dataesr/scientific-paragraphs-categorization huggingface dataset:
Data transformation LPS and IMU data were transformed from WIMU PRO software (SPRO, Real-Track Systems, 2018, version 964  [17] ) format to a comma-separated value (. csv) format. The frequency of positional data (X, Y) ranged from 17 to 18 Hz and the frequency from IMU sensors were at 100 Hz. In the same way than Luteberget, Spencer, et al. [18] , positional data was resampled from 17-18 Hz to 100 Hz using a cubic spline interpolation (i. e. of 3rd order) to have every variables on the same timeline and avoid synchronisation issues. With the same procedure than Guignard et al. [13] , the positioning reference, initially defined outside the boundaries of the court, has been relocated to the centre of the court (on the median lines on both X and Y axes). by using the barycentre of the positions of the two GKs when they were in front of their goal.
Game phase and player specialty recognition algorithm Data validation set. Four handball games were randomly extracted from the sample and sequenced with Dartfish software (Fribourg, Switzerland) using a designed sequencing panel. Two of them were used to develop thresholds to detect game phase (see Game Phase Detection) and two of them were used for validation. A notational analysis  [19]  was performed to record game phases during the game, and then was compared to the LPS automatic data processing: start of the match, offensive phases, offensive transitions, stable defensive phases, defensive transitions, 7 m shots (penalty throws), presence of time-outs (either requested by the coaches or half-time break). Duration of offensive and defensive phases were computed and compared to those obtained from the automatic data treatment.
Computation of physical variables Displacement direction. To characterise player's displacement direction (forward, backward, left, or right), Madgwick's orientation filter was applied on IMU data to get the shoulder orientation in a transverse plane and compare it to the orientation of the displacement of the player. First, to apply Madgwick's orientation filter at a later stage, the units of the sensors data must be changed as follows: accelerometer data in m. s -2 , gyroscope data in rad. s -1 and magnetometer data in millitesla (mT). Secondly, to reduce noise in IMUs' signal, a low-pass Butterworth filter of 4 th order with a cut-off frequency of 10 Hz was applied  [20]. Madgwick orientation  [21, 22]  filter from AHRS (Attitude and Heading Reference Systems) Python  toolbox was then applied to the raw inertial data.
Results A total of 31 handball matches were included in the present study. It corresponds to the analysis of 491 data sets of players (positional and inertial data). If player's playing time was less than 4 minutes, this was considered not representative enough of a classic handball activity. 28 players data were therefore removed from the study bringing the total to 463 raw data. The following distribution on player positions was observed: 120 wing players (Wings), 40 goalkeepers (Goalkeeper), 73 versatile line (Pivot_Both), 160 versatile backs (Backs_Both), 37 defensive backs or defensive line player (Pos3_Def), 33 offensive backs (Backs_Off). There is no offensive line players in this study. As defensive backs and defensive line players were covering the same position on the court (position 3 in defensive play), they were put in the same category.
In the future, with the development of machine learning video analysis, data from both teams and the ball in terms of position could become available which can give more insights to understand physical match demands regarding tactical behaviour (defensive schemes, offensive system), game events (numerical superiority or inferiority), game context (team is winning or losing). Crucial tactical information such as the number of substitutions, the duration of the different offensive and defensive phases of the game would be available continuously. More broadly, it could also automate the detection of shots, passes and correlate that to the player sensors (IMU). All-together, this information would help monitoring modern handball to individualize the cost of each of these actions/sequences. As practical applications, the present information could help the decision-making of the head coach and strength and condition coach to quantify the physical demands for each player in the game, and hence to determine with accuracy the recovery time the players can benefit from.
6  in ref. 39  ). The estimated values of mouse pyramidal dendrites  25  were therefore rescaled by a factor of 1. 5 (Methods). Conversely, PC axons could not be approximated to regular shapes. Differently from rodents in fact, axonal pathways of PCs had to be adapted to the marked sulci and gyrification characterizing the human hippocampal surface (Fig. 4 ). Given the lack of data, we have assumed a similarity with rodents where pyramidal axons preferentially branch towards the subiculum (Fig. 4b ) with a poor backpropagation to the CA3 (see Fig. 4b  inset and the MouseLight database) and a limited longitudinal spread. The axonal probability density functions were generated by calculating transversal planes for every PC (Fig. 4b  and Methods). The transversal planes were used to calculate the pathway of the axon extending towards the SO and running towards the subiculum (Fig.
Neuronal placement Neuronal placement was performed by analysing a dataset of human brain images (Bigbrain  29  ; Fig. 1a ) previously labelled  30  for the subregions of the hippocampal formation (CA1,2,3,4, dentate gyrus (DG) and subiculum (Sub)). Labelled images were segmented and employed to generate the surface of hippocampal regions adopted as external anatomical landmarks. The calculation of hippocampal subregions confirmed the estimates that the volume of CA1 (547. 1 mm 3 ) is about half of the entire hippocampal formation (Sub 289 mm 3 , CA2 41. 6 mm 3 , CA3 55. 6 mm 3 , CA4 110 mm 3 and DG 110 mm 3 ). According to the labels obtained in the first analysis, stacked images were cropped to isolate the regions of interest (Fig. 1b ). Binary images were then generated through dynamic thresholding on hippocampal stained areas (Fig.
From the 18 million cells, 5. 28 million were randomly selected to represent PCs  31, 32  and interneurons  33. The number comes from the estimate of PCs reported in ref. 32   (4,836,111 cells) and subsequently rounded to the closest integer. The GABAergic interneurons have been classified by adopting a 10% ratio between excitatory and inhibitory neurons: a proportion well conserved among species (from 5% (ref. 34  ) to more than 20% (ref. 35  )) and brain regions, including hippocampal circuits  36, 37. We have adopted an intermediate value of 10%, in agreement with the range of interneurons representativeness reported in refs. 36, 37  and in experimental databases such as https://bbp. epfl. ch/nexus/cell-atlas/. The neurons were therefore subdivided into two classes, labelling 90% (4. 8 M) of the overall population as PCs (pink circles in Fig.
Network simulation A model network, using simple integrate and fire neurons and Tsodyks-Markram synapses with default parameters  45  , was implemented with the exclusive intention to have a demo model to test running times and circuit integrity. The computational effort required to run a full-scale simulation was tested using the excitatory network (connection matrix stored in HDF5 format with gzip compressed dataset, file size approximately 86 Gb) implemented in NEST simulator (https://www. nest-simulator. org). For the purpose of this work, neurons were implemented as standard Hill and Tononi point neurons, available in the NEST distribution ('HT_neuron'). The neurons were connected with 'Tsodyks-Markram' synapses, also available in NEST ('Tsodyks_synapse'). Hippocampal activity was stimulated by activating with a single pulse a group of 6,706 PCs distributed in a spherical volume of 500 μm radius.
Since each process was instructed to use five threads to carry out the simulation, the actual number of processors used to simulate the full network was 2,400, the available amount of random-access memory was 19. 2 TB out of which 7. 2 TB was used by NEST. Disregarding the network setup time, 200 ms of simulated time and data saving in the native text file format of NEST (. dat files) required 162 s of CPU time.
The data extracted Resource https://doi. org/10. 1038/s43588-023-00417-2 to implement models of the hippocampus at cellular resolution will be used to run co-simulations, a framework enabling the use of two simulators operating at different scales to run simulations of full brain networks (Supplementary Fig. 6 ). A further embedding in a large-scale network using co-simulation will shed light on the relevance of these structural variances at the large-scale network level (Supplementary Fig. 6 ). Using co-simulation would enable the modelling of the impact of the pathomechanisms on the emergence of temporal lobe epilepsy, which arises at the microscale level  49  , on the full brain network. We expect that those models will lead to new mechanistic hypotheses, as well as to an improved prediction of patient-specific seizure dynamics.
Methods The automatic detection of 2D cell body was designed to analyse singlechannel marker microscopy images, labelled for the identification of the hippocampal subregions. The algorithm, written in MATLAB (v 2019b; The MathworksInc), was conceived to be applied to silverstained sections. The staining procedure, which darkens cell bodies in an unspecific way, did not allow us to assign a specific neuronal labelling to a given population. A two-step procedure has been implemented to analyse labelled and raw images: image labelling and image segmentation.
Image labelling The BigBrain imaging dataset  29  is a high-resolution microscopic full human brain scan at a resolution of 20 μm and labelled for the different hippocampal subregions. The manual labelling, which is available at a 40 μm isotropic voxel resolution, was upsampled to the original 20 μm resolution, using nearest neighbour interpolation. Images labelled for the different hippocampal regions were analysed to reconstruct the meshes that were adopted as anatomical landmarks for the automatic orientation of the axonal and dendritic probability function. The algorithm implemented morphological operation using the built-in function bwconncomp, regionprop2 and bwperim to identify, depending on the resulting binary mask areas, single cells or closely packed somas and connect them. Once the labelled regions were identified, the algorithm performed the 3D reconstruction of the images obtained from different planes.
2  and Supplementary Table  1 ). The 3D surface of the hippocampal volume resulting from image labelling has been divided into a superficial and a deep side corresponding respectively to SR and to the SO (Fig. 2b, c ) through a custom-made nearest neighbour algorithm. Subsequently, inhibitory population was divided in two main classes depending on the relative position from SO and SR calculated as the minimum Euclidean distance. The interneurons predominantly laying in SR and SLM (IVY-like and NGF-like) were shifted radially to account for the fact that image labelling has been performed by considering only SO and SP. The complete scaffolding of the neurons in the 3D volume resulted from the placement procedure was visualized with the visualization software Mayavi (v4. 8. 1; https://hal. science/hal-00502548).
Neuronal morphology The rule to generate neuronal connection pairs was implemented assuming that neuronal classes are characterized by specific morphological properties. These properties, derived from literature  36, 39  or public databases, have been modelled as geometrical probability volumes mimicking the cross-section volume of axons and dendrites. Every neuron belonging to a specific class has been associated with a series of parameters accounting for its position and its orientation with respect to the three canonical axes (transverse, longitudinal and horizontal) allowing a proper orientation. The algorithms were written in MATLAB and were identified as 'positional-morpho-anatomical' modelling  25. All cells were associated with their relative distances from CA2, subiculum and internal subregions (landmarks identification), by calculating the minimum Euclidean distances.
Neuronal connectivity Neuronal connectivity was performed between neurons belonging to a pre-and a post-synaptic class. The axonal and dendritic probability density functions were preliminarily circumscribed within their minimal bounding boxes and to reduce the computational effort, the presynaptic neuron was intersected only against neurons whose dendritic bounding boxes overlapped with its axonal bounding box (Extended Data Fig. 1 ). The overlapping between axonal and dendritic bounding boxes (BB1 3D , BB2 3D ) was preliminarily determined by applying the following set of equations: BB1 3D = (x ∶ (x min1 , x max1 ), y ∶ (y min1 , y max1 ) , z ∶ (z min1 , z max1 )) BB2 3D = (x ∶ (x min2 , x max2 ) , y ∶ (y min2 , y max2 ) , z ∶ (z min2 , z max2 ))  where, given BB1 1D = (x min1 , x max1 ) and BB2 1D = (x min2 , x max2 ) overlap1D (BB1 1D , BB2 1D ) = x max1 ≥ x min2 & x max2 ≥ x min1 The intersection was evaluated only on neurons with overlapping bounding boxes.
Acknowledgements This work has received funding from the  EU Horizon 2020 Framework Program for Research and Innovation  (Specific Grant Agreement  945539 ,  Human Brain Project SGA3 ) to M. M. and E. D. , the  Flag ERA JTC   2019  ( MILEDI  project to M. M. and  SMART-BRAIN  project to J. M. ), the  French National Research Agency (ANR)  as part of the second ' Investissements d'Avenir' program ,  ANR-17-RHUS-0004 ,  EPINOV  (https://anr. fr) to V. J. , and from the  National Recovery and Resilience Plan  (NRRP),  Mission 4, 'Education and Research' Component 2 , ' From research to Business' Investiment 3. 1 -Call  for tender No. 3264 of Dec 28 , 2021 of  Italian Ministry of University  and Research funded by the  European Union  -NextGenerationEU -Award Number: Project code  IR0000011 , Concession Decree No.
availability Source data for Figs. 3 -5 are available with this manuscript. Source images are available from the BigBrain repository  29. The scaffold model resulting from the analysis of the images is available in the EBRAINS knowledge graph  59  in the form of a text file with Global Identification (GID) numbers and x,y,z coordinates that can be used to reproduce Figs. 2  and 6. At the same link  59  the connection pairs can be downloaded as a collection of multiple text files containing the GID numbers of the presynaptic and postsynaptic neurons and a hdf5 file was generated to allocate each txt file as an hdf5 key.
Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information.
We specify the required input data, mention common pitfalls in choosing appropriate parameters, and suggest courses of action for species that are missing estimates of some necessary inputs. We conclude with examples from two species recently added to stdpopsim, which demonstrate some of the main considerations involved in the process of designing realistic chromosome-scale simulations. While the guidelines provided in this paper are intended for any researcher interested in implementing a population genomic simulation using any software, we highlight the ways in which the stdpopsim framework eases the burden involved in this process and facilitates reproducible research.
A key challenge when setting up a population genomic simulation is to obtain estimates of all of these quantities from the literature and then correctly implement them in an appropriate simulation engine. Detailed estimates of all of these quantities are increasingly available due to the growing availability of population genomic data coupled with methodological advances. Incorporating this data into a population genomic simulation often involves integrating this data between different literature sources, which can require specialized knowledge of population genetics theory. Thus, the process of coding a realistic simulation can be quite time-consuming and often error-prone. The main objective of stdpopsim is to streamline this process, and to make it more robust and more reproducible. Contributors collect parameter values for their species of interest from the literature, and then specify these parameters in a template file for the new model.
Adding species to the stdpopsim catalog will allow developers to easily test their methods across a wider variety of organisms. We thus made a concerted effort to recruit members of the population and evolutionary genetics com-munity to add their species of interest to the stdpopsim catalog. This effort involved a series of workshops to introduce potential contributors to stdpopsim, followed by a "Growing the Zoo" hackathon organized alongside the 2021 ProbGen conference. The seven initial workshops allowed us to reach a broad community of more than 150 researchers, many of whom expressed interest in adding non-model species to stdpopsim. The hackathon was then structured based on feedback from these participants. One month before the hackathon, we organized a final workshop to prepare interested participants, by introducing them to the process of developing a new species model and adding it to the stdpopsim code base.
When expanding the stdpopsim catalog during the "Growing the Zoo" hackathon, we considered the possibility of adding species whose genome assemblies are composed of many relatively small contigs, unanchored to chromosome-level scaffolds. Although we had not previously put restrictions on which species might be added, we decided that we would only add species with chromosome-level assemblies. The main justification for this restriction is that species with less complete genome builds typically do not have good estimates of recombination rate, genetic maps, and demographic models, making chromosome-level simulation much less useful in such species. Another issue is the storage burden and long load times involved in dealing with hundreds of contigs. Finally, each species requires validation of its code before it is added to the stdpopsim catalog, as well as long-term maintenance to keep it up-to-date with changes made to the stdpopsim framework.
So, the benefit of including species with very partial genome builds in stdpopsim would be outweighed by the substantial extra burden on stdpopsim maintainers as well as downstream users of these models. Another reason to focus on species with chromosome-level assemblies is that we expect their numbers to dramatically increase in the near future due to numerous genome initiatives  (Lewin et al. , 2022; Rhie et al. , 2021; Cheng et al. , 2018)  and the development of new long-read sequencing technologies and assembly pipelines  (Chakraborty et al. , 2016; Amarasinghe et al. , 2020 Amarasinghe et al. , , 2021)). 2. An average mutation rate for each chromosome (per generation per bp). This rate estimate can be based on sequence data from pedigrees, mutation accumulation studies, or comparative genomic analysis calibrated by fossil data (i.
Extracting parameters from the literature Simulations cannot of course precisely match reality, but in setting up simulations it is desirable to choose parameters that best reflect our current understanding of the evolutionary history of the species of interest. In practice a researcher may choose each parameter to match a fairly precise estimate or a wild guess, which may be obtained from a peer-reviewed publication or by word of mouth. However, values in stdpopsim are always chosen to match published estimates, so that the underlying data and methods are documented and can be validated. Because the process of converting information reported in the literature to parameters used by a simulation engine is quite error-prone, independent validation of the simulation code is crucial. We highly recommend following a quality-control procedure similar to the one used in stdpopsim, in which each species or model added to the catalog is independently recreated or thoroughly reviewed by a separate researcher.
In this manuscript we describe the expansion of stdpopsim in two ways: the addition of new features to the simulation framework that incorporate new evolutionary processes, such as non-crossover recombination, broadening the diversity of species that can be realistically modeled; and the considerable expansion of the catalog itself to include more species and demographic models. We also formulated a series of guidelines for implementing population genomic simulations, based on insights from the community-driven process of expanding the stdpopsim catalog. These guidelines specify the basic requirements for generating a useful chromosome-level simulation for a given species, as well as the rationale behind these requirements. We also discuss special considerations for collecting relevant information from the literature, and what to do if some of that information is not available.
Because this process is quite error-prone, we encourage wider adoption of "code review": researchers implementing simulations should have their parameter choices and implementation reviewed by at least one other researcher. The guidelines in this paper can be followed when implementing a simulation independently for a single study, or (as we encourage others to do) when adding code to stdpopsim, which helps to ensure its robustness and to make it available for future research. Currently, large-scale efforts such as the Earth Biogenome and its affiliated project networks are generating tens of thousands of genome assemblies. Each of these assemblies would become a candidate for inclusion into the stdpopsim catalog, although substantial changes to the structure of stdpopsim would be required to include so many distinct species.
As annotations of those genome assemblies improve over time, this information, too, can easily be added to the stdpopsim catalog. One of the important objectives of the PopSim consortium is to leverage stdpopsim as a means to promote education and inclusion of new communities into computational biology and software development. We are keen to use outreach, such as the workshops and hackathons described here, as a way to grow the stdpopsim catalog and library while also democratizing the development of population genomic simulations in general. We predict that the increased use of chromosome-scale simulations in non-model species will lead to an improvement in inference methods, which traditionally have been quite narrowly focused on wellstudied model organisms. Thus, we hope that further expansion of stdpopsim will improve the ease and reproducibility of research across a larger number of systems, while simultaneously expanding the community of software developers among population and evolutionary geneticists.