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Favorite tools for managing research workflows.Notion vs Obsidian vs Roam (79)

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Growing 130 pts 0 followers
R.V. College of Engineering · Posted
Photonics & Optics Chemical Engineering Financial Engineering

I want to start an honest conversation about something I have been thinking about for a while.

As a fourth-year PhD student, I have watched the academic publication ecosystem up close. The pressure to publish in high-impact journals has created perverse incentives:

  • Researchers cherry-pick results to show positive findings
  • Null results are systematically rejected
  • The same idea gets cut into multiple 'minimum publishable units' to inflate output
  • Citation rings inflate impact scores

What do you think? Is this a structural problem that needs policy-level intervention, or are there practical things individual researchers can do?

I am particularly interested in hearing from senior faculty who have seen this evolve over their careers.

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Chetan Jain Growing 100 pts · Accepted answer

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.

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Jagdish Rawat · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

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Varsha Pillai Growing 60 pts · Accepted answer

Great question.I went through something very similar in my second year.

The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.

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Bindiya Thomas Growing 65 pts · Accepted answer

Flower (flwr) is the most actively maintained and research-friendly federated learning framework right now. My group has been using it for 18 months on a healthcare project with 4 hospital sites.

Strengths:

  • Clean simulation API that lets you test on a single machine before deploying
  • Good support for custom aggregation strategies (FedProx, FedNova)
  • Active Discord community with quick responses from the maintainers

For non-IID data specifically, look at the FedProx strategy.it handles heterogeneous data distributions much better than FedAvg.

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Xena D'Souza Starting 25 pts · Accepted answer

I'd push back slightly on the idea that industry collaboration is universally beneficial for academic research. The incentive misalignment is real.

Industry partners want deliverables on a schedule. Academic research is often exploratory and unpredictable. When the two clash, it is usually the researcher who has to compromise.either by rushing results, constraining publication, or steering away from results that are inconvenient for the sponsor.

This doesn't mean industry collaboration is bad. But the terms matter enormously. IP rights, publication rights, and the ability to pursue negative results should be negotiated upfront and protected in writing.

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Varsha Pillai Growing 60 pts · Accepted answer

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?