Difference between revisions of "Q6"
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Tim Morris (Talk | contribs) (→Comments on the respondents' second most used visualisation tool.) |
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− | + | 47 replies were given. | |
The most commonly used tools, with links to answers are: | The most commonly used tools, with links to answers are: | ||
− | #[[gnuplot]] | + | #[[gnuplot]] - 5 replies |
− | # | + | #[[Materials Studio]] - 3 replies |
− | #[[Paraview]] | + | #Matlab - 3 replies, but is not discussed in this section as the responses are the same as those in question 5. |
− | # | + | #[[Paraview]] - 3 replies |
− | # | + | #PyMol - 3 replies, also not discussed here. |
− | + | #VMD - 3 replies, also not discussed here. | |
− | Other tools had | + | Other tools that had two responses: |
+ | #CCPN Analysis | ||
+ | #Discovery Studio | ||
+ | #IMOD | ||
+ | #JMOL | ||
+ | #Vesta | ||
+ | #Visit | ||
+ | #xmgrace | ||
Line 19: | Line 26: | ||
====Overall==== | ====Overall==== | ||
A few observations can be drawn from these responses: | A few observations can be drawn from these responses: | ||
− | #Users | + | #Users seems to prefer software that is general purpose. |
#Large datasets must be handled efficiently. | #Large datasets must be handled efficiently. | ||
− | # | + | #Good quality documentation/tutorials is required. |
− | # | + | #Ability to read multiple formats is useful. |
Latest revision as of 12:21, 30 January 2015
Comments on the respondents' second most used visualisation tool.
47 replies were given.
The most commonly used tools, with links to answers are:
- gnuplot - 5 replies
- Materials Studio - 3 replies
- Matlab - 3 replies, but is not discussed in this section as the responses are the same as those in question 5.
- Paraview - 3 replies
- PyMol - 3 replies, also not discussed here.
- VMD - 3 replies, also not discussed here.
Other tools that had two responses:
- CCPN Analysis
- Discovery Studio
- IMOD
- JMOL
- Vesta
- Visit
- xmgrace
Overall
A few observations can be drawn from these responses:
- Users seems to prefer software that is general purpose.
- Large datasets must be handled efficiently.
- Good quality documentation/tutorials is required.
- Ability to read multiple formats is useful.