{"contributors":[],"created":"2024-03-28T18:43","description":"At the \u00abOpen Food Data Hackdays\u00bb diverse food-interested actors are united.","homepage":"https://food.opendata.ch/","keywords":["dribdat","hackathon","co-creation"],"licenses":[{"name":"ODC-PDDL-1.0","path":"http://opendatacommons.org/licenses/pddl/","title":"Open Data Commons Public Domain Dedication & License 1.0"}],"name":"event-16","resources":[{"data":[{"aftersubmit":"","boilerplate":"Please answer these questions in your project description.\r\n\r\n- What challenge(s) are you tackling with your project, and why? (3-5 sentences)\r\n- Describe your solution in a short summary of 3-5 sentences.\r\n- Add any screenshots / demo links / documentation / photos of results.\r\n- Enter any links or datasets that were key to your progress.\r\n- What was the starting point (boilerplate, code, API, etc.) for the project?\r\n- What have you accomplished during the Hackdays?\r\n- What frustrations / roadblocks did you experience?\r\n- What are the next steps to continue your project?\r\n- Who is on your team?\r\n\r\nYou can use Markdown formatting in the Description. Update your progress level as you advance. Your team members can make an account as well and click the JOIN button to edit the page.\r\n\r\nIf you host your project on GitLab, GitHub, Bitbucket, DokuWiki or Google Drive (with publish enabled), you can quickly and easily sync it's details here by entering an Autofill link and enabling \"auto-update\" to SYNC changes.\r\n\r\nNeed more help? Get in touch with the organising team using the Community link above.","certificate_path":"","community_embed":"
Connect to our community on\r\nMattermost\r\n| Twitter\r\n| Facebook\r\n
\r\n\r\nThe contents of this website, unless otherwise stated, are licensed under a Creative Commons Attribution 4.0 International License.
","community_url":"","custom_css":"@import url(https://fonts.googleapis.com/css?family=Open+Sans:400,700);\r\n\r\n.navbar-inverse { background: #0C0944; }\r\nbody {\r\n font-family: Open Sans, Helvetica Neue, Helvetica, Arial, sans-serif;\r\n color: #444;\r\n border-top: 10px solid #ff6942;\r\n background-image: url(https://food.opendata.ch/wp-content/themes/food.opendata.ch/images/OpenData_Banner_02.jpg);\r\n background-repeat: no-repeat;\r\n background-position: top center;\r\n background-attachment: fixed;\r\n background-color: #f7f7f7;\r\n}\r\nfooter { background: white; height: 3em; }\r\ndiv[role=main] { \r\n/* overflow-x: hidden; */\r\n padding: 0px;\r\n}\r\n.container .jumbotron {\r\n background: rgba(255,255,255,0.7);\r\n}\r\n\r\n/* Center the logo */\r\n.section.section-centered { text-align: center; }\r\n.section-header .section-header-logo { margin-right:-170px; }\r\n\r\n/* .list-datapackage { max-height: 14.7em; overflow: auto } */\r\n#datacentral .btn { color: white; }\r\n.list-datapackage .list-group-item {\r\n border-left: none; border-right: none; border-top: none;\r\n border-bottom: 1px solid #333; border-radius: 0px;\r\n background: transparent;\r\n}\r\n@media (min-width: 1000px) {\r\ndiv[role=main] { background: white; padding:2em; }\r\n.list-datapackage .list-group-item {\r\n width: 100%;\r\n display: inline-block;\r\n min-height: 3em;\r\n line-height: 2em;\r\n padding-top: 0.4em;\r\n}\r\na.list-group-item:nth-child(2) {\r\n border-top: 1px solid #333;\r\n}\r\n}\r\na.list-group-item:nth-child(1) {\r\n border-top: 1px solid #333;\r\n}\r\n.list-datapackage .list-infos {\r\n font-size: 80%; color: #999;\r\n}\r\n\r\n/* Hexagons and embedding */\r\n.project .hexagontent {\r\n font-size: 1.0rem;\r\n overflow: visible;\r\n}\r\n.embed-event .event-countdown {\r\n bottom: 0px;\r\n position: absolute;\r\n transform: scale(0.5);\r\n display: block;\r\n}\r\n.embed-view .honeycomb { transform: none; margin-top: 20px; }\r\n.embed-event .hexagon { zoom: 0.4; }\r\n.embed-view .section-header { display: none; }","description":"If you care about food, no matter if it\u2019s due to ecological, health, social or gourmet reasons, this is the place to be!\r\n\r\nAt the \u00ab**Open Food Data Hackdays**\u00bb diverse food-interested actors, including big data specialists, software engineers, nutrition experts, farmers, app designers, students and entrepreneurs, are united. This potential is combined with publicly available data on the production, movement, consumption and impact of food, in order to enable groundbreaking projects.\r\n\r\nPlease visit [food.opendata.ch](https://food.opendata.ch) for more information.","ends_at":"2018-01-28T14:00","gallery_url":"","has_finished":true,"has_started":false,"hashtags":"","hostname":"Opendata.ch","id":16,"instruction":"# Data resources\r\n\r\nIn addition to the datasets provided by the challenges above, there is a collection of [community-maintained data packages](https://openfood.schoolofdata.ch) for use at the Hackdays. At the Hackdays, you are free to use these, your own, or other data - just remember to always properly **attribute your sources** and respect each license. Be aware of the *Terms of Use* before you dig in. Your experience and support will help us to open more data according to the [Open Definition](http://opendefinition.org/) and [open licenses](http://licenses.opendefinition.org/)! Find out how to publish your own open data at [schoolofdata.ch](https://schoolofdata-ch.github.io/2018/01/01/Food-Data-Expedition.html), and reach out to [the organisers](mailto:food@opendata.ch) if you have any questions. \r\n\r\n\r\n","location":"EPFL","location_lat":0.0,"location_lon":0.0,"logo_url":"https://food.opendata.ch/wp-content/themes/food.opendata.ch/images/logo.png","name":"Open Food Hackdays Lausanne","starts_at":"2018-01-27T08:00","summary":"At the \u00abOpen Food Data Hackdays\u00bb diverse food-interested actors are united.","webpage_url":"https://food.opendata.ch/"}],"name":"events"},{"data":[{"autotext":null,"autotext_url":"https://github.com/alafanechere/food-hackathon/","category_id":"","category_name":"","contact_url":"https://github.com/alafanechere/food-hackathon/issues","created_at":"2018-01-28T13:34","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"# A Dashboard for Prognolite\r\n\r\n## Abstract\r\n\r\nIn the food hackathon we (@arnaudmiribel - @victor5114 - @jastaehl - @yamash - @ArnaudRobert - @musella - @Ash-1331 - @alafanechere)\r\nhelped Prognolite to prototype 3 data driven use cases for their clients :\r\n1. Turnover forecasting to predict restaurant activities for better handling of food waste, staff management and customer services.\r\n2. Bundling suggestions generated from receipt collection.\r\n3. Realtime restaurant audience measurement by pro...","hashtag":"","id":155,"ident":null,"image_url":"https://prognolite.com/site/wp-content/uploads/2018/01/Juckerfarm.png","is_challenge":false,"is_webembed":null,"logo_color":"#29354a","logo_icon":"","longtext":"# A Dashboard for Prognolite\r\n\r\n## Abstract\r\n\r\nIn the food hackathon we (@arnaudmiribel - @victor5114 - @jastaehl - @yamash - @ArnaudRobert - @musella - @Ash-1331 - @alafanechere)\r\nhelped Prognolite to prototype 3 data driven use cases for their clients :\r\n1. Turnover forecasting to predict restaurant activities for better handling of food waste, staff management and customer services.\r\n2. Bundling suggestions generated from receipt collection.\r\n3. Realtime restaurant audience measurement by probe requests capture. Enabling covers estimation and forecast.\r\n\r\nOur deliverable is a web app that displays data visualisations helping the restaurant staff to take decisions thanks to the three use-cases tackled.\r\n\r\n## Repo organization\r\n\r\nThe repository is structured as :\r\n\r\n* **bundling**: ML notebooks for product bundling from receipts\r\n\r\n* **dashboard**: Docker configurations to run a [Superset](https://github.com/apache/incubator-superset) dashboard\r\n\r\n* **data_ingestion**: Utils for ingesting csv into db\r\n\r\n* **forecasts**: ML notebooks for unit and turnover forecasting\r\n\r\n* **wifi_tracking**: Probe requests capture for wifi tracking\r\n\r\nTemporary link to dashboard: http://lafanechere.me:8088\r\n\r\nSome screenshots :\r\n\r\n\r\n\r\n\r\nCredentials :\r\n\r\n* Username : `demo`\r\n\r\n* Password: `demo75`\r\n\r\nLink to forecasts outputs : https://cernbox.cern.ch/index.php/s/Rr8tSBtwvf679C1?path=%2F\r\n\r\n## Details on our methods\r\n\r\n* __Turnover Forecasting__\r\n\r\nAfter aggregating the training data into per-hour frames, we decided to learn three models :\r\n- one that predicts the number of products that will be sold\r\n- one that predicts the number of clients visiting the shop\r\n- one that predicts how much money will be earned.\r\n\r\nFor this, we tried three models of increasing complexity : Lasso Regression, Gaussian Process Regression and finally XGBoost Regression. The predictions and evaluations of the models are displayed in the dashboard.\r\n\r\n* __Bundle Suggestions__ \r\n\r\nWe wanted to help the farm finding products that could be sold together. For this, we studied the historical data which consists of all the past baskets sold over last year. We applied a clustering algorithm (LDA) that seeks for latent communities inside this basket set e.g. try to find the \u00ab\u00a0I\u2019m on my way to work, just taking a take-away breakfast\u00a0\u00bb basket community for example. We looked for 20 communities of baskets; such that now, when the restaurant wants to create bundles, we look inside the communities where the products have the highest weight, and extract good product candidates that could be sold with them.\r\n\r\n* __Real-time restaurant audience measurement__\r\n\r\nKnowing how many customers visit the restaurant and how much time they spend inside is crucial to determine the visitors/tables turnover in order to optimise the amount of workforce and space for the restaurant owners. To evaluate this, we take advantage of the fact that all connected devices send Wi-Fi signals (called probe requests) by listening to them and counting them. That gives us a live approximation of the traffic in the restaurant, that we display on our dashboard.\r\n","maintainer":"Prognolite","name":"Prognolite","phase":"Share","progress":50,"score":102,"source_url":"https://github.com/alafanechere/food-hackathon","stats":{"commits":0,"during":8,"people":5,"sizepitch":3335,"sizetotal":3453,"total":32,"updates":26},"summary":"Restaurant Managers don't know how many guests are coming next week. We solved this and other demand related problems.","team":"Prognolite, jastaehl, tomcheng, augustin, jsforever","team_count":5,"updated_at":"2018-01-31T13:37","url":"https://hack.opendata.ch/project/155","webpage_url":"http://lafanechere.me:8088/login/"},{"autotext":null,"autotext_url":"https://github.com/vkandy/beerchain","category_id":"","category_name":"","contact_url":"https://team.opendata.ch/food/channels/team-beerchain","created_at":"2018-01-27T15:57","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"## BeerChain\r\n\r\nAnti-counterfeiting beer using blockchain. An easy QR code scan verifies if a beer is legit. The users are incentivized to scan by product gamification and BeerCoin awards. Brewers obtain instantaneous feedback on tampering of their products.\r\n\r\nProject developed using Ambrosus API as part of [Open Food Hackdays 2018](https://food.opendata.ch/#hackdays), January 27th - 28th, 2018.\r\n\r\nOpen data provided by [SwissDeCode](http://www.swissdecode.com).\r\n\r\nRaw beer data (http://www.gen...","hashtag":"","id":152,"ident":null,"image_url":"http://www.pvhc.net/img9/brediwvlihspdigyssbp.png","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"beer","longtext":"## BeerChain\r\n\r\nAnti-counterfeiting beer using blockchain. An easy QR code scan verifies if a beer is legit. The users are incentivized to scan by product gamification and BeerCoin awards. Brewers obtain instantaneous feedback on tampering of their products.\r\n\r\nProject developed using Ambrosus API as part of [Open Food Hackdays 2018](https://food.opendata.ch/#hackdays), January 27th - 28th, 2018.\r\n\r\nOpen data provided by [SwissDeCode](http://www.swissdecode.com).\r\n\r\nRaw beer data (http://www.genome.beer/data).\r\n\r\n## Team\r\n\r\n* Samuel Siegfried\r\n* Quentin Cavillier\r\n* Marek Kirejczyk\r\n* Vijay Kandy\r\n","maintainer":"beerchain","name":"BeerChain","phase":"Prototype","progress":30,"score":76,"source_url":"https://github.com/vkandy/beerchain","stats":{"commits":0,"during":10,"people":2,"sizepitch":603,"sizetotal":631,"total":11,"updates":8},"summary":"Open Food Data Hackdays Demo","team":"beerchain, tomcheng","team_count":2,"updated_at":"2018-01-27T17:20","url":"https://hack.opendata.ch/project/152","webpage_url":"https://github.com/vkandy/beerchain"},{"autotext":null,"autotext_url":"https://github.com/kurt-hectic/openfood-nutricore","category_id":"","category_name":"","contact_url":"https://team.opendata.ch/food/channels/swiss-nutriscore","created_at":"2018-01-28T13:42","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"**The challenge**\r\nWhen we buy food from the supermarkets, we would all like to buy something which is healthy as well as environment friendly. There is unfortunately no convenient way to do this right now for Switzerland.\r\n\r\n**The solution**\r\nWe make an app which can scan bar-coded food items in Switzerland based on the [FoodRepo](https://www.foodrepo.org/) and give a score of how healthy the food item is based on the standard nutriscore criteria, and will eventually be updated to also include ...","hashtag":"","id":156,"ident":null,"image_url":"http://sdmsh.hr/sadrzaj/uploads/2017/12/osnovne_informacije_o_prehrani_zzjzdnz.jpg","is_challenge":false,"is_webembed":null,"logo_color":"#b4e8e9","logo_icon":"","longtext":"**The challenge**\r\nWhen we buy food from the supermarkets, we would all like to buy something which is healthy as well as environment friendly. There is unfortunately no convenient way to do this right now for Switzerland.\r\n\r\n**The solution**\r\nWe make an app which can scan bar-coded food items in Switzerland based on the [FoodRepo](https://www.foodrepo.org/) and give a score of how healthy the food item is based on the standard nutriscore criteria, and will eventually be updated to also include the carbon footprint. Further it will also recommend healthier alternatives to food items which are not healthy, thus providing an easy solution to buying and eating healthy. \r\n\r\n**During the hackdays**\r\nWe already have a working app made during the hackdays, which gives the nutriscore of the items.\r\n\r\n**Roadblocks**\r\nWe are new to making apps and faced a lot of trouble in getting it to work. This experience should serve us well in the future work for the project.\r\n\r\n**Next steps**\r\nThe next step is to add some finishing touches and already the app in the play store within the next few days. Then we can start to add further functionalities based on suggestions and recommendations from users.\r\n\r\n**Team**\r\nKirtan Padh (kirtan.701@gmail.com)\r\nSharbatanu Chatterjee (sharbatanu444@gmail.com)","maintainer":"Swiss Nutriscore","name":"Swiss Nutriscore","phase":"Prototype","progress":30,"score":74,"source_url":"https://github.com/kurt-hectic/openfood-nutricore","stats":{"commits":0,"during":4,"people":1,"sizepitch":1297,"sizetotal":1354,"total":5,"updates":3},"summary":"A nutritional score of barcoded food items in Switzerland","team":"Swiss Nutriscore","team_count":1,"updated_at":"2018-02-15T11:33","url":"https://hack.opendata.ch/project/156","webpage_url":"https://docs.google.com/presentation/d/1kk5m1wOhrmjSShF-Lw3f_SDL7Pj1qKVEqRkwQu7kFqg/edit?usp=sharing"},{"autotext":null,"autotext_url":"","category_id":"","category_name":"","contact_url":"https://team.opendata.ch/food/channels/team-land2door","created_at":"2018-01-27T07:17","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"Presentation Deck Design sketch Mobile app Demo video\r\n\r\n\r\nTEAM:\r\nEmmanuel Muller,\r\nJean...","hashtag":"","id":141,"ident":null,"image_url":"","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"Presentation Deck Design sketch Mobile app Demo video\r\n\r\n\r\nTEAM:\r\nEmmanuel Muller,\r\nJeanDavid Harrouet,\r\nLuca Coranzia,\r\nRagav Lingamaneni,\r\nVeronica Estrada,\r\nR\u00e9mi Tognet Bruchet,\r\nAur\u00e9lie Barberis.\r\n\r\n## CHALLENGE\r\n\r\nToday there is about 3600 farms in the Canton de Vaud. This number has been dropping by 23% in the last 10 years with an agriculture in constant evolution. Smaller producers have been searching for new selling models to cope with the financial pressure. To increase their margin about 20% of them are now selling their product direct to the consumer. Direct selling is tricky to set-up and certainly time consuming for farmers who are already doing more than 70 hours work a week! \r\n\r\nIn parallel, consumers are requesting direct selling! The direct selling model is more and more perceived as the future because a guarantee of quality and traceability! Lack of information on their local producers is judged by consumer as one of the strongest barrier for broader adoption. Today in Canton de Vaud there is no central point of information with up to date information to search for local food producers.\r\nThis why we want to simplify local food access for a sustainable and ecofriendly model.\r\n\r\n## SOLUTION\r\n\r\nHaving this information more accessible could promote more sustainable food habits and built a stronger community. Land2Door is a collaborative and comprehensive database gathering local farmers and direct selling points. It contains data such as localization,products, seasonality, volumes. The data base is enriched either with existing open data source or farmer can directly enter their characteristics (via an App). Land2Door is to be made available via an API to open this information to the consumers or restaurants. \r\n\r\n## EXISTING DATA\r\n\r\nDuring the Open Food Hackdays in 2017, web scraping was used to explore Web data sources. We assembled a list of 200 + local producers and direct selling points in Canton de Vaud. This listing was non exhaustive and some cleaning are needed. Since then, we drafted the data requirements & schema to build the database. An [open source](https://github.com/loleg/land2door) [demo application](http://f.datalets.ch/) to prove the data collection concept and provide an initial [open API](http://f.datalets.ch/api/v2/farms/) was developed in collaboration with Oleg Lavrovsky. \r\n\r\n## SCOPE of the HACKDAYS\r\n\r\nWe tackled 2 objectives during these 2 days :\r\n\r\n1. Fuel the database --> Create an app to enrol and collect data directly from the Producer. \r\n2. Foster local food choice --> Develop front end for users /consumers to locate the closest farmers based on consumer geolocalisation and shopping list.\r\n\r\n## OUTCOMES\r\n\r\n- Design of the mobiles apps have been sketched and shared with the team.\r\nhttp://f.datalets.ch/documents/1/Land2Door_-_consumer.pdf\r\n\r\n- Mobile app prototyped to collect data from farmers\r\nhttps://github.com/raghavSrih/Land2Door\r\n\r\n- Voice app prototyped to connect farm data to end users.\r\nhttps://www.youtube.com/watch?v=TjSPN8zubaw&feature=youtu.be\r\n\r\n- More details and screenshots in our presentation.\r\nhttps://docs.google.com/presentation/d/1TOG-JpoYy7X2_3D1JxvB3Wjocda4onExLnCF86nksNI/edit?usp=sharing\r\n\r\n## NEXT STEPS\r\n\r\n1-Raise awareness and extend network in order to fund our project:\r\n\r\n- Develop a web page to support the database.\r\n- Test the collection mobile app on pilot farmers group.\r\n- Test the end-users apps on pilot consumers group.\r\n\r\n--> Optimize data interface and further populate it. \r\n\r\n2-Develop business model for direct selling in Canton de Vaud first. \r\n\r\nKey questions to tackle : \r\nProve our assumption on consumers to go for local food. \r\nDefine experiments to understand why the consumers will go on the plateform. \r\nFind ways to advertise the idea.","maintainer":"Aurelie.Barberis","name":"Land2Door","phase":"Prototype","progress":30,"score":74,"source_url":"https://github.com/raghavSrih/Land2Door","stats":{"commits":0,"during":41,"people":3,"sizepitch":4199,"sizetotal":4314,"total":53,"updates":49},"summary":"Build database on local farmers & open this information to consumers to foster local food choices (Canton de Vaud).","team":"Aurelie.Barberis, oleg, loleg","team_count":3,"updated_at":"2018-01-29T12:53","url":"https://hack.opendata.ch/project/141","webpage_url":"https://docs.google.com/presentation/d/1TOG-JpoYy7X2_3D1JxvB3Wjocda4onExLnCF86nksNI/edit?usp=sharing"},{"autotext":null,"autotext_url":"https://github.com/MichaelHochstrasser/TooSweetProject","category_id":"","category_name":"","contact_url":"https://team.opendata.ch/food/channels/team-too-sweet","created_at":"2018-01-27T18:53","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"# TooSweet\r\n\r\n#### Challenge\r\nNon-communicable diseases (NCDs) kill 40 million people each year, equivalent to 70% of all deaths globally. One of the main factor who lead to NCDs is an unhealthy diet which is correlated with a high sugar intake. Too high sugar intake leads to chronic inflammation state in our body and therefore to constant immune reactions. Food shopping in the supermarket like Migros is done almost every day, but are you aware of all the nutritional ingredients you are buying? ...","hashtag":"","id":153,"ident":null,"image_url":"http://realnewsmagazine.net/wp-content/uploads/2017/11/sugar-06.jpg","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"cubes","longtext":"# TooSweet\r\n\r\n#### Challenge\r\nNon-communicable diseases (NCDs) kill 40 million people each year, equivalent to 70% of all deaths globally. One of the main factor who lead to NCDs is an unhealthy diet which is correlated with a high sugar intake. Too high sugar intake leads to chronic inflammation state in our body and therefore to constant immune reactions. Food shopping in the supermarket like Migros is done almost every day, but are you aware of all the nutritional ingredients you are buying? We think most of the people don\u2019t check the ingredients list or are not aware about the sugar amount in their favourite daily food like they yogurt for breakfast.\r\n\r\n#### Solution\r\nWe aim to make customer more aware of their main sugar contributor and will present them alternative products with less sugar. TooSweet is a mobile application who gives you a ranking of food by sugar content. The data are provided by uploading your Cumulus Data as CSV File to the App. In the App you see the sugar amount in gram of all receipts together and by clicking on one specific receipt you get the detailed ranking of this receipt by sugar content from high to low. You can click on one product to get more detailed information about the sugar contribution and alternative products ranked by sugar reduction (sugar content low to high). In the overview section you see a your sugar consumption/buying averaged over the last months over shoppings per week.\r\n\r\n#### Demo\r\n\r\nInstall on Android\r\n\r\nRequires allowing unsigned binaries in your Developer settings.\r\n\r\n#### Datasets\r\nProduct data is used by Cumulus CSV upload done by customer. The mapping with the nutritional data about the sugar content is done with the data base of www.foodrepo.org or with Nutritional data from www.migros.ch.\r\n\r\n#### Main Roadblocks \r\nThe hardest task was to implement the Cumulus data into the app, this was very important for us, as it is more convenient for the consumer and also more constant as scan. If the app would work by scan there are already several similar apps on the market and it would be more specific to make a decision in the supermarket. By using all products that a customer did buy its more real (no cheating is possible), more convenient and also an additional value to have a Cumulus Card. Open Questions: How to find alternative products, how to rank them, what are criterias, subcategories like beverages or snaks?\r\n\r\n#### Futures Steps - most promising\r\nCooperation with Migros \u2192 Migros implementing our app idea into their Migros app\r\n\u2192 easier data handling, Migros has unambiguous data of all products\r\n\u2192 additional benefit for costumer to make Cumulus Card\r\n\u2192 possible like with Migusto Recipeis as healthier alternatives\r\n\r\n---\r\n#### Our Team\r\n* Michael Hochstrasser - MSc Computational Science\r\n* Gabriel Hochstrasser- MSc Machine Learning Engineer\r\n* Charlotte Soland - Master Student in Food Engineering\r\n\r\nPlease contact us via the [Issues page](https://github.com/MichaelHochstrasser/TooSweetProject/issues)","maintainer":"charlotte","name":"TooSweet","phase":"Launch","progress":40,"score":71,"source_url":"https://github.com/MichaelHochstrasser/TooSweetProject","stats":{"commits":0,"during":29,"people":1,"sizepitch":3138,"sizetotal":3138,"total":34,"updates":32},"summary":"","team":"charlotte","team_count":1,"updated_at":"2018-02-16T09:49","url":"https://hack.opendata.ch/project/153","webpage_url":""},{"autotext":null,"autotext_url":"","category_id":"","category_name":"","contact_url":"https://team.opendata.ch/food/channels/team-public-theory","created_at":"2018-01-29T12:56","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"Pitch deck\r\n\r\n- Capture an image of food\r\n- Deep Convolutional Neural Network classifies it into several categories \r\n- Health information derived from food category\r\n\r\nReferences:\r\n\r\n1. [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)\r\n2. [Using Pre-trained Deep Learning models for your own dataset](...","hashtag":"","id":159,"ident":null,"image_url":"","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"Pitch deck\r\n\r\n- Capture an image of food\r\n- Deep Convolutional Neural Network classifies it into several categories \r\n- Health information derived from food category\r\n\r\nReferences:\r\n\r\n1. [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)\r\n2. [Using Pre-trained Deep Learning models for your own dataset](https://gogul09.github.io/software/flower-recognition-deep-learning)","maintainer":"oleg","name":"Food Theory","phase":"Prototype","progress":30,"score":58,"source_url":"","stats":{"commits":0,"during":0,"people":0,"sizepitch":568,"sizetotal":587,"total":4,"updates":4},"summary":"Improve Food Habits","team":"oleg","team_count":0,"updated_at":"2018-01-29T13:52","url":"https://hack.opendata.ch/project/159","webpage_url":"https://docs.google.com/presentation/d/1vG77FbGY6sOttr6Gel6kW_OJszjv_JQ1vJgl3FUrxUo/edit#slide=id.g32904d8c6a_0_6"},{"autotext":null,"autotext_url":"","category_id":"","category_name":"","contact_url":"https://team.opendata.ch/food/channels/team-sharper","created_at":"2018-01-28T13:11","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"https://docs.google.com/presentation/d/1CDPpmlWZCj_ZmsCUmv8bkalwJPC1uGc9OlbEmBmL0Ic/edit?usp=sharing \r\n\r\nSHARPER>> is the next step of Self-evaluation and Holistic Assessment of climate Resilience of farmers and Pastoralists(SHARP), the tool developed by Food and Agriculture Organization of the United Nations. Through the data collected by the FAO with Sharp, Sharper will provide you with a selection of analyzed data points.\r\n\r\nStarting point: FAO had developed a macro to provide resilience sco...","hashtag":"","id":154,"ident":null,"image_url":"","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"https://docs.google.com/presentation/d/1CDPpmlWZCj_ZmsCUmv8bkalwJPC1uGc9OlbEmBmL0Ic/edit?usp=sharing \r\n\r\nSHARPER>> is the next step of Self-evaluation and Holistic Assessment of climate Resilience of farmers and Pastoralists(SHARP), the tool developed by Food and Agriculture Organization of the United Nations. Through the data collected by the FAO with Sharp, Sharper will provide you with a selection of analyzed data points.\r\n\r\nStarting point: FAO had developed a macro to provide resilience scores to each farm according to 54 markers. From this macro, general statistics were provided for each dataset. \r\n\r\nProject: Get key insights from the general dataset and provide the next step for the tool that would provide value to the farmers who spend 4-5 hours completing the survey and wait between 3-6months for their data to be analyzed and recommendations to be provided. \r\n\r\nTeam Members:\r\n- Florian Bienefelt\r\n- Vincent Lee\r\n- Lorena Edejer","maintainer":"ledejer","name":"SHARPER>>","phase":"Sketching","progress":20,"score":50,"source_url":"","stats":{"commits":0,"during":4,"people":1,"sizepitch":949,"sizetotal":1002,"total":5,"updates":3},"summary":"SHARPER is the next step of the SHARP tool of the FAO","team":"ledejer","team_count":1,"updated_at":"2018-02-15T11:29","url":"https://hack.opendata.ch/project/154","webpage_url":"https://floriferous.github.io/open-sharp-data/"},{"autotext":null,"autotext_url":"https://drive.google.com/open?id=1Qd77AFtRyvgs2uBOIrT3pxqnjIsL4kiO","category_id":"","category_name":"","contact_url":"https://team.opendata.ch/food/channels/meal-good-team","created_at":"2018-01-28T13:57","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"pre-portioned meal-kits from local residual production is offered on a weekly basis to Lausanne area. To tackle food waste as well as offer gastronomic experiences and convenience to end-users. The meal-kits are ready to be cooked, with detailed and easy recipes to be done in less than half an hour. Creative recipes from all over the world is proposed, for local ingredients for a real delight.\r\nThe idea is to connect farmers with consumers in a sustainable way, and consume ethically.\r\n\r\nOur team...","hashtag":"","id":157,"ident":null,"image_url":"","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"pre-portioned meal-kits from local residual production is offered on a weekly basis to Lausanne area. To tackle food waste as well as offer gastronomic experiences and convenience to end-users. The meal-kits are ready to be cooked, with detailed and easy recipes to be done in less than half an hour. Creative recipes from all over the world is proposed, for local ingredients for a real delight.\r\nThe idea is to connect farmers with consumers in a sustainable way, and consume ethically.\r\n\r\nOur team during this hackdays is:\r\n- Sinan Numan [@sinan_numan1](https://twitter.com/sinan_numan1)\r\n- Sarah Numan\r\n- Marisol Giacomelli\r\n- Francesca Conselvan\r\n- Rosa Castillo\r\n\r\nOur milestones for this hackdays were to work on the market study in order to validate our hypotheses and see if our offer is interesting. At the same time, work on packaging and recipes for research and prototyping.\r\n\r\nStarting pitch: https://drive.google.com/open?id=1JhciJkOQyBCuWASZXCqxE37YYVbVTtbJsOmPl5XmHGc\r\n","maintainer":"sinan","name":"Meal-Good","phase":"Research","progress":10,"score":50,"source_url":"https://drive.google.com/open?id=1JhciJkOQyBCuWASZXCqxE37YYVbVTtbJsOmPl5XmHGc","stats":{"commits":0,"during":3,"people":1,"sizepitch":984,"sizetotal":1024,"total":9,"updates":7},"summary":"Pre-portioned meal-kits in Lausanne area","team":"sinan","team_count":1,"updated_at":"2018-02-15T11:31","url":"https://hack.opendata.ch/project/157","webpage_url":"https://drive.google.com/open?id=1Qd77AFtRyvgs2uBOIrT3pxqnjIsL4kiO"},{"autotext":null,"autotext_url":"","category_id":"","category_name":"","contact_url":"","created_at":"2018-01-29T13:00","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"Pitch deck\r\n\r\nPowered by [Ambrosus](https://hack.opendata.ch/project/144)\r\n\r\n## Current Problem with Recalls\r\n\r\n- In 2017, there were 578 product recalls in Europe.\r\n- The current system (RASFF) is slow and is not readily available to consumers. \r\n- Only \u2153 of all the recalled products were recovered.\r\n- Consumers don\u2019t know if their purchased product is safe.\r\n\r\n## O...","hashtag":"","id":160,"ident":null,"image_url":"","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"Pitch deck\r\n\r\nPowered by [Ambrosus](https://hack.opendata.ch/project/144)\r\n\r\n## Current Problem with Recalls\r\n\r\n- In 2017, there were 578 product recalls in Europe.\r\n- The current system (RASFF) is slow and is not readily available to consumers. \r\n- Only \u2153 of all the recalled products were recovered.\r\n- Consumers don\u2019t know if their purchased product is safe.\r\n\r\n## Our Solution\r\n\r\nInformation on the entire supply chain and recalls of any product readily accessible to consumers, retailers, and manufacturers.","maintainer":"Timon","name":"Total Recall","phase":"Research","progress":10,"score":38,"source_url":"","stats":{"commits":0,"during":0,"people":0,"sizepitch":643,"sizetotal":696,"total":3,"updates":3},"summary":"How Food Traceability helps effective recall handling","team":"Timon","team_count":0,"updated_at":"2018-01-29T13:41","url":"https://hack.opendata.ch/project/160","webpage_url":"https://docs.google.com/presentation/d/1VJLc_3Rtnj1YmcdydVKTqa6qYYLwYO-2ZxzK4LVAbig/edit?usp=sharing"},{"autotext":null,"autotext_url":"","category_id":"","category_name":"","contact_url":"https://syllogismos.github.io/","created_at":"2018-01-30T08:15","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"Pitch deck\r\n\r\n![](https://blog.datalets.ch/workshops/2018/foodhackdays/eschernode/snaq_on_eschernode_1.png)\r\n\r\nUse Cases\r\n\r\n- People will know the nutritional composition instantly.\r\n- Identify the dish, especially helpful when people are travelling, so that they can know what they are eating. eg. Simply take a picture at a\r\nbuffet and know what it is.\r\n- Helpful to people...","hashtag":"","id":161,"ident":null,"image_url":"","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"Pitch deck\r\n\r\n![](https://blog.datalets.ch/workshops/2018/foodhackdays/eschernode/snaq_on_eschernode_1.png)\r\n\r\nUse Cases\r\n\r\n- People will know the nutritional composition instantly.\r\n- Identify the dish, especially helpful when people are travelling, so that they can know what they are eating. eg. Simply take a picture at a\r\nbuffet and know what it is.\r\n- Helpful to people following a diet.\r\n\r\nFirst steps.\r\n\r\n- The data set we were provided is a 700 pictures each of three different classes. Identify these classes automatically.\r\n- Available Classes:\r\n1. Bowl plate\r\n2. Regular plate\r\n3. Soup plate\r\n\r\nTraining using Eschernode\r\n\r\n- Trained a deep learning classifier on Resnet18 architecture.\r\n- Trained on a 60GB machine for 5 hours.\r\n\r\nResults\r\n\r\n- Best Cross entropy error of about 0.863 after training for 42 epochs\r\n","maintainer":"oleg","name":"Snaq on Eschernode","phase":"Research","progress":10,"score":38,"source_url":"","stats":{"commits":0,"during":0,"people":0,"sizepitch":950,"sizetotal":1019,"total":3,"updates":3},"summary":"Estimate food portion size, nutritional value, contents automatically","team":"oleg","team_count":0,"updated_at":"2018-01-30T08:22","url":"https://hack.opendata.ch/project/161","webpage_url":"https://blog.datalets.ch/workshops/2018/foodhackdays/eschernode/snaq_on_eschernode_compressed.pdf"},{"autotext":null,"autotext_url":"","category_id":"","category_name":"","contact_url":"","created_at":"2018-01-24T20:23","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"There is a lot of information available (data, guidelines, resources) on what is healthy food. But they fail to provide an answer which can be used in real time by the consumer to get an answer if specific product is healthier or more risky. The data (e.g. https://health.gov/dietaryguidelines/2015/guidelines/ and http://openfood.schoolofdata.ch/food-allergens/) should be combined with personal physical condition and nutrient table recognition to provide a useful solution for people. ","hashtag":"","id":140,"ident":null,"image_url":"","is_challenge":true,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"There is a lot of information available (data, guidelines, resources) on what is healthy food. But they fail to provide an answer which can be used in real time by the consumer to get an answer if specific product is healthier or more risky. The data (e.g. https://health.gov/dietaryguidelines/2015/guidelines/ and http://openfood.schoolofdata.ch/food-allergens/) should be combined with personal physical condition and nutrient table recognition to provide a useful solution for people. ","maintainer":"smsmetana","name":"Healthy Track","phase":"Challenge","progress":-1,"score":2,"source_url":"","stats":{"commits":0,"during":0,"people":1,"sizepitch":487,"sizetotal":571,"total":2,"updates":0},"summary":"A scanning app providing relevant information on how healthy this product is for me.","team":"smsmetana","team_count":1,"updated_at":"2018-01-24T20:23","url":"https://hack.opendata.ch/project/140","webpage_url":""},{"autotext":null,"autotext_url":"","category_id":"","category_name":"","contact_url":"","created_at":"2018-01-27T08:55","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"","hashtag":"","id":145,"ident":null,"image_url":"","is_challenge":true,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"","maintainer":"oleg","name":"6 Agridea","phase":"Challenge","progress":-1,"score":2,"source_url":"","stats":{"commits":0,"during":2,"people":1,"sizepitch":0,"sizetotal":21,"total":2,"updates":0},"summary":"Foster local sourcing","team":"oleg","team_count":1,"updated_at":"2018-01-27T08:55","url":"https://hack.opendata.ch/project/145","webpage_url":"http://www.agridea.ch/"},{"autotext":null,"autotext_url":"","category_id":"","category_name":"","contact_url":"","created_at":"2018-01-27T08:38","download_url":"","event_name":"Open Food Hackdays Lausanne","event_url":"https://hack.opendata.ch/event/16","excerpt":"The same weekend as the Open Food Hackdays in Lausanne is the Global Game Jam 2018. There are four Swiss locations participating and a big online community of people building games, sharing tips, tricks, content and code. You can learn more about this at the [Swiss Game Developers Association](https://www.sgda.ch/event/global-game-jam-2018/) and at [swissgamejam.ch](http://www.swissgamejam.ch/). \r\n\r\nYour challenge: be inspired by some of the Game Jam projects, use Open Food Data to make a game w...","hashtag":"","id":142,"ident":null,"image_url":"","is_challenge":true,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"The same weekend as the Open Food Hackdays in Lausanne is the Global Game Jam 2018. There are four Swiss locations participating and a big online community of people building games, sharing tips, tricks, content and code. You can learn more about this at the [Swiss Game Developers Association](https://www.sgda.ch/event/global-game-jam-2018/) and at [swissgamejam.ch](http://www.swissgamejam.ch/). \r\n\r\nYour challenge: be inspired by some of the Game Jam projects, use Open Food Data to make a game with real-world data, publish your project both at the Game Jam and at the Hackdays! \r\n\r\nHere is a small selection of food-themed games from past years - click to play/download:\r\n\r\n