Data landscape self review - Spotty Alicorn Lifesciences Digital Strategies

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1. Is your data fully digitalised?

Yes, nothing is recorded on paper
Most of it, some data is recorded on paper before being entered on the computer system
No, we are still using a lot of paper

2. The newbie challenge: 2 weeks in the new job, you are asked to reproduce the experiment previously performed by a former team member who has now left. Is it:

Easy, all the protocols and methods used are well documented with details along with the deviations
Somewhat easy. Most of the protocols and methods are documented but they lack details . Knowledgeable colleagues are available to help.
Quite difficult. There is some documentation but it lacks in details and there aren’t any standard work documents. Team members are not always available
A big challenge. Poor documentation or very difficult to find and/or understand, not much support available.

This might be different for a newbie in the various teams, think of the most challenging case.

3. When several teams use the same common item e.g. Glucose, is the exact same name/identifier used by all the teams?

Yes, Glucose is always written the same way as it is generally picked up from a catalog/drop down list
Most of the time: within each team uses the same naming for every common item
Each scientist can use name/identifier whatever make sense to them for common items as long as it can be understood by others.

4. Assuming that you are using several software systems, how does the data flow between systems?

All the data is linked and can be accessed via the same interface. All the data is transferred programmatically.
Some systems allow export to other systems based on search criteria, programmatically
Data is manually searched and retrieved from each system and put together in Excel or similar software using copy and paste
Some systems only provide data in PDF form or in their proprietary format, not readable by other systems

5. Metadata is…

A nice to have. Sometimes captured. Very manual process.
Only a limited amount of metadata is automatically captured e.g. project ID, User ID
Manually filling endless forms with very fields that are not useful
Relevant to me and other users/providers of the data
Well defined for each type of experiment/assay/instrument and uses drop down lists rather than manual entry
Our systems don’t allow configuring and recording metadata that is relevant to us
Our systems are configured to capture as automatically as possible relevant metadata

Select all that applies

6. Are your teams using the same data sets?

Yes, they are duplicating the data sets to work on them independently
Yes they are accessing the same data sets and modifying them permanently as they go along
Yes they are accessing the same data sets, making new reversible versions of the data
No each team is creating their own data sets and doesn’t exchange them with others

7. Are the various teams communicating and aware of each other's data requirements?

Yes teams communicate a lot formerly and informally. The data needs are understood cross-function. Data catalogs are available globally.
Mostly, guidelines are available for all the teams so they can self serve bothering the other teams without bothering the other teams
Mostly, the teams meet and talk to each other but not mny guidance or global data catalogs are available
Teams are working independently of each other

exact common that

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