Search

Dataset discovery is a frequent task in daily research practice, yet studies are missing that explore the usability of user interfaces (UI) in data portals. In particular, very few user studies exist that analyze whether particular elements in the user interface are useful for search tasks. We aim to address those needs for more specific usability evaluations in dataset search. In this work, wepresent a flexible semantic search over biological datasets with two user interfaces. The search result contains semantically related terms, such as synonyms or more specific terms, obtained from domain ontologies. We evaluated the system in a user study with 20 scholars. We focused on two components, the query input to explore a search in categories (entity types) in comparision to a single input field, and we analyzed textual highlightings in the returned datasets to study whether users are distracted by semantic information such as URIs. Our results show that users prefer interfaces with a single input field for search tasks they are not familiar with, and that users appreciate explanations with terminologies and URIs.

Messages on social media can be an important source of information during crisis situations. They can frequently provide details about developments much faster than traditional sources (e.g., official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks. One challenge for utilizing social media in crisis situations is the reliable detection of relevant messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowdsourced methods. Lately, approaches have shifted towards an automatic analysis of messages. A major stumbling block here is the question of exactly what messages are considered relevant or informative, as this is dependent on the specific usage scenario and the role of the user in this scenario. In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, leading into the concept of use case-dependent actionability that has recently become more popular and is the focal point of the review paper. This is followed by an overview of existing crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning technique. We analyze their suitability and limitations of the approaches with regards to actionability. We then point out particular challenges, such as the linguistic issues concerning social media data. Finally, we suggest future avenues of research and show connections to related tasks, such as the subsequent semantic classification of tweets.

This is the English version of theWhite Paper Citizen Science Strategy 2030. This White Paperpresents a strategy with 94 action recommendations in 15 thematic fieldsfor Germany to strengthen Citizen Science by 2030 in order to unleash its innovative potential for science, society and policy. &nbsp

Data collection strategies vary among different citizen science projects. This complicates the intercomparability of parameter values acquired in different studies (e.g., methodological and scale issues) and results in variable data quality. This creates problems regarding the merging of different data sets and hampers the reuse of data from different projects. Modular designed applications for mobile devices (Apps) represent a framework that helps to foster the standardisation of data collection methods. While they encourage the reuse of the software, they provide enough flexibility for an adjustment in accordance with the research question(s) of interest. The currently developed App FieldMApp offers such a framework running under Android and iOS. The related concept includes predefined frame functionalities, like settings for the user account and the user interface, and adaptable application-related functionalities. The latter comprise several modules that are categorized as sensor test, basic functionality, parameter collection and data quality collection modules. The interdependencies of these modules are documented in a wiki. This enables an individual and context-based selection of functionalities. The FieldMApp is based on open-source software libraries (Xamarin, Open Development Kit (ODK), SQLite, CoreCLR-NCalc, LusoV.YamarinUsbSerialForAndroid, Newtonsoft.Json, SharpZipLib) and will be published as open-source software. Hence, the existing catalogue of functionalities can be augmented in the future. The premise for such extensions is that modules are published together with smart, universally applicable data quality recording routines and a proper documentation in the wiki. In this contribution, we present the concept and the structure of the FieldMApp and some current fields of application that are related to the cultivation of arable land, soil mapping, forest monitoring, and Earth Observation. The extension of the functionality catalogue is exemplified by the newly implemented speech recognition module. A related quality recording routine will be introduced. With this contribution we would like to encourage citizens and scientists to elicit which requirements such an App should fulfil from their point of view.

As citizen science projects heavily rely on their members’ voluntary participation in contributing ideas, co-creating projects and especially collecting data, it is crucial to understand motivations and factors that affect an individual’s decision to take part. This study seeks to understand how people’s personality traits affect their appraisal of citizen science projects and their likelihood of participating in them. We selected five typical citizen science projects that represent a broad range of topics, levels of difficulty and execution modes as a basis for our investigation. While other studies only focus on the motivation of participants who already took part in citizen science activities, this study also targets participants without prior experience, who might volunteer in citizen science projects in the future. By using a structured survey that includes questions on personality traits, functional motives and subjective evaluation of the five sample projects, we seek to report respondents’ most important intrinsic motivations for citizen science. Apart from these intrinsic motivations, we also explore the use of monetary incentives to encourage participation in citizen science tasks, which might be used in the future to recruit participants and improve data quality. The study participants’ expectations regarding monetary rewards can be used to improve existing algorithms and design new methods that fairly and efficiently distribute rewards.