We illustrate our approach with two well-established long-term monitoring datasets in the UK: moth and butterfly data from the UK Environmental Change Network (ECN), and the UK CEH Cumbrian Lakes monitoring scheme. The users can then apply further analysis and filtering as they see fit. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. The meaning of the states is open to specialist interpretation. The term “state” here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. We propose the use of data science techniques to tag each measurement with an identified system state. Such tests take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. The latter usually involves compliance (i.e., format) and conformity (i.e., value) checks that are most likely to be in the form of single parameter range tests. Such procedures generally include standard operating procedures during sampling and laboratory measurement (if applicable), as well as data validation upon entry to databases. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. It allows us to study trends and helps us to develop better models to describe processes in our environment and they, in turn, can provide information to improve management practices. By means of cluster analysis and Bayesian network modelling, I show that patterns of repetition for both languages differ drastically depending on whether participants of the experiment had to communicate their messages being or not being able to see what others had written before them.Įnvironmental data allows us to monitor the constantly changing environment that we live in. In study 2, I explore how priming and antipriming effects work together to produce collective language creativity. In study 1, I discuss the notion of collective language creativity that I understand as a product of two major factors interacting: cognitive priming effects and the unsolicited desire of the discourse participants to be linguistically creative, that is, to say what one wants to say using the words that have not yet been used. By introducing the results of two experiments, for English and Russian native speakers, this paper seeks to explain the mechanisms establishing balance of priming and language creativity. However, it has long remained unclear how priming effects coexist with the creative aspect of language use, and the importance of the latter has been somewhat downplayed. It is now a matter of scientific consensus that priming, a recency effect of activation in memory, has a significant impact on language users’ choice of linguistic means. Based on the discussions, several recommendations are proposed for future improvements in the QC for ocean observations. We also discuss the performances of the various QC schemes and identify the key challenges. The similarities and differences between QC schemes developed by various ocean organizations are introduced. In this review, we briefly summarize the latest progress of QC for oceanic in situ observations, and mainly focus on temperature and salinity data. However, as ocean in-situ observations are obtained using different instruments that offer heterogeneous data qualities, it is paramount that bad data could be accurately and efficiently identified via QC to provide a reliable global ocean database. With the introduction of various advanced instruments in recent decades, oceanographic surveys have expanded from coastal regions to open oceans. The quality control (QC) of ocean observational data, essential to establish a high-quality global ocean database, is one of the basic data pre-processing steps in oceanography research, marine monitoring, and forecasting.

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