Étiquette : Matthew S. Mayernik

Catégories
EN

The Role of Metadata and Vocabulary Standards in Enabling Scientific Data Interoperability: A Study of Earth System Science Data Facilities

Authors : Matthew S. Mayernik, Yauheniya Liapich

Objective

Journal publishers within many sciences are increasingly expecting data to be deposited into repositories that support the FAIR principles. Data repositories are thus needing to determine what implications the FAIR principles have on their existing services and systems. Metadata standards and controlled vocabularies are specifically called out as core components of the FAIR principles related to interoperability.

Methods

This paper looks specifically at the ways that metadata standards and controlled vocabularies are used by Earth system science data repositories. Data sets from 55 data facilities were examined to determine which metadata standards and controlled subject / keyword vocabularies were used.

Results

The findings indicate that only the ISO 19115:2003 and DataCite metadata standards are used by more than 40% of the data facilities, and the NASA Global Change Master Directory (GCMD) keywords are the only keyword vocabulary of broad use within this community.

Conclusions

These findings raise questions about the extent to which metadata standards and keyword vocabularies can facilitate interoperability beyond narrow sub-sections of the data facility communities. This study also points to systematic challenges related to migration to new standards.

URL : The Role of Metadata and Vocabulary Standards in Enabling Scientific Data Interoperability: A Study of Earth System Science Data Facilities

DOI : https://doi.org/10.7191/jeslib.619

Catégories
EN

Risk Assessment for Scientific Data

Authors : Matthew S. Mayernik, Kelsey Breseman, Robert R. Downs, Ruth Duerr, Alexis Garretson, Chung-Yi (Sophie) Hou

Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use.

Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks.

The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship.

The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities.

URL : Risk Assessment for Scientific Data

DOI : http://doi.org/10.5334/dsj-2020-010

Catégories
EN

Using Peer Review to Support Development of Community Resources for Research Data Management

Authors : Heather Soyka, Amber Budden, Viv Hutchison, David Bloom, Jonah Duckles, Amy Hodge, Matthew S. Mayernik, Timothée Poisot, Shannon Rauch, Gail Steinhart, Leah Wasser, Amanda L. Whitmire, Stephanie Wright

Objective

To ensure that resources designed to teach skills and best practices for scientific research data sharing and management are useful, the maintainers of those materials need to evaluate and update them to ensure their accuracy, currency, and quality.

This paper advances the use and process of outside peer review for community resources in addressing ongoing accuracy, quality, and currency issues. It further describes the next step of moving the updated materials to an online collaborative community platform for future iterative review in order to build upon mechanisms for open science, ongoing iteration, participation, and transparent community engagement.

Setting

Research data management resources were developed in support of the DataONE (Data Observation Network for Earth) project, which has deployed a sustainable, long-term network to ensure the preservation and access to multi-scale, multi-discipline, and multi-national environmental and biological science data (Michener et al. 2012).

Created by members of the Community Engagement and Education (CEE) Working Group in 2011-2012, the freely available Educational Modules included three complementary components (slides, handouts, and exercises) that were designed to be adaptable for use in classrooms as well as for research data management training.

Methods

Because the modules were initially created and launched in 2011-2012, the current members of the (renamed) Community Engagement and Outreach (CEO) Working Group were concerned that the materials could be and / or quickly become outdated and should be reviewed for accuracy, currency, and quality.

In November 2015, the Working Group developed an evaluation rubric for use by outside reviewers. Review criteria were developed based on surveys and usage scenarios from previous DataONE projects.

Peer reviewers were selected from the DataONE community network for their expertise in the areas covered by one of the 11 educational modules. Reviewers were contacted in March 2016, and were asked to volunteer to complete their evaluations online within one month of the request, by using a customized Google form.

Results

For the 11 modules, 22 completed reviews were received by April 2016 from outside experts. Comments on all three components of each module (slides, handouts, and exercises) were compiled and evaluated by the postdoctoral fellow attached to the CEO Working Group.

These reviews contributed to the full evaluation and revision by members of the Working Group of all educational modules in September 2016. This review process, as well as the potential lack of funding for ongoing maintenance by Working Group members or paid staff, provoked the group to transform the modules to a more stable, non-proprietary format, and move them to an online open repository hosting platform, GitHub.

These decisions were made to foster sustainability, community engagement, version control, and transparency.

Conclusion

Outside peer review of the modules by experts in the field was beneficial for highlighting areas of weakness or overlap in the education modules. The modules were initially created in 2011-2012 by an earlier iteration of the Working Group, and updates were needed due to the constant evolving practices in the field.

Because the review process was lengthy (approximately one year) comparative to the rate of innovations in data management practices, the Working Group discussed other options that would allow community members to make updates available more quickly.

The intent of migrating the modules to an online collaborative platform (GitHub) is to allow for iterative updates and ongoing outside review, and to provide further transparency about accuracy, currency, and quality in the spirit of open science and collaboration.

Documentation about this project may be useful for others trying to develop and maintain educational resources for engagement and outreach, particularly in communities and spaces where information changes quickly, and open platforms are already in common use.

URL : Using Peer Review to Support Development of Community Resources for Research Data Management

DOI : https://doi.org/10.7191/jeslib.2017.1114