@@ -25,7 +25,9 @@ The Open Analysis Working Group has worked with researchers to capture details o
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@@ -25,7 +25,9 @@ The Open Analysis Working Group has worked with researchers to capture details o
International community data standards - specifically the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io) - have been employed to ensure that shared pipelines are compatible with tools developed elsewhere. This working group is also committed to actively engaging with the future development of BIDS, to ensure lifetime compatibility between FSL and the wide data standards.
International community data standards - specifically the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io) - have been employed to ensure that shared pipelines are compatible with tools developed elsewhere. This working group is also committed to actively engaging with the future development of BIDS, to ensure lifetime compatibility between FSL and the wide data standards.
### Education
### Education
Another large focus of this working group is developing programming literacy among WIN members, to support users in creating robust code to run their analysis. Significant efforts have been made to update the [FSL training material](https://fsl.fmrib.ox.ac.uk/fslcourse/), which is now available for free both internally and externally, and has been updated to include basic training in Unix command line access. The FSL course is being run remotely and at reduced registration rates for the first time in 2020, for improved accessibility and inclusivity.
Another large focus of this working group is developing programming literacy among WIN members, to support users in creating robust code to run their analysis. Significant efforts have been made to update the [FSL training material](https://fsl.fmrib.ox.ac.uk/fslcourse/), which is now available for free both internally and externally, and has been updated to include basic training in Unix command line access. The FSL course was run remotely and at reduced registration rates for the first time in 2020, for improved accessibility and inclusivity.
We run a series of programming workshops as part of our [MRI Graduate Programme](https://www.win.ox.ac.uk/training/mri-graduate-programme) which is open to all WIN members at any career stage. These workshops include our intensive "python for MRI" [pytreat](https://git.fmrib.ox.ac.uk/fsl/win-pytreat) material, which has been developed and refined by our FSL developers and users since 2018.
### FSL - the FMRIB Software Library
### FSL - the FMRIB Software Library
FSL 6.1.0 (due to be released in the second half of 2022) will be based on the [`conda`](https://docs.conda.io/en/latest/) environment and package manager. Conda makes installing FSL, and reproducing a FSL installation straightforward. Conda also allows you to install only the tools you need, instead of a full FSL installation, which is useful for containerising your analysis pipeline. More details can be found [https://git.fmrib.ox.ac.uk/fsl/conda/docs/](here).
FSL 6.1.0 (due to be released in the second half of 2022) will be based on the [`conda`](https://docs.conda.io/en/latest/) environment and package manager. Conda makes installing FSL, and reproducing a FSL installation straightforward. Conda also allows you to install only the tools you need, instead of a full FSL installation, which is useful for containerising your analysis pipeline. More details can be found [https://git.fmrib.ox.ac.uk/fsl/conda/docs/](here).
@@ -27,7 +27,7 @@ After these stages have been considered, one or a combination of the below [repo
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@@ -27,7 +27,7 @@ After these stages have been considered, one or a combination of the below [repo
## Contributor attribution
## Contributor attribution
Before you publish your data you should have an open and honest conversation about who has contributed to the data collection and processing, and agree how these individuals will be recognised and attributed for their work. This need not be the same list of individuals who are authors on any manuscript which references your data, indeed this can be a valuable opportunity to recognise the contributions of those who do not traditionally receive authorship on journal manuscripts (for example project managers, software engineers or data stewards).
Before you publish your data you should have an open and honest conversation about who has contributed to the data collection and processing, and agree how these individuals will be recognised and attributed for their work. This need not be the same list of individuals who are authors on any manuscript which references your data, indeed this can be a valuable opportunity to recognise the contributions of those who do not traditionally receive authorship on journal manuscripts (for example project managers, software engineers or data stewards).
Consider creating a [Contributor Roles Taxonomy - CRediT](https://casrai.org/credit/) statement and sharing this with your published data. [Tenzing](https://rollercoaster.shinyapps.io/tenzing/) is a useful tool for collecting contributor information and generating the CRediT statement.
Consider creating a [Contributor Roles Taxonomy - CRediT](https://credit.niso.org) statement and sharing this with your published data. [Tenzing](https://rollercoaster.shinyapps.io/tenzing/) is a useful tool for collecting contributor information and generating the CRediT statement.
Aim to include the [ORCID](https://orcid.org) of all your contributors in your published metadata, so contributions can be traced back to the individual.
Aim to include the [ORCID](https://orcid.org) of all your contributors in your published metadata, so contributions can be traced back to the individual.
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@@ -72,19 +72,17 @@ You should also be satisfied with the licence conditions of adding your data to
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@@ -72,19 +72,17 @@ You should also be satisfied with the licence conditions of adding your data to
Each version of a dataset added to OpenNeuro is assigned a unique DOI
Each version of a dataset added to OpenNeuro is assigned a unique DOI
### WIN XNAT
### WIN Open Data Portal
The Open Data Working Group is building a searchable, user friendly [XNAT](https://www.xnat.org) database to store MRI, EEG and MEG scan data directly from the scanners. The database also has the capability to store other research data alongside the scans to create a research dataset. Image conversion tools are integrated into the database to convert raw image files to standard formats and the community standard [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io) file structures.
The Open Data Working Group is building a searchable, user friendly Open Data Portal which will contain data transferred directly from the scanners (MRI and MEG) and associate it with other research data (for example behavuoural) for the same project. Image conversion tools are integrated into the database to convert raw image files to standard formats and the community standard [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io) file structures.
The XNAT system will provide managed access at the level prescribed by the researcher (for example fully open, verified researchers only, or by email invitation). You will also be able to specify details such as required authorship, prohibition from attempts to re-identify participants, and any other terms of reuse you require.
The Open Data Portal will provide managed access at the level prescribed by the researcher (for example fully open, verified researchers only, or by email invitation). You will also be able to specify details such as required authorship, prohibition from attempts to re-identify participants, and any other terms of reuse you require.
Data shared on XNAT will be given a DOI and explicit citation text.
Data shared on the Open Data Portal will be given a DOI and explicit citation text.
#### How to use
#### How to use
[WIN XNAT](https://xnat.win.ox.ac.uk)(accessible from the university network or VPN) is currently under development.
While the Open Data Portal is in development, we suggest you use the below wording in your data availability statement in journal submissions:
While the XNAT system is in development, we suggest you use the below wording in your data availability statement in journal submissions:
> "Deidentified data will be made available on the WIN Open Data Portal. This is currently in development. Register here to find out when materials are available for download: [https://web.maillist.ox.ac.uk/ox/subscribe/win-open-data](https://web.maillist.ox.ac.uk/ox/subscribe/win-open-data)"
> "Deidentified data will be made available on the WIN Open Data server. This is currently in development. Register here to find out when materials are available for download: [https://web.maillist.ox.ac.uk/ox/subscribe/win-open-data](https://web.maillist.ox.ac.uk/ox/subscribe/win-open-data)"
#### For external researchers
#### For external researchers
External users will be able to search the database for resources which individual teams have chosen to make openly available. These may be deposited to support publications as supplementary methods material, or they may form the main body of research in data papers.
External users will be able to search the database for resources which individual teams have chosen to make openly available. These may be deposited to support publications as supplementary methods material, or they may form the main body of research in data papers.
@@ -44,9 +44,11 @@ If your code builds on existing material, the originators of that material may h
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## What license to choose
## What license to choose
In general, we recommend a [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/). This enables others to reuse your work as long as they attribute you as the originator. This license also allows the University to retain copyright.
We recommend the following licenses for diffferent types of work. These have been created with specific products in mind, hence licenses written for software might not be well suited to other creative works.
-**Software**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). This license requires attribution (be sure to include a 'Attribution' section in your README, or event better a CITATION.cff), and copyright notice to the University of Oxford. This licence does not restrict reuse to commercial or non-comercial applications.
-**Data or other creative works** (for example documentation): [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/). This enables others to reuse your work as long as they attribute you as the originator. This license also allows the University to retain copyright. This licence does not restrict reuse to commercial or non-comercial applications.
The University also has detailed [recommendations on open source licensing](https://researchsupport.admin.ox.ac.uk/reporting/openaccess#collapse393821). Please review these recommendations if you are unsure whether a CC-BY-4.0 license is appropriate.
The University also has detailed [recommendations on open source licensing](https://researchsupport.admin.ox.ac.uk/reporting/openaccess#collapse393821). Please review these recommendations if you are unsure which license is appropriate.
For a more complete exploration of software licensing, we recommend you visit the [The Turing Way handbook to reproducible, ethical and collaborative data science](https://the-turing-way.netlify.app/reproducible-research/licensing/licensing-software.html)
For a more complete exploration of software licensing, we recommend you visit the [The Turing Way handbook to reproducible, ethical and collaborative data science](https://the-turing-way.netlify.app/reproducible-research/licensing/licensing-software.html)
