Data Literacy in Focus: Using the Learning Objectives Matrix to teach Research Data Management

Data Literacy in Focus: Using the Learning Objective Matrix to teach Research Data Management Franziska Altemeier1, Juliane Jacob2, Jorge Murcia Serra3 1TIB, Hannover, Germany, [email protected] 2University of Hamburg Hamburg, Germany [email protected] 3University Library Mannheim, Mannheim, Germany [email protected] Abstract: Successful skills transfer requires that learning objectives are defined in such a way that they accurately describe the intended learning gain. For this purpose, the Learning Objective Matrix (LOM) for the teaching of Research Data Management (RDM) was developed. The matrix formulates learning topics and objectives for all RDM-relevant skills for four different target groups: Bachelor’s and Master’s students, early career researchers, and Data Stewards. In the present third version, in addition to new and revised content and learning objectives, extensive accompanying material has been developed, including application scenarios, a how-to-use-guide, and a glossary of terms used in the matrix. The glossary contains more than 40 terms and is unique in its scope in the field of RDM. The present article shows practical applications of the LOM for planning RDM training in academic libraries. We discuss how different actors and stakeholders can benefit from the LOM. We identify how the learning objectives can be applied to different target groups and how the levels of competence differ. We show, ultimately, the potential of the LOM for academic libraries and beyond to bring about a further development of RDM competences to the academic community. Keywords: Data literacy, research data management, learning objectives, training, didactics, instructional design, evaluation. 1 Introduction For the past around twenty years, Research Data Management, defined as “a number of different activities and processes associated with the data lifecycle, involving the design and creation of data, storage, security, preservation, retrieval, sharing, and reuse, all taking into account technical capabilities, ethical considerations, legal issues and governance frameworks” [1], has become an important field of activity in academic libraries. This is shown by the increasing development of RDM services at libraries worldwide (especially in high-income countries) to facilitate and empower researchers in their work with research data [2]. This is paired with the increasing demands on qualitative data management posed by research funders and scientific publishers but also by the scientific community itself, especially in relation to good scientific practices for open science to attain reproducibility and reusability. Among the services being offered by academic libraries for research support in relation to RDM are those related to the identification, access, organisation, and sharing of research data (including location and acquisition of data as well as the establishment of data management plans); services related to analysis, publishing, handling, and management of metadata; services concerning data preservation (including institutional repositories) as well as services concerning legal and ethical aspects of research data (typically consultation) [3]. An outstanding research support service though has turned out to be training on RDM. RDM training in academic libraries does not stand on its own but it is to be seen as a further development of the broadening of the field of information literacy, complementing digital and data literacy as key topics for teaching libraries. This is in so far important, as RDM, overall, is still seldom part of curricula in higher education, even though there is an increasing demand for training on this topic for Bachelor’s, Master’s, and PhD students as well as among young (and less young) researchers. Here is where academic libraries can jump in by complementing their portfolio of research supporting services and training. To offer training in a purposeful way though, it is key to clearly define the learning outputs of the intended training programmes, both concerning the topics and the degree of proficiency which are to be acquired. It is with this goal in mind that the LOM was developed in 2022 under the lead of co-workers of the University of Kiel and in cooperation with members of the DINI/nestor Research Data Working Group's subgroup “Training/Further Education” [4, 5]. The last revised and expanded third version, which is in focus of the present paper, was released in March 2025 [6] in extensive collaboration with the German RDM trainer community. An English translation thereof is due to be published in the summer 2025. 2 Teaching RDM in academic libraries Although the collection, analysis and presentation of data are core competences for researchers and are consequently part of the curriculum in higher education, it is less so in the case of the demands posed by the storage of data and the security measures needed to guarantee their integrity, especially in relation to long-term preservation and sharing, the facilitation of their reuse (implying among other things the assignation of appropriate metadata), and the necessary planning of all the above. These competences though, are necessary to meet the increasing demands for transparency in science to confront scientific scepticism, fake news in relation to scientific findings and, not the least, to prevent both scientific misconduct and false accusations thereof. While institutions such as research data centres are well positioned to provide targeted training on RDM, academic libraries hold a particularly advantageous position to do so. Their combined expertise in acquiring, organising, managing, and preserving large volumes of data, along with their proficiency in handling metadata, uniquely qualifies them to support RDM training initiatives. They have also been offering for decades now training on information literacy and more recently also on digital and data literacy. Besides, they are well connected within the faculties on campus so that they can well pinpoint their needs for RDM training. To teach RDM at academic libraries anew it is necessary first to build the corresponding training skills among teaching librarians. Among the necessary competences necessary to achieve this goal are a thorough knowledge of all steps along the research data life cycle: from the planning and collection or acquisition of data and their secure storage, through processing, analysis, publishing and sharing for reuse, up to long-time archiving. Paired with that, it is essential that librarians teaching RDM also have a founded knowledge on legal and ethical aspects of research data as well as on existing initiatives and infrastructures for support and further development of an appropriate RDM. These competences need to be accompanied with skills in the design of appropriate teaching/learning scenarios as well as suitable learning materials, both in accordance with the different target groups. An outstanding help for achieving these goals offers the “Train-the-Trainer Concept on Research Data Management” by Biernacka et al. [7]. The concept addresses most of the relevant topics for RDM and accompanies them with proposals for teaching and learning activities as well as reusable templates for presentation slides. Besides, the concept is suitable both for online and onsite training. All activities are additionally tagged with the intended learning objectives according to the LOM. 3 Learning Objectives Learning objectives describe the desired learning success of a learner in relation to a specific subject matter. Learning objectives can have several advantages for teachers and learners. They can help to focus on the learning outcomes to be achieved and to manage the learning process effectively. Furthermore, they and their formulation are helpful in planning teaching and learning scenarios, as they provide guidelines for the entire didactic approach. Learning objectives should be verifiably, clearly and precisely formulated, and operationalised in such a way that learning success can be achievable, systematically “measured” and evaluated. Operationalised learning objectives consist of a content component and an action component. The content component provides information about the specific skill to be acquired. The action component describes the activity the learner should be able to accomplish to fulfil the learning objective. This should be formulated in a way that it can be measured, for example the learning objective “learners know the criteria …” should be better formulated as “learners can name the criteria …”. Verbs like for example “know, understand, recognise, believe, be familiar with, be interested in, be informed” should be avoided. Whereas verbs like “name, explain, apply, analyse, assess, design” are recommended, because it can be verified whether the learning objective has been achieved or not [6]. Learning objectives have a standardised linguistic structure. They start with “learners are able to…”, followed by a specific verb [8, 9] and a content component. Furthermore, the learning objectives are categorised according to the six levels of objectives by Bloom's Taxonomy [10] or in a revised version, respectively [11]. Bloom's taxonomy divides learning objectives into six different levels: remember, understand, apply, analyse, evaluate and create, which ranges from simple memorisation to more complex processes such as developing or conceptualising. By adding conditions under which observable behaviour should take place (for example under guidance, independently, in group work, in writing, within a time frame, relevant to subject X), the operationalised learning objectives can be refined and tailored to an individual didactic scenario, as done in the LOM for RDM-Training. 4 The Learning objective matrix for RDM-Training The first version of the LOM was published in 2022 by university lecturers and students at the Kiel University as well as members of the DINI/nestor Research Data Working Group's subgroup “Training/Further Education” [5]. In 2023, a second version with editorial adjustments was published. This version contained also an English translation supported by members of NFDI4Health consortium [12, 13]) and by the NFDI (German National Research Data Infrastructure). In early 2024, a community event was held in Darmstadt, Germany, to collaboratively work on the third version of the LOM. An editorial team formed thereupon to implement the suggestions from the community event and to manage a public call for comments. In March 2025, the third version of the LOM was published implementing the numerous contributions [6]. In the third version, the learning objectives in the context of the RDM matrix are assigned to specific topics and these in turn are assigned to six superordinate subject clusters: 1. fundamental and comprehensive concepts, 2. working with data, 3. documentation and metadata, 4. archiving, publication, reuse, 5. law and ethics, 6. metacompetences. The third version contains in addition new features such as unique identifiers (LO-ID), a glossary and an overview of application examples. The learning objective elements have been split into separate columns (learning objective start, learning objective verb, learning objective content) to facilitate machine readability and general reuse. The reuse and adaptation of the LOM is also supported by a Git repository containing the files of the LOM and an implementation of the glossary as a Simple Knowledge Organisation System (SKOS) [14]. Forschungsdaten.org provides an overview of the LOM and adaptations of the matrix [15]. The main file contains the worksheets “How-to-use”, “Index”, “Learning objectives”, “Glossary” as well as a “Changelog” to document the changes attained after the second version. The “How-to-use” worksheet summarises all relevant information in a quick guide. The “Index” worksheet contains an overview of subject cluster, cluster-ID, topic, and topic ID. Neither the clustering nor the order implies any judgement of their relevance; they are merely intended to improve clarity. The “Learning objectives” worksheet lists all subject clusters, topics, learning objectives, Bloom's taxonomy, target groups and levels of objectives. The learning objectives are assigned to four target groups: Bachelor’s students, Master’s students, early career researchers (ECR) and data stewards. ERC are researchers who typically are in the first years of their research careers, and it includes those working on a doctorate thesis as well as post-doctoral researchers. It should be noted that a specific target group is not automatically assigned to a particular level of the taxonomy. In addition to the LOM, the editorial team produced a work-related glossary of more than 40 definitions of RDM-related terms. These can be found in the “Glossary” worksheet. The definitions are aligned with the content and objectives of the matrix and serve to provide a common level of understanding for the use and interpretation of the learning objectives. The LOM provides rough learning objectives, where the sentence structure follows the same pattern; they are concisely formulated, containing no (subject-) specific conditions and using a limited range of verbs for the action component, the structure being “learning objective start”, “learning objective verb” and “learning objective content” (cf. Table 1). With a learning objective matrix, the learning process is thus very well structured because the individual didactic scenario is in a meaningful order and with appropriate levels of objectives. Therefore, the LOM is well suited for subsequent reuse. Those wanting to reuse the LOM to implement own teaching or learning programmes may want to reformulate the learning objectives for their own purposes in accordance to their particular use cases and specific needs [16]. The content components of the existing learning objectives can for example be supplemented with (subject-) specific conditions as well as with conditions relating to forms of learning or examination. The action component verb of a learning objective can also be adapted since there are many alternatives for verbs that specify action components other than those used in the LOM (s. lists of verbs in [8, 9]). Table 1. Examples of learning objectives of all six LOM subject clusters Learning objective start Learning objective verb Learning objective content Learners are able to name the FAIR principles. Learners are able to evaluate the interoperability of nonproprietary file formats. Learners are able to apply methods for data documentation independently. Learners are able to analyse challenges for the long-term usability of data. Learners are able to explain the characteristics of open and restricted licences. Learners are able to design didactic methods. 5 Use Cases The Learning objective matrix has seen substantial development in recent years, significantly driven by the collaboration of various stakeholders and expert communities. It has proven to be a decisive tool in the design of subject-specific RDM training programmes, such as the self-learning course developed by NFDI4ING [17] and the adaptation for subject librarians by BERD@NFDI [18], underscoring its growing relevance in RDM education. While the LOM has seen growing adoption across the academic landscape (for example, [19, 20]); its practical application within academic libraries is still at a relatively early stage. One notable example is the Research Data Management Curriculum developed by the Research Data Services at the University Library of Duisburg-Essen, which systematically employed the LOM to define its learning objectives [21]. Kalová and Hackl [22] provide another example of the application of the LOM in the library domain, recommending its use in the development of training programmes aimed at fostering repository-related competences. The following section presents prototypical application scenarios in which the LOM can be applied within the academic library context, each accompanied by a brief description. These scenarios serve not only to illustrate concrete use cases but also to offer academic libraries inspiration for integrating the LOM into their RDM consultation and training services. Subject-specific adaptation of the learning objective matrix • As a subject librarian for a specific subject area in the library, I would like to use the LOM to identify relevant topics for a training course on research data management and adapt the content to the subject-specific target group. • As a subject librarian, I work together with an NFDI consortium and support the creation of an adapted LOM for the specialist community. Development of training and further education formats • As a teaching librarian, I plan workshops on topics such as data organisation, metadata or FAIR principles. The LOM helps me to define suitable topics and learning objectives for different target groups (for example BA students, MA students). • As a training officer at a university library, I would like to offer internal training courses for library staff. I use the LOM to identify relevant topics for professional RDM-development specifically of librarians. Self-study and skills development for library staff • As an academic librarian, I use the LOM for structured further training in the field of research data management expand my RDM-skills to optimise consultation services for researchers. Creation and quality assurance of teaching materials • As a librarian who creates Open Educational Resources (OER), I use the LOM to develop teaching resources that introduce BA-students to the basics of research data management. • As a librarian who creates OER, I use the LOM to identify gaps in my existing learning materials and develop customised new content to close these gaps. Curriculum development in cooperation with departments • As a contact person for research data management in the library, I work with lecturers in a research department to integrate RDM skills into existing degree programmes. The LOM helps to select suitable content and systematically integrate it into the curriculum. PhD programmes • As the contact person for PhD programmes, I design an accompanying RDM training program for doctoral candidates. The LOM helps me to select suitable modules and learning objectives, particularly on topics such as data publication or repositories. 6 Conclusion As we could show in the present paper, the LOM offers an extremely valuable foundation for academic libraries planning to offer or in need to further develop training programmes on RDM. It offers an extensive catalogue of the most relevant topics, paired with learning objective formulated in such a way, that they make clear, what the output of the training will be. This has a series of benefits: • with the LOM, academic libraries have the possibility of tailoring teaching offers for different target groups in accordance with the current requirements of the faculties and departments on campus. • in this sense, it offers an ideal basis for libraries to collaborate with academic and research staff to define together the key points for future training. • for teaching librarians, it offers both a basis for designing didactic scenarios and formulating training outputs in such a way, that their achievement can be verified. • this is clearly an ideal contribution to quality management for libraries to better evaluate their teaching programmes. • for learners, it offers the possibility of knowing in advance, what exactly the outcome of participating in the training programmes will be. • besides, it makes it possible for them to assess their own degree of proficiency in relation to RDM. With all these, we are deeply convinced that the Learning Objective Matrix for Research Data Management will greatly contribute to thrive on the necessary competences among future researchers for a methodical open handling of research data. This will in turn contribute to better scientific progress as well as a better understanding of scientific findings among the general public. References 1. Cox, A.M., Pinfield, S.: Research data management and libraries: Current activities and future priorities. J. Librariansh. Inf. Sci. 46, 299–316 (2014). https://doi.org/10.1177/0961000613492542 2. Sheikh, A., Malik, A., Adnan, R.: Evolution of Research Data Management in Academic Libraries: A Review of the Literature. Inf. Dev. (2023). https://doi.org/10.1177/02666669231157405 3. Safdar, M., Rehman, S.U., Arif, M., Ashiq, M.: Research Data Services in Libraries: a Systematic Literature Review. Inf. Discov. Deliv. 51, 151–165 (2023). https://doi.org/10.1108/IDD-04-2021-0044 4. UAG Schulungen/Fortbildungen. Forschungsdaten.org, https://www.forschungsdaten.org/index.php/UAG_Schulungen/Fortbildungen 5. Petersen, B., Engelhardt, C., Hörner, T., Jacob, J., Kvetnaya, T., Mühlichen, A., Schranzhofer, H., Schulz, S., Slowig, B., Trautwein-Bruns, U., Voigt, A., Wiljes, C.: Lernzielmatrix zum Themenbereich Forschungsdatenmanagement (FDM) für die Zielgruppen Studierende, PhDs und Data Stewards, https://doi.org/10.5281/zenodo.7034478, (2022) 6. Petersen, B., Altemeier, F., Boße, S., Düvel, N., Engelhardt, C., Fichtner, M., Hastik, C., Haugwitz, J.-M., Jacob, J., Koch, K., Kuntz, A., Manske, A., Mühlichen, A., Murcia Serra, J., Ortmeyer, J., Richter, M., Schranzhofer, H., Slowig, B., Trautwein-Bruns, U., Urbaum, D., Voigt, A., Werner, S., Wiljes, C., Zollitsch, L.: Lernzielmatrix zum Themenbereich Forschungsdatenmanagement (FDM), https://zenodo.org/doi/10.5281/zenodo.15025246, (2025) 7. Biernacka, K., Dockhorn, R., Engelhardt, C., Helbig, K., Jacob, J., Kalová, T., Karsten, A., Meier, K., Mühlichen, A., Neumann, J., Petersen, B., Slowig, B., Trautwein-Bruns, U., Wilbrandt, J., Wiljes, C.: Train-the-Trainer-Konzept zum Thema Forschungsdatenmanagement, https://zenodo.org/doi/10.5281/zenodo.10122153, (2023) 8. Bloom’s Taxonomy Action Verb List for the Cognitive Domain, https://gme.med.ufl.edu/wordpress/files/2020/12/BloomsTaxonomy-cognitivedomain-verbs.pdf 9. Revised Bloom’s Taxonomy Action Verbs, https://www.naeyc.org/sites/default/files/globally-shared/downloads/PDFs/revisedblooms-taxonomy-action-verbs.pdf 10. Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.H.: Taxonomy of Educational Objectives. The Classification of Educational Goals, Handbook I: Cognitive Domain. David McKay, New York (1956) 11. Anderson, L.W., Krathwohl, D.R. hrsg: A Taxonomy for Learning, Teaching, and Assessing: a Revision of Bloom’s Taxonomy of Educational Objectives. Longman, New York (2001) 12. Vivone, M.: NFDI4Health, https://www.nfdi4health.de/en/ 13. Petersen, B., Engelhardt, C., Hörner, T., Jacob, J., Kvetnaya, T., Mühlichen, A., Schranzhofer, H., Schulz, S., Slowig, B., Trautwein-Bruns, U., Voigt, A., Wiljes, C.: Lernzielmatrix zum Themenbereich Forschungsdatenmanagement (FDM) für die Zielgruppen Studierende, PhDs und Data Stewards. (2023). https://doi.org/10.5281/ZENODO.8010617 14. dini-ag-kim/fdm-lernziele, https://github.com/dini-ag-kim/fdm-lernziele, (2025) 15. Lernzielmatrix–Forschungsdaten.org, https://www.forschungsdaten.org/index.php/Lernzielmatrix 16. Manske, A., Petersen, B.: 23 TrainingThings for Writing Learning Objectives, https://zenodo.org/doi/10.5281/zenodo.15043810, (2025) 17. NFDI4ING Trainingsplattform: Basis FDM Trainings für die Ingenieurwissenschaften, https://education.nfdi4ing.de/startpage.html#/ 18. Schumm, I., Murcia Serra, J.: Forschungsdatenmanagement-Grundlagen. Forschungsdatenmanagement in der Fachreferatsarbeit (Wirtschaftsund Sozialwissenschaften). (2024)