Pub Date : 2022-03-04DOI: 10.7191/jeslib.2022.1228
Tina Griffin, Rebecca Raszewski, Holly Beverley
Objective: An underexplored area in Library and Information Science (LIS) is the development of educational offerings and partnerships in Health-Related Informatics (HRI) (e.g., bioinformatics, clinical informatics, health informatics). The purpose of this study is to identify which disciplines are collaborating in HRI education and how partnerships developed. Methods: This study was conducted in two parts: a website review and survey. Seventy-seven North American ALA-accredited and iSchool member websites were searched between November 2019-March 2020 for HRI-related educational offerings and which academic units were involved. Two hundred sixteen individuals involved in LIS and/or HRI education were contacted for a 40-question survey that included: their roles and responsibilities regarding HRI education; the alignment of this education with strategic plans or competencies; and how HRI partnerships developed. The survey also asked those who were not currently partnering in HRI education which factors influenced their circumstances. Results: 352 HRI educational offerings existed within ALA-accredited or iSchool programs. A total of 38 (17.5%) responded to the survey. For almost two-thirds of these, there was no indication of partnership in that education (213/352, 60.5%). LIS or iSchool involvement in HRI is just under one-third of all offerings (111/352, 31%). “Health or healthcare” informatics (35) or “biomedical or bioinformatics” were the most common types of HRI offered from the website review and survey. Conclusions: Opportunities exist for LIS programs to form HRI educational partnerships that will provide richer educational offerings for LIS students and health sciences librarians.
{"title":"Library or iSchool Involvement in Health-Related Informatics Education","authors":"Tina Griffin, Rebecca Raszewski, Holly Beverley","doi":"10.7191/jeslib.2022.1228","DOIUrl":"https://doi.org/10.7191/jeslib.2022.1228","url":null,"abstract":"Objective: An underexplored area in Library and Information Science (LIS) is the development of educational offerings and partnerships in Health-Related Informatics (HRI) (e.g., bioinformatics, clinical informatics, health informatics). The purpose of this study is to identify which disciplines are collaborating in HRI education and how partnerships developed.\u0000\u0000Methods: This study was conducted in two parts: a website review and survey. Seventy-seven North American ALA-accredited and iSchool member websites were searched between November 2019-March 2020 for HRI-related educational offerings and which academic units were involved. Two hundred sixteen individuals involved in LIS and/or HRI education were contacted for a 40-question survey that included: their roles and responsibilities regarding HRI education; the alignment of this education with strategic plans or competencies; and how HRI partnerships developed. The survey also asked those who were not currently partnering in HRI education which factors influenced their circumstances.\u0000\u0000Results: 352 HRI educational offerings existed within ALA-accredited or iSchool programs. A total of 38 (17.5%) responded to the survey. For almost two-thirds of these, there was no indication of partnership in that education (213/352, 60.5%). LIS or iSchool involvement in HRI is just under one-third of all offerings (111/352, 31%). “Health or healthcare” informatics (35) or “biomedical or bioinformatics” were the most common types of HRI offered from the website review and survey.\u0000\u0000Conclusions: Opportunities exist for LIS programs to form HRI educational partnerships that will provide richer educational offerings for LIS students and health sciences librarians.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48020316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-04DOI: 10.7191/jeslib.2022.1226
B. Bishop, A. Orehek, C. Eaker, Plato L. Smith
This study of data services librarians is part of a series of studies examining the current roles and perspectives on Research Data Management (RDM) services in higher education. Reviewing current best practices provides insights into the role-based responsibilities for RDM services that data services librarians perform, as well as ways to improve and create new services to meet the needs of their respective university communities. Objectives: The objectives of this article are to provide the context of research data services through a review of past studies, explain how they informed this qualitative study, and provide the methods and results of the current study. This study provides an in-depth overview of the overall job responsibilities of data services librarians and as well as their perspectives on RDM through job analyses. Methods: Job analysis interviews provide insight and context to the tasks employees do as described in their own words. Interviews with 10 data services librarians recruited from the top 10 public and top 10 private universities according to the 2020 Best National University Rankings in the US News and World Reports were asked 30 questions concerning their overall job tasks and perspectives on RDM. Five public and five private data services librarians were interviewed. The interviews were recorded and transcribed. The transcriptions were analyzed in NVivo using a grounded theory application of open, axial, and selective coding to generate categories and broad themes based on the responses using synonymous meanings. Results: The results presented here provide the typical job tasks of data services librarians that include locating secondary data, reviewing data management plans (DMPs), conducting outreach, collaborating, and offering RDM training. Fewer data services librarians assisted with data curation or manage an institutional repository. Discussion: The results indicate that there may be different types of data services librarians depending on the mix of responsibilities. Academic librarianship will benefit from further delineation of job titles using tasks while planning, advertising, hiring, and evaluating workers in this emerging area. There remain many other explorations needed to understand the challenges and opportunities for data services librarians related to RDM. Conclusions: This article concludes with a proposed matrix of job tasks that indicates different types of data services librarians to inform further study. Future job descriptions, training, and education will all benefit from differentiating between the many associated research data services roles and with increased focus on research data greater specializations will emerge.
{"title":"Data Services Librarians’ Responsibilities and Perspectives on Research Data Management","authors":"B. Bishop, A. Orehek, C. Eaker, Plato L. Smith","doi":"10.7191/jeslib.2022.1226","DOIUrl":"https://doi.org/10.7191/jeslib.2022.1226","url":null,"abstract":"This study of data services librarians is part of a series of studies examining the current roles and perspectives on Research Data Management (RDM) services in higher education. Reviewing current best practices provides insights into the role-based responsibilities for RDM services that data services librarians perform, as well as ways to improve and create new services to meet the needs of their respective university communities.\u0000\u0000Objectives: The objectives of this article are to provide the context of research data services through a review of past studies, explain how they informed this qualitative study, and provide the methods and results of the current study. This study provides an in-depth overview of the overall job responsibilities of data services librarians and as well as their perspectives on RDM through job analyses.\u0000\u0000Methods: Job analysis interviews provide insight and context to the tasks employees do as described in their own words. Interviews with 10 data services librarians recruited from the top 10 public and top 10 private universities according to the 2020 Best National University Rankings in the US News and World Reports were asked 30 questions concerning their overall job tasks and perspectives on RDM. Five public and five private data services librarians were interviewed. The interviews were recorded and transcribed. The transcriptions were analyzed in NVivo using a grounded theory application of open, axial, and selective coding to generate categories and broad themes based on the responses using synonymous meanings.\u0000\u0000Results: The results presented here provide the typical job tasks of data services librarians that include locating secondary data, reviewing data management plans (DMPs), conducting outreach, collaborating, and offering RDM training. Fewer data services librarians assisted with data curation or manage an institutional repository.\u0000\u0000Discussion: The results indicate that there may be different types of data services librarians depending on the mix of responsibilities. Academic librarianship will benefit from further delineation of job titles using tasks while planning, advertising, hiring, and evaluating workers in this emerging area. There remain many other explorations needed to understand the challenges and opportunities for data services librarians related to RDM.\u0000\u0000Conclusions: This article concludes with a proposed matrix of job tasks that indicates different types of data services librarians to inform further study. Future job descriptions, training, and education will all benefit from differentiating between the many associated research data services roles and with increased focus on research data greater specializations will emerge.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47236807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-27DOI: 10.7191/jeslib.2022.1224
Megan N. O'Donnell, Curtis Brundy
Objective: This paper examines a unique data set disclosure process at a medium sized, land grant, research university and the campus collaboration that led to its creation. Methods: The authors utilized a single case study methodology, reviewing relevant documents and workflows. As first-hand participants in the collaboration and disclosure process development, their own accounts and experiences also were utilized. Results: A collaborative approach to enhancing research data sharing is essential, considering the wide array of stakeholders involved across the life cycle of research data. A transparent, inclusive data set disclosure process is a viable route to ensuring research data can be appropriately shared. Conclusions: Successful sharing of research data impacts a range of university units and individuals. The establishment of productive working relationships and trust between these stakeholders is critical to expanding the sharing of research data and to establishing shared workflows.
{"title":"Bringing All the Stakeholders to the Table: A Collaborative Approach to Data Sharing","authors":"Megan N. O'Donnell, Curtis Brundy","doi":"10.7191/jeslib.2022.1224","DOIUrl":"https://doi.org/10.7191/jeslib.2022.1224","url":null,"abstract":"Objective: This paper examines a unique data set disclosure process at a medium sized, land grant, research university and the campus collaboration that led to its creation.\u0000\u0000Methods: The authors utilized a single case study methodology, reviewing relevant documents and workflows. As first-hand participants in the collaboration and disclosure process development, their own accounts and experiences also were utilized.\u0000\u0000Results: A collaborative approach to enhancing research data sharing is essential, considering the wide array of stakeholders involved across the life cycle of research data. A transparent, inclusive data set disclosure process is a viable route to ensuring research data can be appropriately shared.\u0000\u0000Conclusions: Successful sharing of research data impacts a range of university units and individuals. The establishment of productive working relationships and trust between these stakeholders is critical to expanding the sharing of research data and to establishing shared workflows.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46666114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-27DOI: 10.7191/jeslib.2022.1212
Joanna Thielen, Wanda Marsolek
Objective: While diversity, equity, inclusion, and accessibility (DEIA) principles and practices have been incorporated into much of academic librarianship, there has been less focus on the job postings. Methods: In order to quantify ways in which DEIA is being integrated into job postings, we analyzed 48 job positions for engineering librarians posted in 2018 and 2019 via deductive thematic analysis, looking for trends in salary and qualifications related to education and academic or professional experience. Results: Of postings that listed a quantitative salary value, salary ranged from $45,000 to $81,606; the median was $60,750. However, only 33% (n = 16) of positions listed a quantitative salary value. For educational qualifications, we found that 98% of job postings (n = 47) listed a Master’s in Library and Information Science (MLIS) as a required qualification; however, 34% of these postings (n = 16) would accept an equivalent degree in lieu of the MLIS. Additionally, 73% (n = 35) of positions sought candidates with an MLIS and another degree; 91% of these positions (n = 32) wanted the additional degree to be in a science, technology, engineering, and mathematics discipline. For academic or professional experience, 56% of positions (n = 27) sought candidates with previous academic library experience. Conclusions: Using this data, we provide actionable recommendations on how to incorporate DEIA principles into any academic librarian job posting. Our study provides quantitative data and evidence-based recommendations that can be used to make DEIA an integral part of the job postings in academic librarianship.
{"title":"Taking a Diversity, Equity, Inclusion & Accessibility Lens to Engineering Librarian Job Postings: Recommendations from an Analysis of Postings from 2018 and 2019","authors":"Joanna Thielen, Wanda Marsolek","doi":"10.7191/jeslib.2022.1212","DOIUrl":"https://doi.org/10.7191/jeslib.2022.1212","url":null,"abstract":"Objective: While diversity, equity, inclusion, and accessibility (DEIA) principles and practices have been incorporated into much of academic librarianship, there has been less focus on the job postings.\u0000\u0000Methods: In order to quantify ways in which DEIA is being integrated into job postings, we analyzed 48 job positions for engineering librarians posted in 2018 and 2019 via deductive thematic analysis, looking for trends in salary and qualifications related to education and academic or professional experience.\u0000\u0000Results: Of postings that listed a quantitative salary value, salary ranged from $45,000 to $81,606; the median was $60,750. However, only 33% (n = 16) of positions listed a quantitative salary value. For educational qualifications, we found that 98% of job postings (n = 47) listed a Master’s in Library and Information Science (MLIS) as a required qualification; however, 34% of these postings (n = 16) would accept an equivalent degree in lieu of the MLIS. Additionally, 73% (n = 35) of positions sought candidates with an MLIS and another degree; 91% of these positions (n = 32) wanted the additional degree to be in a science, technology, engineering, and mathematics discipline. For academic or professional experience, 56% of positions (n = 27) sought candidates with previous academic library experience.\u0000\u0000Conclusions: Using this data, we provide actionable recommendations on how to incorporate DEIA principles into any academic librarian job posting. Our study provides quantitative data and evidence-based recommendations that can be used to make DEIA an integral part of the job postings in academic librarianship.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48854281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-16DOI: 10.7191/jeslib.2021.1232
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.
Data Soup是《电子科学图书馆学杂志》（JeSLIB）和数据策展网络之间的合作，旨在举办一系列以社区为中心的网络研讨会/讨论，在数据策展人之间交流不同格式或主题领域的研究数据策展实践。首届网络研讨会的阵容包括以下演讲者和最近的JeSLIB特刊的主题：实践中的数据策展：为加拿大Dataverse策展人创建指南：Portage Network的Dataverse馆长指南Alexandra Cooper、Michael Steeleworthy、Éve Paquette Bigras、Erin Clary、Erin MacPherson、Louise Gillis和Jason Brodeur，https://escholarship.umassmed.edu/jeslib/vol10/iss3/2；大型纵向调查的积极控制：案例研究Inna Kouper、Karen L.Tucker、Kevin Tharp、Mary Ellen van Booven和Ashley Clark，https://doi.org/10.7191/jeslib.2021.1210；通过目录进行数据整理：数据发现的独立于存储库的模型Helenmary Sheridan、Anthony J.Dellureficio、Melissa A.Ratajeski、Sara Mannheimer和Terrie R.Wheeler，https://doi.org/10.7191/jeslib.2021.1203.
{"title":"Data Soup Webinar, December 16, 2021: hosted by the Data Curation Network and the Journal of eScience Librarianship","authors":"","doi":"10.7191/jeslib.2021.1232","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1232","url":null,"abstract":"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.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46546605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-12DOI: 10.7191/jeslib.2021.1230
C. Bakker, H. Coates, Sara Mannheimer
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.
{"title":"Special Issue: 2021 Research Data Access and Preservation Summit","authors":"C. Bakker, H. Coates, Sara Mannheimer","doi":"10.7191/jeslib.2021.1230","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1230","url":null,"abstract":"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.\u0000\u0000The 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.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44358073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-10DOI: 10.7191/jeslib.2021.1213
N. Exner, E. Carrillo, Sam A. Leif
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.
{"title":"Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets","authors":"N. Exner, E. Carrillo, Sam A. Leif","doi":"10.7191/jeslib.2021.1213","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1213","url":null,"abstract":"Objective: We consider how data librarians can take antiracist action in education and consultations. We attempt to apply QuantCrit thinking, particularly to demographic datasheets.\u0000\u0000Methods: 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.\u0000\u0000Results: 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.\u0000\u0000Conclusions: 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.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42789956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-10DOI: 10.7191/jeslib.2021.1218
Sara Mannheimer
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.
{"title":"Data Curation Implications of Qualitative Data Reuse and Big Social Research","authors":"Sara Mannheimer","doi":"10.7191/jeslib.2021.1218","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1218","url":null,"abstract":"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.\u0000\u0000Methods: 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.\u0000\u0000Results: 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.\u0000\u0000Conclusions: 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.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41955238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-10DOI: 10.7191/jeslib.2021.1219
A. Gofman, Sam A. Leif, Hannah C. Gunderman, N. Exner
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.
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