The Citizen Science Solution Kit (CSSK) is a set of tools for developing and running Citizen Science (CS) projects, maintained by the Crowd4SDG partners. The tools enable anyone to design and launch their own CS project, and support teams that are developing innovative CS projects. Some of the tools are being enhanced with AI features by the Crowd4SDG partners. The list below is updated regularly, as new tools are added or enhanced. All the tools are open source projects.
GEAR Cycle II participants can use the CSSK tools for a variety of purposes, such as self-organization within their teams, extracting relevant social media content for an ongoing event, utilizing crowdsourcing to annotate gathered images, and interpreting annotations by AI. Before going into the detail of the tools, here is an example case story:
A Case Story
Jane Doe wants to start a Citizen Science project to help her community in Ruritania be better prepared in case a flooding occurs as a result of climate change. In order to understand what the needs of the community are, she uses Decidim4CS, where she can easily set up a homepage and blog for the project, receive the proposals from the community, organize meetings and request citizens to vote. After three months of debating and self-organizing through Decidim4CS, it has been agreed with the community that better data is needed to make adequate decisions. In particular, they have co-decided that a map of the places which are more likely to get damaged when floods occur would be a very valuable asset for the project, and they are willing to work together to make it. They agree to build the map based on information of the last two floods that took place in Ruritania. By means of VisualCit they crawl the tweets containing images from those two floods, filter those images which do not contain images of the floods, and geolocate the tweets. After that, they are left with several thousand images. They create a project in Project Builder, upload the images and request a set of volunteers to connect to Project Builder and help them classify the images by labeling them with one out of five different levels of damage. They decide that, to improve the quality of the data, each image will be labeled by five different volunteers. Once the volunteers have labeled all the images, they use Crowdnalysis to provide them with advanced AI models to go from labels provided by each of the volunteers to a consensus opinion which takes into account the accuracy of the different volunteers, or specific characteristics of the images. After a consensus labeling is established for each image, VisualCit helps visualize a map coloring the different regions of Ruritania with different intensities based on their likelihood to get damage in a flood and visualizing the associated images from the last two floods. Based on the map, Jane decides….
The Citizen Science Project Builder (CSPB) is a web-based tool that allows volunteers to participate in complex data classification tasks that automatic tools cannot handle. It supports projects where citizens can analyze or enrich existing data, typically large sets of images or texts, such as satellite pictures or social media posts, as well as other media formats such as videos and scanned documents.
CSPB also enables the development of CS projects that involve data classification, using a project-building interface that does not require any coding skills. The web interface is based on Crowdcrafting, a project launched in 2011 by Citizen Cyberlab, which with its underlying PyBossa open source software framework was spun out as part of the European SME SciFabric in 2015. The CSPB software is publicly available under the ‘CitizenScienceCenter’ organisation on Github.
Getting first-hand information about an emergency situation while it is happening is important, but is often difficult to obtain in a timely fashion. The image-based social sensing tool VisualCit allows extraction of visual evidence about a situation from Twitter by searching for images posted and geolocating them. Using AI methods, VisualCit enables the user to crawl Twitter with user-defined keywords to search for posts with images.
VisualCit can apply selected filters (e.g. contains photo, occurs outdoors etc.). It can associate locations to posts, even if tweets are not natively geolocated. Posts can be evaluated by crowdsourcing initiatives using the PyBossa platform (same technology as Citizen Science Project Builder above). A collection of images for a location or thematic maps can be created to support interested users.
Co-creation of citizen science projects requires citizens and scientists to self-organize, propose and discuss ideas, schedule meetings, conduct surveys, and much more. decidim4CS is a digital platform for participatory citizen science. It allows citizen scientists to organize themselves democratically by making proposals, attending online meetings, making decisions through different forms of digital voting, and monitoring the implementations of these decisions.
Decidim4cs is based on decidim, a free open-source software originally created by the Barcelona City Hall as a participatory democracy platform for cities and organizations. Open-source code of the tool and further information can be found on GitHub.
CoSo (Collaborative Sonar) is a smartphone application aimed at understanding how team interactions impact team performance and learning. How do team members collaborate? How are subgroups formed? How do these interactions lead to better learning, productivity, creativity, and success? CoSo allows team members to journal the tasks they work on during the course of their project, along with the collaborators involved. In addition, CoSo allows to send longer-form surveys to collect answers about qualitative team features such as diversity (demographic, skills) or organization (roles, relationships). CoSo is combined with a web dashboard for teams to visualize their own data, empowering them with a meta-cognition of their collaborative processes. Developped in the context of the iGEM student competition, CoSo is of general use for any team study geared at understanding how dynamic task allocation and team organization underlies team performance.
The SDG in Progress platform allows project developers to document ongoing projects, or to get inspired by other people’s projects, re-use them or re-purpose them. The platform is based on Build in Progress, originally developed as an open source tool by MIT Media Lab. Compared to other documentation platforms (wikis, Github, etc.), SDG in Progress provides a highly visual overview of how a project is conceived and iteratively improved. It allows for easy visual documentation of the sort of branching that naturally occurs in projects, where different options are explored.
The goal of SDG in Progress is to provide an open repository that charts the step-by-step development of SDG projects, many involving citizen science tools and methodologies, as well as more general crowdsourcing and open science techniques. The idea of SDG in Progress is to document creativity, and support sustainable innovation.