dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We analyzed several winters of snow survey data at two sites that were located less than 1 km apart in the forest-tundra ecotone. One site was covered with sparse forest (FOREST), and one was an Arctic tundra (TUNDRA). We compared the snow properties of the two sites in terms of snow height, stratigraphy, density, thermal conductivity, and snow temperhttps://doi. org/10. 5194/tc-16-3357-2022 The Cryosphere, 16, 3357-3373, 2022 ature. Additionally, we ran simulations with the snow model Crocus to explore model performance in both environments. All the observed snow properties revealed marked differences between the two sites. Snow height was up to twice as high at FOREST than at TUNDRA due to wind-driven snow transport. This strongly affected the temperatures at the bottom of the snowpack as they were at times more than 10 • C colder at TUNDRA than at FOREST.
Acknowledgements. The authors are grateful to  Denis Sarrazin  for technical support of the CEN weather stations and the community of Umiujaq for permission to conduct our research. Financial support. This study was funded by  Sentinel North , a  Canada First Research Excellence Fund , under the theme 1 project titled "Complex systems: structure, function and interrelationships in the North". This research has been supported by the  Natural Sciences and Engineering Research Council of Canada  ( Discovery Grant ), the  French Polar Institute (IPEV)  (grant no. 1042 ), and the  Horizon 2020 program  of the European 70 Commission (INTAROS; grant no. 727890 ). CNRM/CEN is part of  Labex OSUG@2020  ( ANR-10-LABX-0056 ). Review statement. This paper was edited by  Masashi Niwano  and reviewed by  Charles Fierz  and two anonymous referees.
Code availability. The source files of SURFEX code are provided at the Git repository (http://git. umr-cnrm. fr/git/Surfex_ Git2. git, last access: 15 November 2021, Masson et al. ,  2013)  with several code management tools (history management, bug fixes, documentation, interface for technical support, etc. ). Registration is required; a description of the procedure is described at https://opensource. cnrm-game-meteo. fr/projects/ snowtools/wiki/Procedure_for_new_users (last access: 15 January 2022). Data availability. Data will be available on the Pangaea repository (https://doi. pangaea. de/10. 1594/PANGAEA. 946538 (Lackner  et al. , 2022b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-16-3357-2022-supplement.
Data Availability Statement: "MDPI Research Data Policies" at https://www. mdpi. com/ethics. Acknowledgments: Thanks to IMV technologies for the material provided. The present work was supported by Oniris, INSERM, and the French Ministry of Higher Education and Research. This research did not receive any commercial or not-for-profit sector support. Thanks to IFCE (Institut Français du cheval et de l'équitation) for the funding and supporting this project (CS-2021-014-PROAKAP-SPZ-qualité). Conflicts of Interest: Maryse Delehedde and Nicolas Sergeant are cofounders of SPQI company.
FUNDING We acknowledge financial support from  Itmo Cancer  on funds administered by  INSERM  (project  MALMO ) and from  I-SITE MUSE  (project  MEL-ECO ). AH has been funded by the  Wellcome Trust Institutional Strategic Support Fund. ML and LLC are financially supported by the  Ligue Nationale Contre le Cancer , the  Institut National du Cancer (INCa)  and the canceŕopole Grand Sud Ouest.
We manually annotate all the volumes of this series, in order to constitute a dataset allowing us to extract its character network. We perform a descriptive analysis of the network structure and compare it to real-world and fictional social networks. We also study how the process of filtering out minor characters, which is largely spread in the literature, affects the network structure. After this, we leverage complex network analysis tools to answer two research questions identified in the literature, and specifically related to Thorgal. The first concerns its hypothesized similarity with the Íslendingasögur, a Scandinavian corpus of medieval literature  [54]. The second is to know whether Thorgal actually implements a form of soft masculinity  [15] , by studying the position of women in the series.
The rest of the article is organized as follows. In Section 2, we review the literature dealing with the character networks of graphic novels. In Section 3, we describe the methods that we use to annotate our dataset and to extract our character networks. In Section 4, we introduce the Thorgal series and our related research questions. We also describe the raw dataset obtained after our annotation process. Section 5 is dedicated to the descriptive analysis of the character networks, whereas Section 6 focuses on the remaining research questions. We conclude in Section 7 by summarizing our findings and contributions, and discussing the limitations and possible extensions of our work.
Related Work There are only a very few published articles dealing with the extraction and analysis of character networks from graphic novels. As far as we know, the articles discussed in this section and listed in Table  1  constitute a comprehensive survey. Some of them deal with American super-hero comic books, Japanese mangas, Korean webtoons, or European bande dessinée. Alberich et al. [1]  study the Marvel Comics social network. They first extract a bipartite graph using the Marvel Chronology Project a , a fan-made database covering the 1961-2002 period, by linking each significant character to the comic books in which they appear. Then, they perform a one-mode projection to get a character-tocharacter network modeling same-book co-appearances. They compare the resulting unweighted co-occurrence network to a null random model and to real-world collaboration networks, such as the ones resulting from scientific co-authorship or from movie castings.
Manual Annotation The annotation step is generally not described in a very detailed way in the literature, if mentioned at all (cf. Section 2). Yet, the method and nature of the annotation have a direct effect on the constituted dataset, and therefore the results obtained later when processing it. Therefore, this step should not be overlooked. Moreover, the task is not as straightforward as it may seem at first glance, as it requires dealing with various specific situations. These are the reasons why we summarize our annotation approach in this section, and review the most typical of these situations. We illustrate them using excerpts from the Thorgal series, which is studied later in this article. We define the notion of character as any sentient entity able to have a meaningful interaction.
This would not be relevant considering our dataset, because the number of characters appearing in a single volume (34 in average) is large enough to make the extracted graph too dense to be useful. The approach adopted by Murakami et al. [39]  amounts to using a twopanel sliding window with a single-panel overlap, which generally constitutes a finer granularity than ours, as scenes are often longer than two panels. However, such a window is not narratively sound, as two consecutive panels can belong to completely different subplots. Rochat & Triclot [49]  prefer to use a two-page sliding window with a single-page overlap, which generally corresponds to a rougher granularity than ours. Using such a page-based window is questionable when applied to novels, since writers do not control the pagination of the end product, as noted in  [27].
Dataset Description As mentioned before, in this article we study the character network extracted from the Thorgal series of graphic novels. In this section, we first introduce the series itself (Section 4. 1), and list the research questions that arose from its study in the literature. We then describe the main characteristics of the dataset resulting from our annotation process, and discuss the resulting statistics (Section 4. 2). Next, we explain how we filter the character network to retain only meaningful characters (Section 4. 3). In addition, we also compare our dataset to other graphic novel datasets in the Supplementary Material (Section SM1. 3).
The Thorgal Series Our dataset is based on the Franco-Belgian series Thorgal, which was originally created by writer Jean Van Hamme and artist Grzegorz Rosiński in 1977. It takes place during a period reminiscent of the European medieval age, and mixes elements of fantasy, mythology, and science-fiction. It mainly follows the adventures of Thorgal (shown in Figure  2c ), originally a baby adopted by a Viking chief, and his family. The main secondary characters are his wife Aaricia (Figure  1b ), his elder son Jolan (Figure  1a ), his daughter Louve (Figure  2a ), and his antagonist Kriss of Valnor (Figure  3c ). This graphic novel is still going on nowadays with several other authors and artists, as well as three spinoff series. Two of these are centered around secondary characters (Kriss of Valnor and Louve), whereas the third focuses on the youth of Thorgal and Aaricia youth (Young Thorgal ).
Dataset Properties A single annotator identified the characters, interactions, and scene boundaries for the whole series, as explained in Section 3. 1. In order to assess the reliability of this annotation, three independent annotators did the same work on 4 randomly picked volumes. The agreement between these annotators and the main one is strong, with micro-average F -measure scores of 0. 97 for scene boundaries and of 0. 94 for Complex Network of a Graphic Novel 11 character identification (cf. Section SM1. 2 of the Supplementary Material for more technical details). In addition, we also annotated the biological sex of the characters: Female, Male, Mixed (for groups of characters), or Unknown. Each volume follows the 48CC bandes dessinées standard, i. e. a 48-page hardcover color book (of which 46 pages are actually drawn).
It takes approximately one hour to annotate such a book. Our dataset b , including the raw data and the extracted networks, as well as our source code c are both available online. The 63 volumes constituting our dataset contain 21,259 panels distributed over 2,925 pages. They involve a total of 1,480 characters interacting in 4,622 scenes. The average volume and scene include 34. 2 and 3. 1 characters, respectively. One could assume that longer scenes tend to involve more characters, but this is not the case: the correlation between both quantities is rather weak (ρ k = 0. 18). The story is clearly dominated by Thorgal and his family, as illustrated by Table  2 , that shows the most frequent characters. Besides the main protagonist, Thorgal himself, which is present in 38% of the scenes, these include his wife Aaricia (18%); his eldest son Jolan (17%, introduced in vol.
Character Filtering When extracting character networks from works of fiction, many authors apply a filtering step in order to remove very minor characters, namely extras, whose contribution to the story is not deemed sufficient to be taken into account during the subsequent analysis  [27]. Criteria For this purpose, they use mainly three criteria. First, the most widespread is a minimal threshold over the number of occurrences. For instance, Elson et al. [16]  remove characters that appear three times or fewer in a novel, or that amount to 1% or less of all occurrences; Suen et al. [52]  ignore characters that speak fewer than five times over a play or movie; and the Marvel Chronology Project dataset used by Alberich et al. [1]  and Gleiser  [20]  is built by retaining only super-heroes and super-villains, provided they appear in at least two issues.
By comparison, there are 22% women among Marvel characters, with also an improvement when focusing on important characters: 31%  [23]. However, it is worth stressing that only super-heroes and super-villains are taken into account in these statistics, whereas our Thorgal dataset contains all characters appearing in the narrative. When focusing on the 10 most central characters, we find 4 or 5 females, depending on the considered centrality measure. In  [26] , Kagan et al. study gender bias in movies spanning a period of one century, through the analysis of their character networks. By comparison with Thorgal, they find a lower average of 33% women among the 10 most central roles. Note that their dataset includes only named characters. They get a slightly lower proportion when considering the 5 most central roles.
Gender Equality Testing The Bechdel test is a popular method to test whether a narrative represents women fairly. It has three criteria: 1) there must be at least two named female characters; 2) they must talk to each other; and 3) about another topic than men. Out of the 4, 622 scenes constituting our dataset, 441 (10%) involve only women. Among them, 200 have more than one female character, which talk to each other in 172 cases. After a manual assessment of these scenes, it turns out that 87 (51%) comply with the third criterion of the Bechdel test. However, as noted by Kagan et al. [26] , the Bechdel test can be considered as too lenient, as a single interaction is enough for a narrative to pass, independently of the rest of the story. Instead, they propose a quantitative approach relying on a gender-based comparison of the numbers of interactions.
Conclusion In this article, we proposed a new corpus describing the characters of the graphic novel Thorgal, and the interactions occurring between them in the course of the narrative. We then leveraged this dataset to extract the series character network. We conducted a descriptive analysis which showed that it exhibits certain realistic properties (i. e. features also present in real-world social networks): small-worldness, scale-freeness, hierarchical structure, and sensitivity to attacks. By comparison, the comics-based Marvel network studied by Alberich et al. [1]  is less realistic (less transitive, not scale-free, robust to targeted attacks). However, the Thorgal network is also disassortative, a trait generally found in fictional character networks. Moreover, we argue that it exhibits only the appearance of realism: the structure of the network does not necessarily represent realistic social interactions, but rather the outcome of the creative process behind the unfolding of the story, which happens to possess some of the properties observed when modeling real-world relationships.
Second, we showed that the notion of soft masculinity discussed in  [15]  seems to affect the network structure, both intrinsically and when compared to other works of fiction. Our work can be extended in several ways. We plan to expand the dataset by annotating the character interactions in order to include directionality and polarity: this will allow us to extract directed and signed networks, and therefore to apply additional tools to complement our analysis. Another direct extension is to use the narrative smoothing method presented in  [8]  to extract a dynamic version of our network and study its evolution. We also want to conduct a comparative study of character networks extracted from works belonging to the three main graphic novel traditions, namely North American, European, and East Asian.
SM1. 3. Comparison It is worth comparing some characteristics of our dataset with those listed in Section 2, whenever possible. For the European and Asian graphic novels studied by  Rochat & Triclot [49] , we use the dataset provided in the article f to compute the statistics ourselves. For the Marvel comics, we leverage the information provided in Alberich et al. 's article  [1, 20] , and also use the dataset available online g. Unfortunately, the Manga-and Webtoon-based datasets respectively constituted by Murakami et al. [39, 40, 41]  and Lee & Kim  [30]  are not available and consequently could not be included in this comparison. Note that we focus on the filtered version of Thorgal, as only the main characters are considered in the other datasets. Table  SM5  summarizes the main statistics of the mentioned graphic novels.
It reflects some differences in the way these are published in America, Europe and Japan, from the perspective of their format and production pace. These differences are important in the context of our study, as they are known to affect various aspects of the graphic novels, in particular their narrative structure  [31]. In the United f https://github. com/mtriclot/Belfort g http://bioinfo. uib. es/ ~joemiro/marvel. html States, the primary medium of dissemination is the publication of monthly issues of approximately 30 pages, which are generally not systematically collected in omnibus books  [13]. Recent Marvel issues tend to be 20 pages long  [14]. In France and Belgium, bandes dessinées used to be pre-published in specialized weekly magazines collecting various distinct series, before being released as hardcover albums.
Nowadays, such pre-publication still exist, but is less frequent, and the main means of dissemination is the album, typically 46 pages long. Thorgal and Aldebaran follow this format, whereas Metabarons is a bit longer, with 62-page volumes. In Japan, mangas are also pre-published in similar weekly or monthly magazines, under the form of 20-page long chapters, then collected in so-called tankobons  [31]. The number of pages produced is larger than in Europe though, as mangas are generally produced by studios and not by individual authors. The Akira and Gunnm volumes contain approximately 175 pages, and their chapter 20-30 pages. Based on these lengths, manga chapters seem to better fit a comparison with American and European volumes, a priori. The compared datasets vastly differ in their numbers of volumes.
The Marvel dataset  [1]  is the largest, as it describes the work of many people over forty years. By comparison, the main Thorgal series was produced by two persons; this dataset is second in number of volumes only because it is complete. It is not the case of the datasets produced by  Rochat & Triclot [49] : Metabarons is part of the Incal universe, Aldebaran focuses only on the second arc of the larger series, and Akira and Gunnm contain only the first few volumes of their respective series. Despite these differences, the average number of volumes by character is rather similar for all datasets (2-4) except Marvel. The average number of characters by volume is also relatively close for all series  (15) (16) (17) (18) (19) (20) , except for Metabarons  (27). Finally, contrary to our initial assumption, the statistics obtained for the mangas when using chapters instead of volumes are the most different from the other datasets.
The latter type corresponds to a selection of novels and plays, and also includes the Marvel dataset of Alberich et al. [1]. Mac Carron & Kenna identify robustness to targeted attacks as a sign of the network artificiality, in the sense that its structure relies too much on a few important characters to be realistic. All three considered types of social networks tend to be robust to random attacks, however real-world ones are vulnerable to targeted attacks whereas fictional ones are robust to these. The mythological networks studied in  [33]  tend to be on the realistic side. We adopt the same method to study Thorgal. Both the unfiltered and filtered networks are robust to random attacks: removing 5% of the vertices results in giant components containing 89% and 100% of the remaining vertices, respectively.
Data for all snow variables were available for WRFc-NoahMP, CCLM-CLM5. 0, WRFa-NoahMP, RCA, and WRFb-CLM4. 0. However, REMO-iMOVE, RegCMa-CLM4. 5, and CCLM-TERRA did not have data for snow depth, while CCLM-VEG3D could only provide a binary number of 0 and 1 for the snow cover fraction. Snow depth for REMO-iMOVE, RegCMa-CLM4. 5, and CCLM-TERRA was calculated from snow water equivalent using a constant value of 312 kg m -3 for the bulk density as described in  Sturm et al. (2010).
Acknowledgements. In Norway, the simulations were stored on the  NIRD (National Infrastructure  for Research Data) server with resources provided by Uninett Sigma2 -the National Infrastructure for High Performance Computing and Data Storage in Norway. WRFc-NoahMP simulations were performed and stored on resources provided by  Uninett Sigma2 -the National Infrastructure for High Performance Computing and Data Storage in Norway  ( NN9280K ,  NS9001K , and  NS9599K ). WRFb-CLM4. 0simulations were supported by computational time granted from the  National Infrastructures for Research and Technology S. A. (GRNET S. A. )  in the ARIS national high-performance computing facility (project nos. pr005025 and pr007033_thin). Edouard L. Davin  and  Ronny Meier  acknowledge financial support from the  Swiss National Science Foundation (SNSF)  through the  CLIMPULSE (CLimate IMPacts of Utilizing Land in Switzerland and Europe)  project and thank the  Swiss National Supercomputing Centre (CSCS)  for providing computing resources.
Peter Hoffmann  is funded by the  Climate Service Center Germany (GERICS) of the Helmholtz-Zentrum Hereon  in the framework of the  Helmholtz Institute for Climate Service Science (HICSS)  project  LANDMATE (Modelling human LAND surface Modifications  and its feedbacks on local and regional cliMATE). The authors gratefully acknowledge the  WCRP CORDEX Flagship Pilot Study LUCAS (Land use and Climate Across Scales ) and the research data exchange infrastructure and services provided by the  Jülich Supercomputing Centre, Germany , as part of the  Helmholtz Data Federation initiative. Financial support. This research has been supported by the  Norges Forskningsråd  (grant no. 254966 ) and the  Fundação para a Ciência e a Tecnologia  (grant nos. PTDC/CTA-MET/28914/2017  and  UIDB/50019/2020 ).
Data availability. Simulations were forced by the ERA-Interim reanalysis dataset (https://www. ecmwf. int/en/forecasts/ datasets/reanalysis-datasets/era-interim 28 November 2018, Dee et al. , 2011). The source code for the Weather Research and Forecasting (WRF) model can be accessed at https://doi. org/10. 5065/D6MK6B4K (Powers et al. , 2017). The documentation for COSMO-Model is available at https://www. dwd. de/EN/ourservices/cosmo_documentation/ cosmo_documentation. html (last access: 12 August 2021, Sørland et al. , 2021), although a license is required for access (http://www. cosmo-model. org/content/consortium/licencing. htm, COSMO, 2022). The RegCM4 model is available at https://github. com/ictp-esp/RegCM (last access: 8 March 2021;.
Seasonal patterns of dominant PAM-derived clusters at three AERONET sites (GSFC, Kanpur, and Banizoumbou) strongly influenced by U/I, DU, and BB types, respectively, showed a satisfactory agreement with variations of aerosol mixtures reported in the literature. These PAM-derived models augment the National Aeronautics and Space Administration's (NASA's) aerosol models (A2010) applied in its operational AC. To demonstrate the validity and complementary nature of our models, a coupled ocean-atmosphere radiative transfer code is employed to create a simulated dataset for developing two experimental machine-learning AC processors. These two processors differ only in their aerosol models used in training: 1) a processor trained with the A2010 aerosol models (ACI) and 2) a processor trained with both PAM and A2010 aerosol models (ACII).
Recent studies have also demonstrated the utility of the ultraviolet (UV) bands for phytoplankton characterizations and biomass estimations off the coasts of California  (Kahru et al. , 2021) , a spectral region where effects of absorbing aerosols are most confounding  (IOCCG, 2010). The overarching purpose of this contribution is to reduce one major uncertainty component, among others [e. g. , skyglint  (Gilerson et al. , 2018) ], in the AC framework and enhance the quality of retrieved R rs products, particularly in the blue bands  (Gordon et al. , 1997; IOCCG, 2010) , over inland and coastal waters. To date, there is no real-time integration of independent aerosol datasets into AC frameworks. This limitation is due to the lack of concurrent in situ measurements of aerosol properties and/or poor spatial and temporal representation of atmospheric models.