Best practice strategies for bioimage analysis in research.

 

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Advice for naming files and folders:

Start by creating a folder with an unique project-name (e.g. TRPV1-study) or potentially a collaborator name (e.g. WaitheD). When writing folder names (or file names), try to avoid empty spaces “ “ and avoid funny characters (e.g. £$%^&*). This makes it easier for analysis software to open the files and having no spaces makes it easier to paste and interpret the path information. When you come to write a paper it is useful if you can nicely copy/isolate all of the relevant data. By arranging things in terms of collaborators and projects, this becomes straightforward.

Within each project folder it is good to have a folder with your experiment name (e.g. capsaicin-dose-confocal). This should describe an experiment and maybe contain some explanatory words as a description, i.e. confocal as in the microscopy type. During the course of an experiment you will repeat your work several times, most likely on different dates. For each experimental repeat/day create an additional sub-folder, this time with the date e.g. (20190405_exp1). It makes life much easier if you start with the year and then month and then day (i.e. YYYYMMDD). The computer can nicely organise this for and you can find your data more easily.

I recommend the following format for file names. 1) Date 2) condition short form 3) cell-type 4) one or more dyes (in the same order as in file) 5) a file number unique for that experimental session/condition. An example: 20180508_cA_hek_dapi_fitc_01, this says that this file was acquired on the 8th May 2018, it was from condition A, and that the cell-type were HEK cells, the stain was DAPI and fitc and this was the 01 (1st) cell imaged on that day. Although the folder also likely contains the date of the experiment it is important that every file is uniquely identifiable, even at the expense of a long file name. This means you can search for it easily and you are also less likely to confuse your filenames.

e.g. /User/dwaithe/Documents/TRPV1-study/capsaicin-dose-confocal/20180504_condA/20180504_cA_hek_dapi_fitc_01.tiff

e.g. /User/dwaithe/Documents/TRPV1-study/capsaicin-dose-confocal/20180504_condA/20180504_cA_hek_dapi_fitc_02.tiff

My use of underscore “_” and dashes “-” to separate aspects of the files and folders makes it clear to read by eye, but also for the computer. Some systems (e.g. OMERO) can easily interpret this information and this can be used to organise your data within these systems.

If you follow these simple instructions you will find you spend a lot less time fretting over your data, and will be able to remember file locations from memory due to their logical placement.

Data Backup and Archiving

OMERO is a secure repository for imaging data. It can be hosted locally or on a server in your institute or university. It supports more than 150 different microscopy, imaging and graphical formats. You can view and search through metadata collected with your images and dynamically visualise and annotate your images or image volumes. You can upload content to OMERO using a desktop client which will work with Windows, Mac and Linux Operating Systems. Although secure, users can share data with collaborators or within a laboratory. For more details please visit: https://www.openmicroscopy.org/omero/.

Version Control

Github is a website which works in concert with the version control software Git. It allows you to maintain an online repository for your code over-time and simplifies the process of tracking and versioning through a simple graphical interface. For more details please visit: https://github.com/. Git with all its functionality can be quite complex, but there are many starter guides online (e.g. https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners).

What is Zenodo

Zenodo (http://about.zenodo.org/) is funded by a partnership between the EC (https://ec.europa.eu/programmes/horizon2020/), OpenAIRE (https://www.openaire.eu) and CERN (https://home.cern/). Through their website you can upload up to 50 GB of data (more if you apply). What is great about Zenodo is that you can obtain a DOI for your submission which is secure and consistent reference for long-term usage. In addition, you can also reference your collaborators and also link in a github account make it very easy to reference your scripts with DOI also.