I entirely agree with Mike and Scott.

I never smooth data because it is a slippery slope to start on! How much should you smooth? Should you only smooth data if it looks "bad"? What does "bad data" look like anyway?

If the only point of smoothing is to improve the visual look of data, then you are doing it for the wrong reason - it is better to present the true data and let the reader decide if your interpretation is robust. In my view, smoothing data is somewhat scientifically dishonest unless you have a strong justification that extends beyond trying to make the data look "better"...

As someone who works with environmental samples at low concentrations, I'm very used to seeing noisy data. You just have to get used to long count times and lots of replicate scans!

Cheers,

Will

Dr William W. Bennett

Senior Lecturer
Environmental Futures Research Institute
School of Environment and Science
Griffith University
Gold Coast, Queensland, Australia
+61 401 186 488
w.bennett@griffith.edu.au

Assistant Professor
Nordcee, Department of Biology
University of Southern Denmark
Odense, Denmark
M +45 8193 8111
wbennett@biology.sdu.dk


From: Ifeffit <ifeffit-bounces@millenia.cars.aps.anl.gov> on behalf of Mike Massey <mmassey@gmail.com>
Sent: Thursday, 23 January 2020 2:50 PM
To: Carlo Segre <segre@iit.edu>; XAFS Analysis using Ifeffit <ifeffit@millenia.cars.aps.anl.gov>
Subject: Re: [Ifeffit] smoothing XAS data
 
At this juncture, I'd like to bring up a "rule" I've made for myself regarding data quality and smoothing: I'd rather walk away from a beam run with one good spectrum than a hundred poor ones.

Meaning, for me anyway, if the data aren't smooth enough on their own, I'd rather spend more precious time counting than move on. I'd count for three days on a sample if I had to (I've never had to).

While it has been proven that, contrary to the popular saying, one _can_ polish a turd, I'd personally rather not try.

I realize this approach was more difficult when I was a grad student and everyone in the research group was clamoring for data, but even in that situation, good data > not-good data.

Apologies for the tangent, but I hope someone out there might find it useful.


Cheers,



Mike





> On Jan 22, 2020, at 5:44 PM, Carlo Segre <segre@iit.edu> wrote:
>
> 
> Hi Daria:
>
> I smooth (or boxcar average) the data which it is oversampled, such as in a continuous scan where the data in the exafs region is spaced closer than the usual delta k of 0.05.
>
> I generally don't like to smooth XANES data since smoothing does tend to attenuate sharp features such as the ones which exist near the absorption edge.
>
> Carlo
>
>> On Wed, 22 Jan 2020, Daria Boglaienko wrote:
>>
>> Hello,
>>
>> In what case smoothing of the data is OK? My data have a lot of noise and
>> smoothing really helps (visually). I compared fit done in Artemis for
>> smoothed and non-smoothed data sets and the result is very similar, however
>> when I searched about it online, it looks like it is not recommended.
>> What is a good way to justify it?
>>
>> Thank you!
>>
>> -Daria
>>
>
> --
> Carlo U. Segre -- Duchossois Leadership Professor of Physics
> Directory, Center for Synchrotron Radiation Research and Instrumentation
> Illinois Institute of Technology
> Voice: 312.567.3498 Fax: 312.567.3494
> segre@iit.edu http://phys.iit.edu/~segre segre@debian.org
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