dataesr/scientific-paragraphs-categorization huggingface dataset:
E(p i ) is the self-declared prevalence rate in the sample. B is the absolute value of the difference between the self-declared and true prevalence rates. N min is the minimum sample size required so that the mean-squared error of the list experiment is lower than the MSE of the direct question. N survey is the number of observations in the dataset. Let's note (x, y) = (N -1, B) and C = π * (1 -π * ) + 4V ar[Y i (0)] (see Appendix A3). x is obtained by solving the following equation: y 2 x 2 + (2π * -1)yx -4V ar[Y i (0)]x -C = 0. ‡ As an example, here we replace π * , V ar[(Y i (0))], E(p i ) and B by their values and solve the following equation: 0. 028x 2 -2. 591x -2. 852 = 0. Doing so allows us to obtain N -1. N min = (N -1) + 1.
659 0. 789 0. 070 0. 965 0. 010 0. 176 84 372 Burkina dataset (List A) Estimation of physical IPV V ar (Y i (o)) List experiment Direct question В N min N survey π* SE E (p i ) SE All observations 0. 665 0. 215 0. 036 0. 054 0. 005 0. 161 108 1706 Polygamous marriage (=1) 0. 614 0. 179 0. 071 0. 037 0. 009 0. 142 128 436 Ever attended school (=1) 0. 666 0. 244 0. 072 0. 062 0. 012 0. 182 85 418 Did not work every month in the past year (=1) 0. 658 0. 237 0. 039 0. 049 0. 006 0. 187 80 1402 Thinks husband is entitled to beat her wife if she stands up to him (=1) 0. 650 0. 233 0. 041 0. 055 0. 006 0. 178 87 1272 Husband ever attended school (=1) 0. 682 0. 262 0. 059 0. 068 0. 010 0. 194 77 620 Husband consumes alcohol (=1) 0. 668 0. 240 0. 062 0. 078 0. 011 0. 162 107 574 Husband does not approve contraception (=1) 0.
Methods This study consisted of a secondary analysis of data collected from SBS outreached in the course of a systematic audit of the consecutive suicide cases that occurred in the east end of the island of Montreal in the rst 10 months of 2016 (Fortin G, Ligier F, van Haaster I, et al. A systematic method to highlight circumstances of suicide cases and work together for prevention. In: Poster presentation before the Canadian Psychiatric Association, Quebec City, September 12, 2019). The audit was modeled on the one conducted of all the suicide cases that occurred in 2002-2003 in New Brunswick, Canada  (13). The research protocol was approved by the Scienti c Committee of the Montreal Mental Health Institute (November 6, 2018) and the publication of results was approved by its Ethics Research Committee (February 1, 2019).
Data Collection Data were collected through interviews and two self-report instruments. Aside from socio-demographics, data were also collected on risk/protective factors in and signs of pathological grief, relational proximity to the deceased, emotional experiences post suicide death noti cation and their duration, and general physical and mental health and problems/di culties post suicide. Depression and anxiety were assessed through self-reported data on the Patient Health Questionnaire (PHQ-9;  (25, 26) ) and the Generalized Anxiety Disorder (GAD-7; (27-29)), respectively. During the interview, the audit team member explored whether other SBS might be interested in participating in the study, as more than one SBS could be interviewed regarding the same case. Names provided were contacted and invited to be interviewed.
Participants A total of 59 participants (29 controls and 30 ASD participants) were included in the study (see Table  1 ). They were all part of the InFoR cohort. InFoR is a multicentric French longitudinal study, promoted by Fondation FondaMental, INSERM, and the Roche Institute. This cohort is constituted of a total of 117 individuals with ASD, including various age groups and levels of intellectual functioning, and 57 controls. Participants were followed during a 2-year period and undertook various assessments at each yearly visit (clinical, neuropsychological, and biological) allowing for deep phenotyping of the cohort. Only adults from the cohort were included in the present study, using data from the initial visit only. All adult participants were recruited by the Expert Center for Adults with ASD, which is part of the Adult Psychiatry Department of Mondor Hospital (AP-HP, Creteil, France).
This median split methodology is similar to previous dichotomizations of the Eyes Test items  [Domes, Heinrichs, Michel, Berger, & Herpertz, 2007; Feeser et al. , 2015; Radke & de Bruijn, 2015; Woolley et al. , 2014]. At this point, we built a contingency table to check the repartition of items between Valence and Difficulty factors (see Table  2 ). This table shows a mostly even distribution when considering each factor alone. But it shows an unbalanced distribution of items when crossing the two factors. Indeed, the Difficult × Positive cell contains only one item, compared to four to eight items in other cells. That is why we focused our analyses on assessing the effect of each factor alone, without assessing their interaction. Procedure. The Eyes Test was proposed among several other evaluations in the context of the InFoR project (data not reported here, except for IQ, ADOS, and ADI-R data).
Statistical Analysis All analyses were performed using the R software [R Core  Team, 2019]. We computed a Generalized Linear Mixed Model using the glmer command of the lme4 package  [Bates, Mächler, Bolker, & Walker, 2015]  trying to predict performance at each Eyes Test item (1 = success and 0 = failure) by the following fixed factors: Valence (3 levels: Neutral, Negative, Positive), Difficulty (2 levels: Easy, Difficult) and Diagnostic Group (2 levels: Controls, Patients). We used a binomial link function and a maximum likelihood estimation method (Laplace Approximation). The interactions between Diagnostic Group and Valence and between Diagnostic Group and Difficulty were also tested. Due to the unbalanced distribution of items among the factorial design, we did not explore interactions between Valence and Difficulty, nor between Group, Valence and Difficulty.
Participants were entered in the model as a random factor. The α value was set at 0. 05 for all statistical tests. Significant effects were then explored by computing pairwise least square means contrasts, using the multcomp package  [Hothorn, Bretz, & Westfall, 2008 ] and applying a Bonferroni correction for multiple comparisons to protect against Type I error. All reported P values are corrected for multiple comparisons throughout the text, tables and figures. Data were plotted using ggplot2  [Wickham, 2016]. A power analysis was conducted using the G*Power software (Version 3. 1)  [Faul, Erdfelder, Lang, & Buchner, 2007]  and established that a total sample size of 50 participants would be needed to identify an effect of Group on overall Eyes Test performance. For this computation, we set the power parameter to 80% and derived the expected effect size from the Baron-Cohen and colleagues' paper presenting the revised version of the Eyes Test [  Baron-Cohen, Wheelwright, Hill, et al.
study was part of the InFoR project, sponsored by INSERM (French National Institute for Health, Clinical Trial C07-33), through a collaboration with the Roche Institute for Research and Translational Medicine. It was granted approval by local Ethics Committee or "Comité de Protection des Personnes" on November 14, 2008, authorized by the French authorities (ANSM B80738-70 on August 11, 2008) and registered in a public trials registry (NCT02628808). All study participants gave their informed written consent to participation, in line with French ethical guidelines.
Acknowledgments This work has been co-ordinated by  Fondation FondaMental  (www. fondation-fondamental. org) and achieved thanks to the following organisms and establishments: INSERM,  AP-HP, CHU Bordeaux, Hôpital Charles Perrens ,  Robert Debré , and  Henri Mondor 's CIC. It was financially supported in part by the  Institut Roche , in part by the  Investissements d'Avenir program  managed by the  ANR  under reference  ANR-11-IDEX-0004-02.
We operate as if we had to respond to this parameter perturbation (also referred to as disruption) under a tight optimization time budget, aiming at accelerating the descent towards the optimum. Given the modifications that have occurred in the past or that can be simulated a priori, one can build a dataset of resolutions. Through the example of the Facility Location Problem, we study how one can extract information from this dataset to facilitate the resolution of future instances. We cast this problem as a Supervised Learning problem to predict whether a modification will affect and to what extent the reference optimal solution. According to this prediction, additional constraints are added to the MIP formulation of the new instance. In order to evaluate the approach, the two formulations are fed to the solver and the performance are compared.
, 2017; Khalil et al. , 2017; Deudon et al. , 2018) , aiming at learning to build incrementally a solution. Another field of application is the interaction between existing optimization solvers and a priori knowledge on the problem. For instance,  Kruber et al. (2017)  find an approach to automatically handle MIP problem decompositions, by detecting if and what reformulation to apply within a dedicated software. On the same topic,  Basso et al. (2018)  bring evidence to why such an approach can be carried out, showing empirically that the role of an expert needed when using decomposition methods can be, at least in part, automated via learning algorithms. In  Bonami et al. (2018) , the authors present a method to select the best resolution method for a Mixed Integer Quadratic Programming problem from the different algorithms offered within the CPLEX framework.
Machine Learning has proven useful in producing a description of the optimal solution of a yet unseen instance. Fischetti and Fraccaro (2018)  describe a realworld application with the problem of a wind turbine park layout. Minimizing the complex turbulence induced by the positioning of wind turbines generates a series of difficult combinatorial problems. Where different configurations would need to be evaluated at a considerable computational cost, the authors approximate the solution values to these optimizations via ML, thanks to a dataset built a priori. On a similar line lies the work of  Larsen et al. (2018). When the problem of choosing the optimal load planning for containers on freight trains cannot be solved online because of time constraints and insufficient information (tactical level), a set of offline cases can be collected and used to get a description of the solution of a new instance, at an aggregate level.
Generation of Training Data For each of the three instances, we produce three datasets of sizes N A = N B = N C = 10000. Given P ref , we generate our training data by perturbing the capacities of the facilities. We randomly perturb between 5% and 50% of the facilities as follows. 1. Pick uniformly at random between 5% and 50% of the N F = 100 facilities. 2. For each facility j, apply a Gaussian noise to its capacity: s j ← s j + N (0, σ = 0. 2 × s j ). 3. All the remaining data is left unchanged. This yields ∆P k. Figure  2  presents three different scenarios. With instance A we see that on average less than half of the 7 original facilities are kept open, that is, the perturbations affect the new optimal solutions. In the case of B, for most of the data points the original reference solution is still optimal after the perturbations, as seen in the right-most bar of Figure  2b.
Optimization Results We report the performance of our method on the three references A, B and C, generating three batches D A , D B and D C of 50 new instances, unseen during training. We apply the same procedure followed to generate the learning data (Section 5. 1). To measure the effect of the ML bound with respect to resolution time, each instance in D A (but also D B and D C ) is optimized twice (with and without bound) with time limits t lim ∈ {5, 10, 30, 60, 120} seconds. At the end of the time-limited run, we record the objective value and the effective solving time (some instances are optimized before the limit is reached). Our ML constraint cannot make the solution infeasible, as the total number of active facilities is not bounded, but it could cut off the optimal solution. For instance, one could predict all of the reference facilities to be open (γ = 1) while none of them should be.
Thus, this strategy reduced the risk of losing participants throughout the process and increased the chances of collecting more quantitative data for analysis. The generation of statements was based on the focus prompt sentence defined in the preparation step. The aim was to integrate scientific and practical knowledge in the generated statements. The group of experts included researchers in the physical activity for health domain (n = 6), municipal department heads (n = 3) and elected officials (n = 3). Department heads and elected officials were selected by the research team from municipalities strongly engaged in HEPA promotion. First, the researchers conducted a literature review to identify the key determinants and key factors for developing HEPA policies in a municipal context. PubMed, Web of Science, ScienceDirect and Google Scholar databases were used to search for the terms 'physical activity', 'policy', 'local government' and 'municipality' in English and French between 2007 and 2018.
Thus, the group of experts deliberately decided not to invite policy-makers to sort the statements into piles using Concept Systems Global © software. Instead, consensus was found through brainstorming sessions, the group of experts decided to sort statements into categories that would be understandable and relevant, and therefore easier to rate by the group of policy-makers. Sorting is usually used to measure, analyse and map the relationship as well as the perceived similarity between statements  [31]. However, this was not the objective in this study.
These municipalities were initially contacted by email, outlining the purpose of the study and how it would be conducted. Then, if necessary, a phone call or a face-to-face meeting was organised to provide more details on the research project. Municipalities' volunteered to participate in the project. Data on the characteristics of each municipality were collected from the regional health observatory database [45], including number of inhabitants, median income per inhabitant, number of people affected by a chronic illness, average number of new people each year affected by a chronic illness and number of written HEPA policies by sector. Then, department heads and elected officials from these municipalities who were involved in the sport, health and social sectors were invited to participate in the statement rating step.
Statement rating An individual meeting was scheduled with each participant in order to explain the aim of the study and statement rating instructions. Face-to-to face meetings increase the likelihood of participation from this type of informant. A scientific review showed that compliance of the rating step in CMA seemed to be higher with face-to-face meetings compared to those using the webbased Concept Systems Global Max © software  [43]. Participants were requested to complete paper-based surveys, including demographic information and expertgenerated statements at their convenience. Collected participant demographic data included gender (man or woman), age (age category), physical activity level (a single question), training courses on physical activity and health (yes, no, or no but have knowledge), and number of written HEPA policies by sector.
Mapping analysis Using Concept Systems Global MAX © software, data from the statement rating step were computed to generate go-zone maps. Descriptive statistics on the importance and feasibility ratings were also calculated to create a go-zone map. Each point on the graph represented the mean rating value of each statement in terms of its importance and feasibility. The map was divided into four zones by the mean rating value of importance on the vertical axis and the mean rating value of feasibility on the horizontal axis. The upper right zone comprised the statements that were above the mean rating values of importance and feasibility; therefore, these statements were assumed to refer to potential levers. The upper left zone comprised those statements that were above the mean rating value of feasibility but below the mean rating value of importance.
This could be considered a limitation. The brainstorming and rating steps were made by two different groups. The sample size of the expert group who generated and sorted statements was small. Some relevant statements might therefore have been missed in the list proposed by the expert group. Moreover, as statements were not sorted into categories using Concept Systems Global MAX © software, some statistical analysis could not be done. Policy-makers who participated in this study were only from three sectors, although many other sectors, such as the urban, environmental or educational sectors, could be involved in HEPA promotion. Therefore, it was not possible to analyse every position in every sector. Similarly, it was not possible to analyse the perceived barriers and levers of HEPA policy according to the characteristics of municipalities.
Advances in computer-assisted linguistic research have been greatly influential in reshaping linguistic research. With the increasing availability of interconnected datasets created and curated by researchers, more and more interwoven questions can now be investigated. Such advances, however, are bringing high requirements in terms of rigorousness for preparing and curating datasets. Here we present CLICS, a Database of Cross-Linguistic Colexifications (CLICS). CLICS tackles interconnected interdisciplinary research questions about the colexification of words across semantic categories in the world's languages, and show-cases best practices for preparing data for cross-linguistic research. This is done by addressing shortcomings of an earlier version of the database, CLICS2, and by supplying an updated version with CLICS3, which massively increases the size and scope of the project.
One example is the Database of Cross-Linguistic Colexifications, first published in 2014  18  , which offers a framework for the computer-assisted collection, computation, and exploration of worldwide patterns of cross-linguistic "colexifications". The term colexification  19  refers to instances where the same word expresses two or more comparable concepts  20, 21  , such as in the common case of wood and tree "colexifying" in languages like Russian (both expressed by the word dérevo) or Nahuatl (both k w awi-t). By harvesting colexifications across multiple languages, with recurring patterns potentially reflecting universal aspects of human perception and cognition, researchers can identify cross-linguistic polysemies without resorting to intuitive decisions about the motivation for such identities.
The CLICS project reflects the rigorous and transparent approaches to standardization and aggregation of linguistic data, allowing to investigate colexifications through global and areal semantic networks, as in the example of Fig. 1 , mostly by reusing data first collected for historical linguistics. We designed its framework, along with the corresponding interfaces, to facilitate the exploration and testing of alleged cross-linguistic polysemies  22  and areal patterns  [23] [24] [25]. The project is becoming a popular tool not only for examining cross-linguistic patterns, particularly those involving unrelated languages, but also for conducting new research in fields not strictly related to semantically oriented lexical typology  [26] [27] [28] [29] [30]  in its relation to semantic typology  [31] [32] [33].
A second version of the CLICS database was published in 2018, revising and greatly increasing the amount of cross-linguistic data  34. These improvements were made possible by an enhanced strategy of data aggregation, relying on the standardization efforts of the Cross-Linguistic Data Formats initiative (CLDF)  7  , which provides standards, tools, and best practice examples for promoting linguistic data which is FAIR: findable, accessible, interoperable, and reusable  35. By adopting these principles and coding independently published cross-linguistic datasets according to the specifications recommended by the CLDF initiative, it was possible to increase the amount of languages from less than 300 to over 2000, while expanding the number of concepts in the study from 1200 to almost 3000. A specific shortcoming of this second release of CLICS was that, despite being based on CLDF format specifications, it did not specify how data conforming to such standards could be created in the first place.
Thus, while the CLDF data underlying CLICS2 are findable, accessible, interoperable, and reusable, the procedures involving their creation and expansion were not necessarily easy to apply due to a lack of transparency. In order to tackle this problem, we have developed guidelines and software tools that help converting existing linguistic datasets into the CLDF format. We tested the suitability of our new curation framework by conducting two student tasks in which students with a background in linguistics helped us to convert and integrate data from different sources into our database. We illustrate the efficiency of this workflow by providing an updated version of our data, which increases the number of languages from 1220 to 3156 and the number of concepts from 2487 to 2906. In addition, we also increased and enhanced the transparency, flexibility, and reproducibility of the workflow by which CLDF datasets are analyzed and published within the CLICS framework, by publishing a testable virtual container  36  that can be freely used on-line in the form of a Code Ocean capsule  37.
2. 1  Example of a colexification network. A strong link between ARM and HAND is shown, showing that in many languages both concepts are expressed with the same word; among others, weaker links between concepts HAND and FIVE, explainable by the number of fingers on a hand, and ELBOW and KNEE, explainable as both being joints, can also be observed. By using our sets of tools, data can be enriched, cleaned, improved, and made ready for usage in multiple different applications, both current ones, such as CLICS, or future ones, using compliant data. This toolbox of components supports the creation and release of CLDF datasets through an integrated workflow comprising six fundamental steps (as illustrated in Fig. 3 ). First, (1) scripts prepare raw data from sources for digital processing, leading the way to the subsequent catalog cross-referencing at the core of CLDF.
This task includes the steps of (2) referencing sources in the BibTeX format, (3) linking languages to Glottolog, and (4) mapping concepts to Concepticon. To guarantee straightforward processing of lexical entries by CLICS and other systems, the workflow might also include a step for (5) cleaning lexical entries of systematic errors and artifacts from data conversion. Once the data have been curated and the scripts for workflow reproducibility are completed, the dataset is ready for (6) public release as a package relying on the pylexibank library, a step that includes publishing the CLDF data on Zenodo and obtaining a DOI. The first step in this workflow, preparing source data for digital processing (1), varies according to the characteristics of each dataset. The procedure ranges from the digitization of data collections only available as book scans or even fieldwork notes (using software for optical character recognition or manual labor, as done for the beidasinitic dataset 41 derived from  42  ), via the re-arrangement of data distributed in word processing or spreadsheet formats such as docx and xlsx (as for the castrosui dataset  43  , derived from  44  ), up to extracting data from websites (as done for diacl  45  , derived from  46  ).