dataesr/scientific-paragraphs-categorization huggingface dataset:
Recent projects investigate automatic writing of Wikipedia articles in low endowed Wikipedia linguistic versions  [29]. The AbstractWikipedia project aims at solving an automated text generation task from semantic knowledge hosted on Wikidata. This is made possible as Wikidata is one of the highest qualitative multilingual knowledge repository, even if the amount and quality of its knowledge is not always connected to the number of worldwide native speakers  [12]. A survey  [6]  compared these knowledge graphs in terms of quality according to many metrics and gives criteria to find the most suitable graph for the needs of researchers. None of these graphs is dedicated to events. EventKG  [10]  fills this gap. It is based on the Simple Event Model  [28]  (SEM) ontology and intended to store events.
This purpose is striven towards the usage for digital libraries by providing for instance semantic or event based search engines to explore historical news  [23]. Wikipedia articles lead sections offer qualitative data and give an overall picture of events  [20]. They often contain elementary event information: dates, places, and participating entities. The latter have also been used to extract events from real time news, with a particular focus on people, organisations, places and dates when semantically enriching documents  [13]. Semantic labeling thanks to Wikidata and Wikipedia  [5]  has been proven useful in semantic search engines  [21]  with annotated press articles. We notice in the state of the art that exploiting Wikidata and Wikipedia entities and elementary event knowledge is useful, especially with specific use cases as exploring digital libraries.
We propose to associate Wikidata and Wikipedia to exhaustively collect real-world-event qualifiers which are dates, places and participating entities. Ontologies such as EventKG do not provide comprehensive data, some entities may miss. We propose to collect knowledge where it is: from encyclopedias. On another hand, we know Wikimedia projects are multilingual. Some studies on event mentions tracking suggested to focus on hub languages  [22]  (languages with a high number of articles and significant overlap in article coverage) to qualify events, we will propose another approach, based on vernacular languages.
Representing Events from Wikidata and Wikipedia At first, we address the first question: we wish to qualify, in any language, realworld-events based on publicly available resources. We present the method we developed to collect event qualifiers from Wikidata and Wikipedia. We consider they both provide sufficient data to characterize real world events. We act in the continuation of the Automated Content Extraction program  [3]  and existing event ontologies  [28, 24]. Our event qualifiers are used in the same context as ACE's event arguments. We benefit from Wikidata to extract elementary event information and use Wikipedia to aggregate all the entities involved in it.
The Extraction of Elementary Event Information Wikidata supplies two different event identifiers, which point subtleties: some apply for breaking events, others for event with premisses. In this paper, we conform to the Wikidata Event Type (WET)  [21]  definition which accepts both. We refer to events as happenings in the real world which have spatio-temporal anchors and additional entities involved in it. From Wikidata entities, we only collect the event type and date, the locations, participants and labels. We consider these properties discriminate two events: it is unlikely that two distinct events have the same label, type, and occurred at the same place at the same time. As a community project, Wikidata is not an exhaustive data source. Table  1  shows that expecting a comprehensive event qualifiers collection is not possible when only capitalizing on Wikidata.
They respectively gather 1. 120 billion and 128 thousands of articles  [7]. From articles lead sections, we keep people, locations and organisations or geopolitical entities. The number of occurrences found for each entity in all lead sections is counted and represents the entity weight in relation to the event. This weight shows the relevance of the entity in relation to the event. We assume the entities found in multiple lead sections are important entities in the event description. Let us take the example of the assassination of Rasputin event on Wikidata (identified by Q2882749 ). From Wikidata, we retain the date, locations, participants and entity labels in multiple languages. We ignore, for the time being, other properties associated with the event type, such as the target for a political assassination.
Participants and locations are linked entities and identified by their URIs in the ontology. We supplement the event characterization with entities found in Wikipedia articles. There only exists Wikipedia articles written in French and Spanish for this event. After processing, we obtain, among others, these triples: (PER, Q312997 [Felix Yusupov, perpetrator], 3), (PER, Q43989 [Grigori Rasputin, target], 2), (PER, Q34266 [Stanislas Lazovert], 1). The weights, respectively 3, 2 and 1 show that Yusupov is a major player in the event, while Lavozert has a limited implication, even if he is a known plotter. Weights synthesize historical knowledge and give an unbiased information about entities implication in the event. The Lavozert entity is absent from Wikidata and was extracted from the French lead section.
Localizing Event Qualifiers The event description consists of an association of absolute properties such as the date and labels with links to knowledge bases. The description is fundamentally multi-lingual. In most cases, Wikidata provides multiple names in different languages (i. e. with different spellings) for each entity. To continue with the previous example, in French the entity Q312997 on Wikidata is equally written Félix Youssoupoff or Felix Youssoupov. This final step transforms abstract entities, identified by their Wikidata URIs to a language-dependent description. It takes all the alternative spellings for every entity involved in the event and saves them in the targeted language. Our approach makes it possible to get the event description in Italian even if, in this example, only French and Spanish Wikipedias were analyzed.
Conclusion In this section, we proposed a method to characterize real-world events with qualifiers. Our method relies on the Wikidata ontology and Wikipedia to extract all the event participating entities. Our approach is multi-lingual: entities are identified by URIs but can be turned into any existing language. By selecting a subset of all the available Wikipedia languages, we assume we can efficiently collect most of the event entities. With this paper, we release the wikivents tool 1  that implements the method described in this section. It is able to automatically extract the event representation and participating entities with a Wikidata identifier as input. The package can be customized in order to gather data from other resources, out of Wikimedia projects. More information about its API and tutorials are available in the project archive.
Enhancing the Event Representation with Vernacular Languages Although we introduced in Section 3 our method to collect most of the event qualifiers, the arbitrary selection of some hub languages is biased. Widely spoken languages that are less present on Wikipedia are ignored. Numerous languages (i. e. Arabic, Mandarin, Hindi, Bengali, Portuguese or Russian) are concerned. We intend to discover whether the language influences the event representation when processing Wikipedia articles. To answer this second question, we propose to extend the list of processed languages with vernacular languages. In this section, we carry out a comparative experiment to know how useful and pertinent it is to benefit from vernacular language when processing Wikipedia articles. First, we introduce the dataset we built, then the evaluation process and our results.
Datasets description Selected events In order to compare the influence of language, we built two distinct datasets with the same events. As the nature of an event is ambiguous  [26] , we qualify of indisputable an event that is considered as such for people with various backgrounds (history scholars  [25] , psychologists  [19]  or NLP reseachers  [17] , for instance). This led us to restrict events to only three categories, taken as examples: assassinations and attacks, natural disasters and political happenings. On Wikidata, the first two concern breaking events while the last gathers events with premisses. We randomly selected two event types for each category. -Assassination and attacks: political murder (Q1139665 ) and terrorist attack (Q2223653 ). -Natural disasters: earthquake (Q7944 ) and volcanic eruption (Q7692360 ).
-Political happenings: ceremony (Q2627975 ) and election (Q40231 ). We express the same reserve about the exhaustiveness of Wikidata. The number of events reported is not uniform over the years but tends to grow since the beginning of the 21 st century. This increase must not be interpreted as an increase of events happening in the world but as a better data quality, especially with events now better anchored in time  [21]. Therefore, we decided to only focus on events happening in the last fifty years, from January 1970 to December 2019. This ensures to exclude poorly documented events. For the sake of the experiment, it is necessary to process events which are described in at least one Wikipedia article. Thus, we exclude events without any articles. The number of found events is reported in Table  3.
Vernacular languages The difference between the two datasets resides in the number of languages processed to gather participating entities. The first one is called "base language" and built from the five languages mentioned above. We call the other one "all languages" for which vernacular languages are processed in addition to those from the base dataset. In case a Wikipedia project in this language is missing, we fall back to its standard dialect. For instance, American English is "en-us" which does not exist on Wikipedia, but is a dialect of "en". This process introduces another bias: temporality. French was an official language in Algeria before 1950. This information is missing from Wikidata which only provides current information. Moreover, the ontology sometimes provides the languages spoken in the countries, including the non official ones.
We state the hypothesis that if an event occurs somewhere, the event will be better reported on Wikipedia in one of the languages spoken where it happened. It may happen that the political entity changed through the years: the Easter Rising occurred in the United Kingdom of Great Britain and Ireland. This country does no longer exist, as the current United Kingdom exists since 1922. This is not an issue as Wikidata still informs about official or spoken languages. In any case, our dataset comprises events from 1970 to 2019, which limits this risk. Conclusion With this paper, we release the datasets we built. 2 3 They comprise 241 events divided up among six events types, in three categories. The first dataset is produced with selected languages, the other with selected and vernacular languages.
Experimental Metrics The comparison between the two datasets is based on four metrics we consider as relevant to evaluate our hypothesis: processing vernacular languages on Wikipedia supplies additional and more precise information about participating entities. Metrics respectively are the number of participating entities found in the lead section of Wikipedia articles (M1), the number of tokens in the lead section, to the exclusion of tokens shorter than two characters (M2), the ratio between the two previous metrics (M3) and the number of alternative names found for each Wikidata entity, as mentioned in Section 3. 3 (M4). Table  4  records the results for the assassination of John Fitzgerald Kennedy in 1963. As the assassination took place in the USA, an English speaking country, no additional language gets processed in addition to the five default ones.
Experiment Evaluation Although when building the dataset in Section 4. 1 we excluded events without any Wikipedia article, it may happen that some selected events do not exist on Wikipedia in any of the processed languages. They were not filtered out in the first step because they have at least an article, but written in a language which is not a hub or a vernacular language, which we were unaware of at the first step. It is also a necessity to exclude events for which we do not know either any official or spoken language. The two overlap in most cases. We report in Table  6  the number of events excluded by this final selection. When considering vernacular languages, the number of events to analyze increases. This is the first argument in favour of our hypothesis that events are better described in their vernacular languages.
In order to compare the description of events in the two datasets, we apply, for each event, the same computations as shown in Table  4  and Table  5. For each metric, we check whether one of the official languages spoken in the event place is in the best three languages to characterize it. Results shown in Table  7  show, for each metric, for how many events a vernacular language is in the top three of language that best represent the event. Results are significant with only the first language but selecting the best third languages tends to limit the issue described with the Kennedy's assassination example. By doing so, we state that the five languages we previously identified as core languages are not sufficient to accurately extract event qualifiers. This statement refutes the hypothesis we assumed in Section 3 that led us to only consider only five hub languages.
Conclusion In this paper, we described a method to gather event qualifiers coming from Wikidata and Wikipedia. We analysed and described its shortcomings and proposed to include vernacular languages. Our experiments demonstrate that this approach is greatly beneficial when describing events. We release an implementation of our approaches, and actually hereby make publicly available the source code, the analysis as well as the datasets, to be updated regularly. In the near future, we will add features to encode the event model into SEM  [28]  or LODE  [24]. Researchers working on real-world events may already take advantage of our tool to fulfil their needs. It can be used to qualify events for their semantic search engines. We already use the library as the entry point of an event based search engine for a historical news digital library.
The lack of a clear wet-dry pattern to ENSO events in the area  (Schneider et al 2018, Kiefer and Karamperidou 2019)  and the influence of catchment differences on lake sediment records  (Schneider et al 2018)  also reduces the reliability of these records as indicators of IADV and ENSO. Furthermore, the spatial complexity of IADV necessitates regional-scale reconstructions to understand differences in IADV changes. The Gallapagos and Pallcacocha records were a focus for reconstructions of IADV because, compared to most other terrestrial records, they have quasi-annual resolution. Speleothems provide an alternative source of high-resolution data in monsoon regions and can therefore be used to investigate precipitation IADV over land. Speleothems record changes in the strength of the monsoon, via a combination of changes in regional precipitation and circulation changes that cause changes in moisture source and transport pathway  (Sinha et al 2015 , Cheng et al 2019 , Parker et al 2021).
However, there are several factors that influence the oxygen isotopic composition of speleothems and might complicate the interpretation of short-term climate variability, including changing growth rates and the mixing of meteoric water in the epikarst  (Lachniet 2009)  although compositing of speleothem records can be used to minimise the impact of such local factors when deriving regional climate signals  (Comas-Bru et al 2019 , Parker et al 2021). In this paper, we use speleothem oxygen isotope (δ 18 O) records from the second version of the Speleothem Isotopes Synthesis and AnaLysis (SISAL) database  (Atsawawaranunt et al 2018 , Comas-Bru et al 2020a , 2020b)  to reconstruct changes in IADV in monsoon regions through the Holocene. We first investigate the potential influence of confounding factors on δ 18 O variability using multiple regression.
Speleothem oxygen isotope data We use speleothem δ 18 O data from the SISAL database  (Atsawawaranunt et al 2018 , Comas-Bru et al 2020a , 2020b) , selected using the following criteria: • The record is located in a monsoon region, between 40 °N and 35 °S; • The mineralogy is known and does not vary because oxygen isotope fractionation depends on speleothem mineralogy, introducing a non-climatic influence on δ 18 O s. d. ; • The record covers at least 3 000 years of the Holocene; • The record has a mean sampling resolution of at least 20 years; This resulted in the selection of 144 records from 79 sites (figure  1 ). We interpret speleothem δ 18 O records (δ 18 O spel ) as changes in the strength of the summer monsoon, via regional precipitation and moisture source pathway and source changes (including oceanic versus land-derived moisture), which likely reinforce one another to give more negative δ 18 O when the summer monsoon is stronger  (Vuille et al 2003 , Yang et al 2016 , Cai et al 2017 , Kathayat et al 2021).
Regional-scale IADV amplitude evolution We reconstructed IADV amplitude through the Holocene for individual monsoon regions by calculating IADV spel for a 100-year sliding window with 50% overlap (as described in section 2. 2). We corrected for the influence of confounding factors by subtracting the MLR slope and intercept values from the IADV spel values. Regional composites were constructed by fitting a running median and interquartile range through the IADV spel values. These composites represent the evolution of the overall amplitude of IADV represented by numerous sites in each region. We analysed IADV spel changes through the Holocene using breakpoint analysis to distinguish intervals with coherent behaviour  (Bai and Perron 2003). Breakpoint analysis was carried out using the strucchange package in R  (Zeileis et al 2002 , Zeileis et al 2003).
Influence of confounding factors on δ 18 O spel variability The MLR model using growth rate and mean δ 18 O and the alternative model, using n-samples and mean δ 18 O, produced similar results. The models show a significant (P <0. 001) relationship between IADV spel and both growth rate (or n-samples) and mean δ 18 O spel (table 1, table S1 available online at stacks. iop. org/ERC/3/ 121002/mmedia). Growth rate has a significant positive relationship with IADV spel (figures 2, S1). There is a significant negative relationship in the NH and a significant positive relationship in the SH between IADV spel and mean δ 18 O spel (figure  2 ). The models have low R 2 (NH: 0. 06; SH: 0. 27) suggesting that confounding factors only have a limited influence on IADV spel. 3. 2. Regional δ 18 O spel variability evolution Three regions have sufficient data to reconstruct composites of IADV spel : the Indian Monsoon (ISM), East Asian Monsoon (EAM) and South American Monsoon (SAM) (figure  1 ).
Continuous monitoring of water surfaces is essential for water resource management. This study presents a nonparametric unsupervised automatic algorithm for the identification of inland water pixels from multispectral satellite data using multidimensional clustering and a high-performance subsampling approach for large scenes. Clustering analysis is a technique that is used to identify similar samples in a multidimensional data space. The spectral information and derived indices were used to characterize each scene pixel individually. A machine learning approach with random subsampling and generalization through a Naïve Bayes classifier was also proposed to make the application of complex algorithms to large scenes feasible. Accuracy was evaluated using an independent dataset that provides water bodies in 15 Sentinel-2 images over France acquired in different seasons and that covers a large range of water bodies and water color types.
The validation dataset covers a water surface of more than 1,200 km 2 (approximately 12 million pixels) including over 80,000 water bodies outlined using a semiautomatic active learning method, which were manually revised. The classification results were compared to the water pixel classification using three of the major Level 2A processors (MAJA, Sen2Cor and FMask) and two of the most common thresholding techniques: Otsu and Canny-edge. An input mask was used to remove coastal waters, clouds, shadows and snow pixels. Water pixels were identified automatically from the clustering process without the need for ancillary or pretrained data. Combinations using up to three water indices (Modified Normalized Difference Water Index-MNDWI, Normalized Difference Water Index-NDWI and Multiband Water Index-MBWI) and two reflectance bands (B8 and B12) were tested in the algorithm, and the best combination was NDWI-B12.
Reference Maps and Study Area To test and validate the new water extraction methodology, it is necessary to have a good reference dataset. However, obtaining a good reference dataset can be a challenge for large areas because in-situ reference databases are scarcely available at a broad scale and remote sensing databases require costly, time-intensive investment  (Baetens et al. , 2019). Although previous studies have been performed to provide global water datasets, such as Global Surface Water data, provided by the European Commission  (Pekel et al. , 2016) , or Global Water Bodies -GLOWABO data  (Verpoorter et al. , 2014) , our goal is to provide a per-scene pixel classification that is affected by instant climate and water conditions. The approach of ALCD consists of an iterative process in which the operator manually selects the reference points, trains the machine learning model based on random forest and classifies the scene.
After the classification, the operator visually determines the possible imperfections and labels new pixels where the classification went wrong or was uncertain and then repeats the cycle until a desirable result is achieved. The operator performs a manual correction of persistent errors if necessary. The continental water area and percentage were calculated while accounting for only the valid pixels within each scene. For the continental water domain, the ALCD algorithm was used to produce the CNES ALCD Open Water Masks dataset  (Santiago, 2019) , which is available online. This open dataset comprises open water masks for 16 Sentinel-2 scenes over 9 different regions in France that were selected to minimize the presence of clouds while including areas with diverse land coverage (Figure  1 ).
Some areas have reference masks in different seasons, such as the end of summer and the end of winter periods. The masks were generated at a 10 meter resolution, with each tile covering 110 km x 110 km. Among the available scenes, tile T31TGK, from the Alpes region during winter, was discarded because of the unavailability of ancillary data related to this scene that are required to complete the study. The selected dataset has an inland area coverage of 96. 315 km 2 with approximately 1. 239 km 2 of surface water and water quality characteristics that would match most European countries. The complete list of scenes used in this study, with basic characteristics and water coverages and percentages, is described in Table  1.