# Presentation

Magdeburg about to write a grant.
What topic to talk about in the afternoon.


# Thomas Wachtler: NFDI-neuro
(LMU): usual presentation.
Q: what about BRAIN initiative'?

# Michael Denker: reproducible analysis of neuroal activities

tools for electrophysiology and modelling.

## scenario data reuse

- jack wants the data and the code and be able to reproduce the analysis, make new ones and use the same analysis on a different dataset.
-> repeatability
-> replicability (new team)
-> reproducibility (new experiment setup)

## odML, Neo

Making collaboration possible.
metadata standard, computer readable
neo: abstraction from raw data

## Rescience journal

standardised description of specific implementations: elephant library

high potential, but still quite scary to me.

## how to implement these tools in the lab?

- having someone using the tool 
- get ERC/phd students to learn (by doing)

how to write it up ?

```
1. incentives for the data producer

- Who is doing what with my data
- Did someone attempted to use the data ?

Is there a way to implement such a collaboration.

Data curationb after the fact is difficult.

2. Costs (time to invest) to choose/learn the tools

```



# MRI data (Michael Hanke)

http://datalad.org



- imaging data is personal data
- data is too big
- no metadata standards

## challenges

- not open, but FAIR
- reusability and interoperability is way to costly
- no incentives to make data accessible

## what data is important

- there is some investment, where to put the ressources
- answer depends on who you ask (researcher, data steward, tech, librarian,...)

## datalad

- workflow for data input to output archiving.
- capable of making transition to new standards.

handbook.datalad.org

## sharing analysis, not data

- if we get the data in a standard format, we could actually do that.

## today tool disappear: sould become a inconvenience, nothing more

- self contained


#Thorsten TRR135 and nowa

## what we do

- DMP and evaluation of workflows
- software for workflow assistance
- training

## Tools

RDM planning tool (RDMO), 
heesen box (seafile)
Gitlab with quality checks
Research data repository (Dspace)

## Challenges: variability 

- location
- organizational structures
- research methods
- data volume
- standards usage and awareness (and existence)

# Rene Bernard, neurocure

6 institutions -Berlin based, 45% is Charité
25 PIs, clinical and basic research
2 partner institution (BIH, DIFE-potsdam)

value and open science module

## Success and enabler

- ELN (600 active users now)
- reward open data
- QUEST toolbox
- Qumula scalable data storage (2020) - linked to ELN

...

## Ideas

- get one responsible person in the lab - someone easier to access.
- go iterative, small steps first.

- good practive example


# NFDI

- linking tools
- sharing platform

- RDMP = blablabla
- knowledge repository

- financial ressources

- DFG will not tell it loud, but SFB funding depends on data management.

- more focussed discussion
- see the appliaction as objective:
- Get things done, have outputs (strengthen the application)
- dialog with field and sfb, update guidelines for DFG application
- more collaboration : entire field exploiting the existing ressources.
- working group implementations


Tools:
- tutorial based infromation (in context)
- 

openbrainconsent workflow: collect, check what works, make one file that works