## 1 Introduction  
Citizen science incorporates scientific research and monitoring projects for which members of the public collect, categorise, transcribe or analyse scientific data. Citizen science has definitely come of age in recent years in generating knowledge, creating new learning opportunities and enabling civic participation [1]. The marine conservation and biodiversity monitoring potential of citizen science campaigns is increasingly being recognised [2; 3], being deployed, for example, in the monitoring of invasions by marine alien species [4] and in an array of other marine applications summarised in [5]. Despite an increased uptake of marine monitoring protocols based on the citizen science rationale, a number of challenges still undermine the mainstreaming of the same protocols. Chief among these challenges is the need to validate individually submitted citizen science reports in order to avoid the contamination of the main dataset with misidentification reports. In fact, for citizen science projects to fully fulfil their potential, they need to generate good-quality datasets [6]. A decade ago, smart phone apps and the virtual/gaming approach were considered as cutting-edge, emerging technologies which held great promise for the advancement of citizen science [7]. The state-of-the-art has progressed to a state where smart phone apps are being enhanced with a new suite of sensors to enable greater citizen science functionality of the same technologies. Given the image-intensive nature of many ongoing marine citizen science campaigns, the image analyses domains of Artificial Intelligence and Machine Learning have increasingly been applied to automate the validation of the same images [8] and thus to expedite the normally lengthy process. These protocols have been incorporated within fixed-location, jellyfish bloom early-warning systems, including the JellyMonitor prototype deployed on the Scottish seabed and which makes use of both classic computer vision and deep learning neural networks to detect the occurrence of jellyfish blooms [9]. The automated processing of underwater footage, besides static imagery, has also been achieved through the same protocols. For instance, the JellyToring system automatically detects and quantifies different species of jellyfish based on a deep object detection neural network, allowing the user to automatically record jellyfish presence during long periods of time [10]. To date, our University of Malta research group has proposed an image-analysis protocol for the characterisation of Large Microplastics (LMPs) extracted from beach sediment [11]. Jellyfish swarms, or blooms, consist of large numbers of jellyfish which aggregate occasionally, mainly as a result of the direction of prevailing water currents, and the occurrence of such jellyfish aggregations has attracted increased attention in recent years (see, e.g., in [12]) by virtue of their socio-economic impact. The \"Spot the Jellyfish\" initiative, run by the University of Malta and the International Ocean Institute (IOI) since June 2010, follows a citizen science approach and relies on the collaboration of the general public, mariners, divers and especially the younger generations through their teachers and parents, by recruiting their assistance in recording the presence and location of different jellyfish within Maltese waters. The reporting is done by simply matching a sighted jellyfish with a simple visual identification guide, giving the date and time of the sighting, and indicating the number of jellyfish individuals observed. Sightings can also be reported online, through the campaign's social media page, via SMS or email. Citizen scientists are also encouraged to supplement their report with a photo, which is used for verification purposes and to quality control the database. All jellyfish reports within this campaign are also shared with Maltese tourism authorities for beach management purposes.