dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding KAS and AC are supported by the  National Institute for Health Research (NIHR) Oxford Cognitive Health Clinical Research Facility. AC is also supported by an  NIHR   Research Professorship  (Grant  RP-2017-08-ST2-006 ), by the  NIHR Oxford and Thames Valley Applied Research Collaboration  and by the  NIHR Oxford Health Biomedical Research Centre  (Grant  NIHR203316 ). JF is supported by a  University of Manchester Presidential Fellowship  ( P123958 ) and a  UK Research and Innovation Future Leaders Fellowship  ( MR/T021780/1 ). CM is supported by the  Spanish Ministry of Science and Innovation ,  Instituto de Salud Carlos III (ISCIII ,  PI21/01929 ),  Consorcio Centro de Investigación Biomédica en Red (CIBER ,  CB/07/09/0023 ), cofinanced by the  European Union  and  ERDF Funds  from the  European Commission , 'A way of making Europe', financed by the  European Union-Next Generation EU  ( PMP21/00051 ),  Madrid Regional Government  ( B2017/BMD-3740 AGES-CM-2 ),  European Union   Structural Funds, EU Seventh Framework Program, H2020 Program  under the  Innovative Medicines Initiative 2 Joint Undertaking: Project c4c  (Grant Agreement No  777389 ),  Horizon Europe  ( HORIZON-HLTH-2021-STAYHLTH-01-02 No 101057529 ;  HORIZON-HLTH-2021-STAYHLTH-01-02 No 101057454 ;  HORIZON-HLTH-2022-STAYHLTH-01-01 -two-stage No  101080238 ),  National Institute of Mental Health of the National Institutes of Health, Fundación Familia Alonso and Fundación Alicia Koplowitz.
The page https://www. w3. org/wiki/LargeTripleStores (accessed on 6 July 2023) enumerates about 18 such stores, of a capacity ranging from 10 million to 1. 08 trillion (!) RDF triples. The Pragmatic Web we imagine-be it a Pragmatic Web of metadata on Web pages or an Implicit Pragmatic Web of implicatures, presuppositions, and the such, as we suggestmust be firmly connected with the Web pages of the "Syntactic Web". Furthermore, we imagine the Web of Social Representations as a Wikipedia-like structure where one can store observed social representations of given communities at given periods over given subjects. When reading, e. g. , the Wikipedia page of the wolf, the reader should be able to learn about the social representations of the wolf, be it today or in the past. It is time for the hidden aspects of human social life to be exposed to the whole light.
Metaphors Metaphors  [63]  involve a translation from one frame (in the sense of  [64, 65] ) into another, so that various concepts of the first frame get mapped to contexts in the second. Let us take an example from the blog  [20]  that we mentioned in the introduction: […] the Semantic Web was on life-support since its inception, and it continued to survive only with the medical intervention of academic departments In this excerpt, the metaphor maps the concept of computer network (domain A: information technologies) to the concept of human (domain B: life/death, health). "Lifesupport", "survive", and "medical intervention" belong to frame B, and although the word "inception" still belongs to frame A, it is lexically close to the word "conception", which is paradigmatic of frame B. Metaphors have been extensively studied, and data resources are available, such as the database MetaNet (https://metanet.
arts. ubc. ca/, accessed on 6 July 2023), hosted by the University of British Columbia. In this database, we find a metaphor "computers are people", going from the frame "person" (with roles: person, body, body parts, actions, objectives, and with superframes: animate entity and sentient entity) to the frame "computer" (with roles: computer whole, computer parts, computer function, computer purpose, computational process, hardware, software, computer user, power source, and with superframe: machine). When an NLP algorithm encounters "life-support" and "survive", while the context is that of computers, it will search for metaphors, find "computers are people", and consequently replace "life-support" by "bad condition" and "survive" by "remain operational". This is by no means trivial, and, indeed, "life-support" and "survive" are not (yet) explicitly provided as frame elements in MetaNet.
([107]  p. 7) The authors continue on the differences between the two regions (Brittany has the highest suicide in France, namely 25 suicides per 100,000 inhabitants, while Alsace had a suicide of 10. 7/100,000, as of 2015) and conclude with recommendations on an optimal way of achieving suicide prevention, adapted to each region. This is a beneficial application of social representations: to address a population or community efficiently, you need to know how it thinks, and social representations provide a solid account of shared beliefs, very often unconscious and sometimes surprising. The data of the above example would be encoded as follows in a Web of Social Representations: • Subject: Suicide (node Q10737 of Wikidata, and code CUI C0038661 of the Diseases Database (http://www. diseasesdatabase.
Introduction After more than a decade of falling revenue, the music industry is returning to growth. Since 2015, its global revenues are increasing again, and, since 2017, the biggest part of its revenues originate from streaming services, which timidly started We are grateful for the financial and intellectual support we received from Département des Etudes, de la Prospective et des Statistiques (DEPS) from the French Ministry of Culture (Under Convention DEPS/FJJL-TSE, 2018). This project has also been funded by the Agence Nationale de la Recherche (Under Grant ANR-17-EURE-0010, Future Investment program). We thank Luis Aguiar, Tobias Kretschmer, Johannes Loh, and Yann Nicolas, as well as seminar participants at ISTO's Research Days in Munich (2019), AFREN's Digital Summer School in La Rochelle (2019), and AFREN's Digital Summer School in Palaiseau (2021) for their useful comments and suggestions.
Finally, evaluating whether there is a displacement or an enhancement effect between consumption channels is of importance to predict which distributors of recorded music will see their power and influence reinforced by the digitization, and which will see them decline, with a potential impact on the current and future market structure. To address these issues, we use a rich dataset that covers virtually the whole French market for recorded music between 2014 and 2017. A unique feature of our data is that we observe sales and prices for both physical and digital products, as well as streaming consumption. These data enable us to ask whether there is displacement or complementarity between old and new ways of listening to recorded music-what we name consumption channels hereafter. The old consumption channels correspond to physical sales (CDs and Vinyls), while the new ones to digital sales (downloads) or streaming.
Hiller (2016) finds a strong substitution effect of YouTube on physical sales (with YouTube views replacing about a quarter of album sales). In contrast, Kretschmer and Peukert (2020) find that YouTube has generated positive externalities on physical sales (with 20 percent extra revenues from songs available on the platform). 4 Results from Aguiar (2017) also go in that direction as he finds that free streaming also stimulates the consumption of licensed content, i. e. , digital sales. Our work relates most closely to the analysis of streaming by Aguiar and Waldfogel (2018b). They estimate displacement based on weekly data on digital track sales and streams in over 21 countries, between April and December 2013. However, their streaming data covers the top 50 only. They also exploit aggregate sales of tracks and albums, both in digital and physical format, for the period 2012-2013 for the USA which is prior to the true boom of streaming consumption.
We, therefore, suggest an alternative empirical strategy that relies on variation in prices to identify the impact of new music channels on existing ones. Even though the question of substitutability and complementarity in the media industries has attracted important attention from researchers over the last decade, this paper reconciles two strands of empirical evidence in highlighting how they can co-exist-with substitution at the product level, but complementarity at the artist level. It also provides an analysis of the effects in the long tail and across genres, which to our knowledge has not received much attention before. 5 ompared to the existing work, our contribution is substantive in two ways. First, we use a unique dataset that covers the entire market, with product-level sales and streams.
9€. 14 We remove the products priced in between, which represent a negligible number of observations (less than 0. 5 percent). 15 Regarding streams, this always refers to tracks in our dataset. We remove a small number of price outliers: products with a price lower than 0. 9€ or greater than 60€ on the physical channel (which removes very high-end special editions); and products with a price greater than 30€ for the digital albums and greater than 5€ for digital tracks (where this was coded by GfK). These cut-offs are arbitrary but do not affect the results as they remove only a very small number of observations, i. e. , 36,251 observations, what represents 0. 12 percent of observations. Finally, we remove observations with an outlier price variation (beyond +100 percent of the price of a given product).
The creation of the panel data set highlights that some products are not sold or consumed every month since the launch date. GfK confirmed to us that it registers virtually every physical and digital sale. Hence, the absence of sales of a product in a given week indicates a true zero value for the week at which we observe the first sale. For such products with zero sales in some weeks, we do not observe the average price, so we use a simple linear interpolation to impute the missing price information. 16 Regarding streaming, the interpretation of a missing quantity takes another meaning. Indeed, to appear in GfK's panel, a title must be streamed at least 100 times on audio-streaming platforms and at least 1000 times on video-streaming platforms on a given week. All the consumptions below this threshold enter a basket which is provided by GfK.
Even though this basket allows us to compute the total number of units consumed on the market, the data are partially censored at the track-level. 17 To deal with that issue, we use a linear interpolation for tracks with missing values and for which we can reasonably assume that the track was available on the market during the week in question and that the observation was possibly censored by the 100/1000 streams threshold rule adopted by GfK. We interpolate at the track-level. 18 inally, we complement this data with an extensive dataset published by the National Agency of Radio Frequencies (ANFR) that contains information on the number of 2G, 3G, and 4G antennas on the French territory between 1997 and 2019. 19 We use this data to compute the weekly number of active 4G antennas between 2014 and 2017 and use it as an instrument in the market-level analysis.
Number of observations As shown in Table  1 , the final dataset is a panel of 29,672,540 observations, covering 4,330,868 unique products from 894,134 unique artists observed during 152 weeks. Albums make up to 25 percent of all products and relate to either physical or digital sales (i. e. , not streaming). Tracks make up the remaining 75 percent of the products. They mainly refer to digital sales and streaming. For physical sales, there are also singles, i. e. , two tracks on CDs or Vinyls, but these make up a very small number of the observations during our sample, so we exclude them from our analysis.
Volumes and market shares by consumption channel Since our dataset covers virtually the entire French market between 2014 and 2017, we can provide a comprehensive description of changes in the market shares of each channel: physical sales, digital sales, and streams. Because we do not have an unambiguous measure for the average price per stream, we focus on market shares in volume rather than in value terms. To aggregate sales over tracks and albums, we express album sales in track-equivalent units, assuming there are ten tracks per album, which is a common assumption in the literature (see for instance Aguiar &  Waldfogel, 2018b). Figure  3  in Appendix A shows the evolution of market shares over time. Table  2  provides information on the absolute numbers behind these market shares. The total volume of digital sales is on average 827,196 track-equivalent units per week.
We incorporate this feature of the music market in several ways. First, we consider a product-level analysis at the track-level. Here, we focus exclusively on the impact of streaming on digital sales. Hence, our product-level analysis does not study the impact of the digital sales and streaming channels on physical sales. This would not be possible for physical tracks (singles), because these are virtually non-existent in our dataset. Moreover, it cannot be done for physical albums, because we do not observe streams for albums (as GfK counts streams from full albums as individual track consumptions). 20 Second, we implement our analysis at the more aggregate artist-level, by considering track-equivalent units for album sales (based on our conversion of one album into ten tracks). Our artist-level approach enables us to measure the impact of both the digital and streaming channels on the physical channel in an integrated way.
Natural candidates are the product prices at the various sales channels (or the determinants of these prices). 22 ) q phy. tr. equ. it = 0 + 1 q dig. tr. equ. it + 2 q streams it + i + t + phy. tr. equ. it. (3) q phy. tr. equ. t = 0 + 1 q dig. tr. equ. t + 2 q streams t + t + phy. tr. equ. t 21 Finding a proxy for media exposure of a given title, album, or artist is theoretically possible. However, given the nature of our dataset which includes the hits and the long tail, and its granularity (observations are at the week-level), such proxy would be very challenging to find. 22 Additionally, one may use other demand determinants at the consumption channel level, such as the number of active 4G antennas which is likely to influence the consumption of streaming services, but not the number of downloads.
We can conclude from these analyses that the displacement observed between digital sales and streams is not solely fuelled by one specific type of streaming, but by all of them, even though the displacement appears to be stronger with the free audio streaming. Finally, we estimated our main model after excluding the 22 weeks which followed GfK's change in streaming dataset construction. The coefficient of interest we obtained is -1. 30 (standard error of 0. 22), which is very close to the coefficient obtained with all weeks, i. e. , -1. 21 (standard error of 0. 19). This suggests that our results are robust to this change.
Dropped weeks In the data cleaning process, we dropped several weeks for which part of the information on sales or streams was missing. In the figures below (Fig. 9 ), we show how the number of track-equivalent sales or streams compares between (i) weeks kept in the sample and (ii) weeks dropped from the sample. We can observe that the weeks we had to exclude from the final dataset do not exhibit any particularities that would threaten the representativeness of the sample.
Empirical specification In this setting, we regress the physical album sales (observed on the older channel) on the digital album sales (observed on the newer channel). We cannot include streams here because these always refer to tracks in our dataset. Equation (1) presented in the main text can therefore be specified as: (16) q phy. albums it = 0 + 1 q dig. albums it   The first stage of Eq. (  1b ) can be written as follows: + i + t + where the number of digital album sales q dig. albums it is explained by the price of physical and digital albums, as well as product and time fixed effects. In this regression, we can capture the own-and cross-price effects.
ConclusionsThe digitization of the economy impacted virtually all industries -in particular the media. The ability for consumers to consume both physical goods and digital goods raised questions about the revenues that could be generated in the long run, the business models, and the power of the various players involved. One of the central questions is related to the substitution and complementarity of the consumption of the various forms of these products, physical or digital, purchased or streamed. Even though several empirical analyses have been conducted to shed light on this question, the results are somewhat fragmented and rarely give insights on the heterogeneity of the effect across products types. Using a unique dataset that covers virtually the entire French market for recorded music, we measure the displacement between old and new distribution channels.
As a result, by the turn of the nineteenth century there were complete censuses, or at least initiatives to carry them out, in the majority of Europe's polities. For most of the early modern period, however, scholars rely on partial datasets that were put down in writing for purposes other than surveying complete populations. While some of them, like parish registers, provide data that allows a better understanding of demographic features than many early censuses, these early modern sources all survive in highly scattered forms, even in areas with the best source coverage, such as the Low Countries, England, France, or Italy. Even if parish registers have survived, it is challenging to reconstruct demographic processes or family structures from them. Other sources, such as tithe and other tax records, as well as estate conscriptions (such as land registers, manorial rolls, and urbaria-all forms of recording property ownership) also provide information that in many cases censuses do not.
Introduction The population changes of the nineteenth century have been studied exhaustively by historians and demographers alike. States started to govern populations in a biopolitical sense, meaning that they took responsibility for the wellbeing of their subjects, for which they began gathering statistics on a large and increasingly comprehensive scale. Statistical sources also recorded the large and rapid demographic changes to which Europe was subjected over the course of the nineteenth century. The European population grew rapidly as lives lengthened, birth rates decreased, and labour markets changed dramatically. In general, these trends were very similar across Europe, however, the timing and underlying reasons for these demographic changes differed between countries. In the section on new sources, we outline the historical background against which historical population data was gathered and warn against the uncritical study of sources.
Another benefit was that it was possible to write a new song fairly quickly. Producing contrafacta thus also became a way to report and respond to recent news or to create new material for festivities or other occasions. An example of such a tune was 'Fortune my Foe', probably the best-known secular melody in early modern England, where it first appeared. The tune was prominent in all layers of society in England, and was played in taverns as well as court (and even at executions). The tune also travelled abroad. For instance, in the Low Countries, it was known as 'Engelsche Fortuyn' (English Fortune), acknowledging its place of origin. Based on a search of the Dutch Song Database, at least 223 songs were written to this melody. Both composers and playwrights used the song to write new pieces about love or morality, or to express some misfortune.
Recent human comparative sample Data from five discrete cultural groups (n=76) are used to contextualize the dietary signatures of Pech de l'Azé I and Hortus II. These include children from three Neolithic collective burials from the Meuse river system of Belgium, radiocarbon dated to 4. 6 to 3. 8 ka BP. Two additional studies of dental microwear texture analysis of human deciduous molars have been conducted. One of these is from the Roman site Herculaneum, which was destroyed by Mount Vesuvius in A. D. 79  (Remy et al. , 2014; Remy and Schmidt, 2016). The other is a Medieval cemetery population from Canterbury, England  (Mahoney et al. , 2016).
, 2008). The total study area approximated 242 × 182 µm; datapoint spacing was 0. 17 microns in the X-Y plane and 0. 20 microns in the Z plane. Leveling used the least squares algorithm, and there was no S or L filtering. The subsequent data clouds for each individual were "cleaned" of surface debris in SolarMap 5. 1. 1. Two and 3-dimensional surface reconstructions were carefully evaluated to remove from the sample any occlusal surface that presented postmortem artifacts. When the quality of the surface was evaluated as free of taphonomic defects, it was included (figure  4 ) and subjected to scale-sensitive fractal analysis using Sfrax and Toothfrax  (Scott et al. , 2006 (Scott et al. , , 2012;; Ungar et al. , 2012; Schmidt et al. , 2016 Schmidt et al. , , 2019)). This study focused on complexity and anisotropy.
Acknowledgments We are extremely grateful to the museum curators who kindly allowed access to remains featured in this study, including  Aurélie Fort  and  Liliana Huet  at the  Musée de l'Homme (Paris ),  Tony Chevalier  at the  Centre Européen de Recherches Préhistoriques de Tautavel  and  Patrick Semal , Chief of the  Scientific Heritage Service, Institut Royal des Sciences Naturelles de Belgique   (Bruxelles). Funding for this project was generously received by  Fulbright-Belgium , the  Commission for Educational Exchange between the United States, Belgium , and  Luxembourg  and the  National Science Foundation  ( BCS 0922930 ).
Elemental composition Bulk XRF. Elemental compositions were measured from five locations on the corrosion-free metal forming fragment B2P1, using a Bruker Artax X-ray fluorescence spectrometer (XRF) with rhodium (Rh) anode tube operated at 40 kV, 400 µA, 60 s live time, a 0. 65 mm collimator, no filter and all measurements were made in air. Based on counting statistics, limits of detection for elements was estimated to be 0. 1 wt% for iron (Fe), Cu and lead (Pb), and 0. 3 wt% for Ag and Au. Quantitative results were determined by Bruker software using one laboratory Ag standard. Based on previous experience, accuracy for Ag and Au was estimated as ±1 wt% for Cu and ±0. 1 wt% for Pb. The bulk elemental compositions of ~1. 4 g of corrosion products of fragment B1P4 were measured using a Panalytical Epsilon 3 benchtop energy dispersive X-ray fluorescence spectrometer, with rhodium (Rh) anode tube and a 6 mm ellipse measurement area.
Measurement conditions were 50 kV, 60 s, Ag filter; 50 kV, 300 µm Cu filter, 60 s; 20 kV, 200 µm Al filter, 60 s; 12 kV, 50 µm Al filter, 100 s; 5 kV, no filter, 100 s and all measurements were made in air. Limits of detection for elements was estimated to be 0. 001 wt% for iron (Fe), Cu and lead (Pb), and 0. 005 wt% for Au. Quantitative results were determined using the Panalytical Omnian software, trained using a range of MBH reference materials consisting of trace elements in Ag matrices. Accuracies for Fe, Cu and Pb were better than 10% for concentrations >0. 1 wt%. Data reduction and quantification were carried out in the Epsilon 3 software version 1. 1. Data were summed to 100%. Micro-XRF. Elemental maps were produced from a ~9. 1 × 6. 8 mm piece of corrosion product B1P4, using a Bruker M4 Tornado with Rh anode tube operated at 50 kV, 200 µA, twin XʼFlash 30 detectors and measurement conditions of 20 µm spot size, with a dwell time of 20 ms/ pixel, in vacuum.