dataesr/scientific-paragraphs-categorization huggingface dataset:
Any channels that did not contain neuronal data, i. e. channels 133 or higher on NSP 1 and channel 129 or higher on NSPs 2 to 8, were also removed. The temporally aligned data were saved in the. nev and. ns6 formats. The unaligned raw data files are not provided in the data repository due to their large volume, but are available upon reasonable request. The lightweight events files (. nev) are provided in both their aligned and non-aligned form. Eye signal processing. In many human studies on resting state activity, subjects are given blindfolds and asked to keep their eyes closed. For the resting state sessions in our subjects, we recorded the pupil size and included it in the dataset, instead of using blindfolds. The eye position and pupil diameter (channels 129 to 132 on NSP #1) were temporally aligned, labelled, and saved in.
Signal-to-noise ratio (SNR). To quantify the signal quality of the recorded neuronal activity, the signal-to-noise ratio (SNR) for each channel was calculated based on the amount of visually evoked activity that was elicited upon presentation of a full-screen checkerboard stimulus, relative to baseline activity, across a minimum of 30 trials. We calculated the mean and standard deviation (SD) of the MUAe during the 300-ms time window prior to stimulus onset (Mean spontaneous and SD spontaneous of the baseline activity) for each trial. Next, trial-averaged MUAe data were smoothed with a moving average of 20 bins (i. e. at a sampling rate of 1 kHz, each bin comprised 20 ms), and we identified the peak level of activity elicited during stimulus presentation (Peak stimulus_evoked ). The SNR was then calculated following Eq.
Neuronal response latency. In addition to the SNR we estimate the neuronal response latency from the checkerboard stimulus task (i. e. the SNR task). We define the response latency as the time elapsed between stimulus onset and the first time that the trial-averaged MUAe signal is more than 2 times the SD spontaneous in 5 consecutive bins. The spontaneous activity period is defined as 300 ms prior to stimulus presentation, as in the SNR calculation. We require the activity to be above the threshold in several consecutive bins to ensure robustness against rapid fluctuations. Figure  7b, d, f  shows the response latency for a sample session in each monkey. Our measurements are in agreement with previous reports of latency in the visual system  1. The SNR and latency are calculated together and can be found in the same metadata files (Online-only Table  1 ).
= -+ - where D is the diameter of the RF, r and l are the x-coordinates of the right and left boundaries and t and b are the y-coordinates of the top and bottom boundaries. The arrays with RFs located closest to the fixation spot (arrays 1 and 4 in monkey L, and 1, 3, 4, 6, 7, 8 and 15 in monkey A) were mapped using a small, thin, slowly moving bar and the other arrays were mapped using a large, thick, fast-moving bar. Note that during stimulus presentation, data were recorded from all the arrays, including from arrays that were not being mapped by the stimulus. Hence, the datasets obtained using the thick and thin bar stimuli were combined into a unified RF map and the remaining data was discarded. The combined RFs for each monkey can be found in the metadata repository (Online-only Table  2 ).
Data Records Identification of array and channel number. As described in the Methods, of the 16 Utah arrays, 14 were implanted in V1 and 2 in V4. Figure  2a  shows the location of implantation in the visual cortex for each of the 16 arrays. Each Utah array consisted of 64 electrodes, and each NSP recorded the signals obtained from two arrays, i. e. 128 channels (see the section, 'Data collection'). Each electrode was assigned a unique global identifier from 1 to 1024. To link the global identity of individual channels with the numbering within an array (out of 64) and the numbering within an NSP (out of 128 channels) we generated look-up tables (LUTs) for each monkey. Each row in the table represents a single electrode. For each electrode the global (out of 1024), within-NSP (out of 128) and within-array (out of 64) channel indices are indicated.
Additionally, the NSP number (out of 8), array number (out of 16) and cortical area (V1 or V4) are specified. These tables allow the unique identification of the electrodes across indexing systems. (c, d ) from session A_SNR_150817 in monkey A; and (e,f) from session A_SNR_041018 in monkey A. Note that the resting state data in monkey A were recorded several months after surgical implantation of the electrodes, by which time the data quality had decreased (c,d). Hence, we provide an additional, stand-alone checkerboard stimulation dataset, session A_ SNR_041018 (e,f) which was obtained at an earlier date (when the SNR was high on the majority of channels) and does not have a matching resting state session. Description of file formats. All data can be found at this GIN repository (https://doi.
org/10. 12751/g-node. i20kyh)  24. The raw, aligned data are provided in the proprietary Blackrock format,. ns6. The pre-processed signals were stored as. mat and. nix (https://g-node. github. io/nix/) files;. nix files can be loaded using the Python Neo framework  43  (https://neuralensemble. org/neo/). Basic metadata from the recording system are saved in the proprietary Blackrock formats,. nev and. ccf. Note that both. nix and. mat data files (listed in Table  4 ) contain basic metadata; however, for the complete metadata, the metadata files (listed in Online-only Table  2 ) should be used. All additional metadata files are provided in various machine-and human-readable formats, such as. txt,. xls,. csv and. mat. Metadata were diverse and originated from different sources, such as the experimental equipment, subject specifications, electrode identifiers, signal quality (SNR), receptive fields (RFs), etc.
All metadata were organized into a single unified hierarchical structure, using the open metadata markup language (odML)  44  (https://g-node. github. io/python-odml/), a human-and machine-readable file format for reproducible metadata management in electrophysiology. The raw metadata were processed with odMLtables  45  (https://odmltables. readthedocs. io) and custom Python scripts. The generated metadata files are listed in Online-only Table  2 , all of which are integrated into a single odML file per session.
technical Validation Impedance measurements. Post-implantation, electrode impedance was measured at 1 kHz using the Impedance Tester function in the Blackrock Central Software Suite. These measurements were carried out in the same month that the resting state data were collected, yielding one text file (. txt) per NSP. These values were subsequently combined across the eight raw data files (one per NSP), yielding a single. csv file that contains impedance data across all 1024 channels. The impedances were also included in the hierarchically organized odML metadata files. eye closure validation. During the resting state sessions, eye pupil diameter and position were tracked using an infrared camera. The monkeys were head fixed throughout the recordings, and the pupil was within sight of the camera at all times, as verified by inspection of the camera feed by an experimenter.
We examined the data for spurious correlations, as they could potentially contaminate MUAe signals. Determining the precise source of such spurious correlations is beyond the scope of this publication. In order to detect cross-talk artifacts in the data, we band-passed the raw signals at 250-9000 Hz. We then removed the first principal component for the channels that shared a common reference wire, roughly corresponding to the mean of the signals. Threshold crossing events were extracted as described by Quiroga et al. 46  , with a threshold multiplier parameter of 5. Next, we counted synchronous threshold crossings at sampling resolution and in adjacent sampling bins (1/30 ms). The complexity of a synchronous event was defined as the number of near-simultaneous threshold crossings across electrodes  47.
Each point represents a single electrode. As shown by the absence of a positive correlation, the SP was not biased by the FR, due to our method of normalising the SP by the total number of synchronous events. (d) Synchrofact participation of each electrode in the original data (prior to removal of 150 electrodes with high SP). Crosses (X) indicate electrodes with SNR < 2 and hyphens (-) indicate electrodes with FR < 0. 1; these electrodes were excluded from the crosstalk analysis. (e) For the remaining electrodes, those with a high SP were systematically removed, accompanied by a decrease in the largest SP that was observed across the remaining electrodes. (f) SP of each electrode after the removal of 150 electrodes with high SP; removed electrodes are indicated by a circle (O). (c) (d) and (f) use the same colour map.
We systematically removed electrodes with the highest SP one by one, and recalculated the SP and chance levels after the removal of each electrode. Our significance level was not adjusted for multiple comparisons, as this would lead to a high false negative rate and hinder the removal of electrodes with cross-talk from the dataset. We removed up to 250 electrodes (Fig. 8e ), greatly reducing the levels of cross-talk in the data (Fig. 8f ). Note that while the electrode removal process eliminates a large portion of the spurious correlations, it does not eliminate artifacts that occur sporadically or at low rates on a given electrode. We provide a recommended list of electrodes to discard, and the order of their removal. The precise number of electrodes to be discarded can be adjusted as needed by data users, depending on the particular use case.
Detailed plots depicting the complexity histograms and electrode SP for all the resting state sessions are available in our data repository. A reference implementation for synchrofact detection was included in version 0. 10. 0 of the Electrophysiology Analysis Toolkit 26 (Elephant, RRID:SCR_003833, https://elephant. readthedocs. io). The full workflow used for systematic electrode removal was implemented with Python and can be found in the data respository. Palis, Mike Gruenhagen, and others at Blackrock Microsystems for scientific and technical collaborations; and Sebastian Lehmann for assistance with graphic design. Funding: This work was supported by NWO (STW grant number P15-42 'NESTOR' and Crossover grant number 17619 'INTENSE'), the European Union FP7 (ERC grant number 339490 'Cortic_al_gorithms'), the European Union Horizon 2020 Framework Programme for Research and Innovation under the Framework Partnership (HBP FPA agreement number 650003), the H2020 Framework Programme for Research and Innovation (Human Brain Project SGA2 grant number 785907, and HBP SGA3 grant number 945539), the European Union Horizon 2020 Future and Emerging Technologies (FET Open grant number 899287 'NeuraViPeR'), and the Deutsche Forschungsgemeinschaft (German Research Foundation, grant number 368482240/ RTG 2416).
Acknowledgements We thank  Kor Brandsma  and  Anneke Ditewig  for technical support;  Matthew Self  and  Feng Wang  for assistance during surgeries;  Chris Klink  for help with technical validation;  John van Veldhuizen ,  Stephen Super ,  Joop Bos ,  Joost Brand , and  Ruud van der Blom , for help with mechanical engineering;  Cyril Voisard  (Medicoat) for biocompatible coating of the implants;  Florian Solzbacher ,  Marcus Gerhardt ,  Nick Halper ,  Stephen Hou ,  Rob Franklin ,  Saman Hagh-Gooie ,  Kian Torab ,  Sherman Wiebe ,  Charles Dryden ,  Vinh Ngo ,  William Yang , Greg.
Code availability All scripts used for the processing of data and our preliminary analyses are available alongside the data at https:// gin. g-node. org/NIN/V1_V4_1024_electrode_resting_state_data, in the 'code' folder. All experiment control scripts are listed in Table  2  and the data processing scripts are listed in Online-only Table  1. Matlab version R2015b, Python version 3. 7 and Snakemake version 5. 8. 1 were used. The only Matlab dependency was the NPMK toolbox (version 5. 0, Blackrock Microsystems), a copy of which is included in the data repository. Direct Python dependencies include neo 0. 9. 0, nixio 1. 5. 0 elephant 0. 10. 0, odml 1. 4. 5 and odmltables 1. 0. A full list of all Python dependencies and the specific versions used can be found in the Python environment specifications, along with the Python scripts (Online-only Table  1 ).
Sampling Data were collected during the second Acoustics Along Brazilian Coast (ABRACOS2) survey performed onboard the R/V ANTEA from April 9 to  May 9, 2017 (Bertrand, 2017). Fish were collected using a bottom trawl (body mesh: 40 mm, codend mesh: 25 mm, entrance dimensions horizontal x vertical: 28 ×10 m). Trawling was carried out at depths ranging between 15 and 65 m. Each haul lasted approximately 5 min at a mean speed of 3. 2 kt. Tow duration was considered the moment of the arrival Study area with sampling points in northeastern Brazil, in two contrasting zones: one dominated by sand (green dots) and another with more complex substrates such as algae and coral reefs (red dots). AL, Alagoas; PB, Paraiba; PE, Pernambuco; RN, Rio Grande do Norte. of the net on the pre-set depth to the lift-off time, recorded with a SCANMAR system.
, 2018). Zone B, between 7 and 9°S, dominated by coral and algal formations (Figure  1 ), offered many ecological advantages, such as greater availability of resources, resulting in a higher abundance and diversity of species than in zone A, characterized by a prevalence of sand  (Eduardo et al. , 2018). A sediment classification also verified the substrates differences between both areas with data compiled from the Brazilian National Oceanographic Data Bank (BNDO) and literature  (Vital et al. , 2005; Amaral and dos Santos, 2015; de Assis et al. , 2015; de Assis et al. , 2016)  (Supplementary Figure  1 ).
Data analysis We selected the eight most representative species in the catch (74% of the total catch in number) to study trophic competition within the demersal fish community: Acanthurus chirurgus (Acanthuridae), Haemulon aurolineatum, Haemulon pl um ieri i (Haemulidae), Holoce nt rus ads ce nsi onis (Holocentridae), Hypanus marianae (Dasyatidae), Lutjanus synagris (Lutjanidae), Pseudupeneus maculatus (Mullidae), and Sparisoma axillare (Scaridae). Large piscivorous species, which have a more remarkable ability to avoid fishing gear, were not captured. Still, presumably, this does not unduly bias our results as these fish are likely to have a predatory rather than competitive relationship with the selected species  (Vaske and Teixeira Lessa, 2019). The species were classified into trophic guilds, which were attributed by  Ferreira et al.
(2019)  (Supplementary Table  1 ). We aimed to identify the main trophic compartments used by the selected species to get a rough idea of their diet in a poordata context. We used gut content analysis of fish sampled during the cruise combined with literature data from an area as close as possible to our study area (Supplementary Table  2 ) and constructed a network showing the main trophic compartments and their relative importance in the diet of each selected species. The catch per unit of effort (CPUE) was calculated considering the number of individuals of fish caught per trawled area (ind. km -  ). The trawled area was estimated by multiplying the distance covered by the net through the bottom with the estimated gear mouth opening obtained through the SCANMAR sensors. The SCANMAR system was not operated in six trawls, and the average mouth opening (13 m) was used.
The SIBER (Stable Isotope Bayesian Ellipses in R  (Jackson et al. , 2011) ;) package was used to estimate the total isotopic area (TA), the total nitrogen (dN) and carbon (dC) range occupied by the demersal fish community in each zone. We used Bayesian estimates to avoid sampling bias for the TA, dN and dC to propagate sampling error on the estimates of the means of community members and provide measures of uncertainty surrounding these metrics. This Bayesian approach increases the robustness of comparisons between zones by performing resampling simulations within each species and giving a value for each simulation  (Jackson et al. , 2011). The corrected standard ellipse area (SEAc) of each species, containing the 95% prediction ellipse interval, was calculated. As the SEAc incorporates a correction for sample size bias, the slight differences in sample size between our areas are not expected to influence our results (Supplementary Table  3 ).
Social network analysis We built a social network for each studied zone with the ggraph package  (Pedersen et al. , 2017). This network was constituted of nodes (one for each of the eight selected species) connected by arrows (edges) representing competitive interspecific trophic interactions. The size of the nodes was set according to species intraspecific trophic competition (ITP index), and the width of the arrows was a function of the interspecific trophic index (Pov). To quantify the strength of the competitive trophic interactions within the network of each studied zone, we calculated the shortest, largest, and average paths with the tnet package  (Opsahl, 2015). Those metrics, commonly used in social network analysis, help measure network connectivity  (Sosa et al. , 2020). We used the variant of the metric designed for weighted (when interactions have different intensities) and directed (when interactions have directionality from one node to another) networks  (Opsahl et al.
Funding This study is a contribution to the LMI TAPIOCA, program  CAPES/COFECUB  ( 88881. 142689/2017-01 ), EU H2020   TRIATLAS  project (grant agreement  817578 ). We acknowledge the  French oceanographic  fleet for funding the at-sea survey ABRACOS 2 (https://dx. doi. org/10. 17600/17004100 ). We thank the  CNPq (Brazilian National Council for Scientific and Technological Development) , which provided research grant to FL-F.
The GAS work package 2. 1 Work package's raison d'être Work Package 6 of the EURAD European Joint Programme 'Mechanistic understanding of gas transport in clayey materials' (WP GAS) focuses on gas transport in natural and engineered clayey materials. It aims: • to improve the mechanistic understanding of gas transport processes in natural and engineered clayey materials, their couplings with the mechanical behaviour and their impact on the properties of these materials; • to evaluate the gas transport regimes that can be active at the scale of a geological disposal system in clayey host formations and their potential impact on clayey barrier integrity and repository performance. The first raison d'être of this WP is to provide results that apply to a wide range of national programmes in clayey host rock.
Experimental work that is carried out determines, for each identified gas transport regime, the conditions under which that regime is possible in clay materials representative of host formation and clayey EBS components. Data obtained are pertinent from low (diffusion) to high (advection) gas transport rates. This WP also illustrates how knowledge gained from laboratory and in situ experiments can be integrated into the conceptualisation of gas transport through different clayey components of a disposal system and how gas could affect (or not) the performance of the system. This work carried out involves (i) the development of phenomenological descriptions of gas transport and of its likely consequences at the relevant scale and (ii) the testing of different approaches to represent the effects of gas at the repository scale and bounding its consequences in terms of repository performance.
Work package's raison d'être The Work Package 7 of the EURAD European Joint Programme 'Influence of Temperature on Clay-based Material Behaviour' (WP HITEC) aims to develop an improved thermo-hydro-mechanical (THM) understanding of clay-based materials (host rocks and bentonite buffers) exposed at high temperatures (>100 • C) or having experienced high-temperature transients for extended durations. The WP's raison d'être is to evaluate whether or not elevated temperature limits (up to 150 • C) are feasible for various geological disposal concepts for high heat generating wastes (HHGW). HITEC studies clay host rock formations, document and establish the possible extent of elevated temperature damage in the near and far field (e. g. , from over-pressurisation of water due to temperature increase) and also indicates the likely consequences of any such damage.
INTRODUCTION 1. 1 Context The Sentinel constellation is composed of different coupled SAR and optical sensors with short revisit period (five to ten days). However, optical images are frequently polluted by cloud cover. To leverage the problem of optical image reconstruction, various approaches have been proposed over the years. First, approaches based on mathematical, physical or statistical model, have been extensively used to reconstruct the missing parts of the images. A review of these traditional approaches are summarized in  (Shen et al. , 2015). Among them, we can distinguish multispectral based  (Hu et al. , 2015)  methods, multitemporal  (Cheng et al. , 2014)   (Li et al. , 2014) , and methods using optical and SAR data fusion  (Eckardt et al. , 2013). Lastly, machine learning and particularly deep learning have become popular to achieve the task of cloudy images reconstruction, thanks to the unprecedented ability to fuse images of different modalities, and accompanied with state of the art results.
Method In this paper, we provide an insight into various optical image reconstruction methods. In particular, we address the question of which approach to employ for a specific availability of remote sensing products, in comparing a few selected single date based and multiple dates based methods. Since these methods employ inputs of different nature (single optical or SAR image, or optical and SAR images pair), number (single or multiple image or pair or images), and cloud coverage (clean or cloudy images), their comparison is not straightforward. To leverage this, we introduce the acquisitions layout, a descriptor of the available inputs and their properties, for a specific approach. We then use space partitioning data structures to ease the generation of various datasets from specific acquisitions layouts.
These datasets are then used to train networks, and also at inference time for the comparison of the different methods when a common set of inputs can be shared and matches the expected constraints, e. g. cloud coverage. This simple yet generic framework allows to produce datasets tailored for a specific problem and suited to the data availability, i. e. inputs and targets images. We carry out the benchmarks of representative state of the art methods for optical image reconstruction, namely the network presented in  (Meraner et al. , 2020) , which uses a single pair of optical and SAR image acquired near the same date, and a convolutional network that inputs three consecutive pairs to reconstruct the central optical image  (Cresson et al. , 2019 )  (Scarpa et al. , 2018). In  (Scarpa et al.
Sentinel-1 images We have used the so-called S1Tiling tool 1 to automatically download and process the Sentinel-1 images. The tool performs the orthorectification and 1 https://gitlab. orfeo-toolbox. org/s1-tiling/s1tiling the calibration in sigma nought of the VV and VH SAR images channels. It also projects and resamples the final images over the same coordinate reference system and pixel grid as the Sentinel-2 images, at 10m physical spacing resolution.