Frequently Asked Questions

• RDM platforms:

  • BExIS2 by NFDI4Biodiversity at FSU Jena
  • Coscine by RWTH Aachen
    • Coscine is a research data management platform for your research projects. Coscine adheres to the FAIR principles with structured storage, metadata management, collaborative working and long-term storage of your data from research projects in accordance with good scientific practice. To get started simply register with your university account or ORCID and create a project.
    • Coscine is available for use by employees of participating universities or research institutions in North Rhine-Westphalia (NRW). Usage is also permitted by third parties who have been invited by an employee of a participating university or research institution in NRW to collaborate on a Coscine project.
    • For more information check out the Coscine documentation page: About Coscine - Documentation | Coscine
  • GfBio consortium services
  • Research Data Management Competence Base (RDM Compas) by KonsortSWD (social, behavioural, educational and economic sciences)

• Tools:

  • bio.tools (ELIXIR): essential scientific and technical information on software tools, databases and services for bioinformatics and the life science.

  • The Research Data Management toolkit for Life Sciences (RDMkit by ELIXIR)

  • ToolPool Gesundheitsforschung (TMF): The TMF-Portal was launched in 2017 and is operated by the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). It provides a collection of IT infrastructure-related products for networked medical research. There are products from the TMF and from other providers such as companies and research institutions. There are over 80 products, more than half of which are software tools. Other product categories include eServices, reports and expert opinions, working materials and checklists, consultancy services and training courses. Products can be filtered by category, topic, project phase, keywords, provider and year. Similar products can also be compared using a feature matrix. On each product page you will find information about the use of the product in projects, testimonials from other users and references. New products can be submitted by anyone. Each product is then reviewed by a team of TMF members against a set of criteria before being added to the portal.

    To use the portal, follow this link. Many offerings are free and can be accessed directly from the portal. Software products usually require local installation and configuration.

• SOPs:

  • Data management in clinical trials
  • Data management in the National Institute of Neurological Disorders and Stroke

• Caliskan, A., Dangwal, S., & Dandekar, T. (2023). Metadata integrity in bioinformatics: Bridging the gap between data and knowledge. Computational and Structural Biotechnology Journal, 21, 4895–4913. https://doi.org/10.1016/j.csbj.2023.10.006
• Egli, A., Schrenzel, J., & Greub, G. (2020). Digital microbiology. Clinical Microbiology and Infection, 26(10), 1324–1331. https://doi.org/10.1016/j.cmi.2020.06.023
• Kyrpides, N. C., Eloe-Fadrosh, E. A., & Ivanova, N. N. (2016). Microbiome Data Science: Understanding Our Microbial Planet. Trends in Microbiology, 24(6), 425–427. https://doi.org/10.1016/j.tim.2016.02.011
• Nasr, E., Amato, P., Bernt, M., Bhardwaj, A., Blankenberg, D., Brites, D., Cumbo, F., Do, K., Ferrari, E., Griffin, T. J., Gruening, B., Hiltemann, S., Hyde, C. J., Jagtap, P., Mehta, S., Métris, K. L., Momin, S., Oba, A., Pavloudi, C., … Batut, B. (2024). The Microbiology Galaxy Lab: A community-driven gateway to tools, workflows, and training for reproducible and FAIR analysis of microbial data. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.12.23.629682
• Zhou, R., Ng, S. K., Sung, J. J. Y., Goh, W. W. B., & Wong, S. H. (2023). Data pre-processing for analyzing microbiome data – A mini review. Computational and Structural Biotechnology Journal, 21, 4804–4815. https://doi.org/10.1016/j.csbj.2023.10.001

There is no need to use a simple text editor anymore, many different tools are available for writing a DMP. These tools offer similar functions and benefits and mainly differ in DMP specifications requested by different funding agencies. Using a DMP tool makes managing a DMP and collaborating much easier.

The Research Data Management Organiser (RDMO) is the most common DMP tool used in Germany. It is an open-source web application developed to support the structured and collaborative planning and implementation of RDM. It allows users to create DMPs in text format and offers templates for questionnaires, project descriptions, tasks, and DMPs. Input is collected through a structured interview, and all responses are stored in a database. Question catalogues can be modified without losing information, and many questions allow dataset-specific answers. Key features include versioning, import/export functions, collaborative editing, snapshots, a timeline of RDM-related tasks, and notifications for upcoming events. DMP4NFDI offers demonstartions on how to set up DMPS using RDMO.

DMPonline was developed by the Digital Curation Centre in the UK. It is an open-source, web-based tool designed for researchers, primarily those working on UK-funded projects, though it is also used internationally. DMPonline enables users to create, review, and share DMPs that comply with institutional and funder requirements.

The Data Stewardship Wizard (DSW) was developed by ELIXIR Netherlands and ELIXIR Czech Republic. It is an open-source, dynamic web-based system aimed at data stewards who support researchers in creating machine-readable DMPs. The DSW is recommended by the Horizon Europe Programme Guide. It features user-friendly questionnaires, a variety of built-in templates, and the ability to develop custom templates. Various ELIXIR nodes offer training on how to use the DSW.

Other DMP tools include ARBOS, DataPLAN, DataWiz, DMPRoadMap, DMPTool, GFBio DMPT and TUB-DMP. A comprehensive guide to DMP tools is available on Zenodo.

Microbial data are highly heterogeneous, as are the methods used to collect them. The following list comprises examples of microbial data and the collection method(s) associated with each:

• Microbiome data: High-Throughput Sequencing (HTS), Next Generation Sequencing (NGS)
• Crystallographic data for small molecules: Single crystal X-ray diffraction
• Protein sequences: Mass spectrometry, Edman degradation using a protein sequenator
• Nucleic acid sequences: (RT-)PCR, sequencing, …
• Linked genotype and phenotype data: High-throughput genotyping and ongoing patient care/clinical trial 
• Macromolecular structures: Diffraction, electron cryo-microscopy
• Clinical data: Ongoing patient care, clinical trial
• Functional genomics and gene expression data: High-throughput functional genomics experiments
• Standardized bacterial information: Culture collections, species descriptions

To select an ELN, we recommend that you define selection criteria that reflect the needs of your institution and labs. You can then use these criteria to compare the available ELNs with your requirements, for example, by entering the criteria into the ELN Finder. The ELN Finder is a tool developed by the University and State Library Darmstadt and ZB MED – Information Centre for Life Sciences. It is an interactive tool for filtering ELNs based on 40 criteria.

Important criteria to consider include discipline, whether the ELN is proprietary or open-source, whether it is a cloud-computing service (SaaS) or self-hosted, and performance and stability. Other important criteria to consider when selecting an ELN include your lab’s established practices and preferences, your institution’s ELN policy, the security level needed for your data and your budget.

If you would like to find out more about working with eLabFTW, take a look at this demo with eLabFTW or this video tutorial from ZB MED, which explores both eLabFTW and Labfolder. You can also request NFDI4Microbiota training on how to work with eLabFTW if you are interested.

If your home institution does not support an open-source ELN and your research group would like to set up a proprietary solution, we can offer insights and suggestions based on our experience with eLabJournal, for instance.

The most common formats for microbial data are as follows:

• FASTA (*.fasta, .fas, .fa, .fna, .ffn, .faa, .mpfa, *.frn) for nucleotide and protein sequences.
• FASTQ (*.fq, *.fastq) for raw biological sequences and their corresponding quality scores.
• General Feature Format (GFF) (*.gff, *.gff3) sequence annotations.
• Sequence Alignment Map (SAM) (*.sam) and Binary Alignment Map (BAM)  (*. bam) for biological sequences aligned to a reference sequence.
• Variant Call Format (VCF) (*.vcf) for gene sequence variations.

For more information on suitable file formats for long-term archiving, please refer to the Digital Preservation page of our Knowledge Base.

If you would like to learn more about using Bash, Python and R for data analysis, please take a look at our training calendar to see if there are any upcoming training events on these topics, or take a look at the training materials we have published on Zenodo. You can also take a look at The Carpentries’ teaching materials, such as the following:

• The Unix Shell
• Programming with Python
• Plotting and Programming with Python
• R for Reproducible Scientific Analysis

One way to share your scripts is to archive your GitHub repository on Zenodo and assign it a Digital Object Identifier (DOI). To do so, follow these steps:

1. Create a Zenodo account.
2. Create a Binder-ready repository on GitHub (see here for instructions).
3. Make sure your repository is ready to be published.
4. Create a Zenodo DOI for your repository (see here for best practices).
5. Create a Binder link for your Zenodo DOI (see here for the form).

We currently offer a guided evaluation system for provenance standards for your workflows. If you are interested in rating your workflows and tools, please reach out to the helpdesk. We recommend evaluating five different aspects individually to check if they are easily reproducible and verifiable (see below). You can find more detailed information on the guidelines and rating system in this guideline (Provenance Guidelines for Workflow and Tool Developers)

(1) Improve reproducibility
(2) Version report (output file: versions.yml)
(3) Data and metadata management (output file: provenance.yml)
(4) Documentation
(5) Validation, cooperation and sharing

A proper backup and storage strategy for any type of research should include the following:

• Consult your local IT or library staff to learn about backup and storage options.
• Follow the 3-2-1 rule when backing up your data: keep 3 copies of any important file; store your files on 2 different types of media; and keep at least 1 copy offsite or in the cloud.
• Back up versions.
• Use incremental backups or specialized storage systems for large data sets.
• Generate checksums for files and compare them after data transfers to verify data integrity. A checksum is a digit representing the sum of the correct digits in a piece of stored or transmitted digital data, against which later comparisons can be made to detect errors in the data.
• Plan for regular updates and migration to newer technologies.

As a researcher, if you want to ensure that your data is preserved in the long term, you must handle it sustainably. This includes complying with community standards (e.g. your discipline’s metadata standard), providing curated and extensive metadata and contextual information for your data (e.g. comments, detailed descriptions of methods, units and formats, and user licences), organizing your data, validating your data (i.e. cleaning and quality-controlling your data), and using acceptable file formats.