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# System prepended metadata

title: 'RSH 020: data preparation'

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# RSH 020: data preparation

## Intro

- Welcome to the session
- Introduce us
- Purpose of this session: Imagine if someone comes to us and says "I have to release some data".  We'll go over what we would discuss with them.
- How RSH works:
  - hackmd, will be public, ALWAYS at end
  - questions anytime
  - this is like a chat.


## Part 1: data management plans

- Why DMPs?
- What is wrong with historical process?
- I guess this came from the open scinece movement, pressuring you to 
- should a DMP be fully copy-paste from best practices, by sort-of default?
- What should you actually do?
  - First, learn the best way to handle your data
  - Then, write a plan for funder
  - Hopefully, use the plan.  But as they say, plans are useless but planning es essential
- How do you typically do them (what does your university insturctions say?)


## Part 2: tidy data

- I have 2 slides as example of this, maybe I manage to create a slide-less example
- Discuss how to store tabular data
  - excel 
  - standard formats: csv, numpy array, netcdf
  - pandas
- Discuss problems with non-tidy data storage
- Tidy data: [H. Wickham, "Tidy Data"](http://vita.had.co.nz/papers/tidy-data.pdf)
- Ddvantages/disadvantages of tidy data
- Metadata


## Part 3: release

* Findable
  * Does someone know your data exists only if they finds your paper first?  Or can they search directly for it?
  * What's the minimum required to find it?
  * General search engine?  Domain-specific search that understands the concepts of the data?
    * RD: NOMAD repository
* Accessible
  * You can somehow request access to it
  * It's in a place that won't disappear later
  * In a good repository
  * Or maybe, it's in some isolated permanent place, disconnected from a searchable repository
  * re3data.org - find a repository
  * Is your own website acceptable?
  * Is github acceptable?
* Interoperable
  * If someone can get value by combining it with other data, can they?
  * This is mostly covered in 'preparing data for release'
  * If there are no existing standards, you can't make it interoperable
  * There may be standards, but they may not be common enough to be useful.  It depends on what your particular case.
  * Or it may not be worth putting that much effort.
* Reusable
  * Someone has permission to reuse it
  * Licensing


## Next stream

What should we talk about in two weeks?



Possible topics: https://researchsoftwarehour.github.io/