What to expect from the PI?

It’s an honor for Jussi to coach and mentor an amazing team of people who together drive our innovation and accomplishments. My commitment to you is to make our research group a welcoming and supportive environment to conduct good science. Importantly, Jussi is invested in your success—the ability to graduate and/or follow your desired career path either in or outside of academia. Jussi will provide an environment that is emotionally supportive, safe, equitable, intellectually stimulating, and free of harassment. Jussi will help guide your research project and give you feedback on your work to ensure our science is of the best quality possible. A two-way feedback culture is critical to a successful lab environment! Thus, any issues, even small things, should be dealt with swiftly.  You are always welcome to communicate with Jussi and Jussi will respond to you in a timely fashion, the main communication option is email.  Jussi can help guide you to the appropriate resources to deal with this within the AIVI and UEF. If you think that Jussi is not the correct person to handle your concern, you can turn, for example, to other PIs at the department, human resources, or department administration.  You can find a correct contact also through the UEF intranet.  

What is Expected from All Group Members? 

In our research group, we want everyone to be honest, enthusiastic, and happy. You are expected to bring your enthusiasm and curiosity to the lab. We ask everyone to have a proactive attitude and to contribute to the research, both intellectually and operationally. It is up to you to make the most of all the training and opportunities you are given. Group members come from diverse personal and academic backgrounds. All group members should treat each other with respect and dignity. Disrespectful behavior, harassment, and/or scientific misconduct will not be tolerated. Be considerate of others in the lab; it is courteous to keep common spaces clean and organized. Mistakes happen , and that is okay, but if an incident will affect others in the group please tell them. If you do not know how to fix an issue that has arisen or are having trouble with something in the group, ask for help. Make smart choices. Be respectful, kind, safe, and proactive!

What should you know/learn? 

This naturally depends on your academic background. But at the beginning, most people should know/learn the basics  about the brain image analysis/processing (if they are working on brain imaging related topics, see https://github.com/jussitohka/KUBIAC_brainimageanalysis , and especially https://github.com/jussitohka/KUBIAC_brainimageanalysis/blob/main/resources/index.md  ) and applied machine learning, especially about evaluation of the machine learning algorithms (see a tutorial) . 

Most important software and data resources are listed in https://github.com/jussitohka/KUBIAC_brainimageanalysis/blob/main/resources/index.md so please check that page.  

Research integrity

Research Integrity means conducting research in such a way that allows others to have confidence and trust in the methods and the findings of the research. It relates both to the scientific integrity of conducted research and to the professional integrity of researchers. The Finnish National Board on Research Integrity TENK published its updated guidelines on good scientific practice and research integrity in March 2023. These are available here.    

 Please refer to the university and department policies. 

This section concerns mainly those who are salaried by Jussi or whose superior is Jussi. If your salary comes from other sources, all things here may not be valid. Please discuss with your main supervisor/superior.  

Remote work. The group has a history of remote members, and we support the needs of lab members who must work remotely. Remote work must be discussed with and approved by Jussi and arranged in accordance with UEF and AIVI policies.  However, there should not be any problems with remote work. The work location is expected to be in Finland. Please note that even if you're working remotely, the other lab members (including Jussi)  must be able to reach you within a reasonable time (e.g. you generally should answer to e-mails the next working day unless traveling etc.).     

Lab Meetings. Group members are expected to attend the Monday morning remote meeting (9:30 a.m.) and Friday morning MRI community hybrid seminar (8.30 a.m). If you are away (conference, visit) or on holiday, you do not need to attend. If there are any other timing conflicts, please discuss with Jussi.  

Vacations. Please inform Jussi well in time of your planned vacations. 

Illnesses. If you will be away due to an illness more than one or two days, please inform Jussi promptly. 

Travel to conferences/visits. Work related travel. Please see UEF instructions (UEF intranet). Please talk to Jussi well in time about the funding for the traveling.   

The computations in the group are mainly done either on UEF bioinformatics servers (https://bioinformatics.uef.fi/) or on the CSC IT Center for science (https://research.csc.fi/) resources. Your first task as a new group member is to apply for UEF-Bioinformatics center resources as explained here. For  CSC resources you need to apply for a project and resources for it. Before doing that for the first time, please inform and discuss with Jussi. Also, otherwise please keep Jussi informed about your usage of computational services (e.g. consider adding Jussi to the CSC project if you apply by yourself). This is because there may be a need to report the computing units used.

Please familiarize yourself with the use of slurm workload manager .  The slurm of the UEF Bioinformatics Centre is explained here . I have also found the examples in the Aalto university's documentation useful, see here. 

We typically do not collect any primary research data.  Our data comes from public sources, databases that are available by registration, from (internal or external) collaborations, etc.  If the data is from by-registration database or by-collaboration, then typically there are some conditions how the data can be used, where can it be stored, who can access it etc.  These are often described in the data-usage conditions or in the data transfer agreement between the UEF and the data providing partner.  Most of our data is stored in /research/groups/tohkagroup . When you have applied for computing resources, please let Jussi to know and you will be given the necessary permissions to access the data.  However, in some cases, the data may be stored in a different place (for specific projects) and there typically exists project-specific data management plan, which will be provided to you during your onboarding.  As a general rule, no data should be copied to your personal computer/external hard-drives (Copying to external hard-drive might be acceptable in certain cases, but please discuss with Jussi before doing so.)   

We strive to organize the imaging data that we use according to the BIDS standard.   

More detailed information/documentation about data management are available in UEF-biomedicalimageanalysis group files.   

Documenting research activities. Please document as much as humanly possible. This will save you a lot of time in future. 

Code management and sharing. It is a good practice to name you scripts/functions/programs with long enough and descriptive names, so you can locate them later on. In addition, study a style-guide of your favourite programming language and develop your own coding style and learn what version control means. This just makes things much easier in a long run. A good slide-set of scientific programming basics is here and educational material can be found here.   

We typically share our codes under permissive open source licenses. For this, you should have your own github account that is then linkable to github account of the research group.  For major packages, in addition to GitHub, please mint a DOI with e.g. Zenodo.  

Reproducibility. The reproducibility is a cornerstone of the experimental research. However, here I do not aim to go philosophical, but claim that doing everything in a way that is easily reproducible offers substantial practical advantages. Think about, for example, preparing a figure for a paper. Then, a reviewer comments on that figure and requires a minor change to it. Unless the figure has been prepared with the reproducibility aspect in mind, this can take considerable amount of time. Note that reproducibility is more than replicability. It is not sufficient to be able to exactly replicate the results, figures, etc., but to allow also changes.

In my field, the reproducibility usually means that you should have a piece of code that you can edit for everything. This documents exactly what has been done to produce the results and you can easily change some aspects of the work. The same goes for preparing figures. The only exception to this are the software packages that are easier to operate using a GUI and produce detailed logs to show what has been done. 

Open research.  As a general rule, our code is available, we post preprints to either arXiv or bioarXiv when we submit manuscripts for evaluation, and our publications are in openly accessible.    

Writing publications.  We typically use overleaf. However, in collaborative projects, you might be required to work with MS-word and it is good to have a working knowledge of that too.  

A few practical guidelines (some LaTeX specific and some more general):

1. Reference management. The main rule is: Don't build the bibliography section of your paper manually, but let a bibliography management software to do this for you. Please use bibtex when working with LaTeX. Please use human readable keys and keep them consistent. I use google-scholar key style (e.g. tohka2004fast), but  any human readable and consistent style will do equally well. I highly recommend using a reference manager software like Zotero (there are other alternatives also) that usually contain a lot of functionalities beyond standard reference management. .   

2.  Referring to figures or tables or equations in the paper (LaTeX): Use \ref and \label and keep their keys descriptive, e.g. \label{fig:main_flowchart}, so that it is immediately clear based on the key what the figure or table is about. Avoid \label{fig:fig1} type keys. These do not help.    

3. Writing guides. There are many good ones. Please check here for J. Saramäki's blogs about writing.  How to write and publish a scientific paper is a classic book that is recommended. An article by G.D. Gopen and J.A. Swan is also recommended. A number of other links concerning technical writing in English can be found here. 

4. Mathematical writing is requires its own guidelines. A good and concise set of rules can be found in a report of a course by D.E. Knuth (from 1987). The document is 118 pages long, but do not worry, the basic rules are presented in the 6 first ones.

5. Brain imaging researchers should consult also Guidelines for reporting an fMRI study and Ten simple rules for reporting voxel-based morphometry studies. These (mostly) target neuroscientists, but are useful also for method developers.

6. AI as a helper in writing.   I am a strong supporter of using AI-based tools to help your writing and also the university level policies are supportive of this.  However, there are few essential rules: 1) One should disclose/admit the uses of AI tools; 2) One should always manually check the output and that the facts are correct; 3) Never give information that could be labeled as confidential to the AI tools; 4) You are always responsible about the content; 5) Use these tools wisely, they excel at certain things and are really bad in others.   I will not recommend any AI-tools here because they are developing in fast pace and will probably be replaced by something better quite soon. 

Funding for the research group members salaries comes almost 100% from competed, external funding sources. Therefore,  the group members are expected to be active in getting funding for their research if possible.  For example, PhD students are expected to apply for funded graduate school positions (deadline usually in May - Jussi will let you know well in time).  Group members are generally expected to seek funding for longer research visits (several months). PhD students are also funded through various foundations. However, please discuss with Jussi  before planning any funding application and let him know whether your application was accepted or not. These are case-by-case and applying for funding that is difficult to get may be not the best use of your time.  

Doctoral Programme in Molecular Medicine . Most of the PhD students enroll to the doctoral programme in molecular medicine. This  is very interdisciplinary doctoral programme that will and can host PhD students with computational/engineering/computer science backgrounds.  Most of the PhD students enroll for the degree of  Doctor of Philosophy

Studies. In brief, in addition to the thesis, a PhD student must complete 30 ECTS of course work. Please see the page in Kamu for more details.  There is much freedom in selecting the courses that are useful just for you. But please consult your supervisor about your study plan.    Please see also this page and  this page of some possible (transferrable skills and language) courses to take.  

Thesis. A PhD thesis in Finland is typically an Article based dissertation,  which comprises a sufficient number of publications, or manuscripts accepted for publication dealing with the same set of problems. In addition to the articles, the dissertation includes a summary (article-based dissertation).  Typically, the thesis includes four or five articles, majority of which have been accepted for publication and are first author papers. But the number of required papers is not strict. The official requirements are available here.  Examples of PhD theses from Jussi's group can be found here.