dataesr/scientific-paragraphs-categorization huggingface dataset:
Training and Validation Data Inspired by previous studies  [19]  and based on the National Land Cover Database  [68, 69] , we defined a total of 14 major LULC classes. For classification purposes, some of these classes were subdivided in order to catch local variations in land cover spectral signature (Table  2 ). To ensure comparability with previous 1979 to 2009 decadal classifications  [19] , these subclasses were merged back into their parent wider classes when statistics were calculated (but detailed subclasses could be kept if need be).
Riparian Forest Areas dominated by woody vegetation (>20%) greater than or equal to 5 m in height. Mostly cottonwoods in arroyos. 1 Subclasses to catch local variations of their parent class for the classification but merged after. As our goal was to use supervised classification algorithms, we created a set of 1923 polygons spread across our study area, totaling 10,579 hectares and representing all classes and subclasses (see Supplementary Materials Table  S1  for detail). These polygons were drawn using a self-made grid mimicking Landsat pixel to ensure their homogeneity in composition, leading to heterogeneity in size. LULC class assignation for each polygon was conducted by a single trained interpreter, based on human visual analysis of different high resolution orthophoto sources, including National Agriculture Imagery Program (NAIP) aerial imagery or Google Earth current and historic repositories.
The latter were used to ensure that these polygons had consistent LULC from 1986 to 2021, allowing us to use the same sample for each year. These "ground-truth" polygons were then randomly and equally split into training and validation datasets, later used to acquire a random stratified sample of 4874 training and 4960 validation points evenly distributed between our LULC classes and subclasses (about 277 points per class). Sampling was conducted at a minimum resolution of 30 m to ensure that two points could not fall within the same Landsat pixel.
Classification and Post-Processing Each year's 124 variable datasets and stratified sample training points were then provided as inputs for a supervised 10 trees Random Forest (RF) classification. RF is currently reported as one of the best classifiers, producing high accuracy while staying relatively computationally light  [70] [71] [72] [73]. RF classifications operate by constructing multiple decision trees and outputting for each pixel the mode of all predicted classes  [74]. Thus, this method is less sensible to overfitting training data than single decision tree predictors, which they generally outperform  [75]. This characteristic of RF classifiers and the tremendous computing capacities of GEE allowed us to feed all 124 variable datasets for the computation of LULC maps for each year, without the need for prior feature selection.
Main Drivers of Inaccuracies in Our Yearly Classifications As noted before, inaccuracies in our model are strongly correlated with time, LULC classification being less performant as we go back in past years. This is most likely due to the fact that we favored an automated process over the entire 1986-2020 period, thus using the same training and validation polygons for the entire time series. While high-resolution satellite imagery can be used as "ground truth" in recent years, available aerial imagery in the 1970s and 1980s is scarcer and of much coarser resolution. Our training and validation datasets are less accurate back then, as they may contain uncorrected errors for the LULC classes they actually cover on the ground at these times. While Landsat data provide opportunities for consistent time series of satellite imageries spanning several decades, its medium-high resolution of 30 m can complicate the identification of smaller targets, such as the isolated houses characteristic of some urbanization dynamics in the Tucson area.
One way to further improve the accuracy of our model would be to manually refine our training and validation datasets, as well as Landsat scene selection, for each year. So far, we accepted images with a rather high cloud cover, and we selected rather large time windows for each of the two seasons we considered. This was necessary in order to not have too many NoData pixels-i. e. , areas masked out by our image pre-processing technique removing clouds, cloud edges and saturated pixels. Raw image quality or season-specific spectral signature detection might be improved by manually picking Landsat scenes and thus lowering both these filters, but without the confidence to gain full coverage images for each year. However, this would drastically increase human intervention while we favored an automated process which allows us to rapidly process long time series.
Conclusions We propose in this study an automated multi-temporal and multi-sensor classification method to extract yearly LULC data for the Tucson area since the mid-80s. Hosting the entire workflow in the GEE platform allowed us to quickly process very large datasets and to maintain a consistent methodology for the entire 1986-2020 period and on to oncoming years. We used a number of post-processing techniques to mitigate inevitable issues in temporal LULC change monitoring, such as smoothing the influence of interannual climate variations or addressing possible misclassifications associated with the lack of availability of robust ground truth imagery to build our model training datasets as we go further back in time. For most years, we achieved very satisfying classification accuracy (over 90 to 95%), allowing us to monitor landscape transformations in southern Arizona from then and in the future.
Funding: This research was funded by a grant from the  LabEx DRIHM ,  French Programme "Investissements d'Avenir"  ( ANR-11-LABX-0010 ), which is managed by the  French National Research Agency (Agence Nationale de la Recherche , or ANR). M. L. V. and L. M. N. are supported by the  Land Change Science Program of the U. S. Geological Survey.
Data Availability Statement: The yearly LULC classification results, the GEE scripts and other ancillary data supporting this study are open access; See Dubertret, F. , Le Tourneau, F. M. , Villarreal, M. L. and L. M. Norman. 2022. Annual (1986-2020) land-use/land cover maps of the Tucson metropolitan area, Arizona: U. S. Geological Survey data release, https://doi. org/10. 5066/P9XC5Y36 (accessed on 14 April 2022).
Patient and FD Characteristics All patients treated with FDs at our institution and followed with both MRA and DSA were prospectively included in a data base. Patients who underwent both DSA and 3T MRA within a maximum interval of 4 weeks, between November 2020 and August 2021, were included. Exclusion criteria were patients with a contraindication to MR imaging, patients who refused to undergo examinations (MR imaging or DSA), and those who did not undergo one or more of the 4 MRA sequences listed below. The Online Supplemental Data summarize the main technical characteristics of the devices used and evaluated in this study.
Image Analysis Studies from all patients were placed in an anonymized folder on the PACS, with NC-MRA (LAVA-Flex MRA and 3D-TOF-MRA with HyperSense), CE-MRA (TRICKS MRA and delayed CE-MRA), and DSA studies forming 3 anonymized folders. The entire original acquired data set, including both source images (mask, subtracted, and unsubtracted images) and standard reformats (MIP and MPR) for each technique, was made available for review when evaluating that technique. LAVA-Flex MRA and 3D-TOF-MRA with HyperSense were evaluated together to form the NC-MRA evaluation of parent artery patency, and TRICKS MRA and delayed CE-MRA were evaluated together to form the CE-MRA evaluation of parent artery patency. NC-MRA, CE-MRA, and DSA were evaluated separately without knowledge of the MRA or DSA examination results, by 1 interventional neuroradiologist and 1 diagnostic neuroradiologist (both MD-PhD, with.
Combining the information at the 4 distances, the phase map for each angle was obtained running inhouse developed GNU Octave scripts. With the ESRF PyHST software package  [36]  , the obtained phase maps were reconstructed to 3D digital images. For each sample, two resolutions were used with the corresponding voxel sizes of 100 and 30 nm. The volume of view (VoV) of the 30 nm scan is selected within the VOV of the 100 nm scan, allowing one to compare the microstructure of a same region with different resolutions. The 100 nm and 30 nm scans yield reconstructed volumes of 321. 6 × 321. 6 × 321. 6 μm 3 and 96. 48 × 96. 48 × 96. 48 μm 3 , respectively. The VoVs to be analysed were selected by on-line prior inspection of the X-ray images. The VoV should contain at least one grain boundary perpendicular to the loading direction, to show some form of creep damage and to be fully contained in the sample, preferably as close to the central axis as possible.
Phase segmentation and quantitative analysis The subsequent tomographic rendering and phase segmentation are performed with the FEI Avizo software. From the microstructure characterisation and in line with previous electron microscopy studies  [22]  , 4 phases are detected in the creep-failed Fe-Au-W samples, namely the bcc matrix, the fcc Au-rich precipitates (with an Au concentration of about 60 at. %), the W-rich precipitates with the composition of Fe 2 W, and the cavities. The occasional FeWO 4 particles are not taken into consideration since they do not affect the damage formation and self-healing behaviour. The electron densities of the precipitates can be estimated based on their composition and crystal structure. Since the electron densities of the precipitates (3. 71 e / Å 3 for Au 60 Fe 40 and 3.
After the phase identification, the binary images were used for further analysis in MATLAB using the DIPimage toolbox  [37]. For each object (cavity or precipitate), its shape was determined by the shape complexity, its elongation and flatness  [ 15 , 21 ]. The elongation and flatness are calculated from the three semi-axes of an equivalent ellipsoid with the same moments of inertia as the object itself. It is assumed that the volume occupied by a precipitate was previously occupied by an open cavity. By checking the contact between the precipitates and the open volume for each cavity, we determine how many precipitates are contributing to fill the cavity, how much volume has been filled, and how much volume remains open. The total volume of a cavity is therefore V cav + V prec , where V prec is the volume of the precipitates in contact with the open volume V cav.
Acknowledgements We thank  C. Kwakernaak  for providing the SEM facilities. We acknowledge  D. Ngan-Tillard  and  Y. Li  for technical assistance with the data analysis. The X-ray synchrotron nanotomography experiments were performed on beamline ID16A-NI at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. We are grateful to the beamline staff at the  ESRF  for providing assistance in using beamline ID16A-NI. Y. Fu acknowledges the financial support provided by the  China Scholarship Council (CSC).
Spatio-temporal pattern of urbanization The spatio-temporal dynamics of urbanization in the Cap-Vert peninsula was assessed using: (i) historical data to identify the localization of built-up areas at twelve time points over a 156 year-period, from 1862 to present, and (ii) results of a recent classification of Dakar socioeconomic habitats based on satellite images and census data from  Borderon et al. (2014)  to describe the current features of the cityscape. Historical data were gathered from eleven historical maps and one recent map that were retrieved from various web sources or from paper maps provided by the Senegalese national geographical institute. All images were georeferenced as "rasters" using the QGis software  (QGIS Development Team, 2018). As coordinates were not always available or reliable, easily identifiable features such as coastlines, major road crossings and outstanding buildings were used as control points for the georeferencing process (see Appendix).
Genetic diversity and differentiation Population genetic analyses were done on a dataset comprising 557 house mice ( (HWE) and genotypic linkage disequilibrium (LD) were tested by locus and by sampling site using GENEPOP v4  (Rousset, 2008). Corrections for multiple tests were performed using the false discovery rate (FDR) approach  (Benjamini and Hochberg, 1995)  implemented in the Bioconductors qvalue R package  (Storey, 2002). Heterozygote deficiencies are often found in house mouse populations and are classically attributed to their social system  (Ihle et al. , 2006). We calculated the kinship coefficient (ρ,  Loiselle et al. , 1995)  between all pairs of individuals within sampling sites, using SPAGeDI v1. 4  (Hardy and Vekemans, 2002) , in order to evaluate whether heterozygote deficiencies in the dataset may be due to the oversampling of closely related individuals or to the pool of individuals sampled from different social groups  (Parreira and Chikhi, 2015).
Genotype data from all localities were used as the dataset of reference for allelic frequencies. We also examined whether null alleles may alternatively explain heterozygote deficiencies, by estimating null allele frequencies for each sampling site at each locus using the software FreeNA  (Chapuis and Estoup, 2006). For each sampling site, genetic diversity was estimated using the expected heterozygosity H s  (Nei, 1987) , the inbreeding coefficient (F IS ), and the allelic richness (a r ) calculated using the rarefaction procedure implemented in FSTAT v. 2. 9. 3  (Goudet, 2001)  for a minimum sample of 19 diploid individuals. Genotypic differentiation among sampling sites was tested using Markov chain methods in GENEPOP (using the FDR approach to correct for multiple tests). Genetic differentiation between sampling sites was summarized by calculating pairwise F ST estimates  (Weir and Cockerham, 1984)  using FSTAT.
Spatial patterns of genetic variation and cityscape heterogeneity The relationship between the spatial patterns of genetic diversity and differentiation and the spatio-temporal urbanization process within the Cap-Vert peninsula (i. e. Gorée Island, locality IDG, was excluded from the dataset) was analyzed using both a punctual (i. e. population-based genetic estimates) and pairwise (i. e. pairwise genetic estimate) approach. For the punctual approach, we considered three genetic estimates for each sampling site: the allelic richness, the expected heterozygosity and a local-F ST value. The latter was computed using the program GESTE  (Foll and Gaggiotti, 2006)  and represents the average level of differentiation of one site from all the others. To relate these genetic estimates to the characteristics of the cityscape in the surrounding of each sampling site we used nested circular buffers, of radius equal to 300m, 600m, 1000m and 1500m, centered on the barycenter of the individual coordinates at each site.
, 2008 (Strobl et al. , , 2009)). Analyses were done considering different value (2 to 13) for the "mtry" parameter, which controls the number of variables to possibly split at in each node. For each value of "mtry", 20 replicates were performed using different seed values. The shape of the relationship between genetic estimates and each cityscape feature were determined from partial dependence plots. We also used the R package "ranger"  (Wright, Ziegler, et al. , 2017)  to estimate variable importance p-values based on unconditional RFs (see Supplementary material, Section 3 for details on RFs analyses). For the pairwise approach, we used the MAPI method, which has a low sensitivity to IBD and is not based on a predefined population genetic model  (Piry et al. , 2016 , implemented in the R package "mapi").
Finally, we also checked that model residuals were not overly structured in space. MAPI is a smoothing procedure that performs better when the sampling maximizes the spatial coverage of the study area  (Piry et al. , 2016). To evalu-ate whether the observed genetic structure can be biased, due to the smoothing of a relatively small network (i. e. clumped sampling of 13 localities in the Cap-Vert peninsula), we applied the approach combining the MAPI analysis and Bayesian CAR model on: 1) individual microsatellites genotypes simulated under a landscape configuration corresponding to a spatial gradient from a favorable to an unfavorable habitat as fully described in  Piry et al. (2016) , and 2) subsampling of the initial dataset, with a number of sampling sites (N) varying from 6 to 12 (see Supplementary Material, section 4, for details on the simulation and subsampling framework).
65 and 0. 16, respectively) and negatively correlated to all other variables (R s varied from -0. 32, for the variable "Spontaneous", to -0. 65 for the variable "Connection"; Supplementary material, section 3, Table  S3. 3). The ranking of the conditional importance of the cityscape features from the RFs trained on 100 randomly resampled datasets (50% of the MAPI grid cells) was very consistent whatever the setting of the analysis: 1) "Connection", 2) "Vegetation", 3) "Industrial", 4) "Spontaneous", 5) "Residential", 6) "Old_City" and 7) "Built-up" (Supplementary material, section of the CAR model (Table  3 ) showed that the posterior probability was significant (at a 5% threshold) for all cityscape variables but "Old_city". The most important variables to explain the variation in MAPI smoothed-F ST values were "Connection" and "Industrial", which both had a negative effect (regression coefficient = -0.
081±0. 007 and -0. 066±0. 020, respectively). The two other important variables, "Residential" and "Vegetation" had a positive effect (regression coefficient = 0. 059 ± 0. 010 and 0. 028 ± 0. 007, respectively). Lastly, the variables "Builtup" and "Spontaneous" had a slight positive and negative effect, respectively (regression coefficient = 0. 017 ± 0. 005 and -0. 017 ± 0. 007). Subsampling: The analysis of subsampled datasets (with a number of sampling sites N varying from 6 to 12) showed that the gradient-like pattern in smoothed-F ST and the main effects of cityscape variables were observable even with N=6 sampling sites: high posterior probability for a negative effect of "Connection" and "Industrial" and positive effect of "Vegetation", "Residential" and "Built-up" (Supplementary material, Section 4, Fig  S4.
, 2017). For such a reason, sampling size and coverage may be limited which make it difficult to precisely analyse the relationships between genetic patterns and urban features. In this study, despite the suboptimal coverage of the sampling within the Cap-Vert peninsula, the analysis of simulated and subsampled datasets gave confidence that the MAPI method allowed retrieving the main spatial genetic structure of the house mouse and to relate it to important historical and current features of the cityscape. However, we do believe that, in this particular case, the approach was facilitated by the strength and simplicity of the main structure (i. e. a gradient-like pattern with a high level of genetic differentiation). More complicated or fine-scale genetic structure would be hard to detect without further increasing the sampling size and spatial coverage through a more intensive individual-based sampling design within and across urban habitats  (Combs et al.
15 nuclear microsatellites loci (D1Mit291, D2Mit456, D3Mit246, D4Mit17, D4Mit241, D6Mit373, D7Mit176, D9Mit51, D10Mit186, D11Mit236, D14Mit66, D16Mit8, D17Mit101, D18Mit8 and D19-Mit30: available from the MMDBJ database: http://www. shigen. nig. ac. jp/mouse/mmdbj/top. jsp), according to Lippens et al. (2017).
high genetic continuity and discontinuity. The MAPI grid was then superimposed on the layers describing the historical and current cityscape and the value of each variable within each cell of the grid was computed as detailed in the section "Spatio-temporal pattern of urbanization". As for the punctual approach, Spearman correlation coefficient was calculated for all variable pairs associated to the cells of the MAPI grid. To identify which cityscape features can better explain variations in MAPI smoothed-F ST values we used conditional RFs, considering different seed and "mtry" (2 to 5) values, to analyze 100 independent training datasets built by randomly subsampling 50% of the MAPI grid cells. Result from each training dataset was used to predict smoothed-F ST within the remaining MAPI cells (i.
e. not included in the training dataset). Model accuracy was assessed by computing the Pearson correlation coefficient (r) between predicted and observed MAPI smoothed-F ST as well as the R 2 and RMSE (root mean square error). Finally, we used a conditional autoregressive model (CAR) to further analyze the relationships between MAPI smoothed-ST values and the cityscape variables computed within each cell. To account for spatial autocorrelation, the error term of the model was assumed to have a conditional intrinsic Gaussian autoregressive (CAR) distribution (Besag et al. , 1991) with an inverse-distance neighborhood weight matrix. The model was fitted in a Bayesian framework with an INLA approach (Blangiardo and Cameletti, 2015) using the R-INLA package (Martins et al. , 2013) (specification of the model for R-INLA is provided in Supplementary material, Section 3).
Acknowledgements We thank all the people in Senegal who welcomed us into their homes for the purposes of small mammal trapping, as well as the  Senegalese Head Office of Waters and Forests , the  Ministry of Health and Social Action in Senegal , the National hygiene service, and the management director of the Dakar harbour for allowing us to do fieldwork and to export samples. Samples are preserved in the CBGP-Small mammal collection (DOI:  10. 15454/WWNUPO ) and referenced in the Small Mammal Database associated. Molecular data were generated at the molecular biology platform of CBGP and through the GenSeq technical facilities, with the support of the  LabEx Ce-MEB. We are grateful to  Nathalie Sarr  for data capture,  Caroline Tatard  for helping with molecular laboratory work,  Julien Papaïx  and  Thomas Opitz  for helpful comments on the Bayesian CAR model,  Jean-Marc Duplantier  for fruitful discussions on urbanization history and rodent colonization of Dakar.
We thank  Tuomas Aivelo ,  Torsti Schulz  and an anonymous reviewer as well as the recommender,  Michelle DiLeo , for their constructive comments on this work. Funding was provided by the  Institut de Recherche pour le Developpement (IRD). Version 4 of this preprint has been peer-reviewed and recommended by  Peer Community In Ecology (Recommendation DOI :  10. 24072/pci. ecology. 100044 ).
DOIF high genetic continuity and discontinuity. The MAPI grid was then superimposed on the layers describing the historical and current cityscape and the value of each variable within each cell of the grid was computed as detailed in the section "Spatio-temporal pattern of urbanization". As for the punctual approach, Spearman correlation coefficient was calculated for all variable pairs associated to the cells of the MAPI grid. To identify which cityscape features can better explain variations in MAPI smoothed-F ST values we used conditional RFs, considering different seed and "mtry" (2 to 5) values, to analyze 100 independent training datasets built by randomly subsampling 50% of the MAPI grid cells. Result from each training dataset was used to predict smoothed-F ST within the remaining MAPI cells (i.
Simulations: When combining MAPI and a CAR model to analyze 200 datasets of individual genotypes simulated under a landscape configuration corresponding to a spatial gradient of habitat quality, we found the expected significant positive effect of the unfavorable habitat on the MAPI smoothed-F ST for 95. 5% of the simulated datasets. None of the remaining datasets showed an unexpected significant negative effect (Supplementary material, Section 4, Fig S4. 1).