dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This project was partially funded by the  German National Cohort  and the  1000BRAINS -Study of the Institute of Neuroscience and Medicine, Research Centre Jülich, Germany. We thank the  Heinz Nixdorf Foundation (Germany)  for the generous support of the  Heinz Nixdorf Study. We thank the investigative group and the study staff of the  Heinz Nixdorf Recall Study  and 1000BRAINS. ML would like to thank  Paul Triebkorn  for the Visualization support and both  Viktor Sip  and  Lionel Kusch  for their insights on programming with HPC facilities. The authors also wish to acknowledge the financial support of the following agencies: the  French National Research Agency  (ANR) as part of the second " Investissements d'Avenir" program  ( ANR-17-RHUS-0004 ,  EPI-NOV ),  European Union 's  Horizon 2020 research and innovation program  under grant agreement nos.
Key resources table Reagent or resource Source Identifier Deposited data Structural connectivity data of the 1000 BRAINS dataset EBRAINS https://doi. org/10. 25493% 2F6640-3XH Software or algorithms The virtual aging brain This paper https://github. com/ins-amu/ virtual_aging_brain The virtual brain The virtual brain foundation https://github. com/the-vir tual-brain/tvb-root https:// www. thevirtualbrain. org/tvb/ zwei/home CAT12 toolbox.
In order to take ecosystems into account within natural capital, multiple measurement and quantification approaches are being developed. Among these tools, the Artificial Intelligence For Ecosystem Services (ARIES) developed by the University of Vermont makes it possible to map and quantify ecosystem services and their beneficiaries at the scale of a territory  (IONESCU et al. , 2019). Another example is the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), a suite of software models based on ecosystem services. These models are designed for different types of ecosystems (terrestrial, aquatic, coastal. ). Governmental" natural capital accounting tools account for the variation and evolution of ecosystems at the scale of an entire territory. These integrated accounts seek to aggregate biophysical and socio-economic information in order to guide and provide a decision-making tool for public policies on ecosystems and also to inform the population on the ecosystem services provided by a territory  (Feger & Mermet, 2021).
These are based on the Hydroshed 10 (HYBAS 10) level retrieved from the HydroBASINS database which is composed of a series of vectorised polygon layers that follow the boundaries of sub-catchments of different sizes  (Lehner & Grill, 2013). ENCA requires a large amount of collected data (freely available online) with different resolutions (from 10-30 m to 250 m). A spatial resolution of 100 m by 100 m was chosen over the territory of the Republic of Guinea. As a result, all geographic data are resampled to a 1 ha grid corresponding to the project grid. A common projection system is also chosen for the project, which may lead to a reprojection of some geographical data. SAGA software  (Conrad et al. , SAGA, 2015)  is used to carry out most of the ENCA process. ENCA is produced from various types of national and international data.
Geographic reference layers (administrative boundaries, road networks, etc. ) are easily accessible online via national mapping agencies or ministries, such as those in charge of spatial planning  (Weber, 2014). Depending on the availability of the data, the scale of the information is adapted. Thus, for local applications, more precise monitoring data or even field observation data can be integrated to enrich the calculation of the accounts. If the data is not available on a local/regional scale, the use of national and international databases is preferred. The latter are constantly being improved and updates are to be expected over time, particularly with the availability of regularly updated highresolution satellite images (Sentinel, Landsat 8, SPOT, etc. ) and the development of more efficient monitoring systems.
Land accounts : the foundation for the account In the ENCA system, land cover data are central, as they reflect biophysical characteristics (vegetation cover, density, height. ) and land use. According to the FAO, land cover indicates the physical cover of the land such as forests and wetlands. Land use indicates the use of the land cover by humans activities (such as crops or fallow land). Within the framework of the Guinea agro-ecological zoning project, land use maps are produced based on the CORINE Land Cover nomenclature (Jaffrain in Feranec, European landscapes dynamics, 2016) and integrating modifications linked to regional specificities, notably by relying on the Yangambi classification  (Aubréville, 1957). Two land coverages were carried out by remote sensing and photointerpretation for the years 2005 and 2015.
SPOT 4/5 and SPOT 6/7 satellite data were used for 2005 and 2015, respectively, over the entire territory of Guinea  (Jaffrain et al. , 2021). The land account based on information from the land cover databases provides a first assessment of land cover changes over a period of time. It is the basic account required for the construction of the ENCA. Based on the gains and losses of areas (in ha) between two dates, it first allows the establishment of a matrix of changes in terms of land cover classes. This matrix is produced from the SAGA GIS software. The interpretation of land cover classification changes allows the identification of the processes at their origin and thus to group them into fluxes. A land cover flux is thus 'a grouping of changes of the same nature'  (Jaffrain et al. , 2021)  which allows a better understanding of the changes that have occurred by integrating information on the land cover prior to the change.
Forestry data from ESACCI (ESA Climate Change Initiative) and Hansen (University of Maryland) are used to estimate forest biomass at both dates. The International Soil Reference and Information Centre (ISRIC) provides soil biocarbon data per hectare at different depths. Terrestrial animal biocarbon is also taken into account, starting with livestock (FAO data). All data produced is converted to tonnes of carbon using the equivalence of 50% biocarbon in biomass. The account then describes biocarbon flows within ecosystems. Inputs are mainly based on net primary production (NPP) data. Biocarbon outputs include agricultural and forestry harvests (tree felling), soil erosion, losses due to land use changes (such as artificialisation), wood burning, forest fires. FAO data and national statistics are used to determine agricultural crops.
It is defined as a 1 km long stretch of watercourse (river, stream) that has a flow of 1 m³/s at any point along its course. The basic balance then describes the inflow and outflow of rivers and groundwater following the series "precipitation, evapotranspiration, infiltration and runoff"  (Weber, 2021). Data are mainly taken from global meteorological databases (such as WorldClim, n. d. ) but local monitoring stations can also be used. Natural inflows and outflows from HYBAS 10 catchments (on which the SELU is based) are measured, as well as water flows related to irrigation and other uses (hydropower dams, cooling water, etc. ), based on national statistical data where available. The water use account summarises all recorded water uses. In order to assess the sustainability of the water resource, it is important to know the accessible water resource, i.
Some indicators of water accounts The ENCA water account is calculated from available input data on stocks (lakes and reservoirs, rivers) and input and output flows (rainfall, evapotranspiration, irrigation abstraction). The main input to the water account is rainfall. An increase in average annual rainfall in Guinea between 2005 and 2015 was observed, which partly explains the increase in net primary productivity (see Fig. 10 ) observed in the area. The distribution of rainfall is heterogeneous and varies across the country (Fig. 11 ). The coast is wetter, while the north-east of the country is drier. In order to smooth out interannual variations, 3-year averages were used.
These ecological debts could be measured to estimate the restoration value of the degraded ecosystem  (Babin & Weber, 2019). In addition, this experimentation makes it possible to advance on As the ENCA method depends on the data used, the latter play an essential role in the smooth running of the method. The quality and availability of the data for the periods studied  (2005 and 2015)  sometimes raise difficulties. Indeed, some data, such as the OpenStreetMap road network map, are too heterogeneous and/or undated, making the resulting indicator (of landscape fragmentation) less representative of the reality on the ground and more static (available only for one date). The ENCA protocol is currently being improved, in particular to better integrate the morphology of the territory and the relief in the definition of socio-ecological landscape units (SELU), or to better take into account marine coastal ecosystems in the accounts.
, 2017; Lutz et al. , 2018; Meakem et al. , 2018). This under-sampling is particularly problematic when assessing impacts of climate change because upper-canopy trees are most vulnerable to periods of water shortage  (Gora & Esquivel-Muelbert, 2021; Stovall et al. , 2019)  which are increasing in frequency  (IPCC, 2021). Remote sensing has the potential to overcome these sampling challenges by providing wall-to-wall maps that can be used to monitor millions of upper-canopy trees. Remote sensing of individual trees has mostly focused on airborne lidar data, which are used by the forestry industry to map trees at landscape scales  (Zhen et al. , 2016). Delineating individual trees from airborne lidar datasets is most successful for conifer because their apical dominance results in clear local height maxima that make tree crowns easily distinguishable  (Dalponte & Coomes, 2016; Hastings et al.
, 2017; Wu et al. , 2019). We trained and evaluated Detectree2 on four tropical forest sites. In total, 3797 manually delineated tree crowns were used of which 1530 spatially separated crowns were reserved to evaluate the model. We evaluated the performance with F 1 scores, which quantify the skill of the method in delineating individual tree crowns accurately. We expected a model trained at one site to drop in performance when transferred to making predictions of crowns at the other sites and that supplying a greater variety of training data would boost performance. As an example ecological application, we deployed the trained model across 14 km 2 of airborne RGB imagery, automatically delineating 65 786 tree crowns. For context, this area is approximately 40% the total area of forest inventory plots in the main plot networks across the tropics  (Davies et al.
, 2021; ForestPlots. net et al. , 2021). We then combined these crowns with repeat airborne lidar data to investigate the growth and mortality rates of uppercanopy trees in relation to their height. Regional and global syntheses of forest inventory data suggest that growth slows down and mortality rates increase with tree size  (Coomes et al. , 2003; Hurst et al. , 2011; Iida et al. , 2014; Muller-Landau et al. , 2006; Richardson et al. , 2009). We therefore expected to find the tallest trees we sampled to have lower growth rates and higher mortality rates than shorter trees. The Detectree2 Python package is available to install and apply on new regions. 1.
Remote sensing data Airborne RGB surveys were conducted at all four sites using manned aircraft (Table  1 ). Repeat lidar surveys were also conducted at all four location (see Table  1 , noting different sensors and altitudes between flights in Sabah). We analysed RGB imagery from 3. 85 km 2 of Malaysian forest, with a ground resolution of 10 cm. In Paracou, we sampled 10. 2 km 2 of imagery, with an 8 cm ground resolution. The raw imagery was orthorectified, georeferenced and collated into homogeneous mosaics using structure from motion in AgiSoft Metashape  (Agi-Soft, 2021; Westoby et al. , 2012)  in Sabah. In Paracou the imagery was orthorectifed using TerraPhoto to the Canopy surface model derived from simultaneously acquired lidar data.
Manual tree crown data To train and evaluate our automatic delineation approach, we created a manually labelled dataset of trees at all four sites. We generated our delineations using both The exact location of the sites is described in "Study sites" section. Resolution is given as ground resolution for the RGB imagery and as the processed CHM resolution for the lidar scans. Beam divergence is given at the 1/e 2 points. Sepilok West and Sepilok East were separated for analysis due to the different characteristics of the Sepilok forest in these two areas. RGB and lidar data and, in the case of Paracou, supplementary hyperspectral layers. We used several techniques to improve the accuracy of crown delineation, including manipulating the contrast and saturation of the RGB image to exaggerate differences between the crowns, and using a mask of the lidar data to remove irrelevant parts of the RGB imagery.
, 2014). Additionally, the training data were augmented by applying several randomly applied transformations to the training cases including vertical and horizontal flips, rotation, and varying the saturation and contrast of the image. The model hyperparameters (Table  S1 ) were tuned with a Bayesian hyperparameter sweep implemented on wandb. ai. 4 This is an automated process that allows an automated agent to iteratively adjust hyperparameters to optimize accuracy. The best performing models and optimal confidence threshold for a given model (see "Model architecture and parameterization" section) were selected based on the F 1 score (see "Performance evaluation" section) on the validation fold. See Supplementary Note 4 for more details on model architecture, training and validation. The Colab (Jupyter) notebooks in the GitHub repository 1 illustrate the best practices for training and selecting models.
We took a statistical approach defining a mortality event as a negative change in height of more than three standard deviations below the robust least squares fit. This allowed for the possibility that a mortality event may uncover another layer of vegetation rather than the forest floor. This choice was ratified by manual inspection of trees meeting this threshold, and confirming that they constituted mortality events. Annual rates were determined by dividing by the time between lidar scans. Differences in lidar scanning parameters (pulse density, scanning angle, flight height etc. ) can bias height estimates  (Roussel et al. , 2017). For this reason, we resisted reporting a direct comparison of reported growth and mortality rates between sites. As our focus here was on demonstrating the use of Detectree2 for locating crowns, we considered that a detailed exploration of the potential biases from the lidar data beyond the scope of the current paper.
However, we found that Detectree2 can be quickly trained to perform well on new areas of forest using around 10 images (each $ 1 ha in scale) with all visible tree crowns manually delineated. This manual delineation represents approximately 4 h work. The best performing models were those that were exposed to training data from all the sites and then 'honed' with a limited number of training iterations on the site to be predicted on. This suggests that our trained models (provided freely with the Python package 5 ) could be transferred to a new site with very little manual data or training iterations. We note that the manual delineations were done by different people focusing on different parts of the sites. There was no clear effect of different delineators on the results but this would be somewhat confounded with site differences.
Uncertainty estimates were determined by bootstrapping. assess liana infestation occurrence. Previously, hyperspectral data have been employed to address this problem but with limited success due to the phylogenetic and spectral diversity of lianas and relatively low spatial resolution of hyperspectral imagery  (Grabska et al. , 2020; Wessel et al. , 2018). The availablility of Detectree2 as an opensource Python package means other research groups can test its efficacy on their own research questions.
Acknowledgements We thank the  NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS)  for supplying data and computational resources for this study. We thank  CEDA  for the 2014 Sepilok and Danum remote sensing data. S. H. M. H. received funding from the  Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks  ( AI4ER ,  EP/S022961/1 ), which is supported by the  Engineering and Physical Sciences Research Council (EPSRC). J. G. C. B. was supported by the  NERC C-CLEAR  doctoral training programme ( PDAG/501 ). T. D. J. and D. A. C. were supported by  NERC  grant ( NE/S010750/1 ). D. A. C. was supported by the  Franklinia Foundation. We thank  Stephen Goult  for contributing to discussions. Data collection in French Guiana was supported by  CNES  who funded the 2016 hyperspectral, RGB and lidar data over Paracou and  Labex CEBA  ( ANR-10-LABX-25 ) for contributing financial resource for the field validation of manual crown segmentations.
Context We use text documents produced by students on the LabNbook platform 1  , which is a digital environment for learning experimental sciences in secondary and higher education (d  'Ham et al. , 2019). It provides useful tools for writing collaborative scientific documents (text and equations, drawing, data processing, etc. ) and allows students to interact with each other (team building, shared workspace, internal messaging and chat, etc. ). LabNbook operates in a locked co-editing mode  (Wang et al. , 2017) , i. e. , students can work simultaneously in the shared workspace but each document, called LabDoc, composing it can be edited only by one student at a time. For instance, a collaborative report may be composed of different sections, and each section corresponds to a LabDoc.
Data Collection We use a questionnaire to measure to what extent the strategies and indicators are interpretable by teachers (RQ3). Our objective is to verify whether the definitions make sense to them. We selected 12 LabDocs edited on the LabNbook platform by groups of students during physics and chemistry courses (undergraduate level) in Grenoble Alpes University (France). To distinguish student contributions, we highlight in each LabDoc with a color-code the text written by each student. We choose LabDocs uniformly with different writing strategies (integrative, summative, and mixed strategies). Respondent teachers are a total of 15, situated in IMT Atlantique in Brest (France), from Computer Science discipline and in Grenoble Alpes University in Grenoble (France), from Experimental Sciences disciplines.
Co-writing Indicator Similarly to the balance of contribution indicator, which measures students' contributions at a word level, the co-writing indicator measures students' contributions at a sentence level (6). Thus, this indicator requires splitting the text into sentences. To this end, we use a rule-based (heuristic) approach for sentence segmentation, for its implementation simplicity  (Sadvilkar and Neumann, 2020). We use a sentence boundary detection tool based on a set of rules called Golden Rule Set 3 , hand-designed to determine sentence boundaries, such as punctuation. 3 https://github. com/diasks2/pragmatic segmenter When all sentences are written by a single student, this indicator equals 0 and it equals 1 when all sentences are co-written, in a balanced way, by all students. The co-writing indicator is based on the balance of contribution indicator, but on a sentence level.
Maize responses to heat and cold stress To decipher maize responses to temperature stress, heat and cold-stress-related transcriptomic data of B73 genotype were collected from the literature. 22  Seedlings were subjected to cold (5 0 C for 16 h) or heat (50 0 C for 4 h) stresses, compared to a control condition (24 0 C). All plants were grown in the autoclaved soil collected from field. While heat stress drastically affected transcriptional response compared to control conditions (Figure  1A ), the effect of cold stress was similar to control conditions (Figure  1B ), indicating a difference of plant adaptations to temperature stresses (Figures  1C  and 1D ). To explore the components of temperature stresses, we performed K-means clustering (Figure  1E ) on both heat and cold stress data, identifying four distinct clusters followed by gene enrichment analyses (Figures  S1-S4 ).
EXTREAM algorithm Our previous study used the E-flux algorithm  25  to accurately predict the maize root phenotype under nitrogen starvation based on transcriptomic data. This work involved photosynthetic tissues, having additional metabolic complexities compared to roots. Thereby, the efficacy of the E-flux algorithm was re-assessed. In a previous study, the maize leaf was divided into 15 cross-sections, and generated transcriptomics data for each of these sections. 26  For this study, we collected transcriptomics data of cross-based on the greenness of the leaf blade (less green to greener). These data were integrated in the leaf GSM 27 using E-flux algorithm. The highest growth rates in maize leaves occur at the base of the leaf, where new cells are actively dividing and elongating. As the cells grow toward the tip, they undergo expansion and maturation, leading to a decrease in the growth rate.
Eventually, the cells at the tip stop elongating altogether. The contextualized leaf model correctly predicted higher biomass growth rate of leaf base compared to leaf tip (Figure  1F ). The model also predicted rate of carbon fixation correctly, however, it incorrectly predicted the starch distribution profile along the leaf (Figure  1F ). Thus, modification of the E-flux algorithm was required to fit the starch distribution profile. To achieve this, we proposed the EXTREAM algorithm, which equally distributed transcript levels for each gene based on the number of cognate biochemical reactions. Once we contextualized the leaf model with the EXTREAM algorithm, it predicted the correct biomass growth rate, carbon fixation, and starch distribution profiles along the selected leaf sections (Figure  1F ).
Multi-organ genome-scale metabolic model of maize In this study, we reconstructed the first multi-organ maize GSM, iZMA6517 (Figure  2A ) for the B73 genotype. The model is based on the previously reconstructed root and leaf-specific GSMs,  19, 27  and newly reconstructed stalk and kernel GSMs, connected via vascular tissues (Table  S1 ). To further refine our existing leaf model, we identified thermodynamically infeasible cycles (TICs) and resolved those using our previously developed OptFill 28 pipeline. For stalk and kernel GSMs, we assigned gene-protein-reaction (GPR) relationships to all known reactions based on the MaizeCyc database. 29  The GPR relationships used Boolean logic to determine if enzymes associated with metabolic reactions are encoded by isozymes (OR relationship) or protein complexes (AND relationship).
After these modifications, the new algorithm, EXTREAM, predicted the correct carbohydrate profile across leaf cross-sections. We also wanted to check which aspect of EXTREAM contributed more toward the correct prediction of starch distribution. To address that, we formulated two different optimization problems. In one optimization problem (OPT-01), we used the distributed transcriptomics data with biomass maximization, and in the other optimization problem (OPT-02), we used non-distributed transcriptomics data with the modified objective function. OPT-01 predicted the right starch distribution, whereas OPT-02 predicted inaccurate starch distribution. Thereby transcript distribution played a crucial role in the correct prediction of starch profile. The optimization problems can be accessed through the GitHub directory (data and code availability section).