dataesr/scientific-paragraphs-categorization huggingface dataset:
It focused on the different stages of bibliographic search, the description and the use of MeSH thesaurus, Boolean operators and subheadings. The evaluation took place in two adjacent rooms of the Rouen University Medical School, at the same time. Residents allocated to the first room had access to the French version of the query builder, residents allocated to the second one had access to the English version. Twelve short medical questions were designed with three levels of complexity. The clinical questions were drafted by an experienced medical librarian (GK). They were chosen arbitrarily in the logs of a document database developed in the Rouen University hospital (http:// www. chu-rouen. fr/cismef/). They were then validated by two physicians (MS and NG) for medical relevance. The level of complexity was decided in regard to the number of MeSH terms (descriptors or subheadings) required for a correct query.
Discussion of the main results Many barriers to query building and information retrieval among healthcare professionals have previously been identified in the literature. Currently, the most reported obstacles are: (i) the amount of time required to find information, (ii) difficulties in reformulating the original question and finding an optimal search strategy, (iii) lack of a good source of information, uncertainty as to whether all relevant information has been found and (iiii) inadequate synthesis of any pieces of evidence into a clinically useful approach  [10, 11]. The literature also shows that physicians, and especially primary care doctors, express a need for database training, regardless of their first language  [6, 12]. Physicians' difficulties in building search queries are well known.
In 2007, a web log analysis was undertaken in a metasearch engine covering 150 health resources and a variety of guidelines. It showed that most queries were built using a single search term and no Boolean operator  [13]. A similar study was conducted on PubMed queries. If PubMed queries had a median of three terms, only 11% of them contained Boolean operators  [14]. Many factors can influence the physicians' ability to build relevant search queries, including the level of English skills  [9, 15]. These findings suggest that our query builder may be of significant value for non-native English speaking healthcare professionals. As previously mentioned, our data suggest that the impact of using the first language increases with the complexity of clinical questions. Complexity appears to play a key role in physicians' difficulties in information retrieval.
They fail to master the use of Boolean operators and, when dealing with complex clinical questions, GP trainees tend to refer to their colleagues more than electronic sources  [13, 16]. Participants querying in their first language took less time to build each query than participants querying in English. This appears to be an important finding, as time constraints are always cited as a major obstacle when seeking information and may improve the PubMed's use as searches with PubMed are not as frequent as searches with Google or UpToDate  [10, 17]. Nevertheless, only one-third of the queries were considered perfect, even among participants querying in their first language. Irrelevant MeSH terms and the lack of specification in descriptors and subheadings lead to poor precision and recall  [9].
Querying in first language will not solve all the problems faced by researchers and physicians, especially as the overwhelming majority of PubMed references remain in English. Some research tools already provide an automatic translation of a biomedical text, including titles and abstracts and using the MEDLINE database  [18]. Other tools increase the information retrieval task performance by allowing non-native-English speakers to access PubMed references written in their native language: BabelMeSH  [3, 4] , Patient, Intervention, Comparison, Outcome (PICO) Linguist  [4]  and LiSSa  [5]. According to Gagnon et al. , educational meetings currently seem to be the only type of interventions showing a significant positive effect on clinical information retrieval technologies adoption by healthcare professionals  [19].
Strengths and limitations This study has several limitations. First, only the quality of the query was assessed and not the quality of the results. However, the quality of the query is strongly associated to the quality of the results. Vanopstal et al. demonstrated that under-specified queries led to an increase of noise and our data show that under-specification is the main error compensated by querying in users' first language  [9]. An evaluation of the queries is planned as a second step. A sample of discrepant results (using queries built by English group versus French group) will be rated by a group of physicians. This will allow us to assess the impact of the multilingual query builder on the quality of the results. Second, this study only involves French residents in general medicine and this could affect the external validity of the results.
Perspectives As might be expected, this study clearly demonstrates that querying in first language is easier than querying in English. This study will soon be repeated among Argentinean healthcare professionals, comparing the use of Spanish and English. The multi-lingual query builder permits to overcome the obstacle of English when building queries, and could be of major interest for students, clinicians and researchers worldwide. The query builder is already available in more than fifteen languages including Dutch, English, Finnish, French, German, Italian, Portuguese and Spanish. Some translations of MeSH terms or of the web site interface may still be lacking and we actively encourage all the teams working on MeSH translation, i. e. INSERM and Inist-CNRS in France. Our tool permits physicians and medical researchers to perform a request in the most relevant PubMed database fields.
Conclusion Physicians often feel incompetent when seeking medical information, especially when using bibliographic databases. This sentiment is sometimes associated with a feeling of illegitimacy, as these databases were not created to meet their needs. This shows that there is a gap between an idealized academic search model and the practical requirements of everyday life  [20]. This multi-lingual query builder is an effective tool to improve the quality of PubMed queries and should narrow the gap, particularly for physicians and researchers whose first language is not English.
Force-plate data was recorded at an acquisition frequency of 500 Hz. Data acquisition and stimulus display were controlled by a custom-made program written in Matlab TM (R2009b), The MathWorks (Inc. , USA). Mechanical model. We used the same mechanical model as in a previous study to determine the stance leg stiffness along the mediolateral direction during the GI execution phase (for details on this model and related equations of motion, see Yiou et al. 5  ). In brief, the human body was modeled as a single conic inverted pendulum which rotates around a fixed point corresponding to the stance ankle. It was considered that the COM was falling laterally under the influence of two forces: the gravity force P = mg (where m is the mass of the solid, and g is the gravitational acceleration) and an elastic restoring force T that reflects active muscular control of the movement  63, 64  , with T = k|yM| where k is the stance leg stiffness and |yM| is the absolute value of the mediolateral COM shift, which was systematically oriented towards the swing leg (positive values) during the execution phase.
Data reduction. Force-plate data were low-pass filtered using a Butterworth filter with a 15 Hz 65 and a 10 Hz 29 cut-off frequency, respectively. The mediolateral (yCOP) and anteroposterior (xCOP) coordinates of the COP were computed from force-plate data as follows:  where Mx and My are the moments around the anteroposterior and mediolateral axes, respectively; Fy, Fx and Fz are the mediolateral, anteroposterior and vertical ground reaction forces; and dz is the distance between the surface of the force-plate and its origin. Instantaneous COM acceleration along the anteroposterior and mediolateral axes was determined from the ground reaction forces (GRF) according to the Newton's second law. COM velocity and displacement was computed by successive numerical integrations of COM acceleration using integration constants equal to zero, i.
e. initial velocity and displacement null  1. Dependant variables of the main experiment. Timing. The following instants were determined from biomechanical traces: gait initiation onset (t 0 ), swing heel-off, swing toe-off, swing foot-contact and rear foot-off. These instants were determined from force-plate data  1, 17, 66. Two t 0 times were estimated, one for the mediolateral axis and one for the anteroposterior axis. The t 0 times corresponded to the instants when the mediolateral or anteroposterior COP trace deviated 2. 5 standard deviations from its baseline value. Initial posture, APAs and foot lift phase. The mediolateral and anteroposterior COM position in the initial upright static posture was estimated by averaging the COP position during the 250 ms period preceding the "all set" signal.
Sociodemographical and organizational variables Data were collected on age, gender, seniority of position, workplace (i. e. , public or private hospital, non-profit organization, governmental administration, private and independent practice, company), type of contract (i. e. , permanent contract, fixed-term contract, self-employment, mixed contracts, voluntary work), and working pattern (i. e. , fulltime, part-time, full-time with different employers, specific cases).
Hierarchical cluster analysis For our purposes, we followed the procedure proposed by  Clatworthy et al. (2005)  for research in health psychology. More specifically, we first of all ran a hierarchical Cluster Analysis using Ward's method and the squared Euclidean distance to determine profiles of participants according to their z scores on each subscale of the MBI (with Statistica 7 ® )  (Hair, Black, Babin, & Anderson, 2009 ). 8 The hierarchical Cluster Analysis run on the participants suggested a fourcluster solution as shown by an examination of the dendogram (the distance between a four-and a five-groups solutionbottom of the figurewas small). The Bayesian Index Criterion  (Schwarz, 1978)  confirmed the four-cluster solution as the lowest value was observed for this solution (calculated with SPSS17 ® ).
This procedure was replicated on two subsamples chosen arbitrarily on the base of age: those with an even age (N = 359) and those with an odd age (N = 305), as advised by  Clatworthy et al. (2005). Because the Bayesian Index Criterion confirmed the four-cluster solution for both subsamples and because the distribution of participants into those four clusters was about the same  4 The title "psychologist" is protected by French law. Professionals have to complete five years of studies in psychology and be registered in a national database in order to practice psychology (they receive a registered personal number). They can practice their profession in any area of psychology (e. g. clinician, psychotherapeutic, neuropsychologist, human relation). Researchers in psychology (who do have the title of psychologist, but most of whom do not practice psychology) were excluded from the database.
Discussion The aim of this study was to emphasize that the use of cut-off scores that are not specific to a professional population and/or extracted from another linguistic version of the scale has little sense and can lead to very different results according to which scores are used. The aim was also to propose an alternative technique, namely Cluster Analysis, which can be used instead of cut-off to group together participants of a specific profession according to their responses on the MBI (see Appendix A for a detailed procedure using Statistica 7; see also  Yim & Ramdeen, 2015  for a detailed and easy procedure using SPSS 9 ). We did this by surveying a large sample of French psychologists whose working conditions suggest that they may suffer from burnout or be at risk on some dimensions (i.
3. Percentage of psychologists in each cluster according to workplace. 9 Using z scores and 1 standard deviation as standpoints which, as will be discussed, can be criteria but are not restrictive enough. 10 Practitioners may find all kinds of advice on the Internet on how to run Cluster Analyses using different tools such as Statistica, SPSS, SAS, or R. The procedure using the Statistica 10 software is displayed in the Appendix A. companies or in private hospitals than when they are independent  (Farber & Heifetz, 1982; Rupert & Kent, 2007). It is important to note, however, that those results are only indicators of links between some of our sociodemographic variablesin particular the type of practiceand burnout. It is also possible that the lower prevalence of burnout among those psychologists in independent practice may be explained by other individual variables that may have influenced their resistance to burnout and/or their personal choice of practice  (Garden, 1987; Garden, 1989; Rzeszutek & Schier, 2014).
Cluster analysis -Step two: K-means clustering Once you have determined the number of clusters, go back to the Statistics menu, and again select "Multivariate Exploratory Techniques", then "K-Means Clustering" (which corresponds to the clustering). Once in this menu, enter your three variables (again, the standardized ones) and choose to cluster on "Cases" (not variables). Then select the number of clusters on which you want to perform the analysis (here four), and click OK. The next menu gives you access to all the statistics you need. Of interest is the Graph of Means (see Fig. 2 ), which gives you the standardized scores of each of your clusters on each variable. Scores that are around zero are average, those near or above 1 are high and those near 1 or under are low. You can also obtain descriptive statistics including the number of participants in each cluster and save the classification in you dataset so as to have a variable with the cluster's number for each of your participants.
Finally the purpose of quality control is also to contribute to a databank used to investigate in case of bad clinical in patients. In this last case, for the reason explained above, analytical control is superior to archive video recordings because the concentration of the active drugs is effectively determined in the final product with a validated and known accuracy. However, in case of inquiry the video archives could be very useful if a second determination of the concentration cannot be performed for example for stability issues or because a sufficient volume of the remaining sample is not available. As a conclusion, we can say that in-process controls, especially video control, which is reliable and allows archiving, are a complement to analytical control of the final product. In function of the level of information he needs, the responsible pharmacist will have to choose between these techniques or associate them.
Our proposed method consists of extracting an input-output transfer function, linking the exposure induced by multiple plane waves to the parameters contributing in the variability of their total electric field, using a regression metamodel approach in the postprocessing of a finite subset of FDTD simulations. This study is a part of an European project called LEXNET  [10] , supported by the European Commission under the FP7, was established to minimize the exposure induced by wireless systems. The rest of this paper is organized as follows. The second section presents the methodology used in the modeling of the uplink power and the electric field received from a fixed base station. The third section illustrates the materials used to prepare the simulations. Section 4 is reserved to discuss the results as well as the statistical analysis of the uplink exposure variability.
Statistical Modeling of a Realistic Exposure Scenario This section is devoted to applying the methodologies presented as well as the obtained results to a real network. For this reason, various measurements of the uplink power   (the power emitted by the user system) and the downlink power   (the power received at the user system) are performed inside the Lyon city (France) and its surroundings cities, including basically NLOS scenarios. We note that the user system is the couple composed by the human body and the personal device. For emissions at 900 MHz, a hand-held probe connected to various base stations is configured to measure the radio traffic in both uplink and downlink paths. Concerning the tarfics at 1940 MHz, the data are taken from a measuring campaign conducted using two mobile phone trackers.
Introduction If human motion analysis in laboratory can be considered as matured regarding the extensive research that has been performed in terms of technology and methodology, it is not the case for human motion analysis based on inertial sensors. Basically, for 3D movement analysis, angular velocities and linear accelerations measured by respectively the 3 gyroscopes and the 3 accelerometers present in one 3D-Inertial Measurement Unit (IMU) are sent to a data fusion algorithm in order to obtain orientation and, sometimes, also position (see Picerno 2017 for a review). Several challenges remain to be solved for this technology to be really effective and spread and this includes the sensor-to-segment calibration. Indeed, to estimate joint kinematics, the rotational matrix that enables to obtain the body segment referential frame orientation from that of the sensor is required for each sensor.
). Albergel et al. (1988)  and  Gudiksen et al. (1989)  reported that the first release must have reached 2000 m or more. A similar profile of the Chernobyl emissions was proposed by  Lange et al. (1988)  and  Hass et al. (1990). The goal of this paper is to reconstruct and assess the source term based on inverse modeling techniques. We focus on the temporal variations and the altitude of the releases. Although reportedly 500 000 deposition measurements were used to create the Atlas map, only 5000 deposition measurements were made available to the public in the REM database, and very few of these data referred to the FSU countries, where the highest contamination occurred. Therefore, inverse modeling studies for the quantification of the source term of Chernobyl were mainly based on atmospheric concentrations only (e.
g. ,  Davoine and Bocquet, 2007). For the first time, we perform inverse modeling calculations using an extended data set of deposition observations for 134 Cs, 137 Cs and 131 I  (Evangeliou et al. , 2016)  together with surface atmospheric activity concentrations. The data set that we used consists of 3000 observations for 134 Cs and 11 000 observations for 137 Cs, 60 % of which were made in the FSU countries. The data originate from the public REM database of the Joint Research Centre, enriched with measurements from Ukraine, Belarus and Russia and a few other countries. All of these data were used for creating the original Atlas map, but they were not included in the public REM database and were thus inaccessible. These data have been recovered in a recent data rescue effort (see  Evangeliou et al.
Inverse modeling We used the inversion algorithm previously used to calculate the source term of 133 Xe and 137 Cs in the recent accident in Fukushima NPP (Japan) in 2011  (Stohl et al. , 2012) , the emissions of greenhouse gases  (Stohl et al. , 2009)  and volcanic sulfur dioxide and ash emissions  (Kristiansen et al. , 2010; Stohl et al. , 2011). The algorithm is based on original work by  Seibert (2000) , incorporates different types of observation data and can be based on forward or backward calculations with FLEXPART. A full description of the algorithm has been given elsewhere  (Seibert et al. , 2011). The inversion setup is almost identical to that described by  Stohl et al. (2012)  for determining the Fukushima emissions as a function of time and altitude. We determine radionuclide emissions as a function of time for 96 3-hourly intervals between 00:00 UTC on 26 April and 00:00 UTC on 8 May.
Vector σ o is the standard error of the observations and vector σ x is the standard error of the a priori values. The operator diag(a) yields a diagonal matrix with the elements of a in the diagonal. D is a matrix with elements equal to -2 or 1, giving a discrete representation of the second derivative, and is a regularization parameter determining the weight of the smoothness constraint compared to the other two terms. Equation (4) implies normally distributed, uncorrelated errors, a condition that is not generally fulfilled. To quantify the model errors, we used an ensemble of FLEXPART simulations using two meteorological data sets (ERA-40 and ERA-Interim) for all three radionuclides of interest. The inversion algorithm reads the source-receptor relationships calculated in FLEXPART simultaneously in order to evaluate a range of prior modeled concentrations and deposition densities.
Surface activity concentration and deposition observations Measurements of surface activity concentrations and deposition densities from all over Europe were adopted from  Evangeliou et al. (2016). The database consists of surface air activity concentration measurements (in Bq m -3 ) of 134 Cs (1927 observations), 137 Cs (1601) and 131 I (2041) and deposition density observations (in kBq m -2 ) of 134 Cs (2966) and 137 Cs (11 334) as shown in Fig. 3. Of the 11 334 deposition observations for 137 Cs, 4077 were adopted from the public REM data set, and the remainder were made available from  Talerko (2005a, b)  and  Kashparov et al. (2003). The data of 137 Cs deposition over the FSU countries were collected using the standardized method adopted previously in the former USSR  (Tsaturov et al.
, 1996). The samples were collected within national framework programmes for the determination of radioactive deposition in settlements; they were included into the database used for the creation of the Atlas map but not in the public REM database. Air concentrations in areas closer to the vicinity of the plant were determined using airborne gamma spectrometers mounted on aircraft or helicopters capable of flying at low altitudes (25-100 m) during the initial period after the accident. In countries where concentrations were lower, surface air was sucked through filters for a long time (e. g. , hours to days depending on the relevant detection limits and the air concentrations) using high-volume samplers. Then, the filters were measured with gamma spectrometry. As regards to the relative measurement errors (it is combined with model error to give observation error, Sect.
RMSE values spatially were esti- mated for areas where deposition of radionuclides has shown large discrepancies from observations. The RMSE values averaged for all Scandinavian countries were estimated to be 47 kBq m -2 for 134 Cs and 36 kBq m -2 for 137 Cs using the ERA-Interim data set and only 36 and 27 kBq m -2 (for 134 Cs and 137 Cs) using the ERA-40 fields. In Germany, RMSEs for 134 Cs and 137 Cs were 49 and 43 kBq m -2 using the ERA-Interim and 41 and 32 kBq m -2 using the ERA-40 fields, whereas in Austria they decreased from 48 and 40 kBq m -2 to 44 and 35 kBq m -2 , respectively. A different representation of deposition was also achieved for 131 I, although there are not enough measurements to clearly decide which data set gave better results (Fig. 3 ). According to  Evangeliou et al.