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For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.
Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.
Just to make sure I understand the Zotero workflow correctly.is the .bib file synced to Overleaf automatically every time you add a new paper, or do you need to trigger an export manually?
Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?
I have successfully received PMRF (lateral entry, second year). Happy to share what worked in my proposal.
On the research proposal:
On citations: Yes, cite your MTech thesis. Framing your PhD as an extension of prior work you did shows continuity of thought.
For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.
+1 to everything said above. My experience was identical.
I am in a very similar situation. Would you be willing to share the outline of your PMRF proposal? Not the content.just the section headings and approximate word allocation.
As a senior faculty member (25 years in the system), I want to offer a different perspective. Yes, the pressures are real. But I have also seen the other side: students who produce excellent work, publish honestly, and build reputations over time. The system is imperfect but not entirely broken.
What has actually helped my students: choosing venues carefully (a few strong papers rather than many weak ones), developing genuine collaborations rather than transactional coauthorships.
This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.
If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.
This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?
I think you are conflating two separate problems here. The publish-or-perish culture creates pressure, yes.but the solution is not to criticize researchers who are navigating an unfair system. The real problem is with evaluation committees who treat publication count as a proxy for research quality. Fix the evaluation, and the incentives change.
Also worth noting: open review has been implemented in some fields and has its own problems. It can disadvantage early-career researchers who fear retaliation from senior colleagues they have reviewed critically.
FDR (Benjamini-Hochberg) is widely accepted and preferable to Bonferroni when you have many tests and limited power. The key is to be explicit about which correction you used and why.
On replication: No independent cohort is a genuine limitation but not a dealbreaker for rare disease research, where cohort availability is a known constraint. Many high-quality papers acknowledge this and still get published in good journals. Frame it as future work.
Power analysis: Yes, report it. Showing you are aware of the limitation is better than not mentioning it.
I have a slightly different take from my experience in industry research. The reproducibility crisis is real but unevenly distributed. Fields with strong engineering culture (computational biology, ML with benchmarks) have actually improved significantly in the last 5 years. The bigger problem is in fields where data sharing is structurally difficult.clinical medicine, behavioral economics.
The ML community's move toward open code and reproducibility checklists has been genuinely effective.
[Moderator note] Reminder to keep discussion collegial. We can disagree on ideas.and productive disagreement is valuable.but please avoid personal characterizations. Thank you.
This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.
Check whether your institution has a Springer/Elsevier waiver agreement.many NITs and IITs do.
Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.
If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.
Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?
If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.
Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.
This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.
Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.
This is very reassuring. I was worried the null result would kill the paper but your framing around transparency and effect sizes makes a lot of sense.
Marking this as the accepted answer. Incredibly helpful and specific. Really appreciate you taking the time.
I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.
For Q1 IEEE journals, expect:
As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.
Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.
The ethics review situation in Indian universities is actually more complex than most people realize.
There is no uniform national framework equivalent to the US Common Rule. Each institution has its own Institutional Ethics Committee (IEC) or Institutional Review Board (IRB).
For anonymous survey research with no identifiable data and no vulnerable populations: most Indian IECs would classify this as exempt, but 'exempt' still needs a formal determination from the IEC.it is not self-certifying.
My practical advice: go to your institution's research ethics office now, describe what you did, and request an exempt determination in writing. Better to have documentation than to discover the issue during thesis review.