dataesr/scientific-paragraphs-categorization huggingface dataset:
3D Patch-based Local Fine Alignment Let us note with Pnew,G and P old,G the 3D point clouds representing the ground for the recent and the historical dataset, respectively. In order to refine the 3D global patch alignment, each 3D patch match is injected into the local alignment procedure which includes rigid pose estimation via the ICP  (Besl and McKay, 1992)  algorithm, barycenter compensation and outlier rejection. Let Pnew,G = {P 3D new,i , i = 1,. , N } and P old,G = {P 3D old,i , i = 1,. , N } be the list of N homologous 3D patches representing the ground. For each 3D patch match, (P 3D new,i ←→ P 3D old,i ), the local 3D patch alignment procedure estimates iteratively the optimal transformation Ti = [ R, t] which best aligns the 3D points belonging to each patch by minimizing the Residual Mean Square Error (RMSE), noted Q [ R, t] , expressed in the following equation: Q [ R, t] = 1 nG n G k=1 p new,k -R-1 p old,k -t (4) The accuracy of the obtained 3D matches is evaluated in terms of RMSE computed between the reference and the optimally aligned homologous 3D points.
The accuracy of the algorithm is measured in terms of fitness score  (Rusu and Cousins, 2011) , noted f [ R, t] , defined as the mean of squared distances between each 3D point in the reference patch (recent DSM) to its closest point belonging to the target (historical DSM). The current ICP implementation  (Rusu and Cousins, 2011) DoD [ R, t] (k) = p new,k (z) -pnew,k [ R, t](z) (8) where, pnew,k [ R, t] = R-1 p old,k -t, k = 1,. , nin denote the historical 3D points registered with respect to the reference (recent) dataset. Figure  6  (c) displays the altimetric residual values obtained for the dataset Fabas. A more detailed analysis of the obtained result in presented in the following section. In order to illustrate the final alignment result in terms of geometry,  Figure 4(d)  displays the initial reference 3D patches (recent DSM) superposed with the optimally aligned patches (historical DSM) generated via the 3D local alignment procedure.
EXPERIMENTAL RESULTS AND PERFORMANCE EVALUATION This section presents the experimental results together with the performance evaluation in terms of accuracy and computation time. The first part is dedicated to a description of the dataset, while the second part focuses on experimental results and their evaluation. Historical and Recent Datasets. Experimental results are performed on the historical dataset acquired in 1962 over the Fabas area, situated in France, which is essentially composed of natural and rural areas. Land-cover changes are mainly due to forest evolution and to agricultural parcels clustering. The reference dataset is represented by recent orthoimages and DSMs, acquired in 2010 and extracted from the French national image databases. A detailed description of the dataset can be found in  (Giordano et al.
, 2018). Experimental results performed on several datasets extracted from the Fabas study area allowed us to conclude on the effectiveness of the proposed approach. In order to provide a consistent analysis of the output generated by each composing stage of the algorithm, the experimental results illustrated throughout this paper exploit the same dataset extracted from the Fabas survey. Accuracy evaluation. The first measure for qualifying the accuracy of the proposed approach is the Difference of Digital Elevation Models (DEMs), noted DoD, expressed in Equation  8. Figure  7  illustrates the DoDs (elevation residual values) obtained for each homologous patch P 3D new,i ←→ [P 3D old,i , Ti], i = 1,. , N , indicating that the proposed method generates accurate registration results in terms of elevation values.
Therefore, the interrelation of fluctuating context-specific dynamics with species' intrinsic evolving ecologies are expected to result in lineage-specific diversification rates that themselves evolve and can vary at any point in time. The mammal radiation started at around 200 Mya  (Upham et al. , 2019; Álvarez-Carretero et al. , 2021)  and resulted in an estimated present-day diversity of ca. 6450 currently recognized species  (Mammal Diversity Database, 2022). Distinct mammalian evolutionary routes led to marked differences in ecomorphologies, reflected in body size (10 8 fold differences), generation time, litter size and habitat (aquatic, arboreal, terrestial, fossorial, etc. ) variation, concomitant with an uneven distribution of richness across clades  (Davies et al. , 2008; Meredith et al.
As expected, small datasets (e. g. , 50 tip trees) are particularly affected by the choice of prior: a Γ -1 (0. 05, 0. 05), for instance, will enforce very low probabilities to small values (e. g. , σ λ < 0. 1), overestimating 8 the diffusion rate. Conversely, a Γ -1 (0. 1, 0. 001) prior, for instance, will capture small values of σ λ but will underestimate higher diffusion rates (Fig  S2 ). While the prior choice becomes asymptotically irrelevant as more data is included, we emphasize that, for inference, some rationale should be taken for the choice of prior (Fig  S2 ).
Mammal Birth-Death Diffusion We characterize the across-lineage temporal diversification of mammals using our new diffusion models. We used the recent time-calibrated phylogenetic tree integrating phylogenomic data from  Álvarez-Carretero et al. (2021) , which comprises 4705 genetically represented extant lineages. Because many of these tips are still considered subspecies, we used only recognized species as tips for taxonomic consistency in the patterns observed, yielding a tree with 4071 species. We estimated clade-specific sampling fractions for each of 16 subclades we identified and which broadly correspond to those divisions used to infer the full mammal phylogenetic tree in  Álvarez-Carretero et al. (2021). For this, we matched current mammals diversity following the Mammal Diversity Database ('MDD'; 2022), which at the time of the analyses (May 2022) recognized 6457 total extant mammal species.
, 2021). Given that all these processes could be present to some degree in empirical datasets, comparing models with exclusive processes may not be particularly meaningful. A promising and exciting perspective of the data augmentation methods introduced here is that they can easily be recruited to develop models that would combine those hitherto disconnected processes into a joint model, where their respective contributions could be directly estimated. Our new model development uncovered a clear signal of an early-rise of modern mammals, refuting the idea that suppression before the K-Pg was limiting the diversification of those mammal lineages that survived to the present, but rather suggest the angiosperm radiation opened substantial ecological opportunities for these lineages to diversify in the Late Cretaceous and up to the present.
Co-variations in resting state activity are thought to arise from a variety of correlated inputs to neurons, such as bottom-up activity from lower areas, feedback from higher areas, recurrent processing in local circuits, and fluctuations in neuromodulatory systems. Most studies have examined resting state activity throughout the brain using MRI scans, or observed local co-variations in activity by recording from a small number of electrodes. We carried out electrophysiological recordings from over a thousand chronically implanted electrodes in the visual cortex of non-human primates, yielding a resting state dataset with unprecedentedly high channel counts and spatiotemporal resolution. Such signals could be used to observe brain waves across larger regions of cortex, offering a temporally detailed picture of brain activity.
overlap between V1 and V4 RFs), spanning the central 6-9 degrees of visual angle across one quadrant of the visual field. Our dataset  24  covers the full range of spectral components from raw signals sampled at 30 kHz to local field potentials (LFP, at 1-100 Hz) to multiunit spiking activity (MUA, at 500-9000 Hz). We expect these resting state data to be of interest to neuroscientists in the fields of computational and systems neuroscience. Potential applications include correlation analyses, large-scale modelling  25  , detection of activity waves  26  , teaching material, and more. For example, the strength and anatomical distribution of co-variations in activity could shed light on the anatomical and functional connectivity between or within the areas under examination  27  , including the retinotopic organisation of V1 and V4.
In the visual system, resting state correlations have been used to calculate functional correlations between brain regions in order to identify the borders of visual cortical areas such as V1, V2 and V3  15, 28, 29  , and for the estimation of the retinotopic layout within individual visual areas  [30] [31] [32]. Across visual areas, the lateral geniculate nucleus has been observed to exhibit higher levels of correlated activity with primary visual cortex than with higher-order visual areas, whereas activity in V2 and V3 is more closely correlated with V4 and hMT+  11. Retinotopically corresponding locations across areas V1, V2 and V3 show increased functional connectivity, and a similar pattern has been observed for corresponding brain regions in the two hemispheres. This dataset could further be used to compare electrophysiologically recorded neuronal activity to that obtained using non-invasive techniques.
To give an example, we recently compared population RF estimates obtained with multiple-channel electrophysiology and fMRI-generated BOLD activity  33. Indeed, MRI  [8] [9] [10] 15  and invasive electrophysiology  [1] [2] [3] [4] [5] 27, [34] [35] [36] [37]  provide complementary approaches to examining correlations, including during resting state: fMRI offers a large-scale perspective, revealing the interplay between multiple brain areas and permitting the examination of entire resting state networks, via fluctuations in the MRI signal which have a relatively coarse spatial and temporal resolution. By contrast, electrophysiology yields direct recordings of neuronal activity from a smaller set of brain regions, but at a high spatial and temporal resolution. The present dataset could also serve as a template for future publications of electrophysiology datasets, providing standardized methods and tools for the description, preparation, and organization of both data and metadata, thereby contributing to the present era of open data sharing and collaboration.
The data are available on the G-Node Infrastructure (GIN, https://gin. g-node. org/), an open-access data-sharing platform. The dataset version described in this publication can be found at https://gin. g-node. org/ NIN/V1_V4_1024_electrode_resting_state_data. The dataset follows the FAIR principles  38  , i. e. it is designed to be findable, accessible, interoperable, and reusable.
Methods Our subjects were two male rhesus macaque monkeys (Macaca mulatta, monkeys A and L). Each animal received two cranial implants during two separate surgical procedures. The first of these implants was a customized, in-house-designed, 3D-printed head post for head fixation  39. The head post was affixed to the setup to stabilize the head throughout the recordings. This ensured that the eye tracker captured the eye data (pupil diameter and position) accurately throughout the recordings (see Chen et al. , 2017, for a detailed description of these methods). The second was a 1024-channel implant for the visual cortex, consisting of 16 Utah electrode arrays (Blackrock Microsystems) attached via 7-cm-long wire bundles to a customized, in-house-designed, 3D-printed pedestal (referred to in the rest of the manuscript as a '1024-channel pedestal, ' Fig.
Datasets. In this study, we present 1) resting state data from the two monkeys, collected across three recording sessions per animal. In addition to this main dataset, we collected two supplementary datasets to allow further interpretation of the resting state data: 2) a dataset acquired during a visual fixation task, collected across three recording sessions per animal (on the same days as the acquisition of the resting state dataset), for quantification of the size and signal-to-noise ratio of the neuronal responses elicited by a visually presented checkerboard stimulus (Fig. 3a ); and 3) a dataset acquired during a fixation task, collected across two recording sessions per animal, in which we presented moving light bars to map the receptive fields (RFs) of the neurons (Fig. 3b ). Table  1  provides a list of all the recording sessions for the three datasets.
Resting state. For the resting state recordings, the monkey was seated, head-fixed, in a room next to the operator room, with the lights turned off. The room was silent during the recordings (although it was not acoustically isolated). Note that although the lights were off, the setup was not completely dark, due to the presence of small LED lights on our recording equipment, and a small amount of light coming under the door from the adjacent room. The monkey did not carry out a task and was allowed to stay awake or fall asleep at any point in time during the recording, and was free to shift its gaze and centre of attention. We recorded the pupil diameter and the eye camera also allowed us to determine whether the eyes were open or closed. Our aim was to provide a resting state dataset in which the signals were likely to be derived from the same or similar groups of neurons, allowing for pooling of data across the recording sessions.
Therefore, the three resting state sessions were recorded within a short time span (across consecutive working days where possible). Visually evoked activity. For each resting state dataset we also collected a dataset with visually evoked activity on the same day in order to provide an assessment of the quality of the neuronal signal on each channel that day. This dataset consisted of at least 30 trials in which the monkey viewed a grey screen (with a luminance of 14. 8 cd/ m 2 ) before a full-screen checkerboard stimulus was presented for 400 ms while the monkey maintained fixation on a dot located at the centre of the screen (Fig. 3a ). The levels of visually evoked activity (relative to baseline activity) provided a measure of the quality of the neuronal signal obtained on each channel.
We determined the 'signal-to-noise-ratio' (SNR) as the amplitude of the visually driven response divided by the standard deviation of activity in a time window before stimulus onset (see below for details). If desired, the SNR may be used during subsequent analyses to select only the channels from the corresponding resting state dataset that clearly have stimulus-evoked responses and to discard those that show poor or no signal. The size of the checkerboard squares was 1 degree of visual angle (dva), and the luminance values of the black and white squares were 0 and 92. 1 cd/m 2 , respectively. Fig. 2  (a) Numbering of the 16 arrays that were implanted in the visual cortex. LS: lunate sulcus. STS: superior temporal sulcus. The yellow dot on each array indicates the side on which the wire bundle exits the array.
Right: Numbering of channels on each array, as viewed from the top of the array after implantation, rotated 90 degrees CCW relative to the left panel. (b-e) RF map, showing the coordinates of the V1 (b,c) and V4 (d,e) RF centres for channels with an SNR of more than 2 for each condition (N = 893 and 679 in monkeys L and A, respectively). Channels are colour-coded by array number, using the same colour code as in a. The receptive fields are located in the lower-right quadrant of the visual field. In monkey A, the resting state data and matching visually evoked data were collected 1 year after surgical implantation, following the completion of other (unrelated) experiments. By this time, the number of channels with high SNR had decreased, compared to the number observed soon after surgery. To allow future users of the data to carry out analyses of visually evoked responses across close to 1024 channels (independently of the  resting state data), we provide an 'extra' dataset of visually evoked activity from monkey A, which was obtained 10 weeks after implantation.
This 'early' SNR dataset was collected using an identical task design to that of the other SNR datasets, while providing a larger number of channels with high SNR. Note that this additional dataset is stand-alone and is not paired with a resting state session. Receptive field mapping. The subjects viewed moving light bars that appeared at specific locations on the screen, allowing us to identify the RF location of the neurons recorded on each channel. To characterize the receptive field properties on each channel, we recorded the responses evoked by white sweeping bar stimuli that moved in each of four possible directions (top to bottom; bottom to top; left to right; and right to left)  40. RF size scales with eccentricity 41 : the farther away an RF is from the fixation, the larger its size.
Neurons with small RFs respond best to small stimuli, whereas neurons with larger RFs show a more pronounced response to large stimuli. Hence, the RF mapping task included two stimulus sets: 1) RFs of low eccentricity were mapped out using a small, thin, slow-moving bar (4 degrees of visual angle [dva] in length, 0. 04 dva in thickness, moving at a rate of 4 dva per second) that was positioned close to the fixation spot. 2) RFs of higher eccentricity were mapped out using a long, thicker, faster-moving bar (20 dva in length, 0. 19 dva in thickness, 20 dva/s) that was positioned farther from the fixation spot (see Fig. 3b ). Stimulus presentation was controlled using custom-written Matlab scripts (Table  2 ) that were run on the stimulus control computer. The two types of visual stimuli elicited spatially and temporally well-defined neuronal responses, which allowed for the measurement of RFs closer to and farther away from fixation.
4), with one instance of the software being run for each NSP, i. e. a total of eight instances of the software ran simultaneously during data acquisition. Each PC was connected to four NSPs, and four instances of the software were run on each PC. Each NSP stored the raw neuronal signals from 128 channels in a single raw data file (corresponding to channels 1 to 128), giving rise to a total of eight raw data files across the eight NSPs. The data recorded from the eight NSPs were temporally aligned as described in the section, 'Temporal alignment of raw data. ' Due to the high volume of data being processed and stored by each NSP, the onset of recording was controlled manually with a temporal offset of several seconds between NSPs. Before starting the recording on any given NSP, the operator checked to ensure that on-going recordings were running smoothly on the other NSPs, thereby avoiding buffer overflow and dropped packet issues due to system overload at the start of recording.
Automatic updates were disabled to prevent unwanted disruptions during recording. Eye tracking. During each recording, an infrared eye tracker (TREC ET-49B, version 1. 2. 8, Thomas Recording GmbH) was used to sample the eye position and pupil diameter for both the X-and Y-axes with a frame rate of 230 Hz, and the data were stored at a sampling rate of 30 kHz. The eye tracking hardware was controlled by a dedicated PC (PC #4) using Eyetracer software (Thomas Recording), which forwarded the analog signals regarding the eye position and pupil diameter directly to NSP Table  2. List of Matlab scripts used for experimental control and stimulus presentation, with script names and descriptions. The experimental control scripts are highly specific to the hardware and are not designed to run without the equipment.
We provide the scripts for completeness. #1. X and Y eye positions were recorded on channels 129 and 130 of NSP #1, respectively, while X and Y pupil diameter were recorded on channels 131 and 132, respectively. On all NSPs, the data collected on channels 1 to 128 comprised the neuronal signals. On NSP #1, channels 129 to 132 additionally contained the eye signals. Furthermore, analog synchronization signals were recorded on the NSP channel 144; these can be ignored as the synchronization pulses were also registered as digital events in the. nev files. All other NSP channels (133-143) did not record any data. We stored the raw data from the relevant channels (containing neural and eye signals) as specified in the configuration files that were loaded into the Blackrock Central Software Suite for each NSP.
To encode a bit change initiated by the stimulus control computer, a 500-ms voltage pulse was sent on the desired pin. On each NSP, the event codes were recorded in the events file (. nev) as a sequence of numbers that ranged in value from 1 to 8. Note that since the DAS board used zero-based indexing, when instructions were sent from the Matlab script to the DAS board, the sequence of pin numbers specified in the Matlab script ranged from 0 to 7, instead of 1 to 8. Table  3  provides a list of the bit identities and their interpretations. As the precise times at which recording was initiated or terminated varied across the eight NSPs, the duration of the raw data traces also varied slightly between NSPs. Hence, the common digital signal that was sent to all eight NSPs simultaneously via their digital input ports was used to precisely align the raw data traces between NSPs during data processing (described in the section, 'Temporal alignment of raw data').
During the resting state sessions, the digital signal consisted of a randomly generated sequence of numbers (ranging in value from 1 to 8), which were sent at 1-second intervals using a custom Matlab script that was run on the stimulus control computer. A list of experimental-control scripts is provided in Table  2. During the SNR and RF mapping tasks, a series of trial-related event codes were sent. Event codes were sent upon stimulus onset and offset and during reward delivery via the same system as that used to send sync pulses during the resting state. Additionally, during the RF task, the stimulus condition used on that particular trial (the direction of bar movement) was sent as an event code. PC #3 also received a copy of the X and Y eye position to check the gaze fixation and determine the success or failure of a trial.
Instructions for fluid delivery were then sent to the reward system (Crist Instruments) (Table  3 ). A summary of the digital codes that were used for the three datasets is provided in Table  2. During post-hoc analysis of the raw data, trial-related events could be identified with high temporal precision (with 30-kHz resolution) and were used for precise temporal alignment of data across NSPs. Data pre-processing. The datasets are comprised of temporally aligned raw data, as well as data that have been pre-processed to facilitate their usage. The pre-processing steps included the extraction of local field potential (LFP) signals and envelope multiunit activity (MUAe, which represents the aggregation of spiking activity across multiple units recorded via one electrode -details on how we computed MUAe are provided below)  40  from the raw recording traces, and a systematic registration of metadata.
These steps were executed after the recording session and implemented into a Python workflow using the Snakemake workflow management system  42. In addition to this fully integrated workflow, standalone Matlab pre-processing scripts are also included. The metadata integration is only provided based on Python. A full list of data-processing scripts is given in Online-only Table  1. See a schematic description of the data pre-processing workflow in Fig. 5. Temporal alignment of raw data. The onset and offset of recording were not synchronous across NSPs. Hence, the raw neuronal data were temporally aligned across the files that were generated by the eight NSPs. Excess data at the beginning and end of each file that were not common to all eight NSPs were removed, yielding files of the same duration.