add proton drive (#1819)

Author: bedroesb

_data/tool_and_resource_list.yml

@@ -3542,3 +3542,7 @@
   registry:
     fairsharing: uBpQ1q
   url: https://panoramaweb.org/home/project-begin.view
+- description: Proton Drive is a secure cloud storage platform for protecting critical files and information.
+  id: proton-drive
+  name: Proton Drive
+  url: https://proton.me/drive

pages/your_tasks/storage.md

@@ -62,8 +62,8 @@ When looking for solutions to store your data during the collection or generatio
 * Ask for a tiered storage solution that gives you easy and fast access to the data for processing and analysis. Explain to the IT support what machine or infrastructure you need to access the data from and if other researchers should have access as well (in case of collaborative projects).
 * Ask if the storage solution includes an automatic management of versioning, conflict resolution and back-tracing capabilities (see also our [Data organisation page](data_organisation)).
 * Ask the IT support in your institute if they offer technical solutions to keep a copy of your (raw)data secure and untouched (snapshot, read-only access, backup…). You could also keep a copy of the original data file in a separate folder as “read-only”.
-* For small data files and private or collaborative projects within your institute, commonly accessible Cloud Storage is usually provided by the institute, such as {% tool "nextcloud" %} (on-premises), {% tool "microsoft-onedrive" %}, {% tool "dropbox" %}, {% tool "box" %}, etc. Do not use personal accounts on similar services for this purpose, adhere to the policies of your institute.
-* For large datasets consider cloud storage services, such as {% tool "sciencemesh" %}, {% tool "openstack" %} and cloud synchronisation and sharing services ({% tool "cs3" %}), such as {% tool "cernbox" %} or {% tool "seafile" %}
+* For small data files and private or collaborative projects within your institute, commonly accessible Cloud Storage is usually provided by the institute, such as {% tool "nextcloud" %} (on-premises), {% tool "microsoft-onedrive" %}, {% tool "dropbox" %}, {% tool "box" %}, {% tool "proton-drive" %}, etc. Do not use personal accounts on similar services for this purpose, adhere to the policies of your institute.
+* For large datasets consider cloud storage services, such as {% tool "sciencemesh" %}, {% tool "openstack" %} and cloud synchronisation and sharing services ({% tool "cs3" %}), such as {% tool "cernbox" %} or {% tool "seafile" %}.
 * It is a requirement from the funders or universities to store raw data and data analysis workflows (for reproducible results) for a certain amount of time after the end of the project (see our [Preserve page](preserving)). This is usually a requirement. Check the data policy for your project or institute to know if a copy of the data should be also stored at your institute for a specific time after the project. This helps you budget for storage costs and helps your IT support with estimation of storage resources needed.
 * Make sure to generate good documentation (i.e., README file) and metadata together with the data. Follow best practices for folder structure, file naming and versioning systems (see our [Data organisation page](data_organisation)). Check if your institute provides a (meta)data management system, such as {% tool "irods" %}, {% tool "dataverse" %}, {% tool "fairdom-seek" %} or {% tool "osf" %}.