International Journal of Digital Curation
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Research Data Management Practices at the University of Namibia: Moving Towards Adoption
Full text linkThe management of research data in academic institutions is increasing across most disciplines. In Namibia, the requirement to manage research data, making it available for the purposes of sharing, preservation and to support research findings, has not yet been mandated. At the University of Namibia (UNAM) there is no institutional research data management (RDM) culture, yet RDM may nevertheless be practiced among its researchers. The extent to which these practices have been adopted is, however, not known. This study investigated the extent of RDM adoption by researchers at UNAM. It identifies current or potential challenges in managing research data, and proposes solutions to some of these challenges that could aid the university as it attempts to encourage the adoption of RDM practices. The investigation used Rogers’ Diffusion of Innovations (DOI) theory, with a focus on the innovation-decision process, as a means to establish where UNAM researchers are in the process of adopting RDM practices. The population under study were the UNAM faculty members who conduct research as part of their academic duties. Questionnaires were used to gather quantitative data. The study found that some researchers practice RDM to some extent out of their own free will, but there are many challenges that hinder these practices. Overall, though, there is a lack of interest in RDM as the knowledge of the concept among researchers is relatively low. The study found that most researchers were at the knowledge stage of the innovation-decision process and recommended, among other things, that the university puts effort into creating RDM awareness and encouraging data sharing, and that it moves forward with infrastructure and policy development so that RDM can be fully adopted by the researchers of the institution. 
Curating for Accessibility
Full text linkAccessibility of research data to disabled users has received scant attention in literature and practice. In this paper we briefly survey the current state of accessibility for research data and suggest some first steps that repositories should take to make their holdings more accessible. We then describe in depth how those steps were implemented at the Qualitative Data Repository (QDR), a domain repository for qualitative social-science data. The paper discusses accessibility testing and improvements on the repository and its underlying software, changes to the curation process to improve accessibility, as well as efforts to retroactively improve the accessibility of existing collections. We conclude by describing key lessons learned during this process as well as next steps
Building LABDRIVE, a Petabyte Scale, OAIS/ISO 16363 Conformant, Environmentally Sustainable Archive, Tested by Large Scientific Organisations to Preserve their Raw and Processed Data, Software and Documents: LABDRIVE - Petabyte scale OAIS/ISO 16363 conformant archive for all types of digital information
Full text linkVast amounts of scientific, cultural, social, business and government, and other, information is being created every day. There are billions of objects, in a multitude of formats, semantics and associated software. Much, perhaps the majority, of this information is transitory but there is still an immense amount which should be preserved for the medium and long term – perhaps even indefinitely.
Preservation requires that the information continues to be usable, not simply to be printed or displayed. Of course, the digital objects (the bits) must be preserved, as must the “metadata” which enables the bits to the understood which includes the software.
Before LABDRIVE no system could adequately preserve such information, especially in such gigantic volume and variety.
In this paper we describe the development of LABDRIVE and its ability to preserve tens or hundreds of petabytes in a way which is conformant to the OAIS Reference Model and capable of being ISO 16363 certified
DBRepo: a Semantic Digital Repository for Relational Databases
Full text linkData curation is a complex, multi-faceted task. While dedicated data stewards are starting to take care of these activities in close collaboration with researchers for many types of (usually file-based) data in many institutions, this is rarely yet the case for data held in relational databases. Beyond large-scale infrastructures hosting e.g. climate or genome data, researchers usually have to create, build and maintain their database, care about security patches, and feed data into it in order to use it in their research. Data curation, if at all, usually happens after a project is finished, when data may be exported for digital preservation into file repository systems.
We present DBRepo, a semantic digital repository for relational databases in a private cloud setting designed to (1) host research data stored in relational databases right from the beginning of a research project, (2) provide separation of concerns, allowing the researchers to focus on the domain aspects of the data and their work while bringing in experts to handle classic data management tasks, (3) improve findability, accessibility and reusability by offering semantic mapping of metadata attributes, and (4) focus on reproducibility in dynamically evolving data by supporting versioning and precise identification/cite-ability for arbitrary subsets of data. 
Automation is Documentation: Functional Documentation of Human-Machine Interaction for Future Software Reuse
Full text linkPreserving software and providing access to obsolete software is necessary and will become even more important for work with any kind of born-digital artifacts. While usability and availability of emulation in digital curation and preservation workflow has improved significantly, productive (re)use of preserved obsolete software is a growing concern, due to a lack of (future) operational knowledge. In this article we describe solutions to automate and document software usage in a way, such that the result is not only instructive but also productive
OpenStack Swift: An Ideal Bit-Level Object Storage System for Digital Preservation
Full text linkA bit-level object storage system is a foundational building block of long-term digital preservation (LTDP). To achieve the purposes of LTDP, the system must be able to: preserve the authenticity and integrity of the original digital objects; scale up with dramatically increasing demands for preservation storage; mitigate the impact of hardware obsolescence and software ephemerality; replicate digital objects among distributed data centers at different geographical locations; and to constantly audit and automatically recover from compromised states. A realistic and daunting challenge to satisfy these requirements is not only to overcome technological difficulties but also to maintain economic sustainability by implementing and continuously operating such systems in a cost-effective way. In this paper, we present OpenStack Swift, an open-source, mature and widely accepted cloud platform, as a practical and proven solution with a case study at the University of Alberta Library. We emphasize the implementation, application, cost analysis and maintenance of the system, with the purpose of contributing to the community with an exceedingly robust, highly scalable, self-healing and comparatively cost-effective bit-level object storage system for long-term digital preservation. 
Fostering the Adoption of DMP in Small Research Projects through a Collaborative Approach: A case study
Full text linkIn order to promote sound management of research data the European Commission, under the Horizon 2020 framework program, is promoting the adoption of a Data Management Plan (DMP) in research projects. Despite the value of a DMP to make data findable, accessible, interoperable and reusable (FAIR) through time, the development and implementation of DMPs is not yet a common practice in health research. Raising the awareness of researchers in small projects to the benefits of early adoption of a DMP is, therefore, a motivator for others to follow suit. In this paper we describe an approach to engage researchers in the writing of a DMP, in an ongoing project, FrailSurvey, in which researchers are collecting data through a mobile application for self-assessment of fragility. The case study is supported by interviews, a metadata creation session, as well as the validation of recommendations by researchers. With the outline of our process we also outline tools and services that supported the development of the DMP in this small project, particularly since there were no institutional services available to researcher
Capturing Data Provenance from Statistical Software
Full text linkWe have created tools that automate one of the most burdensome aspects of documenting the provenance of research data: describing data transformations performed by statistical software. Researchers in many fields use statistical software (SPSS, Stata, SAS, R, Python) for data transformation and data management as well as analysis. The C2Metadata ("Continuous Capture of Metadata for Statistical Data") Project creates a metadata workflow paralleling the data management process by deriving provenance information from scripts used to manage and transform data. C2Metadata differs from most previous data provenance initiatives by documenting transformations at the variable level rather than describing a sequence of opaque programs. Command scripts for statistical software are translated into an independent Structured Data Transformation Language (SDTL), which serves as an intermediate language for describing data transformations. SDTL can be used to add variable-level provenance to data catalogues and codebooks and to create "variable lineages" for auditing software operations. Better data documentation makes research more transparent and expands the discovery and re-use of research data
Automatic Module Detection in Data Cleaning Workflows: Enabling Transparency and Recipe Reuse
Full text linkBefore data from multiple sources can be analyzed, data cleaning workflows (“recipes”) usually need to be employed to improve data quality. We identify a number of technical problems that make application of FAIR principles to data cleaning recipes challenging. We then demonstrate how transparency and reusability of recipes can be improved by analyzing dataflow dependencies within recipes. In particular column-level dependencies can be used to automatically detect independent subworkflows, which then can be reused individually as data cleaning modules. We have prototypically implemented this approach as part of an ongoing project to develop open-source companion tools for OpenRefine.
Keywords: Data Cleaning, Provenance, Workflow Analysi
An Approach for Curating Collections of Historical Documents with the Use of Topic Detection Technologies
Full text linkDigital curation of materials available in large online repositories is required to enable the reuse of Cultural Heritage resources in specific activities like education or scientific research. The digitization of such valuable objects is an important task for making them accessible through digital platforms such as Europeana, therefore ensuring the success of transcription campaigns via the Transcribathon platform is highly important for this goal. Based on impact assessment results, people are more engaged in the transcription process if the content is more oriented to specific themes, such as First World War. Currently, efforts to group related documents into thematic collections are in general hand-crafted and due to the large ingestion of new material they are difficult to maintain and update. The current solutions based on text retrieval are not able to support the discovery of related content since the existing collections are multi-lingual and contain heterogeneous items like postcards, letters, journals, photographs etc. Technological advances in natural language understanding and in data management have led to the automation of document categorization and via automatic topic detection. To use existing topic detection technologies on Europeana collections there are several challenges to be addressed: (1) ensure representative and qualitative training data, (2) ensure the quality of the learned topics, and (3) efficient and scalable solutions for searching related content based on the automatically detected topics, and for suggesting the most relevant topics on new items. This paper describes in more details each such challenge and the proposed solutions thus offering a novel perspective on how digital curation practices can be enhanced with the help of machine learning technologies