Area Z

INF | Developing standards for data quality, reproducibility, and accessibility

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Complex, multidimensional data, including behavioral readouts imaging and electrical recordings, will be collected within the TRR from animals and humans. Our overarching goal is to incrementally integrate these data into comprehensive computational models that will allow inference of the underlying rules governing experimental observations. To meet the challenges involved in data collection, storage, and exchange in a standardized and efficient way, the INFproject will develop ReTune Commons, a central data management platform. We will harmonize and standardize data acquisition and storage, develop concepts for quality assurance, establish rules for data sharing, and set up hard- and software infrastructure to support data management.

ReTune is collecting complex multimodal human neuromodulation research data. This is spanning electrophysiological (e.g. ECoG, LFP, MEG and EEG), neuroimaging (e.g. anatomical MRI, electrode localizations, functional and structural MRI) and human clinical data (e.g. behavioral, clinical, and routine clinical care data). Specific data standards are needed, to meet the substantial challenges involved in collecting, sorting, and exchanging the different types of data collected within ReTune. The human neuroscience INF project aims to develop computational methods to secure FAIR (www.go-fair.org) and reproducible data handling through the ReTune Commons as a central element of the ReTune consortium.

  1. Pursuing three intersecting work packages, we will:
  2. Harmonize and standardize experimental data by adhering and extending major international initiatives, such as the Brain Imaging Data Structure (https://bids.neuroimaging.io/).
  3. Develop concepts for data quality assurance.

Establish consensus principles for data policy, privacy, access, use, and integration of human experimental, imaging, and clinical data, within the set-up hardware and software infrastructures to support data management, including mapping of public data, within the TRR.

The three intersecting work packages for establishing ReTune Commons comprise:

  • common data standards
  • common data infrastructure
  • common data exchange and integration

The overarching aim of the INF project is to ensure the highest quality research output within the TRR, and to facilitate replicability of results across different research settings, which will build the basis for integrating data of basic animal, human, and clinical origin and generating hypotheses for new research projects and prospective clinical studies.

Team

Publications

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