dataesr/scientific-paragraphs-categorization huggingface dataset:
The entire procedure was also approved by Eric-Jan Wagenmakers via personal communication in 2013 (email correspondence from  August 19, 2013). A one-sample ttest (one-tailed) was performed on a regular basis, at least once at the end of every semester during periods of data collection, to test whether the actual sample's mean score of negative stimuli presentations were below chance (50%). For all Bayesian analyses, the statistical software tools R (Version 3. 5. 1) and JASP (Version 0. 10. 1  [39]  and previous versions) were used. This was repeated over several years from October 2013 to March 2019. Although at this time the stopping criterion had not been met, all members of the participating research teams agreed to cease data collection primarily because the financial and human resources were exhausted.
These post-hoc analyses support our interpretation of the original data analysis that a true null effect was detected there. The raw data can be found here: https://osf. io/yqqfz/files/. Variations across labs. Next, several Bayesian one-sample t-tests (one-tailed) with the original prior were performed separately for each lab to test any variations in the effects between the different participating labs. Table  1  presents the sample size, the final BFs, mean scores, and standard deviations for each lab. As the table above indicates, none of the individual labs produced strong evidence for or against the effect. The strongest trend in line with the prediction was observed at the lab in Moscow University. By contrast, the two most powerful sub-samples (from Padua and Munich) show moderate evidence for H 0.
To test the homogeneity of the results across labs, we also calculated a meta-analysis with a random-effects model. The heterogeneity estimates were not significant (tau =. 0007; I 2 =. 01%; Q(4) = 4. 36, p =. 36), indicating a rather homogenous set of data. Mean overall effect size was ES =. 008 (SE =. 02, p =. 76) also revealing a null finding. In sum, the non-significant heterogeneity test and the low sample size in most sub-samples prevents any further interpretation of the variations. However, given the high sample size in the German data collection one could assume that this partial result might be strongest related to the true population parameter.
The following analyses are therefore purely exploratory and are proposed here for testing during future research into effects of this nature. Temporal analyses. We performed three post-hoc analyses to achieve a better understanding of the effects' development over time  [37]. For these analyses, we included the initial data and our later replication data. These were the only two studies to have been run with a QRNG within this specific retroactive avoidance paradigm and were the only studies relevant to the theory outlined above, since only quantum-based random mechanisms were addressed in this research framework. To this end, we examined the sequential Bayesian analyses of all retroactive avoidance data obtained from these two studies, i. e. a combination of the 324 data files from  [1]  (Exp.
4; unfortunately, three original E-Prime files were corrupt and could not be used for the present analysis; the original n was 327) and the 2,004 participants in the replication attempt, which were arranged in the exact temporal order of data collection. We compared this dataset of 2,328 participants to 10,000 simulated datasets of the same size. That is, these simulations consisted of 139,680 random bits each (2,328 participants � 60 trials) aggregated in the same fashion as the experimental data. Subsequently, 10,000 sequential Bayesian ttests with the same parameters as the experimental data (one-tailed; δ ~Cauchy (0, 0. 1)) were conducted based on 2,328 data points each. These simulations represent an experimental nulleffect data set. Alpha error probability was set at the. 05 level for all analyses.
The human sample FFT revealed a more pronounced amplitude pattern than 98. 53% of the simulations. In sum, analyses of the temporal change of the effect across time, expressed by the sequential BF analysis revealed across three different types of methods, show a clear non-random variation of the effect across time. The BF curve variation of human data across time seems to be more pronounced than those obtained from most simulated data sets. This fluctuation in the human data creates an anomaly that fits with von Lucadou et al. 's  [23]  propositions and is in line with Dechamps and Maier's predictions  [37]. The data files including the simulation data and the original data from Maier et al. (Exp. 4)  [1] , the R code and the jsPsych code can be found here: https://osf. io/yqqfz/files/.
In Section 3 we establish the efficiency properties of these estimators, discuss the verification of key assumptions and describe the use of the proposed estimators on parallel processors in light of results from for example  Glynn and Heidelberger (1991). In Section 4 we describe how to couple some important MCMC algorithms and illustrate the effect of dimension on algorithms' performance with a multivariate normal distributions target. Section 5 contains more challenging examples including a multimodal target, a comparison with regeneration methods, sampling problems in large dimensional discrete spaces arising in Bayesian variable selection and Ising models, and an application to modular inference. We discuss our findings in Section 6. Scripts in R (  R Core Team, 2015)  are available from https://github.
Gibbs sampling Gibbs sampling is another popular class of MCMC algorithms, in which components of a Markov chain are updated alternately by sampling from the target conditional distributions (chapter 10 of Robert and Casella (  2004 )), implemented for example in the software packages JAGS  (Plummer, 2003). In Bayesian statistics, these conditional distributions sometimes belong to a standard family such as the normal, gamma or inverse gamma distributions. Otherwise, the conditional updates might require MH steps. We can introduce couplings in each conditional update, using either maximal couplings of the target conditionals, if these are standard distributions, or maximal couplings of the proposal distributions in MH steps targeting the target conditionals. Controlling the probability of meeting at the next step over a set, as required for the application of proposition 4, can be done case by case.
Changing the threshold for social distancing has a nonlinear aspect. This can be evinced more clearly by plotting the weeks lost due to lockdown as a function of the (logarithmic) changes in threshold. Weeks lost was quantified in terms of the number of weeks, over 18 months, during which the probability of being at work was less than 8%. The lower left panel of Figure  13  highlights the switch from lockdown strategies-which preclude a long-term return to work-to those that permit near-normal social distancing in the long term. Interestingly, the empirical (posterior expectation) estimate of social distancing based upon the data is characterised by a threshold that is close to the transition between the two kind of strategies. Again, as one might intuit, these simulations speak to a trade-off between lives and weeks lost due to the pandemic-and our adaptive response (see lower right panel).
In the final report of this series, we will estimate the period of immunity by comparing models over a range of short-term immunity, in terms of their model evidence. At some point over the next month or so, there should be sufficient data to render this model comparison sufficiently definitive to estimate the effective period of immunity one can expect. As with all dynamic causal modelling studies, everything is entirely conditioned upon the models that have been evaluated in terms of their evidence. In this report, the aim of the modelling is not to provide predictions or guidance per se-it is to show that such predictions are possible under a suitably configured model using state-of-the-art variational Bayesian model inversion and reduction. In other words, one can reproduce these analyses under different models or prior assumptions in a few minutes on a personal computer.
Software note The figures in this report can be reproduced using annotated (MATLAB/Octave) code that is available as part of the free and open source academic software SPM (https://www. fil. ion. ucl. ac. uk/spm/), released under the terms of the GNU General Public License version 2 or later. The routines are called by a demonstration script that can be invoked by typing >> DEM_COVID_X at the MATLAB prompt. At the time of writing, these routines are undergoing software validation in our internal source version control system-that will be released in the next public release of SPM (and via GitHub at https://github. com/spm/). In the interim, please see https://www. fil.
Acknowledgements This work was undertaken by members of the  Wellcome Centre for Human Neuroimaging ,  UCL Queen Square Institute of Neurology. The  Wellcome Centre for Human Neuroimaging  is supported by core funding from  Wellcome  [ 203147/Z/16/Z ]. A. R. is funded by the  Australian Research Council  (Refs:  DE170100128  and  DP200100757 ). A. J. B. is supported by a  Wellcome Trust  grant  WT091681MA. CL is supported by an  MRC   Clinician Scientist award  ( MR/R006504/1 ). The authors declare no conflicts of interest.
ion. ucl. ac. uk/spm/COVID-19/. The data used in this technical report are available for academic research purposes from the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE, hosted on GitHub at https://github. com/CSSEGISandData/COVID-19. The time series tables from States in America used in this report are stored in files time_series_COVID19_confirmed_US. csv and time_series_COVID19_deaths_US. csv.
Movement of the mouse was imaged in the infrared to prevent light interference with the non-descanned detection in the visible using a 1 Megapixel NIR security camera (ELP-USB100W05MT-DL36, Amazon. com, USA) at 30 frames per second. Velocity of body movement was estimated using the Farneback algorithm coded in Matlab 67 (Mathworks, was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 23, 2020. ; https://doi. org/10. 1101/2019. 12. 14. 876201 doi: bioRxiv preprint USA). Measurement of body movement with a single camera gives limited information. Five mice were used for the go-no go experiments. Four mice were imaged when they were naïve. The window became opaque for two of the mice preventing MLI imaging for the reversal experiment.
Data analysis Raw imaging data was first surveyed in ImageJ (NIH, USA) to exclude image sequences exhibiting axial movement. We did not find evidence of axial movement while the animal was engaged in the go-no go task. In addition, we performed control imaging where we excited GCaMP6f at 820 nm, a two photon excitation wavelength where fluorescence emission is Ca 2+ -independent  68. Supplementary Fig. 16b, c  shows that we did not detect transient changes in GCaMP6 fluorescence in a mouse engaged in the go-no go task when the cells were excited at 820 nm. If we found horizontal drift due to motion, we applied cross correlation-based image alignment using the Turboreg, Image J plugin. The data were then analyzed with CaImAn Matlab software that uses constrained nonnegative matrix factorization to define independent spatial and temporal components corresponding to changes in GCaMP fluorescence in individual MLIs 31.
Statistical analysis Statistical analysis was performed in Matlab (Mathworks, USA). Statistical significance for changes in measured parameters for factors such as learning and odorant identity (S+ vs. S-) was estimated using a generalized linear model (GLM), with post-hoc tests for all data pairs corrected for multiple comparisons using false discovery rate  33. The post hoc comparisons between pairs of data were performed either with a t-test, or a ranksum test, depending on the result of an Anderson-Darling test of normality. 95% CIs shown in the figures as vertical black lines or shading bounding the lines were estimated by bootstrap analysis of the mean by sampling with replacement 1000 times using the bootci function in MATLAB. Principal component analysis was calculated using the Matlab Statistics Toolbox.
Classification of trials using DF/F measured from all components in the FOV was was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 23, 2020. ; https://doi. org/10. 1101/2019. 12. 14. 876201 doi: bioRxiv preprint accomplished via linear discriminant analysis (LDA) in Matlab. We obtained similar result with perceptron analysis (not shown). DF/F for all components for every trial except one were used to train the LDA, and the missing trial was classified by its fit into the preexisting dataset. This was repeated for all trials and was performed separately for analysis where the identity of the odorants was shuffled. Fluorescence intensity traces, lick rates and kinematics in Fig.
Generalized linear modelling estimate of contribution of different variables to changes in DF/F In order to quantify the contribution of different variables to neural activity, we used GLM as described by Engelhard et al. 37. We used the Matlab fitglm function to fit the per trial DF/F time course for mice proficient in the go-no go task with a GLM. We included "event" variables, "whole trial" variables and "continuous" variables. Continuous variables quantified kinematics including lick rate, the derivative of lick rate and the velocity and acceleration of movements made by base of the tail of the head fixed animal during the trial (body velocity and body acceleration, see Supplementary Fig. 3 ). The identity of the odorant (S+/S-odorant) was an event variable that increased from zero to one during the time for S+ or S-odorant application.
Data availability The data supporting the findings of this study are available in GigaDB (http://gigadb. org/) (we are depositing the data in GigaDB, and this line will be replaced with a link to the data), the model is deposited in ModelDB (https://senselab. med. yale. edu/modeldb/)(we are depositing the data in ModelDB, and this line will be replaced with a link to the data) and the code is available in https://github. com/restrepd/CaImAnDR. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 23, 2020. ; https://doi. org/10. 1101/2019. 12. 14. 876201 doi: bioRxiv preprint 74-199 This calculation was performed in time series that included at least one Miss or FA trial when the animal was proficient (percent correct >=80%).
01, 2040 observations, 2028 degrees of freedom, n=170 ROIs, 1 mouse, GLM F-statistic 234, p<0. 001). *Post-hoc ranksum p<pFDR=0. 048. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 23, 2020. ; https://doi. org/10. 1101/2019. 12. 14. 876201 doi: bioRxiv preprint  f-g. DF/F for four mice averaged over all the ROIs for all trials falling within different proficiency windows. DF/F average was calculated for one second before odorant application (f) and for the last second during odorant application (g). For the data during odorant application (g) a generalized linear model (GLM) analysis indicated that there is a statistical significance for the interaction between percent correct and the identity of the odorant (p<0.
All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 23, 2020. ; https://doi. org/10. 1101/2019. 12. 14. 876201 doi: bioRxiv preprint d. Odorant-induced changes in DF/F recorded from three animals computed for trials when the animal was behaving >=80% correct. Vertical lines are 95% CIs. Red: Hit, Blue: CR. A GLM analysis indicates that there is a significant difference in odor-induced changes in DF/F for both odorant and reversal (p<0. 001, 12 observations, 8 d. f. , n=3 sessions, 3 mice, GLM F-statistic=14. 8, p<0. 01). e. Time course for decoding accuracy computed with LDA analysis of DF/F data for all ROIs in the FOV. The shade is the 95% CI of decoding accuracy calculated with shuffled odorant identity. The vertical black lines are odorant onset and removal and the red lines bound the reinforcement period.
Acknowledgments: We thank  Ms. Nicole Arevalo  for animal husbandry,  Ms. Dnate' Baxter  for laboratory support,  Ms. Arianna Gentile-Polese  for help with the summary figure and  Mr. Jesse Gilmer  with help coding dimensionality. We thank  Mr. Matt I. Becker ,  Mr. Jesse Gilmer  and  Dr. Abigail Person  for discussions. This research was supported by  NIDCD   R01 DC000566 ,  NINDS U01 NS099577 ,  NSF   CBET-1631912 ,  NSF   BIO-1926676  and  ANR-18-CE16-0010-01.
Thus, this research proves a database of Inuit perspectives to inform future collaborations. To gauge local perception of climate change impact on hunting and fishing, we conducted qualitative interviews and participated in a hunting camp along with multigenerational Inuit hunters and fishers in Nunavut. The main objective of the study was to understand how the impacts of climate change are perceived locally in the Arctic, in the communities of Kanngiqtugaapik, Pangniqtuuq, and Qikiqtarjuaq and then to understand (1) the sociocultural context and Inuit observation system; (2) Inuit perceptions of climate change, including weather, impacts on the ice, and invasive/disappearing species; and (3) changes in hunting practices, fishing practices, and eating habits. A final perspective was addressed regarding the needs of the communities to develop more robust, complementary scientific and Inuit sharing of knowledge.
Face-to-face interview process Face-to-face interviews with Inuit hunters and fishers were conducted. In order to recruit participants, several events were organized. The first was a presentation of the project in May 2016 during a public gathering with the elders along with HTO. The second took place in May 2016 in Qikiqtarjuaq and in May 2017 in Pangniqtuuq and Kanngiqtugaapik, where the Mayor or HTO liaison officers made a radio call to invite the hunters and fishers to participate in the project, using a script provided by the research team. Finally, through informal discussions and word of mouth, the project became more public. The research team collected qualitative data through semi-structured interviews from May 7 to 26, 2016, in Qikiqtarjuaq and from May 1 to May 16, 2017, in Kanngiqtugaapik and Pangniqtuuq.
A qualitative analysis of the interviews was conducted twice using the NVivo software (NVivo for Mac-Version 11). Two research evaluators then analyzed the data, which made it possible to verify agreements between the evaluators. A system of thematic coding of the qualitative data was particularly useful in attempting to determine the range of experiences, perceptions, information, and attitudes within the study population. The technique consisted of classifying and categorizing text data segments into a set of nodes, theme, and subtheme categories. The interpretations were based on observed empirical data. We chose thematic analysis because it allows considerable flexibility in interpreting the data and allows to approach large data sets more easily by sorting them into broad themes. To ensure that the theory was based solely on observed evidence, the approach required that researchers suspended any preexisting theoretical expectations before data analysis.
After the analyses, nodes were organized in a hierarchical structure with themes and subthemes. As the written analysis was conducted according to the qualitative analysis, the current structure of the results is very influenced by the thematic and subthematic coding. We used noteworthy quotations from the transcript in order to highlight major themes within findings. This approach incorporated Inuit perspective directly into our study, as we used their words through quotes extracted from the aforementioned conducted interviews not simply as a complement but rather as a central part of the final and written analysis. Visual representations were created using Adobe Premiere Pro and Illustrator software to inform our qualitative data  (Sansoulet et al. , 2019). We first chose to visualize data using word clouds and maps (data not shown), but we found more pertinent to present here a more designoriented infographic (Figure  2 ) that represents the inventory of the different current species in Qikiqtaaluk region, as well as the new and unusual ones.
This inventory was discussed in detail with the participants. Next, infographics and a 13-min documentary called "Inuit Belief" were produced and published on an educational website entitled AOA-Arctic Ocean Arctique, accessible at http://aoa. education/. The 13 videos were translated in Inuktitut: https:// vimeo. com/showcase/6726889. This website is intended for students 11-16 years of age. The platform contains vignettes (web documentaries), each one associated with interactive educational modules (photos, infographics, interactive activities, teaching files). Our partners from Qikiqtarjuaq hamlet were included in the process from beginning to end, and they presented with great enthusiasm this final work locally (especially films and videos). We would like to suggest that, even though this work was conducted with Inuit participants, views of the histories and cultures of Inuit societies from a Western perspective can sometimes lead to a misrepresentation of existing realities: We tried to avoid/minimize this effect by showing and validating our texts and multimedia supports and by having our texts reread to the involved communities.
Acknowledgments This project would not have been possible without the support of the  Hamlet of Qikiqtarjuaq  and the members of the community, as well as the  Inuksuit School and its Principal Jacqueline Arsenault. The project was conducted under the scientific coordination of the  Canada Excellence Research Chair on remote sensing of Canada's new Arctic  frontier and the  CNRS and UniversiteLaval Takuvik Joint International laboratory  ( UMI3376 ). The field campaign was successful, thanks to the contributions of  J. Ferland ,  G. Becu ,  C. Marec ,  J. Lagunas ,  F. Bruyant ,  J. Larivie  `re,  E. Rehm ,  S. Lambert-Girard ,  C. Aubry ,  C. Lalande ,  A. LeBaron ,  C. Marty ,  J. Sansoulet ,  D. Christiansen-Stowe ,  G. Weissenberger ,  A. Wells ,  M. Benoı  ˆt-Gagne,  E. Devred , and  M.
(2015)  to study the intrinsic inter-annual variability. Every DSO model simulation used in the present study (the control run and all sensitivity runs) is initialized with the state of the global run on 1 January of year 72 and is run for a period of 5 years (until year 76). The atmospheric forcing is the same as in the global run and is also repeated every year. The data used at the open boundaries of the DSO domain are extracted from years 72 to 76 of the global simulation (5 d mean outputs), so the open boundary forcing is fully consistent with the atmospheric forcing and the initial state. We have chosen such a simulation scenario because several decades have passed from the initialization of the global run, and the model has reached a dynamical equilibrium and is close to thermodynamical equilibrium, which results in a negligible drift in the mass field.