Cargando…

FAIRly big: A framework for computationally reproducible processing of large-scale data

Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wagner, Adina S., Waite, Laura K., Wierzba, Małgorzata, Hoffstaedter, Felix, Waite, Alexander Q., Poldrack, Benjamin, Eickhoff, Simon B., Hanke, Michael
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917149/ https://www.ncbi.nlm.nih.gov/pubmed/35277501 http://dx.doi.org/10.1038/s41597-022-01163-2

Ejemplares similares

Reproducible big data science: A case study in continuous FAIRness
por: Madduri, Ravi, et al.
Publicado: (2019)

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics
por: Levinson, Maxwell Adam, et al.
Publicado: (2021)

Correction: Reproducible big data science: A case study in continuous FAIRness
por: Madduri, Ravi, et al.
Publicado: (2023)

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps
por: Zhao, Chenying, et al.
Publicado: (2023)

Personality and local brain structure: Their shared genetic basis and reproducibility
por: Valk, Sofie L., et al.
Publicado: (2020)

Evaluation of thresholding methods for activation likelihood estimation meta‐analysis via large‐scale simulations
por: Frahm, Lennart, et al.
Publicado: (2022)

What Can Computational Models Contribute to Neuroimaging Data Analytics?
por: Popovych, Oleksandr V., et al.
Publicado: (2019)

Differential Fairness: An Intersectional Framework for Fair AI †
por: Islam, Rashidul, et al.
Publicado: (2023)

bigSCale: an analytical framework for big-scale single-cell data
por: Iacono, Giovanni, et al.
Publicado: (2018)

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models
por: Ciric, Rastko, et al.
Publicado: (2022)

Big Data in Laboratory Medicine—FAIR Quality for AI?
por: Blatter, Tobias Ueli, et al.
Publicado: (2022)

Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns
por: Wu, Jianxiao, et al.
Publicado: (2022)

Impact of sample size and regression of tissue‐specific signals on effective connectivity within the core default mode network
por: Silchenko, Alexander N., et al.
Publicado: (2023)

Natural and anthropogenic factors drive large-scale freshwater fish invasions
por: Milardi, Marco, et al.
Publicado: (2022)

Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible
por: Loftus, Tyler J., et al.
Publicado: (2022)

Reproducibility and FAIR Principles: The Case of a Segment Polarity Network Model
por: Mendes, Pedro
Publicado: (2023)

Reproducibility and FAIR principles: the case of a segment polarity network model
por: Mendes, Pedro
Publicado: (2023)

A General Framework for Fair Regression
por: Fitzsimons, Jack, et al.
Publicado: (2019)

A framework for FAIR robotic datasets
por: Motta, Corrado, et al.
Publicado: (2023)

The adaptive immune receptor repertoire community as a model for FAIR stewardship of big immunology data
por: Scott, Jamie K., et al.
Publicado: (2020)

Towards a human self-regulation system: Common and distinct neural signatures of emotional and behavioural control
por: Langner, Robert, et al.
Publicado: (2018)

Editorial: Reliability and Reproducibility in Functional Connectomics
por: Zuo, Xi-Nian, et al.
Publicado: (2019)

A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research
por: Gorgolewski, Krzysztof J., et al.
Publicado: (2016)

Corrigendum: Editorial: Reliability and Reproducibility in Functional Connectomics
por: Zuo, Xi-Nian, et al.
Publicado: (2019)

Empirical examination of the replicability of associations between brain structure and psychological variables
por: Kharabian Masouleh, Shahrzad, et al.
Publicado: (2019)

Tract-specific statistics based on diffusion-weighted probabilistic tractography
por: Reid, Andrew T., et al.
Publicado: (2022)

Algorithmic fairness in computational medicine
por: Xu, Jie, et al.
Publicado: (2022)

A computational framework to explore large-scale biosynthetic diversity
por: Navarro-Muñoz, Jorge C., et al.
Publicado: (2019)

Strategies to enable large-scale proteomics for reproducible research
por: Poulos, Rebecca C., et al.
Publicado: (2020)

ALFA: The new ALICE-FAIR software framework
por: Al-Turany, M, et al.
Publicado: (2015)

Stochastic Cooling in the Framework of the FAIR Project at GSI
por: Nolden, F, et al.
Publicado: (2008)

A design framework and exemplar metrics for FAIRness
por: Wilkinson, Mark D., et al.
Publicado: (2018)

FACT and FAIR with Big Data allows objectivity in science: The view of crystallography
por: Helliwell, John R.
Publicado: (2019)

Data science and big data computing: frameworks and methodologies
por: Mahmood, Zaigham
Publicado: (2016)

Large scale and big data: processing and management
por: Sakr, Sherif, et al.
Publicado: (2014)

Big science: the growth of large-scale research
por: Hevly, Bruce, et al.
Publicado: (1992)

Scalable Infrastructure Supporting Reproducible Nationwide Healthcare Data Analysis toward FAIR Stewardship
por: Kim, Ji-Woo, et al.
Publicado: (2023)

Large-Scale Protein-Protein Interactions Detection by Integrating Big Biosensing Data with Computational Model
por: You, Zhu-Hong, et al.
Publicado: (2014)

An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases
por: Pokutnaya, Darya, et al.
Publicado: (2023)

A Maximal Correlation Framework for Fair Machine Learning
por: Lee, Joshua, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_gereso7s3r1miht3cbluajv5r6