Balance between Open Data and Privacy
Data management is the collection, management, processing and disclosure of data. You must take into account (a) your technical capabilities, (b) the privacy and ethical restrictions and (c) the purpose of your project for which you are collecting the data.
Citizen science is more complex than usual scientific research in this respect, because of the apparent contradiction between privacy and the pursuit of an 'Open Data' structure. There is no 'one size fits all' system in this. Every initiator of a citizen science project must carefully determine the balance between these two aspects. Scivil offers support in this regard and deals with data-related topics through the Data Management workgroup. For more information about this you can contact email@example.com.
By interoperability of data we mean the possibility to combine different data sources (e.g. from other projects) with each other. The use of (meta) data standards promotes the interoperability of databases. This way you ensure that the data from your project can also be used to answer other research questions than just those for which the database was initially created.
Data standards for both data and (project) metadata have not yet been established within citizen science. Internationally, the Data & Metadata working group of the Citizen Science Association is working on the development of standards for metadata, based on the PPSR_Core. Europe is also working on (meta) data standards for citizen science; within the COST action CA15212 the same goals are being built as the Citizen Science Association. The progress of this is regularly reported.
Tip: Even though the data standards for projects and data are still a work in progress, you should still inquire about the latest developments before starting your project. Check the above websites, and try to follow the shared suggestions already.
Tip: If it is not feasible within the budget and scope of your project to follow the above tip, try to choose a data structure when setting up your project that optimally matches frequently used citizen science data platforms, preferably platforms that are also linked. can be linked to scientific or policy-oriented databases. For example, there is the biology platform GBIF, to which various citizen science initiatives contribute indirectly via sharing platforms such as eBird, Natusfera or DigiVol.
There are various active European platforms. Click here for an overview of the largest/most used platforms at national or European level.
In every citizen science project, the quality of the data obtained remains a point that must be properly monitored. Some studies show that the results collected by citizens are more variable than those collected by professional scientists, while other studies measure an equal data quality in both groups. In any case, monitoring quality must be a main goal within the project.
Tip: You can increase the accuracy of your data by (a) collecting reference data at the start of your project, (b) allowing participants to participate in, for example, monitoring studies, (c) offering the participants a thorough training beforehand and testing this training, (d) increasing the number of participants and data points, and (e) obtaining more information about the profile of the participants themselves, so that you can tailor the protocol to the skills of the group of citizens you want to involve in your research.
Tip: Few studies make a quantitative comparison between data collected by/with citizens and data collected by scientists. If the project permits, it is advisable to have a sample or small test project run beforehand as a way of 'groundtruthing' and thus testing the quality of the proposed protocol. This also increases the likelihood that publications resulting from the project will be cited, as the number of studies on the quality of citizen science data is currently still scarce, and also differs greatly from research branch to research branch. It has also been found that qualitative estimates of data quality often yield more optimistic results than quantitatively measured controls.
Aceves-Bueno E, Adeleye AS, Feraud M, Huang Y, Tao M, Yang Y, Anderson SE. 2017: The accuracy of citizen science data: a quantitative review. Bulletin of the Ecological Society of America, 98 (4), 278-289. [online]
COST Action 15212, 2019. Minutes of WG5 workshop in Enschede: "On citizen-science ontology, standards and data", Enschede, March 21-22; 22pp. [online]
Kosmala MA, Wiggins A, Swanson A, Simmons B. 2016. Assessing data quality in citizen science. Frontiers in Ecology and the Environment, 14, 551-560. [preprint online]
Wiggins A, Bonney R, Graham E, Henderson S, Kelling S, LeBuhn G, et al. 2013: Data management guide for public participation in scientific research. DataOne Working Group, 1-41. [online]