Data Soup Webinar, December 16, 2021: hosted by the Data Curation Network and the Journal of eScience Librarianship

Data Soup is a collaboration between the Journal of eScience Librarianship (JeSLIB) and the Data Curation Networkto host a series of community focused webinars/discussions to exchange practices for curating research data of different formats or subject areas among data curators. The lineup of the inaugural webinar includes the following speakers and topics from the recent JeSLIB Special Issue: Data Curation in Practice: Creating Guidance for Canadian Dataverse Curators: Portage Network’s Dataverse Curation Guide Alexandra Cooper, Michael Steeleworthy, Ève Paquette-Bigras, Erin Clary, Erin MacPherson, Louise Gillis, and Jason Brodeur, https://escholarship.umassmed.edu/jeslib/vol10/iss3/2; Active Curation of Large Longitudinal Surveys: A Case Study Inna Kouper, Karen L. Tucker, Kevin Tharp, Mary Ellen van Booven, and Ashley Clark, https://doi.org/10.7191/jeslib.2021.1210; Data Curation through Catalogs: A Repository-Independent Model for Data Discovery Helenmary Sheridan, Anthony J. Dellureficio, Melissa A. Ratajeski, Sara Mannheimer, and Terrie R. Wheeler, https://doi.org/10.7191/jeslib.2021.1203.

Special Issue: 2021 Research Data Access and Preservation Summit

The Journal of eScience Librarianship has partnered with the Research Data Access and Preservation (RDAP) Association for a fourth year to publish selected conference proceedings. The fully-virtual 2021 Research Data Access and Preservation (RDAP) Summit focused on the theme of Radical Change and Data. This editorial introduces the 2021 RDAP Special Issue of the Journal of eScience Librarianship.

Data Curation Implications of Qualitative Data Reuse and Big Social Research

Objective: Big social data (such as social media and blogs) and archived qualitative data (such as interview transcripts, field notebooks, and diaries) are similar, but their respective communities of practice are under-connected. This paper explores shared challenges in qualitative data reuse and big social research and identifies implications for data curation. Methods: This paper uses a broad literature search and inductive coding of 300 articles relating to qualitative data reuse and big social research. The literature review produces six key challenges relating to data use and reuse that are present in both qualitative data reuse and big social research—context, data quality, data comparability, informed consent, privacy & confidentiality, and intellectual property & data ownership. Results: This paper explores six key challenges related to data use and reuse for qualitative data and big social research and discusses their implications for data curation practices. Conclusions: Data curators can benefit from understanding these six key challenges and examining data curation implications. Data curation implications from these challenges include strategies for: providing clear documentation; linking and combining datasets; supporting trustworthy repositories; using and advocating for metadata standards; discussing alternative consent strategies with researchers and IRBs; understanding and supporting deidentification challenges; supporting restricted access for data; creating data use agreements; supporting rights management and data licensing; developing and supporting alternative archiving strategies. Considering these data curation implications will help data curators support sounder practices for both qualitative data reuse and big social research.

Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets

Objective: We consider how data librarians can take antiracist action in education and consultations. We attempt to apply QuantCrit thinking, particularly to demographic datasheets. Methods: We synthesize historical context with modern critical thinking about race and data to examine the origins of current assumptions about data. We then present examples of how racial categories can hide, rather than reveal, racial disparities. Finally, we apply the Model of Domain Learning to explain why data science and data management experts can and should expose experts in subject research to the idea of critically examining demographic data collection. Results: There are good reasons why patrons who are experts in topics other than racism can find it challenging to change habits from Interoperable approaches to race. Nevertheless, the Census categories explicitly say that they have no basis in research or science. Therefore, social justice requires that data librarians should expose researchers to this fact. If possible, data librarians should also consult on alternatives to habitual use of the Census racial categories. Conclusions: We suggest that many studies are harmed by including race and should remove it entirely. Those studies that are truly examining race should reflect on their research question and seek more relevant racial questions for data collection.

Preparing a Data Archive or Repository for Changing Research Data and Materials Retention Policies

Archival expectations and requirements for researchers’ data and code are changing rapidly, both among publishers and institutions, in response to what has been referred to as a “reproducibility crisis.” In an effort to address this crisis, a number of publishers have added requirements or recommendations to increase the availability of supporting information behind the research, and academic institutions have followed. Librarians should focus on ways to make it easier for researchers to effectively share their data and code with reproducibility in mind. At the Cornell Center for Social Sciences, we have instituted a Results Reproduction Service (R-Squared) for Cornell researchers. Part of this service includes archiving the R-Squared package in our CoreTrustSeal certified Data and Reproduction Archive, which has been rebuilt to accommodate both the unique requirements of those packages and the traditional role of our data archive. Librarians need to consider roles that archives and institutional repositories can play in supporting researchers with reproducibility initiatives. Our commentary closes with some suggestions for more information and training.

Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle

Objective: Existing studies estimate that between 0.3% and 2% of adults in the U.S. (between 900,000 and 2.6 million in 2020) identify as a nonbinary gender or otherwise gender nonconforming. In response to the RDAP 2021 theme of radical change, this article examines the need to change how datasets represent nonbinary persons and how research involving gender data should approach the curation of this data at each stage of the research lifecycle. Methods: In this article, we examine some of the known challenges of gender inclusion in datasets and summarize some solutions underway. Using a critical lens, we examine the difference between current practice and inclusive practice in gender representation, describing inclusive practices at each stage of the research lifecycle from writing a data management plan to sharing data. Results: Data structures that limit gender to “male” and “female” or ontological structures that use mapping to collapse gender demographics to binary values exclude nonbinary and gender diverse populations. Some data collection instruments attempt inclusivity by adding the gender category of “other,” but using the “other” gender category labels nonbinary persons as intrinsically alien. Inclusive change must go farther, to move from alienation to inclusive categories. We describe several techniques for inclusively representing gender in data, from the data management planning stage, to collecting data, cleaning data, and sharing data. To facilitate better sharing of gender data, repositories must also allow mapping that includes nonbinary genders explicitly and allow for ontological mapping for long-term representation of diverse gender identities. Conclusions: A good practice during research design is to consider two levels of critique in the data collection plan. First, consider the research question at hand and remove unnecessary gendering from the data. Secondly, if the research question needs gender, make sure to include nonbinary genders explicitly. Allies must take on this problem without leaving it to those who are most affected by it. Further, more voices calling for inclusionary practices surrounding data rises to a crescendo that cannot be ignored.

Reflections from Transitioning Carpentries Workshops Online

Objectives: As certified Carpentries instructors, the authors organized and co-taught the University of Montana’s first in-person Carpentries workshop focused on the R programming language during early 2020. Due to the COVID-19 pandemic, a repeated workshop was postponed to the fall of 2020 and was adapted for a fully online setting. The authors share their Carpentries journey from in-person to online instruction, hoping to inspire those interested in organizing Carpentries at their institution for the first time and those interested in improving their existing Carpentries presence. Methods: The authors reflected on their experience facilitating the same Carpentries workshop in-person and online. They used this unique opportunity to compare the effectiveness of a face-to-face environment versus a virtual modality for delivering an interactive workshop. Results: When teaching in the online setting, the authors learned to emphasize the basics, create many opportunities for feedback using formative assessments, reduce the amount of material presented, and include helpers who are familiar with technology and troubleshooting. Conclusions: Although the online environment came with challenges (i.e., Zoom logistics and challenges, the need to further condense curricula, etc.), the instructors were surprised at the many advantages of hosting an online workshop. With some adaptations, Carpentries workshops work well in online delivery.

Using Customer Journey Mapping and Design Thinking to Understand the Library’s Role in Supporting the Research Data Lifecycle

Objective: Customer journey mapping and design thinking were identified as useful tools for identifying deeper insights into the research data service needs of researchers on our campus with their direct input. In this article we discuss ways to improve the process in order to identify data needs earlier in the project life and at a more granular level. Methods: Customer journey mapping and design thinking were employed to get direct input from researchers about their research processes and data management needs. Responses from mapping templates and follow-up interviews were then used to identify themes to be explored using design thinking. Finally, a toolkit was created in Open Science Framework to guide other libraries who wish to employ these techniques Results: Outcomes from the customer journey mapping and design thinking sessions identified needs in the areas of data storage, organization and sharing. We also identified project-management lessons learned. The first lesson was to ensure the researchers who participate adequately represent the range of data needs on campus. Another was that customer journey mapping would be more effective if the responses were collected in real time and researchers were allowed more flexibility in the mapping process. Conclusions: Modifications to the customer journey mapping and design thinking techniques will provide real-time responses and deeper insights into the research data service needs of researchers on our campus. Our pilot identified some important gaps but we felt that more subtle and useful outcomes were possible by making changes to our process.

Data Management for Systematic Reviews: Guidance is Needed

Data management practices for systematic reviews and other types of knowledge syntheses are variable, with some reviews following open science practices and others with poor reporting practices leading to lack of transparency or reproducibility. Reporting standards have improved the level of detail being shared in published reviews, and also encourage more open sharing of data from various stages of the review process. Similar to project planning or completion of an ethics application, systematic review teams should create a data management plan alongside creation of their study protocol. This commentary provides a brief description of a Data Management Plan Template created specifically for systematic reviews. It also describes the companion LibGuide which was created to provide more detailed examples, and to serve as a living document for updates and new guidance. The creation of the template was funded by the Portage Network.

Introduction to the Special JeSLIB Issue on Data Curation in Practice

Research data curation is a set of scientific communication processes and activities that support the ethical reuse of research data and uphold research integrity. Data curators act as key collaborators with researchers to enrich the scholarly value and potential impact of their data through preparing it to be shared with others and preserved for the long term. This special issues focuses on practical data curation workflows and tools that have been developed and implemented within data repositories, scholarly societies, research projects, and academic institutions.