dataesr/scientific-paragraphs-categorization huggingface dataset:
All task instructions and stimuli were displayed and button responses were collected via the Psychtoolbox extension  84  for Matlab (Mathworks, Natick, MA, USA). The experimental design consisted of two modified SARTs  85  in which participants were instructed to pay attention to a series of pictures of human faces in the Face SART blocks or digits in the Digit SART blocks. The order of Face or Digit blocks was pseudo-randomized for each participant. Each block lasted ~12-15 min. Participants were allowed to rest between blocks. Participants performed three Face SART blocks and three Digit SART blocks for a total duration of 103 ± 19. 7 min (mean ± standard deviation) from beginning to end. Each type of the Face and Digit SART was preceded by a brief training session (27 trials) on each SART.
During this SART training session, feedback on the proportion of correct trials and average RTs was provided to participants. Participants were encouraged to prioritize accuracy over speed. Face and digit stimuli were presented continuously, each stimulus appearing for a duration of 750-1250 ms (random uniform jitter). Face stimuli were extracted from the Radboud Face Database  86  and consisted of eight faces (four females) with a neutral facial expression and one smiling female face. Digits from 1 to 9 were displayed with a fixed font size. For the Face SART, participants were instructed to press a button for all neutral faces (Go trials) but to avoid pressing the response button for the smiling face (NoGo trials). The order of faces was pseudorandomized throughout the entire task (i. e.
The eye tracker was calibrated at the start of each recording using the EyeLink acquisition software. The EEG signal was analysed in Matlab with a combination of the SPM12, EEGlab  88  and Fieldtrip 89 toolboxes. The raw EEG signal was first high-pass filtered >0. 1 Hz using a two-pass fifth-order Butterworth filter. A notch filter was then applied (stopband: [45, 55] Hz, fourth-order Butterworth filter) to remove line noise. Electrodes that were visually identified as noisy throughout the recording were removed and interpolated using neighbouring electrodes. Finally, the continuous EEG data were segmented according to probe onsets on a 64 s window ([-32, 32] s relative to the probe onset); the average voltage over the entire window (64 s) was then removed for each electrode and probe. Pupil size was analysed with custom functions in Matlab and corrected for the occurrence of blinks (see ref.
34  and Supplementary Methods). Pupil size was averaged over the stimulus presentation window for each trial (window length: 0. 75-1. 25 s). Pupil size values in Fig. 2e  were computed by averaging the pupil size in all trials within 20 s preceding the probe onset and then by discretizing them into 5 bins across all probes for each participant and task, to normalize pupil size across participants  34. Raw behavioural, eye-tracking and EEG data are publicly available  90  as well as the codes used to preprocess and analyse these data  91. Behavioural analyses. Go trials were considered incorrect (Miss) if no response was recorded between stimulus onset and the next stimulus onset. Conversely, NoGo trials were considered incorrect (FA) if a response was recorded between stimulus onset and the next stimulus onset.
Hierarchical Bayesian Drift-Diffusion Modelling (HDDM) was used to extend our analysis beyond simple behavioural metrics and examine the impact of mental states (Supplementary Fig. 9 ) and slow waves (Fig. 6 ) on the sub-processes of decision-making. The DDM is a sequentialsampling model of 2AFC decision-making that can be considered an extension of signal detection theory into the time-domain, accounting for full reaction-time distributions as well as choice behaviour  38. The HDDM package 39 in Python 2. 8 was used to fit the drift-diffusion model to the SART data. DDM parameters were estimated using a hierarchical Bayesian method that uses Markov-chain Monte Carlo (MCMC) sampling to generate full posterior distributions of model parameters. The following DDM parameters were estimated: the drift rate for Go trials (v Go ), the drift rate for NoGo trials (v NoGo ), the decision threshold (a), the decision bias (z) and the NDT (t).
Drift bias (v Bias ) was computed by taking the difference between the absolute values of v Go (positive) and v NoGo (negative), where greater values indicate stronger v Go drift bias (Supplementary Fig. 7 ). To examine whether the model could reproduce key patterns in the SART data, posterior predictive checks were undertaken by simulating 100 datasets from the joint posteriors of model parameters and comparing these to the observed data  93  (Supplementary Fig. 8 ). To estimate HDDM parameters 8000 samples from the posterior were generated with MCMC methods and the initial 2000 were discarded as burn-in to reduce autocorrelation. HDDM models were first to fit on the trials preceding subjective reports to examine the influence of mental states on decision parameters (Supplementary Fig. 9 ).
To indicate the magnitude and direction of the effects, we report the estimates (β) and CI for the contrasts of interest (MW vs ON, MB vs. ON, MB vs. MW). All models performed are described in Supplementary Table  1. For topographical maps, clusters were identified using a clusterpermutation approach. In practice, for each electrode, we extracted the t values and p values for the effects of interest. Clusters of neighbouring electrodes were defined as electrodes with p values below 0. 025 (cluster alpha). Once the clusters were defined, a comparison was performed with permuted datasets and we used a Monte Carlo p value threshold of 0. 05 to identify the significant clusters (p cluster , see Supplementary Methods for details). This threshold was corrected for multiple comparisons when several non-independent cluster permutations were performed (e.
Acknowledgements T. A. and N. T. were supported by  Australian Research Council Discovery Projects  ( DP180104128  and  DP180100396 ) and  National Health Medical Research Council Ideas Projects  ( APP1183280 ). T. A. was supported by a  Long-Term Fellowship  from the  Human Frontier Science Programme  ( LT000362/2018-L ). J. W. was supported by  Australian Research Council Discovery Early Career Researcher Awards  ( DE170101254 ). This work was supported by  JSPS   Grant-in-Aid for Transformative Research Areas (B) Grant Number   20B101. We thank  Devangna Tangri  for her help in data collection and  Giulio Bernardi  for sharing his slow-wave detection algorithm.
Barcodes are specific to each bead and prime RNA reverse transcription. d Compositional landscape of 1837 unique networks  48    We next aimed to interpret the diversity of observed responses based on network connectivity. The kinetic model does not provide such a direct interpretation as it consists of a mixture between two regimes, where either only noncovalent or only covalent ribozymes are active. For noncovalent ribozymes only, product fractions should be predicted by in-degree centrality, a network-theoretic measure which accounts only for catalysis by directly upstream catalysts  33. In contrast, for covalent ribozymes only, product fractions should be predicted by eigenvector centrality, which accounts for longer catalytic chains  33. Despite these differences, the in-degree centrality was found to be a good approximation of the eigenvector centrality for our dataset (R = 0.
83, p-value < 10 -5 , Supplementary Fig. 8a ), as is already known in general  39  , except for species strongly catalyzing their own production, whose fraction is underestimated (13% of the dataset, Supplementary Fig. 8b ). The in-degree centrality approximation allowed an analytical derivation of the total perturbation (Supplementary Note): p G!G 0 ¼ 2n 1 À n m σ G =e σ G =e þ n ð1Þ The predicted perturbation depends on four parameters, which characterize the initial network and the added catalytic species (Fig. 3a ): (i) n, which we call perturbation breadth, is the number of catalytic species whose formation is catalyzed by the new catalyst a added to the network; (ii) m, which we call catalytic novelty, is the number of species already present in G that catalyze the same reactions as the newly added catalytic species a (i.
Methods Materials. All experiments used DNAse/RNase free water (Thermo Fisher Scientific Product No. : 10977035). Chemicals were purchased from Sigma-Aldrich unless specified otherwise. 4-(2-Hydroxyethyl)-1-piperazinepropanesulfonic acid (EPPS) was purchased from Alfa Aesar (Product no. : J60511, CAS no. : 16052-06-5). Denaturing polyacrylamide gels (12%) were prepared using gel stock solution (Roth, Product no. : A431. 1) and run in 1X Tris-Borate EDTA buffer (TBE). DNA/RNA oligonucleotides (Supplementary Data 1) were obtained from IDT DNA technologies. Software packages. Sequencing data processing and analysis was performed using custom software written in Python 2. 7. 16. The compositional landscape (Fig. 1d ) was prepared used 'Circos' software  48  (Version 0. 69-6).
:98-0212-2928-5) and a fluorosurfactant (RAN Biotechnologies, Product No. : 008-FluoroSurfactant). Devices were designed with AutoCAD software (Supplementary Data 3), printed at Selba SA. PDMS (polydimethylsiloxane) devices were fabricated by soft-lithography as described in ref. 49. For electrocoalescence, indium tin oxide coated glass slides were used (Delta Technologies, Product No. : CG-90IN-S215), with channels containing 3 M NaCl liquid electrodes  50. After plasma bonding, channels were made hydrophobic by immediately flushing them with 2% 1H,1H,2H,2H-perfluorodecyltrichlorosilane (ABCR, Product No. : AB111155) in HFE 7500 with the help of syringe. Dropletbased microfluidics experiments were performed using an inverted microscope (Nikon Eclipse Ti) as described earlier  51. Flows were controlled by either syringe pumps (Harvard Apparatus Inc.
All 24 emulsions were collected together in a 1. 5 mL collection tube (with a PDMS cap) containing oil with 2% surfactant and mixed thoroughly. A 1. 6 µM solution of Z RNA fragment in 1× Azoarcus reaction buffer (30 mM EPPS pH 7. 4, 20 mM MgCl 2 ), was folded in the same manner as WXY RNA, and encapsulated in 50 pL droplets using a 50 pL drop-making device (Supplementary Data 3), at flow rates of 350 µL/h for the aqueous (RNA) and 250 µL/h for the oil (HFE 7500 with 2% surfactant) phases, droplets being collected in a 0. 5 mL collection tube (with a PDMS cap). All flows were driven by syringe pumps. (B) Electrocoalescence of initial emulsions: The 5 pL droplets (containing WXY fragments) and 50 pL droplets (containing Z fragment and reaction buffer) were reinjected into separate channels of a droplet fusion device (Supplementary Data 3).
Sequencing data processing. Custom software was written to process sequencing data and further data analysis steps and is available at https://doi. org/10. 5281/ zenodo. 4016905. The libraries were sequenced in paired-end mode with read 1 and read 2 of 180 bp and 120 bp, respectively. Read 1 was used to determine the sample barcode (used for multiplexing samples for sequencing) and the RNA molecule identity (either an Azoarcus ribozyme with specific IGS-tag pair or a hairpin RNA reporter). Read 2 was used to determine the UMIs  56  and the droplet barcode. The structure of read 1 and 2 is summarized in Supplementary Fig. 3b. UMI normalization: Four meta fields were associated for each pair of reads: sample barcode, droplet barcode, UMI and RNA molecule identity (see below 'Droplet barcode, UMI, IGS, tag, and hairpin RNA reporter identification from sequencing data').
Reads missing any of these were discarded (~30%). The reads with the same meta information were merged and the read count was added as an extra meta field. This allows to filter out noise due to sequencing errors, given that read counts resulting from such errors are significantly lower (threshold indicated on Supplementary Fig. 3e ). The filtering thresholds for the minimum number of reads per UMI were determined separately for the Azoarcus ribozymes and hairpin RNA reporters based on the shape of the distribution of number of reads per UMI (Supplementary Fig. 3e-f ). The resulting dataset is available at https://doi. org/10. 6084/m9. figshare. c. 5103962. Final data processing: Filtered UMI-normalized reads were then clustered by droplet barcode to count the number of UMIs per type of RNA molecule per droplet barcode (Supplementary Fig.
1 bin of Fig. 1d  (Supplementary Fig. 3j ). Imposing this probability to be >95% requires of threshold of 28 ribozyme UMIs per droplet. Measurements, which do not fulfill the latter criterion are indicated in Supplementary Data 2. Processed network compositions and mean species fractions (species fraction is the proportion of a species [WXYZ catalyst], relative to other species [WXYZ catalysts] in the network) are publicly available for all unique networks (Supplementary Data 2). To determine network yield per droplet, first we computed a droplet-specific UMI to concentration conversion rate by considering total hairpin reporter UMIs as well as number of different hairpin reporters identified. This was then used to convert total number of ribozyme UMIs into a yield value. Controls for droplet-level sequencing biases.
Acknowledgements We thank  Eörs Szathmáry  for critical feedback. We also thank  Matthew Deyell  for the help in designing droplet barcodes indexes, and  Estelle Mendes  for her technical assistance in the realization of the experiments. This work was supported by the  European Union   Seventh Framework Program  ( FP7/2007-2013 , grant agreement  294332  EvoEvo), the  OCAV  project from  PSL Research University , the  Défi Origines CNRS program ,  Institut Pierre-Gilles de Gennes (laboratoire d'excellence, Investissements d' avenir  ANR-10-IDEX-0001-02   PSL ,  ANR-10-LABX-31 ,  ANR-10-EQPX-34 ),  Human Frontier Science Program  (Grant  RGY0077/2019 ) and the  Indo-French Centre for the Promotion of Advanced Research (IFCPAR/CEFIPRA ). S. Arsène. acknowledges  Ecole Polytechnique  for his PhD fellowship (AMX) and the  Ecole Doctorale FdV  ( Programme Bettencour ).
Simulating dynamics For each model, we simulated 1000 independent iterations of 100 time-units each, and, after allowing a 20 time-unit "burn-in" period, measured resilience, resistance, and invariance following the empirical methods described below. Simulations were conducted in R version 3. 6. 1 (R Development Core Team, 2019). Source code is archived on Zenodo (https://doi. org/10. 5281/zenodo. 4626668). For the single species, single patch model, we varied temporal scale by "sampling" the simulated timeseries at frequencies ranging from every 0. 01 to every 10 timeunits, with growth rate r = 1, standard deviation of disturbance effects σ = √0. 1, and average frequency of disturbances λ = 1. Note that because both this model and the multi-species model in Equation 5 can be expressed fully in terms of standardised abundances x or x i , dynamics can be simulated without directly including effects of carrying capacity K.
, 2018) , average temporal variance of x in the single species, single patch model in Equation 3 is Thus, variance decreases, and invariance increases, with higher resistance (lower σ), higher resilience (larger r), and slower disturbance frequency (lower λ). Building on this formula, we can derive an estimate for how the distance of x from equilibrium changes over time, yielding where t represents the time of an observation, t + τ is the time of the observation made immediately after t, p(t + τ) is the number of disturbances that occurred between t and t + τ, and 1/Λ is the average waiting time between disturbances. For our simulations, we applied this function to observations of individual species and patches to obtain unbiased estimates of r and σ. Because this expression is non-separable, we estimated parameters using a nonlinear optimiser (either nls in the stats package, or gnls in the nlme package in R;  Pinheiro & Bates, 2000).
Jointly, these methods allow empirical estimates of resilience, resistance, and invariance measured at one set of scales to be extrapolated out to other, unobserved scales. These results are therefore important both because they highlight the fact that stability measurements are scale dependent, and because they demonstrate how to correct for this scale-dependence when comparing and interpreting empirical results. The general implications and limitations of these methods are discussed below, and a guide for practitioners showing how to apply our methods using the ecostatscale R package is presented in Box 1.
All results are for a system with 30 interacting species, but measured at different ecological scales. (a) Resilience varies with ecological scale in this model because of compensatory dynamics among species. (b) Resistance depends only on instantaneous responses to the disturbance regime, and therefore varies with ecological scale, but not with species interactions (n. b. dashed and dotted lines overlap perfectly). (c) Invariance varies quadratically with ecological scale as a function of average species-level variance in abundance, and between-species correlation-negative correlation implies a concavedown relationship (as shown here), whereas positive correlation implies a concave-up relationship (see Figure  S2  in the appendix for other examples from empirical datasets). Note that the vertical axis is log-transformed for easier visualisation.
For example, if stability varies among plots in different regions of a site (e. g. northwest vs. southeast corner of a field), or among different species groups (e. g. legumes vs. grasses), then these deviations indicate spatial or ecological heterogeneity, respectively. Similarly, if stability varies between estimates derived from the full dataset vs. for example, a subset of the dataset including every second sampling event, then this indicates that temporal sampling was potentially too infrequent to produce unbiased estimates of resilience and resistance, and a correction such as that in Equation  7 , or the accompanying xt2fun R function, should be applied. See function help documentation for example implementations. smaller scale match those at the larger scale). Thus, the scaling definitions should be applicable for most studies that randomly sample species from a larger community, or plots from a larger landscape-which includes a substantial fraction of existing ecological timeseries datasets.
Funding information TULIP  French Laboratory of Excellence , Grant/Award Number:  ANR-10-LABX-41  and  ANR-11-IDEX-0002-02 ;  Swedish Research Council , Grant/Award Number:  VR 2017-05245 ;  Deutsche Forschungsgemeinschaft , Grant/Award Number: FZT 118;  NSF, Grant/Award Number :  EPS-1655726 ;  Horizon 2020 Framework Programme , Grant/Award Number: 666971.
DATA AVA I L A BI L I T Y STAT E M E N T All data used in this study are already available through published, publicly available databases, cited by DOI in this manuscript. Full source code and data used in analyses are available via Zenodo (https://doi. org/10. 5281/ zenodo. 4626668), and the ecostatscale R package is available via CRAN, GitHub (github. com/adamt clark/ ecost atscale), and Zenodo (https://doi. org/10. 5281/ zenodo. 4626672).
Results and discussion Mobility of susceptible and infected populations. Using our framework, we estimate that in Bangkok, susceptible individuals spend 76% (95% CI, 57-95%) of their time within their home cell as compared to 42% for adults (95% CI, 41-43%) (Fig. 2A  and Supplementary Figs. 7 8 9 ). As dengue susceptibility is concentrated in children (Supplementary Fig. 10 ), our findings of reduced mobility in susceptible individuals suggest that children are less likely to travel far from their home than adults. To explore the consistency of this finding with observed differences in mobility by age, we use data from a separate study from Thailand that asked individuals (N = 2011) of all ages about their daily travel. Consistent with our findings of reduced mobility in susceptible individuals, we find that there is a strong relationship between age and reporting having stayed within 1 km of their home in the prior week (Supplementary Fig.
Data sources Sequence data and associated metadata. We use all available full genome sequence data from all four serotypes from Thailand covering the period 1994 and 2012 where province-level spatial information is available. All sequences are available from GenBank. The accession numbers are set out in Supplementary Data 1. For sequences from Bangkok, we also have household coordinates for a subset of 432 sequences. In Bangkok, the sequences come from Queen Sirikit National Institute of Child Health, a large children's tertiary care hospital based in the centre of the city. Outside Bangkok, most sequences come from the national surveillance system of dengue run by the Ministry of Public Health. They perform confirmatory testing and viral isolation and sequencing of samples from sentinel hospitals based around the country.
The hospitals are based in the following five provinces: Lampang, Ratchaburi, Songkhla, Pathum Thani, Nakhon Ratchasima. In addition, there are sequences available on GenBank from Kamphaeng Phet province. Case data. All cases of dengue are notifiable and are reported to the Thai Ministry of Public Health. We extract the number of cases per year for each year and each of the 76 provinces in the country between 1994 and 2012. To estimate serotypespecific case data in each year, we used the serotype distributions of geocoded cases from QSNICH for Bangkok (N = 11,583 cases). For the rest of the country, we used serotype-specific case data from the Ministry of Public Health. Outside the five sentinel surveillance sites, these data mainly come from ad hoc samples sent to the ministry for testing.
Altogether, this represents a serotype-specific database of 27,586 cases covering 67 of the 76 provinces. For each province and year, we calculated the proportion of cases that were caused by each serotype. Where there were no samples from that province-year, we used data from the closest province in that same time period where data were available. Population data. In Bangkok, we initially placed a 1 × 1-km grid cell over the central part of the city (337 grid cells) and estimated the population size using population-size estimates from LandScan for 2010  17. We also identified the grid cell for each of the Bangkok sequences. At the province level, we used population-size estimates from the 2010 national census. We note that the Thai population has been relatively stable over the study period (rising from 60 million to 64 million between 1995 and 2012).
Call detail records (CDR). To estimate adult mobility across provinces and within Bangkok, we used the call detail records (CDR) of over 11 million mobile phone subscribers between August 1, 2017 and October 19, 2017 from the third-largest mobile phone operator in Thailand (N = 11. 4 million subscribers, 26% market share). These data are described in more detail elsewhere  18. Briefly, each subscriber was assigned a daily home location based on their most frequently used cellular tower. Travel between locations was estimated by tabulating the subscriber's home location on 1 day relative to the day before. The location-to-location transition probability matrix was estimated by using the average travel from location i to location j (weighted by the population at location i), and normalising travel such that the sum of travel from location i is equal to 1.