# Data Dojo Würzburg 16

## DataDojo@Lunch - live

## September 2022
- **When:** Thursday, September 8^th, 2022 at 11:30am until 1:00pm (90 minutes)
- **Where:** [CCTB](https://www.google.de/maps/place/Center+for+Computational+and+Theoretical+Biology+(CCTB),+University+of+W%C3%BCrzburg/@49.7850742,9.9723819,19z/data=!3m1!4b1!4m5!3m4!1s0x47a28fc802e5e8d9:0x6b62d2cbd2e6f094!8m2!3d49.7851122!4d9.9730135)
- **Info:** [DataDojo Website](https://ddojo.github.io/), [Repo](https://github.com/ddojo/ddojo.github.io)

## Participants
> Please add your name to the list (click the pen icon at the top left to edit) if you plan to come. And please remove it if you can not make it. Feel free to add your preferred tool or programming language.
- Markus (python/scikit-learn)
- **» Add your name here «**

## Dataset

### Machine Learning Series

We are doing a series of Data Dojos on machine learning. The task is to classify tree species by their traits (e.g. height, stem diameter, geographic location). :deciduous_tree::evergreen_tree::palm_tree: We use a subset of the recently published database:
[Tallo](https://zenodo.org/record/6637599)

The full dataset contains measurements for almost 500k individual trees from more than 5k species.

In the first dojo of the series, we filtered the full set to 3 species with reasonable overlap (*Fagus sylvatica*, *Pinus pinaster*, *Quercus ilex*). Now we want to try different Machine Learning methods to classify tree species from traits.

In the second dojo we created our first models.
A very simple "Majority Vote" model and some K-Nearest-Neighbor (KNN) models with scikit-learn.

#### Session 2 - Data Scaling

Question Pool:
- Generic
- What is supervised machine learning?
- How to evaluate the performance of our model(s)?
- What kinds of (classical) models exist?
- Specific
- Why is scaling the features important for KNN?
- What kinds of scaling are available?
- How does scaling the features impact the model performance?
- **Add your own questions**
- Further Ideas
- TBD
- **Add your own ideas**

## Collaborative Tools and Workflow

For Notebooks (R, python, julia, js, ...) with real time collaboration [CoCalc](https://cocalc.com) seems to be the best option right now. It worked great the last couple of times so we'll stick to it for now. You need to register an account there (it is free).

## Future Suggestions
> Add your suggestions to the list and :+1: to the end of a line you are interested in

### Data Sets
- [Tree Sizes](https://onlinelibrary.wiley.com/doi/10.1111/gcb.16302) :deciduous_tree::evergreen_tree::palm_tree:
- [Wordbank](https://wordbank.stanford.edu/) - data of children learning to talk
- [All Birds](https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13898) :bird:
- Results of the [Bundestagswahl 2021](https://www.bundeswahlleiter.de/bundestagswahlen/2021/ergebnisse/opendata.html)
- Weather data throughout Germany over time (incl. temperature, precipitation, ...): https://www.dwd.de/DE/leistungen/cdc_portal/cdc_portal.html
- German [Mikrozensus](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Haushalte-Familien/Methoden/mikrozensus.html)
- Kaggle [Titanic](https://www.kaggle.com/c/titanic) or [Tabular Playground](https://www.kaggle.com/competitions?hostSegmentIdFilter=8) or [Meta Kaggle](https://www.kaggle.com/kaggle/meta-kaggle)
- World Trade Data ([Open Trade Statistics](https://tradestatistics.io))
- [Open Citation Data](http://opencitations.net/download#coci)
- [Top 100 charts + Audio Features](https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-09-14/readme.md)
- [Emoji Usage :hugging_face::heart::laughing:](https://observablehq.com/@jenniferdaniel/unicode-emoji-mirror)
- [Observable Curated Datasets](https://observablehq.com/@observablehq/curated-datasets)

### Tools/Languages
- R/tidyverse
- python
- [Power BI](https://www.microsoft.com/en-US/download/details.aspx?id=58494)
- [Tableau](https://www.tableau.com)
- [KNIME](https://www.knime.com/)
- javascript
- julia
- [visidata](https://www.visidata.org/)

### Skills
- interactive maps
- dashboards
- animations

### Data Sources
> all data types are welcome, including tables, images, videos, sounds, DNA, ...

- [TidyTuesday](https://github.com/rfordatascience/tidytuesday)
- [Our World in Data](https://ourworldindata.org/) (R package: [owidR](https://github.com/piersyork/owidR)), [Sustainable Development Goals](https://sdg-tracker.org/)
- Open Data Initiatives ([Würzburg](https://opendata.wuerzburg.de/), [Germany](https://www.govdata.de/), [Statistisches Bundesamt](https://www.destatis.de/), [Europe](https://data.europa.eu/en), [APIs](https://bund.dev/))
- [Data is plural](https://docs.google.com/spreadsheets/d/1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk/htmlview#)
- [Awesome Public Datasets](https://github.com/awesomedata/awesome-public-datasets)
- [Kaggle Datasets](https://www.kaggle.com/datasets) or [Competitions](https://kaggle.com/competitions), e.g. [SLICED](https://www.kaggle.com/search?q=Sliced+in%3Acompetitions)
- [tsibbledata](https://tsibbledata.tidyverts.org/reference/index.html): Time Series Datasets
- [R-text-data](https://github.com/EmilHvitfeldt/R-text-data): Text Datasets, ready to use in R
- [data.world](https://data.world/)
- [Statista](https://de.statista.com/) - the University of Würzburg has a campus license
- [Open Legal Data](https://de.openlegaldata.io/)
- [Bundestag Data](https://github.com/bundestag) (e.g. poll results, deputies, wahl-o-mat, [inspirational blog post](https://jollydata.blog/posts/2021-03-14-bundestag-part-iii/))
- [Deutsche Digitale Bibliothek](https://www.deutsche-digitale-bibliothek.de/newspaper) ([API](https://labs.deutsche-digitale-bibliothek.de/app/ddbapi/), old newspapers from Germany)
- [Earth Observation: Satellite Image Time Series](https://e-sensing.github.io/sitsbook)
- [Machine Learning Datasets](https://paperswithcode.com/datasets)
- Internation (Student) Assessment Data ([TIMSS, PIRLS, PISA, ...](https://pirls.bc.edu/databases-landing.html))
- [(Medical) Imaging Datasets](https://radiopaedia.org/articles/imaging-data-sets-artificial-intelligence), [MedMNIST](https://medmnist.com/)
- [Inspirational Notebooks on Observable](https://observablehq.com/@tomlarkworthy/notebooks2021)
- [Ski resort statistics](https://ski-resort-stats.com/) :skier:

## Cross Links
- [previous pad](https://hackmd.io/RzSQOMptQcWwQunEzCmyCA)
- [next pad](https://hackmd.io/4QAkxPHvTQK03aaq3adtsw)