Discover the Group: TA 4!
I'm the big cheese - the head honcho of the Bioinformatics and Scientific Data Research Group at the Leibniz Institute of Plant Biochemistry in Halle (Saale). I've got a background in statistical mass spectrometry data analysis and metabolite identification. I'm all about open data and open standards, pushing for community-wide e-infrastructures to drive functional annotation through computational metabolomics analyses. I'm a board member of the French metabolomics infrastructure MetaboHUB, and I also serve as an associate editor for BMC Bioinformatics, MDPI Metabolites, and Nature Scientific Data.
In our consortium, I'm co-lead with Chris Steinbeck on task area 4: Metadata, Data Standards, and Publication Standards. This is a massive, long-term endeavor that requires persistence and determination.
I especially dig coordinating the Lead-by-example initiatives. Here, we join forces with the research community to collect research data associated with recent or upcoming manuscripts. We help gather, organize, and publish these datasets for you, enabling upcoming manuscripts to include a "Data availability statement" that points to citable datasets in a suitable resource such as the Chemotion Repository or RADAR, depending on the data types and content involved.
Ensuring Data Accessibility with the Lead-by-Example Approach
The “Lead-by-example” strategy emphasizes transparency, reproducibility, and collaboration by demonstrating how research data can be effectively managed and shared. Key aspects include:
- Data Availability: Making data from research projects readily available is crucial. This means ensuring datasets are accessible for verification and further analysis, which boosts the integrity and reproducibility of scientific findings.
- Suitable Data Repositories:
- Chemotion Repository: This platform is designed for storing and sharing chemical experiment data. It supports FAIR data principles (Findable, Accessible, Interoperable, Reusable) by providing a structured way to document and share experimental details.
- RADAR (Research Data Repository): Although not explicitly covered, RADAR encompasses various generic data repositories. For specific disciplines, like chemistry, repositories such as Chemotion are highlighted.
- Benefits: By setting a good example, researchers inspire others to adopt similar practices, fostering collaboration, improving research efficiency, and accelerating scientific progress.
- Implementation: This approach requires setting clear data management and curation standards, using tools like metadata extractors and schema mappers to automate data organization, and advocating for established repositories.
In a nutshell, the “Lead-by-example” approach focuses on demonstrating best practices in data management and accessibility using repositories like Chemotion. This helps promote transparency, collaboration, and reproducibility in scientific research.
- Leveraging the Chemotion Repository and RADAR, our "Lead-by-example" initiatives in the bioinformatics field aim to promote data availability and adherence to FAIR data principles, ensuring research data can be easily found, accessed, interpreted, and reused.
- By following the lead of our team in implementing the "Lead-by-example" approach, the research community can enhance the transparency, reproducibility, and collaboration within health-and-wellness, science, and the development of therapies-and-treatments.
- Embracing data-and-cloud-computing technologies and adopting this strategy will empower researchers to share and access essential data in a timely manner, facilitating the efficiency of science and accelerating groundbreaking discoveries in various disciplines such as health-and-wellness, science, and therapies-and-treatments.
