dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018; Diminescu, 2012) ; hence completing the evolution of earlier forms of diasporic organizations. As a natural result, the science of migration evolved just as much as its own subject of research. Scientific publications' online databases are scaling up and one can now browse through the amazing richness of multidisciplinary archives. Researchers also integrate large and heterogeneous data sets within their own observations' scope like scrapped Web contents or late-digitized corpora of texts  (Ben-David, 2012; Ceserani et al. , 2017). However, if dealing with digital sources remains a technical and methodological issue, we must not forget that guaranteeing the quality of a sparse and mixed set of documents might be even more difficult. Dedicated fields of research have thus emerged and settled at the crossroad between social and computational sciences.
, 2014). Various mapping tools are thus already available and some strong research protocols have yet been successfully fulfilled in the context of migration studies. Among others, the e-Diasporas Atlas  (Diminescu, 2012)  used maps of migrant Web sites to reconstruct the online representation of thirty different diasporas: the relevant Web sites were semi-manually collected 2 before being aggregated as a set of collective networks through the software Gephi  (Bastian et al. , 2009). As for them, semantic maps are maps dedicated to the visualization of networks of terms, expressions or concepts. They can be reconstructed within the software Gargantext where large corpora of documents are processed by means of text-mining algorithms  (Delanoë et al. , 2020). Extracted words are thus projected in a multi-dimensional semantic landscape and late visualized through Gephi (see section "The scientific landscape of digital diasporas").
Phylomemies. The phylomemy reconstruction aims to investigate the temporal dynamics of a given corpus of textual documents  (Chavalarias and Cointet, 2013; Chavalarias et al. , 2020)  by revealing the internal evolution of its vocabulary. As well as being constructed upon strong mathematical foundations, phylomemies have also been influenced by social sciences concerns as they are part of the co-word analysis framework  (Callon et al. , 1991). Phylomemies have recently been enriched with a set of multiscale properties and can now encompass several levels of aggregation  (Chavalarias et al. , 2020). To that end, the researcher first chooses the scope of her corpus O by constructing an home-made database or by requesting an existing one (e. g. , an online collection of scientific publications).
Within the free software Gargantext 3 , she semi-manually selects a set of terms called map list that she later uses as foundations to the upcoming reconstruction. Next, text-mining algorithms find groups of terms jointly used during a shared period of time. An inter-temporal matching is then applied between these groups from one period to another in order to reconstruct their kinship relations R. This results in a complex formal object called foliation. The researcher is finally able to reduce this foliation to visualize all the branches of inter-connected groups as an evolutionary representation V of the internal dynamics of her corpus. Within Gargantext, the researcher can browse the phylomemy from a neophyte to an expert-oriented visualization (see section "The emergence of a socio-technical branch").
, 2020). Corpus. We start by broadly defining the scientific borders of digital diasporas with a set of generic keywords such as migration, diasporas, smartphone or digital technologies before building a more complex query 4. This query is then used for browsing through the related literature available in academic databases. We first elect the Web of science (Wos) as a starting point for reconstruct a landscape of digital diasporas' research-the Wos has the advantage of being accessible online and covers a wild variety of international journals in the field of social sciences and sociology. We consolidate this primary perimeter by combining it to the English language section of the french Hyper Articles en Ligne (Hal) database. We limit our harvesting to English written papers because, in its current stage of development, Gargantext cannot deal with cross-language contents.
Furthermore, though scientific databases provide poor quality meta-data for papers published before the 1990s, we think that digital diasporas' publications are barely concerned by this bias 5 as they have been mainly written after the 2000s. By doing so, we retrieve from Wos and Hal a collection of 2106 papers and their meta-data (titles and abstracts) that we later reduce with a manual pruning to a definitive corpus of 1562 documents 6 published between the 1980's and April 2020. As most of the scientific databases are unfortunately still not open-access, we recognize that our corpus couldn't be considered as fully representative (English written papers only, well established journals, etc. ), but we make the assumption that the resulting landscape will yet give evidence of the main digital diasporas' research trends.
1), while interests for digital self-spaces and identities had to wait until the present days to become incredibly investigated by the community (group No. 2). For its part, the socio-technical branch we now want to focus on (group No. 3) has grown alongside it's own subject of research; namely, the diasporic Web. At the beginning of the 2000's, the Web and its constitutive elements (sites, forums, databases, etc. ) slowly became a source of scientific materials for the comprehension of migrations  (Tyner and Kuhlke, 2000; Van den Bos and Nell, 2006). Simultaneously, the dedicated research group TIC-Migration appeared under the coordination of the sociologist D. Diminescu  (Diminescu, 2008). Altogether, they wanted to investigate the new communication habits of diasporic groups and, among others, the singular status of hundreds of migrant Web sites and their digital-born mechanism of citation: the hypertext links.
An e-Diaspora was, for its part, a network of migrant Web sites connected one another by hypertext links. It was a migrant collective that organized itself first and foremost on the Web. In order to build the Atlas, the researchers made up an innovative socio-technical protocol at the crossroad between social and computational sciences. For instance, the software Gephi came out of these dedicated developments and, from that moment, has kept influencing many reconstruction methodologies like semantic maps or to a lesser extend phylomemies. But back in the early 2000's, while they were investigating the diasporic Web, researchers frequently witnessed the total or partial disappearance of some of their targeted Web sites. Indeed, the Web has always been an ephemeral environment where entire online collectives can vanished in the blink of an eye.
As a matter of fact, it was decided to enrich the e-Diasporas Atlas with a Web archival section  (Laflaquière et al. , 2005). Thanks to the french National Audiovisual Institute (INA), all the sites indexed by the Atlas were thus automatically archived in dedicated databases. Since 2015, new researches have finally begun to dive into this corpus of millions of migrant Web archives. The resulting studies question the evolution of the divers e-Diasporas regarding Web uses, languages and historical events. The Web fragment method makes it now possible to navigate across the internal dynamics of migrant Web sites' archives and follow online cultural changes throughout time.
Broadly speaking, a Web archive can be pictured as a screenshot of a Web page taken by a robot-called crawler-at a specific moment of time and stored within a dedicated database. The resulting shape of a corpus of Web archives depends on both the crawling mechanism and the storage technique 14. In what follows, we refer to the specific INA's crawler and DAFF file format  (Drugeon, 2005). At the INA, in order to archive an entire Web site, the crawler has to frequently collect all its constituent pages provided they evolve since their last harvesting  (Lobbe, 2018). For instance, a comment might have been added and a post might have been deleted. A Web archival process thus requires considerable engineering handling and might also induce quality, coherence or timestamping issues. We first extract from INA's corpus the archives of the blogs captured between 2010 and 2014.
Cartesian forces are extracted from an Unsteady Reynolds Averaged Navier-Stokes (URANS) simulation and then injected in an Actuator Force model, where they are adjusted with the local velocity. In the first simulation, the Darrieus turbine is represented by a cylinder; in the second simulation, the mesh is simplified and is Cartesian everywhere in the domain: URANS forces are then projected in the simplified mesh. The method presents good and accurate results for a 3D-simulation, both for wake and power production predictions. However, improvements need to be made for 2D cases, even if the general trend for power production can be approximated. In this paper, the Actuator Cylinder method is implemented in a Computational Fluid Dynamic (CFD) software to study wake and performance of a double column contrarotating vertical-axis turbine and then applied to two hypothetical 4-turbines farms.
( 3 ) The method is implemented in ANSYS Fluent v14. 5. Reynolds Averaged Navier-Stokes equations (Equations (  4 ) and (  5 )) are resolved, and total forces (Equation (  3 )) are injected in by way of a volumetric source term S i. Simulations are performed in a 2D steady-state, considering an incompressible flow. ∂U i ∂x i = 0 (4) ∂ ρU i U j ∂x j = ∂p ∂x i + ∂ ∂x j µ ∂U i ∂x j + ∂U j ∂x i + R ij + S i. ( 5 ) R i,j = µ t ∂U i ∂x j + ∂U j ∂x i is the Reynolds tensor, and µ t the turbulent viscosity. Relative velocity #» W is obtained with local velocity computed by Fluent with the following formula: #» W = U local,N. # » e N + (U local,T -ωR). #» e T (for a counterclockwise rotation direction of the rotor); lift and drag coefficients C L and C D are extracted from hydrodynamic tables. Geometry is first realized in DesignModeler, and mesh in Meshing, both parts of ANSYS software.
Data Availability Statement: Not applicable. Acknowledgments: This work was funded by l'Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME) [26]  in the frame of the OCEANQUEST project. Computational resources were provided by the Manche county council. The authors would like to thank Hydroquest  [27]  for the data provided concerning the Hydroquest/CMN tidal turbine.
Using inverse filtering to obtain the original signal is not an option given the acquisition noise, especially for recursive filters. A deep neural network (DNN) can learn a complex non-linear mapping between input and output data, and in our case, to predict the true RT from filtered signals. However, DNNs usually require a large amount of training examples with known labels. The lack of known RT labels makes applying DNN on clinical applications particularly challenging, but this can be resolved by using synthetic data from physiologically-informed mathematical models  ( Corral-Acero et al. , 2020 ) , as well as from data augmentation techniques for machine learning  ( Bizopoulos and Koutsouris, 2019 ). In this article, we first study and highlight the error in estimating RT from a filtered UE using synthetic UEs filtered with the default CARTO high-pass filter.
Clinical patient UEs acquired by CARTO We obtained 18 CARTO datasets with UEs recorded from both the ventricular endocardium and epicardium. The patients were 17 male and 1 female with age 40. 5 ± 15. 29 (mean ± standard deviation (s. d. )), all without structural heart disease. Their pathologies were 11 idiopathic ventricular fibrillation, 4 Brugada syndrome, 1 hypertrophic cardiomyopathy, 1 laminopathy and 1 arrhythmogenic right ventricular cardiomyopathy. UEs were 2500 ms in duration, and recorded at a sampling frequency of 1 kHz, with resolution of 0. 003 mV. The mapping protocol was approved by the institutional review board, and all patients gave written informed consent before the EP procedures. UEs were selected for analysis if the signal-to-noise ratio was > 10 dB, where the signal and noise power spectral densities were computed over the frequency intervals 1-40 Hz and 40-100 Hz, respectively.
Synthetic patient UEs Since we did not have a set of ground-truth measurements of RTs for the CARTO-acquired UEs, we needed to generate a large set of synthetic data. For this, we constructed a comprehensive set of UE morphologies, corresponding to early and late, activation and repolarization. First, patient-specific reference signals which formed the basis of the UE models, were computed using the data from each of the 18 patients (2. 2. 1). Then, in order to create a patient population from our cohort of only 18 patients, we used a data augmentation technique to generate new reference signals, each corresponding to a new patient physiology (2. 2. 2) ; this has the effect of interpolating between the patient's physiologies. From these reference signals, a wide range of UEs were computed (2.
Computing UEs from the augmented reference signals For each of the 32 augmented reference signals, we then computed 625 UEs over a range of 25 ATs and 25 RTs using the model described in Section 2. 2. 1 , yielding 20,0 0 0 UEs for the training dataset. For very extreme RTs, the morphologies of the UEs changed very little. Hence, to avoid any redundancy in the morphologies, the range of RT was restricted such that the absolute amplitude of the T-wave was within 50% of the amplitude of the reference signal. Similarly, the AT range was a non-linear distribution centered at the mid-point of the upstroke of the reference signal with a width of 60 ms , where fewer points occurred near the edges of the distribution. The RT of each UE was estimated via the Wyatt method, which was considered to be the true RT.
Synthetic patient UEs validation The morphologies of the training UEs are assumed to be physiologically realistic. Although the reference signals described above were based on CARTO patient datasets, having only filtered CARTO datasets available for constructing the reference signals meant we could only approximate the input AT and RT distributions. Comparing the morphologies of the training UEs with the CARTO UEs used to construct the UE model would therefore indicate whether each of the CARTO UEs was well represented in the training set, and that the model is able to produce physiologically-reasonable morphologies. In our case, the UE model requires only a general distribution of AT and RT, but not necessarily accurate estimations.
The optimum n h and cp were then used to train the DNN on the full training set. We used a one-hot encoding to transform the ground-truth RT into a 500-dimensional vector with the corresponding entry being 1 and others being 0. We computed cross entropy between the ground-truth RT and the DNN output as the loss. An AdaMax optimizer  ( Kingma and Ba, 2014 )  with a learning rate of 0. 001 was used to update the weights of the DNN layers. The DNN was implemented in Python using the PyTorch package (version 1. 2. 0)  ( Paszke et al. , 2019 ) , and configured to run on a graphical processing unit (GPU) via CUDA (version 10. 2). It took around 2h for each split to train on the Nvidia Quadro M40 0 0 GPU.
Such procedures provide a reliable and extensive source of retrospective UEs, which would be appropriate for reuse in further RT studies if the filtering was minimal (given that the data was acquired reliably and it is suitable for the intended study). However, CARTO's highly-distorting, yet seemingly low default filter frequency of 2 Hz led us to believe that the effects of filtering could be overlooked. In addition, the datasets appeared to give no details of the filter setting used at acquisition, hence, without direct confirmation from the acquisition team, these settings could not be known. We have seen several studies that consider UEs that appear to exhibit distortions similar to the filtered UEs in our catalog, suggesting that the effects of filtering are under-acknowledged in the field.
If filtering was an issue, repeating the study with minimally-filtered UEs should produce a better match of the repolarization phase of the UEs. Even if filter settings are known, trying to reverse the filter relies on amplifying low-amplitude components of the signal, which could also amplify signal noise. Mizuhiki et al. (2020)  overcame this issue by first averaging signals with their close neighbors to attenuate any noise before reverse-filtering, but given the sparsity of the CARTO data, the spatial morphological variation was too large for this to be an option. For some patients, the option of re-performing a clinical procedure with broader filter settings for future studies exists. Endocardial recordings are frequently performed and new minimally-filtered data could be analyzed. However, the percutaneous subxiphoid puncture that yields the epicardium data  ( Sacher et al.
A DNN approach to estimate the RT distribution from filtered UEs We adopted a DNN to learn the mapping from different combinations of AT and RT to the resulting filtered UE morphology. By matching the morphology of the signal, the DNN was able to capture ground-truth RT, which, in our training set, was calculated by the Wyatt method. However, the labeling of RT is independent of the DNN architecture, thus, other RT estimation methods can also be applied. The key to applying the DNN successfully was to perform supervised learning over a large variety of training data. However, we had access to only a small number of CARTO datasets to use as training examples, and of these their true RTs were not known. Modeling tools allowed us to generate a virtual patient cohort of synthetic yet physiologically-realistic UEs, across a range of clinically observed AT and RT.
The DNN was, thus, trained with the synthetic UEs. The ATs and RTs from each of the CARTO UE datasets were used to model the patient, from which a set of synthetic UEs could be computed. Although not in exact accordance with the model in  Potse et al. (2009)  , our validation step showed that the UEs nevertheless proved sufficiently physiological to form the training dataset. These UEs depended on the model's reference signal that characterizes patient-specific electrophysiology. To ensure a reasonable electrophysiological variability in the training set from only 18 patients, we generated a larger set of synthetic reference signals sampled uniformly from a two-dimensional PCA-reduced subspace of the reference signals, which we constrained to lie within the envelope of the measured points representative of our patient characteristics.
The computation of RT maps We presented an example of an RT (ARI) map in Fig. 8  that was computed from CARTO UEs using the DNN. These UEs were acquired originally for AT map construction, hence, this example demonstrates how the CARTO acquisition system can be extended to generate both AT and RT maps from the same data without compromising the quality of the AT map. Since the UE recordings were acquired sequentially, the recordings did not always correspond to the same beat. Variability in the cycle length could lead to spurious variations in the RT estimations. Although this is not an issue for individual RT estimations, our UE selection process mitigated this factor in order to compare local estimations in the map. The quality of an interpolated map from UE features relies heavily on the spatial density of the recording sites and the quality of the local UEs.
Acknowledgments PL would like to thank  Veronique Meijborg  and  Ruben Coronel  for the discussions about the filtering of CARTO electrograms. This project was supported by the  Leducq Foundation  and  GENCI  computing resources, allocation  A0080310517. This project has received funding from the  European Union Horizon 2020 Research and Innovation programme "Personalised In-silico Cardiology (PIC) " under the  Marie Skłodowska-Curie  grant agreement no. 764738 , and from the  French Government  as part of the  "Investments of the Future" program  managed by the  National Research Agency (ANR) , Grant reference  ANR-10-IAHU-04.
, 2020; Kirsten et al. , 2020). This emerging regional dataset is enabling a more refined understanding of the spatial and temporal complexity of climate and vegetation responses to a range of forcing mechanisms, and suggests that substantial variability in inferred climatic trends/trajectories occurs across relatively short distances (e. g. Chase et al. , 2015a; Chase et al. , 2017; Chase and Quick, 2018; Chase et al. , 2020). Even though more -and indeed more detailed -records are now available, most of these records only cover the Holocene (11. 7 -0 cal ka BP) or portions thereof. In particular, greater spatial and temporal coverage is still needed to fully document the Last Glacial Maximum (LGM; 19 -26. 5 ka  (Clark et al. , 2009) )and last glacial termination (Termination I; the period from the end of the Last Glacial Maximum to the early Holocene, ~19 -11.
Chronology The age-depth model for the PB1 core was constructed from eight radiocarbon ages (Table  1 ). Subsamples of approximately 1 g were taken from various positions along the PB1 core and sent to Beta Analytic Inc for AMS radiocarbon dating. These ages were calculated using the Libby half-life of 5568 years following  Stuiver and Polach (1977). The ages were corrected for isotope fractionation using the AMS-measured δ 13 C, which accounts for both natural and machine fractionation. The age-depth model was developed in the software package rbacon (v. 2. 3. 6; Blaauw and Christen, 2011). Using a Bayesian framework, Bacon divides the core into sections and models the accumulation rate for each section through multiple Markov Chain Monte Carlo (MCMC) iterations. The ages were calibrated with the SHCal13 data  (Hogg et al.
Defining plant-climate relationships To objectively define the plant-climate relationships associated with the Pearly Beach pollen assemblage, we used the CREST (Climate REconstruction SofTware) method and software  (Chevalier et al. , 2014; Chevalier, 2019). For our analyses, we used botanical data obtained from the Global Biodiversity Information Facility (GBIF) database (Global Biodiversity Information Facility, https://www. gbif. org/), specifically the curated dataset of Chevalier (2020), and we used a regional subset of the Worldclim 2 climatology  (Fick and Hijmans, 2017).
Pollen and microcharcoal A total of 102 pollen taxa were identified from the 78 samples analysed. The pollen, non-pollen palynomorphs, microcharcoal and particle size data (Fig. 4 , Fig. 5, Appendix A ) were divided based on CONISS analysis results and assemblage variability into four pollen assemblage zones (and three subzones). Pollen concentrations range between 2x10 3 and 33x10 3 grain g - 1  , with peaks in concentration 22. 5 -19 cal ka BP and 14 -12 cal ka BP. No pollen was preserved from 11. 6 -8. 5 cal ka BP. and a smaller peak in Aizoaceae. Just prior to this phase, the highest counts and greatest concentrations of microcharcoal were recorded (from 23 -22 cal ka BP). There is a small peak in Juncaceae percentages at the base of the sequence and peaks in Cyperaceae around 22 cal ka BP, although wetland taxa percentages are generally somewhat reduced for PB1-A.