Open Cultural Data Hackathon 2020

Challenge: COVID19 is restricting and changing the way we can access buildings and we can experience space. For safety reasons, access to museums and cultural sites is strictly regulated and complex.

Vision: The outdoors represent a meaningful extension of museums and cultural sites. Connecting a building with the outdoors allows a different experience of the museum, its physical space and its collection.

Objective: Visualizing the ecosystem of connections of museums and cultural sites, by linking the building of the museum and cultural site to the outdoors. Combining data related to the collections to the original location of the objects, existing itineraries, potential venues, morphology of the territory, municipalities, mobility, infrastructure. Generating opportunities to create new itineraries (i.e. ethnographic, artistic, historical itineraries…) and new temporary exhibitions, dislocation of objects… We will focus on Ticino and on a series of examples.

Data available

Data we are currently uploading on openswiss.data

List of cultural operators in Ticino released by Osservatorio culturale del Cantone Ticino (DECS, Repubblica e Cantone Ticino) https://it.wikipedia.org/wiki/Wikipedia:Raduni/Prova_il_tasto_modifica/Elenco

Data on Val Piora released by the Cantonal Museum of Natural History of Lugano (rocks, flora, fauna, landscape, water)

Data on the Verzasca Valley released by the Museum of Val Verzasca (ethnographic itineraries, past exhibitions, museum collection)

Data on Leventina released Museum of Leventina (museum collection, past exhibitions)

Data on Neuralrope#1, permanent interactive installation at the pedestrian tunnel of Besso, Lugano, released by the municipality of Lugano (interaction of the public and daily passages).

We have already uploaded in 2018

On OpenStreetMap the repository made by the Osservatorio culturale del Cantone Ticino enriched with locations suggested by citizens

On Wikidata a selection of cultural institutions part of the repository made by Osservatorio culturale del Cantone Ticino

In our project want to focus on the beauty of nature, that‘s why we decided to use the dataset «The Call of the Mountains».
Our aim is to generate an interactive map, which allows the user to quickly get an overview of the mountains around «Graubünden».

Frontpage (Map of Mountains)
-The map consists of several districts. After selecting one area, the user is able to see different suggestions - inside the selected district.

Subpage1 (Variety of Cards)
-After clicking the "See More"-button the user is directed to the first subpage.
-There will be shown a variety of different mountains - located in the chosen district and displayed as cards.

Subpage2 (Personal Choice)
-Once the user made his choice, he can get more details by using the "See Profile"-button.
-This leads to a second subsite, where the image of the selected mountain-card appears in full size
-Also more information about the background and location are visible.

Contact: Mirjam Rodehacke & Nicole Nett (students at FHGR)
Data Source: Fundaziun Capauliana

As we work for two days on this challenge, we've decided to determine, thanks to an open data set, the five most popular family names for the female and male population in 26 cities, in 26 cantons, in Switzerland.

We want to visualize this data on an interactive map which shows what family names are predominantly represented throughout the country.

We want to visualize this data on an interactive map which shows what family names are predominantly represented throughout the country.
An example is to find here: https://codepen.io/aomyers/pen/LWOwpR

We have not looked at the data set closely enough . it is always only the top 5 surnames per postal code. Sorted by woman and man. So we couldn't just say: These are the top 5 surnames per canton, because we would falsify the evaluation.
So we decided to simply choose one postcode per canton and selected the data in Google docs and then converted it into a Jason.
The goal is to create a One pager where you can see one city per canton, which is divided into top 5 men's last names and top 5 women's last names.

The dataset we used: https://opendata.swiss/de/dataset/nachnamen-pro-plz

First steps:
1. Some big cities in Switzerland are not mentioned. That means that the swiss post didn’t collect names there.
1. Some rangs are doubled because of the same number of names on the ranglist. Example: in Kloten are 35 surenames Mueller and 35 surenames Jäger -> so they show both as rang 5.
For our project we only can use one name. Jäger and not Mueller for example.

1. We made our own list. We had to find out wich is the next big city in the canton.
   Check if there are double rangs and delete it if needed.

2. After that, we converted the excel list to a json object. That json-list is now input of our javascript-file.

Next steps and problems:
4. We recognized, that we need should use a databank. This is unfortunately way out of our programm skillz and would take to much time.
5. RETHINK: How can we still manage that the lists appears?
6. SOLUTION, (thank you brain), -> its possible to show the list female and male on each side, with flexboxes. For that we need to make excel lists for each canton.
We’ve been working with atom and the integrated teleport package in order to be able to work together.. However, the team member who’s the owner of the files must always be online and always active, or else the invitees cannot access the files.

To be more precise: First of all, typed in a connection from the list in HTML to the pictures. They're changinge everytime they got hovered/unhovered. In a next step, we had to implement the .json, what wasn't easy because we didn't use a database so far in the reason of the time range. So we had to create a function, where the locations have to pass through our .json file. forEach() we used also to gather the gender and similar data out of JSON. Right after, we had to create a function for the table title. We had to create a function to read and parse the JSON:

function loadJSON(callback) {
var xobj = new XMLHttpRequest();
xobj.overrideMimeType("application/json");
xobj.open('GET', 'convertcsv.json', true);
xobj.onreadystatechange = function() {
console.log(xobj.readyState);
if (xobj.readyState == 4 && xobj.status == "200") {
// Required use of an anonymous callback as .open will NOT return a value but simply returns undefined in asynchronous mode
callback(xobj.responseText);
}
};
xobj.send(null);
}

This was the most important step to set it up without a database.

LEARNINGS:
We got the hint, that it's also possible to work on Github together and we'll definitely going to do that by the next project.
The programming was kept very easy, as we are only Newbies.
We made a map with pictures, every canton was one. For the hover we made an overlayer to color the canton red.
We connected it with a class to the right name button.

The Question: How to sift through the millions of objects in museums to identify top priorities for intensive research by humans?

The Goal: Automatically Classify and Rank 70,000 art provenance texts by probability that further research will turn up a deliberately concealed history of looting, forced sale, theft or forgery.

The Challenge: Analyse texts quickly for Red Flags, quantify, detect patterns, classify, rank, and learn. Whatever it takes to produce a reliable list of top suspects

For this challenge several datasets will be provided.



1) DATASET:70,000 art provenance texts for analysis

2) DATASET: 1000 Red Flag Names

3) DATESET: 10 Key Words or Phrases




TRIAGE: You're the doctor and the texts are your patients! Who's in good health and who's sick? How sick? With what disease? What kind of tests and measurements can we perform on the texts to help us to reach a diagnosis? What kind of markers for should we look for? How to look for them?

The PTT archive is increasingly digitising its collections with the aim of not only making documents more easily available, but also of making them accessible, linked and analysed in new ways. Many of these Documents can be assigned to one or more geographical points. Three such records are presented below, with the idea of trying out new approaches and concepts:

Poststellenchroniken: This OpenRefine dataset contains an extract of the archive database with metadata on the post office chronicles stored in the PTT archive. The post office chronicles contain collected information, partly also newspaper clippings, photos and plans of all historical post offices of the PTT. The archive database entries for each dossier were prepared in OpenRefine, assigned to a post office (there are sometimes several dossiers per post office) and then linked to the corresponding current administrative communities on Wikidata via OpenRefine's Reconciliation Service. This linkage is currently not yet fully completed. The aim is firstly to record entries for the post offices on Wikidata itself, and secondly to enable a rough geo-referencing of the post offices via the link to the municipalities, which would otherwise only be possible via a time-consuming, manual re-listing.

Postkurskarten: The dataset contains a selection of retro-digitized "Postkurskarten" of the Swiss PTT between 1851 and 1941, providing information on postal connections and the respective departure and arrival times. The older maps depict the entire network of postal connections (stage coaches and later also railway and post bus services), while the more recent maps are somewhat more schematic. The selection presented here gives an impression of the maps archived in the PTT archive. However, there is a large number of other maps (also on telecommunications) with a different geographical focus and covering the entire period of existence of the PTT. The file names correspond to the archive signature of the course charts.
The "Postkurskarten" are to be digitally annotated. Where possible, this should be done automatically (e.g. via text recognition) or via easy to use public collaborative tools. Maps prepared in this way could then be used for assigning/linking other archive documents on them geographically.

Postauto Postkarten: The data set contains a selection of retro-digitized postbus postcards from the PTT archive. These postcards each show Postbuses on known routes, especially pass lines. They were produced by the PTT for advertising purposes. Also here, possible methods for georeferencing shall be experimented with. These references can then be linked and localized with the annotated maps.