I never stopped taking notes in physics classes throughout my undergraduate and graduate coursework. As I recall, I wasn't the only one taking notes, either. Knowing, in general, how to setup and attack a problem is important, yes, and something you might be able to remember without writing things down. But learning how to put the right tools to use at the right time to make the mathematics produce something akin to a reasonable answer was something that required active note taking. For me and most of my classmates, anyway.
Past a certain point, tools like Maple and Mathematica weren't even very useful for the more difficult problems. Or even for the "easy" problems in certain frameworks. Having a detailed example of how to work through the mathematics as part of my class notes was never a detriment to my higher education.
Also, past a certain point, textbooks aren't so well refined from being used extensively like their undergraduate cousins. Many are written more like references than learning devices. A detailed example worked out in the classroom for the benefit of educating the audience was, to me, often more helpful than the reference material found in the text.
Of course, everyone's experience can be different.
It was two physicists, who were testing their new radio astronomy receiver. And they really did have bird shit in it--the "noise" just didn't go away when they mucked it out.
However, raw data is useless without the calibration.
Global climate change skeptics like to cherry pick specific thermometers and point out that significant modifications were made to their raw readings in order to somehow "fake" the final result that comes out of peer-reviewed papers. Those same skeptics then imply that this kind of data manipulation runs rampant within peer-reviewed science and is the only reason for the increasing temperature trend.
But raw temperature readings from individual thermometers are not what you want to examine in order to test for a global trend. You want to remove as much bias as possible from that reading when including it in your global fit to the rest of the world's data--and you want to do this to every single thermometer in the world! Was the thermometer above grass before 1956, and above a concrete pad after? Was it subject to direct sunlight before 1941 and subject to permanent shade after? Was the physical instrument used to generate the temperature readings ever changed out for a different one? Is the reading from that specific make and model of thermometer subject to larger random fluctuations than that of a different model, or was it calibrated incorrectly in 1978 and subsequently showed a -0.5 C offset for the next three years? Was the least significant digit truncated or rounded?
How exactly did you choose to adjust the raw data to compensate for each of these individual effects in order to produce your calibrated data?!?
All of those questions are important to answer for EVERY data set included in a global fit.
And how is an amateur climatologist, skeptical of the theory of man-made global climate change or not, going to handle all of these important corrections to the "raw" data? How are they going to fit their own model? Do they understand the statistical problems behind dealing with large data sets, with differing statistical and systematic uncertainties inherent in each individual thermometer's readings?
I think people who think "releasing all the raw data will fix everything" assume far too much of the amateur climatologists that they expect to interpret all of this raw data. But these are exactly the kinds of things expected of peer-reviewed scientific publications.
Would you ask an amateur to answer all of these questions if they processed all of the raw data on their own, or would you simply accept their analysis as "correct" if they told you the answer that you "intuitively" expect, regardless of which side of the fence you are on?
Slashdot Mirror
Perhaps you were the exception.
I never stopped taking notes in physics classes throughout my undergraduate and graduate coursework. As I recall, I wasn't the only one taking notes, either. Knowing, in general, how to setup and attack a problem is important, yes, and something you might be able to remember without writing things down. But learning how to put the right tools to use at the right time to make the mathematics produce something akin to a reasonable answer was something that required active note taking. For me and most of my classmates, anyway.
Past a certain point, tools like Maple and Mathematica weren't even very useful for the more difficult problems. Or even for the "easy" problems in certain frameworks. Having a detailed example of how to work through the mathematics as part of my class notes was never a detriment to my higher education.
Also, past a certain point, textbooks aren't so well refined from being used extensively like their undergraduate cousins. Many are written more like references than learning devices. A detailed example worked out in the classroom for the benefit of educating the audience was, to me, often more helpful than the reference material found in the text.
Of course, everyone's experience can be different.
It was two physicists, who were testing their new radio astronomy receiver. And they really did have bird shit in it--the "noise" just didn't go away when they mucked it out.
However, raw data is useless without the calibration.
Global climate change skeptics like to cherry pick specific thermometers and point out that significant modifications were made to their raw readings in order to somehow "fake" the final result that comes out of peer-reviewed papers. Those same skeptics then imply that this kind of data manipulation runs rampant within peer-reviewed science and is the only reason for the increasing temperature trend.
But raw temperature readings from individual thermometers are not what you want to examine in order to test for a global trend. You want to remove as much bias as possible from that reading when including it in your global fit to the rest of the world's data--and you want to do this to every single thermometer in the world! Was the thermometer above grass before 1956, and above a concrete pad after? Was it subject to direct sunlight before 1941 and subject to permanent shade after? Was the physical instrument used to generate the temperature readings ever changed out for a different one? Is the reading from that specific make and model of thermometer subject to larger random fluctuations than that of a different model, or was it calibrated incorrectly in 1978 and subsequently showed a -0.5 C offset for the next three years? Was the least significant digit truncated or rounded?
How exactly did you choose to adjust the raw data to compensate for each of these individual effects in order to produce your calibrated data?!?
All of those questions are important to answer for EVERY data set included in a global fit.
And how is an amateur climatologist, skeptical of the theory of man-made global climate change or not, going to handle all of these important corrections to the "raw" data? How are they going to fit their own model? Do they understand the statistical problems behind dealing with large data sets, with differing statistical and systematic uncertainties inherent in each individual thermometer's readings?
I think people who think "releasing all the raw data will fix everything" assume far too much of the amateur climatologists that they expect to interpret all of this raw data. But these are exactly the kinds of things expected of peer-reviewed scientific publications.
Would you ask an amateur to answer all of these questions if they processed all of the raw data on their own, or would you simply accept their analysis as "correct" if they told you the answer that you "intuitively" expect, regardless of which side of the fence you are on?