When we collaborate with others on research and in general, we often develop different frameworks and expectations to ensure that all involved in the partnership get what they expect. To that end, I want to discuss the general types of expectations that you will encounter in your research career and provide you with a text template that you can adapt for your own project regarding your data-sharing needs.
Mutual expectations
As you embark on your research career, regardless of the job or stage of your journey, you must establish expectations of yourself and the team, supervisors or even staff you work with. Now, this is normal and necessary in industry as well. This is where your supervisors outline what they expect you to do, such as the administrative component, such as days in the office, sickness and holiday leave, and pay. They also outline how your meetings will work, how these are structured, when you are expected to chair them, and who will oversee them.
This is also where you find out about the nitty gritty of your PhD and what they, as your supervisor, will provide you with:
Academic development concerns further training, whether in a particular experimental area unique to your field or more general but related to your general skills, such as coding or time management. Some universities will have doctoral colleges to help you train in this area, and some will partner with adjacent universities to pool resources. For example, the academic courses I've been involved with range from teaching undergraduates research impact centring around communication and professional development to looking at CVs and networking.
The area is around your well-being and health. This is where you discuss your work-life balance and any reasonable adjustments you require, such as for learning differences such as dyslexia, dyspraxia and dyscalculia. This area is also where you discuss your holidays, general well-being, and when you expect to take leave. Managing your relationship, how you and your team want to work, expectations, preferred communication, and anchor days, such as when you expect to be in this, will largely depend on your experimental design. If you are laboratory-based, you must be on-site more than someone working on more qualitative studies, which may have an entirely different hybrid format. This will also be how your supervisor/s will communicate what they feel is essential for your development. Professional development in this regard is more what you need to be a professional academic supporting you with the publication of your work, where to publish and the other side of academia, which is sharing your work with other audiences such as conferences and societies to network in.
Data Sharing
When working in areas where you are going to share data on a project within your institution and with other professionals, you need to be clear on what your institution or work needs and in the text that you share between you, you should outline how the work is shared, stored and the outputs are used.
So, what are these terms?
For Sharing: This is the core data that you have. This is essential whether you are sharing personal data protected due to the subject's privacy or professional data that needs to be safeguarded for intellectual property. From your perspective, this need is where you must decide if any data that will not be shared can be collected. Consider what is essential to the study, but remember that further projects could be working in tandem that are not a part of the same agreement.
For Storage: This is more of a practicality. You need to consider where the information is stored in terms of the platform you use to collect data. Now, this will often be dictated by your university ethics committee, which will have some form of surveying systems such as Qualtrics or Survey Monkey, and depending on the level of security and dissemination, this will be how the ethics committee decides on how appropriate it is. The other thing to remember is the universities will likely have a license and, therefore, have free access to you as a researcher. This would not be the case if you are an independent researcher or self-funded. The other question you need to ask is how long you will hold the data. This will generally be up to 10 years, but if a partner institution has a shorter expected storage time, then to facilitate the project, you will have to go with your partner's needs.
Finally, Outputs: without getting into the specifics of your project collection, which will partially dictate your outputs, you need to consider the following: where do you intend to publish your work?
This is important for journals and conferences regarding analysing who has conducted this and how you will refer to them. When running a collaborative project, you must consider data and credit sharing. The individuals involved will often be referred to in published works as co-authors or at least acknowledged if they have a more reduced role. This is worth recognising if you intend to publish a specific paper using the data as the core evidence. This would differ from whether it is secondary data or used in other research projects. The level of expectation would be reduced if the area of research looks at other, perhaps adjacent, areas of research, but it is still useful to remember to discuss the uses of the data with your partners.
Data Sharing Agreement
Now for a template text on data sharing suitable for editing in your ethics application.
This Data Sharing Agreement (this "Agreement") is effective between the following dates ________ to _________. The agreement is between the following parties: [insert name] of [insert institution] and [insert name] of [insert institution]. The researchers agree to collect the following data ________ as part of this study and agree that the data will be collected using the following program [insert name]. The data collected from the study will stored on the following account, with the lead researcher acting as the administrator and sharing the raw data and/or the analysed data with all partners. The partners agree that researchers may also collect additional data that is not specifically part of the project, such as ________, which will not be shared with partners. The partners agree that the data collected in this project will be stored for ________; after such date, the information will be deleted. The partners agree that the data can be used for the following proceedings and publications and agree to refer to the other partners and institutions by ____________.
To note, this is a general brief data-sharing agreement. More detail is often needed, but this should help get you started.
Final Thoughts
Effective communication is vital when starting a collaborative project. Being upfront with your partners and discussing everyone's expectations and motivations can help ensure the project proceeds smoothly. Remember to consider all aspects of the project, not just the collection and storage of data. By working together and establishing clear guidelines, you can achieve success and create a positive working relationship with your partners.
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