dataesr/scientific-paragraphs-categorization huggingface dataset:
See "Methods" for a more detailed description of each of these steps. Model validation against synthetic data. To test the accuracy and identifiability of the proposed generative model in inferring the spatio-temporal seizure propagation patterns, first it is tested against a synthetic data set (see "Synthetic data" in "Methods"), so that the inferred seizure propagation pattern can be validated against the ground truth at the source level. Inferred seizure propagation obtained by maximizing over the posterior density (Eq. (  5 )) of the synthetic data is shown in Fig. 2. State space dynamics of the simulated and inferred source activity are shown in Fig. 2a. All the six regions recruited by the seizure, with two regions in EZ and four regions in PZ, in the ground truth are accurately inferred to be recruited by seizure in the inferred source activity.
Out of the five brain regions in the clinical EZ hypothesis, the model predicted four regions to be recruited by the seizure with one subcortical region (right hippocampus) in EZ and three regions (right thalamus proper, right amygdala, and ctx-rh-G-oc-temp-med-Parahip) as part of PZ. To quantify this at the group level, we calculated the confusion matrix between the clinical hypothesis-based classification and the model predictionbased classification of the regions. The clinical classification is a binary classification labeling each region as part of EZ or not part Fig. 1  End-to-end workflow for identifying spatio-temporal seizure propagation patterns in focal drug-resistant epilepsy patients. Diffusion MRI and SEEG data are collected for each patient. From this data, structural connectome and the transformation from source-to-sensor space is computed.
SEEG recordings are preprocessed to extract data features such as log power over a sliding window, which could be modeled using 2D Epileptor. A generative model is defined over the 2D Epileptor parameters and observed SEEG log power using a PPL called Stan. Model inversion is performed using maximum a posterior techniques to identify 2D Epileptor parameters that best explain the observations. PPL, probabilistic programming language; SEEG, stereotactic electroencephalography; TVB, The Virtual Brain. of EZ, whereas the model prediction-based classification consists of three classes: (i) regions that are part of EZ, (ii) regions in PZ, and (iii) regions that are not recruited by the seizure. At an onset tolerance of 10 s, 88. 2% of regions in clinical EZ hypothesis are predicted to be recruited by the seizure, with 38.
Methods Patient data. Model predictions are tested against a retrospective patient cohort containing 25 patients with drug-resistant epilepsy. SEEG and diffusion MRI data were collected from all 25 patients before surgery. Non-invasive T1-weighted images (magnetization prepared rapid acquisition gradient echo sequence, either with repetition time = 1. 9 s and echo time = 2. 19 ms or repetition time = 2. 3 s and echo time = 2. 98 ms, voxel size 1. 0 × 1. 0 × 1. 0 mm) and diffusion MRI images (diffusion tensor imaging-MR sequence, either with angular gradient set of 64 directions, repetition time = 10. 7 s, echo time = 95 ms, voxel size 1. 95 × 1. 95 × 2. 0 mm, b-weighting of 1000 s mm -2 , or with angular gradient set of 200 directions, repetition time = 3 s, echo time = 88 ms, voxel size 2.
0 × 2. 0 × 2. 0 mm, b-weighting of 1800 s mm -2 ) are acquired using a Siemens Magnetom Verio 3T MR-scanner. SEEG data are collected using a 128-channel Deltamed system with a sampling rate of at least 256 Hz. SEEG recording are bandpass filtered between 0. 16 and 97 Hz by a hardware filter. Patient details such as age, gender, preliminary clinical diagnosis, and Engel scores (clinical classification for epilepsy surgery) are given in Supplementary Table  1. The patients signed an informed consent form according to the rules of local ethics committee (Comiteé de Protection des Personnes Sud-Meéditerranée I). Accuracy of inferred EZ is evaluated by comparing it with the clinical EZ hypothesis that was used for the surgery. Clinical EZ hypothesis is generated by the clinicians by aggregating the information from semiology, non-invasive MRI, EEG, visual inspection of SEEG (invasive), and a data-driven signal-processing method metric called EI  18.
Structural connectome. From the diffusion MRI data, a SC is built using the same reconstruction pipeline as in Hashemi et al. 22  but using a different parcellation with a finer spatial resolution. Briefly, the pipeline involves the following: (a) parcellation of brain anatomy from T1-weighted images using FreeSurfer v6. 0. 0 32 ; (b) coregistration with diffusion-weighted images using flirt 33 from FSL package in version 6. 0; (c) estimating fiber orientation distributions using dwi2fod tool  34, 35  ; (d) generate fiber tracts using iFOD2 probabilistic tractography algorithm  36  ; and (e) building the connectome using tck2connectome tool. Tractography is performed using MRtrix package in version 0. 3. 15. In this study, we used the Destrieux parcellation 37 containing 164 brain regions with 74 cortical regions per hemisphere and 16 subcortical regions.
04 ms for 2500 ms. f 1 ðx 1;i ; x 2;i Þ ¼ x 3 1;i À 3x 2 1;i if x 1;i < 0 ðx 2;i À 0:6 z i À 4 À Á 2 Þx 1;i if x 1;i ≥ 0 ( f 2 ðx 2;i Þ ¼ 0 i f x 2;i < À0:25 6ðx 2;i þ 0:25Þ if x 2;i ≥ À0:25 ( gðx 1;i Þ ¼ Z t Data preprocessing/feature extraction. A spatio-temporal seizure propagation can be characterized in terms of seizure-onset time and seizure length of all the regions recruited by the seizure. The log power profile of all brain regions captures both these features and it can be modeled using the reduced 2D Epileptor 3. Inference over reduced 2D Epileptor allows for faster inversion, compared to inversion of 5D Epileptor, while enabling us to predict the envelope of fast discharges during the ictal states  22. Hence, raw SEEG data are preprocessed to extract SEEG log power. Preprocessing involves high-pass filtering raw SEEG data from 10 Hz, computing the power over a sliding window, applying a log transformation, and finally a low-pass filter, tuned per each patient, is applied for smoothing out any short spikes in the data.
Convergence is monitored according to the following three criteria: (a) density convergence: change in unnormalized log posterior density is <10 -12 ; (b) gradient convergence: Euclidean norm of gradient is <10 -8 ; and (c) parameter convergence: change in parameter value is <10 -8. Numerical implementation of the generative model and model inversion are performed using a PPL Stan  13. On a Linux workstation with 3. 0 GHz quad core processor model inversion, using MAP took, on average, 1 h for optimization to converge. PðSjY; θÞ ¼ Y M i¼1 Y T Identifying EZ and PZ. EZ is the network of brain regions where seizure originates and PZ is the network of brain regions that are later recruited by the seizure due to the coupling with regions in EZ. Although in an isolated Epileptor node seizure would originate when the excitability parameter (x 0 ) is greater than te bifurcation threshold value -2.
Acknowledgements This research was supported by the  Fondation pour la Recherche Médicale  (Grant  DIC20161236442 ),  European Union 's  Horizon 2020 research and innovation program  under grant agreement number  945539  ( SGA3 ),  Human Brain Project , and the  SATT Sud-Est  ( TVB-Epilepsy ). This work has been carried out within the  Fédération Hospitalo-Universitaire EPINEXT  with the support of the  Recherche Hospitalo-Universitaire EPINOV  (Grant  ANR-17-RHUS-0004 ) funded by the " Investissements d'Avenir" French Government program  managed by the  French National Research Agency (ANR).
, 2014; Stocker and Joos, 2015) ; however, despite being based on the same land use dataset, this leads to comparably lower f LULCC estimates, in particular for later LULCCs  (Stocker and Joos, 2015). Historically and globally, environmental changes led to an increase in land C stocks, therefore present-day environmental conditions are associated with taller trees in our scheme. When a LULCC occurs that reduces C stocks (box 2) the higher C stocks will cause a higher f LULCC (box 3: red line (presentday) higher than blue line (pre-industrial); yellow line (transient) increasing with time). The f LULCC is derived by subtracting the net biome productivity from a simulation without LULCCs from one with LULCCs. Additionally, the different f LULCC estimations can be compared to each other (box 4): the loss of additional sink capacity (LASC; green line; Eq.
Models and simulations We use 12 TRENDY v8 DGVMs that provide gridded output of NBP with and without LULCCs under both transient (historically observed) and pre-industrial (constant) environmental conditions (called S0, S2, S3, S4 in the TRENDY v8 protocol; see Table  1 ) to calculate the LASC on a regional level (see Table  2  for a comparison of the model output and relevant processes included in the DGVMs; additional information can be found in Table  A1  in  Friedlingstein et al. , 2019). Note, for SDGVM, model output from TRENDY v9 was used due to erroneous merging of land cover and LULCC datasets in earlier versions that caused a C loss over the period ∼ 1900-1970 mainly in semi-arid regions. For eight models that provided simulations under constant present-W. A. Obermeier et al. :  Towards spatio-temporally standardized emissions from land use and land cover changes 639 day environmental forcing (S5, S6), the f LULCC was also calculated under present-day environmental conditions.
Models used land use change data from the History Database of the Global Environment (HYDE), which provides annual, half-degree, fractional data on cropland, rangeland, and pasture areas based on annual statistics of the Food and Agriculture Organization of the United Nations (FAO; Klein  Goldewijk et al. , 2017; Goldewijk et al. , 2017)  or the updated harmonized land use change data (LUH2;  Hurtt et al. , 2011 Hurtt et al. , , 2020)). While HYDE agricultural areas are used in LUH2, the main difference lies in LUH2 additionally adding wood harvest from the Global Forest Resources Assessments of the FAO and sub-grid-scale ("gross") transitions to capture shifting cultivation in the tropics. For pre-industrial simulations, the CO 2 concentration and LULCC data from 1700 were applied, and climate was derived by recycling the mean and variability from 1901-1920.
For present-day simulations, the CO 2 concentrations from 2018 were taken as constant, and climate was derived by recycling the mean and variability from 1999-2018. 2. 2 Data processing 2. 2. 1 Three alternative f LULCC estimates and their differences We infer the three different DGVM-based f LULCC s each from the differences in NBP of a simulation with and one without LULCCs (Eqs. 1 to 3; see Table  1  for description of simulations S0 to S6 and Fig. 1  for a schematic of resulting carbon fluxes). For example, we derive the f LULCC under transient environmental conditions by subtracting NBP in S3 from NBP in S2 (Eq. 1). Using yearly aggregated NBP values, the f LULCC is derived for each DGVM, time step, and grid cell under transient (subscript trans), constant preindustrial (pi), and constant present-day (pd) environmental conditions from the TRENDY v8 simulations as follows: f LULCC_trans = NBP S2 -NBP S3 (1) f LULCC_pi = NBP S0 -NBP S4 (2) f LULCC_pd = NBP S6 -NBP S5.
A13 ). Note, for grid point-wise comparison, all model output was regridded to 720×360 grid boxes using first-order conservative remapping  (Jones, 1999). Due to high interannual NBP variability, the resulting regional and global f LULCC estimates were smoothed by a Savitzky-Golay filter using 5 % of the spatially summed annual data points (16 years). Savitzky-Golay smoothing was applied to preserve peak heights and widths, which are known to be removed by other smoothing practices such as moving averages. All data pre-processing and statistical analysis were performed using Climate Data Operator software  (CDO, v1. 9. 3; Schulzweida, 2019), netCDF Operators (NCO, v4. 7. 7; Rew et al. , 1997), and raster-(v2. 8-4; Hijmans and van Etten, 2014), ncdf4-(v1. 16. 1; Pierce, 2019), matrixStats-(v0.
3  and 4 , showing the multi-model mean ±1 SD). This pronounced model spread can be explained by intertwining issues, such as the low quality of historical LULCC data (with different databases), the consideration or neglection of relevant processes (e. g. nitrogen fertilization), the simplified representation and uncertainty in the parameterization of management and natural processes, uncertainties in soil and vegetation C stocks, and the lack of observational constraints  (Friedlingstein et al. , 2019; Gasser et al. , 2020; Lienert and Joos, 2018; Goll et al. , 2015; Li et al. , 2017). The global EED and PTD were higher than in the other models for LPJ-GUESS, ORCHIDEE-CNP, and DLEM and lower for CLASS-CTEM, LPX-Bern and SDGVM. The PTD and EED show the highest model spread at the time of maximum LULCCs and towards the end of the simulation period, in particular in regions where vast areas of land were transformed  (Brazil, equatorial Africa, Central, and South and Southeast Asia).
Zooarchaeological studies of fossil bone collections are often conducted using simple spreadsheet programs for data recording and analysis. After quickly summarizing the limitations of such an approach, we present a new software solution, TIPZOO, which uses a FileMaker touchscreen interface combined with R scripts and QGIS files. This tool was specifically designed for zooarchaeologists needs, and is freely available at http://tipzoo. cnrs. fr. In this article, the key features of TIPZOO are highlighted, and we present some of the tools developed in order to (1) simplify the use of recently published complex coding systems, (2) better control intra-and inter-analyst variation, and (3) allow faster and more reliable data entry, analysis and visualization in Palaeolithic zooarchaeology.
Introduction Performing a zooarchaeological study of a faunal collection involves several steps, including anatomical and taxonomical identification, observation of bone surface modifications, and, as with most archaeological disciplines, data entry and analysis. For the last two steps, the vast majority of zooarchaeologists use spreadsheet software, such as Microsoft Excel, despite the inherent limitations of such programs. These software packages are, in fact, poor solutions for data recording and analysis as they are not adapted for the complexity of modern zooarchaeological analyses and integration within global archaeological datasets. The most significant problems that the use of such spreadsheet programs does not adequately address are outlined below: 1) The multiplication and complexification of recorded variables and their coding.
) involves more than 400 codes, and the NDE landmark system proposed by  Morin et al. (ibid. ) relies on more than 100 variables. The simple memorization and/or training required to efficiently employ one of these systems is already difficult, with the difficulty obviously being amplified by the use of several systems, and this necessarily renders data entry and analysis in simple spreadsheet programs quite arduous. This is in part ultimately why these useful coding systems are still not commonly used by zooarchaeologists, despite their obvious and inherent qualities for reconstructing past butchering activities and skeletal-part representation. Advances in techniques of analysis entail a cumulation of complex coding systems, which ultimately impedes their broad use because datasets become increasingly unmanageable, especially for large faunal collections.
This lack of real-time verification is therefore another source for error. 4) Database connection between zooarchaeological data with the field database. In many recent excavations, a field database is produced during excavation, recording the three-dimensional provenience for each artefact, artefact layer attributions, etc. Connecting a field database with zooarchaeological data, requiring the use of shared object keys, can seem complicated to the uninitiated. The result is that some analysts do not even attempt spatial analysis of their data as obtaining the coordinates for their sample is often seen as too cumbersome, even if spatialization of data can be particularly useful for taphonomic analyses. In order to tackle these issues, we developed a new solution for data acquisition and analysis in Palaeolithic zooarchaeology, TIPZOO, which is freely available on the http://tipzoo.
TIPZOO general overview This tool, called TIPZOO (Touchscreen Interface for Palaeolithic Zooarchaeology), is based on (Fig. 1 ): -TIPZOO-FMP: its core element, a FileMaker solution connected to a relational database specifically designed for bone recording (identifications, taphonomic observations, minimum number of elements, refits, etc. ), with a graphical and touchscreen interface adapted for tablets; -TIPZOO-R: R scripts for analysis and statistical processing of the preformatted data exported by TIPZOO-FMP, linked with a collection of pre-entered zooarchaeological reference datasets (amount of bone occurrences per skeleton, bone mineral density values, food utility marrow indices, etc. ); -TIPZOO-QGIS: QGIS project and shapefiles designed for quick and easy representation of skeletal-part profiles on skeleton outlines, using preformatted data exported by TIPZOO-R (inspired by comparable efforts by Orton, 2010 using ESRI ArcGIS).
10) Dual recording system for teeth. A recurrent issue with teeth is whether they should be recorded individually (one record per tooth, which would entail, for example, three records for one mandible fragment with three teeth), or at the scale of the fragment (one record for one mandible with three teeth). Both have their advantages and disadvantages. In TIPZOO-FMP, the "tooth creator" tool allows for a dual recording system. First, the fragment itself is recorded as a single object, and second, teeth are individually entered in a separate but linked table (Fig. 2c ). This makes it easy to record individual information for each tooth (e. g. use-wear class, crown height) without altering the main fragment database (meaning there are no duplicate entries). Furthermore, TIPZOO-FMP offers the possibility to export a summary table of all teeth recorded (either isolated or included within a cranium/mandible fragment) for a given taxon, making MNI calculations and the computation of mortality profiles much easier.
g. H12-43-1, H12-43-2, and so forth). This provides a quick and easy way to keep track of remains that have previously been numbered and avoids duplicating subID fields. A similar system is provided for refit groups (unique refit group numbers are automatically created) to easily keep track and record fragments that refit together. 2) Live connection with the field database. TIPZOO-FMP offers a simple interface to import all relevant contextual information recorded in the field. This may include, for example, object coordinates, layer attributions, squares, object descriptions, or fabric measurements (orientation and plunge of objects twice as long as wide, see  Lenoble and Bertran, 2004). When the zooarchaeologist enters a fragment in the database, using the individualized field ID, a portal shows all relevant information that was recorded in the field and verification scripts are run.
The direct connection between field data and the faunal database allows for the real-time identification of problems in the former (e. g. TIPZOO-FMP will show an error message if a bone was plotted during excavation as a "lithic" in the field database), as well as the verification of some information (e. g. if a fabric measurement was taken in the field for a bone, the appearance of a new variable [elongated?] will remind the user to verify if the fragment is elongated enough to justify the valid use of this type of measurement, forcing the user to fill in this variable and therefore cleaning the fabric measurement database). Examples of three layouts from the TIPZOO-FMP interface, for recording a) bone portions of a tibia fragment (note the touchscreen box system for landmarks on the right), b) cut mark codes (with its graphically aided recording system), and c) a mandible with three enclosed teeth (note the "tooth creator" in light blue on the right, and the dual recording system for the whole mandible fragment -top part -and the three enclosed teeth -bottom part).
C. Data analysis Finally, TIPZOO is also intended to help with the analysis of data recorded, and therefore also has the following analytic features: 1) Automated production of summary tables, basic graphs, and statistical tests. Buttons in the TIPZOO-FMP interface allow for the export of preformatted record data that can then be opened in an Renvironment, TIPZOO-R (available on http://tipzoo. cnrs. fr). This R-environment includes scripts for the easy production of summary tables, graphs, and basic statistics. This is notably achieved via the inclusion of reference data tables, such as species' Latin and common names, number of portion occurrences per skeleton, bone mineral density values (e. g. Lam et al. , 1999) , body-part utility indices (e. g. Binford, 1978; Morin, 2007) , or correspondences between cut mark codes and butchering activities  (Soulier and Costamagno, 2017; Costamagno and Soulier, 2019) , within the TIPZOO-R environment.
The user therefore does not have to look up correspondences between the database codes and the corresponding information. TIPZOO-R automatically searches for this information in pre-entered reference datasets and automatically produces the summary tables (Fig. 3a ), graphs, and statistical tests (Fig. 3b-d ). Analytical scripts and reference datasets are continuously and gradually being integrated, thus TIPZOO-R is still far from complete, but should be considered a continuously evolving analytical tool. Zooarchaeological analysis is made faster by adding reference datasets to TIPZOO-R once and by subsequently scripting the necessary calculations for future use. TIPZOO-R scripts can also be used without relying on TIPZOO-FMP for data entry, for example with spreadsheet software, but only if TIPZOO-FMP's formatting rules are followed (as described in the manual).
2) Automated creation of body-part skeletal figures. Once datasets are processed in TIPZOO-R, they can be exported and opened in a pre-designed QGIS project, with integrated shapefiles (TIPZOO-QGIS), that allows for rapid schematic representations of body-part profiles using skeletal outline templates. Different species, and other types of data visualization, will gradually be developed and integrated into the system and made available on the TIPZOO website.
Discussion TIPZOO is a further step towards encouraging accurate and standardized recording practices among zooarchaeologists, building upon many previous efforts. It does this by attempting to reduce inter-analyst differences and by limiting undesired variability that can be introduced during data entry, using practical solutions such as bone and skeletal templates, relational databases with preset fields, landmark codes, etc. (e. g. Watson, 1979; Dobney and Rielly, 1988; Marean et al. , 2001; Harland et al. , 2003; Popkin, 2005; Thompson, 2005; Fischer, 2007; Smith, 2010; Abe et al. , 2012; Dibble, 2015; Garcia-Moreno et al. , 2015; Morin et al. , 2017 Morin et al. , , 2019)). TIPZOO differs from previous attempts, however, by proposing a comprehensive solution that accompanies researchers through data entry, analysis, and visualization, as well as by using a touchscreen interface that was specifically designed to facilitate and accelerate data entry in Palaeolithic zooarchaeology.