dataesr/scientific-paragraphs-categorization huggingface dataset:
We find that August 22, 1914 was indeed the deadliest day for these cohorts. In any case, this point illustrates the potential uses of the MPF database for the historiography of WWI in France. 47 This selection is to avoid measurement error due to the fact that we do not know when soldiers first joined the front. From 1,270,943 records, this selection reduces the sample to 976,885 observations. V. Gay and P. Grosjean (where the excluded category is Enlisted), whether they were in a colonial unit, and whether they enrolled voluntarily. We then progressively include a set of fixed effects for soldiers' military region of recruitment, département of birth, and municipality of birth. 48 We report results in Appendix Table  B. 21. Soldiers from the infantry had considerably shorter survival times.
On average, soldiers in the artillery survived one more year (350 days) compared to infantrymen, while those in engineering corps survived 231 more days, and those in the cavalry, 314 more days. Soldiers in other units, such as train personnel or marines, survived 434 more days. These findings are qualitatively similar to those in  Guillot and Parent (2018) , although the magnitudes of differences between the infantry and other units are larger when using the full MPF database. Additionally, officers survived longer than enlisted soldiers: 108 days for non-commissioned officers and 134 days for commissioned officers. Younger soldiers died faster: even conditional on rank and unit, (deceased) soldiers a year older could expect to survive 4 more days. Finally, soldiers from colonial units survived longer, though only by about a month, while the (few) volunteers died faster, by about three months.
Data availability The dissemination of MPF records through the MdH web platform was authorized by the decrees of  October 3, 2003 and July 24, 2014 (Aufray, 2014). 49 The collection, digitization, indexation, conservation, and online diffusion of the personal data contained 48 Selecting on cases for which all these control variables are available further drops close to 23 thousand records, resulting in a sample of 953,382 observations. 49 The texts of these two decrees are accessible at https://www. legifrance. gouv. fr/eli/arrete/2003/10/3/DEFS0302096A/jo/texte and https://www. legifrance. gouv. fr/jorf/id/JORFTEXT000029361926. , 1914 and November 11, 1918  among non-civilian MPFs born in France and for which the date of death is available (1,213,086 MPFs in total). Some notable, particularly deadly dates are highlighted with accompanying events on the front.
Other dates are provided for reference. in the MPF records was further authorized by the National Commission on Informatics and Liberty (CNIL) on July 10, 2014, for historical, statistical, and scientific purposes. 50  We disseminate the MPF database under the CC-BY 4. 0 license on the Harvard Dataverse at https://doi. org/10. 7910/DVN/ 2APQZ6 in both Stata data format (dta) and text delimited format (txt) under the heading mpf_database  (Gay and Grosjean, 2023). We also disseminate two additional datasets discussed in Section 4: the département-and municipality-level military death rates datasets in both Stata data format (dta) and text delimited format (txt) under the headings mpf_deathrate_dep and mpf_deathrate_municip, respectively, together with relevant shapefiles, under the headings DEATHRATE_DEP and DEATHRATE_MUNICIP, respectively (see Table  4  for a summary of the content of these data files).
Conclusion This article describes the Morts pour la France (MPF) database. It includes a host of individual-level information for each of the 1. 3 million French individuals who died as a result of the war: their first and last names, dates of birth and death, circumstances of death, recruitment status, military rank and unit at death, and locations of birth and recruitment. We also provide datasets of military death rates at the level of 87 départements and 35 thousand municipalities along with relevant shapefiles. Matching records from the MPF database to auxiliary individual-level information constitutes a promising avenue of research. Given the availability of soldiers' names, dates of birth, and locations of birth and recruitment, it would be possible to match these records to military service records, which include information on height, weight, occupation, and other physiological characteristics as well as migration and military unit histories  (Kesztenbaum, 2013).
Missing (1), Location of death (2), Type of death (3). Class available and in bounds pm 4 years (1), Class missing and inputted from birth year (2), Class out of bound (pm 3 years), inputted from birth year (4), Class missing (5). Values for rank_flag: Rank available (1), Rank missing (2), Civilian (3). Values for rank_gen: Enlisted (2), NCO (3), CO (4), Rank missing (. a), Civilian (. b). Values for unit_flag: Unit available (1), Unit missing (2), Civilian (3). Values for unit_type_gen: Infantry (1), Artillery (2), Engineering (3), Cavalry (4), Other (5), Unit missing (. a), Civilian (. b). The MPF database is available in the Harvard Dataverse at https://doi. org/10. 7910/DVN/2APQZ6 (Gay and Grosjean, 2023).
27 Appendix TableB. 4 provides the list of the 50 most frequent last names along with their frequencies and Appendix TableB. 5 provides the distribution of unique last names. 28 To construct this comparison, we use a subset of INSEE's (2012) file of last names, which provides the total number of last names among births registered in France over the cohorts 1891-1900. More precisely, in Appendix TableB. 6, we compare the 10 most frequent last names in the MPF database among all MPF Category Variable Description Values Birth place birth_place_flag Birth place flag See table notes birth_cassini Birth municipality identifier [Cassini] Numeric birth_insee_2015 Birth municipality identifier [INSEE] String birth_geofla_2015 Birth municipality identifier [GEOFLA] String birth_x_geofla Birth municipality latitude [GEOFLA] Numeric birth_y_geofla Birth municipality longitude [GEOFLA] Numeric birth_dep_YYYY Birth département identifier [INSEE, TRF-GIS] String birth_dep_name_YYYY Birth département name [INSEE, TRF-GIS] String birth_com_name_YYYY Birth municipality name [INSEE, TRF-GIS] String Recruitment bureau bureau_flag Recruitment bureau flag See table notes bureau_id Recruitment bureau identifier Numeric bureau_name Recruitment bureau name [TRF-GIS] String bureau_mreg Recruitment bureau military region identifier [TRF-GIS] String bureau_mreg_name Recruitment bureau military region name [TRF-GIS] String bureau_mregsub Recruitment bureau military region-subdivision identifier [TRF-GIS] String bureau_msub Recruitment bureau military subdivision identifier [TRF-GIS] String bureau_msub_name Recruitment bureau military subdivision name [TRF-GIS] String bureau_cassini Recruitment bureau municipality identifier [Cassini] Numeric bureau_insee_2015 Recruitment bureau municipality identifier [INSEE] String bureau_geofla_2015 Recruitment bureau municipality identifier [GEOFLA] String bureau_x_geofla Recruitment bureau municipality latitude [GEOFLA] Numeric bureau_y_geofla Recruitment bureau municipality longitude [GEOFLA] Numeric bureau_dep_YYYY Recruitment bureau département identifier [INSEE, TRF-GIS] String bureau_dep_name_YYYY Recruitment bureau département name [INSEE, TRF-GIS] String bureau_com_name_YYYY Recruitment bureau municipality name [INSEE, TRF-GIS] String Notes: Values for birth_place_flag: Commune available (1), Commune available, Alsace-Lorraine (2), Département available only (3), Birth place missing (4).
Values for bureau_flag: Current recruitment bureau (1), Former recruitment bureau (2), Split recruitment bureau (3), Recruitment bureau département only (4), Recruitment bureau city only (5), Former recruitment bureau, Alsace-Lorraine (6), Recruitment bureau out of France (7). YYYY: information relative to the geographic information systems of 2015 (INSEE, 2015; IGN, 2015 ), 1911, and 1921 (Gay, 2020a,d, 2021). The MPF database is available in the Harvard Dataverse at https://doi. org/10. 7910/DVN/2APQZ6 (Gay and Grosjean, 2023). The final MPF database contains 1,270,943 records, among which 198 women (mostly nurses and civilians), 1,450 civilian victims, and 53,735 soldiers who were denied the MPF recognition (158 cases are still currently unsettled). 26    First and last name. The MPF database contains 182,434 unique last names.
Their distribution is highly skewed: the most frequent last name, MARTIN, counts 5,281 records (0. 42 percent of MPF records), while 83,180 last names (45. 59 percent of all unique last names) count only one entry. 27  Comparing the distribution of last names in the MPF database to that of all contemporaneous last names reveals that the last names composition of MPFs is representative of the general population (Appendix Table B. 6). 28 Likewise, the distribution of first names is highly skewed. Over 227,701 unique first names, the most frequent, JEAN, counts 31,574 records (2. 48 percent of MPF records), while 170,535 first names (74. 89 percent of all unique first names) count only one entry. 29 Both the last name and first name variables have nearly full coverage as all MPF records have a last name and only 176 have no first name mentioned.
30 Dates of birth and death. records and among the MPFs born between 1891 and 1900 to the 10 most frequent last names in INSEE's (2012)   database. We find that the ordering of these 10 most frequent last names is almost identical in all three samples. For more details on the representativeness of last names in the MPF database, see Darlu  and Chareille (2020, 99-102). 29 Appendix Table B. 7 provides the list of the 50 most frequent first names along with their frequencies and Appendix Table B. 8 provides the distribution of unique first names. Because the INSEE's (2012) first names file only contains first names given after 1900, we cannot make a comparison to gauge the representativeness of first names in the MPF database.
Study design and procedure A cross-sectional study using anonymous and self-administered questionnaires was conducted in a healthcare student cohort. All data collection occurred in the last quarter of 2022. The study was approved by the Institutional Review Board of the School of Public Health, Hangzhou Normal University (Reference No. 20190076). The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines were followed  (40). Participants were enrolled via a stratified random sampling method. Students were not retained if they were unwilling to participate, unable to understand Chinese, or not attending school on the day. As the minimum required sample size is 15 participants for each variable, a total of at least 285 subjects were needed for this study. The final sample size of 348 exceeds this requirement.
Statistical analysis EpiData (version 3. 1) and R (version 4. 1. 2) were used for data organization and data analysis. Given that researchers quickly checked the questionnaires at the time of collection and requested respondents to fill in missed questions, there were no missing values in this dataset. The "MVN v. 5. 9" package was used for multivariate normality  (51). The "dplyr v. 1. 0. 10" package was used for descriptive analysis: means and standard deviations (SD) were used to summarize the continuous variables while counts and proportions were used to summarize the categorical variables in descriptive statistics  (52). Since the scale scores were ordinal categorical variables and the data was not normally distributed, the "Hmisc v. 5. 1-0" package was used for Spearman correlation to investigate the relationship among sleep quality, well-being, anxiety and depression  (53).
Xu et al. employed a nomogram prediction model to analyze the clinical features and risk factors for poor outcomes of severe community-acquired pneumonia (SCAP) in children. This approach allows for a more personalized and precise assessment of individual patients. By incorporating this predictive tool into clinical practice, healthcare professionals can make informed decisions regarding the management and treatment of children with SCAP, ultimately improving patient outcomes. In resource-limited settings, the availability of pulse oximeters can be limited. To address this challenge, Schuh et al. developed clinical scores to identify hypoxemia in children with pneumonia based on two outpatient datasets of 3-35-month-old children with pneumonia in Bangladesh and Malawi. The authors found that those scores were more potent to identify outpatient hypoxemic pneumonia cases than the WHO guidelines.
Methods In each hospital participating in DetectIP, a clinical ward was selected whose data on the occurrence of hyperkalemia and AKI would be compared before and after the introduction of the new CDSS rules. The services were selected to vary the specialties as much as possible while ensuring that they accommodate patients older than 65 years. Semi-structured interviews were conducted with representatives of professionals involved in the prevention and management of hyperkalemia and AKI: physicians (senior and resident), nurses, clinical pharmacists, medical biologists, with the possibility of other profiles being added if they were identified during the analysis. Interviews were conducted individually or with a group of representatives of the same profile, depending on their availability.
The interview guide investigated the components of the work systems and processes as described by the systems engineering initiative for patient safety (SEIPS 2. 0  [5] ): persons, tasks, technology and tools used to perform them, internal environments where they are performed, work organization, external environment (e. g. , policies), and the interaction between those elements. Interviews were recorded and transcribed. Transcripts were analyzed by two researchers in ergonomics. In each unit, for each profile, the components of the work system were listed as well as their characteristics. The way in which these elements interact to give rise to the work process was also extracted in a narrative manner. The data were then merged for a given unit so that the entire processes of preventing and managing hyperkalemia and AKI could be described and current barriers and facilitators to AKI and hyperkaliemia prevention and management could be identified.
Statistical analysis Statistical analyses were carried out using SPSS (Version 22). Data was expressed as mean ± standard deviations or 95% confidence interval (CI). For criterion validity, Pearson correlation analysis was used to evaluate the correlation between the items of the Arabic SBQ and the total sitting time of the Arabic SBQ with both IPAQ-SF and IPAQ-LF. A one-sample t-test was used to determine systematic bias in the mean difference between sitting time of the Arabic SBQ on weekdays (min/day) and sitting time of IPAQ-SF on weekdays (min/day). A one-sample t-test was also used to determine systematic bias in the mean difference between the total sitting time of the Arabic SBQ and the total sitting time derived from IPAQ-SF and IPAQ-LF. For constructive validity, Pearson correlation was used to evaluate the association between BMI and the items of the Arabic SBQ.
Strengths and limitations To the best of our knowledge, the current study is the first to adapt the SBQ to the Arabic language. We also determined a high correlation between answers of the English and Arabic SBQ version, and moderate to good test-retest reliability for most of the Arabic SBQ items. Finally, we provided a reliable multi-item questionnaire for Arabicspeaking countries to assess sedentary behavior. However, our study is not without shortcomings. The main limitation of our study is that we did not use device-assessed tools such as accelerometers or other devices to objectively assess sedentary behaviors. Therefore, the criterion validity evolution is highly recommended for future research to compare the Arabic SBQ to a gold-standard criterion measure. The data was collected only from one city, which is considered another limitation.
Acknowledgements The authors are grateful to the Researchers Supporting Project ( RSP2023R21 ) for their support. The authors would also like to thank  Samiah Alahmadi ,  Hatoon Alahmadi ,  Shahd Alrafai ,  Rahaf Alwafei ,  Atheer Almuhammdi , and  Rama Najjar  for providing valuable assistance in the data collection phase of this study. Special thanks also go to all students for participating in our study.
Study design and participants A French multicenter survey was conducted from 24 September 2021 to 8 April 2022 among patients diagnosed with JIA (according to the International League of Associations for Rheumatology classi cation  [24] ), aged 18-45 years and their parents, in nine rheumatology centers. To optimize the recruitment, three patient and parent associations were also approached: "Association France Spondyloarthrites" (AFS, patients with spondylarthritis), "Association Nationale de Défense contre L'Arthrite Rhumatoïde" (ANDAR, patients with rheumatoid arthritis) and KOURIR (parents of children and adolescents with JIA). Participants completed anonymous questionnaires on SH during the adolescence of patients. On the advice of two child psychologists and a psychiatrist, we chose to interview an adult population (age 18-45 years) rather than adolescents to obtain strong data from their recollections of adolescence with necessary insight from the subject.
Statistical analysis A descriptive analysis of the patient and parent responses was carried out. Categorical variables are presented as numbers and percentages, continuous variables as means and standard deviation  (SD). Responses to open-ended questions were analyzed by AC, a team member with experience in qualitative analysis, and CSa, a therapeutic education nurse sexologist involved in the study design, and classi ed by verbatim reports, without pre-determined themes. The responses of patients and parents were compared using chi-squared tests (or Fisher's exact tests as appropriate). All statistical analyses were performed using SAS® Version 9. 4 (SAS Institute, Cary, NC, USA). All the tests were two-sided. Statistical signi cance was set at p < 0. 05.
Nevertheless, they can give precious insights regarding the adaptation of human movement to novel joint dynamics mimicking non-Earth gravity fields. This follows a general path toward the exploitation of robotics in neuroscience. 47  Here, we used the ABLE robotic exoskeleton  48  to induce various gravity-like torques at the participant's elbow, ranging from normal gravity (1g) to reversed gravity (À1g) and passing through microgravity (0g). A gradual change of gravity with a 0:2g-step ranging from À1g to 1g was also applied on the forearm of participants, thereby including Marsand Moon-like gravity torques. The gravity-exploitation theory was then tested against experimental data for single-joint pointing movements. The predictions of a representative model, termed Smooth-Effort (SE) model,  21  are illustrated in Figure  1.
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: (acromion), elbow (epicondyle and epitrochlea), middle of the forearm, wrist (styloid process of the radius), base of the index proximal phalanx and tip of the index finger (the 3mm marker). The other markers placed on the robot were used during the identification process. d KEY RESOURCES The EMG activity was recorded with bipolar surface electrodes (Wave Plus, Wireless EMG, 2000Hz; Cometa, Bareggio, Italy). The QTM interface (Qualisys, Gothenburg, Sweden) allowed recording synchronously kinematic data and EMG activity. Participants were first locally shaved and a hydroalcoholic solution was applied. The electrodes were then placed on the following four muscles: triceps (long and lateral heads), biceps brachii (long head) and brachioradialis.
Data processing Kinematics processing Three-dimensional position data of the marker taped on the tip of the index finger was used to assess the human kinematics. Data from the other reflective markers was used as a control. Position data was filtered (low-pass Butterworth, 5Hz cutoff, fifth-order, zero-phase distortion, ''butter'' function from the ''scipy'' package) before differentiation as in previous studies. 29, 46  The computed kinematic parameters were defined as in Figure  2C. The threshold allowing to segment movements was set at 5% of PV in agreement with previous studies,  18, 20, 46  which allowed to define MD and movement amplitude. PV and PA were respectively defined as the maximum value of the velocity and the maximum positive value of the acceleration reached during each movement.
The significance level of the corrected p-value was set at p< 0:05. All statistical analyses were performed using custom Python 3. 8 scripts and the Pingouin package. 98  Given the number of participants and conditions in Experiment 3, only quantitative comparisons between the model predictions and the average experimental data were provided there. Mean absolute errors between the model and the experimental data for rtPV, and Pearson correlation coefficients were computed to quantify the agreement between the model and the data when varying gravity incrementally.
Data collection The survey information documents contained web addresses and links where the persons could access the survey questionnaire and get all the information about the survey. If they wished to participate, they were then invited to complete an inclusion questionnaire to verify whether they were eligible to participate. Informed consent was also provided by respondents. Then, the eligible ones were directed to a web-based epidemiological questionnaire. Participation was voluntary. The current analyses concerned the 475 participants who answered the questions about MSPC initiation and about attacks exposure (Fig. 1 ).
Acknowledgements The  ESPA 13 November  survey is part of a transdisciplinary research programme (Programme 13-Novembre in French) focusing on the construction and evolution of individual and collective memory after the terror  attacks in Paris and Saint Denis in November  2015, which is headed by  Denis Peschanski  and  Francis Eustache. The authors thank the 13 November survey team and in particular  Denis Peschanski ,  Francis Eustache , and  Carine Klein-Peschanski  for their support (more information available at www. memoi re13n ovemb re. fr). Our sincere thanks to all the survey participants. We also thank all the people that helped to design and implement this study: APHP and  CUMP  ( G. Barbry-Abgrall ,  L. Bernard-Brunel ,  A. Botero ,  C. Dendoncker ,  M. Grohens ,  J. Geneste ,  S.
Availability of data and materials The data for this study are available on request from Santé Publique France, but restrictions apply. More specifically, some data were used under license and so are not publicly available. Other data are however available from the authors upon reasonable request and with the permission of Santé publique France. The protocol and questionnaire of the survey are available on this DOI link https:// doi. org/ 10. 6084/ m9. figsh are. 20552 292.
Infections related data were included: i) for HCV infection (viral load, time since HCV infection diagnosis, transmission route, treatment follow up and treatment of HCV infection), and ii) for HIV infection (CDC classification, transmission route, CD4 count, ART duration, and time since HIV infection diagnosis. Behaviors related data from the SQR-12 questionnaire included prevention issues as knowledge about the risk of transmission and condom use. There were questions that explored satisfaction with health care support, and communication with health professional about sexuality. Sexual dysfunction among was assessed (Vaginal dryness, sexual desire, and pain during penetration).