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The algorithm to find bad segments of data is missing a lot, particularly cases where we end up identifying many closely-spaced peaks.
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![image](https://user-images.githubusercontent.com/9632322/74774514-eb3a6700-5261-11ea-8030-f233d0e25fbe.png)
The appearance above will occur if:
1. Open `mp2rage`
2. Open another model.
a…
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Thinking that aggregating the most common destinations could be a way around this.
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Study the different possible approaches to store Biolog data:
- [x] Use BioSamples and have multiple tables (too much much scrolling?)
- [x] Use BioStudies to store data and create external xref to Bi…
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The datascikit is a DIY Data Science self paced learning modules that takes students through the steps of generating transport data maps of their city of choice.
We started this at the Unconf in Br…
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Some functions on the Shift-Invariant Wavelet Packet Decomposition (SIWPD) have been implemented, but the tree structure and the best basis algorithm are quite different from the usual implementations…
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Refactoring vulkan swapchain. By abstracting this inside the `Window` class.
The current design was quite messy in the sense of simply creating a window using Vulkan and made it very difficult to fo…
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On the data cleansing, can we have worse data where the zip does not match the address, street name spelled wrong, two of the same people listed with different spellings – Mike Jones vs Michael Jones?…
edsar updated
9 years ago
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So its more like real world data analysis work, 80% cleaning.
see by plotting
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ddbi Compendium Content
- The updated/migrated data is messy.
- Are we assuming that we are moving away from ddbi completely?
- How to deal with stuff not available (at the moment) as Premium Con…