IASSIST quarterly最新文献

{"title":"Who is counted? Ethno-racial and indigenous identities in the Census of Canada, 1871-2021","authors":"Kevin K. K. Manuel, R. Orlandini, Alexandra L. Cooper","doi":"10.29173/iq1016","DOIUrl":"https://doi.org/10.29173/iq1016","url":null,"abstract":"Finding data on race, racialized populations, and anti-racism in Canada can be a complex process when conducting research. One source of data is the Census of Canada which has been collecting socio-demographic data since 1871. However, the collection of racial, ethnic, or Indigenous data has changed throughout the years and from Census to Census. In response to the need for more support in finding ethno-racial and Indigenous data, the Ontario Council of University Libraries’ Ontario Data Community has created an online guide to provide guidance, in part, about the terminology used for Indigenous and racialized identities over time in the Census. In this article, the modifications to how ethno-racial origin questions have been asked, and the ongoing changes to sociocultural perceptions impacting the Census are reviewed.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48113111","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}

{"title":"Systemic racism in data practices","authors":"T. Watkins, J. Cain","doi":"10.29173/iq1079","DOIUrl":"https://doi.org/10.29173/iq1079","url":null,"abstract":"Positionality statement \u0000As we begin to discuss this issue, its origins, and its importance in contemporary society, I wanted to acknowledge my positionality and the role that it may play in the formation of this issue.  Jonathan O. Cain is an African-American male working in the LIS field. Before moving into administration, I taught data and digital literacy and worked on developing programs that focused on improving access to these critical skills at zero cost to learners.\u0000It is important to acknowledge my positionality and the lens through which I see the data science field. Trevor Watkins is an African American male working in the LIS field at an academic institution in an academic library. I teach critical data literacy workshops and engage in diversity and BIPOC-related digital projects with faculty, students, and the broader academic community across the country. I am also a researcher and practitioner in artificial intelligence (AI) and data science.\u0000The global pandemic, its impacts, and why it matters\u0000We first met in August 2020 to discuss the possibilities of this special issue about five months into the pandemic. We spent a good chunk of that meeting getting to know each other and, most importantly, discussed the toll the pandemic placed on our communities and us. It is probably safe to say that many of you, at some point, were uncertain of the future. Like most people worldwide, we lost family and friends or knew of people who succumbed to Covid-19 and other illnesses that weren't treated because the focus shifted to Covid-19. We get it. At one point, Covid-19 killed over three thousand people per day (Centers for Disease Control and Prevention (CDC), 2022). According to data from the CDC, 90% of the 385,676 people who died between March and December 2020 had Covid-19 listed as the underlying cause of death on their death certificate. The murders of Ahmaud Arbery in February, Breonna Taylor in March, and George Floyd in May 2020 sparked civic unrest across the United States (US) and protests across the globe in solidarity against racial injustice. When we announced this special issue and initiated a call for papers, we didn't get much of a response initially. We expected and acknowledged that it would probably take some time before we received inquiries or proposals about the issue, the intent to submit, or any submissions.\u0000Like many of you, we are still picking up the pieces from 2020 and dealing with the aftermath of Covid-19. The pandemic may be over now, depending on whom you ask, but the emotional scars are still there and may remain so for quite some time. Patience was the one quality we all had throughout this process, which is why we can present this publication today.\u0000Data and liberatory technology\u0000Liberatory technology. This is a concept that invited contemplation as we sat down to record our reflections on this special issue. In drawing together scholars, educators, and practitioners to address the issue of data and its rel","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44720119","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}

{"title":"We talk data. We do data.","authors":"K. Rasmussen","doi":"10.29173/iq1065","DOIUrl":"https://doi.org/10.29173/iq1065","url":null,"abstract":"Welcome to the third issue of IASSIST Quarterly for the year 2022 - IQ vol. 46(3).  \u0000In Denmark we sometimes retrieve an old quote from a member of the Danish Parliament: 'If those are the facts, then I deny the facts'. We have laughed at that for more than a hundred years, but now fact denial is apparently the new normal in many places. And we are not amused. Data can become dangerous as facts can be fabricated. Therefore, a critical approach to data is fundamental to producing reliable information: facts. The articles in this issue are about teaching students good data behavior, and how researchers with great care and attention can carry out the task of fact production. \u0000The first article is about improvement in teaching data: 'Investigating teaching practices in quantitative and computational Social Sciences: a case study' by Rebecca Greer and Renata G. Curty. The authors are both at the University of California, Santa Barbara Library, where Rebecca Greer is director of Teaching & Learning and Renata Curty is social science research facilitator. They are investigating data education and present some of the findings from a local report - part of a national project - into how instructors adapt curricula and pedagogy to advance undergraduates computational and statistical knowledge in the social sciences. The core goal of the instructors concerns 'data thinking' - the critical understanding and evaluation of data. Many students have a preconceived fear of mathematics that influences other areas. Personally, I feel that data thinking is essential to live and participation in society, and I believe that it should be achievable even with a background of math fear. However, for social science students I also expect they have acquired some level of 'data doing'. I agree with the authors that the necessary support for data is more often found in the areas of Science, Technology, Engineering and Mathematics than it is in Social Sciences. However, many IASSIST members successfully work to relate data to social science students. And the implicit relationship via data to STEM areas will furthermore often improve job success for social science students. The local study interviewed instructors and the article presents among other things the learning goals and the explicit skills contained in these goals. The study uses many quotations from the interviewees, including quotes on sharing among the instructors. This leads to how the instructors can be further supported and how the library can support them, including a partnership between the library's Research Data Services and Teaching & Learning. \u0000With the second article we continue at a university. Now the focus shifts from teaching to research - the other main area of university work, and more specifically the data in research. The article 'Research data integrity: A cornerstone of rigorous and reproducible research' is by Patricia B. Condon, Julie F. Simpson and Maria E. Emanuel. All three are in positions ","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44153564","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}

{"title":"Investigating teaching practices in quantitative and computational Social Sciences: A case study","authors":"Rebecca Greer, R. Curty","doi":"10.29173/iq1039","DOIUrl":"https://doi.org/10.29173/iq1039","url":null,"abstract":"Data education is gaining traction across disciplines and degree levels in higher education. Teaching data skills in the Social Sciences in today's data-driven world is vital for preparing the next generation of data literate and critical social scientists. The ability to identify, assess, analyze, and communicate well and responsibly with data is key for scholars and professionals to navigate dynamic and expansive information ecosystems. This paradigm shift demands instructors to adapt their curricula and pedagogy to advance students’ computational and statistical knowledge. This paper presents some of the findings from a local report of a larger national project which explored pedagogical techniques and instructional support needs for teaching undergraduates with quantitative data in the Social Sciences. Results revealed that the core learning goal of instructors is to develop students' critical thinking skills with data, including the conceptual understanding of the research methods employed in the field; the ability to critically evaluate research methodologies, findings, and data sets; and prowess using quantitative and computational tools and technologies. A recurring theme across interviews was students’ fear of math and technology and challenges these fears pose to data-related instruction. Instructors value participation in a community of practice and are eager for more institutional support to advance their computational skills. Based on these findings, we suggest avenues for academic libraries to further develop services, activities, and partnerships to aid data instruction efforts in the Social Sciences.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41952778","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}

{"title":"Research data integrity: A cornerstone of rigorous and reproducible research","authors":"Patricia B. Condon, Julie Simpson, Maria Emanuel","doi":"10.29173/iq1033","DOIUrl":"https://doi.org/10.29173/iq1033","url":null,"abstract":"Research data integrity provides a strong foundation for high quality research outcomes, and it is an essential part of the research data lifecycle due to its critical role in research rigor, reproducibility, replication, and data reuse (the four Rs). Understanding research data integrity is therefore imperative in collaborative interdisciplinary research and collaborative cross-sector research where different norms, procedures, and terminology regarding data exist.\u0000Research data integrity is closely associated with data management, data quality, and data security. Producing data that are reliable, trustworthy, valid, and secure throughout the research process requires purposefully planning for research data integrity and careful consideration of research data lifecycle actions like data acquisition, analysis, and preservation. In addition, purposeful planning enables researchers to conduct rigorous research and generate outcomes that are reproducible, replicable, and reusable. To advance this conversation, we developed two tools: a concept model that visually represents the relationship between data management, data quality, and data security as components of research data integrity, and a schema for implementing these components in practice. We contend that disentangling research data integrity and its components, developing a standardized way of describing their interplay, and intentionally addressing them in the research data lifecycle reduces threats to research data integrity.\u0000In this paper, we break down the complexity of research data integrity to make it more understandable and propose a practical process by which research data integrity can be achieved in a way that is useful for data producers, providers, users, and educators. We position our concept model and schema within the larger dialog around research integrity and data literacy and illuminate the role that research data integrity and its components (data management, data quality, and data security) play in the four Rs. In this paper, we present a concept model and schema for use as tools for instruction/training and practical implementation. Using these tools, we examine the role of research data integrity in rigorous and reproducible research and offer insight into ensuring research data integrity throughout the research process.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43613681","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}

{"title":"Factors contributing to repository success in recruiting data deposits","authors":"Michele Hayslett, M. Jansen","doi":"10.29173/iq1037","DOIUrl":"https://doi.org/10.29173/iq1037","url":null,"abstract":"What factors make data repositories successful in recruiting research data deposits from scholars? While quite a few studies outline researchers’ data management needs and how repositories can meet those needs, few have assessed the success of various approaches. This study examines infrastructure for accepting data into repositories and identifies factors influential in recruiting data deposits.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44104862","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}

{"title":"Deposit data - including qualitative data - and support students in obtaining the skills for data-driven research","authors":"K. Rasmussen","doi":"10.29173/iq1047","DOIUrl":"https://doi.org/10.29173/iq1047","url":null,"abstract":"","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49395911","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}

{"title":"Going qual in: Towards methodologically inclusive data work in academic libraries","authors":"J. Hagman, Hilary Bussell","doi":"10.29173/iq1022","DOIUrl":"https://doi.org/10.29173/iq1022","url":null,"abstract":"Data literacy and research data services are a growing part of the work of academic libraries. Data in this context is often presumed to mean only numeric data or statistics, leaving open the question of what role qualitative research plays in services and programming for research data and data literacy. In this paper, we report on the results of interviews with academic librarians about their understanding of data literacy, qualitative research, and academic library infrastructure around qualitative research. From the interviews, we propose a model of data literacy that incorporates both interpretive and instrumental elements. We conclude with suggestions for incorporating qualitative data and analysis methods into academic library programming and services around data literacy and research data.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48548431","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}

{"title":"Developing data literacy: How data services and data fellowships are creating data skilled social researchers","authors":"V. Higgins, J. Carter","doi":"10.29173/iq1027","DOIUrl":"https://doi.org/10.29173/iq1027","url":null,"abstract":"This paper describes two successful approaches to quantitative data literacy training within the UK and the synergies and collaborations between these two programmes. The first is a data literacy training programme, being delivered by the UK Data Service, which focuses on training in basic data literacy skills. The second is a Data Fellows programme that has been developed to help undergraduate social science students gain real-world experience by applying their classroom skills in the workplace. The paper also discusses next steps in the global development of data literacy skills via the EmpoderaData project, which is trialling the Data Fellows programme in Latin America. ","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42988970","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}

{"title":"Open geospatial data: A comparison of data cultures in local government","authors":"Karen L Majewicz, Jaime Martindale, Melinda Kernik","doi":"10.29173/iq1013","DOIUrl":"https://doi.org/10.29173/iq1013","url":null,"abstract":"Public geospatial data (geodata) is created at all levels of government, including federal, state, and local (county and municipal). Local governments, in particular, are critical sources of geodata because they produce foundational datasets, such as parcels, road centerlines, address points, land use, and elevation. These datasets are sought after by other public agencies for aggregation into state and national frameworks, by researchers for analysis, and by cartographers to serve as base map layers. Despite the importance of this data, policies about whether it is free and open to the public vary from place to place. As a result, some regions offer hundreds of free and open datasets to the public, while their neighbors may have zero, preferring to restrict them due to privacy, economic, or legal concerns. \u0000Minnesota relies on an approach that allows counties to choose for themselves if their geodata is free and open. By contrast, its neighboring state of Wisconsin has passed legislation requiring that specific foundational geospatial datasets created by counties must be freely available to the public. This paper compares the implications and outcomes of these diverging data cultures.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46410405","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}