[{"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":64,"title":"Piracy and (Digital) Economy - Beneficial side-effects of outlawed acts and black market economies for societies. ","sha":"2c6e126f305abe33c168c66d25be24f8","user_id":244,"state":"published","body":"There are various pointers in recent research that suggest that product piracy as a means of distribution is a mayor factor in not only innovation but also education and economic advancement. Independent studies also show  positive effects for the establishment of the class of goods that have been pirated on local markets. \r\n\r\nThis is especially true if looked at in combination with weak copyright protection mechanisms.The book market in Germany in Early Modern Europe - for example- could not have had such an explosive growth in comparison to the book market Great Britain at the same time without widespread and cheap illegal reprints available for a low price, no effective censorship nor copyright protection (Eisenstein_1980, Gieseke_1998). While Great Britain had a strict copyright regime in place around 1710, Germany did not establish a common copyright until around 1830. This lead to a wide-spread distribution of knowledge, a highly skilled workforce, a vibrant civil society and to Germany closing up and surpassing other industrial nations in the 19th century (Höffner_2010).\r\n\r\nThis research idea aims at linking these findings to the recent debate on internet piracy, copyright protections, pricing of goods and measures to foster innovation and economic growth.\r\n\r\nElizabeth_Eisenstein,1980:The.Printing.Press.as.an.Agent.of.Change.\r\nMichael_Gieseke,1998:Der.Buchdruck.in.der.frühen.Neuzeit.(Printing.in.Early.Modern.Europe)\r\nEckhard_Höffner,2010:Geschichte.und.Wesen.des.Urheberrechts.(History.of.Copyright.and.Related.Rights)","created_at":"2015-02-19T10:20:08.942Z","updated_at":"2018-05-21T14:50:35.520Z","zenodo_id":23127,"doi":"http://dx.doi.org/10.5281/zenodo.15519","subject":null,"tags":["piracy","software","torrent","information","data_exchange","knowledge","history_of_ideas","education","workforce","distribution_channels","logistics","piracy_of_goods","black_market_economy","product_piracy"],"vote_count":8,"view_count":1,"score":8.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":164,"title":"Capturing reuse in altmetrics","sha":"8a5d2aecc5bbc5e751f41ad0e8d0b9c3","user_id":580,"state":"published","body":"Altmetrics are aimed at capturing the amount of attention a paper gets. The adoption has been significantly encouraged by services provided by, for example, Altmetric.com, ImpactStory, and PlumX, which serve the same role as Google Scholar for traditional metrics. The traditional metrics cover formal citations from primary and secondary information sources (papers, reviews, books), while altmetrics capture a wider range of sources to measure the amount of attention, like bookmarks, comments in blogs, PubMed comments, review sites, etc. Personally, I value not the count, but the overview of this attention. This attention is roughly divided into views, discussions, saves, citations, and recommendations, though various tools use related terminologies. However, both traditional citation metrics and altmetrics do not sufficiently cover reuse: citations reflect attention, but a paper can be cited for many reasons. The citing paper may agree, disagree, confirm, refute, or just cite the paper because to reflect some authority. Reuse, however, captures impact more directly: software that implements a method, databases that contain findings from a paper. Therefore, altmetrics services should start using altmetrics data from such sources, to capture knowledge reuse. Technologically, this is a no-brainer, but some social barriers have to be taken first.","created_at":"2015-05-23T09:30:30.735Z","updated_at":"2016-07-28T15:26:47.351Z","zenodo_id":30101,"doi":"http://dx.doi.org/10.5281/zenodo.17892","subject":null,"tags":["altmetrics","software","literature"],"vote_count":5,"view_count":1,"score":5.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":238,"title":"Software Discovery Dashboard","sha":"88ecd484a365e705557771cdd20e8a0e","user_id":null,"state":"published","body":"This project’s objective is to create an open source web dashboard capable of searching multiple code hosting services, such as Zenodo, Figshare, and GitHub, for the benefit of the research community. The goal of this dashboard is to demonstrate the usefulness of a new metadata standard by utilizing it for easy and effective software discovery and analysis.  \r\n\r\nThis project allows for furthering open science on the web by creating a single location to search across multiple repositories and services in order to find software and the DOI to use with research.  We are leveraging a currently evolving metadata standard to make such searching possible.  Our goal is to implement the ability to search as well as detail some different metrics and information gathered from the services using the standardized metadata to show its potential.\r\n\r\nhttps://github.com/mozillascience/software-discovery-dashboard","created_at":"2015-09-24T21:13:41.824Z","updated_at":"2016-05-14T05:17:49.407Z","zenodo_id":62352,"doi":"http://dx.doi.org/10.5281/zenodo.31400","subject":null,"tags":["open research accelerator","software","web","research","mozilla","metadata"],"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}]