dataesr/scientific-paragraphs-categorization huggingface dataset:
Effect of diesel exposure on microbial functional potential in marine beach sediment To investigate the effect of diesel treatment on functional gene abundance, Pearson's correlation coefficient was calculated for gene abundance data annotated using the KEGG database. Multiple KEGG pathways were strongly and significantly (p < 0. 0001) correlated to an increasing diesel concentration pattern (1-2-2-3), irrespective of nutrient amendment (Fig. S4 ). In particular, pathways associated with hydrocarbon degradationlimonene and pinenewere more present in the high diesel treated samples (Figs. S4; 4 ). Metabolism of amino acids was also affected as genes associated with phenylalanine metabolism were more abundant at higher diesel concentrations (Figs. S4; 4 ). Fatty Fig. 3. The 16S rRNA gene amplicon sequencing analysis.
Genus (and phylum or class) level taxonomy abundances of Allen Bay sediment bacterial communities (at 5 • C) across select diesel concentrations. The taxonomic analysis was performed for 0 %, 0. 01 %, 0. 1 % for nutrient amended and unamended samples, as well as 0. 5 % for nutrient amended samples. We were unable to get high quality sequencing data from the 0. 5 % nutrient unamended samples. acidlinoleic acid, alpha-linolenic acid metabolism genes (KEGG 71)were more abundant at higher diesel concentration, as were genes from the biosynthesis of unsaturated fatty acids pathway (Fig. S4 ). In addition, genes associated with pathways for degradation and metabolism of organic compounds (geraniol, benzoate, caprolactam, inositol phosphate, lipoic acid) were also more abundant in samples with higher diesel concentrations (Fig.
S4 ). Similar pathways have been shown to be present in microorganisms with hexadecane degradative ability from petroleum enrichments  (Sierra-Garcia et al. , 2014). This suggests that these pathways may be important in hydrocarbon degradation; their larger abundance in the samples exposed to higher diesel suggests that they may aid in survival of the strains detected in the highest diesel concentrations. Dombrowski et al. in a 2016 study reconstructed seven MAGs using metagenomic data from hydrocarbon-degrading enrichments of sea water and oil from the Deepwater Horizon spill. The reconstructed MAGs also contained genes for benzoate degradation, alkane utilization, and phosphate metabolism  (Dombrowski et al. , 2016). We also consistently recovered benzoate degradation genes in the MAGs reconstructed from our Arctic beach sediment samples (see below section).
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no 871124  Laserlab-Europe. This work is supported by the  Cluster of Excellence 'CUI: Advanced Imaging of Matter' of the Deutsche Forschungsgemeinschaft (DFG) -EXC 2056-project  ID  390715994 and Grant-No. HIDSS-0002   DASHH  ( Data Science in Hamburg-HELMHOLTZ Graduate School  for the Structure of Matter). L. S. and S. B. acknowledge funding by the  Helmholtz Initiative and Networking Fund. Financial support from the  French IR INFRA-NALYTICS   FR2054  for conducting the research in Orsay MS/IRMPD platform is gratefully acknowledged. Publishing fees supported by  Funding Programme Open Access Publishing of Hamburg University of Technology (TUHH).
An important requirement at the heart of CI is the ability to communicate and share knowledge. Models and their various representations-which are the cornerstone of MDE-are powerful means of addressing this. Therefore, encouraging CI to solve complex problems raises challenges, and difficulties, and needs to develop modeling skills in future engineering professionals. But the widespread adoption of MDE technologies (e. g. , modeling tools, code generators, or even formal analyzers) remains limited due to their use complexity  [2, 3]. Gamification  [4]  is an exciting and promising way to remove these barriers and encourage all stakeholders' commitment to a project team. Within this paper, we present our initiative that started two years ago and is known as PapyGame. 1 We have experimented with the gamification of software modeling tools in a real and robust modeling environment (Papyrus 2 ) to reduce its use complexity.
Our contribution is triple. First, we propose the complete and usable implementation of a gamified modeling environment. Second, we have asked students to use it and get their feedback to have the first evaluation of the user, learner, and gamer experiences of such an approach. Third, to design our solution, we have defined a conceptual architecture that can be used to gamify other modeling environments. The paper presents our approach and these three contributions as follows. Section 2 presents a state of art on gamification in software engineering and modeling. In Sect. 3, we explain the dimensions of gamification we cover and the related mechanisms we have chosen. Section 4 describes the conceptual architecture we have defined and details one per one each of its parts. Section 5 reports on how each technical component of PapyGame has been implemented with regard to the previously defined conceptual architecture.
Conceptual architecture The main goal of our work was to study in real conditions the gamification of software modeling activity. The main concrete objective was then to have a concrete gamified modeling environment (Papyrus). At the same time, we aimed at facilitating the gamification of other modeling environments. The greater the number of tools integrating our approach, the easier it will be to learn how to model. We designed the outlines of PapyGame through a conceptual architecture whose components support the different aspects of our gamification approach. The resulting abstract architecture can then serve as a specification for future implementations. Figure  2  illustrates all the components and their related communications through a UML collaboration diagram. The components are distributed between the Software Modeling Environment (SME) and the Gamification Engine (GE).
Game dashboard and login The Game Dashboard (GD) interactive component is the learners' entry point of the proposed Gamified Software Modeling Environment. After the learner logs to the game framework via the Login interactive component, the GD allows them to access to the various available games, and to check their games status (i. e. , history, achievements, and progresses). It also enables them to personalize their learner profile with new avatars, expertise levels, etc. All this information may be used by the Game Master component to adapt dynamically game scenarios or difficulties to end-user features and experiences.
Server side: the gamification engine The Gamification Engine (described in Sect. 4. 3. 3) is implemented as a standalone application deployed on a remote server. It allows to implement each key concept of the GDF  [31]  through user interfaces accessible from a web browser. For PapyGame, the Gamification Engine plays three main roles: Learner Profile Manager, Series Manager, and Model Quality Assessor. The server on which the Gamification Engine is launched also provides the client with useful remote resources (JSON files, images, JavaScript scripts).
Player profile manager In order to implement gamification mechanisms related to progression, experience, rewards, and more generally to a player's profile, the Gamification Engine provides a set of administration interfaces and endpoint APIs to manage the player database. The Gamification Engine natively manages the notion of player profile by integrating the nickname, the current level of the player, and the storage of the current level of the rewards obtained by the player. However, there is no system for keeping track of the player's game history, nor the history of experience and award acquisition. To keep track of these histories, we use a customData field included in each player profile, in which we store the history of wins and awards in JSON format. User profiles can be created, read, updated, or deleted from REST API endpoints exposed automatically by the Gamification Engine.
Model quality assessor The Model Quality Assessor bridges the client requests issued by the PapyGame plugin containing the player diagram data and the rules of each level defined in the Gamification Engine. For PapyGame, it is the internal action mechanism of the Gamification Engine that plays the role of Model Quality Assessor by executing the rules when a request is received. This module accepts POST requests containing the player's ID, the time taken to complete the level, the content of the diagram parsed in JSON, and possibly the number of errors or unsuccessful attempts. The number of errors can be calculated directly from the client or in the Model Quality Assessor depending on the type of game being run. When executing the rules, the Model Quality Assessor evaluates whether the attempt is a failure or a success and, if so, updates the player profile (progress and history) thanks to the end point exposed by the Player Profile Manager.
The PapyGame plugin Papyrus relies on the Eclipse environment due to its distribution as an Eclipse plugin. Thus, it enables developers to augment it with new modules, by retrieving the proper services and listening to dispatched callbacks. The PapyGame client follows this logic and is distributed as an Eclipse plugin written in Java, that is available for download and install in its own update site. 5 From a client architecture perspective, the plugin is responsible for providing the technical bricks which tie logical components together. This includes creating the Game Area inside Papyrus, and initializing the Api Service. The Game Area is a view container which allows other components to display their own views. In Eclipse, we chose to use a Browser component for the Game Area, so we could use web technologies such as HTML, CSS, and JavaScript to create responsive and user-friendly UI.
The game object In PapyGame, a Game is a set of Java classes and HTML/CSS /JavaScript files which describe the mechanics of the behavior of a game. Each Game contains four views: the Preview View, the InGame view, the GameSuccess view, and the GameOver view. Technically, each view is associated with one Java class and one HTML file. (Styles and scripts can be added to the HTML file. ) The Java classes of the views contain the internal behavior of the Game, e. g. , how to react to a input user event, and define an entry point to be executed by the Game Master component. While the Preview, Game-Success, and GameOver views are mainly static screens to display information, the InGame view is often dynamic, reacting to the modeling progress. This InGame view is also in charge to inform the Game Master when the game should stop, and the remote server should be queried.
Evaluation In order to accurately evaluate PapyGame, we designed two different evaluation procedures. In the first one, we developed an ad hoc user experience questionnaire, which allowed us to gather information about the user experience, the application procedure, comments about PapyGame strengths and weaknesses, and suggestions regarding the software improvement. In the second one, we used a modified version of a preexisting questionnaire in order to consolidate first results and to get an overall evaluation of the software quality according to the user/learner/gamer experiences.
Rationale In developing PapyGame, we made rational design choices that come from serious and validated works. Thus, to evaluate our approach exhaustively, we preferred to firstly evaluate our work with a user experience questionnaire. An ad hoc created questionnaire (Table  2 ) was presented to users with an initial part aimed at the collection of generic data (age, gender and country), a part to differentiate between the two groups ("What is the software you used / are using?") and a final part to give opinion about what is working well and what needs to be changed in the software. Then, the questionnaire evaluates several aspects related to the user experience (i. e. , motivation, engagement, attractiveness, learning perception) and to software usability and perceived efficacy.
Questionnaire results Results were calculated using the statistical software RStudio 8  (v. 2021. 09. 0). Table  3  presents an overview of the results. They suggest medium/high values to positive items, and low values for those items related to negative components ("I find this software annoying," "I think this software has slowed down my learning," "I didn't try very hard to do well in the tasks"). We analyzed also the responses to the open questions "The aspect I appreciated the most" and "The aspect that disturbed me the most. " The most appreciated aspects of the software were the animation of the software, the game aspects, the usability, the gamification elements used (money and levels), the colored belts, the graduation of the software, the efficacy. The most disturbing elements were the lack of information, the design components (dimension of buttons, localization, typing, scrolling), the complexity and problems related to the Eclipse environment.
Rationale The first evaluation provided encouraging results regarding the user experience of PapyGame. In order to strengthen the demonstration of these benefits, we performed a second evaluation, still focusing on the user experience, but taking into account the aspects related to the game principle. First, obtaining the same results in terms of user experience by using two different tests has a positive impact on the validity of the feature under study, as mentioned in the convergent validity theory  [49]. Regarding also the "methodological triangulation"  [50, 51] , it is important to increase confidence in having captured the concept  [52]. Second, there is evidence of differences in the evaluation of gamified software depending on learners' gaming habits  [53]. So, through this second evaluation we aimed at increasing the validity of the scale we adopted in the first evaluation, and investigating any existing difference between gamers and non-gamers.
For this, we used the MEEGA360 9  scale  [54]  which is an evolution of MEEGA+ scale  [55]. Based on a rigorous systematic literature review  [56] , MEEGA+ is a model for evaluating the quality of educational games. It aims at assessing fun, perceived learning, usability and user experience of a software. It uses a full but limited number of items  (35). Moreover, the goodness of the software can be assessed by calculating the total score of the scale. The MEEGA360  [54]  brings more reliability in the calculation of the overall score, if compared to the MEEGA+, due to the introduction of reverse items (excluded from the calculation of the overall score), which allows checking for biases and patterns in users' answers. Also, the introduction of items assessing users' motivation, and the elimination of confusion or unnecessary items, makes the MEEGA360 more suitable for our context.
Participants and procedure A sample of 16 students from the University of Trento in Italy participated in the experiment (age between 18 and 26 years old, 2 females, 14 males). In addition, participants were divided into two groups based on their responses about their gaming habits in order to analyze the influence of gaming habits on the perceived quality of PapyGame. The first group (gamers, n = 8) consisted of students who played video games regularly (daily or weekly), while the second group (nongamers, n = 8) consisted of participants who played video games occasionally (monthly, rarely, never). Students first used PapyGame and then completed the MEEGA360 scale to assess the overall quality of the gamified software. Responses were given on a five-point Likert-type scale (from 1 = disagree to 5 = agree).
MEEGA360 overall quality score The analysis was performed in Jamovi 10  (v. 1. 6. 23. 0). We calculated the mean of the MEEGA360 total score of the 16 participants and compared the results with the three ranges related to the goodness of the software (low quality θ < 42. 5; good quality 42. 5 ≤ θ < 65; and excellent quality θ ≥ 65). The result (mean = 58. 49 ± 4. 46)  shows that PapyGame lies in the middle range, indicating that the software achieves an overall good quality. In addition, we compared the total MEEGA360 scale scores between the gamer and non-gamer groups. We decided to perform a t test for independent samples. The results (t test, t = 1. 51, p >. 05, d =. 76) show that there is no significant difference in the perceived quality of the gamified software and the user experience between the two groups.
Since the goal of gamified solutions is to satisfy experts and novices users alike while addressing deeply held pedagogical assumptions  [57] , addressing the existing differences in the user experience according to the video game expertise  [53] , the data suggest that the software succeeds in the difficult goal of being perceived in a similar way according to the users' video game experience. Overall, the results indicate that PapyGame achieves a satisfactory level of quality and that the gaming habits of the participants do not influence the perceived quality of the software.
Analyses discussion In the first applications, PapyGame showed an overall positive evaluation from the users, which evaluation was consistent for both questionnaires used, showing convergent validity. While the tool showed adequate levels during user experience analysis, it also presented some disturbing elements, including elements of UI, the information provided, and the complexity and problems related to the Eclipse environment. We expect to fix those elements soon. Moreover, users who participated in the evaluation, all belonged to category learning of a modeling language, presented in Sect. 3. 1, thus it is not possible to provide generalization on the utility of PapyGame on the other categories of modeling students (i. e. , learning of modeling principles and learning of a modeling tool).
-Intelligent game adaptation PapyGame allows the definition of games as sequences of mini-games. Our intention is to extend it with a more flexible scenario where this predefined sequence can be altered at runtime based on the monitoring of the personalized student learning process. This is especially useful in a multi-player scenario where we will have available monitoring data to make smarter decisions and reconduct (or at least suggest changes) the gamification based on the players' behavior and results. -Desktop vs web-based client using a " heavy client" is not ideal in the case of academic learning. The installation of the software, of our PapyGame plugin, of the hardware and software compatibility (JVM) complicates the implementation of the gamification experience within the university courses (change of rooms, change of the place of the users of machines).
Conclusion Software models are high value-added elements in a software project. The learning curve concerning modeling is unfortunately quite high and partly prevents a wide adoption of modeling in IT projects. An underlying reason for this problem lies in the fact that this learning process is threefold, since it concerns the very principle of modeling (abstraction, modularity, etc. ), the notation, and the model editor. In this paper, we presented our work on gamifying this process to simplify it. Our primary objective was to experience and experiment this gamification with a real modeling environment and with real learners in order to evaluate the interest of such an approach. We chose Papyrus, the open-source reference environment for UML modeling in Eclipse. After explaining our choices regarding the target audience, the modeling dimensions to consider, and the gamification elements, we also defined an abstract architecture that could be used as a basis for implementation in other modeling environments.
2010; Sulla-Menashe et al. 2019) , 300-m European Space Agency Climate Change Initiative Land Cover Dataset (ESA CCI LC) (ESA 2017), 30-m Finer Resolution Observation and Monitoring of Global Land Cover Plus (FROM-GLC Plus) products  (Gong et al. 2013; Yu et al. 2022) , and 10-m ESA WorldCover products. The spatial resolution and consistency of LULC products have gradually improved in recent years since the public availability of Landsat and Sentinel data; however, challenges remain in keeping LULC products up-to-date, impeding further updates of relevant studies based on these data. Generally, the process of updating large-scale LULC products is hindered by significant issues, such as the high quantity of remote sensing imagery collection and processing and the massive amount of data computation required.
With the rapid development of Earth observation and computer technology in recent decades, remote sensing image interpretation has evolved into a valuable tool for land resource surveying  (Fuller, Sheail, and Barr 1994; Shu and Du 2022). Although remote sensing technology has provided important insights into LULC patterns, image interpretation for land resource surveys relies mainly on manual methods  (Fan et al. 2022; Sannier, McRoberts, and Fichet 2016). Moreover, land resource surveys are typically conducted at the national level, and statistics are generally published in administrative units, such as at the provincial level. While these surveys provide important basic LULC statistics  (Nedd et al. 2021; Wang et al. 2022) , finer-scale LULC distributions are rarely available through land surveys, resulting in inconsistencies with several published gridded socioeconomic and ecological datasets, such as Gross Domestic Product (GDP) (J.
Satellite imagery All available Landsat surface reflectance images from 2000 to 2022 were used for land cover updating in this study, including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). Landsat images were processed by the United States Geological Survey (USGS) using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS)  (Masek et al. 2006 ) and the Land Surface Reflectance Code (LaSRC)  (Vermote et al. 2016). The clouds and shadows were masked using the C Function of Mask (CFMask)  (Foga et al. 2017).
Land cover products CLUDs is a 30-m LULC dataset for the  1980s, 1995, 2000, 2005, 2010, 2015, and 2020  in China. This dataset was developed using Landsat TM/ETM+/OLI imagery and human-computer interactive interpretation method with a classification system of grassland, forest, unused land, cropland, water, and built-up land (J. Liu et al. 2003; J. Liu et al. 2003; J. Liu et al. 2014; J. Liu et al. 2010; Ning et al. 2018). Furthermore, based on the CLUDs dataset, an annual LULC dataset (CLUD-A) was developed from 1980 to 2015 (Y. Xu et al. 2020). This study aimed to update annual land cover maps based on the CLUD-A product.
Evaluation The updated LULC maps were assessed based on LULC classification accuracy and LULC areachange performance. The 30-m China Land Cover Dataset (CLCD) and 300-m ESA CCI LC were utilized to facilitate this evaluation. To ensure comparability, both datasets were converted to the CLUD classification system before being used for comparison. First, annual LULC evaluation samples from 2016 to 2022 were generated using the sample migration method, and the FAST dataset was used for annual training sample generation. To assess the overall accuracy (OA) and uncertainty of the three datasets, an error matrix-based calibration estimator was used with a 95% confidence interval  (Olofsson et al. 2014) , covering the entire region of China. Second, the LULC area change performance of the LULC datasets was conducted to evaluate whether the area changes since 2010 were consistent with CLDA and ESA CCI LC and reasonable, based on visual comparison.