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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.
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This content will become publicly available on December 2, 2025
Sensor Reproducibility Analysis: Challenges and Potential Solutions
The ability to repeat research is vital in confirming the validity of scientific discovery and is relevant to ubiquitous sensor research. Investigation of novel sensors and sensing mechanisms intersect several Federal and non-Federal agencies. Despite numerous studies on sensors at different stages of development, the absence of new field-ready or commercial sensors seems limited by reproducibility. Current research practices in sensors needs sustainable transformations. The scientific community seeks ways to incorporate reproducibility and repeatability to validate published results. A case study on the reproducibility of low-cost air quality sensors is presented. In this context, the article discusses (a) open source data management frameworks in alignment with findability, accessibility, interoperability, and reuse (FAIR) principles to facilitate sensor reproducibility; (b) suggestions for journals focused on sensors to incorporate a reproducibility editorial board and incentivization for data sharing; (c) practice of reproducibility by targeted focus issues; and (d) education of current and the next generation of diverse student and faculty community on FAIR principles. The existence of different types of sensors such as physical, chemical, biological, and magnetic (to name a few) and the fact that the sensing field spans multiple disciplines (electrical engineering, mechanical engineering, physics, chemistry, and electrochemistry) call for a generic model for reproducibility. Considering the available metrics, the authors propose eight FAIR metric standards to that transcend disciplines: citation standards, design and analysis transparency, data transparency, analytical methods transparency, research materials transparency, hardware transparency, preregistration of studies, and replication.
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- Award ID(s):
- 2122195
- PAR ID:
- 10610331
- Publisher / Repository:
- IOP Science
- Date Published:
- Journal Name:
- ECS Sensors Plus
- Volume:
- 3
- Issue:
- 4
- ISSN:
- 2754-2726
- Page Range / eLocation ID:
- 046401
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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