Data management, storage and sharing Managing data at - - PowerPoint PPT Presentation

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data management storage and sharing managing data at

Data management, storage and sharing Managing data at - - PowerPoint PPT Presentation

Nov 13, 2023 271 likes •430 views

Data management, storage and sharing Managing data at institute-level: an example Plateforms MRI neuroimaging Mouse Heterogeneous data Anatomical Diffusion Rat Multiple sources Functional Quantitative Marmoset

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Data management, storage and sharing

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Managing data at institute-level: an example

Plateforms Clinical Large international databases

Human Connectome Project

MRI neuroimaging

Anatomical
Diffusion
Functional
Quantitative
…

Optical Imaging

Bi-photon microscopy
Confocal microscopy
Mesoscopic optical imaging
Spectroscopy
Laser doppler flowmetry
Optical coherence tomography
Histology / tracing

Electrophysiology

EEG/MEG
Multi-electrodes array
SIngle cell recordings
Deep brain stimulation recordings

NeuroBioTools

Genomics
Transcriptomics

Mouse Rat Marmoset Macaque Baboon Chimpanzee Human Microscopic Mesoscopic Macroscopic In Vivo Post-mortem Heterogeneous data Multiple sources Multiple scales Large quantities (~150To) Large need of data processing

What management for such an amount and variety of data ?

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Where’s my data ?

« On a portable hard drive. My PhD student has got it. I’ll email him» Non secure and unreliable storage. No backup. Major risk: Complete data loss Other risks: loss of associated data and impossibility to reprocess. « On a workstation in the experimental room. From time to time I make a copy of the hard

drive. »

Non secure storage. Random backup. Risk: data loss Other risks: loss of associated data and impossibility to reprocess. « On a (professional level) storage server » Secure storage, guaranteed backup Can we find the data, can we proceed to new analyses ?

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss To offer an easy and reliable access to all data using specifric queries databasing, indexation

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss To offer an easy and reliable access to all data using specifric queries To ease or automate data processing Formatage / standardisation du stockage

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss To offer an easy and reliable access to all data using specifric queries To ease or automate data processing Reduce costs

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss To offer an easy and reliable access to all data using specifric queries To facilitate data sharing between researchers, and/or journals requiring an access to experimental data To ease or automate data processing Reduce costs Universal formatting of data

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss To offer an easy and reliable access to all data using specifric queries To facilitate data sharing between researchers, and/or journals requiring an access to experimental data To propose a Data Management Plan to researchers To ease or automate data processing Reduce costs

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss To offer an easy and reliable access to all data using specifric queries To facilitate data sharing between researchers, and/or journals requiring an access to experimental data To propose a Data Management Plan to researchers To promote and facilitate reproducible and open science To ease or automate data processing Reduce costs

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss To offer an easy and reliable access to all data using specifric queries To facilitate data sharing between researchers, and/or journals requiring an access to experimental data To propose a Data Management Plan to researchers To promote and facilitate reproducible and open science To ease or automate data processing Reduce costs

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Rationalizing data magement. Goals and motivations

To eliminate all possibility of data loss To offer an easy and reliable access to all data using specifric queries To facilitate data sharing between researchers, and/or journals requiring an access to experimental data To propose a Data Management Plan to researchers To promote and facilitate reproducible and open science To ease or automate data processing To facilitate scientific projects using heterogeneous multi-modal data, or to facilitate machine learning Reduce costs

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The 3 pillars of good data management

Storage

Must guarantee security and regular data backup All data must be stored as automatically as possible on storage servers

No loss Indexing

Ensures that the data is traceable, and possibly accessible according to specific queries based on descriptive metadata This indexation is usually performed via a database engine.

Access Formatting

Standardised nomenclature defining storage and organization of data and associated metadata. Ensures that data can be exchanged and analysed autonomously

Sharing Automatic processing

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Some solutions exist – many need to be built

MR Neuroimaging

Storage server BIDS formatting Xnat database (partial) automation of processing

Bio-informatics

Storage server TranSMART database

Multi-electrod electrophysiology

NEO formatting, optimised for data transfer and sharing Python API for automatic indexation

Example organization

Clinical and demographic data

Storage server REDCap databse

A tool to join all databases