dataesr/scientific-paragraphs-categorization huggingface dataset:
The closer its value was to 1, the better the match. The ROLAP approach was used for the multidimensional representation of data. The ROLAP, using an SQL query-generated table containing the results (primary keys and structured data of the different tables) (Figure  6 ), allowed all analyses to be performed from several decision angles. Figure  6  describes the architecture of the platform implemented in Gabon as well as the SQL query that allows the raw data to be stored at the finest possible level of granularity in a materialized view, which is assimilated to a fact table and integrates the data contained in the various tables of the database deployed in the platform. This materialized view avoids manual processing (ETL) of data before integration into the DW. It uses the primary keys of all other tables as foreign keys to retrieve data from all the relational tables (Figure  7 ).
Design of the DMIS The DW was implemented using the MySQL relational database management system with a multidimensional ROLAP data model. The implemented system, referred to as Routine Info, has a general interface allowing the user to connect using a log-in and password attached to one of the decision levels (health service, health facility, health district, regional health, and country), which constitute, at the same time, different connection profiles to the tool with functionalities distributed as shown in Table  2.
Implementation of the DMIS Pilot For the implementation and testing of the Routine Info platform, 2 health regions of Gabon were chosen. For 2 months, the platform was routinely used simultaneously in a health center, the regional hospital, and a university hospital to respond to the 3 levels of the health pyramid (department, region, and country). The data managers of these facilities were asked to fill in the platform with the raw data available in the medical visit registers. The data filled in included clinical, demographic, and socioeconomic data. The objective was to compare the quality of the data reported using the Routine Info platform with that of the data produced by the traditional system used within the HIS in Gabon. During the 2 months of use, actual data from various medical consultations were integrated in near real time.
For reporting purposes, Tableau 2020. 1 software was connected to this database for data analysis and visualization. The implementation of the platform in these 2 regions made it possible to obtain data associated with medical consultations recorded from January 20, 2018, to July 12, 2018. The results gathered made it possible to carry out multivariate analyses at all levels of the health pyramid. These results have been shown to support the decisions of decision makers at all levels.
Scalability and Evaluation of the Pilot To scale up the model, a pilot implementation phase was carried out over a period of 2 months in health facilities in 2 of the 10 health regions of Gabon. These 2 health regions serve 49% (8,87,428/1,811,079) of the general population, employ 34% (2125/6250) of health professionals, and have 31. 95% (333/1042) of the health facilities in Gabon  [13]. At the end of this pilot phase, the Routine Info tool was evaluated through a survey interview of all the users who participated in the pilot phase. This evaluation was based on 3 determinants contributing to the achievement of data quality, namely the ability to have fully recorded data, the ability to facilitate the verification of data accuracy, and the ability to have all data available in a timely manner.
Feedback on the Implementation of the Pilot and Evaluation To test and evaluate the proposed system, a set of health professionals were first randomly selected from the participating health facilities in the 2 health regions of Gabon. Then, these professionals participated in various test sessions. Finally, we interviewed them to collect their opinions. In total, 14 health professionals (3 women and 11 men) were interviewed and participated in the various testing and evaluation sessions of the prototype. Overall, they had a mean age of 43 (SD 7) years and 12 (SD 1) years of experience in HIS data management. Specifically, there was no statistically significant difference between women and men in terms of age (P>. 05) or experience in the NHI system (P>. 05). The 14 health professionals interviewed were distributed as follows: statistics specialist, 36% (5/14); data managers, 29% (4/14); activity coordinator, 14% (2/14); physician: 7% (1/14); epidemiologist: 7% (1/14); and computer scientist, 7% (1/14).
RenderX The ability to obtain all health data in a timely manner resulted in an overall response score of 96% (27/28) for all data quality criteria for the Routine Info tool compared with an overall response score of 18% (5/28) for all data quality criteria for the traditional system. Overall, 2 criteria were used to assess the ability of the system to provide all data in a timely manner. These 2 criteria were the ability to produce monthly reports on time and the ability to generate statistics or indicators on time. These 2 criteria had scores of 93% (13/14) and 100% (14/14), respectively, for the Routine Info tool compared with 7% (1/14) and 28% (4/14), respectively, for the traditional system that met these criteria. With regard to the ability to facilitate the verification of data accuracy, the Routine Info tool had an overall response score of 100% (28/28) for all data quality criteria compared with a score of 18% (5/28) for the traditional system.
The 2 criteria that were used to assess this dimension were the ability to compare the tool's data with the registry data and the ability to have the registered data detailed at the finest possible level of granularity. These 2 criteria had scores of 100% (14/14) each for the Routine Info tool and 28% (4/14) and 7% (1/14), respectively, for the traditional system. Regarding the ability to have fully recorded data, the results showed that the Routine Info tool had an overall response score of 93% (39/42) for all data quality criteria, compared with a score of 12% (5/42) for the traditional system. A total of 3 criteria were used to assess this dimension. Measured individually, these criteria had scores of 100% (14/14), 86% (12/14), and 93% (13/14), respectively, for the Routine Info tool, compared with the scores of 14% (2/14), 7% (1/14), and 14% (2/14), respectively, for the traditional system.
Value of the Model Compared With Existing Models The design and implementation of a system based on a relational database approach, which integrates all data production processes, is a real challenge, as it pertains to facilitating the collection and structuring of data available on paper registers. It helps to structure different data and make semantically coherent data recorded in the different tables of the system. Furthermore, it helps to meet another major challenge of improving data quality. This systematically excludes manual and subjective manipulation despite the absence of digitalized operational systems, as is the case in the context of an assertive digital transformation. Indeed, in a context of sufficient digital maturity, many platforms such as i2b2  [20]  and Ehop  [17]  have simply used ETL tools to extract and transform data to the platform model before loading  [16].
Acknowledgments This work was financially supported by the e-Gabon National Health Information System project and the  World Bank. The lead author, a PhD student in public health specializing in medical informatics, received funding from the project to support this work. However , the project played no role in the design of the study, data collection, analysis and interpretation of the data, or writing of the manuscript. The National Health Information System's e- Gabon  project and the  World Bank  found no objections to the publication of the results of this study. The authors would like to thank the  World Bank , the health information system e- Gabon  project, the project team, the managers of the Libreville-Owendo and Ouest-Estuaire health regions, and their respective health staff.
Specifically, JWST instruments such as NIRSpec will cover blueward of 7000 Å and 4500 Å only in objects between z ∼ 6 and z ∼ 10, respectively. As such, a UV toolkit will be essential for characterizing and interpreting the spectroscopic observations of high-z systems. The ideal framework from which a UV toolkit can be built would consist of high S/N spectra with the possibility of extensive wavelength coverage that spans UV-to-optical wavelengths. Each of these essential elements are offered by local high-z analogs. In this context, the Cosmic Origins Spectrograph (COS) Legacy Spectroscopic SurveY (CLASSY) treasury  (Berg et al. 2022; James et al. 2022, Paper I and Paper II hereafter)  represents the first high-quality (S/N 1500Å  5 per resolution element, resel), high-resolution (R ∼ 15,000), and broad-wavelength range (∼1200-2000 Å) UV database of 45 nearby (0.
Then, from their comparison, we provide a set of diagnostic equations to estimate ISM properties only from UV emission lines. In Section 2 we describe the CLASSY sample, covering both the UV and optical data, while in Section 3 we present the spectroscopic analysis, including stellar-continuum and emission-line fitting. In Section 4 we discuss the chemical and physical diagnostics used in our analysis, showing and comparing the derived ISM properties in Section 5. Then, in Section 6, we introduce and discuss our UV-based toolkit, providing also a comparison with previous works. Finally, in Section 7 we summarize our main findings. The data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The specific observations analyzed can be accessed via 10.
In Paper I, we presented our sample, explaining in detail the selection criteria and giving an extensive overview of the Hubble Space Telescope (HST) COS and archival optical spectra. To summarize, from the Hubble Spectral Legacy Archive (HSLA), 101 nearby (z < 0. 2) galaxies were selected on the basis of the high S/N (7 per 100 km s -1 resolution element) COS spectroscopy in at least one medium resolution grating (i. e. , G130M, G160M, or G185M), applying further selection criteria to assemble a high-quality, comprehensive rest-frame set of FUV spectra for a large and diverse sample of star-forming galaxies. Specifically, any targets with secondary classifications or visually confirmed spectra features of quasi-stellar objects (QSO) or Seyferts were removed. The data reduction has been presented in detail in Paper II, including spectra extraction, coaddition, wavelength calibration, and vignetting.
UV Data CLASSY combines 135 orbits of new HST data (PID: 15840, PI: Berg) with 177 orbits of archival HST data, for a total of 312 orbits. In order to achieve nearly panchromatic FUV spectral coverage with the highest spectral resolution possible, CLASSY combines the G130M, G160M, G185M, G225M, and G140L gratings, spanning from 1150 Å to 2100-2500 Å to allow synergistic co-spatial studies of stars and gas within the same galaxy. Each HST/COS grating has a different spectral resolution that must be accounted for when combining data from multiple gratings. This co-addition process is explained in Section 2. 3 of Paper I and in Paper II, which presents all the details of this multistage technical process, concerning extracting, reducing, aligning, and coadding the spectra from the different gratings.
Optical Data High-quality optical spectra have been collected for the entire CLASSY sample to ensure uniform determinations of galaxy properties and to allow comparisons between properties derived from optical and UV diagnostics, thus enabling an accurately calibrated suite of UV diagnostics. Data release (DR) 7 Apache Point Observatory (APO) SDSS spectra with a 3 0 aperture exist for 38 of the CLASSY galaxies  (Abazajian et al. 2009 ), while for one galaxy, J1444 +4237, there are DR13 BOSS spectrograph data with a 2 0 aperture  (Albareti et al. 2017; Guseva et al. 2017 ). These spectra are in the wavelength range of 3800-9200 Å (3600-10,400 Å for BOSS), with a spectral resolution of R ∼ 1500 -2500  (Eisenstein et al. 2011). For the remaining galaxies of the sample (J0036-3333, J0127-0619, J0337-0502, J0405-3648, J0934+5514, and J0144+0453), we used integral-field spectroscopy data, when available, or long-slit spectroscopy, instead of SDSS.
Regarding the long-slit data, the MMT spectrum of J0144 +0453 was taken with the 300 lines mm -1 grating with a 10″ × 1″ slit, oriented along the parallactic angle to minimize slit losses (see  Senchyna et al. 2019  for more details). The wavelength coverage is 3200-8000 Å with a resolution of R ∼ 740. For galaxies J0808+3948, J0944-0038, J1148+2546, J1323-0132, and J1545+0808, instead of the SDSS, we took advantage of the MODS data from LBT obtained using the G400L and G670L. The MODS long-slit data were taken with a 60″ × 1″ slit, with an extraction aperture of 2 5 × 1″, and a slit orientation along the parallactic angle (see Paper V for more details). The wavelength coverage extends from 3200 to 10,000 Å with a moderate spectral resolution of R ∼ 2000. Paper V compares the SDSS, LBT, and MUSE integrated spectra for the galaxies with multiple observations, demonstrating that flux calibration issues or aperture differences do not introduce significant discrepancies in the optical ISM properties in terms of gas attenuation, density, temperature, metallicity, and SFRs.
Conclusions In this work we investigated UV and optical diagnostics of the ISM properties by exploiting the CLASSY survey presented by  Berg et al. (2022)  and  James et al. (2022) , which represents the first high-quality, high-resolution, and broad-wavelength-range (∼ 1200-2000 Å) FUV spectral database of 45 nearby (0. 002 < z < 0. 182) star-forming galaxies. Specifically we focused on the main UV emission lines visible in the COS spectra apart from Lyα (i. e. , N IV] λλ 1483,87, C IV λλ1548,51, He II λ1640, O III]λλ 1661,6, Si III] λλ1883,92, C III] λλ1907,9), and combine them with emission lines comprised between [O II] λλ 3727,9 and [S III]λ9069 from optical spectroscopy of the same pointing. Our aim was to provide a UV toolkit of ISM diagnostics, i. e. , a set of equations to diagnose E(B -V ), n e , T e , 12+log(O/H), and log(U), that use only UV emission lines.
2022 ; JWST User Documentation 2016). On the other hand, the higher spectral resolution of JWST/NIRSpec (up to R ∼ 2700) will provide a resolved C III] doublet (JWST User Documentation 2016). As demonstrated here, the CLASSY survey has provided the ideal ATLAS to create a powerful UV toolkit to explore these high-z spectra in terms of sSFR, direct gas-phase metallicity, ionization level, reddening, and nebular density, as it represents a high-quality UV and optical database for local analogs to reionization-era systems. To complete our toolkit, in the next paper on UVbased diagnostics, we will focus on the ionized gas kinematics, both of UV and optical emission, and on the diagnostics to inspect their main ionization mechanisms, to also understand the origin of the main UV emission lines taken into account.
This research has made use of the HSLA database, developed and maintained at  STScI, Baltimore, USA. Facilities:  HST (COS) ,  LBT (MODS) ,  APO (SDSS) ,  KECK (ESI) ,  VLT (MUSE, VIMOS). Software: astropy (Astropy Collaboration 2013, 2018) BEAGLE (Chevallard & Charlot 2016),  CalCOS (STScI) , dustmaps (Green 2018), jupyter (Kluyver et al. 2016), LINMIX (Kelly 2007) MPFIT (Markwardt 2009), MODS reduction Pipeline, Photutils (Bradley et al. 2021), PYNEB (Luridiana et al. 2012, 2015), python, pysynphot ( STScI Development Team 2013 ), RASCAS ( Michel-Dansac  et al. 2020), SALT (Scarlata & Panagia 2015), STARLIGHT (Fernandes et al. 2005b), TLAC (Gronke & Dijkstra 2014), XIDL.
The SurSaUD system collects daily data from 4 data sources: Emergency Departments (OSCOUR ED network)  1  , emergency general practitioners (SOS Médecins network), crude mortality (civil status data) and electronic death certification including causes of death. 2  Since its inception, the OSCOUR network has recorded more than 130 million ED visits. Data is collected by direct extraction of information from the patient's EHR (Electronic Health Record) in a common format for the whole territory and transmitted to Santé Publique France via the OSCOUR network. 5] [6] [7]  This is one of the tools currently used to monitor responses to COVID-19 epidemic in France. Approximately one-third of emergency department (ED) visits are the result of trauma in France. 8 rauma is a major cause of mortality and morbidity in the world.
7 In 2017 in France, trauma and injury accounted for 7. 01(6. 75-7. 33)% of deaths. 9 Unfortunately, little information is available when it comes to trauma: whereas we can know the nature of the main injury, nothing is known about the mechanism (road accident, assault, suicide. ). However, this information is available in the EHR, but in a free text form. In fact, each time a patient visits the ED, the nurse in charge of reception and orientation and the doctor in charge of the first consultation enter a text called clinical note, which describes the reasons for the patient's visit and the circumstances in which the symptoms occurred. In order to add the trauma mechanism to the data collected by the OSCOUR network, a manual classification by the health professionals would be time consuming and require multiple resources.
11 rench derivatives such as FlauBERT  12  and CamemBERT 13 of the BERT model have been trained on very large and diverse French corpora. FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Mo els of different sizes were trained using the Jean Zay supercomputer of the CNRS (Centre national de la recherche scientifique). Th re are three sizes: small (54 million parameters), base cased (138M) and uncased (137M) as well as large (373M). CamemBERT is based on RoBERTa 14 , an evolution of BERT in several aspects, including the use of the masked language model as the sole pre-training objective. CamemBERT, like FlauBERT, is available with different sizes: base (110M) and large (335M), but also with different training corpora such as OSCAR (either 138GB or 4GB of text)  15  , CCNET (either 135GB or 4GB)  16  or French Wikipedia (4GB).
One of the most interesting examples of transformer architecture is GPT-2, released by OpenAI in 2019. GPT-2 (Generative Pre-Training 2) is a large transformer-based model, composed solely of decoder blocks, with 1. 5 billion parameters on its extra-large version, trained on a dataset of 8 million web pages to predict the next word from the previous words. 17 Three other sizes of GPT-2 have been released before the largest one: with 124 (small), 355 (medium), 774 (large) million parameters. This model's ability for text generation quickly attracted the attention of the community because of the difficulty to distinguish the artificial texts produced from texts written by humans, suggesting that some of the meaning present in natural language was embedded. Moreover, beyond its ability to generate coherent texts, the GPT-2 can perform other tasks such as answering questions or classifying documents.
As with BERT, the conservation of several self-attention blocks weights from a pre-trained model is sufficient to transfer contextual representations into another dataset. The training of the GPT-2 model is thus carried out in two distinct phases: the first phase of selfsupervised generative pre-training, consists of the reading of a corpus of texts. It leads to the ability to generate texts automatically. The second supervised training phase consists in resuming the learning process from a corpus of annotated texts in order to create a system capable of performing specific tasks (classification for example). BelGPT2 is a Belgian small GPT2 pre-trained on French corpus of 60GB (Common Crawl, Project Gutenberg, Wikipedia, EuroPARL. ) that was released at the end of 2020. 18.
23 Recent studies have shown the effectiveness of Transformers on classification tasks for EHR free-text data such as ICD coding  24, 25  , phenotyping  26  or readmission prediction  27. Therefore, within the framework of the TARPON project (Traitement Automatique des Résumés de Passage aux urgences dans le but de créer un Observatoire National), which aims to demonstrate the feasibility of setting up a national observatory of trauma, we propose here to compare the performances of several transformer models for the classification of ED visits for trauma based on clinical notes from the adult emergency department of the Bordeaux University Hospital. We compared the transformers FlauBERT, CamemBERT, BelGPT2 and a French GPT2 model pre-trained on a domain-specific corpus called GPTanam here to the TF-IDF (Terms Frequency-Inverse Document Frequency)/SVM (Support Vector Machine) as a baseline model.
Models and experiment settings The models selected for comparison and freely available as open-source content were a traditional machine learning (baseline model) with TF-IDF/SVM couple as well as 3 transformers type models pre-trained on French corpora: CamemBERT  13  , FlauBERT 12 and BelGPT2  18. We then chose the most performing model and applied a supplementary step of self-supervised training step with the remaining 306,368 unlabeled clinical notes. This model is being called, here, GPTanam. Table  1  gives the size and configuration of each transformer model. For the TF-IDF, tokenization was performed using the nltk v3. 6. 6 package  29  and linear SVC was applied with the use of scikit-learn v0. 24. 1  30. The most frequent words (e. g. , "that", "he", "the") were removed. Tokenization was performed with SentencePiece  31  for CamemBERT, Byte-Pair Encoding (BPE) for FlauBERT and a Byte-level BPE for both GPT-2 models  32.
The data was cleaned using regular expressions with the re package in Python 3. 7. Unicode normalization was performed in UTF-8 (Universal Character Set Transformation Format -8). Linear SVC parameters were as follows: tolerance = 1e-05, penalty = l2, loss = squared hinge, dual optimization = True, C=1. 0, multi class strategy = one versus rest, verbose=0, a maximum of iterations of 1000. For all 3 transformers the optimizer was AdamW with an epsilon of 1e-8 and the maximum length was of 512. GPTanam had training and evaluation batch sizes of 5 and the learning rate was of 2e-5. For FlauBERT and CamemBERT, batch sizes for training was of 16 and 20 for evaluation and the learning rate was of 5e-5. Models were trained with the hugging face library under Pytorch framework on our workstation with a single Titan RTX (Nvidia©) GPU with 24GB of VRAM.
Self-supervised learning and Fine-tuning phase Considering the GPTanam model, a first step comprising a self-supervised learning was performed with 306,368 clinical notes with one epoch. 33 For all models, a random sample of 80% (n= 18166) of the labeled as trauma (n=22481) was dedicated to supervised learning. This dataset was divided into a training sample (n=14532) and a validation sample (n=3634) with an 80/20 ratio. We trained each model 9 times with different seeds on 7 epochs for CamemBERT and FlauBERT and 5 epochs for BelGPT2 and GPTanam. In order to obtain a single prediction for the 9 different executions of the chosen epoch (based on maximum validation micro F1-score) for each model, a vote was taken.
Test phase The test sample contained 20% of the labeled dataset, i. e. 4315 records. The second reading of these clinical notes resulted in 467 being tagged as clinical notes with potentially complex and/or ambiguous content as regard to its classification. The analysis therefore included both the complete test dataset (n=4315) and the dataset without complex and/or ambiguous content (n=3848). In order to obtain the probabilities for each prediction, a softmax activation layer was applied to the 4 transformer models.