dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The data that support this study are available from the corresponding authors upon reasonable request. Informed consents from patients do not cover the deposition of sequencing data from the patient samples. These data are available only upon request from S. M. (stefan. mundlos@charite. de). Data can be shared for research purposes with permission of the patient or his/her legal guardian. The gene annotation from Gencode (v19) was used. TAD annotations for hg19 were downloaded from the website of the 3D genome browser for cell line GM12878  53. A/B-compartment annotation was taken from previously published work  25  (GEO GSE63525), the subcompartments were collapsed to A and B compartments. LAD annotation for T cells was taken from previously published work  26  (GEO GSE94971).
Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e. g. , attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mainly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults ( InLang [Interactive networks of Language] database ), providing the opportunity to assess language features across a wide range of linguistic processes.
Using this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG atlas). The organization of this datadriven neurocognitive atlas of language was examined at multiple levels, uncovering its major components (or crucial subnetworks ), and its anatomical and functional correlates. In addition, we estimated its reconfiguration as a function of linguistic demand ( flexibility ) or several factors such as age or gender ( variability ). We observed that several discrete networks could be specifically shaped to promote key functional features of language: codingdecoding (Net1), control-executive (Net2), abstract-knowledge (Net3), and sensorimotor (Net4) functions. The architecture of these systems and the functional connectivity of the pivotal brain regions varied according to the nature of the linguistic process, gender, or age.
The anatomo-functional substrates associated with language are indeed highly task-dependent  ( Hickok and Poeppel, 2000 ). The task-based FC analyses presented here rely on an fMRI database compiling a broad spectrum of language-related tasks (Interactive networks of Language database: InLang database). InLang comprises thirteen language tasks, performed cross-sectionally by 150 right-handed neurotypical adults. The database is unique in that it covers a broad spectrum of language features: semantic and conceptual processing,  decoding (phonology, sound) , lexico-syntactic formulation (production), dialogality (social aspects of language), monitoring of self and others, and unintentional speech ( Fig. 1 A ; see also the Supplementary Material for more details). Such a database is essential to uncover the functional architecture of the multifaceted language processes in an integrative approach.
Dataset The InLang database contains data of 13 different linguistic tasks from 7 previously published fMRI protocols  ( Baciu et al. , 2016 ; Banjac et al. , 2020 ; Grandchamp et al. , 2019 ; Haldin et al. , 2018 ; Hoyau, 2018 ; Perrone-Bertolotti et al. , 2011 ; Perrone -Bertolotti et al. , 2012 ; Perrone-Bertolotti et al. , 2015 , 2017 ) , as well as respective structural MRIs (T1w). In all, 359 functional scans have been acquired between 2010 and 2019 from 150 healthy adults (all right-handed according to the Edinburgh Handedness Inventory (handedness score strictly greater than + 80;  Oldfield, 1971 ) ; 64 females: F/86 males: M; Table  S1  contains the subjects' characteristics, by tasks). The database includes 114 "young/middle-aged adults " (18-59: mean = 36. 87; SD = 13. 29; 50F/64M) and 36 "seniors " (older adults from 60-85: mean = 70.
Task-based connectomes Regions of interest (ROIs) covering both the brain and the cerebellum were defined from 6 mm radius spherical regions built around the 264 coordinates in MNI space proposed by  Power et al. (2011). The images used for signal extraction (beta values) were the statistical parametric maps containing the linear contrasts between the HRF parameter estimates for the conditions of interest. Nilearn ( https://nilearn. github. io/stable/index. html ;  Abraham et al. , 2014 )  was used to delineate ROIs and extract the signal. The mean signal for each of these ROIs was extracted by participant and task. Functional connectivity (FC) between brain ROIs was derived from correlating (Pearson correlations) the extracted beta values across subjects, within a given task. The process was repeated for each of the tasks separately, allowing to obtain thirteen 264 * 264 matrices of interregional FC based on beta-series correlation.
We extracted the relative spatial layout of regions along the cortical surface by using existing scripts ( https://github. com/margulies/ topography/tree/master/utils ), based on an algorithm developed to approximate the exact geodesic distance from triangular meshes  ( Oligschläger et al. , 2017 ). "Physical " geodesic distances between pairs of nodes, estimated in mm, were quantified from the Power's nodes coordinates projected onto a template surface mesh (fsaverage5). This resulted in a node-by-node matrix of geodesic cortical distance. From the geodesic distance matrix, we averaged the distribution of distance-toconnected-areas of the relevant functional connections identified from the thresholded FC matrices. We thus obtained the global geodesic distance of the functional connectivity for each of the task-related connectomes.
Starting from the partitioning, we computed the FC matrices of each of these main task groups -hereafter referred to as subprocess connectomes -from the scans of the respectively involved tasks and using the same procedure described above ( Section 2. 2. ). In addition, and still following the same procedure, we generated the general task-based language connectome -abbreviated LANG -corresponding to the FC matrix derived from all language tasks. The global FC parameters reported in the previous section ( Section 2. 3. 1 ) were also extracted for the LANG and the respective subprocesses connectomes. Note that only scans corresponding to the young/middle-aged adults of the InLang cohort ( n = 114) were considered for the calculation of the FC matrices (of the tasks, subprocesses, and the general LANG connectome) and the main statistical analyses.
See Table  S5  for a detailed summary of significant differences. database of multi-paradigm language tasks ( InLang [Interactive networks of Language] database), and we applied a state-of-the-art functional connectivity (FC) methodology that provided unique insights on brain networks related to language. The central finding of this research was that the general language connectome (LANG) could be objectively partitioned into four main non-overlapping subnetworks (referred to as "Nets "), possessing distinctive and marked features. Table  1  below provides a complete overview of these Nets. The most extensive subnetwork, Net1, appears to correspond to a "specialized " language system shaped for the encoding-decoding of auditory-verbal signals. Indeed, Net1 consisted primarily of areas belonging to the intrinsic networks previously designated as "auditory " and "language " (CAB-NP RSNs:  Ji et al.
The great benefit of partitioning emerging directly from data is to pinpoint latent mechanisms that transcend our classical cognitive descriptions (see interesting discussions on the current problem of brainbehavior concordance or the blurriness and ambiguity associated with terminology and definition of psychological constructs:  Anderson, 2011 ; Buzsáki, 2020 ). Data-driven ontology provides an independent view, here from a neuro-centric perspective  ( Roger et al. , 2022 ) , and can serve as a "lingua franca across disciplines and theoretical gaps "  ( Eisenberg et al. , 2019 ). However, since they are derived from the observations, these partitions depend directly on the quantity, quality, sensitivity, and validity of the data used. This study has the advantage of being based on a database including a rich diversity of fMRI protocols, varying in a wide range of language characteristics ( Fig.
1  ). However, task paradigms are less easy to implement than the resting state. They are typically very controlled, require multiple repetitions to obtain a robust signal/noise ratio, are more prone to movement artifacts, and induce higher interindividual variability  ( Park et al. , 2020 ). This often leads to smaller final samples, which may be a prominent issue for subsequent analyses. Taking into consideration the need to maximize observations to ensure robust results, we have focused most of our analysis on the investigation of the general connectome or on the subprocesses common to several tasks (and not on individual tasks). The compilation of even larger databases will allow for broader and more detailed investigations. For example, it would be important to consider the pragmatic aspects of language ( Rasgado-Toledo et al.
, 2021 ), which are not specifically valued in the InLang database. Moreover, the current trend is to extend the framework of fMRI paradigms traditionally employed in "laboratory " settings to less controlled and more ecological protocols  ( Verga and Kotz, 2019 ). As evidenced by the recent Neuroimage Special Issue  ( Finn et al. , 2022 ) , several initiatives and datasets are steering toward accounts of cognition in more natural settings (e. g. ,  Bhattasali et al. , 2020 ; LeBel et al. , 2021 ; Nastase et al. , 2020   2017 ) data collections]. Multimodal datasets would then allow more direct confirmation of the neurofunctional relevance of the spatial correspondences we observed regarding anatomo-functional correlates of LANG. In our study, analysis of the links between LANG and anatomical connectivity and neurotransmitter maps could not be performed in the same individuals.
Multimodal data would provide more subtle indicators of the correspondence between structure, function, and neurotransmitter pathways than the simple matching coefficient. It is essential when considering highly interactive complex networks where one-to-one relationships cannot be a viable principle  ( Suárez et al. , 2020 ). In addition, considering behavioral and cognitive performance is crucial for a better description of the functional repertoire. We must acknowledge that one of the main limitations of the proposed database concerns the cross-sectional aspect of the cohort and, consequently, the lack of (homogeneous) behavioral/cognitive data (i. e. , inconsistent response modalities; some tasks were designed without directly measurable behavioral output, without overt speech production or with inner speech, depending on the targeted linguistic processes or to limit movement artifacts).
While databases built from pre-existing data are essential to address current data reuse concerns (see the FAIR principles;  Wilkinson et al. , 2016 ) , building highly multimodal databases -coupled with improved methods of acquisition, processing, and statistical investigation -is a practical approach to tackle the issue of brain-behavior correspondence more effectively. Such multimodal data acquired systematically across subjects would also limit the inter-subject variability inherent in cross-sectional databases, which may lead to greater stability and accuracy of results. However, the presence of functional (language) tasks and/or behavioral data in the multimodal neurocognitive datasets available to date is still generally limited. We have attempted to comply with the best practices mentioned for analyzing and sharing FC data following the guidelines of the COBIDAS report  ( Nichols et al.
The present study offers a language atlas that relies on a thorough topological (i. e. , spatial) analysis of FC. One of the following steps is to identify the causal organization (e. g. , effective hierarchies/heterarchies) and precise timeframes in which its components/regions engage depending on subprocesses/mechanisms at work. MEG (such as in the MOUS dataset:  Schoffelen et al. 2019 )  or electroencephalography (EEG; placed on the scalp, the cortex, or intracranial) are valuable tools for the evaluation of such dynamics. To this end, and using intracranial EEG recordings, a recent study examined the dynamic organization of naming  ( Forseth et al. , 2021 ). They observed that regions were co-activated during extended periods, confirming that complex behaviors such as speech production requires the coordination of discrete network states (defined as a set of reference dynamics that coordinate the generation and transmission of information throughout the cortex; see also the concept of meta-networking of  Herbet and Duffau (2020)  ).
Establishing accurate age-related benchmarks requires more representative databases, both in terms of age and tasks performed, but would mutually benefit models of language and aging. The sample of our cohort of so-called young or middle-aged adults ( < 60 years) includes a wide variety of ages (mean = 36. 87; SD = 13. 29), and it would be interesting to see if there are already significant age-related changes in language networks in this sample (i. e. , before age 60). Studies of aging have indeed long shown that brain structure evolves with age and that certain regions -mainly frontostriatal -experience an early decline  ( Buckner, 2004 ; Head et al. , 2005 ; Raz et al. , 2005 ; Raz and Rodrigue, 2006 ; Ziegler et al. , 2011 ). It could result in subtle but quantifiable neurofunctional changes in the language networks.
Conclusion Language is a multi-faceted cognitive function. To account for the multidimensionality of language, we performed functional connectivity analyses on a multi-paradigm fMRI database ( InLang [Interactive networks of Language] database), gathering thirteen different language tasks. It allowed us to inspect the language connectome in-depth, particularly its spatial properties and functional attributes. This study reaffirms that high-level cognition, such as language, emanates from synergistic exchanges of external and internal information across specialized systems. Our results highlighted the involvement of essential discrete networks (or components) that are settled around a core "languagerelated " system. Furthermore, the flexible engagement of some key regions depending on several modulating factors, such as linguistic demand, pointed to the dynamic nature of language.
Acknowledgments We thank  Philippe Kahane ,  Lorella Minotti ,  Olivier David ,  Arnaud Attyé ,  Laurent Lamalle , and  Jean Francois Démonet  for their support and contribution to the  NeuroCoG  project. We also thank  Cédric Pichat ,  Sonja Banjac ,  Laurent Torlay , and  Eric Guinet  for their help in data collection and their useful insights.
Funding This work was supported by  NeuroCoG IDEX UGA , " Investissements d'avenir " program  [grant number  ANR-15-IDEX-02  ]; and by the  French program  " AAP GENERIQUE 2017  " run by the  "Agence Nationale pour la Recherche "  [grant number  ANR-17-CE28-0015-01  ].
Data availability statement InLang [Interactive networks of Language] Database: Neuroimaging data (raw and preprocessed T1w/ task-fMRI Nifti files) are available upon request. Data sharing requires approval of the request by the ethics committee of the Grenoble Alpes University Hospital (data owner) and the signature of a data use agreement (DUA). De-identified data related to the results reported in this Article: Tables/documents, codes, statistical functional maps (group and individual maps), derivatives to perform FC/networks analyses, and the connectivity-based functional atlas of language (LANG atlas) are hosted on the Open Science Framework (OSF) platform and freely available for download [InLang_database; DOI: 10. 17605/OSF. IO/6XM8N].
Increasing soil organic carbon (SOC) stocks is increasingly targeted as a key strategy in climate change mitigation and improved ecosystem resiliency. Agricultural land, a dominant global land use, provides substantial challenges and opportunities for global carbon sequestration. Despite this, global estimates of soil carbon sequestration potential often exclude agricultural land and estimates are coarse for regions in the Global South. To address these discrepancies and improve estimates, we develop a hybrid, data-augmented database approach to better estimate the magnitude of SOC sequestration potential of agricultural soils. With high-resolution (30 m) soil maps of Africa developed by the International Soils Database (iSDA) and Malawi as a case study, we create a national adjustment using site-specific soil data retrieved from 1160 agricultural fields.
, 2018)  and also excludes large, management-sensitive pools such as particulate organic matter. Alternatively, nearby natural lands are used as a primary data source for mapping stocks or estimating potentials  (Guillaume et al. , 2022; Kempen et al. , 2019). While land use change from agriculture to natural lands may allow a field to reach the ecological potential of carbon stocks, such a tradeoff with food production likely is unacceptable. Estimating a field's potential carbon stock assuming continued agricultural management avoids this tradeoff but lacks an obvious standard. We propose solutions to these dual challenges of map accuracy and the estimation of sequestration potential of agricultural soils, with Malawi as a case study. For the first, we create a national adjustment to the International Soils Database (iSDA) maps of Africa using a dataset of 1160 fields, as suggested by  Hengl et al.
| Study locations, sampling, and data acquisition Data were collected from 1160 smallholder fields as part of the Africa RISING project  (Tu et al. , 2022). Sites were selected by stratified random sampling from two villages within each of eight extension planning areas in three districts. The villages span the variation in soil conditions across Malawi (Figure  1 ). Soils were collected in 2016 as previously described  (Ewing et al. , 2021). Briefly, samples were collected to a depth of 20 cm and composited from eight locations per field. Samples were air-dried, sieved to 2 mm, and stored until analyses. Total carbon and nitrogen concentrations were measured with a Leco Tru-Mac CN Analyzer (Leco Corporation). pH was measured in a 1:2 soil:water slurry. Texture was measured using the micropipette method  (Burt et al.
All properties are estimated from iSDA or the SRTM-90 digital elevation model. Densities indicate the proportion of points along the x-axis. Map lines delineate study areas and do not necessarily depict accepted national boundaries. to avoid compaction, three from the row and three from the interrow to account for management-induced differences in these locations. The six cores were then composited, oven dried, and weighed. Layers of estimates for soil properties in Malawi were downloaded from the Innovative Solutions for Digital Agriculture database  (iSDA; www. isda-africa. com; Hengl et al. , 2021)  following OpenLandMap instructions (https://gitlab. com/openl andma p/afric a-soil-and-agron omy-data-cube) within R 4. 0. 2 for Malawi and transformed to SI units. Soil properties were mass fractions of total carbon, organic carbon, sand, silt, clay, plus bulk density, pH, and carbon stocks for the 0-20 cm depth.
| Estimation of carbon stocks and storage potential All analyses were performed in R 4. 1. 1 (R Core Team, 2021). Unless noted, graphs and maps were made using ggplot2, geospatial analyses were conducted with sf, terra, and stars, and other statistics were completed using base functions  (Hijmans, 2022; Pebesma, 2018 Pebesma, , 2022;; Wickham, 2016). Geospatial analyses were conducted in the WGS84(2007) datum, while maps were projected in NAD83 UTM Zone 36S. Code for adjusting iSDA layers, deriving benchmarks, evaluating prediction error, and predicting potentials is available on Zenodo at https://doi. org/10. 5281/zenodo. We used an equivalent-depth approach to estimate and compare carbon stocks at all measured sites  (Rovira et al. , 2022). We first derived a pedotransfer function to predict soil bulk density at those sites where bulk density was not measured.
7 ± 1. 3 Mg C ha -1 in Nsanama (Machinga district) to 48. 8 ± 3. 7 Mg C ha -1 in Linthipe (Dedza). We further wanted to identify how much carbon soils could store under optimal, agricultural management. In general, higher elevation and finer textured soils contained more SOC, in agreement with previous studies  (Matus, 2021 ; Figure  3 ). Using datasetspecific quantile regression equations (Table  S2 ), we found that potential carbon stocks in agricultural soils also were larger than iSDA predicted and averaged within extension planning area were as high as 54. 4 ± 4. 6 Mg C ha -1 in Linthipe (Dezda). (2) R 2 = 1 -SS error × SS total -1 , F I G U R Finally, we found a consistently large gap between current and potential SOC stocks that was substantially higher than iSDA predicted (4. 42 ± 0.
23 Mg C ha -1 ) and exceeded 10 Mg C ha -1 in Linthipe and Nyambi (Machinga). Investigation at the field level revealed an underprediction of current and potential SOC stocks and gaps in the iSDA dataset, especially in fields with more than 20 Mg C ha -1. This was consistent across the sampled region (Figure  2b ) and consistent with previous, independent ground truthing of continental soils databases based on remote sensing  (Ewing et al. , 2021).
| In-field estimates of soil carbon remain essential for policy, management, and interventions We found that iSDA, which as of writing provides the highest resolution predictions of soil properties in Africa, was able to identify fields with high storage potential in its current form-if stocks are recalculated using iSDA predictions for bulk density and SOC. However, adjusting iSDA slightly improved sensitivity and resolved a systematic underestimation of SOC gaps. Armed with such identifications of fields, we expect that policies and entrepreneurial activity aimed at improving carbon stocks or capturing carbon credits will be more effective at targeting farmer-partners. Additionally, local adjustments to regional databases can provide an accurate prediction of carbon stocks and gaps within administrative districts.
Still, adjustments to regional databases do not obviate the need for on-site sample collection for quantifying carbon sequestration potential in agricultural soils at the field scale. Rather, such sampling remains an important component of farmer engagement toward increasing SOC stocks. One reason for the difficulty in accurately predicting carbon stocks and gaps-even with datasets augmented with locally collected data-may be that in this region, management varies from plot to plot; as a result, land degradation can be both extreme and highly asymmetric at the scale of tens of meters due to soil's sensitivity to management practices  (Li et al. , 2017; Moebius-Clune et al. , 2011; Snapp, 1998; Tittonell et al. , 2005). Continued sampling therefore serves multiple purposes. The first is scientific, to aid the development of regionally specific databases to use in benchmarking to estimate carbon gaps.