Data Matters Newsletter  —  Dec 2018

Data is never self-contained; it comes into existence only when someone expends resources to gather and record it, and its meaning is inextricable from its context. 

Look Back:

Listening to a Glacier on a Warm Summer Day

 

“Climate Change is an abstraction. We read about it, but it’s happening too slowly for us to perceive, and so it’s hard to accept it as something that’s real and urgent. This artwork is an attempt to give a ‘voice’ to a specific glacier in the hopes of making climate change something we can hear and see — something that feels real.” — Ben Rubin

We Recommend:

1. Illustrating Mental Illness

It’s hard to visualise mental illness in the same way we might visualise physical disabilities, and this can make it difficult for people with no experience of mental health problems to empathise or imagine how they affect peoples’ lives.

2. Water, data, art!

“Waterways get polluted. But they can also be cleaned up. It’s a process that’s reversible… Data forces you to work with constraints, at the same time as it gives unexpected results and surprises,” Kildall says.

3. Aeromidd | Earth Square Moon

A neat little fact about the solar system demonstrated in animation. Did you know that the radius of the Earth in ratio to the radius of the Moon is 3:11? This remarkably means that If you create a square of a side length equivalent to Earth’s diameter, it will have the same perimeter length as a circle of diameter equivalent to the Earth and Moons’ diameters together.

4. An Artist Sees Data So Powerful It Can Help Us Pick Better Friends

Frick imagines a future in which your smart watch will know how your body is responding to someone. Then it will combine with Facebook data about their personality. And that will let you know whether that person makes you lethargic, raises your blood pressure or depresses you.

5. Visualize your music DNA with Data 

Let’s retrieve your listening history of the last 100 days. Nothing that the recent GDPR wouldn’t allow though! Just the precise date, genre of the songs listened to and how many times you’ve listened to the same song/artist.

Upcoming Events in New York City:

1. Making Art in the Age of Algorithms Symposium– Friday, December 7, 2018 | Register

2. Culture Shifts in Social Data – How Brands Learn and React to Global Shifts in Thinking – Tuesday, December 11, 2018 | Register

3. NYC #OpenData 102 — Unlocking Open Data through data journeys – Thursday, January 17, 2018 | Register

Throwback Podcast:

Urban Legends with Karl Mamer

Back in 2014, Karl Mamer decoded the data mysteries behind three well known urban legends on Data Skeptic.

Follow:

Instagram @MappingLab.Me

Journey of a Meme: Culture Jamming & Elections

Meme is the basic unit of culture jamming – an idea that utilizes the conventions of mainstream media to disrupt, subvert and plays upon the emotions of political bystanders so as to evoke change and activism.

On Oct 11 2018, the hashtag #JobsNotMobs came into being anchored by a ‘supercut’ viral video of cable news’ use of the word ‘mob’ juxtaposed with footage of various protests that happened last year. This meme slowly made its way through the crevices of social media across 4chan, reddit, facebook and twitter into President Trump’s twitter feed. 

Keith Collins and Kevin Roose in their NY times article, 

visualize the birth and spread of #JobsNotMobs and how it rapidly became part of the Republican campaign narrative in the midterm elections. 

The creator of the meme, who goes by the pen name “Bryan Machiavelli,” told The New York Times he charges $200 an hour for his “memetic warfare consulting” services.

Check out the visualization here.

When Place favors Power: A Spatial Recount of the Ford-Kavanaugh Hearing

TRIGGER WARNING: This article or section, or pages it links to, contains information about sexual assault and/or violence which may be triggering to survivors.

Image result for blasey ford

Continue reading “When Place favors Power: A Spatial Recount of the Ford-Kavanaugh Hearing”

Mapping Technology’s Reach: Anatomy of an AI System

‘Alexa, turn on the hall lights’

The cylinder springs into life. ‘OK.’ The room lights up….

This is an interaction with Amazon’s Echo device. 3 A brief command and a response is the most common form of engagement with this consumer voice-enabled AI device. But in this fleeting moment of interaction, a vast matrix of capacities is invoked: interlaced chains of resource extraction, human labor and algorithmic processing across networks of mining, logistics, distribution, prediction and optimization. The scale of this system is almost beyond human imagining. How can we begin to see it, to grasp its immensity and complexity as a connected form?

The graphic and passage above are excerpts from Kate Crawford and Vladan Joler, “Anatomy of an AI System: The Amazon Echo As An Anatomical Map of Human Labor, Data and Planetary Resources,” The AI Now Institute and SHARE Lab, (September 7, 2018)

In their stunning new work, which comprises both an essay and a vast infographic, Crawford and Joler strip away the smooth exterior of Amazon’s Echo to reveal the global network of human labor, data, and planetary resources that make this technology possible.

…invisible, hidden labor, outsourced or crowdsourced, hidden behind interfaces and camouflaged within algorithmic processes is now commonplace, particularly in the process of tagging and labeling thousands of hours of digital archives for the sake of feeding the neural networks.

Read the essay and see the infographic here.

10th Berlin Biennale: Mapping an Exhibition Network  

by Prof. Dr. Eleonora Vratskidou and Dr. Anne Luther

Introduction

The Berlin Biennale is a contemporary art exhibition first organized by Klaus Biesenbach (Director of MoMA PS1 in New York), Nancy Spector (Chief Curator at the Solomon R. Guggenheim Museum in New York) and Hans Ulrich-Obrist (Artistic Director at the Serpentine Galleries in London) in 1998. The creation of the Berlin Biennale in the mid-1990s stands at the outset of a significant increase in number and geographical dispersal of such large-scale perennial exhibitions ─a phenomenon that fully partakes in the global flows of objects and people, the expansion of neoliberal economic structures, urban development, social engineering, and city branding. No more than ten in number around the early 1990s, biennales are today more than a hundred to take place more or less regularly around the world, [1] becoming the standard format for producing and displaying contemporary art.

With every Biennale a specific network of actors takes shape, involving curatorial and research teams, artists and their galleries, funding bodies, artistic collaborators and other public and private support bodies, institutions and their curators that are invited for a one time facilitation of the exhibition, graphic designers and media experts, technicians, transporters and installation teams, art writers and art historians, mediators and art educators, invigilators, etc. An inquiry into the network of actors that biennales bring together is the foundation to understanding how these exhibitions are made.

The information that is released about the number and kind of actors involved in the production of each show is a conscious decision communicated in press material, their website and publications. This decision is related to the labor politics and work ethos to which each biennial subscribes as well as to the self-image it seeks to broadcast.

The proliferation of biennials has not yet been thoroughly examined. A number of studies, based often on individual cases, focus mainly on curatorial practices and discourses ─or the discrepancies between discourses and practices─, but more empirical approaches regarding the involved actors, issues of connectivity and work ethics are still rare. The acknowledgement of internationally active artistic and curatorial networks is certainly a given, but their actual study is not yet systematically pursued. This inquiry seeks to contribute in this direction, based on the example of the 10th  Berlin Biennale, that took place in summer 2018 (June 9 – September 9, 2018) .

Under the title We don’t need another hero, the last edition of the show was curated by Johannesburg-based curator, artist and art educator Gabi Ngcobo. Upon her appointment by the international selection committee in November 2016, she invited four fellow curators, with whom she had collaborated individually in the past  to join her in the direction of the show: Nomaduma Rosa Masilela, Serubiri Moses, Thiago de Paula Souza, and Yvette Mutumba. The discourse of the exhibition put the emphasis on collectivity, collaboration and collective authorship and promoted dialogue as generative force. To this ethos testify most prominently the “curatorial conversations” published in the catalogue. Instead of an extended curatorial statement, the conversations serve to illustrate the reasoning mode and the collaborative generation of ideas at work among the members of the curatorial team.[2]  Similar attitudes were adopted among the artists: they were manifest in the production of the exhibited works, such as the programmatic installation piece by Dineo Seshee Bopape at the KW, which hosted works by three other artists (Jabu Arnell, Lachell Workman, and Robert Rhee), an initiative which was qualified by the curatorial discourse as “a gesture of hospitality and collaboration”. [3]

Interested in the collaborative ethos promoted by the show, we decided to pursue an investigation into the specific information made visible about the makers of the 10th Berlin Biennale, as an example of communication of a specific biennale network. The article introduces a network visualization based on the information on the collaborating actors mentioned in the website of the Berlin Biennale 10. We collected and structured data from the website in a format that allows to develop a node-link network of the various actors and their relationships. The following will introduce the data collection, node types and link relationships for the exploration of the interactive network graph of the Berlin Biennale 10.

Data Collection

The interactive network graph that we created maps out the relationships between the actors that made the 10th Berlin Biennale based on the information drawn from their website http://www.berlinbiennale.de. More specifically, we collected and manually structured the data that is displayed on the introduction pages of every participating artist. These pages contain the following elements: artist name, image of presented work or installation view and image credit, text to each participating artist and their exhibited work, name of the author, exhibition venue, list of works with or without courtesy and credits of various roles.[4]The artists’ pages communicate specific information on funding, support and art production regarding the making of the 10th Berlin Biennale that are by default linked to an artist’s name. In the structuring of our data, we considered this communication logic and defined the various node types (listed below) according to their corresponding artist.

The information provided on the website concerns the production and funding of the works and projects presented by each artist; the representation of the artists (galleries/courtesy) as well as the production of curatorial discourse (texts), involving 26 invited authors along with the members of the curatorial team.

We focused on artists’ pages, since this is an important point of contact between artistic and curatorial agency. While in the texts, members of the curatorial team and invited authors sought to place/situate the contribution of each participating artist within the larger curatorial project, artists were themselves responsible for the information communicated regarding those implicated in the making of the presented works, their various collaborators and supporters. The amount of contributors in the actual production of the works surely depends on their nature and media: a drawing is in this respect less demanding than an installation, a performance or a video. Diverging attitudes regarding crediting among the artists become most evident in the case of film and video works, which are per se collective enterprises. To cite only one example, Cynthia Marcelle names 48 collaborators (director, camera, steadicam, camera assistant and grip, production and production assistants, sound design, music research, editing, stills, musicians, drivers, etc.) involved in the production of her video Cruzada (2010, 8’35’’), while no collaborators are named for Emma Wolukau-Wanambwa’s video Promised Lands (2015, 22’). (We are not able to account for such differences at this point.)

The Βerlin Biennale is organized by KUNST-WERKE BERLIN e.V. and funded, since its 4th iteration in 2006, by the Kulturstiftung des Bundes (German Federal Cultural Foundation), the amount allotted to the show being augmented from 2,5 to 3 million euro starting with the last iteration.[5] This funding agency will not appear in our graph, since it is a given for every iteration of the exhibition, while what is of interest for us here is the way each curator and/or curatorial team appropriates the institution of the Berlin Biennale anew and shapes a network of actors depending on their own position and connectivity within the art world. Equally not considered are other public and private sponsors mentioned in the catalogue, such as the Berlin’s Senate Department for Culture and Europe or the car industry BMW (as Corporate Partner), since no information is disclosed regarding the concrete way they are connected to the various participating artists and exhibition related projects.

Data structure

The data from the website was structured in the specific format typical for the construction of node-link networks, as required by the digital tool Graph Commons. A node is an actor, entity or object within a network that is connected to other nodes with a specific link, which is called an ‘edge’. The ‘node type’ describes the actor in the network such as art institution or gallery, while the ‘edge type’ describes the relationship between actors. In order to illustrate how this database was structured, let us take as an example the participation of Basir Mahmood. In the credits on the artist page (http://www.berlinbiennale.de/artists/b/basir-mahmood), we find the following information: “Commissioned and produced by Sharjah Art Foundation”. Sharjah Art Foundation is the name of a node with the node type “Institution” that is linked to the node name Basir Mahmood with the node type “Person” by the edge (or relationship) “Commissioned and produced”.

While for the names of nodes and edges we closely followed the vocabulary adopted by the biennale for communicating the roles and relations involved in the making of the exhibition, we proceeded to the necessary classification of nodes and edges into types. We assembled all descriptions in two following node types:

Institutions include:

  • Art institutions: Primary function: supporting, hosting (pe. residency), conserving (pe. museum), archiving (pe. museum) and exhibiting art. Art schools have also been categorized under art institutions.
  • Cultural Institution: Largely educational function – active beyond the field of fine and performing arts. Many of them are involved in international cultural relations/exchange.
  • Political Institution: Its primary function is in the political realm.
  • Enterprise: Its primary function is in the economic realm.
  • Gallery: Its primary function is selling art.
  • Collection: Everything that has been qualified as such by the Berlin Biennale

Persons include:

  • Artists that are participating as such in the Berlin Biennale 10.
  • Curators
  • Authors of artists texts in the catalogue and for the website.
  • Persons active in the production or support of exhibited work.

Regarding the edges, we grouped the various descriptions found in the website into four big categories: Commission, production and support; Courtesy; Art Production and Text. These meta-descriptions are indicated by color coding in the network graph. Concretely, we assembled the following phrasings under:

Commission and production and support: : 15 Commissioned and coproduced; 12 Commissioned and produced; 1 Produced; 3 Commissioned; 6 Coproduced; 4 Coproducer; 1 Produced in partnership; 1 Produced with the support; 1 Existing works as well as commissioned works produced; 1 Existing works as well as commissioned works coproduced; 54 With the support; 3 In-kind support; 1 Funded; 13 Thanks.

Courtesy: 75 Courtesy; 14 In (Collection).

Text: 45 Text.

Art Production: Production, 3 Producer, 1 Production, 5 Production Assistants, 6 Production Team, 2 Artistic Production. Performers:24 Featuring, 7 Performed, 1 Choreography, 16 Musicians, 30 Activator. Director/Camera:1 Director, 2 Assistant Directors, 2 Cinematographer, 3 Camera, 3 Camera Assistant, 1 Camera Assistant and Grip, 1 Grip, 1 Steadicam. Screenplay:2 Screenplay, 1 Screenwriters, 1 Script, Direction, and Editing, 1 Line Producer. Editing: 2 Editor, 1 Editing, 1 Video Editing (Coloring), 1 Video Editing (Editing). Music/Sound: 5 Sound, 1 Sound Assistant, 1 Sound Design, 1 Sound Designer, 1 Sound engineer, 1 Music, 1 Music Director, 1 Music Research, 1 Spatialization and mix by, 16 Musicians. Light/Photography: 7 Light, 1 Film and Lighting Technician, 4 Director of Photography, 3 Stills. Costumes, make-up, design: 1 Costumes, 1 Costumes stitched, 1 Costume Designer, 1 Make-up, 1 Project Design Collaborator, 1 Set Design, 1 Backdrops painted by. Varia: 3 In cooperation, 3 Including works, 2 Collaborator: Vibratory installation, 1 Collaborators: Fluffy sculptures, 1 Printed and published, 1 Poster, 1 Driver, 1 Water Truck Operator, 1 Assistant, 1 Project Liaison.

We chose to adopt the ‘original’ description that the Berlin Biennale displays as information about the making of the exhibition: every link displays the wording that is also displayed on the website of the Berlin Biennale. The node types that we display in the graph use descriptions that are the closest to what we could find on the website. We did not use our own interpretations of roles in the art world but rather chose to display descriptions from the Berlin Biennale website. These descriptions of art world roles are displayed in a network view.

The visual representation of the network of actors is displayed in a Force Directed Graph, which is a visually pleasing method. The nodes are forced in a direction that gives space to comprehend edges and nodes in distinguishable ways. Nodes with a higher degree of centrality, which is determined by the number of edges connected to a node, are displayed closer to the center of the network.

Disconnected nodes are drawn to the outside. In Graph Commons, it is possible to view the Degree Centrality of each node displayed in a chart by in-degree centrality, out-degree centrality and betweenness centrality. In-degree centrality shows the number of edges that are directed towards a node and out-degree centrality shows the number of edges that are directed from a node. In this particular graph, analyzing the nodes by in-degree centrality, we can therefore see how many actors were involved with an exhibiting artist as co-producers, art production or authors (to name but a few). Analyzing the nodes by out-degree centrality, we can ask the network graph questions about the funding bodies who supported the most artists or how many galleries had more than one represented artist in the exhibition.

Clusters are nodes that are connected with each other with a higher number of edges. The betweenness centrality shows nodes that connect clusters with each other. Graph Commons allows the user to view these clusters in detail in the analysis tab.

The visualization of actors of the 10th Berlin Biennale is a platform to ask further questions and develop a broader inquiry into the networking, politics and funding of the exhibition and international biennale structures more generally. The authors will develop a deepened investigation and publish the results in peer-reviewed journals with a focus on art and technology.

 

Notes

[1] Panos Kompatsiaris, The Politics of Contemporary Arts Biennials: Spectacles of Critique, Theory and Art, New York and London, Routledge, 2016, p. 9.

[2] Gabi Ngcobo, Nomaduma Rosa Masilela, Serubiri Moses, Thiago de Paula Souza, and Yvette Mutumba, “Curatorial Conversations”, in: 10th Berlin Biennale for Contemporary Art, We don’t need another hero, exhibition catalogue, p. 31-41  (English part).

[3] Portia Μalatjie, “Dineo Seshee Bopape”, in: 10th Berlin Biennale for Contemporary Art, We don’t need another hero, exhibition catalogue, 2018, p. 62 (English part).

[4] This information is also provided in the catalogue, though not structured in the same way: in the main body of the catalogue one finds the texts on each artist –the website contains only short versions of the printed texts–, but the list of works by artist and information on courtesy, funding and production are given at the end of the essays section. Out of convenience, we used the website as our main source where all relevant information is grouped together.

[5] Gabrielle Horn, “Introduction”, in: 10th Berlin Biennale for Contemporary Art, We don’t need another hero, exhibition catalogue, p. 15 (English part).

 

‘Activating Museums’ Data for Research, Scholarship, and Public Engagement

A vast number of Digital Humanities projects have emerged in the last few decades that digitized museum collections, archives, and libraries and made new data types and sources possible. Interdisciplinary labs such as the medialab at Sciences Po focus on the development of new tools that cater to methods and research in the Humanities. They especially address the regressive gap that has become evident in the current tools needed to analyze, comprehend, and present these new digital sources. Provenance and translocation of cultural assets and the social, cultural, and economic mechanisms underlying the circulation of art is an emerging field of scholarship encompassing all humanities disciplines.

‘With the migration of cultural materials into networked environments, questions regarding the production, availability, validity, and stewardship of these materials present new challenges and opportunities for humanists in contrast with most traditional forms of scholarship, digital approaches are conspicuously collaborative and generative, even as they remain grounded in the traditions of humanistic inquiry, this changes the culture of humanities work as well as the questions  that can be asked of the materials and objects that comprise the humanistic corpus.’

Internationally researchers concerned with the ‘social, cultural, and economic mechanisms underlying the circulation of art’ work with object-based databases that describe, document and store information about objects, operations, movements and provenance of objects. The scholarship working with those databases is grounded foremost in disciplines of the Humanities and Social Sciences: Sociology of Art studying the social worlds that construct a discourse of art and aesthetics; closely related to it the Social History of Art concerned with the social contribution to the appearance of certain art forms and practices; Economics and Art Business exploring global economic flow and the influence of wealth and management strategies on art; Art Theory developing discourse on concepts relating to a philosophy of art and therefore closely related to curating and art criticism; and Art History with research in provenance, image analysis and museum studies.  

Although established methods for collecting, analysing and storing of various data sources differ with the methodological approach each researcher takes, it can be said that the need for new computational tools that allow the analysis, storage, sharing and understanding of the new digital data sources are needed. Digital data collections can not be studied without tools that are appropriate for the analysis of digital textual, pictorial and numeric databases. Borgman (2015) illustrates the current discourse on Big Data/Little Data and the associated methodological approaches in current research communities and identifies much like Anne Burdick (2012) “there exists not one method, but many” for digital data analysis in the Humanities.

The research project uses the infrastructure of digital databases produced with methods in the DH but will advance a primary focus in computer-aided research with the development of current web based software development standards. Only in the past 7 years researchers started to develop “standardized, native application programming interfaces (APIs) for providing graphics, animation, multimedia, and other advanced features.” This standardization allows researchers to develop tools and applications that can be used in multiple different browsers and on different devices. This is to show that the digitization of object based collections (museum collections, libraries and archives etc.) has an established history but nevertheless the development of tools to access, display and analyse these digitized collections is just at the beginning of current  possibilities due to the development of web standards across browsers and devices. The research group will bring this standard to the work with digital museum collections and in general with digital object based collection.

Museums produce phenomenal amounts of data.

They do so by tradition, research about the provenance, narrative, material, production and cultural embeddedness are attached to every piece in a museum collection. By digitizing we do not only consider pictorial surrogates for the works or economic value (acquisition prize, insurance value and other administrative costs that keep a work of art in a specific physical condition) but rather, researchers have produced knowledge, a history and institutional contextualization about works of art in institutions. The mode of shareability of knowledge and access to a corpus of information about works of art in a museum context is, at the moment, unsatisfying but inevitable.

Another development is the creation of a data capitalism that we find in a current museum context especially through applications such as Google’s Art and Culture face detection software in which the private company asks their users to trade a face match with museum portraits for their user data and facial images which is believe to be used to “train and improve the quality of their facial recognition AI technology.” In contrast to this research projects in the Humanities have the aim to find and produce solutions for accessibility to information dissemination of museum collections that work based on the research, methods and data structures of the institution.     

But, the advent of digitization and digital modes of exhibitions have only exacerbated the possible facets of the work of art. Researchers from all disciplines in the humanities analyse and explore object based databases with a variety of quantitative and qualitative methods and data analysis tools. The proposed project aims to produce new digital tools for interdisciplinary and mixed method research in the digital humanities, working with extensive data sets that allow the inclusion of a variety of methods, data types and analysis tools.

In its preliminary phases, the project between three institutions is interested in exploiting databases of cultural material to pave the way for new research tools in contemporary art history. The collaborating institutions, The Médialab, Sciences Po, Paris Translocation Cluster at the Technische Universität Berlin and the Center for Data Arts, The New School, New York City will organize data sprints that started in Paris in Fall 2017.

The Data Sprints

This methodology in the overview of methods in the social sciences was borrowed from the development of free software and has been adapted to the new constraints weigh on researchers venturing into the world of digital data. It takes the form of a data sprint, a form of data-centered workshop designed to deliver a better understanding of datasets and conducive to formulate research questions based on their complex exploration. It is the interdisciplinary development of new digital tools for the humanities, a sequence mixing in a short duration, typically over one week: data mining, their (re) shaping and production of descriptive statistics and data visualizations.

“Data-sprints are intensive research and coding workshops where participants coming from different academic and non-academic backgrounds convene physically to work together on a set of data and research questions.”

The six phases of a data sprint are 1) Posing research questions; 2) Operationalizing research questions into feasible digital methods projects; 3) Procuring and preparing datasets; 4) Writing and adapting code; 5) Designing data visualizations and interface and 6) Eliciting engagement and co-production of knowledge.

History of the project

The Medialab, Sciences Po, in coordination with the Centre national des arts plastiques (CNAP) and videomuseum (Consortium of modern and contemporary public art collections), has already tested the possibility of exploiting a data management infrastructure to tease out research questions in art history and sociology of art during a data sprint organized in September 2016 in Paris. This workshop produced an initial study of the Goûts de l’État – the tastes of the State, for once a better rhyme in English than in French – by exploiting the rich documentation of the acquisition and circulation of contemporary art (about 83,956 works in its Fonds national d’art contemporain or FNAC, managed by the CNAP) by the French State since the revolution. The experience has been positive for both the art historians participating and for the CNAP cadres who have also learned quickly about many aspects of their database from the new formats and visualizations tried by the programmers and designers. Several research projects emerged as a result of this encounter between art historians, programmers and designers.

Data Sprint at the MediaLab at Sciences Po in November 2017

Since April 2017, the researchers involved in the project are working together to connect with museums as partnering institutions to the project, strategize data sprints, and generate a team of researchers that will be invited to the ongoing data sprints. The MediaLab at Sciences Po organized the first data sprint in November 2017 inviting over 25 participants including museum staff of the Centre Pompidou. The participants gathered for one week at Sciences Po and developed over 50 visualizations and two working software prototypes based on their preliminary research and on site data analysis.

Research Questions in the First Data Sprint included:
  • Provenance and the translocation of cultural assets
  • Modalities and temporality of acquisition
  • Exhibition in the museum and circulation outside of the museum
  • Artistic groups and collectives in the museum
  • Social, cultural and historical embeddedness of the collection

Small working groups focused on at least on one of the following approaches:

Mixed-Method approaches

Qualitative and quantitative methods are used on the same issue and with the same priorities. Quantitative methods give insight into a data set and qualitative methods are used for single cases. Both the qualitative approach can lead to research questions that can be utilized for structuring the quantitative analysis and vice versa. Machine learning and quantitative methods are practiced as forms of ‘distant reading’ that allow the qualitative researcher a ‘deep dive’ with an understanding of patterns of the entire dataset.  

Machine Learning: Natural Language Processing

The method in natural language processing that will be focussed on in the workshop is ‘Named entity recognition (NER)’ to detect entities such as people, places or organizations in large text based data sets. NER will make it possible to map these entities according to geo locations, expressions of time or their category (attributes). After the first data sprint, a group of researchers begun to work on a forthcoming focus in image recognition as a tool for authentication in art history:

Machine Learning: Image recognition

In recent years the digitization of cultural collections (archives, museum collections, libraries etc.) has produced a massive amount of digital images. These surrogates for the material objects can be analyzed with qualities (shapes, color, theme, etc.) that the digital object has and can lead to categorization and the identification of patterns in large scale data sets.

The data that the partnering institution, Centre Pompidou in Paris, made available for the interdisciplinary team of researchers contains “over 100,000 works, the collections of the Centre Pompidou (musée national d’art moderne – centre de création industrielle) which make up one of the world’s leading references for art of the 20th and 21st centuries.

 

Dark Source: A Reflection of the Politics of Voting Technology

As the debate on the Midterm Elections of November 2018 gets more heated, senators from both parties have expressed serious concerns over threats to cybersecurity of electoral systems across different states. Several lists of recommendations to fortify the systems have been released at the state and federal levels by the Senate Intelligence Committee, the Secretary and Former Head of Homeland Security and the Federal Elections Commission emphasizing the need for Cyber-security Risk Assessments.
Between the record-number White House resignations and departures and the amorphous allocation of around $700 million in funding, with $380 million towards “rapidly” replacing aging election technology across the country and $307 million towards fighting potential cyber threats due to Russian interference, it is clear that the Trump Administration is not sufficiently prepared for the upcoming elections. These patterns of weak election technology and weaker cyber-security, however, are not a recent phenomenon.
In 2005, CDA Director Ben Rubin expressed through his art installation Dark Source, “the inner workings of a commercial electronic voting machine.”

The artwork presents over 2,000 pages of software code, a printout of 49,609 lines of C++ that constitute version 4.3.1 of the AccuVote-TS™ source code.

In Dark Source the code, which had been obtained freely over the internet following a 2002 security failure at Diebold, has been blacked out in its entirety in order to comply with trade secrecy laws.

In an essay subsequently published in Making Things Public: Atmospheres of Democracy, he elaborates on the complications with proprietary election technology in the context of 2004 elections.
We trust cash machines and gambling machines with our money, and we trust medical devices and autopilots with our safety, so why shouldn’t we also trust electronic voting machines with our ballots?
Proprietary voting technology, subject to no meaningful standards of security, reliability, or accuracy, is inherently vulnerable not only to malicious tampering but also to inadvertent failure.
Election systems must be returned to the public’s control, and one essential step will be to lift the veil of secrecy that cloaks the software.
As we continue to follow the trail of Election Security, here at Data Matters and raise the necessary concerns over the upcoming elections, it could be worthwhile to reflect upon the fallacies of the past.

Lifelong Analytics: Equity, Data Viz & Precision Medicine

About the Author: Emily Chu is a Data Visualization Developer and Motion Designer currently focusing on data visualization engineering, machine learning and interactivity. Her background spans program management, research, business, design and technology. (MS Data Visualization, Parsons School of Design) | Website: 3milychu.github.io

 

The Spotlight on Healthcare Data

The tidal wave of new healthcare technologies is dizzying. Telehealth, artificial intelligence, AR/VR, 3D printing and patient monitoring promise that the future of medicine will be more efficient, affordable, secure and personal. Most advancements spotlight healthcare data as the foundation: we are capturing it, sharing it, making use of it and disseminating it in ways we’ve never done before.

Healthcare Data’s Rising Value and Impending Inequity

Consider this year’s Economic Forum’s meeting in Davos, where key industry leaders stated that using global healthcare data available to them, Machine Learning will be able to uniquely pinpoint the most effective treatment for an individual. At the current rate of data representation, however, health systems will be much poorer at offering efficient and highly accurate treatment for individuals that are not of European or East-Asian descent.

Meanwhile, the momentum behind capturing healthcare information is the heightening awareness of its value and security. Companies like Nebula Genomics, for instance, are offering to sequence your genome and secure it on a blockchain, wherein you will have control over the transfer of this packet of information and it will only go where you send it. In a consumer-driven healthcare world, what types of customers will have the privilege to understand what this even means?

What we can do with healthcare data can level the playing field.

We can make it secure and affordable for everyone, regardless of condition, race, or socioeconomic background, to receive the most effective treatment available. Looking at the typical health system infrastructure, where do we start?

Enter Electronic Health Records

Electronic Health Records or Electronic Medical Record (EHR/EMRs) are now a standard method of healthcare data storage and exchange. Patients are able to view an electronic copy of their medical records and physicians are able to share test results with patients. It can be thought of as the start of healthcare data consumerization. It is perhaps the perfect training ground to help the most vulnerable populations understand –

  1. how valuable their healthcare data is and
  2. how to harness it to improve their health and receive the most affordable, effective treatments in the future.

Since its inception, we now know that approximately half of the U.S. population encounter difficulties in comprehending and utilizing their health information, ushering in the need for a “visual vocabulary of electronic medical information to improve health literacy”. In 2014, a study revealed that 63.9% of of EMR survey respondents complained that note-writing took longer, and as of 2017, 94% of physicians in a survey were overwhelmed by what they believe to be “useless data”.

Visualizing Healthcare Data for the Most Vulnerable: From Collection and Adoption to Accuracy and Feedback

One important step is to get the most vulnerable populations – lower literacy individuals, patients with chronic or debilitating conditions, the elderly – to find a real use in capturing data and finding an enjoyment in doing so. The following demonstrates an example of how this updated electronic health record might function.

From Integrated Treatment Adherence to Responsive Feedback to Lifelong Analytics

In Visual 1.0: Simple Gamification of Healthcare Activities (below), for example, the patient is first shown how medications and healthcare tasks such as “take your blood pressure” can be gamified in a simple user experience to encourage data collection.  

Visual 1.1: Progress Over Time (below) shows how collecting vitals and treatment plan adherence might then be synced and displayed in the record shared with physicians. 

In Visual 1.2 Breakout view of healthcare activity or Biometric Marker (below), consider that the main dashboard view can be broken down and analyzed easily by physicians.  

Visual 1.3 Condensed Progress Summary and Feedback for the Patient (below) then illustrates closing the feedback and health comprehension gap that is often left open after treatment, by condensing the analytics into a simple progress view over time. Recommendations for the medical aspect (i.e. treatment plans) or maintenance behaviors (i.e. exercise) are adaptive. For example, at predetermined check-in intervals or when tracking metrics trigger a certain threshold, the treatment plan adapts based on level of adherence or other care plans that were implemented. Finally, consider that patients should be able to view future states assigned to them by predictive analytics (not pictured). In this scenario, what I would call Lifelong Analytics, individuals securely own all their healthcare information and are able to compare how predictive analytic models place them in the future.

By using the electronic health record as a catalyst to drive data collection and adoption among the most vulnerable, we are securing a pool of representative information for groups that may otherwise be left behind in the race for precise treatment at near-to-no cost. Along the way, through digestible habits and user-friendly actions, patients will be exposed to the power behind documenting their healthcare information. Once individuals are empowered with their data and what it really means, we can imagine a future where people are quick to stand up for the ownership of their data – and ensure that advancements that are made considering their footprint.

Takeaways

The poor, the elderly, the sick and the underrepresented have much to offer to the future of medical practice. They offer interesting challenges and high payoffs in cost efficiencies. When we consider a future where data will be dynamically classified and trends predicted, it is important to concentrate adoption among these groups. Some methods we discussed in this article:

Making treatment plans easy to track and adaptable

Treatment plans should be easy to track. Monitoring can be easily integrated into our routines, or in the future – automatically reported back to us. Providers should be able to understand what adaptive measures need to be taken should we miss a dose, or life interferes with our rehabilitation plan.

Making our medical history secure, transparent and shareable

Technologies currently exist to ensure our healthcare information belongs to us, and we have ownership over where it is transferred virtually. Visualizing healthcare information using a visual vocabulary that demystifies our health history, and shared among all providers in our care network can strengthen this transparency.

From responsive feedback to lifelong analytics

Consider a future where individuals with secure ownership of their healthcare data can access not only responsive feedback from their care providers, but see how their lifelong analytics are affected with each stint of perfect treatment plan adherence or alternative care plan. In other words, imagine what predictive analytics has to say about us is eventually comprehensible and accessible to us as individuals.

By visualizing and making healthcare information for the most vulnerable readily accessible and comprehensible, we make it possible to access the most difficult treatments responsively and potentially risky treatments with transparency. In the end, this can teach an entire population how to better develop an infrastructure that prepares and cares for us when we too age, get sick or fall into disadvantaged circumstances.

Data Stories: What Space Oddity Looks Like

In the land of popular music, there has been little scarcity of fashion experiments. And David Bowie’s visual legacy definitely takes up a large piece. But, what does a David Bowie song look like? Valentina D’Efilippo and Miriam Quick answer this question in their remarkable project.

Outfit by Kansai Yamamoto                                Photo by Masayoshi Sukita 1973
Aladdin Sane Cover

OddityViz – a data tribute to David Bowie is a visualization project that gives ‘form to what we hear, imagine and feel while listening to’ Bowie’s hit number Space Oddity. The project which is a combination of ten engraved records, large-scale prints and projections is deconstructed from data extracted from the song – narrative, texture, rhythm, melody, harmony, lyrics, structure, lyrics, trip and emotion. The inquiries that went into the making of each of these records are even more interesting.

When making this, the ninth disc in the Oddityviz series, we asked ourselves: how can we tell the story of Major Tom so it could be understood by an alien?

The project took inspiration from a variety of references from popular culture, while the colour palette naturally recalls the darkness of space (black) and the stars (white). One can also see a reference to the Voyager Golden Records in the engraved dataviz format.

The final disc of the series illustrates the central themes of the song: the destruction of its main character, the bittersweet nature of triumph, the smallness of humanity in a vast, extended universe.

In her article on Muzli, D’Efilippo breaks down the process of creating this piece, comparing the ‘system’ of data visualization to music – one that is largely subjective and that which becomes more ‘meaningful and legible’ as we learn how to read it.

In my opinion, dataviz is more than a tool to render numbers, it’s a way to make sense of any experience and communicate the underpinning stories.

Read the full article here.

Evolution of the Data Artist

Defining Data Art is tricky. And for good reason. The mediascape that breathes around us is a terrain that shifts, distorts and transforms before it can be drawn. In such a space, defining can only be limiting. Jacoba Urist, in his comprehensive article in The Atlantic in 2015 explored the multifarious ways of the Data Artist.

Art is as much a product of the technologies available to artists as it is of the sociopolitical time it was made in, and the current world is no exception. A growing community of “data artists” is creating conceptual works using information collected by mobile apps, GPS trackers, scientists, and more.

                                                      Liberté (1963) – Joaquim Rodrigo 

In a series called Moodjam, (Laurie) Frick took thousands of Italian laminate countertop samples from a recycling center and created a series of canvases and billboard-sized murals based on her temperament … Frick is adamant that her work is about more than simply visualizing information—that it serves as a metaphor for human experience, and thus belongs firmly in the art world.

As Urist deftly puts it – working with (this) data isn’t just a matter of reducing human beings to numbers, but also of achieving greater awareness of complex matters in a modern world. Fast forward to two years later, Cynthia Andrews speaks about the role of Context in Data Art.

If you look at neural networks created by scientists with a creative eye you might see it as art. If you take it out of context, it could be a subway map or a series of rivers. It could be anything. It’s the non-creative context in which things are placed that makes people think they aren’t be considered art.

Andrews expands on a specific genre of Data Art that Urist mentions –

Artists influenced by self-tracking.

‘Waiting for Earthquakes’ by Moon Ribas. She has a sensor embedded into her skin that, using seismic data, vibrates every time there is an earthquake in the world, from anywhere, any magnitude. ‘Waiting for Earthquakes’ is a performance piece in which she literally just stands on stage and waits for an earthquake to happen and then interprets the feeling that she gets into movement. I don’t know if she considers it data art, but I do.

And then, there are artists like Shelita Burke, a pop musician who decided to use Blockchain and Music Metadata to not only get paid on time – but to organize a centralized system for distributing royalties across the production spectrum to the producers and writers involved.

Burke thinks it also has something to do with her use of data to her advantage, like when she determined  that 90 days was the perfect time to release new music in order to keep fans engaged.

“I really believe that every artist needs to understand data” Burke says.