dataesr/scientific-paragraphs-categorization huggingface dataset:
Observation error on the counts In this article, our fitted models did not include observation error on the counts. We actually planned to do so, and our computer code allows this observation error to be implemented, but we found out through preliminary model fits that an unknown information error seems to induce major identifiability issues. The trajectories of predator and prey counts can still be reproduced when a poorly estimated observation error is present, but the density-dependent functions cannot. In other words, the model then produces good in-sample forecasts, but for the wrong reasons. All α i values are estimated with strong bias in this case, including when all data types are used. Our model belong to the general family of state-space models or SSMs  (Newman et al. , 2014)  and as such, suffers from the same difficulties in implementing unknown observation errors in such models.
Top predators are usually well-studied, notably due to conservation concerns, but their more abundant prey could be difficult/costly to follow through capture-recapture and be simply counted. Our results suggests that a prey fecundity survey adds much value, probably at a small fraction of the cost of a capture-recapture program. In a Bayesian framework, a priori knowledge on some of those parameters can also be gained from the MPM databases  (Salguero-Gómez et al. , 2016) , which will be especially important for poorly studied species. Which parameter(s) should be set with prior information will then depend on a trade-off between the degree of phylogenetic conservatism of the parameter  (Che-Castaldo et al. , 2018)  and the ease which with the information can be collected in the field. Adult survival is a good target (costly to infer, probably fairly conserved between related species), and the analyses that we performed could be attempted again using a mixture of real data and database-derived a priori information.
Introduction The Morris Water Maze (MWM) was first described by Richard Morris in the 80's 1 and is still one of the most commonly used tasks to evaluate spatial learning in rodents, including normal and genetically modified mice. It is validated as an assay for hippocampus-dependent spatial navigation and reference memory. The maze consists of a large circular tank filled with opaque water in which rodents can escape onto a platform hidden just beneath the surface. During a training phase animals perform repeated blocks of 60 second-long trials to find the location of a fixed platform using distant visual cues from semi-random start locations and the escape latency is recorded. Since data are right-truncated at 60 seconds, in contradiction with a normal distribution and causing potentially biased results, statistical guidelines have been published to properly characterize learning behaviors using survival data 2.
During a probe test session, the platform is removed and animals freely navigate into the pool from the same start location and for the same fixed amount of time (e. g. 60 seconds). The path of the animal is recorded using a video camera and an automatic tracking system. Data collected during the probe test session can be classified into three categories: time spent per zone, which can be theoretical quadrants defined on the pool or a theoretical annulus drawn around the platform location; number of crossings of the platform area; or total proximity to the platform center 3. Creating a large database using several published tests from their institute and simulated data, Maei et al. have shown that total proximity allows the best detection for small samples, whereas time spent in quadrants is of great interest for bad performers 4.
Such a correction has been shown to correctly reproduce the first three moments of the asymptotic χ 2 -distribution 6. In order to derive the scaling factor, we needed to compute the expected value of the statistic as a function of the number of samples N and the precision s. To do so, we drew random samples of N observations from uniform Dirichlet distributions with precision s, varying N between 2 and 100 and s between 1 and 1000 (with logarithmicallyspaced values), and measured the mean of the statistic Λ 4. We found that the mean value of the likelihood-ratio statistic Λ depends very little on s in the probed range, and that the 2 https://github. com/ericsuh/dirichlet 3 https://github. com/xuod/dirichlet  4 For each tuple (N, s), the number of samples is increased until the means is measured with relative error below 0.
Comparison with the one-sample Student t-test In order to compare the type 2 error obtained with the Dirichlet distribution with the results obtained using a one-sample t-test on the target quadrant as often done in the literature, we simulated data from a non-uniform Dirichlet distribution with the parameters α = (40; 20; 20; 20). We found that the p-value from the corrected statistic of the Dirichlet distribution is mainly lower than the one obtained with a single t-test on the target quadrant (75% of the p-values are lower for s = 30). This means that for some cases where the target quadrant is preferred, using a one-sample t-test on the target quadrant would not detect this preference whereas the test based on Dirichlet distribution would   detect the divergence from uniformity. Beyond improving the description and the interpretation of data from the MWM, the test we propose extracts more information from the same experiment as it is based on a larger dataset and then decreases the number of false-positives.
Bayesian inference of the parameters of the Dirichlet distribution Bayesian analysis can be used to infer constraints on the parameters α i 's of the Dirichlet distribution used to model the data (and subsequently the means m i 's). Specifically, we performed nested sampling of the parameter space of the Dirichlet distribution using the PyMC3 package with Python 3. 6. For simplicity, we used the Jeffreys prior 5 π(α) which does not depend on the model parametrization (e. g. , sampling over α or (m, s)) and leave the discussion about this choice for future work. The output is a sample of vectors α distributed as the posterior given the data, i. e. P (α | D) ∝ P (D | α)π(α), which enables us to compare confidence regions for different groups and visualize the consistency with uniformity.
For adjacent quadrants we performed two-tailed single sample t-tests. We observed that the fraction of time spent in the target and opposite quadrants were respectively significantly higher (p = 0. 025) and lower (p = 0. 013) than 25%. The fraction of time spent in the adjacent quadrants did not differ from 25%. | ( ) |. I π ∝ α α Using this dataset with usual sample sizes for behavioral studies (N=7), we confirmed that our test is able to discriminate efficient and deficient memory abilities on real data.
Discussion We proposed a statistical approach for the analysis of MWM data based on the Dirichlet distribution as a model for the fraction of time spent by rodents in the quadrants of the maze. In the context of behavioral experiments that usually generate a lot of data with high inter-individual variability, a lot of parameters can be taken into account to extract evidence of memory abilities 3. In the literature, the time spent in quadrants -the target quadrant, but sometimes also the opposite quadrant 9,10 -is commonly used to assess long-term memory. Even if the focus on the time spent in quadrants is broadly accepted as a good index to evaluate reference memory, there is no consensus about the processing of these data. In this context, the Dirichlet distribution has the great advantage to simultaneously take into account the four quadrants and to correctly account for the constant-sum constraint of such data, which implies both deviation from the normal distribution and interdependence.
Conclusion We propose here a new way to analyze MWM probe test data that accurately and simultaneously describes the four variables of time spent in the quadrants and allows to extract more information from the same experiments than the currently used method. All the packages required to perform the statistical test and to draw the corresponding chart are publicly available  7  and can be easily run with R using the reticulate package 11. Minor modifications of this test would allow to apply the same methodology on other behavioural tests facing the same constraints like H-Maze or Y-Maze.
This could lead the child to form the generalization that plural definites have a universal meaning. Some empirical evidence for this state of affairs in the caregiver input may be found in corpus data reported by  Caponigro et al. (2012). These authors examined child-directed speech in the CHILDES database  (MacWhinney, 2000) , and reported that all 6404 instances in their sample of plural definite descriptions headed by the determiner the referred to a maximal element. Note that evidence about negative plural definites may be more intricate, for reasons having to do with scope; if a child only ever hears the negative (2) in homogeneous contexts, the child might be led to liken the plural definite to a universal that must take wide scope with respect to negation. descriptions like (2), a problem that we will address shortly, they nevertheless provide a preliminary suggestion that children may not be sensitive to the truth value gap described above.
Plural Definite Descriptions Figure  6  displays the percentage of yes-responses for the homogeneity targets, in between the true and false definite description controls. While children were adult-like with respect to the definite description controls, the two groups differed in their treatment of the definite plural in GAP contexts. Unlike the adults, the children showed some acceptance of the positive definite descriptions in gap contexts; a mixed effects logistic regression model of responses as predicted by polarity revealed that they accepted the positive targets significantly more than they did the negative targets (p < 0. 001) (lme4 package for R,  R Core Team, 2016 , Bates et al. , 2015). That children behaved differently from adults indicates that the child participants as a group were non-adult-like in their interpretation of the definite plural; but we wished to explore further how they might be interpreting the plural definite descriptions.
Bayesian models were fitted separately for children and adults using JAGS through the rjags package  (Plummer, 2003). The prior for the intercept parameter α was set to a normal distribution with mean 0 and precision 0. 001, while the prior for the slope parameter β was the non-negative half of the same distribution (since a participant cannot plausibly be more likely to judge a sentence true when it is, in fact, false). The prior for γ (s), the group parameter of each participant s, was uniform. 5,000 samples were drawn from each of 4 chains after 5,000 burn-in iterations. The two corresponding groups were thus subsequently dropped and the analysis was re-run with only four possible values for the group parameter. Children were assigned to groups quite unambiguously: the posterior probability of the group with the highest posterior probability (max g p(γ (s) = g|Y)) was > 0.
Each of these groups corresponds to a unique pattern of responses to the different conditions, as described in Table  8. The upcoming analyses fit ordinal regression models which assign each participant to a given group, given this participant's actual responses 31. The models may allow for different groups to be considered, and in order to decide whether it is meaningful to say that some participants belong to a particular group, we ask whether models that include that group are superior to models that do not include that group, all else being equal. One problem is that it is not necessarily possible to reliably estimate the relevant models with the whole dataset while considering all 31 The models were thus similar to the ones used in the previous experiment, except that we performed ordinal regression because the responses could now take 3, and not only 2, values.
Analysis 1: no partial truth, implicatures imply homogeneity In this analysis, we restricted the dataset to the conditions with THE plus the implicature-relevant condition SOME-ALL-POS. There is little reason to think that partial truth was playing any role and, accordingly, this analysis does not consider the possibility of a PT group (the role of PT groups is evaluated separately in Analysis 3). Furthermore, we assume here that every child who had acquired implicatures had also reached an adult-like stage for homogeneity. The only +SI group allowed in these models was thus the HOM/+SI group. Apart from these restrictions, the models in this analysis explore all combinations of HOM/-SI, SA/-SI, and WS/-SI 32. Table  9  shows the estimated log pointwise predictive likelihoods (elpd) and their standard errors for each of these models.
Overall, models that included the HOM/-SI group were superior to those that did not, providing evidence in favor of 32 Since HOM and PT do not differ on the conditions under consideration (but only on ALL-GAP-POS, which was not included), the latter group was excluded from the models. Note that while we did take into account the possibility of scope ambiguity effects, we did not use the ALL-GAP-NEG data points to estimate their prevalence. The effect of the scope ambiguity of the negative universal sentences was very strong in that all possible responses were chosen in a sizeable percentage of cases. Given that this is not so for negative definite description sentences, these effects are clearly more prevalent with the universal all, and we found that the scope ambiguity effect with all dominated the group assignment choices of the models at the expense of an accurate categorization on the basis of the definite description data.
ACKNOWLEDGMENTS For helpful feedback and discussion, we would like to thank  Alexandre Cremers ,  Ewan Dunbar ,  Napoleon Katsos ,  Tal Linzen ,  Jacopo Romoli , and  Jérémy Zehr. We would also like to thank  Monica Barbir  for her assistance with stimuli and piloting,  Anne-Caroline Fiévet  for coordinating testing at local preschools,  Charlotte Hauser  for her role in recording the audiovisual stimuli, and  Florian Pellet  for implementing the adult version of the task. We are grateful to the families of the children who participated in our study, as well as the preschool directors and teachers in Paris, who graciously allowed us to conduct our study in their schools.
Methods The local ethics committee (Comitéde Protection des Personnes "CPP Ile de France V") approved the experiment, and all participants provided written informed consent. Subjects and MRI acquisition. MRI images of subjects without neurological or psychiatric disease were obtained (age mean ± SD 29 ± 6 years, range 22-42 years; 11 females, 9 males) with a Siemens 3 Tesla Prisma system equipped with a 64channel head coil. An axial 3D T1-weighted imaging data set covering the whole head was acquired for each participant (286 slices, voxel resolution = 0. 7 mm 3 , echo time (TE) = 2. 17 ms, repetition time (TR) = 2400 ms, flip angle = 9°). rs-fMRI images were obtained using T2*-weighted echo-planar imaging (EPI) with blood oxygenation level-dependent (BOLD) contrast. EPIs (TR/TE = 2050/25 ms) comprised 42 axial slices acquired with a multiband pulse  [125] [126] [127] [128]  covering the entire cerebrum (voxel size = 3 mm 3 ) including 290 brain scan volumes in one run of 10 min.
A diffusion-weighted imaging (DWI) acquisition sequence, fully optimised for tractography, provided isotropic (1. 7 × 1. 7 × 1. 7 mm) resolution and coverage of the whole head with a posterior-anterior phase of acquisition, with an echo time (TE) = 75 ms. A repetition time (TR) equivalent to 3500 ms was used. At each slice location, six images were acquired with no diffusion gradient applied (b-value of 0 s mm -2 ). In addition, 60 diffusion-weighted images were acquired, in which gradient directions were uniformly distributed on the hemisphere with electrostatic repulsion. The diffusion weighting was equal to a b-value of 2000 s mm -2. This sequence was fully repeated with reversed phase-encode blips. This provides us with two data sets with distortions going in opposite directions. From these pairs, the susceptibility-induced off-resonance field was estimated using a method similar to that described in ref.
129  and corrected on the whole diffusion-weighted data set using the tool TOPUP and EDDY as implemented in FSL  130. rs-fMRI analysis-overview. 2] [133]  ). Then, they were registered to T1 high-resolution individual structural images and normalised to MNI152 standard space using Advanced Normalization Tools (ANTS; http://stnava. github. io/ANTs;  134, 135 ). Preprocessing steps in details: a. fMRI images were first motion corrected using MCFLIRT  132  , then corrected for slice timing, smoothed with a full half-width maximum equal to 1. 5 times the largest voxel dimension and finally filtered for low temporal frequencies using a Gaussian-weighted local fit to a straight line. These steps are available in FEAT, as part of FSL package  133. b. fMRI images were linearly registered to the T1 images, and subsequently to the MNI152 template (2 mm) using affine and diffeomorphic transformations  136, 137.
The DMN map of each participant was obtained by calculating the median of the 40 seed-based correlation maps of each individual using FSL  144. We then calculated the median across the DMN maps of the 20 participants to obtain the median DMN map in the MNI152 space. The median was used, instead of the mean, because it is less sensitive to outliers, being a better centrality measure for small sample sizes  145. Individual DMN maps in the functional space. To achieve the proposed optimised map of the DMN, the same 20 individual maps were functionally aligned with each other in a new functional space using the following steps: Individual DMN maps were aligned with each other using ANTs' script buildtemplateparallel. sh, defining cross-correlation as the similarity measure and greedy SyN as the transformation model  136, 137, 146.
Subcortical regions of interest were defined manually based on the experienced judgement of the neuroanatomists among the authors combined with the careful comparison with previously published atlases  148, 149. Correlation and partial correlation coefficients were determined as measures of functional connectivity. Two matrices, representing the median correlation values in the MNI152 space and in the functional space, were created using BrainNet Viewer  150. A paired t test was calculated for each cell of the two connectivity matrices using Python's Scipy package, version 0. 19. 1 (https://www. scipy. org; scipy. stats. ttest_rel). A p-value corrected for multiple comparisons at a threshold of <0. 0001 (Bonferroni correction) was used. Circos software was used to illustrate functional connections in the functional space (http://circos.
A modified Euler algorithm was used  123. An angle threshold of 35°, a step size of 0. 85 mm and a minimum length of 20 mm were chosen. Diffusion tensor images were registered to the MNI152 standard space and, then, into the functional space applying the affine and diffeomorphic deformation generated in the previous sections, using the tool tractmath as part of the software package Tract Querier  156. The same regions of interest used for functional connectivity calculation (section 2. 2. 3) were also used for the tractography analysis. The command tckedit of MRtrix toolbox (http://www. mrtrix. org/ 157 ) was used to extract the tracts of interest. The -include arguments were composed by combinations of DMN regions of interest to guarantee that the selected streamlines crossed at least two DMN regions of interest.
Brain Connectivity Toolbox for Python (https://pypi. python. org/pypi/bctpy) was used to obtain the network measures. The functions degrees_und and betweenness_bin were run, respectively, for each individual matrix  67. The median values for each measure were obtained. The illustration of the network was made using Surf Ice (https://www. nitrc. org/projects/ surfice/). Statistics and reproducibility. In order to assess reproducibility, the effects of interest were also measured at the individual level (i. e. , replicated for every subject). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements P. N. A. work was financially supported by the  Research Experience Fellowship  grant of the  European Academy of Neurology  and by " PRE ́MIO JOA ̃O LOBO ANTUNES" -SCML. The research leading to these results received funding from the  "Agence Nationale de la Recherche " [grant number  ANR-13-JSV4-0001-01 ]. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 818521 ) and from the  Fondation pour la Recherche Medicale  ( FRM DEQ20150331725 ).
95, 95% CI 0. 70 to 1. 29). When stratified by clinical depression/depressive symptoms versus psychological distress, the pooled ORs were 1. 09 (0. 94 to 1. 26) and 1. 18 (1. 06 to 1. 32) for clinical depression/depressive symptoms and psychological distress, respectively. Meta-regressions did not show any statistically significant differences in the estimates for clinical depression, depressive symptoms and psychological distress. The review by Virtanen et al. was published recently (Online First, 8 February 2018) and two authors of the Virtanen et al. article are also authors of this protocol (RR and IEHM). Therefore, we want to briefly delineate the main differences between the Virtanen et al. article and this protocol. First, our search is broader, Virtanen et al. searched two academic databases whereas we will search seven academic databases and two grey literature databases.
Types of exposures. We will include studies that define long working hours in accordance with our standard definition (Table  1 ). We will prioritize measures of the total number of hours worked, including in both of: main and secondary jobs, self-employment and salaried employment and informal and formal jobs. Cumulative exposure may be the most relevant exposure metric in theory, but we will here also prioritize a non-cumulative exposure metric in practice, because we believe that global exposure data on agreed cumulative exposure measures do not currently exist. We will include all studies where long working hours were measured, whether objectively (e. g. by means of time recording technology), or subjectively, including studies that used measurements by experts (e. g. scientists with subject matter expertise) and self-reports by the worker or workplace administrator or manager.
If a study presents both objective and subjective measurements, then we will prioritize objective measurements. We will include studies with measures from any data source, including registry data, in the same analyses and description. We will include studies on the prevalence of occupational exposure to the risk factor, if it is disaggregated by country, sex (two categories: female, male), age group (ideally in 5-year age bands, such as 20-24 years) and industrial sector (e. g. International Standard Industrial Classification of All Economic Activities, Revision 4 [ISIC Rev. 4])  (United Nations, 2008)  or occupation (as defined, for example, by the International Standard  Classification of Occupations 1988 [ISCO-88] (International Labour Organization, 1987)  or 2008 [ISCO-08] (International Labour Organization, 2012)).