Good Data Management Practices Research Articles

A research data management (RDM) community for ELIXIR

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR’s vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

A bridge between trust and control: computational workflows meet automated battery cycling.

Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We successfully interface off-the-shelf battery cycling hardware with the computational workflow management software AiiDA, allowing us to control experiments, while ensuring trust in the data by tracking its provenance. We design user interfaces compatible with this tooling, which span the inventory, experiment design, and result analysis stages. Other features, including monitoring of workflows and import of externally generated and legacy data are also implemented. Finally, the full software stack required for this work is made available in a set of open-source packages.

COPO - Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project

Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.

Teaching the Best Research Data Management Practices to PhD Students

Good Research Data Management (RDM) practices make it easier for researchers to conduct their research and ensure their data is preserved and stored securely for the long-term. Archiving is one of the last stages of Research Data Lifecycle and deals with depositing and preserving datasets at the end of a project in a suitable storage location (repositories, local server, or hard drive). Students may not find data management topic as interesting as their core course topics. This can result in lack of students’ knowledge of the importance and benefits of good data management practices for their studies. At Warwick, I design and deliver Research Data Management & Planning (RDM&P) case study sessions to PhD students. Students learn different elements of RDM and importance of RDM for long-term preservation. Students work in groups to develop and present Data Management Plan (DMP) and put RDM&P learning into practice. In this presentation, I briefly introduce the above case study, feedbacks received from students across different disciplines and further steps.

Informatics infrastructure in a rural pediatric clinical trials network: Matching specific clinical research needs with best practices and industry guidelines

Children have historically been underrepresented in randomized controlled trials and multi-center studies. This is particularly true for children who reside in rural and underserved areas. Conducting multi-center trials in rural areas presents unique informatics challenges. These challenges call for increased attention towards informatics infrastructure and the need for development and application of sound informatics approaches to the collection, processing, and management of data for clinical studies. By modifying existing local infrastructure and utilizing open source tools, we have been able to successfully deploy a multi-site data coordinating and operations center. We report our implementation decisions for data collection and management for the IDeA States Pediatric Clinical Trial Network (ISPCTN) based on the functionality needed for the ISPCTN, our synthesis of the extant literature in data collection and management methodology, and Good Clinical Data Management Practices.

Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project

Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.

LUSTRE: An Online Data Management and Student Project Resource

We advocate for greater emphasis in training students about data management, within the context of supporting experience in reproducible workflows. We introduce the Lancaster University STatistics REsources (LUSTRE) package, used to manage student research project data in psychology and build capacity with respect to data acumen. LUSTRE provides a safe space to engage students with open research practices—by making tangible different phases of the reproducible research pipeline, while emphasizing its value as a transferable skill. It is an open-source online data catalogue that captures key data management information about a student research project of potential relevance to data scientists. Embedded within a taught programme, it also highlights concepts and examples of data management processes. We document a portfolio of open teaching resources for LUSTRE, and consider how others can implement or adapt them to facilitate data management and open research. We discuss the role of LUSTRE as a; (a) resource and set of activities for promoting good data management practices; (b) framework to enable the delivery of key concepts in open research; (c) an online system to organize and showcase project work.

Changing the Culture on Data Management and Sharing: Overview and Highlights from a Workshop Held by the National Academies of Sciences, Engineering, and Medicine

The U.S. National Institutes of Health (NIH) released a broad new policy on data management and sharing on October 29, 2020, mandating that all data from NIH-funded or conducted research must be managed and most must be shared. This policy requires the submission of data management and sharing plans in all grant applications consistent with good data management practices and sets expectations for sharing of scientific data generated from NIH-funded or conducted research. The policy recognizes the benefits and opportunities in routine and effective data sharing, and therefore seeks to drive a shift in attitudes and practices across biomedicine in the handling, sharing, reuse, and valuation of biomedical data. In anticipation of the implementation of this policy on January 25, 2023, the NIH sponsored the U.S. National Academies of Sciences, Engineering, and Medicine’s Health and Medicine Division to hold a public workshop on “Changing the Culture of Data Management and Sharing.” The workshop was held virtually on April 28 and 29, 2021, and explored the issue from many angles over the 2 days, hearing from > 30 multidisciplinary speakers on what is needed to make this policy impactful and biomedicine ready for this shift. In the following, we provide a summary of the workshop, summarizing each of the sessions and the major areas that emerged over the course of it.

Rampant Smoothing (RTS) Algorithm: an optimized consensus mechanism for private Blockchain enabled technologies

Blockchain is a distributed database method of storing electronic information in digital form that makes it challenging to transform, hack, or rogue the system. Each time a new operation transpires on the Blockchain, an irreversible information of that transaction is buffered in all connected ledgers. Each distributed ledger is programmable, immutable and timestamped. This paper follows an immutable policy to ensure chronological trust-administration, security and privacy to resolve anomalies within linearly stored blocks. To prove the validity of submitted data, a consensus mechanism is required, which was attained by implementing a novel ‘Rampant Smoothing Algorithm’ that was encoded using Solidity (i.e., aimed at developing smart contracts). Experimental policies were aligned with Good Clinical Data Management Practices that portrayed effective ‘smart contract modeling’ to demonstrate resistance against majority consensus attacks by harnessing the core feature of ‘privacy decentralization’. During implementation of Blockchain network, an interconnected system of six hundred nodes (i.e., data points) were gradually configured.

Urban Data Science Education: A Key Actor towards Improving Data-Driven Policy-Making for Solving Urban Problems


 This paper explains the benefits of Urban Data Science Education in improving data driving policy-making and highlights the impact of vital use of available open data using a practical demonstration from the Summer-Winter School (SWS) at CEPT University Ahmedabad India. Good Urban data policies can be an important driver for smart city research and the implementation of good data management practices. Many authors are also becoming interested in the data revolution that is enhancing the way we study and understand cities. It now becomes appropriate to respond to this need in an evidence-informed manner by working with stakeholders to encourage a more resilient approach via education for the younger enthusiast, leading to more transparent, improved, sustainable and safer urban cities. However, the major challenge we foresee is the inability to utilize the large amount of urban data generated daily. With the drive to mobilize more education towards urban data science and analytics, generated data can be effectively utilized to make informed and evidence-based decisions. This paper points to a practical methodology for reproducibility, the key steps taken, as it views the inclusion of an Urban Data Science curriculum as priceless, and a decision that will bring into notice the importance of engaging the younger generation in scholastic learning on data principles and analytics. Data along with spatial enhancement can be the driving factor towards improving data-driven policy-making for solving urban problems in smart cities.
 

Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience.

Background: Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU's Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance. Methods: As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs. Results & Conclusions: We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being "just" an European Commission (EC) requirement. DMPs are most prominently developed within a project's Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach. The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk. If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs. The study identifies IP and licensing arrangements for DMPs as promising areas for further research.

Informing training needs for the revised certified clinical data manager (CCDMTM) exam: analysis of results from the previous exam.

ObjectiveTo inform training needs for the revised Certified Clinical Data Manager (CCDMTM) Exam.IntroductionClinical data managers hold the responsibility for processing the data on which research conclusions and regulatory decisions are based, highlighting the importance of applying effective data management practices. The use of practice standards such as the Good Clinical Data Management Practices increases confidence in data, emphasizing that the study conclusions likely hold much more weight when utilizing standard practices.MethodsA quantitative, descriptive study, and application of classic test theory was undertaken to analyze past data from the CCDMTM Exam to identify potential training needs. Data across 952 sequential exam attempts were pooled for analysis.ResultsCompetency domain-level analysis identified training needs in 4 areas: design tasks; data processing tasks; programming tasks; and coordination and management tasks.ConclusionsAnalysis of past CCDMTM Exam results using classic test theory identified training needs reflective of exam takers. Training in the identified areas could benefit CCDMTM Exam takers and improve their ability to apply effective data management practices. While this may not be reflective of individual or organizational needs, recommendations for assessing individual and organizational training needs are provided.

Data management matters

The implementation of good data management practices are essential to ensure that scientific data is findable, accessible and usable for verification and reuse.

Better data management, one nudge at a time

How do you help people improve their data management skills? For our team at the University of Illinois at Urbana-Champaign, we decided the answer was "one nudge at a time”.
 A study conducted by Wiley and Mischo (2016) found that Illinois researchers are aware of data services available but under-utilize them. Many researchers do not consider data management as a concern distinct from researching and producing scholarly work products. In 2017, the RDS piloted the Data Nudge – a monthly, opt-in email service to “nudge” Illinois researchers toward good data management practices, and towards utilizing data services on campus. The aim of the Data Nudge was to address the gap between knowing about a service and using it by highlighting best practices and campus resources.
 The topics covered in the Data Nudge center around data. Some topics are applicable to everyone, such as data back-up, documentation, and file naming conventions. Other topics are specific to Illinois, like storage options, events, and conferences.
 After four years, the Data Nudge has accumulated over 400 subscribers through word-of-mouth, marketing channels on campus and inclusion in subject liaisons' instructional workshops. It receives stable open rates averaging at 52% (compared to 19.44% average industry rate for Higher Education*) and many compliments from subscribers. We expect the Data Nudge to continue supplementing workshops and training as an effective means of communication to reach researchers on our campus. In the spirit of re-use, we are in the process of archiving the Data Nudge topics in a reusable format, readily adaptable by other institutions. 
 Data Nudge link: https://go.illinois.edu/past_nudges

Health Data Management Practice and Associated Factors Among Health Professionals Working at Public Health Facilities in Resource Limited Settings.

BackgroundDespite the vast amount of resources invested in the development of health information systems, health professionals in developing countries are still suffering from lack of adequate skill to perform health data management activities. There is a lack of sound evidence to overcome health data management challenges in this setting. This study aimed to assess health data management practice and its associated factors among health professionals working at public health facilities in North Wollo Zone, Northeast Ethiopia.MethodsA quantitative cross-sectional study was conducted at public health facilities in North Wollo Zone, Northeast Ethiopia from March 2 to April 15, 2020. A total of 715 health professionalswere selected using a stratified random sampling technique. EpiData version 4.6 and STATA version 15 were used for data entry and analysis, respectively. Descriptive statistics were computed. Multi-variable logistic regression analyses techniques were carried out to show the association between explanatory and outcome variables. Odd ratio at 95% confidence level was used to describe the strength of association.ResultsA total of 643 health professionals participated in this study. The response rate was 90%. Among them, 56.1% (95% CI: 52.3%–59.9%) demonstrated good data management practice. Working in health center [AOR=1.31 (95% CI: 1.853, 2.003)], having knowledge on data management [AOR=3.74 (95% CI: 2.454, 5.713)], favorable attitude toward data management [AOR=2.64 (95% CI: 1.746, 3.976)], high competency level on data management tasks [AOR=3.12 (95% CI: 1.873, 5.197)], friendliness of data management format [AOR=2.26 (95% CI: 1.478, 3.454)], supervision [AOR=1.78 (95% CI: 1.153, 2.745)] and training [AOR=1.84 (95% CI: 1.115, 3.022)] were significantly associated with good practice of health data management.ConclusionHealth data management practices of health professionals’ were found to be inadequate. Capacity building to enhance health professionals’ data management knowledge, attitude and their competency level, providing continuous supportive supervision, designing friendly data management format, providing comprehensive data management training are necessary measures to improve data management practice in this study setting.

Effectiveness of data auditing as a tool to reinforce good research data management (RDM) practice: a Singapore study

BackgroundInstitutions, funding agencies and publishers are placing increasing emphasis on good research data management (RDM). RDM lapses in medical science can result in questionable data and cause the public’s confidence in the scientific community to crumble. A fledgling medical school in a young university in Singapore has mandated every funded research project to have a data management plan (DMP). However, researchers’ adherence to their DMPs was unknown until the school embarked on routine data auditing. We hypothesize that research data auditing improves RDM awareness, compliance and reception in the school.MethodsWe conducted surveys with research PIs and researchers before and after data auditing to evaluate differences in self-reported RDM awareness, compliance and reception. As it is mandatory to deposit research data in a central data repository system in the school, we tracked data deposition by each laboratory from 2 weeks before to 3 months after data auditing as a marker of actual RDM compliance.ResultsResearch data auditing had an overall positive effect on self-reported RDM awareness, compliance and reception for both research PIs and researchers. Research PIs agreed more that RDM was important to scientific reproducibility, were more aware of proper RDM, had higher RDM strength in their laboratories and were more compliant with the DMP. Both research PIs and researchers believed data auditing helped them to be more compliant with data deposition in the repository. However, data auditing had no significant impact on laboratories’ data deposition rates over time, which could be due to the short sampling period.ConclusionsResearch PIs and researchers generally felt that data auditing was effective in improving RDM practices. It helped to evaluate their RDM practices objectively, propose corrective actions for RDM lapses and spread awareness of the university’s data management policies. Our findings corroborated other studies in medical research, geosciences, engineering and ethics that data auditing promotes good RDM practices. Hence, we recommend research institutions worldwide to adopt data auditing as a tool to reinforce research integrity.

Data management and sharing: Practices and perceptions of psychology researchers.

Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

Background: Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU’s Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance. Methods: As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs. Results & Conclusions: We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being “just” an European Commission (EC) requirement. DMPs are most prominently developed within a project’s Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach. The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk. If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs. The study identifies IP and licensing arrangements for DMPs as promising areas for further research.

Beyond EDC

INTRODUCTION: The last two decades have seen Electronic Data Capture (EDC) scale the adoption curve in the therapeutic development industry. The history of EDC adoption can inform development and implementation of similar, future advances. OBJECTIVE: This synthesis documents the progression to EDC in clinical research towards improving the development, implementation, and use of emerging and similar technology in multicenter clinical studies. METHODS: The 158 full-text sources identified in the Good Clinical Data Management Practices (GCDMP)systematic literature search were used as the basis for this scoping review.  A single reader evaluated this literature to determine rationale for and barriers to EDC implementation as well as remaining gaps in the use and optimization of EDC technology in clinical research. Quantitative evaluations of EDC technology were identified and summarized. Two reviewers examined the findings to identify omitted seminal work and alternate perspectives. RESULTS: Metrics and outcomes used in EDC evaluation are detailed. Three major gaps between current EDC implementation and fully realized electronically supported clinical trials. Similarities and differences between EDC and new clinical study technology highlighted to improve methods in implementation and evaluation of new technology in clinical studies.   CONCLUSIONS: Foreseeable implementation challenges delayed but did not necessarily extend the EDC adoption curve relative to contemporaneous innovation. Multiple adjunct new technologies have potential to surpass the current EDC performance plateau. Of these, direct EHR-to-eCRF data collection parallels EDC in terms of workflow disruption and technology interaction at clinical sites. Thus, similar adoption challenges are expected. Based on our observations, if developing standards are successful, they will make data more quickly and computationally accessible to significantly broaden opportunities for therapeutic development beyond those available in today’s EDC.

Why Won’t They Just Adopt Good Research Data Management Practices? An Exploration of Research Teams and Librarians’ Role in Facilitating RDM Adoption

Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams. Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively. The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management. In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group. We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions. We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective.