dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Results are reported as mean ± standard deviation (StD). Owing to several non-normal distributions in the data, we used non-parametric tests in all analyses. Binomial tests and N-1 χ 2 proportion comparison tests were performed to compare one or two proportions using JASP (JASP Team (2020) Version 0. 14) and MedCalc online tools (MedCalc Software, Ostend, Belgium), respectively. Contingency table analysis was used for testing the interrelation between binary variables using JASP software. When comparing the means of two groups with unequal sample sizes, we used a permutation-based Welch's independent t-test (10,000 permutations) in MATLAB (PERMUTOOLS package, The Math Works, Inc. MATLAB. Version 2020a, The Math Works, Inc. , 2020. Computer Software. www. mathworks. com/).
In order to measure BRE members service contribution (BRE service) as a function of gender, we computed the number of months in which the RE was affiliated with the BRE per year, i. e. the proportion of months of service of women and men REs out of the total number of service months, thus accounting for variability in BRE service contract durations and partial work time (e. g. REs appointed in the middle of the year, being on leave). To investigate whether gender disparities were associated with REs' expertise, as advertised by eLife to prospective authors, we conducted an analysis of the relative scope and reach of the REs' keywords, broken down by the recorded gender of the RE. Keywords for each RE were extracted and strung together using the 'OR' operator and then queried against the PubMed database through NCBI's public API-'Entrez Programming Utilities (E-utilities)' (Entrez Programming Utilities Help  [Internet].
Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www. ncbi. nlm. nih. gov/books/NBK25501/). The number of search results for each set of RE keywords was recorded and used as a measure of the reach of the keywords provided by the REs, as evidenced by published papers related to the keywords in the literature. The E-Utilities API was accessed through a script in Python (Python Software Foundation. Python Language Reference, version 3. 9. 6. Available at.
Diversity statement Recent work in several fields of science has identified a bias in citation practices such that papers from women and other minority scholars are under-cited relative to the number of such papers in the field  [54, [56] [57] [58] [59] [60] [61] [62] [63]. Here we sought to proactively consider choosing references that reflect the diversity of the field in thought, form of contribution, gender, race, ethnicity, and other factors. First, we obtained the predicted gender of the first and last author of each reference by using databases that store the probability of a first name being carried by a woman  [54, 111]. By this measure (and excluding self-citations to the first and last authors of our current paper), our references contain 30. 62% woman(first)/woman(last), 22. 82% man/ woman, 18.
14% woman/man, and 28. 42% man/man. This method is limited in that a) names, pronouns, and social media profiles used to construct the databases may not, in every case, be indicative of gender identity and b) it cannot account for intersex, non-binary, or transgender people. Second, we obtained predicted racial/ethnic category of the first and last author of each reference by databases that store the probability of a first and last name being carried by an author of color  [112, 113]. By this measure (and excluding self-citations), our references contain 6. 21% author of color (first)/author of color(last), 15. 01% white author/author of color, 16. 03% author of color/white author, and 62. 75% white author/white author. This method is limited in that a) names and Florida Voter Data to make the predictions may not be indicative of racial/ethnic identity, and b) it cannot account for Indigenous and mixed-race authors, or those who may face differential biases due to the ambiguous racialization or ethnicization of their names.
Acknowledgments We thank the eLife editorial team for facilitating this study at all stages, and in particular, to:  Andy Collings  and  Daniel Ecer  for help with preparing the dataset;  Jennifer Raymond ,  Chris Baker  and  Jon Roiser  for discussions about the dataset;  Stuart King  for feedback on the manuscript;  Michael B. Eisen ,  Tim Behrens  and  eLife Senior  Leadership for feedback and ongoing support of this study.
•  Agence Nationale de la Recherche  Award Number:  ANR-16-CE37-0005  | Recipient:  Tal Seidel Malkinson , Ph. D. • Wellcome Trust Award Number:  215575/Z/19/Z |  Recipient: Tamar R. Makin •  European Research Council  Award Number:  715022  EmbodiedTech | Recipient:  Tamar R. Makin  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Also, some REs may have been on leave, and therefore may have not been consulted for a certain period. Bottom: Manuscript Dataset: contains information relating to each manuscript submission, detailing the manuscript's outcome in each of the reviewing process stages (identified by gender of the Senior and Reviewing Editors). It includes the following fields: The proportion of men BRE members suggested by the authors (% of Men BRE members); The gender of the Senior Editor handling the manuscript throughout the reviewing process (Gender of Senior Editor); The gender of the RE handling the manuscript in the Full Submission stage (Gender of handling RE); The rate of author appeals at the Initial Assessment stage in which only the Senior Editor identity is revealed to the authors (Initial appeal rate); The rate of author appeals at the Full Submission stage in which both the Senior and Reviewing Editors' identities are revealed to the authors (Initial appeal rate); The two discipline terms the authors chose, out of 18 possible terms (Discipline 1 & Discipline 2; see Table1for details).
A patient was considered fully vaccinated if he/she had received at least either one injection of the Ad26. CoV2-S vaccine (Janssen) or two injections of the ChAdOx1-S vaccine (AstraZeneca) and/or RNA vaccines (tozinameran [Pfizer/BioNTech] and elasomeran [Moderna]), with the last injection administered at least 14 days before the date of PCR. Vaccinated and unvaccinated patients were included concurrently according to the order of hospitalization in the participating centre. Data were anonymously collected with a form similar to the one used in a recent study on SARS-CoV-2 Delta infection  [7] , providing similar variables (supplementary material and Tables  1  and 2 ), with the exception of antiviral treatments, which were not available at the time data were collected for the Delta study.
Even though some data were missing at random, in many cases, this was because they did not exist. Patients could have been too severe to receive a computed tomography (CT) scan before mechanical ventilation or only mildly ill and went back home without any oxygen requirement or CT scan. To manage missing data not at random, multivariate analysis was initially performed only on the dataset with complete cases, after which the model was applied to the full dataset  [9]. Results are presented on the basis of the full dataset. In addition, a multivariate analysis was similarly performed on the dataset of vaccinated patients only to independently assess the weight of comorbidities among vaccinated patients, taking into account the influence of the time elapsed since the most recent injection. Interactions were systematically searched and mentioned if found.
In addition, the availability of antiviral treatment was not comparable between the Delta and the Omicron wave, which could have had an impact on mortality. The main strength of our study is the large number of highly phenotyped patients, making our conclusions probably valid for most populations. In particular, vaccine status was individually checked, ensuring accurate classification of patients. In addition, the inclusion period we chose corresponds to a phase of homogeneous circulation of a unique variant, ensuring more precision to our conclusions. Lastly, the fact that the data collection was aligned with the Delta study performed in 2021 enabled us to conduct the comparison between the two variants with high validity. Our study has several implications. Despite the immune escape by Omicron, vaccination with the currently available vaccines still retains efficacy against severe infections, even among older patients or those with comorbidities, which is an important rationale for vaccine campaigns to be remembered.
The elevation field is extracted from the NASA SRTM dataset,  12  which originally has a 30 m resolution. Fuel parameterization is performed to assign reference fuel parameters to each type of vegetation in the land use data for the ROS computations. Data used for simulation also include 2D fields of wind speed vectors, computed to account for the influence of the elevation field. All these fields are compiled on NetCdf landscape files that are pre-formatted for many latitude/longitude points, providing coverage for Cyprus, the south of France and Corsica, with plans to extend to other European regions. ForeFire simulation is set up as a RESTful service on the server hosting the landscape, with output forecasted fronts generated as JSON data.
Web server The Web server is responsible for linking the mobile apps of the end users with the backend. The Web server uses the HERE HYZ database and its Maps API in order to properly model fire propagation as polygons, in a language understood by the map provider software used by the mobile app. HERE HYZ. 13  is a location data management cloud service, built around standards like GeoJSON. It stores data in multiple levels. The top level is called a space Each space is being used to describe one fire, modelled as GeoJSON geometry objects. These are objects like polygons, multi-polygons or line strings. When accessing the service through HERE Maps API, communication is primarily through GeoJSON files.
Historic wildfire in Amarillo, Texas As a second evaluation option, a historic fire was considered, namely the wildfire of the Tanglewood Complex, as it is known, which happened on the 27th February 2011, at the outskirts of Amarillo, Texas. The National Institute of Standards and Technology (NIST), as part of its Disaster and Failure Studies Program, gathered electronic data for the incident as the Tanglewood fire front moved through the Palisades community towards Lake Tanglewood  [42]. This wildfire was selected because it was captured in its entirety in GIS software, plus it offers progression intervals of the wildfire every 15-30 min, which is an important feature based on our requirements and design considerations (see Section 3). The progression of the wildfire during its lifetime is illustrated in Fig.
e. fire management tool, see Section 4. 4) by a system operator. The use of advanced technology (e. g. drones, satellites, forest sensors) to automatically detect and locate wildfires is beyond the scope of this work, but it could be easily combined with our proposed system. In terms of forest fire predictions, a number of limitations exist due to the datasets or models employed in the current version of the proposed system. A conservative parameterization of ForeFire code  [46]  was selected in the EscapeWildFire system applied in the study of A. Kamilaris et al. Cyprus, for reasons of robustness, using the most common fire velocity model  (Rothermel [47] , used in the US Forest Service since 1990) and the CORINE land dataset  [48]  (EU-provided land cover at 80 m resolution). Nevertheless, more detailed land-use datasets and more specific fire velocity models may be considered and will likely lead to better forecasts.
Future work As for future work, efforts will mainly focus on improving the mobile app along the lines of the feedback received by the participants and observations of the authors (see Section 6. 5 above). Land type distribution data with higher spatial resolution than the CORINE LandCover dataset  [48]  will be acquired, plus we intend to investigate various fire velocity models, towards better accuracy on modelling fire propagation. Modelling of smoke's diffusion in the 3D space will also be considered. A comparison of the island of Cyprus with the island of Corsica, for which a relevant case study has been performed in  [54] , would be an interesting initiative. Multiple modes of transport and dynamic escape routes will be supported, performing re-routing along the way based on the actual status of the wildfire and all relevant contextual information collected, e.
Archaeological surveys Pedestrian surveys were conducted in two phases: exploratory surveys (Phase 1) and archaeological surveys (Phase 2). Limitations encountered included difficulty of access to the survey region, which lacks a road system, and the absence of power sources, cellular coverage or internet access. ©Garmin CX GPS devices were used to record locations and artefact positions using a "catch and release" strategy in Phase 1 (exploratory surveys) and Phase 2 surveys carried out in 2016, when the techno-typological identification of artefacts was recorded in the field. Phase 2 surveys conducted in 2019 adopted a "tag and bag" strategy and lithic artefacts were transported to the University of Lusaka for metrical, techno-typological and taphonomic analysis. GPS data was downloaded daily and processed using ©Garmin Basecamp software.
8 m intervals, recording all surface finds. Survey tracks and artefacts were recorded using hand-held Garmin © GPS devices. Spatial data from the GPS units, including survey tracks and waypoints, were processed in the field using Garmin ©Basecamp software and later exported to a geographic information   [27] , landcover from Sentinel 2 data processed by the Zambian ESA-CCI (European Space Agency-Climate Change Initiative) Land Cover (LC) team, mean rainfall (WorldClim)  [32]  and rainfall erosivity (GlobalR) from the European Soil Data centre (ESDAC)  [33]. Topographically derived layers (slope, hydrology) were produced using ArcMap tools. Regional-scale administrative boundaries, hydrology, rainfall and erosivity data were sourced from HydroAtlas_Zambia v. 1. 0  [16, 34]. Phase 1 surveys were used to provide a broad picture of the regional geological and geomorphological context within which artefact scatters occur.
Descriptive statistics were produced using ggplot and regression analyses were performed using the AICcmodavg package in R  [35]. For Phase 2, survey tracks were merged and buffered using a 2-m buffer and superimposed on a 1x1 meter grid in order to calculate the spatial extent of the surveys and artefact densities using standard arcMAP tools. A kernel density function was used to identify artefact clusters within the survey areas. Statistical summaries were produced for the survey areas and larger artefact clusters using zonal statistics. Techno-typological identification of lithic artefacts was conducted in the field in 2016. In 2019 artefacts were georeferenced and collected for analysis at the University of Lusaka, including typological identification and the recording of detailed morphological, metrical and taphonomic attributes following Barham  [36]  and Clark et al  [37].
Acknowledgments We would like to thank the following people for their invaluable assistance with the logistical planning and active participation in the fieldwork. Martha Nchimunya Kayuni  ( University of Zambia, Department of Historical and Archaeological Studies ) and  Margaret Katongo  ( Assistant Keeper of Archaeology, Livingstone Museum ) participated in fieldwork in 2019. Mike Thomas  ( Emeritus Professor, Stirling University ),  Brandon Peecook  (Asst. Curator and  Professor ,  Idaho Museum  of  Natural History & Biological Sciences ) and  Neil Tabor  ( Prof. of  Earth Sciences, Southern Methodist University ) and  Lawrence Barham (Prof. of Archaeology, Classics and Egyptology, University of Liverpool ), shared their field observations and insights into the geomorphological and archaeological history of the  Luangwa Valley.
Using ET in these devices can help better understand attention mechanisms, facilitate navigation and interactions, and optimize graphical performance by adapting resolution based on gaze point. An example of this synergy between MR and ET is found in the Microsoft HoloLens 2 device. This technology combines MR with ET to provide accurate information on gaze position, allowing a better understanding of attention mechanisms in individuals with ASD  [31]. The source code of the ARRET eye-tracking toolkit developed by the University of Kaiserslautern is freely provided and must be adapted to the experimental design "https://github. com/AR-Eye-Tracking-Toolkit (accessed on 3 June 2023)". Integrating ET into MR devices allows the collection of valuable data for studying neurodevelopmental disorders, such as ASD.
Eye Tracking The HoloLens 2 headset has an application programming interface (API) to acquire a single gaze ray (origin and direction of gaze) at about 30 Hz. The predicted gaze is about 1. 5 degrees in the visual angle around the target. We reused source code from the ARETT toolbox to retrieve the raw data from Microsoft's Eye Tracking API for research  [31]. The raw data obtained represents an x, y and z position of the gaze direction with a frequency of 30 Hz. To study the participants' visual activity as a specific eye movement, we used.
Eye Tracking The HoloLens 2 headset has an application programming interface (API) to acquire a single gaze ray (origin and direction of gaze) at about 30 Hz. The predicted gaze is about 1. 5 degrees in the visual angle around the target. We reused source code from the ARETT toolbox to retrieve the raw data from Microsoft's Eye Tracking API for research  [31]. The raw data obtained represents an x, y and z position of the gaze direction with a frequency of 30 Hz. To study the participants' visual activity as a specific eye movement, we used the functions provided by the ARRET toolkit using the R programming language and its RStudio environment. All raw data were processed based on the study by Llanes-Jurado et al. [32]  to categorize eye fixations in a 3D environment:.
Outcome Measures The familiarization success criteria are determined by the stages reached throughout the sessions. For evaluating the use of the tutorial, we focus on two dependent variables: the fixation duration (in milliseconds) on relevant cues (holograms in Figure  3a ) and the execution time (in milliseconds) of each trial. The calculation of the fixation duration on relevant cues is based on the gaze fixation time within Areas of Interest (AOIs), defined around the holograms and instructions. AOIs are specified for each task involved and are dynamic, following any movements of the referent objects. Data processing is performed solely on fixation durations within the AOIs. The remainder of the perceptual field, the Mesh Figure  3b , accumulates all parts of the percept outside the AOIs.
Hypothesis 3 (H3): Execution times and fixation durations for each trial differ between groups only during task 1 of the tutorial. This is analyzed using the Wilcoxon non-parametric test on unpaired series between the control group and the group with ASD. A Durbin-Conover post-hoc test will also be used to identify trials that show significant differences with a threshold of 5%. All statistical analyses are performed using the R software and its RStudio environment. The figures are created using Excel software.
Qualitative data analysis We analyzed our qualitative data (interview transcripts and event memos) using inductive coding techniques. Epistemologically, we used guidelines from the grounded theory tradition to understand how our subjects attached meaning to entrepreneurial activities and their organizational position, and the actions they took while performing these activities  (Glaser and Strauss, 1967; Creswell and Poth, 2016). In doing this, our ground assumption was that organization members are knowledgeable members and construct their organizational reality which we were then seeking to understand from our interviews and observations  (Gioia et al. , 2013). Using the software package Atlas-Ti, we first openly coded all of our text for instances where the participants provided information about key aspects of the entrepreneurial process: their motivation and steps taken for initiating an entrepreneurial project, their perceived relationship with the organization and changes thereto, development of the venture, and personal goals and outlook.
Data collection Data were collected via semi-structured interviews and participant observation (i. e. , observation and participation in community activities), namely via participating in activities related to the transformation of cassava tubers at swidden and fallow fields and observing each step of the operational sequence, or chaîne opératoire. This study was conducted within the scope of a wider research project whereby interview questions related to knowledge, attitudes and practices pertaining to diabetes and its treatment. Nomenclature and terminology were recorded in Parikwaki. Because various writing systems have been developed over time,.
Data analyses All interviews were transcribed verbatim. Transcripts were then stored and handled with NVivo qualitative data analysis software  (51)  to identify, manage, link, and retrieve coded data. Thematic analysis and sequential focused coding centered on the cultivation, transformation and consumption of cassava, as well as narratives regarding its connection with diabetes. Results from thematic analyses distinguishing cassava products and their perceived impact on diabetes can be found in a supplementary data file. Verbatim quotes translated into English by the authors are used to illustrate narratives.
Acknowledgments The authors express their thank to all participants for their implication in this study, special thanks to the  Parikwene of Saint-Georges and Macouria  for sharing their knowledge, thank to  Marc-Alexandre Tareau ,  Mélissa Cardozo ,  Marquilène Narciso Felicio ,  Fabrice Labonté ,  Emanuel Guiome ,  Jean Narcis ,  Roger Labonté ,  Antonio Felicio  for field assistance, and many thanks to  Guillaume Odonne  and  Richard Raymond  for long discussions in the ethnosciences, to  Sandrine Ruhlmann  for guidance in constructing the operational sequence of couac production. The content of this paper has appeared, for the most part, in the thesis of Rapinski (69). 10. 3389/fnut. 2023. 1061611Frontiers in Nutrition 14 frontiersin. org.
Funding Financial support was provided by a PICS (N° 07779 "Gestions des territoires, des ressources et systèmes de santé autochtones comparés (Guyane française-Québec)") from France's CNRS to AC and DD, the OHM Oyapock, Labex DRIIHM (ANR-11-LABX-0010) and Labex CEBA (ANR-10-LABX-25-01), as well as a CIHR Doctoral Research Award (201510DAR-358297-196992) and a travel award from the Université de Montréal to MR.
Our study utilizes a within-subject set-up with a sample representative of the (mainland) French adult population in terms of age, gender, and living area. Participants answered both survey questions and incentivized experimental tasks to assess their time and risk preferences. To measure risk preferences, we adopt the portfolio choice task by  Gneezy and Potters (1997) , while for time preferences, we rely on the Convex Time Budget (CTB) method introduced by  Andreoni and Sprenger (2012a, 2012b). We compare these experimental measures to self-reported data using established and validated questions on patience (based on  Vischer et al. , 2013)  and risk attitudes, including general attitudes and those specifically pertaining to health and finance  (Dohmen et al. , 2011). Our main findings are as follows: Based on survey measures, risk tolerance and impatience decline with age.