dataesr/scientific-paragraphs-categorization huggingface dataset:
As a demonstration of the broad scope of the ICSMP data, published studies cover a broad range of psychological disciplines, including social psychology  13, 14  , cognitive psychology  15, 17  , political psychology  16  , moral psychology  16, 18  , economic psychology  19  and health sciences  20  , among others. They explore different populations in reference to the COVID-19 pandemic in terms of age (e. g. , older adults see  21  , marital status  19  or nationality (e. g. , for a study on the Spanish population, see;  22  for Swedish and Chinese population see  23  ), and other socio-demographic characteristics. These all attest to the great potential of the ICSMP data to inspire further research. In sum, the present dataset affords numerous opportunities for cross-cultural research on a plethora of hypotheses.
Technical Validation To support the technical quality of the dataset, we conducted an analysis to showcase its reliability (and its diverse applicability to research questions in social sciences and beyond). For completeness, in the analyses that follow, we examined all samples-including those with very few observations, such as Puerto Rico (N = 2), Brazil_3 (N = 6), and Panama (N = 12). We evaluated the adopted survey methodology utilised by national teams by conducting an internal survey to ensure the accuracy of reported sample types. The inspection showed that 28 samples were quota-based nationally representative samples (36%), 6 used post hoc weights to achieve an approximate level of national representation (8%) which nonetheless should be seen as convenience samples, and 43 were convenience samples (56%), many of which were from low and middle-income countries  60.
" Participants who wrote "213" were coded as passing the attention check, participants who wrote anything else were coded as failing the attention check, and those who did not reach this screen of the survey were coded as missing data. Figure  6  also shows (bottom plot) a world map coloured according to the rate of attention-check fails across countries. Overall, 90. 1% of participants passed the attention check (1. 0% failed), and 8. 0% did not reach the final screen with the attention check. The full dataset presents N = 51,404 cases across 69 countries (from 77 samples, 28 of which are quota-based nationally representative), with an average sample size of 745 (SD = 549) and a proportion of valid answers of 95%. The mean age of respondents was 42. 93 (SD = 16. 04) years, and 50. 9% were women (44% males, 0.
Usage Notes The datasets are shared, cleaned, and ready for analysis. We recommend that interested researchers use the cleaned version of the data (available at https://doi. org/10. 17605/osf. io/tfsza)  56. The use of the labelled data is also suggested for convenience as it has all variable levels encoded, thus eliminating the need to consult the codebook when using the. csv format. The Data were imported and cleaned using the R software for statistical analysis  62  and packages readr  63  , haven  64  , readxl  65  , dplyr 66 , psych 67 , htmltools 68 , mime  69  , xfun  70  , labelled  71  , sjlabelled  72  , codebook 73 , lubridate  74. As previously noted  5  , those wishing to approximate national representativeness can apply the appropriate survey weights to demographic and countries of interest when random sampling is used (e.
g. , sex: https://ourworldindata. org/gender-ratio; age: http://data. un. org/Data. aspx?d=POP&f=tableCode%3A22; education: https://ourworldindata. org/global-education; marital status: https://ourworldindata. org/ marriages-and-divorces). To minimize misclassification of text-based responses to the cognitive reflection test (CRT) and the attention check, we used multiple steps of data cleaning using REGEX (regular expressions) as fully detailed in (ICSMP official data. Rmd; osf. io/dwpng) located in the folder named Code. First, we coded the predefined numerical and  text values as correct (in the case of CRT, also the values predefined as intuitive). Then, iteratively, we screened the remaining responses and, using REGEX, updated answers. Remaining responses were recoded as incorrect.
Sample size, average proportion of valid answers, age of respondents and the number of data collections in 69 countries (A-M). Note: Country = country names in accordance with ISO3 codes, N = number of respondents in each country. <50% and <90% = average proportion of valid (non NA) answers that are below 0. 5 and 0. 9 respectively in the subject level. μ Age = mean age and sd Age = standard deviation of the age, Multiple datasets = whether there were multiple data collections in the country. Tables.
Sample Country N % Valid Answers Age Multiple datasets <50% <90% μ Age sd Age per country NP Nepal 563 0. 78 0. 61 28. 06 7. 58 1 NL Netherlands 1297 1. 00 0. 99 49. 63 16. 83 1 NZ New Zealand 510 1. 00 1. 00 45. 76 17. 62 1 NI Nicaragua 16 1. 00 1. 00 42. 75 14. 84 1 NG Nigeria 608 0. 93 0. 87 32. 08 10. 81 1 NO Norway 532 1. 00 1. 00 47. 04 17. 39 1 PK Pakistan 565 0. 90 0. 85 26. 94 8. 38 1 PA Panama 18 1. 00 1. 00 44. 11 17. 32 1 PY Paraguay 16 1. 00 1. 00 38. 94 9. 33 1 PE Peru 91 1. 00 1. 00 46. 21 14. 44 1 PH Philippines 524 0. 98 0. 96 36. 74 12. 27 1 PL Poland 1817 1. 00 1. 00 46. 44 17. 09 1 PR Puerto Rico 2 1. 00 1. 00 64. 00 16. 97 1 RO_1 Romania_1 500 1. 00 1. 00 42. 26 13. 45 2 RO_2 Romania_2 505 1. 00 0. 99 42. 53 14. 50 2 RU Russian Federation 558 1. 00 1. 00 45. 02 15. 46 1 SN Senegal 552 0.
41 0. 15 0. 24 0. 16 0. 03 0. 01 0. 00 Pakistan 0. 51 0. 10 0. 24 0. 14 0. 66 0. 07 0. 07 0. 02 0. 01 0. 01 0. 15 Panama 0. 33 0. 17 0. 50 0. 00 0. 44 0. 11 0. 28 0. 11 0. 00 0. 06 0. 00 Paraguay 0. 56 0. 31 0. 12 0. 00 0. 44 0. 06 0. 31 0. 06 0. 12 0. 00 0. 00 Peru 0. 40 0. 14 0. 46 0. 00 0. 35 0. 20 0. 29 0. 13 0. 01 0. 02 0. 00 Philippines 0. 44 0. 15 0. 38 0. 03 0. 46 0. 21 0. 17 0. 09 0. 02 0. 02 0. 03 Poland 0. 29 0. 21 0. 50 0. 00 0. 33 0. 22 0. 31 0. 10 0. 03 0. 01 0. 00 Puerto Rico 0. 00 0. 50 0. 50 0. 00 0. 00 0. 00 0. 00 0. 50 0. 50 0. 00 0. 00 Romania_1 0. 32 0. 13 0. 55 0. 00 0. 65 0. 22 0. 11 0. 01 0. 00 0. 00 0. 00 Romania_2 0. 27 0. 19 0. 54 0. 00 0. 40 0. 32 0. 22 0. 04 0. 01 0. 01 0. 00 Russian Federation 0. 41 0. 15 0. 44 0. 00 0. 39 0. 28 0. 26 0. 05 0. 01 0. 01 0. 00 contains the merged and cleaned dataset, which is provided in a non-proprietary (ICSMP_cleaned_data.
csv; osf. io/ypkrc) and a labelled (ICSMP_cleaned_data. sav; at osf. io/8tyj9) file formats. In addition, we included in a sub-folder a dataset that removes observations failing the attention check or filled out less than 50% of the items, both in a non-proprietary (ICSMP_cleaned_data_nobots. csv; osf. io/98fex) and a labelled (ICSMP_cleaned_ data_nobots. sav; at osf. io/3yjga) file formats. The Metadata sub-folder provides a thorough itemised description of the data cleaning process in both text (Data Cleaning. docx; osf. io/7udpt) and human-readable change-log (human-readable change log ICSMP. xlsx; osf. io/fydx2).
Methods This study describes an iterative saturation approach, using randomly selected batches from a systematic literature search to develop a long list of candidate outcomes to evaluate healthcare. This approach can be preceded with an optional benchmark review of relevant registries and Clinical Practice Guidelines and data visualization techniques (e. g. as a WordCloud) to potentially decrease the number of iterations. The development of the International Consortium of Health Outcome Measures Heart valve disease set is used to illustrate the approach. Batch cutoff choices of the iterative saturation approach were validated using data of 1000 simulated cases.
Methods The methodology is based on a iterative saturation approach, which can be preceded by benchmark review and/or data visualization techniques to screen (using frequent words in abstracts) the studies that have been extracted from a systematic search using a scoping search. We illustrate practical use of the methodology in the ICHOM standard set for heart valve disease (HVD). Data saturation techniques to identify candidate outcomes have been proposed and used before, nevertheless in literature several algorithms have been used to achieve saturation, and it is unknown if these algorithms reach full saturation or if several candidate outcomes are missed altogether  [7, 11]. This method can be used to both identify outcomes (what is being measured e. g. pain) and outcome measures (how it is being measured e.
Iterative saturation approach of literature A systematic literature review is an essential component in identifying candidate outcomes for a standardized outcome set, also called core outcome set in COMET and OMERACT initiatives. It is recommended to conduct a systematic search in collaboration with a medical information specialist  [7]. Multiple medical literature databases should be included. Duplicate articles should be removed before selecting relevant articles. In case of ICHOM, they focus on outcomes (and subsequent outcome measures) of importance to patients, as such PROMs have been incorporated as an integral part of all ICHOM sets. A separate search was done for PROMS specifically, and this yielded a relatively small number of studies that could all be reviewed. In case of clinical outcomes, this searched yielded 17,166 articles.
The Wordcloud of the HVD search is presented in Fig. 2  as an illustration. A quick scan of this Wordcloud suggests that outcomes such as mortality, valve regurgitation and stenosis are commonly discussed in literature. Note that the script abbreviates words so that identical words with different suffixes (e. g. valve and valves) are counted as one word. An example of R code to develop a Wordcloud is provided in Supplementary Text 3. This method is also optional and should performed preceding the iterative saturation approach, to potentially reduce the number iterations needed to achieve maximum saturation.
Discussion In this paper, we have validated a structured approach for the identification of candidate standardized outcomes from literature. This methodology is based on a iterative saturation approach, and can be preceded by initial benchmark review and data visualization to decrease the number of iterations needed to achieve full saturation. We validated this approach using a simulation module with an average of 98% (range 92-100%) saturation reached using a 100-article initial batch, with 25 articles in the subsequent batches. The R code for this simulation is attached in the supplement and can be used by applied researchers. This approach can be used to obtain a list of candidate outcomes obtained from literature which can be supplemented using qualitative measures (e. g. focus groups, interviews) and used in voting processes such as the modified Delphi method to develop a standard outcome set, or a core outcome set.
Prior research has been performed to filter searches to limit the search to relevant articles  [17]. In this study the filters are applied when the articles are randomly selected. Of note, in HVD ICHOM set we have limited ourselves to original research, since the main interest was in outcomes that are already used and measured. Nevertheless, one could expand to perspectives and reviews, especially in the setting of designing a standard (or core) outcome set for research purposes. Data visualization and benchmark review could optionally be performed to reduce the number of iterations needed in the saturation approach. Simulation studies showed that it indeed reduced the number of iterations needed to achieve maximum saturation. In this study we did not use natural language processing (NLP) models.
Patterns in the NISP sample Because they provide the foundation of a large number of faunal interpretations, our comparisons begin with an analysis of the NISP sample (all layers are combined here to increase sample size, an analysis by layer is presented below). A chi-square test of independence shows no statistical difference in the taxonomic profiles produced by the two methods at all three sites (Crvena Stijena: χ 2 = 14. 0, p = 0. 1709; Saint-Césaire: χ 2 = 9. 7, p = 0. 2843; Le Piage: χ 2 = 3. 1, p = 0. 6801, Fig. 1 , data from Table  S1 ), an indication that the counts are not affected by the method of identification. Out of 26 pairwise comparisons of taxa, only one shows a taxonomic difference larger than 7% (reindeer at Saint-Césaire, difference of 10. 8% between the two methods, Table  S1 ).
Because errors may cancel one another in comparisons of whole samples, an additional test consists in determining if the morphological and ZooMS identifications are consistent for the same specimen. In this case, we are ascertaining whether a given morphological identification-a Bos/Bison femur for instance-matches the taxon given by ZooMS for the same specimen. The percentage of agreement, that is, the proportion of matches relative to the sum of compared specimens, is moderate at Saint-Césaire (69. 6%), whereas it is high at Crvena Stijena (91. 8%) and Le Piage (93. 2%, Table  3 ). A plot of the data suggests that errors of identification are more common when species are poorly represented, although the trend is not significant (r = 0. 69, p = 0. 0707, Fig. 2a , n > 4). The percentage of agreement in our dataset also seems inversely correlated with species diversity as measured by the reciprocal of Simpson's Index, a pattern that is observed regardless of whether the identifications are based on the morphological or ZooMS counts (Fig.
Acknowledgements The authors would like to express their gratitude to  Aline Galland ,  Vasilije Marojević  and  Đuro Pribilovic  for their humor and patience while processing the samples. The faunal analysis at all three sites was supported by a  SSHRC Insight Grant  ( 435-2013-0993 ). The excavations at  Saint-Césaire  ( PCR La Roche-à-Pierrot ) are funded by the  Direction régionale des affaires culturelles of the Nouvelle-Aquitaine Region  and by the  Charente-Maritime Department, France , while those at  Le Piage  are made possible thanks to support received from the  Direction régionale des affaires culturelles of the Occitanie Region , also in France. The excavations at  Crvena Stijena  benefit from the material and financial support provided by the  Montenegrin Ministry of Culture , the  Montenegrin Academy of Sciences  and the  National Science Foundation  ( BCS 1758285 ).
(2022)  by allowing basis decomposition to interpolate nonlinear covariate effects using smoothing functions. The different methods were compared for their ability to harmonize batch effects and preserve the quality of covariate effects on simulated data (Section 4). Furthermore, we performed an evaluation on derived phenotypes from MRI-brain images from nine cohorts (A4, ABIDE-I, ABIDE-II, ADNI, AIBL, MIRIAD, OASIS3, PDPB, PPMI) comprising controls and people with different brain disorders: patients with different subtypes of Parkinson's disease (PD), Alzheimer's disease (AD) and Autism spectrum disorder (ASD). Using synthetic data, we find that Fed-ComBat achieves a 35% improvement in reconstructing the target unbiased function compared to d-ComBat (RMSE = 0. 9162), and a 12% improvement compared to our federated extension of ComBat-GAM, d-ComBat-GAM (RMSE = 0.
Synthetic data We created simulated datasets under three main scenarios: i) when cohorts have similar distributions for covariates and sample size effects (IID), ii) when covariate distributions slightly overlap but follow different patterns, and when there are differences in sample sizes (mid-non-IID), and iii) when there is no overlap between covariate distributions between cohorts and there are evident differences in sample sizes (non-IID). We simulated ComBat parameters following the methodology outlined in  Reynolds et al. (2022) , using the graphical model depicted in Figure  2. The primary difference between our approach and that of  Reynolds et al. (2022)  was the model used to relate the covariates and phenotypes ϕ. We used a nonlinear function in this step, which was employed as the unbiased target function to be learned after the harmonization process for the synthetically biased phenotypes.
Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (https://adni. loni. usc. edu/aibl-a ustralian-imaging-biomarkers-and-lifestyle-study-of-ageing-1 8-month-data-now-released/). Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (https://adni. loni. usc. edu/). Data used in this article were provided by the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (https://adni. loni. usc. edu/aibl-australian-imaging-bio markers-and-lifestyle-study-of-ageing-18-month-data-now-relea sed/). Data used in the preparation of this article were obtained from the Autism Brain Imaging Data Exchange (ABIDE) I database.
Data used in this article were obtained from the Autism Brain Imaging Data Exchange (ABIDE) II database (http://fcon_1000. projects. nitrc. org/indi/ abide/abide_II. html). The AIBL researchers contributed data but did not participate in the analysis or writing of this report. AIBL researchers are listed at https://www. aibl. csiro. au. Data used in the preparation of this article were obtained from the MIRIAD database. Data used in the preparation of this article were obtained from the Open Access Series of Imaging Studies (OASIS) database. Data used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative ration of this article were obtained from the Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (A4) study.
Data collection Data was collected on the dispensing of the dTcaP booster vaccine by pharmacies and pro-pharmacies within 12 months that the information by the CPAM had been disseminated. Outcomes included the dTcaP booster vaccine delivery (which was assessed using the Club Inter Pharmaceutic (CIP) codes corresponding to the vaccine in the CPAM database) and a consultation with a GP at least once within 12 months after the start of the awareness campaign for those who had already received the dTcaP booster vaccine. A GP consultation was an endpoint because the French national health insurance running the awareness campaign suggested that patients consult with their GP.
g. , Microsoft Silverlight and Adobe Flash). However, the advent of HTML5-based media playback systems encouraged the media industry to make DRM integration more seamless. In 2012, Google and Microsoft partnered with Netflix (a content provider) to propose a "built-in" DRM extension for the Web: the W3C Encrypted Media Extensions (EME)  [11] , with the overarching goal to define a standard DRM API (Application Programming Interface) that would work across multiple browsers or operating systems on a broad of range of devices. The EME specification does not create yet-another DRM key system. Instead, it allows browsers to discover, select and interact with any DRM module automatically. Thus, EME removes the burden of implementing content protection from browsers, whose role is now restricted to only redirecting DRM messages to the right native key system.
In our analysis, we focus on the Widevine distinctive identifier, namely Client ID, and session-related stored data, namely persistent sessions. In that sense, we find that all desktop browsers fail to encrypt the Client ID containing identifying information about the user's device. Ironically, we find that some mobile privacy-focused browsers, such as Ghostery, do not encrypt the Client ID, which can be obtained by a few JavaScript calls with no or little explicit users consent, as desired by EME designers  [61]. In addition, we highlight some mobile browsers storing Widevine data as cookies even when first-party cookies are not enabled. Fourth, we continue our study and inspect Widevine device fingerprinting and resulting user tracking. Here, among others, we examine the nature of the Client ID and look deeper into its different fields, uniqueness, and stability.
g. , Netflix, Disney+), can decrypt it, implying that they can effortlessly fingerprint all devices. Widevine does not publicly state the required terms for being a partner OTT. This could be concerning, but we consider a wider threat model: an attacker (website) with no Widevine partnership, meaning not having a Widevine-signed server certificate, that can call EME JavaScript API (see subsection 5. 1 for required permissions) acting as a mirrored proxy between a client and a license server. In this paper, we show that this attacker can achieve three goals: (1) Fingerprint users on Android devices if they are on a browser not applying the privacy mode (e. g. , firefox). (2) Get identifying, but not unique, attributes if the privacy mode is not always enforced (e. g. , all desktop browsers).
EME Components. The EME API brings together multiple entities. Here, we list the main ones used during the control flow. User-Agent. The EME implementation lies in the user-agent (e. g. , a web browser) and is called through JavaScript to communicate with a CDM and distant servers using opaque messages. In the rest of the paper, we use the terms browser and user-agent interchangeably. CDM. The Content Decryption Module DRM system in the EME specification. It is responsible for license protection and usage enforcement to decrypt media content on the user's device. CDN. The Content Delivery Network is a distant server providing encrypted media to the user device on demand. It also offers the necessary information for license requests and DRM configurations. License Server. License requests generated by the CDM are sent to the related license server of the given key system.
Protocol Workflow. EME defines a uniform API centered on CDM session management and license acquisition while abstracting message contents. Henceforth, the EME API relies on multiple objects and events to implement the proprietary protocols of CDMs. We exclude user authentication to license server and CDN from EME workflow due to it being out of scope of the EME specification. License Acquisition. An overview of the protocol can be seen in Figure  1. For the execution, we assume that the EME user-agent has already received all the required information about the encrypted media from the CDN, such as supported codecs and used DRM system. The EME workflow starts in step 1 by the JavaScript running in the user-agent, taking as arguments the requested DRM as well as configurations for media decryption and playback.
Reverse Engineering Settings We conducted a two-stage approach to inspect Widevine-based EME. The first settings are as follows: we leveraged some web extensions that monitor all calls to EME API. Then, we requested media from various content providers, and noticed that EME works similarly for both paid services (e. g. , Netflix and Disney+) and experimental ones (e. g. , Bitmovin). Finally, we examined all the collected messages to determine which parts are Widevine-defined and which are content provider-defined As for the second settings, we developed a script calling EME APIs, and hosted it on our web server. Our script implements the workflow in Figure  1  with Widevine as the underlying DRM system. To this end, we leveraged the integration platform made available by Widevine for external developers to perform license acquisition/renewal  [65].