|What||Creating quality resources for a hackathon|
|How||Orienteer through a data jungle and package the good stuff|
|When||March 18 - 24, 2020|
|Where||Open Internet + @opendatazh|
|Requirements||Ability to comfortably navigate the Web, some experience with spreadsheets and web apps.|
|I have a question Get started|
Let’s set up a monitoring of the societal response to the COVID-19 epidemic and the actions of the authorities! 🤞 If you would like to contribute to making an impact with open data and analysis, we would be happy to hear from you. We will support you with sharing data wrangling skills, advice and practical opportunities for action. This is a continuously developing hackathon initiated by Statistik Kt. Zürich and the Swiss open data community. We are counting on your support.
The School of Data is a working group of the non-profit association Opendata.ch - Swiss chapter of Open Knowledge. If you are not yet a member of Opendata.ch, please consider joining the association as an individual or institution, however you would like to more actively participate in the Working Group and other activities. Donations are also welcome.
(ENGLISH) In a Data Expedition, you can learn to work with open data and collect vast amounts of information from legal, authentic sources. Think about some questions you would like to ask, issues you care about. We will run searches on data portals and search engines together, introduce you to petitioning for data and crowdsourcing on- and offline. The association Opendata.ch, Swiss chapter of Open Knowledge, runs hackathons and expeditions year round. We are always looking for sources of data which are relatively up to date, with potential for new applications, and which are as free as possible of restrictions and accessibility issues - in the ideal case, open according to the Open Definition. From the best data we discover together, we aim to create high quality Data Packages and free, online tools accessible to the general public.
(GERMAN) In einer Datenexpedition kannst Du lernen, mit frei verfügbaren “offenen Daten” zu arbeiten und große Mengen an Informationen aus legalen, authentischen Quellen zu sammeln. Überlege dir Fragen, die du an Themen, welche dir wichtig sind, stellen möchtest. Wir führen dann gemeinsam Recherchen auf Datenportalen und Suchmaschinen durch, führen dich in die Petitionierung und das Crowdsourcing (on- und offline) von Daten ein. Der Verein Opendata.ch veranstaltet Hackathons und Expeditionen in der ganzen Schweiz, wir suchen nach relativ aktuellen Datenquellen mit Potenzial für neue Anwendungen, die so frei wie möglich von Einschränkungen und Zugänglichkeitsfragen sind - im Idealfall nach der Open Definition. Aus den besten Daten, die wir gemeinsam entdecken, wollen wir hochwertige Datenpakete und/oder Online-Tools/Visualisierungen erstellen, die für die breite Öffentlichkeit zugänglich sind.
With the following steps, you can help us to prepare data resources, or even if you are not able to join the expedition or hackathon - your contributions will be very appreciated! 💓
We will be sharing updates on our progress in this forum thread.
1. Discover data
Using web sites like opendata.swiss, datahub.io and datacentral, you can find datasets published in accessible and carefully published form. Look around to understand what is meant by Open Data. Think about some questions you would like to ask, issues you care about, or datasets which could help us to be better citizens of the digital society. Run searches on open data portals or search engines. If you find something interesting, or have an idea of something that is missing, hold on to it for the next step. You may skip this step if you just want to help prepare data packages, but keep in mind that it is your personal interest that makes activism worthwhile.
2. Make a suggestion
We are looking to support a moderated, public process of raising attention to possibilities in the open data spaces. In particular, we are looking for sources of data which are relatively up to date, with potential for new applications, and which are as free as possible of restrictions and accessibility issues - in the ideal case, open according to the Open Definition. From the best data we discover together, we aim to create high quality Data Packages.
Our master list of incoming datasets is a spreadsheet where we are managing requests coming in throughout the year. After checking each of the recommendations, we include them in a public list published here. Data suggestions are shared on the issue tracker, in our forum, via social media, or by contacting our office or working group directly.
3. Let’s package the data
Each of the #covid19mon challenges will be using tools to keep track of progress. You can download and test datasets, clean them up as needed, re-publish and make them readily available to hackathon participants. If there is a dataset you wish to see supported, you can start making a Data Package yourself. Ask for help at any step if you need it!
- Check the licenses, make sure you know that this is open data and can be freely reused. Be aware of any restrictions so that you can re-apply them and make the users aware. If no licenses are stated, or the legality of the data is unclear, make an attempt to contact the owner and clarify the situation.
- Fork the boilerplate and/or use the Frictionless Data create tool to make a Data Package. It would ideally have all the metadata which you would typically find on the open data portal. Again, try to do some online research and contact the data owners to find the details. If the data is tabular or fiscal, please make an attempt to document its schema, at least using data tools.
- Validating the data is a really important step, please do not skip this. Document any issues we encounter in an issue tracker or README. Use Goodtables.io or similar to enable automatic verification of the data and attach validation badges. If you are using a scraper, make it easy for data users to check the status of the last crawl. Most importantly, suggest improvements whether they are about the legal, linguistic, technical or qualitative aspects of the data.
- Finally: if you publish it online, please send us the link for review, ideally by starting a pull request if you’re editing an existing repository, creating an issue here, or by mentioning @schoolofdata-ch in a “please review” issue on your own repo - or just contacting us otherwise.
Good Tables == Good Times!
Tips and tools
There are lots of resources in our Forum, and you may also find our data wrangling Toolbox and information on Data Packages useful. If you any other things to share or ask, so let’s chat. Join our next Data Expedition or Hackathon, and let’s make something amazing!
• School of Data in Switzerland