2,865 Matching Annotations
  1. Feb 2016
    1. Great explanation of 15 common probability distributions: Bernouli, Uniform, Binomial, Geometric, Negative Binomial, Exponential, Weibull, Hypergeometric, Poisson, Normal, Log Normal, Student's t, Chi-Squared, Gamma, Beta.

    1. Since its start in 1998, Software Carpentry has evolved from a week-long training course at the US national laboratories into a worldwide volunteer effort to improve researchers' computing skills. This paper explains what we have learned along the way, the challenges we now face, and our plans for the future.

      http://software-carpentry.org/lessons/<br> Basic programming skills for scientific researchers.<br> SQL, and Python, R, or MATLAB.

      http://www.datacarpentry.org/lessons/<br> Managing and analyzing data.

  2. Jan 2016
    1. While there are some features shared between a university repository and us we are distinctly different for the following reasons: We offer DOIs to all content published on The Winnower All content is automatically typeset on The Winnower Content published on the winnower is not restricted to one university but is published amongst work from peers at different institutions around the world Work is published from around the world it is more discoverable We offer Altmetrics to content  Our site is much more visually appealing than a typical repository  Work can be openly reviewed on The Winnower but often times not even commented on in repositories. This is not to say that repositories have no place, but that we should focus on offering authors choices not restricting them to products developed in house.

      Over this tension/complementary between in house and external publishing platforms I wonder where is the place for indie web self hosted publishing, like the one impulsed by grafoscopio.

      A reproducible structured interactive grafoscopio notebook is self contained in software and data and holds all its history by design. Will in-house solutions and open journals like The Winnower, RIO Journal or the Self Journal of Science, support such kinds of publishing artifacts?

      Technically there is not a big barrier (it's mostly about hosting fossil repositories, which is pretty easy, and adding a discoverability and author layer on top), but it seems that the only option now is going to big DVCS and data platforms now like GitHub or datahub alike for storing other research artifacts like software and data, so it is more about centralized-mostly instead of p2p-also. This other p2p alternatives seem outside the radar for most alternative Open Access and Open Science publishers now.

    1. 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 di fferent 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'.

    1. Discussion about Obama's computer science for K-12 initiative. CS programs in high school are about 40 years overdue. It is a valid concern that much of this money may be wasted on overpriced proprietary software, hardware, and training programs. And of course, average schools will handle CS about like they handle other subjects -- not very well.

      Another concern raised, and countered, is that more programmers will mean lower wages for programmers. But not everyone who studies CS in high school is going to become a programmer. And an increase in computer literacy may help increase the demand for programmers and technicians.

    1. educators and business leaders are increasingly recognizing that CS is a “new basic” skill necessary for economic opportunity. The President referenced his Computer Science for All Initiative, which provides $4 billion in funding for states and $100 million directly for districts in his upcoming budget; and invests more than $135 million beginning this year by the National Science Foundation and the Corporation for National and Community Service to support and train CS teachers.
    1. open Science

      Die Auswirkungen des digitalen Wandels in der Forschung erforschr der Leibniz-Forschungsverbund Science 2.0. Die derzeit 37 Partner bearbeiten die Forschungsschwerpunkte „Neue Arbeitsgewohnheiten“, „Technologieentwicklung“ und „Nutzungsforschung“. Damit untrennbar verbunden sind die aktuellen Entwicklungen im Hinblick auf die Öffnung des gesamten Wissenschaftsprozesses oder Teilen davon („Open Science“)

      http://www.leibniz-science20.de/

    1. This has implications far beyond the cryptocurrency

      The concept of trust, in the sociological and economic sense, underlies exchange. In the 15th-17th centuries, the Dutch and English dominance of trade owed much to their early development of instruments of credit that allowed merchants to fund and later to insure commercial shipping without the exchange of hard currency, either silver or by physically transporting the currency of the realm. Credit worked because the English and Dutch economies trusted the issuers of credit.

      Francis Fukuyama, a philosopher and political economist at Stanford, wrote a book in 1995, Trust: The Social Virtues and the Creation of Prosperity, on the impact of cultures of trust on entrepreneurial growth. Countries of ‘low trust’ have close family culture who limit trust to relations: France, China, S. Italy. Countries of ‘high trust’ have greater ‘spontaneous sociability’ that encourages the formation of intermediate institutions between the state and the family, that encourage greater entrepreneurial growth: Germany, England, the U.S. – I own the book and (shame on me!) haven’t yet read it.

      I thought of this article in those contexts – of the general need for trusted institutions and the power they have in mediating an economy, and the fascinating questions raised when a new facilitator of trust is introduced.

      How do we trust? Across human history, how have we extended the social role of trust to institutions? If a new modality of trust comes available, how does that change institutional structures and correspondingly the power of individuals, of institutions. How would it change the friction to growth and to decline?

      Prior to reading this article, I had dismissed Bitcoin as a temporary aberration, mostly for criminal enterprises and malcontents. I still feel that way. But the underlying technology and it’s implications – now that’s interesting.

    1. Category Theory for the Sciences by David I. Spivak<br> Creative Commons Attribution-NonCommercial-ShareAlike 4.0<br> MIT Press.

    1. "A friend of mine said a really great phrase: 'remember those times in early 1990's when every single brick-and-mortar store wanted a webmaster and a small website. Now they want to have a data scientist.' It's good for an industry when an attitude precedes the technology."
    1. paradox of unanimity - Unanimous or nearly unanimous agreement doesn't always indicate the correct answer. If agreement is unlikely, it indicates a problem with the system.

      Witnesses who only saw a suspect for a moment are not likely to be able to pick them out of a lineup accurately. If several witnesses all pick the same suspect, you should be suspicious that bias is at work. Perhaps these witnesses were cherry-picked, or they were somehow encouraged to choose a particular suspect.

    1. Stupid models are extremely useful. They are usefulbecause humans are boundedly rational and because language is imprecise. It is often only by formalizing a complex system that we can make progress in understanding it. Formal models should be a necessary component of the behavioral scientist’s toolkit. Models are stupid, and we need more of them.

      Formal models are explicit in the assumptions they make about how the parts of a system work and interact, and moreover are explicit in the aspects of reality they omit.

      -- Paul Smaldino

    2. Microeconomic models based on rational choice theory are useful for developing intuition, and may even approximate reality in a fewspecial cases, but the history of behavioral economics shows that standard economic theory has also provided a smorgasbord of null hypotheses to be struck down by empirical observation.
    3. Where differences between conditions are indicated, avoid the mistake of running statistical analyses as if you were sampling from a larger population.

      You already have a generating model for your data – it’s your model. Statistical analyses on model data often involve modeling your model with a stupider model. Don’t do this. Instead, run enough simulations to obtain limiting distributions.

    4. A model’s strength stemsfromits precision.

      I have come across too many modeling papers in which the model – that is, the parts, all their components, the relationships between them, and mechanisms for change – is not clearly expressed. This is most common with computational models (such as agent-based models), which can be quite complicated, but also exists in cases of purely mathematical models.

    5. However, I want to be careful not to elevate modelers above those scientists who employ other methods.

      This is important for at least two reasons, the first and foremost of which is that science absolutely requires empirical data. Those data are often painstaking to collect, requiring clever, meticulous, and occasionally tedious labor. There is a certain kind of laziness inherent in the professional modeler, who builds entire worlds from his or her desk using only pen, paper, and computer. Relatedly, many scientists are truly fantastic communicators, and present extremely clear theories that advance scientific understanding without a formal model in sight. Charles Darwin, to give an extreme example, laid almost all the foundations of modern evolutionary biology without writing down a single equation.

    6. Ultimately,the theory has been shown to be incorrect, and has been epistemically replaced by the theory of General Relativity. Nevertheless, the theory is able to make exceptionally good approximations of gravitational forces –so good that NASA’s moon missions have relied upon them.

      General Relativity may also turn out to be a "dumb model". https://twitter.com/worrydream/status/672957979545571329

    7. Table 1.Twelve functions served by false models. Adapted with permissionfrom Wimsatt

      Twelve good uses for dumb models, William Wimsatt (1987).

    8. To paraphrase Gunawardena (2014), a model is a logical engine for turning assumptions into conclusions.

      By making our assumptions explicit, we can clearly assess their implied conclusions. These conclusions will inevitably be flawed, because the assumptions are ultimately incorrect or at least incomplete. By examining how they differ from reality, we can refine our models, and thereby refine our theories and so gradually we might become less wrong.

    9. the stupidity of a model is often its strength. By focusing on some key aspects of a real-world system(i.e., those aspectsinstantiated in the model), we can investigate how such a system would work if, in principle, we really couldignore everything we are ignoring. This only sounds absurd until one recognizes that, in our theorizing about the nature of reality –both as scientists and as quotidianhumans hopelessly entangled in myriad webs of connection and conflict –weignore thingsall the time.
    10. The generalized linear model, the work horse ofthe social sciences, models data as being randomly drawn from a distribution whose mean varies according to some parameter. The linear model is so obviously wrong yet so useful that the mathematical anthropologist Richard McElreathhas dubbed it “the geocentric model of applied statistics,”in reference to the Ptolemaic model of the solar system that erroneously placed the earth rather than the sun at the center but nevertheless produced accurate predictions of planetary motion as they appeared in the night sky(McElreath 2015).

      A model that approximates some aspect of reality can be very useful, even if the model itself is flat-out wrong.

      But on the other hand, we can't accept approximation of reality as hard proof that a model is correct.

    11. Unfortunately, my own experience working with complex systems and working among complexity scientistssuggests that we are hardly immune to such stupidity. Consider the case of Marilyn Vos Savantand the Monty Hall problem.

      Many people, including some with training in advanced mathematics, contradicted her smugly. But a simple computer program that models the situation can demonstrate her point.

      2/3 times, your first pick will be wrong. Every time that happens, the door Monty didn't open is the winner. So switching wins 2/3 times.

      http://marilynvossavant.com/game-show-problem/

    12. Mitch Resnick, in his book Turtles, Termites, and Traffic Jams, details his experiences teaching gifted high school students about the dynamics of complex systems using artificial life models (Resnick 1994). He showed them how organized behavior could emerge when individualsresponded only to local stimuli using simple rules, without the need for a central coordinating authority. Resnick reports that even after weeks spent demonstrating the principles of emergence,using computer simulations that the students programmed themselves, many students still refused to believe that what they were seeing could really work without central leadership.
    1. Maybe digital history is at the midway point on the continuum between art and science

      I find the realm of data science, and particularly data visualisation, really interesting in this context. Visual renderings of complex data sets can bridge the divide between art and science, while also requiring a certain, new, literacy in order to decipher and decode them. At the same time, increasingly we see these aesthetics in museums and galleries, co-opted by 'art' proper: http://o-c-r.org/portfolio/listening-post/

      https://vimeo.com/59622009

  3. Dec 2015
    1. As part of EFF’s 25th Anniversary celebrations, we are releasing “Pwning Tomorrow: Stories from the Electronic Frontier,” an anthology of speculative fiction from more than 20 authors, including Bruce Sterling, Lauren Beukes, Cory Doctorow, and Charlie Jane Anders. To get the ebook, you can make an optional contribution to support EFF’s work, or you can download it at no cost. We're releasing the ebook under a Creative Commons Attribution-NonCommercial-No Derivatives 4.0 International license, which permits sharing among users. 
    1. We believe that openness and transparency are core values of science. For a long time, technological obstacles existed preventing transparency from being the norm. With the advent of the internet, however, these obstacles have largely disappeared. The promise of open research can finally be realized, but this will require a cultural change in science. The power to create that change lies in the peer-review process.

      We suggest that beginning January 1, 2017, reviewers make open practices a pre-condition for more comprehensive review. This is already in reviewers’ power; to drive the change, all that is needed is for reviewers to collectively agree that the time for change has come.

    1. Part of Galileo’s genius was to transfer the spirit of the Italian Renaissance in the plastic arts to the mathematical and observational ones.
    1. Big Sur is our newest Open Rack-compatible hardware designed for AI computing at a large scale. In collaboration with partners, we've built Big Sur to incorporate eight high-performance GPUs
    1. Similarly, in science there exists substantial expertise making brilliant connectionsbetween concepts, but it is being conveyed in silos of English prose known as journalarticles. Every scientific journal article has a methods section, but it is almost impossibleto read a methods section and subsequently repeat the experiment—the English languageis inadequate to precisely and concisely convey what is being done.

      This issue of reproducible science is starting to be tackled but I do believe formal methods and abstractions would go along way to making sure we adhere these ideas. It is a bit like writing a program with global state vs a functionally defined program, but even worse, since you may forget to write down one little thing you did to the global state.

    2. As mentioned above category theory has branched out into certain areas of scienceas well. Baez and Dolan have shown its value in making sense of quantum physics, itis well established in computer science, and it has found proponents in several otherfields as well. But to my mind, we are the very beginning of its venture into scientificmethodology. Category theory was invented as a bridge and it will continue to serve inthat role.
    3. In 1980 Joachim Lambek showed that the types and programs used in computerscience form a specific kind of category. This provided a new semantics for talking aboutprograms, allowing people to investigate how programs combine and compose to createother programs, without caring about the specifics of implementation. Eugenio Moggibrought the category theoretic notion of monads into computer science to encapsulateideas that up to that point were considered outside the realm of such theory.
    4. The paradigm shift brought on by Einstein’s theory of relativity brought on the real-ization that there is no single perspective from which to view the world. There is nobackground framework that we need to find; there are infinitely many different frame-works and perspectives, and the real power lies in being able to translate between them.It is in this historical context that category theory got its start.
    5. Agreementis the good stuff in science; it’s the high fives.But it is easy to think we’re in agreement, when really we’re not. Modeling ourthoughts on heuristics and pictures may be convenient for quick travel down the road,but we’re liable to miss our turnoff at the first mile. The danger is in mistaking ourconvenient conceptualizations for what’s actually there. It is imperative that we havethe ability at any time to ground out in reality.
    1. For me the interesting part of Stiegler’s work is the idea that it is technics that invents human beings; it is technics that constitutes the human.

      We make the tools. Then the tools make us.

      "Careful With That Axe, Eugene" -- Pink Floyd

  4. Nov 2015
    1. Les représentants de la Bibliothèque nationale de France (BnF) annoncèrent leur objectif de ramener le délai de traitement des documents à six semaines en moyenne

      C’était long, en 2002! Où en est la BnF, aujourd’hui? D’une certaine façon, ce résumé semble prédire la venue des données, la fédération des catalogues, etc. Pourtant, il semble demeurer de nombreux obstacles, malgré tout ce temps. Et si on pouvait annoter le Web directement?

    1. “Many random number generators in use today are not very good. There is a tendency for people to avoid learning anything about such subroutines; quite often we find that some old method that is comparatively unsatisfactory has blindly been passed down from one programmer to another, and today’s users have no understanding of its limitations.”— Donald Knuth; The Art of Computer Programming, Volume 2.

      Mike Malone examines JavaScript's Math.random() in v8, argues that the algorithm used should be replaced, and suggests alternatives.

    1. TPOT is a Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. Think of TPOT as your “Data Science Assistant”: TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines, then recommending the pipelines that work best for your data.

      https://github.com/rhiever/tpot TPOT (Tree-based Pipeline Optimization Tool) Built on numpy, scipy, pandas, scikit-learn, and deap.

    1. Max Planck, when asked how often science changes and adopts new ideas, said, “with every funeral.” And for better or worse, they happen pretty regularly.
    2. Who among us could predict anything five years into the future? What kind of science would science be if it could make reliable predictions about stuff five years out? Science is about what we don’t know yet and how we’re going to get to know it.

      GREAT!

    3. I know how crazy that sounds, but it is of course exactly the right way to proceed. If you are reviewing a grant, you should be interested in how it will fail—usefully or just by not succeeding. Not succeeding is not the same as failing. Not in science.

      Yep. Every day we learn new ways that doesn't work because of this and that... It's sad that this is not "formally" discussed as part of the process!

    4. Too often you fail until you succeed, and then you are expected to stop failing. Once you have succeeded you supposedly know something that helps you to avoid further failure. But that is not the way of science. Success can lead only to more failure. The success, when it comes, has to be tested rigorously and then it has to be considered for what it doesn’t tell us, not just what it does tell us. It has to be used to get to the next stop in our ignorance—it has to be challenged until it fails, challenged so that it fails.

      A great interpretation on the way of science.

    1. According to Mark T. Mitchell, professor of political science at Patrick Henry College in Virginia: Gratitude is born of humility, for it acknowledges the giftedness of the creation and the benevolence of the Creator. This recognition gives birth to acts marked by attention and responsibility. Ingratitude, on the other hand, is marked by hubris, which denies the gift, and this always leads to inattention, irresponsibility, and abuse.
  5. Oct 2015
    1. Davidson shocked his professors by taking off for India to explore meditation practice and Buddhist teachings. After three months there and in Sri Lanka, he came back convinced he would do meditation research. He was quickly disabused of this notion by his professors, who let him know that if he had any hope of a career in science, he’d better stow the meditation and follow a more conventional path of research. He became a closet meditator and an affective neuroscientist—a deep student of the emotions.

      This seems to be the theme for scientific pioneers in recent decades.

    1. as Brent Tully (known for his discovery of supergalaxies) observed: “It’s disturbing  that there is a new theory every time there is a new observation.”

      "When the facts change I can my mind, don't you, sir?"

  6. Sep 2015
    1. when the built environment ceases to accommodate behavioral requirements, people seek to correct the problem through construction, renovation, or moving to a different building

      Stauffer Science Building transitioning into the new and improved Science and Learning Center is an example on Whittier College's campus of this idea.

  7. Aug 2015
    1. However, if an open access version of a text is available, this must always be treated as the primary text. Here the commercial version of the text becomes the secondary version and it should always be cited second and in a manner that makes this completely clear. For instance, after the primary reference to the full text, you could write: ‘Also available as: ….’

      Would be interesting to write a tool that could take a paper as input and replace all citations with references to freely available versions

    1. Here, on page 2, a study on infrasound conducted by Mr. Richard James is referenced. Mr. Richard James references Nina Pierpont's "Wind Turbine Syndrome" in articles he has written, namely "Wind Turbine Infra and Low-Frequency Sound: Warning Signs That Were Not Heard," see this link. Wind turbine syndrome is not a real medical syndrome, see this link and this link. In fact, Mr. Richard James and his methodologies for measuring sound has been discredited in a Michigan court, see Rick James – A Technical Discussion of His Deposition and Testimony in the Spencer / Kobetz Lawsuit.

      On page 7, we learn that Mr. Richard James trained a field technician to set up sound measuring equipment at a dozen homes within the Shirley Wind Farm. It's unclear if Mr. Richard James was present to ensure set up and staging of equipment was per professional protocol. The trained field technician is stated to live within the Shirley Wind Farm. Mr. Richard James also collected weather data using a website called wonderground.com [sic]. Note that the field technician didn't record weather data via actual observation while domiciled within the Shirley Wind Farm. Also to consider is the likelihood of gaps in the collection of data, "On many occasions, there was an observer recording the events of the turbines..." This sounds fuzzy. Brings doubt to the reliability of collected data.

      On page 13, the Brown County Board of health declares the Shirley Wind Farm a human health hazard.

      As a result of Brown County's declaration, the Governor of Wisconsin will spend $250,000 to study health effects of wind power.

  8. Jun 2015
    1. Laboratory analysis of those samples found compounds that are toxic to humans, including acetone and methylene chloride — powerful industrial solvents — along with oil.

      In what concentrations? "Toxic" is pretty meaningless.

    1. warns us against equating changes in scientific understanding of a sense such as smell, what is called “osmology,” with experiential transformations. Attending to the history of smell, he tells us, is also valuable in undermining simple binary oppositions between boundaried individuals and their englobing environ- ment, the basis of Cartesian subject/object dualisms. Instead, it helps situate us in a more fluid, immersive context, where such stark oppositions are understood as themselves contingent rather than necessary

      This reminds me of our Monday discussion of Spinoza re: how expanded "scientific understanding" changes (or doesn't change) sensory experiences.

    1. There’s a scale for how to think about science. On one end there’s an attempt to solve deep, fundamental questions of nature; on the other is rote uninteresting procedure. There’s also a scale for creating products. On one end you find ambitious, important breakthroughs; on the other small, iterative updates. Plot those two things next to each other and you get a simple chart with four sections. Important science but no immediate practical use? That’s pure basic research — think Niels Bohr and his investigations into the nature of the atom. Not much science but huge practical implications? That’s pure applied research — think Thomas Edison grinding through thousands of materials before he lit upon the tungsten filament for the lightbulb.
  9. May 2015
  10. www.jstor.org.mutex.gmu.edu www.jstor.org.mutex.gmu.edu
    1. Annual Reviews is collaborating with JSTOR to digitize, preserve and extend access to Annual Review of Anthropology. http://www.jstor.org The Globalization of Pentecostal and Charismatic Christianity

      Finally an open-source, open access option for sharing research!

  11. Apr 2015
    1. Wouldn’t it be useful, both to the scientific community or the wider world, to increase the publication of negative results?
  12. Jan 2015
    1. Unlike most popularizers (at least of mine), this post didn’t describe a completed piece of research. It just served just an opportunity to riff about an idea I found interesting. But blogging made me realize this idea could be more interesting than I had realized. A “motivated view” of empathy could, for instance, help in understanding illnesses like autism and psychopathy, or thinking up techniques to “grow” empathy. I figured it’d be worth sinking some more effort into it, and wrote a long form academic article on the subject. After much work and a long (but productive) peer review process, that article was published just last week! More importantly, the ideas in that piece—taken over by my students—now drive much new work in my lab that might not have happened otherwise.

      A very interesting point, especially considering the fact that in my own research of science bloggers (#MySciBlog research at LSU), this seems to be a common approach to blogging by scientists/scholars. A scientist/scholarly blogger often starts a blog post with an idea, nugget or concept that they are curious about or interested in learning more about. Many 'intellectuals' also say that blogging helps them collect and clarify their thoughts on a topic or question. The natural result for those engaged in scholarship is for some blogged topics/questions to blossom into larger and more complex ideas and even research questions. I know for myself, several of my blog posts - and especially my some of my freelance science journalism work - has prompted me to pursue complimentary research in my role as a science communication PhD student.

      As an added bonus of being public on the web, the 'blogged' content can elicit feedback from readers and scholars that further pushes the blogger's own ideas and scholarship in new directions.

  13. Feb 2014
    1. developed a specific coding scheme for volunteers to follow. As a supplement to that scheme, the following tutorial can be used to gain more facility with identifying article structure and und

      this is cool

  14. Sep 2013
    1. Hence the man who makes a good guess at truth is likely to make a good guess at probabilities

      At first, I didn't like this quote, then I thought back to good ol' Oakley's stats class. We make scientific theories based on what idea is most likely to happen (we reject/do not reject the null hypothesis, but we do not say we accept the null hypothesis). Science: putting me in my place since I had a place to be put.

    1. "science" which can teach us to do under all circumstances the things which will insure our happiness and success.

      Happiness has far too much variability to be considered scientific, in my opinion. It's all a matter of opinion and personal experience. Hence my disagreement with his "judgment not knowledge" bit.