Nistara Randhawa, PhD's Avatar

Nistara Randhawa, PhD

@nistara.bsky.social

Data Science | Epidemiology | Wildlife Health & Conservation | Veterinary Science University California, Davis nistara.net

111 Followers  |  186 Following  |  12 Posts  |  Joined: 30.08.2023  |  2.0986

Latest posts by nistara.bsky.social on Bluesky

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Springer Nature retracts, removes nearly 40 publications The dataset contains images of children’s faces downloaded from websites about autism, which sparked concerns at Springer Nature about consent and reliability.

Springer Nature retracting papers based on bonkers dataset
www.thetransmitter.org/retraction/e... Good. #autism

08.12.2025 17:55 β€” πŸ‘ 100    πŸ” 41    πŸ’¬ 3    πŸ“Œ 12
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Sharks and Rays Gain Sweeping Protections From Wildlife Trade

From @nytimes.com: Sharks and Rays Gain Sweeping Protections From Wildlife Trade

These represent the first time there’s been recognition that some sharks and rays are wildlife, just like elephants or rhinos, WCS’s @lukedavid301.bsky.social tells the paper.

🌎 www.nytimes.com/2025/12/02/s...

02.12.2025 16:37 β€” πŸ‘ 43    πŸ” 21    πŸ’¬ 0    πŸ“Œ 2
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Sea anemones and hermit crabs form a mutualistic relationship in Japan Japanese researchers have described a new species of sea anemone that appears to share a mutually beneficial relationship with hermit crabs. The pale pink sea anemones, now named Paracalliactis…

Japanese researchers have described a new species of sea anemone that appears to share a mutually beneficial relationship with hermit crabs.

The pale pink sea anemones, now named Paracalliactis tsukisome, were found attached to the shells of hermit crabs.

16.11.2025 03:24 β€” πŸ‘ 90    πŸ” 14    πŸ’¬ 0    πŸ“Œ 3
[Scene is a kitchen - a middle aged woman called JANET is boiling peas at the stove. A younger more colourfully dressed woman named LIZ approached her.]

JANET:
Ugh...

LIZ:
What's up?

JANET:
I am so bored of cooking peas!

LIZ:
Have you tried...

AI peas?

JANET:
AI peas?

LIZ:
They're peas with AI!

[Liz holds up to us a packet of peas labelled: Pea-i AI - Peas with AI].

LIZ:
Al-powered peas harness the potential of your peas

JANET:
What

LIZ [Now a voiceover as we cut to a whizzy technology diagram of peas all connected by meaningless dotted lines]

Why not take your peas to the next level with Al Peas' new Al tools to power your peas?

[Show a techno diagram of a pea with a label reading 'AI' pointing to a random zone in it]

LIZ:

Each pea has Al in a way we haven't quite worked out yet but it's fine

[Show Janet and Liz now in a Matrix-style world of peas]

LIZ:
With Al peas you can supercharge productivity and make AI work for your peas!

JANET:
What

LIZ:
Shut up

LIZ:
Our game-changing Pea-Al gives you the freedom to unlock the potential of the power of the future of your peas workflow

From opening the bag of peas

to boiling the peas

to eating the peas

To spending millions on adding Al to the peas and then having to work out what that even means.

JANET:
Is it really necessary to-

LIZ [Grabbing Janet by the collar]:
THE PEAS HAVE GOT AI, JANET

[Cut to an advert ending screen, with the bag of peas and the slogan:

AI PEAS: Just 'Peas' for god's sake buy the AI peas.

[Ends]

[Scene is a kitchen - a middle aged woman called JANET is boiling peas at the stove. A younger more colourfully dressed woman named LIZ approached her.] JANET: Ugh... LIZ: What's up? JANET: I am so bored of cooking peas! LIZ: Have you tried... AI peas? JANET: AI peas? LIZ: They're peas with AI! [Liz holds up to us a packet of peas labelled: Pea-i AI - Peas with AI]. LIZ: Al-powered peas harness the potential of your peas JANET: What LIZ [Now a voiceover as we cut to a whizzy technology diagram of peas all connected by meaningless dotted lines] Why not take your peas to the next level with Al Peas' new Al tools to power your peas? [Show a techno diagram of a pea with a label reading 'AI' pointing to a random zone in it] LIZ: Each pea has Al in a way we haven't quite worked out yet but it's fine [Show Janet and Liz now in a Matrix-style world of peas] LIZ: With Al peas you can supercharge productivity and make AI work for your peas! JANET: What LIZ: Shut up LIZ: Our game-changing Pea-Al gives you the freedom to unlock the potential of the power of the future of your peas workflow From opening the bag of peas to boiling the peas to eating the peas To spending millions on adding Al to the peas and then having to work out what that even means. JANET: Is it really necessary to- LIZ [Grabbing Janet by the collar]: THE PEAS HAVE GOT AI, JANET [Cut to an advert ending screen, with the bag of peas and the slogan: AI PEAS: Just 'Peas' for god's sake buy the AI peas. [Ends]

Every ad now

13.11.2025 17:38 β€” πŸ‘ 5317    πŸ” 2350    πŸ’¬ 66    πŸ“Œ 95

Please take some time to read about the origin of the 1.5C target. It didn't originate from naivety or stupidity. Those who put it forward certainly didn't see it as "pointless" then, and would probably still not.

@climatehome.bsky.social

www.climatechangenews.com/2015/12/10/a...

18.11.2024 12:05 β€” πŸ‘ 457    πŸ” 163    πŸ’¬ 6    πŸ“Œ 10
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Painted bats belong alive in the wild β€” not dead on walls. Tell Amazon to stop selling painted woolly bats so their populations can again flourish in the wild β€” where they belong.

Painted woolly bats are listed as Near Threatened on the IUCN Red List. Ornamental trade is the top threat they face, and several lines of evidence point to steep declines in at least some populations.

We are doing everything we can to stop the trade.

act.biologicaldiversity.org/_M_d3tlOQ0G4...

31.10.2025 22:57 β€” πŸ‘ 4    πŸ” 1    πŸ’¬ 0    πŸ“Œ 1
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Fredston Lab: Postdoctoral Scholar University of California, Santa Cruz is hiring. Apply now!

POSTDOC JOB AD: I'm hiring a Bayesian ecologist to build a (IMO, extremely fun) model of humpback whale spatiotemporal dynamics in California

2-year position starting fall 2026. in-person in Santa Cruz; collab w with Mevin Hooten's lab at UT Austin.

ask me Qs or apply: recruit.ucsc.edu/JPF02003

22.10.2025 17:14 β€” πŸ‘ 75    πŸ” 71    πŸ’¬ 4    πŸ“Œ 1
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A national recognition; but science and open source are bitter victories I have recently been awarded France’s national order of merit, for my career, in science, in open source, and around AI. The speech that I gave carries messages important to me (French below;...

A speech about what drives me, how science and open source are bitter victories, unable to make improve the world if society does not embrace them for the better:
gael-varoquaux.info/personnal/a-...

10.10.2025 11:37 β€” πŸ‘ 115    πŸ” 37    πŸ’¬ 5    πŸ“Œ 5

If you've ever wanted to learn how to make beautiful websites with #QuartoPub and #rstats , check out this workshop I'm giving in a couple weeks! It'll be a blast (and we're covering Quarto's brand new _brand dot yaml system!)

03.10.2025 18:36 β€” πŸ‘ 87    πŸ” 26    πŸ’¬ 2    πŸ“Œ 1
Large-scale seizure offers glimpse into the hidden hornbill trade in West Africa | Oryx | Cambridge Core Large-scale seizure offers glimpse into the hidden hornbill trade in West Africa

Here is a note we have just had published in Oryx Conservation News, in support of the proposal to list African hornbills in CITES Appendix II. Please share this far and wide!

www.cambridge.org/core/journal...

09.10.2025 14:48 β€” πŸ‘ 3    πŸ” 2    πŸ’¬ 0    πŸ“Œ 0

tl;dr

    Collinearity is a form of lack of information that is appropriately reflected in the output of your statistical model.
    When collinearity is associated with interpretational difficulties, these difficulties aren’t caused by the collinearity itself. Rather, they reveal that the model was poorly specified (in that it answers a question different to the one of interest), that the analyst overly focuses on significance rather than estimates and the uncertainty about them or that the analyst took a mental shortcut in interpreting the model that could’ve also led them astray in the absence of collinearity.
    If you do decide to β€œdeal with” collinearity, make sure you can still answer the question of interest.

tl;dr Collinearity is a form of lack of information that is appropriately reflected in the output of your statistical model. When collinearity is associated with interpretational difficulties, these difficulties aren’t caused by the collinearity itself. Rather, they reveal that the model was poorly specified (in that it answers a question different to the one of interest), that the analyst overly focuses on significance rather than estimates and the uncertainty about them or that the analyst took a mental shortcut in interpreting the model that could’ve also led them astray in the absence of collinearity. If you do decide to β€œdeal with” collinearity, make sure you can still answer the question of interest.

Was asked about collinearity again, so here's Vahove's 2019 post on why it isn't a problem that needs a solution. Design the model(s) to answer a formal question and free your mind janhove.github.io/posts/2019-0...

01.10.2025 05:29 β€” πŸ‘ 115    πŸ” 35    πŸ’¬ 3    πŸ“Œ 4
A graphic showing the concept of mapply in R, with multiple input vectors being paired and processed by a function returning a single output vector.

A graphic showing the concept of mapply in R, with multiple input vectors being paired and processed by a function returning a single output vector.

Just published my new R article: 'Mapply: When You Need to Iterate Over Multiple Inputs'! πŸš€ If `sapply` doesn't quite cut it for your multi-variable iterations, `mapply` is your friend. Learn to pair inputs beautifully. #RStats #Mapply
https://drmo.site/bhXeDb

02.10.2025 13:02 β€” πŸ‘ 30    πŸ” 8    πŸ’¬ 0    πŸ“Œ 0

Thank you @gaborcsardi.org

30.09.2025 23:51 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Been loving its cli_progress_along() function lately. Super helpful package!

#rstats #cli

30.09.2025 23:51 β€” πŸ‘ 3    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

I read "Some of your monsters will be like this" and then wondered why...

29.09.2025 20:29 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
a table about lemurs

a table about lemurs

a table about students and schools

a table about students and schools

a table about wines

a table about wines

{tinytable} 0.14.0 for #RStats makes it super easy to draw tables in html, tex, docx, typ, md & png.

There are only a few functions to learn, but don't be fooled! Small πŸ“¦s can still be powerful.

Check out the new gallery page for fun case studies.

vincentarelbundock.github.io/tinytable/vi...

29.09.2025 12:44 β€” πŸ‘ 135    πŸ” 38    πŸ’¬ 1    πŸ“Œ 5
Post image

Whoaβ€”my book is up for pre-order!

𝐌𝐨𝐝𝐞π₯ 𝐭𝐨 𝐌𝐞𝐚𝐧𝐒𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 π’π­πšπ­ & πŒπ‹ 𝐌𝐨𝐝𝐞π₯𝐬 𝐒𝐧 #Rstats 𝐚𝐧𝐝 #PyData

The book presents an ultra-simple and powerful workflow to make sense of Β± any model you fit

The web version will stay free forever and my proceeds go to charity.

tinyurl.com/4fk56fc8

17.09.2025 19:49 β€” πŸ‘ 278    πŸ” 84    πŸ’¬ 9    πŸ“Œ 4
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New global collaboration uses experts and AI to spot the next pandemic | CEPI Combining AI programmes and disease knowledge can help more accurately rank the viruses that have the potential to cause the next pandemic.

We've joined UC Davis Grand Challenges and UC Davis Health to partner with @cepi.net and @bostonu.bsky.social's BEACON project to integrate #AI with expert knowledge for ranking #viruses with the pandemic potential.

cepi.net/new-global-c...

05.09.2025 21:29 β€” πŸ‘ 5    πŸ” 2    πŸ’¬ 0    πŸ“Œ 0
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Specimen Inactivation Methods for Proteomics─Comparisons of Irradiation, Chemical, and Heat Treatments on Downstream Serum Analyses Multiomic techniques, including proteomics, can provide novel insights for both pathogen detection and assessment of host responses to infection. Numerous studies have described the efficacy of heat i...

Specimen Inactivation Methods for Proteomics─Comparisons of Irradiation, Chemical, and Heat Treatments on Downstream Serum Analyses #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

19.09.2025 18:06 β€” πŸ‘ 2    πŸ” 2    πŸ’¬ 0    πŸ“Œ 0

Aww thank you, and the pleasure was all mine as I've said before 😊 Look forward to seeing more great work from you!

23.09.2025 05:44 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Congratulations on getting your first chapter out @brookegenovese.bsky.social!! What a special milestone and such great work!

23.09.2025 05:41 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
Screenshot from https://posit.co/conf-pricing-2025/ showing the posit conf virtual ticket pricing. $99 for standard virtual ticket, $49 for Government and Non-profit employees, and Free for students, academics, and needs-based attendees. There are hand-drawn doodles by Libby that highlight each option and one note says "Plus, Virtual Day!"

Screenshot from https://posit.co/conf-pricing-2025/ showing the posit conf virtual ticket pricing. $99 for standard virtual ticket, $49 for Government and Non-profit employees, and Free for students, academics, and needs-based attendees. There are hand-drawn doodles by Libby that highlight each option and one note says "Plus, Virtual Day!"

People keep telling me they can't afford to attend #PositConf2025 next week, so I need to make a public service announcement: attending virtually is very affordable (or free!) & is amazing! Get registered & hangout with me on the Discord server!! #databs #rstats #python

09.09.2025 19:32 β€” πŸ‘ 38    πŸ” 21    πŸ’¬ 2    πŸ“Œ 0

Just made this little diagram and it's so pretty! {ggdiagram} is really neat for these types of graphics #rstats

(code here: gist.github.com/andrewheiss/... )

08.09.2025 13:32 β€” πŸ‘ 64    πŸ” 10    πŸ’¬ 3    πŸ“Œ 0

Huge congratulations, Joanna!!

14.04.2025 07:04 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
A screenshot of the paper

A screenshot of the paper

A screenshot of the paper

A screenshot of the paper

A screenshot of the paper

A screenshot of the paper

A screenshot of the paper

A screenshot of the paper

πŸš€ New preprint! "Spatial Data Science Languages: Commonalities and Needs" 🌍

Exploring challenges & insights from #Rstats #Python & #JuliaLang for spatial data handlingβ€”geodetic coords, data cubes, and more!

arxiv.org/html/2503.16...

#SpatialDataScience #rspatial #geopython #juliageo

09.04.2025 14:00 β€” πŸ‘ 15    πŸ” 6    πŸ’¬ 1    πŸ“Œ 0

chatgpt has fully broken-through to every facet of the corporate environment. I'm spending a not-insignificant amount of my time every week now explaining to people that just because chatgpt told you that something was possible doesn't mean that it a) is or b) even exists

21.03.2025 13:44 β€” πŸ‘ 11325    πŸ” 1563    πŸ’¬ 286    πŸ“Œ 154

Please get in touch if you are up for chatting with recent U.S. fed employees about career paths!

14.03.2025 21:52 β€” πŸ‘ 11    πŸ” 8    πŸ’¬ 0    πŸ“Œ 0
Post image Post image

Maybe the crisis in seabirds with multi-organ failure from exposure to plastics will help raise awareness of their serious hazard and lead to action
science.org/doi/10.1126/...

12.03.2025 20:52 β€” πŸ‘ 373    πŸ” 102    πŸ’¬ 18    πŸ“Œ 8
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Rapid butterfly declines across the United States during the 21st century Numerous declines have been documented across insect groups, and the potential consequences of insect losses are dire. Butterflies are the most surveyed insect taxa, yet analyses have been limited in ...

Our paper is out, and it is distressing.

Rapid butterfly declines across the United States during the 21st century | Science www.science.org/doi/10.1126/...

06.03.2025 19:38 β€” πŸ‘ 511    πŸ” 249    πŸ’¬ 22    πŸ“Œ 18
Abstract
Introduction
A key step in the Bayesian workflow for model building is the graphical assessment of model predictions, whether these are drawn from the prior or posterior predictive distribution. The goal of these assessments is to identify whether the model is a reasonable (and ideally accurate) representation of the domain knowledge and/or observed data. There are many commonly used visual predictive checks which can be misleading if their implicit assumptions do not match the reality. Thus, there is a need for more guidance for selecting, interpreting, and diagnosing appropriate visualizations. As a visual predictive check itself can be viewed as a model fit to data, assessing when this model fails to represent the data is important for drawing well-informed conclusions.

Demonstration
We present recommendations for appropriate visual predictive checks for observations that are: continuous, discrete, or a mixture of the two. We also discuss diagnostics to aid in the selection of visual methods. Specifically, in the detection of an incorrect assumption of continuously-distributed data: identifying when data is likely to be discrete or contain discrete components, detecting and estimating possible bounds in data, and a diagnostic of the goodness-of-fit to data for density plots made through kernel density estimates.

Conclusion
We offer recommendations and diagnostic tools to mitigate ad-hoc decision-making in visual predictive checks. These contributions aim to improve the robustness and interpretability of Bayesian model criticism practices.

Abstract Introduction A key step in the Bayesian workflow for model building is the graphical assessment of model predictions, whether these are drawn from the prior or posterior predictive distribution. The goal of these assessments is to identify whether the model is a reasonable (and ideally accurate) representation of the domain knowledge and/or observed data. There are many commonly used visual predictive checks which can be misleading if their implicit assumptions do not match the reality. Thus, there is a need for more guidance for selecting, interpreting, and diagnosing appropriate visualizations. As a visual predictive check itself can be viewed as a model fit to data, assessing when this model fails to represent the data is important for drawing well-informed conclusions. Demonstration We present recommendations for appropriate visual predictive checks for observations that are: continuous, discrete, or a mixture of the two. We also discuss diagnostics to aid in the selection of visual methods. Specifically, in the detection of an incorrect assumption of continuously-distributed data: identifying when data is likely to be discrete or contain discrete components, detecting and estimating possible bounds in data, and a diagnostic of the goodness-of-fit to data for density plots made through kernel density estimates. Conclusion We offer recommendations and diagnostic tools to mitigate ad-hoc decision-making in visual predictive checks. These contributions aim to improve the robustness and interpretability of Bayesian model criticism practices.

New paper SΓ€ilynoja, Johnson, Martin, and Vehtari, "Recommendations for visual predictive checks in Bayesian workflow" teemusailynoja.github.io/visual-predi... (also arxiv.org/abs/2503.01509)

04.03.2025 13:15 β€” πŸ‘ 64    πŸ” 20    πŸ’¬ 4    πŸ“Œ 0

@nistara is following 20 prominent accounts