dataesr/scientific-paragraphs-categorization huggingface dataset:
Calculation of wetted cross-sectional area Discharge estimates using the stage-velocity radar data assume that there is a stable cross-section. Changes to the bathymetry can have a negative effect on the discharge estimates. Therefore, at first, surveys are conducted to find a stable channel bottom for each site and then the cross-sections are established at the selected location of the channel. A cross-section of any channel is measured using a conventional total station surveying method. The vertical elevations along the line where the radar makes stage measurements are recorded by a total station survey relative to the reference level (e. g. , point of zero flow, corresponding to the channel bottom or lower to allow for scour). However, in the case of Mill Creek, the elevation of the floodplain at its left bank is complemented from a 1-m resolution digital elevation model (DEM) (USGS, 2020) and then merged with the surveyed datapoints to complete the cross section.
Cross-section data (e. g. , station and elevation with respect to North American Vertical Datum of 1988) of a channel are further loaded into an online calculator (NWS, 2020) that gives the corresponding wetted area for any water depth. For each site, wetted areas corresponding to a range of water depths are determined, where the range varies from the minimum recorded stage value for that station to the maximum one with an increment of 0. 03-0. 06 m. Finally, an equation is developed from the best fit of these data points and for any i th stage value, A i is derived from the equation. For Cherry Creek, a stage-area rating curve is provided by the USGS. It is noted that stage-area and stage-discharge ratings would be affected by changes to the channel bathymetry caused by sediment deposition or scour, vegetation growth, etc.
526 and 0. 804, which indicates a moderate, positive correlation between stage and air temperature. Although Falls Creek has a cable-mounted radar, the r values during the two base-flow periods are 0. 155 and -0. 139, indicating no correlation. As previously mentioned, this is due to the effect on base-flow conditions caused by the pumping from the water treatment plant. As for the two box culverts at Sessom Creek and Austin North, they both show weaker, negative correlations. The stage records at Austin South have little correlation with air temperature (r = 0. 023 and 0. 330) during stable base-flow conditions due to the added vibration of the bridge. The analysis conducted herein was to assess potential effects of the mounting structures on the stage data measured by the radars. Given that Walnut Gulch is a normally dry wash, we analyze the stage and temperature data during time periods for which there was zero observed flow.
Further, many low flow cases were not considered in the analysis due to poor quality velocity measurements from the stream radar. The sample size at Walnut Gulch is even smaller due to the episodic nature of flows in the dry wash during the monsoon. In all cases, the radar sensors yield NSE and r values close to 1. 0, and the NB values are generally small with a slight high bias at the Cherry Creek site. The data are also presented in the form of scatterplots in Fig. 5. These results are representative of the sensors' capability of estimating discharge without any parameter tuning on streams with a measured or known, stable cross-section. One objective of the ANCHOR project is to assess the sensors' capabilities of detecting rapid-onset flooding in vulnerable areas to provide early alerting to local authorities.
Discussion and Conclusions To evaluate the effectiveness of remote-sensing technologies in noncontact measurement of stream variables (water level and surface velocity), eight stream radars have been installed at high-priority locations across the United States. The radars are mounted on bridges, culverts, and cables and have provided measurements for at least 1 year with a temporal resolution of 5-10 min. The eight streams represent a variety of hydrologic, hydraulic, and hydroclimatological characteristics, and three of them are collocated with conventional streamgauges. This study is part of the Automated NonContact Hydrologic Observations in Rivers (ANCHOR) project and aims to identify and characterize errors in the raw measurements, to optimize correction factors so the data can be used to estimate discharge, and to assess whether the realtime transmission of alerts based on stage and surface velocity measurements can be useful in an early alerting context.
Potential biases were assessed on stage measurement due to diurnal temperature changes causing expansion and contraction of the supporting bridges, cables, and culverts. For bridge-and cable-mounted systems, these stage measurements would be biased in direct correlation with temperature. In the cases of bridges and cables, the sensor could sag closer to the water surface giving the impression of an increase in stage. The opposite could occur with the culvert-mounted units. We compared stage data from the stream radars to observations from collocated, conventional bubbler streamgauges at Cherry Creek, Colorado, and Mill Creek, Oklahoma. These independent USGS streamgauges also showed a diurnal fluctuation in stage, which is attributable to outdoor water uses during the summer months. The stream radar had the same fluctuations as the USGS observations at Cherry Creek and had about 1 cm more variation than the USGS stage measurements at Mill Creek.
Based on experience gained throughout the ANCHOR project, we provide some insights that can maximize data quality and quantity. Siting an instrument is key to its success. The channel would need to have uniform, relatively straight flow at the chosen site and a stable cross-section. Further, a site would need to have adequate cellular network coverage for transmitting the data. There are instances in which radio transmitters are being used to send the signal up to a ridge location where there is better cellular data coverage. The batteries are charged with a solar panel, so having good visibility toward the equator improves the power budgeting. Optimal siting of the units also needs to consider the degree of public access to reduce the instances of vandalism. In closing, this study provides experiences gained at diverse locations and will hopefully motivate future studies and efforts to promote additional sensing of earth's freshwater resources.
Acknowledgements Funding was provided by  NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma  Cooperative Agreement # NA16OAR4320115  and # NA16OAR4590234 ,  U. S. Department of Commerce. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government. The authors declare no conflicts of interest, and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The manuscript was greatly improved following the constructive comments from three independent reviewers and internal reviews supplied by the  USGS.
Data availability The air temperature data used in this study were obtained from the NOAA/National Center for Environmental Information at the following location: https://www. ncdc. noaa. gov/data-access/land-based-stationdata/land-based-datasets/quality-controlled-local-climatological-dataqclcd. All USGS data used for validation can be obtained online (https:// doi. org/10. 5066/F7P55KJN). The time series data for all eight stations analyzed in this study (and their specific locations) have been placed in the public domain (doi:10. 5281/zenodo. 4526127).
Action 2: Develop clinical Diagnostic Reference Levels (DRLs) for adults and children The ESR was awarded the EC funded EUCLID project (European Study on Clinical DRLs for X-ray Medical Imaging) that started in August 2017 and was completed in June 2020. The main goals were: (i) to collect data needed to establish DRLs for the most important x-ray imaging tasks in Europe with respects to radiation protection; (ii) to stipulate up-to-date DRLs for these clinical tasks; and, to convene a workshop to discuss the project's results, differing perspectives on DRLs, and how DRLs will affect clinical practice. Following the successful completion of the EUCLID project, EuroSafe Imaging intends to advocate for a similar project for paediatric patients. A EuroSafe Imaging working group on Clinical DRLs has been established to continue the work of the EUCLID project (Table  1 ).
Action 3: Develop image quality assessment based on clinical indications. As different radiological imaging tasks require different levels of image quality, image data of the highest quality achievable is not necessarily essential for confident diagnosis. Standardised, indication-specific clinical criteria are necessary to objectively judge required image quality. The EuroSafe Imaging Appropriate Image Quality Working Group has, since its launch in 2016, been active in identifying indication-specific clinical criteria and compiling them into sets suited for the assessment of image quality of radiological examinations. In 2019, it was decided to expand the scope of the working group to include "Artificial Intelligence" (AI), thus renaming the Working Group to "Strategies for Image Quality of the Future".
How Covid-19 Changed the Way to Do Research? Olivier Telle from CNRS India raised a question about how COVID-19 had really changed the way we work. As a health geographer, it was very difficult to get access to public health data. During his Ph. D. , he spent 1-2 years to convince municipalities and government of India to give him dengue data. Recently, we can get access to all the COVID-19 data online. They have already been geo-referenced, which means that they are already specialized. We therefore have work that has already been done. It is then possible to compare, for example, the diffusion of COVID-19 influence in Japan with other countries. In the past, it was very difficult for social scientists to access data without collaboration with biologists. Thanks to the COVID-19 pandemic, these datasets are more accessible nowadays.
They will do the antibody test and social science study in parallel. They will start with the sociological survey. The measurement of antibody will take place later. Once they get the samples, they can measure the samples at any time, as we can keep the materials in good condition. Lastly, there is a question on the ethics of using people movement data from the Facebook application. Samuel Benkimoun explained that Facebook is aware of this concern. They provided the data in an aggregated form. There is no individual data and no data of underage individuals (less than 18 years old). And even more important, the data is only collected from people who agreed to share their location with the Facebook application. For example, they provided data in the form of a grid. They did not provide data if there are fewer than ten people inside a grid.
1. Montreal Neurological Institute (MNI) coordinates are provided in Supplementary Tables  1  and 2. Disconnectivity fingerprints of alexia subtypes. Here we were especially interested in identifying the white matter disconnectivity fingerprint of each alexia subtype. To this end, 2. 5mm (in radius) spherical VOIs (representing each responsive white matter site) were individually positioned in the 152-MNI space and embedded into the HCP-1021-1mm dataset. Fibers intersecting with each VOI (i. e. the "disconnected" fibers) were reconstructed (see "Materials and Methods" for details regarding the parameters used for the tracking). Next, pairwise white matter disconnectivity matrices using cortical parcels from the Brainnetome atlas  33  were generated. The resulting left-hemispheric disconnectograms are displayed in Fig.
In the same way, these findings may help better predict the severity of reading disorders in multiple conditions wherein cerebral disconnections are the rule (e. g. stroke, traumatic brain injuries or brain neoplasms) and thus guide rehabilitation strategies to enhance cognitive recovery. Several notes of caution must be mentioned not to overstate the current findings. First, as the electrostimulation dataset has been obtained in a particular clinical context, the behavioral paradigm used intraoperatively is necessarily reductionist. Although the stimulation trains were applied following a welltried procedure allowing to control for false negative or positive outcomes  59  , it remains that the number of stimulations per white matter locus was limited, thus implying that alexia classification was established on the basis of categorical diagnoses rather than dimensional diagnoses  21.
As the spatial topography of each tumor could differ within the left hemisphere, the same stimulation sites were not tested strictly equally across all the subjects, potentially inducing a sampling bias. Last, tumors spreading in the vicinity of the VWFA are to the very least rare, explaining why only a limited number of white matter sites for alexia were available. Although the present dataset is unique, a larger one is needed to better dissociate the disconnectivity patterns related to Ls-A and Pu-A. To conclude, our results shed light on the disconnective mechanisms underlying the main forms of alexia, with important clarifications regarding the contributions of the ILF, the VOF, the lsAF and the pAF. Taken together, these findings provide a strong anatomical background for current cognitive and neurocomputational models of reading processing.
Methods Study design and patient selection. Data used in this study were gained in a clinical context and the protocol described below reflects our standard clinical approach. All patients gave their informed consent. All methods of this study were performed in compliance with the tenets of the Declaration of Helsinki. Data and imaging were studied following anonymization in agreement with the Personal Data Protection Act and the code of conduct for responsible use of Human Tissue and Medical Research. Approval for the study and its methods was granted by the Institutional Review Board of Montpellier University Medical Center (No. 202000557). We screened the intraoperative brain electrostimulation data obtained from consecutive awake surgeries performed in our institution from 2010 to 2020.
The tumor volume was measured with a dedicated software (Myrian, Intrasense, Montpellier, France). All 3-month postoperative MRI datasets were conformed to the standardized MNI space, using SPM12 implemented in MATLAB environment (Release 2018a, The MathWorks Inc. , Natick, NA, USA); normalized MRIs were individually and systematically checked to ensure accuracy. Resection cavities were then drawn using the semiautomatic drawing tool in MRIcron software (NITRC, University of Massachusetts Medical School in Worcester, MA, USA). Spatial positioning of DES positive sites. The MNI coordinates of each stimulation point were recorded using operative reports and intraoperative photographs. A similar method was previously described with a high level of inter-rater reliability  56. To increase the accuracy of this positioning work, a 3D pial-mesh reconstruction of each normalized 3-month postoperative MRI was generated using BrainVISA/Anatomist Software (Version 5.
0, CEA I2BM, CATI Neuroimaging, Inserm IFR49, and CNRS, France). This mesh allows a 3D navigation through anatomical structures and an automatic MNI coordinates-surface-matching of the 3D model (Fig. 1 ). Then, using the MarsBar Toolbox implemented in MATLAB, spherical volumes of interest (VOI) were generated for each stimulation point. To account for the different intensities of stimulation, VOI of 2. 5 mm (equivalent to the spatial resolution of the bipolar probe), 3. 0 mm and 3. 5 mm were created. Disconnectivity analyses. Individual VOIs of 2. 5-mm were embedded into the population-averaged, HCP-1021-1mm diffusion dataset  66  in DSI studio Software (http://dsi-studio. labsolver. org). This dataset was obtained from a multishell diffusion scheme with b-values of 1000, 2000, and 3000 s/mm 2.
Statistical comparisons resulted in n 123 × 123 matrix in which each matrix coordinate contained a Zstatistics further plotted in the form of heatmaps. Note that no adjustment for multiple correction was applied due to the exploratory nature of this complementary analysis. Tract-level analyses. The total number of generated fibers using 2. 5-mm VOIs were collected and projected onto the MNI space along with the HCP populationaveraged atlas of the human connectome  34  using DSI studio software. The "recognize" function was used to filter fibers intersecting the ROIs and belonging to the main associative white matter pathways assumed to be involved in reading processes (i. e. the long segment [lsAF] and the posterior segment (pAF] of arcuate fasciculus [pAF], IFOF, ILF, and VOF). In this way, the number of "disconnected" fibers by tract could be estimated.
Disconnected streamlines by tract were regrouped by alexia subtype (the number of disconnected streamlines by alexia subtype is provided in Supplementary Data 2) or language impairments (Ph-A vs Ls-A vs Pu-A vs phonological paraphasia vs semantic paraphasia vs anomia) and statistically compared using two-sided non-parametric ANOVAs (i. e. Kruskall-Wallis). Further pairwise comparisons were performed and corrected for multiple comparisons with the Dunn's test. Adjusted p values < 0. 05 were considered statistically significant. These analyses were repeated using VOIs of different sizes (3. 0 and 3. 5 mm) to account for the different intensities of stimulations (see Supplementary Fig. 2 ). In addition, tracts generated with DSI studio were converted into 3D binarized maps and regrouped according to the clinical manifestations.
Between-group voxel-based comparisons were then performed at the brainwide level using twosided chi-square tests (Matlab in-house script). The subsequent statistical maps were statistically corrected using a FDR procedure with a q = 0. 05. Statistics and reproducibility. Non-parametric tests were systematically used. A significance level of 0. 05 was used for all statistical tests. Serial pairwise comparisons between n 123 × 123 matrices were performed with two-sided rank-based Mann-Whitney tests without adjustment for multiple comparisons due to the limited number of observations and the considerable number of comparisons. Tract analyses (based on streamline counts) were performed with two-sided Kruskall-Wallis tests and corrected for multiple comparisons with the Dunn's test. Tract analyses (3D binarized maps) were performed with two-sided chi-square tests and FDR-corrected with a q = 0.
, 2020; Shugart et al. , 2015; van der Sande et al. , 2017; Zuidema et al. , 2013). While fundamentally relying on the basic knowledge developed through theoretical considerations or empirical studies, models themselves represent an efficient tool to, under given assumptions, generate virtual data or perform virtual experiments out of reach of empirical investigations in terms of temporal and spatial scope as well as number of replicates (e. g. ,  Fyllas et al. , 2017; Morin et al. , 2018; Schmitt et al. , 2019). For example, using a forest dynamics model,  Bohn and Huth (2017)  created a database of 500,000 virtual forest plots varying in forest composition and structure, allowing to explore the drivers of the temperature sensitivity of productivity in temperate forests. In addition, providing anticipatory predictions of possible futures, models can be used to test hypotheses about processes (explanatory or corroboratory predictions;  Maris et al.
, 2021; Taubert et al. , 2020). From the 1990s onwards, models that keep track of the positions of each tree in a finer-grained grid (i. e. , they are spatially explicit) and thus allow for a more detailed computation of tree light availability have been developed  (Chave, 1999; Maréchaux & Chave, 2017; Pacala et al. , 1996; Pretzsch et al. , 2002). Other developments have led to a more explicit representation of processes, for example by including a more detailed temperature and CO 2 dependence of photosynthesis and respiration, or more detailed water and carbon cycles or site fertility  (Fischer et al. , 2016; Maréchaux & Chave, 2017). Similarly, by taking advantage of comprehensive trait databases or long-term inventories and the detailed information they provide on tree life histories, novel parameterizations have allowed for simulating hundreds of species within diverse forest communities  (Maréchaux & Chave, 2017; Rüger et al.
2 ). Global plant trait databases (e. g. , TRY,  Kattge et al. , 2011)  gather data of commonly measured traits (e. g. , leaf mass per area or wood density) for a wide range of species, and this effort is being expanded to other traits (e. g. , stem and leaf drought tolerance,  Bartlett et al. , 2012; Choat et al. , 2012;  fine root traits,  Iversen et al. , 2017;  litter decomposition rates,  Brovkin et al. , 2012). This fosters a systematic model trait-based parameterization for a range of plant species and individuals. If data coverage remains incomplete however  (Kattge et al. , 2020)   Takoudjou et al. , (2018)  optimality or entropy maximization to constraint plant and ecosystem behavior can alleviate the data demand for parameterization while improving model predictive ability  (Franklin et al.
, 2020). Simultaneously, networks of forest plot inventories are being complemented by remote-sensing data, offering novel opportunities to initialize and/or validate model simulation over large spatial scales  (Shugart et al. , 2015)  or complement predictors of SDMs  (Fedrigo et al. , 2019). Recent advances in remote-sensing tools, such as the possibility to derive tree-level information within dense canopies  (Ferraz et al. , 2016)  or fuse spectrometer data with co-registered LiDAR data  (Jucker et al. , 2018) , provide new ways to parameterize models (e. g. , allometries,  Fischer et al. , 2019; Jucker et al. , 2017). New tools of data processing have been developed to leverage these new sources of data. The development of machine learning techniques offers new possibilities to use the resulting huge datasets for model development and evaluation  (Botella et al.
, 2020) , and thus foster new model developments as well as novel empirical investigations. Although the increasing complexity of models makes the interpretation of model inter-comparison results challenging (Fisher & Koven, 2020; Appendix B), model benchmarking is facilitated by new tools of code and data sharing (e. g. ,  Ram, 2013)  as well as the availability of detailed standardized databases  (Collier et al. , 2018; Reyer et al. , 2020)  Model comparisons have a long history within each model community (e. g. , among forest gap models -  Bugmann et al. , 1996 -, forest landscape models -Petter et al. , 2020 -, stand-based ecophysiological models -Kramer et al. , 2002; Morales et al. , 2005 -, DGVMs -Cramer et al. , 2001; Sitch et al. , 2008 -, or SDMs -Araújo & New, 2007). More recently, the increasing ability of different model types to use inputs and provide outputs of similar nature and structure has allowed to compare models across model types  (Cheaib et al.
ACK N OWLED G EM ENTS The authors represent different forest model communities. They gathered during a workshop series "Perspectives of forest modeling" supported by  COST Action   FP1304   PROFOUND (Toward Robust Projections of European Forests under Climate Change ), supported by  COST (European Cooperation in Science and Technology). IM acknowledges funding from an " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ). FL acknowledges funding from the program Evropské strukturální a investiční fondy,  Operační program  Výzkum, vývoj a vzdělávání. CPOR acknowledges funding from the  German Federal Ministry of Science and Education (BMBF  grant  01LS1711A ). RS acknowledges funding from the  Austrian Science Fund (FWF)  through  START  grant  Y895-B25.