NFDI4Agri: National Research Data Infrastructure for Agricultural Sciences
Agriculture is central to all people. One focus of agricultural research is on ensuring food security through resource-efficient, sustainable food production. Among various disciplines, high diversity of data is generated in agricultural research in terms of content. In agricultural field measurements, data is collected to describe the physical condition and development of plants as a function of soil, management and climate.
Agricultural research data originating from laboratory analysis, landscape monitoring, sequencing, breeding, phenotyping, spectrophotometer measurements, remote sensing, economic market data or modelling results and is usually both, spatial and temporal referenced as geodata (e.g. soil maps) or time series (e.g. yield data). Additionally, data from agricultural research have to deal with sensitive information about landowners’ areas and aspects of data protection must be considered.
As a result of increasing use of sensor technology and the upcoming digitization in agriculture (Farming 4.0), the production of spatially and/or temporally high-resolution data increases considerably. The NFDI4Agri approach meet the needs of the agricultural research community and connect agricultural disciplinary repositories and, hence, make publicly funded and yet isolated research data inter- and transdisciplinary available and thus reusable.
NFDI4BioDiversity: National Research Data Infrastructure for Biodiversity, Ecology and Environmental Data
Biodiversity is more than just the diversity of living species. It includes genetic and phenotypic diversity of organisms, functional diversity, interactions and the diversity of populations and whole ecosystems. Mankind continues to dramatically impact the earth’s ecosystem which is the foundation of human well-being. A general understanding of the status, trends, and drivers of biodiversity on earth is urgently needed to determine management options and devise conservation responses.
Answers to scientifically and socially relevant questions can only be found following the FAIR data principles, open science and through availability of data integrated from multiple sources. As digitalization and liberation of data proceeds, NFDI4BioDiversity will foster community standards, data management as an integral part of research and documentation as well as the harmonization and synthesis of heterogeneous data. It will pro-actively engage the user community to build a coordinated data management platform for all types of biodiversity data as a dedicated added value service for all users of NFDI.
NFDI4Health: National Research Data Infrastructure for Personal Health Data
Access to detailed information on a large and unselected number of patients is pivotal to advance patient stratification, to support personalized medicine, find new therapy options and to improve patient care. In Germany, there exist a high number of different health data resources such as clinical, epidemiological or public health studies distributed over various research institutions and public authorities.
Access to and use of this data is restricted by data protection laws due to the legitimate interests of the study subjects. Besides these important restrictions, access to research data is still hampered mainly by missing interoperability of data types, poor data linkage opportunities, and the lack of protected data sharing environments and settings, which would allow automatic access to data.
In the interest of improving population health through better and broader data re-use in Germany, we map out a national research strategy focusing on personal health data. Main objectives are (1) the implementation of a federated health data infrastructure in Germany for searching and accessing health care data and health databases, (2) to enhance data sharing and data linkage of personal health data in compliance with privacy regulations and ethics principles, (3) to enable the development and deployment of new consent management mechanisms and augmented data access services, and (4) to foster data sharing and cooperation between clinical research, epidemiological and public health communities.
NFDI4Microbiota: National Research Data Infrastructure for Microbiota Research
A large fraction of existing microorganisms is associated with plants, animals, and human beings, where they typically exert essential functions. However, the complexity of these microorganisms and their symbiotic interactions is yet poorly understood. Microbiota (including viruses) have a strong impact on many aspects of human life, starting from health to ecologically relevant processes. The biggest challenge in the understanding of microbiota lies in the complexity of numerous biotic interactions between the specific strains of a microbiota and their abiotic environmental factors. Mapping and deciphering those molecular interactions and the underlying regulatory mechanisms is a crucial step towards an understanding and usage of microbiota.
NFDI4Microbiota will assist researchers with different scientific challenges to understand microbial communities and the interaction between the species in them. For this purpose the consortium will provide the computational infrastructure as well as analytical tools for the community to compile, analyze and store various types of data with the aim to decipher the interspecies interactions on a molecular level.
The consortium will enable efficient and reproducible processing of metagenomes, meta-transcriptomes, meta-proteomes and meta-metabolomic information as well as of data from single cell sequencing. It will enrich this data by metadata from databases and by knowledge automatically extracted from literature and make the data interoperable. The increased understanding of microbiota interactomes and bacterial interspecies interactions facilitated by this infrastructure will be beneficial for biotechnology, agriculture, ecology, and medicine.
GHGA The German Human Genome-Phenome Archive
Human genome sequencing and other omics data modalities are of critical importance for biomedical research and the future development of healthcare. GHGA will provide the infrastructure to meet both the desire to handle omics data in an open and FAIR manner and the need to keep personal data safe and secure. Other than other European infrastructures, as a National consortium, GHGA will have the ability to address the legal requirements specific to Germany, enabling German researchers to help shape future international standards for data exchange and take on leading roles in international research consortia.
NFDI Neuro National Research Data Infrastructure for Neuroscience
Heterogeneity, complexity and growing volumes of neuroscience data, paired with the demand to re-use valuable experimental data, make it increasingly important to provide infrastructural support for the data management tasks of neuroscientists. This may include very diverse aspects, for example, tools to facilitate the acquisition of experimental data and metadata, standards to improve interoperability of data from multiple sources, services to store and organize the provenance of data acquisition and processing workflows, or enhancing accessibility and interoperability of databases, repositories, and other resources. Some solutions have started to emerge but need to be integrated and further developed to achieve a coherent and efficient data management framework for neuroscientists. This process will have to consider all subdomains of neuroscience and all methodological approaches with their requirements on research data, addressing the specific needs of the researchers.
The NFDI Neuroscience consortium works to build a community to develop the conceptual and practical basis of a research data management infrastructure for the neurosciences. The consortium is supported by the Neurowissenschaftliche Gesellschaft (NWG) and the Bernstein Network for Computational Neuroscience.
NFDI4AIRR National Research Data Infrastructure for Adaptive Immune Receptor Repertoires
The adaptive immune system plays a fundamental role in health and disease and normally efficiently protects vertebrate hosts from infections and cancer. On the downside however, failures in its regulation are causative for autoimmunity, allergy, immunodeficiencies and lymphoid malignancies. To perform the critical task of self/non-self recognition, the adaptive immune system utilizes millions of randomly generated immunoglobulins/antibodies and T-cell receptors. The entireness of this highly dynamic set of receptors present in a host at a given time is referred to as Adaptive Immune Receptor Repertoire (AIRR). Due to the fixed (i.e., genetic) linkage of receptor reactivity to individual cell or clones, the structure of the AIRR represents key processes of the adaptive immune system: Diversification, selection, antigen recognition and clonal expansion. A comprehensive understanding of these processes will facilitate mechanistic insights and allow the development of diagnostic markers and novel therapeutic strategies. To this end, it is necessary to obtain the capability to combine data and metadata from diverse experimental technologies that provide complementary viewpoints on these processes. Thus, the main objective of NFDI4AIRR is to build together with the German immunological community a network of federated repositories for data describing the state of the adaptive immune system and to provide tools and services that will facilitate integrated data analyses across these repositories.
Christian Busse (The German Cancer Research Center DKFZ, Heidelberg) E-Mail: firstname.lastname@example.org
DataPLANT NFDI Fundamental Plant Research
In modern hypothesis-driven science, researchers increasingly rely on effective research data management services and infrastructures that facilitate the acquisition, processing, exchange and archival of research data sets, to enable the linking of interdisciplinary expertise and the combination of different analytical results. The immense additional insight obtained through comparative and integrative analyses provides additional value in the examination of research questions that goes far beyond individual experiments. Specifically, in the research area of fundamental plant research that this consortium focuses on, modern approaches need to integrate analyses across different system levels (such as genomics, transcriptomics, proteomics, metabolomics, phenomics). This is necessary to understand system-wide molecular physiological responses as a complex dynamic adjustment of the interplay between genes, proteins and metabolites. As a consequence, a wide range of different technologies as well as experimental and computational methods are employed to pursue state-of-the-art research questions, rendering the research objective a team effort across disciplines. The overall goal of DataPLANT is to provide the research data management practices, tools, and infrastructure to enable such collaborative research in plant biology. In this context, common standards, software, and infrastructure can ensure availability, quality, and interoperability of data, metadata, and data-centric workflows and are thus a key success factor and crucial precondition in barrier-free, high-impact collaborative plant biology research.
NFDI4Chem – Chemistry Consortium in the NFDI
The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation.