Data Management Plan Research Articles

Boosting I/O and visualization for exascale era using Hercule: test case on RAMSES

It has been clearly identified that I/O is one of the bottleneck to extend application for the exascale era. New concepts such as ‘in transit’ and ‘in situ’ visualization and analysis have been identified as key technologies to circumvent this particular issue. A new parallel I/O and data management library called Hercule, developed at CEA-DAM, has been integrated to Ramses, an AMR simulation code for self-gravitating fluids. Splitting the original Ramses output format in Hercule database formats dedicated to either checkpoints/restarts (HProt format) or post-processing (HDep format) not only improved I/O performance and scalability of the Ramses code but also introduced much more flexibility in the simulation outputs to help astrophysicists prepare their DMP (Data Management Plan). Furthermore, the very lightweight and purpose-specific post-processing format (HDep) will significantly improve the overall performance of analysis and visualization tools such as PyMSES 5. An introduction to the Hercule parallel I/O library as well as I/O benchmark results will be discussed.

Lessons from a Marine Spatial Planning data management process for Ireland

ABSTRACT This paper presents a framework containing ten components to deliver a data management process for the storage and management of data used for Marine Spatial Planning (MSP) in Ireland. The work includes a data process flow and a recommended solution architecture. The architecture includes a central data catalogue and a spatial storage system. The components of the process are presented to maximise the reuse potential of any dataset within an MSP context. The terms ‘Suitability’ and ‘Readiness’ in the MSP context are offered as both formal and considered assessments of data, as is the applicability of a data stewardship maturity matrix. How data contained in such a storage system can be published externally to potential consumers of these data is also explored. The process presents a means of managing data and metadata to ensure data lineage is optimised by carrying information about the origin of and the processing applied to the data; to evaluate the quality and relevance of geospatial datasets for use in MSP decisions in Ireland. The process was piloted in the National Marine Planning Framework for Ireland in the development of draft map products; feedback from the public consultation is ongoing and not presented.

Virtual exchanges as complex research environments: facing the data management challenge. A case study of Teletandem Brasil

Although there is a move toward open data, with research funding bodies more frequently requiring data management plans and dissemination strategies, the data management challenges inherently linked to virtual exchange research are understudied. Data collection is often reported upon in papers addressing interaction analysis or language development, but little attention has been paid to offering critical discussion of data collection and structuration methods or practical advice to encourage data/corpora dissemination. This paper reports on two phases of the Multimodal Teletandem Corpus project (Aranha & Lopes, 2019) that structured 581 hours of video data from Portuguese-English teletandem sessions, 351 chat logs, 956 written productions exchanged between the partners (original, revised, and corrected versions), 91 initial and 41 final questionnaires, and 666 learning diaries. We describe the data management problems faced that included the organization of data collected, ethical consent, management of a large quantity of data, inclusion of sociolinguistic information, expansion of learning theories, and the solutions found. We then outline data management planning steps that, consequently, are being introduced for future telecollaboration instantiations.

Research Data Management for Master’s Students: From Awareness to Action

This article provides an analysis of how sixteen recently graduated master’s students from the Netherlands perceive research data management. It is important to study the master’s students’ attitudes towards this, as students in this phase prepare themselves for their career. Some of them might become future academics or policymakers, thus, potentially, the future advocates of good data management and reproducible science. In general, students were rather unsure what ‘data management’ meant and would often confuse it with data analysis, study design or methodology, or ethics and privacy. When students defined the concept, they focussed on privacy aspects. Concepts such as open data and the ‘FAIR’ principles were rarely mentioned, even though these are the cornerstones of contemporary data management efforts. In practice, the students managed their own data in an ad hoc way, and only a few of them worked with a clear data management plan. Illustrative of this is that half of the interviewees did not know where to find their data anymore. Furthermore, their study programmes had diverse approaches to data management education. Most of the classes offered were limited in scope. Nevertheless, the students seemed to be aware of the importance of data management and were willing to learn more about good data management practices. This report helps to catch an important first glimpse of how master’s students (from different scientific backgrounds) think about research data management. Only by knowing this, accurate measures can be taken to improve data management awareness and skills. The article also provides some useful recommendations on what such measures might be, and introduces some of the steps already taken by the Delft University of Technology (TU Delft).

Research Data Management Status of Science and Technology Research Institutes in Korea

Recent advances in digital technology and the data-driven science paradigm has led to a proliferation of research data, which are becoming more important in scholarly communications. The sharing and reuse of research data can play a key role in enhancing the reusability and reproducibility of research, and data from publicly funded projects are assumed to be public goods. This is seen as a movement of open science and, more specifically, open research data. Many countries, such as the USA, UK, and Australia, are pushing ahead with implementing policies and infrastructure for open research data. In this paper, we present survey results pertaining to the creation, management, and utilization of data for researchers from government-funded research institutes of science and technology in Korea. We then introduce recent regulations stipulating a mandated data management plan for national R&D projects and on-going efforts to realize open research data in Korea.

Process Data-Based Knowledge Discovery in Additive Manufacturing of Ceramic Materials by Multi-Material Jetting (CerAM MMJ)

Multi-material jetting (CerAM MMJ, previously T3DP) enables the additive manufacturing of ceramics, metals, glass and hardmetals, demonstrating comparatively high solid contents of the processed materials. The material is applied drop by drop onto a substrate. The droplets can be adapted to the component to be produced by a large degree of freedom in parameterization. Thus, large volumes can be processed quickly and fine structures can be displayed in detail, based on the droplet size. Data-driven methods are applied to build process knowledge and to contribute to the optimization of CerAM MMJ manufacturing processes. As a basis for the computational exploitation of mass sensor data from the technological process chain for manufacturing a component with CerAM MMJ, a data management plan was developed with the help of an engineering workflow. Focusing on the process step of green part production, droplet structures as intermediate products of 3D generation were described by means of droplet height, droplet circularity, the number of ambient satellite particles, as well as the associated standard deviations. First of all, the weighting of the factors influencing the droplet geometry was determined by means of single factor preliminary tests, in order to be able to reduce the number of factors to be considered in the detailed test series. The identification of key influences (falling time, needle lift, rising time, air supply pressure) permitted an optimization of the droplet geometry according to the introduced target characteristics by means of a design of experiments.

Implementing mHealth Interventions in a Resource-Constrained Setting: Case Study From Uganda.

BackgroundMobile health (mHealth) interventions are becoming more common in low-income countries. Existing research often overlooks implementation challenges associated with the design and technology requirements of mHealth interventions.ObjectiveWe aimed to characterize the challenges that we encountered in the implementation of a complex mHealth intervention in Uganda.MethodsWe customized a commercial mobile survey app to facilitate a two-arm household-randomized, controlled trial of home-based tuberculosis (TB) contact investigation. We incorporated digital fingerprinting for patient identification in both study arms and automated SMS messages in the intervention arm only. A local research team systematically documented challenges to implementation in biweekly site visit reports, project management reports, and minutes from biweekly conference calls. We then classified these challenges using the Consolidated Framework for Implementation Research (CFIR).ResultsWe identified challenges in three principal CFIR domains: (1) intervention characteristics, (2) inner setting, and (3) characteristics of implementers. The adaptability of the app to the local setting was limited by software and hardware requirements. The complexity and logistics of implementing the intervention further hindered its adaptability. Study staff reported that community health workers (CHWs) were enthusiastic regarding the use of technology to enhance TB contact investigation during training and the initial phase of implementation. After experiencing technological failures, their trust in the technology declined along with their use of it. Finally, complex data structures impeded the development and execution of a data management plan that would allow for articulation of goals and provide timely feedback to study staff, CHWs, and participants.ConclusionsmHealth technologies have the potential to make delivery of public health interventions more direct and efficient, but we found that a lack of adaptability, excessive complexity, loss of trust among end users, and a lack of effective feedback systems can undermine implementation, especially in low-resource settings where digital services have not yet proliferated. Implementers should anticipate and strive to avoid these barriers by investing in and adapting to local human and material resources, prioritizing feedback from end users, and optimizing data management and quality assurance procedures.Trial RegistrationPan-African Clinical Trials Registration PACTR201509000877140; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=877

Current Status and Issues of Data Management Plan in Korea

Current Status and Issues of Data Management Plan in Korea

Ankara Üniversitesi Araştırmacılarının Araştırma Verilerinin Yönetimine Yönelik Tutumları ve Veri Paylaşımı Önündeki Engeller

Araştırma verisi, bilim dünyası için doğrulama amaçlı kullanımları, ekonomik ve toplumsal değerleri ile kilit bir öneme sahiptir. Bu nedenle verilerin açık hale getirilmesi ve paylaşımı önündeki engellerin kaldırılması gerekir. Bu çalışma, Ankara Üniversitesi bünyesinde Bilimsel Araştırma Projeleri’nde (BAP) yürütücü olarak görev yapan araştırmacıların araştırma verilerinin yönetimine yönelik tutumlarını ortaya çıkartmayı ve veri paylaşımı önündeki engelleri belirlemeyi amaçlamaktadır. Çalışmada betimleme yöntemi kullanılmış ve veri toplama tekniği olarak anket tercih edilmiştir. Toplam 194 araştırmacıdan bulguların toplandığı çalışmada, katılımcıların veriyi etkili bir şekilde yönetemedikleri ortaya çıkmıştır. Araştırmacıların büyük bir bölümü; yazılı bir veri yönetim planı hazırlamamakta, araştırmanın tamamlanmasından sonra veriye ilişkin devam eden uygulamaları kapsamlı bir şekilde planlamamakta, büyük miktarda veriyi kurumsal depolama alanları dışında tutmakta ve üst veri oluşturmada kabul görmüş standartları kullanmamaktadırlar. Veri yönetim süreçlerinde bilimsel alana özgü etkinin de ortaya konduğu çalışmada; oluşturulan veri türleri, verinin tutulduğu dosya türleri, üretilen veri miktarı, üst veriye aşinalık ve veri paylaşımını etkileyen nedenler gibi konularda bilimsel alanlar arası istatistiksel açıdan anlamlı farklılıklar olduğu saptanmıştır. Bu çalışmada ortaya çıkan en önemli sonuçlardan biri; katılımcıların verilerini yayın yolu ile paylaşmayı yeterli bulmalarıdır. Dolayısı ile araştırmacılar, araştırma verisini değil, sonuçlarını/bulgularını paylaşmayı tercih etmektedirler. Bu durum, açık araştırma verisi düşüncesi ile çelişmektedir. Araştırmacıların veri paylaşımı önündeki engeller; mevcut akademik sistem ve araştırma fikirlerinin/yöntemlerinin çalınma endişesi ile yakından ilgilidir. Bunların yanı sıra, veri yönetim süreçlerindeki problemler, konuya ilişkin hukuki düzenleme ve hizmet eksiklikleri gibi meseleler veri paylaşımı önündeki diğer engellerdir.

Researchers at Arab Universities Hold Positive Views on Research Data Management and Data Sharing

A Review of:
 Elsayed, A. M., & Saleh, E. I. (2018). Research data management and sharing among researchers in Arab universities: An exploratory study. IFLA Journal, 44(4), 281–299. https://doi.org/10.1177/0340035218785196
 Abstract
 Objective – To investigate researchers’ practices and attitudes regarding research data management and data sharing.
 Design – Email survey.
 Setting – Universities in Egypt, Jordan, and Saudi Arabia.
 Subjects – Surveys were sent to 4,086 academic faculty researchers.
 Methods – The survey was emailed to faculty at three Arab universities, targeting faculty in the life sciences and engineering. The survey was created using Google Docs and remained open for five months. Participants were asked basic demographic questions, questions regarding their research data and metadata practices, and questions regarding their data sharing practices.
 Main Results – The authors received 337 responses, for a response rate of 8%. The results showed that 48.4% of respondents had a data management plan and that 97% were responsible for preserving their own data. Most respondents stored their research data on their personal storage devices. The authors found that 64.4% of respondents reported sharing their research data. Respondents most frequently shared their data by publishing in a data research journal, sharing through academic social networks such as ResearchGate, and providing data upon request to peers. Only 5.1% of respondents shared data through an open data repository. Of those who did not share data, data privacy and confidentiality were the most common reasons cited. Of the respondents who did share their data, contributing to scientific progress and increased citation and visibility were the primary reasons for doing so. A total of 59.6% of respondents stated that they needed more training in research data management from their universities.
 Conclusion – The authors conclude that researchers at Arab universities are still primarily responsible for their own data and that data management planning is still a new concept to most researchers. For the most part, the researchers had a positive attitude toward data sharing, although depositing data in open repositories is still not a widespread practice. The authors conclude that in order to encourage strong data management practices and open data sharing among Arab university researchers, more training and institutional support is needed.

Data management plans: time wasting or time saving?

As a Research Data Librarian, one of my responsibilities is to raise awareness about the requirements for academic researchers to write data management plans for their projects. These awareness activities, often in the form of a presentation at staff meetings or departmental seminars, are frequently followed by a sceptical response from the academic staff along the lines that “This is just something else for us to try to fit into our already over-busy lives”. My answer is usually: “Yes, but it might prevent data management problems slowing up your project”. So, what are data management plans and how can they be of value to your project?

A Guide and Toolbox to Replicability and Open Science in Entomology.

The ability to replicate scientific experiments is a cornerstone of the scientific method. Sharing ideas, workflows, data, and protocols facilitates testing the generalizability of results, increases the speed that science progresses, and enhances quality control of published work. Fields of science such as medicine, the social sciences, and the physical sciences have embraced practices designed to increase replicability. Granting agencies, for example, may require data management plans and journals may require data and code availability statements along with the deposition of data and code in publicly available repositories. While many tools commonly used in replicable workflows such as distributed version control systems (e.g., ‘git’) or script programming languages for data cleaning and analysis may have a steep learning curve, their adoption can increase individual efficiency and facilitate collaborations both within entomology and across disciplines. The open science movement is developing within the discipline of entomology, but practitioners of these concepts or those desiring to work more collaboratively across disciplines may be unsure where or how to embrace these initiatives. This article is meant to introduce some of the tools entomologists can incorporate into their workflows to increase the replicability and openness of their work. We describe these tools and others, recommend additional resources for learning more about these tools, and discuss the benefits to both individuals and the scientific community and potential drawbacks associated with implementing a replicable workflow.

Research data management in Turkey: A survey to build an effective national data repository

Research data management is an important topic for funding agencies, universities and researchers. In this context, the main aim of this study is to collect preliminary information for Aperta, which is being developed by the Scientific and Technological Research Council of Turkey, to fulfil the following goals: determine the research data management awareness levels of researchers in Turkey; understand current research data management practices in their research environments; and find out their experiences of policy issues. For this, a questionnaire was distributed to 37,223 researchers, with 1577 researchers completing it. The results indicated that researchers who spend more time with data have more concerns about data management issues. The levels of experience of creating a data management plan were quite low. The importance of this study lies in how it is able to show the current research data management practices of Turkish scholars during the new repository’s foundational development stage.

Research Data Sharing in Spain: Exploring Determinants, Practices, and Perceptions

This work provides an overview of a Spanish survey on research data, which was carried out within the framework of the project Datasea at the beginning of 2015. It is covered by the objectives of sustainable development (goal 9) to support the research. The purpose of the study was to identify the habits and current experiences of Spanish researchers in the health sciences in relation to the management and sharing of raw research data. Method: An electronic questionnaire composed of 40 questions divided into three blocks was designed. The three Section s contained questions on the following aspects: (A) personal information; (B) creation and reuse of data; and (C) preservation of data. The questionnaire was sent by email to a list of universities in Spain to be distributed among their researchers and professors. A total of 1063 researchers completed the questionnaire. More than half of the respondents (54.9%) lacked a data management plan; nearly a quarter had storage systems for the research group; 81.5% used personal computers to store data; “Contact with colleagues” was the most frequent means used to locate and access other researchers’ data; and nearly 60% of researchers stated their data were available to the research group and collaborating colleagues. The main fears about sharing were legal questions (47.9%), misuse or interpretation of data (42.7%), and loss of authorship (28.7%). The results allow us to understand the state of data sharing among Spanish researchers and can serve as a basis to identify the needs of researchers to share data, optimize existing infrastructure, and promote data sharing among those who do not practice it yet.

Assessing the state of research data publication in hydrology: A perspective from the Consortium of Universities for the Advancement of Hydrologic Science, Incorporated

AbstractMany have argued that datasets resulting from scientific research should be part of the scholarly record as first class research products. Data sharing mandates from funding agencies and scientific journal publishers along with calls from the scientific community to better support transparency and reproducibility of scientific research have increased demand for tools and support for publishing datasets. Hydrology domain‐specific data publication services have been developed alongside more general purpose and even commercial data repositories. Prominent among these are the Hydrologic Information System (HIS) and HydroShare repositories developed by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). More broadly, however, multiple organizations have been involved in the practice of data publication in the hydrology domain, each having different roles that have shaped data publication and reuse. Bibliographic and archival approaches to data publication have been advanced, but both have limitations with respect to hydrologic data. Specific recommendations for improving data publication infrastructure, support, and practices to move beyond existing limitations and enable more effective data publication in support of scientific research in the hydrology domain include: improving support for journal article‐based data access and data citation, considering the workflow for data publication, enhancing support for reproducible science, encouraging publication of curated reference data collections, advancing interoperability standards for sharing data and metadata among repositories, developing partnerships with university libraries offering data services, and developing more specific data management plans. While presented in the context of CUAHSI's data repositories and experience, these recommendations are broadly applicable to other domains.This article is categorized under: Science of Water > Methods

Exploring research data management planning challenges in practice

AbstractResearch data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP.

Researchers' attitudes and perceptions towards data sharing and data reuse in the field of food science and technology

This work analyses the perception and practice of sharing, reusing, and facilitating access to research data in the field of food science and technology. The study involved the coordination of a focus group discussion and an online survey, to understand and evince the behaviour of researchers regarding data management in that field. Both the discussion group and the survey were performed with researchers from several institutes of the Spanish National Research Council. The lack of a data sharing culture, the fear of being scooped, and confusion between the concepts of the working plan and the data management plan were some of the issues that emerged in the focus group. Respondents' previous experience with sharing their research data has been mainly in the form of appendices to peer‐reviewed publications. From the survey (101 responses), the most important motivations for publishing research data were found to be facilitating the reproducibility of the research, increasing the likelihood of citations of the article, and compliance with funding body mandates. Legal constraints, intellectual property, data ownership, data rights, potential commercial exploitation, and misuse of data were the main barriers to publishing data as open data. Citation in publications, certification, compliance with standards, and the reputation of the data providers were the most relevant factors affecting the use of other researchers' data. Being recent or recently updated, well documented, with quality metadata and ease of access were the most valued attributes of open research data.

Over-Professional Skills and Professional Knowledge of Library Specialist: Demands of the Time

Library specialists having competencies in the field of modern information technologies and knowledge of information resources, capable to analyse and synthesize heterogeneous information, process data, solve non-standard tasks, are able to develop innovative trends, increase the importance and competitiveness of libraries in the information space. The purpose of this study is to determine the most important skills and knowledge of librarians for the development of new forms and trends in the activities of research libraries: assistant services to scientists, work with research data, creation of intellectual centres, centres of intellectual leisure, organization of communication platforms, etc. The author highlights the key knowledge necessary for librarian: knowledge of modern and advanced information technologies (social networks, cloud, mobile technologies, new generation analytics, etc.), knowledge of the world market of information resources, as well as technologies of collection and processing of information/data. The article presents competences of librarians in the research data management, who provide consulting and assistant services to scientists in the life cycle of research. It is determined that the research data management librarian should know the methods of data management plan preparation, management methods, categories, metadata standards and schemes, data classifications and identifiers, data citation requirements, copyright, data repositories, long-term data preservation technologies, etc. The author concludes that the possession of non-specialized over-professional (“soft”) skills (communication skills, emotional intelligence, thinking by “results” and “processes”, etc.) along with the complex of professional knowledge is the key to the improvement of efficiency and demand of libraries in the conditions of intensively developing environment.

Data Curation Profiling to Assess Data Management Training Needs and Practices to Inform a Toolkit

The purpose of this paper is to explore current data management training needs and practices for Belmont Forum member agencies and researchers to inform a Toolkit. Fourteen Belmont Forum affiliated individuals were interviewed following a predetermined set of questions to create data curation profiles of their funded work. The data curation profile questionnaire includes questions related to data management, storage, stakeholders, costs, training, and credentials. The interview findings highlight gaps in existing knowledge of data management theory and practice that could impact data re-use. Results of these interviews were used to populate a Toolkit of data management training and effective practice resources specifically developed to train Belmont Forum grant awardees. The results also highlight some attitudes and behaviours of current scientists, researchers, and agency representatives, and the impact of the implementation of data management plans on the open science movement.

Data Management Planning: How Requirements and Solutions are Beginning to Converge

Effective stewardship of data is a critical precursor to making data FAIR. The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions (DMP). We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles, we describe the background, context and historical development, as well as major driving forces, being research initiatives and funders. Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics. Next, we elaborate on emerging common standards for DMPs, especially the topic of machine-actionable DMPs. As sound DMP is not only a precursor of FAIR data stewardship, but also an integral part of it, we discuss its positioning in the emerging FAIR tools ecosystem. Capacity building and training activities are an important ingredient in the whole effort. Although not being the primary goal of this paper, we touch also the topic of research workforce support, as tools can be just as much effective as their users are competent to use them properly. We conclude by discussing the relations of DMP to FAIR principles, as there are other important connections than just being a precursor.