And by that I don’t mean “get nature” but rather, “get Nature, the magazine.” I do get Nature, which is very expensive, so maybe you don’t have to. A recent newsletter from the Mother Mag includes a list of great new science books, and I was pretty impressed with the books, so I’m giving you the list*. Take the money you saved on not subscribing to Nature and get one! Continue reading Amazing science books→
Every thing, be it a tall skyscraper, a lofty mountain, or a mere mole hill, has a single destiny: To become flat, to fall, wear or settle down into flatness. This is the way of the world because the world warps the spacetime in which those things stand in a way that pulls the atoms they are made of towards the center of the planet. That this is true is evidenced by the fact that the largest region of the Earth that is made of molecules that are not well attached to each other is basically flat. (The oceans and seas.) Even the harder stuff such as rock and dirt is mostly flat around the earth. Be impressed with the jagged and broad Front Range of the majestic Rocky Mountains, but after you are done looking at them turn around and behold the essential flatness of the Plains and Midwest. Most of Asia is pretty flat as is most of Africa. The biggest thing going in South America is the Amazon Basin. Again, flat. Obviously, “flat” is a somewhat subjective term, but we can truly and scientifically divide the surface of the land of the Earth into regions of mountain building and regions of continuous, relentless, enflattening. The only reason that everything isn’t more flat is because, even though the destiny of all the atoms is to be part of one great flatness is real, there are also other effects.
If two continents run into each other, you get mountains. If a big bank provides the financing and a corporation has the will, you get a sky scraper. If a department of transportation gets the funding, and there is a river, there will be a bridge somewhere. These short term effects upon the earth create the bumps and high spots. Temporarily.
So yes, a bridge or a building falls down because of gravity, and now you are annoyed at me because I just spent 389 words stating the obvious. But wait, there’s more.
I state the obvious here not because you need to be reminded of this great truth (though we can all use that reminder now and then), but because the reality of gravity generates a bureaucratic situation that is the more proximal reason for the collapse of a condo.
Everything is broken. Some things are only barely broken, possibly invisibly broken, so maybe not technically broken by some mundane human standard, but at the molecular level, there is an atom here or there out of place (a flaw) or a vulnerability that is more of a broken design element than an actual break. Things like buildings and bridges, and a wide range of important machines, are regularly inspected to find these broken elements, in order that failure does not happen unexpectedly. But since everything is broke at some level, the bridge or building or machine is not discarded or rebuilt every time a problem is found. Rather, there is a threshold of how many breaks, or how bad the breaks are, beyond which we try to not let the brokenness pass.
But the ideal threshold is not known, merely estimated. And, there is a more conservative and a less conservative approach. Then there are errors and flaws in the system of looking for and keeping tracks of the breaks. There are corporate, institutional, and political pressures to not acknowledge that there is a problem. Sometimes that gets to the point of an enigmatic fedora wearing dog having a cup of coffee in a flaming restaurant.
And then the condo collapses, or the bridge falls down, and there is a … well, reassessment.
It happens in stages. First you build all the bridges such as the numerous bridges built across rivers and streams as part of the US Federal Interstate projects of the 1950s. Inspections happen, but the threshold is not sufficiently conservative, or the methods of inspection are not as good as they could be, or maybe there are pressures to ignore the data or move the threshold. Then the Schoharie Bridge collapses. From Wikipedia:
The Schoharie Creek Bridge was a New York State Thruway bridge over the Schoharie Creek near Fort Hunter and the Mohawk River in New York State. On April 5, 1987 it collapsed due to bridge scour at the foundations after a record rainfall. The collapse killed ten people. The replacement bridge was completed and fully open to traffic on May 21, 1988. The failure of the Schoharie Creek Bridge motivated improvement in bridge design and inspection procedures within New York and beyond.
That entry is a little misleading, suggesting that an unusual flood did something unusual to the bridge. Yes, it was a record flood, but records for that stream post date the building of a major reservoir upstream. The previous record was only from 1955, and most years the highest floods were nearly this high. In other words, no one was that surprised about the water level coming off the dam of the big reservoir, and no one was surprised about the big rainfall that happened downstream from the dam and upstream from the bridge. It was the fact that they happened over the same few days that rose the level to a record high, but not an outlandish record high. The bridge was built broken, in the sense that it was vulnerable to scouring. Today, interstate bridges are built with better foundations so this happens less, and they are inspected more.
But here’s the thing: As noted, this led to better design and inspection. But it also led to a lot of bridges being repaired all of the sudden.
I have not found a study that links major news-worthy failure to policy changes. But I can tell you that in the decade after 1987, there was a huge push to rebuild and update bridges to the degree that for a few years, I made a living on it, since most bridges in New York and New England pass by historic homes, old mills, or threaten Native American sites, as a function of how rivers, streams, roads, paths, hydrology, and settlement patterns work. I’m pretty sure similar things happened after the collapse of the I-35W bridge in Minneapolis a few years ago. And now, condos.
I think it works like this. At any moment in time there are identified problems with all the buildings or bridges of a certain class. By class I mean “Condos on barrier islands in Florida” or “Interstate bridges” and so on. The number of problems increases over time, but of course, many of those problems are dealt with as they are found, or at least, eventually. But the number of outstanding problems tends to increase because absent outside forces, the institutional, economic, and political forces that tend to lead to problems not being addressed tend to work a little at a time to enhance complacency, and sometimes, just plain corruption or stupidity.
While this is happening the public perception is essentially null. It isn’t on anyone’s radar screen. Even if you know about this or that problem, regular members of the public are not tuned in to a steadily ageing infrastructure that is associated with a steadily growing set of problems. Expensive problems. Annoying and time consuming problems. Problems that are easy to ignore, and really, not even know about to begin with. So, we are dogs with fedoras sipping coffee in a burning building. Everything is fine.
But then the condo collapses, or the bridge falls into the ravine. The public is astounded, shocked, made fearful, angry, and demands action, but generally, remains focused on that one event, that one structure, that one failure. Then, that is over and everyone forgets, and never really knew that there were a dozen condos or bridges at that level of broken, but only one failed because failures tend to come one at a time. The public is also mostly unaware (though certainly not everyone) of a response by the powers that be, the inspecting agencies and so on, that involves the sudden increase in inspection rate, the betterment of standards, and ultimately the application of jackhammers and pouring of concrete and leveling of footings and so on. The number of inspection issues suddenly drops to an acceptable level (but they are of course still there, again, unperceived by the public) and start to build again.
The improvements in engineering, materials, and inspection procedures hopefully lasts longer than public concern. The industry behind the infrastructure improves. But the social and political infrastructure seems to not improve much, or does so only temporarily. I put this pattern in a chart:
I am a little disappointed in Neil deGrasse Tyson. He has long pointed out the very correct truth that many astronomers, including professionals and avocational astronomers, have spent a lot of time looking at the sky, and have failed to find Aliens flying about. This suggests that there are not aliens flying about. Recently he added to this the observation that the UFOs recently discussed in the media and subjected to a certain amount of government scrutiny seem only to be seen by Navy pilots in remote areas, which leaves him with no interest in making them a subject of research. I agree with his observation, but in fact, his statement about UFOs can be easily reformulated as a hypothesis that fits nicely with his own area of expertise as an actual scientist (as opposed to the part of his professional activates that are more about science outreach and education).
I am a little disappointed with Ari Melber, though his transgression is forgivable since he is a law expert and not a UFO expert. He makes the same mistake as NdGT when he distills the range of possible explanations for UFOs to three possibilities, apparently presented as exhaustive: 1) they are natural phenomena (but not natural Aliens); 2) they are associated with secret non-Alien technology of some kind; and 3) Aliens.
Obviously there is another explanation that is not quite “natural phenomenon” because that usually means swamp gasses or lights formed by some geological process: they are an artifact of the mode of observation. A smudge on the windshield or lens, as it were, but presumably a somewhat enigmatic or at lest inobvious smudge.
(I’m leaving aside the explanation that they are a hoax perpetuated by a number of loosely connected Navy pilots, on the assumption that the recent Government Report would have ruled that out.)
Many of these things — some of the most important recent examples of these things — are seen with some sort of seeing technology, and the light energy that this technology collects is then processed by some more technology. I can not offer a detailed idea of how these technologies would produce a smudge on the lens of some sort, and this is not the appropriate time to do so. But I am suggesting that the technology produces an artifact that we mistake for a UFO. I would guess that the Government Report, which I admittedly have not read, has not addressed this issue, or some reporter or another would have mentioned it by now. Assuming they read the Government Report.
Here is what I would do. I’d catalog the optical or energy grabbing equipment (the “eyes,” which may be as simple as the window of a jet or the lens of a sighting device) of military vehicles (mainly jets?) into meaningful categories, and I’d catalog the processing machines (the technology that makes the HUDs of the aircraft work etc) into meaningful categories, and see if there is a subset of these devices, by specific technology, manufacturer, or whatever, that is producing the UFO signals, as opposed to others that do not.
That won’t provide an answer to what these UFOs are, but it would generate thought that might lead to this. I said this was a hypothesis, and I do not use that term lightly. My null hypothesis is that the observations are distributed randomly among the various visualization technologies used by all aircraft. If that is falsified because a biased subset of the technology produces UFOs, then the next step of research is warranted.
And this might interest Neil deGrasse Tyson, since his own early PhD (and other) research, which looked at solar flares and magnetics, required a deep and detailed understanding of machines that see things, other than the human eye. This should be something he would find interesting.
Unless, of course, he has made some deal with the Aliens to through us all off the scent…
With Covid-19 limitations on so many activities, we are doing so much reading there is a threat that we will wear out all the books!
I have four items here that are deep, and intellectually engaging. A scholarly look at literature by one of the great living American authors, two addressing the history of science in Victorian England by two of the leading experts, and an engaging deep dive into the way the human brain comes to grip with mathematics and numbers in general.
13 Ways of Looking at the Novel by Jane Smiley consists of 279 pages with narrow margins and small type providing 13 different views of novels as a phenomenon. This is the best modern dissection of the art I’ve seen. These rich and engaging pages are then followed by almost the same exact number of pages of commentary and (to a lesser extent) synopsis of 100 novels. If you ever want a list of the great novels over time, from which to chose new material to read, this list is excellent, but be warned: It is a fairly uniform sampling, and you know what that means.
An essential guide for writers and readers alike, here is Smiley’s great celebration of the novel. As she embarks on an exhilarating tour through one hundred titles—from classics such as the thousand-year-old Tale of Genji to recent fiction by Zadie Smith and Alice Munro—she explores the power of the form, looking at its history and variety, its cultural impact, and just how it works its magic. She invites us behind the scenes of novel-writing, sharing her own habits and spilling the secrets of her craft, and offering priceless advice to aspiring authors. Every page infects us anew with the passion for reading that is the governing spirit of this gift to book lovers everywhere.
If you don’t know Jane Smiley as an author (and academic) you should. One of my favorite novels of all time is by her: JANE SMILEY: MOO* (That is the Amazon link, but it is been around a long time, so look for a used copy. This version on Amazon is just under one thousand dollars. Must be some kind of mistake!)
A Brain for Numbers: The Biology of the Number Instinct (The MIT Press) by Andreas Nieder* “Nieder explores how the workings of the brain give rise to numerical competence, tracing flair for numbers to dedicated “number neurons” in the brain. Drawing on a range of methods including brain imaging techniques, behavioral experiments, and twin studies, he outlines a new, integrated understanding of the talent for numbers. Along the way, he compares the numerical capabilities of humans and animals, and discusses the benefits animals reap from such a capability. He shows how the neurobiological roots of the brain’s nonverbal quantification capacity are the evolutionary foundation of more elaborate numerical skills. He discusses how number signs and symbols are represented in the brain; calculation capability and the “neuromythology” of mathematical genius; the “start-up tools” for counting and developmental of dyscalculia (a number disorder analogous to the reading disorder dyslexia); and how the brain processes the abstract concept of zero.”
This blog,for a while, was called “The X Blog” in celebration of “The X Club,” which was a thing of the Darwin-Huxley ilk. Turns out there is a book about The X Club, and this is it: The X Club: Power and Authority in Victorian Science by Ruth Barton. Those of you who know this blog, and my Facebook community, well know Ruth’s husband. Anyway, do not google “The X Club” in mixed company, but do read the book.
“In 1864, amid headline-grabbing heresy trials, members of the British Association for the Advancement of Science were asked to sign a declaration affirming that science and scripture were in agreement. Many criticized the new test of orthodoxy; nine decided that collaborative action was required. The X Club tells their story.*
These six ambitious professionals and three wealthy amateurs—J. D. Hooker, T. H. Huxley, John Tyndall, John Lubbock, William Spottiswoode, Edward Frankland, George Busk, T. A. Hirst, and Herbert Spencer—wanted to guide the development of science and public opinion on issues where science impinged on daily life, religious belief, and politics. They formed a private dining club, which they named the X Club, to discuss and further their plans. As Ruth Barton shows, they had a clear objective: they wanted to promote “scientific habits of mind,” which they sought to do through lectures, journalism, and science education. They devoted enormous effort to the expansion of science education, with real, but mixed, success.
?For twenty years, the X Club was the most powerful network in Victorian science—the men succeeded each other in the presidency of the Royal Society for a dozen years. Barton’s group biography traces the roots of their success and the lasting effects of their championing of science against those who attempted to limit or control it, along the way shedding light on the social organization of science, the interactions of science and the state, and the places of science and scientific men in elite culture in the Victorian era.”
And, in the spirit of inquiry, consider The Spirit of Inquiry: How one extraordinary society shaped modern science by Susannah Gibson*. “Cambridge is now world-famous as a centre of science, but it wasn’t always so. Before the nineteenth century, the sciences were of little importance in the University of Cambridge. But that began to change in 1819 when two young Cambridge fellows took a geological fieldtrip to the Isle of Wight. Adam Sedgwick and John Stevens Henslow spent their days there exploring, unearthing dazzling fossils, dreaming up elaborate theories about the formation of the earth, and bemoaning the lack of serious science in their ancient university. As they threw themselves into the exciting new science of geology – conjuring millions of years of history from the evidence they found in the island’s rocks – they also began to dream of a new scientific society for Cambridge. This society would bring together like-minded young men who wished to learn of the latest science from overseas, and would encourage original research in Cambridge. It would be, they wrote, a society “to keep alive the spirit of inquiry”.
Their vision was realised when they founded the Cambridge Philosophical Society later that same year. Its founders could not have imagined the impact the Cambridge Philosophical Society would have: it was responsible for the first publication of Charles Darwin’s scientific writings, and hosted some of the most heated debates about evolutionary theory in the nineteenth century; it saw the first announcement of x-ray diffraction by a young Lawrence Bragg – a technique that would revolutionise the physical, chemical and life sciences; it published the first paper by C.T.R. Wilson on his cloud chamber – a device that opened up a previously-unimaginable world of sub-atomic particles. 200 years on from the Society’s foundation, this book reflects on the achievements of Sedgwick, Henslow, their peers, and their successors. Susannah Gibson explains how Cambridge moved from what Sedgwick saw as a “death-like stagnation” (really little more than a provincial training school for Church of England clergy) to being a world-leader in the sciences. And she shows how science, once a peripheral activity undertaken for interest by a small number of wealthy gentlemen, has transformed into an enormously well-funded activity that can affect every aspect of our lives.”
Check out Primates: The Fearless Science of Jane Goodall, Dian Fossey, and Biruté Galdikas by Jim Ottaviani and Maris Wicks*, a graphic style book** about Jane Goodall, Dian Fossey, and Birute Galdikas. These were, as you probably know, the three women that dispersed around the world to study major great ape species (chimps, gorillas, orangs, respectively) in order to better understand human evolution.
These are three reasonably good biographies (and a fourth, of Louis Leakey, linked to all three life stories), presented in an entertaining (and graphic, as in drawing) fashion. Adults will enjoy it, suitable for children.
**I struggled with what to call it. It is “graphic novel” format but it is not a novel, It is non fiction. So, is it “graphic non fiction”? The material from the publisher calls it “nonfiction graphic novel” which is clearly not a phrase I want to use unironically. Suggestions welcome.
The Battle of New Orleans, one of the major battles of the War of 1812, was fought on January 8th, 1815. The War of 1812 had ended the previous December. Awkward. In South Africa, the “Second Boer War” broke out for a number of reasons, but the common thread was about how the various territories of the region should be organized and governed. War was declared in October 1899, and formally ended on May 31st, 1902. The political and ideological struggle continued, and it was not until 1910 that the first official agreement to address the initial reasons for the war emerged. But even after that the struggle continued. The American Civil War ended on April 9th, 1865. A half dozen major battles and 16 months later, the fighting in that ended war petered out. The ideological struggle related to that war continues today, and thousands have died over it, after it was over.
A purely ideological war (though not without material casualties) is the war against the teaching of evolution in American public schools. There was a lot of action in that war throughout much of the 20th century. On December 20th, 2005, the United States District Court for the Middle District of Pennsylvania decided Tammy Kitzmiller vs. Dover Area School District in favor of the science of evolution being taught unfettered, and identified the last breath of a pseudo-scientific creationist doctrine as an expression of religion. Sure, people still continued to fight over the issue, but after the Dover decision, there were very few significant fights in public schools over evolution, the battles being brought to state legislatures, where they never took root because of Dover. Fighting continued, ideological battles continued, just like in all those other wars, but the war on evolution in the US public school system ended in December 2005.
I declare the war on science over this month, July, 2020. Nice round patriotic number. We can pick a date later after history has sorted out some details. But the war ended when this happened: American anti science forces having spent months telling people that Covid-19 was a hoax, not really deadly, not really as bad as it seemed, and that masks did not really matter … well, they started wearing masks. Pence and Trump surrendered the war when they said wear masks. The people in my local grocery store, that had been not wearing masks, masked up. The end. War over.
Most of my friends are pedantic skeptics, just like you dear reader, and you won’t let me say that the war on science is over because bla bla bla bla. That is why I wrote the little introduction at the beginning of this blog post. If we treat every thing like we were Wikipedia editors, than every thing would be slightly to very warped and things like wars would never be over. Get over it. This war is over, even if sporadic fighting continues until the Sun expands.
By the way, did you notice that there are some wars that actually unambiguously end, like World War II? Do you know why they get to end but other wars, from a pedantic perspective, never do? I’m not sure but I think those are wars started by individuals, or small groups of different kings or leaders, then when the opposition (usually, the good guys) catch up to them and put them down, the war ends, more or less instantly. But I digress.
There is still a fight, there are still more fights over science and justice and all that. But the systematic Republican controlled war on science in America got won. By us.
If you read a lot of books about cosmology and the universe, you will not find much new in this book, but you will find newways to think about all that old stuff. If you really do have a newtheory of everything, this book will give you some useful advice on how to buy your ticket into the physics game. Like, that you have to make sure your theory of everything works in a way that does not result in the night sky being as bright as the day sky, or makes light do something it does not do, and so on. Also, do not use many different TYPEFACES AND all caps in your write-up.
Interestingly, one of the things the actual-cosmologists-authors do NOT say is something I often hear from pro-physicists about TOE-pushers. They don’t say “if you don’t have a mathematical formula for your theory, it isn’t a theory.” I hear that all the time and I always thought there was something wrong with that. Seems to me that a totally wrong mathematical theory is too much of a likelihood.
The best overview of this book, which you SHOULD read, is from the authors themselves who made a video talking about the book. Here:
… will be written in about three years from now. Meanwhile …
We labor under a number of falsehoods about how science works. Even scientists do. There are considerable differences among the panoply of scientific disciplines, and these are important enough that I would never trust the practitioners of one scientific discipline to, say, review research procedures or grant proposals from another discipline, by default.
These differences are even more significant outside of science itself. A common example is this one. A lay person evaluating peer reviewed research claims that a certain scientific conclusion can not be supported because there have been no double blind studies. That person may be unaware of the fact that almost no science uses double blind studies. This is a methodology used only in some areas of research. A study of earthquake hazards, genetic phylogeny of chickadees, or how long a particular virus lingers on a surface will not have a double blind methodology.
In some fields of study, a single idea will often be represented by a single major publication (sometimes a book) and will not be seen elsewhere unless it is being criticized. This is not common in the true sciences, per se, but this does happen in the peer reviewed literature. In other fields of study, a single idea may be addressed in hundreds of peer reviewed papers. In some fields of study, if a published peer reviewed paper presents a conclusion that is thought to be wrong, because of some flaw, the scholars in that field are expected to learn of this problem and thereafter avoid citing that paper. In other fields, when this happens, the paper is withdrawn from the literature after the invocation of complex rituals that might or might not involve the sounding of trumpets.
There seems to be two falsehoods affecting some of our thinking about COVID-19. One is the idea that a “study” or “publication” about some detail of the disease tells us something that we can take as fact. Yes, Covid-19 stays on a certain kind of surface for N days, therefore we can’t do X! That sort of thing. However, this research is, firstly, not peer reviewed. There may very well be no peer reviewed papers on COVID-19 at this time. This Pandemic has lasted less time that the typical peer review process takes. Maybe there are a few out there, but mostly, we are dealing with non-reviewed work, or work in review. This is good work, and important work, but it is more like a set of “emergency results” that address specific pressing questions in a provisional way.
It has been important to decide which of a small number of broad categories COVID-19 can be placed in, and the work on persistence on various surfaces has provided that rough and ready guide. There are pathogens that can find their way out of an exam room, go 20 feet down the hall, and infect a person sitting in a different exam room. There are pathogens that are so unlikely to infect another person that you practically have to lick the inside of their mouth five times to catch the disease. COVID-19 is in the in between category, where it sheds into the air and hangs around on surfaces for long enough that surfaces are found to have the virus on them. Is COVID-19 more or less surface-contaminating than, say, norovirus? Rotavirus? Nobody knows, because the research to determine that, and the publication array that would be necessary to lead to policy and recommendations about that, will take time. Someday there will be a study that looks at how much of the virus persists for how long on various surfaces, integrated with the other important question of how can the virus on a given surface actually infect a human, in order to allow for a realistic and useful statement about how to go about keeping a home, and ICU, an examining room, or a school relatively safe. COVID-19 has the potential to be the most studied pathogen in recent history, but not today.
So, that is the first fallacy: that a handful of quick and dirty, rough and ready, studies designed to get a clue about this disease constitute a well tempered and developed peer reviewed literature from which we can glean an accurate characterization of most o fhte important details of this disease. Nope.
One cost of this fallacy is the second fallacy, that we can evaluate models of either COVID-19’s behavior, or the efficacy of our reaction to it, based on a solid knowledge of the disease. That is backwards. We will eventually be able to evaluate ideas like “curve flattening” by understanding a lot about COVID-19, but that will happen after we have actually seen what various curve flattening efforts have done. A recent proposal that certain areas of the world may have seen a prior passage of COVID-19, causing some local immunity. One well meaning expert (not an actual expert) on social media responded that given the way COVID-19 operates, this is simply impossible. But that is backwards. The way we will eventually be able to describe how COVID-19 actually works is by observing it, measuring it, developing good explanations for what we see, strengthening and tempering those explanations by further hypothesis testing, replication, critique in the formal peer review process as well as the less formal but sometimes more important conversations at the conference-bar setting, and time. Time to just think. Then, we will be able to say things like “X is pretty much impossible because this is how COVID-19 works.” Now, we have an expansive void where some good theory and data will eventually reside, and the job of the scientists focused on this problem is to carefully and thoughtfully fill that void with what they come to know. To get a sense of how this works, read up on the literature that came out of the 2013 Ebola epidemic. Many key known things about the pathogen and its effects were not nailed down until months or years after the last patient was identified. These things take time.
The BBVA Foundation has awarded climate scientist Kerry Emanuel the Frontiers of Knowledge Award in Climate Change.
MIT’s press release:
Emanuel’s research has provided fundamental contributions to understanding of tropical cyclones and how they are affected by climate change.
The BBVA Foundation — which promotes knowledge based on research and artistic and cultural creation, and supports activity on the analysis of emerging issues in five strategic areas: environment, biomedicine and health, economy and society, basic sciences and technology, and Culture — recognizes MIT Cecil and Ida Green Professor of Atmospheric Science Kerry Emanuel’s body of research on hurricanes and their evolution in a changing climate, as well as his effectiveness for communicating these issues. The annually bestowed Climate Change award acknowledges “both research endeavors in confronting this challenge and impactful actions informed by the best science.”
“By understanding the essential physics of atmospheric convection…he has unraveled the behavior of tropical cyclones – hurricanes and typhoons – as our climate changes,” cites the foundation’s conferring committee.
Throughout the 1980s and 1990s, after completing degrees at MIT and later joining the Department of Earth, Atmospheric and Planetary Sciences (EAPS) faculty, Emanuel pinned down the mechanisms behind hurricanes and how warming surface oceans fuel storms and increase intensity as the climate changes. This issue is of particular concern to humanity because, of the natural events, tropical cyclones cause many deaths and bring about high economic costs. Further research has probed connections between anthropogenic global warming and cyclone frequency, intensity, development time, and geographical expansion of hurricane occurrence.
The selection committee noted Emanuel’s exceptional theories and research that “has opened new approaches for assessing risks from weather extremes.” He has expanded this work by co-founding the MIT Lorenz Center, a climate think tank which fosters creative approaches to learning how climate works.
For Bjorn Stevens, BBVA Foundation committee chairman and Director of the Max Planck Institute for Meteorology, “it is hard to imagine an area of climate science where one person’s leadership is so incontestable.”
OpenSource science means, among other things, using OpenSource software to do the science. For some aspects of software this is not important. It does not matter too much if a science lab uses Microsoft Word or if they use LibreOffice Write.
However, since it does matter if you use LibreOffice Calc as your spreadsheet, as long as you are eschewing proprietary spreadsheets, you might as well use the OpenSource office package LibreOffice or equivalent, and then use the OpenSource presentation software, word processor, and spreadsheet.
OpenSource programs like Calc, R (a stats package), and OpenSource friendly software development tools like Python and the GPL C Compilers, etc. do matter. Why? Because your science involves calculating things, and software is a magic calculating box. You might be doing actual calculations, or production of graphics, or management of data, or whatever. All of the software that does this stuff is on the surface a black box, and just using it does not give you access to what is happening under the hood.
But, if you use OpenSoucre software, you have both direct and indirect access to the actual technologies that are key to your science project. You can see exactly how the numbers are calculated or the graphic created, if you want to. It might not be easy, but at least you don’t have to worry about the first hurdle in looking under the hood that happens with commercial software: they won’t let you do it.
Direct access to the inner workings of the software you use comes in the form of actually getting involved in the software development and maintenance. For most people, this is not something you are going to do in your scientific endeavor, but you could get involved with some help from a friend or colleague. For example, if you are at a University, there is a good chance that somewhere in your university system there is a computer department that has an involvement in OpenSource software development. See what they are up to, find out what they know about the software you are using. Who knows, maybe you can get a special feature included in your favorite graphics package by helping your new found computer friends cop an internal University grant! You might be surprised as to what is out there, as well as what is in there.
In any event, it is explicitly easy to get involved in OpenSource software projects because they are designed that way. Or, usually are and always should be.
The indirect benefit comes from the simple fact that these projects are OpenSource. Let me give you an example form the non scientific world. (it is a made up example, but it could reflect reality and is highly instructive.)
Say there is an operating system or major piece of software competing in a field of other similar products. Say there is a widely used benchmark standard that compares the applications and ranks them. Some of the different products load up faster than others, and use less RAM. That leaves both time (for you) and RAM (for other applications) that you might value a great deal. All else being equal, pick the software that loads faster in less space, right?
Now imagine a group of trollish deviants meeting in a smoky back room of the evile corporation that makes one of these products. They have discovered that if they leave a dozen key features that all the competitors use out of the loading process, so they load later, they can get a better benchmark. Without those standard components running, the software will load fast and be relatively small. It happens to be the case, however, that once all the features are loaded, this particular product is the slowest of them all, and takes up the most RAM. Also, the process of holding back functionality until it is needed is annoying to the user and sometimes causes memory conflicts, causing crashes.
In one version of this scenario, the concept of selling more of the product by using this performance tilting trick is considered a good idea, and someone might even get a promotion for thinking of it. That would be something that could potentially happen in the world of proprietary software.
In a different version of this scenario the idea gets about as far as the water cooler before it is taken down by a heavy tape dispenser to the head and kicked to death. That would be what would certainly happen in the OpenSource world.
You collect and manage data. You write code to process or analyze data. You use statistical tools to turn data into analytically meaningful numbers. You make graphs and charts. You write stuff and integrate the writing with the pretty pictures, and produce a final product.
The first thing you need to understand if you are developing or enhancing the computer side of your scientific endevour is that you need the basic GNU tools and command line access that comes automatically if you use Linux. You can get the same stuff with a few extra steps if you use Windows. The Apple Mac system is in between with the command line tools already built in, but not quite as in your face available.
You may need to have an understanding of Regular Expressions, and how to use them on the command line (using sed or awk, perhaps) and in programming, perhaps in python.
You will likely want to master the R environment because a) it is cool and powerful and b) a lot of your colleagues use R so you will want to have enough under your belt to share code and data now and then. You will likely want to master Python, which is becoming the default scientific programming language. It is probably true that anything you can do in R you can do in Python using the available tools, but it is also true that the most basic statistical stuff you might be doing is easier in R than Python since R is set up for it. The two systems are relatively easy to use and very powerful, so there is no reason to not have both in your toolbox. If you don’t chose the Python route, you may want to supplement R with gnu plotting tools.
You will need some sort of relational database setup in your lab, some kind of OpenSource SQL lanaguge based system.
You will have to decide on your own if you are into LaTex. If you have no idea what I’m talking about, don’t worry, you don’t need to know. If you do know what I’m talking about, you probably have the need to typeset math inside your publications.
Finally, and of utmost importance, you should be willing to spend the upfront effort making your scientific work flow into scripts. Say you have a machine (or a place on the internet or an email stream if you are working collaboratively) where some raw data spits out. These data need some preliminary messing around with to discard what you don’t want, convert numbers to a proper form, etc. etc. Then, this fixed-up data goes through a series of analyses, possibly several parallel streams of analysis, to produce a set of statistical outputs, tables, graphics, or a new highly transformed data set you send on to someone else.
If this is something you do on a regular basis, and it likely is because your lab or field project is set up to get certain data certain ways, then do certain things to it, then ideally you would set up a script, likely in bash but calling gnu tools like sed or awk, or running Python programs or R programs, and making various intermediate files and final products and stuff. You will want to bother with making the first run of these operations take three times longer to set up, so that all the subsequent runs take one one hundredth of the time to carry out, or can be run unattended.
Nothing, of course, is so simple as I just suggested … you will be changing the scripts and Python programs (and LaTeX specs) frequently, perhaps. Or you might have one big giant complex operation that you only need to run once, but you KNOW it is going to screw up somehow … a value that is entered incorrectly or whatever … so the entire thing you need to do once is actually something you have to do 18 times. So make the whole process a script.
Aside form convenience and efficiency, a script does something else that is vitally important. It documents the process, both for you and others. This alone is probably more important than the convenience part of scripting your science, in many cases.
Being small in a world of largeness
Here is a piece of advice you wont get from anyone else. As you develop your computer working environment, the set of software tools and stuff that you use to run R or Python and all that, you will run into opportunities to install some pretty fancy and sophisticated developments systems that have many cool bells and whistles, but that are really designed for team development of large software projects, and continual maintenance over time of versions of that software as it evolves as a distributed project.
Don’t do that unless you need to. Scientific computing often not that complex or team oriented. Sure, you are working with a team, but probably not a team of a dozen people working on the same set of Python programs. Chances are, much of the code you write is going to be tweaked to be what you need it to be then never change. There are no marketing gurus coming along and asking you to make a different menu system to attract millennials. You are not competing with other products in a market of any sort. You will change your software when your machine breaks and you get a new one, and the new one produces output in a more convenient style than the old one. Or whatever.
In other words, if you are running an enterprise level operation, look into systems like Anaconda. If you are a handful of scientists making and controlling your own workflow, stick with the simple scripts and avoid the snake. The setup and maintenance of an enterprise level system for using R and Python is probably more work before you get your first t-test or histogram than it is worth. This is especially true if you are more or less working on your own.
Another piece of advice. Some software decisions are based on deeply rooted cultural norms or fetishes that make no sense. I’m an emacs user. This is the most annoying, but also, most powerful, of all text editors. Here is an example of what is annoying about emac. In the late 70s, computer keyboards had a “meta” key (it was actually called that) which is now the alt key. Emacs made use of the metakey. No person has seen or used a metakey since about 1979, but emacs refuses to change its documentation to use the word “alt” for this key. Rather, the documentation says somethin like “here, use the meta key, which on some keyboards is the alt key.” That is a cultural fetish.
Using LaTeX might be a fetish as well. Obliviously. It is possible that for some people, using R is a fetish and they should rethink and switch to using Python for what they are doing. The most dangerous fetish, of course, is using proprietary scientific software because you think only if you pay hundreds of dollars a year to use SPSS or BMD for stats, as opposed to zero dollars a year for R, will your numbers be acceptable. In fact, the reverse is true. Only with an OpenSource stats package can you really be sure how the stats or other values are calculated.
This book focuses on Python and not R, and covers Latex which, frankly, will not be useful for many. This also means that the regular expression work in the book is not as useful for all applications, as might be the case with a volume like Mastering Regular Expressions. But overall, this volume does a great job of mapping out the landscape of scripting-oriented scientific computing, using excellent examples from biology.
Mastering Regular Expressions can and should be used as a textbook for an advanced high school level course to prep young and upcoming investigators for when they go off and apprentice in labs at the start of their career. It can be used as a textbook in a short seminar in any advanced program to get everyone in a lab on the same page. I suppose it would be treat if Princeton came out with a version for math and physical sciences, or geosciences, but really, this volume can be generalized beyond biology.
Stefano Allesina is a professor in the Department of Ecology and Evolution at the University of Chicago and a deputy editor of PLoS Computational Biology. Madlen Wilmes is a data scientist and web developer.
As is the case with the other kits, the Solar System includes a book, a large format big flat thing to which one might attach stickers, stickers, and a unique on-topic object, in this case, those cool stars you can attach to your ceiling or walls, and they glow in the dark. Continue reading The Solar System from The Smithsonian→
This is not a book that fully explores the alliance and overlap between war and makers of war on one hand and science and scientists on the other. Authors Neil deGrasse Tyson and Avis Lang focus on one part of that relationship, the link between astrophysics and related disciplines (really, astronomy at large) and the military.
Back when I was working in or near the Peabody Museum, in Cambridge, the museum’s assistant director, Barbara Isaac, hired me to work with the NAGPRA database. NAGPRA was the North American Graves Protection and Repatriation Act. Ultimately, large swaths of the Peabody Museum’s collection would be turned over, or some other thing done to it, as per the wishes of the various Native American groups associated with that material. Most of the work had already been done. But, Barbara is a meticulous person and wanted to make sure the dotting of each i and crossing of each t was double checked. So, I was one of two people charged with going over the printouts, on that old green and less green striped paper, bound in large blue cardboard books. Each line (or two) was an item or collection of items, with notes, and an indication of what was going to happen to the material. There were just a few options, but the basic idea was this: An item listed was either going to be returned to a tribal group, or not. My job was mainly to look at stuff that was not going to be returned and, given my ongoing scan of what was going to be returned, and my knowledge of North American prehistory, ethnography, and archaeology, to earmark things that said “do not return” but where maybe we should be returning it. So, for example, after noting that a particular South Dakota Lakota tribe would have this, and that, and this other, soapstone tobacco pipe returned to them, when I saw that the ninth pipe on the list, several lines down and all by itself, is labeled to not be returned, I’d earmark that. Nearly 100% of the time, that ninth pipe was just something that nobody wanted, or it didn’t really exist (not all museum databases are exactly accurate). But, it would be earmarked.
Many items on the list had information as to how the item had originally gotten to the museum.
Many, many items, especially items taken from Native Americans living in what was the frontier between about 1840 and 1900, were taken by medical doctors who, as we all know, also stood in for naturalists, or some kind of traveling scientist, on military and quasi military expeditions (Like Darwin).
And many of those items were taken for use as medical specimens.
We initially learned that Native Americans have a particular blood type because, in part, of studies done on blood stains on shirts of slain warriors, collected after various battles with the US Army units accompanied by such scientists. There are a few famous cases of Native American bodily remains, mostly but not all skeletal remains, sitting in the anatomy teaching rooms of this or that college. But a lot more, a lot not noticed by either historians or even the all seeing all knowing Wikipedia, are or were sitting in museums around the world. Collected, by scientists wearing military uniforms, on military ventures, with a scientific twist.
So the science-military link is not exclusive to astronomy and astrophysics.
I wrote elsewhere about the person I met who was taking Pentagon funding to build an object that would help cure cancer. An example of a scientist subverting the military funding process. And so on.
OK, my complaint.
The authors have two long chapters (and references elsewhere) covering the early history of human endeavor in general (not limited to military) and the evolution of astronomy, mainly as it related, over a very long period of time, to navigation. One chapter covers land, the other the sea.
Staring somewhere along the way in each chapter, we get a very nice, well done, and pretty full description of the process of humans learning about the stars, about the earth and how to find one’s way, etc. But prior to that, the authors do what so many authors do and I so much dislike. I’ve written about this before. We get a version of human prehistory, and indeed, current human variation (or at least, ethnographically recent), that is bogus. For example, the authors speak of the first modern humans wandering around in the Rift Valley of Africa. There is no evidence that modern humans evolved there. Using just the archaeology, southern Africa is a more likely origin, and the physical anthropology record is simply incomplete. There are early fossils there, but that is because the rift valley is and was a big hole that made fossils. The entire rest of the continent is big, and the evolution probably happened there, not in the rift.
Similarly, ethnographic variation we see in the present and recent times is stripped out. For example, most rain forest dwelling foragers are not known to have a sky oriented cosmology, or to use the sky for much information about seasonal change in ecology, or navigation. And, there have always been a lot of rain forest dwelling foragers.
Suman Seth is associate professor in the Department of Science and Technology Studies, at Cornell. He is an historian of science, and studies medicine, race, and colonialism (and dabbles as well in quantum theory). In his new book, Difference and Disease: Medicine, Race, and the Eighteenth-Century British Empire, Seth takes on a fascinating subject that all of us who have worked in tropical regions but with a western (or northern) perspective have thought about, one way or another.
As Europeans, and Seth is concerned mainly with the British, explored and conquered, colonizing and creating the empire on which the sun could never set no matter how hard it tried, they got sick. They also observed other people getting sick. And, they encountered a wide range of physiological or biosocial phenomena that were unfamiliar and often linked (in real or in the head) to disease. A key cultural imperative of British Colonials as to racialize their explanations for things, including disease. The science available through the 18th and 19th century was inadequate to address questions that kept rising. Like, why did a Brit get sick on his first visit to a plantation in Jamaica, but on return a few years later, did not get as sick? If you have a model where people of different races have specific diseases and immunities in their very nature, how do you explain that sort of phenomenon? How might the widely held, or at least somewhat widely held, concept of polygenism, have explained things? This is an early version of the multi-regional hypothesis, but more extreme, in which god created each type of human independently where we find them, and we are all different species. (Agassiz, with his advanced but highly imperfect geological understanding, thought the earth was totally frozen over with each ice age, and repopulated with these polygenetic populations of not just humans, but all the organisms, after each thaw).
Seth weaves together considerations of slavery and abolition, colonialism, race, geography, gender, and illness. This is an academic book, but at the same time, something of a page turner. Anyone interested in disease, colonial history, and race, will want to re-excavate the British colonial world, looking at disease, illness, and racial thinking, with Suman Seth as your guide. I highly recommend this book.
I no longer call myself a skeptic. Well, actually, I probably never really did, but now I’m more explicit about that. Why? Two reasons. 1) Global warming and other science deniers call themselves skeptics, and I don’t want any confusion. 2) The actual “skeptics movement” is described as…
…a modern social movement based on the idea of scientific skepticism (also called rational skepticism). Scientific skepticism involves the application of skeptical philosophy, critical-thinking skills, and knowledge of science and its methods to empirical claims, while remaining agnostic or neutral to non-empirical claims (except those that directly impact the practice of science). The movement has the goal of investigating claims made on fringe topics and determining whether they are supported by empirical research and are reproducible, as part of a methodological norm pursuing “the extension of certified knowledge”. The process followed is sometimes referred to[by whom?] as skeptical inquiry.
That’s all nice and all, but I discovered that the actual skeptics movement is made out of people not quite so cleanly guided by a philosophy, roughly one third of whom are not really skeptics (such as Penn Jilette and James Randi, who allowed their libertarian philosophy to drive “skepticism” of anthropocentric global warming long after the scientific consensus was established), “mens rights activists” (MRAs) who vigorously attacked anyone speaking out in favor of women’s rights, against rape, etc., and #MeToo movement poster boys, who have for years used skeptical conferences as their own private meat markets.
Besides, I’m an actual scientist, so I can be a fan of science without having to be a fanboy, which makes it easier for me.
I started writing publicly, blogging, partly to be an on-line skeptic, to take on politically charged topics, especially as related to evolutionary biology, but other areas of science as well (and more recently, climate change), addressing falsehoods and misconceptions. But I very quickly discovered that there multiple and distinct kinds of “skepticism” make up the larger conversation.
There is a lot of very low level, knee jerk skepticism that is little more than uninformed reactionism, based on, at best, received knowledge. That is about as unskeptical as it gets. The Amazing Randy says Global Warming is nothing other than natural variation. Therefore, I will believe that. Uncritically. Some of this is what I long ago labeled as “hyperskepticism.” This is where potentially valid skepticism about a claim is melded with hyperbole. “There is not a single peer reviewed study that shows the bla bla bla bladiby bla” coming from the mouth of a person who has never once even looked for a peer reviewed study about any thing. They hyperskeptic may create entire categories of things that include claims worthy of debunking, and put all of the thing into the debunked category even if they are not.
A fairly benign example of this relates to CAM medicine. “CAM” refers to “complementary and alternative medicine” like acupuncture, rolfing, and the like. These are mostly forms of treatment that have no basis in science, and probably don’t do anything useful even if they sometimes cost real money. Hypersketpics put all CAM into the same category and light a match to it. But, there is a subset of CAM that is legit … the very fact that I wrote that sentence just there will disqualify me, and my entire post, and everything I ever say — there will be comments below that say “I stopped reading when you said “there is a subset of CAM that is legit”. OK, hold on a second, count to four. One two thee four. Now that all the hyperskeptics have gone off in a huff I can continue … and I can give you an example. There are people who undergo regular, uncomfortable, sometimes painful or sick-making treatments as part of their normal medical routine. Chemotherapy, dialysis, that sort of thing. We know that the quality of an individual’s life can be improved, their stress levels, reduced, and thus, probably, the outcome of their treatments improved or made less complicated, if the environment in which they get the treatments are more comfortable. This is why dentists put ferns and pictures of the ocean in their waiting rooms. There is evidence to suggest that surroundings should be considered in design of treatment rooms, waiting room, etc. (See for example, Brown and Gallant, 2006, “Impating Patient Outcomes Through Design: Acuity Adaptable Care/Universal Rom Design. “Critical Care Nursing Quarterly. 29:4(326-341) and Ulrich, Zimring, and Zhu, 2008, “A Review of the Research Literature on Evidence-Based Healthcare Design. HERD 1(3). They hyperskeptic wants divide the world into evidence based double blind study proven and everything else, with everything else being always wrong in all ways. (Perhaps I exaggerate a little, but only for the irony.) This concept, of considering room and environmental design, now standard, did exist before CAM (those dentists and their ferns) but the study an implementation of stress reducing design as we now know of it comes from the CAM movement. What is needed is not closing down CAM, but making it accountable. It would probably get much smaller if that happened, but what is left of it would be useful.
Four others contributed to this volume, Bob Novella, Cara Santa Maria, Jay Novella, and Evan Bernstein.
I do not agree with everything in this book. For example, although the discussion of placebo effect is excellent, I have a different take on it. I like to divide the effect up into different categories than I do, and I want to make a more explicit connection between the phenomenon called placebo effect and the role and meaning of a control. But for the most part, every single one of the more than 50 topics covered in this book is well treated, informative, and enjoyable to read. (See what I did there? I was a little skeptical of the book, so now, you know it must be good!)
Do get and read this book, get one for a friend for a holiday gift, and enjoy. But right now, before you even do that, to tho the Amazon page and find the negative reviews. There are only two now (the book just came out) but they are a hoot.
There is a great deal of overlap and integration between government agencies, private corporations including Big Pharm and Big Ag and Big Whatever, university and other research institutions, and the scientist and others who work in these places.
This topic is addressed in the latest episode of the science podcast Ikonokast, which also includes details about a recent minor scandal involving GMO research and squirrels. And maybe bears.