# SWAT4HCLS hackathon suggestions
Hackathon Chair: Andra Waagmeester
The SWAT4(HC)LS hackathon is a recurring hackathon. After an intense set of tutorial and 2 conference days, there is the time to relax and use the tranquility and informal space provided to play, experiment, create new collaborations, prepare a new project or simply work on your own work.

This document collect some idea's to use for inspiration. At the beginning of the hackathon there will be time allocated to pitch idea's after which the hacking starts. the final 30 minutes of the day will provide space to demonstrate of show of a developed app

## Submit your pitch
New pitches are welcome, submit yours [here](https://docs.google.com/forms/d/e/1FAIpQLSf9JYSHMS5q6fv_Mbi5k_jOzI7tmAjpljrOMwBjM_YavJkwSw/viewform?usp=sf_link)

## General
### Bioschemas Markup and Support Tools
**Driver**
Alasdair Gray ([e-mail])

**Introduction**
Bioschemas aims to make life sciences resources on the web more Findable.

**Aim**
* Generate markup for life sciences resources
* Enable validation of generated markup

**Prerequisites**
Markup: none, although an awareness of JSON-LD and schema.org is an advantage
Validation: ShEx and Javascript
Expected Results
Deployment of markup in web resources

### WikiCite, Wikidata, and Scholia: Linking Publications to Topics
**Driver**
Egon Willighagen <[e-mail]>
**Introduction**
Keywords, MeSH terms are simple solutions to link topics to publications. However, our collective knowledge is not captured in single articles, they build on the effort explained in other articles. Citation networks are intrinsic to the provenance of our
knowledge. Wikidata, WikiCite, and Scholia provide a FAIR and Open
solution. Wikidata allows linking topics with articles about them.
WikiCite provides the citation graph, and Scholia visualizes it all.

**Aim**
- add BioSchemas annotation to Scholia pages
- Generate tools to automate linking articles with their 'main subjects'
- Develop SPARQL queries that find common topics in articles cited
by some article
- Classify articles cited by some article, based on their 'main subjects'
- Develop SPARQL queries that find authors that specialize in some topic
- Write a new extension for Scholia for a type of topic (existing topics
are proteins, genes, chemicals, etc)
- Write a Scholia extension that shows missing topic annotation of cited
article
- Write code to generate `main subject` annotation coverage for the
various Scholia topics (aspects)
- Develop a Scholia topic (aspect) for clinical trials

---
## Agriculture
### Schemas and Vocabularies for Agriculture and Plant Biology

**Driver**
Marco Brandizi ([e-mail])

**Introduction**
Schemas and Vocabularies are gaining momentum in the field of agriculture, plant research and food.

**Aim**
This hackathon activity aims at using simple and practical extensions of existing schemas/ontologies, like schema.org and bioschemas.org, to cover the typical entities used in the agriculture, plant biology and food field.

**Prerequisites**
Domain knowledge on agriculture, plant research and food.

**Expected Results**
* A collection of relevant entity types and their relations
* Ideas on how to leverage existing frameworks like schema.org and bioschemas
* Possibly some initial implementation examples.
* Deployment of a validation tool

---
### Writing Shape Expressions for Lasagne
**Drivers**
Matthew Lange, Eric Prud'hommeaux, Tom Baker, Egon Willighagen, Andra Waagmeester

**Introduction**
Shape Expressions provide a powerful way to describe linked data sets. In this hackathon we will focus on food, specifically lasagna.

**Aim**
To create an initial collection of Shape Expressions to describe food phenotypes, the processes that produce and transform their ingredients, and the machines/people that engage in these processes.

**Prerequisite**:
Knowledge on food and agriculture data sets (recommended but not required). Having prepared, eaten or smelled lasagna.

**Expected results**:
Cooking up seed corpus of Shape Expressions by modeling lasagna. Providing some of the food linked data landscape, and serving as a nucleation site for further schema curation.

---

## Healthcare
### Metadata Interoperability - Mapping Metadata from Health Care, Clinical Research and EHR
**Driver**
Matthias Löbe ([e-mail])

**Time frame**
Half day (12:30pm)

**Introduction**
Soon all university hospitals in Germany are required to provide access to their health data for research purposes. Making this happen brings major challenges for technical infrastructure, legal frameworks but certainly with respect to semantic interoperability.

**Aim**
The aim of the hackathon is to **map exemplary data elements** from clinical research and care data, into a common metamodel of ISO 21526 from TC 215.

**Prerequisites**
* Familiarity with ISO 11179-3 or ISO 21526 is required
* Knowledge on data modelling in health care
* Knowledge on common data modelling standards (CDA, CDISC, FHIR)

**Expected results**
To find and state weaknesses in the ISO 21526 draft and to develop best practices to translate mappings into a common metamodel.

**Material**
[Google Drive Folder](https://drive.google.com/open?id=1hvElFmA8fuJ7oghDj-ju3VSp8XEk4zg8)

---
# Technical
## Data2services, converting your data to a standard data model.
**Driver**
Vincent Emonet & Alexander Malic (Maastricht University)
**Introduction**
Today an increasingly amount of data is available on the Web, but this data usually comes in a myriad of formats (XML, CSV, RDB...) with no inherent semantic representation. Inspired by the FAIR data principles (Findable, Accessible, Interoperable, Reusable), we propose Data2services, a framework built with scalability in mind to convert any type of data to a standard data model that follows the Semantic Web standards.
This data is then accessible as RDF through a SPARQL endpoint. We are also working on automatically generating web services based on the data model for simplified access.

**Aim**
- Execute the data2services pipeline on your machine using Docker to convert your data to a generic RDF, where the data representation is based on the input data structure.
- Craft SPARQL queries to map the relevant data from the generated generic RDF to the target data model of your choice.
- Draft and start to develop web services that consume and expose the refined data model to simplify its access.

**Prerequistes**
- Docker already installed, or admin rights on your machine to install Docker
- Bring data you want to transform (relational database and/or XML-, CSV-, TSV-, PSV-files)
- Provide a target data model to convert your data to, or we can propose you one

**Expected outcome**
- Data2services running on your hardware using Docker
- Your data now complying to a standard data model, and accessible from a SPARQL endpoint and auto-generated API.
- A draft or a prototype of a web service that expose data2services RDF