{"title":"Insights on Sustainability of Earth Science Data Infrastructure Projects","authors":"A. Virapongse, James Gallagher, B. Tikoff","doi":"10.5334/dsj-2024-014","DOIUrl":null,"url":null,"abstract":"We studied 11 long-term data infrastructure projects, most of which focused on the Earth Sciences, to understand characteristics that contributed to their project sustainability. Among our sample group, we noted the existence of three different types of project groupings: Database, Framework, and Middleware. Most efforts started as federally funded research projects, and our results show that nearly all became organizations in order to become sustainable. Projects were often funded for short time scales but had the long-term burden of sustaining and supporting open science, interoperability, and community building–activities that are difficult to fund directly. This transition from ‘project’ to ‘organization’ was challenging for most efforts, especially in regard to leadership change and funding issues.\nSome common approaches to sustainability were identified within each project grouping. Framework and Database projects both relied heavily on the commitment to, and contribution from, a disciplinary community. Framework projects often used bottom-up governance approaches to maintain the active participation and interest of their community. Database projects succeeded when they were able to position themselves as part of the core workflow for disciplinary-specific scientific research. Middleware projects borrowed heavily from sustainability models used by software companies, while maintaining strong scientific partnerships. Cyberinfrastructure for science requires considerable resources to develop and sustain itself, and much of these resources are provided through in-kind support from academics, researchers, and their institutes. It is imperative that more work is done to find appropriate models that help sustain key data infrastructure for Earth Science over the long-term.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/dsj-2024-014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
We studied 11 long-term data infrastructure projects, most of which focused on the Earth Sciences, to understand characteristics that contributed to their project sustainability. Among our sample group, we noted the existence of three different types of project groupings: Database, Framework, and Middleware. Most efforts started as federally funded research projects, and our results show that nearly all became organizations in order to become sustainable. Projects were often funded for short time scales but had the long-term burden of sustaining and supporting open science, interoperability, and community building–activities that are difficult to fund directly. This transition from ‘project’ to ‘organization’ was challenging for most efforts, especially in regard to leadership change and funding issues.
Some common approaches to sustainability were identified within each project grouping. Framework and Database projects both relied heavily on the commitment to, and contribution from, a disciplinary community. Framework projects often used bottom-up governance approaches to maintain the active participation and interest of their community. Database projects succeeded when they were able to position themselves as part of the core workflow for disciplinary-specific scientific research. Middleware projects borrowed heavily from sustainability models used by software companies, while maintaining strong scientific partnerships. Cyberinfrastructure for science requires considerable resources to develop and sustain itself, and much of these resources are provided through in-kind support from academics, researchers, and their institutes. It is imperative that more work is done to find appropriate models that help sustain key data infrastructure for Earth Science over the long-term.
期刊介绍:
The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.