Data Stewardship Research Articles

The need for climate data stewardship: 10 tensions and reflections regarding climate data governance

The need for climate data stewardship: 10 tensions and reflections regarding climate data governance

Digital transformation and data ecosystem: implications for policy actions and competency frameworks

The article discusses EU policy for digital transformation and the associated development potential. The article aims to critically analyse the current progress of the operationalisation and implementation of the relevant policies. It is followed by the recognition of the challenges that can contribute negatively to the necessary strategic objectives and obstacles that may hinder reaching the policy goals. In particular, a significant obstacle may be a major deficiency of adequately prepared experts ready to work in new roles in a dynamically developing data ecosystem. A remarkable example is the role of the Data Steward. This role is essential for fostering the rapid development of the data ecosystem in the EU. We propose creating a universal competence framework for Data Stewards to streamline human resource allocation. The article proposes a basic outline of the necessary skills and competencies ensuring effective data stewardship.

Why Can’t I just use Dropbox? A comparison of cloud file storage platforms used for research

Objective: Many researchers use cloud file storage platforms such as Box and Google Drive as the sole data management platform for all of their research data throughout the course of their projects. Researchers have lost access to their preferred platforms due to changes in licensing agreements and cost to their institutions, leaving researchers to figure out a new system on their own. This paper describes differences between these platforms that affect research data workflows. Methods: We selected four commonly used cloud file storage vendors (Box, Dropbox, Google Drive, and Microsoft SharePoint/OneDrive) to assess. The authors read public user documentation for the platforms and identified several differences, such as maximum file size, that could affect research data workflows. For each difference, we recorded the specifics of each platform then narrowed the scope to features that vary across the platforms. Results: We identified three areas where cloud platforms differed in ways that affect data management: data stewardship, storage capacity, and file organization. Data stewardship is affected by variations in the platforms’ approaches to individual vs. group ownership of files and how user roles are defined and assigned. The platforms also differ in limits on total capacity, individual file size, total number of files, and the amount of data that can be moved in and out of the platform. Some of these limits vary by access method (e.g. API vs web interfaces). Finally many differences affect how data can be organized, such as number of files per folder, synchronization, and file sharing restrictions. Conclusions: Some research data workflows may be negatively affected by moving between cloud file storage platforms. Researchers will need assistance identifying differences between the platforms and modifying disrupted data workflows. 

How to avoid the “infrastructural blues”? Studying‐while‐caring for data stewardship

AbstractWhen it comes to climate crisis research, current debates are increasingly thematizing the needs but also the challenges of collaborative, transdisciplinary work. Geophysical characterizations of climate change are increasingly deemed insufficient to respond to the challenges that vulnerable communities face worldwide. In this paper, I describe the work of studying‐while‐caring for an environmental data infrastructure in order to address this issue. I suggest framing “data management” anthropologically as a question of collective stewardship that is better conceived as a “knowledge infrastructure” (Edwards 2010) instead of a formal approach to automated data curation. To examine the sociotechnical blindspots of data management, I elaborate on the anthropological concept of “infrastructural blues” based on the data engineering work I conducted. For the conclusion, I discuss the concept of “common” as a substitute for “open” technologies and address the broader implications of the proposed shift toward community stewardship and self‐determination as guiding practices for socio‐environmental data governance.

Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study.

Secondary investigations into digital health records, including electronic patient data from German medical data integration centers (DICs), pave the way for enhanced future patient care. However, only limited information is captured regarding the integrity, traceability, and quality of the (sensitive) data elements. This lack of detail diminishes trust in the validity of the collected data. From a technical standpoint, adhering to the widely accepted FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship necessitates enriching data with provenance-related metadata. Provenance offers insights into the readiness for the reuse of a data element and serves as a supplier of data governance. The primary goal of this study is to augment the reusability of clinical routine data within a medical DIC for secondary utilization in clinical research. Our aim is to establish provenance traces that underpin the status of data integrity, reliability, and consequently, trust in electronic health records, thereby enhancing the accountability of the medical DIC. We present the implementation of a proof-of-concept provenance library integrating international standards as an initial step. We adhered to a customized road map for a provenance framework, and examined the data integration steps across the ETL (extract, transform, and load) phases. Following a maturity model, we derived requirements for a provenance library. Using this research approach, we formulated a provenance model with associated metadata and implemented a proof-of-concept provenance class. Furthermore, we seamlessly incorporated the internationally recognized Word Wide Web Consortium (W3C) provenance standard, aligned the resultant provenance records with the interoperable health care standard Fast Healthcare Interoperability Resources, and presented them in various representation formats. Ultimately, we conducted a thorough assessment of provenance trace measurements. This study marks the inaugural implementation of integrated provenance traces at the data element level within a German medical DIC. We devised and executed a practical method that synergizes the robustness of quality- and health standard-guided (meta)data management practices. Our measurements indicate commendable pipeline execution times, attaining notable levels of accuracy and reliability in processing clinical routine data, thereby ensuring accountability in the medical DIC. These findings should inspire the development of additional tools aimed at providing evidence-based and reliable electronic health record services for secondary use. The research method outlined for the proof-of-concept provenance class has been crafted to promote effective and reliable core data management practices. It aims to enhance biomedical data by imbuing it with meaningful provenance, thereby bolstering the benefits for both research and society. Additionally, it facilitates the streamlined reuse of biomedical data. As a result, the system mitigates risks, as data analysis without knowledge of the origin and quality of all data elements is rendered futile. While the approach was initially developed for the medical DIC use case, these principles can be universally applied throughout the scientific domain.

Data Stewardship – Austrian National Strategy and Alignment

Within the FAIR Data Austria project, supported by the Federal Ministry for Education, Science, and Research (BMBWF), a national strategy has been established to advance the creation of tailored Data Stewardship solutions for the Austrian context. The strategy, formalized as a toolbox, delineates various Data Steward models, corresponding competencies, and accessible training resources. Despite the crucial role of Data Stewardship in supporting data-driven scientific research, Austrian universities encounter challenges in its implementation. Issues include lack of consensus on the skills, roles, and responsibilities of Data Stewards, coupled with insufficient funding for these positions. This article explores these challenges and emphasizes the importance of addressing them to promote effective Data Stewardship within the Austrian academic landscape.

Cytokine Release Syndrome: The Patient, Caregiver and Healthcare Professional Experience

Background: Cytokine release syndrome (CRS) is a supraphysiologic response to T-cell engaging immunotherapy that can negatively impact the treatment experience of patients with non-Hodgkin lymphoma (NHL) and other blood cancers. We explored patient, caregiver and healthcare professional (HCP) perspectives on key burdens and unmet needs associated with CRS. Methods: Existing insights from disease-level patient journeys (follicular lymphoma, diffuse large B-cell lymphoma, multiple myeloma [MM]) and reports from focus groups with a CRS patient working group from the USA, UK, Canada, and Belgium were assessed to identify key questions. Further interviews were conducted (November 2022-January 2023) in the USA, UK, Germany, and Austria to address these questions with patients, caregivers and HCPs. HCP insights were collected via 60-minute virtual interviews; patient and caregiver insights were collected via video homework tasks and 60-minute virtual interviews. Outputs were validated by the working group, consisting of patients with CRS experience and representatives of patient organizations. Results: A total of 34 HCPs (hemato-oncologists, n=24; nurses, n=10; Tables 1 & 2) with varying degrees of experience of managing CRS were interviewed, along with 9 patients and 3 caregivers (associated diagnoses: NHL, n=6; MM, n=5; acute lymphocytic leukemia, n=1). All patients had experienced CRS (Grade 1-3) as a result of CAR T-cell (n=6) or bispecific antibody (n=3) therapy. Insights were based on five focus areas: 1) patient education and information; 2) CRS diagnosis and management; 3) CRS in different healthcare archetypes; 4) digital literacy and the role of technology in mitigating/monitoring/managing CRS; and 5) the caregiver perspective. CRS education was a primary unmet need, but overall awareness and knowledge of CRS was strong among the patient sample. Patients noted the life-threatening potential of CRS, but preferred to be on active treatment. HCP communication to patients focused on CRS being an expected occurrence rather than a marker of treatment failure, the need for in-patient admission in some cases and/or close monitoring, and the critical role of caregivers. Generally, HCPs were familiar with CRS and the procedures for its monitoring and management, but the variability in time to onset, severity of symptoms, and potential impact on quality of life (QOL) warranted caution. Logistics emerged as a common barrier, with CRS treatment often administered at major hospitals, as local, community-based clinics do not currently have the required capacity/capability to manage CRS; this could be achieved via the provision of training and support. Uncertainty remains regarding the broader roll out of bispecific antibodies, given the variability in CRS treatment between countries. Patients, HCPs and caregivers were aligned on their preferences for remote monitoring for CRS. All three groups expressed the need for simple, easy-to-use solutions that integrate seamlessly with existing systems and do not cause undue concern with false alarms, complex technical set-up and patient user experience. Patients emphasized the need for prompt and assured access to their medical team in case of an emergency. HCPs expressed a range of opinions on the feasibility of adopting such solutions. Some were open to using new tools and saw the potential for alleviating resource challenges. Others had reservations about interoperability and questions around over-informing patients with detailed data. Patients were open to adopting new technologies, if they added tangible benefit to their QOL, avoided additional logistical burden and improved their experience. Conclusions: There is strong awareness among patients, caregivers, and HCPs of the risks associated with CRS and the potential impact on QOL. HCPs with experience in administering cellular immunotherapies are confident in monitoring and managing CRS; however, its unpredictability and logistical burden need to be addressed. There is value in adopting digital solutions for CRS monitoring and management. Concerns remain around interoperability, data stewardship, and integration and validation within current hospital systems. Patients' concerns regarding the useability of such digital solutions and assured data privacy also need to be addressed as part of solution development in the CRS space.

IoT Techniques: Exploring Edge Computing Challenges and Ethical Implications in Interconnected Device Systems

The Internet of Things (IoT) denotes a system where various devices and items are interconnected, gathering and sharing data through built-in sensors and communication systems. Edge Computing: The emergence of edge computing within IoT calls for immediate data processing at its origin, introducing complexities in effective distributed computing and consistent data management. Ethical Concerns: With the expanding use of IoT across various industries, navigating ethical issues related to data privacy, user consent, and appropriate utilization becomes more intricate and essential. The proposed system integrates advanced IoT techniques with optimized edge computing strategies to address immediate data processing challenges and implement robust ethical safeguards, ensuring efficient distributed computing and responsible data management across interconnected devices. Two Advantages of purposed system are IoT Benefits: The Internet of Things (IoT) fosters smooth connections between devices, boosting efficiency in operations and facilitating instant monitoring and decision-making. Utilizing IoT functionalities allows enterprises to extract valuable insights from data analytics, resulting in enhanced product innovation and enriched customer interactions. Edge Computing Benefits: Edge computing diminishes delays by handling data near its origin, improving response speeds and enabling swift decisions without over-reliance on centralized infrastructures. Integrating edge computing into IoT setups reduces data traffic, resulting in financial savings and streamlined network performance. Advantages of Ethical Considerations: Tackling ethical issues within IoT builds user confidence, encouraging wider acceptance of interconnected systems and emphasizing conscientious data handling. Giving precedence to ethical aspects can pave the way for consistent regulations and standards, establishing a basis for trustworthy and clear IoT implementations. Benefits of the Proposed System: Merging sophisticated IoT methodologies with edge computing approaches bolsters system dependability, adaptability, and efficiency, catering to dynamic data handling needs adeptly. By embedding strong ethical measures, the envisioned system reassures stakeholders, upholding adherence to standards while championing ethical data practices. The suggested system embodies a holistic strategy that combines cutting-edge IoT and edge computing methods, emphasizing ethical aspects, to tackle pivotal issues related to data handling, network optimization, and ethical data stewardship in interconnected device environments.

Clearing the way for participatory data stewardship in artificial intelligence development: a mixed methods approach

Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligence (AI) requires massive amounts of data, research must assess what factors predict consumers’ willingness to provide their data to AI. This mixed-methods study applied the extended Technology Acceptance Model (TAM) with additional predictors of trust and subjective norms. Participants’ data donation profile was also measured to assess the influence of individuals’ social duty, understanding of the purpose and guilt. Participants (N = 322) completed an experimental survey. Individuals were willing to provide data to AI via PDS when they believed it was their social duty, understood the purpose and trusted AI. However, the TAM may not be a complete model for assessing user willingness. This study establishes that individuals value the importance of trusting and comprehending the broader societal impact of AI when providing their data to AI. Practitioner summary: To build responsible and representative AI, individuals are needed to participate in data stewardship. The factors driving willingness to participate in such methods were studied via an online survey. Trust, social duty and understanding the purpose significantly predicted willingness to provide data to AI via participatory data stewardship.

An Overview of Data Management in Human Subjects Research.

Research studies generate data in various forms. Data can be quantitative or qualitative. Research involving human subjects requires protection of data to ensure privacy. Various regulations and local policies need to be followed to ensure data security. Data management plans are critical for effective data stewardship and include details plan on data collection, management, storage, and formatting. This paper will review data collection tools, data security strategies, file management, data storage, government regulations, prepping data for analysis, reference management, and file management.

Adopting population health management in PHC. Bridging needs and response

Abstract Population health management can substantially contribute to realizing some of PHC's central attributes, including person-centeredness; accessibility; comprehensiveness; attention to health problems in their physical, mental, social, cultural and existential dimensions; continuity; coordination and community orientation. It can do this by supporting PHC providers in: • moving from a one-size-fits-all approach to targeted and tailored approaches that account for the needs of different groups within local catchment populations or population clusters with similar needs or health conditions; • moving from passive and reactive to proactive care, ensuring that people with different risks are identified and have their care anticipated; • moving from a narrow focus on clinical needs to a holistic approach focusing also on psychosocial needs and the social determinants of health; and • moving from fragmented and poorly coordinated care to better coordination and integration with secondary and tertiary care and partnership with other sectors and actors in the community. Although moving towards system-wide implementation of population health management requires several years of development work and takes time to achieve impact, population health management can promote improvements in PHC even before the full benefits of population health management are realized. Key and common success factors to enable population health management in PHC are described here as system (such as information governance arrangements that promote information sharing within and outside the health system, data stewardship capacity and skills or support of regulatory agencies); organizational (multidisciplinary and networked PHC models and integration and close collaboration with public health and social care agencies); and clinical-level factors (such as comprehensive and systematic data collection in PHC or patient and community engagement).

Data Note: The Digital Ludeme Project Database.

This document outlines the types of data collected for the Digital Ludeme Project, an ERC-funded research project that aims to improve our understanding of the development of games throughout human history through computational analysis of the available (partial) historical data of games. This document outlines how this data is collected, formatted and stored, and how it can be accessed. It is the aim of the Digital Ludeme Project to provide a data resource of unprecedented depth and scope for the benefit of historical games researchers worldwide. Special attention is paid to the FAIR Guiding Principles for scientific data management and stewardship.

Differentially Private Methods for Releasing Results of Stability Analyses

Data stewards and analysts can promote transparent and trustworthy science and policy-making by facilitating assessments of the sensitivity of published results to alternate analysis choices. For example, researchers may want to assess whether the results change substantially when different subsets of data points (e.g., sets formed by demographic characteristics) are used in the analysis, or when different models (e.g., with or without log transformations) are estimated on the data. Releasing the results of such stability analyses leaks information about the data subjects. When the underlying data are confidential, the data stewards and analysts may seek to bound this information leakage. We present methods for stability analyses that can satisfy differential privacy, a definition of data confidentiality providing such bounds. We use regression modeling as the motivating example. The basic idea is to split the data into disjoint subsets, compute a measure summarizing the difference between the published and alternative analysis on each subset, aggregate these subset estimates, and add noise to the aggregated value to satisfy differential privacy. We illustrate the methods using regressions in which an analyst compares coefficient estimates for different groups in the data, and in which analysts fit two different models on the data.

Developing inclusive digital health diagnostic for schistosomiasis: a need for guidance via target product profiles

IntroductionThe INSPIRED project aims to develop inclusive Digital Optical Diagnostic Devices (DODDs) for schistosomiasis, to support disease management by enabling rapid diagnostic results, to improve efficient data management to guide decision-making and to provide healthcare workers with critical health information to facilitate follow-up action. Due to the non-availability of Target Product Profiles (TPPs) for guiding the development of digital diagnostics for schistosomiasis, we explored existing diagnostic TPPs.MethodsUsing a curated open access database (Notion database), we studied a selection of TPPs for diagnosing infectious diseases, focusing on specifications related to digital health products for Neglected Tropical Diseases (NTDs).ResultsEighteen TPPs originating from 12 documents, covering 13 specific diseases, were selected and their characteristics were labeled and entered into the database. Further exploration of the database revealed several gaps, including a lack of stakeholder input, sustainability, and TPP availability. Other significant gaps related to digital health platform interconnectivity and data stewardship specifically in relation to digital diagnostics, including DODDs.DiscussionThese findings reflect two possible scenarios: (1) there is currently no need for digital diagnostic devices for schistosomiasis and, by extension for other NTDs; or (2) those needs are not yet covered by TPPs. Therefore, we recommend that digital health diagnostics are included in the use cases for schistosomiasis control and elimination, at least in the ideal/desirable scenario, as this will guide research and incentivize investment in digital health diagnostics for schistosomiasis.

Testing Open Science Tools

Data management plans (DMP) are an intrinsic part of planning for and implementing openness in scientific research. DMPs contain integral information on research projects: IT requirements, legal and ethical considerations and strategies for opening data. However, all too often, the DMP is seen as a chore drawn up at the request of external funders rather than a tool adopted for the benefit of project members. Similarly, the information gathered into DMPs is rarely utilised by other stakeholders such as institutional research services, nor linked to external parties such as data repositories. Machine-actionable DMPs aim to change this landscape by offering better guidance and support for researchers preparing their plans, and facilitating a research data management system that allows data and information to be shared across institutions and repositories. In fact, one of the recommendations of the EOSC Nordic project, which studied FAIR incentives and expected impact in the Nordics, Baltics and EOSC, was the implementation of machine-actionable DMPs for seamless information workflow and supporting FAIR adoption in everyday research work. Tools such as Data Stewardship Wizard and Argos do this by taking researchers through a set of simple structural questions on research data management from which answers are exported into funder templates resulting in downloadable DMP documents. These machine-actionable online tools transform tick-the-box answers that are easy to fill in into full-text DMPs that comply with funder requirements. This poster introduces a pilot project testing the Data Stewardship Wizard and localising its Knowledge Template to the needs of Aalto University. It presents the project’s findings and stages, including the process of creating a template for the Research Council of Finland DMPs and testing the tool with researchers. In doing so, it examines machine-actionable DMPs from a practice-based perspective and comments on the process of institutional implementation.

Umbrella Data Management Plans to Integrate FAIR Data: Lessons From the ISIDORe and BY-COVID Consortia for Pandemic Preparedness

The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 research infrastructures (RIs) and networks to provide a broad range of services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners. Here, we describe how, to achieve that aim, we undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the use of knowledge models when writing DMPs and, how, in the future, an inter-RI network of data stewards could contribute to the establishment of a community of practice, to be integrated subsequently into planned trans-RI competence centres.

Ethical pinch-points: Effectively communicating ethical consideration in research design

This presentation aims to communicate the first two year’s work of the UK Statistic Authority’s Centre for Applied Data Ethics (CADE), present key ethical considerations to researchers working with population, administrative and other secondary data, and describe the innovative methods that have been used to review over 700 pieces of research in those two years.
 The CADE was established in February 2021 with the aim of enabling researchers and statisticians, both within government and outside of it, to effectively address potential ethical issues in their use of data for the public good. To assist researchers in practically applying data stewardship principles to their research, an ethics self-assessment tool has been developed. The ethics self-assessment process aims to offer researchers an easy-to-use framework to review the ethics of their own projects throughout the research cycle, whilst promoting a culture of “ethics by design”.
 In two years’ worth of use, the self-assessment tool has been widely adopted across government, academia and the commercial and charities sectors. Analysis of the work that has been supported by this tool has revealed several topics where researchers could further develop their own research. Well designed and communicated research that demonstrates consideration of these factors enables quicker and safer access to data. In response to these findings, CADE have developed specific guidance pieces to assist the analytical community in considering these ethical concerns in their research design and communicating them effectively to ethics bodies and the wider public to ensure effective and consistent data stewardship practices.
 This presentation will support delegates by going into detail in the defining and realising of public good in research, engaging public audiences with research, and considering (and demonstrating) the public view in research design. It will also aid delegates by providing them with a framework that they can use to demonstrate consideration of traditional ethical concerns and also ethical concerns in new, emerging and currently unknown fields.

I Know Something You Don’t Know: The annotation saga continues…

Over the past 20 years, the biodiversity informatics community has pursued components of the digital annotation landscape with varying degrees of success. We will provide an historical overview of the theory, the advancements made through a few key projects, and will identify some of the ongoing challenges and opportunities. The fundamental principles remain unchanged since annotations were first proposed. Someone (or something): (1) has an enhancement to make elsewhere from the source where original data or information are generated or transcribed; (2) wishes to broadcast these statements to the originator and to others who may benefit; and (3) expects persistence, discoverability, and attribution for their contributions alongside the source. The Filtered Push project (Morris et al. 2013) considered several use cases and pioneered development of services based on the technology of the day. The exchange of data between parties in a universally consistent way necessitated the development of a novel draft standard for data annotations via an extension of the World Wide Web Consortium’s Web Annotation Working Group standard (Sanderson et al. 2013) to be sufficiently informative for a data curator to confidently make a decision. Figure 2 from Morris et al. (2013), reproduced here as Fig. 1, outlines the composition of an annotation data package for a taxonomic identification. The package contains the data object(s) associated with an occurrence, an expression of the motivation(s) for updating, some evidence for an assertion, and a stated expectation for how the receiving entity should take action. The Filtered Push and Annosys (Tschöpe et al. 2013) projects also considered implementation strategies involving collection management systems (e.g., Symbiota) and portals (e.g., European Distributed Institute of Taxonomy, EDIT). However, there remain technological barriers for these systems to operate at scale, the least of which is the absence of globally unique, persistent, resolvable identifiers for shared objects and concepts. Major aggregation infrastructures like the Global Biodiversity Information Facility (GBIF) and the Distributed System of Scientific Collections (DiSSCo) rely on data enhancement to improve the quality of their resources and have annotation services in their work plans. More recently, the Digital Extended Specimen (DES) concept (Hardisty et al. 2022) will rely on annotation services as key components of the proposed infrastructure. Recent work on annotation services more generally has considered various new forms of packaging and delivery such as Frictionless Data (Fowler et al. 2018), Journal Article Tag Suite XML (Agosti et al. 2022), or nanopublications (Kuhn et al. 2018). There is risk in fragmentation of this landscape and disenfranchisement of both biological collections and the wider research community if we fail to align the purpose, content, and structure of these packages or if these fail to remain aligned with FAIR principles. Institutional collection management systems currently represent the canonical data store that provides data to researchers and data aggregators. It is critical that information and/or feedback about the data they release be round-tripped back to them for consideration. However, the sheer volume of annotations that could be generated by both human and machine curation processes will overwhelm local data curators and the systems supporting them. One solution to this is to create a central annotation store with write and discovery services that best support the needs of all stewards of data. This will require an international consortium of parties with a governance and technical model to assure its sustainability.

Implementing CARE Principles to Link Noongar Language and Knowledge to Western Science through the Atlas of Living Australia

Implementing CARE Principles to Link Noongar Language and Knowledge to Western Science through the Atlas of Living Australia

Data Steward Service Center (DSSC): FAIRagro RDM-Expertise Hub

The Data Steward Service Center (DSSC) is the central institution within FAIRagro to develop data management tools based on the needs of the scientific community. The DSSC organizes the continuous exchange of RDM knowledge and experience with other institutions, channels user requests from the community, and transfers knowledge from the FAIRagro task areas to the FAIRagro data stewards. FAIRagro data stewards are experts in the field of RDM for agrosystems research supervising and will train data curators in our community. Data stewards have core competencies in research data management (e.g., cross-scale from genes, phenomics, management to region; sensitive data, remote sensing, time series, plant, soil and related FAIRagro data). Knowledge and expertise is pooled to provide the full range of expertise to the community in one place to foster the coalescence of the community. The DSSC is headed by a coordinator and will house five data stewards, who are active in the community e.g. train data curators, give legal support. In the course of the project, further institutional or project data stewards will be integrated and the pool of experts will be further expanded. The network to the other NFDI consortia is continuously growing.