Kevin K. K. Manuel, R. Orlandini, Alexandra L. Cooper
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.
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Positionality statement As 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. It 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. The global pandemic, its impacts, and why it matters We 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. Like 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. Data and liberatory technology Liberatory 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
立场声明当我们开始讨论这个问题、它的起源及其在当代社会中的重要性时，我想承认我的立场及其在这个问题的形成中可能发挥的作用。Jonathan O.Cain是一名在LIS领域工作的非裔美国男性。在进入行政部门之前，我教授数据和数字素养，并致力于开发项目，重点是以零成本提高学习者获得这些关键技能的机会。重要的是要承认我的立场和我看待数据科学领域的视角。Trevor Watkins是一名非裔美国男性，在一家学术图书馆的学术机构从事LIS领域的工作。我教授关键的数据素养研讨会，并与全国各地的教职员工、学生和更广泛的学术界一起参与多样性和BIPOC相关的数字项目。我还是人工智能（AI）和数据科学的研究员和实践者。全球疫情、其影响以及为什么重要我们于2020年8月首次会面，讨论在疫情爆发约五个月后发行这期特刊的可能性。在那次会议上，我们花了很大一部分时间相互了解，最重要的是，讨论了疫情给我们的社区和我们带来的损失。可以肯定地说，你们中的许多人在某个时候对未来感到不确定。像世界上大多数人一样，我们失去了家人和朋友，或者知道有人死于新冠肺炎和其他没有得到治疗的疾病，因为重点转移到了新冠肺炎。我们明白了。有一次，新冠肺炎每天导致3000多人死亡（美国疾病控制与预防中心，2022年）。根据美国疾病控制与预防中心的数据，在2020年3月至12月期间死亡的385676人中，90%的人的死亡证明中已将新冠肺炎列为潜在死亡原因。2020年2月Ahmaud Arbery、3月Breonna Taylor和5月George Floyd的谋杀案在美国引发了内乱，并在全球范围内引发了声援种族不公正的抗议活动。当我们宣布这期特刊并发起论文征集时，最初并没有得到太多回应。我们预计并承认，我们可能需要一段时间才能收到有关该问题、提交意向或任何提交材料的询问或建议。和你们中的许多人一样，我们仍在收拾2020年的残局，处理新冠肺炎的后果。疫情现在可能已经结束，这取决于你问谁，但情感创伤仍然存在，而且可能会持续很长一段时间。在整个过程中，耐心是我们所有人的一种品质，这就是为什么我们今天能够发表这份出版物。数据和解放技术解放技术。当我们坐下来记录我们对这个特刊的思考时，这个概念引起了沉思。在召集学者、教育工作者和从业者来解决数据及其与种族、族裔和代表性的关系问题时，我们作为合著者，就数据的重要性以及这个看似抽象和空灵的物体能够而且确实对个人和社区生活产生的物质影响发表了声明。思考这种影响将解放性技术带到了我们的脑海中。IDA B.Wells Just Data Lab提供的解放者技术的定义吸引了我们，并邀请我们讨论这个话题。他们将解放定义为“支持边缘化人群，特别是资本主义和定居者殖民权力结构之外的黑人，增加自由和福祉”，将技术定义为“用于完成任务的工具”。当我们思考这组定义时，我们不得不质疑数据是否是一种解放技术。（《解放技术与数字婚姻》，n.d.）在《解放技术：富兰克林时代的黑人抗议》一书中，理查德·S·纽曼将其与新通信技术和黑人解放活动的所有权和掌握权相提并论。在反思印刷技术的变革性时，他写道：“如果1793年孔多塞侯爵认为印刷将欧洲从中世纪的思想和行动模式中解放出来是正确的，那么印刷也许是第一种将黑人从长期困扰他们的西方文化中的卑躬屈膝的形象中解放出来的技术。并引用了一个19世纪的例子，说明它如何明确地与解放后的黑人生活联系在一起。杜波依斯当然认为黑人历史和印刷历史是相辅相成的。他观察到，在南北战争后的南方，无论在哪里找到报纸，都能找到某种形式的黑人自由”（Richard S.Newman，2009，第175页）。他甚至注意到，学者们注意到，黑人活动家接受了摄影等其他传播技术，“以重塑19世纪文化中非裔美国人的形象”。”（理查德S。 Newman，2009，第175页）我们不乏数据和数据驱动技术未能支持“资本主义和定居者殖民权力结构之外边缘化人群的自由和福祉增加”的例子。2016年，ProPublica发表了一份关于传讯和判刑中使用的风险评估技术的报告《机器偏见》。他们报告称，“该公式特别有可能错误地将黑人被告标记为未来，错误地将他们标记为白人被告的比率几乎是白人被告的两倍”，“白人被告比黑人被告更经常被错误地标记为低风险”（Julia Angwin，2016）。2021年的一篇文章《刑事司法风险评估中的公平：最新技术》在其分析中指出，“白人的假阴性率要高得多，因此暴力的白人罪犯比暴力的黑人罪犯更有可能被错误地归类为非暴力罪犯。黑人的假阳性率要高得多。因此非暴力的黑人犯罪者比非暴力的白人犯罪者更有可能被不正确地归类为暴力罪犯黑人预言会有暴力倾向。这种差异可以支持种族不公正的说法。在这个应用程序中，两种不同类型的公平性之间的权衡具有实际意义。“（Berk等人，2021，第33页）这些只是这些技术发展如何因其自身优点而未能达到Ida B。Wells Just数据实验室。反思纽曼所描绘的技术道路，拥有和掌握工具的工作为其解放提供了潜力。通过这个镜头，正义数据实验室的工作是这种冥想的典范；它与技术、教育、掌握和解放技术有着直接的联系。高等教育中的数据数据素养教育是我们图书馆事业中关注的一个领域。在这个空间里，我们看到了图书馆产生有意义影响的能力。数据对大学校园产生了巨大影响，从研究的进行方式到大学感受到的来自利益相关者群体的压力：学生、政府、资助者、捐赠者和雇主，让学生掌握数据和技术技能，在知识经济中就业。随着学院和大学转向满足这些社区的需求（取得了不同程度的成功），对这些边缘化社区在这些系统中的代表性的重要性进行了无数次探索，以打击和消除我们所看到的嵌入驱动社会的系统中的有害做法及其产生的潜在削弱性后果。这就是为什么本期特刊中的作品在这个时刻如此重要的部分原因。这些学者和学者从业者正在参与这些推动我们周围不透明结构的问题。希望他们的工作能给我们提供另一个视角，让我们了解如何参与这些结构，并将其转化为支持解放实践。本期的文章我们有一些精彩的文章供您阅读。我们以Kevin Manuel、Rosa Orlandini和Alexandra Cooper的一篇文章开场，他们讨论了自1871年以来加拿大人口普查中种族、族裔和土著数据的收集过程是如何演变的，从人口普查中删除少数民族和土著公民，以及恢复和准确识别和分类种族化群体的工作。在下一篇文章中，Leigh Phan、Stephanie Labou、Erin Foster和Ibraheem Ali通过设计和实施两个数据伦理研讨会，为非专家提供了一个数据伦理指导模型。他们对学术界未能将数据的道德使用纳入课程和数字素养培训提出了重要观点，并展示了学术图书馆如何成为学术界的重要资源。他们的工作室结构可以为任何试图为其社区提供类似服务的学术图书馆建模。在第三篇文章中，Natasha Johnson、Megan Sapp Nelson和Katherine Yngve质疑了机构数据收集的集体和地方目的及其对学生归属感的影响，并提出了一个基于数据女权主义的框架，将学生作为一个人而非商品。最后，我们来自Thema Monroe White的闭幕文章聚焦于数据科学领域中被边缘化和代表性不足的人群。作者提出，为了从这些
{"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}
Welcome to the third issue of IASSIST Quarterly for the year 2022 - IQ vol. 46(3). In 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. The 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. With 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
{"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}
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.
{"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}
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. Research 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. In 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.
{"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}
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.
{"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}
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.
{"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}
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.
{"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}
Karen L Majewicz, Jaime Martindale, Melinda Kernik
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. Minnesota 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.
{"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}
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