dataesr/scientific-paragraphs-categorization huggingface dataset:
4. Dispersion of z-scores corresponding to estimates of independent analysts using the same dataset to test Hypothesis 2 ("Higher status participants are more verbose than lower status participants"), together with some details on each specification. may lead to different analytical choices. Thereby, a data analysis not only incorporates statistical or computational steps, but also cognitive processes  (Grolemund & Wickham, 2014; Paglieri, 2004). The four meta-categories derived from our qualitative coding form the core of a model of the cognitive processes involved in data analysis. What (setting). This meta-category covers the elements of the process which are given and objective in nature. The dataset structure and characteristics and (for this crowdsourced project) the specific hypothesis they are tasked with testing are the same for different data analysts.
) can be a consequence of person factors, leading to variability. How (analysis). The "how" meta-category captures actions or methods which are performed during data analysis. These can either be exploratory or confirmatory in nature. We refer to exploratory data analysis (EDA) as the process of data exploration, as well as attempts to understand the logic of the problem and summarize its main characteristics. Confirmatory data analysis (CDA) refers to the analytic choices to confirm the emerged models (i. e. , systematically assess the strength of evidence). Note that this is a different definition of a confirmatory analysis than seen in scholarship on pre-registration of analyses, in which strictly confirmatory analyses are planned out and "frozen" online prior to having the dataset  (Wagenmakers et al.
(2018). Although IV and DV choices do matter, their effect is small. Surprisingly (at least to us), the outcome of the analysis was only weakly related to one analytic choice or a combination of analytic choices. There are several possible causes for this unpredictability of the outcome of the analysis. First, the task for the analyst, testing a hypothesis with a dataset with yet unspecified variables, may have been so broadly formulated that the universe of potential analyses was enormous. This is confirmed by the actual analyses that differed in all respects; no two analyses were similar with respect to all analytic choices or the number of observations. Second, it may also be that the unpredictability of the outcome of the analysis reflects the nature of research in the social sciences; arbitrary choices may result in arbitrary outcomes of the analysis (see Figure  6 ).
Of course, it is of paramount importance for social science research to distinguish the most important cause of diverging outcomes of multi-analyst projects. Does social science research have an intrinsically low rate of successful conceptual replications and reproducibility  (Iso-Aloha, 2017; cf. Heino, Fried, & LeBel, 2017) , or is it merely the characteristics of the current project that is responsible for the larger heterogeneity of results? Analyses of data of pre-registered many-lab studies suggest that minor changes to sample population and settings often do not affect the results and conclusions of experimental research (Olsson-Collentine,  Wicherts, & van Assen, 2020). Hence, further crowdsourced data analysis initiatives testing many research hypotheses with many datasets, as well as further manylab studies, are needed to address this question systematically.
More limitations and future directions The analysts in the study were confronted with an unconventional research environment; a guiding theoretical framework was not directly provided by the project coordinators, and the dataset was sizeable with many variables that could potentially be used as operationalizations of the constructs in question (e. g. , professional status). We therefore should be careful with generalizing the results to other research environments where, for instance, the theory is fully articulated and the dataset contains fewer variables and statistical choices to be made. Like any other research, crowd projects can and should be subjected to replication  (Landy et al. , 2020) , and we believe a long series of crowdsourcing data analysis projects are necessary before drawing strong inferences from this line of research.
At the same time, it is worth noting that the present dataset and the one leveraged by  Silberzahn et al. (2018)  are less complex than many archival datasets used by organizational scholars, economists, and others. With the present dataset, further operationalizations could have involved for example additional coding to quantify the amount of meaningful information conveyed per unit of text as a measure of verbal contributions to the debate. To the extent that complexity and ambiguity are positively correlated with dispersed results across different analysts, our findings may have wide implications for the conclusions drawn from analyses of complex datasets (see also  Bastiaansen et al. , 2020; Botvinik-Nezer et al. , 2020; Silberzahn et al. , 2018). We fully acknowledge that our final crowd of analysts was relatively small (14 or 15 per hypothesis, for a total of 29 sets of analytic results), and heterogeneous in terms of job rank.
Our results would perhaps be  more convincing to some if more senior scholars, such as tenured faculty at highly ranked universities, were involved. The small final number of analysts is partly attributable to the scope of the task, specifically operationalizing and testing two hypotheses using a complex dataset while simultaneously explaining each decision taken (and not taken) using an online portal. The pool of potential analysts was further restricted to individuals well versed in R. The heterogeneity of seniority is a more general property of crowd research, which tends to attract interested parties from a diversity of career stages, something we see as a strength. Although our sample is far too small to draw strong inferences, an internal exploratory analysis suggests effect size dispersion in the present project was not driven by either more junior or more senior scientists (see Supplement 10).
In  Silberzahn et al. (2018) , which featured a larger number of analysts (N = 29 teams), there was likewise no correlation between indices of seniority (e. g. , job rank) and effect size estimates. Although the smaller sample in the present project facilitated carefully tracking of decisions with DataExplained as well as in-depth qualitative coding of each analysis (see more below), this came at the expense of running meaningful tests of the potential moderating roles of expertise and other analyst characteristics. Future projects with larger samples of analysts are needed to explore potential individual differences. To that end,  Delios et al. (2020a)  have recruited over 80 analysts to test four hypotheses from the field of strategic management using the same complex longitudinal dataset, assessing both statistical and topic expertise as potential moderators.
The results of recent investigations suggest archival findings may be less robust than hoped when the same set of observations is used but a different analytic strategy is employed  (Murphy & Aguinis, 2019; Orben & Przybylski, 2019; Silberzahn et al. , 2018; Simonsohn et al. , 2020; Steegen et al. , 2016). It is also of interest to hold archival studies to the same replication standard to which experimental work is held-in other words, employing the same methodology and statistical analyses, but using new A crowd of researchers was provided with a data descriptor and asked to nominate research questions for testing. A second crowd then voted on which hypotheses to test. Crowd-generated hypotheses received independent ratings for scientific value as high as those generated by the project coordinators.
StudySwap Online platform for posting research "needs" and "haves" (e. g. , "needing" 200 participants from a particular nation or "having" a subject pool with participants of that nationality). Laboratories successfully matched for replication projects and other collaborations (see https://osf. io/meetings/StudySwap). Study design  Landy et al. (2020)  Up to 15 independent research teams designed brief online experiments testing up to 5 research questions. For 4 out of 5 hypotheses, independent research teams designed experiments that returned significant estimates in the opposite direction from each other. Meta-analysing across the effect size estimates from the different designs, 2 of 5 hypotheses were robust across conceptual replications. Data collection  Stewart, Chandler, and Paolacci (2017).
Broadly consistent with the present findings,  Landy et al. (2020)  found that when up to 13 independent research teams designed their own experimental studies to address the same research question (e. g. , "Are individuals who work in the absence of any material need to do so morally praised?"), the different study designs returned statistically significant effects in opposite directions for four out of five original ideas examined (see also  Baribault et al. , 2018). This converging evidence suggests that the link between subjective researcher choices and support for a given conclusion may be stronger than intuition suggests (see  Botvinik-Nezer et al. , 2020, who  found that forecasters in a prediction market underestimated the impact of analytic choices on fMRI results). The effort of a crowdsourced approach is most justified when dealing with controversial issues about which organizational scholars possess different prior beliefs  (Leavitt, Mitchell, & Peterson, 2010) , for research questions with important implications for public policies or organizational decision making, and for complex datasets in which a variety of defensible analytic approaches could be employed.
Up to 15 individual analysts per hypothesis Different analysts reported statistically significant results in opposite directions for both Hypothesis 1 and Hypothesis 2 (see Table  2 ). Boba multiverse analysis demonstrates that variable operationalizations contribute to radical dispersion in estimates, above-and-beyond statistical choices. of the wisdom of the crowds, in which aggregating estimates reduces individual level biases  (Galton, 1907; Lorge, Fox, Davitz, & Brenner, 1958; Surowiecki, 2004) , the central tendency of the effect size estimates calculated by many different analysts may provide a less subjective and error-prone estimate of the effect. For datasets that do not contain sensitive information, firms may consider websites like  Upwork. com, Guru. com, StudySwap, Kaggle. com , and academic partners to help obtain independent perspectives.
For academics, another option is asking different researchers on the same team, or better yet members of an independent team, to separately conduct the analyses, then report both approaches in the article. Whether conducted individually, as independent copilots, or as a crowd, data analysis decisions should be rendered explicitly (e. g. , using carefully commentated code, or the DataExplained platform at (https://dataexplained. net/) which can also be recreated and modified using the code provided in Supplement 9). This study and other meta-scientific investigations into the robustness of research methodologies and results  (Banks et al. , 2016; Bedeian et al. , 2010; Begley & Ellis, 2012; Bergh et al. , 2017; Camerer et al. , 2016 Camerer et al. , , 2018;; Chang & Li, in press; Ebersole et al.
Dataset of red card decisions across four major European football (soccer) leagues, with 146,028 referee-player dyads Are soccer referees more likely to give red cards to dark-skin-toned players than to lightskin-toned players?29 analysis teams 69% of analysis teams reported a statistically significant relationship such that light skin toned players received more red cards than dark skin toned players, whereas 31% did not. Estimates ranged from 0. 89 to 2. 93 in odds ratio units. No analysis team reported a statistically significant effect such that light skin toned players received relatively more red cards. Positive parametric effect of losses in the amygdala (equal range group) Hypothesis 9: Greater positive response to losses in amygdala (equal range group vs.
of dataset Hypotheses or research question tested Number of analysts Degree of dispersion in results Dataset of red card decisions across four major European football (soccer) leagues, with 146,028 referee-player dyads Are soccer referees more likely to give red cards to dark-skin-toned players than to lightskin-toned players?29 analysis teams 69% of analysis teams reported a statistically significant relationship such that light skin toned players received more red cards than dark skin toned players, whereas 31% did not. Estimates ranged from 0. 89 to 2. 93 in odds ratio units. No analysis team reported a statistically significant effect such that light skin toned players received relatively more red cards. Positive parametric effect of losses in the amygdala (equal range group) Hypothesis 9: Greater positive response to losses in amygdala (equal range group vs.
Acknowledgements The authors are also very grateful for the useful suggestions and comments of the two anonymous referees which led to significant improvements of this article. The remaining errors, of course, should be attributed to the authors alone. This paper also benefits from fruitful discussions at meetings of the  French chair DIALog -Digital Insurance And Long -term risks -under the aegis of the  Fondation du Risque , a joint initiative by  UCBL  and  CNP.
The objective of this work is to study the coupling of two efficient optimization techniques, Aerodynamic Shape Optimization (ASO) and Topology Optimization (TO), in 2D airfoils. To achieve such goal two open-source codes, SU2 and Calculix, are employed for ASO and TO, respectively, using the Sequential Least SQuares Programming (SLSQP) and the Bi-directional Evolutionary Structural Optimization (BESO) algorithms; the latter is well-known for allowing the addition of material in the TO which constitutes, as far as our knowledge, a novelty for this kind of application. These codes are linked by means of a script capable of reading the geometry and pressure distribution obtained from the ASO and defining the boundary conditions to be applied in the TO. The Free-Form Deformation technique is chosen for the definition of the design variables to be used in the ASO, while the densities of the inner elements are defined as design variables of the TO.
With these surrogate models the computational burden of ASO can be relieved at a cost of generating large databases. The computational cost of the latter can overpass the former if only a reduced number of optimization iterations are needed as noted by Bouhlel et al. [25]. Despite these optimization algorithms having been employed in several wing and airfoil applications, their combined application is much less common in the literature. ASO is normally done before the TO as in  [26] , although there are some papers where two optimizations process are carried out simultaneous for performance  [27]  or aeroelastic applications  [28]. Maute and Reich  [26]  developed a sequential framework to design morphing structures based firstly on ASO and secondly on TO. James et al. [27]  coupled both optimization processes in their Multidisciplinary Design Optimization (MDO) framework and compared it with the sequential approach for the design of the Common Research Model (CRM) wing.
These models, which are based on extensive reviews of the project management and project complexity literature, are used as inputs to construct the framework proposed in this article. Several database (Web of Science, Project Management Institute, ScienceDirect, etc. ) searches were conducted iteratively to retrieve articles related to project complexity describing and assessment. Search terms included "project complexity," "complexity measure," "complexity management," "complexity assessment," "complexity evaluation," etc. Eighty journal articles were retrieved and the reference lists of each article were reviewed in detail to find additional articles. Project complexity factors were retrieved from these articles and recorded in a table with the associated reference and assessment method.
Due to the project's dynamic aspect, repeated use during the different phases of a project is expected. Establishing an objective and standardized measure permits retrospective analysis of previous projects. That is needed to assess the impact of complexity sources on achieving project goals and their influence on the cost and the staffing level. Applying the proposed framework in the project's upstream stage permits to highlight areas with high complexity. Consequently, it allows anticipating their impact by comparing to other projects and planning mitigation actions to reduce complexity-induced risks. Some examples of risk mitigation actions are adopting a simpler process, choosing a more stable supplier, or increasing communication frequencies between actors. A vital improvement of the proposed framework would be introducing more precise evaluation aids by enumerating more objective questions for each factor and developing a shared database of results that improve and grow with every use.
Dataset All hospital discharge summaries for women over 45 years old admitted for ACS in France from March 2009 to December 2014 were extracted from the French NHDDB. Since 1986, all public and private French healthcare facilities caring for medical, surgical, and obstetric patients have been required to submit anonymous patient data to the NHDDB. Information in these discharge summaries includes both medical and administrative data. Each discharge summary submitted to the NHDDB is linked to a national grouping algorithm leading to a French DRG. This data is deidentified with a secure hash algorithm allowing to link discharge abstracts related to a given patient. 25 This data includes the diagnoses (principal and related), which are coded according to the International Classification of Diseases 10th Revision (ICD-10).
Investigations on different health topics have shown the reliability and validity of this data since 2009. 26, 27 This study was conducted according to the approval given by the Commission Nationale de l'Informatique et des Libertés (National Commission for data protection and freedom): agreement No. 1375062 (www. cnil. fr/en/ home). All patient records were de-identified and analyzed retrospectively, and as such, no informed consent was required in accordance with the terms of January 6th, 1978, relative to Informatics files and freedom (www. legifrance. gouv. fr/affichTexte. do?cidTexte=JORFTEXT00 0000886460). This law has been adapted on June the 20th 2018 to be following the new European regulation (https://eugdpr. org/): the General Data Protection Regulation (GDPR). All methods were performed under the relevant guidelines and regulations.
Longitudinal data clustering Longitudinal data are measured repeatedly over time for the same individual. One way to analyze this data is to partition them with methods like k-means or variants of this method. 28 In this article, we are interested in the evolution of a phenomenon rather than in its moment of occurrence. Table  1. The encoded database. The sequential database for each patient at each timestamp with its hospitalization code. Consequently, we used the kmlShape method which is shape-respecting. 21 In the following, we present its operating principle, and we define the two key concepts of distance and mean this method is based on. The k-means algorithm is a partitioning algorithm. It has been used extensively used for longitudinal data. 29, 30 The R package kml is dedicated to it.
Characterization process of hospital healthcare flows The hospital healthcare flow characterization process, illustrated by Figure  1 , includes three main phases. The encoding phase generates the sequential database. Then, the processing phase clusters trajectories-highlights care trajectories-time gaps, and tariff profiles. This phase is divided into three steps: Step (a) extracts and sorts spatio-temporal patterns from the NHDDB. These patterns will correspond to care trajectory profiles. Step (b) provides an overview of all the patient's pathways, identified in the previous step, in a visualization tool. These results are the hospital healthcare flows. Step (c) clusters the time gaps between hospitalizations and tariff trajectories to identify trends. Finally, the distribution phase describes the distribution of healthcare flows according to the above-mentioned clusters, in terms of time gaps between hospitalizations and tariffs.
Flow This study had several limitations. First, the choice of the database: the NHDDB is a budget allocation tool, so it presents some pitfalls for epidemiological studies. 38 Yet, they are undeniably an important source of information. There are some examples of successful recent investigations that underline the interest of this data for medical research: elaboration of a prognostic score of postoperative mortality,  39  highlighting a spatial overlap between obesity and depression  40  and establishing an increase in acute kidney injury incidence in France. 41 The comparison with other studies is another limitation. Time gap is mostly investigated  42  as a precise event such as readmission for heart failure, but less frequently for the more general event of heart disease. Moreover, most direct cost studies take into account emergency cost and drug consumption.
43 Access to the SNIIRAM (National health insurance system of inter-scheme information) database would allow a similar analysis to be carried out. A significant parameter is the length of a stay (LoS), it is also an indicator of the level of severity of the hospitalization. This parameter is not considered in this study. However, we suggest two ways to consider it, by including the LoS in the definition of time gap or by associating the LoS to the hospital event and mine spatio-temporal patterns in trajectories of couples (ICD-10 code, LoS). In this way, the mining algorithm can be used in its original version with spatial coordinates, here represented by the event and the time spent in the event. Finally, our observation period was limited: most patients (67%) had between 1-year to almost 4-years of follow-up.
The High Luminosity phase of the LHC, which aims for a tenfold increase in the luminosity of proton-proton collisions is expected to start operation in eight years. An unprecedented scientific data volume at the multiexabyte scale will be delivered to particle physics experiments at CERN. This amount of data has to be stored and the corresponding technology must ensure fast and reliable data delivery for processing by the scientific community all over the world. The present LHC computing model will not be able to provide the required infrastructure growth even taking into account the expected hardware evolution. To address this challenge the Data Lake R&D project has been launched by the DOMA community in the fall of 2019. State-of-the-art data handling technologies are under active development, and their current status for the Russian Scientific Data Lake prototype is presented here.
Introduction Modern high energy and nuclear physics experiments, which are being carried out at the accelerator complexes of the LHC (CERN, Switzerland)  [1] , SuperKEKB (KEK, Japan), RHIC (BNL, USA), and in the coming years at NICA (JINR, Russia) and FAIR (GSI, Germany), deal with hundreds of petabytes of scientific data located in billions of files. Starting from 2014 the LHC experiments analyze annually more than two exabytes of physics data processing millions of files per day, and the total permanent storage capacity for the experiments has exceeded the exabyte level. The next project at CERN, the High Luminosity LHC (HL-LHC), aims to increase the intensity of proton-proton collisions tenfold, which will lead to an even greater amount of incoming scientific information. Such enormous data volumes and the amount of files require new techniques for their storage and processing  [2]   [3] , and one of the proposed approaches is to use the "data lake" concept  [4]   [5].