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Is citation count still a valid metric for research impact?

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Starting 0 pts 0 followers
Indian Institute of Science · Posted
Curriculum & Pedagogy Pharmaceutical Sciences Quantum Computing

Background

I have been an Assistant Professor for three years now. Looking back, the most valuable thing in my career so far has been the research network I built.not my publications, not my grants, not my teaching record.

Here is what actually worked for building that network as a new faculty member:

  1. Email cold introductions after reading someone's paper.specific, about their work, not a generic 'let's collaborate' message. Response rate is surprisingly high (~40%) if the email shows you actually read the paper.

  2. Conference conversations.not the formal talks, but the coffee breaks and poster sessions. I have gotten more collaborators from standing next to someone at a coffee station than from any structured networking event.

  3. Reviewing papers.every paper you review is an invitation to learn what leading researchers in your area are working on before it is published.

  4. Social media presence.LinkedIn for professional connections, Twitter/X for research community discussions. One thread I posted on my research got picked up by a researcher at Cambridge who is now a collaborator.

What has worked for others?

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18 Replies

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Gopal Naidu · Accepted answer

Check whether your institution has a Springer/Elsevier waiver agreement.many NITs and IITs do.

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Replying to Gopal Naidu
Uday Bose Starting 5 pts · Accepted answer

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?

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Replying to Gopal Naidu
Kiran Yadav Growing 130 pts · Accepted answer

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.

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Replying to Gopal Naidu
Vikram Bhatia Active 420 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

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Arjun Venkatesan Growing 195 pts · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

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Om Prakash Growing 55 pts · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.

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Royal Dsouza Starting 30 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

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Replying to Royal Dsouza
Jagdish Rawat · 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?

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Replying to Royal Dsouza
Meena Sharma · Accepted answer

The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.

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Replying to Royal Dsouza
Meghana Rao Growing 170 pts · Accepted answer

Strongly agree with the point about choosing venues carefully. One paper in NeurIPS > five papers in obscure workshops.

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Jagdish Rawat · Accepted answer

+1 to everything said above. My experience was identical.

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Asha Pillai Distinguished 880 pts · Accepted answer

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?

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Hema Suresh Starting 20 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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Replying to Hema Suresh
Revathi Chandrasekaran Active 410 pts · Accepted answer

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.

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Replying to Hema Suresh
Dinesh Kulkarni Distinguished 730 pts · Accepted answer

This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.

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Replying to Hema Suresh
Sanjay Kumar Growing 145 pts · Accepted answer

Thank you! The reference to MuRIL is particularly useful.I had not considered it as an alternative to XLM-R for Indic languages.

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Asha Pillai Distinguished 880 pts · Accepted answer

When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?

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Eshan Patil Growing 70 pts · Accepted answer

The short answer: it depends heavily on the journal and field, but here is a general framework.

If your null result is well-powered and the hypothesis was reasonable: Absolutely publish it. Null results in well-designed studies are as valuable as positive results. Journals like PLOS ONE explicitly welcome them.

If your study is underpowered: Be transparent about it. Report effect sizes and confidence intervals, not just p-values. A small, underpowered study with honest reporting is still a contribution.

What kills papers is when limitations are obvious but the authors have not addressed them.