dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of the parcellation method To validate our method, given the lack of a clinical ground truth, we used the deformation method to generate a synthetic dataset including local and global remodelling. Since we imposed the remodelling, we know the specific areas as well as the exact amount of remodelling, computed as the global difference in volume between the template and the remodelled meshes. For each RV part (apical, inlet, RVOT), we generated two local remodelling patterns: one elongating in the longitudinal direction, and the other one affecting the circumferential direction. These remodelled meshes were generated using the log-domain reconstruction method. As explained in the methodology section, this method needs as input the mesh topology, the rotation between faces R ij (derived from the dihedral angles) and the in-triangle coordinates for each node a ti.
Reproducibility of the parcellation method In this section we study the stability of the regional volumes and EFs. Reproducibility was estimated using the inter-and intra-observer reproducibility test dataset. Table  2  shows the mean intra-and inter-observer errors and the mean value of the variables for the regional and global ED volumes and EF. We can observe large errors in the interobserver case (>12%), but lower for the intraobserver case ( 7 -12%). The outlet segment had the biggest error for volume(12%), and regarding function, the apical EF had the biggest error. Both registration and geodesic approaches present similar reproducibility in the apex and inlet segments. Registration was found to be more reproducible in the RVOT. We tested the stability of both methods to remeshing: Table  3  shows the regional volumes difference between the original and remeshed RV surface: registration presented less robustness.
Generation of synthetic remodelling We used the synthetic remodelling method to generate different types of localised (apex, inlet and RVOT) and global remodelling, all four cases including pure longitudinal and circumferential dilatations, as well as a homogeneous global scaling. For each RV in the dataset, we generated a total of 9 synthetic remodelled meshes (3 global and 6 regional). To assess the reconstruction quality of the local remodelling (the global ones were obtained by applying a linear transformation and therefore their reconstruction is exact), we computed the strain between the synthetic and original mesh, and compared it to the imposed. Table  5  shows the obtained errors. We observed that the error is below 3% of the maximal imposed strain in the circumferential cases, and less than 1.
Discussion In this paper we proposed a mesh-independent method to volumetrically parcellate the RV in three clinically relevant regions: inlet, outlet and apex, with the aim of quantifying inter-or intraindividual remodelling in regional morphology. For validation, we additionally presented a method to synthetically remodel meshes in a localised and global manner by specifying a desired strain field, thus generating a synthetic dataset resembling closely monitoring clinical remodelling. In order for a remodelled mesh associated to a strain field to exist, the strain field needs to verify some conditions (Coddazzi-Mainardi equations, Wang et al. 2012 )). Since it is not easy to generate strain fields that satisfy these conditions we generated arbitrary fields, and quantified the reconstruction error and found it below 3% for the circumferential and 1.
Acknowledgements This study was partially supported by the  Spanish Ministry of Economy and Competitiveness (Maria de Maeztu Units of Excellence Programme  - MDM-2015-0502 ), the  European Union  under the  Horizon 2020 Programme for Research, Innovation  (grant agreement No. 642676 CardioFunXion). We thank doctors Duchateau and  Nuñez-García  for fruitful discussions.
INTRODUCTION Interfaces for digitised, historical newspapers offer great opportunities for many different user groups, such as academic researchers, lay historians, students, teachers, etc. Interfaces steer what users can learn from digitised newspapers, and they influence workflows by offering functions and tools [cf. Jarlbrink and Snickars 2017]. On the other hand, users are often not aware of biases in the search results, which are caused by the processing and datafication of newspapers. The main aim of this paper is to discuss the opportunities and pitfalls of research using digital newspaper interfaces. We argue that integrating tools for analysis and visualisation of data and metadata would not only address the needs of various user groups, but that libraries could also profit from expanding their digital interfaces.
In order to be able to make such assertions, a DH team 1 made up of researchers from Austria, Finland and France in the EU-funded research project 'NewsEye: A Digital Investigator for Historical Newspapers' 2 experimented extensively with existing newspaper search interfaces of the three national libraries using specific humanities case studies. The interfaces used were ANNO (ONB -Austrian National Library), Digi (National Library of Finland) and Korp (The Language Bank of Finland), as well as Gallica and Retronews (BNF -National Library of France). In addition, the involved DH researchers set out to use various tools that are publicly available online to analyse the newspaper corpora outside the interface environments. Since the corpora are way too large to export, they created subcorpora of the data they used for their researcha task that turned out to be a major and tricky first step in newspaper research.
I DIGITAL HISTORY PRACTICE While most humanities scholars use digital sources, databases or applications for their research, only a fraction of these scholars have embraced the digital age wholeheartedly. This has to do with more traditional disciplinary approaches as well as with the complexity of the research in question. Historical scholarship, for example, traditionally assumes that there is too little material available from the past to reconstruct historical events, and that it is one of the tasks of historical research to fill the gaps with the methods of hermeneutics. Traditional historical research therefore sometimes seems like a form of insider knowledge available only to those who have the necessary expertise or language skills, and privileged access  [Abbott, 2001]. Over the past three decades, however, the quantity of available historical data has grown exponentially.
Overall, the discussion shifted towards multidisciplinary approaches and several fundamental works were edited in the following years  [Gold, 2012 [Gold, & 2016 [Gold, & 2019;; Terras, 2013; Jones, 2014; Koller, 2016]  that allow for a tracing of shifting modes, arguments, tools and methods. The debate has also been kept alive by journals such as the Digital Humanities Quarterly  [2007] [2008] [2009] [2010] [2011] [2012] [2013] [2014] [2015] [2016] [2017] [2018] [2019] [2020]  or Digital Scholarship in the Humanities  [2015] [2016] [2017] [2018] [2019] [2020] , (formerly known as  Literary and Linguistic Computing [1986-2015] ) and others. These also reflect the deeply interdisciplinary nature of the methods of digital humanities and digital history: Qualitative analysis applications come from the social sciences, databases arose in computer science, text encoding and language research grew in linguistics and literary studies.
Creating a subcorpus Considering the amount of hits usually received when performing keyword searches on the newspaper interfaces, it quickly became clear that it is imperative for researchers to be able to create subcorpora of the available datasets. Limiting the number of results, however, is imperative when humanities researchers work qualitativelya research approach that can never be ignored in these disciplines. None of the tested interfaces offer tools to select, structure, or build datasets, and the DH researchers were forced to create their own collections manually, an extremely time-consuming task. Methods to support the automatic creation of collections, e. g. using text mining methods, are still experimental and can at this point not be used on newspaper interfaces. [Nanni, Ponzetto and Dietz, 2017] , for example, used named entities and word embeddings to automatically build entity-centric event collections in order to address the needs of humanities and social science scholars who work qualitatively on specific topics.
Third, without knowledge of the historical context, the research area, and the factors of changing language use over longer time periods, it is not possible to decide whether a subcorpus is suitable for the research in question. For the case study on return migration, a subcorpus from the ANNO dataset was created, focusing on questions about types of returnees, nationalities, countries, and other topics related to remigration. As a first steps, the keywords in table 3 were used to find articles in ANNO about remigration issues: keywords 'heimat zurückkehren'∼20 ('returning home') 'heimat rückkehr'∼20 ('return home') 'Heimkehrenden' ('returnees') 'Heimgekehrten' ('returnees') 'emigranten rückkehr'∼20 ('emigrants return') 'Heimkehrer' ('returnees') 'Rückwanderer' ('returnees') 'Rückkehrer' ('returnees').
• Workspace: Since there is no specific place on the ANNO interface that allows for a collection and/or further analysis of found articles, all the material had to be downloaded one by one and processed using other software. Metadata and context are lost in the course of this process. • Metadata: While information about the newspapers itself (title, publication year, etc. ) can be found in ANNO, details about the content (number of words, pages, languages etc. per year/month/day, document length) are not available.
Subcorpus on 'women's suffrage' (1890-1940) The subcorpus on woman suffrage contains 628 articles from Le Matin and Le Petit Parisien published from 1890 to 1940 and available in the BNF newspaper dataset. The corpus includes the articles containing the French expression 'vote des femmes' ('women's suffrage') in these two daily newspapers. In this case, it was possible to extract articles thanks to the Optical Layout Segmentation done by the Europeana Newspaper projects in 2011-2014: Paragraphs and text blocks were grouped into articles. This digital reconstruction of articles is not perfect and is especially faulty for advertisements, which tend to be printed in messy sections, regardless of the actual segmentation between ads. Nevertheless, standard articles with a title are recognised correctly enough in order to be suitable for research purposes.
The complete digital archives of the two newspapers were already available within the programming interface of the Numapresse Project, hosted on the French national digital infrastructure for the humanities and social sciences, Huma-Num. The original dataset includes numerous supplementary pieces of metadata at the word level, for instance, the coordinates, the size or the font style for each token. This contextual approach to text mining has been described by  [Langlais, 2019]. Here, only the aggregated raw text for each article published in Le Matin and Le Parisien from 1890 to 1940 was used and the 628 publications with the string 'vote des femmes' retained. This selection is only a small portion of available material and shows a tiny sample of the journalistic debate on women's suffrage.
The method of focusing on a set window or flexible context is not as accurate as using identified articles as context for analysis. As the Finnish newspaper dataset does not include reliable article segmentation, this, however, remains our best method of doing contextual analysis without hand-curating the subcorpus. Using a set window has the benefit of producing symmetrical contexts for each keyword search, but this method is ignorant of sentence structure or stylistic features in the text. If the window is large, say one hundred words before and after, it is also more likely that the chosen context includes text from articles that precede or succeed the target article. Focusing on a sentence or a paragraph has the benefit of rarely transgressing article boundaries, but this method sometimes gets the context wrong due to OCR errors regarding punctuation.
Frequency analysis For historians working with large quantities of digitised texts from longer periods of time, the possibility to not only note the introduction of new vocabulary, but also to be able to quantify the frequency of words or other features of language has become a way of illustrating changes in how people conceptualised the world. Newspapers form an especially interesting data set for this, as they are, relatively speaking, a rather stable corpus that deals with a large selection of topics. The first step when working with an adequate corpus using digital tools is therefore usually a simple frequency analysis. Next to co-occurrences or topic modelling tools, this can be a first entry point for more in-depth investigations of larger and complex datasets, since it can give researchers a quick overview of word frequencies (or at least frequencies of strings) and trends in the selected dataset.
It is common practice in humanities scholarship to trace the first occurrence of a word relating to the topic of a study. As  [Dufuoix, 2019]  puts it, this can be called 'the religion of the first occurrence'. Digitised sources are obviously helpful in identifying new candidates for first uses of particular terms through keyword searches, but more importantly, they make it possible to shift focus from early uses to the analysis of frequency and answering questions of when it became more common in general to use a particular word or when only some people chose to use a particular terminology. Digitised newspapers are a very good dataset for diachronic analysis of word frequency for many research questions, as it is relatively easy to select subcorpora based on particular newspapers (perhaps with different political leanings), particular places of publication, or the publication frequency of newspapers.
In the case of newspapers, this might, for instance, be repeated advertisements that are surely part of the historical dataset, but would be cleaned out if the data set was seen as balanced in a corpus-linguistic sense. Of these examples, the issue of polysemy is perhaps the most complicated one, as semantic change can happen in many different ways and be about rather different things. For instance, semantic change might be about new things (referents) that need to be described, but might also be about changes in the valuation of a word, or, indeed, something else. If we take into account different types of meaning ranging from conceptual meaning to social meaning and affective meaning  [Leech, 1974]  and also different types of reasons for change in them  [Ullman, 1962] , we end up with a rather complex model for lexical semantic change.
Frequency analysis on nationalism using Digi and Korp The subtopic on nationalism relies on digitised newspapers as a massive text dataset that can be used to trace long-term changes in the vocabulary of 'nation', 'national', 'nationalism' and related terms. As such, it provides an example of the possibilities and limits of using frequency analysis in the digitised Finnish newspapers to study complex historical phenomena. One first step is to understand that words like 'nation' and 'nationalism' should not be studied alone, but in the context of related terms, but at the same time it is clear that individual words do have their own trajectories and that choosing to talk about 'nationalism' and not just 'nation' has most probably been a conscious choice by a historical actor. Typically, the first uses of the words 'nation' and 'nationalism' have been mapped thoroughly, for instance by  [Kemiläinen, 1964].
This made it easier to estimate the quality of the OCR, the number of issues published, and the volume of the dataset per newspaper. The newspaper Illustrierte Kronen  Zeitung (1905 Zeitung ( -1944) )  seemed to be suitable for this, as the quantity and the OCR quality remained more or less the same throughout the entire time period. As it turned out, however, there is no OCR text available for the years 1936 and 1937. This limited search delivered different results. In contrast to figure 6, figure  7  shows a clear increase in the usage of the search terms at least for the beginning of the Second World War: Therefore, figure  7  reveals that missing keywords are not the main problem here. Instead, two issues were identified: • Absolute frequencies can show an unrealistic picture, as they only count the number of times a word or a page containing the word appears in a corpus without putting the results in relation, e.
Creating topics on return migration using STM -R package for the structural topic model STM was used with the corpus on return migration (472 articles). For pre-processing, stop words were removed and the corpus was prepared using naive lemmatisation with lookup tables: This is not a significant issue for text classification based on thousands of individual tokens. The following eight topics were generated for the whole corpus (figure  9 ): At a first glance and without being familiar with the content of the corpus, it is difficult to tell what the topics really stand for. In order to provide more accurate interpretations, a better insight into the corpus is needed. After reading, the following conclusions can be reached: • Only topic 4 seems to be very clear: the return of people from America.
The program removes stopwords automatically (German can be chosen as a language). In addition, it is also possible to remove irrelevant words manually (such as recurring titles of the newspapers etc. ). The program then compares every pair of documents (535 pages in total). Just like STM, Overview recognised some of the main themes, such as the Serbo-Turkish War from 1876 to 1878, or the return from America. The topic about the return from America, for example, was found in 55 articles and the folder was labelled with words such as 'Rückwanderer' ('returnees'), 'Auswanderer' ('emigrants'), 'Arbeiter' ('workers'), 'Rückwanderung' ('return migration') and 'Amerika' ('America'). The same folder was split into several sub-folders with even more specific topics (each folder splits into smaller and smaller sub-folders, each of which contains a smaller number of documents).
Simply put, text mining options would be welcomed by newspaper researchers. This is also true for metadata: Number of words, pages, languages etc. per year/month/day are needed for in-depth analysis and interpretation. 6. Although libraries might struggle to offer appropriate functions for quantitative analysis and visualisations on their newspaper interfaces, it has to be taken into consideration that these can point to errors in the dataset, such as bad OCR quality, missing data, incorrect metadata, etc. To implement such a tool could therefore ultimately be to the advantage of the libraries. 7. Frequencies are needed if topic modelling approaches are considered as alternative ways to analyse the datasets. The DH teams of NewsEye tested various topic modelling tools, some of them implemented on the newspaper interfaces, but most of them available on other sites.
It can be concluded that despite the ongoing issues with the available tools, topic modelling could be advantageous when dealing with the mass of data that is available on newspaper interfaces. 8. It is not enough to create powerful tools: Their accessibility should be guaranteed by the mediation of professionals (e. g. librarians). Many different user groups are rather proficient at using the interfaces, but there is ample opportunity for improvements. As an overall assessment, it can be underlined that digital tools combining quantitative macroanalysis (big data analysis) and qualitative microanalysis (reading) have some significant advantages over other methods. Especially when dealing with massive amounts of data, the sheer quantity makes the traditional practice of reading in context untenable and an inadequate method of evidence gathering.
Analysing this data with traditional micro-analysis approaches often turns out to be impossible, and researchers get the impression that they are missing out on important things. At the same time, the arguments vouching for quantitative approaches can be used against macro-analysis: By looking at the data from afar, crucial things could be missed. Agreeing with  [Jockers, 2013] , it must be said that the two ways of analysis, therefore, should and must co-exist. The digital newspaper interfaces studied for this paper, however, do not yet offer opportunities for both approaches. The tools and methods developed within the NewsEye project could therefore highlight the chances and challenges of complex digital interfaces.
Set-up and organisation In order to provide an added value with respect to the various existing but mostly regional or local DEM comparison studies (e. g. ,  Grohmann 2018 , Hayakawa et al. , 2008 , Hawker et al. , 2019 , Hirt et al. , 2010) , DEMIX is striving to have a global outreach to include all datasets which have at least continental coverage and are available under a free and open data policy (FAIR). These should include but are not limited to: ▪ NASADEM 1" (NASA, JPL, most recent release of the SRTM product line based on a full re-processing)  (Crippen, 2016)  ▪ SRTM, 3" and 1" to assess its adequacy and the need for a re-evalution of the results from the many studies that have used it. ▪ ASTER-GDEM v3, (METI, NASA) (Abrams, 2020) ▪ AW3D30 1" (JAXA, free and open version of the Japanese ALOS based global DEM)  (Tadono, 2016 ) ▪ TanDEM-X90, (DLR, free version of the TanDEM-X mission for scientific use) (Rizzoli, 2017) ▪ Copernicus DEM 3" and 1" (EC/ESA, free and open version of WorldDEM TM , the commercial version of TanDEM-X produced by Airbus Inc.
If there are no ties, all the integers between 1 and k are used, attaching each one to one DEM. If there are ties, the equivalent DEMs will have each an averaged rank, computed in order that the sum of the ranks equals k*(k+1)/2. Thus, a valid ranking for k=4 will be any permutation of {1, 2, 3, 4}, or when ties are present it might be {1, 2. 5, 2. 5, 4} or {2, 2, 2, 4}, etc. In all cases the sum of the ranks should be 10=4*5/2. In the comparison we will use a number of independent Test sites. In each of them we have access to a reference dataset, deemed to be of higher accuracy. From the reference points we can compute an interpolated elevation at DEM grid points, and then we can use the discrepancies as a figure of merit. One familiar choice for the criteria is to compute the RMSE. In this case the criteria will be to rank the DEMs according to the value of the RMSE, being better ranking attached to smaller RMSE.
Different test sites might render different rankings, and we will show later how to deal with this. Different criteria might require different reference datasets; for example, if we want to compare DEM based in their computed slope, the reference dataset might be derived from topographical local information without a high absolute elevation accuracy. The use of the RMSE is not the single option, even considering just the elevation. In this protocol we will show that more than one criteria can be used under the modest requirement that it can provide a ranking among the available DEMs. The procedure might be based upon the familiar RMSE, or might be another rule which for example considers the discrepancy of the drainage network. We should require from the criteria that higher performing DEMs will have the better rankings (whereby "better" usually will be lower numbers), just in order to be able to compare.