ASWI - Pokročilé softwarové inženýrství » ASWI 2021 » OpenData vlastní téma (KIV) - Tři Mušketýři

h1. Initially Proposed Applications

Here is a list of all applications proposed during the 0th iteration.

h2. University Campus navigation

h3. Proposed datasets:

* http://opendata.zcu.cz/Obsazeni-mistnosti.html

* https://opendata.plzen.eu/dataset/gis-doprava-mestska-hromadna-doprava-zastavky-mhd

h3. Abstract:

Create an application to navigate guests and freshmen around university classrooms and buildings.

h3. Risks:

There is not any map of the interiors.

h2. Various data visualizations / predictions

h3. Proposed datasets:

* https://data.gov.cz/datov%C3%A9-sady?dotaz=d%C5%AFchod

* https://data.gov.cz/datov%C3%A1-sada?iri=https%3A%2F%2Fdata.gov.cz%2Fzdroj%2Fdatov%C3%A9-sady%2Fhttp---vdb.czso.cz-pll-eweb-package_show-id-190037

* https://data.gov.cz/datov%C3%A9-sady?kl%C3%AD%C4%8Dov%C3%A1-slova=covid-19&kl%C3%AD%C4%8Dov%C3%A1-slova=koronavirus

* https://data.europa.eu/euodp/en/data/dataset/non-pharmaceutical-country-response-measures-to-covid-19

* https://data.europa.eu/euodp/en/data/dataset?tags=drugs&vocab_theme=http%3A%2F%2Fpublications.europa.eu%2Fresource%2Fauthority%2Fdata-theme%2FHEAL&sort=views_total+desc

h3. Abstract:

Create some useful visualizations from aggregated data about covid-19, drug use in the EU or retirement home capacity predictions.

h3. Risks:

TBD

h2. University Attendance Estimation Based On Weather Conditions

h3. Proposed datasets:

* http://opendata.zcu.cz/Obsazeni-mistnosti.html

* http://opendata.zcu.cz/Snimace-JIS.html

* http://opendata.zcu.cz/Energeticky-dispecink.html

Using RSS weather data from suitable weather forecast websites

* https://www.yr.no/nb/v%C3%A6rvarsel/daglig-tabell/2-3068160/Tsjekkia/Plze%C5%88sk%C3%BD%20kraj/Okres%20Plze%C5%88-m%C4%9Bsto/Plze%C5%88

h3. Abstract:

Create an application that estimates the attendance of lectures based on weather conditions. Weather conditions can be set manually or automatically using RSS data.

h3. Risks:

Inaccurate predictions.

h2. Gender Equality across the European Union

h3. Proposed datasets:

* https://ec.europa.eu/eurostat/databrowser/view/trng_lfse_01$DV_242/default/table?lang=en

* https://ec.europa.eu/eurostat/databrowser/view/edat_lfse_14/default/table?lang=en

* https://ec.europa.eu/eurostat/databrowser/view/edat_lfse_03$DV_596/default/table?lang=en

* https://ec.europa.eu/eurostat/databrowser/view/educ_itertc$DV_440/default/table?lang=en

* https://ec.europa.eu/eurostat/databrowser/view/tsc00005/default/table?lang=en

* https://www.europeandataportal.eu/data/datasets/she-figures-2018-gender-in-research-and-innovation?locale=en

* https://www.europeandataportal.eu/data/datasets/zdjzliveooitauzvvpgmg?locale=en

* https://data.oecd.org/pisa/reading-performance-pisa.htm#indicator-chart

h3. Abstract:

Create a (web) app where a viewer can visualize and compare various statistics about gender equality in Europe, especially in STEM.

h3. Risks:

Unclear purpose, low demand.

Data would need to be processed on a per dataset basis due to inconsistent format.

Some datasets are not possible to mass-process due to complex format or may be provided only as a specific visualization.

Lack of data scientist involvement.