Doctoral Students’ Research Data Management Competencies Based on the Quality of Their Data Management Plans

Many international, national, and institutional principles and policies as well as a growing number of funders and publishers recommend or mandate researchers to write data management plans (DMPs) or share the underlying research data upon which the research articles are based. At the core of these alignments is to enhance research transparency, reproducibility and reliability, and the reuse of research data by bringing the data findable, accessible, interoperable, and reusable (FAIR). To help researchers fulfill this task, they need education in research data management (RDM). The goal of this preliminary paper is to find out the quality of the DMPs developed by doctoral students (DSs) in a 10-week, 3 ECTS credits multi-stakeholder Basics of Research Data Management (BRDM) course. Moreover, we aim to identify differences between DMPs in relation to background variables such as year, discipline, course track or other variables. The course is held in two multi-faculty research-intensive universities in Finland since 2019. In this ongoing study, 130 DSs’ DMPs have been assessed and rated from 2020 and 2021 so far, using the criteria of the Finnish DMP Evaluation Guide (FDEG). The quality of the DMPs appeared to be satisfactory. The differences between DMPs developed in separate years, course tracks or disciplines were statistically insignificant. However, DMPs that contained a data type specific classification (a data table) differed statistically highly significantly from DMPs without a data table. DMPs with a data table acknowledged better than DMPs without a data table the data handling needs of different data types and improved the overall quality of a DMP. DMPs illustrated how well DSs had learned RDM competencies and how the course had furthered comprehension of the importance of sound data management practices to the integrity and reliability of the research, to the reusability of data, and to the reproducibility of the research

Doctoral Students' Educational Needs in Research Data Management: Perceived Importance and Current Competencies

Sound research data management (RDM) competencies are elementary tools used by researchers to ensure integrated, reliable, and re-usable data, and to produce high quality research results. In this study, 35 doctoral students and faculty members were asked to self-rate or rate doctoral students’ current RDM competencies and rate the importance of these competencies. Structured interviews were conducted, using close-ended and open-ended questions, covering research data lifecycle phases such as collection, storing, organization, documentation, processing, analysis, preservation, and data sharing. The quantitative analysis of the respondents’ answers indicated a wide gap between doctoral students’ rated/self-rated current competencies and the rated importance of these competencies. In conclusion, two major educational needs were identified in the qualitative analysis of the interviews: to improve and standardize data management planning, including awareness of the intellectual property and agreements issues affecting data processing and sharing; and to improve and standardize data documenting and describing, not only for the researcher themself but especially for data preservation, sharing, and re-using. Hence the study informs the development of RDM education for doctoral students

Closing the Skills Gap - the Basics of the Research Data Management (BRDM) Course: Case University of Turku

The current challenge for researchers at the University of Turku is that there is a substantial gap between the level of targeted and present research data management (RDM) skills. In order to better understand this challenge and to develop a training course in RDM, we examined the importance of RDM competencies vs. perceived competencies of doctoral students in different stages of research data life cycle. The RDM importance and competencies were examined by interviewing doctoral students, supervisors and biostatisticians. So far, 34 interview sessions on RDM skills have been conducted, covering research data life cycle topics such as collection, organization, documentation, processing and sharing the data. The interviewees’ average estimate of the importance of RDM skills in different stages of research data life cycle was 4.1 (very important) on Likert scale 1 to 5. An average estimate of the competencies of doctoral students was 2.6 (have somewhat skills). Targets for competencies have been set – besides by the interviewees themselves – by the Data Policy of the University of Turku, Finnish and EU level Open Science principles and research literature. Based on the results we developed a three-credit RDM course for doctoral students and post-doctoral researchers. The course was developed by a working group consisting of university teacher-researchers, lawyers, library\u27s open science specialists, data protection officer, IT Services, and biostatisticians. Three different study programmes of the BRDM are initiated: Health Sciences programme, Natural Sciences programme and Survey and Interview Studies programme. Each study programme has 7 modules, of which 3 are mutual for all the study programmes. During the course, students complete a study plan and build a data management plan for a research project and learn e.g. to take care of data privacy and to collect, store, protect, process, document and share data. In this preliminary paper we will discuss the conducted interviews and their key results, the RDM course planning and implementation, the student feedback and the lessons we have learned so far

Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

To ensure the quality and integrity of data and the reliability of research, data must be well documented, organised, and described. This calls for research data management (RDM) education for researchers. In light of 3 ECTS Basics of Research Data Management (BRDM) courses held between 2019 and 2021, we aim to find how a generic level multi-stakeholder training can improve STEM and HSS disciplines’ doctoral students’ and postdoc researchers’ competencies in RDM. The study uses quantitative, descriptive and inferential statistics to analyse respondents’ self-ratings of their competencies, and a qualitative grounded theory-inspired approach to code and analyse course participants’ feedback. Results: On average, based on the post-course surveys, respondents’ (n = 123) competencies improved one point on a four-level scale, from “little competence” (2) to “somewhat competent” (3). Participants also reported that the training would change their current practices in planning research projects, data management and documentation, acknowledging legal and data privacy viewpoints, and data collecting and organising. Participants indicated that it would be helpful to see legal and data privacy principles and regulations presented as concrete instructions, cases, and examples. The most requested continuing education topics were metadata and description, discipline specific cultures, and backup, version management, and storage. Conclusions: Regarding to the widely used criteria for successful training containing 1) active participation during training; 2) demand for RDM training; 3) increased participants’ knowledge and understanding of RDM and confidence in enacting RDM practices; and 4) positive post-training feedback, BRDM meets the criteria. This study shows that although reaching excellent competence in a RDM basics training is improbable, participants become aware of RDM and its contents and gain the elementary tools and basic skills to begin applying sound RDM practices in their research. Furthermore, participants are introduced to the academic and research support professionals and vice versa: Stakeholders will get to know the challenges that young researchers and research students encounter when applying RDM. The study reveals valuable information on doctoral students’ and postdoc researchers’ competencies, the impact of education on competencies, and further learning needs in RDM.</p

Datanhallinnan merkitys, tutkijoiden osaaminen ja kirjaston rooli kulttuurinmuutoksessa

Tutkijat pitävät hyvää datanhallintaa eli datan järjestelmällistä käsittelyä erittäin tärkeänä datan eheyden, tutkimustulosten luotettavuuden ja tutkimuksen toistettavuuden kannalta. Silti monet arjen tutkimuskäytännöt eivät palvele tätä tavoitetta. Kulttuurinmuutokseen tarvitaan koulutusta, palveluja ja kannustimia. Akateemisilla kirjastoilla tiedonhallinnan ja tiedonlähteiden hallinnan ammattilaisina on yksi avainrooleista muutoksen johtamisessa

”A Change Is Gonna Come” – Avoimen tieteen palveluita rakentamassa Turun yliopiston kirjastossa

Yliopistojen toimintaympäristö on muuttunut monin tavoin 2010-luvulla. Sen myötä tutkimuksen arvioinnille, näkyvyydelle ja vaikuttavuudelle asettavat tavoitteita niin yhteiskunnalliset päättäjät kuin yliopistot itse. Yliopistokirjastoissa tämä näkyy siinä, että luodaan uusia tehtäviä ja jo olemassa olevia tehtäviä uudistetaan, kuten julkaisutiedonhallinta, metriikka- ja arviointipalvelut sekä avoimen tieteen palvelut

AVOTT-linjausten vaikutuksia yliopistoissa

Avoimen tieteen ja tutkimuksen (AVOTT) kansallisen koordinaation työryhmät ovat tuottanut viime vuosien aikana useita linjauksia, suosituksia ja muita dokumentteja, jotka ovat luoneet perustaa tutkimusorganisaatioiden avoimen tieteen yhtenäisille käytännöille. Mutta millaisia vaikutuksia AVOTT-koordinaation dokumenteilla on ollut käytännön tasolla? Tähän artikkeliin on koottu tietoa neljän eri yliopiston tilanteesta

Datatukea rakentamassa – Katsaus koulutuksiin ja palveluihin

Korkeakouluissa on viime vuosina kehitetty ahkerasti datanhallintaan liittyviä koulutuksia ja palveluja. Toiminnalleon leimallista moniammatillisuus, yhteistyö ja jatkuva kehitys. Palvelutarjonnassa on paljon yhtäläisyyksiä, mutta myöspaikallisia ratkaisuja

Finnish DMP evaluation guidance

This guide gives some general tips for evaluators. It can be used when evaluating DMP by students, peer reviewing or when evaluation is conducted by a data steward. The working group hopes you develop the guidance further in order to meet your specific needs and policies.Ideally data management plan will be read and evaluated together with the research plan. In the DMP context, ‘data’ is understood as a broad term. Data covers all the information and material research results are based on (like codes, software, notes, etc). </p