Make scientific data FAIR

Stall, Shelley; Yarmey, Lynn; Cutcher‐Gershenfeld, Joel; Hanson, Brooks; Lehnert, Kerstin; Nosek, Brian A.; Parsons, M. A.; Robinson, Erin; Wyborn, Lesley

doi:10.1038/d41586-019-01720-7

Cited by 189 publications

(163 citation statements)

References 7 publications

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“…Once the Armenian Data Cube will be fully operational and will generate "official" products, they will be complied with the FAIR (Findable, Accessible, Interoperable, Re-usable) data principles [42,43], which will include adding a license such as Creative Commons and having a Digital Object Indentifier (DOI) for each generated datasets/products. It could be stressed that Armenian DC has a potential to transform the EO into useful information for users and represents a prospective solution for remotely sensed environmental monitoring in Armenia.…”

Section: Discussionmentioning

confidence: 99%

Paving the Way towards an Armenian Data Cube

Asmaryan

Muradyan

Tepanosyan

et al. 2019

Data

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Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

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Section: Discussionmentioning

confidence: 99%

Paving the Way towards an Armenian Data Cube

Asmaryan

Muradyan

Tepanosyan

et al. 2019

Data

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“…Additional solutions have also been proposed to provide appropriate recognition and credit for researchers who share their clinical data. [43][44][45][46] For instance, researchers who generated and shared their data could be designated as authors-data authors-on the manuscripts published via the reuse of their shared data. 43 Additionally, several data repositories that generate a data citation, have been developed (i.e., Dryad and Harvard Dataverse).…”

Section: Concerns About Losing Creditmentioning

confidence: 99%

Open Data Revolution in Clinical Research: Opportunities and Challenges

Shahin

Bhattacharya

Silva

et al. 2020

Clinical Translational Sci

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Efforts for sharing individual clinical data are gaining momentum due to a heightened recognition that integrated data sets can catalyze biomedical discoveries and drug development. Among the benefits are the fact that data sharing can help generate and investigate new research hypothesis beyond those explored in the original study. Despite several accomplishments establishing public systems and guidance for data sharing in clinical trials, this practice is not the norm. Among the reasons are ethical challenges, such as privacy of individuals, data ownership, and control. This paper creates awareness of the potential benefits and challenges of sharing individual clinical data, how to overcome these challenges, and how as a clinical pharmacology community we can shape future directions in this field.

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“…It is to be noted that in the last two cases, the restart files containing solvent information should be kept for reusability/reproducibility purposes, since keeping only dry trajectories many interesting aspects of macromolecular dynamics cannot be understood, as they are coupled with the solvent …”

Section: Compression Strategies and Data Transfermentioning

confidence: 99%

Surviving the deluge of biosimulation data

Hospital

Battistini

Soliva³

et al. 2019

WIREs Comput Mol Sci

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New hardware, massively parallel and graphical processing unit-based computers in particular, has boosted molecular simulations to levels that would be unthinkable just a decade ago. At the classical level, it is now possible to perform atomistic simulations with systems containing over 10 million atoms and to collect trajectories extending to the millisecond range. Such achievements are moving biosimulations into the mainstream of structural biology research, complementary to the experimental studies. The drawback of this impressive development is the management of data, especially at a time where the inherent value of data is becoming more apparent. In this review, we summarize the main characteristics of (bio)simulation data, how we can store them, how they can be reused for new, unexpected projects, and how they can be transformed to make them FAIR (findable, accessible, interoperable and reusable).

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Make scientific data FAIR

Cited by 189 publications

References 7 publications

Paving the Way towards an Armenian Data Cube

Paving the Way towards an Armenian Data Cube

Open Data Revolution in Clinical Research: Opportunities and Challenges

Surviving the deluge of biosimulation data

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