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In order to develop a discipline-specific data management plan (DMP) template, it is important to obtain information from researchers. For a chemistry-specific template, NFDI4Chem conducted a series of interviews with 27 participants and used data from the RDA WG Discipline-specific Guidance for DMP online survey. The interviews showed that the implementation of research data management in everyday work is a big challenge. Key findings from the interview series highlight challenges in implementing FAIR principles, with a focus on “Findability” and “Reusability.” The importance of linking physical samples and data in chemistry is emphasised, with discussions on storage, archiving, and the use of tools like electronic lab notebooks and repositories. However, documentation methods, software tools, and naming conventions commonly used in chemical research are also addressed. Overall, the study underscores the need for improved resources and strategies to enhance data management practices in the field of chemistry. All the gathered information and examples will be used to develop a DMP template in line with chemistry-specific requirements. The results provide a comprehensive outlook on the future developments of research data management (RDM) in chemistry.

RADAR is a cross-disciplinary internet-based service for long-term and format-independent archiving and publishing of digital research data from scientific studies and projects. The focus is on data from disciplines that are not yet supported by specific research data management infrastructures. The repository aims to ensure access and long-term availability of deposited datasets according to FAIR criteriaWilkinson et al. 2016 for the benefit of the scientific community. Published datasets are retained for at least 25 years; for archived datasets, the retention period can be flexibly selected up to 15 years. The RADAR Cloud service was developed as a cooperation project funded by the DFG (2013-2016) and started operations in 2017. It is operated by FIZ Karlsruhe - Leibniz-Institute for Information Infrastructure.As a distributed, multilayer application, RADAR is structured into a multitude of services and interfaces. The system architecture is modular and consists of a user interface (frontend), management layer (backend) and storage layer (archive), which communicate with each other via application programming interfaces (API). This open structure and the access to the APIs from outside allows integrating RADAR into existing systems and work processes, e. g. for automated upload of metadata from other applications using the RADAR API. RADAR's storage layer is encapsulated via the Data Center API. This approach guarantees independence from a specific storage technology and makes it possible to integrate alternative archives for the bitstream preservation of the research data.The data transfer to RADAR takes place in two steps: In the first step, the data is transferred to a temporary work storage. The ingest service accepts individual files and packed archives, optionally unpacks them while retaining the original directory structure and creates a dataset. For each file found, the MIME Type (see Multipurpose Internet Mail Extensions specification)) is analysed and stored in the technical metadata. When archiving and publishing, a dataset is created in the second step. The structure of this dataset - the AIP (archival information package) in the sense of the OAIS standard - corresponds to the BagIt standard. It contains, in addition to the actual research data in original order, technical and descriptive metadata (if created) for each file or directory as well as a manifest within one single TAR ("tape archive", a unix archiving format and utility) file as an entity in one place. This TAR file is stored permanently on magnetic tapes redundantly in three copies at different locations in two academic computing centres.The FAIR Principles are currently being given special importance in the research community. They define measures that ensure the optimal processing of research data, accessibility for both humans and machines, as well as reusability for further research. RADAR also promotes the implementation of the FAIR Principles with different measures and functional features, amongst others:Descriptive metadata are recorded using the internal RADAR Metadata Schema (based on DataCite Metadata Schema 4.0), which supports 10 mandatory and 13 optional metadata fields. Annotations can be made on the dataset level and on the individual files and folders level. A user licence which rules re-use of the data, must be defined for each dataset. Each published dataset receives a DOI which is registered with DataCite. RADAR metadata uses a combination of controlled lists and free text entries. Author identification is ensured by using an ORCID ID and funder identification by CrossRef Open Funder Registry. More interfacing options, e.g. ROR and the Integrated Authority File (GND) are currently implemented. Datasets can be easily linked with other digital resources (e.g. text publications) via a “related identifier”. To maximise data dissemination and discoverability, the metadata of published datasets are indexed in various formats (e.g. DataCite and DublinCore) and offered for public metadata harvesting e.g. via an OAI-provider.These measures are - to our minds - undoubtedly already significant, but not yet sufficient in the medium to long term. Especially in terms of interoperability, we see development potential for RADAR. The FAIR Digital Object (FDO) Framework seems to offer a promising concept, especially to further promote data interoperability and to close respective gaps in the current infrastructure and repository landscape.RADAR aims to participate in this community driven approach also in its role within the National Research Data Infrastructure (NFDI). As part of the NFDI, RADAR already plays a relevant role as a generic infrastructure service in several NFDI consortia (e.g. NFDI4Culture and NFDI4Chem). With RADAR4Chem and RADAR4Culture, FIZ Karlsruhe for example offers researchers from chemistry and the cultural sciences low-threshold data publication services based on RADAR. We successively develop these services further according to the needs of the communities, e.g. by integrating and linking them with subject-specific terminologies, by providing annotation options with subject-specific metadata or by enabling selective reading or previewing options for individual files in existing datasets.In our presentation, we would like to describe the present and future functionality of RADAR and its current level of FAIRness as possible starting points for further discussion with the FDO community with regard to the implementation of the FDO framework for our service.

RADAR is a cross-disciplinary internet-based service for long-term and format-independent archiving and publishing of digital research data from scientific studies and projects. The focus is on data from disciplines that are not yet supported by specific research data management infrastructures. The repository aims to ensure access and long-term availability of deposited datasets according to FAIR criteriaWilkinson et al. 2016 for the benefit of the scientific community. Published datasets are retained for at least 25 years; for archived datasets, the retention period can be flexibly selected up to 15 years. The RADAR Cloud service was developed as a cooperation project funded by the DFG (2013-2016) and started operations in 2017. It is operated by FIZ Karlsruhe - Leibniz-Institute for Information Infrastructure. As a distributed, multilayer application, RADAR is structured into a multitude of services and interfaces. The system architecture is modular and consists of a user interface (frontend), management layer (backend) and storage layer (archive), which communicate with each other via application programming interfaces (API). This open structure and the access to the APIs from outside allows integrating RADAR into existing systems and work processes, e. g. for automated upload of metadata from other applications using the RADAR API. RADAR's storage layer is encapsulated via the Data Center API. This approach guarantees independence from a specific storage technology and makes it possible to integrate alternative archives for the bitstream preservation of the research data. The data transfer to RADAR takes place in two steps: In the first step, the data is transferred to a temporary work storage. The ingest service accepts individual files and packed archives, optionally unpacks them while retaining the original directory structure and creates a dataset. For each file found, the MIME Type (see Multipurpose Internet Mail Extensions specification)) is analysed and stored in the technical metadata. When archiving and publishing, a dataset is created in the second step. The structure of this dataset - the AIP (archival information package) in the sense of the OAIS standard - corresponds to the BagIt standard. It contains, in addition to the actual research data in original order, technical and descriptive metadata (if created) for each file or directory as well as a manifest within one single TAR ("tape archive", a unix archiving format and utility) file as an entity in one place. This TAR file is stored permanently on magnetic tapes redundantly in three copies at different locations in two academic computing centres. The FAIR Principles are currently being given special importance in the research community. They define measures that ensure the optimal processing of research data, accessibility for both humans and machines, as well as reusability for further research. RADAR also promotes the implementation of the FAIR Principles with different measures and functional features, amongst others: Descriptive metadata are recorded using the internal RADAR Metadata Schema (based on DataCite Metadata Schema 4.0), which supports 10 mandatory and 13 optional metadata fields. Annotations can be made on the dataset level and on the individual files and folders level. A user licence which rules re-use of the data, must be defined for each dataset. Each published dataset receives a DOI which is registered with DataCite. RADAR metadata uses a combination of controlled lists and free text entries. Author identification is ensured by using an ORCID ID and funder identification by CrossRef Open Funder Registry. More interfacing options, e.g. ROR and the Integrated Authority File (GND) are currently implemented. Datasets can be easily linked with other digital resources (e.g. text publications) via a "related identifier". To maximise data dissemination and discoverability, the metadata of published datasets are indexed in various formats (e.g. DataCite and Dubli nCore) and offered for public metadata harvesting e.g. via an OAI-provider. These measures are - to our minds - undoubtedly already significant, but not yet sufficient in the medium to long term. Especially in terms of interoperability, we see development potential for RADAR. The FAIR Digital Object (FDO) Framework seems to offer a promising concept, especially to further promote data interoperability and to close respective gaps in the current infrastructure and repository landscape. RADAR aims to participate in this community driven approach also in its role within the National Research Data Infrastructure (NFDI). As part of the NFDI, RADAR already plays a relevant role as a generic infrastructure service in several NFDI consortia (e.g. NFDI4Cultu re and NFDI4Chem). With RADAR4Chem and RADAR4Culture, FIZ Karlsruhe for example offers researchers from chemistry and the cultural sciences low-threshold data publication services based on RADAR. We successively develop these services further according to the needs of the communities, e.g. by integrating and linking them with subject-specific terminologies, by providing annotation options with subject-specific metadata or by enabling selective reading or previewing options for individual files in existing datasets. In our presentation, we would like to describe the present and future functionality of RADAR and its current level of FAIRness as possible starting points for further discussion with the FDO community with regard to the implementation of the FDO framework for our service. [ABSTRACT FROM AUTHOR]

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. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM. [ABSTRACT FROM AUTHOR]