dataesr/scientific-paragraphs-categorization huggingface dataset:
The association tests were performed using a multi-locus mixed-model, MLMM, proposed by  Segura et al. (2012). The model is based on a classic GWAS model  (Yu et al. , 2006)  on which marker selection is performed using a forward stepwise approach. At each step the SNP with the smallest p-value is added to the model and the p-value thus as the residual variance are re-estimated for all cofactors. The forward selection analysis stops when the proportion of variance explained by the model is close to zero. The analysis has been conducted thanks to the MLMM code written by  Segura et al. (2012)  and with ASReml-R package  (Butler et al. , 2009)  and the modifications provided by  (Bonnafous et al. 2017). The method used is now available on CRAN with mlmm. gwas package.
Globally, the SUNFLO crop model has about 50 equations and 64 parameters: 43 plant-related traits, among which eight are genotype-dependent, and 21 environment-related. A report that summarizes the equations and parameters used in the model is available as supplementary information in  Picheny et al. (2017). The source code is available on INRA software repository [https://forgemia. inra. fr/record/sunflo. git]. The INRA VLE-RECORD software environment  (Bergez et al. , 2013; Quesnel et al. , 2009)  was used as simulation platform. The model inputs are split in four categories: genotype, climate and soil, management actions and initial conditions. In the model, a genotype is represented by a combination of eight genotype-dependent parameters whose values are assumed to be constant among environments, thus trying to mimic genetic information  (Boote et al.
Needed management practices includes sowing date, planting density, irrigation and nitrogen fertilization. The SUNFLO simulation model was evaluated both on specific research trials (40 trials, 110 plots) and on agricultural extension trials that were representative of its targeted use (96 trials, 888 plots). Over these two datasets, the model was able to simulate significant genotypes-environment interaction and rank genotypes  (Casadebaig et al. , 2011; Casadebaig, Mestries, et al. , 2016). The prediction error for grain yield was 15. 7% when estimated over all data (9% -30% in individual trials). From these two evaluations, we determined that the model was accurate enough to discriminate between two given genotypes.
Impact of parameterization method on model accuracy Here, we evaluate how the model accuracy is impacted by using the Heliaphen phenotyping platform to estimate the value of two genotype-dependent parameters, the response of leaf expansion and plant transpiration to water deficit. In the parameterization dataset, three genotypes were common between two outdoor experiments and six greenhouse experiments  (Casadebaig et al. , 2008). The test dataset was a subset of these three genotypes from a multi-environmental network (described in section plant material) were grain yield was observed in 10 locations. We assessed the model prediction capacity using the bias, the root of mean square error (rmse), and relative rmse, which are different metrics commonly used to evaluate the predicting ability of models.
The data related to soil properties were obtained by local soil analysis for 13 of 42 trials and completed with the European soil database (ESDAC, esdac. jrc. ec. europa. eu, Panagos et al. (2012)  ;  Hiederer (2013) ). The climatic dataset was provided by the Agri4cast data portal (Gridded Agro-Meteorological Data in Europe). For each considered location in the study, meteorological data from the nearest grid (25x25 km) point was used, over the 1996-2016 period. For each genotype x location x year combination (n=3528), six output variables were simulated by the crop model: seed yield at harvest, oil content at harvest, and four time-series of the impact of selected abiotic stress on the simulated photosynthesis rate (water stress, nitrogen deficiency, low temperature stress and high temperature stress).
Similar cropping condition are then grouped together to identify broad environment-types were stress dynamics are more predictable (also known as envirotyping,  Xu, 2016). Using this method, we identified the major stresses occurring on the various sampled locations. The accuracy of this method depends on the uncertainty of data used to describe the locations (climate, soil properties, crop management systems). Even if soil and climate databases exists, we found that the method was particularly sensitive to the estimation of soil available water capacity. With finer environmental descriptions available in the future, we hope to increase the resolution and span of the simulations. This would be a key step to extend classical variety evaluation, and work on recommendation zones based on environment-types identified with simulation studies.
Acknowledgements Research grants were provided by the  French Ministry of Research  ( ANR SUNRISE ANR-11-BTBR-0005 ). We thank all partners of the  SUNRISE  project of the  French National Research Agency: Biogemma , Caussades semences, Maisadour semences, RAGT 2n, Soltis,  Syngenta , and  Terres Inovia  for field trials and the provision of genetics materials. We especially thank  Stéphane Muños  for the provision of the genetic map on the RILs population. We are also grateful to the students ( Claire Barbet-Massin ,  Ewen Gery ,  Bertrand Haquin ) and staff from  INRA, ENSAT  ( Michel Labarrère, Pierre Maury, Colette Quinquiry ) that helped to constitute the phenotypic database, and to the RECORD team from  INRA  that helped in modeling and simulation. Florie Gosseau PhD was funded by  MAS Seeds and Caussades Semences.
Functional Magnetic Resonance Imaging (fMRI) has furthered brain mapping on perceptual, motor, as well as higher-level cognitive functions. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a ne spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multitask fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a cohort of 12 participants performing many dierent tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The present article gives a detailed description of the rst release of the IBC dataset.
Establishing an atlas of brain functions calls for probing many dierent cognitive tasks. Yet, such coverage has only been possible by compounding dierent studies, like in meta-analyses. This approach is however susceptible to many sources of variability, such as between-protocol and inter-subject variability. The latter problem is common to all group-level analyses. It has been shown to undermine the estimation of statistical signicance  [1, 2]  and the ascription of brain locations to specic functions  [3, 4]. A handful of studies in neuroimaging has adopted individual analysis, in order to overcome both functional and anatomical inter-subject variability  [3, 4, 5, 6, 7, 8, 9]. To avoid sources of inter-subject plus inter-site variability across tasks, the Individual Brain Charting (IBC) project aims at providing an heterogeneous task-fMRI dataset acquired in a xed environment.
The data are obtained from a permanent cohort of 12 participants during the performance of a dozen of tasks. Given its task-wise organization, the IBC dataset encompasses a wide range of psychological domains that will yield a rened characterization of the neurocognitive mechanisms underlying human behavior. Raw and postprocessed data from within-subject level analysis are made available along with task-stimuli. The data are acquired at high-spatial resolution (1. 5mm for fMRI data) to enable accurate delineations of brain regions and elicit ner macroscopic representations of functional networks. There has been a strong uptake in large-scale brain imaging datasets. Nonetheless, existing ones dier signicantly from the IBC dataset. Many of these collections are centered on resting-state fMRI data  [10, 11, 12, 13, 14] , including largescale databases devoted to research in clinical neuroscience  [15, 16].
Further, the Human Connectome Project (HCP) accounts for a considerable amount of subjects scanned with dierent neuroimaging modalities  [17] , including not only resting-state but also task-fMRI data. This component of the project sought to delineate and characterize representative functional territories, according to their implication in task performance on a large population size  [18, 19]. It comprises however a limited amount of fMRI tasks. The brain atlas was mostly developed from resting-state fMRI data  [19]  and emphasis was given to population aspects, like the investigation of between-subject variability with respect to behavior  [20]  or genetics  [21]. Another example is the studyforrest initiative, which provides a multi-modal brain-imaging dataset comprising task-fMRI data on the continuous presentation of audio descriptions from the Forrest Gump movie.
It has given rise to many studies investigating the neurocognitive encoding of complex auditory information, like the ability to perceive language, music or social interplay  [6, 22, 23, 24]. The tasks employed across these studies were nevertheless restricted to naturalistic stimuli, that can not be easily integrated in more classical controlled experiments. Finally, a forerunner of the IBC project  [25]  relates to the collection of a functional localizer task on 81 participants. This task, named ARCHI Standard, is used in this rst release of the IBC dataset. Part of the data are openly available in the Brainomics/Localizer database  [26]. However, this study was dedicated to between-subject comparison  [27, 28] , rather than ne cognitive mapping, which requires greater amount of tasks. Here, we provide a description of the rst release of the IBC dataset, comprising 59 conditions on several cognitive domains, such as somatotopy, calculation, language comprehension and social cognition.
Thus, it accounts for 59 independent contrast maps per participant. To achieve a comprehensive collection of task-fMRI data, acquisitions are continuously ongoing and more releases are expected over the next years. Although every participant is subject to many Magnetic Resonance Imaging  (MRI)  sessions in the course of approximately seven years, the project does not stand for a longitudinal study. Repeated measurements of the tasks will not be systematically undertaken. Lastly, the IBC dataset is a free and open dataset devoted to providing a comprehensive characterization of brain systems within the scope of cognitive neuroscience. It is intended to become a reference for human neuroscience and to clarify the relationships between function and structure.
Stimulation For all tasks (see Section 2. 4 for details), the stimuli were delivered through custom-made scripts that ensure a fully automated environment and computercontrolled collection of the behavioral data. Two software tools were used for the development of such protocols: (1) E-Prime 2. 0 Professional (Psychological Software Tools, Inc. ); and (2) Expyriment (version 0. 7. 0, Python 2. 7). The visual and auditory stimuli presented in the protocols obtained from the HCP consortium  [18]  were translated to french. The auditory material for the Language processing task of HCP (see Section 2. 4, HCP Language task of  [18] ) was synthesized using the Acapela Text To Speech software (Acapela Group). The corresponding material is publicly available, as described in Section 2. 7.
MRI Equipment The fMRI data were acquired using an MRI scanner Siemens 3T Magnetom Prisma t along with a Siemens Head/Neck 64-channel coil. The Screening ses-sion (see Table  2  for denition) was performed on a Siemens 3T Magnetom Trio with a 32-channel coil for two participants. In order to register the behavioral responses from participants during the scanning sessions, two MR-compatible, optic-ber response devices were interchangeably used according to the type of task employed: (1) a ve-button ergonomic pad (Current Designs, Package 932 with Pyka HHSC-1x5-N4); and (2) a pair of in-house custom-made sticks featuring one-top button. MR-Confon package was utilized as audio system in the MRI environment. All sessions were conducted at the NeuroSpin platform of the CEA Research Institute, Saclay, France.
Experimental Procedure Since the IBC initiative stands for a long-term project involving a xed set of participants, they were subject to a complete assessment before their inclusion in the cohort. The MRI sessions dedicated to the collection of the present dataset were preceded by three sessions of evaluation. The rst two sessions were dedicated to general medical assessment of the participants. They include a checkup following all inclusion criteria as well as additional exams, such as blood sampling, auditory exams and questionnaires. Blood samples were taken from the subjects during these sessions and, regularly, afterwards at ten time points throughout the study. This procedure is intended to look over potential long-term eects due to repeated MRI acquisitions. An inquiry to assess for prior and/or current consumption of psychotropic substances was performed at the rst session; the tetrahydrocannabinol (THC) urine-testing procedure has been employed afterwards, concretely in the beginning of every session, to detect for cannabis usage during the last three days preceding the session.
The third session, i. e. the Screening session, was subsequently dedicated to a neurological assessment i. e. detection of potential neurological issues and brain abnormalities and behavioral inspection i. e. small motion amplitude and compliance with given instructions as well as the quality check of the MRI data collected. All remaining sessions pertained to MRI acquisitions entirely devoted to data collection (see Table  2  for details on the organization of the MRI sessions). The participants rst learned about the execution plus timing of the behavioral tasks related to the ongoing MRI session. This short training period took place in a dedicated room outside of the scanner environment. During practice, the participants were seated in a chair in front of a laptop placed on a desk. While the laptop displayed the stimuli following a predetermined sequential order, the participants complied with the corresponding task-related instructions.
In addition, ear plugs were used to reduce scanner noise and headphones were used to allow for verbal communication with the experimenters. Behavioral tasks were carried out across several runs within the same session (to learn more about the paradigms of the tasks, consult Section 2. 4). Each taskrelated run was repeated in multiples of two, alternating the phase-encoding direction (see Section 2. 5. 2 for details). Each session was dedicated to one or more tasks. The overall temporal structure of the sessions according to the MRI modality employed is detailed in Table  2 ; specications about imaging parameters of the referred modalities are described in Section 2. 5. 2. The acquisitions were separated by short pauses, ranging between one and three minutes. During these technical breaks, the well-being of the participants was veried through informal interactions and visual inspection of the newlycollected MRI data was carried out for quality-checking.
There were two BOLD runs, corresponding to PA-and AP-phase-encoding directions for each task during a session. As an exception, the session dedicated to the RSVP Language task included three runs for each phase-encoding direction. In addition, the experimental paradigm of this task displays dierent stimuli and has dierent onsets across repetitions. The 2D Spin-Echo AP/PA maps were always acquired before the runs dedicated to the collection of BOLD fMRI data and repeated afterwards. employed in the study. Note that the material used for stimulus presentation has been made publicly available (see Section 2. 7), together with video annotations of the corresponding protocols. For each subject, the onset les describing the actual occurrence of events are part of the dataset, following BIDS Specication.
Preprocessing Source data were preprocessed using PyPreprocess. This library oers a collection of Python tools to facilitate pipeline runs, reporting and quality check (https://github. com/neurospin/pypreprocess). It is built upon the Nipype library  [43]  v0. 12. 1, that in turn launched various commands used to process neuroimaging data. These commands were taken from the SPM12 software package (Wellcome Department of Imaging Neuroscience, London, UK) v6685, and the FSL library (Analysis Group, FMRIB, Oxford, UK) v5. 0. All fMRI images, i. e. GE-EPI volumes, were collected twice with reversed phase-encoding directions, resulting in pairs of images with distortions going in opposite directions (see Section 2. 5. 2 and Table  2  for details). Susceptibilityinduced o-resonance eld was estimated from the two Spin-Echo EPI volumes in reversed phase-encoding directions.
The images were corrected based on the estimated deformation model, using the topup tool  [44]  implemented in FSL  [45]. Further, the GE-EPI volumes were aligned to each other within each participant. A rigid body transformation was employed, in which the average volume of all images was used as reference  [46]. The mean EPI volume was also coregistered onto the corresponding T1-weighted MPRAGE (anatomical) volume for every participant  [47]. The individual anatomical volumes were then segmented into tissue types to nally allow for the normalization of both anatomical and functional data  [48]. Concretely, the segmented volumes were used to compute the deformation eld for normalization to the standard MNI152 space. The deformation eld was then applied to the EPI data. In the end, all volumes were resampled to their original resolution, i.
fMRI Model Specication The fMRI data were analyzed using the General Linear Model (GLM). Regressors of the model were designed to capture variations in BOLD response strictly following stimulus timing specications. They were estimated through the convolution of temporal representations referring to the task-conditions with the canonical Hemodynamic Response Function (HRF), dened according to  [49]  and  [50]. The temporal prole of the conditions was characterized by boxcar functions. To build such models, paradigm descriptors grouped in triplets (i. e. onset time, duration and trial type according to BIDS Specication) were determined from the log les' registries generated by the stimulus-delivery software. To account for small uctuations in the latency of the HRF peak response, additional regressors were computed based on the convolution of the same taskconditions prole with the time derivative of the HRF.
Nuisance regressors were also added to the design matrix in order to minimize the nal residual error. To remove signal variance associated with spurious eects arising from movements, six temporal regressors were dened for the motion parameters. Further, the rst ve principal components of the signal, extracted from voxels showing the 5% highest variance, were also regressed to capture physiological noise  [51]. In addition, a discrete-cosine transform set was applied for high-pass ltering (cuto=128 seconds). Model specication was implemented using Nistats library v0. 1, a Python module devoted to statistical analysis of fMRI data (https://nistats. github. io), which leverages Nilearn  [52] , a Python library for statistical learning on neuroimaging data (https://nilearn. github. io/).
Model Estimation In order to restrict GLM parameters estimation to voxels inside functional brain regions, a brain mask was extracted from the mean EPI volume. The procedure implemented in the Nilearn software simply thresholds the mean fMRI image of each subject in order to separate brain tissue from background, and performs then a morphological opening of the resulting image to remove spurious voxels. Regarding noise modeling, a rst-order autoregressive model was used in the maximum likelihood estimation procedure. A mass-univariate GLM t was applied separately to the preprocessed GE-EPI data of each run with respect to a specic task. Parameter estimates pertaining to the experimental conditions were thus computed, along with the respective covariance at every voxel. Various contrasts (linear combinations of the eects), were then dened, referring only to dierences in evoked responses between either (i) two conditions-of-interest or (ii) one condition-of-interest and baseline.
Summary Statistics Because data from each task were collected, per subject, at least in two acquisitions with opposite phase-encoding directions (see Section 2. 5. 2 for details), statistics of their joint eects were calculated in all aforementioned contrasts with as Fixed-Eects (FFX) model. Specically, t-tests were computed at every voxel for each contrast, in order to assess the statistical signicance of the dierences among evoked responses. For further analyses (see Sections 4. 2 and 4. 3), all images are conned to an average mask of the grey matter across subjects, keeping all voxels with more than 25% average grey matter density across subjects, as obtained from the SPM12 anatomical segmentation. This masking procedure yields 372k voxels at the chosen resolution.
Code Availability Metadata, concerning the stimuli presented during the BOLD fMRI runs, were made available publicly at https://github. com/hbp-brain-charting/public_ protocols. They include:  (1)  the task-stimuli protocols; (2) demo presentations of the tasks as video annotations; (3) instructions to the participants; and (4) scripts to extract paradigm descriptors from log les for the GLM estimation. The scripts used for data analysis were also made available through Github under the Simplied BSD license: https://github. com/hbp-brain-charting/ public_analysis_code.
Source data Raw data (aka source data according to BIDS Specication) of the present release can be accessed from the OpenfMRI public repository  [53]  under the data accession number ds000244 (https://openfmri. org/dataset/ds000244/; see Data Citation 1 for details). This release counts in total for 306GB of MRI data. The corresponding NIfTI les as well as paradigm descriptors and imaging parameters were organized per run for each session following BIDS Specication: • The data repository is organized in twelve main directories sub-01 to sub-14. Note that sub-03 and sub-10 are not part of the data (see Table  1 ); • Data from each subject are then numbered on a per-session basis, following the chronological order of the corresponding acquisitions. Note that this order is not the same for all subjects; • Acquisitions are organized within session by modality: func, fmap are part of all sessions, whereas anat and dwi are only part of the Screening session; • Image volumes of BOLD fMRI data are provided as gzipped NIfTI 4D les, with an identier corresponding to the following pattern: sub-XX_ses-YY_task-ZZZ_acq-AA_bold.
Relevance of the IBC dataset for brain mapping Following the aforementioned procedures, one map per subject was obtained for each condition and each phase-encoding direction (PA/AP), with two exceptions:  (1)  some experiments were repeated between the Screening and ARCHI sessions, in which case only the latter was systematically considered; and (2) the RSVP Language task yields six maps, i. e. 3 in PA and 3 in AP phase-encoding direction respectively. There are, in overall, 59 conditions across tasks, which amounts to approximately 176 maps per subject, i. e. 2112 activation maps in total. Two high-level analyses of the activation maps, obtained upon processing and analysis of the individual data, are herein presented in order to assess whether they actually capture relevant cognitive networks.