Hi!
I'm involved in a animal experiment that will generate a lot of data over several years.
There will be e.g. blood parameters that are investigated over several weeks, cell culture work from these animals, some are operated on, some are used to generate huge pictures.
Is there a beginner-friendly database program to organize all those different data types? Right now every parameter has its own excel file. We just started and it's already confusing.
Best,
Juliane
Hi, I'm not familiar with animal experiments (and just starting to learn about Python) but maybe you can use programming language for data analysis eg Python (or R). For example, in Python there are Pandas package for data 'management', Numpy for data manipulation, and Matplotlib (or others like Seaborn or Bokeh) for visualisation.
Hi Juliane Salbach-Hirsch, you can try using GraphPad Prism, its a simple, easy and widely acceptable statistical software were you can do any type of analysis. This software guides you what kind of analysis you whats to do with your data. It also allow you to learn basics of your analysis.
Regards
Sundarajan K
Hey! Thanks for all your answers but i am not looking for something to just have a look at one dataset and do statistics on but to organize a complete project. and evaluate I was recommended te pSomeone KNIME to me.t is freeware and i will ck out if iis applicable to my needs in the next weeks.
My advise to you is a general one: name your files (usually tables) appropriately, so you know what data it contains right away when you see its name. Then (very) carefully draw a tree diagram (schema or schematic diagram) that clearly shows the relationship of one table to the other tables, and vice versa. This is how one would create a schema for a relational database. One look at this tree diagram should be able to ell which table to refer to if you need to retrieve specific information, or if you want to include new data into any of your tables. This method should work regardless of the particular software you use. Plus, creating such tree diagrams is always a good research habit whichever area of science you are working on. C'est tout, merci, au revoir.
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