International Journal of Digital Curation
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Out of the Jar into the World! A Case Study on Storing and Sharing Vertebrate Data
Get PDFIn 2018, the Deep Blue Repositories and Research Data Services (DBRRDS) team at the University of Michigan Library began working with the University of Michigan Museum of Zoology (UMMZ) to provide a persistent and sustainable (i.e., non-grant funded, institutionally supported) solution for their part of the National Science Foundation’s (NSF) openVertebrate (oVert) initiative. The objective of oVert is to the digitize scientific collections of thousands of vertebrate specimens stored in jars on museum shelves and make the data freely accessible to researchers, students, classrooms, and the general public anywhere in the world. The University of Michigan (U-M) is one of five scanning centers working on oVert and will contribute scans of more than 3,500 specimens from the UMMZ collections (Erickson 2017).
In addition to ingesting scans, the project involved developing methods to work around several significant system constraints: Deep Blue Data’s file structure (flat files only, no folders) and the closed use of Specify, UMMZ’s specimen database, for specimen metadata. DBRRDS had to create a completely new workflow for handling batch deposits at regular intervals, develop scripts to reorganize the data (according to a third-party data model) and augment the metadata using a third-party resource, Global Biodiversity Information Facility (GBIF).
This paper will describe the following aspects of the UMMZ CT Scanning Project partnership in greater detail: data generation, metadata requirements, workflows, code development, lessons learned, and next steps.
[This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.]
 
The CODATA-RDA Data Steward School
Get PDFGiven the expected increase in demand for Data Stewards and Data Stewardship skills it is clear that there is a need to develop training, education and CPD (continuous professional development) in this area.
In this paper a brief introduction is provided to the origin of definitions of Data Stewardship. Also it notes the present tendency towards equivalence between Data Stewardship skills and FAIR principles. It then focuses on one specific training event – the pilot Data Stewardship strand of the CODATA-RDA Research Data Science schools that by the time of the IDCC meeting will have been held in Trieste in August 2019. The paper will discuss the overall curriculum for the pilot school, how it matches with the FAIR4S framework, and plans for getting feedback from the students.
Finally, the paper discuss future plans for the school, in particular how to deepen the integration between the Data Stewardship strand with the Early Career Researcher strand.
[This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.
When Scientists Become Social Scientists: How Citizen Science Projects Learn About Volunteers
Get PDFOnline citizen science projects involve recruitment of volunteers to assist researchers with the creation, curation, and analysis of large datasets. Enhancing the quality of these data products is a fundamental concern for teams running citizen science projects. Decisions about a project’s design and operations have a critical effect both on whether the project recruits and retains enough volunteers, and on the quality of volunteers’ work. The processes by which the team running a project learn about their volunteers play a critical role in these decisions. Improving these processes will enhance decision-making, resulting in better quality datasets, and more successful outcomes for citizen science projects. This paper presents a qualitative case study, involving interviews and long-term observation, of how the team running Galaxy Zoo, a major citizen science project in astronomy, came to know their volunteers and how this knowledge shaped their decision-making processes. This paper presents three instances that played significant roles in shaping Galaxy Zoo team members’ understandings of volunteers. Team members integrated heterogeneous sources of information to derive new insights into the volunteers. Project metrics and formal studies of volunteers combined with tacit understandings gained through on- and offline interactions with volunteers. This paper presents a number of recommendations for practice. These recommendations include strategies for improving how citizen science project team members learn about volunteers, and how teams can more effectively circulate among themselves what they learn
Understanding the Data Management Plan as a Boundary Object through a Multi-stakeholder perspective
Get PDFA three-phase Delphi study was used to investigate an emerging community for research data management in Norway and their understanding and application of data management plans (DMPs). The findings reveal visions of what the DMP should be as well as different practice approaches, yet the stakeholders present common goals. This paper discusses the different perspectives on the DMP by applying Star and Griesemer’s theory of boundary objects (Star & Griesemer, 1989). The debate on what the DMP is and the findings presented are relevant to all research communities currently implementing DMP procedures and requirements. The current discussions about DMPs tend to be distant from the active researchers and limited to the needs of funders and institutions rather than to the usefulness for researchers. By analysing the DMP as a boundary object, plastic and adaptable yet with a robust identity (Star & Griesemer, 1989), and by translating between worlds where collaboration on data sharing can take place we expand the perspectives and include all stakeholders. An understanding of the DMP as a boundary object can shift the focus from shaping a DMP which fulfils funders’ requirements to enabling collaboration on data management and sharing across domains using standardised forms.
[This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.
Implementing a Research Data Policy at Leiden University
Get PDFIn this paper, we discuss the various stages of the institution-wide project that lead to the adoption of the data management policy at Leiden University in 2016. We illustrate this process by highlighting how we have involved all stakeholders. Each organisational unit was represented in the project teams. Results were discussed in a sounding board with both academic and support staff. Senior researchers acted as pioneers and raised awareness and commitment among their peers. By way of example, we present pilot projects from two faculties. We then describe the comprehensive implementation programme that will create facilities and services that must allow implementing the policy as well as monitoring and evaluating it. Finally, we will present lessons learnt and steps ahead. The engagement of all stakeholders, as well as explicit commitment from the Executive Board, has been an important key factor for the success of the project and will continue to be an important condition for the steps ahead
A Review of the History, Advocacy and Efficacy of Data Management Plans
Get PDFData management plans (DMPs) have increasingly been encouraged as a key component of institutional and funding body policy. Although DMPs necessarily place administrative burden on researchers, proponents claim that DMPs have myriad benefits, including enhanced research data quality, increased rates of data sharing, and institutional planning and compliance benefits.
In this article, we explore the international history of DMPs and describe institutional and funding body DMP policy. We find that economic and societal benefits from presumed increased rates of data sharing was the original driver of mandating DMPs by funding bodies. Today, 86% of UK Research Councils and 63% of US funding bodies require submission of a DMP with funding applications. Given that no major Australian funding bodies require DMP submission, it is of note that 37% of Australian universities have taken the initiative to internally mandate DMPs. Institutions both within Australia and internationally frequently promote the professional benefits of DMP use, and endorse DMPs as ‘best practice’. We analyse one such typical DMP implementation at a major Australian institution, finding that DMPs have low levels of apparent translational value. Indeed, an extensive literature review suggests there is very limited published systematic evidence that DMP use has any tangible benefit for researchers, institutions or funding bodies.
We are therefore led to question why DMPs have become the go-to tool for research data professionals and advocates of good data practice. By delineating multiple use-cases and highlighting the need for DMPs to be fit for intended purpose, we question the view that a good DMP is necessarily that which encompasses the entire data lifecycle of a project. Finally, we summarise recent developments in the DMP landscape, and note a positive shift towards evidence-based research management through more researcher-centric, educative, and integrated DMP services
Developing Culturally Competent Data Publication Resources
Get PDFIt is increasingly common for researchers to publish their datasets in open, mediated, or restricted form as a research output. The process of publishing data is complex and there are few guides that are practical or easy to understand. To bridge this gap, the Digital Curation and Data team at the University of Sydney Library initiated a project to develop applied data publication resources. To reflect current University and Library strategies, the team expanded the project to include a cultural competence review of each newly developed resource in order to reflect on the cultural biases that had gone into its creation and the ways in which the resource might appear through alternative cultural lenses, initially focusing on the data publication needs for research relating to Aboriginal and Torres Strait Islander peoples. It may be especially challenging for researchers who are working with culturally sensitive data to find resources that are easy to use and relevant. One initial goal of this project is to ensure that researchers, students and staff can support the ethical and culturally appropriate publication of data relating to Aboriginal and Torres Strait Islander peoples, communities, and knowledges. The team reviewed prototyped resources with researchers who conduct research with Aboriginal and Torres Strait Islander peoples to assess their usefulness and identify gaps. Resources are now being updated to reflect feedback, and additional resources will also be created. The intention is to continue to integrate cultural competence into the University of Sydney’s data curation and publication services and to expand this process to include wider cultural perspectives.
 
Designing and Building Interactive Curation Pipelines for Natural Hazards in Engineering Data
Get PDFTo design data curation pipelines within DesignSafe-CI, we gathered requirements and sought regular guidance from a group of experts in different aspects of natural hazards engineering research. Upon achieving understanding of experimental, simulation, hybrid simulation and field reconnaissance research workflows, we created four data models to guide data organization and developed specialized vocabularies as metadata. We then translated the models and metadata to interface design (front-end), and selected the infrastructure resources that would support curation and publication functions (back-end). We used iterative design and testing, including the use of interactive mockups of the GUI, to communicate and elicit feedback from the experts, and mapped real datasets to the mockups to evaluate the fitness of the data models, the clarity of the curation tasks. To address the problem of big data interfaces, we provide data representations that highlight the structure of the datasets and the possibility to browse their components in relation to provenance
Curating Humanities Research Data: Managing Workflows for Adjusting a Repository Framework
Get PDFHandling heterogeneous data, subject to minimal costs, can be perceived as a classic management problem. The approach at hand applies established managerial theorizing to the field of data curation. It is argued, however, that data curation cannot merely be treated as a standard case of applying management theory in a traditional sense. Rather, the practice of curating humanities research data, the specifications and adjustments of the model suggested here reveal an intertwined process, in which knowledge of both strategic management and solid information technology have to be considered. Thus, suggestions on the strategic positioning of research data, which can be used as an analytical tool to understand the proposed workflow mechanisms, and the definition of workflow modules, which can be flexibly used in designing new standard workflows to configure research data repositories, are put forward
Data Communities: Empowering Researcher-Driven Data Sharing in the Sciences
Get PDFThere is a growing perception that science can progress more quickly, more innovatively, and more rigorously when researchers share data with each other. However many scientists are not engaging in data sharing and remain skeptical of its relevance to their work. As organizations and initiatives designed to promote STEM data sharing multiply – within, across, and outside academic institutions – there is a pressing need to decide strategically on the best ways to move forward. In this paper, we propose a new mechanism for conceptualizing and supporting STEM research data sharing.. Successful data sharing happens within data communities, formal or informal groups of scholars who share a certain type of data with each other, regardless of disciplinary boundaries. Drawing on the findings of four large-scale qualitative studies of research practices conducted by Ithaka S+R, as well as the scholarly literature, we identify what constitutes a data community and outline its most important features by studying three success stories, investigating the circumstances under which intensive data sharing is already happening. We contend that stakeholders who wish to promote data sharing – librarians, information technologists, scholarly communications professionals, and research funders, to name a few – should work to identify and empower emergent data communities. These are groups of scholars for whom a relatively straightforward technological intervention, usually the establishment of a data repository, could kickstart the growth of a more active data sharing culture. We conclude by offering recommendations for ways forward.
[This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.