Invitation to project
Natalia Bielczyk & OHBM Student and Postdoc Special Interest Group,
Edited by AmanPreet Badhwar
Early career researchers in different parts of the world face similar challenges, but not everyone has the same access to mentoring and career development resources. While online mentoring programmes, such as the OHBM International Online Mentoring Programme, are available, it is hard to cover the needs of the whole population of early career researchers in the natural sciences.
In order to tackle this issue within the OHBM Student and Postdoc Special Interest Group, we are developing a set of advice relevant to early career researchers in the natural sciences. The aim of this project is to empower early career researchers to positively influence their future career opportunities on a daily basis - regardless of the circumstances. The main points which we aim to cover are the following:
The project will take the form of a full-length manuscript. The draft working paper has been posted under the link https://osf.io/53yrv/.
We would, however, like to further develop the manuscript before submitting this work for peer review. Therefore, to provide advice that can be generalizable to a wide variety of situations, demographics, and countries, we are seeking contributions from the OHBM community. We would like to welcome everyone willing to participate to join us and discuss this subject on the associated Google group: https://groups.google.com/forum/#!forum/effective-self-management-for-ecrs
We look forward to your contributions. The manuscript is dedicated to early career researchers but we welcome the expertise and experience of seniors researchers on this topic as well. We hope the group endeavour will not only make for a better manuscript but also serve as a platform where early career researchers can give each other support and advice, and make new friends! Depending on the traffic, the Google group might be sustained after the final form of the manuscript is formed. The official starting date of the Google group is Monday, January 14th 2019, and we close collecting contributions on Thursday, February 28th 2019.
Active participation in the Google group will be interpreted as an academic collaboration. Therefore, we will bring together feedback received through the group, invite the significant contributors to co-author this work and reupload the working paper as a preprint with a new, extended list of authors. Invitation will be determined on the basis of: (1) constructive comments / additions to the manuscript and (2) being active and helpful to other members of the Google group. This material will be subsequently submitted for peer review.
You can find all the details about the project, together with the Code of Conduct and the rules for establishing authorship, on the Google group. We hope to meet you soon and chat about careers together!
By Ilona Lipp and Jean Chen
Edited by: Nils Muhlert
In science, the term “work-life balance” may seem like a holy grail for some and a conundrum for others. Its easy matter-of-factness belies deep self-examination. Today’s research communities are larger and more competitive than ever with regard to permanent positions and funding, with the success rate for many grants being as low as 5%. For this reason many leave academia after finishing their PhDs (according to a recent report by the Royal Society). For those who choose to stay, the clock starts ticking from the very moment one starts a job, and the counting begins --- for grants, for journal articles, for trainees, for experience in international labs, etc. So who are the people that, despite everything seemingly being against the odds, persevere and manage to stand out in a world of stressed early-career researchers? Do they purposely dedicate their lives to science? Do they have a life outside of work? Are they even human?
To find out, we talked to a diverse group of seven early-to-mid-career researchers, all highly successful for their career stage in terms of their funding situation, publication list and professional recognition (below, ordered by first name). We asked them how important work-life balance is to them and what strategies they take to achieve it, and have summarized their answers for you.
By Chris Gorgolewski & Ekaterina Dobryakova
Reproducibility and transparency are core to all branches of science. Two years ago, OHBM established the Reproducibility Award. The purpose of this award is to honor researchers who conducted a ‘successful’ or ‘unsuccessful’ replication study, while adhering to rigorous standards of study design, data collection and analysis. The second recipient of the Reproducibility Award is Benedikt Sundermann, who received the award during OHBM 2018 in Singapore for his study that was published in the Journal of Neural Transmission. Chris Gorgolewski, one of the initiators of the OHBM Reproducibility Award, interviewed Benedikt about his experience related to this replication study.
Chris Gorgolewski (CG): I am joined here today by Benedikt Sundermann, the recipient of 2018 OHBM Replication Award. Benedikt, thank you for joining us and congratulations on the award.
Benedikt Sundermann (BS): Thank you.
CG: The first question I want to ask you is how would you describe the study if you met a stranger a bar?
BS: In previous studies, people have tried to apply artificial intelligence technologies that are frequently used in face recognition to functional brain imaging data in order to try and diagnose people, for example, with depression. In our study, we wanted to see whether this replicates in a larger and more clinically realistic sample, featuring various comorbidities, heterogeneous age, sex etc. Surprisingly, most of the previously reported results did not replicate in this larger, more heterogeneous and clinically realistic sample. Only when we looked at a subgroup of people could we replicate some models but, still, at a diagnostic accuracy that would not be clinically useful.
CG: I see. So that is quite a controversial statement, especially considering how much we discuss about the application of neuroimaging methods to the clinic. Did you experience challenges in publishing this work?
BS: Yes, definitely. If I remember correctly, it was the fourth or fifth submission of this work that we finally managed to publish. Generally, when you try to publish a replication study with a negative result, you should be prepared to be interrogated much more critically by the reviewers, about the sample, about the methods and so on. But there was also criticism that our work was not scientifically solid. For example, there were comments like “We know from previous studies that this works, the fact that you couldn’t replicate it means that you must have made a mistake.”
CG: I see, but you must have, in a way, anticipated that it’s going to be somehow challenging. I want to understand a bit more what motivated you to do this wonderful work to begin with.
BS: I have a clinical background in radiology and I am mostly interested in actual diagnostic tools and to expand the spectrum of diagnostic tools that we can use in the clinic. In major disorders, we are usually limited to the exclusion of larger structural lesions but we cannot really get the diagnostic information about the actual disease mechanisms and disease correlates in these people. So my main motivation was to work on these technologies to improve these diagnostic imaging techniques based on functional imaging and machine learning.
CG: So after having completed the study - you know they say ‘hindsight is 20/20’ - if you were to take the trip back to the past in a time machine, what would you do differently? What advice would you give to people who are planning to do a replication study?
BS: First, you need to expect frustration. We did not expect that much frustration that we experienced during the study and when we tried to publish it. So just be prepared and then it will probably be easier, I think. The other thing is, in a replication setting you might be interrogated more strictly about your sample and about your analyses. Having a good structure of your analysis and your data will make it a lot easier. This is also one of the points that you are also working on in your initiative, so I think this is pretty important and we should focus more on that.
CG: Great. So what’s next for you? What are you excited about? What are you working on right now?
BS: One thing that I am working on right now is more clinical than scientific. I will be working on project optimisation/project standardization from a clinical point of view. Scientifically, I am currently interested in multi-modal integration of functional and structural imaging data in diagnostic models.
CG: So do I understand correctly that the main implication of your finding is that resting state fMRI is not ready for clinical use?
BS: I think it is currently not ready for clinical use. But I think it is important to realize that this is not an endpoint. Our findings do not suggest that there is zero information about depression in these data. We looked at it with machine learning techniques that were available two to three years ago and there has been a lot of improvement in this field since. Also, the current classification system of patients into major disorders has room for improvement. For example, just saying whether somebody has unipolar depression or not, may not be sufficient to capture the wide spectrum and large heterogeneity of these patients.
CG: So you think that the main reason for the lack of diagnostic ability are noisy labels and poor definitions of the phenomena that we are trying to predict?
BS: I think that it is at least one major determinant.
CG: So as a clinician how do you improve classification of methods?
BS: Currently, the main classifications are based on clinical interviews. You have to ask standardized questions, and you have a checklist to see whether certain criteria are fulfilled or not. But maybe we can use biological information itself to further sub-categorize patients’ mental disorders, and to see some commonalities or links between different mental disorders.
CG: That sounds very exciting! The OHBM replication award will run next year so I encourage everyone to submit their replication studies and see you next year.
Please note, the submission deadline to be considered for the Replication Award is January 11th, 2019.