dataesr/scientific-paragraphs-categorization huggingface dataset:
In most retained studies, these factors were used to match and/or adjust analyses, a required feature to increase the robustness of results. Use of such statistical approaches allowed the level of evidence to be raised to moderate but not high as residual confounding by hidden factors could not be ruled out; for instance, detailed data on lifestyle behaviours (such as smoking, diet and physical activity) are Fig. 4  distribution of papers by quality rating within selected PICOT questions. TBC refers to a minority of papers that got only one rating, so that final rating remained to be consolidated frequently lacking and body mass index is absent in several databases. The results of observational studies considered of sufficient quality (all 11 primary items fulfilled) suggest several conclusions regarding the corresponding PICOT questions.
Influence of ICS particle size on asthma outcomes (Tables 6, 7) [32, 40, 67-72] Several analyses, mostly by the same group  [32, 40, [67] [68] [69] [70] [71] , were performed in various populations (children, adults) using various databases (UK, US) and found superiority of extra-fine versus larger particle ICS administered alone or as part of an ICS-LABA fixed dose combination in asthma  [32, 40, [67] [68] [69] [70] [71] [72]. Accordingly, economic analyses using these results suggested extra-fine ICS was a dominant strategy as compared with larger particle ICS alternatives  [32, 69].
One major challenge in that respect is to distinguish between a methodological flaw and insufficient reporting. The latter No No can be overcome (e. g. by contacting the authors for clarifications), but may also be improved through awareness of the RELEVANT tool. Four PICOT questions were selected through a poll among the Task Force and members of REG and EAACI, and related observational studies identified through a systematic literature search among bibliometric research databases. Retrieved records were then quality appraised using the RELEVANT tool. Several of the papers were assessed as being of sufficiently good quality to allow an increase in their level of evidence from low to moderate following a GRADE-like process. For all PICOT questions of interest, assessed observational studies yielded results with possible impact on clinical practice in areas where similar evidence from RCTs is lacking.
Literature search: rationale and quantitative results The initial steps of the process confirmed that conducting a search specific to observational effectiveness research is challenging given the lack of standardised and specific terminology/nomenclature and requires the input of specialists of bibliometric research databases. Despite this, the number of published articles available for each of the PICOT questions was surprisingly low given the long period that was scrutinized (10 years), especially for PICOT questions 2 (influence of device type on the effectiveness of maintenance ICS therapy, n = 7 papers) and 3 (influence of current active smoking on the effectiveness of ICS-containing maintenance therapy regimen, n = 3 papers). This illustrates that observational effectiveness research in asthma is still infrequently performed or reported despite the clear need for real-life data to complement classical RCTs by answering different study questions including to, better assess the extent to which their results can be generalised, and identify the best target populations.
Quality assessment A common limitation of analysed papers was the lack of precise clinical data available to ensure that patients of compared groups were 100% comparable at baseline. To minimize the impact of confounding (bias), most studies used statistical strategies including matching (exact or propensity score-based) and adjustments for variables differing between groups during the baseline period. These strategies could not, however, account for variables that were entirely absent from the databases. For instance, in most studies lung function data (e. g. , FEV1) or questionnaire-measured level of asthma control were not available; so proxies for control were used although they were not necessarily strictly concordant with questionnaire results, e. g. , due to time frame differences. In addition, some studies did not report sufficient detail on baseline population characteristics, making it difficult to assess their quality with a high level of confidence.
Influence of ICS particle size on asthma outcomes (PICOT question 3) Several studies (most performed by a single research group) found that asthma outcomes were better in reallife patients receiving ICS or ICS-LABA combinations delivered by extra-fine rather than fine particle formulations  [32, 40, [67] [68] [69] [70] [71] [72]. These studies followed strict processes to minimize severity biases, including matching and adjustment strategies. However, as always in observational database research, it is not possible to totally exclude residual confounders that would be detected only if full clinical data were available. Importantly, the selection and quality assessment processes used here were subsequently applied to a recently published systematic review and metaanalysis on this topic  [93].
Opportunities for future research and initiatives Comprehensive quality assessment tools are now available, not only for efficacy trials (e. g. , CONSORT Statement)  [94] , but also for their pragmatic counterparts (CONSORT Statement extension)  [95] , for observational studies in epidemiology (e. g. , STROBE statement)  [96]  and, more specifically, for pharmacoepidemiology and pharmacovigilance studies (EMA-ENCePP checklist for study protocols)  [22]. Another useful initiative called SPIRIT has recently published recommendations for describing clinical trials protocols  [23]. Quality criteria and minimal datasets requirements for observational studies are also the topic of the UNLOCK initiative  [24]. For meta-analyses, the QUOROM (quality of reporting of meta-analysis) reporting guideline and the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidance seeks to improve the reporting of key information  [19, 97].
[43] Adherence and SABA use are better D-M Moderate No No with MF than FP DPIs, with no differ- ence in other clinical outcomes Campbell et al. [44] Shifting drug costs to patients D Moderate Yes No decreases adherence and impairs asthma outcomes Tan et al. [38] In adherent patients, ICS > LTRA In non-adherent patients, ICS < LTRA D + S D + S Moderate (D), low (S) Yes Moderate (D), low (S) Yes In part (pragmatic RCT) In part (pragmatic RCT) SABA short-acting beta2 agonist, LABA long-acting beta2 agonist, ICS inhaled corticosteroid, LTRA leukotriene-receptor antagonist, MF mometasone furoate, FP fluticasone propionate, D database, PC prospective cohort, S survey, M matched, A adjusted, RCT randomized controlled trial, MPR medication possession ratio, ED emergency department, ACQ asthma control questionnaire, TF task force a Opposite finding regarding the risk of severe asthma exacerbation in several trials.
Temporal representation and reasoning are important facets in the design of many Semantic Web applications. Several approaches exist to represent and reason about precise temporal data in ontology. However, most of them handle only time intervals and associated qualitative relations. Besides, to the best of our knowledge, there is no approach devoted to handle imprecise temporal data (e. g. , "late 1970s"). In this paper, we propose an ontology-based approach for representing and reasoning about precise and imprecise temporal data. Quantitative temporal data (i. e. , time intervals and points) and qualitative ones (i. e. , relations between time intervals, relations between a time interval and a time point and relations between time points) are taken into consideration. Our approach is three folds: (i) extending the 4Dfluents approach with new crisp and fuzzy components, to represent precise and imprecise temporal data, (ii) extending the Allen's interval algebra to enable reasoning about precise and imprecise temporal data, and (iii) creating a Fuzzy-OWL 2 ontology TimeOnto that, based on the extended Allen's interval algebra, instantiates our 4D-fluents-based representation.
g. , "John married to Maria in 1982 and he remarried to Béatrice in 2006", "I traveled to India in 1980" and "My sister married before moving to Paris". This kind of data is not supported by PersonLink. Therefore, it is necessary to extend it to handle quantitative and qualitative temporal data. Temporal data given by humans are often imprecise. This issue becomes worse in the context of Captain Memo. Indeed, imprecise temporal inputs are especially numerous when given by Alzheimer disease patients. For instance, if they give the information "John married to Béatrice from early 2000s to by 2016", two measures of imprecision are involved. On the one hand, the temporal data "early 2000s" is imprecise in the sense that it could mean, with a decreasingly possibility, from 2000 to 2004; on the other hand, the temporal data "by 2016" is imprecise in the sense that it could mean from 2014 to 2016.
However, it does not support qualitative temporal relations and path consistency checking. Furthermore, it is not compatible with existing OWL editing, reasoning and querying tools. Temporal RDF proposes extending RDF by the annotation of properties with the data about the time interval they hold on. It uses only RDF triples and requires extending the RDF syntax. Therefore, it does not have all the expressiveness of OWL. For instance, it is not possible to employ qualitative relations. A number of temporal representations are based on this approach. In  [44] , the authors introduce a constraint data model called stRDF. It extends RDF to represent temporal data. The authors extend SPARQL to propose a language called stSPARQL that query stRDF. StSPARQL does not express qualitative temporal data and does not have any reasoning support.
Searching requires exhaustive searches in all created versions  [55]. Most implementations adopt a variety of optimization strategies to ensure that entire copies of the ontology are not generated for each new version. In  [57] , the authors introduce an RDF-centric versioning approach and an implementation named SemVersion. In  [58] , the authors propose an approach for schema versioning in OWL 2. T-SPARQL  [59]  is a query language that uses the versioning approach for the representation of temporal data. The N-ary Relations § approach suggests representing an N-ary relation as two properties each related to a new object (rather than as the object of a property, as the reification does). It requires only one additional object for every time interval, and it maintains property semantics. However, it suffers from data redundancy in the case of inverse and symmetric properties.
In SOWL, the 4D-fluents approach, is extended with qualitative temporal relations holding between time intervals whose beginning and ending bounds are not specified. The qualitative relations are supported via SWRL rules. In  [67] , the authors present a specialized temporal reasoner in conjunction with the SOWL ontology. In  [47] , a SPARQL-like query language supporting the SOWL ontology is introduced. In  [56] , the 4D-fluents approach is extended to represent precise time points. In  [48]  and  [68] , reasoning over temporal relations is achieved by means of SWRL rules embedded into an ontology based on the 4D-fluents approach. Temporal relation may be one of the Allen's relations. The MUSING system  [39]  uses both a 4D-fluents approach and an alternative approach based on extending RDF to represent temporal data.
However, MUSING does not provide any qualitative reasoning support. PROTON  [37]  is an ontology that is based on the 4D-fluents approach. TOQL  [69]  is a SQL-like temporal query language for temporal ontologies using 4D-fluents for the representation of temporal data. It supports the Allen's relations, and the relations that allow comparisons between time points or time intervals. The Named Graphs approach represents the temporal context of a property by the inclusion of a triple representing the property in a named graph (i. e. , a subgraph into the RDF graph of the ontology specified by a distinct name). The main RDF graph contains definitions of interval beginning and ending bounds for each named graph, thus a property is stored in a named graph with beginning and ending bounds corresponding to the time interval that the property holds.
Extending the Allen's Interval Algebra In the literature, a number of approaches extend the Allen's interval algebra. Most of them are proposed in the context of the Artificial Intelligence and Databases fields. A number of approaches extend the Allen's interval algebra to propose temporal relations between precise time intervals. Dubois and Prade  [70]  discuss the approximate reasoning on dates and time intervals. They represent a precise time interval as a pair of possibility distributions that define the possible values of the bounds of the interval. This approach proposes some imprecise relations such as "long before". Based on the possibility theory, Ryabov and Trudel  [71]  propose an approach to model uncertain interval relations by assigning a preference degree to every basic Allen's relation.
Our TimeOnto Ontology for Representing and Reasoning about Precise and Imprecise Temporal Data in OWL 2 In this section, we introduce the proposed temporal ontology called TimeOnto. This ontology implements our extension of the 4D-fluents approach which is instantiated based on our extension of the Allen's interval algebra. It is implemented using Fuzzy-OWL 2. We use the ontology editor PROTÉGÉ version 4. 3 and the Fuzzy OWL 2 PROTÉGÉ's plug-in † † version 2. 21. This plug-in allows representing fuzzy classes and relations as annotations. For reasoning, we use the Pellet  [76]  precise reasoner and the FuzzyDL fuzzy reasoner. We instantiate the crisp object properties {"CrispRelationIntervals", "CrispRelationIntervalPoint", "CrispRelationPointInterval" and "CrispRelationPoints"} based on our extension of the Allen's interval algebra.
For instance, using N = 3, the crisp object property "BeforeIntervals" is fuzzified into 3 fuzzy object properties {"BeforeIntervals_1", "BeforeIntervals_2" and "BeforeIntervals_3"} (see Fig. 6 ). In the same way, we implement the other imprecise properties relating a time interval and a time point or time points. TimeOnto proposes a set of SWRL and fuzzy IF-THEN rules to infer missing qualitative temporal relations. Crisp rules are passed to Pellet and fuzzy rules are passed to FuzzyDL. SWRL rules are associated to the crisp part of TimeOnto. They deduce precise temporal relations. We associate a SWRL rule for each precise temporal relation to deduce it from the precise quantitative temporal data given by the user. Based on transitivity tables, we associate a SWRL rule for each transitivity relation that implies only precise temporal relations.
TimeOnto-Based Prototype We propose a prototype that implements the proposed approach. This prototype, implemented in JAVA, is based on the TimeOnto ontology and allows users to interact through user interfaces. It uses Fuzzy Owl API (for managing fuzzy ontologies), FuzzyDL API (for managing the FuzzyDL fuzzy reasoner) and SPARQL-DL API *** (for querying crisp ontology). The architecture of the prototype includes four main components: "TimeOnto Population", "Qualitative Temporal Data Inference", "Configuration" and "Querying". First, the user instantiates the TimeOnto ontology via the user interface shown in Fig. 10. This interface enables them to enter imprecise temporal data using the R-Function, L-Function, Trapezoidal, Triangular and Linear membership functions. We are limited to these functions as they are the only membership functions supported by the Fuzzy OWL 2 PROTÉGÉ's plug-in.
We propose a set of search criteria to show selected parts of the saved temporal data. For each search criterion, a pre-established query is associated. For instance, in Fig. 10 , we show all saved time intervals using the following fuzzy query: (all instances? TimeInterval). After each user's new temporal data input, the "Qualitative Temporal Data Inference" component is automatically executed to infer missing data. This component is based on the proposed SWRL and fuzzy IF-THEN rules embedded into TimeOnto. The third component allows configuring the prototype. By default, we use the following parameters: N = 1, α = 0, β = 0. It also allows the domain expert to associate the meaning of each imprecise temporal relation. The fourth component allows users to query TimeOnto. However, querying this ontology is a tedious task.
It requires using constructs specific to the extended 4D-fluent approach (e. g. , "TsTimeSliceOf" and "TsTimeInterval"). This leads to complicated expressions and the user has to be familiar with the extended representation model. To address the issue, we introduce two temporal languages to query TimeOnto. The first one extends SPARQL to query the crisp part of the TimeOnto. It uses the same clauses as SPARQL and it supports all SPARQL features. The second language extends the syntax related to the FuzzyDL reasoner to query the fuzzy part of TimeOnto. For both languages, we propose "simple" temporal operators e. g. , "BEFORE", "DURING" and "EQUALS". For each operator, we associate the corresponding SPARQL/syntax related to the FuzzyDL translation. For instance, in the context of PersonLink, the following query will retrieve all the countries visited by a person from 2005 to 2018.
Application to the Travel Ontology The proposed approach is also applied to the Travel ontology. It allows representing and reasoning about data related to the tourism domain. In this context, temporal data are especially numerous e. g. , "The journey starts by June 5th and finishes by the end of July", "The accommodation in the hotel starts in June 05th and ends in June 12th" and "We should leave the hotel after lunch time". Managing temporal data is necessary. For this reason, we merge it with the TimeOnto ontology. An interesting point in this work is to deal with temporal entities "with finer granularity" e. g. , time clocks, date and vernacular names for months as well as days. To handles such data, we extend TimeOnto. We present two examples. Further details are presented in  [79]. The first example concerns representing dates.
Inferences are done using a set of SWRL and fuzzy IF-THEN rules. As a fuzzy ontology is an extension of crisp ontology, TimeOnto may be integrated in crisp or fuzzy ontologies to represent and reason about temporal data. Finally, we proposed a TimeOnto-based prototype that was integrated in the prototype of the Captain Memo memory prosthesis to handle temporal inputs in the context of the PersonLink ontology. The extension of the Allen's interval algebra that we proposed in this work in not limited to the semantic web field but may be applied to other research fields. For instance, it may be applied to the databases field. In this case, inferences may be made using database querying languages such SQL. Furthermore, users/researchers who do not want to learn technologies related to fuzzy ontology may benefit from our approach.
Indeed, they can use only the crisp part of TimeOnto to represent and reason about precise temporal data. Future works are categorized in four main axes: • We plan to test our approach in the ANR DAPHNE project that aims to allow Middle Ages specialized historians to deal with prosopographical databases storing Middle age academic's career histories. • We plan to allow the TimeOnto ontology to be referenced by the Linked Open Vocabularies (LOV) † † † platform. • We plan to semi-automate the choosing values process of the setting parameters (N, α and β). The idea is to propose a fuzzy inference system that takes as input a set of criteria and returns the parameters values. These criteria are set by the domain expert. They differ from one application to another. For instance, in the context of Captain Memo, we plan to take into consideration the rate of progression of the Alzheimer's disease when setting the value of the number of graduality N.
OBSERVATIONS: DEFINING THE SAMPLE 2. 1 Data calibration In this work, the Kepler light curves analyzed for rotation were generated from the long-cadence (29. 4244 min) pixel files available at the Mikulski Archive for Space Telescopes (MAST) website. The integration of the stellar signal is done in masks larger than the usual ones used for exoplanet research in order to reduce the drifts induced by the displacement of the targets on the Kepler CCD. To do so, we take all the pixels centered at the coordinates of the star up to a radius for which a reference value per pixel, calculated as the 99. 9 percentile of the weighted flux, is greater than 100. Then to correct for the remaining drifts, jumps and to stitch all the quarters together we follow the Kepler Asteroseismic Data Analysis and Calibration Software (KADACS) 1 methods described in García et al.
Target selection The original sample was selected based on the target list of the Working Group 1 (WG1) of the Kepler Asteroseismic Science Consortium (KASC 2 ) that focuses on solar-like stars. During the first 10 months of the mission, a survey phase was led in order to help the selection of the most promising targets for planet search or for asteroseismic analyses. As there were only 512 slots available to download the short-cadence data (due to the limited downlink bandwith), each month a different sample of stars was targeted. Thus we start with a sample of 2,576 targets for which data was collected with a 58. 85 s cadence. First we remove four stars that do not have any effective temperature values. By studying the power spectrum, we can see that a fraction of these stars are classical pulsators or red giants.
This leads to a sample of about 1,900 stars that should most probably be on the main sequence based on the lightcurves. Some of these stars that we discarded are not necessarily misclassified but could be the result of some pollution as we use larger aperture than the ones optimized for planetary research. As we want to measure the rotation and level of magnetic activity of the stars, we prefer to remove any star whose lightcurve has some pollution to be sure that our study is not biased. In this work we are interested in studying solar-like stars for which acoustic modes have not been detected. Thus we removed from the sample the solar-like stars that have detection of acoustic modes from Chaplin et al. [20] , leading to a sample of 1,380 stars. In addition, we re-analyzed the latest data release (DR25) with the best calibration available.
Indeed, the latest lightcurves processed by the Kepler Science office corrected some smearing effect that non-negligibly improved the noise at high-frequency leading to a higher signal-to-noise ratio and allowing us to detect acoustic (p) modes in additional stars. In order to search for acoustic modes in main-sequence stars, we use short-cadence data. Given that none of these 1,380 stars had oscillations detected, they were dropped off the short-cadence program to give priority to other targets and only have short-cadence data available for one month. We performed an asteroseismic analysis with the A2Z pipeline  [49]  and the FliPer  [11]  to look for signature of p modes. We confirmed the detection of oscillations in 42 stars and still have 92 candidates. The detailed analysis of these new detections will be presented in  Mathur et al.