IASSIST Quarterly (Journal)
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Deficit, asset, or whole person? Institutional data practices that impact belongingness
Full text linkGiven the capitalist model of higher education that has developed since the 1980s, the data collected by institutions of higher education on students is based on micro-targeting to understand and retain students as consumers, and to retain that customer base (i.e. to prevent attrition/dropouts). Institutional data has long been collected but the authors will question how, why, and for whom the data is collected in the current higher education model. The authors will then turn to the current higher education focus on equity, diversity, inclusion, and particularly on the concept of belongingness in higher education. The authors question the collective and local purposes of institutional data collection and the fallout of the current practices and will argue that using existing institutional data to facilitate student belongingness is impossible with current practices. We will propose a new framework of asset-minded institutional data practices that centers the student as a whole person and recenters data collection away from the concept of students as commodities. We propose a new framework based on data feminism that intends to elevate qualitative data and all persons/experiences along the bell-shaped curve, not just the middle two quadrants.
Research data integrity: A cornerstone of rigorous and reproducible research
Full text linkResearch 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
A model for data ethics instruction for non-experts
Full text linkThe dramatic increase in use of technological and algorithmic-based solutions for research, economic, and policy decisions has led to a number of high-profile ethical and privacy violations in the last decade. Current disparities in academic curriculum for data and computational science result in significant gaps regarding ethics training in the next generation of data-intensive researchers. Libraries are often called to fill the curricular gaps in data science training for non-data science disciplines, including within the University of California (UC) system. We found that in addition to incomplete computational training, ethics training is almost completely absent in the standard course curricula. In this report, we highlight the experiences of library data services providers in attempting to meet the need for additional training, by designing and running two workshops: Ethical Considerations in Data (2021) and its sequel Data Ethics & Justice (2022). We discuss our interdisciplinary workshop approach and our efforts to highlight resources that can be used by non-experts to engage productively with these topics. Finally, we report a set of recommendations for librarians and data science instructors to more easily incorporate data ethics concepts into curricular instruction
A tool to promote research planning and conceptualization: SoDaNet research infrastructure’s scientific dictionary of social terms
Full text linkThis article examines the contribution of SoDaNet research infrastructure’s Scientific Dictionary of Social Terms to empirical social research. The article records the dictionary functional specifications in regarding to terms, definitions and bibliographic records and analyzes the management issues in user access in relation to the basic functions (search, import, modification and deletion of digital content). In addition, the functions of the dictionary as a research planning tool are analyzed (providing opportunities to search for scientific information necessary to design a new research), conceptualization (providing access to the different meanings of a term through the different definitions given) and scientific documentation. Finally, the function of the dictionary as an element of a research infrastructure is evaluated
Developing data literacy: How data services and data fellowships are creating data skilled social researchers
Full text linkThis 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. 
Factors contributing to repository success in recruiting data deposits
Full text linkWhat 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
Informed consent for linking survey and social media data - Differences between platforms and data types
Full text linkLinking social media data with survey data is a way to combine the unique strengths and address some of the respective limitations of these two data types. As such linked data can be quite disclosive and potentially sensitive, it is important that researchers obtain informed consent from the individuals whose data are being linked. When formulating appropriate informed consent, there are several things that researchers need to take into account. Besides legal and ethical questions, key aspects to consider are the differences between platforms and data types. Depending on what type of social media data is collected, how the data are collected, and from which platform(s), different points need to be addressed in the informed consent. In this paper, we present three case studies in which survey data were linked with data from 1) Twitter, 2) Facebook, and 3) LinkedIn and discuss how the specific features of the platforms and data collection methods were covered in the informed consent. We compare the key attributes of these platforms that are relevant for the formulation of informed consent and also discuss scenarios of social media data collection and linking in which obtaining informed consent is not necessary. By presenting the specific case studies as well as general considerations, this paper is meant to provide guidance on informed consent for linked survey and social media data for both researchers and archivists working with this type of data
Establishment of data centre at Mzuzu University: A survey of anticipations and aspirations of key project stakeholders
Full text linkMzuzu University lost its Library as a result of a fire that took place on December 18, 2015. In response, the university established two processes to ensure the library services were not interrupted. The first process was to restore information services within six months by creating an interim Library. The second was to design a new library in collaboration with Virginia Tech’s School of Architecture and Design in the United States. A total of three conceptual designs were developed, from which Mzuzu University selected a final design. One key aspect of each conceptual design was a dedicated space for a data centre. The initial concept was that the data centre would support research activities at the University, within Malawi, and with international partners outside Malawi, such as Virginia Tech. This paper captures the anticipations and aspirations of the key stakeholders involved with the library design project at Mzuzu University in Malawi and Virginia Tech in the USA. Data were captured by a survey that was shared via email with 29 stakeholders. A total of 10 responded at Mzuzu University, and 12 responded at Virginia Tech. A key finding from the survey was the need to create clear plans for each aspect of the project to ensure the effective implementation of the data centre. Critical aspects to the project include staffing, equipment procurement, the management of the data centre, data literacy programming, and the long-term sustainability of the data centre. Developing a policy/process to guide the operations of the data centre was also found to be critical. The library construction began in February 2021 and is expected to end in February 2023. Having a clear plan for how the data centre could be operationalized will be essential to ensuring the centre is successful. The data centre will be a new facility for the university and this paper is a first step towards shaping the requirements of, and potential for, this new facility
A recommendation to the SSH community: Take a linguist on board
Full text linkIn this paper we address how Natural Language Processing (NLP) approaches and language technology can contribute to data services in different ways; from providing social science users with new approaches and tools to explore oral and textual data, to enhancing the search, findability and retrieval of data sources. By using linguistic approaches we are able to process data, for example using Automated Speech Recognition (ASR) and named entity recognizers (NER), extract key concepts and terms, and improve search strategies. We provide examples of how computational linguistics contribute to and facilitate the mining and analysis of oral or textual material, for example (transcribed) interviews or oral histories, and show how free open source (OS) tools can be used very easily to gain a quick overview of the key features of text, which can be further exploited as useful metadata
The development of institutional repositories in East Africa countries: A comparative analysis of Tanzania, Kenya, and Uganda
Full text linkThis paper aims to examine the growth of IR in the East Africa region (Tanzania, Kenya, and Uganda) from 2010-2020. This study adopted a content analysis methodology. Data for this study was extracted from OpenDOAR (Directory of Open Access Repository), ROAR (Registry of Open Access Repository) and repositories websites to identify the language used, subject covered, software used and types of content that are found in East African repositories. The findings of this study reveal that East Africa region had a total number of 66 repositories, which are registered in OpenDOAR. Kenya is a leading country in the region by having 42 repositories, followed by Tanzania with 14 repositories and Uganda have 10 repositories. The findings show that there is an increase number in the of repositories in the region from 4 in 2010 to 66 in 2020, however the growth is low compared to other parts of the world like Europe, Asia, and America. The study shows the need of librarians, researchers, stakeholders, and East Africa governments to come together to overcome the challenges that hinder the growth of repositories in the region. Mandate policies formulation, training, fund support, OA awareness and technical support are needed in overcoming those challenges.
Keywords: Institutional Repository, Open Access, Content growth, Institutional Repository software, Items types, Institutional Repository language, and subject covered in repository, East Africa region