Entries tagged science from Fulcra

→ Microsoft wants to capture all of the carbon dioxide it’s ever emitted Jan 19, 2020 highlights & tech & science & climate change

The most audacious commitment from Microsoft is its push to take carbon out of the atmosphere. The company is putting its faith in nascent technology, and it’s injecting a significant investment into a still controversial climate solution. Proponents of carbon capture, like Friedmann, say that the technology is mature enough to accomplish Microsoft’s aims. It’s just way too expensive right now. Microsoft’s backing — and its $1 billion infusion of cash — could ultimately make the tech cheaper and more appealing to other companies looking for new ways to go green.

Fantastic news. Carbon capture is a key opportunity for decelerating climate change. Hopefully more companies follow suit.

→ Science Conferences Are Stuck in the Dark Ages Jan 4, 2020 highlights & science & facilitation & collaboration

Dr. Ngumbi and Dr. Lovett outline the issues with modern research conferences that are stuck in the 20th (or even 19th) century.

By the end of each conference, you’ve heard dozens of people dispense all their knowledge in 10-minute bursts, and you sometimes leave feeling less informed than before you arrived. Where’s the dialog? Where’s the questioning? Where’s the innovation? It’s beyond time that scientific conferences themselves undergo the scientific process, and move forward.

I shouldn’t ever be surprised by these events, but every time I go to one, I am shocked by how boring the facilitation is. Some might defend the format. After all, sage-on-a-stage has worked for hundreds of years.

The question isn’t whether it works, though. It’s whether it could be better. Surely, in an age of cloud technologies and the Internet and social media—not to mention better recognition of soft power and inclusivity and the processes of scientific revolution—there are modes of conference programming that can leapfrog the conventional format.

Having led a number of events over the years that have shirked tradition for more interesting facilitation formats, I know firsthand how disruptive facilitation mistakes can be. But I’ve also seen some incredible results from shaking up the structure. Radhoc’s Unpanel, for instance, turns the structure of a panel upside-down. Instead of having a group of “experts” on a stage speaking to an anonymous crowd, the format puts those invited guests in subgroups that get to introduce one another. The audience becomes the panel, and the expert an anchor in the conversation. It gives everyone a chance to connect with the quasi-celebrities anointed by these events. As a bonus, it’s easier for the guests, too—they don’t need to prepare keynotes, only business cards.

→ The Demon Haunted World Nov 21, 2019 highlights & science & systems & change

I have a foreboding of an America in my children’s or grandchildren’s time—when the United States is a service and information economy; when nearly all the manufacturing industries have slipped away to other countries; when awesome technological powers are in the hands of a very few, and no one representing the public interest can even grasp the issues; when the people have lost the ability to set their own agendas or knowledgeably question those in authority; when, clutching our crystals and nervously consulting our horoscopes, our critical faculties in decline, unable to distinguish between what feels good and what’s true, we slide, almost without noticing, back into superstition and darkness…

Carl Sagan, as quoted by @Andromeda321 in this interesting Reddit thread on the regretful trends of the 2010s.

The thread discusses the growth of anti-intellectualism and conspiracy theories. I’m reminded of this timeless Medium post about how hating Ross in Friends became a meme in and of itself, reinforcing the persecution of science in the ’90s. From David Hopkins:

I want to discuss a popular TV show my wife and I have been binge-watching on Netflix. It’s the story of a family man, a man of science, a genius who fell in with the wrong crowd. He slowly descends into madness and desperation, led by his own egotism. With one mishap after another, he becomes a monster. I’m talking, of course, about Friends and its tragic hero, Ross Geller.

[…]

If you remember the 1990s and early 2000s, and you lived near a television set, then you remember Friends. Friends was the Thursday night primetime, “must-see-TV” event that featured the most likable ensemble ever assembled by a casting agent: all young, all middle class, all white, all straight, all attractive (but approachable), all morally and politically bland, and all equipped with easily digestible personas. Joey is the goofball. Chandler is the sarcastic one. Monica is obsessive-compulsive. Phoebe is the hippie. Rachel, hell, I don’t know, Rachel likes to shop. Then there was Ross. Ross was the intellectual and the romantic.

Eventually, the Friends audience — roughly 52.5 million people — turned on Ross. But the characters of the show were pitted against him from the beginning (consider episode 1, when Joey says of Ross: “This guy says hello, I wanna kill myself.”) In fact, any time Ross would say anything — about his interests, his studies, his ideas — whenever he was mid-sentence, one of his “friends” was sure to groan and say how boring Ross was, how stupid it is to be smart, and that nobody cares. Cue the laughter of the live studio audience. This gag went on, pretty much every episode, for 10 seasons. Can you blame Ross for going crazy?

People in the Reddit thread point out that these seemingly recent trends have been taking root for a long time. While this is true, it’s also true that (just like seemingly everything else) these phenomena have been moving much faster and growing much larger in recent years. Which leads to a curious tangent: how do accelerated scales of change play on our biases? Does the interaction between these biases and our accelerated experiences change our perception of the world?

→ That’s why, starting on January 14th, we’ll be publishing Better Worlds: 10 original fiction stories, five animated adaptations, and five audio adaptations by a diverse roster of science fiction authors who take a more optimistic view of what lies ahead in ways both large and small, fantastical and everyday Dec 5, 2018 highlights & Science & Social That’s why, starting on January 14th, we’ll be publishing Better Worlds: 10 original fiction stories, five animated adaptations, and five audio adaptations by a diverse roster of science fiction authors who take a more optimistic view of what lies ahead in ways both large and small, fantastical and everyday.
Growing up, I was surrounded by optimistic science fiction — not only the idealism of television shows like Star Trek, but also the pulpy, thrilling adventures of golden age science fiction comics. They imagined worlds where the lot of humanity had been improved by our innovations, both technological and social. Stories like these are more than just fantasy and fabulism; they are articulations of hope. We need only look at how many tech leaders were inspired to pursue careers in technology because of Star Trek to see the tangible effect of inspirational fiction. (Conversely, Snow Crash author Neal Stephenson once linked the increasing scarcity of optimistic science fiction to “innovation starvation.”)
Better Worlds is partly inspired by Stephenson’s fiction anthology Hieroglyph: Stories and Visions for a Better Future as well as Octavia’s Brood: Science Fiction Stories from Social Justice Movements, a 2015 “visionary fiction” anthology that is written by a diverse array of social activists and edited by Walidah Imarisha and adrienne maree brown. Their premise was simple: whenever we imagine a more equitable, sustainable, or humane world, we are producing speculative fiction, and this creates a “vital space” that is essential to forward progress. — https://www.theverge.com/2018/12/5/18055980/better-worlds-science-fiction-short-stories-video

→ Scientist He Jian Kui allegedly used the gene-editing tool CRISPR cas-9 to disable the CCR5 gene in 31 embryos with the goal of making children who were more resistant to HIV Nov 30, 2018 highlights & Science Scientist He Jian Kui allegedly used the gene-editing tool CRISPR cas-9 to disable the CCR5 gene in 31 embryos with the goal of making children who were more resistant to HIV. He claims that two of the embryos were implanted, resulting in the female babies “Lulu” and “Nana.” (His work has since been halted by the Chinese government, reports The Associated Press.) — https://www.theverge.com/2018/11/29/18116830/crispr-baby-he-jiankui-genetics-ethics-science-health-mutation

→ I actually have a specific anomaly that I saw the other day, where I’m hiring a new data scientist in Denver Nov 23, 2018 highlights & Science I actually have a specific anomaly that I saw the other day, where I’m hiring a new data scientist in Denver. Particularly wanted a senior data scientist in Denver, so I posted a job opening on LinkedIn for a Denver data scientist. I got something like 30 applications in a few days. 11 were from one company…. I ask some of my colleagues that are in Denver, saying “What’s wrong with company X? I just got 11 applications from data scientists from this company.” First of all I didn’t even know they had a lot of data scientists, and they said … because [they] are data scientists, and they [said] “Yeah, they’re job openings are all over the place. They hired a crazy number of … hundreds of data scientists over the past two years.” … now obviously they’re hemorrhaging, because they probably didn’t actually think about how to communicate. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ the biggest problem here about this lack of process around management, around data engineering, the communication between data engineering and data science, this lack of management, if you want to specialize, you want to have a data liaison…do you want to have a data engineer specialist, because the earliest data science project, like the smallest one, data scientist is doing the data engineering work too Nov 23, 2018 highlights & People & Science the biggest problem here about this lack of process around management, around data engineering, the communication between data engineering and data science, this lack of management, if you want to specialize, you want to have a data liaison…do you want to have a data engineer specialist, because the earliest data science project, like the smallest one, data scientist is doing the data engineering work too. And probably the platform architecture work too, and the application development.
Once you start specializing, which is why we have data engineers and data scientists now, these two people need to have a process to communicate.
When you have an application developer, now they need a process to communicate and work together.
You have the platform architecture, you got management, you got the advisory liaison person, you got the rest of the business, all is about process and, honestly, I don’t think anybody really knows what they’re doing. I think the number one thing that’s holding us back in this industry, is building large data science teams and organization. The most successful data science teams I see right now are like three people… it could be a massive organization, but those three people are getting a lot of work done, and if they wanted to scale up to 20 people, 40 people, it’s not going to work. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ When queried to unpack the idea of a “data liaison” more and provide additional clarity and whether this person could be a “project manager”, Miner indicated “…in a consulting construct, that both myself and Niels [co-founder] provides in some of our larger projects Nov 22, 2018 highlights & Science When queried to unpack the idea of a “data liaison” more and provide additional clarity and whether this person could be a “project manager”, Miner indicated
“…in a consulting construct, that both myself and Niels [co-founder] provides in some of our larger projects. And it’s a really necessary role and some of the other customers we work with, we’ve made this recommendation for them to do this, it’s actually two reasons. One is that, data science requires a lot of focus. When you’re working on data science problem and you’re fumbling with some machine learning thing, you’re messing with the data, an interruption can break down a house of cards in your head that you’ve been building for multiple hours and if you’re responsible for going around to random meetings to discuss use cases and things, you’re never going to get anything done…what you need to do, is you need to kind of pick somebody. I mean honestly, these are some personality types that are better than others, but really it needs to be somebody that could do it if they had to, that understands the real problems, that can represent the data scientists that are actually going to do the work in these meetings. But due to the focus requirement you kind of need to pick somebody to be the sacrificial person to do it, that’s okay going around and talking from experience so that the others can focus. It’s a really important role… in a large organization with a large team.” — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ something that we advise our clients on all the time, and is a major portion that I think takes people by surprise sometimes, is that most organizations is that their default is to treat their data science projects like software engineering projects that they’re currently running at the organization Nov 22, 2018 highlights & Science something that we advise our clients on all the time, and is a major portion that I think takes people by surprise sometimes, is that most organizations is that their default is to treat their data science projects like software engineering projects that they’re currently running at the organization. So if they want their data scientists to be filling out Jira tickets and have Sprints. Not only the data scientists, but data engineering is not a similar task like that either. And the platform architecture too, is similar. They all share something in common. in data science, data engineering, and platform architecture, it’s one of those things where you can spend forever on something and it won’t be done. So, it’s all about, “When do I feel like stopping?” Or, “When do I run out of money?” Rather than, “Okay, this application is done. I’ll ship it, it’s in a box. It’s all good to go. We release it to the world and we sell it. It’s great.” On the data science side it’s hard to tell how long something’s going to take until you do it. So there’s this chicken and egg problem. I can’t write the Jira ticket it’s going to take two weeks, until I actually spend the two weeks to do it, and realize it’s actually going to take four weeks. And so when you try to apply these traditional software engineering project management things on these projects it doesn’t work. It actually causes harm in a lot of cases….there’s actually a new discipline that needs to arise. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ I’ve actually heard a project manager say, “You know, any line of code that my developers write to audit what they’re doing, to put stuff in a database, is a line of code that they’re not putting in developing the application Nov 21, 2018 highlights & data & Science I’ve actually heard a project manager say, “You know, any line of code that my developers write to audit what they’re doing, to put stuff in a database, is a line of code that they’re not putting in developing the application.” And so they frequently encourage a huge technical debt as they’ve got this great application now, but when it comes time for phase two of the project, to do something interesting with the data that this application should have stored somewhere but didn’t, we’re kind of left holding the bag because the application developers were kind of short sighted. And to my mind this is the kind of short term thinking that hinders really good data science. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ [it] is a symptom of really bad project management Nov 21, 2018 highlights & Science [it] is a symptom of really bad project management. It seems to me that the way to solve this problem is to have everybody in the room when the project is being designed … It’s sort of like life insurance. You know, you don’t really need it until you need it, but you’ve got to keep having it, even when you don’t need it. The projects that I’ve seen that have been most successful are the projects in which the data scientists, the data engineers, and… the application developers are all there in the room from the beginning, with the customer talking about what the problem is they want to solve, what a minimal product is, what the final solution should be, what the users expect out of this. And if you start from that place you’re much more likely to get empathy. …That’s the first thing. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ I don’t think that data scientists and data engineers at most organizations that I’m working with have figured out how to communicate with anybody Nov 21, 2018 highlights & Design & Science I don’t think that data scientists and data engineers at most organizations that I’m working with have figured out how to communicate with anybody. So, not even with each other, but how does a data scientist and a data engineer fit into, a modern one, that’s building some new systems, how are they interacting with different lines of business? How are they interacting with marketing, sales? How are they interacting with product design? ….even this at a fundamental level, there’s major problems in the industry. And how they’re interacting with each other? — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ in our consulting engagements, and also two other data science consulting companies that I know and work with, if we have a pure play data science project, meaning that the data engineering’s not in scope, the customer said that they were going to take care of it, we won’t start work until we have proof that the data’s been loaded Nov 20, 2018 highlights & Science in our consulting engagements, and also two other data science consulting companies that I know and work with, if we have a pure play data science project, meaning that the data engineering’s not in scope, the customer said that they were going to take care of it, we won’t start work until we have proof that the data’s been loaded. We’ve been burned so many times by them saying like, “Oh, you know what? You guys can start on Monday. We’ll get the data loaded sometimes next week.” We’re not even going to start until that data’s there….that’s the other issue too with the data engineer. I actually ran into this issue….on the younger side of the data engineers, one of the issues that we run into is that they don’t have the seniority to stand up to some ancient Oracle DBA that’s not willing to play nice. …it’s a really hard role to fill because, you’re right,… the interpersonal skills, and the political navigation skills are really important for the data engineer. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ I’ve never heard of anybody having a data engineering undergrad class, but you’re starting to hear data science classes pop up Nov 20, 2018 highlights & Science I’ve never heard of anybody having a data engineering undergrad class, but you’re starting to hear data science classes pop up. … I have some ideas about why that is, but I think where we’re at right now is data science is a pretty fairly well defined career path and profession. People generally know what that means.…there’s a lot of impact from hype still that’s starting to wear down a little bit. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ There is a seemingly myriad of terms to describe people who interact with models Nov 19, 2018 highlights & Learning & Science There is a seemingly myriad of terms to describe people who interact with models. Just a few terms that are currently in usage include researchers, data scientists, machine learning researchers, machine learning engineers, data engineers, infrastructure engineers, DataOps, DevOps, etc. Both Miner and Presser commented upon and agreed that before any assignment of any term, the work itself existed previously. Presser defines data engineering as embodying the skills to obtain data, build data stores, manage data flows including ETL, and provide the data to data scientists for analysis. Presser also indicated that data engineers at large enterprise organizations also have to be well versed in “cajoling” data from departments that may not, at first glance, provide it. Miner agreed and indicated that there is more thought leadership around the definition of data science versus data engineering which contributes to the ambiguity within the market. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models Nov 19, 2018 highlights & Science Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models. Unfortunately, we have also heard that many companies have internalized the model myth, or the misconception that data science should be treated like software development or data assets. This misconception is completely understandable. Data science involves code and data. Yet, people leverage data science to discover answers to previously unsolvable questions. As a result, data science work is more experimental, iterative, and exploratory than software development. Data science work involves computationally intensive algorithms that benefit from scalable compute and sometimes requires specialized hardware like GPUs. Data science work also requires data, a lot more data than typical software products require. All of these needs (and more) highlight how data science work differs from software development. These needs also highlight the vital importance of collaboration between data science and engineering, particularly for innovative model-driven companies seeking to maintain or grow their competitive advantage.
Yet, collaboration between data science and engineering is a known challenge. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models Nov 19, 2018 highlights & Science Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models. Unfortunately, we have also heard that many companies have internalized the model myth, or the misconception that data science should be treated like software development or data assets. This misconception is completely understandable. Data science involves code and data. Yet, people leverage data science to discover answers to previously unsolvable questions. As a result, data science work is more experimental, iterative, and exploratory than software development. Data science work involves computationally intensive algorithms that benefit from scalable compute and sometimes requires specialized hardware like GPUs. Data science work also requires data, a lot more data than typical software products require. All of these needs (and more) highlight how data science work differs from software development. These needs also highlight the vital importance of collaboration between data science and engineering, particularly for innovative model-driven companies seeking to maintain or grow their competitive advantage. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ About the MIT Institute for Data, Systems, and Society Oct 30, 2018 highlights & Science & Systems

Spanning all five schools at MIT, IDSS embraces the collision and synthesis of ideas and methods from analytical disciplines including statistics, data science, information theory and inference, systems and control theory, optimization, economics, human and social behavior, and network science.

The mission of IDSS is to advance education and research in state-of-the-art analytical methods and to apply these methods to address complex societal challenges in a diverse set of areas such as finance, energy systems, urbanization, social networks, and health.

IDSS comprises a number of academic programs, including those offered by the Statistics and Data Science Center (SDSC), two online education programs, and the IDSS research entities Laboratory for Information and Decision Systems (LIDS) and Sociotechnical Systems Research Center (SSRC).

- https://www.prweb.com/releases/noted_mit_scientist_muncher_dahleh_joins_the_enterworks_executive_advisory_board_to_help_guide_company_s_vision_for_artificial_intelligence/prweb15872695.htm

→ Complex adaptive systems thinking is an exciting approach, and the popularity of the theory in the policy literature is testimony to this Oct 13, 2018 highlights & Science & Systems Complex adaptive systems thinking is an exciting approach, and the popularity of the theory in the policy literature is testimony to this. But a number of issues are yet to be resolved. There needs to be clearer indication of the practical changes that it implies (if any) for policy research and practice. What are we saying that is different from “we need to take the wider context into account”, and “it’s hard to predict all possible consequences of any given action”? A widely accepted definition of complexity in the context of policy would add weight to evidence found to support the theory. We also need to clarify whether there is good cause to apply a natural science theory to political science. Most importantly, we should be wary of accepting the approach first and then looking for evidence to support it, rather than following the normal social science method of evaluating whether there is evidence in favour or against a given hypothesis. — http://blogs.lse.ac.uk/impactofsocialsciences/2018/10/12/what-are-the-implications-of-complex-systems-thinking-for-policymaking/

→ there’s a lot of potential in collaborating to illuminate the systems that create data Sep 1, 2018 highlights & Science & Systems there’s a lot of potential in collaborating to illuminate the systems that create data. Part of that potential, I think, will be realized by leveraging the different epistemological assumptions behind our respective approaches. For example, there is unquestionable value in using statistical models as a lens to interpret and forecast sociocultural trends—both business value and value to growing knowledge more generally. But that value is entirely dependent on the quality of the alignment between the statistical model and the sociocultural system(s) it is built for. When there are misalignments and blind spots, the door is opened to validity issues and negative social consequences, such as those coming to light in the debates about fairness in machine learning. There are real disconnects between how data-intensive systems currently work, and what benefits societies. — https://www.epicpeople.org/data-science-and-ethnography/

→ TYE: We touched on data provenance earlier, but I want to come back to it from the perspective of quantitative data Aug 31, 2018 highlights & Science & Systems TYE: We touched on data provenance earlier, but I want to come back to it from the perspective of quantitative data. In particular, I think it is critical to keep in mind that the systems that generate quantitative data are necessarily embedded in socio-technical systems. The technological elements of those systems (electronic sensors, software-based telemetry, etc.) are designed, manufactured, and maintained by sociocultural factors. So, a data scientist who is diligently trying to understand where their data comes from in order to interpret it, will sooner or later need to understand sociocultural phenomena that produced data, even if that understanding is more meta-data than data. It would make sense to co-develop rubrics for assessing the quality of data generated by socio-technical systems. Shining a bright light on the deepest lineage of data that impacts business or design decisions is important for everyone involved. Such assessments could lead to more cautious ways of using data, or be used in efforts to improve the explainability of technical systems. — https://www.epicpeople.org/data-science-and-ethnography/

→ DAWN: I’m always curious about how data scientists measure the consistency or sensitivity of results from datasets Aug 31, 2018 highlights & Science DAWN: I’m always curious about how data scientists measure the consistency or sensitivity of results from datasets. You have a notion of confidence intervals that communicates in a shorthand way “this is the size of grain of salt you have to take.” Ethnography doesn’t look at the world probabilistically, so we can never say, “9 of 10 times this will be the case.” But there are patterns, and those patterns can be relied upon for some purposes but not others. Even though we have messy complicated debates about how culture “scales” (which isn’t the same thing as reliability of results, but it’s related), we still don’t have clear ways to communicate to clients “this is the size of the grain of salt you need to take.” — https://www.epicpeople.org/data-science-and-ethnography/

→ https://www. Aug 31, 2018 highlights & Science But a key difference is that ethnographic work critically assesses the role of the researchers as an explicit, expected part of the research process. If data science projects were truly determined by the data alone (sensor data, click data and so forth), then repeated analyses should yield identical results. They don't. More light has been shed on this recently and is captured by concepts like "p-hacking". Minimally, it's clear that data science processes could benefit from more documentation and critical reflection on the effect of the data scientist themselves. The ethnographer's ability to identify and explicate researcher biases and social pressures could be helpful. — https://www.epicpeople.org/data-science-and-ethnography/

→ TYE: One thing I’ve observed about ethnography is that ethnographers often collect metadata simultaneously to collecting data—e Aug 31, 2018 highlights & Science TYE: One thing I’ve observed about ethnography is that ethnographers often collect metadata simultaneously to collecting data—e.g., taking notes on why they might have made certain observations instead of others, how the observations align or conflict with their expectations, etc. Provenance is built-in. The equivalent metadata about provenance might be recorded post hoc for the data scientist, or she might have to create it by talking to the stakeholders who did the collection.
DAWN: We don’t make hard distinctions between metadata and data because you don’t know which is which until you do the analysis, but the provenance is definitely still there. — https://www.epicpeople.org/data-science-and-ethnography/

→ the research process is somewhat similar, from what I have experienced. Aug 30, 2018 highlights & Science

the research process is somewhat similar, from what I have experienced. The three main steps in the data science process are:

data sourcing—more than mere access, it’s also about understanding lineage and assessing quality and coverage;
data transformation—from filtering and simple arithmetic transformations to complex abductions like predictions and unsupervised clustering; and
results delivery—both socially and programmatically (i.e., as lines of code).

— https://www.epicpeople.org/data-science-and-ethnography/

→ While both areas have a core set of expectations, they both have to extend beyond their core in order to deal with data about social life—data which has very real social consequences Aug 30, 2018 highlights & Science & Social Dawn: … While both areas have a core set of expectations, they both have to extend beyond their core in order to deal with data about social lifeâ€”data which has very real social consequences.

TYE: This is all the more true in industry contexts, where we often have to make social decisions, or design decisions, regardless of expertise.

DAWN: One difference is that in many data science scenarios, the available data has already been collected, whereas most ethnographic projects include field research time to gather new data.

TYE: Although this tendency doesnâ€™t hold true all the time, it is a common expectation, and that expectation results in a divergent initial perspective on projects: data scientists often think about working within the available datasets while ethnographers tend to begin their projects by thinking expansively about what dataset could be created, or should be created given the state of the art of the relevant discipline (anthropology, sociology and so forth). This difference in perspectives leads to different attribution models for the results. Data scientists will often describe their results as derived from the data (even if the derivation is complex and practically impossible to trace). Data scientists will readily recognize that they made decisions throughout the project that impacted the results, but will often characterize these decisions as being determined by the data (or by common and proven analyses of the data). You have a totally different way of dealing with that.

DAWN: Yes, for sure. Itâ€™s all coming from â€œthe dataâ€ but ethnographers themselves are a part of the data. A crucial part. If you were an active part of its creationâ€”if you were there, having conversations with people, looking them in the eye as they try to make sense of your presenceâ€”you just canâ€™t see it any other way. Itâ€™s unavoidable. Youâ€™re also aware of all of the other contingent factors involved in the data you collected in that moment. So we have to be explicitly reflective and critical of how our social position influenced the results.

https://www.epicpeople.org/data-science-and-ethnography/

→ Data science, across its variety of forms, is rooted in statistical calculations—involving both the technical knowledge and skill to assess the validity and applicability of these calculations, and the knowledge and skill to implement software or programming functions that execute the calculations Aug 29, 2018 highlights & Science Data science, across its variety of forms, is rooted in statistical calculations—involving both the technical knowledge and skill to assess the validity and applicability of these calculations, and the knowledge and skill to implement software or programming functions that execute the calculations. Underpinning the application of statistical calculations are assumptions about systemic structures and their dynamics—e.g., whether or not entities or events operate independently from one another, whether the variability of measurements, relative to an assumed or imputed trend or structure, is “noise” adhering to a separate set of rules (or not), and so on. Historically, these skill sets and conceptions of reality have been most heavily utilized in scientific inquiry, in finance and insurance, and business operations research (e.g., supply chain management and resource allocation). More recently, data science has expanded into a much larger set of domains: marketing, medicine, entertainment, education, law, etc. This expansion has shifted a large portion of data scientists toward data about people—some of that data is directly generated, like emails and web searches, some of it is sensed, like location or physical activity. — https://www.epicpeople.org/data-science-and-ethnography/

→ Ethnography is now used across anthropology, sociology, marketing, strategy, design, and other fields, but regardless of where it’s used, the core is about understanding people’s beliefs and behaviors and how these change over time Aug 29, 2018 highlights & People & Science & Systems Ethnography is now used across anthropology, sociology, marketing, strategy, design, and other fields, but regardless of where it’s used, the core is about understanding people’s beliefs and behaviors and how these change over time. Ethnography is a research skill that makes it possible to see what the world looks like from inside a particular context. If “man [sic] is an animal suspended in webs of significance he himself has spun" (Geertz), this skill involves systematically tracing out the logic of those webs, and examining how those webs structure what people do and think. Depending on the domain of study, these webs can be large scale or small, and in applied work they are often about people’s multidimensional roles as customers, users, employees, or citizens. Ethnographers look at the social world as dynamically evolving, emergent systems. They are emergent systems because people reflexively respond to the present and past, and this response shapes what they do in the future. Years of building ethnography from this core has generated both analytical techniques and a body of knowledge about sociocultural realities. — https://www.epicpeople.org/data-science-and-ethnography/

→ Research combining quantitative and qualitative methods have been around for a while, of course Aug 29, 2018 highlights & Science Research combining quantitative and qualitative methods have been around for a while, of course. There’s a clichéd logic to mixed methods research–“quant” + “qual”, “hard” + “soft”. EPIC people have broken down assumptions about the quant/qual divide and reframe the relationship between ethnography and big data, but the fact is, mixed methods research combining ethnographic and data science approaches is still rare.2 Some examples are Gray’s (et al.) study of Mechanical Turk workers, Haines’ multidimensional research design, and Hill and Mattu’s investigative journalism, and Bob Evans’ work on PACO — https://www.epicpeople.org/data-science-and-ethnography/

→ The work of data science is increasingly ubiquitous—computational systems are there “in the wild” when ethnographers go into the field, and have consequences for the human experience that is so central to ethnographic understanding Aug 29, 2018 highlights & Science The work of data science is increasingly ubiquitous—computational systems are there “in the wild” when ethnographers go into the field, and have consequences for the human experience that is so central to ethnographic understanding. Data science also offers new opportunities for mixed methods research, for example to generate a multidimensional understanding of human experience both digital/online and offline.
For data scientists, meanwhile, ethnography can offer a richer understanding of data and its provenance, and the sociocultural implications of data science work. As Nate Silver has written, “Numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning…. Before we demand more of our data, we need to demand more of our selves.” There is huge potential when we demand “more of ourselves”, but to realize that potential, people from both fields have to be in the room — https://www.epicpeople.org/data-science-and-ethnography/

→ The work of data science is increasingly ubiquitous—computational systems are there “in the wild” when ethnographers go into the field, and have consequences for the human experience that is so central to ethnographic understanding Aug 28, 2018 highlights & Science The work of data science is increasingly ubiquitous—computational systems are there “in the wild” when ethnographers go into the field, and have consequences for the human experience that is so central to ethnographic understanding. Data science also offers new opportunities for mixed methods research, for example to generate a multidimensional understanding of human experience both digital/online and offline. — https://www.epicpeople.org/data-science-and-ethnography/

→ We regularly see data science and ethnography conceptualized as polar ends of a research spectrum—one as a crunching of colossal data sets, the other as a slow simmer of experiential immersion Aug 28, 2018 highlights & Science We regularly see data science and ethnography conceptualized as polar ends of a research spectrum—one as a crunching of colossal data sets, the other as a slow simmer of experiential immersion. Unfortunately, we also see occasional professional stereotyping. A naïve view of “crunching” can make it seem as if all numerical work was brute computational force, as if data scientists never took the time to understand the social context from which data comes. A naïve view of ethnography can make it seem as if ethnography were casual description, “anecdotal” rather than systematic research and analysis grounded in evidence. Neither discipline benefits from these misunderstandings, and in fact there is more common ground than is obvious at first glance. — https://www.epicpeople.org/data-science-and-ethnography/

→ “So,” wondered science journalist Caroline Williams, “if brain training isn’t the way to apply it, what should we be doing?” Williams is the author of My Plastic Brain: One Woman’s Yearlong Journey to Discover if Science Can Improve Her Mind Aug 23, 2018 highlights & Science “So,” wondered science journalist Caroline Williams, “if brain training isn’t the way to apply it, what should we be doing?” Williams is the author of My Plastic Brain: One Woman’s Yearlong Journey to Discover if Science Can Improve Her Mind. She picked areas in which she wanted to improve — everything from attention to anxiety to creativity to navigation — and spent a year trying new techniques to see how much she would really pick up. — https://www.theverge.com/2018/8/22/17770652/caroline-williams-my-plastic-brain-neuroscience-self-improvement-interview

→ We can build software to eat the world, or software to feed it Mar 21, 2016 Design & Science & systems & design & highlights We can build software to eat the world, or software to feed it. And if we are going to feed it, it will require a different approach to design, one which optimizes for a different type of growth, and one that draws upon – and rewards – the humility of the designers who participate within it. — Kevin Slavin, Design as Participation. MIT’s Journal of Design and Science.

→ Loren Grush writing for The Verge on the potential impact of the Laser Interferometer Gravitational-Wave Observatory (LIGO)’s gravitational wave discovery on research and innovation in science. Feb 17, 2016 science & highlights If LIGO's measurements hold up, the collaboration could start its own ripple effect — one within the scientific community. —

Loren Grush writing for The Verge on the potential impact of the Laser Interferometer Gravitational-Wave Observatory (LIGO)’s gravitational wave discovery on research and innovation in science.

It’s the simple things in writing.

→ The data mindset is good for some questions, but completely inadequate for others. Oct 24, 2015 science & highlights

The data mindset is good for some questions, but completely inadequate for others. But try arguing that with someone who insists on seeing the numbers.

The promise is that enough data will give you insight. Retain data indefinitely, maybe waterboard it a little, and it will spill all its secrets.

There’s a little bit of a con going on here. On the data side, they tell you to collect all the data you can, because they have magic algorithms to help you make sense of it.

On the algorithms side, where I live, they tell us not to worry too much about our models, because they have magical data. We can train on it without caring how the process works.

The data collectors put their faith in the algorithms, and the programmers put their faith in the data.

At no point in this process is there any understanding, or wisdom. There’s not even domain knowledge. Data science is the universal answer, no matter the question.

— From Maciej Cegłowski’s talk at the Strata+Hadoop 2015 conference in NYC.

→ Kepler’s astronomers decided to found Planet Hunters, a program that asked “citizen scientists” to examine light patterns emitted by the stars, from the comfort of their own homes. Oct 22, 2015 science & highlights

Kepler’s astronomers decided to found Planet Hunters, a program that asked “citizen scientists” to examine light patterns emitted by the stars, from the comfort of their own homes.

In 2011, several citizen scientists flagged one particular star as “interesting” and “bizarre.” The star was emitting a light pattern that looked stranger than any of the others Kepler was watching.

— Citizen scientists provide the backbone for the latest viral astronomical headline.

→ Get a rat and put it in a cage and give it two water bottles. Jul 20, 2015 science & highlights

Get a rat and put it in a cage and give it two water bottles. One is just water, and one is water laced with either heroin or cocaine. If you do that, the rat will almost always prefer the drugged water and almost always kill itself very quickly, right, within a couple of weeks. So there you go. It’s our theory of addiction.

Bruce comes along in the ’70s and said, “Well, hang on a minute. We’re putting the rat in an empty cage. It’s got nothing to do. Let’s try this a little bit differently.” So Bruce built Rat Park, and Rat Park is like heaven for rats. Everything your rat about town could want, it’s got in Rat Park. It’s got lovely food. It’s got sex. It’s got loads of other rats to be friends with. It’s got loads of colored balls. Everything your rat could want. And they’ve got both the water bottles. They’ve got the drugged water and the normal water. But here’s the fascinating thing. In Rat Park, they don’t like the drugged water. They hardly use any of it. None of them ever overdose. None of them ever use in a way that looks like compulsion or addiction. There’s a really interesting human example I’ll tell you about in a minute, but what Bruce says is that shows that both the right-wing and left-wing theories of addiction are wrong. So the right-wing theory is it’s a moral failing, you’re a hedonist, you party too hard. The left-wing theory is it takes you over, your brain is hijacked. Bruce says it’s not your morality, it’s not your brain; it’s your cage. Addiction is largely an adaptation to your environment.

[…]

We’ve created a society where significant numbers of our fellow citizens cannot bear to be present in their lives without being drugged, right? We’ve created a hyperconsumerist, hyperindividualist, isolated world that is, for a lot of people, much more like that first cage than it is like the bonded, connected cages that we need.

The opposite of addiction is not sobriety. The opposite of addiction is connection. And our whole society, the engine of our society, is geared towards making us connect with things. If you are not a good consumer capitalist citizen, if you’re spending your time bonding with the people around you and not buying stuff—in fact, we are trained from a very young age to focus our hopes and our dreams and our ambitions on things we can buy and consume. And drug addiction is really a subset of that.

—

Johann Hari, Does Capitalism Drive Drug Addiction? (via bigfatsun).

Hm. A brief skim of some of the research done on Bruce Alexander’s “Rat Park” in the last few decades and the Wikipedia article on the subject seems to indicate that the conclusion drawn here isn’t as straightforward as we’d like, but overall, it looks like this subject should be studied more. Disappointing that the SFU studies ran out of funding.

Still, it’s an interesting thought, and an important contrast to prevailing views on addiction (as Johann Hari suggests).

→ http://t. Jul 8, 2015 science & highlights Ageing rates vary widely, says study http://t.co/qoiVutPlk0 —

@BBCWorld (http://twitter.com/BBCWorld/status/618225162744410113).

In hindsight, this seems obvious to me. If the aging process is biological (which it obviously is) then there has to be differences in how it happens in people. Still, the implications are huge. I’m reminded of Google’s anti-aging startup, Calico. Maybe Calico can develop treatments for the people that age “quickly” as an early type of aging intervention… Hm.