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Data reuse is a major focus for institutional research groups and their funders and it\u2019s easy to see why. After the (often) expensive process of collecting, analyzing, and mining research data for valuable new knowledge, any additional attention, such as the publication, reference, or reuse of that data, multiplies its value.<\/p>\n
But understanding researchers\u2019 behaviors and needs when it comes to data sharing and reuse is challenging. Each discipline has unique norms and practices for how they collect and manage data, when (and if) they share their data, and how they determine a dataset\u2019s fitness for reuse. Data curators\u2014as information science practitioners\u2014make a wealth of decisions and take well-informed actions to ensure that selected data have meaningful and enduring value to future research.<\/p>\n
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It is nearly impossible, though, for any institution to hire curators skilled in all the format types and disciplinary backgrounds needed to properly ensure that data are complete and well-structured for reuse. Which is why the Data Curation Network<\/a> (DCN) team set out to create a cross-institutional network that would enable researchers to share their data in ethical and reusable ways, regardless of their final repository destination.<\/p>\n The DCN was born from a simple reality: many data repositories receive a wide range of datasets (from neuroscience to entomology data) and in all conceivable file types\/formats (such as Python files or 3d Images).<\/p>\n The network expands the range of data curation experts that repositories rely on to work with data authors to clean, validate, document, and transform their data files for future users. Since our launch,\u00a0more than\u00a0100 data sets<\/a> have been matched to a curator<\/a> across our ten partner institutions<\/a>. Each year we hold annual in-person training events for data curators, we present webinars to share our training and skill development with the broader librarian audience, and we teach no-charge \u201cspecialized data curation\u201d workshops that have been attended by\u00a0more than 150 librarians and curators in the last three years.<\/p>\n Networked curation is not always easy. Each of our repository platforms\u00a0is unique. We use a variety of software (including DSpace, Samvera, Fedora, Dataverse) and we have distinct policies and ingest workflows. This is why we modeled the DCN as a seamless \u201chuman layer\u201d in the local repository stack. Each partner institution receives\u00a0its own data and archives it locally.\u00a0Partners decide what to accept and if\/when to send data to the DCN for curation. They also provide repository services of storage, DOI minting, and long-term preservation.<\/p>\n By running curation pilots (Johnston et al., 2017<\/a>), surveying data repositories nationwide (Hudson Vitale et al., 2017<\/a>), and hosting a series of researcher focus groups, we identified a set of 47 data curation activities that were happening at various repositories. These included actions such as reviewing code, contextualizing data with links to related publications, creating documentation files, and setting\/enforcing embargoes (see full list at Johnston et al., 2016<\/a>). In our focus groups, we asked researchers to rate each activity on a scale of 1-5 for importance.<\/p>\n We observed several areas that are of great importance to researchers \u2026 but were simply not happening for the majority of them. For example, having DOIs for data, auditing files for completeness, or contextualizing the data within a larger body of work. This was where the DCN could provide new value-add services. Additionally, there were several very important activities where researchers were not satisfied with work that was being done, and here we saw an opportunity for the DCN to provide better tools and services to assist with documentation, quality assurance, data visualization, and file format transformation.<\/p>\n Based on this early research and planning prior to launching the DCN, we developed our CURATE(D) steps (Data Curation Network, 2018<\/a>). This conceptual workflow is the basis of our training (both for DCN curators and our workshops for the community). Each step has its own checklist outlining actions that any dataset might need, such as auditing the contents of a dataset and verifying the accuracy of all metadata provided. The steps represent a baseline set of actions to take for any given dataset, but the specific approach may vary.<\/p>\n For other specialized formats, we use \u201cdata curation primers\u201d to capture format-specific actions. These primers are community-authored protocols developed by attendees of our Data Curation Network workshops and published on Github<\/a> so they may be updated and edited over time. So far our colleagues have authored primers on Microsoft Excel, Microsoft Access, netCDF, ATLAS.TI, Confocal Microscope images, Geodatabases, Jupyter Notebook, netCDF, SPSS, \u00a0R, Tableau, and WordPress.com.<\/p>\nLibrarians are well-positioned to help leverage research data in valuable new ways. #OCLCnext <\/a><\/span>Share on X<\/a><\/span>\n Faniel, Frank,\u00a0and Yakel\u2019s \u201cContext from the Data Reuser\u2019s Point of View\u201d (2019<\/a>) is an excellent case study from which to aid these curatorial decisions. Looking across three data-intensive domains (archeology, zoology, and qualitative social science), the project collected evidence for what data reusers look for when evaluating data produced by others. Based on that evidence, this paper assembles a framework of contextual information that data repository curators and developers can use to design better, more user-driven systems and curatorial processes.<\/p>\n I found it very useful to compare the findings from the Faniel et al. study with the steps taken by data curator practitioners in the Data Curation Network to think about how we could update our checklists and provide evidence of the value of our current activities.<\/p>\nMany policies, many platforms: one seamless \u201chuman layer\u201d<\/h2>\n
Adding value to the process<\/h2>\n
CURATE Steps and Context for Reuse<\/h2>\n
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