dataesr/scientific-paragraphs-categorization huggingface dataset:
ISO 14258  [6]  says that semantic interoperability can be achieved in three ways:  Integrated: "With integrated models there is a standard model form. " The main limit there is the standardisation of models.  Unified: A meta-model exists to represent all existing models. Hence, one can establish correspondence between the various models.  Federated: Models are diverse and rely on the establishment of dynamic correspondence. PLM migration from multiple initial systems to one target system can be considered as one way to achieve integrated semantic interoperability. Hence, enabling a better understanding of data migration could enable, in a near future, the transition of integrated systems towards a unified or even a federated ICT system. However, we need to better understand what has already been done concerning PLM data migration.
PLM data migration Research review through Scopus and Web of Science highlights the following:  In Müller et al. [10] , "PLM migration project" is only in the abstract; in the rest of the articles, the authors talk about "PLM deployment". Unfortunately, data migration is not mentioned throughout this article. However, project management in interestingly discussed in one paragraph, providing precious insight about the management of such projects: Even if led by equal rights between the business unit and IT, PLM upgrade still had been perceived as a pure IT project.  Singh  [11]  challenges only the transition to the cloud and does not address data migration itself. It is understood that this is an isomorphic migration and that systems are unlikely to evolve between on-premise PLM and cloud PLM.
Data Model Structuring Choice Indeed, the PLM data is more than often stored in a relational database for speed and reliability purpose. However, graph databases take benefit from these similarities to propose full-featured graph applications with interfaces including real world object/link-based data. In this regard, we consider switching the data stored into PLM towards a graph. In the graph theory, everything is either a vertex or an edge and an edge is necessarily between two vertices. In PLM, any artefact is either an object or a link. An object is a standalone entity holding attributes and a link is an entity relaying objects, generally 2 objects identified by their role; links can hold attributes. As one may realise, PLM data model systems have strong similarities with graph theory models.
By default, Business Lot Dividers produce Business Lots and Mathematical Dividers product Technical Lots but this default settings can be overridden by parameterization depending on clustering strategy needed. Metis  [18]  is a graph partitioning tool taking as input a graph and desired number of partitions N. It produces N sub-graphs optimised to reduce number of cross-partition vertices. Metis is used by "Better than Big Bang Divider" intended to split data in most possible number of independent lots, to reduce data migration complexity. The experimental approach is based on scenarios. A scenario is a pipe of Lot Dividers that allocates data entities [0] (objects, links) to entities [M] (lots and lot dependencies), M being the number of clustering passes. Scenario outcomes are evaluated by verifying they are compliant to rules enounced previously.
Optimizing an ETL Data Load ETL (Extract/Transform/Load) tools aim to migrate data from source to target system by applying a pattern. However, it is flow driven and unaware of data topology, e. g. decomposition of data in loosely coupled lots. Some approaches to ETL processes such as  [19]  in the data warehouse and big data contexts be graph-based and semantic however these do not fit the PLM needs as expressed here. During ETL elaboration, loading error rate may be dramatic for PLM data structures because of the re-use object factor -an object is re-used by other objects through a link. If the re-used object loading is broken, all dependent links and all recursively dependent links will fail, a. k. a. explosive errors spreading. As a result, data is not business testable and the error analysis task is complex because valuable error root causes are flooded by useless dependent errors.
Optimizing a migration strategy A data migration project needed to evaluate migration strategy of data package containing 10 million objects and links. ETL direct approach had failed because of "explosive errors spreading" phenomenon and the duration of a test increment which was too long to bear iterative development mode. Data is extracted from a PLM source system. Data extraction has been made by source object types into a set of CSV flat files. Files data sets are not organized by programs nor any business organization. Loading data type by type in a Big Bang migration strategy is not acceptable because this loading strategy would be testable only at end of process and subjected to cascading errors phenomenon. The question raised was: Is there a more optimal than the Big-Bang migration strategy? Data Systemizer was parameterised to perform two BOM Dividing and Metis BOM Dividing: Fig.
5. Result of Data Systemizer application: data is separated in 12 different lots Fig. 5  shows input data which has been clustered into 12 lots; 11 separated lots all depending on a single component lot. Data Systemizer can restitute this monolithic data package in 12 lots. The lot in orange contains some data referential integrity errors; i. e. orphan links. Some elaboration specific test data lots can be provided given needed volumetry and error rate. Test data lots are functionally significant, they contain testable end-to-end business data.
Choosing right migration strategy An aircraft company needs to migrate a certification application from a PLM system to an ERP system. Volumetry is low but data model is complex. Drastic system changes and model complexity requires important end users testing hence big-bang migration is not acceptable because on one hand, it would retain tests at the end of the migration project and on the other end, users would redo costly tests. To enable a fine-grained migration strategy, two lot dividing strategies are studied: at Family level (top level) and at Baseline level (middle level). Business users would like to know what level is best. As data model is complex, some specific algorithms have been developed to extract Family and Baseline information. Two graphs are produced to compare both migration strategies.
Conclusion and future work This article has outlined the issues related to PLM data migration as well as why this is crucial regarding being able to tackle one company's own data in the current context of Big Data. Methodology was described and use cases improving existing were outlined. Discussion focus on possible reusability of the underlined methodology as well as possible improvements. Indeed, the method outlined could be reused in any information system whose object representation is similar, such as ERP, MES or IoT platforms. Improvements to the method could be made regarding the treatment of circuits. Indeed, they are currently temporarily put away to avoid blocking the process. However, in some scenarios, their number would be too non-negligible, and one would need to tackle them head on.
Sleep assessment In the present study sleep duration was calculated as "time in bed" which represent the difference between bedtime and awakening time. In both CONT and PSD condition, sleep duration was assessed subjectively using the sleep diary. By, eliciting detailed information on daily sleep duration over a specific time period, this subjective sleep duration measure was recognized as an accepted standard for self-reported sleep duration  [27]. In contrast to objective measures (e. g. , Polysomnography, wrist actigraphy), which are time-intensive and resourceconsuming, sleep diary was also recognized as practicable means of collecting sleep duration data in typical at-home sleep  [28]. Besides time diary, time to go bed and time to wake-up in CONT (i. e. , 22:30 h and 06:30 h) and PSD (00:30 h and 04:30 h) conditions were controlled using the observation by the experimenters and the artificial illumination (i.
Statistical analyses Statistical tests were conducted using STATISTICA Software (Statistica version 13, Maisons-Alfort, France). All data are presented as mean ± standard deviation (SD). The Shapiro-Wilk W-test revealed that VE, VO2, VCO2, HR, POMS, reaction times (SRT and CRT) and speed were normally distributed. Physiological variables (HR, VE, VO2 and VCO2) and speed were analyzed using a two-way repeated measures ANOVA [2 sleep conditions (CONT and PSD) × 6 times (every 2 minutes of exercise)]. All ANOVA effect sizes were calculated as partial eta squared (np 2 ). POMS items and reaction times (SRT and CRT) and BT were analyzed using repeated measures ANOVA [2 sleep conditions (CONT and PSD) × 2 times (before and after exercise)]. When significant main or interaction effects were observed, pairwise comparisons were performed using Bonferroni post-hoc test.
It is a two-tiered pre-hospital system with a re department ambulance ( rst professional aid provider) available for prompt intervention and basic life support (BLS), and a mobile emergency and resuscitation service (MERS) including a MICU for advanced life support (ALS). The RéAC form meets the requirements of the French Emergency Medical Service organizations and is structured according to the Utstein universal style. 18 Data is entered in the secured RéAC database (www. registreac. org). 19 Several quality controls were performed on this database (online and o ine tests). The present study was approved by the French Advisory Committee on Information Processing in Health Research (CCTIRS) and the French National Data Protection Commission (CNIL, authorization no. 910946). It was approved as a medical assessment registry without requirement for patient consent.
Patient population We selected in the RéAC database all witnessed OHCA in adults (≥ 18 years of age) with lay bystanderinitiated cardiopulmonary resuscitation (CPR) before the arrival of professional rst responders. Exclusion criteria were unwitnessed OHCA, CPR without chest compressions (ventilation only), OHCA in the presence of professional rescuers and traumatic OHCA. We assessed all medical etiologies of OHCA and classi ed them in asphyxial and non-asphyxial etiologies, including a subgroup of cardiac causes. The OHCA etiology was determined by the MICU physician in charge of the patient. Subjects were also classi ed in two groups, according to the bystander-initiated CPR method: chest compression only (CO-CPR) and chest compression with mouth-to-mouth ventilation (i. e. S-CPR).
21 The variables included for the propensity score were therefore limited to variables related to the outcome, i. e. whether or not related to exposure (CO-CPR or S-CPR). For each analysis (main and in subgroups), we constructed a different propensity score. In order to minimize the impact of missing data, we performed multiple imputation using chained equations (MICE) with predictive mean matching for continuous data (only one variable included in the propensity score with missing data was imputed: "Time between the emergency call and arrival of rst professional rescuers at the scene" with 19. 8% of missing data). The primary endpoint was assessed with a logistic regression model adjusted on the previously calculated propensity score. Results are expressed as adjusted relative risks with 95% CI.
Patient characteristics During the ve-year study period, 51 638 OHCAs were registered in the database from 57 centers. There were 35. 1% unwitnessed OHCA, the bystanders were professional rescuers in 22. 7% of cases and in 22. 7% of the cases no resuscitation was undertaken by non-professional witnesses. Finally, 8 619 patients were included, of which 6 742 had a non-asphyxial etiology (including 5 904 cardiac causes), and 1 710 had an asphyxial OHCA etiology (Fig. 1 ). The main characteristics of the patients are reported in Table  1   I  in the supplemental material. The proportion bystander-initiated S-CPR was higher at the beginning of the period (linear regression, P < 0. 001; R 2 = 0. 44) (Fig. 2 ); the same trend was observed in other subgroups (Figure I in the supplementary material).
Study design Patients The identification of hospital stays was based on the national hospitalisation, Programme de Médicalisation des Systèmes d'Information (PMSI) database, which covers hospital care, inspired by the US Medicare system. In the PMSI system, identified diagnoses are coded according to the International Classification of Diseases, Tenth Revision (ICD-10). Hospital stays with a principal diagnosis (i. e. the health problem that justified admission) of myocardial infarction (codes I21, I22) or French diagnosis-related group (DRG) of birth (codes 14Z13, 14Z14) were identified. Data collection was performed on 10 April 2020 by the department of Medical Information, Hospices Civils de Lyon, France. The study was conducted retrospectively and, as patients were not involved in its conduct, there was no effect on their care.
Recruitment and Questionnaire Design We conducted a multicenter observational single-week survey involving all of the patients infected with HIV who were consulted during the week of October 15-19, 2018 in French hospitals via regional coordination structures. The patients were involved in the design, implementation, reporting, and dissemination of this survey. The inclusion criterion was aged over 18 years. Exclusion criteria were inability to reply to the questionnaire, inability to speak French, and refusal to participate. This observational survey was compliant with the MR003 reference specification  [15]. The protocol was filed with the French data protection agency (CNIL, No. M18009).
Data Collection Study data were collected and managed using Research Electronic Data Capture (REDCap) electronic data capture tools hosted at the teaching hospital Centre Hospitalier Universitaire (CHU) de Clermont-Ferrand (Clermont-Ferrand, France)  [16, 17]. REDCap is a secure, web-based software platform designed to support data capture for research studies, providing an intuitive interface for validated data capture, audit trails for tracking data manipulation and export procedures, automated export procedures for seamless data downloads to common statistical packages, and procedures for data integration and interoperability with external sources. Both patients and health care professionals generated their own anonymous data using the REDCap app created for this survey. Access to the survey was provided through a QR code or internet link, followed by entry of an access code.
Data Analysis All statistical analyses were performed using Stata software (version 13, StataCorp, College Station, TX, USA) and R 3. 3. 3 software (R Foundation for Statistical Computing, Vienna, Austria). All tests were two-sided with a type I error set at. 05. Baseline characteristics (physicians and patients) are presented as frequencies and associated percentages for categorical parameters, and as mean (SD) or median (interquartile range) for continuous variables, according to the data distribution. Multiple correspondence analysis followed by mixed unsupervised classification (k-means clustering applied to the partition obtained from an ascending hierarchical classification using Ward distance) were implemented to (i) examine the relations between the modalities of the variables and (ii) determine PLHIV profiles (clusters of individuals sharing closely similar characteristics).
Overall Response The analysis included 219 physicians for whom no data were missing among the variables selected for analysis. Multiple correspondence analysis identified three groups (G): G1 (95/219, 43. 4%), termed "strongly confident in eHealth;" G2 (80/219, 36. 5%), termed "strongly opposed to eHealth;", and G3 (44/219, 20. 1%), termed "open to eHealth" (Figure  3 ). The eHealth perception characteristics of the physicians are summarized in Multimedia Appendix 2.
Acknowledgments The study was supported by funding from  CHU Clermont-Ferrand. The authors acknowledge all of the collaborators of this study at the participating hospitals across France for offering the online survey to PLHIV and physicians and participating in the study themselves. C Faudon  and  T Allegre  (Aix-en-Provence);  M Lombard  and  JL Schmit (Amiens) ;  S Rehaiem  and  V Rabier  (Angers),  A Valran  and  E Piet (Annecy) ;  J Gerbe  and  F Caby  (Argenteuil),  E Chevalier  and  A Foltzer  (Besancon);  L Traore  and  O Bouchaud  (Bobigny);  JC Duthe ,  S Ansart , and  L de Saint Martin  (Brest);  P Goubin  and  R Verdon  (Caen);  J Prouteau  and  C Jacomet  ( Clermont-Ferrand );  C Chesnel ,  L Richier ,  S Gallien , and  B Elharrar  (Créteil);  I Calmont  and  A Cabié  ( Fort-de-France );  M Marcou  and  P de Truchis (Garches) ;  C Sautron  and  MP Moiton  ( La Réunion Saint-Denis );  L Lainé , and  S Leautez Nainville  ( La Roche-sur-Yon );  P Camps  and  M Roncato-Saberan  ( La Rochelle );  D Bornarel  and  A Greder-Belan  ( Le Chesnay );  AS Keita  and  A Cheret  ( Le Kremlin-Bicêtre );  F Dangeul-Potier  and  H Hitoto  ( Le Mans );  B Montoya  and  JJ Laurichesse  ( Mantes-la-Jolie );  S Tassi  and  E Frogel  ( Marne-la-Vallée );  A Suzel-Ritleng  and  O Zaegel-Faucher  ( Marseille Sainte-Marguerite );  C Fernandez  and  J Reynes  (Montpellier);  M Benomar  and  G Beck-Wirth  (Mulhouse);  A Soria  and  F Raffi  (Nantes);  I Touitou  and  P Pugliese  (Nice);  P Gougeon  and  S Sunder  (Niort);  MJ Dulucq  and  L Slama  ( Paris Hôtel-Dieu );  C Louisin ,  C Duvivier , and  PH Consigny (Paris Necker Institut Pasteur) ;  M Pauchard ,  D Beniken ,  C Katlama ,  A Simon  ( Paris Pitié-Salpêtrière );  C Tran  and  D Bollens  ( Paris Saint-Antoine );  A Adda Lievin ,  G Thibaut , and  P Pialoux  ( Paris Tenon );  L Pinheiro  and  H Aumaitre  (Perpignan),  L Gerard  and  H Masson  (Poissy);  D Plainchamp  and  G Lemoal  (Poitiers);  JC Duthe  and  P Perfezou  (Quimper),  I Kmiec  and  F Bani-Sadr  (Reims);  C Guennoun  and  C Arvieux  (Rennes);  G Unal  and  A Lesourd  ( Rouen) ;  V Ronat  and  A Fresard  ( Saint-Etienne );  B Montoya  and  Y Welker  ( Saint-Germain-en-Laye );  C Grand Courault  and  C Michau  ( Saint-Nazaire );  P Fisher  and  D Rey  (Strasbourg);  V Lambry  and  A Lafeuillade  (Toulon);  M Digumber  and  O Robineau  (Tourcoing);  E Racamier  and  H Champagne  (Valence);  L Richier  and  S Dellion  ( Villeneuve-Saint-Georges ).
This paper presents the first version of the regional-scale personal exposure model EXPLUME (EXposure to atmospheric PolLUtion ModEling). The model uses simulated gridded data of outdoor O 3 and PM 2. 5 concentrations and several population and building-related datasets to simulate (1) space-time activity event sequences, (2) the infiltration of atmospheric contaminants indoors, and (3) daily aggregated personal exposure. The model is applied over the greater Paris region at 2 km × 2 km resolution for the entire year of 2017. Annual averaged population exposure is discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposure. By contrast, considering the infiltration of PM 2. 5 in buildings decreases annual average exposure by 11 % (population average).
Further discretizing in time, we calculate exposure as the sum of the product of time spent by a person in different microenvironments and the time-averaged pollutant concentrations occurring in those locations: E i = m j =1 C ij T ij. (1) Here, T ij is the time spent in microenvironment j by person i with units in minutes, C ij is the air-pollutant concentration person i experiences in microenvironment j in units of µg m -3 , E i is the integrated exposure for person i (µg m -3 min), and m the number of different microenvironments. In this formulation, concentration C ij is averaged over the corresponding time period T ij. The general structure of the model with the necessary input datasets for the exposure calculation is illustrated in Fig. 1. Outdoor pollutant concentrations are simulated with a regional-scale chemistry-transport model.
This survey questioned 43 000 individuals and identified 143 000 journeys. Each journey is characterized by the origin and destination points, the motive for travelling, the duration, and the means of transportation used. The mobility of the sample population is simulated with a Monte Carlo model that matches the simulated data with the EGT (2010) data (Sect. 4). 3 Pollutant concentrations 3. 1 Outdoor O 3 and PM 2. 5 concentration predictions Pollutant concentrations are modelled with the CHIMERE model  (Mailler et al. , 2017)  at a horizontal resolution of 2 km × 2 km. Four-level one-way nesting is used for the CHIMERE simulation with grids of 60, 20, 7, and 2 km spacing between cells at both west-east and south-north directions. Overall, 15 vertical layers are used from 998 up to 300 hPa, with layers becoming thicker with distance from the surface level.
Climatological conditions, temperature, pressure, and outdoor pollutant concentrations are simulated with atmospheric models (WRF for meteorology and CHIMERE for ozone and PM 2. 5 concentrations) at a 4×4 km 2 horizontal resolution for a 10-year period from 1991 to 2000. Atmospheric fields are spatially averaged over the eight departments of the region. So, the atmospheric conditions database input for the SIREN model consists of 10-year period hourly data for the eight departments of the Île-de-France region. For each Île-de-France department, eight SIREN simulations are conducted (five for dwellings and three for offices/schools) at a 3 min time step. The penetration factor is fixed to 0. 8 through the building shell and 1 through air inlet based on the state of the art  (Chen and Zhao, 2011; Monn, 2001; Stephens et al.
, 2012; Thatcher et al. , 2003). Confronting numerical simulations with SIREN and I / O ratio measurements, the deposition rate was fixed to 0. 1 h -1. The SIREN model output consists of a decade-long database of I / O ratios for ozone and PM 2. 5 at 3 min resolution for each of the eight Île-de-France departments, for five construction date classes for dwellings and three construction date classes for offices and schools. This database is further processed to provide seasonal I / O ratios for each pollutant, building type, construction date, and geographical zone as shown in Fig. 4. Indoor / outdoor ratios for the personal exposure calculation are drawn randomly from the corresponding seasonal distribution depending on the personal profile and month.
Generation of the sample population The population data implemented in the model are public census data published by the INSEE (Institut Nationale de la Statistique et des Études Économiques). The current version of the model implements datasets for the year 2009. The administrative unit chosen for the current study is the commune. The Île-de-France region has 1300 communes and a population of 11 726 743. The most densely populated communes are located at the outer rings of the Paris agglomeration, followed by a second circle of high population density in the suburbs directly attached to the agglomeration. A third highly urbanized ring is distinguished before reaching the rural areas at the outskirts of the Île-de-France region (Fig. 6 ). The size of the sample population is fixed at 250 000 individuals (≈ 2 % of the actual population).