Hello. OK. I give. I'm sick of twitter. I'm looking for cool people to follow. Point me at good accounts?
My interests are #design #neuroscience #3dimaging #datascience #softwaredevelopment #research #uiux #hci and #art
datascience
What can Inka Quipus teach us about data management?
A blog post about the ingenious methods used by the ancient Inca culture to encode information and what insights we might draw from them today for #datascience
The value of metadata, human readibility, schema flexibility and append-only #database? Its all there 🤓
https://www.openriskmanagement.com/what-inka-quipus-teach-us-about-data-management/
Datasci.social is a Mastodon server for researchers & practitioners in human-centric data science, including network science, social data science, computational social science, geospatial data science etc.
You can find out more at https://datasci.social/about or contact the admin @mszll
#FeaturedServer #DataScience #DataSci #NetworkScience #SocialDataScience #Geospatial #Mastodon #Fediverse #FreeFediverse
What's been going on with Monteverdi?
- There's now an output plugin with BadgerDB as the first type. My next output plugin will be MIDI, as a direct output or as a file, so the pulse sequences can be heard.
- A second Transformer plugin gives it the ability to read endpoints with just JSON instead of strict KV, which opens it up to anything that publishes telemetry in JSON blobs.
- OTel instrumentation has given me some wild and amazing insights into how robust and concurrent this is. I'm using a supervisor pattern and keeping data structures simple, because they traverse a lot of layers and hierarchies. And all of this is decoupled from the ring pulse UI.
- The inline web UI for hot reloading the configuration is maybe the coolest part. You can run this in a container and browse to the UI and change up the configuration, add endpoints, replace the entire thing if you want.
💾 From Duplicity to Borg — a backup journey
After more than a decade with Duplicity, I finally switched to BorgBackup — and wow, that difference.
📂 Source data: 719 GB in my /home
📦 Packed size: 419 GB
📉 Actual storage: 390 GB after compression
That’s more than a 45% reduction, and the upload to remote storage is much faster now.
For someone who handles geospatial and scientific data daily, this is huge.
It’s not just about saving space — it’s about making the workflow lighter, faster, and more sustainable.
🧠 Curious — what backup tools do you trust most?
#Linux #BorgBackup #OpenSource #DataManagement #Backups #DigitalResearch #IndependentResearch #DataScience #CyberSecurity #ResearchWorkflow #Geodata #Automation
Massive compute power applied to massive data sets can produce outcomes that are worse at the task they’re (ostensibly) intended for than much simpler, easier to understand, less wasteful, and less intrusive data-light methods. It requires an extreme form of bias to believe that big compute + big data is always better.
#AI #GenAI #GenerativeAI #LLMs #tech #dev #DataScience #science #ComputerScience #EcologicalRationality
#AI #GenAI #GenerativeAI #LLMs #tech #dev #DataScience #science #ComputerScience #EcologicalRationality
Edited 4d ago
Mel Andrews on the connections between a naive belief in scientific objectivity (facts and data are "real" and "correct" and "neutral") and eugenics:
I've lost the reference, but I suspect it was Meredith Whittaker who's written and spoken about the big data turn at Google, where it was understood that having and collecting massive datasets allowed them to eschew model-building.
The core idea being critiqued here is that there's a kind of scientific view from nowhere: a theory-free, value-free, model-free, bias-free way of observing the world that will lead to Truth; and that it's the task of the scientist to approximate this view from nowhere as well as possible.
#AI #GenAI #GenerativeAI #LLMs #science #DataScience #ScientificObjectivity #eugenics #ViewFromNowhere
Francis Galton, pioneering figure of the eugenics movement, believed that good research practice should consist in “gathering as many facts as possible without any theory or general principle that might prejudice a neutral and objective view of these facts” (Jackson et al., 2005). Karl Pearson, statistician and fellow purveyor of eugenicist methods, approached research with a similar ethos: “theorizing about the material basis of heredity or the precise physiological or causal significance of observational results, Pearson argues, will do nothing but damage the progress of the science” (Pence, 2011). In collaborative work with Pearson, Weldon emphasised the superiority of data-driven methods which were capable of delivering truths about nature “without introducing any theory” (Weldon, 1895).
I've lost the reference, but I suspect it was Meredith Whittaker who's written and spoken about the big data turn at Google, where it was understood that having and collecting massive datasets allowed them to eschew model-building.
The core idea being critiqued here is that there's a kind of scientific view from nowhere: a theory-free, value-free, model-free, bias-free way of observing the world that will lead to Truth; and that it's the task of the scientist to approximate this view from nowhere as well as possible.
#AI #GenAI #GenerativeAI #LLMs #science #DataScience #ScientificObjectivity #eugenics #ViewFromNowhere
If anyone reading this is in the Data Analysis industry and would be interested in facilitating some sort of unorthodox remote apprenticeship - even unpaid, if it gets me experience - please do kindly let me know.
I don't have the self-discipline for self-study right now. In fact I failed out of college twice because I couldn't handle working and attending and that was a traditional classroom experience.
Yes.. I'm asking for a shortcut. It's desperation, not hubris.