Planning a Community Science (AKA Citizen Science) Research Project

Cite as:

DOI

10.25815/ktnw-y834

Citation format: The Chicago Manual of Style, 17th Edition

Generation Research. ‘Planning a Community Science (AKA Citizen Science) Research Project’, 2019. https://doi.org/10.25815/ktnw-y834.

Generation Research Dossier #1

The GenR dossier is designed as a conclusion of the initial cluster of articles for the Generation Research theme ‘Post-Digital Community Science‘ which ran over May/June 2019 and is accompanied by a collaboratively built ‘Community Science Index’ of projects and tools.

Intro

The conventional role and partner in a research project would be — a PI, a Co-Investigator, co-authors, a community, partner institution, an SME, or data provider — and their roles are not always fixed and quite often can overlap. Similarly this is the case with how a Community Science project design can shape the roles and types of participation by the public. And as with any module or work package you design for a research programme the goals and activities need to be carefully planned. For this dossier we have commented on six projects using Community Science that have lessons that can be widely applied. Additionally there is a collaboratively built ‘Community Science Index’ with further projects, collaborative tools, and spaces and event formats, etc.

Benefits for research projects

GenR has collated a dossier from its editorial theme of Community Science to show areas where research projects can hugely benefit from creating programmes for participation by the public — for science, learning, and society (Hecker et al. 2018). In the dossier we have profiled well-designed integration of Community Science in research programmes that have been able to generate significant outcomes: creating first of kind data sets, opening up new fields of research as in health monitoring, democratizing environmental policies with new large scale data sets, or enabling scalable knowledge transfer via new outreach channels for schools.

Why do we prefer the term Community Science instead of Citizen Science?

Enabling participation by the public in science and scholarship holds a wealth of benefits but these can only be realized by academia being welcoming and inclusive. If the aim of Open Science is to remove impediments to participation in knowledge then a more inclusive term than ‘citizen’ to denote the public need to be used. After all science is universalist and internationalist and doesn’t require citizenship to take part. At GenR we choose to use Community Science as a term for involving the public, you could equally use ‘participatory science’ as well, or another term. As pointed out in the blogpost ‘Is it Time to Retire the Word “Citizen”?’ (Reed Petty 2017) from the Los Angeles Review of Books about the question or finding a replacement for ‘citizen’ being used to mean the public, is that finding a replacement is a very necessary change that many are calling for, but that it’s hard job to do and not fully resolved. The article covers the examples of many groups working through alternatives — panels, council meetings, conferences, and editorial style guides — on a replacement that could be accepted by a wide array of stakeholders, but none yet reaching a satisfactory replacement.

The Community Science spectrum

It is worth noting that there are many types of Community Science and one vector to take into consideration is the levels of participation.

Levels of participation

Crowdsourcing > Interpretation > Participation > Leadership

These could be broken down into four levels, with ‘leadership’ being the highest where the participant can set the research questions.

The most comprehensive source of literature for the field in general is the recently publishing book Citizen Science: Innovation in Open Science, Society and Policy (Hecker et al. 2018) from UCL Press an open access press. The book is written from a policy perspective and has a lot of example of evaluation and ways of categorizing participation and validation of engagement.

Six examples projects

These six example projects represent question that came come up in development of the GenR editorial theme on Community Science as having the most to offer for a researcher planning a research project. Covering questions that relate to: privacy, machine learning, personal sport and health trackers, user experience and platform design, mass participation, or real-time sensor data.

  • Privacy and sensor data. Project: DECODE, https://decodeproject.eu/ Twitter: @DECODEproject – The DECODE research project is a large scale endeavor looking at a wide array of issues that impact on data in the modern city for data and algorithmic sovereignty. What is relevant in DECODE for the purposes of a Community Science project is their work on privacy for anyone using sensors managed by members of the public. Consulting their publications, cryptographic technology, and community would be the best starting place:
  • Machine learning. Project: Flora Incognita, https://floraincognita.com/ Twitter: @Flora_Incognita – Machine learning (ML) has been used by the Flora Incognita plant identification research project to enable the quick identification of images sent in by the public using a mobile app. The researchers have narrowed down the possible matches of a plant types to a posted image by cleverly narrowing the number matches the ML system would have to sort through by using data from the device mobile — time of year, geographic position — to determine what plants are in season, and from geographical location know the geology and so types of plants that are more likely to grown at a given location. Understanding how to use ML and data sets has in turn enabled Flora Incognita to engage a large amount of participants and no double given them the ability to grow an enriched data set, which is then able to be used for many different useful purposes.
  • Health and fitness body data tracking. Project: Open Humans, https://www.openhumans.org/ Twitter: @OpenHumansOrg – Open Humans is a FOSS community platform that allows the public and researchers to use personal tracking data while supporting privacy and security. The types of personal tracking data are varied and the platform is extensible. Current data sources and types include examples such as: fitness trackers, genetics tests, microbiome data, sleep and blood pressure monitoring, etc. Using the platform data can be worked on with visualizations and collaboratively used in ‘notebooks’ for example. It is important to note is that Open Humans also holds data sets that can be accessed. Not only is Open Humans interested in maintaining privacy, but its mission is also to help make use of personal tracking data for the benefit of science in a responsible and accountable way. In May 2019 Open Humans published a paper on the platforms research which is an important read for anyone interested in this field of study. ‘Open Humans: A platform for participant-centered research and personal data exploration’ (Tzovaras et al. 2019).
  • Designing participatory platforms. Project: Ring-a-Scientist, https://www.ring-a-scientist.org/ Twitter @RingAScientist – Ring-a-Scientist is a good example of creating a communications channel between academics and the public, in this case focusing on schools. Ring-a-Scientist is a platform that makes it easy for scientists and schools to organize a web video conference. The idea of technology bringing experts and teachers into the classroom or to the public is not a new one and has often been the promise that has preceded each new communications revolution – from the paper based internet réseau mondial of the librarian Paul Otlet in early 20C (Wright 2014) to satellite communications and live TV of the 1960s.

    From a perspective of making a Community Science platform into a reality is the work that it has put into ‘user experience’ and ‘product/market fit’ that can be made use of here. It is these qualities, among others, that has made Ring-a-Scientist a success. The research team, with the support of the Wikimedia Deutschland/Open Science Fellows Program, that have taken the time to understand their stakeholders and design the system to make the goal of ‘video conference’ easy for all involved. In the interview with GenR, Kerstin Göpfrich (Generation R 2019), co-founder of Ring-a-Scientist, discusses the platform design process.
  • Large scale participation. Project:  Curieuze Neuzen, https://curieuzeneuzen.be/ Twitter: @CNVL2018 – In May 2018 twenty thousand people in Flanders, the northern region of Belgium, took part in monitoring air quality organized by the Curieuze Neuzen research project. This example clearly shows how a large scale and well-designed Community Science experiments can have significant reach — proving that projects can scale and setting new benchmarks.

    But for anyone designing a research programme the questions are going to immediately to be concerned about the costs and quality of data: how do you process so many samples, what are the checks, the costs, and how can the process be sustained or repeated, etc. As an example this one round of research and samples in 2018 cost 880,000€ (CurieusNeuzen 2018), working out at 44€ a sample, with the participants paying 10€ or this cost. Curieuze Neuzen are transparent about their research design and findings, and anyone attempting a project at scale should thoroughly work though their blog and publications.
  • Large scale real-time open data. Project: Safecast, https://blog.safecast.org/ Twitter: @safecast – Safecast was started as an environmental monitoring project in 2011 in response to the disasters in Japan of the earthquake/tsunami and following meltdown Fukushima Daiichi Nuclear Power Plant. The project initially focused on radiation monitoring with a volunteer group being made up of a number of a researchers and the public from a citizen science programme. From the very beginning the project has been an open data research project using networked electronic sensors. As the project has grown, it has encompassed other types of environmental data and its geographical reach has spread. It now maintains one of the largest data sets of its kind. The project continues to grow (Safecast 2019) and for researchers who want to create large scale data sets from electronic sensors using FOSS and open data methodologies there is a lot to learn from Safecast.

GenR collaborative built listing of projects and tools

Community Science Index

The index has been organised to represent a number of areas and questions that were felt to be important for researchers looking to organise and plan research projects making use of Community Science. The categories in the index are: projects, collaborative tools and open access, FOSS for open hardware, and spaces.

Collaborative working tools

Community Science Index: Collaborative tools

FOSS collaborative tools can also mean that designing and launching a platform for a research project is increasingly of low cost, efficient, speedy to implement, and customisable.

Gen R

Posted by Gen R

Generation Research editorial büro

Leave a Reply

Your email address will not be published. Required fields are marked *