dataesr/scientific-paragraphs-categorization huggingface dataset:
The results confirmed that the distribution of power versus frequency components of the recorded signal in anaesthetised or awake animals were accurately reproduced by our model since no difference could be detected between real and simulated signals (Fig. 6b ). Interestingly, we could illustrate the utility of our parameterisable benchmark generator by looking at the effect at different contamination levels on electrophysiological signals. We explored the effects of application of different perturbation levels of artefacts using our annotated datasets for the two scenarios, in anaesthetised and awake subjects. Our model predicted that the amount of artefact contamination would differentially affect the extracellular signals from anaesthetised or awake conditions. The evidence shows that, for the same level of signal contamination; the power-spectrum distribution was altered more in the anaesthetised than in the awake condition (Fig.
Discussion We are currently witnessing an exponential increase of neural data collection paradigms with massive simultaneous recordings brought forward by the progress of microfabrication techniques and integrated sensors. The collection and use of such large amounts of neural information has stimulated the development of a number of hardware and software tools. Examples are signal acquisition devices, signal processing algorithms, or software for the calibration of brain-computer interfaces. To date, despite the necessity of benchmark datasets to test these kind of applications, there are surprisingly few ground-truth datasets available, and most of these are not parameterisable. Thus, there is an urgent need of such benchmarks to assess the validity of recently developed toolboxes and algorithms aiming to analyse neural data.
Parameters Awake Anaesthetised Firing rate [Hz] 51,61,62 0. 5-12 0. 5-5 Percentage of active neurons  49  10 4 Population [neurons/mm 3 ] 46,47 300 000 300 000 % of artefact contamination 1 0. 1 Table  2. Simulation parameters selected for both awake and anaesthetised experiments. A random distribution of point sources was set for both simulations. The refractory period was 2 ms, sampling rate was 20 kHz and the simulation duration was 10 s. illustrate the flexibility of our methodology, we simulated two distinct experimental conditions; extracellular signals extracted from anaesthetised or awake rodents. We challenged our generated benchmark dataset by comparing the simulated signal with real experimental recordings. Our results showed that the synthetic signals generated bore a close resemblance in terms of frequency properties and spike proportions to the recorded ones, and this held for our two different conditions.
In this configuration, a series of devices and software analysis modules interact to form the closed-loop chain. Between the key elements of the chain, online sorting algorithms and on-chip real-time modules (e. g. Field Programmable Gate Arrays (FPGAs) and Complex Programmable logic devices (CPLDs)) are key elements for online analysis. To correctly evaluate and compare the performance of these systems, the use of reliable benchmark datasets, such as the ones presented here, are essential. Ideally, this should be done by generating the datasets via the simulator and streaming them directly to the acquisition systems. The datasets generated could be useful to evaluate the performance of various tools such as denoising and pattern recognition modules or spike sorting algorithms, implemented either in hardware or software.
Such disturbances can proceed from the subject itself (e. g. physiological background noise produced by the subject's activity, additive and variable sources of current from other cells that are capture by the electrodes), the recording site (e. g. dimension, neural density, whether it be a preparation or not), the electronic instrumentation and the electrodes that couple to the tissue (e. g. thermal noise, shot noise, dielectric noise), external sources (e. g. electromagnetic and electrostatic coupling between the circuitry and external devices), and from the digital conversion itself (e. g. aliasing). Although there are metrics to measure their average contribution, it is still a major challenge to replicate every source of noise. We present here a library of common artefacts found during recordings that can be used to complete the benchmark datasets.
In our design, the dataset used to extract the non-stationary slow oscillation component could be modified by the experimenter according to the nature of the signal intended to be simulated as well as the filter cut-off frequency. For our experiments, we used the real recording datasets previously reported  14, 26  , low-pass filtered with a cut-off frequency of 300 Hz. Spike contamination in this frequency band was verified according to  37  to have minimal effects on the extracted LFP. The resulting non-stationary extracted components were linearly added into the simulated signals. Artefact library. To extract the artefacts and create the library, we analysed neural data recorded from several different experiments. To detect artefacts, the signal had to cross the pre-defined amplitude threshold on at least 80% of the channels simultaneously (within an artefact window of 300 μ s).
The head collision artefacts were recorded from mice during a behavioural task  42. The original data consisted of seven single tetrode files recorded with a Cheetah160 Acquisition System with a total of 28 valid channels and a total recording duration of 3635. 6 s. Each tetrode file is the result of a previous preprocessing analysis of the raw data, band pass filtered between 600-6000 Hz and a preset voltage threshold described in Supplementary Table  S1. Individual waveforms were extracted and saved with their corresponding timestamps (Supplementary Table  S1  shows the total number of detected waveforms for each tetrode file). Each waveform is 1142 μ s in length with a pre-threshold period of 285 μ s. The data was sampled at 28 kHz and stored at 32 points per waveform with their corresponding timestamp values and 16 bit A/D resolution.
The different grooming phases described in ref. 54 in the syntactic behavioural chain were annotated together with the artefacts (See Supplementary Table  S4 ) in the library. The chewing artefacts were extracted from electrophysiological recordings in rat. In this case, the animal was moving freely in a square arena chasing solid food rewards. We explored the recordings using NeuroScope software  55  to visually identify abnormal augmented activity that stood out significantly from the background noise. We explored the data in the time-frequency domain and calculated the chewing cycle duration (1/mean chewing rate) and duration of the chewing sequence. We compared time-frequency analyses with the high-pass filtered data (300 Hz cut-off frequency) (See Supplementary Fig. S2 ). Real databases.
The reference neural databases for real signals were recorded from separate groups of awake and anaesthetised animals  14, 26, 34, 35, 38. Case 1: Anaesthetised subjects. Real data consists of extracellular recordings in the hippocampus of anaesthetised rat  14, 34, 35  with experimental procedures fully described previously  27, 35  and have been used by various laboratories as a benchmark for spike sorting algorithms. Animals (Sprague-Dawley rats) were anaesthetised with urethane (1. 5 g/kg; Sigma). Extracellular electrodes were lowered into the CA1 layer of the hippocampus by monitoring for the presence of single unit activity. Case 2: Awake subjects. The datasets include multichannel extracellular recordings from layer CA1 of the right dorsal hippocampus of Long-Evans rats during an open field task.
In the task, the animal was placed on an elevated square platform and was looking actively for water rewards. Full details of the surgical and experimental procedures in awake recording were previously reported  26, 45  and are only briefly described here. Multi-layer simulation. The overall firing rate distributions of the SO, SP and SR layers were described in a previous study  45  and reproduced here (see Supplementary Fig. S5 ), using the Matlab Distribution fitting tool  33  with a logistic distribution and the following mean and scale parameters: Statistical analysis. We computed the Bartlett's power spectrum density estimation (PSD) method  50  of simulated and real signals to reduce the variance introduced by the periodogram while maintaining the frequency resolution  56. The original benchmark datasets of 10 s duration were split into 10 non-overlapping 1 s length data segments.
Software access. Matlab code for generation and use of datasets described here, as well as the artefact library are available at http://bebgteam. net/resources. As previously described, the design is fully modifiable to simulate any specific experimental scenario that the experimenter wants to reproduce (e. g. , amount of signal contaminated with artefacts, number of electrodes, distance between electrodes, etc. ). To facilitate changes of the model, an XLM file and a Matlab configuration file are available where all the parameters can be rapidly modified.
Data for dimensions and contours of rat hippocampal regions were measured from Swanson's rat brain atlas to determine the dimensions of our volume, particularly between the atlas levels 30-= 35. A parasagittal view in the Figure shows atlas level 31 59  (AP = -3. 70 mm relative to Bregma, ML 2-3 mm, DV 2-3 mm). (c) Signal samples from one extracellular virtual tetrode. (d) Polytrode simulation datasets over one second are shown for each virtual polytrode. The activity across channels reflects the signal location of the virtual recording sites.
Acknowledgements We thank  Luis Alejandro Camuñas-Mesa  and  Rodrigo Quian Quiroga  for sharing their simulator for detailed neuron models on the dedicated website http://www2. le. ac. uk/centres/csn/software/neurocube. We would like to thank  Mizuseki K. ,  Sirota A. ,  Pastalkova E. György Buzsáki ,  Darrel A. Henze ,  Hajime Hirase ,  Zsolt Borhegyi ,  Jozsef Csicsvari ,  Akira Mamimya  and  Kenneth D. Harris  for sharing their hc-1 and hc-2 datasets through the  Collaborative Research in Computational Neuroscience (CRCNS ) data sharing platform. We would like to extend our thanks to  Brian Lau  for scientific advice and to  Christiane Schreiweis  whose comments greatly improved this manuscript. The research leading to these results received funding from the programmes " Investissements d'avenir "  ANR-10-IAIHU-06 , from the  CONACYT  graduate programme fellowship and from the  CARNOT institute.
) Figure 2. Configuration parameters of the benchmark dataset generator. For each computing module in the benchmark dataset generator, the user has rapid access to allow its modification through a unique file descriptor (. xml file) to easily create different datasets. The parameters are listed inside the text boxes next to each computing module. • Artifact rate per artifact type • Amplitude distribution • Insertion mode (probability function a(n) described by amplitude distribution) Simulation environment • Edge • Duration • Sampling rate Layers • Depth • Density • Firing rates x(n) Σ s(n • Refractory period • Proportion of firing neurons Electrodes • Number • Coordinates • Diameter • Margin • Window size • Low-pass filter cutoff frequency w(n) • Number of electrodes.
The specific features of the systemic and mechanistic models related to the four case studies are detailed in the four paragraphs below. They are also listed and compared in Table  2. More mathematical details on the models are also provided in Appendices A. 3, A. 4, A. 5 and A. 6. The parameterization of the four different models has also been achieved following two distinct approaches. For the case studies of demersal mixed fishery of the Bay of Biscay and the Northern prawn fishery in Australia, calibrations were derived from available stock assessments and economic data. In Solomon Islands and French Guiana case studies for which no assessments were available, specific stock and bio-economic models were developed and fitted to the available data. data is provided at the bottom of Table  2.
Data from the literature  (Leopold, 2004)  and Fishbase (http://www. fishbase. ca/) were used to provide qualitative trophic information concerning the sign of the relationship between species and intrinsic growth rates, and to initiate parameter estimations. Demersal mixed fishery of Bay of Biscay: As detailed in  Doyen et al. (2012)  and  Gourguet et al. (2013) , population dynamics of the three species included in the analysis (hake, nephrops and sole) were modeled using an age-structured population model. Parameters were derived from stock assessments carried out by ICES (2009) using a virtual population model  (Darby & Flatman, 1994; Shepherd, 1999). The dynamic model was then fitted for each species separately, using data on catch and abundance from surveys or derived from commercial CPUEs.
) underlying the uncertainties. In other words, we consider the following K replicates ω k (. ) over time t 0 ,. , T            ω 1 (t 0 ),. , ω 1 (T ). ω K (t 0 ),. , ω K (T ), and we approximate the expected value by the mean over the K replicates as follows E ω (NPV(e, ω)) ≈ 1 K K k=1 NPV(e, ω k ) Using the scientific software SCILAB available online http://www. scilab. org/en, the replicates are obtained from the function entitled grand. Regarding the way to compute the optimal control e, we have to distinguish between the case studies. For Bay of Biscay and NPF case studies, the control is kept fixed during the whole period t 0 ,. T. But for French Guiana and Solomon Islands, the control can change and adapt to the uncertainty at several periods using optimal feedback controls and non anticipative strate-gies.
The indicator SR is computed as follows: h data i,k (t) -h i,k (t) 2. (19) SR(t) = i 1 i (x i (t)), (20) with the boolean function 1 i (x) =      1 if x ≥ B lim,i 0 otherwise. The marine trophic index MTI(t) of an ecosystem is computed as follows MTI(t) = 1 N (t) 13 i=1 T i N i (t) with abundances N i (t) = x i (t) υ i (21) where υ i is a fixed average weight by species. The total catches H(t) within the fishery plays the role of food supply: H(t) = k i h i,k (t). ( 22 ) The profit Profit k (t) of each fleet k is derived from the landings of each species h i,k , the landing prices p i,k , fixed costs c f k , variable costs c v k and the crew share earnings c L k as follows: Profit k (t) = (1 -c L k ) i p i,k h i,k (t) -c v k e k (t) -c f k. (23) Prices, variable costs and fixed costs are those collected for year 2010.
, T. , for every k = 1,. , 4. The gross income from catches of each sub-fleet denoted by Inc k (t) is then estimated by introducing the market price of the species along with the estimates of discard rates, such that: Inc k (t) = i A i a=1 p i,a (t)υ i,a,k h i,a,k (t)(1 -d i,a,k ). ( 33 p i (t) N (µ P i , σ P i ). ( 34 ) Gaussian laws are calibrated from ex-vessel prices for the three species for the 2000-2009 period, recorded in French harbours (data from Ifremer, SIH, DPMA). Prices by species p i (t) are assumed to be independent by species and by year. The profit Profit k of a sub-fleet k is estimated as follows: Profit k (t) = Inc k (t)+α k K k (t)e k (t 0 ) (1-τ k )-V f uel k p f uel (t)e k (t 0 )+c v k e k (t 0 )+c f k K k (t). ( 35 ) Here the parameter α k corresponds to the income per unit of effort of sub-fleet k derived from catches of species not explicitly modelled.
Acknowledgment This work has been initially carried out with the financial support of the  ANR (French National Research Agency)  through the  ADHOC program. The support from the  Belmont Forum  through the funding of the network SEAVIEW, the  IHP (Institut Henri Poincaré)  through the organization of the trimester 'mathematics of bio-economics' in the framework of Mathematics of Planet Earth 2013 Initiative as well as the research projects  VOGUE  and  ECOPE (PIG CNRS ) are also gratefully acknowledged. We compute the maximal viability probability as well as optimal controls associated with viability scenario using again the optimizing function in scilab entitled optim -ga.
Statistical Analysis Data were collected in Excel (Microsoft Corp. , Redmond, WA), and statistical analysis was conducted in R 3. 2. 3 (R Core  Team, 2015). Bayesian latent class analyses were conducted using JAGS version 4. 0. 0 (http://mcmc-jags. sourceforge. net/) and the package coda  (R Core Team, 2015). The R code for the models is available in Supplemental File S1 (https:// doi. org/10. 3168/jds. The SCC values were log 10 -transformed before analysis. We checked data for missing values and normality, and then computed medians and means. The BC results were classified into 3 groups to further analyze the effect of pathogen group on MAA and SCC: (1) cultures with at least 100 cfu/ mL of a minor pathogen (CNS, C. bovis, Bacillus spp. , or Streptomyces spp. ); (2) cultures with at least 1 colony (100 cfu/mL) of a major pathogen (E.
Additional data include a high-resolution digital elevation model (DEM) that can be used together with the snow physical information for snowpack modeling and a panchromatic image. This paper presents the data set produced so far, explains instrumentation, calibration, processing and data quality control, as well as the sources for various resulting data sets. The resulting data set is unique in the Arctic and serves as a baseline for future studies. The mean permafrost temperature is -2. 8 • C, with a zero-amplitude depth at 5. 5 m (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Since the data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as snow depth and soil moisture content, they are suitable for use in integrating, calibrating and testing permafrost as a component in earth system models.
Data description The Bayelva site was powered by a solar panel and wind generator during the period from 1998 to 2013 and data were retrieved manually through site visits every 4 to 6 weeks, when visual inspections were made of the sensors. Since 2013 the site has been connected to the main electricity supply and the Ny-Ålesund data network, and data have since then been transferred automatically resulting in much improved data collection with almost no data gaps. The data gaps prior to 2013 resulted mainly from problems with the energy supply for the site, such as problems with the solar and wind charge regulator. The current online data transfer and real time data visualization enables real time data checking and identification of possible sensor failure, as well as remote programming. Major problems that have affected the continuous data collection or the data quality include reindeer disturbance (e.
Rainfall An unheated and unshielded tipping bucket rain gauge (R M Young, 52203) was installed on a pole in August 1999, with the top of the bucket about 1. 6 m above ground level. The instrument measures only liquid precipitation (rainfall) and not winter snowfall. The tipping bucket was regularly checked in summer by pouring a known volume of water into the bucket and by frequent visual inspections for dirt or snow during each site visit. At wind speeds above 12. 5 m s -1 (hourly or half-hourly average) the rain gauge was found to record precipitation even when there was none, due to vigorous shaking of the pole; these data were flagged accordingly within the data series. Until 2010, precipitation was only recorded as hourly totals, but the provided data set uses 30 min temporal resolution (half-hours are presented as "NaN", since the hourly total is given at full hour).
Snow depth The snow depth around the station has been continuously monitored since 1998 with an SR50 sonic ranging sensor (Campbell Scientific Ltd. ) and with an additional SR50 sensor at a nearby (about 85 m away; Fig. 2 ) eddy covariance station since 2006. The sensor recorded raw distance data from the sensor to the object (in this case the ground or snow surface). The data obtained from the sonic sensor were corrected using the speed of sound at 0 • C and the air temperature measured at the Bayelva site, according to the formula provided by the manufacturer (Campbell Scientific Ltd. ; Appendix D, Sect. D4). To obtain the snow depth the distance of the sensor from the ground surface was recorded annually and subtracted from the corrected distance data. Snow depths are provided in the data supplement for this paper.
Due to seasonal thawing of the ground, the surface can subside over the season  (Overduin and Kane, 2006) ; thus, negative distance rates of a few centimeters are computed (and not set to zero). The data are not removed from the series since the opposite process, frost heave, takes place during fall freeze-back and thus the surface presumably bounces back to the original height (prior to ground thaw and subsidence). Since the ground surface has little or no vegetation cover the signal is returned with a high degree of accuracy (Table  2 ). An additional snow depth laser distance sensor (Jenoptik SHM30) was installed at the Bayelva site climate tower in August 2013. The data from the three different snow depth sensors were combined to obtain a continuous record and fill any gaps left by the failure of individual instruments (level 2 data containing all original snow data from the three sites merged into one data product can be found in Sect.
Dielectric number of snow The dielectric number (also referred to as relative permittivity) of the snow was measured using a vertically installed time-domain reflectometry (TDR) probe. The length of the TDR probe was 0. 5 m between 1998 and 2007 and 0. 33 m from 2009 onwards. Dielectric numbers are provided in the data set for both the snow-covered and the snow-free periods (recording only air dielectric numbers). When the snow cover depth exceeds the length of the TDR probe the dielectric number data can be used to infer dry snow densities using empirical relationships  (Schneebeli et al. , 1998). In wet snow, the measured snow dielectric number can be used for the calculation of the snow liquid water content using the composite approach with ice added as an additional phase (e. g. ,  Roth et al.
A comprehensive description of the initial results is given by  Hauber et al. (2011a, b). Data processing from the raw images to the final data products, including digital photogrammetric processing, was performed with the VICAR (Video Image Communication and Retrieval; http://www. mipl. jpl. nasa. gov/external/vicar. html) software developed at the JPL (Jet Propulsion Laboratory, Pasadena, USA) and the DLR. We provide HRSC-AX data in the form of a digital elevation model and as individual channels (panchromatic nadir channel; red, green, and blue color channels; and CIR, color-infrared, false-color channels). The CIR channels are computed by merging a false-color image (an RGB image where R, G and B correspond to the original infrared, red and green channels, respectively) with the nadir channel.
The sum of these scores was expressed as total microscopic score as previously reported  [27]. The degree of intestinal inflammation was scored as absent, mild, moderate or severe, according to the density and extent both of the acute and chronic inflammatory infiltrate, loss of goblet cells, and bowel wall thickening. Intestinal fibrosis was scored as mild, moderate or severe, depending on the density and extent of trichrome-positive connective tissue staining and disruption of tissue architecture, as previously described  [27]. Quantitative comparison of Masson's stainings were measured by the ImageJ digital image analysis public domain software (W. S. , Rasband, Image J, U. S. National Institutes of Health, Bethesda, MD; http://rsb. info. nih. gov/ij/, 1997-2011). The staining was expressed as a percentage of the total software-classified areas and the data obtained were plotted as histograms.