dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , a merger) is not clearly known and the event window needs to incorporate a number of days prior to the known event date in order to account for a possible information spill. Despite the full availability of cryptocurrency data as part of the normal operation of public blockchain systems, spotting a 51% attack on a cryptocurrency requires technical skills not available to the general public, which introduces a potential delay between the actual attack and the reporting of it. For example, the Bitcoin Gold (BTG) attack in 2020 was discovered by a researcher at MIT's Digital Currency Initiative  [51]. Sometimes, it is once the information is spread by the media, that the public and the markets become aware of it. Based on the finding in Ref. [52]  that cryptocurrency prices react quickly to "bad news" in the press, and the magnitudes of cumulative abnormal returns (CARs) are larger for negative events than for positive events, implying that the market reaction to negative events is stronger than to positive announcements, we attempt to go further than Ref.
Another particularity we observe in the data is that some attacks last a few hours while some last for multiple days, as can be seen in Table  3. Although some of the longer incidents can be classified as separate block reorganisations, i. e. , subsequent distinct attacks of short duration, we choose to aggregate them into a single attack with a longer duration for two main reasons: first, these strings of events are usually reported by the media as "an attack", and second, from a technical perspective, a number of block reorganisations happening subsequently are most probably caused by a same attacker (or organised group of attackers) trying to abuse the network. For example, Bitcoin Gold was hit by two chain reorganisations of over 10 blocks of length on January 23 th and 24 th in 2020. Although the reorganisations were 6 h apart, this situation was described as a single attack both by the media and by the cryptocommunity.
Data collection The survey was administered in 'self-report' and anonymous form through an identification code via an online platform (Microsoft Azure). The dissemination of the link related to the online completion of the survey took place through the universities' communication systems, online forums (e. g. , through virtual learning environments, Facebook or WhatsApp accounts, etc. ) or WeChat groups. Data collection began on 8 March 2020 (publication date of DPCM) and ended on 4 May 2020 (suspension date of the restrictive measures introduced by DPCM). Of all the project respondents, the individuals who had recounted a dream and completed the DASS-21 and RS scales were included in this work.
Data analysis The dreams were analysed by two researchers (LB and FB), who independently read and analysed them by using a dream content thematic analysis  (Boyatzis, 1998). Thematic analysis is a process for qualitative coding of texts that identifies, analyses, and aggregates content patterns (themes) within the data  (Braun & Clarke, 2006). The analysis involved several distinct moments: i) reading of the entire dream corpus by the two researchers and extrapolation from each dream of the emerging 'raw' categories or sub-categories. In this phase, starting from the dream text, they tried to identify the different sub-categories that described the different elements of the dream. ii) grouping of the sub-categories into superordinate conceptual categories, with a higher level of abstraction, which would help to organise the elements of the dream.
This step was accomplished through several meetings between the researchers. Any disagreements were resolved through a discussion with a third researcher involved (GL). This shared work led to the development of a coding grid (via Excel database) consisting of categories and sub-categories. iii) application of the coding grid to the entire dream corpus, after an initial co-coding between researchers of 10% of the dreams. In this stage, the sub-categories were used as binary coding variables with respect to the presence (0=no, 1=yes) of the relevant content in each individual's dream. The subcategories were applied to dreams in a mutually exclusive manner. iv) identification of dream extracts as exemplifications of the identified sub-categories. This first stage of qualitative analysis led to the identification of the categories and their sub-categories, which were then analysed from a quantitative point of view, noting their frequencies.
The data analysis revealed the explicit presence of the Emotions in the dream category, which includes the positive or negative emotional tone of the dream. More than half the dreams analysed reported negative emotions, such as anxiety and panic, sadness, pain, helplessness or confusion. This result is in line with other studies  (Schredl & Bulkeley, 2020; MacKay & DeCicco, 2020; Mota et al. , 2020; Scarpelli et al. , 2021) , which, by collecting and analysing the dreams of the general population during the pandemic period, have observed a change in dream activity, with a higher frequency of dream content with negative significance compared to normal. Nevertheless, our analysis revealed a relationship between Emotions in dreams and the other dream sub-categories. Negative emotions are predicted by Relationality that divides, Persecutory actions (both active and passive), Blocked actions, Danger, Death, Negative processes and Transformation processes.
This data therefore suggests that dreams with negative significance are linked with relational break-ups, danger or the deadly aspects of the pandemic and to the disease, but also to the feeling of being persecuted or blocked and helpless. In addition, dreams with a negative significance seem to be characterized by difficulties in coping or processing; in fact, destructive mechanisms or negative transformation prevail. Our data would therefore seem to suggest that dreams with a negative emotional experience reflect the psychological suffering experienced by the Italian population and the pandemic situation faced. At the same time, Positive emotions are predicted by Open places or places of escape, Relationality that unites and Positive processes. We can hypothesise that these dreams testify to the population's need for escape and contact during lockdown, but also a capacity for processing and transforming the situation in a sense of renewal or rebirth.
, 2019)  and to identify benthic vegetation using multispectral sensors  (Traganos and Reinartz, 2018a, 2018b). To our knowledge, machine learning classifiers, such as Support Vector Machine, k-Nearest Neighbor, and Random Forest  (Zhang, 2015) , have not yet been tested to discriminate MPB from other types of benthic vegetation. In this study, we first want to test the suitability of S2 MSI imagery for mapping the distribution of microphytobenthos in French estuaries. We hypothesize that 1) the S2 spatial resolution is suited to mapping smaller estuaries with riverine morphology, and 2) the S2 spectral resolution allows MPB to be discriminated from macrophytes colonizing the intertidal zone. Physico-chemical data were collected to describe each estuary and we used Multidimensional Scaling (MDS) multivariate analysis to identify groups sharing common characteristics.
An area-proportional allocation of training samples per class was carried out  (Colditz, 2015). Reflectance data for all classes were obtained from quasi-synchronous S2 images. The RF model was created using the Caret package of R  (Kuhn, 2008). Two parameters need to be set up: the number of trees (ntree) and the number of variables to be selected and tested for the best split when growing the trees (mtry). The number of trees was set to 500  (Belgiu and Drăgu, 2016). A range from 250 to 1000 trees was tested in a previous step and showed that classification accuracy did not improve beyond 500. All 10 and 20 m (spatially resampled at 10 m) S2 bands mentioned previously were used as predictors. The other predictors were two multispectral indices used by  Murray et al. (2019)  for tidal flats: the NDVI and the Normalized Different Water Index (NDWI), and two additional vegetation indices, the Atmospherically Resistant Vegetation Index (ARVI) and the Modified Chlorophyll Absorption Ratio Index (MCARI)  (Daughtry et al.
RF classification accuracy assessment The intertidal vegetation of the 26 French estuaries and bays was classified using the RF classifier described above. To assess the accuracy of these classifications, an additional field campaign, synchronous with an S2 image acquisition, was conducted in June 2019. Vegetation present in the intertidal zone of Bourgneuf Bay was delineated to create a validation dataset of 66 polygons (Supplementary  S3 ). The data consisted of the area (m 2 ) and position (latitudelongitude) of each type of vegetation. A standard remote sensing error matrix was obtained  (Congalton, 1991)  (2003b) for S2.
Environmental data When available for all sites, environmental variables expected to have an effect on the microphytobenthos were extracted for each TWB from the Naïades database http://www. naiades. eaufrance. fr/ of the French Biodiversity Agency and the Geological and Mining Research Bureau (BRGM). A set of physical variables (water temperature, pH, turbidity, oxygen, and day length) was retrieved, as well as nutrient data (concentrations of nitrate, ammonium, phosphate, and silicate). All data were measured in the water column. For each variable, the annual mean was calculated and averaged for the period 2015-2018. The two main types of intertidal sediment, mud and sand, expressed in percent cover for each intertidal area, were obtained from a report by the Algae Technology and Innovation Center (CEVA, https://www.
Statistical analysis First, ordinations based on remotely sensed intertidal vegetation variables (NDVI-MPB, NDVI-Green macroalgae, % cover MPB, % cover green macroalgae) were performed to analyze similarities between sites. For this analysis, the two main types of intertidal vegetation identified by the RF classifier were retained: microphytobenthos and green macroalgae. We used the metric Multidimensional Scaling approach (mMDS,  Clarke and Warwick, 2001)  with Euclidean distance to plot each site in a 2D space with a low projection distortion (stress of 0. 05). Groups statistically significant at p<0. 05 were identified through an analysis of similarity (ANOSIM). Bubble plots were used to highlight differences between groups of estuaries  (Clarke and Warwick, 2001). This ordination analysis was performed using the PRIMER® software package.
The groups of sites identified through mMDS were compared with Box and whisker plots. After checking normality with the Shapiro-Wilk test, a non-parametric Kruskal-Wallis followed by a posteriori Mann-Whitney pairwise comparisons were applied to test for statistically significant differences between groups. All univariate tests were performed using PAST 3. 25  (Hammer et al. , 2001). Relationships between environmental variables and remotely sensed data were first analyzed with univariate Spearman correlations (non-normality checked with Shapiro-Wilk). Finally, a multivariate analysis was performed using the two-table coupling method of Redundancy Analysis (RDA)  (Legendre and Legendre, 1998) , using the R package, Vegan. This was used to elucidate MPB and green macroalgae patterns in relation to environmental variables and study sites.
The RF classification was developed to discriminate each type of intertidal vegetation, but was also used in the preliminary stage of image selection, based on cloud coverage and water height. Thick, opaque clouds, such as cumulus, were efficiently detected, but thin, transparent clouds, such as cirrus, were more problematic. To avoid selecting images with this type of cloud over intertidal areas, we applied conservative thresholds, which probably reduced the number of images retained that could have been exploited. In total, 918 Sentinel-2 images were downloaded and classified for 2018 and took ca. 200 hours using the Caret R package on a standard PC to process. Ranger is a new package developed for R, implementing a C++ version of the RF algorithm  (Wright and Ziegler, 2017) , providing a faster runtime and more efficient memory usage.
The number of trees used to create the RF model was set to 500 after testing different numbers  (100, 250, 500, 1000, and 2000) , as the same accuracy was obtained compared to higher values while limiting processing time  (Thanh Noi and Kappas, 2017). In fact, RF accuracy is more sensitive to the mtry parameter, which sets the number of predictive variables to be selected and tested when growing the trees  (Kulkarni and Sinha, 2012). Increasing mtry can improve the accuracy of the final model, but it greatly increases the processing time, so we kept a value of 4  (Ghosh et al. , 2014). The accuracy of the RF classifier was estimated using field data collected from three distinct sites. Overall, high accuracy was achieved for the identification of MPB, in terms of both User's and Producer's accuracies, which were 94% and 84% respectively.
, 2018) , as has been shown for seagrass  (Duffy et al. , 2018; Roelfsema et al. , 2014). Among the latest state-of-the-art methods, extreme gradient boosting (Xgboost) has been described as outperforming other machine-learning methods for very high-resolution remote sensing data  (Georganos et al. , 2018 a,b). However, this method needs a huge training dataset and corresponds to long computation times  (Georganos et al. , 2018b). LightGBM is another promising approach to classify objects using optical remotely-sensed data  (Zhong et al. , 2020) , but was not compared to RF. The RF model accuracy could be improved by increasing the training and validation dataset across a higher number of sites, notably sandy estuaries. The size of the training data is related to the overall accuracy of the model  (Du et al.
, 2015) , and training data from each class should represent at least 0. 25% of the studied area  (Colditz, 2015; Thanh Noi and Kappas, 2017). In our case, this rule was respected for all classes, except for brown algae. The RF was identified as the most stable machine learning algorithm in terms of overall accuracy when the model is trained on multiple study sites, as we did  (Vetrivel et al. , 2015). The current RF algorithm can certainly be fine-tuned, but the present study already demonstrates that an off-theshelf methodology (RF is available in commercial software such as ENVI©, or on the Sentinel Application Platform (SNAP) from the European Space Agency) can quantitatively assess the spatial distribution of MPB in estuaries using S2 images. Along with the fact that these images are provided atmospherically corrected via the Copernicus platform, this increases the interest of developing such a bioindicator for transitional waters as it reduces the processing complexity.
We expect all environmental/anthropogenic data to become available for the 43 French TWBs, as a national database is currently being organized by the French Biodiversity Agency, and additional S2 data could be obtained to perform an analysis of sedimentary type of each estuary. A more ambitious outlook would be to expand this approach to other European estuaries identified as TWBs by the WFD. Despite the almost thousand S2 images processed for a single year, it was not possible to get a clear picture of the MPB seasonal cycle for each estuary. A pattern of maximum biomass peaking in early spring, followed by a summer depression was observed for the Loire and Sèvre-Niortaise sites using MODIS (Oiry S. pers. comm. ), but no information is available for the remaining sites. MPB seasonal variations observed in Dutch and United Kingdom estuaries and coastal sites using MODIS  (van der Wal et al.
Conclusion The 10 m spatial resolution of S2 enables the observation of all French TWBs for the WFD. With a machine learning classifier, it was possible to discriminate MPB from intertidal macrophytes (macroalgae and angiosperms) despite the multispectral resolution. S2 can, therefore, provide spatial metrics on MPB as a group of unicellular photosynthetic organisms at the macroscale. S2 image data is publicly available through ESA's sci-hub and EU Copernicus portals, and is therefore a good compromise to map estuarine microphytobenthos at no cost. This improved spatial and temporal coverage is a strong argument for the use of satellite data in complement to conventional sampling for the WFD statutory monitoring and reporting  (Papathanasopoulou et al. , 2019). This work is a first step toward a possible satellite water quality metric using MPB percent cover, as a bioindicator of high nutrients loads.
Acknowledgments. This study was supported by a grant from the  Agence Française pour la Biodiversité (AFB) , grant number: 180906 -Convention Microphytobenthos Phase II. The  European Space Agency  is acknowledged for the provision of Sentinel-2 images. This study also benefited from CoastObs, a  European Union   Horizon 2020 research and innovation program  (grant agreement number:  776348 ). The authors are much obliged to  Philippe Rosa  for his assistance during fieldwork and  Bruno Jesus  who helped with R codes for selecting images at low tide. We would like to thank  Stephanie Palmer  for her comments on the manuscript and for revising the English.
The dataset combined images from the two S2 satellites with different orbital cycles (#137 and #94), allowing a revisit time of 3 to 5 days. A preliminary selection was applied to all images with a relaxed constraint of less than 80% of cloud cover for the entire S2 tile. A subsequent selection considered clouds over the intertidal areas and low tide conditions (see paragraph 2. 4). Four 10 m spatial resolution bands from the MSI sensor were used to calculate the multispectral indices: B2 (490 nm), B3 (560 nm), B4 (665 nm), and B8.
Recent examples of BIR workshop publications have shown the potential of informing the information retrieval process with bibliometrics. These examples comprise topics like IR and recommendation tool development, bibliometric IR evaluation and data sets, and the application and analysis of citation contexts for instance for cluster-based search. As an example of recommendation tool development utilising bibliometrics, Wesley-Smith et al. [18]  describe an experimental platform constructed in collaboration with the repository Social Science Research Network (SSRN) in order to test the effectiveness of different approaches for scholarly article recommendations. Jack et al. [19]  present a case study on how to increase the number of citations to support claims in Wikipedia. They analyse the distribution of more than 9 million citations in Wikipedia and found that more than 400,000 times an explicit marker for a needed citation is present.
As an application of citation analysis, Abbasi and Frommholz  [21]  investigate the benefit of combining polyrepresentation with document clustering, where representations are informed by citation analysis. The evaluation of the proposed model on the basis of the iSearch collection shows some potential of the approach to improve retrieval quality. A further application example reported by Nees Jan van Eck and Ludo Waltman  [22]  considers the problem of scientific literature search. The authors suggest that citation relations between publications can be a helpful instrument in the systematic retrieval process of scientific literature. They introduce a new software tool called CitNetExplorer that can be used for citation-based scientific literature retrieval. To demonstrate the use of CitNetExplorer, they employ the tool to identify publications dealing with the topic of "community detection in networks".
Meanwhile, data science becomes a domain of multipurpose and multi-object knowledge as "today, data science, in the spirit of data + science, has become an interdisciplinary enterprise about data-enabled discoveries and inference with scientific theory and methods, algorithms, and systems"  [6]. According to  [7] , big data comprises a large volume of complex structured, semistructured, and unstructured data that goes beyond the processing capabilities of conventional databases. The processing and analysis of big data now plays a central role in decision making, forecasting, business analysis, product development, customer experience, and loyalty, to name just a few  [7]. The characterization of the question "what is big data?" is, therefore, to be understood relative to specific contexts.
In addition, there are no standards and only a few ready-made solutions. It is also a huge hassle because, in addition to technical issues, users also need to consider their data organization and associated processes. The large amount of information as well as the variety and the short topicality also pose challenges for companies  [14]. Innovative big data strategies are of great importance in order not to sink into the data jungle. The creation of transparency in the database, in the data sources, and in the variety of data is very important in order to be able to manage, validate, and analyze data effectively at all. Anyone who does not know what information is available in what form is doomed to failure.
Data Processing with Hadoop as Big Data Technology Hadoop is a technology that meets the needs of big data. It is horizontally scalable and designed as a software framework for processing very large amounts of data  [15]. Hadoop is an open-source implementation for Google MapReduce and is based on the simple programming model MapReduce  [16]. The Hadoop software is part of the Apache project and is a framework written in Java for scalable, distributed software  [16]. The Hadoop Framework handles the processing of details and allows developers to concentrate freely on the application logic. In addition, Hadoop is a well-known technology that is used for realizing big data. Large corporations such as IBM, Intel, and Oracle also rely on an extension of Hadoop support for their big data solutions.
It divides the files into blocks and saves them redundantly on the cluster, which is only slightly influenced and perceived by the user. If files are stored in it, it is generally not noticed even if individual files are stored on several computers. This implicit distribution of the data makes the file system interesting because it reduces the administrative effort of data storage in a big data system. The MapReduce model is supported by Hadoop and is also Java-based. It was introduced by Google as a method of solving a class of petabyte/terabyte magnitude problems with large clusters of inexpensive machines. New, alternative algorithms, frameworks, and database management systems have been developed to resolve the rapidly growing data and its processing. The MapReduce framework is used for the distributed and parallel processing of large amounts of structured and unstructured data, which Hadoop typically stores in HDFS, clustered across large computers  [16].
g. , cancer). Research doctors are primarily interested in ensuring that as much knowledge as possible, also from external data sources (e. g. , databases on pharmacovigilance or clinical studies), flows into a research infrastructure. In this case, time-critical aspects are rather rare. However, interpretations of the results play a greater role here because medical questions are about the detection of novel patterns and not about the constant use of health data in a complex but well-known diagnostic process. Specifically, this means that the integration of methods and visualizations is very important in this case. The focus on new hypotheses also means that a lot more data and intermediate results or results may be saved than might appear appropriate for economic reasons. Further literature in the context of the processing of big data in medicine and health care can be found in papers  [21] [22] [23].