Data Management Plan Research Articles

FAIR data management: a framework for fostering data literacy in biomedical sciences education.

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.

From Policy to Practice: Data Sharing Challenges and Opportunities for Mixed Methods Research in the Era of U.S. Open Science Initiatives

The “Year of Open Science” initiative, spearheaded by the White House in 2023, catalyzed the adoption of national open science policies across U.S. Federal Government agencies, but open science is an international movement. This paper elucidates the evolving landscape of open science policies, particularly focusing on the Data Management and Sharing Plans by agencies like NIH, NSF, and CDC. While quantitative research (and qualitative research to some degree) has received considerable attention in data sharing, this paper highlights the underexplored terrain of mixed methods research. Through detailed analysis and examples, it advocates for tailored guidelines and collaborative efforts to bridge the gap in data sharing practices, emphasizing the unique challenges and opportunities inherent in mixed methods research.

Exploring Open Science practices at a national and research library in Central Europe

The paradigm of Open Science (OS) is increasingly pivotal in contemporary scholarly research, promoting transparency, accessibility, and collaboration. This study, conducted as part of an Erasmus+ internship by a trainee at the National Széchényi Library of Hungary (OSZK) assesses the level of Open Science awareness among its researchers and contributes to the formulation of an Open Science-compliant Data Management Plan policy. By examining researchers' perspectives and current practices, the survey identifies some gaps and areas for improvement in data management and sharing. These insights are instrumental in developing strategic initiatives to enhance Open Science practices within the OSZK researchers. Furthermore, the findings have prompted the Office for GLAM Standards at NSL to develop a specialized Open Science course for OSZK researchers. This study not only supports the advancement of a more open and collaborative research culture within Hungary but also aligns with global efforts to democratize scientific knowledge.

Data science model canvas for health research: a systematic review

Data science in the healthcare sector is rapidly growing and attracting significant investments. Over the past 10 years, the number of research and publications on artificial intelligence has tripled. However, many data science projects fail due to poor planning. Canvas models emerge as effective tools for planning. A systematic review was conducted to analyze canvas models tailored for data science and focused on healthcare research. This analysis uncovered gaps in the models, particularly in addressing essential issues like ethics, security, data preservation, and sharing. We identified opportunities to enhance the models by integrating project management elements and the Data Management Plan (DMP) to address these shortcomings.

Assisting the Learning of Clinical Reasoning by Veterinary Medical Learners with a Case Example.

Effective clinical reasoning is essential for veterinary medical education, particularly in managing complex cases. This review explores strategies for learning clinical reasoning by veterinary medical learners, using a case example of mastitis to illustrate key concepts. Clinical reasoning encompasses cognitive, metacognitive, social, and situational activities, yet the literature on practical applications in veterinary education remains limited. The review discusses various stages of clinical reasoning, including data collection, problem representation, differential diagnosis, and management planning. It emphasizes the importance of integrating client-centered care and iterative evaluation into the clinical decision-making process. Key learning strategies include facilitation in using the domains of clinical reasoning-concepts, data collection, and analysis, taking action, and reflection on encounters. This review highlights best practices such as forward and backward reasoning, reflective practice, and the use of practical examples to enhance learners' diagnostic accuracy and patient outcomes. The insights provided aim to enhance the training of veterinary learners, ensuring they can navigate day 1 as well as complex cases with improved diagnostic accuracy and patient outcomes.

The CUISINES Framework for Conducting Exoplanet Model Intercomparison Projects, Version 1.0

As JWST begins to return observations, it is more important than ever that exoplanet climate models can consistently and correctly predict the observability of exoplanets, retrieval of their data, and interpretation of planetary environments from that data. Model intercomparisons play a crucial role in this context, especially now when few data are available to validate model predictions. The CUISINES Working Group of NASA's Nexus for Exoplanet Systems Science supports a systematic approach to evaluating the performance of exoplanet models and provides here a framework for conducting community-organized exoplanet model intercomparison projects (exoMIPs). The CUISINES framework adapts Earth climate community practices specifically for the needs of the exoplanet researchers, encompassing a range of model types, planetary targets, and parameter space studies. It is intended to help researchers to work collectively, equitably, and openly toward common goals. The CUISINES framework rests on five principles: (1) define in advance what research question(s) the exoMIP is intended to address, (2) create an experimental design that maximizes community participation and advertise it widely, (3) plan a project timeline that allows all exoMIP members to participate fully, (4) generate data products from model output for direct comparison to observations, and (5) create a data management plan that is workable in the present and scalable for the future. Within the first years of its existence, CUISINES is already providing logistical support to 10 exoMIPs and will continue to host annual workshops for further community feedback and presentation of new exoMIP ideas.

Perception and Practice of Data Management Plans in Health: An Exploratory Study

Perception and Practice of Data Management Plans in Health: An Exploratory Study

The Meise Botanic Garden Herbarium Data Management Plan

This Data Management Plan outlines a comprehensive strategy for handling, storing, and sharing of data generated by digitisation projects of the herbarium at Meise Botanic Garden with Index Herbarium code BR. Its purpose is to establish clear guidelines for both staff and external users, specifying the terms governing data usage and storage. It aims to prioritise the FAIR principles (Findable, Accessible, Interoperable and Reusable), ensure responsible data management, facilitate long-term preservation, uphold legal and ethical obligations, all while aligning with the research excellence mission of Meise Botanic Garden. This plan serves as a guiding document to effectively and efficiently achieve these goals.

Data Management Plan for a rapid response sub-study of an existing cohort. The Irish Longitudinal Study on Ageing (TILDA) COVID-19 sub-study

A Data Management Plan (DMP) is a formal document that outlines the management and stewardship of data generated over the lifecycle of a research project from data collection, and governance structures, to the long-term preservation of data outputs. DMPs are an important feature of good research practice. Our aim is to provide details of the development of a DMP that others can learn from and adapt to their specific needs. Our DMP was developed as part of a COVID-19 sub-study of The Irish Longitudinal Study on Ageing (TILDA), titled “Altered lives in a time of crisis: preparing for recovery from the impact of the COVID-19 pandemic on the lives of older adults”. TILDA is a longitudinal study of community-dwelling older adults. In 2009/2010, an initial nationally representative sample of 8,500 adults aged 50 years and older were selected. The sample for the COVID-19 study were recruited from this existing sample. The objective of the sub-study was to document the lives and experiences of older adults during the COVID-19 pandemic to better understand the effect of the pandemic and public health responses on their well-being. This DMP describes the study design and objectives; data collection tools and procedures; data preparation; data storage and security; data sharing and preservation; and ethical and legal considerations within the European Union and Irish Health Research legislative context. Responsible data governance in Ireland is complex, requiring adherence to both European and Irish legislation. Implementation of the Health Information Bill (2023) may bring further complexities to this context. It is therefore crucial that researchers, data stewards, and other practitioners, share their expertise freely, as we have done here, so that others can learn from their experiences and the health research community can develop standards of best practice.

DMPs as Management Tool for Intellectual Assets by SMART-metrics

Data Management Plans (DMPs) are vital components of effective research data management (RDM). They serve not only as organisational tools but also as a structured framework dictating the collection, processing, sharing/publishing, and management of data throughout the research data life cycle. This can include existing data curation standards, the establishment of data handling protocols, and the creation, when necessary, of community curation policies. Therefore, DMPs present a unique opportunity to harmonise project management efforts for optimising the formulation and execution of project objectives. To harness the full potential of DMPs as project management tools, the SMART approach (i.e., Specific, Measurable, Achievable, Relevant, and Time-bound) emerges as a compelling methodology. During the initial stage of the project proposal, drafted SMART metrics can offer a systematic approach to map work packages (WPs) and deliverables to the overarching project objectives. Then, the Principal Investigators (PIs) can ensure the consortia that all the project potential intellectual assets (i.e., expected research results) were considered properly, as well as their necessary timelines, resources, and execution. It becomes imperative for data stewards (DSs) and governance policymakers to educate and provide guidelines to researchers on the advantages of developing well-curated DMPs that align results with SMART metrics. This alignment ensures that every intellectual asset intended as a research result (e.g., intellectual properties, publications, datasets, and software) within the project is subject to rigorous drafted planning, execution, and accountability. Consequently, the risk of unforeseen setbacks and/or deviations from the original objectives is minimised, increasing the traceability and transparency of the research data life cycle. In addition, the integration of Technology Readiness Levels (TRLs) into this proposed enhanced DMP provides a systematic method to evaluate the maturity and readiness of technologies across scientific disciplines. Regular TRL assessments will allow PIs: (1) to monitor the WP progress, (2) to adapt research strategies if required, and (3) to ensure the projects remain in line with the drafted SMART metrics in the enhanced DMP before the project started. The TRLs can also help PIs maintain their focus on project milestones and specific tasks aligned with the original objectives, contributing to the overall success of their endeavours, while improving the transparency for the reporting and divulgation of the research results. The paper presents the overall framework for enhancing DMPs as project management tools for any intellectual assets using SMART metrics and TRLs, as well as introducing suggested support services for data stewardship teams to assist PIs when implementing this novel framework effectively.

Standardisierter Datenmanagementplan für die Bildungsforschung (Stamp) – Ein projektbegleitendes Tool für das Forschungsdatenmanagement

ZusammenfassungDie empirische Bildungsforschung zieht ihre Erkenntnisse aus der Erhebung und Auswertung von Daten. Einheitliche Standards sind notwendig, um den steigenden Anforderungen an das Forschungsdatenmanagement im Kontext einer zunehmenden Kultur des Datenteilens zu begegnen. Das Verbundprojekt Domain-Data-Protokolle für die empirische Bildungsforschung (DDP-Bildung) entwickelte hierzu den Stamp (Standardisierter Datenmanagementplan für die Bildungsforschung). Der Stamp basiert auf dem „Domain Data Protocol“-Konzept von Science Europe und dient der Sicherung der Datenqualität und der Nachnutzbarkeit von Forschungsdaten. In die Entwicklung des Stamps flossen Expertisen aus der Forschungsdateninfrastruktur sowie von der Scientific Community der Bildungsforschung geäußerte Bedarfe ein. Ein zentraler Unterschied zu klassischen Datenmanagementplänen besteht darin, dass der Stamp Informationsquellen verknüpft und fachspezifische Vorgehensweisen in Form von Praxisbeispielen unter Beachtung rechtlicher Vorgaben illustriert. Hierbei begleitet der Stamp wissenschaftliche Projekte von der Planungsphase (inklusive Projektanträgen) über die Erhebung und bis zur Archivierung der Daten. Die vorliegende Arbeit beschreibt den Entwicklungsprozess des Stamps und stellt dessen Bestandteile vor.

The use of data management planning among researchers in higher learning institutions: The case of the Nelson Mandela African Institution of Science and Technology in Tanzania

This study assessed the use of data management plans among researchers at a selected higher learning institution (HLI) in Tanzania. A pretested structured questionnaire was administered to registered postgraduate students. Many of the respondents reported that a data management plan (DMP) was required before writing a research project and when a research project was submitted. The results also demonstrated that many respondents did not use any online DMP template tools to formulate their DMP although most of them were aware of available DMP template tools such as OpenDMP. Many respondents stated that the requirement of using a DMP were selection of a DMP format, updating the DMP regularly, having a short and to-the-point DMP and a well-structured DMP specifying the kinds and formats of the data to be acquired, generated, produced, and preserved. Meeting funders’ institutions, and publishers’ requirements, and ensuring that data are accurate, complete, and reliable were among the DMP benefits in HLIs identified by the respondents. Several challenges were revealed including a lack of awareness, competence, and guidelines to assist researchers using a DMP for their research projects. The conclusion is that researchers need to develop and use DMP template tools to plan, organize, and work on their research projects in addition to ensuring that they meet funders' requirements. It is recommended that HLIs should provide extensive training programs for raising awareness about DMPs among the researchers and to make DMPs a mandatory requirement for finalizing research projects among researchers, and not only for funding purposes.

A practical guide to data management and sharing for biomedical laboratory researchers

Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and knowledge to address this challenge. This article provides an introductory guide into research data management (RDM), and the importance of FAIR (Findable, Accessible, Interoperable, and Reusable) data-sharing principles for laboratory researchers produced by practicing scientists. We explore the advantages of implementing organized data management strategies and introduce key concepts such as data standards, data documentation, and the distinction between machine and human-readable data formats. Furthermore, we offer practical guidance for creating a data management plan and establishing efficient data workflows within the laboratory setting, suitable for labs of all sizes. This includes an examination of requirements analysis, the development of a data dictionary for routine data elements, the implementation of unique subject identifiers, and the formulation of standard operating procedures (SOPs) for seamless data flow. To aid researchers in implementing these practices, we present a simple organizational system as an illustrative example, which can be tailored to suit individual needs and research requirements.By presenting a user-friendly approach, this guide serves as an introduction to the field of RDM and offers practical tips to help researchers effortlessly meet the common data management and sharing mandates rapidly becoming prevalent in biomedical research.

Impact evaluation of a cash-plus programme for children with disabilities in the Xiengkhouang Province in Lao PDR: study protocol for a non-randomised controlled trial

IntroductionMore than 170 countries have implemented disability-targeted social protection programmes, although few have been rigorously evaluated. Consequently, a non-randomised controlled trial is being conducted of a pilot ‘cash-plus’ programme implemented...

Personalizing Customer Connections: Optimizing CRM Strategies in Levi's Retail Outlet in Ahmedabad city of Gujarat

Effective consumer Relationship Management (CRM) is essential in the current competitive retail landscape to comprehensively comprehend consumer requirements, provide tailored experiences, and cultivate loyalty. This research examines the intricacies of customer relationship management (CRM) processes at Levi's retail stores, with the goal of identifying areas of strength, weakness, and providing suggestions to improve the efficacy of CRM. The study employs a qualitative methodology, using purposive sampling to pick Levi's branch managers based on their experience and the importance of their outlets. Interviews are conducted to gather data. Semi-structured interviews provide a thorough investigation of CRM strategy, data management, personalisation efforts, difficulties, and future plans. These interviews are conducted while adhering to ethical rules. Theoretical contributions focus on the actual implementation of CRM ideas, enhancing the current body of knowledge by providing valuable insights into CRM practices that are relevant to the retail industry. The report recommends more investigation into the influence of developing technologies on customer relationship management (CRM) and the analysis of tactics in various retail sectors. To summarise, this study highlights the crucial function of CRM in cultivating customer connections inside Levi's retail stores. The text highlights the significance of technology, personalisation, and customer-centric strategies in attaining retail success via good CRM practices. It offers practical insights for managers and researchers.

An Overview of Data Collection in Health Preference Research.

This paper focuses on survey administration and data collection methods employed for stated-preference studies in health applications. First, it describes different types of survey administration methods, encompassing web-based surveys, face-to-face (in-person) surveys, and mail surveys. Second, the concept of sampling frames is introduced, clarifying distinctions between the target population and survey frame population. The discussion then extends to different types of sampling methods, such as probability and non-probability sampling, along with an evaluation of potential issues associated with different sampling methods within the context of health preference research. Third, the paper provides information about different recruitment methods, including web-surveys, leveraging patient groups, and in-clinic recruitment. Fourth, a crucial aspect addressed is the calculation of response rate, with insights into determining an adequate response rate and strategies to improve response rates in stated-preference surveys. Lastly, the paper concludes by discussing data management plans and suggesting insights for future research in this field. In summary, this paper examines the nuanced aspects of survey administration and data collection methods in stated-preference studies, offering valuable guidance for researchers and practitioners in the health domain.

Road to a Chemistry-Specific Data Management Plan

Road to a Chemistry-Specific Data Management Plan

Assessing Quality Variations in Early Career Researchers’ Data Management Plans

This paper aims to better understand early career researchers’ (ECRs’) research data management (RDM) competencies by assessing the contents and quality of data management plans (DMPs) developed during a multi-stakeholder RDM course. We also aim to identify differences between DMPs in relation to several background variables (e.g., discipline, course track). The Basics of Research Data Management (BRDM) course has been held in two multi-faculty, research-intensive universities in Finland since 2020. In this study, 223 ECRs’ DMPs created in the BRDM of 2020 - 2022 were assessed, using the recommendations and criteria of the Finnish DMP Evaluation Guide + General Finnish DMP Guidance (FDEG). The median quality of DMPs appeared to be satisfactory. The differences in rating according to FDEG’s three-point performance criteria were statistically insignificant between DMPs developed in separate years, course tracks or disciplines. However, using content analysis, differences were found between disciplines or course tracks regarding DMP’s key characteristics such as sharing, storing, and preserving data. DMPs that contained a data table (DtDMPs) also differed highly significantly from prose DMPs. DtDMPs better acknowledged the data handling needs of different data types and improved the overall quality of a DMP. The results illustrated that the ECRs had learned the basic RDM competencies and grasped their significance to the integrity, reliability, and reusability of data. However, more focused, further training to reach the advanced competency is needed, especially in areas of handling and sharing personal data, legal issues, long-term preserving, and funders’ data policies. Equally important to the cultural change when RDM is an organic part of the research practices is to merge research support services, processes, and infrastructure into the research projects’ processes. Additionally, incentives are needed for sharing and reusing data.

The DMPTool NIH DMSP Templates Project.

The DMPTool NIH Data Management and Sharing Plan (DMSP) Templates Project was launched in response to the 2023 NIH Data Management and Sharing (DMS) Policy. This new policy introduced a more structured framework for DMS Plans, featuring six key elements, a departure from the 2003 NIH DMS policy. The project aimed to simplify the process for data librarians, research administrators, and researchers by providing a template with curated guidance, eliminating the need to navigate various policies and guidelines. The template breaks out each Plan section and subsection and provides related guidance and examples at the point of need. This effort has resulted in two NIH DMSP Templates. The first is a generic template (NIH-Default) for all ICs, complying with NOT-OD-21-013 and NOT-OD-22-198. More recently, an NIMH-specific template (NIH-NIMH) was added based on NOT-MH-23-100. As of October 2023, over 5,000 DMS Plans have been written using the main NIH-Default template and the NIH-NIMH alternative template.

The Puzzles in Research-Ethics Review

There are puzzles in research-ethics review that confound researchers in the social sciences. First, how did the biomedical research ethics process capture the research-ethics perspectives in the social sciences? Second, why has it proved impossible to recognize the relevance of stepping outside the biomedical paradigm? Third, what institutional agencies have not permitted researchers in the social sciences to abandon the biomedical paradigm and adopt one that is relevant for social scientists? There are particular elements that characterize the ethics-review process. Some phrases as ‘rigor’ dissuade inductive researchers, the power of hierarchical nature of research-ethics committees inevitably impose the biomedical stance in research, the colonizing nature of research-ethics betrays its American bias, and the insistence that all researchers implement a data-management plan is not relevant to the social sciences. Some of the more common suggestions to improve the situation are unworkable.