skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on April 1, 2026

Title: FAIR principles in workflows: A GIScience workflow management system for reproducible and replicable studies
Scientific workflow management systems (WfMS) provide a systematic way to streamline necessary processes in scientific research. The demand for FAIR (Findable, Accessible, Interoperable, and Reusable) workflows is increasing in the scientific community, particularly in GIScience, where data is not just an output but an integral part of iterative advanced processes. Traditional WfMS often lack the capability to ensure geospatial data and process transparency, leading to challenges in reproducibility and replicability of research findings. This paper proposes the conceptualization and development of FAIR-oriented GIScience WfMS, aiming to incorporate the FAIR principles into the entire lifecycle of geospatial data processing and analysis. To enhance the findability and accessibility of workflows, the WfMS utilizes Harvard Dataverse to share all workflow-related digital resources, organized into workflow datasets, nodes, and case studies. Each resource is assigned a unique DOI (Digital Object Identifier), ensuring easy access and discovery. More importantly, the WfMS complies with the Common Workflow Language (CWL) standard to guarantee interoperability and reproducibility of workflows. It also enables the integration of diverse tools and software, supporting complex analyses that require multiple processing steps. This paper demonstrates the prototype of the GIScience WfMS and illustrates two geospatial science case studies, reflecting its flexibility in selecting appropriate techniques for various datasets and research goals. The user-friendly workflow designer makes it accessible to users with different levels of technical expertise, promoting reusable, reproducible, and replicable GIScience studies.  more » « less
Award ID(s):
2327844
PAR ID:
10633623
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
https://www.sciencedirect.com/science/article/pii/S1569843225001244
Date Published:
Journal Name:
International Journal of Applied Earth Observation and Geoinformation
Volume:
138
Issue:
C
ISSN:
1569-8432
Page Range / eLocation ID:
104477
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. This perspective article presents the vision of combining findable, accessible, interoperable, and reusable (FAIR) Digital Objects with the National Science Data Fabric (NSDF) to enhance data accessibility, scientific discovery, and education. Integrating FAIR Digital Objects into the NSDF overcomes data access barriers and facilitates the extraction of machine-actionable metadata in alignment with FAIR principles. The article discusses examples of climate simulations and materials science workflows and establishes the groundwork for a dataflow design that prioritizes inclusivity, web-centricity, and a network-first approach to democratize data access and create opportunities for research and collaboration in the scientific community. 
    more » « less
  2. Abstract Open science and open data within scholarly research programs are growing both in popularity and by requirement from grant funding agencies and journal publishers. A central component of open data management, especially on collaborative, multidisciplinary, and multi-institutional science projects, is documentation of complete and accurate metadata, workflow, and source code in addition to access to raw data and data products to uphold FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although best practice in data/metadata management is to use established internationally accepted metadata schemata, many of these standards are discipline-specific making it difficult to catalog multidisciplinary data and data products in a way that is easily findable and accessible. Consequently, scattered and incompatible metadata records create a barrier to scientific innovation, as researchers are burdened to find and link multidisciplinary datasets. One possible solution to increase data findability, accessibility, interoperability, reproducibility, and integrity within multi-institutional and interdisciplinary projects is a centralized and integrated data management platform. Overall, this type of interoperable framework supports reproducible open science and its dissemination to various stakeholders and the public in a FAIR manner by providing direct access to raw data and linking protocols, metadata and supporting workflow materials. 
    more » « less
  3. AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research. 
    more » « less
  4. Abstract Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants. 
    more » « less
  5. VREs are predestined to support many aspects of FAIR because of their characteristics to provide a workspace for collaboration, sharing data and simulations and/or workflows. The FAIR for VRE Working Group has worked on a checklist to measure FAIRness in science gateways. This list considers how to address the complexity in regard to which target group is addressed – developers or users – and the granularity such as VREs as software frameworks, services, APIs, workflows, data and simulations. We assume that not only VREs as software frameworks are FAIR but that they also are FAIR-enabling for the digital objects they contain. The objective of this session will be how to recognize and incentivize that providers, developers and users are actively working towards FAIRness of digital objects. The idea for this session is to address this via badges. It probably makes sense to split the badges for the four principles Findable, Accessible, Interoperable and Reusable. There are many open questions beyond this granularity such as how to create badges, who gives such badges, what are the rules for the duration of badges? 
    more » « less