dataesr/scientific-paragraphs-categorization huggingface dataset:
The more primitive Formosan landraces belong to japonica To reveal the genetic relationships of Formosan rices with other rice groups, we performed a phylogenetic analysis (see the section on materials and methods below). To that end, we selected fourteen accessions with different awn lengths and shattering degree from the three populations in our STRUCTURE analysis. To these, we added one primitive Formosan landrace collected from an aboriginal village in 2014: Rui Yan Shiang Mi. This landrace has purple palea and lemma, red caryopsis, long awns and is shattering. The names of these accessions and their early domestication-related phenotypes are shown in Additional file 3: Table  S1. We included in the phylogenetic analysis published NGS data for forty more accessions: five O. nivara, five O.
Linguistic fieldwork In the summer and fall of 2017, we visited 16 Taiwanese villages where the Formosan languages Amis, Atayal, Bunun, Kanakanabu, Kavalan, Kaxabu, Paiwan, Rukai, Saaroa, Saisiyat, Sediq and Thao are spoken (Additional file 3: Table  S3 ). There we collected lexical data relevant to eight words reconstructed at the earliest level (Proto--Austronesian) in an online reference work on the Austronesian vocabulary  (Blust and Trussel 2016) , all of them with attributed rice-specific meanings: *Semay "cooked rice", *beRas "dehusked rice", *qeCah "rice husk/bran", *bunabun "rice seedling", *bineSiq "seed rice", *qemu "sticky rice cake", *bayu "to pound rice", *iŋsuŋ "rice mortar". The data were collected as part of a larger survey of the Formosan vocabulary of traditional agriculture.
2 ). In a further phylogenetic comparison, we used the genome sequence data from 55 accessions: the set of 15 Formosan landraces described above, plus 40, consisting of: 10 Asian AA genome wild rice, including 5 Oryza nivara and 5 O. rufipogon; 7 temperate japonica; 6 tropical japonica; 7 indica, 4 Aus, and 6 aromatic. Each subtype contained landraces and modern varieties. The sequence data for these accessions were gathered from  Xu et al. (2012) , our previous work  (Wei et al. 2016a; Wei et al. 2016b ) and from results obtained for this study. The names, types, origins, and DNA accession numbers are shown in Additional file 3: Table  S4.
Whole-genome sequencing and data interpretation Genomic DNA from rice plants was extracted from healthy leaves of a single-seed-descent plant by using the DNeasy Plant Mini Kit (Qiagen). After quality assessment, genomic DNA was randomly fragmented and size-fractionated. DNA fragments with the desired lengths were gel-purified. For whole-genome resequencing, paired-end libraries with 450-to 500-bp inserts were constructed and sequenced by using a GA2 or HiSeq2000 system (Illumina). Adaptor sequences, low-quality bases and reads < 20-bp long were discarded. The trimmed paired reads were aligned to the reference rice Nipponbare genome sequence (IRGSP v1. 0)  (Project, 2005 , Kawahara et al. 2013). SAMtools and VCFtools  (Danecek et al. 2011a; Li et al. 2009 ) were used to manipulate and transform the sequence alignment/map format (SAM) and variant call format (VCF)  (Danecek et al.
Phylogenetic analysis To reveal the position of the Formosan rice accessions relative to other Asian rice, including five cultivated subtypes and two wild rice species, we performed a phylogenetic analysis with next-generation sequencing (NGS) data. Our 15 primitive Formosan accessions plus 40 accessions, including wild rice and five cultivated rice subgroups, were used in the phylogeny analysis. Additional file 3: Table  S4  lists the names, types, origins and sequence information for these lines. The clean reads were mapped to the Nipponbare reference genome (IRGSP v1. 0) by using BWA v0. 7. 13-r1126 mem with default parameters  (Li and Durbin 2010; Kawahara et al. 2013). The mapped results were merged and low mapping quality (q < 20) data were removed as BAM files by using Samtools v1. 3  (Li et al.
2009; Li 2011 ). Picard v2. 1. 1 MarkDuplicates was used to identify and remove duplicate reads originating in the same DNA fragments (http://broadinstitute. github. io/picard/). The Genome Analysis Toolkit v3. 5-0-g36282e4 RealignerTar-getCreator was used to identify regions around indels, then the Genome Analysis Toolkit IndelRealigner was used to execute local realignment  (McKenna et al. 2010). Samtools and Bcftools were used to call for variant calling including SNPs and indels with filter by depth and mapping quality. Genetic distance with the p-distances model was calculated, and a neighbor-joining tree was constructed with 1000 bootstraps by using PHYLIP v3. 695 (http://evolution. genetics. washington. edu/phylip. html). MEGA v7  (Kumar et al. 2016 ) was used to display the phylogenetic tree.
Study design and study sample Within the SPOTLIGHT ('sustainable prevention of obesity through integrated strategies') project, a cross-sectional study was carried out among inhabitants of urban areas in five different European countries: Ghent and suburbs (Belgium), the Randstad (a conurbation including Amsterdam, Rotterdam and The Hague and Utrecht in the Netherlands), Greater London (UK), Paris and suburbs (France) and Budapest and suburbs (Hungary). Sampling of neighborhoods and recruitment of participants has been described in detail elsewhere  [35]. Briefly, neighborhood sampling was based on a combination of residential density and socioeconomic status (SES) data at neighborhood level. This resulted in four types of neighborhoods: low SES/ low residential density, low SES/ high residential density, high SES/ low residential density and high SES/ high residential density.
A previous study within the same project has demonstrated the reliability of these domains in the current dataset  [42]. The domains were treated as continuous variables, with probabilities that a street segment would contain them ranging from 0. 01 to 0. 71. In addition to the domains, we assessed the percentage of streets alongside bodies of water (e. g. river, canal, lake) and green spaces as a single continuous variable ranging from 0. 01 to 1. 00.
Covariates and potential moderators We obtained data from the survey on age, gender, presence of children in the household, level of education, employment status, urban region of residence, years of residency and whether most of the participants' leisure time was spent in the neighborhood (yes/no). Age (18 to 65, 65+) and years of residency in the neighborhood (0-9, 10+) were dichotomized. Since education systems differed between the participating countries, level of education was also dichotomized (lower: from less than primary school to higher secondary education, higher: college or university level). We divided employment status into three groups: employed/in education, not employed/not in education, and retired.
Statistical analysis Participants for whom no address was known (n = 162), who lived in a neighborhood that was not covered by GSV at the time of data collection (n = 84), or who could not be allocated to one of the remaining 59 neighborhoods (n = 586) were excluded from analysis. This left 5,205 participants available for analysis. The analyses were conducted using Stata version 12  [47]. Descriptive characteristics and univariate comparisons of the total sample and by four levels of happiness were assessed using One-way ANOVAs and Chi-Square tests. Comparisons for non-normally distributed continuous variables were assessed using the Kruskal-Wallis Oneway ANOVA. We separately examined the relations between the fourteen independent variables and happiness, by conducting multivariable multinomial logistic regression analyses with clustered standard errors at the neighborhood level.
3% (perceived functionality) and were handled using the multiple imputation technique, based on an assumption that values were missing at random. With the multiple imputation technique, missing values are replaced by plausible values multiple times to account for the uncertainty of these values  [48]. Given the percentage of missing values, ten complete datasets were generated using predictive mean matching, which were, after analysis, pooled into one final data set  [49, 50]. As a sensitivity check, all final analyses were repeated using complete cases only. After multiple imputation, we tested for moderation by age, gender, presence of children in the household, level of education, employment status, urban region of residence and the amount of leisure time spent in the neighborhood in all associations of the independent variables with happiness.
Previous studies, including a study using the same dataset as this study, have also found differences between objective and subjective measures of the environment  [63, 64]. The objectively verifiable presence of certain environmental characteristics in a neighborhood increases the probability that an individual will be exposed to them, but this is not a certainty. The strength of the association may depend on the level of 'conscious' exposure and may be influenced by personality, experiences and cognitive capacities  [30]. Some environments may be objectively functional for some individuals, but less so for other individuals who have different requirements of their environment. Alternatively, some objectively assessed 'safe' neighborhoods may actually be perceived as unsafe. We found negative associations between objectively assessed traffic safety and functionality and happiness.
Strengths and limitations Our study has several strengths. We made use of a dataset from a large survey that was conducted using consistent methods across five European urban regions, which increased its generalisability. This study was the first to combine factors of different contextual domains in relation with happiness, with several physical as well as social environmental factors included. Furthermore, our study contributed to understanding the associations between perceived and objective measures of the physical environment, and how they vary within different subgroups of the population. The objective measures of the environment were obtained using an innovative and validated tool  [39]. There are also some limitations to our study. Firstly, this was a cross-sectional study, thus causality cannot be determined.
Data Collection and Case Studies The first pilot site was a brownfield site (La Lainière site) located in the European Metropole of Lille (MEL). An important objective for MEL was to enhance City assets, particularly as they were undergoing a period of economic uncertainty. It was considered that the concept of diversity, as defined under C2C philosophy, and its application offered the prospect of achieving this objective through the future-proofing it offers and the economic, sociocultural and ecological benefits that accrue to the local community. A brownfield site located in the MEL that urgently needed to be redeveloped (La Lainière) was used as a pilot site for testing the processes and resultant tools. The second pilot site was a greenfield site (Strawberry Field site) earmarked for future development in Bielefeld, Germany.
[INSERT FIGURE 2 HERE] Figure  2 : Methodological approach for developing diversity ambitions on the Strawberry Field site. In both cases, data were gathered from observations of the process of developing and implementing the diversity plans. These observations were documented in memos kept by researchers. Interactions with all stakeholders were also recorded electronically and by note-taking. The fora for such interactions were workshops and seminars at which stakeholders brainstormed and collaborated to develop 'road-maps' for integrating diversity in the proposed business sites (Figures  1  and 2 ). Observations of developments on the pilot sites, specifically at La Lainière site, also provided useful data which informed the findings presented below. The methods of data collection, using the descriptions of  Johnson (2011)  thus encompassed 'conferences' (workshops, seminars, and brainstorming sessions), field observations (including site visits), research journals and documentary analysis.
Data gathered included field notes, digital recordings of the conferences, promotional documents and other artefacts created during the interventions. On the La Lainière project there were six 'conferences' at the planning stage and seven at implementation stage. On the strawberry fields project there were five 'conferences' at the planning stage and development of an experimental masterplan. This combination of methods provided "a series of quick looks taken at different times and in a variety of ways"  (Johnson, 2011)  at C2C diversity implementation. Thematic analysis was undertaken by coding and categorizing the key concepts that emerged from the memos and recorded information. Findings in relation to the efficacy of the process, the resultant tools and lessons learnt are shared below in relation to the three broad phases of project progression: planning, procurement and execution.
Procedure The study described here was part of a longitudinal data collection project extending over a 13-month period that was designed to study the timecourse of parental burnout and its links to other emotional variables. Only data recorded at the first measurement point were used in the present study. Participants responded to an online questionnaire, distributed over social networks, entitled Emotions and Parenting. Parents could only participate in the study if they had at least one child still living at home.
Research results and discussion First, between 2007 and 2009, the IT project team succeeded in designing the tool. The first implementation attempts witnessed few resistance behaviors from some users. This, however, did not impact the success of the implementation process at 39 cooperatives. The tool was called COOPERFIC. The first outcomes of the tool fit with the initial objectives expected by the FRCA LR. Namely, an online dashboard of indicators has been created, along with a benchmark module and an online directory of agriculture cooperatives in the region. Indeed, the first results provide us with an illustration of the translation process according to ANT. COOPERFIC was launched online by the end of 2009, after few ergonomic developments. Second, from late 2009 until the end of 2011, the number of member cooperatives using the tool had increased from 39 to 89 and, ever since, these cooperatives have been called "interactive.
Specifying learning Managers of opposing cooperatives stated that they cannot trust that their confidential information would be safely protected in the tool's imperfect database. Hence, they decided to reject the tool. However, during unofficial interviews, few of the opposing managers stated that they do not trust the management of the FRCA LR and prefer to "follow" the decision of the many opposing cooperatives that decided to reject the tool, for "there must be a reason for which many of them are rejecting the tool. " Only 39 cooperatives have adopted the tool at the end of Cycle 1. The first implementation attempts have witnessed a few resistance behaviors from some users. The first outcomes of the tool fit the initial objectives expected by the FRCA LR. Indeed, the first results provide us with an illustration of the translation process according to actor-network theory.
This level therefore reflects information already available inside cooperatives, and already known by managers. Information included accounting data (income statements and balance sheets), or information about stocks, current and previous sales activities, etc. Level 2, "what I do not know": this second information level matched with the goals set by the FRCA LR. These goals reflected the needs of the member agricultural cooperatives to deal with the crisis that they were facing. The COOPERFIC tool provided each of the cooperatives with online secured access to a range of economic indicators, dashboards of different axes that allowed cooperatives to compare themselves with the regional average of the other cooperatives, and detain an online directory that would facilitate networking. Achieving these goals triggered a process of collection, classification and data analysis to disseminate the information needed.
Railways are a key infrastructure for any modern country. The reliability and resilience of this peculiar transportation system may be challenged by different shocks such as disruptions, strikes and adverse weather conditions. These events compromise the correct functioning of the system and trigger the spreading of delays into the railway network on a daily basis. Despite their importance, a general theoretical understanding of the underlying causes of these disruptions is still lacking. In this work, we analyse the Italian and German railway networks by leveraging on the train schedules and actual delay data retrieved during the year 2015. We use these data to infer simple statistical laws ruling the emergence of localized delays in different areas of the network and we model the spreading of these delays throughout the network by exploiting a framework inspired by epidemic spreading models.
We name the first kind of delays as "exogenous delays", while the second ones stemming from the interaction between trains as "endogenous delays". While the occurrence of exogenous delays can be detected and analysed from our data, the endogenous delays, on the contrary, since the (supposed) direct interaction between trains cannot be inferred from the dataset we collected, will be modeled with a one parameter interaction. The effects and consistency of such endogenous delay modelling scheme will be checked a posteriori by means of numerical simulations. Our model is close to an SIS model of epidemic spreading  [24] , since trains can either get infected by delay, recover from it and then get infected again. We show that this model is capable of reproducing the empirical distribution of delays measured in the data as well as the emergence of large congested areas.
Moreover, we show that the removal of the delay propagation mechanism prevents the modeled system from generating large disruptions, hence strongly suggesting the existence of this kind of interaction in the real system. Finally, we propose an application of our model in studying a scenario where the propagation of a localized delay leads to the emergence of a vast non-functioning area in the Italian Railway Network. The paper is structured as follows: in the first section we describe the dataset and some empirical findings that inspired the assumptions used in the development of the model; in the second section we discuss the model in detail and will show its capability of reproducing empirical findings such as the distribution of delays and the size of congested areas; we will also demonstrate the usefulness of the model as a scenario simulation tool; in the last section we will summarize our findings and discuss possible future developments.
Data In order to extract relevant patterns related with the emergence of train delays, we collected and analysed data about the daily operations of the Italian and German Railway Systems during the year 2015. Such data were collected in the first case through the ViaggiaTreno website  [25]  and for the second one through the OpenDataCity website  [26]. The information we acquired allowed for a complete reconstruction of the schedules of the trains, the structure of the Railway Network and the delays that affected the trains during their movements. For more information about the data collection please refer to Supplementary Information (SI). The choice of the Italian and German Systems as subjects for our studies lies in their similarities in structure and management. In fact, their networks have remarkable and comparable sizes  (41,315 and 16 ,723 km, respectively) and densities  (8.
More than 10% of the train stops are on-time. The distributions of both countries exhibit an asymmetric pattern: the right tail (labeled Delay) shows a power-law like behaviour compatible with a q-exponential distribution  [32] , while the left tail (labelled Advance) has an exponential steeper slope. We expect this distribution to be the result of the interplay between the occurrence of adverse conditions and the interaction between trains, influencing their dynamics. Despite the fact that the microscopic details in our data do not allow for a precise investigation of possible interactions between trains, we can highlight how the possibility of interaction might affect the delays in railway networks. Hence, we study the relation between the first order co-activity of a link, i. e. , the probability that at a certain time t (with time steps of 30 minutes) a link which is active has at least one active neighbouring link, to the average delay of the link itself.
B) C) A) Fig. 2  Delays and train interactions. (A) Distributions of the delay at the final and at the intermediate stations trains in both Italian and German datasets. Departure delays at the initial station has been subtracted, hence these delays originated during the travel. As a guide for the eye, a power-law has been added in the positive delay area and an exponential in the advance area (negative delay). (B) Average delay of a link as a function of the link k-order coactivities, with Spearman's correlation coefficients showing the existence of a relation between the co-activity and the average delay. The curves have been obtained by binning the data on the x-axis at steps of 0. 2 and reporting the average and the standard error. (C) Cross-Correlations C(dt) from equation (  1 ) as a function of dt for the average delay time series of pairs of nearby links in the railway networks in the "Forward" and "Backward" configurations (shown in Fig.
Exogenous generation of delay We define two kinds of delays: endogenous and exogenous. By "endogenous" we mean that its origin is inside the railway system dynamics, i. e. it has been caused by another train. Conversely, by "exogenous" we mean that its cause is of a different nature: strikes, malfunctioning, bad weather or anything else which is not the result of the interaction with another train. We measure directly this types of delay in our datasets. Let us consider a train i travelling from a station A to a station B on the link e and further to a station C on the link e. It will travel first on the link e and then on the link e. The delay are indicate respectively as ∆t i (e) and ∆t i (e ). If there is a increase in the delay ∆t i (e ) > ∆t i (e) it might be "endogenous" or "exogenous".
Generation of delay at departure Departure delays, i. e. the delay a train acquires right before leaving the first station on its route, cannot be considered in principle completely exogenous. In other words, due to the fact that different trains in our datasets can actually be the same physical train (e. g. , the same convoy travelling back and forth along the same path on the railway network -this is denoted as "rotation" -), the delay at departure might suffer from the influence of the traffic. However, railway administrators envisage suitable time buffers at the endpoints of the paths of each train so that it is reasonable to assume, at least as crude approximation, that departure delay is exogenous in character. It has already been shown that this kind of delay can be described by a q-exponential distribution in  [32].
Negative departure delays were never reported in the German dataset and hence we assume they are not present. Despite the fact that better proxies for station complexity than kout might exist (weighting each link with the actual number of railway lines on it is a valuable alternative A) B) C) Fig. 6  Best fits of the q-distribution parameters of the departure delay distribution as a function of the out-degree of the nodes kout. Panels A and B show the parameters for the positive and negative departure delays in the Italian railway network, while panel C the positive departure delay distribution for the German case. No negative departure delays were reported in the German dataset. The function expressing b was sought of the form b = b 0 e -k out /k 0 , while the parameter q of the constant form q = c.