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Enregistrement W4362589316 · doi:10.1162/leon_r_02389

Giving Bodies Back to Data: Image Makers, Bricolage, and Reinvention in Magnetic Resonance Technology

2023· article· en· W4362589316 sur OpenAlexaff
Roberta Buiani

Notice bibliographique

RevueLeonardo · 2023
Typearticle
Langueen
DomaineNeuroscience
ThématiqueAesthetic Perception and Analysis
Établissements canadiensFields Institute for Research in Mathematical Sciences
Organismes subventionnairesnon disponible
Mots-clésIconCitationBricolageComputer scienceDownloadSearch engine optimizationWorld Wide WebInformation retrievalLibrary scienceArtSearch engineVisual arts

Résumé

récupéré en direct d'OpenAlex

Giving Bodies Back to Data takes us on a fascinating journey into the history and complexities of MRI technologies. Specifically, the book emphasizes the role that aesthetics, affectivity, and craft practice play in the processes leading to the production of and the artistic responses to these technologies.The introduction presents readers with three compelling scenes that immediately grab their attention and set the tone for the entire book. In the first, Santiago Ramon Cajàl, equipped with pen and ink, sketches neurons by hand while sitting at his desk in Madrid in 1882. For the second scene the author fast-forwards to 1974, when physicist James Hutchinson, biologist Margaret Forster, and the biomedical physics laboratory team in Aberdeen produced the iconic first MRI image of a mouse with a broken neck. The data resulting from a small magnetic system were translated into an easily readable color-coded image with the help of color crayons. The third scene transports the reader to an exhibition by Mark Didou, where the apparently abstract form of a sculpture features data recast from an MRI scan. The sculpture in turn is reflected onto an anamorphic mirror, where it rematerializes as a skull. The three scenes introduce us to different ways of approaching, producing, and processing images as complex and unequivocally relational: Cajàl’s scientific image is not just a representation of a phenomenon but a way to “interrogate the object made visible” (p. xiii). Hutchinson’s image is the product of an interdisciplinary effort. It manifests a “striking copresence of numbers and pictorial elements” showing how “scientists combine physics, mathematics, and aesthetic choices” (p. xv); it is an effort to make visually legible a phenomenon that the technology is expressing in the form of data. Didou’s Skull I takes data extracted from the MRI data-visualization scanning process and turns them into an image again. The installation inverts the scanning process, starting by showing the abstracted data-turned-sculpture, then displaying in the mirror the body from which these data originated. In these scenes, data are never just data but are always the product of technological interfacing and mediation and human processes. They come to life when they are processed and transformed into something else; they must be interpreted, translated, and reworked in order for them to make sense to different scientists and practitioners, as well as to a primarily vision-oriented culture.The three scenes introduce three important themes, extending across the book’s seven chapters: Casini weaves together a well-documented cultural-historical account of MRI technologies; an analysis of the transformation of vision and cognition as it emerges from the techno-mediated image; and a multifaceted critical discussion on how art, aesthetics, and science intersect and collide both in the work of artists collaborating with scientists and working in labs and in the work of scientists themselves.The book is composed of two distinct parts linked through an Intermezzo. To a certain extent, the two parts could be read as separate books, depending on whether readers are interested in technoscience, technological history, and scientific ethnography, or are rather attracted by interdisciplinary collaborations or art-and-science explorations. Whereas the first part maps the technoscientific history of MRI technologies and MRI data visualization as they were conceived and developed during long-term experiments and fabrication conducted at the University of Aberdeen, the second part focuses on the interpretation of MRI technologies and data visualization by artists and curatorial teams working at the intersection of art and science and on the difficult yet surprisingly productive dialogues that artists and scientists establish when engaging in collaboration. The central intermezzo, exploring the significance of the grid in transforming vision and the perception of one’s body (be it the body of the patient or the body of the artist), cleverly connects the two parts of the book, pulling readers back in and compelling them to see the grid transversally, that is, as a crucial cross-disciplinary structure used in the arts as much as in the sciences.Casini’s historical and ethnographic analysis covers two important periods in the history of MRI technologies: the data visualization challenges emerging at the Aberdeen’s biomedical physics lab between the 60s and 80s leading to the development of the first whole-body MRI scanner; and the latest (in-progress) research on the FFC-MRI, which Casini discusses as a product of the recent discourse, imagery, and practice of personalized/predictive medicine. This is a rich and multi-faceted history: The author carefully excavated archival material including official documentation, detailed description of equipment, and its technical developments.Importantly, archival material revealed the “intimate process” behind technological development: Lab notes and personal notes by scientists revealed their epistemological approaches and their preoccupations not only with more obvious scientific aspects but also with the well-being of the potential patient. Notes also contained important reflections on the relationship between data and imaging. Casini uses the important concept of bricolage to describe the ecology of instruments, tools, wires, and cables, but also DIY creativity and problem-solving, as essential components of any scientific innovation (p. 63). This is an interdisciplinary history, demonstrating how scientific innovation always involves people with different disciplinary trainings and agenda, different ways of seeing and understanding, and different ideas on the role of imaging.Among the thematic threads traversing the book, the reader finds an important examination of the role of images, not only as they pertain to the realm of MRI technology but also in the context of data visualization used in the medical field and in the arts. For Casini, operational images based on machine learning algorithms have come to challenge our vision and cognition, becoming “spaces of possibilities of human intervention” (p. 207). She demonstrates this by reflecting on the transformative arc from the first image appearing to demonstrate the effectiveness of MRI (the image of the dead mouse with a broken neck), where static data were associated to pictorial clues to facilitate effective comprehension, all the way to the latest images produced with the assistance of algorithms, where dynamic data are connected with probability and uncertainty.A very important aspect of Giving Bodies Back to Data is its sustained reflection on the often uneven and nonetheless very rich relationship between art and science. Casini’s engagement with the many roles played by artistic expression is particularly refreshing. She is well aware of the power-relations involved in art and science collaborations, as the arts have traditionally been considered subordinate or accessory to science: In the book, she narrates such relation with minutiae through a series of examples that flesh out the different stages of art-making, starting from the encounter with the scientists (and the first impression of the artist and the ethnographer) to the actual production of the artwork. Art-making is a long process, encompassing dialogues with scientists, negotiations, misunderstanding, and research. It certainly is not just the object displayed in the gallery exhibition. Here, Casini uses the same ethnographic method she employed while unraveling the history of MRI technology, made of notes, drawings, and dialogues. She speaks about this relationship personally, as an observer coming from a humanities background being parachuted into a lab during her ethnographic research; she also speaks about it as a curator involved in the delicate process of mediation between the artist and the scientist during an arts residency leading to an exhibition at Suttie Art Space.In both cases, she reclaims the important role of the arts in the fields of art and science. Art is not just a way to reveal and explain, literally opening the black box of MRI technologies; it is not just a way for the scientific team to communicate science, or to elevate its exposure outside of the lab. The artists featured in this book are certainly not scientific communicators or tools at the service of science. They demonstrate a deep understanding of what working with data means. The artistic practices by artists such as Ilona Sagar, Mark Didou, Liz Orton, and Beverley Hood (all artists working with data) emerge from intense research into the technoscientific, historical, and social aspects of MRI technologies. Their works play the important role of illuminating the profoundly relational features of data by showing how the scientific process leading to visualization is connected to the body, the emotional journey of patients, and their relationship with the practitioner. In addition, they explicate the changing role of data (from static to dynamic, from exact to probabilistic, etc.) in today’s machine learning era.Giving Bodies Back to Data is a must-read book for a range of readers: Whether interested in understanding the journey leading to the development of MRI technology, or the processes of art-making in an art and science context, they might find themselves becoming inextricably entangled with and benefiting from both approaches.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,568
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,053
Tête enseignante GPT0,312
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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