dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Silvia Fre ,  Spyros Artavanis-Tsakonas  and  Tasuku Honjo  for the Hes1-GFP and floxed RBPJ mouse lines,  Marc Dalod  for the B16-Flt3L cell line,  Patricia Marchal  for technical assistance,  Claudine Ebel  and  Muriel Philipps  for cell sorting,  Cathy Herouard  for microarray experiments,  Michael Gendron  for mouse husbandry, and members of the  Kastner-Chan lab  for discussions.
This work was supported by grants from the  Agence Nationale de la Recherche  ( ANR-07-MIME-018-02  to PKa), the  Conference de Coordination Inter-Regionale du Grand-Est of the Ligue contre le Cancer  ( CCIRGE-2014  to  PKi  and  0001K-2016  to SC) and institutional funds from TGFβ pathway to inhibit cDC differentiation. Our results thus identify Ikaros as a key player in the early steps of DC development. INSERM,  CNRS ,  University of Strasbourg  and the  ANR-10-LABX-0030-INRT  grant. JM received predoctoral fellowships from the  Ministry of Technology and Research and Fondation ARC ; CS a master fellowship from the  Fondation pour la Recherche Medicale  ( FRM-DEA20140630557 ) and  GC a pre-doctoral fellowship  from the  IGBMC PhD programme  ( ANR-10-LABX-0030-INRT ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Clusters of genes similarly expressed between Ik L/L CDPs and DC progenitors (Imm) or mature DCs (DC) are indicated. (C) K-means clustering of 70 genes differentially expressed between WT and Ik L/L CDPs, and deregulated by GSI (FC>1. 5). (D) Top 5 putative upstream regulators related to the 70 genes from (C), as identified by the Ingenuity Pathways Analysis software. (E) GSEA enrichment plots of genes specifically down-regulated [clusters I and III in (A)] and (F) up-regulated (clusters II and IV) in Ik L/L CDPs. The ranked gene list corresponds to TGFβ1-regulated genes in CDPs, as identified by Felker et al (2010). NES: normalized enrichment score; FDR: false discovery rate. (G) Genome browser tracks showing Ikaros binding to loci associated with TGFβ activation in pre-B cells (BH1-Ik1-ER-Bcl2 cell line) and immature DN3 thymocytes (GEO GSE114629 and GSE61148 accession numbers).
Acknowledgments We thank  S. Lambert ,  O. Hyrien ,  F. De Carli ,  M. Hennion ,  L. Lacroix  and  V. Besic  for critical reading of the manuscript. The authors would like to acknowledge the  Cell and Tissue Imaging Platform -PICT-IBiSA  (member of  France-Bioimaging ) of the  Genetics and Developmental Biology Department  ( UMR3215/U934 ) of  Institut Curie  for help with light microscopy, the  Flow Cytometry Platform Imagoseine of Institut Jacques Monod, Université Paris Diderot , and the  Imaging and Cytometry Platform (PFIC) of Institut Gustave Roussy  for assistance with cell sorting. M. D. team is supported by the  Agence Nationale de la Recherche  ( ANR-13-BSV6-0008-01/FRA-Dom ), the  Association pour la Recherche sur le Cancer  ( Subvention Libre Sl220130607073 ) and the  Institut National du Cancer (INCa subvention   2013-103 ).
M. N. P. team is supported by the  Association pour la Recherche sur le Cancer  ( Labellisation PGA120150202272 ) and the  Agence Nationale de la Recherche  ( ANR-15-CE12-0004-01 ). M. B. was supported by fellowships from the  Ministère de l'Enseignement Supérieur et de la Recherche  and the Ligue contre le cancer.
We apply our method on a subset of the IMDb (Internet Movie Database) citation network consisting of around 47,000 international movies, and we derive a list of films that can be considered milestones in the history of cinema. For each movie we also collect data on its year of release, genres and countries of production, to analyze trends and patterns in the film industry according to such features. We also collect data on 20,000 directors and almost 400,000 performers (actors and actresses), and we use the network of references and our score of movies for evaluating their career, and for ranking them. Since the IMDb dataset we employ is highly biased toward European and North American movies and personalities, our findings can be considered relevant principally for Western culture.
2015; Spitz and Horvát 2014; Wasserman et al. 2018; Canet et al. 2016) : the network of citations among movies is collected, and a success score for each movie is computed through graph centrality algorithms, or other techniques borrowed from Network Analysis. The key intuition behind these methods is that a successful movie -it does not matter whether for economic or artistic merits -will be probably known and referenced by some of the successive ones, for honoring it or for trying to reproduce its outstanding performance. Such analyses are made possible thanks to the availability of data about the citations patterns among movies: the best-known public platform for collecting such (and other) data on movies is IMDb (Internet Movie DataBase), an online database storing several information about films and tv shows.
The network of references we use for calculating our score has been inferred from the publicly available subset of IMDb: it is composed by more than 47,000 films produced in several countries from 1920 to 2010. We apply our technique on this network to compute a score of importance for each movie in dataset: this score is then used to obtain a global ranking of the most significant movies. It is worth noting that, since the dataset employed in this study is highly biased toward films produced in Western countries (European and North-American ones), our results can be considered relevant preeminently for Western culture. For each movie, we also collect data on its year of first release, genres and countries of production, and analyze the patterns that emerge in film industry according to these features.
The remainder of the paper is organized as follows: "Related works" section presents some related work; in "Analysis of the dataset" section we introduce the data used in our analysis and report some statistics about them; "Ranking method" section introduces our ranking method, and explains why we have chosen those particular centrality scores; we report the results of our analysis on films in "Results of ranking" section and those concerning the study of directors and performers' careers in "Analysis of careers of directors, actors and actresses" section; finally, "Conclusions" section provides some concluding remarks.
Relations and differences with authors' previous work The present paper is an extension of the analysis we proposed in  Bioglio and Pensa (2018). In our previous paper, we presented our method for ranking movies and we applied it on IMDb dataset, discussing the relevance of movies in the global ranking; we also studied the ranking of movies labeled with certain tags, such as a genre or a country of production, interpreting the result. Here we briefly present the results of the same method, but applied on the IMDb dataset purged of movies labeled as "adult" or "short", because the former is weakly related to the other genres (with its own industry and "appreciators"), and we focus on full-length and medium-length movies only. In addition, here we propose a different analysis of the tags associated to movies and, above all, we introduce a new technique for evaluating the career of personalities involved in film production: such technique is strongly linked to the rank of movies obtained from our score, because it considers only persons involved in top-ranked productions.
Analysis of the dataset IMDb is an online database of movies and TV series, featuring metadata such as year, country, genre, cast, production crew, budget, box office revenue, and so on. As of April 2018, IMDb includes information on more than 4 million titles (including episodes of TV series) and 8 million personalities (cast or crew) 3. The site allows users to register and to expand the database in a collaborative way, by submitting new material, editing existing information and rating movies stored in the database. Registered users can also record references between entries, choosing among several kinds of relationships, from remakes to acknowledged source of inspiration: such data on connections between movies are the core of our method. For our analyses, we use data extracted from a subset of the entire IMDb database, made available by the website for research and non-commercial use 4.
For each movie, we collect its title, year of release, countries of production, genres, references, directors and crew (only actors and actresses), removing all movies not involved in a reference connection, i. e. that neither references nor are referenced, and all movies labeled with genres "short" (to study only full-length and medium-length films) and "adult": this filtering procedure is the main difference on the ranking method with respect to the dataset employed in our previous analysis  (Bioglio and Pensa 2018). Some basic statistics on the filtered dataset are summarized in Table  1. For each entity, the number single occurrences and the number of connections it creates with movies are reported The distributions of years of first release (aggregated by decades), genres and countries of movies in dataset are reported in Fig.
1. We observe that the dataset contains data on releases in a range from the last years of Nineteenth century, when cinema has born, to the early years of 2020, as it also includes some movies already announced as in production, such as sequels of famous franchises. The number of films increases over the years (Fig. 1c ), both because the industry of cinema has growth through the ages, and because users of IMDb could be more interested in adding information about brand new releases with respect to older ones. Many movies have been labeled with only one or two genres (Fig. 1a ): the most employed one is "drama", followed by "comedy": with respect to our previous analysis in  (Bioglio and Pensa 2018)  the number of movies labeled with the latter has greatly reduced, suggesting that a huge number of "short" movies,   1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000   removed from the dataset, were also labeled as "comedy".
Finally, around 90% of movies have been produced in only one country, and United States lead the ranking as the country with the largest film production over time (Fig. 1b ): almost half of the movies in dataset have been produced there, while no one of the other countries is able to reach the 10% of entries. This result is probably affected by the fact that IMDb's website is in English and is owned by an American company, but it leads to an important consideration: movies and references contained in the dataset are significant for an American audience. If we group the productivity by continent instead of country (Fig. 1c ) we notice that also Europe can compete with United States in terms of movie production: then we can suppose that the dataset includes also movies significant for an European audience.
Since the dataset is highly biased toward European and North American productions, we can consider the analysis contained in this paper as relevant principally for Western countries. We continue on analyzing the dataset by studying the relationships emerging between tags of the same item (country of production, year of release and genre): Fig. 2  graphically summarizes the relations found in genres and countries (also grouped by continents). It is worth noting that each movie can be labeled with more than one tag for a single item (for example, it can belong to multiple genres), then rows of heatmaps in Fig. 2  sum to values bigger than one. We start by analyzing the co-occurence between tags of the same item (Fig. 2a, d, g ), that is the number of times the tag on y-axis and the one on x-axis label the same movie, divided by the total number of movies labeled with the tag on y-axis; values on diagonal represents the fraction of times the tag is the unique one for a movie.
The distance d(v, u) between two nodes v, u ∈ V is calculated as the length of the shortest path from v to u; if u can not be reached from v the distance is ∞. As in  Bioglio and Pensa (2018) , we use IMDb data on references between films for calculating a score of each movie, based on how much it has been referenced by other movies released after its production: the idea is that a reference made to another movie is a sort of clue left by some member of the crew, suggesting that the referenced film has influenced him or her in some way. In order to calculate our ranking, we construct a references network G = (V , E), where nodes V are films, and there exists a directed edge (u, v) ∈ E between two nodes u and v if the first one makes a reference to the second one. The score of a movie is calculated by combining four different centrality scores: indegree, closeness, harmonic and PageRank centrality.
For a given node v its PageRank score is defined as: P(v) = da v P(v) + (1 -d) n where d =[ 0, 1] is the damping factor (the 1d quantity is also known as restart probability), and a v = [a 1v ,. , a nv ] is a vector such that each element a iv = 1/|O(u)| if there exists a directed edge (v i , v) (a iv = 0 otherwise). For more details on centrality and centrality scores in graphs see  (Boldi and Vigna 2014). Each one of these centrality scores is calculated singularly, and normalized, obtaining a value between 0 and 1: our score is the weighted sum of these scores, where each one has the same weight of 0. 25. The calculation of centrality scores has been performed by means of Python scripts 5 , using the networkx library 6  (Hagberg et al. 2008).
Ranking of movies Even after excluding two genres ("adult" and "short", as described in "Analysis of the dataset" section) from the dataset, the top 20 movies, reported in Table  2 , are the same previously discovered in  (Bioglio and Pensa 2018) , even if some title is in a slight different position. As expected from the distribution of movies according to their country of production in Fig. 1b , most movies in the list have been produced in the United States, and the few produced outside such country have been released in the early years of cinema. Interestingly, all the movies in this list have been released before the 80's, and most of them even before the 40's: such a result is not completely surprising, because our score measures the influence of movies in history, then classical films, representing the first steps and experimentations in cinematic arts, have more probability of having influenced the following ones.
For "western", we can also notice that two directors in the list belong to the sub-genre called 'Spaghetti Western' , that emerged in Italy during the mid-60s, showing the importance of this style for the genre. Finally, we also filter the results by country, limiting our study to the four countries having more movies in the dataset, as depicted in Fig. 1b , i. e. , the United States, the United For each individual, we report the number of movies he has directed during the decade that reached top 5%, 10% and 15% influence in their year of release, and their range of release years Kingdom, France and Italy: results are summarized in Table  7. India has been excluded because, as showed in Figs. 2i  and 4a , its influence on other countries is too weak, then the identities of Indian filmmakers are relatively unknown outside their native country.
Actors and actresses Table  8  provides the ranking of actors and actresses according to our method: as done with directors, the analysis is limited to movies released before 2010, even if, for columns "career" and "number of movies", we have also taken into account films released after such year. Looking at the list, there are several interesting results. First, all personalities come from the United States or the United Kingdom, showing both the great impact that movies in English have in the industry of cinema, and the bias of the dataset. As second point, the great majority of individuals are still in activity or, at least, appeared in a movie released after 2010, suggesting that the most influential movies of modern times are the ones starring most famous actors and actresses. Probably such result is due to little bias that affect modern movies released after 90's: since they are too recent for having already deeply influenced other ones, their score is heavily influenced by the presence of sequel or other movies belonging to the same franchise.
We could select a fixed number of movies instead of a certain percentage, but in this case we would foster films released in less productive years, to the detriment of more prolific ages. Finally, and most crucially, the number of top movies starring a male actor is several way greater than the number of top movies starring an actress: among them, only Lois Maxwell, who appeared in most movies of James Bond film series with the role of M's secretary, can compete with her male colleagues, entering in the bottom part of the top ten ranking. This result can be partially justified by the differences in number of actors and actresses in our dataset, reported in Table  1 , with the former almost doubling the second. The same happens to the number of connections with movies: however the list shows only top personalities, that in case of gender equality should be comparable.
Conclusions In this paper we have employed the data available in the official repository of IMDb movie dataset for analyzing trends and patterns involving the production of movies, and for identifying the most important films and personalities (directors, actors and actresses) in the history of cinema. As initial analysis, we have combined some data on single movies, such as year of release, genres and countries of production, with data on references between films for analyzing patterns that emerge according to each of these features. We have observed collaboration patterns between countries, for example among France, Italy and Spain, between China and Hong Kong, and in Scandinavian countries, and reference relations between different genres.
, 2016a)  have been studied in the case studies. Stakeholder engagement  (García-Nieto et al. , 2015) , governance  (Primmer et al. , 2015)  and the linkages between ES and human wellbeing  (Kelemen et al. , 2015; Tenerelli et al. , 2016)  are arguably less well researched. In the literature there are many similar examples where researchers draw on theory-based argumentation, large datasets and/or case studies, to test the utility of the ES concept. However large scale case study comparisons on how the ecosystem service concept can be operationalised, and how the knowledge is applied in practical terms are lacking. Few studies have assessed the impact of such research on the ES knowledge users  (Posner et al. , 2016; Saarela and Rinne, 2016) , whose perspectives are vital if we are to make these concepts useful in real-world planning and decision-making.
In total 186 responses were analysed (i. e. in a quarter of the returned questionnaires this question was left blank). In addition to these blank boxes, in twelve responses no limitations were specified, i. e. the respondents simply acknowledged the process of the project implementation and the results achieved. Categorisation of the 256 statements revealed 13 categories (See Supplementary Material 5), with 'shortages in method used or its application' being the most commonly mentioned limitation (61 mentions). The 13 categories can usefully be clustered into four groups: limitations linked to implementation of results, limitations in methodology, data limitations, and case-studyrelated limitations (Table  5 ). Most of the statements from the responses related to limitations linked to implementation of results.
Factors associated with a reported 'change in action' The stepwise regression analysis involving all factors found that, from the full dataset of 31questions, only six were significantly associated with the respondents' score for the question 'The OpenNESS research resulted in a change in actions' (61% of the variance accounted for by the model). The stepwise regression (Table  6 ) revealed significant associations with the factor 'OpenNESS Case Study' and the responses to the statements (i) 'Change in decision-making' (ii) 'All the relevant stakeholders were represented' (iii) 'I have changed my understanding' (iv) 'The process was inclusive and provided opportunities to get involved' and (v) 'Change in the way information and tools are used to support decisions'. All associations were positive.
Acknowledgement This research was funded by the  European Union  EU  FP7  project  OpenNESS -'Operationalisation of Natural Capital and Ecosystem Services: From Concepts to Real-world Applications'  (Grant agreement no. 308428 ). The  OpenNESS  project is solely responsible for the content of this publication. It does not represent the opinion of the  European Union , nor is the  European Union responsible  for any use that might be made of information appearing herein. The goals of the project could not have been achieved without the active, willing participation of over 300 local stakeholders across all the case studies who although not acknowledged as authors, were pivotal to the work reported. The authors are indebted to them Dick et al. 2018.
Electroweak ZZ production in association with two jets is generated with PHANTOM v1. 2. 8  [27]. A sample of Higgs boson events is produced in the gluon-gluon fusion process at NLO with POWHEG. The Higgs boson decay is modeled with JHUGEN 3. 1. 8 [28] [29] [30]. Its cross section is scaled to the NNLO prediction with a K factor of 1. 7  [26]. Samples for background processes containing four prompt leptons in the final state, ttZ and WWZ production, are produced with MADGRAPH5 aMC@NLO v2. 3. 3  [31]. The qq → WZ process is generated with POWHEG. Samples with aTGC contributions included are generated at LO with SHERPA v2. 1. 1  [32]. Distributions from the SHERPA samples are normalized such that the total yield of the SM sample is the same as that of the POWHEG sample. The PYTHIA v8. 175  [23, 33, 34]  package is used for parton showering, hadronization, and the underlying event simulation, with parameters set by the CUETP8M1 tune  [35] , for all samples except the samples generated with SHERPA, which performs these functions itself.
0 PDF sets and fixed scales µ F = µ R = m Z. The MATRIX prediction uses PDFs calculated at NNLO, while the MCFM prediction uses NLO PDFs. The uncertainties are statistical (inner bars) and statistical and systematic added in quadrature (outer bars). The band around the MATRIX predictions reflects scale uncertainties, while the band around the MCFM predictions reflects both scale and PDF uncertainties. The measurement of the differential cross sections provides detailed information about ZZ kinematics. The observed yields are unfolded using the iterative technique described in Ref. [52]. Unfolding is performed with the RooUnfold package  [53]  and regularized by stopping after four iterations. Statistical uncertainties in the data distributions are propagated through the unfolding process to give the statistical uncertainties on the normalized differential cross sections.
Patients and methods Between January 2003 and February 2012 a total of 1101 patients with metastatic bone disease (identified in the hos-K The following inclusion criteria were established for the selection of eligible patients: the presence of an actual or impending pathological fracture of a long bone caused by bone metastasis treated with surgery and followed by post-operative EBRT; bone metastasis proven by biopsy; the presence of a metal stabilizing device with or without the use of bone cement implanted on surgery; and no previous RT to the fractured site. The medical history had to contain detailed information on complete personal data, complete data on the patient's functional and general status with a fully described orthopedic examination, outcomes of postoperative RT, and medication used.