Data and Model Sharing Group

Description & purpose of resource

Data and Model Sharing

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Science is a social activity requiring transparency, collaboration, and critical evaluation of published research. The bulk of research today is still communicated via peer-reviewed publication, and journals represent the primary gateway by which research is disseminated. One of the core aspects of the scientific method is the need to reproduce results. This is one of the primary distinguishing features of the scientific method and is what makes it such a successful enterprise. 

One of the consequences of this is that the reproducibility of published research becomes less important. This diminishes the stature of science as a reliable methodology and can affect policymakers in government and industry and instill a lack of trust by the general population. 
 

One approach to remedy this situation is to encourage journals to make sure that published work is reproducible. This means developing 
policies and a more open culture to freely  share empirical data, and models alongside the publication. 

Data- and model-sharing

To make it easier to conduct reproducible biochemical modeling, the Center for Reproducible Biomedical Modeling is developing tools that simplify model building, annotation, simulation, and visualization. However, if the model, results, and documentation associated with published modeling studies are not accessible, and the modeling workflow is not transparent, reproducing the model and its simulation results remains difficult and reuse is impossible.

Therefore, we recommend that all model artifacts produced during the modeling workflow be publicly shared to facilitate reproducibility and reuse, as emphasized by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Following the FAIR principles will ensure that models can be downloaded and manipulated by independent research groups, allowing published results to be validated, and will allow complex models to be built by integrating previously-published work on components of the system. We encourage modelers to disseminate packages of artifacts alongside publications with an open-source license and to deposit these packages in version-controlled public repositories.

 

Alignment with the NIH Strategic Plan for Data Science

 

 

Spatial scales
molecular
cellular
tissue
organ
whole organism
Temporal scales
<10-6 s (chemical reactions)
10-6 - 10-3 s
10-3 - 1 s
1 - 103 s
hours
days
weeks to months
This resource is currently
mature and useful in ongoing research
Has this resource been validated?
N/A
Key publications (e.g. describing or using resource)

https://blogs.plos.org/plos/2019/07/plos-computational-biology-announce-reproducibility-pilot/

DOI link to publication describing this resource

Not applicable, resource is a blog post

Link to resource
Center for Reproducible Biomedical Modeling Homepage
Service Request Form
Collaborators
Herbert M. Sauro
PI contact information
hsauro@uw.edu
Related Documents
curation.pdf528.31 KB
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Publications from: Integrating Machine Learning with Multiscale Modeling for Biomedical, Biological, and Behavioral Systems (2019 ML-MSM)

Integrating Machine Learning with Multiscale Modeling for Biomedical, Biological, and Behavioral Systems (2019 ML-MSM)

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Bethesda, Maryland (NIH Campus)

Editorial: Bio-inspired Audio Processing, Models and Systems

Submitted by mounya on

Bio-inspired systems look at biology to inspire engineering solutions that help explain, emulate and complement the intricate processes that take place in a biological system. As such, they operate at the intersection of biology and engineering and leverage advantages from both disciplines. When applied to brain sciences, bio-inspired systems often use non-conventional approaches to solve complex sensory and cognitive tasks.

A Study of a Cross-Language Perception Based on Cortical Analysis Using Biomimetic STRFs

Submitted by mounya on

For those in the early stage of learning a foreign language, they commonly experience difficulties in understanding spoken words in the second language, while they have no problem in recognizing words spoken in their mother tongue. This paper examines this phenomenon using biomimetic receptive fields that can be interpreted as a transfer function between acoustic stimulus and cortical responses in the brain.

Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection

Submitted by mounya on

Sound event detection is a challenging task, especially for scenes with multiple simultaneous events. While event classification methods tend to be fairly accurate, event localization presents additional challenges, especially when large amounts of labeled data are not available. Task4 of the 2018 DCASE challenge presents an event detection task that requires accuracy in both segmentation and recognition of events while providing only weakly labeled training data.

A Gestalt inference model for auditory scene segregation

Submitted by mounya on

Our current understanding of how the brain segregates auditory scenes into meaningful objects is in line with a Gestaltism framework. These Gestalt principles suggest a theory of how different attributes of the soundscape are extracted then bound together into separate groups that reflect different objects or streams present in the scene. These cues are thought to reflect the underlying statistical structure of natural sounds in a similar way that statistics of natural images are closely linked to the principles that guide figure-ground segregation and object segmentation in vision.

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