Commit 6fc662f2 authored by Cassandra Gould van Praag's avatar Cassandra Gould van Praag
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fix typos

parent c639da0b
......@@ -17,17 +17,17 @@ principles](https://www.go-fair.org/fair-principles/)
<a name="ethics-rdm"></a>
## Process 1 - Data management, data security and ethics
Good data management is essentail for effective data sharing, for audit purposes and to assess the security and privacy risks of your data. This process ensures that you have completed all governance reviews as required by central Information Compliance. It also checks that you have the correct ethical approvals in place to chare your data.
Good data management is essentail for effective data sharing, for audit purposes and to assess the security and privacy risks of your data. This process ensures that you have completed all governance reviews as required by central Information Compliance, and that you have the correct ethical approvals in place to share your data.
![](../img/p1.png)
<a name="protected"></a>
## Process 2 - Protected features of the data
Different modalities, participants and data sources require differential processing. This process will assess how your data should be handelled based on what you intend to share.
Different modalities, participants and data sources carry differential risks. This process will assess how your data should be handelled based on what you intend to share.
![](../img/p2.png)
<a name="deidentify"></a>
## Process 3 - Deidentification
Most of the data we handel at WIN cannot be fully anonymised as each participant contributes unique information. Instead we aim to maximally deidentify the data while maintining sufficient granularity for analysis. This process highlights which parts of your data may contain identifiable features and how they should be handelled. It also brings in community standards in data structure and quality control.
Most of the data we handle at WIN cannot be fully anonymised as each participant contributes unique information. Instead we aim to maximally deidentify the data while maintining sufficient granularity for analysis. This process highlights which parts of your data may contain identifiable features and how they should be handelled. It also suggests community standards in data structure and quality control.
> See also:
> - [Appendix 1 - DICOM headers to be scrubbed](./decision-tree-appendicies.md#appendix-1-dicom-headers-to-be-scrubbed)
......@@ -41,7 +41,7 @@ Most of the data we handel at WIN cannot be fully anonymised as each participant
<a name="metadata"></a>
## Process 4 - Metadata
High quality metadata enable others to reuse your data effectively. This process suggests where metadata may be valuable and how you can arrange them.
High quality metadata enable others to reuse your data effectively. This process suggests where metadata may be valuable and how they can be compiled.
> See also:
> - [Appendix 2 - BIDS compliance](./decision-tree-appendicies.md#appendix-2-bids-compliance)
......@@ -50,7 +50,7 @@ High quality metadata enable others to reuse your data effectively. This process
<a name="sharing-attribution"></a>
## Process 5 - Sharing and attribution
Your shared research data are a valuable output from your work. This stage walks through the process of uploading your data to the XNAT repository and generating a citable digital object identifier (doi). It also guides you through the selection of an appropriate data usage agreement (dua) which users of your data will agree to follow.
Your shared research data are a valuable output of your work. This process describes adding your data to the XNAT repository and generating a citable digital object identifier (DOI). It also guides you through the selection of an appropriate data usage agreement (DUA) which subsequent users of your data will agree to follow.
> See also:
> - [Appendix 6 - Upload to XNAT](./decision-tree-appendicies.md#appendix-6-upload-to-xnat)
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