FAIR data management: a framework for fostering data literacy in biomedical sciences education
Abstract Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-11-01
|
| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-024-02404-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846165154875572224 |
|---|---|
| author | Rocio Gonzalez Soltero Debora Pino García Alberto Bellido Pablo Ryan Ana I. Rodríguez-Learte |
| author_facet | Rocio Gonzalez Soltero Debora Pino García Alberto Bellido Pablo Ryan Ana I. Rodríguez-Learte |
| author_sort | Rocio Gonzalez Soltero |
| collection | DOAJ |
| description | Abstract Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022–2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master’s thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge. |
| format | Article |
| id | doaj-art-f0d4dd222bc540dbaff1e0e915b4edec |
| institution | Kabale University |
| issn | 1471-2288 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj-art-f0d4dd222bc540dbaff1e0e915b4edec2024-11-17T12:34:13ZengBMCBMC Medical Research Methodology1471-22882024-11-012411910.1186/s12874-024-02404-1FAIR data management: a framework for fostering data literacy in biomedical sciences educationRocio Gonzalez Soltero0Debora Pino García1Alberto Bellido2Pablo Ryan3Ana I. Rodríguez-Learte4Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de MadridFacultad de Ciencias Biomédicas y de la Salud, Universidad Europea de MadridFacultad de Ciencias Biomédicas y de la Salud, Universidad Europea de MadridFacultad de Ciencias Biomédicas y de la Salud, Universidad Europea de MadridFacultad de Ciencias Biomédicas y de la Salud, Universidad Europea de MadridAbstract Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022–2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master’s thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge.https://doi.org/10.1186/s12874-024-02404-1Biomedical educationFAIR principlesData literacyData stewardshipMaster’s thesisAcademic research |
| spellingShingle | Rocio Gonzalez Soltero Debora Pino García Alberto Bellido Pablo Ryan Ana I. Rodríguez-Learte FAIR data management: a framework for fostering data literacy in biomedical sciences education BMC Medical Research Methodology Biomedical education FAIR principles Data literacy Data stewardship Master’s thesis Academic research |
| title | FAIR data management: a framework for fostering data literacy in biomedical sciences education |
| title_full | FAIR data management: a framework for fostering data literacy in biomedical sciences education |
| title_fullStr | FAIR data management: a framework for fostering data literacy in biomedical sciences education |
| title_full_unstemmed | FAIR data management: a framework for fostering data literacy in biomedical sciences education |
| title_short | FAIR data management: a framework for fostering data literacy in biomedical sciences education |
| title_sort | fair data management a framework for fostering data literacy in biomedical sciences education |
| topic | Biomedical education FAIR principles Data literacy Data stewardship Master’s thesis Academic research |
| url | https://doi.org/10.1186/s12874-024-02404-1 |
| work_keys_str_mv | AT rociogonzalezsoltero fairdatamanagementaframeworkforfosteringdataliteracyinbiomedicalscienceseducation AT deborapinogarcia fairdatamanagementaframeworkforfosteringdataliteracyinbiomedicalscienceseducation AT albertobellido fairdatamanagementaframeworkforfosteringdataliteracyinbiomedicalscienceseducation AT pabloryan fairdatamanagementaframeworkforfosteringdataliteracyinbiomedicalscienceseducation AT anairodriguezlearte fairdatamanagementaframeworkforfosteringdataliteracyinbiomedicalscienceseducation |