Camille Roth
email: roth@cmb.hu-berlin.de

Digital, Digitized, and Numerical Humanities

Keywords: Digital, digitized, and numerical humanities

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Introduction

The "digital humanities" label may appear as an ideal keyword to scholars who empirically study online social systems using methods borrowing to both social and computational sciences. Their work is indeed connected with three potential acceptations of the term: they do digital research on digital worlds using digital datasets. Put differently, they may construe their activity as "digitized humanities" (relying on digitized data), "numerical humanities" (using numerical and, more broadly, formal models) and "humanities of the digital" (studying computer-mediated interactions). 1In general, however, academics who identify themselves as "digital humanists" may be more or less sensitive to this polysemy, as it often covers distinct epistemological endeavors. The purpose of this contribution is to shed some light on this trichotomy and to make some remarks on the corresponding scientific differentiation. We also aim at discussing the conditions of a better articulation between these streams.

An overarching dichotomy

We first focus on the two most obvious acceptations of "digital humanities" which, as we shall later show, correspond to what appears to be in practice an overarching dichotomy between digitized and numerical humanities.

Digitized humanities

The perhaps most widespread acceptation of "digital humanities" relates to the creation, curation and use of digitized datasets in human sciences and, to a lesser extent, social sciences. 2 In broad terms, these approaches include the development and application of computer tools to, inter alia, digitize, store, process, gather, connect, manage, make available, mine and visualize text collections and corpuses, image banks or multimedia documents of various origins.

In practice, these tasks raise a series of fundamental infrastructure-related issues, in terms of dataset construction (involving the design of appropriate ontologies), storage (featuring questions such as long-term availability) and access (requiring ergonomic and remotely accessible interfaces). Yet, they also prompt the integration of diverse methods stemming from more formal disciplines, most importantly artificial intelligence (AI). This ranges from established and rather low-level tools such as optical character recognition (OCR) to higher-level data processing techniques, which are connected to open research questions in their origin fields such as named entity detection and classification [START_REF] Nadeau | A survey of named entity recognition and classification[END_REF], optimal graph visualization [START_REF] Tamassia | Handbook of Graph Drawing and Visualization[END_REF] or the more recent techniques of crowdsourced archive curation [START_REF] Carletti | Digital humanities and crowdsourcing: An exploration[END_REF]. We discuss below the links between these AI techniques and the older field formerly denoted as "humanities computing".

This integration leads, in turn, to epistemological advances in the humanities by enabling novel questions, thanks to productivity gains as well as genuinely new ways of looking at traditional data. Examples abund in linguistics, as shown by the development of "corpus linguistics" [see e.g., [START_REF] Kennedy | An introduction to Corpus Linguistics[END_REF], [START_REF] Moretti | Conjectures on world literature[END_REF]'s notion of "distant reading" and further illustrated by the long dominant position of literature and linguistics in digital humanities journals [START_REF] Huggett | Core or periphery? digital humanities from an archaeological perspective[END_REF]. This prominence of linguistics should not come as a surprise. The field that became called as "humanities computing" has emerged long before the term of "digital humanities" has been coined. Humanities computing started in the late 1940s on the premises and promises of applying quantitative approaches on text corpuses [START_REF] Hockey | The history of humanities computing[END_REF]. For instance, statistical methods have been developed in order to automatically analyze stylistic features, under a field denoted as "stylometry", using lexical, syntactic, semantic markers and thus making it possible to provide, for instance, stastistical confidence in author attribution [START_REF] Stamatatos | A survey of modern authorship attribution methods[END_REF] or in the chronological ordering of the works of some author [START_REF] David | The evolution of stylometry in humanities scholarship[END_REF]. The field became further institutionalized from the late 60s to the mid-80s through the creation of various associations, such as the Association for Literary and Linguistic Computing (ALLC) or the Association for Computers and the Humanities (ACH), publication venues, such as "Computers and the Humanities" (1966) and "Literary and Linguistic Computing" (LLC, 1986; which recently took a broader name as "Digital Scholarship in the Humanities", DSH), or normalization efforts, such as the Text Encoding Initiative, towards the end of the 1980s.

Nonetheless, many other disciplines beyond humanities also have an established record of contributions which fit into this "digitized humanities" acceptation, by relying on archives which are not, at least primarily, in a purely textual format. Several social sciences have pioneered the use and computer-assisted exploration of digital archives, including first and foremost archaeology and its old interest in "digital archeology", or geography and its longstanding development of geographical information systems (GIS) -the intersection of these two digital humanities subfields, GIS & archeology, is already a vast domain in itself. The comprehensive textbook of [START_REF] Conolly | Geographical Information Systems in Archaeology[END_REF], for one, reviews many of the application domains of these endeavors, ranging from data acquisition, the computer-assisted analysis of regions of interest or various types of routes, to data management, for instance through elevation models. Political sciences have relied on the mining of public discourses in a variety of manners, including the study of nationalist reference frames in digitized newspaper corpuses [van den [START_REF] Van Den Bos | Mining public discourse for emerging dutch nationalism[END_REF], a material that communication research increasingly exploited in a digital humanities perspective [START_REF] Nicholson | Exploring the methodological possibilities of digital newspaper archives[END_REF], while applications at the interface of history and sociology also emerged, such as the reconstruction of historical social networks by extracting names and inferring relations from the Oxford Dictionary of National Biography [START_REF] Warren | Six degrees of francis bacon: A statistical method for reconstructing large historical social networks[END_REF]. (I will come back to the specific expectations of social sciences with respect to archives and, more broadly, datasets, in Section 2.1.)

Numerical humanities

Another perspective on "digital humanities" puts the emphasis on mathematical abstraction and modeling. Undoubtedly, digitized corpuses are already, to some extent, abstractions of the original empirical material. In what I shall denote in the remainder of this chapter by the term "numerical humanities", however, researchers primarily appear to develop mathematical frameworks and computer science methods with the specific goal of formalizing and stylizing some systematic social processes. In other words, the objective is to capture the possibly general mechanisms at the root of the observed data. In this sense, this acceptation does not relate to the computer-assisted realization of tasks which are difficult or impossible to do by hand -as achieved by OCR, or the application of GIS tools, clustering or statistical methods on large datasets, for example. Rather, it aims at developing numerical models of human or social behavior per se. In this respect, in practice, it generally builds more often on social science research issues than humanities.

In part, this type of approach relies again strongly on artificial intelligence -for instance when pattern detection methods are used to confirm or refute the existence of some regularity in a given dataset, such as using cell phone call records to exhibit some mobility trends or showing the presence of a specific type of interaction preferences [START_REF] Sobolevsky | Delineating geographical regions with networks of human interactions in an extensive set of countries[END_REF][START_REF] Miritello | Limited communication capacity unveils strategies for human interaction[END_REF].

Yet, the distinctive element here is that a given digital dataset merely constitutes one among many possible empirical realizations. In other words, what matters is the theorizing of an underlying process which may account for some sort of empirical measurements on a whole class of comparable datasets. In this case, scholars usually aim at designing mathemetical frameworks where theory-based hypotheses and their empirical manifestations can be formalized. They then develop numerical or algebraic models aimed at reconstructing some key characteristics of the studied systems. These key features are often construed as universal classes of empirical configurations, independently of a specific archive. They include systematic spatial and/or temporal correlations between some types of variables, typical over-or under-representations and, more broadly, distributions of values rather than specific values. For instance, one may look for the systematic emergence of configurations where "few have much, many have little" (e.g., so-called Pareto laws, Zipf laws, power laws) predicted by some theory and generic process (e.g., so-called Matthew effect, or "rich-getricher" reinforcement). By contrast, knowing who precisely is "rich" and possesses a lot in a given case study is of lesser importance.

Sometimes, the abstraction goes as far as modeling entire artificial societies and describing a typology of stylized facts emerging from their very simulation -an approach epitomized by the eponymous field of "social simulation" [START_REF] Epstein | Growing artificial societies: social science from the bottom up[END_REF]Axtell, 1996, Gilbert andTroitzsch, 1999], where models include a thorough description of social or behavioral processes (they are usually "agent-based" models [Bonabeau, 2002, Helbing and[START_REF] Helbing | Agentbased modeling[END_REF]) while the computer provides its own experimental data. More often, real-world empirical observations are used, yet again to validate an abstract class of processes that the chosen digital dataset is supposed to document. The data helps confirming an a priori model, or appraising the divergence between the model and reality. For instance, the above-mentioned mobility trends and interaction preferences are actually being described in regard to models which play the role of laws -that is, the description of these patterns makes sense within a certain modeling and theoretical framework. These laws are either (i) unknown and have to be determined from the data, or (ii) they are hypothesized and thereby provide a baseline or a benchmark from which the data may diverge (for instance, expected mobility based on a gravity model [START_REF] Sobolevsky | Delineating geographical regions with networks of human interactions in an extensive set of countries[END_REF], expected interaction patterns influenced by a demographic bias [START_REF] Miritello | Limited communication capacity unveils strategies for human interaction[END_REF]). In both cases, case studies play no further role than helping to validate (or parameterize) a target (and rather generic) model.

The epistemological setting is thus slightly different, for datasets are not anymore exploited as singular recordings corresponding to given empirical case studies, but simply as exemplar instances of a much wider and, more importantly, interchangeable phenomenon. This approach is not dissimilar from the one usually ascribed to natural sciences, in that they seek nomothetic rather than idiographic regularities 3 , i.e. general laws rather than local patterns. In this context, datasets are considered as equivalent, possibly reproducible empirical instances of an "identical" underlying process. One implicitly assumes that there exists an isomorphism between various data stemming from distinct yet similar empirical situations -for instance, any dataset based on cell phone records should, in principle, confirm the existence of similar phenomena.

This second perspective on digital humanities corresponds to an epistemological breakthrough illustrated by the recent surge of interest in so-called "computational social sciences" (CSS) [START_REF] Lazer | Computational social science[END_REF][START_REF] Conte | Manifesto of computational social science[END_REF], building upon the older fields of mathematical sociology (mainly animated by social scientists) and social complex system modeling (leaning more towards applied mathematics, computer science and statistical physics). Over the last decade, numerous conferences, journals and even departments devoted to these approaches have emerged. Many have been initially connected to quantitative social science research programs, especially in mathematical sociology (featuring the Journal of Mathematical Sociology since 1971) and the Social Network Analysis (SNA) community (around an international association founded in the late 70s [INSNA, Intl. Network for SNA] and yearly international conferences since the early 80s). The field of "Social Simulation" [Gilbert and Troitzsch, 1999] has been relatively institutionalized since around the 1990s, with international conferences currently drawing hundreds of attendants, supported by core journals such as JASSS (Journal of Artificial Societies and Social Simulation) and several international associations, the oldest being ESSA (European Social Simulation Association). Complexity sciences have also a clear connection with numerical humanities, through the study of social complex systems, which most complex system journals and conferences devote a significant attention to, and which spreads to neighboring disciplines as well (for example, the German Physics Society [DPG -Deutsche Physikalische Gesellschaft] has a special chapter on Socio-Economic Systems which is very much aligned with this program).

Venues explicitly referring to CSS have emerged in the last few years, both at the national level (in Germany, let us mention the series of GESIS symposia on CSS since 2015 which attract an international crowd) and international level (including SocialCom and SocInfo, both since 2009, and since 2016 the freshly launched ICCSS, all international conferences respectively on "Social Computing", "Social Informatics" and CSS). Their success owes to the joint and commonplace development of unprecedented computational resources and, most importantly, large-scale databases -either digitized datasets stemming from archives (rather related to human sciences) or natively digital datasets, produced through the recording of human behavior by digital devices (not least from the internet, as will be evident in the latter sections of the present chapter), in the wake of the currently fashionable and prevailing "big data" movement.

An overlapping dichotomy 2.1 Digital divides and overlaps

While digitized and numerical humanities have been defined as distinct perspectives, they may well coexist within the same scientific community, for numerical approaches may inform digitized humanities, and digitized datasets may feed numerical humanists. In this light, what might nevertheless remain unexpected is the relative lack of explicit connections between the communities who respectively identify themselves as digital humanists or computational social scientists. For instance, explicit references to "digital humanities" within computational social science communities remain relatively rare; and vice versa. By contrast, it is not rare to meet social scientists who identify better the signification of keywords such as "mathematical sociology", "social computing" and "computational social sciences" than "digital humanities".

General epistemological causes can and will be invoked, here, to explain this peculiar and perhaps temporary situation. Social sciences and humanities certainly had markedly distinct development paths with respect to digital and numerical methods, as regards both data collection and processing, and epistemological attitudes.

Experimental and nomological social sciences

On the one hand, the empirical material of social sciences is often made of either (i) textual records (essentially based on interviews or ethnographic observations) or (ii) tabulated data (be it, for instance, demographic, query-based, relational or geographical databases). Each kind of data corresponds, in turn, to noticeably distinct approaches.

(i) The analysis of the former, typical of the ethnographic approach and particularly prevalent in sociology and anthropology, generally relies on human interpretation and manual induction of typologies of discourses or behaviors from a collection of interview transcripts. There is nonetheless a small yet long tradition of quantitative text analysis [Popping, 2000, especially in chapter 8] related, in part, to the so-called "grounded theory" which notably advocates an inductive approach from (field) data [START_REF] Barney | The Discovery of Grounded Theory[END_REF] to discover social categories. While this branch concurrently led to significant software development [START_REF] Klein | Overview of text analysis software[END_REF], it remains infrequently applied in the field, and manual text processing is still widespread.

Regardless, there is nowadays a widespread culture of digitization, as many researchers arrange their ethnographical records into computer files. Yet, there are few efforts aiming at normalizing the format of archives, securing their conservation or publicizing them, essentially for privacy reasons -transcripts are certainly not made available to a wider audience, outside of the small circle of scholars who primarily designed and carried out the empirical protocol.

As a result, even though they do share a common epistemological and technological background in terms of discourse and narrative analysis with digital humanists working on text corpuses, such social scientists appear to be rarely concerned by the issues commonly addressed in the "digitized humanities", especially its subfields related to text processing.

(ii) The latter type of data admittedly lends itself readily to numerical analysis. But it also resonates with a long-standing stream of research devoted to formalization rather than digitization. While tabulated and structured databases indeed enable social scientists to apply systematic procedures to detect or assert the existence of relevant patterns and phenomena -an objective which is aligned with the epistemological promises ascribed in the previous section to digitized humanities -they have also played a key role, from early on, in fostering the development of a nomological stance. Durkheim, for one, in his study of pan-European suicide at the end of the 19th century, sought the statistical signature of anomie as a general social process, rather than discussing the specific national or seasonal figures per se.

In this nomological context, empirical data has an instrumental and even, one may say, almost accessory status. It is not uncommon to witness the predominance of a normative attitude over a descriptive attitude: some approaches even assume the existence or relevance of some abstract structures or formal designs before observing them in real datasets (let alone archives). In concrete terms, for instance, the quantitative study of sociological communities relied first on conceptual constructs such as cliques [Luce and [START_REF] Luce | A method of matrix analysis of group structure[END_REF] or structurally equivalent sets [START_REF] Lorrain | Structural equivalence of individuals in social networks[END_REF]]. Many of the seminal works in this area dealt with a variety of issues without resorting to empirical data, spanning otherwise concrete topics such as information propagation in social systems [START_REF] Rapoport | Spread of information through a population with socio-structural bias: I. Assumption of transitivity[END_REF], social field configurations [START_REF] Cartwright | Structural balance: A generalization of Heider's theory[END_REF], urban segregation dynamics [START_REF] Thomas | Dynamic models of segregation[END_REF], conditions of the emergence of riots [START_REF] Granovetter | Threshold models of collective behavior[END_REF], cultural epidemiology in cognitive anthropology [START_REF] Claidière | Commentary: The role of attraction in cultural evolution[END_REF], or even a significant portion of economic theory [START_REF] Samuelson | Foundations of Economic Analysis[END_REF]. Studies diversely affiliated with the paradigms of agent-based and multi-agent modeling [START_REF] Axelrod | The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration[END_REF], artificial societies and social simulation [Gilbert and Troitzsch, 1999] and social self-organization [Helbing, 2012], are practically dataset-free or primarily rely on artificial datasets, what is admittedly diametrically opposed to digitized humanities.

Here, real-world datasets are admittedly useful to empirically validate the prediction of the existence of some stylized or so-called "emergent" phenomena (i.e. macro-scale phenomena which are not obvious from micro-level behavior and seem to be only describable through a macro-level observation framework) -but they are far from being a requisite.

The long-lasting presence of this approach in many social sciences [in sociology, see [START_REF] Edling | Mathematics in sociology[END_REF] plausibly facilitated its affiliation to modern-day computational social science (CSS). It is also no wonder that CSS gathers fields traditionally associated with natural sciences, such as statistics (for obvious reasons), discrete mathematics (including classification, graph, game theory), system science (to configure social processes as dynamic iterative systems), and statistical physics (to study large interactional systems). By contrast, this stance appears to have much less appeal in fields traditionally associated with human sciences.

Archive-based humanities

On the other hand, humanities plausibly pay a marked attention to the specificities of singular datasets e.g., by focusing on a given author or a given historical case study, where social sciences tend to assume, as said before, the interchangeability of datasets within a similar empirical context. This may explain why textual and even visual archives are more ubiquitous in human science and, as a result, why "digitized humanities" have a stronger urgency than in fields more related to social science: sophisticated archiving issues regarding, for instance, indexation, data processing and availability. On the contrary, social scientists may feel less concerned with the preservation of specific datasets since (i) their increased focus on reproducible and/or interchangeable observations of a social system makes them less dependent on unique archives, and (ii) they often have the option of being in control of the empirical protocol and, thereby, of the conditions of data construction. Their interest in archives is further complicated when datasets cannot be released because of privacy concerns (typically, small-scale field research such as interviews) or commercial interests (typically, when a dataset provided by a private company remains a current business asset). This relative lack of enthusiasm (and, perhaps, of good practices) regarding archives, on the part of social science, is further reinforced by a certain preference for the present. Social science studies appear to focus more often on contemporary issues4 or, at least to some extent, atemporal research questions (as regards the most nomological and naturalized part, such as the above-mentioned works on cultural transmission, kinship structure, or urban segregation). This warrants the collection of recent and even new data to fulfill the needs of a research study: in practice, empirical validation consists in proposing a protocol and then creating the conditions to build a(ny) new dataset, or hunt for an(y) existing database fulfilling some criteria essential to the protocol, without necessarily being peculiar to a specific spatio-temporal empirical situation. In other words, studying the current sociological impact of the introduction of IT in German, French, European or worldwide university departments is certainly less idiographic and dependent on a unique set of observations than writing the history of the development of digital humanities specifically in Germany, and articulating its key actors, dates and institutions.

Furthermore, the fact that numerical humanists often rely on a genuinely experimental approach (i.e. doing experiments) is not to be underestimated in their relative disaffection with archives. In effect, the assessment of the future value and relevance of a raw dataset collected for a given case study is intrinsically linked to the question of the experimental protocol, on at least two levels:

• The first one relates to the collection protocol. A given database may indeed become more or less obsolete for further research: either because it has revealed most of its mysteries at a certain level of granularity (resolution limit), or because new questions require a richer data collection protocol (ontology limit). For example, in order to advance the above-mentioned works of [START_REF] Miritello | Limited communication capacity unveils strategies for human interaction[END_REF] and Sobolevsky et al.

[2013], one can expect that more detailed datasets would be needed, and therefore built.

• The second one touches the complexity of the pre-processing protocol. Oftentimes, very big datasets need to be pre-processed before being manageable -think, here, of the readily-available digital dumps of the complete history of the Wikipedia, documenting all revision and interaction histories. Most scholars would only be interested in a subset of the data: interaction networks between pages [START_REF] Zlatic | Wikipedias: Collaborative web-based encyclopedias as complex networks[END_REF] or contributors [START_REF] Capocci | Preferential attachment in the growth of social networks: The internet encyclopedia wikipedia[END_REF], dynamics of reference lists on pages [START_REF] Chen | {{citation needed}}: The dynamics of referencing in wikipedia[END_REF], edition diversity of contributors [START_REF] Lada | Individual focus and knowledge contribution[END_REF], votes for administrators [START_REF] Leskovec | Governance in social media: A case study of the wikipedia promotion process[END_REF], to cite a few.

For both these reasons, there will most likely be eventually as many archives and associated tools as there are classes of research questions, complicating further a convergence of interests around a given huge original database, as could be the case for digitized humanities.

Two epistemic communities?

To summarize, we first observe a disjunction between a contemporary (or non-historical) focus, encouraging the creation of relevant data from scratch, and a historical focus, impos-ing a heavy dependence on archives. Second, we may differentiate a nomological stance (and a relative interchangeability of datasets) from a more idiographic stance (and relative singularity of datasets). We contend that this double dichotomy creates a combination of factors which partially help explain the relative disconnection between the two scientific communities dealing respectively with digitized and numerical humanities.

Despite this, it is important to recognize the shared interests of these overlapping epistemic communities, at least from an instrumental viewpoint. The two perspectives are indeed intertwined. Numerical models of humanities data require digitally-available corpuses in the first place, while the evaluation and detection of specific patterns from such datasets fuels the design of hypotheses which, in turn, are used as the basis for the modeling of artificial societies and eventually the confrontation of simulations to empirical data.

Some interdisciplinary wide-ranging endeavors have already successfully pursued this kind of bicephalous research under a single umbrella -the most prominent and perhaps oldest such research program being the field of "bio-informatics", which features a virtuous mix of data visualization, pattern analysis, and modeling of artificial evolutionary processes. Why the junction between computational social sciences and digital humanities has not yet fully occurred, in terms of scientific communities, remains to be discussed. While there is no obvious de jure epistemic dichotomy between fields rather associated with human sciences which would be dedicated to "digitized humanities", and fields leaning towards social sciences and more dedicated to "numerical humanities", my experience seems to indicate that there is a de facto social split. Empirical evidence will support this intuition in section 4.

An obvious remedy would consist in encouraging the development of areas at the interface between the two stances. The recent agenda of several humanities scholars definitely reflects the preoccupation of appraising existing corpuses with a fundamentally nomological angle. In linguistics, we may evoke the work of [START_REF] Ghanbarnejad | Extracting information from s-curves of language change[END_REF] which aims at discovering language evolution laws based on the adoption of spelling innovations, including orthographic variations, at the scale of an entire population, by relying on an extremely large corpus covering millions of digitized books from the two last centuries [described by [START_REF] Michel | Quantitative analysis of culture using millions of digitized books[END_REF]. Focusing on so-called adoption "s-curves", they distinguish innovations related to exogenous causes (e.g., imposed reforms) from those due to endogenous causes (e.g., progressive regularization of verbs, or spelling simplifications) by positing the existence of distinct dynamical processes, evidenced in turn by different categories of time series. In an archeological context, [START_REF] Knappett | Modelling maritime interaction in the aegean bronze age[END_REF] focus on maritime interactions in the Aegean sea during the Bronze Age. They assume a process of cost minimization empirically based on geographical distances between known settlement sites. Their model, which owes very much to Hamiltonian mechanics, enables them to propose a probable interaction network whose shape fruitfully questions existing field knowledge [as presented, in particular, by [START_REF] Broodbank | An island archaeology of the early Cyclades[END_REF]. In anthropology, [START_REF] Roth | Random alliance networks[END_REF] assess the contribution of chance to the morphological properties of kinship and marriage alliance networks. They hypothesize and analytically study a random baseline of alliance formation to empirically scrutinize the matrimonial role of social groups as asserted by native or ethnological theory. To this end, they use around 20 digital corpuses of genealogical networks from varied temporal and spatial origins [most of them available on kinsources.net, see [START_REF] Bringé | Kinsources and puck: Open data and free tools for analyzing kinship networks[END_REF] and represent them in the uniform framework of weighted networks. Using historical archives, [START_REF] Boulet | Batch kernel SOM and related Laplacian methods for social network analysis[END_REF] discuss the relevance of a variety of significantly formal notions on graph structure for the purpose of defining communities in interaction graphs stemming from agrar contracts made during the 14th century in a French seigniory. Even though they eventually illustrate their discussion using a specific digital dataset in a specific region at a specific time, it clearly appears that not only any similar database would have been a valid candidate as well, but they also seem to suggest that the conclusions drawn from this very case study are in essence extendable to any other similar dataset -a stance which is typical for the normal science of social network analysis [START_REF] Hummon | Social networks as normal science[END_REF]. Similarly, [START_REF] White | Oscillatory dynamics of city-size distributions in world historical systems[END_REF] statistically study a large historical database describing city sizes over the period of a thousand years [START_REF] Chandler | Four thousand years of urban growth: an historical census[END_REF]. They aim at validating a diverse set of assumptions coupling demographic and socio-political dynamics. Here again, the observed stylized features are discussed in terms of universal, almost natural phenomena.

This very partial selection of examples showcases instances of strong interlock between digitized and numerical humanities, as we presented them so far. They highlight the largescale, cross-temporal empirical validation made possible by numerical digitized humanities. They also illustrate how often digital archives may be considered as an interchangeable instrument -the idea of preparing archives for a third-party use is indeed not so frequent among numerical humanists. As said before, beyond privacy and commercial concerns, a certain appetence for renewed experiments might help explain why numerical humanists rarely devote additional time to systematic and concerted efforts to define common formats, ensure long-term data availability and convene on normalized analysis procedures and tools (possibly based on such common formats and meta-databases). In this respect, the objectives of, say, national libraries archiving web content on a massive basis (and possibly with an undocumented selection bias) might not be entirely aligned with those of numerical humanists inclined to use this kind of data, especially for nomological purposes.

This last example brings us to another area of fertile interface between digitized and numerical humanities. It relates to the study of the online world, a third and entirely distinct perspective on "digital humanities", perhaps even orthogonal to our dichotomy so far. This domain, where digital archives are native, attracted many scholars both from digital humanities and computational social science; it will be the focus of the next section.

An overlapping trichotomy

Humanities of the digital

A third understanding of "digital humanities" pertains to what may be called the humanities of the digital. This acceptation focuses on computer-mediated interactions and societies, such as the Internet and other online communities. It principally gathers sociologists, ethnologists and, to a lesser extent, linguists, political scientists and geographers, among others.

There has been a slow yet steady recognition of online communities as a legitimate investigation field per se -"a sense of the Internet as simply another context where social life is lived, where research methods are applied, and where contemporary social issues are addressed", as [START_REF] Hine | Social research methods and the internet: A thematic review[END_REF] nicely puts it. This may be originally attributed to two non-exclusive movements. First, the progressive use of electronic devices to "digitize" the classical toolbox of conventional field research. [START_REF] Murthy | Digital ethnography: An examination of the use of new technologies for social research[END_REF] explains how scholars started using digital cameras, web-based questionnaires, emails and, more recently, social networking sites, to carry out interviews or make surveys. He further underscores the role of online platforms and communities as a means of recruiting and observing participants (especially, in the earlier times, to help connect with normally hard-to-reach populations and underground groups). Second, the construal of computer networks as something essentially social. [START_REF] Flichy | Internet or the ideal scientific community[END_REF] recounts how networked technologies were historically meant to foster the emergence of social interactions. Inter-networks were originally developed as a social tool (and even a socio-cognitive tool) rather than a technical tool, following the vision of [START_REF] Licklider | The computer as a communication device[END_REF] where computers should, more than anything else, be human communication devices facilitating distributed cognition within groups of common interest.

Scholars have essentially looked at the integration of online communication in everyday life, and at the everyday life in online communities. Regarding the former, sociologists in particular have been questioning the nature of electronic interactions and their intertwinement with offline communication channels, studying for instance the socio-demographics of the Internet and its usage (in terms of civic engagement [START_REF] Norris | Digital Divide: Civic Engagement, Information Poverty, and the Internet Worldwide[END_REF] or access to information [START_REF] Kayahara | Searching for culture-high and low[END_REF]), or the share of computer networks in overall communication [START_REF] Wellman | Computer networks as social networks[END_REF], emphasizing the increased social reality of these virtual interactions -"the more virtual the more real" [START_REF] Woolgar | Virtual society, chapter Five rules of virtuality[END_REF]. With respect to the latter, [START_REF] Rheingold | Virtual Communities: Homesteading on the Electronic Frontier[END_REF]'s essay on "The Well", a San Francisco-based virtual community founded by two former hippies, contributed greatly to the acknowledgment of the existence of selforganized online societies of a novel nature, originally related to a certain digital utopia [START_REF] Turner | From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism[END_REF]. This opened the way to a surge in ethnographic research on online communities [START_REF] Wilson | The anthropology of online communities[END_REF]. [START_REF] Hine | Virtual Ethnography[END_REF] describes the specificity of this research, where digital devices constrain the ethnographer's behavior in a manner not experienced before in the offline world, especially with respect to tracking conversations flowing in a very different manner than a face-to-face encounter) -as well as its commonality, in the sense that ethnographers must, as usual, adapt themselves to the culture they wish to study, understand their codes and their rules.

More broadly, each online community configures a potentially unique socio-technical system, in that each specific socio-technical setting can define a distinct society, with its own artificial rules. Consider again Wikipedia: its success made it a lively and hierarchized community of thousands of regular contributors evolving within an environment subject to its own (self-appointed) rules, enforced by a technical interface whose functioning is decided by the community itself. As a result and as said before, the collaborative encyclopedia has been the focus of many studies at the interface of various disciplines: sometimes leaning more towards natural sciences, by considering Wikipedia's website(s) as an almost physical system [START_REF] Zlatic | Wikipedias: Collaborative web-based encyclopedias as complex networks[END_REF][START_REF] Capocci | Preferential attachment in the growth of social networks: The internet encyclopedia wikipedia[END_REF], sometimes leaning more towards sociological issues, by exploring Wikipedia as a radically new community where the interface plays a strong role in disciplining both socialization and content creation (for instance by emphasizing the existence of either entirely novel interaction rules [Adamic et al., 2010, Chen and[START_REF] Chen | {{citation needed}}: The dynamics of referencing in wikipedia[END_REF] or rather traditional social processes, such as apprenticeship [START_REF] Bryant | Becoming wikipedian: Trans-formation of participation in a collaborative online encyclopedia[END_REF] and internal democracy [START_REF] Leskovec | Governance in social media: A case study of the wikipedia promotion process[END_REF]). Blogs define a different ecosystem [START_REF] Karpf | Understanding blogspace[END_REF], being both a public space [START_REF] Etling | Mapping the Arabic blogosphere: Politics, culture, and dissent[END_REF] and a conversation arena [START_REF] Herring | Conversations in the blogosphere: An analysis "from the bottom up[END_REF] animated by peculiar temporal patterns [START_REF] Goetz | Modeling blog dynamics[END_REF]; micro-blogs (such as twitter) borrow partly to the blogsphere but define yet again a distinct socio-cognitive environment [START_REF] Kwak | What is twitter, a social network or a news media?[END_REF], with its own information diffusion processes [START_REF] Weng | Competition among memes in a world with limited attention[END_REF] and reputation dynamics [START_REF] Marwick | I tweet honestly, i tweet passionately: Twitter user, context collapse, and the imagined audience[END_REF]. It is far beyond the scope of this paper -let alone this section-to provide a comprehensive overview on the wealth of themes currently structuring the humanities of the digital, even in a very succinct manner. Let us nonetheless close this partial enumeration with the evocation of gaming, whose practice also belongs in full to the scope of this stream [START_REF] Mäyrä | Introduction to Game Studies: Games in Culture[END_REF], for it offers a perhaps extreme illustration of the particularity of online socio-technical systems where entire universes, often driven by unrealistic rules are being populated by agents who spontaneously dialogue, form groups (guilds), solve problems (quests) together [START_REF] Williams | The virtual worlds exploratorium: Using large-scale data and computational techniques for communication research[END_REF].

A bridge to digitized and numerical humanities

On the whole, this perspective on digital humanities seems to be apparently orthogonal to the two previous ones: although interdisciplinary, this study field has a very specific and bounded object focusing on human-computer and computer-mediated interactions. Yet, it actually touches to the respective cores of digitized and numerical humanities:

1. First, because internet corpuses, i.e. corpuses produced by the web itself, are readily available and, almost by design, already digitized: virtually all online actions and interactions are recordable in a native format, theoretically at any level of granularity.

The impact of online data is for instance significantly visible in linguistics, as emphasized by [START_REF] Kilgarriff | Introduction to the special issue on the web as corpus[END_REF] in their introduction to the special issue of Computational Linguistics dedicated to the web as a corpus (for an overview, see also [START_REF] Hund | Corpus linguistics and the web[END_REF]). It is also an established means, as [START_REF] Rogers | Das ende des virtuellen -digitale methoden[END_REF] expresses it, to "study culture and society with the Internet". [START_REF] Ruppert | Reassembling social science methods: The challenge of digital devices[END_REF] give a comprehensive account of its contribution to the evolution of social science methods as a whole, including the possibility of observing actions and transactions (instead of documenting and interpreting stories), on well-delimited populations (featuring easier uniform recruitment procedures), with possibly as many timepoints as needed.

In practice, online communities also appear to constitute the bulk of the data exploited by numerical humanities [START_REF] Lazer | Computational social science[END_REF][START_REF] Lazer | Data ex machina: Introduction to big data[END_REF]. Here, however, just as for the other types of datasets that they are using, computational social scientists are again rarely aware of the good practices developed in digitized humanities regarding the questions of archive formatting or long-term availability. This data is not less interchangeable, it is a priori reproducible, but, what is more, it can often be easily re-collected or re-exported from a given platform or set of websites as long as they still exist -the web sometimes appears, rather illusorily, to archive itself automatically: why would one worry about securing a specific dataset?

2. Second, because the understanding of computer-mediated interactions is profitable for both stances. As regards digitized humanities, it sheds light on the prospects and limitations of the usuability and viability of interfaces and online research communities in dealing with digitized archives. Of prime interest, here, are the social and technical aspects of virtual collaboration groups dedicated to the collaborative constitution or curation of archives. It is indeed key to have a good knowledge of the sociological phenomena occuring in communities driven by voluntary participation [START_REF] Mockus | Two case studies of open source software development: Apache and Mozilla[END_REF][START_REF] Marlow | HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, ToRead[END_REF][START_REF] Gallant | Five heuristics for designing and evaluating web-based ties[END_REF], as well as being aware of the advances in human computing and "games with a purpose" [START_REF] Luis Von Ahn | Games with a purpose[END_REF] and participative archives [START_REF] Huvila | Participatory archive: towards decentralised curation, radical user orientation, and broader contextualisation of records management[END_REF] or crowdsourced curation [START_REF] Oomen | Crowdsourcing in the cultural heritage domain: opportunities and challenges[END_REF].

As more and more digital humanists consider that it is also essential to be a connected scientist e.g., by managing a research blog or Twitter account, the relevance of studies on information dynamics and social hierarchization in blogspace also increases.

With respect to numerical humanities, the analysis of the dynamics of online communities is often considered as a proxy for the in vivo observation of societies on an unprecedented scale. It enables, in principle, the formulation of testable and sometimes replicable hypotheses on social processes and human behavior at large. Many of the above-studies on Wikipedia, blogs, Twitter or gaming platforms belong indeed to a computational social science endeavor: looking at the evolution of the structure of a self-organized system (of Wikipedia webpages), the behavioral regularity of information producers (in the case of bloggers), content epidemiology across a network (of Twitter users), and typical interaction graph topology (of online gamers).

While online life offers a fertile research playground to both of the above-described perspectives, one may thus wonder which place occupies this strand in venues commonly affiliated with either "digital humanities" or "computational social science". Let us now examine this situation in concrete terms and, more precisely, the respective share of each stance in the various venues.

Digital overlaps in practice DH and the digital, digitized, and numerical

Admittedly, the three above-described understandings induce distinct epistemological underpinnings and, possibly, distinct scientific objectives and audiences. I would like to briefly sketch their concrete presence within fields which are explicitly related to the DH label. To this end, I shall first look at two distinct types of venues for "digital humanities". The choice of the empirical material could be debated, as it relies on relatively arbitrary decisions. Even if I intend to make no claim of strong statistical representativity (to be fair, it should be the aim of a rigorous scientometrics paper devoted to this issue), I contend that the following rough assessment already provides a convincing glimpse on the coexistence of the three acceptations of DH, and their instantiation in different scientific communities.

To start with, I chose the journal "Digital Humanities Quarterly" (DHQ) which has one of the oldest history in the field as a review specifically referring to the DH label. As mentioned in the introduction, "Digital Scholarship in the Humanities" (DSH) also has a long history, it operated however under the name "Literary and Linguistic Computing" (LLC) until the end of 2013. I also harvested recent data for this journal. It is published by the Association for Computers and the Humanities (ACH), which organizes the yearly international DH conference. For this reason, my third venue is the 2015 edition of this conference, held in Sydney, Australia, later denoted by DH2015.

I then examined the representativity of the three stances by carrying out a manual categorization of papers published in recent issues over five years in DHQ and DSH/LLC (covering both 2012-16), to the exclusion of editorial / introductory papers. I also considered abstracts of talks presented at DH2015. 1. For DHQ, I specifically considered seventeen issues 6.2 to 10.4, i.e. from the second issue of 2012 up to the last issue (included) of 2016. This makes a total of 113 articles, of which 16 were providing reflexive viewpoints on the history, epistemology or carreers of DH as a field; they were thus excluded from the categorization.

The final selection of 97 articles consists of:

• 84 articles (86.6%) attributed to "digitized humanities", with topics such as: archive curation, management, access, formatting, status of digital artifacts, digital art; sixteen of them also deal with the application of existing AI techniques on archives for the purpose of a specific case study (PCA, OCR, SNA, and lexicometry);

• 4 articles (4.1%) attributed to "numerical humanities": here, namely, one paper focused on agent-based modeling, one on information contagion models, two on the possibility of a statistical characterization of style or automatic assignment of gender;

• and 9 articles (9.3%) for the "humanities of the digital", featuring three discussions on the Web 2.0, one on game studies, two on coding practices, one on the use of hashtags in Twitter, one on using mobile apps in research, and one paper on online reading groups.

2. For DSH/LLC, I considered the first issues of each volume published between 2012 and 2016, i.e. a total of five issues. DSH/LLC issues contain more articles than DHQ, so this made a total of 47 articles, or 46 after excluding one paper discussing the history of DH.

• 34 articles (73.9%) to "digitized humanities". A large proportion of them deals however with the development of maps or linguistic classifiers (e.g., detection of verbs or some morphemes) often through the application of existing clustering or statistical methods on digitized corpora. Although relying more on numerical methods than in the case of DHQ, the main focus still lies on a specific corpus or collection of corpuses, rather than the abstraction of new models or the development of new quantitative methods per se. In contrast to DHQ, only few in this category focus principally on a specific archive, dataset, or visualization software or approach.

• and 2 articles (4.3%) to the "humanities of the digital". One addresses the use of mobile apps to do language research, the other proposes a case study on the use of a text mining software.

3. For DH2015, I subsequently considered 48 (long) abstracts, randomly chosen within the list of talks found in the conference program. I then attributed:

• 39 articles (81.2%) to "digitized humanities", including 10, this time, featuring the use of quantitative or AI tools;

• 7 papers (14.6%) to "numerical humanities", which were almost systematically involving narrative models: even though they often deal with classification tasks (e.g. authorship attribution), these papers make the assumption that text production may be described with the help of generic models);

• and 2 papers (4.2%) to the "humanities of the digital": while one paper deals with Twitter, the other one analyzes crowdsourcing communities for a task of database curation.

To summarize, an overwhelming majority of papers had to deal with the "digitized" understanding of DH. Very few papers dealt with internet life and corpuses, and not many more had to do with the "numerical" stance -when they did, they essentially had to do with stylometry (8 out of 14). In this respect, if DSH/LLC and, to a lesser extent, DH2015 featured more numerical humanities than DHQ, the corresponding papers were almost all framed within a literary research endeavor. This seems to support further the notion that, in spite of occasional epistemological similarities, there are two distinct communities of practice (humanists and social scientists).

Computational social science and DH

By comparison, I also looked at a large and plausibly representative computational social science venue. To this end, I considered the most recent international conference explicitly referring to this subject: the International Conference on CSS, which a bit more than 400 participants attended in Helsinki during the summer of 2015. I focused on talk titles in the conference program for which the corresponding papers (i.e. with an identical title) were readily available as an open-access article: in order to eventually categorize 48 talks, I had to randomly pick 62 talks (i.e. more than three quarters of the original random selection of titles had been self-archived prior to the conference).

The empirical results yield the contours of a CSS landscape which stands in stark contrast with that of DH. A majority of the presentations (29, i.e. 60.4%) had to do with the "humanities of the digital", including 24 talks (exactly half of the whole sample) which featured a markedly numerical approach, from multi-agent models to behavioral modeling, through the development of original and flexible machine learning models. Of the remaining 19 papers not related to online communities, 17 (35.4%) could be ascribed to "numerical humanities" proper. While no talk focused on archiving issues per se, two consisted in applying existing quantitative tools on archives -digitized humanities thus accounted for a small share of 4.2% of all talks.

On the whole, a total of 5 + 2 talks did not qualify as a truly numerical approach (as defined earlier). They had nonetheless a distinguishable numerical flavor, by contrast with the similarly-categorized DHQ-DSH/LLC-DH2015 papers. Here, these seven papers often consisted in applying a series of existing pattern recognition, regression or classification tools (especially when internet data was involved). The used toolbox was virtually always discussed, i.e. a significant part of the paper was devoted to presenting and adapting the quantitative method to the case study (e.g. working on a binary classification algorithm and discussing its performance in machine learning terms), while many coauthors had a primary background in computer science or statistical physics. This exploration of recent research self-labeled as CSS clearly shows that the humanities of the digital have become, in this field, a somewhat essential category, in contrast to the situation observed in DH-labeled venues. This is all the more visible by drawing Venn diagrams depicting the intersections of papers relying on internet corpuses and borrowing to either digitized or numerical humanities approaches -see Fig. 1.

Concluding remarks

This last empirical examination of a bit more than 250 research studies diversely affiliated to DH or CSS confirms the existence of two non-overlapping epistemic communities with respect to digital methods. In basic terms, this partition may be characterized by a double dichotomy based, on the side of objects, on the attention given to the humanities of the digital and, on the side of methods, on the status both of models and of archives.

It is admittedly a conceivable situation. The existence of two distinct epistemological settings and objectives may indeed suffice to explain the de facto historical development of two disconnected streams, sometimes reinforced by a de jure delimitation between two understandings where either qualitative or quantitative approaches are put forward [START_REF] Porsdam | Digital humanities: On finding the proper balance between qualitative and quantitative ways of doing research in the humanities[END_REF]. Yet, it is also a non-desirable situation. CSS could certainly improve their curiosity for the variety of objects addressed by digital humanists (about 15% of the ICCSS'15 communications were singularly focused on Twitter) as well as profit from their field expertise and, perhaps, finer knowledge of the conditions of construction of the datasets. DH would not be last to benefit from the nomological attitude of computational social scientists and their flexible modeling skills. In this regard, DH might also be currently at the periphery of a revolution which is related in a nomological way to big data (especially big internet data) and which enjoys a notable momentum in social sciences. It is also one of the teachings of the share of the "humanities of the digital" in CSS topics.

By making this state of affairs visible, this contribution hopes to give some hints about the causes of this relative yet definitely perceivable divide, by articulating the connections between the various understandings. Beyond the rather natural idea of encouraging the emergence of more joint venues and networks appealing to both scientific communities, this chapter finally intends to insist on the possible broker role of the scholarship on online communities -that is, humanities of the digital bridging the gap between digital humanities and numerical humanities.

Figure 1 :

 1 Figure1: Intersection diagram representing the share of papers dealing with humanities of the digital and relying on an approach typical of either digitized or numerical humanities, for each of the three considered venues referring respectively to the label "digital humanities" (DHQ and DSH/LLC over2012-16, and DH 2015 in Sydney) or "computational social science" (ICCSS 2015 in Helsinki).

This ambiguity may be even stronger in the French-speaking realm where "digital humanities" are generally translated as "humanités numériques": "numériques" means both "digital" and "numeric" -besides, the translation of "digitized" is "numérisé", which is not far from "numeric" either, even though no numbers are usually involved.

In all generality, we employ the term "dataset" without assuming any level of post-processing: data may consist of raw digitized corpora or measurements which may be completed or documented with comprehensive metadata. The term "archive" is in this respect less generic, as it often relates to digital material which is based on a unique source and where the conservation of the original material particularly matters.

This argument builds upon the classical dichotomy proposed by Windelband and Rickert to distinguish nomothetic and idiographic descriptions. While the latter is usually associated to natural sciences and the former to human and social sciences, both natural and social sciences alternatively tend to look for both types of descriptions (see also[START_REF] Bouvier | Connaissance de l'individuel et sciences du général. une comparaison entre sciences de l'homme en société et sciences de la nature[END_REF]).

Clearly, few sociologists would be interested in decade-old interviews, except for historical comparative purposes (thereby leaning again towards humanities).

• 10 articles (21.7%) to "numerical humanities". This category principally contains papers which introduce a stylometric model, rather than just applying it -often by proposing a model of language generation, hence involving a behavioral mechanism. Only one paper focuses on the modeling of cycles in a given society, while two papers discuss laws behind shot length in films.


Jade Eva Guisiano

Raja Chiky

Jonathas De Mello

SDG-Meter : a deep learning based tool for automatic text classification of the Sustainable Development Goals

Keywords: NLP, Deep learning, Sustainable Development Goals

The 17 Sustainable Development Goals (SDGs) are a "shared blueprint for peace and prosperity for people and the planet, now and into the future". Since 2015, they help pointing out pathways to solve interlinked challenges being faced globally. The monitoring of SDGs is essential to assess progress and obstacles to realise such shared goals. Streams of SDG-related documents produced by governments, academia, private and public entities are assessed by United Nations teams to measure such progress according to each SDG, requiring labelling to proceed to more in-depth analyses. Such laborious task is usually done by the experts, and rely on personal knowledge of the links between the documents contents and the SDGs. While UNEP has experts in many fields, links to the SDGs that are outside their expertise may be overlooked. In this context, we propose to solve this problem with a multi-label classification of texts using Bidirectional Encoder Representations from Transformers (BERT). Based on this method, we designed the SDG-Meter, an online tool able to indicate to the user in a fully automatic way the SDGs linked to their input text but also to quantify the degree of membership of these SDGs.

Introduction

The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, provides a "shared blueprint for peace and prosperity for people and the planet, now and into the future". At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries -developed and developing. They recognize that ending poverty and other deprivations must go hand-in-hand with strategies that improve health and education, reduce inequality, and spur economic growth -all while tackling climate change and working to preserve our oceans and forests. The UN Secretary General presents every year an Annual SDG progress report, aiming to identify challenges and achievements from different countries and regions.

As part of the work of the United Nations Environment Programme (UNEP), multiple documents (policy recommendations, toolkits, reports, project submissions, progress reports, etc.) are analysed by SDG experts for properly indexing them according to SDGs. Experts have to read the document and identify which SDGs are mentioned, or are related to it. These mapping exercises are timeconsuming and subjective to personal knowledge of the links between the document contents and the SDGs. While UNEP has experts in many fields, links to the SDGs that are outside their expertise may be overlooked. This work, based on the subjectivity of each expert, introduces a bias in the overall mapping work, which therefore does not allow for a comparison of the associations made for each text. This association work (labeling) based on text analysis therefore requires increased knowledge of the terminology of each SDG to achieve an optimal analysis. The improvement of the whole text labeling process consists firstly in adopting automation of this process (time saving for the experts), and secondly, in designing a single intelligence with a solid knowledge of the terminologies of each SDG (bias reduction).

Aiming to address these issues, we propose the online tool SDG-Meter which allows to automatically classify a text -without expert intervention -according to the 17 SDGs but also to quantify each link between the SDGs and a text. The SDG-Meter is based on the use of a powerful deep learning algorithm which is currently one of the most powerful and efficient algorithms in the field of Natural Language Processing. Following a series of tests, it was confirmed that our tool is able to perform a classification identical to that of an expert.

This paper starts by presenting the state of the art of multi-label classification methods applied to SDGs, and detailing the functioning of the BERT (Bidirectional Encoder Representations from Transformers) algorithm which is at the heart of our method. The functioning of the SDG-Meter via the implementation of the BERT algorithm in a web solution will also be presented. The paper will then explain the method used to build our training database. Finally, we present the results of our SDG-Meter in comparison with an expert manual classification on 5 different texts.

State-of-the-art

Various methods have recently been developed with the objective of classifying text according to the 17 SDGs. (i.e. multi-label classification task).

The Organisation for Economic Co-operation and Development (OECD) has developed several methods for classifying texts according to the SDGs. For example, in order to assess the volume and alignment of financial flows with the SDGs, OECD has developed the online tool SDG tracker [START_REF] Pincet | Linking aid to the sustainable development goals -a machine learning approach[END_REF] 4 . This tool links the project description of financial contributions from bilateral providers or multilateral organizations to the SDGs they address. Their approach is based on a combination of the XGBoost (Boosted Gradiant) [START_REF] Chen | Xgboost[END_REF] and ULMFiT [START_REF] Howard | Universal language model fine-tuning for text classification[END_REF] (Universal Language Model Fine-Tuning for Text Classification) algorithms followed by a manual verification by experts for each output. This tool by tracking global progress towards the SDGs, permits to the users to explore progress on all of the SDG indicators for which data is available at the global, regional and country-level. OECD has also recently proposed the Sustainable Development Goals Business Actions Identifier (SDG-BAI) [START_REF]OCDE: Industrial Policy for the Sustainable Development Goals[END_REF] allowing to identify, now automatically, the contributions of companies to the SDGs. The latter is composed of 17 XGBoost sub-algorithms, each of which provides a binary classification of whether the text is related to a specific SDG. Finally, the SDG Pathfinder5 online tool still from OECD combine automatic and manual approaches for associating OECD and its partners texts with SDGs which rely on the use of UNSILO Classify6 commercial algorithms which permits to extract automatically all the concepts from text and then compare these concept to each SDG terminology. However, in this case, the goal is not to predict a specific label, but rather to define clusters in the data. The features of these clusters are then manually mapped to labels (form of ontology). This method thus provides a comprehensive list of topics related to the SDGs and a model to detect topics in new texts and thus link new documents to the SDGs. This tool explores the key themes underlying the SDGs and the links between them, using analysis and data from six international organizations.

On the side of the United Nations agencies, various and similar methods have also been developed. The online tool Open-Source Approach to Classify Text Data by UN Sustainable Development Goals (OSDG)7 [START_REF] Pukelis | Osdg -open-source approach to classify text data by un sustainable development goals[END_REF], developed by the UNDP SDG AI Lab, aims to integrate various existing attempts to classify research according to Sustainable Development Goals based on the principles of ontology and domain recognition. The input texts of the OSDG tool are labeled with fields of study defined by the Microsoft Academic Graph tool and are cross-referenced with an evolving ontology to determine the degree of correlation between the two. And finally, via the determination of a specific threshold the numerical value of the relationship between the SDG and the text is interpreted as strong or moderate. This tool allows users to detect SDGs mentioned in the texts of their choice either by entering the DOI of an article or by manual entry in a dedicated insert. There is also the online tool. LinkedSDG8 [START_REF] Joshi | A Knowledge Organization System for the United Nations Sustainable Development Goals[END_REF], developed by United Nations Department of Economic and Social Affairs (UN DESA), provides a tool to link a text to the SDGs it addresses and to establish the degree to which the text is linked to the target of each SDG. This method is based on the use of an ontology and the extraction of concepts from the input texts to classify them according to the distribution of concepts.This tool permits to users to detect the SDGs present in an input text (by entering a text link or by manual entry) but also to detect the precise targets of each SDG. Finally, based on the same will as ours to be able to provide a method [START_REF] Lafleur | Art is long, life is short: An sdg classification system for desa publications (05[END_REF] capable of classifying document flows received by the United Nations, the UN Department Of Economic And Social Affairs (UNDESA) proposes an unsupervised classification method based on the Latent Dirichlet Allocation (LDA) [START_REF] Blei | Latent dirichlet allocation[END_REF] algorithm. The LDA automatically labels texts by statistically identifying logical subgroups in the data, i.e. classes/SDGs, each related to specific word lists. These results in a statistical model that can be used to classify another data set. For a given text the method is also able to assign a similarity score between the words of a text and the words related to each SDGs previously defined by the LDA.

There are also similar others methods developed independently of official organizations like Unitez [START_REF] Körfgen | It's a hit! mapping austrian research contributions to the sustainable development goals[END_REF] which has been designed to evaluate how SDGs are represented in Austrian research. The method is based on a manual selection of keywords list for each SDG, then a search algorithm is applied to scientific texts to retrieve one or more keywords linked to the SDGs. More related to the use of deep learning algorithms, a study [START_REF] Matsui | A natural language processing model for supporting sustainable development goals: Translating semantics, visualizing nexus, and connecting stakeholders[END_REF] proposes SDG-based text classification by training the BERT classification algorithm on a Japanese corpus to identify SDGs related to input sentences and vectorize the semantics of the input sentences. Another methodSOPA20, has the goal to detect UN General Assembly (UNGA) resolutions implicitly related to SDGs. The authors propose a method to apply deep learning methods for text classification with the constraint of a small labelled dataset. They built an ensemble method that effectively combines generic (non domain-specific) deep learning based document obtained from Global Vectors for Word Representation (GloVe) [START_REF] Pennington | Glove: Global vectors for word representation[END_REF] and Universal Sentence Encoder (USE) [START_REF] Cer | Universal sentence encoder for English[END_REF] with domain-specific Term Frequency-Inverse Document Frequency (TF-IDF) document similarities.

Although these methods are efficient in term of classification, they have some limitations such as the unsupervised methods always require time-consuming intervention of experts to map detected features to each SDG. Moreover, these methods are generally based on keyword analysis and do not take into account the context of the texts. Also in most of the case, tool and methods for SDG classification no not provide quantification of the link between a text and an SDG, which is useful to establish a more precise classification.

For these reasons, we decided to carry out additional research in order to build a method that would allow us to link a text to one or more SDGs that it deals with and also to quantify its degree of belonging to these different SDGs. Our tool, called SDG-Meter, is also based on the will to exclude any form of human intervention during the process of associating SDGs to texts (even for the training step). In addition to quantifying the text-SDGs relationships and saving time for our experts, our method will also allow external users to link their project/thoughts to the large fields of action by learning the SDGs that their project deals with and thus give them the possibility to better fit their project into the framework of the SDGs in question.

Multi-labeled text classification with BERT

As our objective is to link and also quantify the degree to which a text belongs to all 17 SDGs, a multi-labeled Text classification algorithm (supervised learning) was selected, which makes it possible to link a text not only to one label (SDG) but to several labels. More specifically, the recent and powerful algorithm Bidirectional Encoder Representations from Transformers (BERT) [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF], which is a neural network-based technique for natural language processing was chosen. As shown in the Figure 1 The input to the encoder for BERT is a sequence of tokens, which are first converted into vectors and then processed in the neural network. But before processing, BERT needs as input a sequence of tokens, for that 3 elements have to be added to the initial input:

BERT: Bidirectional Encoder Representations from Transformers

-Token embeddings: A [CLS] token is added to the input word tokens at the beginning of the first sentence and a [SEP] token is inserted at the end of each sentence.

-Segment embeddings: A marker indicating Sentence A or Sentence B is added to each token. This allows the encoder to distinguish between sentences. -Positional embeddings: A positional embedding is added to each token to indicate its position in the sentence.

To solve NLP tasks, BERT perform a 2-steps process, starting by training a language model on a large unlabelled text corpus (unsupervised or semisupervised). Then it Fine-tune this large model to specific NLP tasks (multi-label classification in our case) to utilize the large repository of knowledge this model has gained (supervised). BERT has two principal advantages compared to others NLP models. First, it is a "deeply bidirectional" model which means that BERT learns information from both the left and the right side of a token's context during the training phase. This bidirectionality is important for truly understanding the meaning of a text. Secondly, BERT is pre-trained on a huge corpus of unlabelled text including the entire Wikipedia and Book Corpus which confers with him a very extensive vocabulary base. But BERT also has a considerable drawback, indeed his memory footprint which grows quadratically with the number of words, as well as the use of pre-trained templates which have a fixed size. Then, BERT accepts a maximum of 512 words (approximately one full PDF page) because beyond that the limits of modern GPUs are quickly reached.

Concerning the data processing part, the dataset (Describe in section4.1) was split in train and test, the splitting ratio is 80% for train and 20% for the test. Then, the data need to be transform into a format which BERT can understand. For that the class "InputExample's" was create using the constructor provided in the BERT library with variable "text_a" containing the text to classify and "label" is the labels corresponding to our texts. In a second step, the text data was pre-process so that it matches the BERT's requirements (text in lower case, tokenization, etc.) Then, the BERT module was loaded and a single new layer was created that will be trained to adapt BERT to our multi-label classification task. The training phase has the goal to train the model to learn the main characteristics (words and their context) of the texts contained in our 17 classes (17 SDGs). Then the model keeps its training parameters to be applied to texts where their class is not given to the algorithm (But they was keep apart.), the algorithm with its parameters must be able to classify them correctly and once the classification is established, the class to which the algorithm classifies the text was compare to the one to which it is really assigned. Finally, to quantify the belonging of a text to a SDG, the prediction output was set in probability thanks to the use of a Logit function. Dropout was also included to prevent overfitting 9 And finally the loss was computed between predicted and real label which permits to assess the accuracy of our model which has an accuracy of 98% (based on 400 tests).

SDG-Meter tool

Once the BERT was trained on our database (using a 32GB GPU), the trained model and its parameters was saved so that it could be reapply for the classification of new texts. Then it was implemented in a web based tool that allow end users to achieve interaction through a website as shown in the Figure 2. The library PyTorch10 has been used for implementing the model. Pytorch is suitable for saving the model in python and deploying it on places such as websites and mobile applications. The used model is Fast-Bert11 wich is based on Pytorch and consists in an encapsulated version of original BERT algorithm 12 . This library supports BERT and XLNet for both multi-Class and multi-Label text classification which is the main purpose of our approach.The front-end is written in HTML/CSS and for the back-end website, Flask 13 was used as a lightweight webpage to connect with our application based on python. The code can be found on the UNEP's Git repository 14 . As shown in the figure, users can enter the text of their choice in the dedicated box (with a strict maximum limit of 512 words) and in less than a second the list of detected SDGs appears in a decreasing order according to their probability of belonging to the text (expressed in %).

Experimentation

Dataset

Web-scrapping process Deep learning methods often require a large dataset for training (of the order of a few thousand) in order to achieve a good level of accuracy in the classification task. However, it is very often difficult to obtain a large set of labeled data and difficult to mobilize an expertise for the manual annotation of so much data (very time-consuming task).

In a previous study [START_REF] Guisiano | Automatic classification of multilabel texts related to Sustainable Development Goals (SDGs)[END_REF] we chose to start with a 169 texts database describing the targets of each SDG (ref site/data) and to extend the database to 6000 texts via manual incorporation of text related to each SDG, integration of the definition of synonyms of the keywords of each SDG and finally the use of Markov chain (NLG) for automatic generation of text for the themes of each SDG. Despite a classification accuracy of 94% we found the quality and diversity of the generated texts to be very poor. Moreover, by generating texts for each SDG theme, the majority of the texts in our database were linked to a single SDG, which is problematic in the context of a multi-label classification. To do this, we chose to re-construct our database by focusing on the quality of the texts and their labels rather than on the quantity of the latter. We have therefore sought a data source containing quality texts associated to one or more SDGs (multilabeled dataset). We also impose the condition that each text of the data source has to be associated to SDG manually by experts but not any other form of classification algorithm in order to not introduce additional bias that we cannot directly manage. After many researches and collaborations with the One Planet linguist team, we finally choose to exploit data coming from IISD "SDG Knowledge Hub" website 15 . Indeed, this website has all the required criteria; it provides users with labelled articles such as "News", "Guest Article" and "Policy Brief" so three different writing styles (important for the diversification of vocabulary and writing style). In order to exploit these data, we had to extract the texts and the labels of each article, for that we had to resort to the techniques known as "Webscrapping". This process allows, via the HTML encoding of the web site, to identify the contents of the texts and labels in order to extract them. We included this process in an algorithm written in Python allowing us to carry out this operation for each category of text, for each web page and for each article. We also add the condition that each article must have less than 512 words to be extracted. (explanation of this limitation in the section below) Data set description thanks to the web-scrapping process, we could extract 724 labeled texts from "News", "Guest Article" and "Policy Brief" format. The texts in our database have an average length of 374 words and have an average of 4 labels(SDGs) with a minimum of one label and a maximum of 17 labels. It can be noticed on the Figure 3 that the distribution of the SDGs, thus of our classes, is unbalanced. Indeed, the texts extracted from IISD were written and labeled during the years 2020-2021, the main period of the COVID epidemic. The distribution of SDGs clearly shows a peak for SDG 3 "Good Health and Well Being". During these period various themes emerged in the run-up to Cop26, all of which are mainly related to SDG 13 "Climate Action", which is reflected in the distribution of classes. The largest peak is attributed to SDG 17 "Partnership for the Goals", which is explained by the fact that SDG groups a large number of sub-themes that are almost automatically associated with all texts. In contrast, it can be note that for this period and the selected texts, SDG 3 "Quality Education" is under-represented.

Test and results

In order to test our SDG-Meter, new texts labeled by experts from IISD-SDG was selected. These have 1 to 4 labels including also under-represented labels in the training data (e.g. SDG 4 and 7) so that it can be test if despite their underrepresentation the SDG-Meter manages to identify them. It should be specify that the labels coming from IISD do not have an order according to their relation with the text, the order is numerical according to the SDG numbers. As for the SDG-Meter results, these indicate the probability of an SDG belonging to the text expressed as a percentage. Each class (SDG) is independent, so the sum of the probabilities for different SDGs can be different from 1. Text 2 : "UN Embarks on Preparations for Education Summit" The second text, which is uniquely related to SDG 4 "Quality Education", addresses the following areas: Means of Implementation, Capacity Building Education. SDG-Meter: SDG 4 has a probability of belonging to the 92% text, so a strong relationship is established with the former. SDG 17 "Partnerships for the Goals" is also detected with a probability of 2% and finally SDG 8 with a probability of 1%. According to the SDG-Meter, the text deals mostly with SDG 4.

Text 3 : "UNGA Event Highlights Potential Sources of Financing for Road Safety" The third text is related to SDG 3 "Good Health and Well-Being" and SDG 11 "Sustainable Cities and Communities" deals with the following areas: Health, Transport. SDG-Meter: SDGs 11 and 3 are detected with respective membership percentages of 29% and 6%. A relationship is also found with SDG 16 "Peace, Justice and Strong Institutions" with a probability of 3.

Text 4 : "Environmental Leaders Selected for Contributions to Ecosystem Restoration" The third text is related to SDG 13 "Climate Action", SDG 14 "Life Below Water" and SDG 15 "Life on Land" and covers the following areas: Atmosphere, Biodiversity, Energy, Health, Conservation, Ecosystem Approach, Oceans Coasts, Stakeholder Participation, Climate Change. SDG-Meter: In this case, the 3 SDGs mentioned by the IISD are all found by the SDG-Meter without additional SDGs. SDG 15 appears in first position with a probability of belonging to 83%, then SDG 14 with a probability of 4% and finally SDG 13 with a probability of 2%.

Text 5: "Environmental Leaders Selected for Contributions to Ecosystem Restoration" The third script is related to SDG 1 "No Poverty", SDG 3 "Good Health and Well-Being", SDG 8 "Decent Work and Economic Growth" and SDG 10 "Reduce Inequalities" and covers the following areas: Health, Poverty Eradication, Economics Investment SDG-Meter: SDG 1 is detected with a high 89% probability of belonging to the text, SDGs 3 and 8 are detected with a 2% probability, and SDG 10 in last place with a 1% probability. The SDG 13 is also detected with a probability of 4%.

These results show us that the SDG-meter is able to provide a text analysis very similar to that of an expert. Indeed, we have seen that the SDG-Meter manages to find all the SDGs selected by the experts for each text. In the case of text 4, the results are identical. For the other texts, the SDG-Meter in addition to the SDGs indicated by the IISD finds by itself other SDGs related to the text that the IISD does not mention. In this case, it is difficult to know on which side the right answer lies, it is also possible that the SDG-meter via its deep learning is able to detect SDGs that the experts would not have noticed. As for example in text 4, the SDG 13 is detected with a probability of 4% which represents a significant percentage. On the other hand, it is possible to question the significance threshold for texts 1 and 2, which mention SDGs with probability percentages of around 1%. Text 5 also presents a threshold of 1% but for SDG 10 which is common between the IISD and SDG-Meter analyses and which therefore remains significant.

Conclusion

Within the various UN entities, experts regularly spend significant time classifying documents according to the 17 SDGs. In addition to being repetitive and time consuming, this approach introduces a large bias as the view from one expert to another varies, meaning that for the same text two experts may assign very different SDGs. Aiming to unify this classification system but also to automate this task, we propose the online tool SDG-meter. The latter is based on the use of the BERT deep learning algorithm which is able to provide excellent results (accuracy of 98%) with a limited amount of training data. Indeed, we have seen that for various texts our method is able to find the same SDGs as an expert, which means that this type of solution could in the future completely replace the current manual work of experts.

However, it is possible to contribute to its improvement on various points such as the improvement of training data sets and BERT itself. Indeed, as shown in Figure 3 our initial dataset can be qualified as imbalanced because some SDGs are less represented than others in our dataset. This can lead to low predictive accuracy for the infrequent class tending to decrease the overall accuracy of the model. Increasing our dataset could therefore contribute to smooth the differences between the representations of each SDG and thus obtain a better accuracy. It would also be preferable, in the context of increasing our dataset, to obtain labeled texts from various sources in order to enrich the vocabulary related to the SDGs, but also to train our model on the various semantic structures. (research articles, official reports, news, etc.) Finally, via our database, the BERT model has been able to learn to recognize SDGs through the specificity of each text, but it does not know what an SDG is not. Indeed, if a text not related to the SDGs is introduce, the SDG Meter will still try to make the link. This leads to a noise during classification which is manifested by the presence of a very small percentage of SDGs not linked to a text. To remedy this problem, it would be necessary to include in the training database various texts that are certainly not related to the SDGs and to stipulate their non-membership in the SDGs by assigning a value of 0 to each class (SDG). Another important aspect concerns the word limit of the input texts imposed by BERT. Indeed, this limit allows for the moment to analyze only the equivalent of a full PDF page. The advanced version of BERT named "SMITH" is currently being designed and should allow to extend this limit to 2048 words. However, it would be better to avoid any word limit. Some methods [START_REF] Wang | Multi-passage BERT: A globally normalized BERT model for open-domain question answering[END_REF] permit to avoid this limit as for example those based on the principle of sliding window but these last ones are known as being less powerful because they have insufficient long-range attentions. Another method, COgnize Long TeXts (CogLTX) [START_REF] Ding | Cogltx: Applying bert to long texts[END_REF], allows to overcome the BERT limit by identifying key sentences by training a judge model, concatenates them for reasoning, and enables multi-step reasoning. This last solution presents excellent results and seems to be a way to improve BERT.

Fig. 1 .

 1 Fig.1. BERT input representation[START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF]

Fig. 2 .

 2 Fig. 2. SDG-Meter web interface

Fig. 3 .

 3 Fig. 3. Frequency of SDGs in the database

Fig. 4 .

 4 Fig. 4. SDG-Meter & IISD-SDG true labels comparisons

https://sdg-pathfinder.org

https://unsilo.ai

https://osdg.ai

https://linkedsdg.officialstatistics.org/

Overfitting occurs when the algorithm over-learns (overfit.)in other words, when it learns from data but also from patterns (diagrams, structures) which are not related to the problem, such as noise, thus degrading the performance of the algorithm.

https://pytorch.org/

https://pypi.org/project/fast-bert/

The original version of BERT is no longer available for the moment because its improved version "SMITH" is under development.

https://flask.palletsprojects.com/

https://github.com/UNEP-Economy-Division/SDG-Meter


Olivier Baledent

Pan Liu

Serge Metanbou

Cyrille Capel

Sidy Fall

Roger Bouzerar

Cerebro spinal uid dynamic front of cardiac and breathing in uence

it is still debated how breathing with the CSF. New Phase contrast MRI sequence based on Echo Planar imaging (EPI-PC) can now produce during minutes a velocity map, more less every 100 ms. We did found the literature quantitative evaluation of the CSF stroke volume change breathing. The aim of this work is to quantify CSF dynamics change in the aqueduct and in the spinal canal during the breathing and cardiac period using EPI-PC.

Background

Conventional cine Phase contrast MRI (Conv-PC) can quantify Spinal Fluid (CSF) by using cardiac synchronization [START_REF] Feinberg | Human brain motion and cerebrospinal uid circulation demonstrated with MR velocity imaging[END_REF]. This sequence can reconstruct only one curve from all the cardiac cycles of the acquisition, acquisition that can take minutes. Since the 1990ies many works have demonstrate how the CSF dynamics interact with the arterial and venous ows [START_REF] Enzmann | Cerebrospinal uid ow measured by phase-contrast cine MR[END_REF][START_REF] Greitz | On the Pulsatile Nature of Intracranial and Spinal CSF-Circulation Demonstrated by MR Imaging[END_REF][START_REF] Balédent | Cerebrospinal uid dynamics and relation with blood ow: a magnetic resonance study with semiautomated cerebrospinal uid segmentation[END_REF]. Nevertheless it is still debated how breathing interact with the CSF. Some authors have shown that breathing in uence more or less the CSF dynamics [START_REF] Klose | Detection of a relation between respiration and CSF pulsation with an echoplanar technique[END_REF][START_REF] Aktas | Spinal CSF ow in response to forced thoracic and abdominal respiration[END_REF][START_REF] Lloyd | Respiratory cerebrospinal uid ow is driven by the thoracic and lumbar spinal pressures[END_REF][START_REF] Yamada | Dynamics of respiratory and cardiac CSF motion revealed with real-time simultaneous multi-slice EPI velocity phase contrast imaging[END_REF]. Some of them postulate that CSF does not change its velocities direction during many cardiac cycles but only by the in uence of breathing. Whereas others continue to think that in normal condition, CSF goes up and down in respons of the systolic and diastolic period of the cardiac cycle. CSF stroke volume (SV) is a useful parameter well known by the persons working in this eld that represents the CSF volume moving up down during the cardiac cycle. New Phase contrast MRI sequence based on Echo Planar imaging (EPI-PC) can now produce continuously during minutes a velocity map, more or less every 100 ms function of the machine and of the quality wanted (9). We did not found in the literature quantitative evaluation of the CSF stroke volume change during breathing. The aim of this work is to quantify CSF dynamics change in the aqueduct and in the spinal canal during the breathing and cardiac period using EPI-PC.

Methods

The study was performed on a scanner with a 32 channels head coil was used. Ten healthy adults volunteers signed the ethical agreement consent (ID-RCB : 2019-A02130-57) to investigate CSF ows in the aqueduct and at the cervical level ( g 1). Two di erent acquisitions were used ( g 2):

• Conventional 2D Cine-PC (Conv PC) using retrospective plethysmograph gating. • 2D EPI PCMRI sequence free of any synchronization. Post processing was done by homemade software to calculate CSF ow curves during cardiac cycle. Conv-PC provided only one ow curve to represent all the cardiac cycles of the acquisition whereas EPI-PC provided continuous ow dynamics curves including all the cardiac cycles of the acquisition. Then it was possible to study the change of CSF ows between inspiration and expiration. Each CSF ows curve obtained from EPI PC signals (8-12 points) per cardiac cycle was extrapolate to 32 points to be compared with conv PC. Respiratory physiological signal was recorded during the MRI acquisition using a pneumatic belt sensor. This respiratory signal was used in the post processing to divide the inspiration and expiration periods and reconstruct CSF ows dynamic of these two di erent breathing periods. Then it was possible to study the change of CSF ows between inspiration and expiration. SV was calculated for conv PC and EPI PC and compared together. Statistical spearman correlation and paired t-wilcoxon tests were used to compared EPI-PC and Conv-PC results.

Results

Both for Conv PC and EPI PC, CSF ows were present and simply identi able in the spinal spaces and in the aqueduct in all the PCMRI of the subjects. SV of the CSF in the aqueduct measured in the Conv PC and EPI PC were well correlated (Rs=0.94, p=0.0001) but the SV of CSF in the aqueduct measured by EPI PC were higher than those measured by Conv PC (p=0.009). SV of the CSF in the spinal canal measured in the Conv PC and EPI PC were little less correlated (Rs=0.67, p=0.039) and the SV of CSF in the spinal canal measured by EPI PC were this time smaller than those measured by Conv PC (p=0.032) ( g 3). An example of the Curves obtained by EPI PC is presented in g (4-5). For all the subjects it was possible to calculate the SV of the CSF during inspiration and expiration periods. In the aqueduct and in the spinal spaces CSF SV were respectively 9% and 8% higher during the inspiration periods in comparison with the expiration periods. In all the subjects, even if exist a small di erence in the SV during breathing, CSF presented all times a change in the velocities direction during cardiac cycle corresponding to existence of a ush period during cardiac systole and a cranium lling period during diastole.

Discussion-Conclusion

Without any cardiac or respiratory gating, EPI PC o ers possibility to explore continuously for the rst time the ows during many cardiac and respiratory cycles. Even if the spatial resolution is still limited these results shows good correlation with the conv PC, actually the gold standard. The good thing it is that EPI PC well correlated with the conv PC but the strange thing was that in the small aqueduct, EPI overestimate SV comparison with conv PC, whereas in the large spinal spaces the SV was under estimated. Nevertheless, in conclusion we have shown that physiological breathing modulate only the CSF ows signals by around 10% whereas the cardiac power is the force able to change direction of the ow during cardiac cycle.

Figures

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Acknowledgements

Thanks to the Arbeaumont -Trocmé -Julien Van Gysel and Héléna Freulet Thanks to the French Research agency: ANR-FIGURES & Hanuman Thanks Région Haut de France Thanks to MRI Research GIE-FF and CHU Amiens Picardie Thanks to all the volunteers who trust us and let us working with their spins. Thanks to David Chechin for his scienti c support.