dataesr/scientific-paragraphs-categorization huggingface dataset:
Survey data collection and analysis Women can request a telemedicine abortion by completing an online consultation form  4  , which includes demographic questions about age and place of residence, medical questions about gestational age, contraceptive behavior, and medical contraindications, and research questions about reasons for wanting an abortion and for choosing telemedicine abortion through WoW. When submitting the online consultation form, women agree with the Terms of Use 5  and thereby consent to the fully anonymized use of their information for scientific research purposes. Consent can be withdrawn at any time by emailing WoW. Hence fully anonymized data was obtained from WoW. The dataset included a total of 2078 requests for telemedicine contraception and abortion between 18 March 2020 and 18 March 2021.
Discussion We assess our data against the concepts of structural violence and intersectionality. Structural violence is explained as the force of social, economic, and health-related policies that act as an invisible force influencing peoples' lives and health in negative ways  [12]. People who are already vulnerable are more likely to become affected by such policies. De Maio and Ansell argue that "structural violence" has insufficient explanatory power alone and should be applied in combination with a larger theoretical framework  [12]. Intersectionality-theory helps explain how multiple overlapping forms of disadvantage may intersect to obstruct access to health services, in this case to abortion services in Germany during the COVID 19 pandemic. This study shows that access to abortion was made more difficult during the pandemic and draws attention to the ways in which access was particularly difficult for some women due to socio-cultural circumstances in their lives.
Study strengths and limitations One of the main limitations of this study is the lack of qualitative data on the experiences and perceptions of women seeking abortion services. Another limitation is the qualitative sample size, as well as the limited number of participants from outside Berlin. Recruiting more HPs was made difficult due to abortion stigma and the significant work-related stress caused by the pandemic. The quantitative data is also not representative of all women seeking abortion services in Germany, and may involve selection bias, since it consists of people seeking abortion services outside the formal heath system. Another limitation is that the dataset consists of self-reported data. We acknowledge that our findings cannot make an overall assessment of abortion service delivery in Germany during the pandemic.
The selection of these treatments was based on current industry practices and ongoing research interests identified by Yan et al. [20]. The aim is to investigate their impacts on microbial quality and physicochemical properties of mealworms. Contents of dry matter, protein, fat, ash and water activity (a w ) were measured, and the results were analyzed by one-way analysis of variance with Tukey's test. In addition, a microbiological predictive model was used to assess the efficacy of heat treatment steps on eight significant hazards. The results of this study can provide preliminary data and proof for the application of the HACCP system in the insect food industry.
Search strategy and databases A systematic literature search of PubMed, Cochrane, and HINARI was performed for studies published in peerreviewed journals from 1 January 2012 up to 25 June 2022. The databases were searched using the keywords of "Alzheimer's Disease, " "AD, " "music therapy, " "music intervention, " "cognitive functions, " and "cognition. " Keywords were combined using the Boolean operators "OR" and "AND. ".
Participants Participants were 468 regular smokers (i. e. , individuals who smoked at least 5 cigarettes per day over the last year) and 121 nonsmokers (i. e. , never smokers) recruited via the research online platform Prolific (https://www. prolific. co/, see Table  1  for sample demographics). Participants were eligible for the study if they were residents from the UK or the USA, and if they were native English speakers. In an effort to oversample individuals who smoked cigarettes for several years, only participants older than 35 were recruited. Smokers completed the study on March 3 rd 2022 (Time 1). About 30 days later, on April 4 th 2022, the participants were recontacted via the platform Prolific and invited to respond to a follow-up survey (Time 2). A total of 286 participants completed the second wave of data collection (182 participants could not be reached, were not available, or did not respond to the survey).
Procedure Participants were initially classified as smokers or nonsmokers based on information in the Prolific database. After a few demographic questions (gender, age, nationality, etc. ), smokers provided information about their tobacco consumption (the number of cigarettes smoked per day), quit attempts and motivation to quit followed by a self-reported questionnaire on craving  27. Then all participants completed the PIC task. Finally, smokers were asked to complete the Fagerström test for nicotine dependence  28. These measures are described in detail below.
The presence of the following exclusion criteria lead to the exclusion of the participant: contraindication for physical activity due to health status, dementia (Mini Mental State Examination score ≤24), or motor dependence (≤24 stage 5 on the Hoehn and Yahr scale). Statistical assumptions associated with MEM, including normality of the residuals, linearity, multicollinearity (variance inflation factors), and data points with undue influence (e. g. , Cook's distances) are checked. 7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. Given the small number of patients usually included in the exercise program of the Swiss private clinic in which the study is conducted (i. e.
Data Collection and Operational Definitions Data were collected daily (July-September 2019, from around 10:00 a. m. to 6:00 p. m. ) via video-recordings, when the bonobos were outdoor. Behavioral data were extracted from videos-via the all occurrences sampling method  [43] -on arousal/anxiety related behavior (i. e. , scratching and yawning), sociosexual behavior, play behavior, agonistic behavior and requesting gestures (see Table  S2  for a full description of these behavioral categories; Figure  1 ). Video-coding was performed by one person (E. R. ). The coder underwent a period of training with the trainer (E. D. ) before starting the video-analysis. The training period ended when the observer and the trainer reached 100% agreement. years old; see Table  S1 ). Group management was entrusted to one out of four keepers (two men and two women) per day.
Data Collection and Operational Definitions Data were collected daily (July-September 2019, from around 10:00 a. m. to 6:00 p. m. ) via video-recordings, when the bonobos were outdoor. Behavioral data were extracted from videos-via the all occurrences sampling method  [43] -on arousal/anxiety related behavior (i. e. , scratching and yawning), sociosexual behavior, play behavior, agonistic behavior and requesting gestures (see Table  S2  for a full description of these behavioral categories; Figure  1 ). Video-coding was performed by one person (E. R. ). The coder underwent a period of training with the trainer (E. D. ) before starting the video-analysis. The training period ended when the observer and the trainer reached 100% agreement. We randomly selected two-minute time slots and we extracted data under three different conditions: (1) bonobos could see the keeper with food (in a basket) either just before or during the food distribution session (when bonobos had finished the first ration of food and were waiting for the next one), but food was not yet available in their enclosure (keeper-present/food-unavailable); (2) bonobos could see the keeper and food was available (keeper-present/food-available); (3) the keeper was absent and no food was available (keeper-absent/food-unavailable).
The condition of keeper-absent/food-available did not occur in the visible part of the enclosure and/or in the foreseen time window. In total, we were able to analyze 118 video sequences, equally distributed across conditions and food and keeper features, for a total of 236 min. To check for the possible effect of keeper familiarity on bonobo behavior, we labelled the keepers (a man and a woman) regularly working with bonobos for at least 5 We randomly selected two-minute time slots and we extracted data under three different conditions: (1) bonobos could see the keeper with food (in a basket) either just before or during the food distribution session (when bonobos had finished the first ration of food and were waiting for the next one), but food was not yet available in their enclosure (keeper-present/food-unavailable); (2) bonobos could see the keeper and food was available (keeper-present/food-available); (3) the keeper was absent and no food was available (keeper-absent/food-unavailable).
Statistical Elaboration For each two-minute time window/condition, we recorded the occurrence of all target behavioral patterns (anxiety behavior, sociosexual behaviors, requesting gestures, play behavior, agonistic behavior) performed by all bonobos. We used the R-function fitdist  [44]  to plot and compare if the count data followed a Poisson or a negative binomial distribution  [45, 46]. In addition, we compared the two Bayesian Information Criteria (BIC corrections) (Poisson and negative binomial), and we checked for the lowest BIC  [47]  and selected the negative binomial distribution. We ran a first Generalized Linear Model (GLM ) (GLM a ; N = 84) on each target behavior (numeric variables; anxiety behavior: GLM 1 ; sociosexual behavior: GLM 2 ; requesting gestures: GLM 3 ; play behavior: GLM 4 ; agonistic behavior: GLM 5 ) to investigate the impact of the keeper presence and the type of food on bonobos' social behavior and dynamics.
All the GLMs were fitted in R  [48]  by using the function glm. nb of the R-package MASS  [49]. At first, we performed the likelihood ratio test  [50]  (analysis of variance with argument Chisq) to verify if the full model significantly differed from the null model  [51]. Subsequently, via the R-Function drop1, we extracted the p values of predictors basing on the likelihood ratio test between the full and the null model  [52]. When the multinomial predictor 'type of food' had a significant effect, we performed all pairwise comparisons with the Tukey test  [53]  using a multiple contrast package (multcomp). In this case, we reported p values, estimate (Est), standard error (SE), and the Z value, adjusted for the Bonferroni correction. We reported the best effect size of each variable, calculated via the function effectsize from the package effectsize.
An alternative interpretation could be that bonobos become more emotionally aroused when a familiar keeper is in front of them just because they share with this person a stronger social bond. Moreover, sociosexual contacts were more frequent when the keeper was present and the food was unavailable, probably to reduce food expectation anxiety. Previous studies showed that in bonobos sociosexual contacts could increase when food was available, even though some sociosexual interactions were found to increase while waiting for food  [33, 41]. In our case, data were collected just before and during the feeding session (including pauses, in which food was not available) within a brief time slot, which probably led to the necessity of using sociosexual contacts as an arousal buffering mechanism. Interestingly, we found no significant difference in the frequency of sociosexual interactions between food absence and high-quality food presence.
Experimental procedure The study was composed of two visits that took place one week apart. Visit 1: Upon enrolment, sociodemographic data such as gender, age, and level and years of education were collected. Then, each participant underwent a 24h dietary recall to study food intake according to the manual adaptation of the Automated Multiple-Pass Method  (Raper et al. , 2004). After obtaining a list of all food items eaten on the previous day, the interviewer probed for frequently forgotten foods such as savory snacks or candies. Then, the time and name of the eating occasion (e. g. , breakfast) and a detailed description of the type of food item, cooking method, and other relevant aspects. Lastly, a final revision of the dietary intake took place  (Raper et al. , 2004). Whenever possible, visit 1 was scheduled so that seasonal changes (e.
After listening and reading the suggestions they rated 15 food stimuli of varying tastiness and healthiness on whether they wanted to eat this food for real at the end of the experiment. Each choice trial started with the display of a food stimulus, and participants had three seconds to rate whether they wanted to eat the food item for real at the end of the experiment by using a 4-point Likert scale ranging from strong no to strong yes. Trials were separated by a jittered fixation cross (Figure  4 ). A short break was planned after 75 trials (i. e. , 5 blocks) to reduce head movement. A brief practice session occurred outside the MRI beforehand, and a second practice session occurred within the MRI to adjust the headphones' volume. All food stimuli were selected from a database of 600 food pictures (contact Dr.
Hilke Plassmann for more information). The food pictures were presented on a computer screen in form of high-resolution pictures (72 dpi). MATLAB and Psychophysics Toolbox extensions  (Brainard, 1997)  were used for stimulus presentation and response recording. Participants saw the food stimuli via a head-coil-based mirror and indicated their responses using an fMRI compatible response box system. The hand used to make choices and the display of the 4-point Likert scale (from left to right or right to left) was counterbalanced across participants. The task was incentive compatible, because one food was chosen out of chance for consumption at the end of the experiment  (Hare et al. , 2009; Hare et al. , 2011).
Dietary intake assessment To ascertain the required nutrition-related variables, once the 24h-recall was collected, energy intake (in Kcal), sodium and fatty acids quantities were ascertained through Nutrilog SAS software (Nutrilog-Online 20231010). Additionally, to measure diet quality, the healthy eating index (HEI) was computed  (Krebs-Smith et al. , 2018). The HEI-2015 is based on the Dietary Guidelines for Americans 2015-2020 (for a comparison between the American guidelines and French guidelines see SI table  4 ). It has 13 items whose scoring follows adequacy-or moderation-based energyadjusted intakes cut-offs, except for the fatty acids components. Each components weights differently into the final scoring (0-100, where higher scores indicate a healthier dietary pattern): total fruits (5 points), whole fruits (5 points), total vegetables (5 points), greens and beans (5 points), whole grains (10 points), dairy (10 points), total protein foods (5 points), fatty acids (polyunsaturated fatty acid plus monounsaturated fatty acid to saturated fatty acid ratio; (10 points), refined grains (10 points), sodium (10 points), added sugars (10 points), and saturated fats (10 points).
Imaging data acquisition Brain images were collected on a 3 T Verio Siemens scan, using a 32-channel receiveonly head coil. For the structural acquisition a whole-brain high-resolution T1-weighted structural scans were acquired for all participants with a magnetisation prepared rapid gradient echo (MPRAGE) sequence with the following parameters: TR = 2. 4 s; TE = 2. 05 ms; flip angle=9°, slice thickness=0. 8mm. For the functional acquisition, a multiband T2*-weighted multi-echoplanar images (mEPI) with BOLD contrast were acquired. To cover the whole brain with a repetition time of 1. 25 seconds, the following sequence parameters were applied: echo times 14. 8 ms; 33. 4 ms and 52 ms; 48 slices collected in an interleaved manner; 3-mm slice thickness, FOV = 192 mm; flip angle = 68°, and an average of 646 volumes per participate session.
Three echos were acquired to better accommodate for the compromise between spatial resolution and signal of the orbitofrontal context  (Posse et al. , 1999; Poser et al. , 2006). To further minimise the signal drop out in the orbitofrontal cortex, an oblique acquisition orientation of 30° above the anterior-posterior commissure line was applied  (Deichmann et al. , 2003). Imaging data pre-processing Anatomical MPRAGE and functional mEPIs were processed using the Statistical Parametric Mapping -SPM12 -(Welcome Department of Imaging Neuroscience, Institute of Neurology, London, United Kingdom) using MATLAB version R2020b (The MathWorks Inc. , United States). Each participant's anatomical image was segmented into gray, white matter and cerebrospinal fluid using the SPM12 segmentation tool. The three echo images of each volume were summed into one EPI volume using the SPM 12 Image Calculator  (Gowland and Bowtell, 2007; Poser and Norris, 2009; Kettinger et al.
Acknowledgments We thank  Hilke Plassmann  and  Nathalie George  for their insightful project discussion. We thank the  CENIR team  for their support and collaboration throughout the project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement n°  945298. BR is a Fellow of the  Paris Region Fellowship Programme  supported by the  Paris Region. LK acknowledges funding from an  ERC Starting Grant  ( SOCIALCRAVING ,  101041087 ). However, the views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible. The funders had no role in study design, data analysis, manuscript preparation, or publication decisions.
Data availability Behavioral data is available on the study's OSF website. Code availability Code to analyse behavioral data and food decision making task scripts are available on study's OSF website. The NCS and code to apply it to any fMRI data (apply_ncs. m function) are publicly available on Github (https://github. com/canlab/Neuroimaging_Pattern_Masks/tree/master/Multivariate_si gnature_patterns/2022_Koban_NCS_Craving).
A large sample of data (N = 292) was received from users with experience in dancing in various VR platforms, with various experiences and motivations. Based on the responses, we categorized the data based on six users-motivations: fun, fitness, social activity, pandemic, escape from reality, and professional activities. Using recent advances in OpenAI data analysis, and more specifically the Open AI's GPT3 language model  [13] , we analyzed the data to understand the user requirements and expectations. We concluded that, even though further improvements could be applied to overcome the current hardware, tracking, and technical limitations (including better and more affordable full-body tracking capabilities, lighter hardware, safe spaces in Social VR platforms, better avatars, etc. ), VR is still an inclusive platform that helps with mental and physical well-being, especially in the current pandemic.
Note that, in this work, we used an unsupervised classification model to remove any bottlenecks of human labeling of the data, such as bias  [49]. There have been similar recent surveys on SocialVR adoption, but they have not explicitly dealt with dance in VR. We contribute to the field of dance in VR by understanding the profiles of many different dance VR users. This precise understanding will help build a segue between the research community and the VR users, leading to the building of more target-oriented and user-friendly applications for dancing in VR. This online survey led to the acquisition of a large, freely available data set (https://forms. gle/q9VHe9sUkoeEzDcf9), aiming to provide the research community and companies with relevant information about the expectations and future developments required to satisfy the expectations of such a large audience.
Our times' increased use of social media platforms gives researchers the required access to data from many VR users around the world, enabling in-depth analysis. The conventional method of processing and analyzing data for small sample sizes do not apply to the masses. Hence, it is crucial to find efficient ways to analyze and extract large data sets using context-oriented deep learning techniques, such as OpenAI. Automatic data processing, such as machine learning, can retrieve interesting complex relations between variables that traditional heuristic-based methods could miss. However, further research is needed to understand the limitations of such "black box" approaches, especially in interpreting some of the results. OpenAI's text-based models like GPT3 offer advantages for natural language processing tasks, but they also have disadvantages for text labeling.
Conclusion In conclusion, our survey is based on the need to understand WHY and HOW people use virtual reality platforms for dancing, to help design user-centered future applications. We collected responses from a large set of users (n = 292), across a wide spectrum of population (varied gender, age, geographical location, and physical and mental disabilities), with different motivations and experiences. The quality of the VR dancing experience may vary depending on the specific VR setup, the game app being used, and the physical condition of the user. The preliminary analysis of the data showed that the user experience, usage of the system, and the user's expectations of future VR systems are based on their motivations and scope for using VR as a platform for dancing, i. e. , fun, fitness, social activity, escape, pandemic, and professional.
Acknowledgements The work was funded as part of the  JPICH Call on Cultural heritage. Funding agencies are the  Agence National pour la Recherche  ( Projet-ANR-17-JPCH-0004 ), and the  Cyprus Research & Innovation Foundation  (with protocol number  P2P/JPICH_DH/0417/ 0052 ). Funding Open access funding provided by the  Cyprus Libraries Consortium (CLC).
Movement registration and optical sensor Underneath the index finger of the participant, approximately 3 millimeters proximal to the hole that the rod connected to the plastic ring passed through, there was another small hole (2 mm in diameter). In this smaller hole, a fiber optic cable attached to an optical sensor (Omron E3X-HD11, Omron Industrial Automation, Osaka, Japan) was placed, which was able to register when the participant's index finger was lifted off the table and when it returned to the table during the tapping movements. The optical sensor registered the luminance from the fiberoptic cable with preset thresholds so that it recorded dichotomic on/off data (finger on or lifted off) with a sampling frequency of 100 Hz and saved this to a text file. This allowed us to record the frequency of taps, the duration of each tap and the total number of taps in each participant and in each condition.
fMRI data acquisition The experiment was conducted using a 3 Tesla GE MR750 scanner equipped with an 8-channel head coil. T2*-weighted gradient echo EPIs with BOLD contrast were used as an index of brain activity  (Logothetis, 2003; Logothetis et al. , 2001). Each functional volume consisted of 43 continuous slices with a slice thickness of 3 mm and an interslice space of 0. 5 mm. The field of view (FOV) was defined as a matrix with dimensions of 72x72 (3x3 mm in plane resolution, TE=30 ms), thus ensuring coverage of the whole brain. One volume was collected every 2. 048 s (TR=2048 ms), and a total of 1812 functional volumes were collected for each participant, divided into 4 runs of 453 volumes each. A high-resolution structural image was collected for each participant at the end of the experiment (3D MPRAGE sequence, 1x1x1 mm voxel size, FOV 240 x 240 mm, 180 slices, TR=6404 ms, TE=2808 ms, flip angle = 12°).