Tutorial:What to do with data - Revision history

KevinYager at 15:31, 1 December 2017

2017-12-01T15:31:46Z

KevinYager at 14:31, 24 January 2015

2015-01-24T14:31:17Z

KevinYager at 23:08, 16 December 2014

2014-12-16T23:08:16Z

KevinYager at 13:23, 16 October 2014

2014-10-16T13:23:39Z

KevinYager at 13:22, 16 October 2014

2014-10-16T13:22:46Z

KevinYager at 13:20, 16 October 2014

2014-10-16T13:20:56Z

KevinYager at 14:05, 18 June 2014

2014-06-18T14:05:33Z

KevinYager at 18:06, 11 June 2014

2014-06-11T18:06:11Z

KevinYager at 12:57, 10 June 2014

2014-06-10T12:57:00Z

KevinYager at 12:56, 10 June 2014

2014-06-10T12:56:42Z

← Older revision		Revision as of 15:31, 1 December 2017
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	* '''[[Tutorial:Modeling\|Modeling]]''': You can use various pieces of [[Software#Data_Modeling_and_Fitting\|modeling software]] to predict the scattering patterns for candidate structures. By comparing these to your experimental data, you can eliminate some possibilities, while demonstrating that others are consistent with your data. This doesn't ''prove'' that you have identified the correct realspace structure, but it provides good evidence. This is most successful if you have identified a likely structure using other means (e.g. electron microscopy), in which case comparing the experimental scattering to the output of a theoretical model gives you good confidence that the proposed structure was present in the measured sample.		* '''[[Tutorial:Modeling\|Modeling]]''': You can use various pieces of [[Software#Data_Modeling_and_Fitting\|modeling software]] to predict the scattering patterns for candidate structures. By comparing these to your experimental data, you can eliminate some possibilities, while demonstrating that others are consistent with your data. This doesn't ''prove'' that you have identified the correct realspace structure, but it provides good evidence. This is most successful if you have identified a likely structure using other means (e.g. electron microscopy), in which case comparing the experimental scattering to the output of a theoretical model gives you good confidence that the proposed structure was present in the measured sample.
	* '''[[Tutorial:Fitting\|Fitting]]''': Theoretical models can be quantitatively fit to your experimental data. This allows you to quantify parameters of interest (lattice spacing, disorder, etc.), assuming you've selected the right model!		* '''[[Tutorial:Fitting\|Fitting]]''': Theoretical models can be quantitatively fit to your experimental data. This allows you to quantify parameters of interest (lattice spacing, disorder, etc.), assuming you've selected the right model!
		+
		+
		+	==See Also==
		+	* [[Data Correction]]
		+	* [[Background]]

@@ Line 2: / Line 2: @@
 The main ways of using scattering data are:
-* '''[[Tutorial:Qualitative inspection|Qualitative inspection]]''': By just looking at the scattering, and using some 'rules of thumb', you can infer much about the structure of your sample (e.g. differentiating between ordered vs. disordered; isotropic vs. anisotropic). This is especially valuable when you're measuring samples, to get a rough idea of what's going on. For some purposes, this may even be sufficient for publishing (i.e. you simply qualitatively describe your data in the paper).
+* '''[[Tutorial:Qualitative inspection|Qualitative inspection]]''': By just looking at the [[scattering features|scattering]], and using some 'rules of thumb', you can infer much about the structure of your sample (e.g. differentiating between ordered vs. disordered; isotropic vs. anisotropic). This is especially valuable when you're measuring samples, to get a rough idea of what's going on. For some purposes, this may even be sufficient for publishing (i.e. you simply qualitatively describe your data in the paper).
 * '''[[Tutorial:Linecuts|Linecuts]]''': A more sophisticated analysis involves converting your data from the raw [[detector]] image into ''q''-space ([[reciprocal-space]]), and then taking 'linecuts' through this space to learn more about your sample. For instance, you can quantify a [[Q value|peak position]] (by fitting to a Gaussian), and thereby measure the realspace repeat-spacing of the associated structure. You can also quantify [[Scherrer grain size analysis|grain sizes]], orientation distributions, etc.
 * '''[[Tutorial:Indexing|Indexing]]''': For ordered materials (atomic crystals, [[superlattices]], etc.) that generate many peaks on the detector, indexing can be used to prove that you understand the structure of your sample. That is, you compare the predicted peak positions for a candidate [[unit cell]] to the experimental data, and show that your model is correct. The peak positions can also be fit, in order to quantify the unit cell parameters, and the unit cell [[orientation]].
 * '''[[Tutorial:Modeling|Modeling]]''': You can use various pieces of [[Software#Data_Modeling_and_Fitting|modeling software]] to predict the scattering patterns for candidate structures. By comparing these to your experimental data, you can eliminate some possibilities, while demonstrating that others are consistent with your data. This doesn't ''prove'' that you have identified the correct realspace structure, but it provides good evidence. This is most successful if you have identified a likely structure using other means (e.g. electron microscopy), in which case comparing the experimental scattering to the output of a theoretical model gives you good confidence that the proposed structure was present in the measured sample.
 * '''[[Tutorial:Fitting|Fitting]]''': Theoretical models can be quantitatively fit to your experimental data. This allows you to quantify parameters of interest (lattice spacing, disorder, etc.), assuming you've selected the right model!

@@ Line 3: / Line 3: @@
 The main ways of using scattering data are:
 * '''[[Tutorial:Qualitative inspection|Qualitative inspection]]''': By just looking at the scattering, and using some 'rules of thumb', you can infer much about the structure of your sample (e.g. differentiating between ordered vs. disordered; isotropic vs. anisotropic). This is especially valuable when you're measuring samples, to get a rough idea of what's going on. For some purposes, this may even be sufficient for publishing (i.e. you simply qualitatively describe your data in the paper).
-* '''[[Tutorial:Linecuts|Linecuts]]''': A more sophisticated analysis involves converting your data from the raw detector image into ''q''-space ([[reciprocal-space]]), and then taking 'linecuts' through this space to learn more about your sample. For instance, you can quantify a [[Q value|peak position]] (by fitting to a Gaussian), and thereby measure the realspace repeat-spacing of the associated structure. You can also quantify [[Scherrer grain size analysis|grain sizes]], orientation distributions, etc.
+* '''[[Tutorial:Linecuts|Linecuts]]''': A more sophisticated analysis involves converting your data from the raw [[detector]] image into ''q''-space ([[reciprocal-space]]), and then taking 'linecuts' through this space to learn more about your sample. For instance, you can quantify a [[Q value|peak position]] (by fitting to a Gaussian), and thereby measure the realspace repeat-spacing of the associated structure. You can also quantify [[Scherrer grain size analysis|grain sizes]], orientation distributions, etc.
 * '''[[Tutorial:Indexing|Indexing]]''': For ordered materials (atomic crystals, [[superlattices]], etc.) that generate many peaks on the detector, indexing can be used to prove that you understand the structure of your sample. That is, you compare the predicted peak positions for a candidate [[unit cell]] to the experimental data, and show that your model is correct. The peak positions can also be fit, in order to quantify the unit cell parameters, and the unit cell [[orientation]].
 * '''[[Tutorial:Modeling|Modeling]]''': You can use various pieces of [[Software#Data_Modeling_and_Fitting|modeling software]] to predict the scattering patterns for candidate structures. By comparing these to your experimental data, you can eliminate some possibilities, while demonstrating that others are consistent with your data. This doesn't ''prove'' that you have identified the correct realspace structure, but it provides good evidence. This is most successful if you have identified a likely structure using other means (e.g. electron microscopy), in which case comparing the experimental scattering to the output of a theoretical model gives you good confidence that the proposed structure was present in the measured sample.
 * '''[[Tutorial:Fitting|Fitting]]''': Theoretical models can be quantitatively fit to your experimental data. This allows you to quantify parameters of interest (lattice spacing, disorder, etc.), assuming you've selected the right model!

@@ Line 4: / Line 4: @@
 * '''[[Tutorial:Qualitative inspection|Qualitative inspection]]''': By just looking at the scattering, and using some 'rules of thumb', you can infer much about the structure of your sample (e.g. differentiating between ordered vs. disordered; isotropic vs. anisotropic). This is especially valuable when you're measuring samples, to get a rough idea of what's going on. For some purposes, this may even be sufficient for publishing (i.e. you simply qualitatively describe your data in the paper).
 * '''[[Tutorial:Linecuts|Linecuts]]''': A more sophisticated analysis involves converting your data from the raw detector image into ''q''-space ([[reciprocal-space]]), and then taking 'linecuts' through this space to learn more about your sample. For instance, you can quantify a [[Q value|peak position]] (by fitting to a Gaussian), and thereby measure the realspace repeat-spacing of the associated structure. You can also quantify [[Scherrer grain size analysis|grain sizes]], orientation distributions, etc.
-* '''[[Tutorial:Indexing|Indexing]]''': For ordered materials (atomic crystals, [[superlattices]], etc.) that generate many peaks on the detector, indexing can be used to prove that you understand the structure of your sample. That is, you compare the predicted peak positions for a candidate [[unit cell]] to the experimental data, and show that your model is correct. The peak positions can also be fit, in order to quantify the unit cell parameters, and the unit cell orientation.
+* '''[[Tutorial:Indexing|Indexing]]''': For ordered materials (atomic crystals, [[superlattices]], etc.) that generate many peaks on the detector, indexing can be used to prove that you understand the structure of your sample. That is, you compare the predicted peak positions for a candidate [[unit cell]] to the experimental data, and show that your model is correct. The peak positions can also be fit, in order to quantify the unit cell parameters, and the unit cell [[orientation]].
 * '''[[Tutorial:Modeling|Modeling]]''': You can use various pieces of [[Software#Data_Modeling_and_Fitting|modeling software]] to predict the scattering patterns for candidate structures. By comparing these to your experimental data, you can eliminate some possibilities, while demonstrating that others are consistent with your data. This doesn't ''prove'' that you have identified the correct realspace structure, but it provides good evidence. This is most successful if you have identified a likely structure using other means (e.g. electron microscopy), in which case comparing the experimental scattering to the output of a theoretical model gives you good confidence that the proposed structure was present in the measured sample.
 * '''[[Tutorial:Fitting|Fitting]]''': Theoretical models can be quantitatively fit to your experimental data. This allows you to quantify parameters of interest (lattice spacing, disorder, etc.), assuming you've selected the right model!

@@ Line 5: / Line 5: @@
 * '''[[Tutorial:Linecuts|Linecuts]]''': A more sophisticated analysis involves converting your data from the raw detector image into ''q''-space ([[reciprocal-space]]), and then taking 'linecuts' through this space to learn more about your sample. For instance, you can quantify a [[Q value|peak position]] (by fitting to a Gaussian), and thereby measure the realspace repeat-spacing of the associated structure. You can also quantify [[Scherrer grain size analysis|grain sizes]], orientation distributions, etc.
 * '''[[Tutorial:Indexing|Indexing]]''': For ordered materials (atomic crystals, [[superlattices]], etc.) that generate many peaks on the detector, indexing can be used to prove that you understand the structure of your sample. That is, you compare the predicted peak positions for a candidate [[unit cell]] to the experimental data, and show that your model is correct. The peak positions can also be fit, in order to quantify the unit cell parameters, and the unit cell orientation.
-* '''[[Tutorial:Modeling|Modeling''': You can use various pieces of [[Software#Data_Modeling_and_Fitting|modeling software]] to predict the scattering patterns for candidate structures. By comparing these to your experimental data, you can eliminate some possibilities, while demonstrating that others are consistent with your data. This doesn't ''prove'' that you have identified the correct realspace structure, but it provides good evidence. This is most successful if you have identified a likely structure using other means (e.g. electron microscopy), in which case comparing the experimental scattering to the output of a theoretical model gives you good confidence that the proposed structure was present in the measured sample.
+* '''[[Tutorial:Modeling|Modeling]]''': You can use various pieces of [[Software#Data_Modeling_and_Fitting|modeling software]] to predict the scattering patterns for candidate structures. By comparing these to your experimental data, you can eliminate some possibilities, while demonstrating that others are consistent with your data. This doesn't ''prove'' that you have identified the correct realspace structure, but it provides good evidence. This is most successful if you have identified a likely structure using other means (e.g. electron microscopy), in which case comparing the experimental scattering to the output of a theoretical model gives you good confidence that the proposed structure was present in the measured sample.
-* '''[[Tutorial:Fitting|Fitting''': Theoretical models can be quantitatively fit to your experimental data. This allows you to quantify parameters of interest (lattice spacing, disorder, etc.), assuming you've selected the right model!
+* '''[[Tutorial:Fitting|Fitting]]''': Theoretical models can be quantitatively fit to your experimental data. This allows you to quantify parameters of interest (lattice spacing, disorder, etc.), assuming you've selected the right model!

@@ Line 2: / Line 2: @@
 The main ways of using scattering data are:
-* '''Qualitative inspection''': By just looking at the scattering, and using some 'rules of thumb', you can infer much about the structure of your sample (e.g. differentiating between ordered vs. disordered; isotropic vs. anisotropic). This is especially valuable when you're measuring samples, to get a rough idea of what's going on. For some purposes, this may even be sufficient for publishing (i.e. you simply qualitatively describe your data in the paper).
+* '''[[Tutorial:Qualitative inspection|Qualitative inspection]]''': By just looking at the scattering, and using some 'rules of thumb', you can infer much about the structure of your sample (e.g. differentiating between ordered vs. disordered; isotropic vs. anisotropic). This is especially valuable when you're measuring samples, to get a rough idea of what's going on. For some purposes, this may even be sufficient for publishing (i.e. you simply qualitatively describe your data in the paper).
 * '''Linecuts''': A more sophisticated analysis involves converting your data from the raw detector image into ''q''-space ([[reciprocal-space]]), and then taking 'linecuts' through this space to learn more about your sample. For instance, you can quantify a [[Q value|peak position]] (by fitting to a Gaussian), and thereby measure the realspace repeat-spacing of the associated structure. You can also quantify [[Scherrer grain size analysis|grain sizes]], orientation distributions, etc.
 * '''Indexing''': For ordered materials (atomic crystals, [[superlattices]], etc.) that generate many peaks on the detector, indexing can be used to prove that you understand the structure of your sample. That is, you compare the predicted peak positions for a candidate [[unit cell]] to the experimental data, and show that your model is correct. The peak positions can also be fit, in order to quantify the unit cell parameters, and the unit cell orientation.
 * '''Modeling''': You can use various pieces of [[Software#Data_Modeling_and_Fitting|modeling software]] to predict the scattering patterns for candidate structures. By comparing these to your experimental data, you can eliminate some possibilities, while demonstrating that others are consistent with your data. This doesn't ''prove'' that you have identified the correct realspace structure, but it provides good evidence. This is most successful if you have identified a likely structure using other means (e.g. electron microscopy), in which case comparing the experimental scattering to the output of a theoretical model gives you good confidence that the proposed structure was present in the measured sample.
 * '''Fitting''': Theoretical models can be quantitatively fit to your experimental data. This allows you to quantify parameters of interest (lattice spacing, disorder, etc.), assuming you've selected the right model!