diff --git a/docs/awesome/awesome-agi-cocosci.md b/docs/awesome/awesome-agi-cocosci.md
index f235a8d6e8..55890c995b 100644
--- a/docs/awesome/awesome-agi-cocosci.md
+++ b/docs/awesome/awesome-agi-cocosci.md
@@ -318,7 +318,7 @@ Contributions are greatly welcomed! Please refer to [Contribution Guidelines](ht
* [Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory](https://psycnet.apa.org/record/2012-12791-001) - ***Psychological Bulletin***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=11218217347365817167&hl=en&as_sdt=0,5)]. Alison Gopnik's review on the constructivism idea of developmental research, including the theory theory of concepts.
-* [Similarity involving attributes and relations: Judgments of similarity and difference are not inverses](https://groups.psych.northwestern.edu/gentner/newpdfpapers/MedinGoldstoneGentner90.pdf) - ***Psychological Science***, 1990. [[All Versions](https://scholar.google.com/scholar?cluster=13205938250772079784&hl=en&as_sdt=0,5)]. Theory on similarity judgement by attributes and relations.
+* [Similarity involving attributes and relations: Judgments of similarity and difference are not inverses](https://groups.psych.northwestern.edu/gentner/newpdfpapers/MedinGoldstoneGentner90.pdf) - ***Psychological Science***, 1990. [[All Versions](https://scholar.google.com/scholar?cluster=13205938250772079784)]. Theory on similarity judgement by attributes and relations.
#### Human Concept Representation
diff --git a/docs/awesome/awesome-angular.md b/docs/awesome/awesome-angular.md
index de6a388ac5..807d2409e1 100644
--- a/docs/awesome/awesome-angular.md
+++ b/docs/awesome/awesome-angular.md
@@ -1120,6 +1120,7 @@ become an Angular expert.
* [ngx-mat-split-button](https://github.com/feature23/ngx-mat-split-button) - A split button component for Angular Material. Split buttons (also known as "split button dropdowns" in Bootstrap) have a primary action button with a conjoined dropdown toggle that opens a menu for secondary actions.
* [ng-select2](https://github.com/Harvest-Dev/ng-select2) - This Angular CLI module is a fork of [select2-component](https://github.com/plantain-00/select2-component).
* [ngx-duration-picker](https://github.com/FrancescoBorzi/ngx-duration-picker) - Angular component for ISO_8601 durations.
+* [ngx-super-select-tree](https://github.com/HesamKashefi/ngx-super-select-tree) - A single/multiple choice drop down tree for Angular!
##### Validation
@@ -1652,6 +1653,7 @@ for the creation of web applications developed with Angular.
* [rxjs-typewriter](https://github.com/omnedia/rxjs-typewriter) - A lightweight and easy-to-use NPM library for creating typewriter effects in your web applications. It uses RxJS to manage the typewriting effect, ensuring smooth and customizable animations.
* [ngx-safe-subscribe](https://github.com/Badisi/ngx-safe-subscribe) - Easy way to automatically unsubscribe from RxJS observables in Angular components.
* [rxjs-collection](https://github.com/henryruhs/rxjs-collection) - RxJS enhanced Array, Map, WeakMap, Set and WeakSet.
+* [ng-rxjs-safe-subscribe](https://github.com/piotrekn/ng-rxjs-safe-subscribe) - Implementation of Angular's repeatable OnDestroy pattern.
#### TypeScript
diff --git a/docs/awesome/awesome-artificial-intelligence.md b/docs/awesome/awesome-artificial-intelligence.md
index 16b191e827..48b549eec2 100644
--- a/docs/awesome/awesome-artificial-intelligence.md
+++ b/docs/awesome/awesome-artificial-intelligence.md
@@ -210,6 +210,8 @@ Contributions are welcome. Connect on [LinkedIn](https://www.linkedin.com/in/owa
## Newsletters
* [Superhuman.ai](https://www.superhuman.ai/) A daily AI newsletter
+* [Alpasignal.ai](https://alphasignal.ai/) AI newsletter for developers
+* [Therundown.ai](https://www.therundown.ai/) Get the latest AI news, understand why it matters, and learn how to apply it in your work.
## Misc
diff --git a/docs/awesome/awesome-cpp.md b/docs/awesome/awesome-cpp.md
index 8bb29d7ed6..e2e6be141c 100644
--- a/docs/awesome/awesome-cpp.md
+++ b/docs/awesome/awesome-cpp.md
@@ -1096,8 +1096,8 @@ A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny
* [LibYAML](https://github.com/yaml/libyaml) - A C library for parsing and emitting YAML. [MIT] [website](https://pyyaml.org/wiki/LibYAML)
* [mini-yaml](https://github.com/jimmiebergmann/mini-yaml) - Single header YAML 1.0 C++11 serializer/deserializer. [MIT]
-* [yaml-cpp](https://github.com/jbeder/yaml-cpp) - A YAML parser and emitter in C++. [MIT]
* [rapidyaml](https://github.com/biojppm/rapidyaml) - Rapid YAML is a C++ library to parse and emit YAML. [MIT]
+* [yaml-cpp](https://github.com/jbeder/yaml-cpp) - A YAML parser and emitter in C++. [MIT]
## Miscellaneous
*Useful libraries or tools that don't fit in the categories above or maybe just not categorised yet*
@@ -1112,6 +1112,7 @@ A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny
* [Cheat Sheets of HackingCPP](https://hackingcpp.com/cpp/cheat_sheets.html) - Cool cheat sheets and infographics for algorithms, views, containers, randomness, etc.
* [Concord](https://github.com/Cogmasters/concord) - A Discord API wrapper library written in C. [MIT] [website](https://cogmasters.github.io/concord)
* [CPPItertools](https://github.com/ryanhaining/cppitertools) - Range-based for loop add-ons inspired by the Python builtins and itertools library. [BSD-2-Clause]
+* [CPP-JWT](https://github.com/arun11299/cpp-jwt) - JSON Web Token library for C++. [MIT]
* [cpp-lazy](https://github.com/MarcDirven/cpp-lazy) - a fast and easy lazy evaluation library for C++11/14/17/20. [MIT]
* [CRCpp](https://github.com/d-bahr/CRCpp) - Easy to use and fast C++ CRC library. [BSD-3-Clause]
* [cxx-prettyprint](https://github.com/louisdx/cxx-prettyprint) - A pretty printing library for C++ containers. [Boost]
diff --git a/docs/awesome/awesome-datascience.md b/docs/awesome/awesome-datascience.md
index 443bae0401..1551d93028 100644
--- a/docs/awesome/awesome-datascience.md
+++ b/docs/awesome/awesome-datascience.md
@@ -76,9 +76,7 @@ How do you learn data science? By doing data science, of course! Okay, okay - th
- [Data science your way](https://github.com/jadianes/data-science-your-way)
- [PySpark Cheatsheet](https://github.com/kevinschaich/pyspark-cheatsheet)
- [Machine Learning, Data Science and Deep Learning with Python ](https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python)
-- [How To Label Data](https://www.lighttag.io/how-to-label-data/)
- [Your Guide to Latent Dirichlet Allocation](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d)
-- [Over 1000 Data Science Online Courses at Classpert Online Search Engine](https://classpert.com/search/data-science)
- [Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard](https://github.com/handcraftsman/GeneticAlgorithmsWithPython)
- [Tutorials to get started on signal processing for machine learning](https://github.com/jinglescode/python-signal-processing)
- [Realtime deployment](https://www.microprediction.com/python-1) Tutorial on Python time-series model deployment.
@@ -99,12 +97,10 @@ How do you learn data science? By doing data science, of course! Okay, okay - th
- [Genetic Algorithms OCW Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-1-introduction-and-scope/)
- [AI Expert Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an Artificial Intelligence Expert
- [Convex Optimization](https://www.edx.org/course/convex-optimization) - Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
-- [Skillcombo - Data Science](https://skillcombo.com/courses/development/data-science/free/) - 1000+ free online Data Science courses
- [Learning from Data](https://home.work.caltech.edu/telecourse.html) - Introduction to machine learning covering basic theory, algorithms and applications
- [Kaggle](https://www.kaggle.com/learn) - Learn about Data Science, Machine Learning, Python etc
- [ML Observability Fundamentals](https://arize.com/ml-observability-fundamentals/) - Learn how to monitor and root-cause production ML issues.
- [Weights & Biases Effective MLOps: Model Development](https://www.wandb.courses/courses/effective-mlops-model-development) - Free Course and Certification for building an end-to-end machine using W&B
-- [Python for Machine Learning](https://globalaihub.com/courses/introduction-to-python-the-road-to-machine-learning/) - Start your journey to machine learning with Python, one of the most powerful programming languages.
- [Python for Data Science by Scaler](https://www.scaler.com/topics/course/python-for-data-science/) - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive curriculum will give you a solid foundation in statistics, programming, data visualization, and machine learning.
- [MLSys-NYU-2022](https://github.com/jacopotagliabue/MLSys-NYU-2022/tree/main) - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
- [Hands-on Train and Deploy ML](https://github.com/Paulescu/hands-on-train-and-deploy-ml) - A hands-on course to train and deploy a serverless API that predicts crypto prices.
@@ -144,7 +140,7 @@ How do you learn data science? By doing data science, of course! Okay, okay - th
- [Coursera Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing)
- [Coursera GAN Specialization](https://www.coursera.org/specializations/generative-adversarial-networks-gans)
- [Codecademy's Data Science](https://www.codecademy.com/learn/paths/data-science)
-- [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) - Linear Algebra course by Gilbert Strang
+- [Linear Algebra](https://ocw.mit.edu/courses/18-06sc-linear-algebra-fall-2011/) - Linear Algebra course by Gilbert Strang
- [A 2020 Vision of Linear Algebra (G. Strang)](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/)
- [Python for Data Science Foundation Course](https://intellipaat.com/academy/course/python-for-data-science-free-training/)
- [Data Science: Statistics & Machine Learning](https://www.coursera.org/specializations/data-science-statistics-machine-learning)
@@ -181,7 +177,7 @@ How do you learn data science? By doing data science, of course! Okay, okay - th
- [Msc in Data Science @ The University of Edinburgh](https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=902)
- [Master of Management Analytics @ Queen's University](https://smith.queensu.ca/grad_studies/mma/index.php)
- [Master of Data Science @ Illinois Institute of Technology](https://www.iit.edu/academics/programs/data-science-mas)
-- [Master of Applied Data Science @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science-online)
+- [Master of Applied Data Science @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science)
- [Master Data Science and Artificial Intelligence @ Eindhoven University of Technology](https://www.tue.nl/en/education/graduate-school/master-data-science-and-artificial-intelligence/)
- [Master's Degree in Data Science and Computer Engineering @ University of Granada](https://masteres.ugr.es/datcom/)
@@ -382,7 +378,6 @@ These are some Machine Learning and Data Mining algorithms and models help you t
**[`^ back to top ^`](#awesome-data-science)**
- [altair](https://altair-viz.github.io/)
-- [addepar](https://opensource.addepar.com/ember-charts/#/overview)
- [amcharts](https://www.amcharts.com/)
- [anychart](https://www.anychart.com/)
- [bokeh](https://bokeh.org/)
@@ -393,7 +388,6 @@ These are some Machine Learning and Data Mining algorithms and models help you t
- [d3plus](https://d3plus.org/)
- [Data-Driven Documents(D3js)](https://d3js.org/)
- [dygraphs](https://dygraphs.com/)
-- [ECharts](https://echarts.baidu.com/index-en.html)
- [exhibit](https://www.simile-widgets.org/exhibit/)
- [gephi](https://gephi.org/)
- [ggplot2](https://ggplot2.tidyverse.org/)
@@ -401,7 +395,6 @@ These are some Machine Learning and Data Mining algorithms and models help you t
- [Google Chart Gallery](https://developers.google.com/chart/interactive/docs/gallery)
- [highcarts](https://www.highcharts.com/)
- [import.io](https://www.import.io/)
-- [jqplot](https://www.jqplot.com/)
- [Matplotlib](https://matplotlib.org/)
- [nvd3](https://nvd3.org/)
- [Netron](https://github.com/lutzroeder/netron)
@@ -416,7 +409,7 @@ These are some Machine Learning and Data Mining algorithms and models help you t
- [vida](https://vida.io/)
- [vizzu](https://github.com/vizzuhq/vizzu-lib)
- [Wrangler](http://vis.stanford.edu/wrangler/)
-- [r2d3](https://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
+- [r2d3](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [NetworkX](https://networkx.org/)
- [Redash](https://redash.io/)
- [C3](https://c3js.org/)
@@ -456,17 +449,17 @@ These are some Machine Learning and Data Mining algorithms and models help you t
| [Wolfram Data Science Platform](https://www.wolfram.com/data-science-platform/) | Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generate rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language. |
| [Datadog](https://www.datadoghq.com/) | Solutions, code, and devops for high-scale data science. |
| [Variance](https://variancecharts.com/) | Build powerful data visualizations for the web without writing JavaScript |
-| [Kite Development Kit](https://kitesdk.org/docs/current/index.html) | The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem. |
+| [Kite Development Kit](http://kitesdk.org/docs/current/index.html) | The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem. |
| [Domino Data Labs](https://www.dominodatalab.com) | Run, scale, share, and deploy your models — without any infrastructure or setup. |
| [Apache Flink](https://flink.apache.org/) | A platform for efficient, distributed, general-purpose data processing. |
| [Apache Hama](https://hama.apache.org/) | Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. |
-| [Weka](https://www.cs.waikato.ac.nz/ml/weka/) | Weka is a collection of machine learning algorithms for data mining tasks. |
+| [Weka](https://ml.cms.waikato.ac.nz/weka/index.html) | Weka is a collection of machine learning algorithms for data mining tasks. |
| [Octave](https://www.gnu.org/software/octave/) | GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) |
| [Apache Spark](https://spark.apache.org/) | Lightning-fast cluster computing |
| [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) | a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services. |
| [Data Mechanics](https://www.datamechanics.co) | A data science and engineering platform making Apache Spark more developer-friendly and cost-effective. |
| [Caffe](https://caffe.berkeleyvision.org/) | Deep Learning Framework |
-| [Torch](https://torch.ch/) | A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT |
+| [Torch](http://torch.ch/) | A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT |
| [Nervana's python based Deep Learning Framework](https://github.com/NervanaSystems/neon) | Intel® Nervana™ reference deep learning framework committed to best performance on all hardware. |
| [Skale](https://github.com/skale-me/skale) | High performance distributed data processing in NodeJS |
| [Aerosolve](https://airbnb.io/aerosolve/) | A machine learning package built for humans. |
@@ -486,7 +479,6 @@ These are some Machine Learning and Data Mining algorithms and models help you t
| [Lambdo](https://github.com/asavinov/lambdo) | is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation. |
| [Feast](https://github.com/feast-dev/feast) | A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving. |
| [Polyaxon](https://github.com/polyaxon/polyaxon) | A platform for reproducible and scalable machine learning and deep learning. |
-| [LightTag](https://www.lighttag.io/) | Text Annotation Tool for teams |
| [UBIAI](https://ubiai.tools) | Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling |
| [Trains](https://github.com/allegroai/clearml) | Auto-Magical Experiment Manager, Version Control & DevOps for AI |
| [Hopsworks](https://github.com/logicalclocks/hopsworks) | Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale. |
@@ -559,7 +551,6 @@ This section includes some additional reading material, channels to watch, and t
- [Artificial Intelligence with Python - Tutorialspoint](https://www.tutorialspoint.com/artificial_intelligence_with_python/artificial_intelligence_with_python_tutorial.pdf)
- [Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html)
- [Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html)
-- [A Comprehensive Guide to Machine Learning](https://www.eecs189.org/static/resources/comprehensive-guide.pdf)
- [How to Lead in Data Science](https://www.manning.com/books/how-to-lead-in-data-science) - Early Access
- [Fighting Churn With Data](https://www.manning.com/books/fighting-churn-with-data)
- [Data Science at Scale with Python and Dask](https://www.manning.com/books/data-science-with-python-and-dask)
@@ -579,14 +570,13 @@ This section includes some additional reading material, channels to watch, and t
- [Applied Data Science: Lessons Learned for the Data-Driven Business](https://www.springer.com/gp/book/9783030118204)
- [The Data Science Handbook](https://www.amazon.com/Data-Science-Handbook-Field-Cady/dp/1119092949)
- [Essential Natural Language Processing](https://www.manning.com/books/getting-started-with-natural-language-processing) - Early access
-- [Mining Massive Datasets](https://www.mmds.org/) - free e-book comprehended by an online course
+- [Mining Massive Datasets](http://www.mmds.org/) - free e-book comprehended by an online course
- [Pandas in Action](https://www.manning.com/books/pandas-in-action) - Early access
- [Genetic Algorithms and Genetic Programming](https://www.taylorfrancis.com/books/9780429141973)
- [Advances in Evolutionary Algorithms](https://www.intechopen.com/books/advances_in_evolutionary_algorithms) - Free Download
- [Genetic Programming: New Approaches and Successful Applications](https://www.intechopen.com/books/genetic-programming-new-approaches-and-successful-applications) - Free Download
- [Evolutionary Algorithms](https://www.intechopen.com/books/evolutionary-algorithms) - Free Download
-- [Advances in Genetic Programming, Vol. 3](https://www.cs.bham.ac.uk/~wbl/aigp3/) - Free Download
-- [Global Optimization Algorithms: Theory and Application](https://www.it-weise.de/projects/book.pdf) - Free Download
+- [Advances in Genetic Programming, Vol. 3](http://www0.cs.ucl.ac.uk/staff/W.Langdon/aigp3/) - Free Download
- [Genetic Algorithms and Evolutionary Computation](https://www.talkorigins.org/faqs/genalg/genalg.html) - Free Download
- [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) - Convex Optimization book by Stephen Boyd - Free Download
- [Data Analysis with Python and PySpark](https://www.manning.com/books/data-analysis-with-python-and-pyspark) - Early Access
@@ -600,14 +590,13 @@ This section includes some additional reading material, channels to watch, and t
- [Regression, a Friendly guide](https://www.manning.com/books/regression-a-friendly-guide) - Early Access
- [Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing](https://www.oreilly.com/library/view/streaming-systems/9781491983867/)
- [Data Science at the Command Line: Facing the Future with Time-Tested Tools](https://www.oreilly.com/library/view/data-science-at/9781491947845/)
-- [Machine Learning - CIn UFPE](https://www.cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20Tom%20Mitchell.pdf)
- [Machine Learning with Python - Tutorialspoint](https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_tutorial.pdf)
- [Deep Learning](https://www.deeplearningbook.org/)
- [Designing Cloud Data Platforms](https://www.manning.com/books/designing-cloud-data-platforms) - Early Access
- [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)
- [The Elements of Statistical Learning: Data Mining, Inference, and Prediction](https://hastie.su.domains/ElemStatLearn/)
- [Deep Learning with PyTorch](https://www.simonandschuster.com/books/Deep-Learning-with-PyTorch/Eli-Stevens/9781617295263)
-- [Neural Networks and Deep Learning](https://neuralnetworksanddeeplearning.com)
+- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
- [Deep Learning Cookbook](https://www.oreilly.com/library/view/deep-learning-cookbook/9781491995839/)
- [Introduction to Machine Learning with Python](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/)
- [Artificial Intelligence: Foundations of Computational Agents, 2nd Edition](https://artint.info/index.html) - Free HTML version
@@ -628,7 +617,7 @@ This section includes some additional reading material, channels to watch, and t
- [Machine Learning For Absolute Beginners](https://www.amazon.in/Machine-Learning-Absolute-Beginners-Introduction-ebook/dp/B07335JNW1)
- [Unifying Business, Data, and Code: Designing Data Products with JSON Schema](https://learning.oreilly.com/library/view/unifying-business-data/9781098144999/)
-#### Book Deals (Affiliated) 🛍
+#### Book Deals (Affiliated)
- [eBook sale - Save up to 45% on eBooks!](https://www.manning.com/?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=ebook_sale_8_8_22)
@@ -653,12 +642,10 @@ This section includes some additional reading material, channels to watch, and t
- [Data Science Trello Board](https://trello.com/b/rbpEfMld/data-science)
- [Medium Data Science Topic](https://medium.com/tag/data-science) - Data Science related publications on medium
- [Towards Data Science Genetic Algorithm Topic](https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3#:~:text=A%20genetic%20algorithm%20is%20a,offspring%20of%20the%20next%20generation.) -Genetic Algorithm related Publications towards Data Science
-- [all AI news](https://allainews.com/) - The AI/ML/Big Data news aggregator platform
### Newsletters
**[`^ back to top ^`](#awesome-data-science)**
-- [AI Digest](https://aidigest.net/). A weekly newsletter to keep up to date with AI, machine learning, and data science. [Archive](https://aidigest.net/digests).
- [DataTalks.Club](https://datatalks.club). A weekly newsletter about data-related things. [Archive](https://us19.campaign-archive.com/home/?u=0d7822ab98152f5afc118c176&id=97178021aa).
- [The Analytics Engineering Roundup](https://roundup.getdbt.com/about). A newsletter about data science. [Archive](https://roundup.getdbt.com/archive).
@@ -667,22 +654,18 @@ This section includes some additional reading material, channels to watch, and t
- [Wes McKinney](https://wesmckinney.com/archives.html) - Wes McKinney Archives.
- [Matthew Russell](https://miningthesocialweb.com/) - Mining The Social Web.
-- [Greg Reda](https://www.gregreda.com/) - Greg Reda Personal Blog
+- [Greg Reda](http://www.gregreda.com/) - Greg Reda Personal Blog
- [Kevin Davenport](https://kldavenport.com/) - Kevin Davenport Personal Blog
- [Julia Evans](https://jvns.ca/) - Recurse Center alumna
- [Hakan Kardas](https://www.cse.unr.edu/~hkardes/) - Personal Web Page
- [Sean J. Taylor](https://seanjtaylor.com/) - Personal Web Page
-- [Drew Conway](https://drewconway.com/) - Personal Web Page
+- [Drew Conway](http://drewconway.com/) - Personal Web Page
- [Hilary Mason](https://hilarymason.com/) - Personal Web Page
-- [Noah Iliinsky](https://complexdiagrams.com/) - Personal Blog
+- [Noah Iliinsky](http://complexdiagrams.com/) - Personal Blog
- [Matt Harrison](https://hairysun.com/) - Personal Blog
- [Vamshi Ambati](https://allthingsds.wordpress.com/) - AllThings Data Sciene
- [Prash Chan](https://www.mdmgeek.com/) - Tech Blog on Master Data Management And Every Buzz Surrounding It
-- [Clare Corthell](https://datasciencemasters.org/) - The Open Source Data Science Masters
-- [Paul Miller](https://cloudofdata.com/) Based in the UK and working globally, Cloud of Data's consultancy services help clients understand the implications of taking data and more to the Cloud.
-- [Data Science London](https://datasciencelondon.org/) Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science.
- We are the largest data science community in Europe.
- We are more than 3,190 data scientists and data geeks in our community.
+- [Clare Corthell](http://datasciencemasters.org/) - The Open Source Data Science Masters
- [Datawrangling](http://www.datawrangling.org) by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE
- [Quora Data Science](https://www.quora.com/topic/Data-Science) - Data Science Questions and Answers from experts
- [Siah](https://openresearch.wordpress.com/) a PhD student at Berkeley
@@ -697,7 +680,7 @@ This section includes some additional reading material, channels to watch, and t
- [Spenczar](https://spenczar.com/) a data scientist at _Twitch_. I handle the whole data pipeline, from tracking to model-building to reporting.
- [KD Nuggets](https://www.kdnuggets.com/) Data Mining, Analytics, Big Data, Data, Science not a blog a portal
- [Meta Brown](https://www.metabrown.com/blog/) - Personal Blog
-- [Data Scientist](https://datascientists.net/) is building the data scientist culture.
+- [Data Scientist](https://datascientists.com/) is building the data scientist culture.
- [WhatSTheBigData](https://whatsthebigdata.com/) is some of, all of, or much more than the above and this blog explores its impact on information technology, the business world, government agencies, and our lives.
- [Tevfik Kosar](https://magnus-notitia.blogspot.com/) - Magnus Notitia
- [New Data Scientist](https://newdatascientist.blogspot.com/) How a Social Scientist Jumps into the World of Big Data
@@ -706,16 +689,13 @@ This section includes some additional reading material, channels to watch, and t
- [Kaggle Past Solutions](https://www.chioka.in/kaggle-competition-solutions/)
- [DataScientistJourney](https://datascientistjourney.wordpress.com/category/data-science/)
- [NYC Taxi Visualization Blog](https://chriswhong.github.io/nyctaxi/)
-- [Learning Lover](https://learninglover.com/blog/)
-- [Dataists](https://www.dataists.com/)
- [Data-Mania](https://www.data-mania.com/)
- [Data-Magnum](https://data-magnum.com/)
-- [P-value](https://www.p-value.info/) - Musings on data science, machine learning, and stats.
- [datascopeanalytics](https://datascopeanalytics.com/blog/)
- [Digital transformation](https://tarrysingh.com/)
- [datascientistjourney](https://datascientistjourney.wordpress.com/category/data-science/)
- [Data Mania Blog](https://www.data-mania.com/blog/) - [The File Drawer](https://chris-said.io/) - Chris Said's science blog
-- [Emilio Ferrara's web page](https://www.emilio.ferrara.name/)
+- [Emilio Ferrara's web page](http://www.emilio.ferrara.name/)
- [DataNews](https://datanews.tumblr.com/)
- [Reddit TextMining](https://www.reddit.com/r/textdatamining/)
- [Periscopic](https://periscopic.com/#!/news)
@@ -724,7 +704,6 @@ This section includes some additional reading material, channels to watch, and t
- [Data Science Lab](https://datasciencelab.wordpress.com/)
- [Meaning of](https://www.kennybastani.com/)
- [Adventures in Data Land](https://blog.smola.org)
-- [DATA MINERS BLOG](https://blog.data-miners.com/)
- [Dataclysm](https://theblog.okcupid.com/)
- [FlowingData](https://flowingdata.com/) - Visualization and Statistics
- [Calculated Risk](https://www.calculatedriskblog.com/)
@@ -745,7 +724,6 @@ This section includes some additional reading material, channels to watch, and t
- [floydhub](https://blog.floydhub.com/introduction-to-genetic-algorithms/) - Blog for Evolutionary Algorithms
- [Jingles](https://jinglescode.github.io/) - Review and extract key concepts from academic papers
- [nbshare](https://www.nbshare.io/notebooks/data-science/) - Data Science notebooks
-- [Deep and Shallow](https://deep-and-shallow.com/) - All things Deep and Shallow in Data Science
- [Loic Tetrel](https://ltetrel.github.io/) - Data science blog
- [Chip Huyen's Blog](https://huyenchip.com/blog/) - ML Engineering, MLOps, and the use of ML in startups
- [Maria Khalusova](https://www.mariakhalusova.com/) - Data science blog
@@ -774,9 +752,7 @@ This section includes some additional reading material, channels to watch, and t
- [AI at Home](https://podcasts.apple.com/us/podcast/data-science-at-home/id1069871378)
- [AI Today](https://www.cognilytica.com/aitoday/)
- [Adversarial Learning](https://adversariallearning.com/)
-- [Becoming a Data Scientist](https://www.becomingadatascientist.com/category/podcast/)
- [Chai time Data Science](https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x)
-- [Data Crunch](https://datacrunchcorp.com/data-crunch-podcast/)
- [Data Engineering Podcast](https://www.dataengineeringpodcast.com/)
- [Data Science at Home](https://datascienceathome.com/)
- [Data Science Mixer](https://community.alteryx.com/t5/Data-Science-Mixer/bg-p/mixer)
@@ -791,13 +767,11 @@ This section includes some additional reading material, channels to watch, and t
- [Linear Digressions](https://lineardigressions.com/)
- [Not So Standard Deviations](https://nssdeviations.com/)
- [O'Reilly Data Show Podcast](https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/)
-- [Partially Derivative](https://partiallyderivative.com/)
+- [Partially Derivative](http://partiallyderivative.com/)
- [Superdatascience](https://www.superdatascience.com/podcast/)
- [The Data Engineering Show](https://www.dataengineeringshow.com/)
- [The Radical AI Podcast](https://www.radicalai.org/)
-- [The Robot Brains Podcast](https://www.therobotbrains.ai/)
- [What's The Point](https://fivethirtyeight.com/tag/whats-the-point/)
-- [How AI Built This](https://how-ai-built-this.captivate.fm/)
- [The Analytics Engineering Podcast](https://roundup.getdbt.com/s/the-analytics-engineering-podcast)
### YouTube Videos & Channels
@@ -951,7 +925,6 @@ Below are some Social Media links. Connect with other data scientists!
### Slack Communities
- [DataTalks.Club](https://datatalks.club)
-- [Women Who Code - Data Science](https://www.womenwhocode.com/datascience)
### GitHub Groups
- [Berkeley Institute for Data Science](https://github.com/BIDS)
@@ -983,10 +956,10 @@ Some data mining competition platforms
| [
](https://i.imgur.com/xLY3XZn.jpg) | By [Data Science Central](https://www.datasciencecentral.com/) |
| [
](https://i.imgur.com/0TydZ4M.png) | Data Science Wars: R vs Python |
| [
](https://i.imgur.com/HnRwlce.png) | How to select statistical or machine learning techniques |
-| [
](https://scikit-learn.org/stable/_static/ml_map.png) | Choosing the Right Estimator |
+| [
](https://scikit-learn.org/1.5/_downloads/b82bf6cd7438a351f19fac60fbc0d927/ml_map.svg) | [Choosing the Right Estimator](https://scikit-learn.org/1.5/machine_learning_map.html#choosing-the-right-estimator) |
| [
](https://i.imgur.com/uEqMwZa.png) | The Data Science Industry: Who Does What |
| [
](https://i.imgur.com/RsHqY84.png) | Data Science ~~Venn~~ Euler Diagram |
-| [
](https://www.springboard.com/blog/wp-content/uploads/2016/03/20160324_springboard_vennDiagram.png) | Different Data Science Skills and Roles from [this article](https://www.springboard.com/blog/data-science-career-paths-different-roles-industry/) by Springboard |
+| [](https://www.springboard.com/blog/wp-content/uploads/2016/03/20160324_springboard_vennDiagram.png) | Different Data Science Skills and Roles from [Springboard](https://www.springboard.com) |
| [
](https://data-literacy.geckoboard.com/poster/) | A simple and friendly way of teaching your non-data scientist/non-statistician colleagues [how to avoid mistakes with data](https://data-literacy.geckoboard.com/poster/). From Geckoboard's [Data Literacy Lessons](https://data-literacy.geckoboard.com/). |
### Datasets
@@ -1028,14 +1001,13 @@ Some data mining competition platforms
- [Open Data Sources](https://github.com/datasciencemasters/data)
- [UNICEF Data](https://data.unicef.org/)
- [undata](https://data.un.org/)
-- [NASA SocioEconomic Data and Applications Center - SEDAC](https://sedac.ciesin.columbia.edu/)
+- [NASA SocioEconomic Data and Applications Center - SEDAC](https://earthdata.nasa.gov/centers/sedac-daac)
- [The GDELT Project](https://www.gdeltproject.org/)
- [Sweden, Statistics](https://www.scb.se/en/)
- [StackExchange Data Explorer](https://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network.
-- [SocialGrep](https://socialgrep.com/datasets) - a collection of open Reddit datasets.
- [San Fransisco Government Open Data](https://datasf.org/opendata/)
- [IBM Asset Dataset](https://developer.ibm.com/exchanges/data/)
-- [Open data Index](https://index.okfn.org/)
+- [Open data Index](http://index.okfn.org/)
- [Public Git Archive](https://github.com/src-d/datasets/tree/master/PublicGitArchive)
- [GHTorrent](https://ghtorrent.org/)
- [Microsoft Research Open Data](https://msropendata.com/)
diff --git a/docs/awesome/awesome-elixir.md b/docs/awesome/awesome-elixir.md
index 0739ecf601..472cd98d58 100644
--- a/docs/awesome/awesome-elixir.md
+++ b/docs/awesome/awesome-elixir.md
@@ -422,6 +422,8 @@ There are [other sites with curated lists of elixir packages](#other-awesome-lis
*Libraries for creating visualizations with data.*
* [plox](https://github.com/gridpoint-com/plox) - Server-side rendered SVG graphing components for Phoenix and LiveView.
+* [tucan](https://github.com/pnezis/tucan) - An Elixir plotting library on top of VegaLite.
+* [vega_lite](https://github.com/livebook-dev/vega_lite) - Elixir bindings for Vega-Lite.
## Date and Time
*Libraries for working with dates and times.*
diff --git a/docs/awesome/awesome-go.md b/docs/awesome/awesome-go.md
index 75bcd5fa0a..64efd50992 100644
--- a/docs/awesome/awesome-go.md
+++ b/docs/awesome/awesome-go.md
@@ -798,6 +798,7 @@ _Packages that help with building Distributed Systems._
- [bedrock](https://github.com/z5labs/bedrock) - Provides a minimal, modular and composable foundation for quickly developing services and more use case specific frameworks in Go.
- [capillaries](https://github.com/capillariesio/capillaries) - distributed batch data processing framework.
- [celeriac](https://github.com/svcavallar/celeriac.v1) - Library for adding support for interacting and monitoring Celery workers, tasks and events in Go.
+- [committer](https://github.com/vadiminshakov/committer) - A distributed transactions management system (2PC/3PC implementation).
- [consistent](https://github.com/buraksezer/consistent) - Consistent hashing with bounded loads.
- [consistenthash](https://github.com/mbrostami/consistenthash) - Consistent hashing with configurable replicas.
- [dht](https://github.com/anacrolix/dht) - BitTorrent Kademlia DHT implementation.
diff --git a/docs/awesome/awesome-mac.md b/docs/awesome/awesome-mac.md
index 3d792116cf..00d5492fe5 100644
--- a/docs/awesome/awesome-mac.md
+++ b/docs/awesome/awesome-mac.md
@@ -203,6 +203,7 @@ Any comments, suggestions? [Let us know!](https://github.com/jaywcjlove/awesome-
* [texifier](https://www.texifier.com/) - Great LaTeX editor for Mac with auto-update PDF and autocomplete LaTeX commands.
* [UPDF](https://updf.com/) - Free PDF editor for reading, annotating, and editing PDFs. ![Freeware][Freeware Icon] [![App Store][app-store Icon]](https://apps.apple.com/app/id1619925971)
* [Zotero](https://www.zotero.org/) - Free tool to collect, organize, annotate, cite, and share research. [![OSS][OSS Icon]](https://github.com/zotero/zotero/) ![Freeware][Freeware Icon]
+* [Collate](https://collate.one/get-started) - Free tool to collect, summarize and query PDFs with AI. All offline and free [![App Store][app-store Icon]](https://apps.apple.com/us/app/collateai/id6447429913) ![Freeware][Freeware Icon]
## Developer Tools
@@ -409,6 +410,8 @@ highlighting. [![Open-Source Software][OSS Icon]](https://github.com/dbcli/pgcli
* [React Native macOS](https://github.com/ptmt/react-native-desktop) - Build OS X desktop apps using React Native and Cocoa. [![Open-Source Software][OSS Icon] ![Freeware][Freeware Icon]](https://github.com/ptmt/react-native-desktop)
* [react-desktop](http://reactdesktop.js.org) - React UI Components for macOS Sierra. [![Open-Source Software][OSS Icon]](https://github.com/gabrielbull/react-desktop) ![Freeware][Freeware Icon]
* [ReactXP](https://microsoft.github.io/reactxp/) - Microsoft platform for Web, iOS, Android, and Windows UWP. [![OSS][OSS Icon]](https://github.com/microsoft/reactxp) ![Freeware][Freeware Icon]
+* [Tauri](https://tauri.app/) - Create small, fast, secure, cross-platform applications [![Open-Source Software][OSS Icon] ![Freeware][Freeware Icon]](https://github.com/tauri-apps/tauri)
+
### Version Control
diff --git a/docs/awesome/awesome-pocketbase.md b/docs/awesome/awesome-pocketbase.md
index c807eb010f..6bae527922 100644
--- a/docs/awesome/awesome-pocketbase.md
+++ b/docs/awesome/awesome-pocketbase.md
@@ -46,6 +46,7 @@ PocketBase is an open source backend consisting of embedded database (SQLite) wi
- [Ngrok](https://github.com/iamelevich/pocketbase-plugin-ngrok) - Expose local PocketBase to the internet with ngrok. 
- [Proxy](https://github.com/iamelevich/pocketbase-plugin-proxy) - Proxy requests to another other host. Can be useful when you want to use separate server as frontend (like Next.js), but serve everything with same port. 
- [Webhooks](https://gist.github.com/cugu/9e74f75dcad3df74370c71ff3c02085a) - Add webhook support in the admin UI to send `create`, `update` and `delete` events on selected collections via POST request to other systems.
+- [Typescript Generator](https://github.com/Vogeslu/pocketbase-ts-generator) - Typescript generator standalone / library with auto generation hook or command 
## React
diff --git a/docs/awesome/awesome-rust.md b/docs/awesome/awesome-rust.md
index 1de4e5a9cb..cf0b13b9d8 100644
--- a/docs/awesome/awesome-rust.md
+++ b/docs/awesome/awesome-rust.md
@@ -291,6 +291,7 @@ See also [Games Made With Piston](https://github.com/PistonDevelopers/piston/wik
See also [A comparison of operating systems written in Rust](https://github.com/flosse/rust-os-comparison).
* [0x59616e/SteinsOS](https://github.com/0x59616e/SteinsOS) - An OS for armv8-a architecture.
+* [adamperkowski/highlightos](https://github.com/adamperkowski/highlightos) - x86_64 OS kernel written in Rust & Assembly.
* [Andy-Python-Programmer/aero](https://github.com/Andy-Python-Programmer/aero) - A modern, unix-like operating system following the monolithic kernel design.
* [DragonOS-Community/DragonOS](https://github.com/DragonOS-Community/DragonOS) - An operating system with a self-developed kernel from scratch and Linux compatibility.
* [redox-os/redox](https://gitlab.redox-os.org/redox-os/redox) - A Unix-like general-purpose microkernel-based operating system with a focus on security, stability, performance, correctness, simplicity and pragmatism that aims to be a complete alternative for Linux and BSD.
diff --git a/docs/awesome/awesome-scriptable.md b/docs/awesome/awesome-scriptable.md
index 23d4cccc41..810567cbcf 100644
--- a/docs/awesome/awesome-scriptable.md
+++ b/docs/awesome/awesome-scriptable.md
@@ -13,6 +13,10 @@
+## Art
+
+- [Random-Canvas](https://github.com/RiccStella/Random-Canvas) - Generates random images.
+
## Cars
- [BMW Linker](https://github.com/opp100/bmw-scriptable-widgets) - Information about your BMW, like location or mileage.