symptoms causing the unfitness.
Main variables of interest The main variables of interest were the number and age of seafarers per year who were declared unfit for work at sea in France. The different diseases or symptoms linked to the unfitness decisions (ICD-10 coding) was the secondary variable of interest. If two or more affections were potentially contributing to the cause of medical unfitness, only the main reason was retained, that is the affection entered first by the SD in the Aesculapius® database. Because of the large number of diseases and symptoms identified, the causes were grouped together by chapter of diseases as set out in the ICD-10. There are twenty-two (XXII) chapters in this 10th version of the ICD, but only fifteen of these were necessary to rank the diseases of the seafarers in our study. They were as follows: Neoplasms (chapter II) Endocrine, nutritional and metabolic diseases (chapter IV) Mental and behavioural disorders (chapter V) Diseases of the nervous system (chapter VI) Diseases of the eye and adnexa (chapter VII) Diseases of the ear and mastoid process (chapter VIII) Diseases of the circulatory system (chapter IX) Diseases of the respiratory system (chapter X) Diseases of the digestive system (chapter XI) Diseases of the skin and subcutaneous tissue (chapter XII) Diseases of the musculoskeletal system and connective tissue (chapter XIII), where many MSDs can be ranked Diseases of the genitourinary system (chapter XIV) Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (chapter XVIII) Injury, poisoning and certain other consequences of external causes (chapter XIX).
Other variables Four different professional seafaring sectors were listed to cover every seafarer in France, that is to say fishing, merchant shipping, shellfish farming and professional sailboating. As we mentioned previously, the annual number of active seafarers was available in the national reports published by the ENIM. We read all the annual national reports published during the study period to find out the distribution of seafarers across each professional sector. We then combined these data with the data in the Aesculapius® database to categorise the professional sector of each unfit seafarer and to calculate the proportion of unfit seafarers among all seafarers for each sector. It was sometimes difficult to categorise the seafarers into one single professional sector. In these cases, we chose to classify them according to the sector (fishing, merchant shipping, shellfish farming or professional sailboating) that accounted for more than 50% of their career.
Analysis In order to carry out the quantitative analysis of the data, we extracted the required information from the Aesculapius® database and transferred the coded elements into Excel® software. The incidence rate was calculated by dividing the annual number of cases concluded as permanently unfit for work at sea by the CMRAs by the annual number of registered active seafarers. We consequently calculated annual incidence rates of permanent unfitness for work at sea. Where data comparisons were necessary, we ran a Student's t-test using the Statgraph® data analysis software. The significance threshold was set at p < 0. 05. The null hypothesis assumed that no difference existed between the annual rates of unfitness for work at sea decisions concerning a particular disease. The sample size for establishing statistical significance was calculated at 30 subjects or more.
In addition, the fact that the disease codes had been entered by different SDs, who had no protocol and who based their decisions solely on the ICD-10's dictionary of specialised terms, meant there was an inherent risk of potentially different interpretations. This could without doubt have influenced the results by over-or under-representing one particular affection. One major bias resulted from the fact that that an individual's health status was limited to one code. There was thus an interpretation bias in the data because some affections were closely related. For example, the consequences of chronic alcohol poisoning could be coded under F10 'Mental and behavioural disorders due to use of alcohol', F32 'Depressive episode', F33 'Recurrent depressive disorder', K70 'Alcoholic liver disease', K74 'Fibrosis and cirrhosis of liver', and so on.
Nevertheless we did not realised an age adjusted analysis that could highlight the evolution of diseases by age categories in the French seafarers population. We can only suppose that some degenerative diseases (most of all degenerative MSD) occur earlier in our studied population than in others working population. Therefore the role of maritime work factors in the appearance of such diseases needs to be assessed in further studies. Finally, while the system of collecting medical information using the Aesculapius® database meant a large amount of data could be gathered, the healthy worker effect and the self-selection effect were highly likely. Seafarers with disabling affections may have ended their maritime activity of their own volition without going through the medical unfitness process.
Sequencing reads, bam files, and mtDNA fasta sequences are available at the European Nucleotide Archive under accession no. PRJEB40573. Data Availability. The data have been deposited in the European Nucleotide Archive, https://www. ebi. ac. uk/ena/browser (accesson no. PRJEB40573). Previously published data were used for this work  (10, (35) (36) (37) 54).
ACKNOWLEDGMENTS. We thank  Logan Kistler  for use of facilities at the  Smithsonian Institute ;  Victoria E. Mullin  and  Matthew D. Teasdale  for helpful reading of the text;  Julien Riel-Salvatore  for help with images of Ganj Dareh;  F. Biglari  ( Paleolithic Department ) and  O. B. Ghafouri  (curator of the Osteology Department) of the National Museum of Iran; and  H. Laleh  and  A. Aliyari  from the  Bioarchaeology Laboratory of the University of Tehran  for their help. New excavations at Ganj Dareh were carried out with the permission from the  Iranian Centre for Archaeological Research. This project has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (Grant Agreement 885729-AncestralWeave and 295729-CodeX).
Study populations The current study included data from the following 4 European birth cohorts participating in the MeDALL project: BAMSE (Sweden), Prevention and Incidence of Asthma and Mite Allergy (PIAMA [The Netherlands]), Influences of Lifestyle-Related Factors on the Human Immune System and Development of Allergies in Childhood (LISA [Germany]), and GINIplus (Germany). Participants were recruited from 1994 to 1999. [23]  Air pollution exposure assessment Annual mean concentrations of ambient particulate matter with an aerodynamic diameter less than 2. 5 mm (PM 2. 5 ) or 10 mm (PM 10 ), coarse particulate matter (PM 2. 5-10 ), PM 2. 5 absorbance (an indicator for black carbon particulate matter content), nitrogen dioxide (NO 2 ), and nitrogen oxides (NO x ) at the residential addresses of the study participants were estimated through land use regression (LUR) models developed for each study area within the framework of the ESCAPE project; they have been extensively described elsewhere,  24, 25  as well as in the Online Repository.
RESULTS The population of the present study comprises individuals with available data on repeated IgE measurements throughout childhood and adolescence and air pollution exposure at birth (n 5 6163) or at the time of biosampling (n 5 5771). A short description of selected characteristics related to the enrolment and follow-ups of the included cohorts is provided in Table  E2  (available in the Online Repository at www. jacionline. org). The distribution of potential risk factors at the ages of 4 to 6, 8 to 10, and 15 to 16 years in each cohort is presented in Tables E3 to E5, respectively (available in the Online Repository at at www. jacionline. org). Notably, in the subset of children with IgE measurements in the BAMSE cohort, the proportion of children with allergic heredity was lower than in the other cohorts.
jacionline. org). Further, we evaluated the association of air pollution exposure with IgE sensitization to microarrayed allergen molecules at the age of 15 to 16 years as determined with the MeDALL chip. In the initial discovery microarray-wide association meta-analysis based on the BAMSE and GINIplus cohorts, no allergen molecule appeared to be significantly associated with air pollution exposure after multiple testing corrections (data not shown). In the candidate analysis, which was limited to 4 ''risk molecules'' previously linked to the risk of respiratory allergy,  20  we observed positive associations of sensitization against these molecules with PM 2. 5 exposure at birth address that did not reach the level of statistical significance (see Table  E15  in the Online Repositoryat www.
The 4 birth cohorts included in this study were specifically designed to examine the development of allergic diseases during childhood. Participation in the European collaborative projects ESCAPE and MeDALL provided a unique opportunity to utilize standardized individual-level exposure data generated from dedicated monitoring campaigns and area-specific LUR modeling, along with repeatedly measured outcome and confounder data harmonized across all the included cohorts. In addition, high-throughput MeDALL microarray measurements in 2 of the participating cohorts enabled investigation of IgE sensitization against allergen molecules in relation to air pollution exposure. We assigned the exposure levels to the birth addresses and the addresses at which children resided at the time of biosampling to investigate potential critical time periods of susceptibility to air pollution.
The research leading to these results has received funding from the  European Community 's  Seventh Framework Program  under grant agreement numbers: 211250 (European Study of Cohorts for Air Pollution Effects [ESCAPE]), and  261357  (Mechanisms of the  Development of ALLergy [MeDALL]). Children, Allergy, Milieu, Stockholm, Epidemiology (BAMSE)  was supported by The  Swedish Research Council , The  Swedish Heart-Lung Foundation ,  Region Stockholm  ( ALF  project, and database maintenance), the  Strategic Research Programme in Epidemiology at Karolinska Institutet , the  Swedish Research Council Formas  and the  Swedish Environment Protection Agency , the  Swedish Asthma and Allergy Research Foundation , the  Cancer and Allergy Foundation. E. M. is supported by a grant from the  European Research Council  (grant agreement  757919 , TRIBAL).
, 2015). We defined ancient forests as sites already forested in the middle of the 18 th century (when the overall forested area was at its minimum over the French territory,  Cateau et al. , 2015)  and recent woodlands as forests resulting from farmland abandonment during the 20 th century. Therefore, ancient forests have an uninterrupted forest state since at least 230 years and recent woodlands are not older than 120 years. Our dataset included 20 plots in ancient forests (from six different forest sites) and 12 plots in recent woodlands (from two different forest sites); see Figure  S1. The site configuration, the habitat structure and the ecological conditions were quite similar between recent and ancient forests: there were no differences of site area, canopy cover, basal area and Ellenberg Indicator Values (EIV) for moisture degree (Table  S1 ).
Accepted Article We conducted floristic surveys in June-July 2014 and 2015 to sample the understorey plant communities of the selected plots (i. e. below 2 meters high and including woody species). We used 50-m² quadrats (  10 x 5 m) and we noted all species encountered belonging to the herbaceous and shrub strata. A total of 99 species was recorded (see  Morel et al. , 2019b  for more details on the dataset). To sample spider communities, we compiled data from a regional database which included individual sampling conducted within the same 32 plots that we used for the floristic surveys. Sampling was made using a standardised protocol based on 3 pitfall traps spaced 10m apart and located at the centre of the plot. The trap data were pooled per plot in the further analyses. Sampling was conducted between April and June either in 2013, 2014, or 2015 (see  Morel et al.
Functional characterisation of species We selected 9 functional traits from the LEDA database  (Kleyer et al. , 2008)  to measure the functional variability of plant species. These traits relate to the plants' ecological strategy for resource acquisition, competition, regeneration and dispersal (Table  S2 ). We selected two traits of the leaf economics spectrum informing about resource acquisition, resource conservation and competition: the specific leaf area (SLA) and the leaf dry matter content (LDMC). We selected four regenerative traits related to growth and dispersal in space and time: dispersal syndrome, pollination type, seed dry mass and start of flowering. We also selected two integrative traits informing about the overall ecological strategy of plants: plant height and life form.
All of these traits are response-effect traits  (Lavorel & Garnier, 2002)  because they both respond to environmental conditions and also influence ecosystem functions. Since traits were not overly correlated (all r < 0. 60), we kept the 9 selected traits. The dataset comprised 16 missing values, that is, 1. 8% of the dataset. We selected 4 life-history traits available in the literature to characterise the functional variability of spider species (Table  S3 ): body size, guilds, phenology and circadian activity. These traits relate to the ecological strategy of spiders, in particular their diet and hunting specialisation, foraging method, the habitats they exploit and their dispersal abilities. They hence represent key features illustrating the assemblages of predator arthropods at local scale  (Cardoso et al.
We run the further analyses with these groups for plants and spiders, but note that we also run the analyses with groups defined a priori, to ensure that our results were not trivially the reflection of group selection. For plants, we adapted the "biological types" of species recorded in the French flora database (Baseflor;  Julve, 1998) , which are derived from the classification of Raunkier, and we partitioned the species into 12 groups. For spiders, we used the guilds' typology developed by Cardoso et al. (  2011 ), and we partitioned the species into 7 groups. With this alternative group selection, we obtained the same results hereafter for both plants and spiders (Figure  S4 ).
Sampling effort Before calculating species co-occurrence probabilities to estimate dark diversity, we tested the sampling effort of our datasets, to validate the fact that the data satisfactorily mirrors the structure and composition of sampled communities. This is particularly advised when sampled units are limited  (Lewis et al. , 2016; Pärtel, 2014)  because in such cases assessment of co-occurrence probabilities may provide spurious results  (De Cáceres & Legendre 2008). Sampling effort estimation is assessed as the average ratio between observed and estimated richness with several richness estimators  (Soberón et al. , 2007).
Measuring dark diversity and completeness First of all, we measured the taxonomic and functional, observed diversity of communities, that is, species and functional group richness. Then, we calculated dark diversity using the Beals co-occurrence index  (Beals, 1984) , which is the most widely used method  (Belinchón et al. , 2020; Lewis et al. , 2016). This method estimates dark diversity by calculating the probability that a species occurs in a plot based on its co-occurrence with the observed species in the rest of the dataset. A species is thus considered to be part of the dark diversity of a plot if it is absent in the plot but regularly co-occurs with species which are present in the plot. Beals' probabilities increase with species frequency: the more a species occurs in the dataset, the higher the probability of identifying associations with others (De Cáceres & Legendre 2008;  Münzbergová & Herben 2004).
Data analysis We had a dataset with a nested structure: replicate plots nested into forest sites, nested into forest types. Therefore, we used generalised linear mixed models (GLMMs with Poisson distribution family) for discrete variables (observed and dark diversity) and linear mixed models (LMMs) for continuous variables (completeness) to test differences among forest types (i. e. ancient versus recent). We used the forest type as a fixed factor and the hierarchical structure (plots nested within sites) as a random effect, to remove any potential effect of autocorrelation. All analyses were performed using R software (R Core Team, 2020). The handling of trait matrices and identification of functional groups were done using the package "cluster"  (Maechler et al. , 2019)  and the "species_to_FE" and "FE_metrics" functions  (Mouillot et al.
Perspectives and limitations We argue for the development of the framework of functional dark diversity for both researchers and practicioners, notably in the study of biodiversity responses to land-use changes. We acknowledge some limitations of our results: our recent study forests might be in somewhat good conditions compared to other ones elsewhere in the study region, because they have not undergone a particularly excessive anthropogenic pressure during their regeneration. We also note that we studied dark diversity on a Accepted Article relatively small dataset (i. e. eight forests of Brittany), but we think it was sufficiently robust to analyse the different facets of dark diversity and test their dissemblances. Overall, we think that our method assessing directly functional dark diversity, with its simplicity, can easily be applied to many other issues of conservation and restoration.
Acknowledgements The project was part of the PhD program "Biodiversity recovery in feral woodlands", funded by the  "Région Bretagne" , the " Département des Côtes d'Armor" , the " Département d'Ille-et-Vilaine " and the  "Communauté de communes de Plouha-Lanvollon". We also are very grateful to  Benoit Dujol  for fruitful early discussion about dark diversity, as well as the two anonymous reviewers for their insightful comments.
Lakes shown here were chosen for representativeness; the same plots for all 139 lakes are available at https://doi. org/10. 5281/zenodo. 4686874. NATuRE CLIMATE CHANGE | www. nature. com/natureclimatechange in thermal non-overlap to determine which lake characteristics are most strongly related to variability in thermal habitat change. Over the past several decades, volume-weighted, whole-lake temperatures have increased in 77% of lakes with a mean trend of +0. 12 °C per decade. However, whole-lake warming rates failed to reveal the complexity of thermal habitat change across each lake's temperature spectrum-most lakes exhibited serial losses and gains along their temperature gradients (Fig. 2 ) with implications for which species would be most affected. After controlling for differences in the length and seasonal coverage of each lake's temperature time series and standardizing against a null estimate (Methods), we found that the mean thermal non-overlap across lakes from 1978 to 2013 was 6.
Overall, these analyses suggest that lake ecosystem sensitivity to climate change, as characterized by thermal non-overlap, differs considerably from the global pattern in the magnitude of lake surface warming. Despite slower surface warming rates, habitat changes are likely to be felt most strongly in tropical lakes where biodiversity may be most affected. The global pattern in thermal non-overlap may have differed from that of whole-lake warming rates (Extended Data Fig. 4 ) because transparency changes sometimes shield deep waters from downward transmission of heat  25. Shielding would counterbalance calculations of whole-lake warming rates when integrating across the entire water column because some depths warm whereas others cool. When calculating whole-lake warming rates, lakes which exhibit deep-water cooling often have moderate or no significant differences in whole-lake mean annual temperature  25.
Methods Overview. We used long-term time series of lake temperature profiles to determine the magnitude of thermal habitat change in 139 widely distributed lakes. Time series were interpolated across depth and season to generate data with consistent resolutions across lakes. To assess temperature change, we used a metric, 'thermal non-overlap' , based on the percentage of two kernel density estimations of lake temperature which are non-overlapping. We calculated the metric for a range of plausible seasonal and depth habitat restrictions for aquatic species in the face of climate change. We used BRT to explain variability across lakes in their thermal habitat non-overlap as a function of lake characteristics (mean depth and latitude), characteristics of the time series for each lake (starting day of the year, ending day of the year, starting year and ending year, average number of sampling dates per year, long-term trend in the number of sampling dates per year, long-term trend in the yearly seasonal range of sampling dates), the habitat restriction values (season and depth) and the location of the time series delineation for thermal non-overlap calculations (30th, 50th and/or 70th quantiles of the years included in each lake's time series).
Study sites. We compiled long-term lake temperature data from 139 lakes across the globe. Temperature variations in many of these lakes have already been linked to climate change  1, 2, 19, 20, 57, 58  , but temperature change in at least one lake may be partially due to background climate variation in addition to anthropogenic climate change (Atlantic Multidecadal Oscillation in Lake Annie)  59. The lakes included in our analysis represent a wide range of surface area (0. 02 to 68,800 km 2 ), maximum depth (2. 3 to 1,642 m), latitude (60 °S to 69 °N) and elevation (-212 to 1,987 m above sea level) (see Supplementary Table  1  for more information). Temperature data. In total, we used more than 32 million lake temperature measurements for our analyses. The number of observations per lake ranged from 368 (Lake Stensjon) to 7,636,767 (Lake Superior) with approximately 232,000 observations per lake on average.