dataesr/scientific-paragraphs-categorization huggingface dataset:
CAMAs are "community-augmented" because researchers can contribute to such databases, for instance by reporting the information necessary to calculate effect sizes that might not have been part of their manuscript, by adding new experiments and thus updating the CAMA to contain the most recent evidence, or even by reporting unpublished null-results. We have created now a number of CAMAs covering various levels of infant speech perception (e. g. ,  [13, 14] ), and have recently started combining CAMAs into standardized meta-metaanalyses using a shared platform called MetaLab  [15, 16]. In previous work, we have investigated single phenomena when building the individual meta-analyses and CAMAs, and have thus demonstrated how such quantitative overviews of the literature can provide us with interesting answers to theoretical questions within the description of those phenomena; for instance, in  [13] , we measured the age at which discrimination for native versus non-native vowels differed.
Methods We drew from two meta-analyses publicly available on MetaLab, one on native and non-native vowel discrimination (henceforth Vowels-Native and Vowels-Nonnative, respectively)  [13]  and one covering word segmentation from native speech (WordSeg)  [14]. The construction of both meta-analyses has been described elsewhere, so we limit ourselves to providing some general statistics on each dataset in Table  1 , given that they have been updated after publication  [12]. All scripts underlying the present analyses along with a snapshot of the data can be found at https://git. io/vHCzM. The outcome measure is Hedges' g, a standardized effect size corrected for small sample sizes. All analyses were conducted in R  [17]  using the package metafor  [18]  implementation of multivariate random effects models.
We inspected both datasets to make sure that all data points that we would eventually analyze should be conceptually included. We excluded from Vowels points where nativeness was ambiguous (6 effect sizes), either because it was an allophonic distinction or because the contrast was instantiated in a dialect that was different to the infants' own. We further excluded from WordSeg data points corresponding to experiments where the stimuli were not in the infants' native language or accent (29) and where target words to be segmented were not content words  (17). Experiments where infants were not typically developing and monolingual were generally excluded (21 in Vowels, 14 in WordSeg). Finally, effect sizes that were more than three standard deviations from the mean of the meta-analysis were also excluded (3 from Vowels, 1 from WordSeg).
Preliminary Analyses: Dataset Comparison Our research questions directly relate to age effects. As a first step, we investigated whether it is necessary to restrict analyses to studies where the exact same stimuli and procedure have been used with multiple age groups to avoid confounds. This restriction takes into account that studies targeting children of specific age groups might adjust task difficulty (e. g. , by changing the stimuli or aspects of the procedure) or aim to tap into general abilities in younger infants and investigate more subtle capabilities in older children. This is not unlikely, especially when looking at Vowels, where infants between 0 and 16 months are included (cf. Figure  1 ). Such a restricted analysis has been used in a previous study of developmental changes in vowel discrimination  [13] ; and was reported complementary to full-scale analyses of word segmentation  [14].
We assessed to what extent developmental conclusions are affected by the analysis strategy, using as a test case the theory-independent expectation that effect sizes should increase for native vowel discrimination and word segmentation, and decrease for non-native vowel discrimination. Results are shown in Table  2. Note that both the full datasets and the subsets cover the same respective age ranges. The results for the moderator age differ across datasets and depending on whether or not we consider a restricted subset of the data. Both in the case of native vowel discrimination and _ _ _ word segmentation we find a significant positive trend only if we use the subset of studies testing multiple age groups. Neither analysis yielded a significant effect for non-native vowels, counter to our predictions; nonetheless, the confidence interval is narrower and covers proportionally larger negative regions when using the subset compared to the whole dataset.
Comparison of Age Effects across Datasets Figure  1  plots effect sizes against age; each point refers to an effect size stemming from a study in the respective subset of studies that tested multiple age groups in the same task. Since we used standardized effect sizes for both analyses, we can directly compare the estimates, both in the linear models and for specific ages. We observe that effect sizes are consistently higher for native vowel discrimination than for word segmentation. This pattern is consistent with the hypothesis that vowel discrimination skills emerge earlier than and independent of word segmentation skills, and also with a bottom-up interactive model where vowel categories affect word segmentation; they are not, however, consistent with top-down acquisition models.
A second strength of the present paper lies in the novelty of the approach of re-using meta-analytic data to answer developmental questions which are at the heart of theoretical debates on infant speech perception. In previous work  [16] , we investigated broader patterns of development across a range of levels in language acquisition, and we expect that, as MetaLab grows, similar studies will be conducted on other aspects of development. Naturally, this extension may not be straightforward and will require careful selection of the data and reflection on potential confounds. For instance, in order to ascertain the viability of our datasets, we made sure that we could detect improvements with age predicted by all contending theories before moving on to further analyses. The data we are using have not been collected for this purpose, and since researchers have been studying a range of speech perception dimensions, it is difficult to read regression lines as purely tagging development.
However, recent work suggests that vowels are more important than consonants in lexical recognition for infants: When 5-month-olds are presented with mispronunciation of the onset of their own name, which either began with a vowel or a consonant, infants' looking patterns suggested that own-name recognition is disrupted to a greater extent by the vowel change than the consonant change  [21]. Thus, at least at young ages, vowels seem a reasonable choice for assessing potential phonology-lexicon interactions. Third, using a vowel meta-analysis might be insufficient because in many languages, and certainly for the languages most infants in our databases were learning, consonants form the majority of the phonological system. We therefore are not measuring acquisition of the whole phonological system.
In the whole database, only 6 studies delay the test phase and/or use exposures in the child's home; and none of them were part of the final analyses given our constraint of including only studies that measured two age groups with the same stimuli. Further, since infants are usually familiarized and tested in close succession, it might be sufficient for them to perform a sort of phonetic pattern matching, which need not employ symbolic representations and/or their long-term memory  [24]. We hope future work will help address that this important gap in the current literature. Regarding our methodological approach, there is one issue that tempers our enthusiasm for the findings, namely that one cannot be sure that tasks across domains have been matched in difficulty. Indeed, what we observe as a developmental lag could be an index of the fact that vowel discrimination tasks (at least in the ways in which they have been implemented in this literature) are intrinsically easier than word segmentation tasks (with the same caveat applying here).
If tasks were truly matched and only skills varied with time, we might expect effect sizes for word segmentation to eventually be at exactly the same level as for native vowel discrimination. We cannot test this empirical question, however, because word segmentation has been tested with familiarization-based paradigms exclusively, while native vowel discrimination specifically has not been tested using familiarization for infants older than 9 months (at least in our database). Nonetheless, we do not see our approach discredited by this open empirical question, and it is not the case that one can only meta-analyze for developmental effects when the task has been held constant. In fact, this would make the study of precedence across phenomena impossible: The exact same task can never be used to test vowel discrimination and word segmentation.
Acknowledgements The present work was supported by the H2020 European Research Council [Marie Skłodowska-Curie grant Nos 660911 and 659553], the Agence Nationale pour la Recherche [ANR-14-CE30-0003 MechELex, ANR-10-IDEX-0001-02 PSL*, ANR-10-LABX-0087 IEC], the Fondation de France, the Berkeley Initiative for Transparency in Social Sciences and the Laura and John Arnold Foundation.
Questionnaire and Type of Data Collected Participants were asked to provide their age, gender, weight, height, current tobacco smoking status, and current cannabis use status. They were also invited to complete online versions of the Alcohol Use Questionnaire (AUQ)  (Mehrabian and Russell, 1978) , the Eating Disorder Examination Questionnaire (EDE-Q)  (Fairburn and Beglin, 1994) , and the 20-item Urgency -Premeditation -Perseverance -Sensation seeking (UPPS) impulsive behavior questionnaire  (Billieux et al. , 2012).
A sleep diary for at least seven days was obtained. Clinical assessment included: sleep habits and medications, anthropometric measures (weight, height, BMI, waist, neck and hip circumferences), Epworth Sleepiness Scale (ESS), Geriatric Depression Scale (GDS), Mini-Mental State Examination (MMSE), overnight home PSG and on the following day in the sleep laboratory, blood tests and multiple sleep latency tests (MSLT) (four consecutive tests starting two hours after awakening, at two-hour intervals). Standard meals were provided without alcohol or caffeine. Exclusion criteria were: diabetes, glycated hemoglobin ! 6. 5% (47. 5 mmol/mol), fasting glycemia >7mM, C-PAP or oxygen treatment, PSG recording time < 180 minutes, central sleep apnea, missing data for fasting insulin, glucose or HOMA-IR.
The objective of labelling the photos was also to guarantee quicker identification of the photos during the interview, and to facilitate data collection by the researcher. The labels were chosen so as not to induce an idea of hierarchy between photos (e. g. "1, 2, 3, 4. ") but rather, were defined by combining letters and numbers (N1, U7, T3, R4. ) such that only the researchers could restore the original ranking order. The photos were sized to a postcard format (10x14cm), and laminated. The final set of 60 photographs + 1 blank card contained a diversity of visual content (black and white photos, color photos, symbolic or abstract contents, realistic contents, thematic contents about food, medical environment, care-giving, family, emotions, religion, nature). This diversity is important, since one of the main advantages of the PEI method is to place the participants in favorable conditions to be able to "break their frame"  [14].
Bridging worlds by using images taken from Google images Since the photos were not directly produced by either the researchers or the participants, but rather, came from a database containing billions of photos (indexed from the key words attributed to the photos by the persons who put them online), we can assume that, using photos from Google Images, we were able to create a common reference framework that helped to bridge the backgrounds of both researchers and participants. There was a risk that the pictures thus collected would not offer a sufficiently "unusual angle"  (14)  to help them to go beyond stereotypes and habits of thinking. Our results, and the participants' feedback on the use of the PEI show that the photographs managed to bring them out of their usual framework and allowed them to improve their reflection on ethical issues concerning the situation of persons in PVS.
The use of new visual technologies or services (such as Flickr, Facebook, Instagram, You-Tube, Twitter, blogs) currently constitutes an expanding area of research  [37, 38]. In this context, our experience shows that using image databases like Google Images is an efficient tool, as long as the methodological process takes account of the specific ethical and situational factors related to the project's context and goals. Our photo collection process can easily be reproduced by other research teams using a brainstorming method coupled with searches of Google Images. Thus, this method could be further explored in a variety of contexts. It could also offer the possibility of cross-cultural studies, based on a collectively constructed set of photographs. It would also be of interest to develop an electronic version of a set of photographs in the framework of studies including a large population.
Conclusion Our results suggest that the PEI method is appropriate to explore and record representations of the persons involved in the care of people in PVS. Indeed, this approach fosters investment, reflection and expression of respondents in a context of multiple, challenging obstacles, such as a difficult emotional context and language-related issues. Using Google Images proved to be an efficient method to collect images that made it possible to bridge the backgrounds of both researchers and participants, while offering sufficient diversity to "break the frame" of participants and yield in-depth interviews. Our experience showed that using image databases like Google Images is an efficient tool, as long as the methodological decision process takes account of the specific ethical and situational factors related to the research goals.
This study was supported by a  National Clinical Research Grant  from the  French Ministry of Health  ( Programme Hospitalier de Recherche Clinique  -PHRC 2008  N˚12-04 : http://social-sante. gouv. fr/IMG/pdf/resultats_PHRC_2008_hors_cancer. pdf to RA). The funders had no role in study and introspection, to allow participants to go beyond stereotypes and habits of thinking, and 4) can be replicated in other research areas. The use of visual methods currently constitutes an expanding area of research and this study stressed that this is of special interest to enhance research among populations facing endof-life and ethical issues. design, data collection and analysis, decision to publish, or preparation of the manuscript.
This readout mechanism was fitted to the performance and false alarm rate across change sizes and change times by allowing five free parameters (Figure  8E-F ): the (bandwidth) scale W, the (temporal) rate w, and the decision parameters (l, a, b; see Materials and methods). The parameters that best fitted the human dataset were W = 0. 54 cycle/oct, w = 0. 72 Hz (a rate corresponding approximately to dynamics or an integration time-constant of the order of 1-2 s), and a = 6. 2, b = 10. 8, and l = 1. 14 s (r = 0. 95; p<5. 10 À16 ; MSE = 0. 7%). The scale value corresponds to a full width at half-maximum for the scale filter of approximately 0. 56 octave, very close to the frequency region spanned by localized changes (0. 55 octave). This may indicate that subjects preferentially used a single scale value for monitoring the frequency modulation and that they estimated the most common frequency modulation across trials since half of the trials contained localized changes.
Experimental setup Acoustic Stimulation Subjects were seated in front of a screen with access to a response box in an acoustically-sealed booth (Industrial Acoustics Company GmbH). Acoustic stimulus presentation and behavioral control were performed using custom-written software in MATLAB (BAPHY, from the Neural Systems Laboratory, University of Maryland, College Park; available upon request). The acoustic stimulus was sampled at 100 kHz, and converted to an analog signal using an IO board (National Instruments, PCIe-6353) before being sent to diotic presentation using high-fidelity headphones (Sennheiser i380, calibrated flat, i. e. ±5 dB, within 100-20000 Hz). Reaction times were measured via a custom-built response box and collected by the same IO card sampled at 1 kHz. Electroencephalogram (EEG) acquisition EEG recordings were collected in a separate set of 18 normal-hearing subjects while listening and responding to the texture change stimuli.
Continuous EEG data were recorded using a 64-channel system (ActiCap, BrainProducts, Gilching, Germany) at a sampling rate of 500 Hz with one reference and one ground electrode. In order to standardize electrode placement on the skull, we used a default fabric head-cap that holds the electrodes (Easy-Cap, Equidistant layout, 60 scalp, four ocular channels). The analysis of EEG responses was carried out offline (see section Data analysis).
As detailed above, a correct detection had to occur within 2 s of the change time. Similarly, the false alarm rate FAR(t) was computed as the number of false alarms that occurred over all 2 s windows (starting at t), in which no change in statistics occurred. This created an artificial reaction time for each false alarm, that we used for comparing the distributions of the actual reaction times resulting from the Hits  (Yin et al. , 2010). d' was computed for different times and change sizes, yielding only a limited number of trials per condition. To avoid degenerate cases (i. e. d' would be infinite for perfect scores), the analysis was not performed separately by subject, but over the pooled data. Confidence bounds (95%) were then estimated by bootstrapping within the dataset. The analysis was verified on surrogate data from a random responder (binomial with p=0.
Reaction times We obtained reaction times by subtracting the change time from the response time in each hit trial. For each condition, the distribution of reaction times was assembled and the median reaction time computed. Note that very early and late reaction times will in some cases not correspond to actual reaction to the change in statistics, but are coincidental, which cannot be distinguished on a trial-bytrial level. The results presented for the effect of change size on performance and reaction time were computed using only the data with change in contiguous bins (localized change). Results for the trials with non-localized bins (at 110% change size) were qualitatively the same, however, they were excluded from this analysis to keep the number of trials per condition equal. These measures were computed as a function of change size and change time.
Analysis of EEG recordings We analyzed two signals based on the EEG: the classical auditory event-related potential (ERP), and the centro-parietal positive potential (CPP). First, slow trends were removed from all electrodes using a low-dimensional polynomial fit ('nt_detrend', from the NoiseTools Matlab toolbox by de Cheveigneand Parra, 2014). We verified that a classical high-pass filter (Matlab: filtfilt, 0. 1 Hz, 15th order, 50 dB attenuation in the stop band) gave very similar results. Electrodes were low-pass filtered below 30 Hz with a 45th order Chebyshev filter using the 'filtfilt' function in MATLAB to avoid phase distortion. All electrodes were referenced to the common average potential. All trials with at least one scalp channel exceeding 500 mV at any time after referencing were discarded as artifacts.
All subjects had a low or moderate rate of blinks and eye movements and could thus be included for a total of 18 subjects. Classical auditory ERPs were estimated from the central electrode (El. one in the equidistant layout of EasyCap, equal to Cz; corresponding to the center in  Nie et al. , 2014). The CPP signal was based on a set of centro-parietal electrodes  (El. 14, 27, 28, similar to Twomey et al. , 2015). Trials were then extracted in the period encompassing 0. 5 s before and 3 s after the sound presentation and either time-shifted to the onset or their corresponding change times (see Figure  5 ). EEG data were segmented into shorter epochs locked on stimulus onset or response time for display. The epochs were baseline-corrected relative to a 150 ms interval prior to onset, and a 200 ms interval before change time for alignments to both change and response time.
Statistical analysis If not specified otherwise, nonparametric tests were used. When data were normally distributed (for performance), we checked that statistical conclusions were the same. One-way analysis of variance was computed with the Kruskal-Wallis' test; two-way using Friedman's test. Error bars are ±2 SEM (standard error of the mean), unless specified otherwise. All statistical analysis was performed using Matlab (The Mathworks, Natick).
Acknowledgements We would like to thank the  Equipe Audition , the  Donders Center for Cognitive Neuroscience , and the  NSF -funded Neuromorphic Cognition Engineering Workshop in Telluride, CO,  USA , for allowing us to use sound booths and EEG equipment, with helpful discussions. Funding was provided through the  ERC   ADAM  project. ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL*  supported the research unit. SS was also supported by an  ARO  grant  63113-LS. BE was supported by a  European Commission 's  Marie Curie Grant  ( 660328 ).
Le dispositif d'enquête a consisté en un auto-questionnaire de 20 pages (caractéristiques sociodémographiques, conditions de travail, santé et qualité de vie), envoyé avec relance à un échantillon de 5000 enseignants des 1 er degré, 2 nd degré et du supérieur, et complété par des informations administratives (corps professionnel, modalité de service, caractéristiques de l'établissement d'affectation principale, épisodes de congés maladie) issues des bases de données de gestion des personnels de l'Éducation nationale. 2) Population d'étude : les enseignants des 1 er et 2 nd degrés Les 5000 enseignants concernés par l'enquête ont été tirés au sort avec une stratification sur le sexe, la classe d'âge, le type d'établissement et le secteur d'enseignement croisé avec l'appartenance à l'éducation prioritaire (public hors prioritaire / public prioritaire / privé sous contrat) dans l'annuaire des personnels du MENESR.
De plus, cette analyse s'est faite sur un échantillon de plusieurs milliers d'enseignants, qui grâce au tirage stratifié couplé au redressement par pondération, peut être extrapolé à l'ensemble de la population enseignante française des 1 er et 2 nd degrés. Ensuite, la richesse des données disponibles dans l'enquête QVE a permis à la fois de considérer plusieurs indicateurs de bien-être complémentaires, au travail et en général, et de prendre en compte de nombreux facteurs de confusion potentiels dans les modèles statistiques. Enfin, les données professionnelles du questionnaire ont été enrichies par des informations administratives provenant des bases de données ministérielles, ce qui permet d'en renforcer la fiabilité et de limiter les données manquantes. Par contre, le dispositif d'enquête transversale par auto-questionnaire en population occupée présente inévitablement des faiblesses.