[{"id":207,"title":"GeoTag-X: crowdsourced photo analysis for humanitarian crises","sha":"060c0b8f513844e21c7db2f0063c6243","user_id":null,"state":"published","body":"Photos taken in disaster situations and other humanitarian crises by different people on the ground can potentially be a powerful resource for the response teams. Unfortunately, the manpower needed to process the incredible number of photos coming out of these situations makes this duty impossible for a single organization. Therefore we are turning to the crowd to help us rapidly extract meaningful, relevant and structured data from these photos.\r\n\r\nThis is why we launched the [GeoTag-X platform](http://geotagx.org), which gathers a series of pilot projects covering different disaster related events, asking volunteers to answer some short and strictly structured questions about photos related to each event. \r\n\r\nOur final aim is to have an open source platform, with a set of tools, projects, and methodology that can be taken by anyone working in a humanitarian crisis and quickly and easily adapted to their needs and redeployed.\r\n\r\nGeoTag-X relies almost entirely on volunteers. By actively pursuing an open science methodology GeoTag-X can be flexible and adapt quickly and easily to users’ needs, and volunteers benefit and learn from the process. GeoTag-X code, tools, and methodology is open and accessible via the platform, [GitHub](https://github.com/geotagx), and events. \r\n\r\n[Template for creating GeoTag-X projects](https://github.com/geotagx/geotagx-project-template)\r\n\r\n","created_at":"2015-08-26T09:12:19.846Z","updated_at":"2015-09-29T19:55:30.363Z","zenodo_id":57551,"doi":"http://dx.doi.org/10.5281/zenodo.29582","subject":null,"tags":["open research accelerator","humanitarian","open science","disasters","open source","crowdsourcing"],"vote_count":2,"view_count":1,"score":2.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":"SimpleGeoTag-X.png","attachment_content_type":"image/png","attachment_file_size":99268,"attachment_updated_at":"2015-08-26T09:12:18.268Z"},{"id":541,"title":"AutoLab: Can an AI Replace Human Data Collectors?","sha":"0ef39e22b61491bdb64d4127d53668c4","user_id":null,"state":"published","body":"Can an autonomous AI conduct experiments on real humans in a lab safely and efficiently? With [Automated Laboratory (AutoLab)](https://devpost.com/software/autolab#updates), our open-source ubiquitous computing project, we will do just that. Imagine a Star Trek-like computer infused into a lab room. Participants enter the smart lab by activating an electronic lock, converse naturally with an AI during the study, and are compensated digitally. This will greatly speed up and standardize data collection in the health and social sciences. Currently, unpaid and overworked assistants must spend inordinate time and money to collect data. This results in human errors that waste tax dollars, lost time that could be spent on more creative tasks, and small datasets that limit generalizability to the larger population.\r\n\r\nTypical human experiments in the socio-behavioral and health sciences span 1-2 hours and need one more hour to prep and manage data. For sample sizes of 50-200, this requires at least 100-600 hours for one study (~ $2,000-12,000 tax dollars annually). Not long ago, statistical/visualization software, word processors, and communication tools advanced science by standardizing tasks, reducing errors, increasing speed, and fostering more creative pursuits. Ubiquitous computing can do the same by collecting large datasets from human subjects in social and health science labs 24/7/365.","created_at":"2018-12-08T00:07:27.531Z","updated_at":"2018-12-09T06:01:18.768Z","zenodo_id":2092676,"doi":"https://doi.org/10.5281/zenodo.2092676","subject":null,"tags":["ai","artificial intelligence","research cycle","internet of things","data collection","open source","productivity","software","ubiquitous computing","ubicomp"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":449,"title":"A service to assess the quality of documentation of open source software","sha":"4baaee8f0f211a3c98c3d2be18156fee","user_id":null,"state":"published","body":"Could one build a service that checks the completeness and quality of documentation of an open source repository?\r\n\r\nPotentially a group could build up a set of repositories with documentation ratings, which could then be used to train a ML/DL model, which could then be used to provide the service?\r\n\r\nThe service could be used by a developer to iterative improve documentation, then check the quality of the changes.\r\n\r\nIt could also be used by a potential user of the software as a factor in deciding whether to use the software or not.\r\n","created_at":"2018-03-27T11:18:15.933Z","updated_at":"2021-01-28T15:47:00.031Z","zenodo_id":1208715,"doi":"https://doi.org/10.5281/zenodo.1208715","subject":null,"tags":["open source","machine learning","documentation"],"vote_count":3,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]