Mapping data stewardship in Italy: Findings from the first national survey

Giulia Caldoni; Vittorio Iacovella; Emma Lazzeri; Liise Lehtsalu; Francesca Marchegiani; Mauro Paschetta; Valentina Pasquale; Giulia Pedonese; Andrea Tarallo

doi:10.12688/openreseurope.22183.1

. 2026 Feb 13;6:45. [Version 1] doi: 10.12688/openreseurope.22183.1

Mapping data stewardship in Italy: Findings from the first national survey

Giulia Caldoni ^1,^a, Vittorio Iacovella ², Emma Lazzeri ³, Liise Lehtsalu ⁴, Francesca Marchegiani ^5,^b, Mauro Paschetta ⁶, Valentina Pasquale ⁷, Giulia Pedonese ⁸, Andrea Tarallo ⁹

PMCID: PMC12972714 PMID: 41816254

Abstract

Research data management plays a key role in ensuring research reproducibility and transparency by supporting data sharing in line with Open Science principles. Professional figures supporting data management practices, such as data stewards, are emerging in the international landscape, following different paths. The Italian Computing and Data Infrastructure, with the support of the Skills4EOSC project, conducted a national survey between April and December 2023 to map the presence, roles, and activities of data stewards or equivalent professionals across universities and research institutions, to provide an initial and systematic overview of the national landscape, which is known to be fragmented and lacking of clear professional recognition for research support professionals. The survey, administered in Italian through the EUSurvey platform, consisted of 13 closed-ended questions addressing institutional organisation, professional background, and support activities related to research data management. Data cleaning was performed on the 77 valid entries collected (out of 88 responses) before conducting the descriptive analyses. Results indicate that most institutions employ staff performing data management support tasks which map to the definition of data stewardship, though these professionals are rarely formally recognised with that title. Hence, the variety of job titles observed reflects the absence of a standardised professional profile. Among the data management support tasks investigated, the most common include FAIR data management support, data management plan writing, researcher training, and policy consultancy. These findings provide the basis to establish the Italian Data Steward Community in order to overcome the fragmentation through peer-to-peer knowledge sharing and support, and to advance the definition of a professionalisation pathway for data stewards in Italy. The findings of this survey can serve as a case study to help data stewards, research data professionals, and policymakers in other countries to map and understand their local landscape.

Keywords: survey, data stewardship, Italy, data steward, professional profile, mapping

Plain Language summary

Good research depends on data that are well organised, well documented, and easy to share. Managing research data properly helps make research more transparent, trustworthy, and reusable, in line with Open Science principles. To support researchers with data management tasks, new professional roles are emerging. One of these roles is often called data steward, although this job title is not used consistently.

This study explores how data management support is organised in Italian universities and research institutions. Between April and December 2023, the Italian Computing and Data Infrastructure, supported by the Horizon Europe Skills4EOSC project, carried out a national survey to understand who provides data management support, what they do, and how they are recognised within their institutions.

The survey included 13 multiple-choice questions and was completed by 88 respondents, of whom 77 were suitable for analysis. The results show that many institutions already have staff members who support researchers with tasks such as writing data management plans, applying FAIR data principles, training researchers, and advising on data policies. However, these professionals often have different job titles and are rarely formally recognised as data stewards.

The study highlights a fragmented landscape and a lack of a clear professional profile in Italy. Based on these findings, the authors propose the creation of an Italian Data Steward Community to encourage knowledge sharing, reduce fragmentation, and support the development of a clear professional pathway. The results may also help other countries better understand and map their own data stewardship landscape.

Introduction

Data stewards (DS) are a cornerstone of the research data ecosystem. Born at the intersection of data management, research support, and policy development, this profession has developed in response to the growing complexity of managing, sharing, and preserving digital research outputs. As research institutions have embraced the principles of Open Science and FAIR data ¹ (Findable, Accessible, Interoperable, and Reusable data), the need for dedicated professionals who can bridge the gap between researchers, IT specialists, and policy-makers has become evident. DS are key actors in ensuring that research data are not only technically well-managed, but also ethically, legally, and contextually sound, and that research-performing institutions have in place policies and workflows that enable FAIR research data management (RDM), turning data into a true institutional and societal asset.

International efforts to professionalise research data stewardship

Several efforts aim to align DS profiles and data stewardship service models at both the European and international levels. Within the Research Data Alliance (RDA), the Professionalising Data Stewardship Interest Group ¹ (endorsed in 2020) has completed global surveys on data stewardship service models ( Ayres et al., 2022) and DS career tracks ( Newbold et al., 2024). Among those, a survey run in 2021 on DS service models found that most respondents considered their service provision still in the early stages of maturity ( Ayres et al., 2022). Another one on DS career tracks, completed in 2022, highlighted that professionals who self-identify as DSs hold many different job titles and job roles – only 33% of the European respondents actually had the job title “data steward” – and underscored the lack of clearly defined career paths for DS ( Newbold et al., 2024). Such surveys are not merely exploratory exercises; they play a crucial role in planning and developing practical implementations. For instance, the results of the RDA surveys were considered and incorporated by the European Open Science Cloud (EOSC) Task Force for Data Stewardship Curricula and Career Paths ( Kalová et al., 2024). Similarly, the Horizon Europe Skills4EOSC project ² built on the DS profiles, skills and competences defined in various national contexts to deliver Minimum Viable Skills profiles for “coordinating data steward” and “embedded data steward” that are intended to have European-level applicability ( Hansen et al., 2025). All those efforts aim at the continued professionalisation of DSs and the maturing of data stewardship services in the research context.

The landscape of research data stewardship in European countries

DS as professionals have emerged only in the last 10 or so years, at least in research contexts ( Wildgaard et al., 2020). Following the publication of the FAIR data principles in 2016 ( Wilkinson et al., 2016), the Turning FAIR into reality report (2018) by the European Commission’s Expert Group on FAIR Data recognised data stewardship as a key service for enabling FAIR data and proposed that new job profiles in data science and data stewardship are needed to support researchers throughout the research data lifecycle ( European Commission. Directorate General for Research and Innovation, 2018). Around the same time, universities in the Netherlands introduced their first dedicated data stewardship programmes, including the 3-year Data Stewardship Project in TU Delft that started in 2018 and introduced faculty-level DSs at the university ( Turkyilmaz-van der Velden, 2018). The years following the Turning FAIR into reality report also saw the launch of national-level projects in several European countries (e.g. the Netherlands, Denmark, Austria, and Germany) that surveyed local data stewardship landscapes, defined DS profiles and service models, identified skills and competences, and outlined training programmes ( Gruber, 2021; Jetten et al., 2021; Seidlmayer et al., 2023; Wildgaard et al., 2020).

The DS profiles defined in these first projects highlight the multiplicity of functions that DS roles encompass. The Dutch and the Danish studies identified three key functional areas for data stewardship – policy, research, and infrastructure – and defined data stewardship roles in these areas ( Schmitz, 2020). The German study identified five DS profiles: (1) generalist, (2) advisory RDM, (3) discipline-specific, (4) coordinating, and (5) infrastructure-focused DS ( Seidlmayer et al., 2023). There are overlaps between the Dutch, Danish, and German DS roles. However, the three studies also highlight that the roles and functions of a DS depend on the professional environment in which they operate. The Austrian study, in fact, directly links DS profiles to the professional environment by introducing professional profiles and related skills and competences through the definition of three data stewardship service models (contact point per faculty, central DS office, cross-organisation DS network) ( Gruber, 2021).

Data stewardship in Italy

In Italy, similarly to other European countries, the role of the DS has not yet been formalised. Following the increasing importance of professional RDM activities to support the research lifecycle, several Italian universities and research institutions have progressively introduced similar institutional roles, training opportunities, and governance models for research data support. In 2019, a group of research and e-infrastructures in Italy signed the “Italian Computing and Data Infrastructure” (ICDI) ³ memorandum of understanding. ICDI is a forum created to promote synergies at the national level, and optimise the Italian participation in European and global challenges in the field of Open Science and FAIR data. By including several Italian research institutions and universities, ICDI progressively became the forum where Open Science and RDM experts could exchange ideas, start collaborations, and propose solutions to policy-makers with a bottom-up approach. The monthly online “Open Science Cafè” webinars, on major Open Science topics and updates, exemplifies ICDI's efforts in community engagement and building in the Italian landscape.

One of the leading organisations in ICDI is the Consortium GARR, which was also the coordinator of the aforementioned Skills4EOSC project. Among its activities, the project included the activation and support of professional networks of DSs to strengthen lifelong learning through peer exchange. Thanks to this favourable collaborative context created between ICDI and Consortium GARR within Skills4EOSC, the idea emerged to launch a public survey to map the state of data stewardship in Italian universities and research-performing institutions. A further objective of the survey was to assess the respondents’ willingness to join a national community of practice on RDM. This article presents the outcome of the survey and summarises the foundation of the Italian DS Community, a result of this first attempt to map data stewardship in Italy.

Materials and methods

Survey development and distribution

Under the umbrella of the Italian Computing and Data Infrastructure (ICDI), Consortium GARR, as the coordinator of the Skills4EOSC project, together with the University of Bologna (UNIBO) and the Italian Institute of Technology (IIT), designed and distributed the “Survey on Data Stewards in Italy: a mapping of professionals supporting research data management”.

The survey was developed as a web-based set of questions using EUSurvey, an open-source platform by the European Commission. It was distributed in Italian to facilitate completion and improve response rates, but an English version was also prepared.

Dissemination followed a snowball sampling approach, beginning with targeted outreach, through two national mailing lists focused on Open Science (“ oa-italia@lists.icdi.it” and “ icdi@lists.garr.it”), and expanding the audience through word-of-mouth, institutional social media channels (e.g., Skills4EOSC linkedin page ⁴), and an article in GARR Magazine ( Colcelli et al., 2023).

The survey was officially launched in April–May 2023 (from 13/04/2023 to 31/05/2023) and reopened in November–December 2023 (from 07/11/2023 to 01/12/2023).

Survey and question structure

To structure the data collection and facilitate analysis, the survey was divided into three main sections:

• 1. Introduction: Provided a general definition of “data steward” to ensure a shared understanding among respondents.

• 2. Respondent Profile: Collected information about the respondent’s institution, role, and whether the response was personal or institutional.

• 3. Institutional Characterisation: Included 13 closed-ended questions to explore how the DS role is defined and how related services are structured in the respondent’s institution.

Only those respondents who reported DSs or equivalent roles in their institutions could access the third section of the survey; for all others, the survey ended after collecting profile information.

The survey consisted of three types of questions, distributed across the sections described above. Open-ended questions were included to capture information that could not be reliably categorised in advance. In addition, we incorporated multiple-choice questions where respondents were required to select one mutually exclusive option, as well as multiple-choice questions that allowed the selection of more than one response.

The survey was designed to collect only anonymous data from participants.

Both templates, in English and Italian, as well as the dataset, have been archived in Zenodo ( Caldoni et al., 2025a).

Data cleaning and visualisation

A total of 84 anonymous responses were collected and exported as a .csv file, on which data cleaning was performed.

OpenRefine (Open Refine, v3.8.7) was used to split multiple-choice responses, correct inconsistencies and cluster roles into three macro-categories: “researcher”, “technologist” and “technical-administrative staff”. These three macro-categories were chosen as they represent distinct and formalised functions in Italian universities and research institutions. Namely, “researchers” are directly responsible for producing scientific knowledge and conducting original research, while “technologists” provide advanced technical and specialist support to research. This second role typically involves managing laboratories, digital infrastructures, data platforms, scientific instruments, or complex technical workflows. Finally, “technical-administrative staff” ensures the day-to-day functioning of the institution by handling administrative, financial, and organisational tasks.

Manual data cleaning operations consisted of:

• adding ROR identifiers when not provided by respondents,

• removing responses not from Italy (used for functionality tests),

• adding a column for institutional acronyms,

• classifying institutions (universities vs. research entities),

• eliminating duplicate answers.

Lastly, two responses referring to the respondents’ affiliated institutions were manually inspected and excluded because the authors could unequivocally verify that these records were incorrect.

After cleaning, 77 valid responses remained. The final dataset was reviewed by all authors to ensure consistency and accuracy.

Given the limited sample size, the analysis was restricted to descriptive statistics, performed using Microsoft Excel (Microsoft Corp., Redmond, WA, USA).

In-house built R code was developed to produce a map of the geographical distribution of the respondents ( Figure 1). The resulting R-plot was replicated, for visualisation purposes, into a multi-level image using vector graphics editor Inkscape ⁵. To improve readability, city-centred pie-charts reflecting geographical provenance were subdivided into 4 categories, according to the number of respondents. Cities with a single respondents were chosen as the unit size. Pie charts for cities with 2–5 respondents were visualised as 1.5 times the size of the unit; for cities with 6–14 respondents, the pie chart size was scaled at 2 times the size of the unit. The only pie chart corresponding to a city with more than 14 respondents was resized to 2.5 times the size of the unit.

The results of the analysis are presented in graphs, maps and tables to visualise the diversity and geographical coverage of the survey.

Analysis of survey results

Institutional roles and personal interests

First, we asked respondents to indicate their institutional affiliation and specify their role within the institution. This allowed us to have a better picture of the professionals involved, which was useful in the qualitative analysis of the data. 33 respondents are affiliated with universities, while 44 are affiliated with public research institutions ( Figure 1).

Regarding the professional role of the respondents, we considered the three main types of employees within the Italian public research institutions: Technical/Administrative staff, Technologist, and Researcher. To highlight possible geographical differences within the respondents' population, we collated responses from different research institutions belonging to the same city. We mounted single-city pie charts on a map of Italian administrative subdivisions ⁶.

The majority of the respondents belong to the Technical/Administrative support staff (n=38), while another important portion of respondents identified themselves as a Researcher (n=27). Only a small number of Technologists (n=8) answered the survey, consistent with the uneven distribution of this professional framework across the national landscape (see the section “Data cleaning and visualisation” in the “Materials and Methods” chapter for the definition of these three different professional roles).

Then, we asked whether the respondents were participating in the survey in a personal capacity or whether they were officially representing their institution. Most of the respondents participated in a personal capacity (about three quarters), as presented in Figure 2.

Formal recognition of Data Stewards

We started our analysis by examining the presence and the role of DSs or analogous figures within the institutions that participated in the survey. In this section, we focus our analysis solely on the responses provided on behalf of the institution (n=20).

About 65% (13 out of 20) of the respondents state that there are professionals who assume the role of DS ( Figure 3).

Interestingly, when participants were asked which type of position is held by individuals acting as DS, only one in 13 respondents reported that the role is formally recognised ( Figure 4a).

Figure 4b shows that the job titles used to identify professional figures working in data management support roles can vary considerably, even within the same institution. Respondents were allowed to select as many job titles as necessary to describe the reality of data management support in their institutions. Respondents were provided with seven possible, non-mutually exclusive answers, partially derived from the roles most represented in the international landscape ( Ayres et al., 2022; Jetten et al., 2021) and had the chance to add more options in a free text box if necessary.

Further evidence of the lack of a clear professional framework for DS is seen in the distribution of the roles for those performing data stewardship activities, including research roles (post-doc, research fellow, etc.), technical roles (e.g. IT support, data processing technician, etc.) and administrative roles (e.g. librarian, research support, project manager and others). As shown in Figure 5, no significant differences were observed across these categories.

Figure 5. — (n=13 respondents, answering on behalf of their institutions and reporting that the institution has staff dedicated to the support functions described in the definition of a “data steward”. Multiple (non-mutually exclusive) responses allowed.)

Background, training and activities

Considering the lack of any officially recognised national training pathway leading to the title of “data steward” in Italy, we asked about the educational background of DSs or equivalent figures.

Figure 6 shows the prevalence of specific disciplinary backgrounds. Indeed, the largest group (18 responses) reported a master’s degree or PhD in a relevant disciplinary field (e.g., biology, physics, social sciences). The second largest group (15 responses) indicated degrees in related or complementary fields (e.g., library science, archiving, information science, statistics). A total of 13 respondents reported degrees in Computing or Computer Science. Finally, 8 responses selected “Other” and 6 selected “I don’t know”.

Data Stewardship activities

After detailing the DSs’ background, we investigated their activities, focusing mainly on support and data management ( Ayres et al., 2022; Basalti et al., 2024; Jetten et al., 2021). Namely, we asked what kind of support the DS or equivalent figure provides within their institution, providing seven possible answers and the option to indicate more functions. The list of proposed answers was:

• “support for research data management and the application of FAIR principles, identification of disciplinary repositories, storage and backup solutions, metadata, etc.;”

• “support for writing the data management plan; assessment of possible legal issues (e.g., privacy, IP and licences, etc.);”

• “drafting of institutional policies for research data management;”

• “advice on national, international and funding body policies and mandates for data management and compliance with any obligations;”

• “training of researchers on Open Science, FAIR data and research data management issues;”

• “support for the use of data management tools made available by their institution (e.g. institutional repository, institutional cloud, etc.);”

• “support for the management and maintenance of the institutional repository (e.g. care and validation of deposited datasets).”

Respondents could select more than one answer in order to capture the extent to which DS activities are characterized by specialization or encompass multiple functions.

The results ( Figure 7) show that DSs or equivalent figures are prevalently involved in RDM support, particularly in applying FAIR principles (34) and assisting with DMPs (31 responses). A second cluster of activities is represented by drafting institutional policies (24 responses), training on Open Science and RDM (25), and support for institutional data management tools (26). More specialised activities like providing advice on national, international and funding body policies and mandates for data management and compliance with any obligations (19), as well as legal assessment (18) and repository maintenance (18) show a moderate engagement.

As shown in Figure 7, most of the respondents (95%) mentioned at least 1 support task out of the 8 we proposed in the survey. Most respondents indicated a combination of tasks: only 6 respondents (16%) mentioned 1-3 related tasks. The remaining part of the respondents (30 out of 38, more than 75% of the cohort) mentioned at least 4 tasks. This multi-task related partition shows a substantial equality between the number of tasks: the number of people mentioning 4 or more tasks (up to 8) is, for all the 5 categories (4,5,6,7,8 tasks), always about 15% (respectively, 6,5,5,7,7 people). By focusing on the 11 (about one third of the cohort) surveys reporting 4-5 tasks, there is a solid pattern (10 out of 11) mentioning both RDM and DMP. This often (7 people) occurs together with training duties. A substantial proportion (50%, 19 respondents) of the entire cohort enumerated 6 or more different tasks. They covered most of the proposed tasks, with slightly fewer mentions (13 out of 19) in "Advice on local and international policies" and "Institutional repository" tasks.

After mapping the type of support provided by DSs or analogous figures, we decided to investigate the target groups of their services. In particular, we asked whether they were limited to researchers with data management requirements derived from projects or if they were extended to all researchers. A striking majority of 29 respondents (76%) indicated that services are primarily aimed at all research staff, regardless of project affiliation or funding source, while in just 6 cases (16%) a restriction was reported.

Analysis of data stewardship services and professional classification areas

After collecting information on the formal recognition, background, and activities of DSs and similar roles, we proceeded to investigate how this role is characterised across institutions. We therefore evaluated both the number of such professional figures employed in each organisation and the professional level at which they are positioned, based on the responses indicating their presence within the institution.

As shown in Figure 8, most respondents reported that their institution had two or more professionals performing roles equivalent to DSs. Only a minority of institutions reported the presence of a single individual in this role.

Figure 9a shows that in approximately 40% of cases (16 out of 38) DS are embedded within research facilities, while a substantial proportion of responses frame these professional figures within central administrative areas. Interestingly, about 25% of respondents reported a hybrid situation, with DS framed at both levels. Figure 9b shows that the majority of DSs positioned within central administration are associated with:

• Research Support Offices or Research Management and Administration (RMA) units;

• IT and Infrastructure Services, especially where data repositories or digital infrastructures are managed;

• Libraries or Documentation Services, in a smaller number of cases.

Since this question allowed for multiple, non-mutually exclusive answers, we can report that a few responses mentioned hybrid or cross-departmental structures, where DSs collaborate between research support, IT, and administrative divisions: this may reflect the interdisciplinary and cross-functional nature of the role, even within centralised structures.

Discussion

The findings of this survey provide an overview of the emerging professional landscape of data stewardship in Italian universities and public research institutions.

We acknowledge the limitations of this analysis, which is based on data collected in 2023 and may not fully reflect the current situation. Although the survey allowed us to gather responses from across the country ( Figure 1), we recognise that they come only from a limited number of Italian research-performing institutions ⁷. Despite these shortcomings, the results highlight that the role is increasingly recognised as essential for supporting research, yet still characterised by fragmented professional identities, heterogeneous role definitions, and limited institutional recognition. In the future, we plan to perform another survey including as many universities and research institutions as possible. It would also be interesting to explore the private sector, which was not targeted in this initial survey, as it was disseminated through mailing lists which primarily reach academic and research personnel. To establish a common basis for the subsequent questions, we defined DS in the introductory section of the survey ( Demchenko & Stoy, 2021; Jetten et al., 2021; Wildgaard et al., 2020). This was also intended to support respondents who might not be familiar with or fully confident with this professional figure. Interestingly, approximately 65% of the respondents reported the presence of professionals performing DS-related activities ( Figure 3). This finding confirms the importance of professionalising this role, as it shows that DS-like tasks are already embedded in the everyday functioning of many Italian universities and public research institutions. However, this is also coupled with a lack of institutional recognition. As shown in Figure 4a, only one out of thirteen respondents indicated that positions performing DS-related tasks are formally labelled as “data steward”. This finding is consistent with the results of previous surveys ( Newbold et al., 2024), which similarly reported limited formal recognition of the role. The variety of job titles associated with DS-like responsibilities, even within the same institution ( Figure 4b), highlights significant fragmentation and aligns with recent literature emphasising the lack of role standardisation at national and European levels ( Ayres et al., 2022; Hansen et al., 2025). Similarly, the distribution across research, technical and administrative roles ( Figure 5) suggests that although the activities associated with data stewardship are increasingly needed, they are often assigned to existing roles without providing adequate formalisation, visibility, or clear career pathways, which can also result in differences in remuneration, as DS performing similar tasks in different institutions may fall under positions with varying salary scales.

Conversely, it is notable that about 35% of respondents ( Figure 3) stated that no specific personnel in their institution is dedicated to any of the RDM activities indicated, suggesting an uneven adoption of DS practices across the national landscape. At this stage, we are not able to determine the exact reasons for this, but we can hypothesise that it may reflect insufficient investment in RDM support services or that DS-like activities are performed informally or distributed across multiple roles, making them difficult for respondents to identify.

Consistent with European trends showing that institutional maturity in RDM correlates with the creation of dedicated professional roles ( Gruber, 2021; Jetten et al., 2021; Seidlmayer et al., 2023; Wildgaard et al., 2020), only a minority of respondents participated on behalf of their institutions ( Figure 2). It is reasonable to infer that institutions demonstrating a higher level of awareness and engagement with RDM topics are those that have designated a representative to respond on behalf of the institution. These institutions are also more likely to be in the process of developing or implementing structured support services for RDM.

On the other hand, the large proportion of respondents who answered in a personal capacity may reflect limited institutional awareness/policies related to RDM needs or a lack of internal communication about existing practices.

These data further support the idea that the concept, functions and activities of a DS often depend heavily on the institution in which they work ( Jetten et al., 2021; Seidlmayer et al., 2023; Wildgaard et al., 2020). While in some organisations DS-like professionals have gradually been integrated, even if their roles are not formally recognised or consistently defined, in other institutions it appears that no one is explicitly responsible for RDM-related tasks. This variability underlines the absence of a coordinated national framework and highlights how institutional context shapes the way data stewardship is interpreted and implemented within the Italian research system.

Part of the problem might be related to the difficulties in recognising who qualifies as a DS, as there is currently no universally accepted definition.

According to the responses that we gathered here, a DS is a professionals with (at least one or more) cross-cutting skills in RDM (disciplinary, IT and technical, legal) and who very often acts as a bridge between researchers (i.e. producers and users of research data), infrastructures and research organisations ( Figure 6 & Figure 7). This scenario is in line with previous national-level surveys conducted in Europe ( Ayres et al., 2022; Gruber, 2021; Jetten et al., 2021; Seidlmayer et al., 2023; Wildgaard et al., 2020).

The analysis of educational backgrounds ( Figure 6) provides further evidence of a fragmented professional identity. In the majority of cases, DSs hold advanced degrees in disciplinary fields related to their data stewardship activities. This observation supports a DS model in which the direct research experience is extremely relevant to shape the dialogue with the main stakeholders of data stewardship services, the researchers, and aligns with previously reported international experiences ( Gruber, 2021; Jetten et al., 2021; Seidlmayer et al., 2023; Wildgaard et al., 2020). Additionally, the notable representation of respondents with training in computing or computer science indicates a growing emphasis on the technical dimension of the role. Nonetheless, the number of responses classified as “Other” or “I don’t know” reflects a lack of consensus regarding the educational pathways necessary for acquiring DS competences and confirms the absence of nationally recognised training standards.

Despite the fragmentation in roles and backgrounds that characterise the figure of a DS, we observed a broad consensus in terms of support activities that they provide within their institution. The survey offered a selection of eight activities that a DS can potentially perform ( Figure 7), and support for RDM and data management plan drafting were the two most frequently selected tasks. Interestingly, these core tasks are often carried out alongside two or more additional activities, usually related to the support in implementing the institutional policies and tools in everyday research work, often complemented by training and capacity-building efforts. These results highlight that the role of the DS requires multiple skills, at the intersection between those specific to the research domain and those typical of administrative functions. This peculiarity differentiates this profession from the fields of project management and IT management and adds another layer of complexity to the role. Notably, only three out of 38 respondents added new activities beyond those proposed, which could still be attributed to them, suggesting that the list provided in the survey was exhaustive and reflects the key functional areas perceived as central to data stewardship in the Italian research context.

Building on the observation that DS-like responsibilities are already embedded in day-to-day activities of many Italian universities and public research institutions, we were interested in reporting the number of DS-like professionals within each organisation ( Figure 8) and their organisational placement ( Figure 9a and 9b).

Most respondents reported that their institution hosts between two and ten DS-like professionals. While this category is very broad and does not allow for an accurate estimation of the number of DS in Italian research institutions, it tells us that there is often more than one figure associated with DS-like activities. The next most common response indicated the presence of only one DS-like professional per institution, suggesting that in many cases, support for RDM remains limited or concentrated in a single individual. It is also noteworthy that five respondents declared that they did not know how many DS-like professionals were present in their organisation. This uncertainty further highlights the limited visibility of these roles and suggests that institutional communication regarding the existence, distribution and responsibilities of DS-like staff is probably still insufficient in many contexts.

The survey findings reveal that DS-like professionals are primarily based in research facilities, yet the overall numbers are similar to those of DS framed in central administration. This dual distribution is coherent with the coexistence of two different organisational models, the “coordinator” and the “embedded” DS, which were recently coded in the Minimum Viable Skillset produced by the Skills4EOSC project ( Hansen et al., 2025). In particular, the project defines the “Coordinator” DS as the one providing support across an organisation’s research domains and units, while “Embedded” DS operates close to a research team and to its domain-specific practices. Interestingly, around 20% of respondents (8 out of 38) reported that DS are framed at both levels in their institutions, proving that some Italian institutions are still experimenting with hybrid organisational frameworks that can benefit the general academic community while also providing specialised support.

Overall, this study offers an overview of the fragmented and still evolving landscape of data stewardship in Italy. The findings may support institutional decision-makers not only in defining and structuring data stewardship services within an increasing number of universities and research institutions, but also in advancing the professionalisation of the role and the development of dedicated career paths. Beyond its immediate results, the survey represents a valuable output in itself, as its methodology can be readily adopted in other national contexts seeking to undertake a similar landscape analysis.

Importantly, the survey also served as the initial step towards the establishment of the Italian Data Stewards Community (Comunità Italiana Data Steward, CIDS) in 2023. The Community has since articulated three overarching objectives, formalised in its 2025 manifesto ( Caldoni et al., 2025a): (i) to create opportunities for sharing and developing new skills; (ii) to support the adoption of best practices in RDM; and (iii) to increase awareness of the DS’s role within the research ecosystem. As a bottom-up initiative, the Community now includes more than 150 members and promotes networking and knowledge exchange through regular meetings, publications, and collaborative activities. The survey thus played a foundational role in fostering the Community’s self-recognition and professional consolidation within the Italian context.

Ethics and consent

Ethical approval and consent were not required for this submission.

Acknowledgements

The authors thank Chiara Basalti (Alma Mater Studiorum - Università di Bologna), Sara Di Giorgio (Consortium GARR) and Monica Forni (Alma Mater Studiorum - Università di Bologna) for their contribution in the initial conceptualisation of the survey.

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

[version 1; peer review: 2 approved, 3 approved with reservations, 1 not approved]

Footnotes

¹ RDA Professionalising Data Stewardship Interest Group, https://www.rd-alliance.org/groups/professionalising-data-stewardship-ig/activity/, URL checked on 2025/11/28

² Skills for the European Open Science Commons: creating a training ecosystem for Open and FAIR science, https://www.skills4eosc.eu/, URL checked on 2025/12/04

³ ICDI, Italian Computing Data Infrastructure, https://www.icdi.it/en/, URL checked on 2025/12/04

⁴ https://www.linkedin.com/company/skills4eosc/, URL checked on 2025/12/11

⁵ Inkscape 1.3 (0e150ed6c4, 2023-07-21), https://inkscape.org, URL checked on 2025-24-10

⁶ https://commons.wikimedia.org/wiki/File:Italy_provincial_location_map_2016.svg, URL checked on 2025/10/24

⁷ https://www.mur.gov.it/it/aree-tematiche/universita/le-universita, URL checked on 2025/12/02 https://www.mur.gov.it/it/aree-tematiche/ricerca/il-sistema-della-ricerca/enti-di-ricerca-pubblici URL checked on 2025/12/02

Data and software availability

Underlying data 

Zenodo: Data and code for Mapping Data Stewardship in Italy: Findings from the First National Survey (1.0). https://doi.org/10.5281/zenodo.17907703 ( Caldoni et al., 2025b)

The project contains the following underlying data: 

Data_Figure1.csv (Portion of the whole dataset, integrated with geographical information related to the institutions of the respondents, ready to be used by “Make_Figure1.R script)

Data_Survey.csv (File containing the whole dataset acquired through the survey)

DS_Survey_Template_EN.pdf (English version of the text of the survey presented to the participants)

DS_Survey_Template_IT.pdf (File containing the text of the survey presented to the participants)

Make_Figure1.R (R code to produce Figure 1 on “Mapping Data Stewardship in Italy: Findings from the First National Survey” manuscript)

Readme.pdf (Readme file describing the content of the dataset)

References

Ayres B, Lehtsalu L, Parton G, et al. : RDA professionalising data stewardship - current models of data stewardship: survey report.2022. 10.15497/RDA00075 [DOI]
Basalti C, Fazekas-Paragh J, Forni M, et al. : Recommendations for data stewardship skills, training and curricula with implementation examples from European countries and universities. Zenodo. 2024. 10.5281/ZENODO.10573891 [DOI] [Google Scholar]
Caldoni G, Coppini S, Ferretti R, et al. : Manifesto of the “Italian data steward community”. Zenodo. 2025a. 10.5281/ZENODO.15240125 [DOI] [Google Scholar]
Caldoni G, Iacovella V, Lazzeri E, et al. : Data and code for mapping data stewardship in Italy: findings from the first national survey. Zenodo. 2025b. 10.5281/ZENODO.17907702 [DOI]
Colcelli MF, Basalti C, Caldoni G, et al. : Il ruolo dei data steward nella ricerca e la comunità italiana. GARR News.2023; (Accessed: 11 December 2025). Reference Source
Demchenko Y, Stoy L: Research data management and data stewardship competences in university curriculum. In: 2021 IEEE Global Engineering Education Conference (EDUCON). 2021 IEEE Global Engineering Education Conference (EDUCON).Vienna, Austria: IEEE,2021;1717–1726. 10.1109/EDUCON46332.2021.9453956 [DOI] [Google Scholar]
European Commission Directorate, General for Research and Innovation: Turning FAIR into reality: final report and action plan from the European Commission expert group on FAIR data. LU: Publications Office,2018; (Accessed: 11 December 2025). Reference Source [Google Scholar]
Gruber A: Kompetenzen von Data Stewards an österreichischen Universitäten. Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 2021;74(1):12–32. 10.31263/voebm.v74i1.6255 [DOI] [Google Scholar]
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Jetten M, Grootveld M, Mordant A, et al. : Professionalising data stewardship in the Netherlands. Competences, training and education. Dutch roadmap towards national implementation of FAIR data stewardship. Zenodo. 2021. 10.5281/ZENODO.4623713 [DOI] [Google Scholar]
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Seidlmayer E, Hoffmann F, Dierkes J, et al. : Forschung unterstützen - Empfehlungen für data stewardship an akademischen forschungsinstitutionen : ergebnisse des projektes DataStew.2023. 10.4126/FRL01-006441397 [DOI] [Google Scholar]
Turkyilmaz-van der Velden Y: Data stewardship at Delft university of technology. International Data Week/SciDataCon. Gaborone, Botswana,2018. 10.5281/zenodo.1477598 [DOI] [Google Scholar]
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Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. : The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016;3(1): 160018. 10.1038/sdata.2016.18 [DOI] [PMC free article] [PubMed] [Google Scholar]

Open Res Eur. 2026 Mar 12. doi: 10.21956/openreseurope.24014.r69758

Reviewer response for version 1

Samsul Farid Samsuddin ¹

Overall, it is a good study that have been conducted. However, as the authors aware and concerns, the findings of the study cannot generalize the whole country findings. It was only small portion of institution involved. The sampling approach (snowball sampling and followed approach) are not suitable for this specific study. The sampling should be conducted in more concise so that all related institutions will be much involved and not left behind, and the authors could find a better finding represent the current objective of the study. Dealing with online responses, always across the common method bias issues. Providing how the authors overcoming the issues will much strengthen the responses gathered throughout the study. The findings of data analysis also a bit confusing. Inconsistent numbers of response being analyzed throughout the study. Also not involving the whole 77 responses without any concrete justifications. Which in the earlier part, the study only involved responses representing their institution and not involving the responses as an individual which are the bigger portion. Not providing any justifications on each decision made on the analyses. The discussion part was very comprehensive and in-depth in contributing to the body of knowledge. Much to be improved if the manuscript to be indexed. Current manuscript not ready to be indexed which it will caused misunderstandings and confused to the readers. Details of comments are as follows:

1. Objectives

Map the state of data stewardship in Italian universities and research-performing institutions.
A further objective of the survey was to assess the respondents’ willingness to join a national community of practice on RDM.

The objective of the study not clear. Should detailed out what the authors meant on the state.
Providing clear several research questions will help readers to relate with all the analysis conducted and findings interpretation.

2. There were no theoretical frameworks or underpinning theories involved in this study seems a bit loose.

3. Materials and methods

3.1 The survey was officially launched in April–May 2023 (from 13/04/2023 to 31/05/2023) and reopened in November–December 2023 (from 07/11/2023 to 01/12/2023).

No explanations or justification why it was reopened.

3.2 Institutional Characterization: Included 13 closed-ended questions to explore how the DS role is defined and how related services are structured in the respondent’s institution.

Not stated how the questions were developed or not based on any developed questionnaire.

3.3 Abstract – Data cleaning was performed on the 77 valid entries collected (out of 88 responses) before conducting the descriptive analyses.

Data cleaning and visualization (in content) – A total of 84 anonymous responses were collected and

exported as a .csv file, on which data cleaning was performed.

The number of responses is not consistent. 88 or 84 responses?

3.4 Dissemination followed a snowball sampling approach, beginning with targeted outreach, through two national mailing lists focused on Open Science, and expanding the audience through word-of-mouth, institutional social media channels, and an article in GARR Magazine.

The dissemination approach of the survey was not compatible with the objective of the study. To map the state of data stewardship in Italian universities and research-performing institutions, a strategic and systematic approach should be conducted at the first place. List out all the Italian universities and research-performing institutions and contacted the related departments or units will benefit the study. The findings will be better and it will help future research to be conducted in clearer path with comprehensive findings.

4. Analysis of survey results

The total number according to the data reported is 73 responses. Not similar with earlier claim on 77 valid entries collected.
The authors did not stated the Italian universities and research-performing institutions involved in the study. This might help future scholars to understand the current state.

5. Authors should consider to have a separated sub-heading of Conclusion section for the two last paragraphs.

6. Ethics and consent: Authors stated that “Ethical approval and consent were not required for this submission.” There was no further justification on this statement. What is the reason of such study did not require any ethical approval and consent when it involved human response. A lot of potential harms might affect when the response was collected, especially when it’s on behalf of the institution and the privacy of the respondents.

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Is the study design appropriate and is the work technically sound?

Partly

Is the work clearly and accurately presented and does it engage with the current literature?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Are all the source data and materials underlying the results available?

Yes

Reviewer Expertise:

Areas of expertise on quantitative research and information science fields (including information and research data management aspects).

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Open Res Eur. 2026 Mar 10. doi: 10.21956/openreseurope.24014.r69762

Reviewer response for version 1

Jan Slifka ¹

This article presents a valuable first national survey of data stewardship in Italian universities and public research institutions. The topic is timely and relevant, the manuscript is well situated within the broader European discussion on research data stewardship, and the exploratory survey design is appropriate for mapping an emerging professional landscape. The paper also has important strengths in transparency: the authors make the underlying dataset, survey templates, and code available. The descriptive analysis is suitable for the size and nature of the sample, and the main message of the paper is useful: many institutions appear to have staff performing data-stewardship-related functions, but these roles are rarely formally recognised under that title.

My recommendation is Approved with Reservations, because the main revisions I would request concern clarity, internal consistency, and the framing of some interpretations, rather than the overall validity of the study. First, the manuscript would benefit from a single, transparent accounting of denominators throughout the Results. Some changing sample sizes are understandable and appear to follow the survey logic, since later analyses are restricted to respondents reporting DS or equivalent roles and several figures explicitly use subset denominators. However, the reporting is not always easy to follow, and one inconsistency seems to require clarification: the paper states that 77 valid responses remained after cleaning; the institutional affiliation counts sum to 77 (33 from universities and 44 from public research institutions); yet the role counts reported in the same section (38 technical/administrative staff, 27 researchers, and 8 technologists) sum to 73. Because the methods state that roles were clustered into three macro-categories, the authors should clarify whether four responses were missing or unclassifiable, whether another category was omitted from the narrative, or whether the relevant denominator for that statement is smaller than 77. More generally, the manuscript would benefit from stating the applicable denominator more explicitly whenever a new subset of respondents is being analysed. Relatedly, there also appears to be some inconsistency in the description of the survey responses across sections: the Abstract mentions 88 responses, while the “Data cleaning and visualisation” section reports 84 collected responses before cleaning. Clarifying this progression of numbers (initial responses, cleaned dataset, and analysed subset) in a consistent way throughout the manuscript would improve transparency and readability.

Second, the Zenodo citations should be corrected and made internally consistent. In the Methods, the survey templates and dataset are cited as Caldoni et al. (2025a), but in the reference list Caldoni et al. (2025a) corresponds to the Manifesto of the “Italian data steward community”. The data and software availability section instead points to Caldoni et al. (2025b) for the data package and lists the survey templates there, but the DOI in that section does not match the DOI given in the reference list. This appears to be a citation and findability issue rather than a lack of data sharing, but it should be corrected so that readers can unambiguously locate the survey templates, dataset, and code.

Third, the methods and limitations would benefit from a little more explanation in places. It would be helpful to explain why the survey was reopened, to clarify more explicitly how the activity list used in Figure 7 was compiled, and to discuss the likely effects of dissemination through Open Science-oriented mailing lists and related channels on the composition of the respondent pool. The paper already acknowledges that the sample covers only a limited number of institutions, and a slightly fuller discussion of representativeness and self-selection would strengthen the manuscript further.

Finally, I encourage the authors to soften a few interpretive statements in the Discussion and Conclusions so that they remain closely aligned with what a descriptive, self-selected survey can support. In particular, some wording could be made more cautious where the manuscript infers broader institutional visibility or communication patterns, suggests a “growing emphasis” on the technical dimension, or describes the proposed activity list as exhaustive. These points do not undermine the usefulness of the study, but a more restrained phrasing would make the conclusions stronger and more precise. With these revisions, I think the article would make a useful contribution to the literature on data stewardship and research support in national contexts.

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Is the work clearly and accurately presented and does it engage with the current literature?

Partly

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Are all the source data and materials underlying the results available?

Yes

Reviewer Expertise:

My research and professional activities focus on research data management, data stewardship, and data management planning. I contribute to the development of the Data Stewardship Wizard tool and participate in ELIXIR and national initiatives related to data stewardship and RDM support in research institutions.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Open Res Eur. 2026 Mar 9. doi: 10.21956/openreseurope.24014.r69757

Reviewer response for version 1

Angeliki Adamaki ¹

Summary

The article presents the results of a national survey exploring the presence, roles, and activities of professionals involved in research data management (RDM) in Italian universities and research institutions. The survey collected 77 valid responses (from 49 distinct institutions based on the CSV file and the ROR IDs provided) and provides an initial overview of how data stewardship-related tasks are currently distributed across institutional roles. The findings show that several institutions (25 based on the ROR IDs provided in the CSV data file and filtered for those knowing that they have DSs in their institutions) rely on staff performing data stewardship activities, such as support/guidance for FAIR data practices, data management plans, training, and policy guidance, although these responsibilities are not always formally recognised under the title of data steward. The study highlights a fragmented professional landscape and emphasises the need for clearer role definitions and professional pathways. At the same time, the work has potential impact beyond the Italian context, as it provides a landscape analysis at national level, contributes to community-building efforts through the creation of the Italian Data Steward Community, and proposes a methodological approach that could support similar mapping exercises in other countries.

General Comments

The article has the potential to generate significant impact, as the survey could contribute to:

landscape analyses of data stewardship practices following a reusable approach,
community-building efforts,
the development of a roadmap for the professionalisation of data stewardship.

Some statements in the article appear to reflect community experience rather than being directly supported by the presented data. Where possible, the authors should clarify this. Given the relatively small sample size (for statistical analysis), some statistical interpretations in the manuscript should be treated with caution. Qualitative expressions such as “a few” or “substantial” or “significant” can be difficult to interpret without corresponding numerical values (e.g. providing absolute numbers alongside percentages) which help readers better assess the significance of the results. Some recommendations for improvements are given below, considering the sample size and the visualisation options.

Throughout the manuscript, the activities of data stewards are frequently described as providing “support” to researchers. While this term is common in RDM contexts, it may underestimate the broader role that data stewards often play (as also discussed in the article). In many cases, data stewards act not only as support staff but also as enablers of research, contributing to the development of infrastructures and relevant policies, and the design of research environments, frameworks and workflows, that make FAIR and sustainable data practices possible.

The authors also mention “policy” in some parts of the text (e.g. “policy consultation”, “policymakers”), it will be good to clarify if they mean data policy, institutional policy, Open Science policy etc. GARR (pg. 5) is mentioned without explanation of the acronym and/or link.

The article builds on the assumption that the importance of RDM is recognised within institutions (e.g. beginning of section “Data stewardship in Italy”, and in “Discussion”). It would be interesting to see (perhaps in a linked, follow up analysis) how this recognition emerged in the Italian context, and which factors contributed to it. For example:

the role of research infrastructures (RIs),
the influence of European policies or funding programmes,
the identification of researchers’ needs perhaps via institutional initiatives.

Future developments could also consider connections with initiatives such as research assessment reform and recognition frameworks, which may influence how such roles are acknowledged within academic institutions.

Questions (Q)

Q1: (related to respondents) Although it becomes clear as the analysis is presented, the authors can clarify early in the survey description that the participants were not necessarily data stewards themselves, and they replied either representing their institutions or based on their personal experiences.

Q2: (related to EUSurvey) Are there technical or ethical considerations that others should know if they want to replicate the methodology?

Q3: (related to Figure 1) Can the authors comment on survey coverage relative to the number of universities and research infrastructures in each region? This would help assess how representative the survey is. The figure helps the reader understand the sample, but not the data coverage. Of course each institution might have diverse departments with different needs in data stewardship, and only one response from a specific organisation might not be representative, so this should be clarified as well.

Q4: Instead of excluding data from part of the analysis, would it be possible to present all responses, while clustering for individuals and institutional representatives? If subset of the dataset eventually is used (as it is now), the authors should explain this choice.

Q5: (pg 7) “The majority of the respondents belong to.”, the numbers in parentheses add up to 73, but total number is 77?

Q6: (Figures 2-3) Would the authors consider combining the figures in e.g. clustered or stacked bar chart? While pie charts are commonly used to visualize percentage, including counts alongside percentages will improve interpretability.

Q7: (Figure 4a) When looking at the free-text responses there is at least one response where the participant writes that there is one officially identified DS and then other cases in their organisation, although they earlier answer “No” to whether there is a formally identified DS. Since the sample size is small, such details change the results presented. Besides this, the data shows that the respondents come from different institutions, which shows good data coverage and the authors could consider mentioning this.

Q8: (General recommendation on figures) The authors may consider adapting the type of visualisation to the number of categories presented. For statistics with only a few categories (e.g., 2–4), pie charts can be effective, provided that absolute counts are shown alongside percentages. For analyses involving larger numbers of categories, horizontal bar charts may be more appropriate (absolute counts shown alongside percentages), as they allow category labels to be placed next to the bars, improving readability.

Q9: (pg 8) The text describing Figure 6 can be improved if rephrased. The numbers in parentheses add up to 60, but these cannot be the respondents (n=38). The authors probably list the answers they collected as each respondent could give multiple answers.

Q10: (Figure 5) Similar comments as in Q8 about the visualisation. If there are 13 respondents, then 1/13 is the minimum we expect. How did the 5% shown on the pie chart result?

Q11: (Figure 7 and corresponding text) The authors mention percentages in the text, which are not shown on the Figure or in a table, and it’s not very easy to follow. On the figure it’s probably not necessary to mention the numbers at each bar, as they don’t add up to the number of respondents (multiple answers). These could be changed to match the text. If the aim of this part of the analysis is to show how the tasks of a DS are distributed, then the authors might consider describing this as e.g. RDM tasks are reported in x% of cases together with DMP, y% also involves training, etc.

Q12: (Figure 8) Would it be interesting to distinguish between Unis and RIs?

Q13: (Figure 9b) Similar comments as in Q8 regarding the visualisation in pie chart.

Q14: (pg 12) “Conversely, it is notable that about 35%..”, although it’s indeed 7/20 responses, those do not correspond to 35% of the survey responses as the observation only refers to those representing their institutions. The authors might consider rephrasing this part, and perhaps also mentioning that these 20 responses seem to be from 20 different organisations.

Q15: (pg 12) “..we can hypothesise..”, is this an assumption based on the authors’ experience from the community activities and fora?

Q16: (pg 12) “..who answered in a personal capacity may reflect..”, could this be also because of weaknesses in the internal structure of each organisation, no mandate, etc.?

Q17: (pg 12) “..difficulties in recognising who qualifies as a DS..”, can the authors elaborate?

Q18: (pg 13) “..Additionally the notable representation of respondents..”, how is the role of the respondents relevant to the survey?

Q19: (pg 13) There are some statements that are given as facts, while they could be better expressed as possible explanations based on the authors’ experience from their work, the DS community, etc. The survey presented here is a snapshot of time, and it would be interesting to see how these observations change and hopefully evolve.

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Is the study design appropriate and is the work technically sound?

Yes

Is the work clearly and accurately presented and does it engage with the current literature?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Are all the source data and materials underlying the results available?

Yes

Reviewer Expertise:

Research Data Management and Open Science, with a focus on Research Infrastructures and community/service development.

Open Res Eur. 2026 Mar 9. doi: 10.21956/openreseurope.24014.r69765

Reviewer response for version 1

Louise Saul ¹

This article describes a survey that was circulated to research institutions in Italy, via a multifaceted snowball approach, within 2 two-month time periods in the year 2023.

It builds upon previous work by other groups to define and professionalise the role of Data Steward in the research setting, particularly looking at work in the Netherlands (1), Germany (2), and Denmark (3) and seeks to undertake the same project in an Italian setting. The introduction of this manuscript frames the narrative and need for this work.

This is an extremely timely piece of work as various EU projects came to a close there was a realisation of the requirement for a continued effort to define the Data Steward role across Europe. However, whilst there needs to be a united effort to ensure that a ubiquitous language is used to describe this role, each nation's research ecosystem will be subtly unique. Understanding this variability is key to ensuring a cohesive approach to embedding Data Stewardship, which necessitates an understanding of the current landscape.

This work seeks to describe the current status of both Data Stewards and people who undertake work that would be completed by Data Stewards at research institutions in Italy, capturing information about their activities, responsibilities, geographical location, official roles, training requirements, recognition for their work, and area in which they are located.

This is a very important piece of work; the survey is comprehensive without being a burden for the user to complete. I was particularly interested in the approach taken to include a description of a 'Data Steward' at the beginning of the survey; it may have been interesting to allow respondent to comment upon this definition, however I appreciate that may complicate the analysis. It would be interesting to understand the rationale for developing the survey and inclusion of questions, however in emerging fields there is often not sufficient information to do so and as stated above, often national landscapes can be unique enough to prevent a direct transposition. I assume that the survey design was based upon previous work (4).

Promoting reproducibility and data re-use for systematic review and meta-analyses, the authors have made the data, survey, and analysis code fully available with persistent identifiers, also including information about the methods used to clean the data. It would have been interesting to include more information about the institution 'types' that respondent worked at (i.e. Independent research institute, higher education, government sponsored institute) in order to fully understand the context in which the answers were given.

The way the responses have been summarised in this manuscript presents an interesting and useful picture that will be useful to compare to other countries' summaries of the same role. I have some questions over the breakdown of responses for figure 4b and figure 5, where descriptive statistic may not be helpful for so few respondents and a table or similar may be more appropriate. I am also unclear as to why only 38 respondents (I presume identified from the 'Technical/Administrative staff' cluster) were included in the analysis for figure 6/7/8/9, but this may be as a result of my own understanding. The results were illuminating and are very valuable for understanding the role of Data Steward in Italian research organisations.

I found the discussion to be very insightful and appreciated the summary that the authors provided. I look forward to seeing outputs from the Italian Data Stewards Community.

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Is the study design appropriate and is the work technically sound?

Yes

Is the work clearly and accurately presented and does it engage with the current literature?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Are all the source data and materials underlying the results available?

Yes

Reviewer Expertise:

Data Stewardship skills and careers

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

References

1. : Professionalising data stewardship in the Netherlands. Competences, training and education. Dutch roadmap towards national implementation of FAIR data stewardship (1.1). https://doi.org/10.5281/zenodo.4623713 .2021;
2. : RDA Professionalising Data Stewardship IG - What does a career track for data stewards look like?. https://doi.org/10.15497/RDA00102 .2024;
3. : National Coordination of Data Steward Education in Denmark: Final report to the National Forum for Research Data Management (DM Forum) (Version 1). https://doi.org/10.5281/zenodo.3609516 .2020;
4. : Forschung unterstützen - Empfehlungen für Data Stewardship an akademischen Forschungsinstitutionen. https://doi.org/10.4126/FRL01-006441397 .2023;

Open Res Eur. 2026 Mar 9. doi: 10.21956/openreseurope.24014.r69761

Reviewer response for version 1

Antti M Rousi ¹

Congratulations to the authors for this interesting research. This work was a pleasure to review. I agree that the present study -- along with its valuable open data -- could serve as a benchmark for similar national questionnaires mapping the state of data stewardship in other countries. Although the number responses may seem small, these respondents represent a highly specialized and rare group of professionals. I believe that the final version of this work could be indexed in ORE.

However, I also believe this work must be improved before it can be accepted for indexing. Although my general sentiment for this research is positive and I wish to support the authors’ research, I recommend a major revision followed by a new round of reviews. I hope the authors find my suggestions helpful.

Work title

The current manuscript is titled “Mapping data stewardship in Italy: Findings from the first national survey”. I understand the use of term “mapping” within this context, but I wonder if the title would work better with search engines without it. Also, the term “first” seems unnecessary in the title. I invite the authors to contemplate whether the title could be shortened into “Data stewardship in Italy: Findings from a national survey”.

Terminology and RQs

The understandability of the article suffers from terminological discrepancies.

The abstract states the research aim as to “map the presence, roles, and activities of data stewards or equivalent professionals across universities and research institutions, to provide an initial and systematic overview of the national landscape”
The introduction, however, seems to be built upon the three following core concepts of data stewardship, data stewardship profiles, and data stewardship service models
The end of the Introduction states that “[…] the idea emerged to launch a public survey to map the state of data stewardship in Italian universities and research-performing institutions. A further objective of the survey was to assess the respondents’ willingness to join a national community of practice on RDM.”
Furthermore, within the methodology section, the structure of the survey questionnaire is based on “Respondent Profiles” and “Institutional Characterisation”.

This terminological looseness is exacerbated by the fact that the research aim and research questions are not addressed in the article text.

Looking at the survey questionnaire, the research aim and questions could perhaps be formulated, for instance, as follows: “The research aim of the present work is to investigate how data stewardship services are organised within Italian universities and research institutions. The research questions are posed as follows.

RQ1 What kinds of professionals undertake data stewardship in Italian universities and research institutions?
RQ2 Which service organizations employ the personnel undertaking data stewardship activities?
RQ3 Which kinds of data stewardship support actions are these professionals currently undertaking?”

I believe the authors must:

Provide a general introduction to their research aim and define their core terminology in the Introduction before the literature review segment
Link the literature review with the core terminology defined in the Introduction (e.g. “The question of what kinds of professionals undertake data stewardship is addressed in the research branch focussing on DS profiles”; note! I am not sure if the latter is factually correct; it is more of a style example)
Define their research aim and research questions in detail after the literature review segment (see the example RQs above)
Make sure the methods section is terminologically aligned with the research aim and RQs
Base the reporting of their survey results around the RQs, e.g., one Findings sub segment per RQ
Briefly summarize the most important findings per RQ in the Discussion segment

Literature review

The authors must improve their literature review. The draft only cites 17 works, which is a small amount for a research article. Furthermore, the draft mostly cites EOSC, RDA and national working group reports when of course the literature on data stewardship is more broad. A simple Google Scholar search with the term “data stewardship” produces several relevant works currently missing from cited references. Besides research articles focussing on data stewardship, also literature on how academic libraries have arranged RDM services are in my view relevant to this work. As a rule of thumb, the authors should strive to double the amount of cited references when adding research articles into the list of references.

Misc. notes and improvement suggestions

For a better flow of the current Introduction text, I suggest the authors experiment with removal of the subtitles (e.g. “International efforts to professionalise research data stewardship”). Furthermore, the authors could experiment with moving the current 2nd paragraph of introduction as the new 4th paragraph of introduction. The latter solution could improve the text flow introducing the concepts of DS profiles and DS service models.
I don’t believe one needs figures to illustrate yes/no questions or other survey questions with only two answer options. Figures 2 and 3 could thus be removed and this information presented in article text only
I would avoid pie charts in research articles (see e.g. https://zenodo.org/records/17550233). I suggest you would use column charts instead throughout the paper
I suggest you state the limitations of your study as the second last paragraph of your discussion section, followed by a concluding paragraph. It is important to acknowledge the limitations of your work, but stating them at the beginning of the discussion can divert the attention from your main results
After thorough revision of the article, I recommend sending it for professional English language editing

If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable

Is the study design appropriate and is the work technically sound?

Yes

Is the work clearly and accurately presented and does it engage with the current literature?

Partly

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Are all the source data and materials underlying the results available?

Yes

Reviewer Expertise:

Data stewardship, Information science, scientific communication

Open Res Eur. 2026 Mar 3. doi: 10.21956/openreseurope.24014.r69766

Reviewer response for version 1

Aoife Coffey ¹, Pádraig Mac Aodhgáin ¹

This manuscript presents the findings of the first national survey on data stewardship in Italian universities and research institutions. The survey responds to the growing need for research data management support in line with Open Research and the FAIR Principles. The key findings suggest that data stewardship in Italy is not formalised, there is a spectrum of job titles and responsibilities and the backgrounds of those in data stewardship roles varies widely. It concludes that there is a growing presence of data stewardship in Italy but that limited formal recognition of the role are preventing clear career pathways and recognition. Highlighting the need for national coordination to strengthen professional identities and recognition and work towards standardize practices.

Is the work clearly and accurately presented, and does it engage with the current literature?

The work is well-grounded in relevant literature and contextualised within the European research landscape. We encourage the authors revisit the statement “only 33% of the European respondents actually had the job title “data steward” The figure mentioned does not seem to be supported by the data underlying the cited report and potentially this needs to be rephrased for accuracy. Data available here https://zenodo.org/records/10117910

Is the study design appropriate and is the work technically sound?

The design is appropriate and socialised in relevant and available channels.

The survey was reopened for a period could the reason and justification for this be included in the methods.
Some additional details of the limitations of the survey would be useful such as an acknowledgement of the limitation of using mailing list which are open science focused so could be seen as self-selecting.
We also suggest including details of how well the 77 responses represent the full Italian research ecosystem – number of eligible institutions vs the number of responses and an estimated national research population.
Could the authors also clarify how the list of data stewardship activities outline in Figure 7 was complied.

Are sufficient details of methods and analysis provided to allow replication by others?

The methodology is described in detail and is sound and appropriate.

However, there could be some improvements.

In figure 1 two of the data points beside the number 17 are the same for consistency should 2 be 1.5 times the size of 1. Pie charts for cities with 2-5 respondents were visualised as 1.5 the size of the units.
Names of major cities could be included in figure 1 for those who are not familiar with the geography of Italy.
Figure 8 – could the axis be explained perhaps include a legend to clarify what each point on the bar chart means.

Are all the source data and materials underlying the results available?

Yes all data and material have been shared

The underlying data provides the survey questions in English but could the column headers in the dataset also be translated to English.

If applicable, is the statistical analysis and its interpretation appropriate?

The analysis is descriptive which is appropriate given the profile of the data and sample size.

It may be helpful to refine some of the wording used in the descriptions of the findings. Softening the phrasing will ensure that the interpretations come across as balanced and precise. An example would be the phrase ‘striking majority’ which seems a bit of an overstatement.

Are the conclusions drawn adequately supported by the results?

The authors acknowledge the limitations of the study in the conclusion, however there are several instances where the discussion appears to over-interpret or extend the findings beyond what can be supported by the data.

The conclusions section would benefit from being more concise and from reframing the content to highlight a small number of clear, well-defined take-home messages.
The conclusion section would also benefit from clarification of the authors definition of DS-like staff, DS- activities, DS-like responsibilities, DS-like professionals as they are mentioned in the discussion but not elsewhere. It would be good to under the meaning of these phases in the context of the survey.
“In the future, we plan to perform another survey including as many universities and research institutions as possible.” We suggest removing this as it is should be an aim from the outset.
We suggest that this statement is revisited “On the other hand, the large proportion of respondents who answered in a personal capacity may reflect limited institutional awareness/policies related to RDM needs or a lack of internal communication about existing practices.” It is an over interpretation of the findings but could be rephrased as question.
For the following line it is not clear how this assertion is supported by figure 6 and 7. “ According to the responses that we gathered here, a DS is a professionals with (at least one or more) cross-cutting skills in RDM (disciplinary, IT and technical, legal) and who very often acts as a bridge between researchers (i.e. producers and users of research data), infrastructures and research organisations (Figure 6 & Figure 7).”
The suggestion that the list of data stewardship task was ‘exhaustive’ seems to be an overreach considering the sample size and the early stage of data stewardship socialization and adoption in Italy in 2023 as described by the author themselves. Also in light of earlier statement such as “ DS-like activities are performed informally or distributed across multiple roles, making them difficult for respondents to identify.”
While the broad range of backgrounds among data stewards is often viewed as a strength, enabling them to contribute effectively across a diverse research landscape the phrase “fragmented professional identity” and the following paragraph seems to imply a negative interpretation of the diverse educational backgrounds. You may want to consider rephrasing.
It is also not clear that the data indicates that there is a ‘growing emphasis’ on the technical dimension of the role given the sample size.
We suggest the last paragraph in the conclusion is giving the heading/title Next Steps

Notes:

There minor typos which need to be corrected and acronyms which should be clarified . Such as

“ Consortium GARR”

"a DS is a professional s with"

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Is the work clearly and accurately presented and does it engage with the current literature?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Are all the source data and materials underlying the results available?

Yes

Reviewer Expertise:

Research data stewardship

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

Caldoni G, Iacovella V, Lazzeri E, et al. : Data and code for mapping data stewardship in Italy: findings from the first national survey. Zenodo. 2025b. 10.5281/ZENODO.17907702 [DOI]

Data Availability Statement

Underlying data 

Zenodo: Data and code for Mapping Data Stewardship in Italy: Findings from the First National Survey (1.0). https://doi.org/10.5281/zenodo.17907703 ( Caldoni et al., 2025b)

The project contains the following underlying data: 

Data_Figure1.csv (Portion of the whole dataset, integrated with geographical information related to the institutions of the respondents, ready to be used by “Make_Figure1.R script)

Data_Survey.csv (File containing the whole dataset acquired through the survey)

DS_Survey_Template_EN.pdf (English version of the text of the survey presented to the participants)

DS_Survey_Template_IT.pdf (File containing the text of the survey presented to the participants)

Make_Figure1.R (R code to produce Figure 1 on “Mapping Data Stewardship in Italy: Findings from the First National Survey” manuscript)

Readme.pdf (Readme file describing the content of the dataset)

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PERMALINK

Mapping data stewardship in Italy: Findings from the first national survey

Giulia Caldoni

Vittorio Iacovella

Emma Lazzeri

Liise Lehtsalu

Francesca Marchegiani

Mauro Paschetta

Valentina Pasquale

Giulia Pedonese

Andrea Tarallo

Roles

Abstract

Plain Language summary

Introduction

International efforts to professionalise research data stewardship

The landscape of research data stewardship in European countries

Data stewardship in Italy

Materials and methods

Survey development and distribution

Survey and question structure

Data cleaning and visualisation

Figure 1. Map of institutional roles represented among respondents to the national survey.

Analysis of survey results

Institutional roles and personal interests

Figure 2. Distribution of respondents according to the capacity in which they completed the survey.

Formal recognition of Data Stewards

Figure 3. Presence of professional figures performing research support functions described in the definition of “data steward”.

Figure 4.

Figure 5. Breakdown of the roles of data stewards or analogous figures within institutions.

Background, training and activities

Figure 6. Breakdown of the types of training possessed by data stewards or equivalent figures before their recruitment within institutions.

Data Stewardship activities

Figure 7. Breakdown of the support functions carried out by data stewards or analogous figures within institutions.

Analysis of data stewardship services and professional classification areas

Figure 8. Count of “data stewards” or equivalent figures within institutions.

Figure 9.

Discussion

Ethics and consent

Acknowledgements

Funding Statement

Footnotes

Data and software availability

Underlying data

References

Reviewer response for version 1

Samsul Farid Samsuddin

Roles

Reviewer response for version 1

Jan Slifka

Roles

Reviewer response for version 1

Angeliki Adamaki

Roles

Reviewer response for version 1

Louise Saul

Roles

References

Reviewer response for version 1

Antti M Rousi

Roles

Reviewer response for version 1

Aoife Coffey

Pádraig Mac Aodhgáin

Roles

Associated Data

Data Citations

Data Availability Statement

Underlying data

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Underlying data 

Underlying data 