dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). In order to address these identified issues, we developed Biod-ivAR 1 , a free and open source (GNU GPLv3. 0) web application using a user-centered design process  (Mercier et al. , 2023). 1 The web application is released under the GNU General Public Li-It was built using the web framework A-Frame 2 , for which we also created a custom library 3 for the creation of WebXR location-based objects in A-Frame. We used the Leaflet 4 library for the interactive maps. BiodivAR enables the creation and visualization of geolocated POIs in AR (see Figure  1 ) as well as a cartographic authoring tool for the collaborative management of AR environments (see Figure  2 ). They can be shared publicly with or without editing privileges. The application allows anyone without technological know-how to create AR environments by importing/exporting geospatial data and styling POIs by attaching medias to them.
Participants The sample includes 54 participants ( = 21, = 33), with a mean age of M = 25. 72 (SD = 4. 80). They are students and collaborators of the School of Engineering and Management Vaud, and they each signed an informed consent form for the use of the data collected. Login credentials (identifier + password) were created for each participant to record their data separately and facilitate comparison. Among them, 47 agreed to wear eyetracking glasses, of which 41 successfully recorded data. They were randomly assigned to each group. The control group's (GNSS) mean age is M = 27. 5 (SD = 6. 09), and it includes 12 and 15. The experimental group's (RTK) mean age is M = 24. 2 (SD = 2. 22) and it includes 9 and 18. The first participant eventually had to be excluded from the final results because they experienced numerous crashes due to a bug that was fixed for the subsequent participants.
Geolocation data accuracy During the test, participants' geographical coordinates were logged at 1 Hz. Each log also contains an attribute for location accuracy, user ID and a timestamp. The resulting users' trajectories can be visualized in the application (see Figure  4 ) and downloaded as GeoJSON files for further analysis. The color of the trajectory changes when the AR session is stopped and resumed again. We downloaded the data and calculated the mean location accuracy each participant was exposed to. As shown in Figure  4 , the trajectories-in particular that of the RTK group-contained outliers, which were removed manually using the free and open source software QGIS to get a more accurate estimate of the actual distance travelled (as an indicator of our "exploration quantity" variable, see 4.
3. 3). By calculating the different amount of points before and after this manual processing, the outliers were summed for each participant. Once the data was cleaned, we calculated the total distance walked by each participant. Because there were variations in the duration of each participant's test (min = 9 ′ 14, max = 24 ′ 11 s), the data was normalized for a duration of 15 minutes. This allowed us to calculate: 1. The average geolocation data accuracy 2. The amount of outliers in the data 3. The standard deviation of the geolocation data accuracy • SUS (System Usability Scale) is a generic, technologyindependent 10 item questionnaire on a 5 point Likert scale, frequently used for generic evaluation of a system  (Brooke, 1996). The Cronbach's alpha of the SUS questionnaire is 0. 79, showing an appropriate internal consistency.
Exploration During the test, various in-app, usertriggered events were recorded by the application. These included: when the AR session was initiated or exited, when the 2D map was opened or closed, and when the triggering radius of a POI was entered or exited. Each log also contains the coordinates the action took place at, the user ID and a timestamp. The resulting users' action log can be visualized in the application and downloaded as GeoJSON files. Events are represented with red circles on the 2D map (see Figure  4 ). We downloaded the data and calculated the number of POIs each participant visited as well as how many times they opened the 2D map. These values (POIs visited, 2D map opened) were normalized for a test duration of 15 minutes. This allowed us to calculate: 1. The amount of POIs visited 2.
Focus The goal of using eye tracking glasses and data in our study is to determine for how long participants were looking in or out of the tablet screen. 47 out of 54 participants were able-and agreed-to wear eye trackers (Tobii Pro Glasses 3), recording their gaze for the duration of the test. The 7 participants that didn't either choose not to or couldn't because they had prescription glasses. Despite rigorous implementation, 6 recordings did not work as expected and no files were saved. The 41 remaining recordings were imported in Tobii's analysis software. Unfortunately, its tools do not support tracking of moving areas of interest (i. e. the surface of the tablet). We exported the videos with the overlaying gaze point and extracted 10 frames per second, resulting in a dataset of 380K images, an instance of which is shown in Figure  5.
We attempted to classify the data with openCV pattern recognition, but the variability prevented from obtaining any results. We resolved to train a deep learning multiclass image classifier model by fine-tuning a pretrained vision transformer (ViT) model with our dataset  (Dosovitskiy et al. , 2020). We first had to manually label a random selection of 10K frames with "in" or "out" labels corresponding to whether the point was in or out of the tablet screen (see Figure  5 ). After training for only one epoch using Google's colaboratory and obtaining a satisfying validity of 95%, we inferred the whole dataset which provided a label for every frame 10. They were encoded in order to calculate the ratio of time each user spent looking at the tablet screen versus outside of it, at the real world.
Outliers As shown in Figure  7 , the GNSS group trajectories contained M = 7. 2 (SD = 7. 55) outliers, and these of the RTK group M = 46. 8 (SD = 40. 1). A Welch t-test was used. The results show a significant difference between the two groups (t(27. 9) = -5. 04, p = <. 001). 1 The data is available here: https://zenodo. org/record/7845707.
CONCLUSIONS The purpose of the study was to assess the impact of geolocation data on the usability of our location-based AR system. To test our hypotheses, we exposed the participants to different geolocation data sources with significantly different accuracies. While we expected RTK data to be more accurate and that it would enable us to improve usability, analysis highlights that it was significantly less accurate and less continuous than GNSS data. This appears to be due to the fact that the embedded GNSS sensor contains filters that preprocess data and remove most of the outliers. In contrast, RTK data purposefully remains "raw", which is valuable for an advanced user. RTK data accuracy is very efficient when used on an isolated basis (ie. at a 2D map scale), but not particularly suitable for a real-time continuous usage (where location is measured several times per second) on a 1:1, tridimensional scale, at least without any filters applied onto it.
Despite this contingency, both the quality and continuity of the geolocation data accuracy the two groups were exposed to was significantly different, which is the essential premise for testing our hypothesis and addressing our research questions. Regarding our main research question, results reveal that the GNSS group, who used the AR application in combination with more accurate and continuous data, reported higher scores in all usability scales, of which five out of nine were statistically significant. This supports our initial hypothesis that poor data accuracy negatively impacts the usability of a location-based AR system. Futures studies should however investigate whether RTK data with proper outlier processing may actually better usability. Our results further highlight that the GNSS group walked more than the RTK group, revealing that the accuracy of geolocation data was partially related to exploration, at least for the quantity indicator.
Although the two experiments cannot be properly compared, because the tests took place 5 years apart under different conditions, we note that participants spent 69. 2%-73. 3% of the time looking at the tablet screen, which seems to be a meaningful longitudinal progress from the measurement that was made on our 2017 proof-of-concept, where participants interacted with the screen for 88. 5 % of the time  (Ingensand et al. , 2018). While we are not aware of a method to determine the ideal proportion, this measure overall remains an interesting indicator of the importance of the tablet in this type of activity. In a wide review of mobile learning projects, technology was found to dominate the experience in a problematic way in 70% (28/38) of the cases  (Goth et al. , 2006). While using RTK data did not allow us to positively impact the usability of our system, our study however demonstrated the impact of varying geolocation data accuracy on usability and exploration.
ACKNOWLEDGEMENTS The authors thank  Yoann Douillet  for his help with the organization of the tests and the eye tracking data collection. Study participation was voluntary, and written informed consent to publish this paper was obtained from all participants involved in the study. Participants were informed that they could withdraw from the study at any point. The data presented in this study is openly available on Zenodo at https://zenodo. org/record/7845707. This research was funded by the  Swiss National Science Foundation (SNSF)  as part of the  NRP 77 "Digital Transformation"  (project number  407740_187313 ) and by the  University of Applied Sciences and Arts Western Switzerland (HES-SO) :  Programme stratégique "Transition numérique et enjeux sociétaux ". The authors declare no conflict of interest.
The code used to fine-tune the ViT model is accessible in following Jupyter Notebook: https://colab. research. google. com/drive/1sYxbJQ-7FrScr7R87qwmqKZcb837LWhJ#scrollTo= JLseEgvycDGy. The dataset and the trained model are available here: https://huggingface. co/julienmercier.
Analyses The effects of individual, society, and culture cluster contributions to differences on ASR scales were tested with hierarchical linear modeling (HLM) estimated using PROC MIXED in SAS 9. 4. (SAS Institute, 2013). Mental health problem scores are positively skewed in general population samples (where many people obtain relatively low scores), but HLM has been found robust to deviations from normality, especially for large samples  (Ketelsen, 2014; Man, Schumacker, Morell, & Wang, 2022; Schielzeth et al. , 2020). Each ASR scale was tested separately in a multilevel model. Individual differences (i. e. , differences between individuals within a society) and unspecified effects (i. e. , measurement error) were entered at Level 1. Societal differences were entered at Level 2. Culture cluster differences were entered at Level 3.
Limitations Our study's strengths included: 1) Use of a standardized mental health measure that has been adopted and tested extensively across many societies; and 2) Data from 16,906 adults across 28 societies that represent 7 culture clusters. There are also limitations to consider. Our samples were collected under varying conditions in diverse societies by indigenous researchers. Some of our samples were of unknown representativeness because they were obtained using convenience rather than random sampling methods. Second, our findings are limited to the specific problems and strengths assessed by the ASR. Many societies and cultural groups were not included in the study. The inclusion of additional societies and cultures might yield different results. Also, other ways of classifying cultures might yield different results.
Acknowledgements Dr. Copeland  had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Copeland ,  Achenbach , and  Ivanova  contributed to the concept and design, all authors contributed to the acquisition, analysis and interpretation of the data,  Drs. Copeland ,  Achenbach , and  Ivanova  contributed to the manuscript, all authors critically revised the manuscript for important intellectual content, and all authors provided final approval for the submitted manuscript. This work was funded in part by the nonprofit  University of Vermont Research Center for Children, Youth , and Families which publishes the Adult  Self-Report  and from which authors  Copeland, Achenbach , and  Ivanova  receive funding.
Trial design The MAKASI study, whose protocol was validated in a pilot study  (Gosselin et al. , 2019) , used a stepped wedge cluster randomised design  (Hemming et al. , 2018; Hussey and Hughes, 2007). Participants were included into clusters according to the day on which they were recruited by our mobile teams of health mediators and social workers. The same participants were followed over six months, in a closed cohort design. The evaluation was based on data collected through face-to-face questionnaires administered at baseline (Month 0) and then at M3 and M6. The intervention was rolled out sequentially over two three-month periods (Fig. 1 ). Clusters were randomly allocated to two sequences: those in the immediate intervention sequence received the intervention directly after answering the M0 questionnaire, while those in the delayed intervention sequence received the intervention three months later (directly after answering the M3 questionnaire).
Participant flow A total of 2117 persons were assessed for eligibility through the outreach activities of the two community-based organisations, on which 821 eligible participants were randomised into the two sequences (406 and 415 participants in the immediate and delayed intervention sequence, respectively). The flowchart of the study is shown in Appendix D. Participants were recruited between April 2018 and December 2020. Each participant was followed up for six months, and the last follow-up ended in July 2021. As expected, attrition rates were high, with only 55. 8% of participants remaining at M3 (458 of 821) and 33. 3% at M6 (273 of 821). A full description of missing data over follow-up, overall and stratified by allocated sequence and gender, is provided in Appendix E. There was no significant difference between the two sequences at any time point in the incidence of missing data.
Funding The MAKASI intervention research project was sponsored by the ANRS | Emerging Infectious Diseases (ANR | MIE), the Agence Régionale de Santé (ARS) Ile-de-France, a postdoctoral grant from the French Collaborative Institute on Migrations, and ANR-18-IDEX-001 (Université Paris Cité). Part of the Article Publishing Charge was financed by the Faculté Sociétés & Humanités of Université Paris Cité.
5% (i. e. there is a 97. 5% probability that the value of the true mean lies within the confidence interval), and we seek a percentage deviation of maximum 2. 5%. The percentage deviation depending on the number of replications for the first 150 replications is presented in Figure  6. According to the calculation of the percentage deviation, it takes at least 85 replications per scenario. Therefore, in order to obtain significant and homogeneous results, we performed 100 replications for each scenario (i. e. disturbed patient), so a total of 14,000 replications since there are 140 patients. The model and the simulations were made using Python scripts to facilitate the exchange between the different files and applications.
MATERIALS AND METHODS Study design. All infection control teams from French hospitals were invited to participate in the study. The study, conducted from January to September 2022, consisted of four phases: (1) an evaluation of HH practices was conducted in each participating center, with direct observation of HCWs placing PVCs and the study of the microbial ora present on the ngers of the observed HCWs; (2) the national-level development of an educational tool was conducted using the data obtained during the rst phase, including the results of the HH practices evaluation and microbiological data; (3) the local infection control teams trained the HCWs observed during the rst phase using the tool; and (4) a second observation of HH practices was conducted by the local infection control teams.
The observation sheets were sent to the national level for analysis. The results were rstly used for the initial evaluation of practices. Secondly, the practices of the HCWs observed before and after training were compared to measure the impact of training on HH compliance. In this study, only the data related to HH and glove use are presented. Microbiological study. During the rst phase, the local infection control teams used sterile systems (Amies transport media, Mast, Copan-Brescia, Italy) to swab the ngertips and palm of the hands of the HCWs immediately prior to PVC insertion, following a standardised procedure. The swabs were stored at room temperature and sent to the national laboratory. Upon receipt, they were labeled and paired with observation sheets, and then inoculated onto Trypticase Soja sheep blood agar (bioMérieux, Marcy l'Etoile, France).
All visible microbial colonies after a 48-hour incubation at 37°C under aerobic conditions were identi ed using MALDI-TOF technology (Bruker Daltonics, France). S. aureus, Enterobacteriales and Enterococci were tested for antibiotic susceptibility according to French guidelines  (14). The microbiological data rstly provided an overview of the contamination of the ngers of the HCWs just before PVC placement. Secondly, the microbiological data, along with the data obtained from the observation of practices, were entered into an Excel table to investigate the correlation between the presence of pathogens on the ngers and the level of compliance of HH among the HCWs. Design and use of the educational tool. A training tool was developed by the national team and distributed with a technical guide.
It was found that one in three HCWs donned gloves prematurely, either at the start of the procedure or before applying the antiseptic. Consequently, in theses cases, HCWs inserted the PVC without conducting the second HH gesture. These data demonstrate how wearing gloves can be a major barrier to implementing HH guidelines, as previously suggested  (10). Considering the signi cance of this barrier, efforts to improve HH should fully address the issue of glove usage and its interaction with HH practices. To our knowledge, there is a lack of recent data demonstrating how the hands of HCWs performing PVC insertions become contaminated throughout the procedure when HH practices are suboptimal. We conducted a study examining the microbial ora present on the ngertips of observed HCWs immedately before PVC insertion.
ACKNOWLEDGEMENTS. The members of the CleanHand4 collaboration group areKimberley ALEXANDRE (SSC, Béziers), Alexandra ALLAIRE (GH, Saint-Lo), Karine AMANDIER (GH, Nogaro), Nathalie AUDRAIN (Univ. H, AP-HP, Paris), Amina AZZAM (OC, Villejuif), Mehdi BASTARD (HCC, Marseille), Mélika Berrahal (GH, Salon-de-Provence), Yasmina BERROUANE (Univ. H, Nice), Marie-Camille BETTI (SSC, Vendôme), Claire BIANCHI (GH, Lens), Mathilde BLANIÉ (GH, Périgueux), Laetitia BORRELYS (GH, Blaye), Caroline BOSCHET (GH, Ponteils-et-Brésis), Myriem BOUNOUA (SSC, Aubervilliers), Alexandre BOURDET (GH, Cayenne), Frédérique.
Conclusion: As described in the conceptual model, asthma affects multiple aspects of sleep that can cause next-day fatigue and other subsequent negative HRQOL impacts. This study demonstrates that the ASDQ, Sleep Diary, and PROMIS SRI SF8a items are comprehensive, relevant, and appropriate for patients with moderate-to-severe, uncontrolled asthma. Evaluation of psychometric properties for the ASDQ, Sleep Diary, and PROMIS SRI SF8a based on clinical trial data in patients with moderate-to-severe, uncontrolled asthma will further support their use.
Analysis A thematic analysis approach was used to analyze the results of the interviews, aided by field notes and interview transcripts. 33 Results of the first round of interviews were compared with those from subsequent interviews to identify themes or patterns in the data collected during the CE portion of the interview. The occurrence and frequency of participant-reported nocturnal asthma symptoms and the key areas of asthma-related impacts on sleep were summarized in a tabular format. Additionally, concept saturation (ie, the point at which no new aspects of sleep affected by asthma were reported during the interviews) was documented using a saturation grid. 19 A conceptual model of the hypothesized impact of asthma on sleep was developed based on the CE data. Conceptual models provide a method for visualizing the patient population and potential treatment effects based on known or hypothesized relationships.
Discussion The findings from this study, the first qualitative data on this topic that we are aware of, provide evidence that the ASDQ, Sleep Diary, and PROMIS SRI SF8a items are relevant and appropriate as outcome measures for assessment of sleep disturbances and related next-day impacts in patients with moderate-to-severe, uncontrolled asthma and have been demonstrated to be comprehensive for this purpose. While other measures assessing nighttime awakening from asthma focus on a single aspect of sleep disruption,  14, 15  this study shows that the concept of sleep disruption goes beyond a single aspect. Concept elicitation interview findings across all study participants support the negative impact of nocturnal asthma symptoms on sleep, especially shortness of breath and chest tightness.
We would estimate participants in future clinical trials needing approximately < 1 minute to complete the ASDQ, 2 minutes for the Sleep Diary, and 2 minutes for the PROMIS SRI SF8a. 35 A previous version of the ASDQ (AM Symptom Score) has been used before in the phase 3 QUEST trial,  28  as well as a phase 2b trial  29  to assess symptoms in patients with moderate-to-severe, uncontrolled asthma. Another trial, MORPHEO (NCT04502862), is an ongoing, phase 4, randomized controlled trial with the key objective of evaluating the effect of a therapeutic on sleep disturbance in patients with uncontrolled, persistent asthma. 36 This trial uses the ASDQ, Sleep Diary, and PROMIS SRI SF8a to measure sleep disturbance, and data from the trial will facilitate the development of clinically relevant change scores.
Summary We present primary descriptive data in the form of tables and graphs for patients treated with home hemodialysis in France and French-speaking Belgium in 2022. These data were recorded in the database of the French-speaking Register of Peritoneal Dialysis and Home Hemodialysis (RDPLF). The average age of the patients is similar in both countries: 52. 7 years in France and 54. 7 years in Belgium. The percentage of patients with diabetes is 25. 6% in Belgium and 18. 5% in France. The majority of patients undergo daily hemodialysis 5 to 7 times per week.
INTRODUCTION Ce document a pour but de résumer sous forme graphique les principaux éléments descriptifs obtenus à partir de la base de données du Registre de Dialyse Péritonéale et hémodialyse à domicile (RDPLF) en Belgique francophone (la Belgique neerlandophone ne participe pas au registre hémodialyse à domicile) et en France (France métropolitaine et départements et territoires d'outre mer) en 2022. Aucune discussion n'est prévue dans ce rapport dont le but est avant tout de fournir une documentation de base aux équipes de dialyse pour illustrer leurs propres travaux ou présentations. Une présentation identique a été adoptée pour la dialyse péritonéale dans notre dernier numéro  [1]. Les diapositives originales peuvent être obtenues auprès du secrétariat du RDPLF. Ces tableaux et graphiques peuvent être librement recopiés sous réserve de citer le présent document avec son DOI.