dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods The present study was conducted in a dedicated PR center (Dieulefit Santé) in Dieulefit, France. We extracted for analysis data collected systematically in an ongoing prospective cohort study including PR patients rehabilitated from 2019 to 2022. We analyzed data from all COVID-19 patients admitted to Dieulefit Santé during the current pandemic and from a non-COVID-19 group of patients after ICU admission due to respiratory failure. Both groups performed PR consisting in respiratory exercises, muscle strengthening, balance and walking when possible, cycling and gymnastics according to current ATS/ERS recommendations  (Rochester et al. , 2015). The following multidimensional testing battery was performed at admission and discharge: pulmonary function test, psychosocial evaluations, muscle strength and balance measurements, and six-minute walking test (6MWT, also performed weekly during PR) (Fig.
1 ). Only pulmonary function (pre-PR) and 6MWT data were available in non-COVID-19 patients. The study was part of an ongoing cohort of pulmonary rehabilitation approved by a French independent ethics committee (ID-RCB number: 2019-A02104-53). One-way and two-way ANOVA analysis was used to compare the groups using IBM SPSS Statistics for Windows, version 23 (IBM Corp. , Armonk, N. Y. , USA).
Data gathering The data used for this study were gathered thanks to an initiative promoted by "Roma Capitale" as part of the open data initiative born according to the National guidelines for the enhancement of public information assets  [50]. The purpose of the initiative is to empower citizens to reuse and integrate the data made available to them, to develop services and applications for the benefit of the entire community of users. For the purpose of this study, only a sample of the data available was extracted. The data used for the analysis describe a sample of 174,222 accidents registered in the metropolitan area of Rome (including the location of Ostia) between the 1st January 2014 and the 11th December 2019. The dataset contains the list of road accidents that occurred in the territory of Roma Capitale in 2014.
Data preparation The data were accordingly transformed, removing all the unnecessary variables or the ones with a considerable presence of missing values. The accidents reported during the period of the snowing days in 2018 were removed since they described a statistical anomaly. The resulting dataset contained the following independent variables: • Road Characteristics; • Road Type; • Road Surface; • Paving; • Road Signs; • Atmospheric condition; • Traffic; • Visibility; • Road Illumination. A dependent variable was computed as the number of human casualties involved in the accident with the sum of the number of people injured plus the number of dead people plus the number of people with reserved prognosis. Then, for classification purpose, another dependent binary variable was generated with a value equal to zero, if there were no human casualties recorded for that specific accident, and equal to one, if there were at least one human casualty.
Feature Importance interpretation with gradient boosting classifier Once the dataset has been appropriately transformed was possible to apply the classification algorithm to interpret the feature importance. The Machine Learning Algorithm adopted the Gradient Boosting Classifier and was run on a dataset of 140,250 observations. The 80% of them was used for the training set and the remaining part (20%) for the test set. We used the binomial deviance loss function; the learning rate was equal to 0. 102 and the number of estimators equal to 250. The Gradient Boosting Classifier was Cross Validated with k-fold equal to 10. The chosen configuration seems accurate, as it is possible to see from the learning curve, shown in Figure  1. The resulting accuracy was 57%. One of the objectives of this empirical research is to contribute to the knowledge about car accidents in Rome.
This paper does not aim at delivering practical indications of best practices for road accidents management. Rather it was designed to explore open data that emerged as a solution in many scenarios such as in the service field to better face several issues being a valid alternative for the future. This article helps at improving our understanding of the usage of open data to define the safety in crowded roads of an important metropolis in the Italian context. Nevertheless, our study presents some limitations. For instance, the dataset is not representative of the population of accidents in Rome since the recorded accidents exclude the accidents on the "Grande Raccordo Anulare" of Rome and in those where the parties involved have reached a conciliation. Moreover, some important variables were not measured, such as road improvements and the implemented policies (road maintenance, improvements, etc.
Strengths and limitations of this study ► First study to evaluate risk assessment tools in a French-Canadian population for predicting breast cancer. ► Population based-cohort representative of the French-Canadian urban population of middle-aged and older adults. ► Linkage with administrative health databases and the Quebec Breast Cancer Registry, which improved the outcome quality and accuracy, and made possible to use variables usually difficult to obtain. ► May not apply to younger women under 40 years old. ► Since the genotyping information was not available for all the cohort, the models had to be evaluated on two different subcohorts.
MATERIALS AND METHODS Design and participants selection The CARTaGENE population-based cohort is composed of 43 037 Quebec residents aged between 40 and 69 years, recruited during two phases (2009-2010 and 2013-2014). With a rich collection of data including phenotyping and genotyping data, CARTaGENE is the largest ongoing prospective population cohort and biobank in Québec, Canada. 27 Details on recruitment and sample selection have been described previously. 27 o comprehensively identify participants with an invasive breast cancer and the incidence date, we used two administrative health databases, the Quebec Health Insurance Board (RAMQ) and the Quebec Breast Cancer Registry (see oonline supplemental methods 1), and an algorithm based on a previous report from the Institut National de Santé Publique du Québec  28  and the Tonelli et al  29  algorithm.
Absolute risk using the BCRAT and the IBIS models The absolute risk of breast cancer estimated by BCRAT and IBIS is calculated using baseline hazard functions calculated from the marginal hazard functions (USA and UK incidence rates, respectively), and the attributable risk obtained from the United States population data (BCRAT) and the UK/Swedish population data (IBIS). In this article, the BCRAT and IBIS absolute risks of breast cancer at 5 years were calculated for each woman at the inclusion date using the National Institutes of Health R package 'BCRA', V. 2. 1  33  and the latest version of the 'IBIS Breast Cancer Risk Evaluation Tool' (http://www. ems-trials. org/ riskevaluator/-V. 8. 0b, September 2017), respectively. Death as a competing risk was taken into account for both models.
Absolute risk using a combination of IBIS and PRS As the clinical risk score obtained from the IBIS model is not an output of the software, we cannot estimate the absolute risk associated with a combination of the IBIS clinical risk score and PRS using the iCARE package in the same way we did for BCRAT (see above). In practice, the V. 8. 0b of the IBIS risk evaluation tool allows to compute the absolute risk by incorporating the PRS scores, but these absolute risks are different from the ones that would be obtained with the iCARE package. Keeping in mind this issue, we have used the IBIS breast cancer risk evaluation tool and incorporate the PRS scores. More precisely, and for taking into account the distribution of the PRS, we incorporated a shifted PRS that corresponds to the PRS minus the logarithm of the expected value of the relative risk associated to the PRS in our population.
Despite an overall good mean calibration of the BCRAT model, the calibration across risk subgroups could be improved. The analysis of the four groups of risk shows a significant difference between expected and observed cases with an overprediction in women with a risk equal or higher than 1. 66%. 2] [43]  Opposite results have also been reported in a recent large study with pooled data from two cohorts of women where the BCRAT model underestimated the risk for values between 1. 7% and 3. 4%. 12 owever, in this latter study, eligible women were aged between 20 and 70 years at the enrollment and recruited since 1991, while our population was aged between 40 and 70 years and enrolled since 2009. The overestimation of the BCRAT risk prediction model for women with a risk higher than 1. 66% cannot be explained by differences in age-standardised incidence rates since, based on information retrieved from national cancer databases,  35 44 45  the incidence rates are comparable between the USA and Canada (250.
Some strengths of the present study should be highlighted. First, this validation study relies on the CARTaGENE cohort, which is representative of the French-Canadian urban population of middle-aged and older adults. Moreover, the linkage with administrative health databases and the Quebec Breast Cancer Registry improved the outcome quality and accuracy, and made possible to use variables usually difficult to obtain such as the history of breast biopsy or atypical hyperplasia. Second and to the best of our knowledge, this study is the first to evaluate the BCRAT in a French-Canadian population for predicting breast cancer at 5 years. This study has nevertheless some limitations. First, our findings may not apply to younger women under forty years old. Second, we have limited our study to BCRAT and IBIS risk prediction models.
The ethnicity differences between the two subcohorts could be explained by the divergent ancestry step of the quality control of genotype data. 1] [32]  Even though these two subcohorts were similar, it would be useful to collect all genotype information for the entire cohort to validate the PRS results. Finally, regarding family history included in the IBIS model, we only had maternal and paternal history of breast cancer and maternal history of ovary cancer. However, the IBIS model can handle missing data and the performance of the model remained good without this information. Therefore, the IBIS model should be more accurate with more family history variables.
2022 by guest. Protected by copyright. http://bmjopen. bmj. com/ BMJ Open: first published as 10. 1136/bmjopen-2020-045078 on 12 April 2021. Downloaded from Open access from the RAMQ database for women with at least two claims in 2 years or one hospitalisation with the appropriate International Classification of Diseases, 9th or 10th Revision codes (174 and C50). The Quebec breast cancer registry's data were available from 15 May 1998 to 31 December 2017, while the RAMQ's data were available from 1 January 1998 to 31 March 2016. Adherence to mammography was not available.
on April 13 , 13 2022 by guest. Protected by copyright. http://bmjopen. bmj. com/ BMJ Open: first published as 10. 1136/bmjopen-2020-045078 on 12 April 2021. Downloaded from Open access from the RAMQ database for women with at least two claims in 2 years or one hospitalisation with the appropriate International Classification of Diseases, 9th or 10th Revision codes (174 and C50). The Quebec breast cancer registry's data were available from 15 May 1998 to 31 December 2017, while the RAMQ's data were available from 1 January 1998 to 31 March 2016. Adherence to mammography was not available.
This novel approach provides theoretical and methodological advantages because it gives access to both inter-and intra-specific variability by automatically analyzing a large dataset of individual images. The spatial distribution of morphological traits revealed that large copepods are associated with ice-covered waters, while open waters host smaller individuals. In those ice-free waters, copepods also seem to feed more actively, as suggested by the increased visibility of their appendages. These traits distributions are likely explained by bottom-up control: high phytoplankton concentrations in the well-lit open waters encourages individuals to actively feed and stimulates the development of small copepod stages. Furthermore, copepods located at the ice edge were opaquer, presumably because of full guts or an increase in red pigmentation.
) were calculated for each object/individual in postprocessing with the ZooProcess software  (Gorsky et al. 2010). Images, their metadata, and morphological descriptors were stored in the EcoTaxa web application (https://ecotaxa. obs-vlfr. fr/; Picheral et al. 2017). Morphological descriptors were used to train a random forest (RF) algorithm  (Breiman 2001 ) that suggested a taxonomic classification for each object, which was then validated or corrected by a human annotator (the same user sorted 85% of the data but 9 others contributed). 1. 2 M images were classified into 36 final categories (Supporting Information Table  S1 ). Of these 1. 2 M images, detritus, badfocus and fibers accounted for 96% of the data and are not considered here. The remain data is comprised of living organisms (45,883 images), of which 83% were copepods.
Finally, we used ice concentration estimates derived from the AMSR2 satellite sensor data on a 3. 125 km grid for each station location between 2016/03/01 and 2016/07/14  (Beitsch et al. 2014; Kaleschke and Tian-Kunze 2016). We focused our study on the upper water column because (1) ice melt will mostly influence surface waters, (2) algal bloom occur in surface waters, (3) copepods are mostly located close to the surface to feed in the spring/summer period  (Williams and Poulet 1986; Helaouët and Beaugrand 2007). We vertically segmented the water column by dividing the temperature profiles into six layers: warm surface layer, transition layer, cold intermediate layer, deep thermocline layer, deep cooling layer and deep stable layer (Fig. S1  for more details). Then, a PCA was performed on all planktonic concentrations obtained from UVP images in each layer to characterize the organismal distribution in the water column.
Numerical tools All statistical analyses were conducted in the programming environment R 3. 5. 3 (R Core Team 2019). The package castr was used to clean vertical environmental profiles (https:// github. com/jiho/castr), car for Box-Cox transformation  (Weisberg and Fox 2018) , FactoMineR  (Lê et al. 2008 ) and vegan  (Oksanen et al. 2007 ) for multivariate analysis, tidyverse  (Wickham et al. 2019)  for data manipulations and graphics, morphr (https://github. com/jiho/morphr) for images representation in the morphological space and finally gstats  (Pebesma 2004 ) and fields  (Nychka et al. 2017)  for statistical interpolations. All data (including images) and codes are available in Supporting Information.
4% of the variance (Fig. 3a ). PC2 explains 25. 9% of variance and is influenced by the opacityhow dark  For each factorial plane, morphotypes are represented according to their coordinates in the morphological space: A grid was defined and eight images close to the factorial plane considered were randomly selected, aligned and superimposed at each node of the grid for visualization. Definitions of morphological descriptors are presented in Table  S2 ; code used to project images in the morphological space is available (morphr package, https://github. com/jiho/morphr). they appear in UVP images-of organisms. PC3 (16. 9% of variance) quantifies the elongation of the organisms (Fig. 3b ). Finally, PC4 (9. 7% of variance) was interpreted as the visibility of appendages, as explained above. In our dataset, variations of elongation (PC3) can be explained by copepods' orientation variability on images.
A copepod imaged laterally or dorsally will appear longer (PC3 > 0) than a one seen from the front or the back (PC3 < 0). Although PC3 describes variations that are important within the dataset, we can make the reasonable assumption that orientation of individuals is either random or slightly, but consistently, biased by the nominal flow generated by the UVP while sampling the water column. We will thus ignore PC3 for further analyses as there is no reason to link orientation relative to the camera with a specific ecological interpretation.
Benefits of morphological trait-based approach from individual in situ images We combined individual-based imagery and multivariate statistical analysis to implement a morphological trait-based approach to zooplankton ecology. The combination of these techniques allowed us to analyze many more profiles (N = 163) than would be possible with conventional net or pump sampling. The acquisition of large datasets-typically thousands of images per cast-enabled statistically robust descriptions of trends of morphological traits. This novel description of the ecosystem allowed us to infer community structure and functioning from individual properties. Moreover, this approach offers a continuous representation of traits, which has benefits in comparison to discrete traits especially for further numerical analyses  (Mcgill et al.
Acknowledgments The GreenEdge project was conducted under the scientific coordination of the  CERC program  and of the  CNRS & Université Laval Takuvik Joint International Laboratory  ( UMI3376 ). This work is a contribution to the scientific program of Québec Océan. We thank officers and crew of  CCGS Amundsen ,  Marie-Hélène Forget  and  Joannie Ferland  for organizing the cruise,  Céline Dimier  and  Josephine Ras  for HPLC analyses,  Pascal Guillot  and the  Amundsen Science Data team  for the CTD data processing and quality control, and all other scientists and technicians who contributed to fieldwork. We also thank  Achim Randelhoff  for his expertise on water masses conditions in Baffin Bay. This work was funded mainly by our salaries as French or Canadian state employees and therefore by  French and Canadian taxpayers.
Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations.
Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics.
Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.
Introduction Systematic bibliometric analyses can be useful analytical tools to gain a better understanding of research patterns (e. g. , journal, author, country) and characteristics of research fields  (Wu et al. , 2015). Recent applications have shown that such analyses can be used to recognize emerging topics  (Small et al. , 2014) , study cooperation networking in research  (Wagner et al. , 2015)  or gain in-depth insight into a research topic  (Tang et al. , 2020). Moreover, a joint search in the SCOPUS database for article titles, abstracts and keywords containing "bibliometric analysis" or "citation analysis" in January 2021 yielded over 40, 000 documents with a clear upwards trend in number of published items in the last years. Literature analysis as a tool is gaining popularity among interdisciplinary academic fields such as earth sciences.
For instance,  Liu et al. (2012)  performed a bibliometric study of earthquake research during  1900 -2010 , Wu et al. (2015) )  performed a bibliometric analysis in order to study global research trends in landslides during  1991 -2014 , and Emmer (2018) )  studied research on natural hazards worldwide during 1900-2017. Gariano and Guzzetti (2016)  reviewed published papers that investigated the past, current, and future (expected, projected) impact of climate change on landslides. Moreover,  Reichenbach et al. (2018)  conducted a critical review of statistical methods for landslide susceptibility modelling and associated terrain zonation. They used a database of 565 articles published in peer-reviewed international journals from January 1983 to June 2016 and identified by a systematic search of the Web of Science database using a set of keywords and criteria as evidence.
, 2018; Lizaga et al. , 2019)  to investigating the impact of soil erosion on biogeochemical cycling  (Lugato et al. , 2016; Quinton et al. , 2010; Tan et al. , 2020) , or the modelling of soil erosion  (Batista et al. , 2019; Borrelli et al. , 2018; Panagos and Katsoyiannis, 2019; Ricci et al. , 2018). Moreover, there are other emerging topics such as the use and abuse of biocides on soil erosion, agricultural and forest management practices to reduce soil erosion rates or experimental studies at small scales. A literature review on research trends and hotspots in soil erosion from 1932 to 2013 was performed by  Zhuang et al. (2015)  using the Science Citation Index (SCI) database. According to this study, soil research has rapidly increased since 1990 with major contributions from the USA and Europe before 2001, and additionally from China and Australia since 2001.
They also discovered through co-citation analysis that soil erosion research mainly focuses on three aspects as follows: soil erosion modelling, soil erosion estimates using caesium-137 and the impact of soil erosion on the environment. Niu et al. (2014)  used a keyword analysis to discover that "evolution", "water", "soil(s)", and "model" were consistent hotspots in sediment-related research in earth science during 1992-2011. To investigate how soil erosion model evaluation is approached in soil erosion research,  Batista et al. (2019)  compiled a database of 550 papers published between 1958 and 2018 that were selected by querying the Web of Science using the query "soil erosion model". However,  Batista et al. (2019)  did not conduct a detailed bibliometric investigation and focused on a much smaller number of papers than the GASEMT database  (Borrelli et al.