dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION In our study, we measured the human ability to perform a trend judgment task on a noisy graph (i. e. , "Does this graph go up or down?"). Analyzing the responses of 3943 participants that performed the task online on computers or tactile devices, we found that their accuracy was affected by all three manipulated factors, namely, the steepness of the graph, its noise level and the number of points. In terms of response times, there was a significant effect of steepness and noise but not of the number of points. This finding suggests that participants treated the scatterplots as an ensemble, without serially processing each item: in fact, if that was the case, we should have observed an increment in response times proportional to the number of points in the dataset. Fast intuitive statistical judgments on graphs with Gaussian noise thus seem to operate similarly to ensemble perception, the human ability to rapidly extract the "average" of visually displayed items, without focusing on each particular element in the set  (Cui & Liu, 2021; Whitney & Yamanashi Leib, 2018 ; but see, in the presence of outliers in the graph:  Ciccione et al.
, 2022). Crucially, participants' responses were entirely predicted by the t-value associated to the Pearson coefficient of correlation of the graph, showing that humans' trend judgments approach those of an optimal statistical model  (Peterson & Beach, 1967). Our findings thus challenge the view according to which people are naïve, biased or inaccurate intuitive statisticians. While some data show that people do not include sample size in their variability estimations  (Kareev et al. , 2002) , thus suggesting that they wrongly assume that a small sample is always representative of the entire population  (Fiedler, 2000; Juslin et al. , 2007) , our studies demonstrate that, for datasets represented in a bivariate graphical format, correctly incorporate both variability and sample size in their trend judgments.
In other words, at least at a perceptual level, humans are not naïve in their statistical estimates, but seem to take into account all the parameters of the dataset. It is worth noting, tangentially, that our online study replicated the findings from an identical trend judgment task performed in a laboratory context  (Ciccione & Dehaene, 2021). This piece of evidence is important both empirically and methodologically. It confirms that, unlike popular opinion among many scientists, psychophysical studies do not need to be confined to a controlled laboratory environment and can be successfully performed online. This clearly reduces research times and costs, especially when participants, such as in the present online study, were included on a purely voluntary basis and with no other reward than personal enjoyment.
ACKNOWLEDGMENTS Funders:  INSERM-CEA ,  Collège de France ,  Bettencourt-Schueller foundation ,  ERC  grant " NeuroSyntax ",  Mind Science Foundation  Grant. We thank the 1 st -grade children and teachers of Versailles' academy; the Himba people and the local guides; all participants that took part in the online experiment; and  Dario Mirossi ,  Mynie Zhan  and  Chanel Valera  for translating the instructions of our online task.
Southern blots For each Southern blot, 3-5 μg of genomic DNA digested with Eco RV and Ssp I were loaded on a 1 % agarose gel and electrophoresis was performed overnight at 1 V/cm. The gel was manually transferred overnight in 20X SSC, on a Hybond-XL nylon membrane (GE Healthcare), according to manufacturer recommendations. Hybridization was performed with a 302 bp 32 P-randomly labeled CAN1 probe amplified from primers CAN133 and CAN135  [36]. Each probe was purified on a G50 column (ProbeQuant G50 microcolumn, GE Healthcare) and specific activities were verified to be above 2. 4 × 108 cpm/μg. The membrane was exposed 3 days on a phosphor screen and quantifications were performed on a FujiFilm FLA-9000 phosphorimager, using the Multi Gauge (v. 3. 0) software. Percentages of DSB and recombinant molecules were calculated as the amount of each corresponding band divided by the total amount of signal in the lane, after background subtraction.
Microfluidics and microfabrication Master molds for the microfluidic devices were created using photolitography techniques by adapting the methods described in Ref. [37] : Briefly, designs were created with CleWin software and printed onto high-resolution polymer photomasks. Master molds were then fabricated with negative photoresist SU8 onto silicon wafers, following a double-layer procedure in order to obtain different specific heights for the wells and the chamber. Microfluidic devices were created using two pieces of polydimethylsiloxane (PDMS): one thin ( ∼ 300 μm) layer patterned by the master mold described before and a second blank thick ( ∼ 8 mm) slab where inlet and out- lets were forged. The whole device was assembled, using plasma oxygen, as follows (from bottom to top): a glass slide, the patterned PDMS layer facing up, and the blank PDMS slab closing the microfluidic chamber.
Image acquisition and analysis The whole microfluidic chip was imaged with a 20× objective, every 20 min both in bright-field and in green epi fluorescence. On such an imaging routine, a rectangular lattice was followed by the motorized stage in order to obtain 176 (22 × 8) fields of view (each 600 μm × 600 μm). The images were processed with the open-source software ImageJ  [21]. First, only those image sets with wells that contain one single cell at the beginning of the experiment were selected and cropped. Such image sets were structured into hyperstacks of 73 (73 time points) per two (two color channels: bright-field and green fluorescence) images. Using the ImageJ plugin TrackMate  [20] , the number of cells at every experimental time point both in bright-field and green fluorescence channels was computed: Using the bright-field channel in each time point, round elements (with a specific size ∼ 3.
Bayesian inference Prior selection Bayesian inference of the ODE model parameters from the Southern blot measurements was performed in Julia using Markov Chain Monte Carlo simulations using the Turing. jl library  [38]. Our prior distributions were independent gamma distributions for each parameter. The gamma distribution is parameterized by a shape and scale parameters, denoted α and θ , respec- tively. The mean and coefficient of the gamma distribution are given by µ = αθ and CV = 1/ √ α , respectively. For each parameter, we selected a mean and a CV which constrained the parameters within some physical reasonable range. In particular, we know that the time scale for double-strand breaks to appear in the population is less than the length of the experiment, so β is not likely less than ln(2)/12 h -1.
Funding This project was partially funded by the  Inception program of Institut Pasteur. Equipment was partially founded by DIM ELICIT. LP was supported by a PhD thesis fellowship from  Fondation Blanchecape and Association Française contre les Myopathies (AFM). Work in the GFR laboratory is supported by the  Institut Pasteur  and the  Centre National de la Recherche Scientifique (CNRS). We acknowledge funding support from  NSF  Grant  DMS-1902895  (EL).
Some of the mountain ranges include Mount Graham, Pinal Mountain, the Superstition Mountain, San Francisco Peaks, Mount Taylor (in New Mexico), among many others in Arizona, New Mexico, Colorado and down into Mexico. These places are holy and alive and are like the Vatican to us. We don't have just one place or an enclosed building with four walls, we have whole mountain landscapes that are essential to our well-being. I grew up learning about and going to these places, offering prayers, singing songs and [participating in] ceremonies. The theories taught in my university education went against what I grew up with and what, first and foremost, made me a beautiful, strong person. What made me love my culture was non-existent. Nonetheless, I stayed at university and earned my master's degree in archaeology, focusing on the southwest, learning GIS and database management.
Acknowledgements The authors first met through an event series, 'In their presence: debates on the dignity, display and ownership of human remains' (2020-2021), co-hosted by  Bridget Conley  and  Diane O'Donoghue , to the latter of whom we wish to express our deep gratitude. The series was made possible through the support of  Human Remains and Violence 8/2  (2022),  [4][5][6][7][8][9][10][11][12][13][14][15][16][17 ].
Methods: We performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform.
Literature Gaps and Research Questions Although many studies have been conducted in the last decade that demonstrate the usefulness of LA for health care professions education, we have been able to identify only 2 reviews on this topic  [14, 16]. Their limitations were that both covered literature reviews only up to 2017, and one of the reviews included data from a single database (ie, MEDLINE). Moreover, they did not propose any framework to analyze the reviewed literature. On the basis of our literature review, which included papers from 10 databases, we identified 4 components of LA. Those 4 components were conceptualized in the form of a life cycle. The life cycle is a cyclical process that explains, in 4 stages, what digital learning content is created, what data are gathered from the content, and how the data are analyzed and used to achieve learning purposes.
When designing a course, it is critical to spell out content curation (the totality of what is to be taught in a course 1. What types of content are used in LA studies in health care professions education? 2. What types of data are collected in health care professions education through content engagement? 3. What approaches are used for data analytics in health care professions education? 4. What are the purposes of analyzing the collected data in health care professions education?.
Data Sources and Searches We searched the following databases for papers written in English from January 2010 to October 2021: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. The year 2010 was used as the starting point for our search, as the field of LA was formally recognized after the first International Conference on LA and Knowledge in 2011  [20]. We developed an initial search strategy based on a past publication on digital health education  [21]. After initial title and abstract screening, we used the Search Refiner and Word Frequency Analyzer tools  [22]  to refine our search strategy further. Finally, we incorporated terms denoting different digital education platforms seen in the initially screened literature (.
Study Selection We included peer-reviewed publications that met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data. We excluded papers that collected data about patient education; educators and not learners; and multimodal LA, abstracts, presentations, reviews, and commentary columns and papers where full texts could not be obtained (Textbox 1). In total, 2 reviewers (IB and MM) performed title, abstract, and full-text screening of all publications retrieved using the first version of our search strategy. After the first round of screening, they defined a detailed set of inclusion and exclusion criteria and clearly explained the defined criteria to the other 4 reviewers (AP, CD, WLT, and CSA). All 6 reviewers worked in pairs sequentially and evaluated all retrieved papers using our final search strategy.
Data Extraction and Synthesis A total of 3 reviewers (IB, MM, and CSA) collaborated on a data charting form to select the variables to be extracted. In an iterative process, the 3 reviewers separately charted the data, reviewed the findings, and modified the data extraction form. The 3 reviewers then grouped study data by type of digital education content, type of data obtained from student interactions with digital education platforms, analytics approaches used to process the collected data, and finally the purposes of the LA. We resolved disagreements on data extraction by consensus and discussion with other reviewers. Upon the completion of data extraction, the data were collated, summarized, and subsequently organized into groups according to similarities and differences. Tentative codes were added to these groups and iteratively discussed and revised to generate a comprehensive map of terms.
Life Cycle of LA In our literature analysis, we identified several components of the LA process. We propose that these components can be conceptualized as part of a larger LA life cycle. Figure  2  shows the life cycle of LA, starting from digital education content creation, followed by the data collection process, the use of analytics to process the collected data, and finally the purposes for which LA was used. Digital education content is mostly created by educators to support student learning, but in certain cases, it can also be created by learners' peers (eg, discussion forum materials). While students are interacting with the provided digital education content, data on their use are collected and stored for further processing. The raw data include the click-level data that contain time-stamped information about every interaction with the digital education platform.
Research Question 2: What Types of Data Are Collected in Health Care Professions Education Through Content Engagement? Data collected from the digital education platform can be broadly divided into 2 categories: raw and preprocessed data. Raw data consist of unprocessed data taken directly from the platform often in the form of log files. Many platforms automatically generate log files as a record of all user interactions with the system. For instance, the Moodle platform maintains a log of all user actions on the system and the time they were made (Figure  3 ). Preprocessed data can then be obtained by processing the raw data and aggregating data points to focus on a specific aspect of it. For example, the same log data can be used to determine how often a student viewed a given course by extracting each instance of a specific user accessing that course.
We found that most preprocessed data types fit into the broader categories of (1) number of connections, (2) time spent, and (3) forum interactions (Table  S4  in Multimedia Appendix 1). Moreover, not all raw data consist of log files. Other types of raw data can be collected directly from relevant learning materials on the digital education platform, which we categorized into (4) test scores and (5) textual data. A number of connections refers to data that measures the number of times users connect to the digital education platform. This is one of the most common data types collected in LA research and can be derived from log data from many platforms. Of articles that collected data on the number of connections to the platform, 32% (21/65) of collected data on multiple content types individually  [24, 26, 27, 29, 36, 37, 39, 56, 58, [63] [64] [65] 67, 73, 76, [78] [79] [80] 82, 85, 86].
Gaining a better understanding of rushing and idling can provide insights into whether students truly engage in digital education content. As with the number of connections, only 3% (2/65) of papers specified the amount of time spent on each content type  [65, 71] , whereas 3% (2/65) of others calculated the overall time spent on the digital education platform  [47, 75]. The remaining (13/65, 20%) reviewed papers examined connections to only 1 content type  [31, 33, [39] [40] [41] [42] [43] 53, 55, 57, 68, 70, 86]. Forum interactions consist of data derived from the students' use of web-based forums  [3, 37, 38, [48] [49] [50] [51] 54, 59, 62, 64]. The data collected typically included the IDs of the sender and receiver, the times that the post was written and modified, the post's content, and the thread and forum where it was written.
A total of 5% (3/65) of articles used forum interactions to observe how students engaged in problem-based learning that involved discussing concepts in web-based groups moderated by a tutor  [48] [49] [50]. Forum interaction data were largely analyzed via social network analysis  [51]. Test scores constitute data from assessments provided on a digital education platform. In most cases, researchers have collected the accuracy of participant performance in web-based clinical cases or e-cases  [52, 82]. The next largest group consisted of participants' scores on the MCQs  [41, 43]. A total of 2% (1/65) of articles did not specify the type of assessment on which they collected data  [75] , and 2% (1/65) of articles featured not only MCQs but also fill in the blank, jumbled sentences, and matching questions  [25].
k-means was the most popular unsupervised algorithm used in 9% (6/65) of papers, mostly as a mechanism for the early detection of at-risk students  [24, 26, 58, 66, 77, 79]. Social network analysis can be divided into individual-level and group/network-level measures and can be either static or dynamic. Individual-level measures are calculated on the level of an individual (ie, a student). Group or network-level data represent a group of students or the whole class (ie, the network). Individual-level measures can be further divided into a quantity of interaction, position in information exchange, and connectedness. Group or network-level measures included centrality, structure, embeddedness, and cohesion. The quantity of interaction denotes how many interactions a student had in total (ie, degree centrality).
Data Analytics LA data are collected in real time, but data analytics is usually performed after course completion. The advantage of conducting real-time analytics is, for example, to implement early detection of at-risk students. Unfortunately, only a handful of studies have investigated how the detection of students at risk in combination with a personalized educational response to each student can be used to minimize students' dropout rates  [58, 79]. The second detected gap is that LA data are mostly collected over a period of up to 1 year or for 1 cohort of students (eg, 10 months in a study by Cirigliano et al  [65] , 1 academic year in a study by Koh et al  [87] , or a cohort of undergraduate students in a study by Kuhbeck et al  [55] ). Even when the data are collected for a longer period, they are mostly either jointly analyzed (eg, 3 years of data in a study by Brands et al  [81] ) or the comparison is made among different years (eg, 2 consecutive cohorts in a study by Chan et al  [56] ) or different courses (eg, 4 different courses in a study by Saqr et al  [49] ) without any changes made between 2 consecutive years or cohorts.
Strengths and Limitations Although this scoping review was conducted according to the scoping review methodology, some limitations are worth noting. This review was guided by a formal protocol. To ensure a broad search of the literature, the search strategy included 10 databases; however, our inclusion criteria limited our review results to only English-language articles and published peer-reviewed literature from January 2010 to October 2021. Furthermore, as the data were extracted by 6 reviewers, we used the standardized extraction form to ensure consistency among reviewers. Nevertheless, the extraction process might be slightly affected by the presence of multiple reviewers. A final limitation is the rapid growth of the field; therefore, it is important to acknowledge that this scoping review is a snapshot at a particular point in time.
Conclusions In this paper, we reviewed 65 papers in which the authors used LA to collect data on health care professions in their classes. From these papers, we extracted some general characteristics of the whole LA process and proposed a framework for the whole LA life cycle that is field independent, meaning it can be used beyond health care professions education. We then used the proposed framework to review the 65 papers. Our study is student-centric, although we acknowledge that, in addition to students, there are other stakeholders such as educators and institutions involved in the entire LA process. We focused on students because most of the reviewed papers focused on students. This is consistent with the findings of  Lee and Recker [133] , where out of 47 reviewed papers, only 3 studies did not focus, at least partly, on students.
Acknowledgments The authors would like to acknowledge the funding support from the  World Health Organization collaborating centre for Digital Health and Health Education. JC's post at  Imperial College London  is supported by the  National Institute for Health and Care Research Northwest London Applied Research Collaboration. Research of DG was in part supported by the  Digital Health Cooperative Research Centre  ( DHCRC0056 ) and the  Australian Research Council  ( DP210100060  and  DP220101209 ).
Similar results were obtained in a retrospective analysis of a single-centre cohort of 150 AAVs, with 94 GPA, for whom relapse-free remission was better predicted by clinical GPA or MPA phenotypes than the ANCA subtype. 17 he limited data available on ANCA-negative and MPO-ANCA-positive GPA, and discordant findings concerning the contributions of AAV and ANCA specificities to the initial phenotype and relapse risk for GPA patients prompted further investigations involving larger patient subsets. The French Vasculitis Study Group (FVSG) Registry is a national database of AAV patients, whose clinical and laboratory information has been collected prospectively and longitudinally, thereby enabling description of clinical characteristics and long-term outcomes of a large cohort including GPA. 18  The data from all GPA patients entered in this database were extracted and analysed to determine whether ANCA-negative and MPO-ANCApositive GPA represent a clinically distinct subsetdiffering from proteinase-3 (PR3)-ANCA-positive GPA-with different outcomes within AAV, and whether ANCA status and specificity should be taken into account to predict relapse and adapt therapy.
Patients This multicentre analysis was conducted on patients with newly diagnosed GPA entered prospectively into the FVSG Registry from 1983 to 2018. This database includes information from vasculitis patients referred to FVSG members, those who participated in our network's trials  19  and whose follow-up was pursued. Investigators from >60 French centres regularly contribute that information. For study enrolment, patients' GPA diagnoses had to meet the 1990 American College of Rheumatology classification criteria and/or revised CHCC Nomenclature. 1 20  All diagnoses of patients who fulfilled those criteria were reassessed independently by two of the authors (XP, MI) considering their entire clinical histories. All FVSG Registry patients gave their informed consent. Only patients entered in the Registry at diagnosis and followed for ≥6 months or who died within 6 months of entry were included.