dataesr/scientific-paragraphs-categorization huggingface dataset:
St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database (IN-CART): is a public 12-lead ECG database sampled at 257 Hz and released on the PhysioNet Website in 2008. It comprises 75 half-hour recordings extracted from32 Holter recordings which were collected from patients undergoing tests for ischemia, coronary artery disease, conduction abnormalities, and arrhythmia. Based on the set analysis window size and stride, 32,925 windows with a total of 175,907 Rpeaks were used in our experiments. Annotations were automatically produced by using an algorithm that detected beat annotations from all 12 leads in the middle of the QRS complexes, and then a few automatic annotations were corrected manually. Hence, there are misaligned annotations in INCART. Figure6illustrates a 12-lead ECG recording from INCART.
2. 2 Lobachevsky University Electrocardiography Database (LUDB): is composed of 200 12-lead ECG recordings with a duration of 10 s, and a sampling rate of 500 Hz, recorded using a Schiller Cardiovit AT-101 cardiograph (Schiller AG, Baar, Switzerland) and released on the PhysioNet Website in 2020. It features a large variety of QRS morphologies, from healthy volunteers to patients with different cardiovascular diseases, some of them with pacemakers. All boundaries and peaks of P-waves, QRS complexes and T-waves were manually annotated by cardiologists for each of 12 leads independently. We considered the annotations of lead II in our experiments. Based on the set segment size and stride, 400 windows with a total of 1831 R-peaks were used.
3. 3 St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database (IN-CART): is a public 12-lead ECG database sampled at 257 Hz and released on the PhysioNet Website in 2008. It comprises 75 half-hour recordings extracted from32 Holter recordings which were collected from patients undergoing tests for ischemia, coronary artery disease, conduction abnormalities, and arrhythmia. Based on the set analysis window size and stride, 32,925 windows with a total of 175,907 Rpeaks were used in our experiments. Annotations were automatically produced by using an algorithm that detected beat annotations from all 12 leads in the middle of the QRS complexes, and then a few automatic annotations were corrected manually. Hence, there are misaligned annotations in INCART. Figure6illustrates a 12-lead ECG recording from INCART.
E. The prior and the Likelihood We perform a full Markov-Chain Monte-Carlo (MCMC) analysis for different experiments. The parameter ϵ Λ quantifies the contribution of the disformal interaction and depends on the orbital frequency of the body. Our prior is a flat prior with β 2 ∈ [0, 1] and Λ -1 ∈ [0, n -1 b ] where n b is the orbital period of the system. We use an affine-invariant MCMC sampler for the minimisation of our likelihoods via the implementation of the open-source package Polychord  [64]. Based on ref. [65] , the Polychord estimates the evidence. One begins by drawing 400 live points uniformly from the prior. After some iterations, the point with the lowest likelihood is replaced by a new live point drawn uniformly from the prior with the constraint. The convergence is reached when the new evidence Z live is some small fraction of the original one.
ACKNOWLEDGMENTS We would like to thank to  Scott Melville ,  Leong-Khim Wong  and the anonymous referee for useful comments and discussions. ACD thanks  Chandrima Ganguly  for discussions and collaboration at an early stage of this work. D. B. thanks  Pasha Fadeev  and  Jenny Wagner  for useful discussions. D. B. gratefully acknowledges the support of the  Blavatnik  and the  Rothschild fellowships. D. B. acknowledges a Postdoctoral Research Associateship at the  Queens' College, University of Cambridge. D. B. have received partial support from  European COST  actions  CA15117  and  CA18108  and the research grants  KP-06-N58/5.
Methods We use item-level data from PISA 2018 from countries that administered the optional ICT questionnaire. Using a sample item and the full set of interactive science items, we develop regression models to estimate differences between daily gamers and other students in information harvesting, time to first action, and procedural science knowledge. We report average associations across countries, country-specific estimates, as well as differences between boys and girls. We report raw differences as well as differences adjusting for background characteristics. To account for the multilevel nature of the data and for the two-stage sampling design, we use replicate weights supplied with the PISA database to estimate robust standard errors using balanced repeated replication (BRR).
Participants Our data come from the 2018 edition of PISA. All cases used in our analyses were extracted from the public-use files for the PISA 2018 computer-based test, which can be downloaded from: http://www. oecd. org/pisa/data/. PISA participants were selected from the population of 15-year-old students in participating countries according to a two-stage random sampling procedure, so that weighted samples are representative of students who are enrolled in grade 7 or above and are between 15 years and 3 months and 16 years and 2 months at the time of the assessment administration (generally referred to as 15-year-olds in this work). In the first stage, a stratified sample of schools was drawn. In the second stage, students were selected at random in each sampled school. Finally, on the day of the test, students are assigned to one of many distinct, but partially overlapping test forms.
After the two-hour test, students were asked to complete a questionnaire (whose total duration never exceeded one hour). Students' response data (e. g. the selected option, in a multiple-choice question), a limited set of "generic" process data including time-on-task, time-to-first-action, the overall number of actions, and a number of task-specific pre-programmed features (e. g. the number of simulation runs in interactive items) were captured by the computer platform.
2  Gaming-related differences in problem-solving behavior, by country. Note: Each bar corresponds to the difference between daily gamers and other students, after adjusting for possible confounding variables (model 2), estimated on 50 national samples (countries/economies that administered the complete ICT familiarity questionnaire in PISA 2018). Statistically significant differences (p <. 05) are marked in a darker tone. Source: PISA 2018 database on interactive tasks than other students, have lower reading achievement, and are more likely to be boys. In fact, the lower reading achievement of daily gamers reflects the gender distribution of gamers and the fact that boys are more likely to lag behind in reading than girls  (Buchmann, DiPrete, & McDaniel, 2008; DiPrete & Buchmann, 2013).
Moderating effects: gender differences in the association between videogaming, behavioral tendencies and achievement We explore gender differences in estimated associations in Table  3. Results reveal that, other things being equal the gender gap in how fast test takers move from seeing an  All models include a constant (not reported). The international average is based on 50 countries/economies which administered the complete ICT familiarity questionnaire in 2018; Austria and Germany, which are included in Fig. 1 , are not included. The coefficient of the "Daily gamer" indicators in the regression of "proportion correct", a binary variable, correspond to a risk difference Source: PISA 2018 database  Avvisati and Borgonovi Large-scale Assessments in Education (2023) 11:29  item to taking their first action is smaller among daily gamers than among other students.
Differences are statistically significant at the 5% level. In contrast, no differences in achievement can be identified between daily gamers and students who play games with less-than-daily frequency. All models include a constant (not reported). The international average is based on 50 countries/economies which administered the complete ICT familiarity questionnaire in 2018; Austria and Germany, which are included in Fig. 1 , are not included. The coefficient of the "Gaming-frequency" indicators in the regression of "proportion correct", a binary variable, correspond to a risk difference Source: PISA 2018 database.
No differences in achievement in interactive science tasks were identified between daily gamers and students who have intermediate levels of use, whereas both groups outperform students who never or hardly ever play videogames, Table  5  Gaming-related differences in behavior and success in simulation-based science items (sample task) All models include a constant (not reported). The international average is based on 50 countries/economies which administered the complete ICT familiarity questionnaire in 2018; Austria and Germany, which are included in Fig. 1 , are not included. The coefficient of the "Daily gamer" indicator in the regression of "proportion correct", a binary variable, corresponds to a risk difference. Source: PISA 2018 database although the difference is quantitatively very small and statistically significant only at the 5% level.
Acknowledgements The authors would like to thank  Elodie Andrieu  for her work analysing the data, participants in seminars at the  Social Research Institute of University College London , the  Paris School of Economics , the  OECD, Bocconi University , the  European University Institute  and  NCME  for their valuable input on previous versions of the manuscript. The opinions expressed do not necessarily reflect the opinions of the OECD or its member countries. 4JS. Avvisati and  Borgonovi Large -scale Assessments in Education (2023) 11:29.
Notes: symbols next to coefficients indicate statistically significant results (*: p <. 05, **: p <. 01, and ***: p <. 001). All models include a constant (not reported). The international average is based on 50 countries/economies which administered the complete ICT familiarity questionnaire in 2018; Austria and Germany, which are included in Fig. 1, are not included. The coefficient of the "Daily gamer" indicator in the regression of "proportion correct", a binary variable, corresponds to a risk difference Source: PISA 2018 database Panel B: Time to first action results for the two parameters of interest -difference in time to first action and number of simulation trials.
Whole-body and segment angular momenta computation The whole-body angular momentum about the body's CoM position was calculated in the three dimensions using a 15-segment rigid body model of the whole body, as previously described  (Maldonado et al. , 2018; Begue et al. , 2021). Briefly, the whole-body geometric model and lower limb, pelvis and upper limb anthropometry are based on the running model of  Hamner et al. (2010). Mass properties of the hands were estimated from the regression equations of de  Leva (1996). The anthropometric description of the torso and head segments were estimated from  Dumas et al. (2007). Kinematics information was retrieved by inverse kinematics, using a multibody kinematic optimization approach in OpenSim software. The resulting kinematics data and the inertial parameters were exported to a custom-made Matlab program for calculation of the angular momentum.
Statistical analysis Synergy index (SI Z ) and variances (V UCM and V ORT ) over the entire stepping movement were time normalized from 0 to100%. Then, synergy index and variance curves were temporally aligned from the continuous curve registration method  (Ramsay et al. , 2009)  using the open-source Functional Data Analysis Matlab Toolbox (www. functiona ldata. org). This method aims to reduce timing variability between the participants and, therefore, allows for time-continuous analyses to provide outcomes based only on magnitude-related differences, rather than changes in timing  (Honert and Pataky, 2021). Briefly, this method consists in transforming time series data into functions that are aligned using dynamic time wrapping functions, thus reducing phase variations between subjects while preserving the curves shape and amplitude  (Ramsay et al.
, 2009). Afterwards, Statistical Parametric Mapping (SPM) was used to evaluate the aging effect on waveforms of synergy index and variances. SPM analysis was conducted by implementing the functions from the open-source spm1D package (www. spm1d. org) in a custommade Matlab program (MathWorks, Inc. , Natick, USA). Normality of the waveforms was beforehand assessed using the normality function of SPM for a two-sample t-test. Depending on data normality, parametric two-sample t-tests (SPM{t}, α = 0. 05) or non-parametric two-sample ttests (SnPM{t}, α = 0. 05) were conducted independently on the waveforms of dependent variables. For all non-parametric t-tests, the number of iterations was set at 10,000  (Pataky et al. , 2015). For each t-test, the critical t-statistic and areas of significance between the two groups were reported.
5 and 8. 5 and ii) determine how climate change may impact the dynamics of streamflow in the McKenzie Creek. Indigenous groups are recognized to face exacerbated levels of vulnerability to climate change  (IPCC, 2022) , and within Canada, Indigenous focused data and information is "woefully insufficient"  (Indigenous Services Canada, 2022). This study will help to provide valuable information required to ensure the security of water resources for Six Nations of Grand River and prepare the community and others in Southern Ontario and the eastern Great Lakes region to deal with climate change impacts.
Model set-up In PRMS, the hydrological processes are computed for each HRU that has a surface grid cell size of 200 m × 200 m. PRMS allows for choice between different modules that were previously described in  Champagne et al. (2019). The parameter values used by PRMS were spatialized to each HRU using Arcpy-GSFLOW under ArcGIS  (Gardner et al. , 2018) , similar to what was previously done in several watersheds in the region by  Champagne et al. (2019). MODFLOW has never been set up in this watershed before. For the MODFLOW, the horizontal grids were the same as the one used in PRMS (200 m × 200 m). In MODFLOW the vertical grids were discretized in 2 layers. The top layer represented the overburdened materials with a depth obtained by the Ontario Geological Survey  (Gao et al. , 2006). The deepest layer represented the bedrock with the surface corresponding to the bottom of the overburdened materials and the bottom fixed at 60 m.
For each layer, the parameters needed in MODFLOW were estimated using the water wells data which provide the type of materials in the subsurface (for example, gravel, sand, silt, clay, etc. )  (Ontario, 2021). The main subsurface material was coded from 1 to 4 from the largest grain (i. e. , gravel) to the finest grind (i. e. , clay). When several wells were present in a grid, the average soil characteristics for the code was calculated. If no well was present in the grid, the code was determined by interpolation of the surrounding averaged material codes. From these material codes, the spatial variability of hydraulic conductivity, specific yield and specific humidity were estimated per grid using literature values for each type of material (for example, gravel, sand, silt, clay, etc. )  (Duffield, 2019).
Because agriculture is an important land use in the watershed, the well package (WEL) from MODFLOW was incorporated into GSFLOW's simulations to account for groundwater well extraction. The WEL package assigned a pumping rate to specific cells and pumping rates can be positive (injection) or negative (extraction). Based on McKenzie Creek data from  Wong (2011)  pumping rates of 9,298 ft 3 day -1 (263 m 3 day -1 ) and -4,649 ft 3 day -1 (131 m 3 day -1 ; for mixed surface/groundwater wells) were used in our study. The stress period was defined from June 1st to September 20th to align with observed agricultural pumping records  (Wong, 2011). It should be noted that water usage records in  Wong (2011)  for McKenzie Creek does not take into account water extraction within the Six Nations reserve, because such information was not available.
Model simulations Daily total precipitation (P tot ) and daily maximum and minimum temperatures (T max , T min ) were used as input data for model simulations. Control period (1951-2020) simulations were forced with observed gridded meteorological data  (NRCANmet, 1951 (NRCANmet, -2013) )   1 ). CMIP5 data before 2006 used observed GHG data, and CMIP5 data from 2006 onwards used projected GHG under RCP 4. 5 and 8. 5 scenarios. RCP 4. 5 is an intermediate climate change pathway in which carbon dioxide (CO 2 ) emissions will continue to increase until the mid-21st century after which emissions begin to level out, while RCP 8. 5 represents a high climate change pathway in which CO 2 emissions will continue to increase throughout the 21st century  (Van Vuuren et al. , 2011). Recent climate warming trends have been following the trajectory of RCP 8.
Moreover, when species are challenging to study they become less desirable targets for researchers whose chances of career advancement may depend on their publication record or the completion of a high-level research dissertation  (Caro, 2007). As with human interest in species (see above), the last decade has seen great advances in our capacity to quantify taxonomic biases in research at scale through the analysis of bibliometric databases such as Scopus, Web of Knowledge or Google Scholar. For example, a recent regional-scale bibliometric analysis of Australian birds demonstrated significantly more publications on species with larger body sizes, larger ranges, higher relative abundance and presence in urban environments  (Yarwood et al. , 2019). A similar study on all extant species of mammals found that research volume was strongly associated with the scientific capacity within the range of species, high body mass and whether the species was non-native, with a very weak effect of conservation threat status  (dos Santos et al.
on the analysis of storm track climatologies and could also contribute to the evaluation and improvement of CDTMs. To cover this gap, we present a new methodological approach that combines overlapping tracks from different CDTMs and produces composite tracks that concentrate the agreement of more than one CDTM. In this study we apply this methodology to the outputs of 10 well-established CDTMs which were originally applied to ERA5 reanalysis in the 42-year period of 1979-2020. We tested the sensitivity of our results to the spatiotemporal criteria that identify overlapping cyclone tracks, and for benchmarking reasons, we produced five reference datasets of subjectively tracked cyclones. Results show that climatological numbers of composite tracks are substantially lower than the ones of individual CDTMs, while benchmarking scores remain high (i.
e. , counting the number of subjectively tracked cyclones captured by the composite tracks). Our results show that composite tracks tend to describe more intense and longer-lasting cyclones with more distinguished early, mature and decay stages than the cyclone tracks produced by individual CDTMs. Ranking the composite tracks according to their confidence level (defined by the number of contributing CDTMs), it is shown that the higher the confidence level, the more intense and long-lasting cyclones are produced. Given the advantage of our methodology in producing cyclone tracks with physically meaningful and distinctive life stages, we propose composite tracks as reference datasets for climatological research in the Mediterranean. The Supplement provides the composite Mediterranean tracks for all confidence levels, and in the conclusion we discuss their adequate use for scientific research and applications.
, 2022). Over the past few decades, several methods have been developed to systematically detect and track cyclone centers in gridded datasets. Cyclone detection and tracking methods (hereafter CDTMs) are based on a series of arbitrary choices about (i) the atmospheric variables that best describe cyclones, (ii) the preprocessing operations applied to their fields, (iii) the criteria that define cyclone centers and (iv) the adopted approaches to track cyclone centers in time. Despite their differences, all CDTMs follow a two-step procedure: first, all methods need to define the representative location of cyclone centers, and, second, tracks need to be built by connecting the identified cyclone centers in consecutive time steps. Methodological approaches in both of these steps are crucial for the quality of the produced cyclone tracks.
In such cases, CDTMs either produce abrupt "jumps" of track points or describe the displacement of single cyclone systems with more than one track. The IMILAST project  (Neu et al. , 2013)  has performed a comprehensive intercomparison of CDTMs showing disagreement and consensus among methods and discussing weaknesses and advantages that depend on the nature of the tracked cyclone. In fact, cyclone climatologies produced by individual methods often differ significantly in the number of cyclones, track densities, cyclone intensities and temporal trends. When combining track datasets, most methods agree to a great extent on basic features of cyclone climatology and when tracking strong well-organized systems like tropical cyclones  (Neu et al. , 2013; Bourdin et al. , 2022). In other cases, however, methods may capture different parts of the same tracks.
In addition, several tracks might be completely missed by individual methods, while a large number of bogus tracks might be produced. The natural question that arises from the above is whether different CDTMs might be combined to build datasets of "high confidence". Such datasets would be expected to include (i) composite tracks that were commonly captured (partly or entirely) by individual methods and (ii) the lowest possible number of bogus tracks. In this study we use a new approach to produce highconfidence datasets for the Mediterranean region based on the recent ERA5 reanalysis  (Hersbach et al. , 2020). Cyclogenesis is frequent in the Mediterranean, producing a high number of shallow and deep cyclones per year  (Trigo et al. , 1999; Campins et al. , 2011; Lionello et al. , 2016; Flaounas et al.
, 2022). However, Mediterranean cyclones are challenging weather features to track, mainly due to their small size, as well as due to the complex geography with sharp landsea transitions and high mountain chains that surround the Mediterranean basin  (Lionello et al. , 2016; Flaounas et al. , 2018). In fact, lee cyclogenesis is frequent, and cyclone systems often cross continental areas, distorting their MSLP and relative vorticity structures  (Buzzi and Tibaldi, 1978; Buzzi et al. , 2020). As a result, atmospheric variables that typically describe cyclones present high spatial variability that challenges the CDTM performance, especially in high-resolution datasets  (Ruti et al. , 2016). The following section presents our methodological approach and the procedure for benchmarking the tracks.
Then, we present the physical characteristics of cyclone tracks pro-duced by individual CDTMs and compare them to the ones of composite tracks. Finally, we discuss the advantages in using composite tracks as reference datasets, compared to tracks from individual CDTMs. This paper ends with dataset availability and conclusions on the use of composite tracks of different confidence levels for scientific research. 2 Datasets and methods 2. 1 Building composite tracks: the methodological approach through two cyclone cases In this study, we use 10 CDTMs (further referred to as M01 to M10), briefly described in the Appendix and summarized in Table  1. All 10 CDTMs were applied to hourly ERA5 reanalysis fields with a regular grid spacing of 0. 25 • × 0. 25 • in longitude and latitude. In contrast to other reanalysis, ERA5 is available in fine-grid spacing, allowing CDTMs to track small-scale cyclones.