dataesr/scientific-paragraphs-categorization huggingface dataset:
The description of the NeuroDecodeR package in this paper is based on the package's initial release, however, we anticipate continuing to develop and extend the package. A few directions we are interested in extending the package include adding additional NeuroDecodeR objects (e. g. , additional data sources and classifiers), and writing code that can give estimates of the memory usage and runtime that particular analysis will take which will enable users to choose decoding parameters based on the computing system they are using for their analyses. For example, users could bin their data at a higher temporal resolution if they are running their analyses on a more powerful computer. We also plan to create a shiny app for the package that will allow users to generate R Markdown documents that contain all the code needed to run an analysis by clicking buttons on a graphical user interface.
This "NeuroShiny" app will further shorten the time it takes to run decoding analyses, and will enable Neuroscientists with little programming experience to run reproducible decoding analyses on their data. Finally, we anticipate creating other types of analyses that use the same binned and raster formats described in this paper, which will allow researchers to easily run a whole range of different types of analyses once they have put their data into the proper format. In summary, we believe that the NeuroDecodeR package will be of great benefit to neuroscientists who are interested in using decoding to analyze their data, and should also offer an entry point for Statisticians who are familiar with the R programming language to become involved in analyzing neural data. We hope that this package will lead to new insights into how the brain processes information and will help to speed up the pace of discovery in neuroscience.
Acknowledgements We would like to thank  Tomaso Poggio  for his continued support, and  Xinzhu Fang ,  Sukolta Rithichoo ,  Xinyao Lu  ,  Boming Zhang ,  Yongyi Peng ,  Elisa Loy , and  Jenny Pan , for helping to test the NeuroDecodeR package. We would also like to thank  Doris Tsao ,  Winrich Friewald ,  Christos Constantinidis ,  Robert Desimone  and  Ying Zhang  for their willingness to share data they collected.
1. Our project ith a few exceptions-focusing on journalism (Compagno 2017), on works of art  (Chartier, Pulizzotto, Chartrand & Meunier 2019 , Dondero 2020) , on deep fake videos  (Leone 2021 , Dondero 2021 ) and on data epistemology (Bachimont 2018) -semiotics has been late to approach big data. In contrast to information and communication studies, that have been quick to recognize the big data revolution in medical, biological, political, urbanistic, and journalistic practices, and while digital humanities and digital art history continue to offer crucial insight with their analysis of large collections of artworks and heritage objects, semiotics has yet to live up to the big data challenge. This tardiness is due to several factors: (i) the prevalence of the structuralist tradition's textualism, which encourages sophisticated analyses of either single objects of study, such as the production of a single author, or limited corpora of works; (ii) the belief that general semiotic theory is applicable to any cultural object, and the consequent assumption that there is no need to adapt semiotic models to new media or practices; (iii) the habit of working with objects endowed with motivated meaning (myths, paintings, novels, political discourse, etc.
Furthermore, semiotics needs to contribute to the analysis of large collections in both diachronic and synchronic terms, and explore the extension or even the overall revision of some of its own key concepts such as enunciation, isotopy, semi-symbolism, etc. In summary, this Punctum issue has attempted to: 1. contribute to the global discussion of possible semiotic approaches to big data theory and methodology; 2. explore specifically an epistemological approach to visualizations of big data, i. e. to their manipulation, design, display, and interpretation; 3. study, from a critical vantage point, the ideologies and epistemic perspectives that underlie the acts of collecting and visualizing big data, prior to their analysis. Our goal is to trace the path that leads from visualization, regarded as a unifying representation of disparate data, to its full elevation to an interpretative device.
Data visualization and augmented reality The relationship between data visualization and augmented reality is rarely understood as crucial, as if the mass of available information could itself direct our "seeing in. " However, an archive of data or even a well constituted and carefully selected corpus of data is never self-sufficient. Forms of "augmentation" are always necessary in order for us to liberate ourselves from the debilitating idea that a framework can never be fully reconstituted. This augmentation, relating an object to a cultural reality, will also serve to activate a "seeing as", if not a "seeing through", that is indispensable if we are to make fertile comparisons between domains of knowledge, as well as between constituted domains and a cross-domain that questions meaning itself.
And it is left to artificial intelligence and its deep learning to offer salient visualizations. Meaningful results (namely pregnant results) occur when the expression plane of emergent forms can be associated with content, and the latter is rarely already immediately available. Instead, it requires "augmentation", collateral knowledge, other corpora already instituted. This does not indicate a relativization or a downgrading of the contribution of the digital moment, but more simply signals that the latter must have confidence in its own re-entries, i. e. in the circuit between visualization results and augmentation possibilities, which immediately entrusts the entries with interpretive depth. For example, visual Big Data do not constitute a totalizing, homogeneous, synchronic frame of History.
Big Data Analyses allow us to discover a new nexus of cultural tensions  (technical, conceptual, social, artistic etc. ) , but this does not prevent, indeed it fosters, the recognition that images are responses to other images that constitute themselves, sometimes unpredictably, as dialogic and polemical spaces. Finally, there is the need to compose, in the analysis of visual Big Data, a diachronic perspective that can recognize genealogies of transformations of forms. If such a project is called "the life of forms", it is because forms are not conceived as following a linear history. As a matter of fact, several transversal dynamics have to be acknowledged: (a) influences coming from different genealogies of forms, (b) evolutions in techniques and in materials, (c) the contingency of reception.
The intersection of autonomous factors (i-iii) with heteronomous ones (a-c) implies, for example, that  Focillon's work (1934)  in art history is both emancipated from formalism and determinism as well as deeply pragmatic, from top ("the tool awakens form from matter") to bottom (the impact of a form on its era is always controversial). Forms question, and are questioned by, a horizon of intentions (production) and of expectations (reception). 1 1 The life of forms is at the heart of the project ANR Augmented Artwork Analysis (https://anr. fr/Projet-ANR-20-CE38-0017). Regarding the epistemology of artwork interpretation, between analysis of large visual databases and augmentation of the object in its usual location, namely the museum, the AAA project stands in contrast to three major forms of reductionism.
Within this perspective, the art image is always a response to other works; it is meant to be an alternative to other efforts, or a critique of them  (Baxandall 1985; Damisch 1987). The issue opens with Johanna Drucker's acknowledgement and analysis of Jacques  Bertin's 1967  Sémiologie Graphique, a work that is still indispensible for design in general and data visualisation in particular. His description of graphic variables (color, texture, value, pattern, shape, position, and orientation) offers concise categories that help us better grasp visual entities within a semiotic system, no matter what scale. Drucker devises new fundamental categories complementary to Bertin's, in accord with features of dynamic display of today's technology, and suggests their standardisation an instrumentalisation in Big Data visualisation and digital design.
From a different point of view, that of the user of data visualisation rather than the designer, Federico Biggio addresses the "framing of truthfulness" in data images. The "interpretive advantage" offered by visualisation software can turn into its opposite. In an exercise in applied semiotics, Biggio illustrates the need for a constant awareness that subjectivity is being constantly modulated in data images, and therefore also that, as Jacques Fontanille has noted, "every data image is the result of a computational enunciative practice. " A crucial part of this "framing of truthfulness" mentioned in the previous text is the management of "latent space" in a deep-learning system. Cristina Voto analyses the work of the artist-coder Jake Elwes, Zizi-Queering the  Dataset (2019) , in order to flesh out exactly how rhetorical and ideological premises of data visualisation are inherent in Artificial Intelligence software.
Using Elwes' discussion of gender-parameter design bias, Voto points to the fact that the visualisation of latent space, i. e. mathematical map of what the Neural Network has learnt from the training dataset, helps us deconstruct and reveal these presumptions and prejudices. Indeed, data visualisation is hardly transparent or objective, it is hardly a direct representation, as Valeria Burgio confirms. Elaborating on examples of scientific and journalistic images, Burgio employs the semiotic concept of enunciation in a way similar to that in critical design theory and digital humanities, and offers a reading of data visualisation as expression, and as interpretation. She discusses the uncertainty of the visualiser vis-a-vis the data observed, and suggests that the presence of this interpreter/visualiser be enhanced, so that the enunciative traces of visualisation become more evident.
If we assume the existence of such effects of meaning, it is possible to assert that the data image is a textual form, the result of a predicative practice aimed at obtaining an interpretive gain for the human utterer who once a data image has been realised and enunciated, can be configured as a communicative gain. In this perspective, every data image is the result of a computational enunciative practice  (Fontanille 1994). A data analysis session and the enunciation of a data image are guided by the collaboration between a user and a computational entity. An example of this enunciative process is found in the collection of data disseminated over a network. To extract a cluster from the data, the collector manipulates the discontinuity of the data streams. Through the collection process, a set of data is extracted from databases and organised into clusters and features to provide a meaningful reading of a process.
The analysis resulted in image data and, in effect, heat maps depicting clusters of topics in relation to the social position of the pronouncing actor. Much like the Internet, the Web is not a single network but a network of networks, a graph in which densely connected zones are separated by relatively empty spaces. For the most part, these zones correspond to thematic communities: groups of people clustered around interests and points of view. The hyperlinks between sites dealing with human-computer interaction can therefore reveal much about the networks of actors involved in the conservation of human-computer interaction. In other words: If we know which websites are linked online, we can infer which actors are linked offline  (Severo & Venturini 2015: 4). Here, we can clearly observe the meaning that comes from following hyperlinks along users' paths and analysing digital traces.
Moreover, this concept of "reflexivity" implies certain practices that are closely related to the creation of an effect of transparency. The analysis confirmed that national and institutional nodes (especially the overlap of the two categories, i. e. , national institutions) play the role of authorities in the networks, as they receive the most links and are consequently very visible sites in the corpus. [. ] While national institutions occupy the centre of the networks, local and individual nodes tend to be located at the edges of the graphs, and local sites are often densely clustered around specific cultural practices. Moreover, as expected, the network structure of each country reflects the main characteristics of its cultural system  (Severo & Venturini 2015: 15). The data meditation rituals of Salvatore Iaconesi and Oriana Persico (2020), concerning autobiographical and collective writing paths that were graphically organised into data images and multisensory images with strong evocative value using data visualisation software, and also used for meditative purposes, were also in this direction.
Impersonality, encyclopaedia and database According to Paolucci, "impersonality" describes the determination of culturally established (i. e. , encyclopaedic) norms, schemes, and customs on subjective (i. e. , personal) enunciates. In this context, Paolucci cites Guillaume and "the primacy of he/she" in describing impersonality as the quality of the delocutive form as an "evenemential subject that opens up subject positions in which one speaks"  (Paolucci 2020: 73). In such a view, the theory of impersonality not only allows us to describe the general functioning of enunciation, but also to affirm a super-personality at the level of language, which ultimately allows us to describe cultural stereotypes, clichés, and genres. In such a perspective, enunciation is impersonal in so far as to produce an utterance is to keep in the present a set of norms, customs, and differential relations that constitute a schema (Eco's encyclopaedia), i.
These data tell the story of everyday life by recording what each of us has chosen to do. And this is very different from what is put on Facebook; postings on Facebook are what people choose to tell each other, edited according to the standards of the day. Who we actually are is more accurately determined by where we spend our time and which things we buy, not just by what we say we do  (Pentland 2014: 22). In this perspective, recovering Umberto  Eco's (1979; 2007)  concept of the encyclopaedia will be useful to define a paradigmatic semantic horizon that brings together all the knowledge of a community and the information produced during a practice of human-computer interaction with digital media. Consequently, the langue of computer utterances is precisely the set of digital archives (databases) linked to special software capable of scraping such encyclopaedic spaces.
Also, the utterances provided by the methodological-applicative proposals of data science can be called "enunciation of the encyclopaedia", i. e. , utterances that access the archive of a large number of differentiated utterances and make it possible to draw continuity inferences and derive semantic isotopies. If we accept the definition of the encyclopaedia as "the totality of all utterances", the set of "what has already been said", we could consider the database as the totality of all digital utterances that have already been announced and translated into data. According to Lev Manovich, one of the most important theorists of software culture, the database is-to quote Panofsky-a "symbolic form", a structured collection of data. In his most famous book, The Language of New  Media (2001) , he argued that the computerization of culture is accompanied by the advent of "storage mania", data collection, and indexing.
David Bihanic  (2015)  argued that data design is the most important practice that needs to be explored and developed to address today's "information overload", and in the terminology of semiotics, this means exploring the discursive dimension of Big Data. Narratives are not only one of the preferred objects of analysis in text semiotics; they are also and above all a set of models used by people to give meaning to things and experiences. Therefore, the narrators' perspective could be useful for the analysis of the implicit practices that lead an empirical subject to express a certain data image. However, it was precisely Lev  Manovich (2001: 199)  to argue that: As a cultural form, database represents the world as a list of items and it refuses to order this list. In contrast, a narrative creates a cause-and-effect trajectory of seemingly unordered items (events).
Therefore, database and narrative are natural enemies. Competing for the same territory of human culture, each claims an exclusive right to make meaning out of the world. After twenty-one years, however, his ideas about game practices-which he described precisely as narrative and interactive practices like those of data analysis-could maybe be revisited and reformed to explain narratively both the syntax of designers' practices and the interpretations of recipients. 2 hile such an application of the narrative paradigm may appear to be a method of deductive interpretation, it is nonetheless useful in focusing on the internal narrative opened up by data imagery. The provocative article by  Willers (2015)  entitled "Show, do not tell" aimed precisely at noting the "narrative approach [of data imagery] that presents the audience with a linear path to follow, dictated entirely by the author" (ibid: 4).
In this context, I will look at one type of journalistic data image and try to understand the ways in which the subjectivity of the speaker-which would contaminate the authenticity of the communicated truth-is hidden and distorted by the discursive manipulation and the subjectivity of the people that is instead explicitly and rhetorically represented in the image. In this respect, the data image appears as only one of the possible realizations of the virtual database to which the displayed information belongs.
In this way, the subjectivity of the data referents is determined by a realization of the virtual information contained in the reference database. The following data image is the result of a journalistic research conducted by La Stampa newspaper from Turin, Italy. In 2016, the "Torino Multe" project led to the visualization of over 4 million fines issued in the city between 2011 and 2015. For each of these fines, space-time information about the charges, the type of vehicle and the type of violation was organized in a database and an interactive visualization was created on the Infractions platform. Once the realization of the interactive platform was complete, the journalistic research work began. As can be read on the website, 3  the application consists of an API connected to the database of records, which is dynamically queried through the front-end interface that controls the display of the data.
We are thus dealing with a semiotic distortion, that is, the actualization of a virtualized form while empowering a realised form  (Paolucci 2020: 212-4 ). It could be argued, then, that such discourse depersonalised people and personalised territories by achieving in this way the distorted representation of a desired truth. Firstly, such an operation betrays the super-personality of the computational utterance, and does so precisely by publishing a non-truth account. It thus becomes a Trump's utterance and not just a computational utterance. Secondly, it fails to present a transparent panoptic vision of the electoral situation. It does not fulfil the image's purpose of allowing users' self-reflection into the image. The two images analysed are examples of data journalistic imagery, a text typology prototypically used in a social domain highly dedicated to the discovery of truth.
This characteristic does not prescribe a passive reading practice that could be compared to the scientific assertion of referential, but an active role for the reader that ultimately offers the possibility of personalising the viewing experience and modulating the subjectivities that most interest the reader. The two images again fall into the category of journalistic data visualization images: they were used by The Guardian to support the Snowden revelations about NSA surveillance programs in 2013. 4 In the image on the left, you can see an interactive visualization of what some public figures said to the press following those revelations. By clicking on the various characters, you can read and hear what each person said by juxtaposing them in real time. Even if it is not a real numerical data image, it creates the effect of truthfulness in the representation of a plurality that, as a rhetorical consequence, assures the super-personality of the utterer (in this case The Guardian).
From archive to dataset. Visualizing the latency of big data punctum. gr BY: Cristina Voto he objective of the proposal is to analyze what latent space is within a Deep-Learning system and how its visualization is capable of triggering a meaning-effect concerning the epistemology of big data. The latent space is the mathematical space that maps what a Neural Network has learned from the training dataset. It is the result of the compression of the input data and the step before the Neural Network's output, a step that usually remains invisible to the human eye, rendering effective the promise of a transparent effect of reality generally promoted by Artificial Intelligence technologies. Precisely in contrast with this promise, the visualization of this complex spatiality makes accessible, and therefore intelligible, the epistemic and rhetorical relations inscribed within datasets, intended as archives that gather information.
To achieve my objective, I will consider an artistic project realized by multimedia artist and coder Jake Elwes, Zizi-Queering the Dataset (2019), a multi-channel video where different facial portraits are shown in a morphing loop that visualizes what a Generative Adversarial Network has learned from the re-training of a dataset containing portraits with another one containing facial images of drag and non-binary individuals. This artistic gesture has led to a series of epistemic issues concerning big data and their situated and ideological meaning. The author would like to thank Elena Beretta, Jake Elwes and the two anonymous reviewers.
Queering art with artificial intelligence Zizi-Queering the Dataset, by AI artist Jake Elwes, 1 is a multi-channel digital loop video first installed in 2019 as part of the exhibition Preternatural, curated by Drew Hemment within Experiential AI, an artistic event developed at Edinburgh Futures Institute in Scotland. In a visual continuity effect, through a smooth, gradual, and seamless transformation, during the video loop we witness the synthetic unfolding of a series of portraits reproducing artificial faces. The video is, in fact, visually and cognitively connotated through a deep understanding of what it means to be human in the times of Artificial Intelligence. An understanding that for Elwes has to embrace a strong commitment towards a cognition necessarily enacted in the framework of a queer 2  sensitivity.