Research Data Management Research Articles

Perspectivas de archivo en la gestión de datos de investigación

It presents a proposal for a relationship between Integrated Archival Science and Research Data Management based on the development of archival functions recommended by Canadian authors Jean-Yves Rousseau and Carol Couture. It approaches Research Data Management as a multidisciplinary activity and develops the role of Archival Science in Research Data Management. It demonstrates the potential of integrating the two disciplines.

Developing research data management services in a regional comprehensive university: The case of Central Washington University

This study aims to analyze the needs of researchers in a regional comprehensive university for research data management services; discuss the options for developing a research data management program at the university; and then propose a phased three-year implementation plan for the university libraries. The method was to design a survey to collect information from researchers and assess and evaluate their needs for research data management services. The results show that researchers’ needs in a regional comprehensive university could be quite different from those of researchers in a research-intensive university. Also, the results verify the hypothesis that researchers in the regional comprehensive university would welcome the libraries offering managed data services for the research community. Therefore, this study suggests a phased three-year implementation plan. The significance of the study is that it can give some insights and helpful information for regional comprehensive universities that are planning to develop a research data management program.

Toward data lakes as central building blocks for data management and analysis.

Data lakes are a fundamental building block for many industrial data analysis solutions and becoming increasingly popular in research. Often associated with big data use cases, data lakes are, for example, used as central data management systems of research institutions or as the core entity of machine learning pipelines. The basic underlying idea of retaining data in its native format within a data lake facilitates a large range of use cases and improves data reusability, especially when compared to the schema-on-write approach applied in data warehouses, where data is transformed prior to the actual storage to fit a predefined schema. Storing such massive amounts of raw data, however, has its very own challenges, spanning from the general data modeling, and indexing for concise querying to the integration of suitable and scalable compute capabilities. In this contribution, influential papers of the last decade have been selected to provide a comprehensive overview of developments and obtained results. The papers are analyzed with regard to the applicability of their input to data lakes that serve as central data management systems of research institutions. To achieve this, contributions to data lake architectures, metadata models, data provenance, workflow support, and FAIR principles are investigated. Last, but not least, these capabilities are mapped onto the requirements of two common research personae to identify open challenges. With that, potential research topics are determined, which have to be tackled toward the applicability of data lakes as central building blocks for research data management.

Understanding Research Data Practices of Civil and Environmental Engineering Graduate Students

Research data management is essential for high-quality reproducible research, yet relatively little is known about how research data management is practiced by graduate students in Civil and Environmental Engineering (CEE). Prior research suggests that faculty in CEE delegate research data management to graduate students, prompting this investigation into how graduate students practice data management. This study uses semi-structured interviews and qualitative content analysis to explore how CEE graduate students work with data and practice data management in their research, as well as what resources and support would meet their needs. Many respondents touched on data collection, data management, disseminating research outputs, and collaboration and learning in their interviews. Several themes emerged from the interviews: data quality as a concern, as many CEE graduate students rely on secondary data for research; a gap between values and enacted practices; a connection between disseminating data and reproducibility; and a reliance on peer and self-directed learning for data management education. Based on these themes, the study recommends strategies for librarians and others on campus to better support CEE graduate student research data practices.

Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy.

Atom probe tomography, and related methods, probe the composition and the three-dimensional architecture of materials. The software tools which microscopists use, and how these tools are connected into workflows, make a substantial contribution to the accuracy and precision of such material characterization experiments. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced it is useful to join forces. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe microscopy software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.

Polyglot data management

Due to the increasing variety of the current database landscape, polyglot data management has become a hot research topic in recent years. The underlying idea is to combine the benefits of different data stores behind a predefined set of common interfaces and thus address use cases that individual stores cannot meet. This can be accomplished using different approaches which vary greatly in terms of capabilities, functionality, and architectural concepts. This tutorial provides a detailed overview of the current state of research in polyglot data management. We motivate its use by showing the high diversity of existing data stores and discussing three use cases in which individual stores are insufficient. Thereafter, we present different taxonomies for classifying polyglot data systems and give a detailed review of a number of selected systems. Finally, we compare these systems based on their features and discuss open challenges that still need to be addressed in future research.

Structured Data Storage for Data-Driven Process Optimisation in Bioprinting

Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats.

Reflections on my data management research journey (VLDB women in database research award talk)

Data-driven decision making is critical for all kinds of enterprises, public and private. It has been my mission to find more efficient, and effective ways to store, manage, query and analyze data to drive actionable insights. Throughout my career, I worked on many different technologies and systems, including semi-structured query processing, and large-scale data analytics. In this talk, I will talk about lessons learned, both technical and non-technical, using two of these systems as examples.

Evaluation of Research Data Management (RDM) services in academic libraries of India: A triangulation approach

Research is a planned and scientific method of increasing knowledge that is typically funded by a country's government or funding agencies. Research activity produces valuable data. Research Data Services (RDS) or Research Data Management (RDM) services are considered vital services provided by an academic library and are focused on the management, archiving, processing, and reuse of critical research data. This study evaluates the current status of the adaptation of RDS or RDM services in Indian academic libraries (which includes a total of 186 institutions, including all of India's Central Universities (54) and Institutes of National Importance (132)). A method triangulation approach was used for the data collection, including a literature survey, library website study, online survey, and telephonic interview with LIS professionals from Indian academic libraries. Academic libraries in India are yet to keep up with those in developed countries in adopting RDM services owing to a lack of RDM policy, institutional support, and technological challenges, according to the data. The study also presents suggestions to decision-makers, higher authorities of academic institutions, and the government to develop a strong RDM policy at both the institutional and national levels defining the role and duties of the libraries in RDM.

Artifacts Availability & Reproducibility (VLDB 2021 Round Table)

In the last few years, SIGMOD and VLDB have intensified efforts to encourage, facilitate, and establish reproducibility as a key process for accepted research papers, awarding them with the Reproducibility badge. In addition, complementary efforts have focused on increasing the sharing of accompanying artifacts of published work (code, scripts, data), independently of reproducibility, awarding them the Artifacts Available badge. In this short note, we summarize the discussion of a panel held during VLDB 2021 titled "Artifacts, Availability & Reproducibility". We first present a more detailed summary of the recent efforts. Then, we present the discussion and the contributed key points that were made, aiming to assess the reproducibility of data management research and to propose changes moving forward.

Mr Two Initiatives Furthering Open Research

This poster will present two initiatives furthering Open Research
 RSpace, is an electronic lab notebook cum research data management engine that underpins an ecosystem of connected tools enabling Open Research.
 The FAIR Data Podcast, a new podcast devoted to conversations around FAIR data and Open Research.

First Line Research Data Management for Life Sciences: a Case Study


 Modern life sciences studies depend on the collection, management and analysis of comprehensive datasets in what has become data-intensive research. Life science research is also characterised by having relatively small groups of researchers. This combination of data-intensive research performed by a few people has led to an increasing bottleneck in research data management (RDM). Parallel to this, there has been an urgent call by initiatives like FAIR and Open Science to openly publish research data which has put additional pressure on improving the quality of RDM. Here, we reflect on the lessons learnt by DataHub Maastricht, a RDM support group of the Maastricht University Medical Centre (MUMC+) in Maastricht, the Netherlands, in providing first-line RDM support for life sciences. DataHub Maastricht operates with a small core team, and is complemented with disciplinary data stewards, many of whom have joint positions with DataHub and a research group. This organisational model helps creating shared knowledge between DataHub and the data stewards, including insights how to focus support on the most reusable datasets. This model has shown to be very beneficial given limited time and personnel. We found that co-hosting tailored platforms for specific domains, reducing storage costs by implementing tiered storage and promoting cross-institutional collaboration through federated authentication were all effective features to stimulate researchers to initiate RDM. Overall, utilising the expertise and communication channel of the embedded data stewards was also instrumental in our RDM success. Looking into the future, we foresee the need to further embed the role of data stewards into the lifeblood of the research organisation, along with policies on how to finance long-term storage of research data. The latter, to remain feasible, needs to be combined with a further formalising of appraisal and reappraisal of archived research data.

Research Data Management needs assessment of Clemson University

Research data management (RDM) encompasses a wide range of activities that include documenting and managing data during a research project, as well as sharing and preserving data after the research project is completed.  Academic libraries can offer a variety of services that support researchers during the research life cycle which vary among institutions.   The faculty, staff, and graduate students of Clemson University were surveyed by Clemson Libraries about their research data management (RDM) needs in the spring of 2021.  The survey was based on previous surveys from 2012 and 2016 to allow for comparison, but language was updated and additional questions added because the field of RDM has evolved.  Survey findings indicated researchers are overall more likely to back up and share their data, but the process of cleaning and preparing the data for sharing was an obstacle.  Few researchers reported including metadata when sharing, or consulting the library for help writing a Data Management Plan (DMP).  Researchers want RDM resources, and offering and effectively marketing those resources will enable libraries to both support researchers and encourage best practices. Understanding researcher needs and offering time-saving services and convenient training options makes following RDM best practices easier for researchers.  Communicating how RDM benefits researchers and integrating RDS into the research life cycle through partnerships is crucial for ensuring effective data management.

Adamant: a JSON schema-based metadata editor for research data management workflows.

The web tool Adamant has been developed to systematically collect research metadata as early as the conception of the experiment. Adamant enables a continuous, consistent, and transparent research data management (RDM) process, which is a key element of good scientific practice ensuring the path to Findable, Accessible, Interoperable, Reusable (FAIR) research data. It simplifies the creation of on-demand metadata schemas and the collection of metadata according to established or new standards. The approach is based on JavaScript Object Notation (JSON) schema, where any valid schema can be presented as an interactive web-form. Furthermore, Adamant eases the integration of numerous available RDM methods and software tools into the everyday research activities of especially small independent laboratories. A programming interface allows programmatic integration with other software tools such as electronic lab books or repositories. The user interface (UI) of Adamant is designed to be as user friendly as possible. Each UI element is self-explanatory and intuitive to use, which makes it accessible for users that have little to no experience with JSON format and programming in general. Several examples of research data management workflows that can be implemented using Adamant are introduced. Adamant (client-only version) is available from: https://plasma-mds.github.io/adamant.

Research Data Preservation Practices of Library and Information Science Faculties


 Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries.

The Leading Role of University Libraries on the Ethics of Providing Big Data and the Analysis and the Extent of its Impact on Scientific Research: A survey of the libraries of the universities of the Kordofan sector.

Information institutions, especially university libraries, are facing the inevitability of dealing with the big data revolution that emerged as a result of the increasing digitalization in size and quality. Accordingly, university libraries are concerned with dealing big data in many activities in terms of acquiring various sources, making available to search in databases available on knowledge banks, and statistical analyzes of the data used in addition to the tools for managing research data. In the light of the foregoing, the study sought to identify the significant role of university libraries in analyzing big data, and clarifying the ethics of its use in scientific research, as well as making it accessible to researchers to help solve problems and analyze the difficulties that libraries, researchers, and library workers may face in this huge revolution. This study also aimed at confronting the big data revolution and addressing it, taking into consideration the proficiency in dealing with its use software so that workers can analyze big data using sound scientific methods. The descriptive approach was used in relation to the data for presenting and defining the basic concepts of big data ethics. In addition, a survey method was used by studying a sample of library and information specialists in the universities of the Kordofan sector, in addition to the questionnaire. The data collected was analyzed by the SPSS program and reached a number of results in which 90% of the surveyed sample proved the possibility of employing big data analytics to develop university library services and support decision makers in the library, which is directly reflected on researchers and scientific research. The study recommends the need to develop an agreed upon scientific ethical standard to be adopted by the Sudanese Association for Libraries and Information with the Sudanese University Libraries Coalition approved by the Federation of Sudanese Universities and to be signed by the Ministry of Higher Education Scientific Research as a Sudanese scientific and legal specification on how to use big data and its analyzes in Sudanese university libraries.

A unified representation method for interdisciplinary spatial earth data

ABSTRACT Unified representation of spatial earth data is an essential scientific issue. The analysis and mining of interdisciplinary spatial earth data resources can help discover hidden scientific knowledge, and even reveal the intrinsic relationship among different disciplines. However, the different description methods and inner structures among interdisciplinary spatial earth data bring significant challenges to unified data management and collaborative analysis in earth environment research. To address this issue, this paper proposes a unified representation method for interdisciplinary spatial earth data. First, this paper establishes a general metadata model and realizes the unified description of interdisciplinary data. Second, an entity data organization model is presented, which can realize the unified organization of entity data with different inner structures. Finally, we introduce the Spatial Earth Data Format (SEDF), a data format based on HDF5 for implementing the data organization model of interdisciplinary spatial earth data. Data representation experiments and validation are conducted to verify the availability and practicability of the proposed data representation method. The results suggest the powerful ability to represent spatial earth data efficiently and ensure data integrity, which is convenient for data management and application.

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

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

Adamant: a JSON schema-based metadata editor for research data management workflows

The web tool Adamant has been developed to systematically collect research metadata as early as the conception of the experiment. Adamant enables a continuous, consistent, and transparent research data management (RDM) process, which is a key element of good scientific practice ensuring the path to Findable, Accessible, Interoperable, Reusable (FAIR) research data. It simplifies the creation of on-demand metadata schemas and the collection of metadata according to established or new standards. The approach is based on JavaScript Object Notation (JSON) schema, where any valid schema can be presented as an interactive web-form. Furthermore, Adamant eases the integration of numerous available RDM methods and software tools into the everyday research activities of especially small independent laboratories. A programming interface allows programmatic integration with other software tools such as electronic lab books or repositories. The user interface (UI) of Adamant is designed to be as user friendly as possible. Each UI element is self-explanatory and intuitive to use, which makes it accessible for users that have little to no experience with JSON format and programming in general. Several examples of research data management workflows that can be implemented using Adamant are introduced. Adamant (client-only version) is available from: https://plasma-mds.github.io/adamant.

Research Data Management Training in Academic Libraries: A Scoping Review

Purpose: The present scoping review examines research data management (RDM) best practices and empirical studies in academic libraries between 2010 and 2021. Method: The current study developed systematic database searches to locate potential articles for inclusion and designed a detailed and systematic coding scheme to examine the substantive features of RDM and characteristics of RDM practice, with an emphasis on RDM instruction. Results and Discussion: The results from the current study demonstrated that there is great demand for RDM training after 2011. Furthermore, research about RDM training spread across North America, Europe, Asia Pacific, and elsewhere. The findings also proved that RDM training is essential for both STEM and non- STEM subjects but simultaneously indicated that non-STEM subjects such as the social sciences in particular lack RDM training. Results from the current literature also found that a large number of RDM training programs focused on the introduction of RDM or an RDM overview, without in-depth and discipline-based curriculum for researchers across domains. Additionally, this study identified a lack of quantitative research, especially statistical analysis, on the effect of RDM interventions. Conclusion: This study contributes to our comprehensive understanding of some essential elements associated with RDM training, with the primary finding that future practitioners in the RDM field would benefit from stronger collaboration with faculty or researchers to develop more discipline-based curriculums for RDM and more application-based approaches for teaching RDM.