Information is required when planning or designing a study in a movement laboratory. Several considerations have to be taken into account when the data from a project in a movement laboratory is to be shared. Please pay special attention to the following sections in the «Guidelines “MoveD” – Open Research Data in Swiss Movement Laboratories»: «Collect: Informed Consent» with reference to data sharing, «Process: Source code» on the description of codes, «Share: Licences», and «Data Re-Use: Quality Check». Staffing requirements can be found in the standards of GAMMA, Clinical Practice Recommendations from Phillips et al. (2023) or Clinical Movement Analysis Standards by Stewart et al. (2023).
 

Metadata that should be collected as well as marker, sensor model and informed consent considerations. 

Metadata
Metadata should be collected when collecting data for each participant separately. For better reproducibility, having more personal data is beneficial. This results in ethical and legal issues, however, when sharing these data publicly. You must therefore clarify within your institution how much, and which, personal information is to be documented.

Marker / Sensor placement
When data is collected with a marker-based measurement system or body-worn sensors, it is important for the traceability and subsequent use of the data to have a clear understanding of the marker /sensor placement. 

Informed consent
Swissethics provides a decision tree for finding the right template. The templates can be downloaded directly through the links provided in the decision tree. The decision tree can be found here or the German, French or Italian version here.

Metadata 
Metadata that should be collected during processing and the corresponding information. If pre-processing steps have been performed during data collection, please include this information in as much detail as possible.

Source code
The mentioned requirements apply to source code.

Description of source codes/ readme files
The mentioned requirements apply to the description of source codes/readme file.

Open File Format
In this section we present recommendations as to which open data formats the files (exported from the measurement system) should be converted to. It is, however, more important to adhere to the «linked open data» framework as described here. You may be able to export your data in another file format which is not listed in the following table. Please ensure that you adhere to the «linked open data» framework mentioned above. Moreover, if additional information is generated by the measurement system, such as patient reports in the form of a PDF, for example, please add this information as well, since PDF is an open file format.

Metadata
Metadata that should be collected during analyzing and the corresponding information. The metadata should be captured during statistical data analysis.

Sourcecode, if applicable.
The section is identical to the section «Data processing - Source code» and only applicable if the statistical software used produces code.

Description of source codes / readme file, if applicable. The section is identical to section «Data processing - Description of source codes/readme file».

Licenses, informed consent, choosing a data repository, metadata and data. Before a licence can be issued or data shared, the copyright must be owned by the person granting the licence/sharing the data. Clarify with your supervisor or the data security officer what is applicable in your institution.

Laws for the retention period and encoding of research data in Switzerland. Listed below are considerations that have to be made when data is archived. 

Legal requirements
The laws (English, German, French) are relevant for the retention period of (research) data in Switzerland. 

Key management
The laws (English, German, French) are applicable for the encoding of research data in Switzerland.

Data access
Data access should be limited to people who require access to fulfil their work within a study/project. Ideally, data access is granularly managed so that, for example, only a few people have access to the identifying information and most people on the project team only use the encoded or anonymized data.

Search for reuse, example repositories and how to perform a quality check.

The following methods may aid in the search for reusable datasets:

• Review relevant scholarly literature for citations and references of accessible datasets.
• Use the search engine Google Dataset Search (bear in mind that a search via Google Dataset Search does not provide conclusive results about the existence of relevant datasets as metadata determine findability).
• Search directly in a repository via the registry of research data repositories.
• Re3data provides an overview of research data repositories. By navigating through the disciplines, suitable repositories can be narrowed down and browsed through.