| term | definition |
|---|---|
| FAIR | FAIR stands for Findable, Accessible, Interoperable, and Reusable, a set of guiding principles that aim to improve the management and stewardship of scientific data, metadata, and other research outputs. Originally proposed by Wilkinson et al. (2016), the FAIR principles emphasize that data should be findable through rich metadata and persistent identifiers; accessible using standardized protocols, even when access is restricted; interoperable via the use of shared formats, vocabularies, and ontologies; and reusable by ensuring clear licensing, documentation, and provenance. FAIR is not a strict standard but a framework to guide the development of research practices and infrastructure that support transparency, reproducibility, and long-term reuse by both humans and machines. |
| metadata | Metadata is information that describes other data. It helps explain what a dataset or research output is, how it was created, who created it, and how it can be used. Metadata makes data easier to find, understand, and reuse. |
STAPLE Documentation
1 Preface
Scientific research has become increasingly complex, requiring specialized skills, interdisciplinary work, and collaboration among large teams. Managing such projects and tracking data and metadata has become a significant challenge. The need for FAIR (findable, accessible, interoperable, and reusable Wilkinson et al. 2016) open data, materials, and metadata has similarly grown, especially considering recent mandates for required sharing for federally funded projects (Rep. Ryan 2019; Rep. Foxx 2019). While user-friendly software tools that help researchers structure metadata exist, few researchers are aware of their necessity and usefulness, the onus is still on the project team to collect and curate this information. Studies using teams of researchers require sophisticated tracking of all project elements, and therefore, there is a need for scientific project management software that is tailored to the management of all manner of science projects. Software that promotes best practices in FAIR data and metadata would increase openness in all reporting, and tracking, and reporting standards.
Currently, the tools and websites designed for researchers are focused on getting researchers to share their materials, code, and data (i.e., Open Science Framework, FigShare, Zenodo. These repositories represent a necessary resource for long-term storage of outputs, but do not help researchers organize or track information during the life of a study. Project management software, like Asana, Monday, or ClickUp, are designed from a business perspective that is not tailored to scientific research. While the features that scientific research requires may be found in some individual project management software, no current solution provides all the essential features, such as the ability to assign tasks at different scales (e.g., teams, individuals), integrated metadata, fully transparent access to all information in the project manager, and long-term storage that complies with international data privacy regulations. Project management software is designed to get things done rather than document the way a project was completed, so attempts to use existing software for this often involve ‘hacking’ it to extract necessary records.
To this end, we developed STAPLE, which not only helps with the unique challenges of project management of research but includes open and transparent documentation of data and metadata, as well as providing templates for minimum metadata collection. STAPLE allows users to add project components based on research type, assign timelines, delegate tasks to individuals or groups, and link to long-term storage of research outputs. STAPLE helps track authorship credit and contributor roles via tasks or contributors. STAPLE includes default metadata standards for documenting common research outputs and is fully open-source, enabling community input and adaptation. Designed for global reach, STAPLE supports the transition to open science by including researchers and outputs that are often overlooked, regardless of technical expertise. Through its point-and-click interface, and downloadable documentation outputs, STAPLE makes it easier for all researchers to participate in transparent, inclusive, and reproducible science.