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  1. My @11ty plugin, #Uncharted, is an early WIP. Turns CSVs into CSS charts with optional animations and customizable classes. Currently supports five chart types—more to come soon (most likely as I need them for #TidyTuesday). Feedback/PRs welcome!
    github.com/slunsford/uncharted
    #datadon

  2. New year, new #TidyTuesday site. I’m building this on @11ty now (like all my other websites) and developing a new plugin for generating charts. First three weeks of 2026 are up.
    tidytuesday.seanlunsford.com/
    #datadon

  3. My @11ty plugin, #Uncharted, is an early WIP. Turns CSVs into CSS charts with optional animations and customizable classes. Currently supports five chart types—more to come soon (most likely as I need them for #TidyTuesday). Feedback/PRs welcome!
    github.com/slunsford/uncharted
    #datadon

  4. New year, new #TidyTuesday site. I’m building this on @11ty now (like all my other websites) and developing a new plugin for generating charts. First three weeks of 2026 are up.
    tidytuesday.seanlunsford.com/
    #datadon

  5. I kind of had a soft launch of my #TidyTuesday site last week, and I'd taken a stab at the one before that—but this is the first time I'm happy with what I ended up with. Powered by #Evidence and #DuckDB. #datadon
    tidytuesday.seanlunsford.com/2

  6. Only nine months late: my SQL adaptation of @drdrang’s Electoral College script, which has been sitting as a basically finished draft since the beginning of September. #datadon seanlunsford.com/2024/bringing

  7. Only nine months late: my SQL adaptation of @drdrang’s Electoral College script, which has been sitting as a basically finished draft since the beginning of September. #datadon seanlunsford.com/2024/bringing

  8. Hey, #RStats and #DataDon folks. How would you estimate the regression of a normally distributed variable and a variable that is bounded between 0 and 1 (and has many 0s and 1s) (specifically: sea ice concentration). I realised that normal linear regression really doesn't make sense. Is beta regression the better choice? betareg is not fitting, (I get an initial value in 'vmmin' is not finite error).

  9. Hey, #RStats and #DataDon folks. How would you estimate the regression of a normally distributed variable and a variable that is bounded between 0 and 1 (and has many 0s and 1s) (specifically: sea ice concentration). I realised that normal linear regression really doesn't make sense. Is beta regression the better choice? betareg is not fitting, (I get an initial value in 'vmmin' is not finite error).

  10. "The reason you begin tracking your data is that you have
    some uncertainty about yourself that you believe the data
    can illuminate. It’s about introspection, reflection, seeing
    patterns, and arriving at realizations about who you are
    and how you might change."
    —Eric Boyd, self-tracker

    an article by Natasha D. Schüll, 2019, "The Data-Based Self:
    Self-Quantification and the Data-Driven (Good) Life" natashadowschull.org/wp-conten

    #concentration #credit #scores #scoring #reward #rewards #psychology #socioPsych #socioPsychology #selfWorth #universalism #digitalization #recognition #data #AIRisks #AIEthics #gaming #socialization #Tracking #surveillance #selfRegulation #attention #reintermediation #intermediation #enshittification #risk #derisking #vulnerability #morality #selfConfidence #dataDon #Schüll #quotes

  11. "The reason you begin tracking your data is that you have
    some uncertainty about yourself that you believe the data
    can illuminate. It’s about introspection, reflection, seeing
    patterns, and arriving at realizations about who you are
    and how you might change."
    —Eric Boyd, self-tracker

    an article by Natasha D. Schüll, 2019, "The Data-Based Self:
    Self-Quantification and the Data-Driven (Good) Life" natashadowschull.org/wp-conten

  12. @data @datadon 🧵

    In its judgment, the General Court of the European Union strengthened the conciliation and regulatory powers of the European Data Protection Board.

    "The DPC will have to continue its investigations into Meta unless it chooses to refer the matter to the European Court of Justice. Given the timeline of the original investigation, which began in 2018, it may still take several years before a final determination is made regarding the GDPR compliance of Meta’s business model, particularly in relation to behavioural advertising."

    dpoblog.eu/cjeu-and-the-powers by Christina Etteldorf, Institut of European #Media #Law, Saarbrücken (Germany)

    #advertising #GDPR #Meta #WhatsApp #instagram #CJEU #DPC #EDPB #Etteldorf #networkPolitics #targetedAdvertising #targetedAdvertisement #dataProtection #dataMining #dataDon #dataCapture #dataGovernance #retaliation

  13. @data @datadon 🧵

    In its judgment, the General Court of the European Union strengthened the conciliation and regulatory powers of the European Data Protection Board.

    "The DPC will have to continue its investigations into Meta unless it chooses to refer the matter to the European Court of Justice. Given the timeline of the original investigation, which began in 2018, it may still take several years before a final determination is made regarding the GDPR compliance of Meta’s business model, particularly in relation to behavioural advertising."

    dpoblog.eu/cjeu-and-the-powers by Christina Etteldorf, Institut of European #Media #Law, Saarbrücken (Germany)

    #advertising #GDPR #Meta #WhatsApp #instagram #CJEU #DPC #EDPB #Etteldorf #networkPolitics #targetedAdvertising #targetedAdvertisement #dataProtection #dataMining #dataDon #dataCapture #dataGovernance #retaliation

  14. What's easier to interpret as a measure of linear relationship between two variables?

    #Datadon #RStats #AcademicChatter

  15. What's easier to interpret as a measure of linear relationship between two variables?

    #Datadon #RStats #AcademicChatter

  16. Edit: Found one, see reply. Feel free to make any other suggestions though :)

    Anyone know of a function dependency graph visualiser for Python that does the same thing(ish) as foodwebr for R? github.com/lewinfox/foodwebr

    Everything I've found so far do module dependency graphs (e.g. github.com/gauge-sh/tach), which are cool, but I'd really like to get a graph of **function** dependencies, to see more finely "who calls who".

    #rStats #Python #Datadon

  17. Edit: Found one, see reply. Feel free to make any other suggestions though :)

    Anyone know of a function dependency graph visualiser for Python that does the same thing(ish) as foodwebr for R? github.com/lewinfox/foodwebr

    Everything I've found so far do module dependency graphs (e.g. github.com/gauge-sh/tach), which are cool, but I'd really like to get a graph of **function** dependencies, to see more finely "who calls who".

    #rStats #Python #Datadon

  18. @eliocamp

    This link luzmo.com/blog/python-chart-li gives a nice overview.

    In my case I am well served with seaborn and matplotlib in most cases when I use Python.

    Regarding the tutorial I don't know the level you start from and I don't know if you prefer reading or watching but here are some recommendations.

    matplotlib.org/stable/tutorial
    w3schools.com/python/matplotli
    dataquest.io/tutorial/data-vis
    geeksforgeeks.org/graph-plotti

  19. @eliocamp

    #python #Datadon

    This link luzmo.com/blog/python-chart-li gives a nice overview.

    In my case I am well served with seaborn and matplotlib in most cases when I use Python.

    Regarding the tutorial I don't know the level you start from and I don't know if you prefer reading or watching but here are some recommendations.

    matplotlib.org/stable/tutorial
    w3schools.com/python/matplotli
    dataquest.io/tutorial/data-vis
    geeksforgeeks.org/graph-plotti

  20. Hey, #python folks. What is the best current plotting library for static plots (publication to PDF.and HTML)? And what is the best tutorial to learn it?

    #Datadon

  21. Hey, #python folks. What is the best current plotting library for static plots (publication to PDF.and HTML)? And what is the best tutorial to learn it?

    #Datadon

  22. Enteric Fermentation in 2022

    Livestock digestion emits too much methane:
    * Too many bovines in India, Pakistan, Brazil, United States, China;
    * Too many sheep and pigs in China.

    (The bubble sizes depend on the amount of methane sent in 2022.)

    #GreenhouseForcing #methane #emissions #climateChange #climateBreakdown #climateCollapse #dataViz #bubbleChart #dataMining #plotly #featureEngineering #featureSelection #dataDon

  23. Enteric Fermentation in 2022

    Livestock digestion emits too much methane:
    * Too many bovines in India, Pakistan, Brazil, United States, China;
    * Too many sheep and pigs in China.

    (The bubble sizes depend on the amount of methane sent in 2022.)

    #GreenhouseForcing #methane #emissions #climateChange #climateBreakdown #climateCollapse #dataViz #bubbleChart #dataMining #plotly #featureEngineering #featureSelection #dataDon

  24. It's trite to start a blog post share with "A common question I hear is…", but in this case it's *true*! One of the most common things I see people asking how to do is get data from #Postgres to #Snowflake, so I wrote a blog post about how to do with @decodable. You can use a web UI or YAML—your choice.

    ✍️ dcbl.link/postgres-to-snowflak

    #dataEngineering #ELT #ETL #datadon

  25. It's trite to start a blog post share with "A common question I hear is…", but in this case it's *true*! One of the most common things I see people asking how to do is get data from #Postgres to #Snowflake, so I wrote a blog post about how to do with @decodable. You can use a web UI or YAML—your choice.

    ✍️ dcbl.link/postgres-to-snowflak

    #dataEngineering #ELT #ETL #datadon

  26. @data @datadon
    "Obtaining the data is a hard human problem. That is, 'people don’t want to give it to you'."

    You can "use your allies on the bottom (front-line workers) and the top (executives) to squeeze out your opponents in the middle (managers, often in IT or data science departments").

    Sarah Constantin: sarahconstantin.substack.com/p

    #industry #power #factory #processes #dataPrivacy #confidentiality #dataDon #dataGovernance #security #dataAccess #analytics #powerTalk #data #dataAnalysis #losers

  27. @data @datadon
    "Obtaining the data is a hard human problem. That is, 'people don’t want to give it to you'."

    You can "use your allies on the bottom (front-line workers) and the top (executives) to squeeze out your opponents in the middle (managers, often in IT or data science departments").

    Sarah Constantin: sarahconstantin.substack.com/p

    #industry #power #factory #processes #dataPrivacy #confidentiality #dataDon #dataGovernance #security #dataAccess #analytics #powerTalk #data #dataAnalysis #losers

  28. Stats question... x and y are random variables with equal measurement error. I can compute the leading PC as orthogonal regression. The variance explained by this PC is 0.91. However, the R^2 between the two variables is 0.64. What's the interpretation of these two numbers in this context?

    #Rstats #datadon

  29. Stats question... x and y are random variables with equal measurement error. I can compute the leading PC as orthogonal regression. The variance explained by this PC is 0.91. However, the R^2 between the two variables is 0.64. What's the interpretation of these two numbers in this context?

    #Rstats #datadon

  30. Data returned by an observation typically is represented as a vector in machine learning.

    A neural network can be seen as a large collection of linear models. We may represent the inputs and outputs of each layer as vectors, matrices, and tensors (which are like higher dimensional matrices).

    #algebra #linearAlgebra #vectors #matrices #determinants #singularity #ML #DataScience #math #maths #mathematics #mathStodon #ML #data #dataDon #dataScience #machineLearning #DeepLearning #neuralNetworks

  31. Data returned by an observation typically is represented as a vector in machine learning.

    A neural network can be seen as a large collection of linear models. We may represent the inputs and outputs of each layer as vectors, matrices, and tensors (which are like higher dimensional matrices).

    #algebra #linearAlgebra #vectors #matrices #determinants #singularity #ML #DataScience #math #maths #mathematics #mathStodon #ML #data #dataDon #dataScience #machineLearning #DeepLearning #neuralNetworks

  32. This is is 🔥🔥🔥 and the tooling I wish I had when writing custom REST extractors

    Automated pagination is the kind of feature that will make many DEs very happy

    dlthub.com/docs/blog/rest-api-
    #datadon

  33. Data Engineers will literally go to therapy before they break up with their data warehouse

    #data #datadon

  34. 🙋‍♂️Who’s going to be at Data Universe this week in New York City?

    I will be! This will be my first in person data conference since … like 2017?

    LMK if you’ll be there and come say hi!

    #data #datadon

  35. B., the senior officer, claimed that in the current war, “I would invest 20 seconds for each target at this stage, and do dozens of them every day. I had zero added value as a human, apart from being a stamp of approval. It saved a lot of time.”

    According to B., a common error occurred “if the [Hamas] target gave [his phone] to his son, his older brother, or just a random man. That person will be bombed in his house with his family. This happened often. These were most of the mistakes caused by Lavender,” B. said.

    972mag.com/lavender-ai-israeli @israel @data 🧶

  36. It was easier to locate the individuals in their private houses.

    “We were not interested in killing operatives only when they were in a military building or engaged in a military activity. On the contrary, the IDF bombed them in homes without hesitation, as a first option. It’s much easier to bomb a family’s home. The system is built to look for them in these situations.”

    Yuval Abraham reports: 972mag.com/lavender-ai-israeli

    (to follow) 🧶#longThread @palestine @israel @ethics @military @idf @terrorism

  37. Love seeing more git-like collaborative reviews come to the BI layer. One challenge is that BI is visual, and another is that the group of reviewers should include non-technical folks like business stakeholders.

    I’ve never used Hex but this looks slick: hex.tech/blog/reviews/

    #data #datadon #data_viz