International Journal of Digital Curation
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    408 research outputs found

    Data Curation in Interdisciplinary and Highly Collaborative Research

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    This paper provides a systematic analysis of publications that discuss data curation in interdisciplinary and highly collaborative research (IHCR). Using content analysis methodology, it examined 159 publications and identified patterns in definitions of interdisciplinarity, projects’ participants and methodologies, and approaches to data curation. The findings suggest that data is a prominent component in interdisciplinarity. In addition to crossing disciplinary and other boundaries, IHCR is defined as curating and integrating heterogeneous data and creating new forms of knowledge from it. Using personal experiences and descriptive approaches, the publications discussed challenges that data curation in IHCR faces, including an increased overhead in coordination and management, lack of consistent metadata practices, and custom infrastructure that makes interoperability across projects, domains, and repositories difficult. The paper concludes with suggestions for future research

    E-Preservation of Old and Rare Books: A Structured Approach for Creating a Digital Collection

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    Antique books, old and rare documents are fragile and vulnerable to different hazards. Preserving them for an extended period is a real challenge. From ancient times people started expressing their knowledge by writing and keeping records and subsequently started collecting and storing these at later ages as antique materials.  These can be seen in different museums, libraries, archives, individual households, and other places all over the world. Preserving and conserving these antique, old and rare books, documents etc. in good condition is a challenge for librarians, conservators, preservation administrators or persons associated with storing these. In this paper, details of the digital preservation of such a collection available in the Directorate of Historical and Antiquarian Studies (DHAS), Guwahati, Assam, India, are discussed. DHAS is a Government of Assam wing and is mainly mandated to collect, preserve and research historical and antiquarian resources. The collection of DHAS is one of the oldest collections and has been serving as a study and research centre in Assam since 1928. A special drive has been taken for the digital preservation of an identified part of the collection, with grant support from the National Archive of India.  This paper discusses the entire project process starting from the project proposal formulation to the structuring of the digital collection. The paper sequentially discusses the different steps of the entire work of digitization of a collection of 241 old and rare books from the main collection of DHAS

    If Data is Used in the Forest and No-one is Around to Hear it, Did it Happen? a Citation Count Investigation

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    In this article I describe the process and results of tracking a citation from a data repository through the article publication process and trying to add a citation event to one of our DOIs. I also discuss some other confusing aspects related to citation counts as indicated in various systems, including reference managers, the publisher’s perspective, aggregators, and DOI minters. I discovered numerous problems with citations. Addressing these problems is important as citations can be key to determining both the original use and reuse of a dataset, especially for repositories that do not track usage by requiring people to login or provide an email to download a dataset. The lack of transparency in some data citation systems and processes obscures how and where data is being used.&nbsp

    Cluster Analysis of Open Research Data: A Case for Replication Metadata

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    Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from tabular data and program code to audio-visual files. Metadata, or data about data, is critical to making research outputs adequately documented and FAIR. Aiming to contribute to the discussions on the development of metadata for research outputs, I conducted an exploratory analysis to determine how research datasets cluster based on what researchers organically deposit together. I use the content of over 40,000 datasets from the Harvard Dataverse research data repository as my sample for the cluster analysis. I find that the majority of the clusters are formed by single-type datasets, while in the rest of the sample, no meaningful clusters can be identified. For the result interpretation, I use the metadata standard employed by DataCite, a leading organization for documenting a scholarly record, and map existing resource types to my results. About 65% of the sample can be described with a single-type metadata (such as Dataset, Software orReport), while the rest would require aggregate metadata types. Though DataCite supports an aggregate type such as a Collection, I argue that a significant number of datasets, in particular those containing both data and code files (about 20% of the sample), would be more accurately described as a Replication resource metadata type. Such resource type would be particularly useful in facilitating research reproducibility

    Proposal for a Maturity Continuum Model for Open Research Data

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    As a contribution to the general effort in research to generalize and improve the practices of Open Research Data (ORD), we developed a model conceptualizing the degrees of maturity of a research community in terms of ORD. This model may be used to assess the ORD capacity or maturity level of a specific research community, to strengthen the use of standards with respect to ORD within this community, and to increase its ORD maturity level. We present the background and our motivations for developing such an instrument as well as the reasoning leading to its design. We present its elements in detail and discuss possible applications.&nbsp

    Who Writes Scholarly Code?

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    This paper presents original research about the behaviours, histories, demographics, and motivations of scholars who code, specifically how they interact with version control systems locally and on the Web. By understanding patrons through multiple lenses – daily productivity habits, motivations, and scholarly needs – librarians and archivists can tailor services for software management, curation, and long-term reuse, raising the possibility for long-term reproducibility of a multitude of scholarship

    Capturing Data Provenance from Statistical Software

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    We 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

    Data Showcases: the Data Journal in a Multimodal World

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       As an experiment, the Research Data Journal for the Humanities and Social Sciences (RDJ) has temporarily extended the usual format of the online journal with so-called ‘showcases’, separate web pages containing a quick introduction to a dataset, embedded multimedia, interactive components, and facilities to directly preview and explore the dataset described. The aim was to create a coherent hyper document with content communicated via different media (multimodality) and provide space for new forms of scientific publication such as executable papers (e.g. Jupyter notebooks). This paper discusses the objectives, technical implementations, and the need for innovation in data publishing considering the advanced possibilities of today's digital modes of communication. The data showcases experiment proved to be a useful starting point for an exploration of related developments within and outside the humanities and social sciences. It turns out that small-scale experiments are relatively easy to perform thanks to the easy availability of digital technology. However, real innovation in publishing affects organization and infrastructure and requires the joint effort of publishers, editors, data repositories, and authors. It implies a thorough update of the concept of publication and adaptation of the production process. This paper also pays attention to these obstacles to taking new paths

    Fostering the Adoption of DMP in Small Research Projects through a Collaborative Approach

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    In 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

    Data Curation Strategies to Support Responsible Big Social Research and Big Social Data Reuse

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    Big social research repurposes existing data from online sources such as social media, blogs, or online forums, with a goal of advancing knowledge of human behavior and social phenomena. Big social research also presents an array of challenges that can prevent data sharing and reuse. This brief report presents an overview of a larger study that aims to understand the data curation implications of big social research to support use and reuse of big social data. The study, which is based in the United States, identifies six key issues relating to big social research and big social data curation through a review of the literature. It then further investigates perceptions and practices relating to these six key issues through semi-structured interviews with big social researchers and data curators. This report concludes with implications for data curation practice: metadata and documentation, connecting with researchers throughout the research process, data repository services, and advocating for community standards. Supporting responsible practices for using big social data can help scale up social science research, thus enhancing our understanding of human behavior and social phenomena

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    International Journal of Digital Curation
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