Data Curation Activities Research Articles

A framework of data curation competencies

Over the last decade, the library and information science (LIS) field has driven data curation education, research, and practice. As the field continues to grow and new techniques and technologies are developed, there is a need to evaluate current data curation practices to understand the important data curation competencies that should be taught to LIS students. This research expands our understanding of data curation competencies, skills, and technical tools and provides a data curation competencies framework. This data curation competencies framework was developed through 1) a systematic literature review and 2) focus groups with strategic stakeholders. For the systematic literature review, over 1,400 documents were reviewed, with 43 deemed relevant. Relevant documents described the competencies needed to conduct data curation activities, and from these findings, the initial competencies framework was created. The framework consists of twenty specific competencies organized into data curation activities, technical skills, and soft skills related to data curation. Secondly, focus groups were held with strategic stakeholders (educators, practitioners, and community organizations) to provide feedback on the overall structure of the competencies framework and input on the competencies necessary to working with community data. As this framework extends our current understanding of data curation competencies, it can also be utilized in both research and curriculum development. Currently, this framework is being utilized to create and test a pilot community data curation curriculum with LIS students and will be disseminated for LIS educators to incorporate into their curriculum.

Thematic Trends in Data Curation Literature

ABSTRACTThe field of data curation is rapidly changing due to new developments in technologies and techniques for conducting data work. As the field of data curation evolves, researchers, practitioners, and educators need to be able to respond to these developments. One way to understand trends in a field is by examining published literature. This study first gathered data curation literature through a modified systematic literature review with the framing question, ‘What competencies, skill sets, and proficiencies are needed to conduct data curation activities?’. These literatures were then analyzed using bibliometric analysis, visual analysis of the citation data, and topic modeling to understand trends in the data curation field.

Trends in Data Curation: Competencies and Tools

ABSTRACTLibrary and information science has led data curation research and education for the last two decades, providing data curation education, professional development programs, and a robust professional opportunity. To keep current with the latest trends in competencies and tools needed to conduct data curation activities, this research conducted a systematic literature review of literature that captures competencies and tools to build a framework of current trends in data curation work that educators can utilize to ensure up‐to‐date educational materials for the next generation of data curation professionals. This poster presents the preliminary findings of this data curation competencies and tools analysis.

A study on formalizing the knowledge of data curation activities across different fields.

In recent years, with the trend of open science, there have been many efforts to share research data on the internet. To promote research data sharing, data curation is essential to make the data interpretable and reusable. In research fields such as life sciences, earth sciences, and social sciences, tasks and procedures have been already developed to implement efficient data curation to meet the needs and customs of individual research fields. However, not only data sharing within research fields but also interdisciplinary data sharing is required to promote open science. For this purpose, knowledge of data curation across the research fields is surveyed, analyzed, and organized as an ontology in this paper. As the survey, existing vocabularies and procedures are collected and compared as well as interviews with the data curators in research institutes in different fields are conducted to clarify commonalities and differences in data curation across the research fields. It turned out that the granularity of tasks and procedures that constitute the building blocks of data curation is not formalized. Without a method to overcome this gap, it will be challenging to promote interdisciplinary reuse of research data. Based on the analysis above, the ontology for the data curation process is proposed to describe data curation processes in different fields universally. It is described by OWL and shown as valid and consistent from the logical viewpoint. The ontology successfully represents data curation activities as the processes in the different fields acquired by the interviews. It is also helpful to identify the functions of the systems to support the data curation process. This study contributes to building a knowledge framework for an interdisciplinary understanding of data curation activities in different fields.

The readiness to implement digital humanities data curation of four institutional repositories in Indonesia

PurposeThis study aims to identify the readiness of institutional repositories in Indonesia to implement digital humanities (DH) data curation. Data curation is a method of managing research data that maintains the data’s accuracy and makes it available for reuse. It requires controlled data management.Design/methodology/approachThe study uses a qualitative approach. Data collection was carried out through a focus group discussion in September–October 2022, interviews and document analysis. The informants came from four institutions in Indonesia.FindingsThe findings reveal that the national research repository has implemented data curation, albeit not optimally. Within the case study, one of the university repositories diligently curates its humanities data and has established networks extending to various ASEAN countries. Both the national archive repository and the other university repository have implemented rudimentary data curation practices but have not prioritized them. In conclusion, the readiness of the national research repository and the university repository stand at the high-capacity stage, while the national archive repository and the other university repository are at the established and early stages of data curation, respectively.Research limitations/implicationsThis study examined only four repositories due to time constraints. Nonetheless, the four institutions were able to provide a comprehensive picture of their readiness for DH data curation management.Practical implicationsThis study provides insight into strategies for developing DH data curation activities in institutional repositories. It also highlights the need for professional development for curators so they can devise and implement stronger ownership policies and data privacy to support a data-driven research agenda.Originality/valueThis study describes the preparations that must be considered by institutional repositories in the development of DH data curation activities.

Analisis proses bisnis kurasi data penelitian di Pusat Data dan Dokumentasi Ilmiah

Introduction. This study aims to identify research data curation activities and business processes at PDDI LIPI
 Data Collection Methods. This research used a case study approach with interviews and observations of five informants from June to July 2021.
 Data Analysis Three stages of coding are used to sort, identify, and associate categories with existing theories. Presentation of data was conducted through the description of entities from business processes based on the business process model and notation (BPMN).
 Results and Discussion. Four main activities of the curation process were carried out. The data owners and curators become actors in the process, and between the two requires trust, communication, and collaboration in their implementation. In addition, validation of the business processes was conducted to show that the process has been running scientifically.
 Conclusion. The flow of knowledge in these activities is documented in a structured manner based on mapped business procedures and processes. It is necessary to conduct periodic reviews and analyzes of time and resources. Further research should focus on human resources, policy documents, and facilities related to research data curation activities.

Research Librarians' Experiences of Research Data Management Activities at an Academic Library in a Developing Country

University libraries have archaeologically augmented scientific research by collecting, organizing, maintaining, and availing research materials for access. Researchers reckon that with the expertise acquired from conventional cataloging, classification, and indexing coupled with that attained in the development, along with the maintenance of institutional repositories, it is only rational that libraries take a dominant and central role in research data management and further their capacity as curators. Accordingly, University libraries are expected to assemble capabilities, to manage and provide research data for sharing and reusing efficiently. This study examined research librarians' experiences of RDM activities at the UON Library to recommend measures to enhance managing, sharing and reusing research data. The study was informed by the DCC Curation lifecycle model and the Community Capability Model Framework (CCMF) that enabled the Investigator to purposively capture qualitative data from a sample of 5 research librarians at the UON Library. The data was analysed thematically to generate themes that enabled the Investigator to address the research problem. Though the UON Library had policies on research data, quality assurance and intellectual property, study findings evidenced no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in skills and training capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited the managing, sharing, and reusing of research data. The study recommends developing an RDM unit within the UON Library to oversee the implementation of RDM activities by assembling all the needed capabilities (policy guidelines, skills and training, technological infrastructure and collaborative partnerships) to support data curation activities and enable efficient managing, sharing and reusing research data.

Researchers' Perceptions of Research Data Management Activities at an Academic Library in a Developing Country

This study investigated researchers perceptions of RDM at the MGGL to recommend measures to enhance managing, sharing, and reusing data. The study was underpinned by the DCC Curation lifecycle model and the CCMF which enabled the investigator to employ a descriptive research design to purposively capture data from a broad cross-section of 330 researchers. The data was analysed using SPSS to generate descriptive and inferential statistics that enabled the Investigator to answer the problem.Study findings demonstrate no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in the knowledge and skills capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited managing, sharing, and reusing data.The study recommends establishing a CMU within the MGGL to oversee the implementation of RDM activities by assembling all the needed capabilities to support data curation activities and enable efficient managing, sharing and reusing research data

Researchers’ Perceptions of Research Data Management Activities

This study investigated researchers perceptions of RDM at the MGGL to recommend measures to enhance managing, sharing, and reusing data. The study was underpinned by the DCC Curation lifecycle model and the CCMF which enabled the investigator to employ a descriptive research design to purposively capture data from a broad cross-section of 330 researchers. The data was analysed using SPSS to generate descriptive and inferential statistics that enabled the Investigator to answer the problem.Study findings demonstrate no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in the knowledge and skills capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited managing, sharing, and reusing data.The study recommends establishing a CMU within the MGGL to oversee the implementation of RDM activities by assembling all the needed capabilities to support data curation activities and enable efficient managing, sharing and reusing research data

The Impact of Community Happenings in OpenStreetMap—Establishing a Framework for Online Community Member Activity Analyses

The collaborative nature of activities in Web 2.0 projects leads to the formation of online communities. To reinforce this community, these projects often rely on happenings centred around data creation and curation activities. We suggest an integrated framework to directly assess online community member performance in a quantitative manner and applied it to the case study of OpenStreetMap. A set of mappers who participated in both field and remote mapping-related happenings was identified. To measure the effects of happenings, we computed attributes characterising the mappers’ contribution behaviour before and after the happenings and tested for significant impacts in relation to a control group. Results showed that newcomers to OpenStreetMap adopted a contribution behaviour similar to the contribution behaviour typical for the respective happening they attended: When contributing after the happening, newcomers who attended a remote mapping event tended to concentrate on creating new data with lower quality but high quantity in places foreign to their home region; newcomers who attended a field mapping event updated and enhanced existing local data with high accuracy. The behaviour of advanced mappers stayed largely unaffected by happenings. Unfortunately, our results did not reveal a positive effect on the community integration of newcomers through happenings.

Long-Term Data Preservation Data Lifecycle, Standardisation Process, Implementation and Lessons Learned

Science and Earth Observation data represent today a unique and valuable asset for humankind that should be preserved without time constraints and kept accessible and exploitable by current and future generations. In Earth Science, knowledge of the past and tracking of the evolution are at the basis of our capability to effectively respond to the global changes that are putting increasing pressure on the environment, and on human society. This can only be achieved if long time series of data are properly preserved and made accessible to support international initiatives. Within ESA Member States and beyond, Earth Science data holders are increasingly coordinating data preservation efforts to ensure that the valuable data are safeguarded against loss and kept accessible and useable for current and future generations. This task becomes increasingly challenging in view of the existing 40 years’ worth of Earth Science data stored in archives around the world and the massive increase of data volumes expected over the next years from e.g., the European Copernicus Sentinel missions. Long Term Data Preservation (LTDP) aims at maintaining information discoverable and accessible in an independent and understandable way, with supporting information, which helps ensuring authenticity, over the long term. A focal aspect of LTDP is data Curation. Data Curation refers to the management of data throughout its life cycle. Data Curation activities enable data discovery and retrieval, maintain its quality, add value, and allow data re-use over time. It includes all the processes that involve data management, such as pre-ingest initiatives, ingest functions, archival storage and preservation, dissemination, and provision of access for a designated community.
 The paper presents specific aspects, of importance during the entire Earth observation data lifecycle, with respect to evolving data volumes and application scenarios. These particular issues are introduced in the section on 'Big Data' and LTDP. The Data Stewardship Reference lifecycle section describes how the data stewardship activities can be efficiently organised, while the following section addresses the overall preservation workflow and shows the technical steps to be taken during Data Curation. Earth Science Data Curation and preservation should be addressed during all mission stages - from the initial mission planning, throughout the entire mission lifetime, and during the post- mission phase. The Data Stewardship Reference Lifecycle gives a high-level overview of the steps useful for implementing Curation and preservation rules on mission data sets from initial conceptualisation or receipt through the iterative Curation cycle.

Data curation as collective action during COVID‐19

AbstractIn this commentary, the authors, an international group data curation researchers and educators, reflect on some of the challenges and opportunities for data curation in the wake of the COVID‐19 pandemic. We focus on some topics of particular interest to the information science community: data infrastructures for scholarly communication and research, the politicization of data curation and visualization for public‐facing “dashboards,” and human subjects research and policies. We conclude with some areas of opportunity and need, including broader and richer data curation education in the information schools, the establishment of better data management policy implementations by research funders, the award of formal academic credit for data curation activities and data sharing, and engagement in cooperative action around data ethics and security.

Conceptualizing Data Curation Activities Within Two Academic Libraries

INTRODUCTION As funders and journals increasingly create policies that require effective data management and data sharing, many institutions have developed research data management (RDM) programs to help researchers meet these mandates. While there is not a standard set of services for these RDM programs, some institutions, particularly those with repositories that accept data deposits, provide data curation services as a way to add value to research data and help make data more accessible and reusable. Stakeholder communities within the field, such as the Data Curation Network (DCN), are also developing guidelines, procedures, and best practices to support and expand data curation practices. DESCRIPTION OF PROJECT This paper examines the data curation activities defined by the DCN, and describes an activity undertaken by library staff at Duke University and the University of North Carolina at Chapel Hill to create a structured model of these tasks to more easily conceptualize and communicate data curation within these two institutional settings. The purpose of this paper is to describe how this model provided a basis for the implementation and expansion of data curation services at each institution and concludes with overall lessons learned. NEXT STEPS As we develop our services, libraries have an opportunity to make the often-invisible work of curation more transparent. This paper aims to provide a point of reference for other libraries as they consider how to scale up their data curation programs as well as contribute to discussions around prioritization of services, program assessment, and communication with stakeholders.

Peer Review of Research Data Submissions to ScholarsArchive@OSU: How can we improve the curation of research datasets to enhance reusability?

<strong>Objective</strong>: Best practices such as the FAIR Principles (Findability, Accessibility, Interoperability, Reusability) were developed to ensure that published datasets are reusable. While we employ best practices in the curation of datasets, we want to learn how domain experts view the reusability of datasets in our institutional repository, ScholarsArchive@OSU. Curation workflows are designed by data curators based on their own recommendations, but research data is extremely specialized, and such workflows are rarely evaluated by researchers. In this project we used peer-review by domain experts to evaluate the reusability of the datasets in our institutional repository, with the goal of informing our curation methods and ensure that the limited resources of our library are maximizing the reusability of research data. <strong>Methods</strong>: We asked all researchers who have datasets submitted in Oregon State University’s repository to refer us to domain experts who could review the reusability of their data sets. Two data curators who are non-experts also reviewed the same datasets. We gave both groups review guidelines based on the guidelines of several journals. Eleven domain experts and two data curators reviewed eight datasets. The review included the quality of the repository record, the quality of the documentation, and the quality of the data. We then compared the comments given by the two groups. <strong>Results</strong>: Domain experts and non-expert data curators largely converged on similar scores for reviewed datasets, but the focus of critique by domain experts was somewhat divergent. A few broad issues common across reviews were: insufficient documentation, the use of links to journal articles in the place of documentation, and concerns about duplication of effort in creating documentation and metadata. Reviews also reflected the background and skills of the reviewer. Domain experts expressed a lack of expertise in data curation practices and data curators expressed their lack of expertise in the research domain. <strong>Conclusions</strong>: The results of this investigation could help guide future research data curation activities and align domain expert and data curator expectations for reusability of datasets. We recommend further exploration of these common issues and additional domain expert peer-review project to further refine and align expectations for research data reusability. <em>The substance of this article is based upon a panel presentation at RDAP Summit 2019.</em>

Lessons on Data Collection and Curation From the NFL Injury Surveillance Program.

"Research-ready" evidence platforms that link sports data with anonymized electronic health records (EHRs) or other data are important tools for evaluating injury occurrence in response to changes in games, training, rules, and other factors. While there is agreement that high-quality data are essential, there is little evidence to guide data curation. We hypothesized that an EHR used in the course of clinical care and curated for research readiness can provide a robust evidence platform. Our purpose was to describe the data curation used for active injury surveillance by the National Football League (NFL). Dynamic cohort study. Level 2. Players provide informed consent for research activities through the collective bargaining process. A league-wide EHR is used to record injuries that come to the attention of the teams' athletic trainers and physicians, NFL medical spotters, or unaffiliated neurotrauma consultants. Information about football activities and injuries are linkable by player, setting, and event to other sports-related data, including game statistics and game-day stadium quality measures, using a unique player identification designed to protect player privacy. Ongoing data curation is used to review data completeness and accuracy and is adjusted over time in response to findings. The core data curation activities include monthly injury summaries to team staff, queries to resolve incomplete reporting, and periodic external checks. Experiences derived from producing more than 100 reports per year on diverse topics are used to update coding training and related guidance documents in response to missing data or inconsistent coding that is observed. Roughly 20% more injuries were recorded for the same "reportable" injuries after switching from targeted reporting to an EHR. Research-ready databases need systematic curation for quality and completeness, along with related action plans. More injuries were reported through EHR than through targeted reporting. Evidence-driven decision-making thrives on reliable data fine-tuned through systematic use, review, and ongoing adjustments to the curation process.

Standardization and certification save us from the frustrations of the Greek drama

Standardization and certification save us from the frustrations of the Greek drama

Researchers May Need Additional Data Curation Support

A Review of:&#x0D; Johnston, L. R., Carlson, J., Hudson-Vitale, C., Imker, H., Kozlowski, W., Olendorf, R., &amp; Stewart, C. (2018). How important are data curation activities to researchers? Gaps and opportunities for academic libraries. Journal of Librarianship and Scholarly Communication, 6(1), 1-24. https://doi.org/10.7710/2162-3309.2198&#x0D; Abstract&#x0D; Objective – To identify the data curation activities most valued by researchers at universities.&#x0D; Design – Focus group and survey instrument.&#x0D; Setting – Six R1: Doctoral Universities in the United States of America that are part of a Data Curation Network (DCN) project to design a shared data curation service.&#x0D; Subjects – 91 researchers, librarians, and support staff.&#x0D; Methods – The authors used focus group methodology to collect data about valued data curation activities, current practices, and satisfaction with existing services or activities. Six focus groups were conducted at participants’ places of employment. Participants reviewed a list of 35 possible data curation activities, including documentation, data visualization, and rights management. A card-swapping exercise enabled subjects to rank the most important issues on a scale of 1-5, with “most important” activities becoming the subject of a facilitated discussion. In a short paper-based survey, participants also noted whether a data curation practice is in place at their institution, and their satisfaction with the practice.&#x0D; Main Results – Twelve data curation activities were identified as “highly rated” services that academic institutions could focus on providing to researchers. Documentation, Secure Storage, Quality Assurance, and Persistent Identifier were the data curation activities that the majority of participants rated as “most important.” Participants identified the data curation practices in place at their institutions, including documentation (80%), secure storage (75%), chain of custody (64%), metadata (63%), file inventory or manifest (58%), data visualization (58%), versioning (56%), file format transformations (55%), and quality assurance (52%). Participants reported low levels of satisfaction with their institutions’ data curation activities.&#x0D; Conclusion – Academic libraries have an opportunity to develop or improve existing data curation services by focusing on the twelve data curation activities that researchers, staff, and librarians value but that could be implemented in a more satisfactory way. The authors conclude that their organization, the Data Curation Network, has an opportunity to improve data curation services or to offer new or expanded services.

Data Curation Network: A Cross-Institutional Staffing Model for Curating Research Data

Funders increasingly require that data sets arising from sponsored research must be preserved and shared, and many publishers either require or encourage that data sets accompanying articles are made available through a publicly accessible repository. Additionally, many researchers wish to make their data available regardless of funder requirements both to enhance their impact and also to propel the concept of open science. However, the data curation activities that support these preservation and sharing activities are costly, requiring advanced curation practices, training, specific technical competencies, and relevant subject expertise. Few colleges or universities will be able to hire and sustain all of the data curation expertise locally that its researchers will require, and even those with the means to do more will benefit from a collective approach that will allow them to supplement at peak times, access specialized capacity when infrequently-curated types arise, and stabilize service levels to account for local staff transition, such as during turn-over periods. The Data Curation Network (DCN) provides a solution for partners of all sizes to develop or to supplement local curation expertise with the expertise of a resilient, distributed network, and creates a funding stream to both sustain central services and support expansion of distributed expertise over time. This paper presents our next steps for piloting the DCN, scheduled to launch in the spring of 2018 across nine partner institutions. Our implementation plan is based on planning phase research performed from 2016-2017 that monitored the types, disciplines, frequency, and curation needs of data sets passing through the curation services at the six planning phase institutions. Our DCN implementation plan includes a well-coordinated and tiered staffing model, a technology-agnostic submission workflow, standardized curation procedures, and a sustainability approach that will allow the DCN to prevail beyond the grant-supported implementation phase as a curation-as-service model.

Content growth of institutional repositories in South India: a status report

PurposeThe purpose of this study was to evaluate the content growth of institutional repositories (IR) in South India and analyse the type-wise growth of items available in these IRs and also discuss the traits and trends exposed by them.Design/methodology/approachWith the help of Registry of Open Access Repositories and Directory of Open Access Repositories (OpenDOAR), 39 repositories were located in south India. From these, Personal websites, the IRs that are currently not working and the repositories used for journal archiving were excluded. A total of 22 operational IRs at 21 institutions were identified for the study. Within a 15 month period, the data were collected from the 22 IRs twice for monitoring content growth.FindingsThe content of nearly all IRs have grown over the 15 month period, and the overall content growth rate was 7.82 per cent. Journal articles were the important content type of IRs, while thesis and conference papers were the next common. Moreover, item monographs exhibited the highest growth rate. Other categories, conference proceedings, and conference papers also exhibited a high growth rate. The present study revealed that Indian repositories were actively engaged in data curation activities, depositing a wide variety of items in their respective IRs. Overall, South Indian repositories exhibited a slow growth rate and tended to become inactive. Most South Indian Universities had not constituted the IRs, which led to the dominance of English language material in these IRs.Research limitations/implicationsThe study was conducted only in South Indian IRs.Originality/valueThis is the first study in India, attempting to determine the type-wise growth of items in IRs.

How Important is Data Curation? Gaps and Opportunities for Academic Libraries

INTRODUCTION Data curation may be an emerging service for academic libraries, but researchers actively “curate” their data in a number of ways—even if terminology may not always align. Building on past userneeds assessments performed via survey and focus groups, the authors sought direct input from researchers on the importance and utilization of specific data curation activities. METHODS Between October 21, 2016, and November 18, 2016, the study team held focus groups with 91 participants at six different academic institutions to determine which data curation activities were most important to researchers, which activities were currently underway for their data, and how satisfied they were with the results. RESULTS Researchers are actively engaged in a variety of data curation activities, and while they considered most data curation activities to be highly important, a majority of the sample reported dissatisfaction with the current state of data curation at their institution. DISCUSSION Our findings demonstrate specific gaps and opportunities for academic libraries to focus their data curation services to more effectively meet researcher needs. CONCLUSION Research libraries stand to benefit their users by emphasizing, investing in, and/or heavily promoting the highly valued services that may not currently be in use by many researchers.