dataesr/scientific-paragraphs-categorization huggingface dataset:
The official Twitter account provided to be sufficient for retrieving data as it also picked up content around mentions and replied to the account. For the EPL brand, social media data from Twitter was captured on the same day each week, every month, for four months (July to October 2016) using the account name '@ PremierLeague'. We used NodeXL, which draws the Search API, to capture and analyse the data, which enabled us to qualitatively observe key influencers in conversations about the brand, and the connections between both the people and the conversations-which arose from the EPL Twitter posts. We then took systematic random samples of 1,000 tweets for each month from July to October 2016 to produce Figure  2. For Figure  3  and Table  1 , we combined tweets from each month and then extracted an overall subsample for further analysis.
Network mapping undertaken using software such as NodeXL allied to the use of centrality measures, providing some interesting insights, compelling even. We believe that our methodology here does indeed help in deepening our understanding of how information flows around social networks, and who is leading them and creating opportunities for new related studies. Nevertheless, all of us are in the formative stages of social media research; and we, therefore, advocate-at least in the short-termusing multiple data collection methods to triangulate the findings of studies such as ours. To ensure that sporting brands truly understand the social media conversations about them, visualizing networks, understanding who or what influences conversations, how we should interpret data (such as Indegree and Outdegree), the analyzing of contents of the posts and interviewing or observing users should all form the basis of a coherent social media conversation research strategy.
As literature in the field of brands and social media evolves, significant consideration must be given to these issues. In the same way, developing and utilizing data analysis software and techniques appears vital. In our study, NodeXL proved to be an effective way of capturing and visualizing social media data; software is currently effective in gathering and analyzing Twitter data, though that cannot be used when analyzing communications on all social media platforms. Researchers should, therefore, remain vigilant to the possibilities and constraints they might face in this field. Our finding that communications pertaining to a high engagement sports brand take a broadcast network form is an important one because it highlights Twitter's marketing potential as broadcast networks are characterized with a large frequency of retweeting.
Acknowledgements No acknowledgements to declare. LNV reports grants from  Merck , grants and others from  Merck Sharp and Dohme , and grants and others from  Ferring , outside the submitted work. MVW reports leading the  Netherlands Satellite of the Cochrane Gynaecology and Fertility Group. BWM reports grants from the  National Health and National Health and Medical Research Council (NHMRC) , others from ObsEva, others from ObsEva, grants from  Guerbet , and grants from  Merck , outside the submitted work. The other authors declare that they have no competing interests. Monash Women's,  Monash Health, Clayton, Australia. 2Department of  Obstetrics and Gynaecology, Monash University, Clayton, Australia. 3Institute for Women's Health, University College London, London, UK. 4  Department of Obstetrics and Gynecology, Division of Reproductive Endocrinology & Infertility, Penn Medicine/University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Statistical methods Baseline characteristics were compared between included and excluded participants using the chi-square and Wilcoxon rank-sum tests. To keep in the analysis participants with missing baseline data, multiple imputations were carried out using a fully conditional specification method with discriminant function for the nominal variable  [23]. The missing values represented 2% of all data for 28 variables. The SAS PROC MI procedure  [24]  with the FCS DISCRIM option for categorical variables and five imputations was used. The participants' characteristics were compared according to their household/transportation and leisure/sport activity sub-score group, and differences were tested with the Chi-square and Wilcoxon rank-sum tests. Associations between baseline PA groups and changes in cognitive performances were analyzed using linear mixed regression models.
Then, the model was additionally adjusted for the selected covariates (consumption of fruits and vegetables, alcohol, BMI, hypertension, diabetes, cardiovascular disease, APOE carrier, depression and benzodiazepine use) and their time interactions if significant (for BMI, hypertension, diabetes, APOE carrier, depression). Interactions between PA groups and sex, age and educational level were also tested. The mixed model analyses were carried out using the five imputed datasets. The estimates were then combined using the SAS MIANALYZE procedure. Additional sensitivity analyses were performed to address possible reverse causality. Specifically, models were restricted to participants without IADL/ADL limitations at baseline and also to those free of dementia at all follow-up visits. All statistical analyses were carried out with SAS, version 9.
To adjust the sample, we used an adapted version of the Calmar method. Calmar is an acronym for CALibration on MARgins. This method is "an adjustment technique which adjusts the margins (estimated from a sample) of a contingency table of two or more qualitative variables to the known population margins. However, the program is more general than mere 'calibration on margins,' since it also calibrates on the totals of quantitative variables"  (Sautory 1993 , p. 2)(Sataury, 1993, 2). We developed the program using IBM's Statistical Package for the Social Sciences (SPSS) software. The linear method was sufficient, as we did not get negative values. Consequently, we corrected the biases to ensure better representativeness for our sample compared to the adult population of mainland France, who was our target population.
Methodology This article is based on our analyses of data collected from hostesses, stewards and flight directors in several airlines: regular, low cost or charter. We carried out 100 individual qualitative interviews-between 1h30 and 3h30 each, in France, Canada and Germany. The data were analyzed using the techniques of qualitative thematic content analysis by studying common themes or patterns emerging from the participants' discourse  (Ghiglione et al. , 1980; Mucchielli, 1996; Bardin, 2013; Drisko & Maschi, 2016). The interviews were transcribed and analyzed, with some elements of documentary data used for comparative purposes. The themes were reworked with an in-depth and comparative analysis of transcriptions, field notes, and other studies  (Patton, 1999). We also distributed an online questionnaire (with 2000 respondents in the major airlines in Europe (France, Germany, Italy, Portugal, the Netherlands, Spain) and Canada.
The Linearbandkeramik (LBK) culture, which originated in central Europe and expanded rapidly westwards reaching the Paris Basin around 5100 BC  (Salavert, 2017) , was subsequently replaced around 4900 BC by another group known as the Blicquy/Villeneuve-Saint-Germain (BVSG). While we know that cereals played an important role in the diet of these LBK and BVSG populations  (Bakels, 2014; Hamon, 2008; Salavert, 2011) , many questions remain to be answered regarding the real contribution of other plants, as no single dataset can provide the entire spectrum of plants used in the past  (Colledge & Conolly, 2014). For example, what other plants were processed and consumed in western LBK regions, and more specifically the Paris Basin? Did Neolithic populations consume fewer wild resources than those of the Mesolithic (or none, see  Bogucki, 2000, p.
By doing so, we were able to consider the potential and limits of the methodology: what starch grains can we expect to find and identify in the archaeobotanical record? Which domesticated plants, recovered in Chalain Lake (France) macrobotanical form, can we identify to species? Which ones are truly diagnostic? Which plants will we be unable to document through this methodology? We thus share our reference collection in the form of an image database¹ in the hope that similar analyses will multiply in the Early Neolithic contexts of temperate Europe, broadening our perception, and understanding of the exploitation of plants for this period and region.
It was interpreted that tubers of a related taxon (Bolboschoenus glaucus) were used to produce flat bread-like products at Shubayqa 1, a Natufian huntergatherer site in northeastern Jordan (Arranz-Otaegui, Carretero,  Ramsey, Fuller, & Richter, 2018). Late Mesolithic datasets (c. fifth mill. BC), reported from Tybrind Vig and Halsskov, both lacustrine areas in Denmark, testify to the presence of sea beet (Beta vulgaris ssp. maritima), whose roots are rich in starch and sugar, and pignut (Conopodium majus (Gouan) Loret. ) tubers  (Kubiak-Martens, 1999 , 2002) ; we were unfortunately unable to get a hold of these latter two plants. Seeds of waterlilies (Nuphar and Nymphaea) have been reported from Mesolithic and Neolithic contexts, indicating that they were probably consumed (see  Bouby, Dietsch-Sellami, Martin, Marinval, & Wiethold, 2018; Dietsch, 1996; Kirleis et al.
Although the sample will eventually dry, it can be re-hydrated before observation using this afore-mentioned solution. The reference slides were then examined at 600× magnification using a Nikon E600 POL microscope and starch grains were measured using NIS Elements software, which provided statistical data including the mean, standard deviation, and range of sizes. The number of grains counted by different specialists when establishing their reference collection varies greatly, from at least 50 grains  (Hart, 2014; Li, Pagán-Jiménez, Tsoraki, Yao, & Van Gijn, 2020; Musaubach, Plos, & Babot, 2013; Piperno, Weiss, Holst, & Nadel, 2004) , to at least 300  (Mercader et al. , 2018b). We measured 50 simple starch grains whenever possible, and these were randomly chosen on the slide. Photographs were taken under transmitted and cross-polarized light, and attributes such as the shape of the grain, the type and position of the hilum as well as the presence or absence of facets and fissures, were described (see Supplementary materials for photos and descriptions).
To isolate the Roman-eraspecific aspects of this measure, we control for geodesic distance and further geographic controls throughout our empirical analysis. Our first contribution is to document that variation in Roman effective distance influenced the intensity of trade during Roman times. To this end, we draw on geocoded information on more than 242,000 excavated potsherds of Roman fine tableware collated in the hitherto underexploited Samian Research database (Römisch-Germanisches Zentralmuseum in Mainz). A unique feature of the mass-produced and widely used ceramic tableware-subsequently referred to as 'terra sigillata'-is that production sites (i. e. , the origins of the tableware) are precisely identifiable. Combined with information on the location of archaeological excavation sites (i.
g. ,  Disdier and Head, 2008). As a second contribution, we provide evidence that Roman-era-specific transport network connectivity continued to influence the geography of trade at least until the advent of steam power and new transport technologies during the Industrial Revolution. Trade is proxied by the degree of interregional price correlation over the period 1208-1790 as well as differences in the timing of onset of the Black Death (1347-51). Our third contribution is to document that differential connectivity within the Roman network influences the spatial pattern of firm ownership today, despite the fundamental changes in relative transport costs that occurred since the advent of railways and air travel. Drawing on geocoded firm-level data from the Bureau van Dijk's Orbis database, we show that greater connectivity during the Roman Empire intensifies cross-regional parent-subsidiary connections.
Once we account for the SCI, Roman transport network connectivity ceases to have explanatory value for today's ownership links. The identifying assumption underlying our estimation strategy is that, conditional on control variables, Roman effective distance captures Roman-era-specific variation in transport network connectivity that is not correlated with unobserved factors that influence integration. A particularly pressing concern is that connectivity within the Roman transport network could be endogenous to pre-existing cultural and economic exchange. We alleviate this concern by empirically documenting that Roman effective distance does not explain pre-existing patterns of socio-economic integration. These findings are based on a newly compiled database of more than 7,000 prehistoric artefacts with precisely identifiable origins and destinations.
In our case, the intensity of transmission is determined by connectivity within the Roman transport network. The remainder of the paper is structured as follows: In Section 2, we provide background information on the creation of the Roman transport network along with qualitative evidence of its effect on contemporary trade; characteristics of the traded Roman terra sigillata are also described. The data is presented in Section 3. Section 4 describes our empirical framework. Regression results are discussed in Section 5; threats to identification are then addressed in Section 6. We investigate potential channels underlying our main results in Section 7, before concluding with Section 8.
e. , sea or river  (Mees, 2011, p. 260). To date, there is no systematic assessment of the effects of transport costs on interregional trade during the Roman era. The first principal aim of this paper is to fill this research gap. To this end, we require historical data on bilateral transport costs and trade volumes. The former can be inferred from the structure of the Roman transport network and relative freight rate differentials across shipping modes. As outlined below, the spatial distribution of terra sigillata excavations allows for the reconstruction of trade flows. The second aim of this paper is to analyse how differences in connectivity within the Roman transport network influence economic integration today. In this context, it is important to note that today's routing of roads is strongly influenced by the paths chosen by the Roman engineers.
Data Our study covers the regions within Austria, Belgium, England, France, Germany, Italy, Luxembourg, Netherlands, Portugal, Spain, and Switzerland that were once part of the Roman Empire. For the empirical analysis, we divide this area-referred to as 'Western Europe'-into grid cells of 0. 5×0. 5 degrees longitude/latitude (ca. 55×55 kilometres). In our main analysis, we only consider cells that are intersected by the Roman transport network. Illustrating the high density of the network, the 903 intersected grid cells cover 88% of the territory of Western Europe. 14 Based 14 Figure A. 1 in Appendix A depicts the grid cells that are intersected by the Roman transport network. In Tables C. 1-C. 2 in Appendix C, we show that our findings remain unchanged when we incorporate non-intersected cells into the network by creating artificial road connections.
Measuring economic integration during the Roman era: terra sigillata To measure bilateral trade volumes during Roman times, we extract information on terra sigillata finds from the Roman tableware database which has recently been made available online by the Römisch-Germanisches Zentralmuseum in Mainz. 22  The stamped vessels were produced between the beginning of the first century and the middle of the third century. Between 75-125 CE a range of terra sigillata products were not stamped. These unstamped items amount to approximately 30% of total excavated terra sigillata  (Furger and Deschler-Erb, 1992, Fig. 84 ). 23 Crucial for our analysis, there is no indication that shipment and distribution of these types systematically differed from stamped types. Based on the precise information on the site of production, identified via the potters' stamp, and location of excavation, we assign each find to its grid cell of origin and destination.
Measuring economic integration today: cross-regional firm ownership The number of cross-regional firm ownership links is based on the Bureau van Dijk's Orbis database. This database covers around 300 million companies worldwide and contains detailed firm-level information on industry, location, and ownership. For our analysis, we focus on firms with an annual operating revenue of more than 2 million U. S. dollars. The data was downloaded between February-April 2018, and consequently captures a snapshot of ownership patterns at that point in time. To compute the grid-cell-pair number of business links, we first identify all firms that are located within Western Europe. Among these firms we then extract the subset of companies that are in a cross-regional parent-subsidiary relationship. Specifically, we keep all firms that either own a stake of at least 25% in another firm that is domiciled in a different grid cell or that are 25% owned by a company registered in another grid cell.
The location of these firms was geocoded manually. For our analysis, we are left with 106,996 crossregional parent-subsidiary links. These business links are aggregated to the grid-cell-pair level by counting the number of firms located in 'destination grid cell' j that are (part-)owned by firms registered in 'origin grid cell' i. Again, we use shares-i. e. , bilateral inflow divided by total inflows-as measure of interaction intensity. Our final grid-cell-pair-level dataset consists of 731,823 observations, made up of 865 origin and 847 destination grid cells for which we observe at least one non-zero investment flow. For summary statistics see Appendix A. 1. In extensions to our main analysis, we make use of further data sources (price correlations, onset of the Black Death, and night-time light intensities).
From past to present Historical narratives indicate that the Roman transport network continued to influence the trade patterns at least until the Industrial Revolution. Roman roads, for example, were maintained and continuously used during the Middle Ages  (De Luca, 2016). Absent major innovations in transport technologies, this suggests that Roman-era specific differences in transport network connectivity persisted long after the collapse of the Roman Empire. In Appendix B we empirically support the historical evidence. Absent spatially disaggregated and temporally consistent information on economic interaction for the post-Roman period, we use two alternative proxies for market integration. The first proxy uses grid-cell-pair price correlations over the time period 1208-1790 and is constructed from data compiled in Federico, Schulze and Volckart (forthcoming).
Robustness A number of robustness checks show that our findings are not driven by specific assumptions or data construction choices. The results of all subsequently discussed exercises are presented in Appendix C in Tables C. 1 (Roman era) and C. 2 (today). We first show that Roman effective distance reduces trade along the extensive as well as the intensive margin. We then document that our results remain stable if we include grid cells in our analysis that are not intersected by the Roman transport network or use the directed network (in which we differentiate between costs of up-and downstream river transportation) to calculate Roman effective distance. Alternative standard error clustering approaches also produce similar results. Additionally accounting for country-pair fixed effects in the current-day regressions changes little.
Goods trade The first set of measures for pre-Roman interaction is based on archaeological artefacts traded during the Neolithic and Bronze age. In analogy to the terra sigillata data, we define excavation sites as destinations and derive information on origins from provenance studies undertaken in the archaeological literature. For our empirical analysis, we draw on two existing databases of pre-Roman trade. These contain information on Neolithic axeheads that were primarily made from jade, for which the mining sites can be identified. Together the two sources include ca. 3,700 artefacts  (Pétrequin et al. , 2012; Schauer et al. , 2020). We complement the existing databases with our own collection. From a variety of academic sources, we collect information on origin and destination of an additional 3,744 metal-based artefacts that were exchanged during the Bronze Age.
On the other hand, the point estimates of Roman effective distance-i. e. , the Roman transport-technology-specific cost of shipping goods within the not yet existing Roman transport network-are small and non-significant. To investigate whether aggregation across goods and databases masks important heterogeneity, we run separate regressions for each of the three databases in columns (3)-(  5 ). Reassuringly, the picture remains consistent. Across all datasets, the coefficient of Roman effective distance is imprecisely estimated (and even positive in two instances), while geodesic distance continues to exert a strong negative effect on pre-Roman intensity of exchange. To address possible concerns related to low numbers of observations, we extend our dataset to include artefacts for which only the origin falls into Western Europe.
Falsification The exercises presented above suggest that our estimates specifically capture the effects of Roman transport network connectivity rather than pre-existing patterns of exchange or geographic proximity. As a final approach to underpin the credibility of our results, we conduct a falsifica-tion test. Due to data availability, the test is restricted to business link intensity as outcome. For the purpose of this exercise and contrary to all the historical and empirical evidence presented above, we assume the following: (i) The routing of roads is determined by geographical features, meaning that the structure of the Roman transport network is simply reflecting underlying geography. The layout of the network would therefore not be Roman specific. (ii) The vector of relative transport costs α Rome represents general historical transport cost ratios and would therefore not be Roman specific.
39 We also allow for taxi rides when public transport is not available. 40 Note that we use OLS instead of PPML because dependent variables can take negative values. , 2016). The GPS contains information on six preferences: time preferences, risk preferences, positive and negative reciprocity, altruism, and trust. The EVS elicits information on human values and attitudes that are grouped into six categories: life, work, religion, family, politics and society, and nationalism. To bring the individual-level information to the grid cell level, we first purge the data of country fixed effects and then compute grid-cell-level means by averaging across all respondents that reside within a given cell. 41  We generate measures of cultural similarity across grid-cell pairs by computing the absolute difference in preferences and values between any two grid cells.
Acknowledgments We thank the editor and three anonymous referees, as well as  Alina Bartscher ,  Sascha O. Becker ,  Michael Burda ,  Sebastian Braun ,  Alan de Bromhead ,  José De Sousa ,  Hartmut Egger ,  Richard Franke ,  David Jacks ,  Krisztina Kis-Katos ,  Udo Kreickemeier ,  Miren Lafourcade ,  Claire Lelarge ,  Stephan Maurer ,  Eric Melander ,  Luigi Pascali ,  Fabian Wahl ,  David Weil ,  Niko Wolf , and seminar participants at  Bayreuth ,  HU Berlin, Bonn ,  TU Braunschweig, Brown, Cologne ,  George Mason ,  Goethe Frankfurt ,  Göttingen, Hamburg ,  Hong-Kong University, Konstanz, Mannheim, Paris-Sud, Stellenbosch ,  WU Vienna , the  2nd Workshop on Geodata and Economics in Hamburg , the Canadian Economics Association Annual Meeting 2019, and the 2020  RIDGE Virtual Forum  for excellent feedback.