dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Since the Digital Humanities researchers and developers are often about to create somehow industrial applications potentially concerning international business  [12, 28, 43] , it is time for those communities to be aware and make the most of legacy constraints and opportunities. For instance, let us consider the Computer Music state of the art, and particularly the Music Information Retrieval (MIR) community, as it is typically structured and organized by the International Society of Music Information Retrieval (ISMIR, see http://www. ismir. net/). ISMIR is now fifteen years old, and getting out of adolescence. After a fastgrowing childhood, bottle-fed by the best IT algorithms and the most vitamin-rich signal analysis methods, the International Society for Music Information Retrieval is now addressing a wide range of scientific, technical and social challenges, dealing with processing, searching, organizing and accessing music-related data and digital sounds through many aspects, considering real scale use-cases and designing innovative applications, overflowing its academic-only initiatory aims.
What Has Changed within the Big Data? The first radical change is obviously the web. Everyone produces data and personal data. However, the user is not always aware that he provides personal data allowing his identification. For instance, as described by  [41] , when a user tags or rates musical items, he gives personal information about himself. If a music recommender exploits this kind of user data without integrating strong privacy concepts, he faces legal issues and strong discontent from the users. The volume of data has been increasing faster than the "Moore's law". This evolution is known as the concept of "Big Data". New data are generally unstructured and traditional database systems such as Relational Database Management Systems cannot handle the volume of data produced by users and by machines & sensors.
This challenge was the main driver for Google to define a new technology: the Apache Hadoop File System. Within this framework, data and computational activities are distributed on a very large number of servers. Data are not loaded for being computed, and the result stored. Here, the algorithm is close to the data. Databases of personal data are no more clearly identified. We can view the situation as combining five aspects: • Explosion of Data Sources. The number of databases for retrieving information is growing dramatically. Applications are also data sources. Spotify for instance, embedded in Facebook, provides a live flow of music consumption information from millions of users. Data from billions of sensors will soon be added. This profusion of data does not mean quality. Accessible does not mean legal or acceptable for a user.
To stay as little invasive as possible, such a model should provide means to determine which information is worth to ask humans during the production or not compared to the creativity disturbing cost. Without a knowledge model, it would not be possible to interpret the traces or to determine the kind of traces worth capturing. To achieve this model, professional knowledge must be identified, listed and characterized with experts, defining a digital music production Knowledge Level. Within Gamelan, traces from used operating system and from used professional applications are extracted, semantic networks dealing with typical digital audio composition acts are involved towards some abstraction of those traces, but personal data are nowhere considered: some real time digital audio flow are involved, transformed on the fly by creative acts that sign the composer particular style and their artistic singularity.
Style identification algorithms in actual MIR: a classification Among the algorithms that identify artists creating a style of their own, we can distinguish two meta-heuristics groups. The first one is based on interconnections of databases and more generally on the big data. The second one is based solely on the intrinsic properties of music data. In this section we propose a classification of algorithms and heuristics allowing the identification of the creators of musical data exclusively on the basis of this second approach. It is usual to consider the style of an artist is available at three levels: patterns, meanings and feelings (cf. [3] , p. xii). For each of these levels, the algorithms implemented enter the structured list given below which can be used as read gate to analyze style recognition algorithms.
From MIR R&D communities to Digital Humanities ones In the current legal frameworks, Personal Data are still considered as a particular kind of data, as opposed to processing according to the classical Information Technology paradigm. By seriously considering the PbD methods and technologies for mastering and appropriation, the MIR community will probably rediscover that the paradigmatic data/processing separation has been finally overcome, as soon as many MIR algorithms raise their results on the fly from digital music flows, often in real-time: thus the MIR community will naturally join and lead the conducting of the PbD contemporary concept towards more advanced concepts, able to take note that sometimes, per-sonal data can be outputs from some authorship attribution artificial system, made of complex person-machine interactions and act accordingly.
The time where data (on the one hand) and processing (on the other hand) were functionally independent, formally and semantically separated, has ended. Nowadays, MIR researchers currently use algorithms that support effective decision, supervised or not, without introducing 'pure' data or 'pure' processing, but building up acceptable solutions together with heuristic knowledge that cannot be reduced to data or processing. This means that new research fields do not separate data and processing anymore. This can be done in different ways. In many circumstances, the MIR community develops new personal data using the whole range of data analysis and data building algorithms. The MIR community is especially well positioned to identify the new personal data produced through these algorithms. From this respect, MIR is a good emblem of what is currently happening within the whole Digital Humanities R&D communities.
6. 2 When some process may lead to indirect personal data identification through some complex process In many circumstances, the Digital Humanities researchers and developers community develops new personal data on the fly, using the whole available range of data analysis and data building algorithm. Then researchers could apply the Privacy by Design methodology, to insure that no personal data is lost during the system design. Here PbD is not a universal solution because the time when data (on the one hand) and processing (on the other hand) were functionally independent, formally and semantically separated, has ended. Nowadays, Digital Humanities researchers and developers currently use algorithms that support effective decision, supervised or not, without introducing 'pure' data or 'pure' processing, but building up acceptable solutions together with machine learning  [21]  or heuristic knowledge that cannot be reduced to data or processing: The third set of personal data may appear, and raise theoretical scientific problems.
Political Opportunities. The Digital Humanities community has a political role to play in the data privacy domain, by explaining to lawyers -joining expert groups in the US, UE or elsewhere-what we are doing and how we overlap with the tradition in style description, turning it into a computed style genetic, which radically questions the analysis of data privacy traditions, cultures and tools. Future Scientific Works. In addition to methodological and political ones, we face purely scientific challenges, which constitute our research program for future works. Under what criteria should we, as Digital Humanities practitioners, specify when a set of data allows an easy identification and belongs to the second set or on the contrary is too complex or allows a too uncertain identification so that we would say that these are not personal data? What characterizes a maximal subset from the big data that could not ever be computed by any Turing machine to identify a natural person with any algorithm?.
Acknowledgments This experiment was supported by a grant from the  Agence Nationale de la Recherche  (Project  SCAD  #  ANR-09-BLAN-0405-03 ) and the  European Union 's  Seventh Framework Program  ( FP7 ICT 2011 Call 9 ) under grant agreement nu  FP7-ICT-600610. We express our gratitude to the patients and their family for participating in this research and thereby made it possible.
Better Together also launched a new initiative to target undecided voters in the summer of 2013 with the Blether Together campaign. This initiative was the product of Better Together's appointment of Blue State Digital, the agency that worked on the successful first election campaign for US President Obama. The recruitment drive using social media and YouTube was launched in an attempt at recruiting volunteers who were given the personal details of members of the public and asked to cold-call them in order to canvass them for support. All information gleaned by the calls was stored in the campaign database and transmitted to the three parties involved in Better Together. This initiative followed a rather controversial one launched in April 2013 of unsolicited text messages and although its beginnings were similarly surrounded with controversy due to the fact that people's personal details and information were at risk of being disclosed to outside organisations, the Blether Together campaign became rather successful in terms of volunteer recruitment (more than 3,500 volunteers signed up).
Finally, the creditchecking group Experian was also hired by Better Together to devise a new data management tool. The Patriot software database stored all consumer data obtained from lenders and companies who voters have contracts with and allowed Better Together to identify lifestyle indicators, categorise voters and link them with activists of a similar age or with similar social media friends in order to improve voter targeting and canvassing. Third party endorsements also featured highly in the Better Together campaign as it was initially somewhat more successful than its rival at recruiting high-profile non politicians to support its cause. The SNP had made much of third party endorsements during its successful May 2011 Scottish Parliament election campaign and Better Together aimed at following this model and mobilising as much support as possible in order to break away from an all political image.
AGRADECIMIENTOS Los autores quieren agradecer muy especialmente a Ana Rosa Uribe, presidenta de la Agrupación del Museo Etnográfico de Achao, por todas las facilidades y buena disposición para con nosotros durante este estudio; a Felipe Montiel, por su acogida en el museo de Castro y el contacto con Ana Rosa Uribe; al Dr, Juán Eduardo Diaz-Vaz por su ayuda en la identificación de la especie de la madera; y a la Dra. Leonor Adán, directora de la Dirección Museológica de la Universidad Austral de Chile e investigadora responsable del proyecto Fondecyt 1060216 a partir del cual se financió el fechado de radiocarbón. También a la Dra. Ximena Urbina por sus comentarios y observaciones. Este trabajo se enmarca en estudios de doctorado financiados por CONICYT a través del programa Becas Chile.
Measures and Statistical Analyses The analyses were carried out on children's effective responses, that is, on correct word1-word2 sequences that allowed the production of a liaison (i. e. , atypical responses and nonresponses, which represented 14% of the sequences presented to the children, were removed from the analyses). The analyses were thus computed on a total of 2691 data points (for each age group, 2-3 years: 501, 3-4 years: 609, 4-5: 770, 5-6 years: 811 contexts of liaison). All the modalities of children's effective responses were included in the analyses with the correct (i. e. , liaisons correctly produced) versus incorrect responses (i. e. , replacements and omissions) as the dependent variable using binomial mixed logit models (for a detailed review of categorical data analyses, see  Jaeger, 2008).
Generalized Linear Mixed Models (GLMM) were performed using R Software (R Development Core Team, 2015) with the lme4 package  (Bates et al. , 2015). The children's age (four age groups: 2-3, 3-4, 4-5, 5-6 years), sex (male, female) and SES (high, low) were included in the analyses as fixed factors so as to assess their effects and interactions on the children's productions of correct liaisons. The children and the items of the picture naming task were included as random factors. The best fitting model was selected by comparing successively the competing models. In the results section, we provided the Akaike Information Criterion (AIC) and the Bayes Information Criterion (BIC) for each model and the Likelihood ratio test for model comparisons. Post hoc multiple comparisons tests were performed using Tukey contrasts (multcomp package,  Hothorn et al.
Acknowledgements This study was funded by the  French National Research Agency (ANR)  through the  POMEDOR  project (People, Pottery and Food in the Medieval Eastern Mediterranean, www. pomedor. mom. fr. ), directed by  Waksman , and we acknowledge the support of the  ANR  under reference  ANR-12-CULT-0008. It is also part of the activities of the ERAAUB,  Consolidated Group  ( 2014 SGR 845 ) funded by the  Comissionat per a Universitats i Recerca del DIUE  of the  Generalitat de Catalunya  and of the Ramon y Cajal contract ( RYC 2013-13369 ) founded by the  Ministerio de Economía y Competividad of A. Pecci. The samples were analysed at the  Centres Científics i Tecnològics of the Universitat de Barcelona (CCiTUB). We are grateful to the  Department of Antiquities of Cyprus , and in particular to  Dr Eustathios Raptou , for giving us access to the material and to the team of the  Paphos Museum  for facilitating our work; to  Holly Cook , from the  Paphos Theatre Excavation Project of the University of Sydney , for the information regarding the table wares from the assemblage; to  Céline Brun  and  Jacques Burlot  ( CNRS Lyon ) and to the  Paphos Theatre Excavation  project of the  University of Sydney  for the drawings of the samples.
An Investigation of Readers' Perceptions of Proverbial Occurrences To summarize the data collected in the COCA corpus, 77. 9% of the occurrences of the six proverbs investigated are non-canonical. Based on a definition of wordplay with, on the one hand, formal manipulation and, on the other hand, the perception of humour and cleverness, most of the non-canonical occurrences do not seem to constitute wordplay, because only the formal condition is satisfied. They are routine manipulations of a rather mechanical nature. Only nine occurrences (3%) can be seen as cases of wordplay. Some of these were already discussed in the preceding section (  8 ), (  19 ), (  27 )-(  29 ), (  30 ), (31); the other occurrences are: (32) The penis is mightier than the sword. (33) No, everything doesn't have to be spicy.
Acknowledgments We acknowledge funding support from the  European Research council (ERC)  to  MD Petraglia  ( Advanced Grant   295719  ' PALAEODESERTS: Climate Change and Hominin Evolution in the Arabian Desert: Life and Death at the Cross-roads of the Old World ),  G Bailey/GCP King  ( Advanced Grant   269586  ' DISPERSE: Dynamic Landscapes, Coastal Environments and Human Dispersals '), and  N Boivin  ( Starter Grant   206148 , ' SEALINKS: Bridging Continents Across the Sea '), all under the  'Ideas' specific Programme of the 7th Framework Programme  ( FP7 ). MG Thomas is supported by a  Wellcome Senior Investigator Award in Medical Humanities  (Grant number:  100713/Z/12/A ). ELM Scerri and  J Blinkhorn  thank the  Fondation Fyssen. L Lewis  acknowledges the support of the  Arts and Humanities Research Council  (UK, #  513691 ), the  Wenner-Gren Foundation  (# 8684 ) and the  Boise Fund (University of Oxford ).
The interviews lasted from 60 to 90 minutes and were held in English (except John's interviews, which were held in French). All interviews were recorded and transcribed. These data were complemented with the set of messages exchanged between the forecasters and the team leaders. For the Broad Peak expedition, the complementary data set consists of the e-mails (plain texts and charts) and sms sent by the forecasters, the e-mails sent by John to the forecasters, and the three phone conversations that Peter had with John during his two summit attempts. All these data were in English. For the Everest expedition, this data included the e-mails exchanged between the forecasters and Georg, as well as the four conversations Peter had with Georg during his two summit attempts. These data were in Swiss German and so have been translated into English.
2. Data Analysis Confronting the different data sources, we first established a chronology of the expeditions and identified the different phases of summit attempts after acclimatization. These chronolo-gies were validated by the team leaders during the post-expedition interviews. It was possible to approximate the date when the team leader decided to attempt to reach the summit (two attempts during both expeditions) and when they decided to stop (two stops during the Broad Peak, one stop during the Everest expedition). Secondly, we calculated the number of message exchanges during the summit attempts, as these can be taken as an indicator of the intensity of the interactions between the leader and the forecasters. Thirdly, in order to get insights into leadership distribution, we analyzed the content of their interactions.
Eye movement recording and analysis Viewing was binocular. Movements of the right eye were monitored with an Eyelink 1k (SR Research, Mississauga, Ontario, Canada) at 1000 Hz sampling rate. At the beginning of a session, the Eyelink was calibrated with the standard 9-point Eyelink procedure. Before each trial, fixation was checked. If the distance between the fixation check and the calibration was greater than 1. 5°, a new calibration was initiated. Calibration was also automatically renewed every 50 trials. On-line saccade detection was based on a boundary criterion: gaze-contingent changes occurred when the eye position crossed one half of the target eccentricity. Eye movement samples were smoothed with the proprietary algorithms provided by SR Research. Instantaneous velocity and acceleration were computed for each data sample and compared to a threshold (30°/sec and 8000°/sec 2 ).
Statistical analyses Double-step task: We performed repeated-measures ANOVAs on saccade latency and gain with factors Saccade (First versus Second) and Condition (Memory-guided versus Visuallyguided). We measured Pearson correlations between first saccade error and the horizontal component of the second saccade, and between the self-movement index and the PDI score, and tested significance using bootstraps and comparing to the 95% confidence interval. Inflight displacement task: Perceptual performance was quantified by fitting cumulative Gaussian psychometric functions to the response data with the maximum likelihood method. Thresholds were defined as the distance, in degrees of visual angle, between the displacement evoking 50% correct responses and the displacement evoking 75% correct, and expressed as Weber fractions by dividing this distance by 10°(the required saccade amplitude).
The abstracts were downloaded from various Anglophone university websites and from the Proquest dissertations database (Proquest 2014) The sub-corpus of 75 texts written in English by French doctoral students was collected thanks to the French open archive database 'Hyperarchives en ligne' (Hal Archives Ouvertes 2014). An interesting characteristic of this database is that, for each PhD, an abstract written in French and in English is available. Therefore, while the studied corpus only includes texts written in English, for the French writers' English texts, a parallel corpus of abstracts written in French could be used in the future for comparison of the use of labeling nouns in native and non-native language. Table  1  provides quantitative data as to the number of words in each sub-corpus, considering disciplinary and language origin:.
b. Frequency data at corpus and sub-corpus level While occurrences of 'this' could be counted automatically, using the "word list" function of Antconc, the identification of labeling nouns required going back to the whole abstract, using the 'file view' function. Manual analysis allowed the retrieval first of 'this' used as a determiner, then of LN. Example 2 taken from the 'semantic web' corpus: 'Previous works in ontology management, primarily for ontology mapping, ontology integration and ontology evolution, have exploited only one form or another of ontology management in restrictive settings. However, a distributed and heterogeneous environment makes it necessary for researchers in this field to consider ontology interoperability in order to achieve the vision of the Semantic Web. ' The term 'this field' re-categorizes 'previous works in ontology management' since it confers the status of a research domain to all the activities listed under 'ontology management'.
This extract is taken from an abstract of the biology sub-corpus: [Context_beg] (. ) Today, few data are available about the pathophysiology of tularemia and about the virulence mechanisms of F. tularensis. Most of the studies have been conducted with F. novicida, a weak pathogen in humans. Moreover, F. tularensis study has been hampered by the lack of specific genetic tools. [Context_end] [Method_beg]  In this work, we characterised in F. Philomiragia (. ) Then, we developed a directed mutagenesis method of F. Tularensis (. ). The respiratory burst-inhibiting AcpA was recognized as a virulence factor in F. novicida, data further extended to F. tularensis. To check this hypothesis, we developed a mouse model of infection by the respiratory route. The textual segment 'this hypothesis' refers to is very difficult to identify for several reasons.
An AvaLight-HAL was used as an illumination source. The equipment is calibrated with a Halon D65 white reference sample in the same lighting conditions as for the archaeological samples. The colour parameters were obtained by Avasoft 7. 5 software. Particle size analyses. Since particle size distribution may play an important role in determining the UVR protection capacities of clay minerals  [144] , ochre samples were subjected to particle size analyses. We used a Horiba LA950 (0. 01μm-3000μm) laser micro-granulometer and analysed the data with NextSpec 7. 10 software. The Mie solution to Maxwell's equations provides the basis for measuring the size of particles through the scattering of electromagnetic radiation  [145]. We employed this solution to calculate the particle size distribution in aqueous solution (refractive index 1,333).
Clay, silt and sand categories comprise the amounts of particles smaller than 7μm, between 7μm and 63μm and between 63μm and 2mm, respectively. The limit of 7μm is based on the demonstration by  Konert and Vandenberghe (1997)   [146]  that this is the value that correlates better, when laser granulometry is used, to the particle size value of the clay fraction (<2μm) of previous sedimentological methods. Energy dispersive X-ray fluorescence (ED-XRF). The ED-XRF measurements were carried out using a portable SPECTRO xSORT X-ray fluorescence spectrometer from AMETEK equipped with a silicon drift detector (SDD) and a low power W X-ray tube with an excitation source of 40 kV. Samples were positioned above a 7mm diameter spectrometer aperture and analysed from below for 60 seconds. For data treatment, we used the peak count rates of all detected elements and quantitative data for a selection of elements.