Chung-Yi Hou – InfoDoc MicroVeille

Authors : Chung-Yi Hou, Heather Soyka, Vivian Hutchison, Isis Sema, Chris Allen, Amber Budden

Effective management is a key component for preparing data to be retained for future long term access, use, and reuse by a broader community. Developing the skills to plan and perform data management tasks is important for individuals and institutions.

Teaching data literacy skills may also help to mitigate the impact of data deluge and other effects of being overexposed to and overwhelmed by data.

The process of learning how to manage data effectively for the entire research data lifecycle can be complex. There are often multiple stages involved within a lifecycle for managing data, and each stage may require specific knowledge, expertise, and resources.

Additionally, although a range of organizations offers data management education and training resources, it can often be difficult to assess how effective the resources are for educating users to meet their data management requirements.

In the case of Data Observation Network for Earth (DataONE), DataONE’s extensive collaboration with individuals and organizations has informed the development of multiple educational resources. Through these interactions, DataONE understands that the process of creating and maintaining educational materials that remain responsive to community needs is reliant on careful evaluations.

Therefore, the impetus for a comprehensive, customizable Education EVAluation instrument (EEVA) is grounded in the need for tools to assess and improve current and future training and educational resources for research data management.

In this paper, the authors outline and provide context for the background and motivations that led to creating EEVA for evaluating the effectiveness of data management educational resources. The paper details the process and results of the current version of EEVA.

Finally, the paper highlights the key features, potential uses, and the next steps in order to improve future extensions and revisions of EEVA.

URL : Evaluating the Effectiveness of Data Management Training: DataONE’s Survey Instrument

DOI : https://doi.org/10.2218/ijdc.v12i2.508

Authors : Chung-Yi Hou, Matthew Mayernik

As scientific data volumes, format types, and sources increase rapidly with the invention and improvement of scientific capabilities, the resulting datasets are becoming more complex to manage as well.

One of the significant management challenges is pulling apart the individual contributions of specific people and organizations within large, complex projects.

This is important for two aspects:1) assigning responsibility and accountability for scientific work, and 2) giving professional credit to individuals (e.g. hiring, promotion, and tenure) who work within such large projects.

This paper aims to review the extant practice of data attribution and how it may be improved. Through a case study of creating a detailed attribution record for a climate model dataset, the paper evaluates the strengths and weaknesses of the current data attribution method and proposes an alternative attribution framework accordingly.

The paper concludes by demonstrating that, analogous to acknowledging the different roles and responsibilities shown in movie credits, the methodology developed in the study could be used in general to identify and map out the relationships among the organizations and individuals who had contributed to a dataset.

As a result, the framework could be applied to create data attribution for other dataset types beyond climate model datasets.

URL : Recognizing the Diversity of Contributions: A Case Study for Framing Attribution and Acknowledgement for Scientific Data

DOI : http://dx.doi.org/10.2218/ijdc.v11i1.357