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Making the case for the need for digitally native research methodologies.

Excellent statement! Could this be the guiding principle of the current big data boom in biology?

How data structure influences value systems and social practices is a much-needed topic of inquiry.

Big, sweeping statement that helps frame how big data could be better conceptualized as a complex, socially contextualized, temporal artifact.

I don't understand what Burns is arguing here. Who is he referring to claims that DHN is a "savior" or "the solution" to crisis response?

"Big data should therefore be be conceptualized as a framing of what can be known about a humanitarian crisis, and how one is able to grasp that knowledge; in short, it is an epistemology. This epistemology privileges knowledges and knowledge- based practices originating in remote geographies and de- emphasizes the connections between multiple knowledges.... Put another way, this configuration obscures the funding, resource, and skills constraints causing imperfect humanitarian response, instead positing volunteered labor as ‘the solution.’ This subjectivity formation carves a space in which digital humanitarians are necessary for effective humanitarian activities." (Burns 2015: 9–10)

Is it "a lack of relational sense of responsibility" in crisis response (state vs private sector vs public) or is it the wicked problem of power, class, social hierarchies, etc.?

"... ways of framing data and correlations that elicit a greater sense of responsibility and commitment."

That could have a temporal component to it to position urgency, timescape, horizon, etc.

This critique addresses surveillance state concerns about glue-ing datasets together to form a broader understanding of aggregate social behavior without the necessary constraints/warnings about social contexts and discontinuity between data.

Skimmed the Ruppert paper, sadly doesn't engage with time and topologies.

Beyond this quote, Chandler (in engaging crisis/disaster scenarios) argues that Big Data may be more appropriately framed as community reflexive knowledge than causal knowledge. That's an interesting idea.

*"Thus, It would be more useful to see Big Data as reflexive knowledge rather than as causal knowledge. Big Data cannot help explain global warming but it can enable individuals and household to measure their own energy consumption through the datafication of household objects and complex production and supply chains. Big Data thereby datafies or materialises an individual or community’s being in the world. This reflexive approach works to construct a pluralised and multiple world of self-organising and adaptive processes. The imaginary of Big Data is that the producers and consumers of knowledge and of governance would be indistinguishable; where both knowing and governing exist without external mediation, constituting a perfect harmonious and self-adapting system: often called ‘community resilience’. In this discourse, increasingly articulated by governments and policy-makers, knowledge of causal connections is no longer relevant as communities adapt to the real-time appearances of the world, without necessarily understanding them."

"Rather than engaging in external understandings of causality in the world, Big Data works on changing social behaviour by enabling greater adaptive reflexivity. If, through Big Data, we could detect and manage our own biorhythms and know the effects of poor eating or a lack of exercise, we could monitor our own health and not need costly medical interventions. Equally, if vulnerable and marginal communities could ‘datafy’ their own modes of being and relationships to their environments they would be able to augment their coping capacities and resilience without disasters or crises occurring. In essence, the imaginary of Big Data resolves the essential problem of modernity and modernist epistemologies, the problem of unintended consequences or side-effects caused by unknown causation, through work on the datafication of the self in its relational-embeddedness.42 This is why disasters in current forms of resilience thinking are understood to be ‘transformative’: revealing the unintended consequences of social planning which prevented proper awareness and responsiveness. Disasters themselves become a form of ‘datafication’, revealing the existence of poor modes of self-governance."*

Downloaded Chandler paper. Cites Meier quite a bit.

The shift from INGO emergency response/logistics to state-sponsored, individualized resilience via the private sector seems profound here.

There's also a subtle temporal element here of surveilling need and collecting data over time.

Again, raises serious questions about the use of predictive analytics, data quality/classification, and PII ethics.

This seems like a terribly naive conclusion about mass data collection by the state.

Also:

"Yet even if capacities to analyse the haystack for needles more adequately were available, there would be questions about the quality of the haystack, and the meaning of analysis. For ‘Big Data is not self-explanatory’ (Bollier 2010: 13, in boyd and Crawford 2012). Neither is big data necessarily good data in terms of quality or relevance (Lesk 2013: 87) or complete data (boyd and Crawford 2012)."

Primary problems to resolve regarding the use of "big data" in humanitarian contexts: dataset size/sample, predictive analytics are contrary to human behavior, and ethical abuses of PII.

What does this paper mean by infrastructures? Perhaps this is a reference to the traditional scholarly journals and monographs.

Reminds me of The End of Theory. But if we lose the theory, the human understanding, what will be the consequences?

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big data solutions for cardiovascular and oncological research.

Patient adherence strategy and Big Data

reliability and accessibility of big data will help facilitate increased reliance upon outcomes-based contracting and alternative payment models.

I wonder how many profiles in the 3,389 tweets? Did the automate the review and capture of the details? Or did they review each profile by hand?

this is just so awful.

1. The Digital Recovery of Texts. Due to computer assisted approaches to paleography( noun: paleography the study of ancient writing systems and the deciphering and dating of historical manuscripts) and the steady advances in the field of digital preservation "Resurrection can be grisly work, I THINK WE COME TO UNDERSTAND EXTINCTION BETTER IN OUR STRUGGLES." .

2. DH has a public and transformative role to play : Big Data and The Longue Duree

Unable to look at article by Armitage, D & Guldi,j.i on The Return of The Longue Duree - hit by paywall each time,

Look at great article on WWW.WIRED.COM .

https://www.wired.com/2014/01/return-of-history-long-timescales/

"The return of the longue durée is intimately connected to changing questions of scale. In a moment of ever-growing inequality, amid crises of global governance, and under the impact of anthropogenic climate change, even a minimal understanding of the conditions shaping our lives demands a scaling-up of our inquiries. "

What does she mean by BIG DATA? Read Samuel Arbesman article in The Washington Post for easy explanation [] (https://www.washingtonpost.com/opinions/five-myths-about-big-data/2013/08/15/64a0dd0a-e044-11e2-963a-72d740e88c12_story.html?utm_term=.54ff7fdf82fe)

A data lake management service with an Apache licence. I am particularly interested in how well the monitoring features of this platform work.

It's good to understand that exploring a corpus for "brainstorming" or discovering heretofore seen connections is different than a discovery query that is meant to give access to an entire text.

‘Precision Education’ Hopes to Apply Big Data to Lift Diverse Student Groups

Doesn't this obfuscate the process? Literature became digital. Digital enables a wide range of futther activity to take place on top of literature, including, perhaps, it's datafication.

volume: The actual size of traffic

Velocity: How fast does the traffic show up.

Variety: Refers to data that can be unstructured, semi structured or multi structured.

Voilà pourquoi les GAFAM sont aussi puissants (voire plus) que des États.

Persuasion

Simply possessing pools of data is of limited utility. It's like having a space ship but your only access point to it is through a pin hole in the garage wall that lets you see one small, random glint of ship; you (think you) know there's something awesome inside but that sense is really all you've got. Margaret points out that it has to be seen (data visualization), it has to be tangible (relevant to audience) and connected at micro and macro levels (storytelling). For all of the machine learning and AI that helps us access the spaceship, these key points are (for now) human-driven.

There's a bit of a slippery slope here, no? Maybe it's Audrey on that slope, maybe it's data-happy schools/companies. In either case, I wonder if it might be productive to lay claim to some space on that slope, short of the dangers below, aware of them, and working to responsibly leverage machine intelligence alongside human understanding.

Conversations!

Key that there's still a human and human relationships in the equation here.

Did students have access to the same data?

So it's not as if this was done willy-nilly.

Page 14

Rockwell and Sinclair note that corporations are mining text including our email; as they say here:

more and more of our private textual correspondence is available for large-scale analysis and interpretation. We need to learn more about these methods to be able to think through the ethical, social, and political consequences. The humanities have traditions of engaging with issues of literacy, and big data should be not an exception. How to analyze interpret, and exploit big data are big problems for the humanities.

les algorithmes ont besoin de données soi-disant neutres.. c'est un peu aller dans le sens des discours d'accompagnement de ces algorithmes et services de recommandation qui considèrent leurs données "naturelles", sans valeur intrasèque. (voir Bonenfant 2015)

p. 100

Data are not useful in and of themselves. They only have utility if meaning and value can be extracted from them. In other words, it is what is done with data that is important, not simply that they are generated. The whole of science is based on realising meaning and value from data. Making sense of scaled small data and big data poses new challenges. In the case of scaled small data, the challenge is linking together varied datasets to gain new insights and opening up the data to new analytical approaches being used in big data. With respect to big data, the challenge is coping with its abundance and exhaustivity (including sizeable amounts of data with low utility and value), timeliness and dynamism, messiness and uncertainty, high relationality, semi-structured or unstructured nature, and the fact that much of big data is generated with no specific question in mind or is a by-product of another activity. Indeed, until recently, data analysis techniques have primarily been designed to extract insights from scarce, static, clean and poorly relational datasets, scientifically sampled and adhering to strict assumptions (such as independence, stationarity, and normality), and generated and alanysed with a specific question in mind.

Good discussion of the different approaches allowed/required by small v. big data.

We should have control of the algorithms and data that guide our experiences online, and increasingly offline. Under our guidance, they can be powerful personal assistants.

Big business has been very militant about protecting their "intellectual property". Yet they regard every detail of our personal lives as theirs to collect and sell at whim. What a bunch of little darlings they are.

50 Years of Data Science, David Donoho<br> 2015, 41 pages

This paper reviews some ingredients of the current "Data Science moment", including recent commentary about data science in the popular media, and about how/whether Data Science is really different from Statistics.

The now-contemplated field of Data Science amounts to a superset of the fields of statistics and machine learning which adds some technology for 'scaling up' to 'big data'.

Wow!

The “social”, with an embedded emphasis on the data part of knowledge building and a nod to solidarity. Cloud computing does go well with collaboration and spelling out the difference can help lift some confusion.

would like to know more about this term and HHS inititiative