dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We have found tendencies in our corpus that show how semiotic and social norms reduce and channel the technical potentials of a social medium. Twitter gives space to many different individual uses, but just a few families of uses, or "tweeting styles", are actually realized. These styles are not homogeneously distributed among the political parties of the candidates. We have also found further associations between the tweeting styles and some other variables, such as the kind of discourse realized in the tweets' texts or the favoured tweeting interface. The next step of our research will be to perform a similar analysis to other corpora extracted from the TEE2014 database. We want to know if Belgian, English, German, Italian and Spanish candidates use Twitter in a comparable way to the French.
The aim of this paper is to discuss the specificity of the electoral tweets the United Kingdom Independence Party (UKIP) political candidates sent before the elections. The call for action tweets represent a form of strategic and goal-oriented discourse meant to persuade the electorate to vote for them. Particular attention will be paid to the various cognitive and discursive strategies used and to the role they play during the interpretation process. Shedding light on the mechanisms which direct the hearer towards a certain interpretation and the extent to which the linguistic details influence people's attitudes about political candidates is essential to understanding the process of decision-making. To this end, a corpus-based and a corpus-driven analysis is suggested. Starting from the vote UKIP collocation, a relevant corpus of data has been gathered and further analysis using the AntConc software has been conducted in order to discuss the content of the selected tweets.
No retweets have been included in the analysis as the interest lies in the candidates' way of approaching the Alex Frame, Arnaud Mercier, Gilles  Brachotte  and Caja Thimm -978-3-631-69740-5  Téléchargé de PubFactory à06/10/2021 11:57:11AM via free access electorate in their attempt to create a connection with them. The software used for concordancing and text analysis is AntConc  (Anthony 2014). As a result of the corpus-based analysis, a formal analysis of the vote Ukip collocation is suggested while as a result of the corpus-driven analysis a content investigation is put forward.
103 Le terme « requête » désigne la commande en langage informatique nécessaire à l'exploitation des informations que nous cherchons à atteindre dans la base de données. Ceci dans le but de faire ressortir des réponses à nos questions dans la multitude de tweets qui ont été collectés. Les requêtes permettent donc d'extraire les données selon différents critères et de les ordonner pour en faciliter la lecture et l'exploitation. Ce tableau résume nos observations au travers du nombre d'indicateurs présentés et permet d'ores et déjà de faire ressortir des pistes de réflexion et d'analyse quant à l'utilisation faite de Twitter par les protagonistes de l'élection de 2014. On constate tout d'abord, aussi bien pour les candidats que pour les partis, une appropriation et une maîtrise des règles et des codes du dispositif.
Appréhender la circulation de l'information sur Twitter, exemple et perspectives L'étude de l'usage qui est fait du dispositif par les candidats et les partis nous permet de mieux mesurer leur implication, leur intégration et leur appropriation des réseaux socionumériques. Pour compléter notre étude, nous avons souhaité présenter les prémisses d'une étude élargie en complément de l'analyse des utilisateurs et qui concerne les messages et leur circulation. Avant de présenter ces résultats, nous tenons à rappeler qu'il s'agit ici d'une expérience dont les résultats restent hypothétiques dans leur interprétation. En effet, bien que nous ayons suivi une méthodologie stricte dans la recherche sur les données, leur signification reste à éprouver dans la durée. Ainsi, l'exploitation de la base de données nous offre la possibilité de faire ressortir des variables concernant -comme dans le cas présenté dans les lignes suivantes -le nombre de RT de tweets de chaque candidat et des partis qu'ils représentent.
Analyzing campaign strategies of German EU-candidates on Twitter Th e data of the present study consist of tweets sent by the German candidates during the 2014 European Election. Th ey were collected three weeks before and one week aft er the EU election day (4 th of May until 1 st of June, 2014). Th is time frame was chosen in order to document not only tweets during the "hot" phase in the run-up to the election day but also during the following week when the election results are discussed on Twitter. Hence, the data do give some insights into the more general relation between Twitter as a communication tool and permanent campaigning. The tweets were collected via Twitter's streaming and search APIs with the help of the open source solution SNFreezer (http://sourceforge. net/projects/ snfreezer/).
Good that there are young candidates like @SkaKeller @JanAlbrecht @TerryReintke" Self-promotion on Twitter is an element of the increasing personalization of politics, which has been observed for more than a decade  (Holtz-Bacha, 2006). Personalization, however, must not be mistaken for a higher amount of insights into the politicians' private lives. In fact, private tweets were rarely found in the dataset. On the one hand, this can be interpreted as an approach by the politicians to stick to the professional and 'factual' side of the campaign by mainly referring to political issues. On the other hand, this implies that insights into private events like for example family matters, are not part of the self-definition and self-presentational style of German politicians -at least in the Twitter public sphere.
5) Negative Campaigning Another strategy put forward by politicians as well as their parties' accounts on Twitter is that of negative campaigning. In some political cultures negative campaigning is a frequently used means of putting the political opponents to the test, but it is, particularly in the German political sphere, a two-sided sword: Many voters don't like offensive criticism during election times  (Walter et al. , 2013). In the present dataset two main strategies of negative campaigning can be identified: The first has a focus on political content and is strongly connected to political issues, while the second is a strategy to offend the political opponent more on a personal level. Figure  6  shows an example of the first category, in which negative campaigning gets aligned with concrete issues and political positions.
(8) Emphasizing and establishing supranational political alliances Political alliances between parties or parliamentary groups on the European level are not usually very well known to many voters since people feel more tied to their respective national parties and politicians. The desire to strengthen those alliances might be a reason why some politicians mark their supranational party alliance explicitly in their tweets. In the present dataset, we found candidates of the same supranational alliance who often relate and link to each other by using @-mentions, retweets, hashtags, linked multimedia content or even emoticons in order to emphasize their closeness. Rebecca Harms from the Green Party for example did a campaign event where she went from Berlin to Brussels on a boat, accompanied by other "green" politicians from the Czech Republic and from Finland.
The retweets totalled 91, doubling the politician's ones. As previously mentioned, retweets registered their maximum on 23 rd , having another outstanding rise at the beginning of the campaign, on 9 th. The lowest Alex Frame, Arnaud Mercier, Gilles Brachotte and Caja Thimm -978-3-631-69740-5 Téléchargé de PubFactory à06/10/2021 11:57:11AM via free access numbers were counted on the first and last day, and 10 th and 13 th. This last day Pablo Iglesias only retweeted once, while his publications were on the rise, above all those written by his team. Taking into account the information collected, it becomes clear that Pablo Iglesias' account gets its quantitative intensity thanks to his team's publications and not by his own tweets, which really appears to show that the importance the politician gave to Twitter, as a good monitoring tool for being in closer touch with the elector's worries, remained secondary.
They found that the implementation of UAV image in land use survey is viable, low-cost and promising. Jones  IV et al. 2006  proposed characteristics of small UAV to be suitable for management and research tools. They used wingspan UAV to investigate its usefulness for wildlife research applications. Brutto et al. 2014  performed a study on cultural heritage area utilizing different UAV systems. Two different datasets were acquired one for archeological site and another for land art site. They developed 3D model and ortho-images with high level of details. In addition, they conducted some tests to investigate the accuracy of images orientation and 3D models. Tscharf et al. 2015  presented an automated processing pipeline utilizing various images platforms. The developed framework allows for geo-referencing of UAV imagery based on GPS measurements and ground control points (GCPs).
UNMANNED AERIAL VEHICLE (UAV) According to  (Colomina and Molina, 2014) , UAV photogrammetry has witnessed rapid development in the past few years. This can be attributed to the accessibility of drones and a major development of Structure from Motion software. Before UAVs, aerial photogrammetry involved planes or helicopters and metric cameras. It encompassed complex and various processes due to the heights to which the aircraft soared and the expensive cost of metric cameras and flight hours. It now offers an affordable access to precise aerial mapping  (Fraser, 2015). According to  (Küng, 2011) , the developments of UAVs in recent years along with the improvements in Structure from Motion (SFM) software and computer vision enhanced the production of photogrammetry. They made it accessible with centimetrical precision even with bad positioning systems onboard the aircraft.
Canon S110 Panasonic GH4 (with Olympus) It also includes meta-data about the camera's position (x, y, z) in the reference system's coordinates and the roll tilt and yaw of the camera for every picture. It allows the model to be referenced to ground control points. Figure  2  displays the process conducted within Photoscan. In all of these steps, it is possible to set parameters that will influence the process and the quality of the model. Moreover, a Python interface can be used to establish an automated process. A set of images are uploaded enabling the Python interface to generate the results at the press of a button. Figure  2 : Photoscan process. 2. Pix4D is very similar to Photoscan, but it is already automated and the parameter must be set before starting the process. It also provides a tool for vectorizing the model within Pix4D.
3. ArcGIS is the most widely used GIS. It has great capabilities in terms of 3D analysis with the extension 3D analyst. It also includes Arcscene, which enables efficient work on heavy point clouds and 3D models. 4. AutoCAD is CAD software, which is used by most land surveyors and in many other professions. It is efficient for generating 2D topographic plans, but it is not optimized to deal with heavy 3D data exploitation.
Photoscan/Pix4D software A total of 181 images were downloaded on the software after sorting out the useless ones (blurred, out of the area). The first image matching is processed with low quality in order to conduct the georeferencing process, where markers have to be positioned at each reference point. The file is then imported with the coordinates of every reference. The georeferencing level of precision for drone-acquired data or traditional instruments does not change, because the methods used to georeference it are the same: GNSS receiver or traverse to a known reference. This is the reason why local polygonal coordinates were used in order to create the model based on five reference points. Once the markers are placed and the coordinates assigned, a second image matching is processed based on the coordinates, to generate dense cloud points with references.
Yet, RNAse resistance of an mRNA fragment does not necessarily imply translation, since other RNA binding proteins or strong RNA secondary structure can also render RNA resistant to RNAse. Thus, one key challenge when analyzing ribosome profiling data is to efficiently distinguish translation related signal from noise  [10]. Several groups have introduced computational scoring schemes for the analysis of ribosome profile data to overcome the limitations of the TE-score. The principal authors of Ingolia et al. [8]  and others reanalyzed their initial dataset with the "Ribosome Release (RR-) Score", an indicator of the preferential RNAse resistance of coding over 3' untranslated regions  [11]. They concluded that the RR-scores of lncRNAs resemble those of classical noncoding RNAs (e. g. snoRNAs) and that lncRNAs do not code for proteins in murine embryonic stem cells.
Mechanistical studies suggested that translation inhibition by DMDA-PatA is not related to the effect of the drug on eIF4A activity but rather to a perturbation of protein complexes required for translation initiation  [17]. DMDA-PatA does not inhibit translation initiation at internal ribosome entry sites (IRES) that do not require eIF4A  [17]  (e. g. HCV like IRESes). The widespread inhibition of translation of protein coding transcripts suggests that translation initiation at HCV-like IRESes is not the predominant mechanism of translation initiation for coding transcripts in our dataset. DMDA-PatA was reported to affect DNA synthesis  [21] , an effect that might possibly be due to unknown targets of DMDA-PatA or alternatively just reflect an impact of a profound shut down of protein synthesis on DNA replication.
Pateamine A inhibition identifies translated ORFs in noncoding RNAs ORFs are common in lncRNAs. All but one of the 332 mouse lncRNAs with at least 20 transcriptome reads in our dataset have at least one ORF with an AUG start codon. Although certain computational scoring schemes yielded contradictory interpretations on the translational status of lncRNAs  [8, 11] , most recent computational scorings of ribosome profiling data rather suggest that ORFs in lncRNAs are, to certain extent, translated  [12] [13] [14]. Since translation inhibitors, in particular DMDA-PatA, reliably identify translated sequences, we examined how translation inhibitors perform in identifying translated ORFs in lncRNAs when compared to bioinformatics scoring schemes. LncRNAs have two properties that need to be considered when comparing them to protein coding transcripts.
We next compared DMDA-PatA inhibition to three representative computational scorings (RR-, ORF-and Floss score). While DMDA-PatA performed well even with low-expressed transcripts (Fig. 2b ; Additional file 1: Figure  S6A ), we observed that all three computational scores were highly affected by mRNA expression levels. They tended to score low expressed coding transcript as weakly or not translated (Fig. 2e-g ; Additional file 1: Figure  S6B, E, H ). The RR-score clearly performed worst, since coding sequences, in particular the low expressed, were very poorly discriminated from snoR-NAs (Table  1 ; Fig. 2e ; Additional file 1: Figure  S6B, C ). This contradicts Guttman et al. [11]  who reported that RR-scores of annotated coding sequences are much higher than those of structural RNAs or lncRNAs and concluded that lncRNAs are not translated.
Thus, while globally both the Floss and the ORF score suggest that certain lncRNAs are translated, they clearly perform worse than DMDA-PatA in identifying a particular translated ORF in a typically low expressed lncRNA with high confidence. We next sought to define the scope of lncRNA translation in murine ES cells. We initially used the translation efficiency (TE-) score to define transcripts with low coding probability, considering that a very low TE-score indicates a low or no ribosome occupancy. Amongst the 160 lncRNAs in our dataset that have at least 20 transcriptome reads on an ORF, 26. 3 % (42) have no ORF with a TE-score above 0. 1, a score that is exceeded for 99 % of annotated protein coding sequences in our dataset (Fig. 2a , Additional file 2: Table  S1 ). Those RNAs likely represent real noncoding RNAs or are translated with very low efficiency.
Visual inspection of ribosome footprint profiles and examination of the effect of different translation inhibitors is in our opinion currently the most reliable way to examine a particular RNA of interest for translated ORFs. For ORFs with a decent number of footprints, examination of the ORFand the Floss score can further support the translational status of a specific ORF. In order to facilitate this analysis, we have developed a dedicated JavaFX GUI application to access our data (Additional file 1: Figure  S13 , http://caire. ipmc. cnrs. fr/ RibosomeProfileViewer/BMC/). The software allows inspection of ribosome footprints on spliced transcripts, ORFs, drug effects and several bioinformatics scores such as RR-score  [11] , TE-score  [8]  and ORFscore  [14]  in our dataset at single nucleotide resolution.
Transcript database The transcript and gene annotations from Ensembl/EMBL (release 76, mm10), miRBase (release 21), fRNAdb (ver. 3. 4), RefSeq (release 65), and murine ES cell lincRNAs  [11]  were combined into one GTF file. The GTF file we used can be downloaded: http://caire. ipmc. cnrs. fr/RibosomePro fileViewer/BMC/2015Waldmann. zip. Annotated noncoding RNAs (lncRNAs, Refseq other noncoding) with exons that overlap exons of annotated protein coding transcripts were excluded from all analyses.
Transcript selection When multiple transcripts were in the databases for one gene, we selected one transcript: (i) Annotated transcripts were favored over predicted transcripts. (ii) Coding transcripts were favored over noncoding. (iii) Transcripts with both annotated start and stop codons over those with just either one annotated. For annotated coding transcripts, we observed that a simple selection of the one with the longest coding sequence frequently selected the wrong transcript (e. g. cMyc). For most transcripts we observed an accumulation of footprints on the translation start codon. When at least 2 % of the coding sequence footprints of the control sample were on the start codon +/-one nucleotide, we used this characteristic; otherwise we selected the transcript with the longest coding sequence.
RR-score Both whole transcriptome and ribosome profiling raw counts were inter-replica (intra-condition) normalized for the relative library size with the size factors computed by the DESeq2 library of the bioconductor package in R  [41]. We used the RR-score  [11]  used for lncRNAs. RRS ¼ readsCDS W T reads3pUTR W T = readsCDS ribo reads3pUTR ribo. ReadsCDS WT and readsCDS ribo are whole transcriptome and ribosome footprints on the coding sequence, respectively. Reads3pUTR WT and reads3pUTR ribo are the reads matching to the 3'UTR between the nucleotide following the stop codon and the nucleotide preceding the first AUG codon in the 3' UTR or the end of the transcript if no AUG codon is in the 3' UTR. Gutman et al. [11]  used a different RR-score for coding RNAs where they used the entire 3'UTR and not just the sequence until the AUG following the stop codon.
Acknowledgements We are grateful to  Dr. Daniel Romo  ( Texas A&M University ) for providing the DMDA-pateamine A. This work was developed in close collaboration with the functional genomics platform of  Nice Sophia Antipolis , a partner of the National Infrastructure France Génomique, thanks to supports by the  Cancéropôle PACA and Commissariat aux Grands Investissements  ( ANR-10-INBS-09-03  and  ANR-10-INBS-09-02 ). We specially thank  Marie-Jeanne Arguel  and  Virginie Magnone  for their expert assistance in high throughput sequencing and  Agnès Paquet  for help with the data analysis. RW was supported by  Fondation ARC pour la recherche sur le cancer  ( SFI20121205973 ), and PB by  Fondation pour la Recherche Médicale  ( DEQ20130326464 ) and labex  Signalife  ( ANR-11-LABX-0028-01 ).
Analyzing textual data is a very challenging task because of the huge volume of data generated daily. Fundamental issues in text analysis include the lack of structure in document datasets, the need for various preprocessing steps and performance and scaling issues. Existing text analysis architectures partly solve these issues, providing restrictive data schemas, addressing only one aspect of text preprocessing and focusing on one single task when dealing with performance optimization. Thus, we propose in this paper a new generic text analysis architecture, where document structure is flexible, many preprocessing techniques are integrated and textual datasets are indexed for efficient access. We implement our conceptual architecture using both a relational and a document-oriented database.
Introduction A vast amount of textual data is generated daily and it is really challenging to develop efficient models and systems to enhance processing performance while doing accurate text analysis. The most fundamental challenges when working with large volumes of heterogeneous text datasets include the lack of structure of textual corpora, the various required preprocessing steps, the need for efficient access and the ability to scale up. Structural issues may be addressed by resorting to textual data warehousing and On-Line Analytical Processing (OLAP). However, such approaches only partially solve the problem because they use a structured schema that falls short when applied to large, heterogeneous volumes of data. Moreover, using a predefined schema makes them extremely dataset-specific.
Text Cubes and OLAP Extensive work on information retrieval (IR) and text analysis have been done using OLAP. Most proposals use Text Cubes for OLAPing multidimensional text databases  [20]. Lin et al. focus on optimizing query processing and reducing storage costs of Text Cubes  [12]. They experimentally show that average query time and storage cost are related to a cube's number of dimensions. Zhang et al. use Text Cubes for topic modeling  [20]  and experimentally show that their approach is much faster than computing each topic cube from scratch. Finally, Ding et al. address the problem of keyword search and top-k document ranking using Text Cubes  [7]. Their algorithms perform well in terms of query response and memory cost when the number of search terms is small. Ben Kraiem et al. propose a generic multidimensional model for OLAP on tweets  [2].
Their experiments show some promising results for knowledge discovery when applying OLAP on a small corpus, but query performance decreases when data volume increases. Bringay et al. propose a data warehouse model to analyze large volumes of tweets  [4]. They introduce different operators to identify trends using the top-k most significant words over a period of time for a specific geographical location, as well as the impact of hierarchies on such operators. Unfortunately, no time performance and storage cost analysis is provided. In conclusion, research done so far on text analysis and OLAP focuses on small, structured datasets and scaling up is not guaranteed.
Approach Overview The approach we propose (Figure  1 ) is subdivided into four steps: 1) clean and preprocess documents using natural language processing (NLP) and store the information in a database; 2) construct indexes; 3) analyze data, e. g. , with topic modeling, etc. ; 4) query and search data, extract top-k most relevant documents, create visualizations and analyses. We construct the inverted index, vocabulary, PoS and named entities (NE) indexes during the index construction step. Indexes may be used afterward by data mining, text analysis, search and visualization. The search engine sorts documents based on a ranking function (e. g. , TF*IDF, Okapi or BM25) to extract the top-k documents.