By Julien Colomb | September 7, 2018
RDM makes data analysis more effective and efficient
Following the results of our analysis of researcher’s relation to data, we do think that the argument can touch researchers and convince them to seek for help and information about RDM questions. However, we would need a stronger evidence that it is actually true, as well as an understanding of what part of RDM is particularly important for data analysis. So…
What are the advantages RDM brings to data analysis?
Please contact us if you can bring some answer this question or point to existing material that do. Do you have a personal story, do you know of a specific project where RDM was introduced successfully, do you know someone who might contribute to this question? See the contribute page or directly open a github issue to share your experience.
We want to use this website and other distribution means (a publication maybe) to reach as many researcher as possible with this topic. In addition we would like to answer shortly this question in a video (inside the rdmpromotion video series we are preparing).
So far, Julien Colomb came up with these theoretical aspects: comment on github here
Four RDM actions to ease data analysis
1: Make your data computer readable
Digital data can be quite easily transformed and analysed using programming language like R and python. While you do not have to learn these languages (yet), knowing what they require in terms or readability might save you time and efforts.
- Tabular data/metadata shall be tidy
- Keep your primary data (raw data) untouched (i.e. no copy/paste in raw data, NEVER)
- If you have many data-sets, make sure you are able to automate the file imports. An index of data-sets may be a good practical solution.
- Separate raw data, derived data, analysis and analysis results in different folders
- Make sure to document each step of your analysis.
2: Fit your data format to its analyse (during data collection)
The analysis you will do (the statistics you wanna use as well as the software you will use) might require your data to be in a certain format, it will probably affect how much data you need to come to a robust conclusion and may even affect the number of variables you indeed need to record.
This is especially true for metadata and using an existing standard is easier than transforming what you collecting into that standard afterwards.
3: Plan for the unexpected
The data you collect today may be analysed in 2 years and published in 5. During that time, a lot can happen. People may prove that the analysis you planned is not fitted to your problem, or you may realize that a variable you did not plan to collect is crucial. Maybe a new data-set will appear that you will need to compare your own data to, or new people will help you with your project and need access to your data,…
Plan for your data to be re-usable. At best, get some colleague to watch your data and see if they can understand it. The unexpected may also be good, maybe halfway in your tedious manual analysis, you will discover a way to automatize it. So keep track of links between raw and derived data.
4: Be specific: merging is easier than splitting
When recording variables, be as specific as you can. It is very easy to pool two categories into one but very difficult (and sometimes impossible) to separate a group during the analysis.
Similarly quantitative variables are easier to analyse than qualitative ones. You can always create categories from quantitative indications, not the way around.
As an example, if your question is “does obese mice make longer naps”, record the mice weight not its category. Analyzing a correlation between weight and length of naps is more powerful than having the two categories.