dataesr/scientific-paragraphs-categorization huggingface dataset:
All these data have been collected by the SocioPatterns collaboration  [38] , except for the case of the American primary school, in which a different infrastructure was used  [35]. For all these datasets, the distributions of cumulated contact durations are broad and, as also discussed in  [44] , can be modeled by negative binomial functional forms. We show in the Supporting Information that similar parameters of the negative binomial fit are obtained for each dataset and for the combined one. Therefore, to further generalize the procedure and avoid relying on a single dataset, we consider in the following the fit of the five combined datasets. We then assign to the links of the CDN s nz weights drawn at random from this fitted distribution, obtaining CDN s nz;NB. Figs 7 and 6 compare the distributions of epidemic sizes obtained when the SIR model is simulated on the resulting surrogate network and on the CSN (see also the Supporting Information, in which we moreover show the outcome of simulations for different initial seeds, as well as the temporal evolution of the density of infectious individuals in the population when using the CDN s nz;NB.
Here on the other hand, the available dataset is given by diaries, in which population sampling is not uniform (actually, the students who filled in diaries tend to have more contacts than the others) and in which under-reporting implies that many links between participating individuals are also missing. Moreover, we face two additional issues (i) the low sampling rate yields a contact matrix of link densities which contains zeros, in an unrealistic way, and (ii) various possibilities can be considered when assigning weights to the links of the surrogate networks as weights reported in the diaries are strongly overestimated. Despite these issues, the surrogate network we build yields, when used in simulations, a good agreement with simulations performed on the whole contact sensor network in terms of epidemic risk prediction, under the condition of using the list of weights (cumulative contact durations) gathered by the wearable sensors.
In order to get rid of this condition, we argue that this list comes from a distribution that has been shown in previous works to be very robust across contexts  [9]. We therefore consider weights taken at random from a pool of publicly available datasets, and show that using these weights gives also satisfactory results. Overall, we thus have presented a procedure that uses only the information contained in the contact diaries and in public data, which allows to obtain a good prediction of the epidemic risk, as measured by the distribution of epidemic sizes, when used in simulations of a spreading process. In the Supporting Information, we moreover consider the issue of using, instead of contact diaries, data coming from friendship surveys, in order to build the surrogate contact network used in the simulations.
Third, we have considered here static networks. As discussed in  [39] , the outcome of simulations is then close enough to the one of simulations taking into account the full contact dynamics if we consider slow enough processes. For fast processes, the burstiness of contacts becomes very relevant; in this case, it would be crucial to supplement our procedure by the construction of surrogate timelines of contacts and intervals between contacts at high temporal resolution, as done in  [40]. Finally, we cannot at this point investigate the efficiency of our procedure in other contexts, for lack of datasets reporting contacts measured by both wearable sensors and contact diaries in the same context and at the same date. Hopefully such datasets will become more available in the future, yielding new testing grounds for our method.
Data description The datasets we use have been presented and made publicly available in  [21]. They correspond to contacts between students of 9 classes in a high school in France, collected through wearable sensors on the one hand and contact diaries on the other hand. The sensors registered contacts with a temporal resolution of 20s for 327 participating students (out of 379 in the 9 classes, i. e. , a 86. 3% participation rate) during the week of Dec. 2-6, 2013. Contact diaries contain data reported by students about encounters and their cumulative durations for Dec. 5, 2013. In these diaries, students were asked to report the cumulative durations of their contacts choosing among four intervals: at most 5 minutes, between 5 and 15 minutes, between 15 minutes and 1 hour, more than one hour.
The students belong to 9 classes with different specializations: "MP" classes focus more on mathematics and physics, "PC" classes on physics and chemistry, "PSI" classes on engineering studies and "BIO" classes on biology. We collected data among students of nine classes corresponding to the second year of such studies: 3 classes of type "MP" (MP, MPÃ1, MPÃ2), two of type "PC" (PC and PCÃ), one of type "PSI" (PSIÃ) and 3 of type "BIO" (2BIO1, 2BIO2, 2BIO3). Using these datasets, we build two networks of contacts among students for the same day (Dec 5, 2013): the Contact Sensors Network (CSN) and the Contact Diaries Network (CDN). In each network, nodes represent students, and a links is drawn between two students if: 1. sensors register at least one contact during the relevant day (CSN); 2.
This choice takes into account data reported and registered and the school schedule) and, if two students reported different durations for their encounter, we use the average of the reported values. In the CDN S on the other hand, we consider weights randomly drawn from the distribution of contact durations registered by sensors. Results are averaged over 1000 such weight assignments. For the CDN S' finally, we start from CDN and rank the E links in decreasing order of their reported weights (assigned as in CDN D , and with random order for equal weights). We extract E weights from the distribution of contact durations registered by sensors, rank them as well in decreasing order, and assign the weights to the links of CDN in such a way to match the two orderings (i. e. , assigning the largest weights to the links with largest reported weights).
This work was supported by the A Ã  MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program , managed by the  French National Research Agency (ANR) , to AB and RM. AB is also partially supported by the  French ANR  project  HarMS-flu  ( ANR-12-MONU-0018 ) and by the  EU FET  project  Multiplex 317532. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study population The Normative Aging Study (NAS) is a longitudinal study of ageing established by the US Veterans Administration. The original cohort was recruited between 1961 and 1970, and consisted of 2280 community-dwelling men, aged 21-80 years, from the greater Boston, Massachusetts area, who were free of known chronic medical conditions at enrolment. 31 Study participants have, every 3 to 5 years, undergone routine physical examinations and laboratory tests, and responded to surveys on medical history, and lifestyle and psychological factors. The present study analysed data on men participating in the NAS cohort, with psychological measures and DNA methylation measures (average of 2. 2 measures/ individual), collected on between one to four visits between 1999 and 2006. During this period, 765 study participants provided at least one whole blood sample that was used to measure DNA methylation.
33 34 In the Introduction, we cited studies suggesting linkages between psychological exposures and the methylation of NR3C1 and IFN-γ promoters, which in turn might explain chronic inflammatory processes characterising diseases such as CHD. As an example for the third selection criterion, lower early-life socioeconomic status (SES) has been linked to greater expression of both NR3C1 and TLR receptor mRNA in leucocytes. 35 NA was extracted from a stored frozen buffy coat of 7 mL whole blood, using the QiAmp DNA blood kits (QIAGEN, Hilden, Germany); 500 ng DNA (concentration 50 ng/μL) was treated using EZ DNA Methylation-Gold Kit (Zymo Research, Orange, California, USA) according to the manufacturer's protocol. Final elution was performed with 30 μL of M-Elution Buffer. CpG dinucleotide-rich promoter regions were identified using the Genomatix Software Suite (Genomatix, Germany).
Online supplementary table S1 lists the specific CpG positions for DNA methylation that we measured within specified promoter regions for each gene. We had limited information about the CpGs that were analysed (eg, for NR3C1), including their functionality or their proximity to transcription factor-binding sites or other important sequences. Since genomic locations were for the hg18 genome build, the majority of the CpGs that we examined were not assayed by the most common methylation assays (ie, either the 27 K or 450 K assays) that are available in public data sets. The degree of methylation was calculated as the percentage of methylated cytosine residues divided by the sum of methylated and unmethylated cytosine residues (%5mC) in each sample. Built-in controls were used to verify bisulfite conversion efficiency.
Predictor variables We used data on anxiety and depression measured through the Brief Symptom Inventory (BSI), a selfadministered 53-item questionnaire of nine primary psychological symptom dimensions (anxiety, depression, hostility, interpersonal sensitivity, obsessive-compulsive, paranoid ideation, phobic anxiety, psychoticism, somatisation) experienced by the respondent over the previous 30 days; the BSI was included as part of the Health and Social Behavior Survey in the NAS, starting in 1985. 31 36 Happiness (based on the single item 'How happy are you right now?') and life satisfaction (based on the 11-item version of the Life Satisfaction Inventory-A)  37  were also examined as predictor variables. Higher life satisfaction scale scores corresponded to higher self-reported life satisfaction; higher scores on the other scales reflected higher negative psychological symptoms.
This work is supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" programme  within the framework of the  LIEPP centre of excellence  ( ANR-11-LABX-0091 ,  ANR--11--IDEX-0005--02 ) a. Université de Cergy-Pontoise, THEMA , Clersé d. Sciences Po, LIEPP (Laboratoire interdisciplinaire  d'évaluation des politiques publiques).
QCA is also an approach as well as a research technique  (Curchod, 2003; Rihoux & Marx, 2013). In this regard, QCA aids in the identification of causal structures  (Ragin, 1987). QCA is therefore an instrumentation of generic analytical approaches for which qualitative methodologists advocate  (Miles & Huberman, 2003). QCA draws on Boolean algebra and set-theoretic approaches, and operates through two types of software: those based on graphical user interface (GUI) (e. g. , TOSMANA, and fsQCA) and those based on command line interface (CLI) (e. g. , R, and Stata packages). The areas of research that use QCA are mostly political sciences and sociology  (Rihoux et al. , 2013). However, although management research increasingly uses QCA, this method still remains relatively new to a large number of scholars and no critical review and overview on its use in management research exists.
Delimiting the scope of the investigation This study first identified management articles using QCA in Web of Science database and then triangulated the results with the COMPASSS database. The final sample comprised 95 articles from aggregated databases (Cairn, Ebsco and Science Direct) and publishers (Sage and Emerald) (see Figure  1 ). To obtain the articles, the study used six sets of keywords. The first set combined the themes "Qualitative Comparative Analysis", "QCA" and "Management". From this combination, the second, third, fourth, fifth and sixth used "Fuzzyset", "fsQCA", "mvQCA", "Crisp-set", and "csQCA" respectively instead of "QCA".
3). There remain only a few countries which have never conducted a census (e. g. Eritrea, Western Sahara)  (UNSD 2013b). Census data are exploited by politicians, policy advisors, statisticians, local government, lobby groups and national and international organisations for the planning and allocation of resources. Data collected at household level in censuses are used for a wide range of purposes including: electoral arrangements; baseline data for planning local health and education services; to identify locations for future services and infrastructure; as a sampling frame for subsequent surveys; and for the estimation of vital statistics in the absence of other data. Historical and more contemporary census data have been extensively used in academic analyses from diverse disciplinary perspectives to understand different dimensions of social trends and transformations.
ONS funding is a part of, and is reported by, the UKSA, in turn funded by the UK Government. The ONS does not report to an individual Minister or Ministry. INSEE is a Directorate General of the Ministry of the Economy, Finance, and Industry and receives its funding from the State's general budget. INSEE organises the population census, produces the main indicators for the national economy (national accounts, consumer price index) and periodically conducts statistical surveys of households on specific topics (employment, living conditions, housing, Trans: Each and everybody counts/is considered/is important. There is a pun on the word ''compter'' here health, etc. ). Finally, INSEE also uses administrative data (civil registration records, annual income tax returns, etc. ) for statistical purposes.
Managing Fieldwork (Terrain) ONS and INSEE both have to deal with the practicalities of census data collection. In E&W in 2011, census forms were posted to the occupants of each registered address in the Postal Address File: … it's borne out of the practicality really of wanting to be able to enumerate these people and because it is not immediately obvious. It is a front door, the postman's putting something through the door, so we've got no idea whether we've got 5 rooms of people who count themselves individually or not, so we can deliver one questionnaire and we can make clear on there if you all share a kitchen and some living space essentially, a cooking space and living space is what we're after, if you share those facilities we're counting you as one household. (E&W: ONS Survey designer, 2011) A key driver for the change in definition of the household in 2011 was the way in which the census was administered: In 2011 because the questionnaires were posted out and posted back, so we weren't going to see people, that had to change, but part of that came from the experience in 2001 where in the 2001 census the definition was broadly comparable with that which had gone previously so it was a group of people living together sharing common housekeeping was the term, and the questionnaires were hand delivered so if the field staff made contact they could explain that but what we found increasingly was that our field staff who were then supposed to be making that judgement, making that decision, 9 Translation: in the French census we collect nothing about income.
The Census as a Reflection of Society and Daily Life: Perspectives from Key Informants Analysis of E&W census documentation suggests that the household definitions generated by ONS are making substantial efforts to accommodate and represent changing social realities. In fact there was a very substantial shift between 2001 and 2011 motivated by two, very different drivers: practical, methodological considerations, and an attempt to reflect contemporary changes in the organisation of everyday life. The nuanced changes to the definition of the household over the past 50 years were generated by the second of these drivers, whereas getting rid of common housekeeping and the shared meal in 2011 was generated by the practicalities of 2011 census data collection although it does also reflect changing trends in meal consumption.
Two UK-based University academics who not only use household data in their research but have also worked with ONS and other international statistics agencies to analyse census data suggest that the impetus behind past changes to definitions has been primarily to reflect changes in social life: The household, in that respect, is defined as a sort of dwelling space where you're the only person in that space, typically a flat. The rules have changed as to how you're supposed to define household. In the past they've talked about…everyone in the household sitting together  [. ]  down together to eat a communal meal. Um that's sort of…been removed. I think I'm getting this the right way around, I hope so, as that's simply socially become a less common occurrence. (E&W: Academic Data Analyst, 2011) The questions they ask, how do you identify several households within the same dwelling, for example I remember a traditional definition of household in Britain was… people who share, cooking facilities, I think that is the latest definition, because it used to be people who share a meal, and then they realised that nobody shares meals nowadays.
(E&W: Academic working on households, 2011) This is very different from INSEE where the census prioritises very different dimensions: counting individuals and providing legal population counts is what is really important, not how they organise their lives (see quotes at the beginning of the paper) even though census data are used to publish statistics on commune inhabitants and their characteristics: composition by sex and age, occupation, housing conditions, modes of cohabitation, etc. In contrast both British academics suggest that the definition of the household had changed-and that the shared meal requirement, in particular, had disappearedbecause of ONS's attempts to define a household in such a way as that it continues to reflect the reality of the majority of the population to whom the definition is applied.
[to] share the kitchen, that's quite standard, and what we've tried to do is make sure…Those people could go their lives never talking to each other, they just happen to share a cooker, so we count those as separate households, which is why we started saying you have share a lounge or a dining room, there has to be some kind of social space that you're sharing for us to count you as household. (E&W: ONS Survey Designer, 2011) The importance of the social, in what makes a household-the ''hold'' of the house-becomes clear and accounts for the ''and'' in the 2011 definition. The imperative to create a household definition which reflects the changing way in which people live appears to be the rationale for this historical series of subtle changes. In France, things are very different: the census ''household'' is not regarded as a conceptually strong entity for data producers and consumers.
Acknowledgments The research for this paper was funded by  ESRC  as part of the  Survey Design Measurement Initiative (SDMI)  and  ESRC/ANR  ( RES-062-33-0007/ANR-09-FRBR-016 ). We thank  Celine Hiron  who undertook some documentary analysis,  Dr. Sadio Ba Gning  who participated in the majority of the in-depth interviews in France, and  Dr. Emily Freeman  who reviewed a version of this manuscript. With thanks to two anonymous reviewers who provided comments on an earlier version. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
If the resulting parsimonious tree has a high consistency level  [17]  the most likely explanation is that the character states are transmitted vertically along the branches. We may otherwise have to consider that character states are shared through other processes, possibly through horizontal diffusion or convergent evolution. For testing this vertical transmission, we build a musical dataset that consist of the coding of 58 musical patrimonies from Gabon. Patrimonies were analysed through 700 musical pieces collected in Gabon between 2000 and 2011. All musical traits were coded with exogenous and endogenous characters, including metrics, scales, polyphonic processes, instruments and repertoires. To our knowledge, this is the largest corpus of encoded music ever published from firsthand fieldwork data.
Material and Methods Sample Data was collected in groups of farmer and hunter-gatherer Bantu  (28)  and Ubanguian  (1)  speakers from Gabon according to a convention and field work authorizations from the Omar Bongo University in Gabon. Our sampling is representative of rural areas in Gabon and does not include the capital area. We have no sample from the middle of the country where human settlements are scarce (  Fig 1). The data matrix (S1 Dataset) was built using 322 characters identified in previous studies on the characterization and categorization of musical practices in Central Africa  [18, 19, 20, 21]. Each musical patrimony (a set of musical features shared by a population) consists on average of 6 repertoires, from which 2 to 3 pieces have been analysed. In total 700 pieces were analysed in the 58 patrimonies.
A presence/absence data matrix was built for the 322 characters for 58 musical patrimonies collected across 75 localities and among 29 distinct ethnonymic entities (S1 Dataset). The data matrix was constructed following hypotheses of primary homologies  [22] i. e. hypotheses of homology that have not yet been tested through a phylogenetic tree. Homologies confirmed by the tree are called secondary homologies. Our cultural sample spans the diversity of rules of descent that can be found in Gabon: matrilineal (Ma), patrilineal (Pa), and ambivalent  [23]. This is a social system of transmission of filiation; in our case it can be either patrilineal or matrilineal if the transmission comes from the father or from the mother respectively. Ambivalent is the case were both systems co-exist. Popular discourse admits two geographical areas: a patrilineal territory north of Ogooue River, and a southern matrilineal territory.
Missing data were coded with a question mark. All sound parameters (metric, rhythmic, melodic) were considered as intrinsic characters. In musicology, the notion of time relies on two distinct principles, the metrics and rhythm, each of which is made up of constitutive parameters. The metrics gives the time standard  [18]. It is made of beat, subdivision mode (binary or ternary), and period. The rhythmic pattern is composed of a succession of rhythmic cells, each one beat long and considered as a character. Melodic character is represented by musical scale  [18]. In oral tradition music, scales are determined through pieces transcriptions. Almost all intrinsic characters are found in every population, but in each population they can be found in different repertoires. These intrinsic characters are distinctive traits which are used in ethnomusicological categorization  [19].
Ethnomusicological coding We collected data for 58 musical patrimonies from 23 farmer groups and 5 pygmies groups. Table  1  describes the 58 taxa included in the matrix, together with a map (Fig 1 ) showing the localization of the corresponding patrimonies. We identified in total 161 distinct repertoires, according to their endogenous name, context of performance and social or symbolic functions. We listed in total 63 instruments. Using direct observation of musical performance, we were able to identify two vocal techniques, the jodel  [42, 43] , and pseudo-jodel, which is characterized by great intervals jumps and a distinct tone but without changing laryngal configuration. We also identified three polyphonic processes, homorhythmy, counterpoint, and partial-counterpoint  [44] , as well as two vocal forms, antiphonal and responsorial.