Research Data Management

Introduction

Research Data Management (RDM) is an integral part of the research process. It encompasses:

• Organisation and documentation: Systematic arrangement and thorough documentation of data to ensure it is understandable and useable.
• Storage: Storing data in a manner that ensures its integrity and accessibility over time.
• Backup and archiving: Regular backups to prevent data loss and archiving for long-term preservation.
• Sharing and publication: Making data available in accordance with ethical, legal and institutional guidelines.

Legal and formal framework

In Germany

In Germany, research data management is governed by several legal and formal frameworks. The primary framework is the Code of Conduct of the Deutsche Forschungsgemeinschaft (DFG). This code sets the standards for good scientific practice and mandates that universities implement these standards in a legally binding manner.

At Freie Universität Berlin

At Freie Universität Berlin, the following regulations are in place:

Freie Universität Berlin’s Statutes for Safeguarding Good Research Practice (Satzung zur Sicherung der guten wissenschaftlichen Praxis): These statutes are the legally binding implementation of the DFG Code of Conduct and govern principles of good scientific practice, including:

• Obligations of researchers
• Supervision responsibilities
• Usage rights and authorship
• Prevention of abuse of power
• Addressing scientific misconduct

Research Data Policy: This policy outlines the requirements for handling research data, ensuring it is managed in line with legal and ethical standards.

Usage rights

According to German law, no single party owns research data. Instead, data usage and publication rights are regulated to ensure fairness and compliance with institutional policies. Research Data Agreements FU Berlin

At a glance: What is research data and what isn’t

Understanding what constitutes research data is crucial for proper management. In general terms, research data constitute

• Raw data: Original data collected from experiments, surveys, or observations.
• Processed data: Data that has been cleaned, transformed, or analysed.
• Metadata: Information describing the context, content, and structure of data.
• Protocols and methods: Documentation of methodologies and procedures used in data collection and analysis.
• Software and code: Scripts, programs, or algorithms developed for data analysis or as part of a research project (does not encompass standard software).
• Images and visualizations: Photographs, graphs, charts, and other visual representations of data.

More specifically, research data in biodiversity and evolutionary research encompass:

• Sample Metadata and Sampling Information: Includes sample origin data and the various identifiers associated with it.
• DNA Sequences and Derivatives: Includes raw data from sequencing platforms, pherograms, contigs, alignments, and assembled plastid genomes.
• Evolutionary Analysis Results: Includes analysis results derived from DNA sequence data, such as matrices, command blocks, analysis logfiles and outputs, phylogenetic trees or networks.
• Experimental Data and Lab Methodology Data: Includes lab-related data such as PCR protocols, primer information, PCR parameters, gel electrophoresis pictures, DNA concentration or quality results, flow cytometry results measures, genome size measurements, etc.
• Image-based Data: Includes Scanning Electron Microscopy (SEM) images, light microscopy images, photographic data (e.g., field photos, sample photos, macro photos), and herbarium image data.
• Derivatives of Image-based Data: Includes morphometry data derived from measurements of images and statistical analysis thereof, chromosome counts, tables containing characters matrices compiled from image analysis, and phenotypic data recorded from living plants.
• Geospatial data, environmental data, ecological data: Includes coordinates, maps, plot inventories, data logger data, etc.
• Software and scrips: Includes commands, scripts or pipelines developed for research as well as software packages developed in research projects.

Project administration

• General documents: Includes project proposals, work plans, exposes, budget, reports, formal collaboration agreements and any administrative or legal documents related to projects or grants.
• Meeting minutes & e-mail correspondence
• Event-related documents: Includes agendas, participant lists, and other organizational documents related to planning and executing events.

Other non-research data

• Literature and reference materials: Includes articles, books, software manuals, images, illustrations and other reference materials used for research.
• Training and teaching materials: Includes presentations and documents for in-house training and academic education.
• Protocols and SOPs: Includes lab procedures or in-house guidelines.
• Standard software: Includes standard software used for analyses like MrBayes, R, Geneious, etc.

Why this distinction is important

Research data need to be managed for scientific integrity, accessibility and compliance with good scientific practice.

Project administration documents require careful management to meet legal, financial and administrative requirement by the Freie Universität Berlin and the funders.

Other non-research data are important for reference abut do not have the same stringent management and storage requirements as research data or project administration.