Ten Simple Rules for FAIR Sharing of Experimental and Clinical Data with the Modeling Community

Science continues to become more interdisciplinary and to involve increasingly complex data sets. Many projects in the biomedical and health-related sciences follow or aim to follow the principles of FAIR data sharing, which has been demonstrated to foster collaboration, to lead to better research outcomes, and to help ensure reproducibility of results. Data generated in the course of biomedical and health research present specific challenges for FAIR sharing in the sense that they are heterogeneous and highly sensitive to context and the needs of protection and privacy. Data sharing must respect these features without impeding timely dissemination of results, so that they can contribute to time-critical advances in medical therapy and treatment. Modeling and simulation of biomedical processes have become established tools, and a global community has been developing algorithms, methodologies, and standards for applying biomedical simulation models in clinical research. However, it can be difficult for clinician scientists to follow the specific rules and recommendations for FAIR data sharing within this domain. We seek to clarify the standard workflow for sharing experimental and clinical data with the simulation modeling community. By following these recommendations, data sharing will be improved, collaborations will become more effective, and the FAIR publication and subsequent reuse of data will become possible at the level of quality necessary to support biomedical and health-related sciences.

“Measurement and Fair-Sharing Models for Dividing Fractions”

From the Archives highlights articles from NCTM’s legacy journals, as chosen by leaders in mathematics education.

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references —i.e., templates— and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR —findable, accessible, interoperable, reusable— principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.

UnityMol prototype for FAIR sharing of molecular-visualization experiences: from pictures in the cloud to collaborative virtual reality exploration in immersive 3D environments

Motivated by the current COVID-19 pandemic, which has spurred a substantial flow of structural data, the use of molecular-visualization experiences to make these data sets accessible to a broad audience is described. Using a variety of technology vectors related to the cloud, 3D and virtual reality gear, how to share curated visualizations of structural biology, modeling and/or bioinformatics data sets for interactive and collaborative exploration is examined. FAIR is discussed as an overarching principle for sharing such visualizations. Four initial example scenes related to recent COVID-19 structural data are provided, together with a ready-to-use (and share) implementation in the UnityMol software.

A Comparison of Fair Sharing Algorithms for Regulating Search as a Service API

Providers of a Search as a Service (SaaS) environment must ensure that their users will not monopolize the service or use more than their fair share of resources. Fair sharing algorithms have long been used in computer networking to balance access to a router or switch, and some of these algorithms have also been applied to the control of queries submitted to search engine APIs. If a search query’s execution cost can be reliably estimated, fair sharing algorithms can be applied to the input of a SaaS API to ensure everyone has equitable access to the search engine. The novelty of this paper lies in presenting a Single-Server Max-Min Fair Deficit Round Robin algorithm, a modified version of the Multi-Server Max-Min Fair Deficit Round Robin algorithm. The Single-Server Max-Min Fair Deficit Round Robin algorithm is compared to three other fair sharing algorithms, token-bucket, Deficit Round Robin (DRR), and Peng and Plale’s [1] Modified Deficit Round Robin (MDRR) in terms of three different usage scenarios, balanced usage, unbalanced usage as well as an idle client usage, to determine which is the most suitable fair sharing algorithm for use in regulating traffic to a SaaS API. This research demonstrated that the Single-Server Max-Min Fair DRR algorithm provided the highest throughput of traffic to the search engine while also maintaining a fair balance of resources among clients by re-allocating unused throughput to clients with saturated queues so a max-min allocation was achieved.

If future generations had a say: An experiment on fair sharing of a common-pool resource across generations

Through an online experiment with 682 participants, we test how inter-generational resource sharing is affected by granting veto power to later generations. We specifically study the over-use of a common-pool resource (CPR) by early generations at the expense of later generations and examine how the veto empowerment of later generations can be used to restrain egoistic tendencies. We compare sequential ultimatum and dictator games of various lengths and find that (1) the CPR consumption of early generations does not depend on the number of generations that follow them; (2) the veto empowerment of later generations leads to a fairer, but ultimately less efficient use of the CPR across generations; and (3) the vetoes are used more carefully if not only previous generations, but also future generations that do not yet have access to the resource are affected by the veto.