International Journal of Digital Curation
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Data Curation Network: A Cross-Institutional Staffing Model for Curating Research Data
Full text linkFunders 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
A Data-Driven Approach to Appraisal and Selection at a Domain Data Repository
Full text linkSocial scientists are producing an ever-expanding volume of data, leading to questions about appraisal and selection of content given finite resources to process data for reuse. We analyze users’ search activity in an established social science data repository to better understand demand for data and more effectively guide collection development. By applying a data-driven approach, we aim to ensure curation resources are applied to make the most valuable data findable, understandable, accessible, and usable. We analyze data from a domain repository for the social sciences that includes over 500,000 annual searches in 2014 and 2015 to better understand trends in user search behavior. Using a newly created search-to-study ratio technique, we identified gaps in the domain data repository’s holdings and leveraged this analysis to inform our collection and curation practices and policies. The evaluative technique we propose in this paper will serve as a baseline for future studies looking at trends in user demand over time at the domain data repository being studied with broader implications for other data repositories
Incorporating Software Curation into Research Data Management Services: Lessons Learned
No full textMany large research universities provide research data management (RDM) support services for researchers. These may include support for data management planning, best practices (e.g., organization, support, and storage), archiving, sharing, and publication. However, these data-focused services may under-emphasize the importance of the software that is created to analyse said data. This is problematic for several reasons. First, because software is an integral part of research across all disciplines, it undermines the ability of said research to be understood, verified, and reused by others (and perhaps even the researcher themselves). Second, it may result in less visibility and credit for those involved in creating the software. A third reason is related to stewardship: if there is no clear process for how, when, and where the software associated with research can be accessed and who will be responsible for maintaining such access, important details of the research may be lost over time.
This article presents the process by which the RDM services unit of a large research university addressed the lack of emphasis on software and source code in their existing service offerings. The greatest challenges were related to the need to incorporate software into existing data-oriented service workflows while minimizing additional resources required, and the nascent state of software curation and archiving in a data management context. The problem was addressed from four directions: building an understanding of software curation and preservation from various viewpoints (e.g., video games, software engineering), building a conceptual model of software preservation to guide service decisions, implementing software-related services, and documenting and evaluating the work to build expertise and establish a standard service level
Researcher Training in Spreadsheet Curation
Full text linkSpreadsheets are commonly used across most academic discplines, however their use has been associated with a number of issues that affect the accuracy and integrity of research data. In 2016, new training on spreadsheet curation was introduced at the University of Sydney to address a gap between practical software skills training and generalised research data management training. The approach to spreadsheet curation behind the training was defined and the training\u27s distinction from other spreadsheet curation training offering described.\parThe uptake of and feedback on the training were evaluated. Training attendance was analysed by discipline and by role. Quantitative and qualitative feedback were analysed and discussed. Feedback revealed that many attendees had been expecting and desired practical spreadsheet software skills training. Issues relating to whether or not practical skills training should and can be integrated with curation training were discussed. While attendees were found to be predominantly from science disciplines, qualitative feedback suggests that humanities attendees have specific needs in relation to managing data with spreadsheets that are currently not being met. Feedback also suggested that some attendees would prefer the curation training to be delivered as a longer, more in depth, hands on workshop.\parThe impact of the training was measured using data collected from the University\u27s Research Data Management Planning (RDMP) tool and the Sydney eScholarship Repository. RDMP descriptions of spreadsheet data and records of tabular datasets published in the repository were analysed and assessed for quality and for accompanying data documentation. No significant improvements in data documentation or quality were found, however it is likely too soon after the launch of the training program to have seen much in the way of impact.\parIdentified next steps include clarifying the marketing material promoting to the training to better communicate the curation focus, investigating the needs of humanities researchers working with qualitative data in spreadsheets, and incorporating new material into the training in order to address those needs. Integrating curation training with practical skills training and modifying the training to be more hands on are changes that may be considered in future, but will not be implemented at this stage
Tuuli project: accelerating data management planning in Finnish research organisations
Full text linkMany research funders have requirements for data sharing and data management plans (DMP). DMP tools are services built to help researchers to create data management plans fitting their needs and based on funder and/or organisation guidelines. Project Tuuli (2015–2017) has provided DMPTuuli, a data management planning tool for Finnish researchers and research organisations offering DMP templates and guidance. In this paper we describe how project has helped both Finnish researchers and research organisations adopt research data management best practices. As a result of the project we have also created a national Tuuli network. With growing competence and collaboration of the network, the project has reached most of its goals. The project has also actively promoted DMP support and training in Finnish research organisations
Sharing Selves: Developing an Ethical Framework for Curating Social Media Data
Full text linkOpen sharing of social media data raises new ethical questions that researchers, repositories and data curators must confront, with little existing guidance available. In this paper, the authors draw upon their experiences in their multiple roles as data curators, academic librarians, and researchers to propose the STEP framework for curating and sharing social media data. The framework is intended to be used by data curators facilitating open publication of social media data. Two case studies from the Dryad Digital Repository serve to demonstrate implementation of the STEP framework. The STEP framework can serve as one important ‘step’ along the path to achieving safe, ethical, and reproducible social media research practice
Data Curation for Community Science Project: CHIME Pilot Study
Full text linkThis paper introduces a community science project, Citizen Data Harvest in Motion Everywhere (CHIME), and the findings from our pilot study, which investigated potential concerns regarding data curation. The CHIME project aims to build a cyclist community–driven data archive that citizens, community scientists, and governments can use and reuse. While citizens’ involvement in the project enables data collection on a massive, unprecedented scale, the citizen-generated data (cyclists’ video data recorded with wearable cameras in the CHIME context) also presents several concerns regarding curation due to the grassroots nature of the data. Learning from our examination of cyclists’ video data and interviews with them, we will discuss the curation concerns and challenges we identified in our pilot study and introduce our approach to addressing these issues. Our study will provide insights into data curation concerns, to which other citizen science projects can refer. As a next step, we are in the process of developing a data curation model that will consider other factors related to this community science project and can be implemented in future community science projects
Building Tools to Support Active Curation: Lessons Learned from SEAD
Full text linkSEAD – a project funded by the US National Science Foundation’s DataNet program – has spent the last five years designing, building, and deploying an integrated set of services to better connect scientists’ research workflows to data publication and preservation activities. Throughout the project, SEAD has promoted the concept and practice of “active curation,” which consists of capturing data and metadata early and refining it throughout the data life cycle. In promoting active curation, our team saw an opportunity to develop tools that would help scientists better manage data for their own use, improve team coordination around data, implement practices that would serve the data better over time, and seamlessly connect with data repositories to ease the burden of sharing and publishing.
SEAD has worked with 30 projects, dozens of researchers, and hundreds of thousands of files, providing us with ample opportunities to learn about data and metadata, integrating with researchers’ workflows, and building tools and services for data. In this paper, we discuss the lessons we have learned and suggest how this might guide future data infrastructure development efforts
Is Democracy the Right System? Collaborative Approaches to Building an Engaged RDM Community
Full text linkWhen developing new products, tools or services, one always need to think about the end users to ensure a wide-spread adoption. While this applies equally to services developed at higher education institutions, sometimes these services are driven by policies and not by the needs of end users. This policy-driven approach can prove challenging for building effective community engagement. The initial development of Research Data Management support services at the University of Cambridge was policy-driven and subsequently failed in the first instance to engage the community of researchers for whom these services were created.
In this practice paper, we describe the initial approach undertaken at Cambridge when developing RDM services, the results of this approach and lessons learnt. We then provide an overview of alternative, democratic strategies employed and their positive effects on community engagement. We summarise by performing a cost-benefit analysis of the two approaches. This paper might be a useful case study for any institutions aiming to develop central support services for researchers, with conclusions applicable to the wider sector, and extending beyond Research Data Management services
Library Carpentry: Software Skills Training for Library Professionals
Full text linkMuch time and energy is now being devoted to developing the skills of researchers in the related areas of data analysis and data management. However, less attention is currently paid to developing the data skills of librarians themselves: these skills are often brought in by recruitment in niche areas rather than considered as a wider development need for the library workforce, and are not widely recognised as important to the professional career development of librarians. We believe that building computational and data science capacity within academic libraries will have direct benefits for both librarians and the users we serve.
Library Carpentry is a global effort to provide training to librarians in technical areas that have traditionally been seen as the preserve of researchers, IT support and systems librarians. Established non-profit volunteer organisations, such as Software Carpentry and Data Carpentry, offer introductory research software skills training with a focus on the needs and requirements of research scientists. Library Carpentry is a comparable introductory software skills training programme with a focus on the needs and requirements of library and information professionals. This paper describes how the material was developed and delivered, and reports on challenges faced, lessons learned and future plans