Addendum: The FAIR Guiding Principles for scientific data management and stewardship

Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; Axton, Myles; Baak, Arie; Blomberg, Niklas; Boiten, Jan Willem; Santos, Luiz Olavo Bonino da Silva; Bourne, Philip E.; Bouwman, Jildau; Brookes, Anthony J.; Clark, Tim W.; Crosas, Mercè; Dillo, Ingrid; Dumon, Olivier; Edmunds, Scott; Evelo, Chris T.; Finkers, Richard; González-Beltrán, Alejandra; Gray, Alasdair J. G.; Groth, Paul; Goble, Carole; Grethe, Jeffrey S.; Heringa, Jaap; Hoen, Peter A.C. ‘t; Hooft, Rob; Kuhn, Tobias; Kok, Ruben; Kok, Joost N.; Lusher, Scott J.; Martone, Maryann E.; Mons, Albert; Packer, Abel L.; Persson, Bengt L.; Rocca-Serra, Philippe; Roos, Marco; Schaik, René van; Sansone, Susanna Assunta; Schultes, Erik; Sengstag, Thierry; Slater, Ted; Strawn, George; Swertz, Morris A.; Thompson, Mark; Lei, Johan van der; Mulligen, Erik M. van; Velterop, Jan; Waagmeester, Andra; Wittenburg, Peter; Wolstencroft, Katherine; Zhao, Jun; Mons, Barend

doi:10.1038/s41597-019-0009-6

Cited by 180 publications

(153 citation statements)

References 21 publications

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“…Fundamental to that goal, he says, is that data production abides by the FAIR Guiding Principles, which were first published in 2016. 16 Those principles state that data, and associated data objects such as code, should be findable, accessible, interoperable, and reusable by humans and machines alike.…”

Section: Issues Of Trustmentioning

confidence: 99%

Into the Black Box: What Can Machine Learning Offer Environmental Health Research?

Schmidt¹

2020

Environ Health Perspect

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Section: Issues Of Trustmentioning

confidence: 99%

Into the Black Box: What Can Machine Learning Offer Environmental Health Research?

Schmidt¹

2020

Environ Health Perspect

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“…[88][89][90][91][92] These approaches, as outlined by the Tox21 Federal collaboration and the U.S. Environmental Protection Agency's ToxCast program, [93][94][95][96] can be used to focus high-throughput in vitro and in vivo testing on compounds and concentrations that are more likely to be biologically active and appropriate for each model. Finally, computational models and the data used in their development must be made findable, freely accessible, interoperable and reusable (FAIR), 100 just as information for in vitro and preclinical in vivo models must be findable and sufficiently transparent to support replication. 99 Speakers described several critical, often broadly applicable considerations for utilizing in silico models.…”

Section: In Silico Modelsmentioning

confidence: 99%

“…Transgenic mice are commonly employed to enhance construct validity; guidelines for rigor and transparency in research using mouse mutants have been published. 176 Many different approaches to "humanizing" mice have been utilized to enhance predictive validity for specific research disciplines 100 Figure 1): Where the genetic mechanism of interest is unknown, for example in toxicity testing 166,174 or research on neuropathic pain, 175 the genetic heterogeneity of outbred mice 174 may provide important information that is unavailable from inbred strains without otherwise affecting outcome variability.…”

Section: Selection Of a Mouse Modelmentioning

confidence: 99%

“…Fourth, end users must understand the computational (or any other) model's limitations, the extent to which it is similar or dissimilar to the biological system of interest, and how the outputs compare with population-derived data, other real-world data, or with data from other models. Finally, computational models and the data used in their development must be made findable, freely accessible, interoperable and reusable (FAIR), 100 just as information for in vitro and preclinical in vivo models must be findable and sufficiently transparent to support replication.…”

Section: In Silico Modelsmentioning

confidence: 99%

“…Where the genetic mechanism of interest is unknown, for example in toxicity testing 166,174 or research on neuropathic pain, 175 the genetic heterogeneity of outbred mice 174 may provide important information that is unavailable from inbred strains without otherwise affecting outcome variability. 176 Many different approaches to "humanizing" mice have been utilized to enhance predictive validity for specific research disciplines 100…”

Section: Selection Of a Mouse Modelmentioning

confidence: 99%

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Improving natural product research translation: From source to clinical trial

et al. 2019

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While great interest in health effects of natural product (NP) including dietary supplements and foods persists, promising preclinical NP research is not consistently translating into actionable clinical trial (CT) outcomes. Generally considered the gold standard for assessing safety and efficacy, CTs, especially phase III CTs, are costly and require rigorous planning to optimize the value of the information obtained. More effective bridging from NP research to CT was the goal of a Septe mber, 2018 trans disci plina ry workshop. Participants emphasized that replicability and likelihood of successful translation depend on rigor in experimental design, interpretation, and reporting across the continuum of NP research. Discussions spanned good practices for NP characterization and quality control; use and interpretation of models (computational through in vivo) with strong clinical predictive validity; controls for experimental artefacts, especially for in vitro interrogation of bioactivity and mechanisms of action; rigorous assessment and interpretation of prior research; transparency in all reporting; and prioritization of research questions. Natural product clinical trials prioritized based on rigorous, convergent supporting data and current public health needs are most likely to be informative and ultimately affect public health. Thoughtful, coordinated implementation of these practices should enhance the knowledge gained from future NP research. K E Y W O R D S clinical predictive validity, dietary supplements, model systems, rigor and replicability, value of information | 43 SORKIN et al.still provide invaluable guidance, the results of such trials are often met with concerns that different design choiceswhether of product, dose, timing, participant eligibility criteria, outcomes, etc.-might have yielded a substantially different result. Each interventional trial represents a substantial investment, with well-powered Phase III CTs typically consuming more than $10 million and many person-years. 9,10 Additionally, as time and funds are limited, and participant pools may also be constrained, a decision to invest in a CT often precludes pursuing other research questions, 11 which may provide more useful public health-relevant knowledge than an RCT with an insufficient evidence base.

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Closing the loop – the role of pathologists in digital and computational pathology research

Rau,

Cross,

Lastra

et al. 2024

The Journal of Pathology CR

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An increasing number of manuscripts related to digital and computational pathology are being submitted to The Journal of Pathology: Clinical Research as part of the continuous evolution from digital imaging and algorithm‐based digital pathology to computational pathology and artificial intelligence. However, despite these technological advances, tissue analysis still relies heavily on pathologists' annotations. There are three crucial elements to the pathologist's role during annotation tasks: granularity, time constraints, and responsibility for the interpretation of computational results. Granularity involves detailed annotations, including case level, regional, and cellular features; and integration of attributions from different sources. Time constraints due to pathologist shortages have led to the development of techniques to expedite annotation tasks from cell‐level attributions up to so‐called unsupervised learning. The impact of pathologists may seem diminished, but their role is crucial in providing ground truth and connecting pathological knowledge generation with computational advancements. Measures to display results back to pathologists and reflections about correctly applied diagnostic criteria are mandatory to maintain fidelity during human–machine interactions. Collaboration and iterative processes, such as human‐in‐the‐loop machine learning are key for continuous improvement, ensuring the pathologist's involvement in evaluating computational results and closing the loop for clinical applicability. The journal is interested particularly in the clinical diagnostic application of computational pathology and invites submissions that address the issues raised in this editorial.

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Addendum: The FAIR Guiding Principles for scientific data management and stewardship

Cited by 180 publications

References 21 publications

Into the Black Box: What Can Machine Learning Offer Environmental Health Research?

Into the Black Box: What Can Machine Learning Offer Environmental Health Research?

Improving natural product research translation: From source to clinical trial

Closing the loop – the role of pathologists in digital and computational pathology research

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