dataesr/scientific-paragraphs-categorization huggingface dataset:
We present Moliverse, an integration of the molecular visualization framework VIAMD into the astronomical visualization software OpenSpace, allowing us to bridge the two extreme ends of the scale spectrum to show, for example, the gas composition in a planet's atmosphere or molecular structures in comet trails and can empower the creation of educational exhibitions. For that purpose we do not use a linear scale traversal but break the scale continuity and show molecular simulations as focus in the context of celestial bodies. We demonstrate the application of our concept in two storytelling scenarios and envision the application both for science presentations to lay audiences and for dedicated exploration, potentially also in a molecule-only environment.
Exploranation is founded on the synergistic coevolution of tools for exploratory analysis and tools for explanatory analysis. This emerging paradigm shift is built on the realization that the same tools and algorithms that domain experts use to analyze datasets can also be used by the general public to understand the same datasets. Expanding the use-cases of these tools lead to a technological cross-fertilization between domain specific use and science communication, which in turn causes an increased usability that is extremely useful in public outreach activities inside informal science institutions  [11]. The use of Exploranation in immersive display environments, for example power walls, planetariums, or head-mounted displays, has large potential as they increase the notion of scientific context to the presented datasets.
Furthermore, immersive environments also provide the necessary scaffolding of knowledge to the general public, and serve to accelerate scientific discoveries for the domain scientists themselves  [12]. So far, the domain expert tools used to analyze molecular dynamics lack many of the features to make them capable of supporting exploranation. The work presented here aims to reduce this gap, not by making the domain science tool more explanation-capable or the contextualization tool more explorationcapable, but rather by embedding the capabilities of one into the other, thus combining the strengths of each tool. In this work, we utilize the VIAMD software  [4] , a feature-rich molecular dynamics analysis tool as the domain expert tool. It is designed to operate on a single screen, but lacks the capability to render in immersive environments on its own.
For the contextualization framework we utilize OpenSpace  [13] , which is designed for displaying a variety of datasets in immersive environments covering vast scales throughout the universe, but lacks the ability to render molecular datasets. Specifically, our Moliverse system allows us to show molecular data in the context of astronomical science shows to audiences in dome theaters worldwide. Our goal with this approach is to make the vast amounts of molecular simulations accessible to the public in their accurate scientific context while simultaneously opening up the benefits of immersive display environments to domain scientists (see Fig. 1 ). We present two use cases that highlight this acquired functionality: (a) a molecular composition of planetary atmospheres at different altitudes, complementing the already physically-based rendering inside OpenSpace  [14]  and highlighting the change in composition and density and (b) a series of proteins highlighting the variety of conformations and sizes in the context of a scale ladder of other objects.
This last use case is a modern reimagination of a portion of the seminal ''Powers of Ten'' video by Eames and Eames  [15, 16]. The remaining aspects of this video are already present in the OpenSpace software. In summary, Moliverse provides us with the capability to: • render molecular simulations in the context of space, illustrated with the visualization of atmospheric composition of different planets and Titan, • utilize several display modalities, such as laptops, touch table, planetaria, and head-mounted displays, and • facilitate the analysis of domain scientists to present their results in an immersive environment.
Exploranation The large availability of scientific datasets and performant computing materials have led some science centers to explore the possibility of providing visitors with the same visualized datasets or models that domain experts use. To facilitate the understanding of these complex datasets by a lay audience, the interactive exploration is augmented by annotations, figures, and general explanations. For this synergy between exploratory systems, usually used by experts, and explanatory visualization that usually guide readers, Ynnerman et al. [6, 8]  have coined the term ''exploranation''. Exploranative systems provide a meaningful pedagogical opportunity when accessing and interacting with challenging scientific concepts that are beyond the comprehension of a non-expert public. Indeed, as they directly explore visualizations of real scientific data, the tailored additional explanations given to them facilitate self-generation and discovery of the communicated concepts.
A good illustration of the exploranation concept lies in the OpenSpace system  [13] , which is used by experts and laypeople alike to explore astronomical datasets. Today, the system is versatile enough to allow visitors Fig. 2. A scale axis of reference objects presenting the vast scale differences between the molecular world and the observable universe. to explore, alone or guided live by a facilitator such as in a dome-theater, fine-grained and precise images of planetary bodies or data from far-away stars. Beyond the infinitely large, the concept of exploranation has also been used to explore nanotechnology, its benefits, and potential pitfalls. For example, the NanoSim system  [8, 17, 18]  relies on Virtual Reality and exploranative concepts to address misconceptions in nano physics. With our own system, we aim to reuse the versatile OpenSpace system to visualize molecular datasets and convey molecular concepts to a lay audience via the exploranative concept  [19, 20].
Astronomy visualization tools Apart from OpenSpace, there are a plethora of other software tools available to visualize 3D astronomical data sets, the Uniview platform  [36]  is widely used in interactive planetarium shows, as is Evans & Sutherland's Ditistar  [37] , or Digital Sky  [38]. All of these options, however, have the drawback of being closedsource ecosystems which severely limits the ability to use them for research applications. On the open-source end, Celestia  [39] , Gaia Sky  [40]  and the CROSSDRIVE project  [41, 42]  would have all been potential candidates for an integration with MDlib. We chose to focus on OpenSpace as it is both the most mature of these options and continuously being updated.
OpenSpace OpenSpace is an astro-visualization software able to contextualize vastly different scales from planetary surfaces to galaxies and including the whole observable universe. The universe is navigable in both space and time and users can interactively explore any celestial body. It is built upon an aggregate of many data sources from telescopes, space missions, satellites, and simulations. It is highly extensible and user-customizable with modules and configuration files and acts as an environment enabling the implementation of specific visualization methods, such as the ones presented in this work concerning the molecular world. The software is employed in many popular science in museums and shows internationally. It is also not only designed for such large-scale installations but targets a variety of devices, ranging from personal laptops, tiled screen displays, and planetarium domes.
VIAMD and MDlib VIAMD stands for Visual Interactive Analysis of Molecular Dynamics and is a research tool used to visualize and analyze molecular data  [4]. It is designed to integrate both the visualization and the analysis of molecular dynamics in the same environment, while being flexible and interactive. VIAMD comprises the MDlib back-end library that implements the core features of VIAMD. The MDlib library is completely independent from the VIAMD user interface frontend and hence can be reused by other applications. This makes MDlib perfectly suited to process and render molecules in OpenSpace. It provides all the heavy-lifting to integrate molecular loading and rendering in any OpenGL context as well as the interpretation of VIAMD's powerful built-in scripting language.
The global coordinates, with their lost precision, are then transformed again to the camera view-point, where the molecule may be close to the origin, hence would be able to benefit from great numerical precision, but such precision was lost already. This example demonstrates how one has to be careful when manipulating numerical values in OpenSpace. In this case, the problem can be partially solved by pre-multiplying the model and view matrices beforehand. More generally, the issue is circumvented in OpenSpace by using double-precision floating-point numbers, but that also comes at a computational cost, and is also generally not used by external libraries like MDlib. There are other issues with the sub-meter scale specific to OpenSpace. There are some places in the code where a minimum distance of 1 m is assumed and changing this would have snowballing implications that would hinder the portability of our prototype with future versions of OpenSpace.
Implementation We implemented the molecular rendering in OpenSpace as an optional module that internally utilizes MDlib for molecular loading, processing, and rendering. The module exposes two conceptual types of renderable scene-graph nodes in OpenSpace for molecules: RenderableMolecule and RenderableSimu-lationBox-one for out of the box rendering of molecular systems and one for creating a coarse-level simulation box with rigid-body mechanics (rotation, translation, and elastic collisions) from a set of molecules and their corresponding ratios, respectively. Both renderable types support molecular trajectories as input data. In the case of RenderableSimulationBox, however, trajectories represent intra-molecular dynamics. Each scene-graph node renders into an off-screen frame buffer with its own depth buffer and is later drawn as a billboard within OpenSpace.
Molecular trajectories are generally sparsely sampled from their underlying simulation. This imposes discontinuities in atomic coordinates over frames and interpolation is required to reconstruct a plausible trajectory. We employ a cubic interpolation scheme that is C 2 -continuous and respects the periodic boundaries of the trajectories simulation box. This yields a perceptually smooth motion, which mitigates motion-sickness-an important aspect when targeting large projection surfaces such as domes. The integration of both tools had no significant impact on the performance. With the exception of amyloid fibril dataset, the framerate always exceeds 60 frames per second on a powerful desktop computer.
Conclusion With Moliverse we have combined the two ends of the physical scale spectrum by combining the molecular visualization software VIAMD with the astronomy framework OpenSpace. This offers audiences the possibility to explore the microcosm in the context of the macroscopic universe. With the launch of the James Webb Telescope and the discoveries it will grant the scientific communities in terms of the chemical compositions of exoplanets  [44, 45] , our work is particularly timely. It constitutes a first step towards building a versatile tool that assists scientists, teachers, and science centers to produce quality scientific communication and mediation by placing past and future chemical discoveries in context.
Acknowledgments The authors would like to thank the  Swedish e-Science Research Centre (SeRC), Sweden  and the  Knut and Alice Wallenberg Foundation, Sweden  under grant number KAW. 2019. 0024for financial support. The  Moliverse  project combines the  OpenSpace framework (github. com/OpenSpace/OpenSpace )and the Vi-aMD software (github. com/scanberg/viamd).
However, the differences between the annotation projects hinder the evaluation of the progress made and to develop systems that should ideally perform well on the broadest possible range of data. Zeldes et al. (2019)  proposed the first iteration of the shared task of Discourse Relation Parsing and 1 Treebanking (DISRPT) 1  in order to broaden the scope of discourse studies by including datasets and inviting researchers from different discourse theories, to facilitate cross-framework studies. The first edition of the DISRPT shared task was limited to Task 1: discourse segmentationidentifying the elementary discourse units (EDUs) that may be linked by discourse relations; and Task 2: discourse connective detection-identifying specific lexical items, called connectives, that can signal a discourse relation (e.
g. while, because, since, as long as etc. ). In 2021, for the second edition,  Zeldes et al. (2021)  added a third task, Task 3: discourse relation classification-identifying a relation label between a pair of attached discourse units. 2  This year, for the third edition, we maintained the three tasks but expanded the benchmark with 10 new corpora, including datasets from the DEP framework: in total, 26 corpora were made available across 4 frameworks and 13 languages in a unified format. In the last phase of the shared task, we released 6 surprise datasets including data for a new language (Thai), as well as 4 out-of-domain (OOD) corpora for which only dev and test partitions were available. Three teams participated in the shared task, with one team including half of the organizers of the shared task.
Overall, two systems were proposed for Tasks 1 and 2, and three systems for Task 3. Two systems are based on fine-tuning Transformer masked language model encoders, while the third one relies on a generative transformer model for relation classification. Only one team presented results for all tasks and tracks (MELODI), and another team (HITS) reported results for all tasks but were limited to the Treebanked track (i. e. parsed and gold sentence-split data) for Tasks 1 and 2. The third team (DiscoFLAN) focused on relation classification only. For the Treebanked track, MELODI ranked first on the EDU segmentation task, and HITS ranked first on the connective detection task with very similar mean scores. For relation classification, HITS ranked first.
Performance is still far from perfect for full discourse parsing, and systems are mostly developed for English, monologues, and the newswire domain, using the largest news corpus available, the RST-DT  (Carlson et al. , 2001) , with an F1 score of 55. 4 at best for full trees  (Kobayashi et al. , 2022). Recent studies also sometimes report results on other datasets, especially on GUM (e. g. Atwell et al. 2022; Yu et al. 2022b ), the largest RST English corpus to date, which is composed of multiple spoken and written genres  (Zeldes, 2017). Very recently,  Liu and Zeldes (2023)  showed the lack of generalization of existing SOTA RST discourse parsers through a series of experiments, with a significant performance drop when applied to unseen genres, and also demonstrated the importance of heterogeneous training data for robust discourse parsing.
A few attempts have also been made to develop systems for dialogues, especially using the SDRT STAC corpus  (Asher et al. , 2016)  with either supervised methods  (Liu and Chen, 2021; Chi and Rudnicky, 2022; Yu et al. , 2022a)  or transfer learning strategies given the small size of the dataset (e. g. Fan et al. 2022). Finally, multilingual RST discourse parsing has been the topic of a few work  (Braud et al. , 2017a; Liu et al. , 2020 Liu et al. , , 2021) )  involving transfer to tackle data scarcity. Liu et al. (2021)  in particular demonstrated that cross-lingual strategies could even help for English, and also that good segmentation is crucial for full discourse parsing, with a loss of up to 8% when using predicted EDUs. As a matter of fact, an option to better understand the difficulty and low performance of discourse parsing is to examine its constituent subtasks, such as discourse segmentation, but also relation classification, and attachment ('naked' or unlabeled tree building).
Many studies have been dedicated to these subtasks, with a specific focus on the first two. The aim of the DISRPT shared task is precisely to provide benchmarks for these critical steps toward full discourse parsing, to the extent possible in a formalism-neutral way, allowing participants to demonstrate the generalizability of their systems across languages, domains, and frameworks. Discourse segmentation in particular has been seen as a solved task with performance as high as 94% on RST-DT as early as over 10 years ago  (Xuan Bach et al. , 2012). However, systems at the time were trained only on English newswires data with gold information about sentence boundaries and morpho-syntactic features. When facing realistic data in other languages and even in English, with systems based on predicted information, performance drops very substantially  (Braud et al.
, 2017b). Disparities across languages and datasets were later emphasized within the DISRPT shared tasks  (Zeldes et al. , 2019 (Zeldes et al. , , 2021) )  under realistic settings (with predicted sentence splits), with performance above 95% for some corpora, but scores in the 80s for the Spanish SCTB (82. 5% at best), the Chinese SCTB (83. 3), or the Russian RRT (86. 2%). The best-performing system in 2019  (Muller et al. , 2019 ) used a single multilingual BERT  (Devlin et al. , 2019)  based model for every corpus, while in the second edition of DISRPT  (Zeldes et al. , 2021) , the winning system  (Gessler et al. , 2021)  achieved the best performance with an accuracy of around 91. 5% on average, which relied on varied language models, either mono-or multilingual, as well as hand-crafted features.
Connective detection and explicit discourse relation classification have been considered easy tasks, with high performance  (Pitler et al. , 2008) , but it was later shown that performance drops drastically on non-news domains, or in languages with small datasets  (Xue et al. , 2016; Scholman et al. , 2021; Johannsen and Søgaard, 2013). For the first two editions of DISRPT, rather high performance was reported for connective detection: between an F1 score of 92-94 for the English and Turkish corpora, and an F1 score of 87 for the Chinese one, with only a small drop when gold sentence splits are not provided. However, this may be due to the relatively large and homogeneous datasets used in the evaluation. This year's new edition introduces 6 new corpora for Task 2, as well as OOD datasets for which no training data is available: this has made the task more challenging, with the mean scores now under 80% (see Section 5 below for details).
We also report scores for implicit vs explicit relation classification for some corpora, which were not available in DISRPT 2021, and demonstrate low scores for implicit relations as well when data is scarce. The DISRPT Shared Task is among the very few studies to report scores for both implicit and explicit (also including other types such as AltLex markers, see  Prasad et al. 2014 ) relation classification, thus making it more practical for models to be able to recognize any types of relations. Task 3 was introduced in 2021, and the winning system was Transformers-based language-specific models for each target language and a set of hand-crafted features: overall the average performance was nevertheless still rather low (61. 8%), showing room for substantial improvement.
Summary of the Datasets In total, this year's DISRPT shared task included 26 corpora annotated across 4 frameworks and 13 languages. We provide general statistics of each dataset in Table  8  in Appendix B. For more information, please consult the relevant publications provided in the last column of the table. The corpora vary not only in terms of languages and sizes, but also their genres and domains, including news, wiki, scientific documents, conversations, and so on. Corpora vary tremendously in extent: as shown in the upper part of the smallest have about 50 documents and 15k tokens. We note that the Russian corpus has twice the amount of tokens compared to corpora of the same size in terms of documents, which indicates longer documents (scientific papers). The English SciDTB, on the other hand, is very large in document count, with almost 800 documents that seem very short: it is composed of scientific abstracts.
In the lower part of Table  8  (PDTB framework), the difference is even more obvious: the English PDTB dataset contains 2, 162 documents, while the English portion of the TED-Multilingual corpus only contains 6 documents  (Zeyrek et al. , 2018 (Zeyrek et al. , , 2019)). In general, performance is lower for small datasets, and one way to improve performance when facing data scarcity is to take advantage of larger datasets, as attempted by some participants, notably for Task 3, relation classification. The statistics also give insights into the differences between genres or languages and annotation guidelines across different corpora. The number of EDUs varies a lot: for example, the English STAC corpus contains a lot of EDUs relative to its size, likely due to the 'conversational' and abbreviated nature of online chatting.
We can also see that the size of the label set differs between corpora, even within the same framework: between 9 and 23 for PDTB, 14 and 32 for RST, and 12 or 24 for DEP (SDRT has only 2 corpora with a more stable set of 16-18 labels). Label sets are not identical, even within the same framework, due to different relation definitions or granularity as well as variations in naming formats, e. g. with a single or a 2-level convention, as in CONCESSION vs COMPARISON. CONCESSION, or even more minor change such as the use of capital letters or not. Some datasets provide much more fine-grained relations (for example over 70 originally for RST-DT, or over 30 for GUM), but we follow the common practice of collapsing these to fewer coarse classes used in most parsing research (however, original fine-grained labels were retained in an additional column in the.
Sentence and EDU Segmentation Sentences are the basic unit for grouping words in NLP. They correspond to EDU boundaries: in most RST, SDRT, and DEP datasets each sentence starts a new EDU. With sentences given, the segmentation task corresponds to finding intra-sentential EDU boundaries, and corpora in general include these boundaries to some extent, depending possibly on the genre or the annotation scheme: for some corpora with a low rate of intra-sentential EDU boundaries, the task could thus be easier if the sentence splitter already gives good results. As a baseline and an indication of the complexity of the task, we thus report results for a sentence-based baseline, where each sentence is predicted to correspond to one EDU (see Table  2 ). We also report performance using another tool for sentence splitting, namely Trankit  (Nguyen et al.
, 2021) , used by the team MELODI for the Plain track. Sentence boundaries are gold for some corpora (English RST-DT and GUM, and German PCC), for the others, Stanza  (Qi et al. , 2020)  was used to provide sentence splits in the. conllu format (the Treebanked track). A. tok format is also provided, without information about sentences (the Plain track). Our baseline results are computed on the Treebanked data and shown in Table  2. Error Rate: bad performance of sentence splitters We compute the error rate by looking at the tokens that are supposed to start a sentence but are not annotated as beginning an EDU: they thus correspond to errors in sentence segmentation. The error rate is not 0 for RST-DT (gold sentences), because of alignments errors with the Penn Treebank  (Marcus et al. , 1993).