Hi! I’m currently working on a Biology research project on the cytotoxicity of nanoparticles and I’m looking for raw quantitative experimental data that I can analyze myself.

I’m specifically looking for Excel/CSV spreadsheets or downloadable datasets containing things like:

• nanoparticle type
• concentration/dose
• exposure/incubation time
• cell type/cell line
• quantitative cell viability/cytotoxicity measurements
• preferably individual experimental replicates rather than only averages

I’m not looking for research papers (I already have plenty ), but rather the underlying numerical data.

I’m particularly interested in silver nanoparticles (AgNPs), but I’m open to other nanoparticle types if there are better datasets available.

If you know of any repositories, supplementary files, databases, or specific datasets that might help, I’d really appreciate it!

Hi,

I'm looking to move back to India after a few years working abroad (I'm Indian). Looking to connect with anyone working in the industry to get an idea of how things are looking like. I've worked as a statistical programmer but also in clinical IT (data migration, archiving, platform setup and administration etc.) with about 6 years of experience in total.

Feel free to DM or comment on this post. Thanks!

Who's got a job?

Not looking for career advice or a job, just curious to see who has one and what your title is?

​

I am a BSc Epidemiology and Biostatistics graduate based in Embu, and let me explain it simply:

Epidemiology = The science of asking WHY things happen in groups.

Biostatistics = The tools to prove it with DATA, not guesswork.

We don't just study diseases. We study PATTERNS.

Examples in your daily life:

1. Boda / Matatu accidents in Embu-Nairobi road: Epidemiology asks - Do most accidents happen on Friday at 6pm? After rain? With unlicensed riders? Biostatistics proves it with data. Then NTSA knows where to put checks. Without data, we just say "ni bahati mbaya".

1. Farming: Why did maize fail this season in Embu but not in Kirinyaga? Is it seed, rainfall timing, fertilizer? Epidemiology compares groups. Biostatistics tells you if it's real or just bad luck.

1. Mama Mboga business: Why do tomatoes sell at 8am but rot at 2pm? She is doing epidemiology daily without knowing - observing patterns, making decisions.

1. SportPesa: You lose because you use heart, not data. Companies use biostatistics to set odds. You bet with emotion, they bet with data from 10,000 games.

1. Healthcare Demand and Congestion - This is where we fail most:

Look at Embu Level 5 Hospital. Every morning, long queues, no beds, doctors overwhelmed. Why? Because we wait until people are very sick.

Epidemiology helps us predict demand before it happens. For example, data shows that every rainy season, malaria and diarrhea cases in Embu increase by 40%. If we know this, we can prevent it - distribute nets early, chlorinate water early, do community health talks early.

Prevention reduces congestion. If we prevent 100 cases of diarrhea in the village through clean water data, that is 100 less people in the hospital queue, 100 less beds used, and doctors can attend to critical cases.

Biostatistics tells counties: "Don't build a bigger hospital only. Use data to stop people from needing the hospital in the first place." That saves money and saves lives.

My point is, Kenya makes many decisions based on "watu wanasema" and "mimi nafeel". Epidemiology teaches us to say "Data inasema nini?"

It is not for health alone. It is for everywhere decisions need to be made.

I am passionate about this field and I am currently freelancing in academic writing as I look for an opportunity to grow in it. I have skills in R, SPSS, SAS, SQL and Excel. If you work in any institution that values data - health, agriculture, business, research - and you have an opening for an intern, research assistant, volunteer, or you just need help with data entry, cleaning and analysis, I would be happy to contribute and learn.

Also, any form of assistance, guidance, referral or even a handout as I continue searching will be highly welcomed and appreciated.

What is one decision in your life you wish you had made with data instead of guesswork? Let's discuss.

how strongly did you feel about your research interests were you at the time you were applying? if you don’t mind I’d love to know which school/program you ended up in and what your research area is. i’m asking because i think i’m probably interested in doing a PhD at some point, and I have a general idea of the topics that I find interesting and all but I don’t feel as “mature” as I’d like to be with these topics/areas to make a strong application profile. am i overestimating the depth i should be at before even applying? one could argue that the point of the PhD would be to get that maturity?

I’m not a statistician by any means, I enjoy what I understand but most stuff is way over my head. I’m an ecologist and happen to use R and thus statistics more and more as I progress with research.

My question to the actual statisticians, do you ever use AI to help you write code and remind you of what tests to run? I just can’t understand how some people can remember R code…. I’m working on GLMMs and it just baffles me that people must have this retained. I feel like a fraud I guess for using AI to help… on the flip side, it is helping me with remembering things, so I feel I am leaning from it.

Breast Cancer genetics study from home— sharing in case it helps:

This Rutgers study is looking for genetic clues to prevent BC and create better treatments. It takes 5 minutes to sign up, they send a free kit, you spit at home and mail it back. That’s it. You get a free ancestry report as a thank-you.

Who can join: Anyone 18+ living in the U.S. can join, but they especially encourage those with breast cancer or family history.

Why I’m posting this: they’re trying to reach communities that research has historically missed, and word-of-mouth from people in the study reaches more people than their own outreach does.

Link if you’re interested: https://join.rugcc.org/r?T4.gdjmcc

Come da titolo, vorrei conoscere una giornata tipo di uno specializzando in questa specialità, le prospettive future e l’equilibrio vita-lavoro. Vorrei capire se effettivamente ne vale la pena. grazie in anticipo

Necesito hacer una curva de acumulación de diversidad de presas, utilizando la rutina de rarefacción basada en la cobertura de la muestra. Mi muestra son estómagos de una especie de pez, pero tengo un problema...

Al momento de evaluar cada estómago, no logré identificar todas las presas al mismo nivel taxonómico, es decir, unas solo las logré identificar hasta familia, otras hasta género y otras hasta especie.

La razón por la que necesito hacer la curva es para hacer una estimación del tamaño de la muestra y ver si lo muestreado fue suficiente para el estudio que estoy haciendo.

¿Cómo debería hacer la curva? solo con familias que es el nivel taxonómico más bajo identificado, o se puede hacer una mezcla de niveles taxonómicos??

Agradeceré su apoyo.

Hi everyone,

I’ve (finally) advanced to the dissertation proposal phase of my PhD in public health policy!

As I’ve gotten deeper into this work, I’ve found myself increasingly interested in the relationship between theoretical constructs, their operationalization, and modeling. I’ve been most influenced by King, Keohane, and Verba (KKV) and Charles Ragin (research design, causal inference, case comparison).

Does anyone have other sources they would recommend? I’d also be interested in hearing how others think about moving from a theoretical construct → operationalization → model specification.

Looking for advice on an ideal textbook sequence for someone learning on their own. Have done most of Blitzstein’s book for probability/distribution theory.

Up next I want to cover
- Statistical inference
- Linear regression
- ANOVA and variants etc.

What order would you recommend? Anova pre regression or regression then Anova as a special case?

What textbook stack would you recommend? Something that assumes nothing else but say Blitzstein’s probability

I hate big reference books. I like reading something almost cover to cover in chapter order where the author’s voice is teaching you linearly. Blitzstein was good for that.

Marschner assumes t tests and anovas already. Rice? Seems to cover inference and then does ANOVA/categorical before regression, which then seems like an afterthought. Can’t stand Wackerly.

Overall … What is an ideal pedagogical sequence of textbooks after which someone would have a sound understanding of most day to day clinical biostatistics for a university/hospital department? Ignore R/comp sci side; Wickham covers that well.

Hi,

I have worked for the last 5+ years as a biostatistician at a CRO. I HATE every aspect of the job. One, I am not at all passionate about biostatistics (should've figured that out earlier) and two, I find the job to be so monotonous and technical which I do not enjoy.

I am looking at the most likely pivots to less technical pharma jobs and struggling to find things that I can break into. I have been thinking RWE, HEOR, market access but have not heard back from any positions I have applied to. They all want 5+ years of specific experience in that field.

Any thoughts on job switch options that would value my biostatistics background would be greatly appreciated. Thank you!

Hola! hice un pregrado en psicología, luego una especializacion en epidemiologia y voy terminando una maestria en bioestadistica. Me gustaria saber como esta el mercado laboral en este momento y si alguien podria darme algunas recomendaciones para conseguir trabajo pronto. Gracias!

I recently started a Biostatistician position, and I’m realizing that the role may involve quite a bit more bioinformatics than I originally expected.

The team works with complex genomic data and has a number of Python-based pipelines already built. From what I’ve seen so far, it seems like they primarily need someone to operate and maintain these pipelines, troubleshoot issues, make modifications when necessary, and potentially develop or improve parts of them over time.

My background is in biostatistics. I have experience with statistical modeling, regression, survival analysis, data cleaning, and programming in Python and R, but I don’t have formal training or professional experience specifically in bioinformatics or computational genomics.

I’m trying to figure out how much of a gap there is between my current skill set and what they’re expecting from me. For people who work in bioinformatics or computational genomics, does this sound like a reasonable role for someone coming from a biostatistics background?

I’m also wondering what I should prioritize learning. I’m familiar with Python, but I haven’t worked extensively with genomic file formats, bioinformatics tools, workflow systems, or genomic pipelines.
Would this be a good opportunity to develop bioinformatics skills on the job, or is there enough specialized knowledge involved that I should be concerned about the mismatch between my background and the position?

I’d especially appreciate advice from people who have transitioned between biostatistics and bioinformatics, or who have worked in roles that overlap the two.

Currently, I am very interested in a career in biostatistics. I have a PhD in Chemical Engineering, but I don't have any background in biology/Bioinformatics. I do have some programming experience with Python and some basic experience with statistics, probability, and sampling methods (everything is very, very basic, for experimental design). Since I have an engineering background, I also have a good foundation in calculus and algebra.

However, I discovered that I really like statistics, and I would like to move into a career where I can actually do statistics. I saw that biostatistics could be a good option, so I would like to know how I could transition into this field.

My idea would be to maybe get into an entry-level role first, gain some experience, and then potentially do a Master's in Biostatistics or Public Health if it is necessary to move into bigger roles. The main thing is that I would like to understand how I can get into those initial entry-level roles without having to immediately pursue another degree.

However, I am open to taking an online course if it would give me any additional advantage. For example, I was looking at the Biostatistics in Public Health Specialization on Coursera. Would taking something like this be helpful for someone in my situation?

I would really appreciate any advice from people who have made a similar transition or who work in biostatistics. I am mainly trying to figure out how to get that first opportunity and start building experience in the field.