dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The data supporting the findings from this study are included either in the manuscript and its associated supplementary files, or can be extracted from the original referenced publications. The protein-protein interaction data have been deposited to the IntAct database (https://www. ebi. ac. uk/intact/), and will be publicly available with PubMed ID upon publication. All data are also available from the authors upon request. N2H plasmids and their corresponding maps were deposited to the Addgene plasmid repository (https://www. addgene. org/Marc_Vidal/).
Methods This study was based on the cross-sectional data of the Brazilian National Health Survey, called in Portuguese, Pesquisa Nacional de Saúde (PNS), set up in 2013 by the Brazilian Institute of Geography and Statistics (IBGE) and the health ministry  [25]. As already described in one of our previous publications  [26] , this is a household survey among residents 18 years of age and older in Brazil. The three-stage cluster sampling included successive selections of primary sampling units, private households and residents aged 18 years or more using simple random sampling. Details on this survey were published previously  [25]. Several questionnaires were used to collect data on household characteristics, and information for all household residents, and more specifically for the selected resident in each household.
Acknowledgements The study and data analysis were performed using data from the  Brazilian Institute of Geography and Statistics (IBGE). However, the opinions and views expressed do not represent those of IBGE. This article is a condensed version of the first author's PhD thesis from  University Federal of Rio Grande do Sul (Brazil)  and product of research internship at  INSERM  and  University of Angers (France). The authors would like to thank  Julie Bodin ,  Julien Dugas  and  Sylvaine Jego  for their help with the SAS coding.
Estimating and predicting temporal trends in species richness is of general importance, but notably difficult because detection probabilities of species are imperfect and many datasets were collected in an opportunistic manner. We need to improve our capabilities to assess richness trends using datasets collected in unstandardized procedures with potential collection bias. Two methods are proposed and applied to estimate richness change, which both incorporate models for sampling effects and detection probability: (a) nonlinear species accumulation curves with an error variance model and (b) Pradel capture-recapture models. The methods are used to assess nationwide temporal trends  in the species richness of wild bees in the Netherlands. Previously, a decelerating decline in wild bee species richness was inferred for part of this dataset.
For the reanalysis, I chose to reconsider the inference strategy. It applies two promising approaches for assessing species richness trends among four explored (Appendices S1-S3) and compares their merits, assisted by simulations. The first analyses time patterns in species richness by modeling time-dependent asymptotes of species accumulation curves. In an alternative statistical analysis, richness will not be estimated itself for reasons detailed below. There, time patterns in the rates of colonization and local (i. e. , within the Netherlands) extinction will be estimated. Both assume nonexhaustive sampling of the species present and assess richness trends at the level of the entire Netherlands. The opportunistic nature of the data implies the absence of any spatial sampling scheme or design in the data.
| MATERIAL AND ME THODS Species richness is the horizontal asymptote of a species accumulation curve representing the expected number of species in a sample as a function of sample size. Estimators of this asymptote are often biased  (Walther & Moore, 2005) , or the precision of an estimate can be limited  (O'Hara, 2005). In a comparison of two assemblages, species accumulation curves can cross  (Chao & Jost, 2012; Lande, DeVries, & Walla, 2000; Thompson & Withers, 2003; Van Dooren, 2016) , such that patterns in the relative numbers of species found at low sampling efforts or at rarefied sample sizes (e. g. ,  Biesmeijer et al. , 2006)  can be independent of actual species richness differences. Gwinn, Allen, Bonvechio, Hoyer, and Beesley (2016)  found high sensitivity of bias and precision of change estimates to the relative abundances of species in a dataset.
| Data The dataset analyzed here consists of 73 years of opportunistic data. Bee records in the EIS (European Invertebrate Survey Netherlands) database in the period 1945-2018 are analyzed, extending periods investigated in  Biesmeijer et al. (2006) ,  Carvalheiro et al. (2013)  and  Van Dooren (2017). The data were provided by EIS as a list of records with species names, years of collection, and the square kilometer cell where each record was collected. The dataset analyzed in Van Dooren (2017) turned out to contain 150,047 spurious records, presumably due to a query error and therefore had to be redone. Sampling has not occurred in a standardized manner across the study period, and not in a spatially homogeneous or balanced manner either (Figure  1 ). In particular from mid-eighties until around 2005, increasingly larger numbers of records have been collected, more often by observation and with records from a larger number of square kilometer grid cells (Figure  1 ).
The samples per year of non-Bombus species show important time trends in the heterogeneity parameter σ of Poisson lognormal distributions  (Engen, Lande, Walla, & DeVries, 2002). This parameter σ is a compound measure representing abundance variation between species rescaled by species-specific sampling effort if present. Its changes suggest that the heterogeneity in numbers of individuals per species in the samples has increased during the study period. I assessed patterns of species richness change over time in the entire Netherlands and therefore fitted models with explanatory variables that changed over time to all data or data subsets that in principle span the entire Netherlands. Previous conclusions were drawn at that scale and I expect that changes at smaller scales are increasingly affected by (nonrandom) sampling variability and lack of consistent protocol.
(b) Records collected in square kilometer grid cells that were sampled in three periods of approximately equal length. These are the last period where the number of observations and grid cells have drastically increased  (1994-2018;  Figure  1 ), a period of equal length right after the end of  WWII (1945 WWII ( -1969) )  and the years in between. In this subset, spatial locations that contributed samples in a restricted time period only were excluded. This avoided that in one part of the study period richness might have been assessed for a different set of grid cells than in another. To the entire dataset and to the subsets, different models were fitted. Model simplification increases the precision of the remaining individual parameter estimates, but this can come at the cost of larger estimation bias.
| Generalized nonlinear models In a first approach, the number of records and the number of species per year in a dataset were used to estimate nonlinear species accumulation curves  (Gotelli & Colwell, 2011, Appendix S3 ) with parameters that change over time. In other studies, nonlinear power functions without horizontal asymptote have been fitted  (Ugland, Gray, & Ellingsen, 2003)  that predict species richness by extrapolating from a smaller area to a larger fixed range. Here, species accumulation curves are represented by Michaelis-Menten curves a(t)x(t)/ (b(t) + x(t)), estimating species number with two time-dependent non-negative functions a(t) and b(t) and with x(t) the number of records in year t, representing sample size. Function a(t) is the timedependent horizontal asymptote of the curve, hence it is this part of the model which describes the change in species richness over time.
Among the models in this set, I selected the one with the lowest AICc. Then, models with one df added to each spline in this model or with one df removed were also fitted, to check whether these modifications would reduce the AICc further. The model with the lowest AICc among all the ones fitted is called the "minimum adequate" model. This model is compared with the "maximal" model: the model with lowest AICc among those with splines of 8 df for both a(t) and b(t). A bootstrap estimate of estimation bias  (Davison & Hinkley, 1997)  was obtained as follows. One hundred bootstrap pseudodatasets were constructed by randomly drawing for each year a number of individuals equal to the number of records in that year and repeating the analysis above for each of these sets of pseudodata. The covariates calculated from the original data were kept except for the multinomial sampling variances which were recalculated for each sample.
van  Strien et al. (2016)  constructed detection/nondetection records per square kilometer cell with observations and analyzed these per species separately. In this dataset, there is no guarantee that collectors sampled in a manner representative of the species locally present and I therefore refrain from such reconstructions. Furthermore, while  Kéry and Royle (2008)  claimed unbiased estimates of species richness for occupancy models, Guillera-Arroita et al. (  2019 ) found that estimation bias can be substantial, in particular when 15% or more of the total richness has not been sampled. Inference on parameters representing changes in species richness might proceed differently from the estimation of species richness itself and patterns of bias and precision do not need to be the same for different parameters.
Each is a sequence of zeroes and ones indicating for which years at least one record is present for that species in the dataset. Williams, Nichols, and Conroy (2002)  noted that the modeling of such species encounter histories when assuming open communities with colonization and extinction is similar to individual capture-recapture histories in the presence of temporary emigration. However, the dataset and the subsets analyzed here have no repeated within-year occasions where the assemblage can be assumed to be closed, such that "temporary emigration," which here is a component of the colonization and extinction dynamics cannot be estimated because a robust design model cannot be fitted  (Pollock, 1982). Temporary emigration, that is the probability that an individual is absent and cannot be sampled, can also be estimated for individual capturerecapture histories assuming an unobservable state  (Kendall & Nichols, 2002; Schaub, Gimenez, Schmidt, & Pradel, 2004 ).
Such a model as constructed for capture histories of individuals cannot deal with individual species jointly present inside and outside of the Netherlands and we would have to use occupancy models. For parameter estimability, we would further have to assume that there are no time trends in temporary absence and survival  (Schaub et al. , 2004 ) such that the time-dependent models of interest cannot be fitted. Therefore, encounter histories per species were analyzed using capture-mark-recapture analysis for open populations  (Pradel, 1996)  and trends in temporary absence were assessed otherwise as explained below. Models were fitted to species encounter histories across years using Mark software  (White & Burnham, 1999)  called via RMark in R  (Laake, 2013). I parameterized using the "Pradrec" model, which estimates local survival and colonization, where I note that the first is a probability per species (in between zero and one) and the second the number of colonizing species relative to the number present at the beginning of a time interval (non-negative).
| Generalized nonlinear models Prediction intervals of the best maximal models and the models with the most favorable AICc (adequate model) are shown in Figure  2  for the full data and the second subset, that is records from grid cells sampled in three predefined periods (results on collection specimens are available in Figure  S1 , Table  S1 ). The overall impression from Figure  2  is that there are moderate declines of species richness for the non-Bombus genera. For Bombus, the analysis of the complete dataset produces a complex pattern in species richness over time. The complexity disappears when the data are restricted to grid cells that have been sampled in three periods and when we consider the minimum adequate model. Comparison with the analyses on the other grid cells that were not sampled in three periods (Appendix S4, Figure  S2 ) reveals that the complex pattern might originate from this data subset and thus from data heterogeneity.
| D ISCUSS I ON To investigate trends in species richness, a dataset with records and observations of Bombus and non-Bombus wild bee species in the Netherlands spanning 73 years was analyzed using two approaches. In the analysis of such an opportunistic dataset, emerging patterns can be caused by sampling heterogeneity across This was the main source of records before 2000, but now contributes a minor fraction of the data. Neither of the approaches then rejected the hypothesis of a constant decline or no decline at all in favor of a decelerating decline. In fact, the species richness decline is small and maybe absent for non-Bombus bees. For bumblebees, if we take advantage of the larger precision of the capture-recapture analysis, a moderate and significant decline of 19% is inferred. Among the generalized nonlinear models, there was one data subset for which the preferred model had a constant decline in species richness for both bumblebees and other wild bee species, while preferred models for the other subset and the full dataset had smooth regressions of species richness with at least three parameters.
| Methodological developments Risks of species richness loss seem taxon-specific (Bombus vs. non-Bombus), and thus call for trend analysis in smaller taxonomic groups in general. However that requires sufficient data for each of them. Boyd (2013)   Issues with models for encounter histories are probably alleviated when robust design models  (Pollock, 1982)  can be fitted to the data. If records had been collected throughout the study period independently by observations and via specimens deposited in collections, then these two sampling methods could have been used to fit a robust design. Heterogeneity in species detection probabilities can be expected in all methods addressing species richness (e. g. ,  Boulinier, Nichols, Sauer, Hines, & Pollock, 1998)  and it was incorporated into the capture-recapture models by means of mixtures.
We would thus require a FIC for the selection of models that predict species richness trends best. Meanwhile, how can we decide which approach is reliable enough for inference of trends? Here I resorted to comparisons of consistency in the results from different approaches and subsets. Additional simulations with similarities to the dataset were essential and revealed critical estimation bias. Therefore, ranking estimates from different approaches based on consistency across data subsets should never occur without accounting for other criteria. For opportunistic data, there seems to be currently no safe alternative other than a pluralistic but conservative modeling approach assisted by simulations which provide some guidance. To conclude, I want to point out a potential example of how nonrandom data collection might be generated.
This is confirmed for the estimation of time trends in local survival using Pradel models, where estima- tion bias leads to wrong inference and masks differences between datasets. The generalized nonlinear modeling does not need to as- sume a constant presence or absence probability, and the manner in which the sampling process is modeled comes with limited assump- tions. However, the method needs manual work, as model fitting is tedious. Convergence to a global maximum likelihood solution is not guaranteed and results need to be inspected with care and can suf- fer from overfitting. Chao et al. (2014)h analysis presented here urge further methodological developments. Chao et al. (2014)proposed a bootstrap method to estimate variances of extrapolated richness, where the fraction of rare missing species is first estimated and then added to an imputed dataset from which resampling occurs.
g. Sato and Greve, 2012; Seddik et al. , 2012). In this case, because the simulated ice flux divergence is generally far from being balanced by the net mass balance (i. e. surface and basal mass balance), an artificial drift arises when free evolving topography is restored  (Goelzer et al. , 2017). A second category of initialisation methods relies on data assimilation techniques, whose goal is to infer model parameters or poorly known boundary conditions and which are also used to minimise the mismatch between model variables (most often surface velocities) and observations (e. g. Arthern and Gudmundsson, 2010; Gillet-Chaulet et al. , 2012; Morlighem et al. , 2010). However, this approach may lead to internal inconsistencies between the simulated internal conditions  (temperature and velocities)  or between the simulated ice velocities and the observational datasets, such as surface and bedrock topography.
1c. It shows areas with temperature largely below the pressure melting point, associated with frozen bed, and areas with temperature at the pressure melting point (red colours), associated with thawed bed. Compared to the recent synthesis of GrIS basal temperatures (see  Fig. 11 in MacGregor et al. , 2016) , our initial basal temperature generally agrees well with the reconstructions in the north-western and northeastern parts of the GrIS but is probably overestimated, with too large a thawed bed area, in the eastern and central parts of the GrIS (not shown). The impact of ice temperature on the minimisation procedure is discussed in Sect. 5. 1. In order to avoid inconsistencies between the different datasets used as boundary and initial conditions, GRISLI is first run forward (free-evolving surface elevation and temperature) for 5 years (relaxation step, blue box in Fig.
Summary and discussion In order to improve the reliability of Greenland ice sheet simulations under a future transient climate, an accurate evaluation of the present-day trend of ice flow dynamics is required. One of the major difficulties in addressing this need lies in the poorly constrained observational data of the basal conditions that strongly control the ice motion in the entire ice sheet. Here, we present an inverse method to infer the spatial distribution of the basal drag coefficient in such a way that the mismatch between simulated and observed GrIS thickness is minimised. As such, our target criteria are defined for the sets of minimisation procedure parameters providing minimum values of ice thickness RMSE (with respect to observations) and ice thickness trend, which are respectively as low as ∼ 50 m and 15 cm yr -1 for our best fit.
Acknowledgements. We are very grateful to  David Pollard  and  Stephen Price  for their fruitful comments that helped us to refine our approach and improve the paper. The authors would like to thank  Xavier Fettweis  for providing outputs from the MAR model used as climate forcing. Sébastien Le  clec'h,  Masa Kageyama ,  Sylvie Charbit  and  Christophe Dumas  acknowledge the financial support from the  French projects OSCAR (LEFE/INSU)  and the  ANR   AC-AHC2  as well as from the  French-Swedish   GIWA  project. Sébastien Le  chlec'h has been funded by the  CEA. He also acknowledges the ice- MOD  project funded by the  Research Foundation -Flanders (FWO -Vlaanderen). Aurélien Quiquet  is funded by the  European Research Council  grant ACCLIMATE no. 339108  and by the  Louis Bachelier Institute.
Aims/hypothesis Maternal obesity in pregnancy is associated with cardiovascular disease and mortality rate in the offspring. We aimed to determine whether maternal obesity is also associated with increased incidence of type 2 and type 1 diabetes in the offspring, independently of maternal diabetes as a candidate mechanistic pathway. Methods Birth records of 118,201 children from 1950 to 2011 in the Aberdeen Maternity and Neonatal Databank were linked to Scottish Care Information-Diabetes, the national register for diagnosed diabetes in Scotland, to identify incident and prevalent type 1 and type 2 diabetes up to 1 January 2012. Maternal BMI was calculated from height and weight measured at the first antenatal visit. The effect of maternal obesity on offspring outcomes was tested using time-to-event analysis with Cox proportional hazards regression to compare outcomes in offspring of mothers in underweight, overweight or obese categories of BMI, compared with offspring of women with normal BMI.
Methods In this cohort analysis, the exposure was maternal obesity during pregnancy and the outcomes in the offspring were: (1) any diabetes; (2) type 1 diabetes; (3) type 2 diabetes. We created a linked dataset including data from 118,201 mothers and their offspring from the AMND databank linked to the SCI-Diabetes database after excluding neonatal deaths and for missing data for offspring sex, gestational age at delivery and year of diabetes diagnosis (Fig. 1 ). The AMND is a unique databank linking all the obstetric and fertility-related events occurring in women from a defined geographical area with a relatively stable population, between 1950 and the present day  [14]. Completeness of the database is checked annually against the number of deliveries recorded in the National Health Service records office, and validity of data entry is verified by numerous consistency checks.
All women who delivered a live singleton infant at term (≥37 weeks' gestation) between 1950 and 2011, and who had their weight recorded at the first antenatal visit, were identified from the AMND databank. The women were grouped according to their BMI, calculated from height and weight measurements taken at the first antenatal visit, according to WHO criteria: underweight (BMI <18. 5 kg/m 2 ), normal weight (BMI 18. 5-24. 9 kg/m 2 ), overweight (BMI 25-29. 9 kg/m 2 ) and obese (BMI ≥30 kg/ m 2 ). Relevant details about the index pregnancy were retrieved from the AMND, including maternal age, parity, area-based socioeconomic status, prior self-reported history of diabetes, history of hypertension, gestation at which weight was measured, gestation at delivery, and offspring birthweight, sex and date of birth, from which age at linkage was calculated.
Offspring birth records were linked to a 2011 extract of SCI-Diabetes using the Community Health Index number, a unique individual identifier used on health records in Scotland, or, if this was unavailable, by probabilistic matching on surname, date of birth and postcode (zipcode), to identify individuals with a diagnosis of diabetes. The SCI-Diabetes database was established at a national level in 2000 and contains demographic and clinical data relevant to diabetes care. It is populated by daily downloads from primary and secondary care databases across Scotland, with increasing completeness since 2004. A validation study among the subset of people with diabetes mentioned on a hospital record in 2007 found that 99% were included in the diabetes register  [15]. In SCI-Diabetes, type of diabetes (type 1, type 2, etc.