Data reuse and sensemaking among novice social scientists

We know little about the data reuse practices of novice data users. Yet large scale data reuse over the long term depends in part on uptake from early career researchers. This paper examines 22 novice social science researchers and how they make sense of social science data. Novices are particularly interested in understanding how data: 1) are transformed from qualitative to quantitative data, 2) capture concepts not well‐established in the literature, and 3) can be matched and merged across multiple datasets. We discuss how novice data users make sense of data in these three circumstances. We find that novices seek to understand the data producer's rationale for methodological procedures and measurement choices, which is broadly similar to researchers in other scientific communities. However we also find that they not only reflect on whether they can trust the data producers' decisions, but also seek guidance from members of their disciplinary community. Specifically, novice social science researchers are heavily influenced by more experienced social science researchers when it comes to discovering, evaluating, and justifying their reuse of other's data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96429/1/14504901068_ftp.pd

Young children learning with mobile devices: Research on design and implementation

The increasing growth and usage of mobile devices, such as tablets and iPads, by young children has not yet been accompanied by systematic research about the effects they have on children's learning and the conditions that facilitate or hinder learning and engagement. As a result, only few empirically-based guidelines exist to guide parents, educators, and application (app) designers when choosing or designing apps for young children, often leading to non-evidence-based decisions, or the design of apps with little educational value. This symposium aims to bring together researchers from Australia, the UK and USA to discuss what evidence exist about the learning potential of mobile devices and apps for young children and how it could be used to inform relevant stakeholders

What data characteristics are needed for data reuse in the domain of social sciences in Korea?

With the benefits of data sharing and reuse, data reuse have been promoted in various domains. While there are practices and discussions regarding data sharing and reuse, we still have little knowledge on what characteristics of data impact decisions on data reuse. In this sense, we aim to explore data characteristics in the context of data reuse within the domain of social sciences in Korea. For the purpose of this study, we conducted in-depth interviews with twelve re-searchers in the field of social science in terms of six dimensions: data producer, country/language, data type/collection method, procedure, accessibility, size/currency. For the producer dimension, social scientists preferred data that have been produced by an institution rather than an individual researcher. In language used in the data sets, English were more favored because researchers preferred English than any other languages. In terms of data type, quantitative and survey data types are preferred. For the procedure of data, researchers preferred original raw data with plenty of metadata and demographic information for analysis. For accessibility, there was less preference for restricted data. Lastly, for size/currency, researchers showed a preference for big size data and current data. These preliminary findings can provide better understanding about data reuse and guide improved data reuse services

Red flags in data: Learning from failed data reuse experiences

This study examined the data reusers' failed or unsuccessful experience to understand what constituted reusers' failure. Learning from failed experiences is necessary to understand why the failure occurred and to prevent the failure or convert the failure to success. This study offers an alternative view on data reuse practices and provides insights for facilitating data reuse processes by eliminating core components of failure. From the interviews with 23 quantitative social science data reusers who had failed data reuse experiences, the study findings suggest: (a) ease of reuse, particularly the issue of access and interoperability, is the important initial condition for a successful data reuse experience; (b) understanding data through documentation may be less of an issue, at least for experienced researchers to make their data reuse unsuccessful, although the process can still be challenging; and (c) the major component of failed experience is the lack of support in reusing data, which emphasizes the need to develop a support system for data reusers

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

The role of data reuse in the apprenticeship process

The availability of research data through digital repositories has made data reuse a possibility in a growing number of fields. This paper reports on the results of interviews with 27 zoologists, 43 quantitative social scientists and 22 archaeologists. It examines how data reuse contributes to the apprenticeship process and aids students in becoming full members of scholarly disciplines. Specifically, it investigates how data reuse contributes to the processes by which novice researchers join academic communities of practice. We demonstrate how projects involving data reuse provide a unique opportunity for advisors to mentor novices through the process of creating knowledge. In these situations, senior researchers model general reuse practices and impart skills for their students to use in the future when selecting, evaluating, and analyzing data they did not collect. For novices, data reuse constitutes a form of legitimate peripheral participation, a way for them to enter the community of practice by analyzing data that has been previously collected and reflecting on others' methodologies. Our study findings indicate that reuse occurs across each target community studied. They also suggest how repositories can help foster a reuse culture by providing access to data and building trust in research communities.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106839/1/14505001051_ftp.pd

Mapping Participatory Sensing and Community-led Environmental Monitoring Initiatives: Making Sense H2020 CAPS Project

This report presents a summary of the state of the art in urban participatory sensing and community-led environmental monitoring, the types of engagement approaches typically followed, contextual examples of current developments in this field, and current challenges and opportunities for successful interventions. The goal is to better understand the field and possible options for reflection and action around it, in order to better inform future conceptual and practical developments inside and outside the Making Sense project.JRC.I.2-Foresight, Behavioural Insights and Design for Polic

Social scientists’ data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories.

Many disciplines within the social sciences have a dynamic culture of sharing and reusing data. Because social science data differ from data in the hard sciences, it is necessary to explicitly examine social science data reuse. This study explores the data reuse behaviors of social scientists in order to better understand both the factors that influence those social scientists' intentions to reuse data and the extent to which those factors influence actual data reuse. Using an integrated theoretical model developed from the theory of planned behavior (TPB) and the technology acceptance model (TAM), this study provides a broad explanation of the relationships among factors influencing social scientists' data reuse. A total of 292 survey responses were analyzed using structural equation modeling. Findings suggest that social scientists' data reuse intentions are directly influenced by the subjective norm of data reuse, attitudes toward data reuse, and perceived effort involved in data reuse. Attitude toward data reuse mediated social scientists' intentions to reuse data, leading to the indirect influence of the perceived usefulness and perceived concern of data reuse, as well as the indirect influence of the subjective norm of data reuse. Finally, the availability of a data repository indirectly influenced social scientists' intentions to reuse data by reducing the perceived effort involved

Factors Influencing Research Data Reuse in the Social Sciences: An Exploratory Study

The development of e-Research infrastructure has enabled data to be shared and accessed more openly. Policy mandates for data sharing have contributed to the increasing availability of research data through data repositories, which create favourable conditions for the re-use of data for purposes not always anticipated by original collectors. Despite the current efforts to promote transparency and reproducibility in science, data re-use cannot be assumed, nor merely considered a ‘thrifting’ activity where scientists shop around in data repositories considering only the ease of access to data. The lack of an integrated view of individual, social and technological influential factors to intentional and actual data re-use behaviour was the key motivator for this study. Interviews with 13 social scientists produced 25 factors that were found to influence their perceptions and experiences, including both their unsuccessful and successful attempts to re-use data. These factors were grouped into six theoretical variables: perceived benefits, perceived risks, perceived effort, social influence, facilitating conditions, and perceived re-usability. These research findings provide an in-depth understanding about the re-use of research data in the context of open science, which can be valuable in terms of theory and practice to help leverage data re-use and make publicly available data more actionable.