Open Science Transparency, openness, and reproducibility is what makes open science so popular

These are initiatives with the general goal to create open science communities and projects in order to enhance the quality of research and collaboration. These initiatives concern efforts to help and support open science communities and research project infrastructure, as well as online platforms to share tools, code, software and data for scientific projects.

• Center for Open Science

  The mission of Centre for Open Science (COS) is to provide expertise, tools, and training to help researchers create and promote open science within their teams and institutions. COS supports and conducts research on scientific practices, helps connect and build open science communities of researches, their affiliated institutions, their founders, and the publishers of research outcomes. In addition, COS developed a variety of software tools based on their free Open Science Framework (OSF). COS works with research institutions, researchers & scientists, publishers & societies, and software developers.

• Allen Institute for Brain Science

  The Allen Institute for Brain Science is a medical research organization that focusses on understanding how the human brain works. The Institute has a commitment to an open science model within its research institutes and promotes the advance of brain research by providing free data and tools. The Institute provides online public resources for exploring the nervous system, such as gene expression data and neuroanatomy, which are accessible via the Allen Brain Atlas data portal.

• Why Open Research?

  Erin McKiernan is a researcher working primarily in experimental and theoretical neuroscience and cellular biophysics, in addition to be the founder of the Why Open Research? project. Why Open Research? is an educational site for researchers to learn how to share their work, funded in part by the Shuttleworth Foundation. The main points of Why Open Research? are version control, verification and validation, open licensing, preprints, open code, and web presence.

• Collaborative Research in Comp Neuro

  Collaborative Research in Computational Neuroscience (CRCNS), by Fritz Sommer (UC Berkeley), provides a marketplace and discussion forum for sharing tools and data in neuroscience. Information about the aims and scope of this initiative can be found here. To date they host experimental data sets of high quality that will be valuable for testing computational models of the brain and new analysis methods. The data include physiological recordings from sensory and memory systems, as well as eye movement data. The model organisms are mouse, rat, and macaque in addition to human data.

Open research projects are specific for a research theme, such as an animal model, specific brain region, or an experimental technique. Multiple research groups work on an open research project by sharing data, protocols, and code. In this way, the collaborative efforts ensure high quality research that lead to reproducible and high impact hypothesis.

• Neuronal Reconstruction

  Reconstructing neuronal morphology in 3D models is important to obtain more insight into neuronal connections, functioning, and physiology.

  • NeuroMorpho

    NeuroMorpho.Org is a centrally curated inventory of digitally reconstructed neurons associated with peer-reviewed publications. The repository now provides access to 62,304 cells from 40 species and 278 labs. Glia arbors are included in the database in addition to neurons. The database is continuously updated as new morphological reconstructions are collected, published, and shared. To date, NeuroMorpho.Org is the largest collection of publicly accessible 3D neuronal reconstructions and associated metadata.

• Model Organisms

  There are a couple open source projects that contain data from specific model organisms, such as the worm, fly, and mouse.

  • Worm

    The OpenWorm is an open source project dedicated to creating a virtual C. elegans nematode in a computer.

  • Fly

    The FlyBase is an open source project for the Drosophila, and contains atlases, data sources, and meta-data.

    NeuroNLP provides a modern web-based portal for navigating fruit fly brain circuit data. It enables in-depth exploration and investigation of brain structures using plain English queries.

  • Mouse

    The MouseAtlas is a quantitative and comprehensive atlas of gene expression in mouse development. The gene expression methodology data is from Serial Analysis of Gene Expression (SAGE).

    The Allen Brain Institute provides a comprehensive Mouse Brain Atlas, which is a genome-wide, three-dimensional map of gene expression throughout the adult mouse brain. In addition, In addition, the Cell Types Database contains electrophysiological, morphological, and transcriptomic properties gathered from individual cells in mice, and models simulating cell activity.

• Human Studies

  The open science web for human studies involve projects that collected data from neuroimaging, clinical recordings, and genetics.

  • The Human Connectome Project

    The Human Connectome Project (HCP) is a project to construct a map of the complete structural and functional neural connections in vivo within and across individuals. The HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics infrastructure to link these data and connectivity models to detailed phenomic and genomic data, building upon existing multidisciplinary and collaborative efforts currently underway.

  • The Virtual Brain

    The The Virtual Brain takes a network approach on the largest scale: by manipulating network parameters, in particular the brain’s connectivity, The Virtual Brain simulates its behavior as it is commonly observed in clinical scanners (e.g. EEG, MEG, fMRI).

A research project has a lifecycle, starting at developing an idea to publish the report. Although the eventual scientific paper is what we see as the outcome of the research project, the infrastructure involves many more steps such as designing the study, data collection, and data analysis. All these steps use different tools, software, and coding scripts, which are hard to understand and most of the time stored in different locations. The management of this infrastructure is key for the progress of the research project as well as understanding the results of the project and collaboration.

• Open Science Framework

  Open Science Framework (OSF) is a free, open source service of the Center for Open Science (COS). They are an online platform to support the whole research cycle. The idea is to structure projects by keeping all your files, data, and protocols in one centralized location, which therefore helps with collaborating with the scientific worldwide community. It allows to manage your project on a dashboard, archive your data, share files, control access and collaboration, see project changes due to historical file versions, view project analytics, and helps with an efficient workflow and lifecycle overall.

Storing data is not as intuitive as you would think, especially when the complexity and uniqueness of the data increases. Therefore, data storage systems are fundamental for the research lifecycle, as it allows to easily compare, use, and analysis the data.

• DataJoint

  DataJoint for MATLAB and Python3 is a high-level programming interface for MySQL databases to support data processing chains in science labs. DataJoint is built on the foundation of the relational data model and prescribes a consistent method for organizing, populating, and querying data. For more information, read their Whitepaper.

• NeuroML

  NeuroML contains computational models based on detailed neuroanatomical and electrophysiological data that have been used for many years as an aid for understanding the function of the nervous system. The NeuroML project focuses on the development of an XML (eXtensible Markup Language) based description language that provides a common data format for defining and exchanging descriptions of neuronal cell and network models. The current approach in the project uses XML schemas to define the model specifications.

Actually, this is just what is says and what you would expect: here you can find web sources where code and description for computational models are stored. These websites include a detailed description of the models, as well as when and how to use it.

• ModelDB

  ModelDB provides an accessible location for storing and efficiently retrieving computational neuroscience models. ModelDB is tightly coupled with NeuronDB. Models can be coded in any language for any environment. You can find models of realistic networks, neurons, electrical synapses (gap junctions), chemical synapses, ion channels, neuromuscular junctions, and axons.

• Open Source Brain

  Open Source Brain is a resource for sharing and collaboratively developing computational models of neural systems. Open Source Brain contains models for neocortex, cerebellum, hippocampus, olfactory bulb, and invertebrates.

• NeuroDebian

  Neuro Debian provides a large collection of popular neuroscience research software for Debian distributions (Linux operating system) as well as Ubuntu and other derivatives. Popular packages include AFNI, FSL, PyMVPA and many others.

Software tools mainly imply that the developed code is accompanied by a graphical user interface (GUI) to make the process more intuitive for the user. Most of the tools concern analysis of experimental data and theoretical model development.

• Analyzing Papers

  The ContentMine provides open source software to download and analyze papers en masse, helping you filter by many custom criteria and feed the data into your preferred analysis and visualization workflows. Their software stack provides tools for getting papers from many online sources, normalizing them, then processing them to lookup and/or search for key terms, phrases, patterns, statements, and more. The stream of facts is available in a standardized, easy-to-reuse way and that makes it share-able.

• Imaging Tools

  Imaging and electrophysiological recordings are the main used data types in computational modeling. Imaging studies measure the structure and function of the brain and neurons (the brain cells). In humans, functional imaging is used to measure the activity of the brain in specific areas. In animal models, imaging is used to measure calcium fluctuations in the neurons, which is also an indication for their activity.

  • FreeSurfer

    FreeSurfer is an open source software suite for processing and analyzing (human) brain MRI images. Also contains acquisition protocols, such as Morphometry Protocols ( for more information, see their wiki.

  • Analysis of Funtional NeuroImages

    Analysis of Functional NeuroImages (AFNI) is a set of C programs for processing, analyzing, and displaying human functional MRI (FMRI) data. It runs on Unix+X11+Motif systems, including SGI, Solaris, Linux, and Mac OS X. They developed Matlab libraries to read, write, and process AFNI datasets.

  • FSL

    FSL is a comprehensive library of analysis tools for FMRI, MRI and DTI brain imaging data. It runs on Apple and PCs (both Linux, and Windows via a Virtual Machine). Most of the tools can be run both from the command line and as GUIs ("point-and-click" graphical user interfaces).

• Simulations

  NetLogo is a multi-agent programmable modeling environment. It is used by students and teachers, and powers HubNet participatory simulations. You can either download NetLogo (all the models in the model library are included) or run each model in your browser. A description of the specific neuroscience models is published in support for teaching undergraduate student.

• Statistics

  PyMVPA is a Python package intended to ease statistical learning analyses of large datasets, mainly in neuroimaging. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn, shogun, MDP, etc.