dataesr/scientific-paragraphs-categorization huggingface dataset:
05) using phyloseq. [38] The significance of the separation between study groups in the PCoA space was assessed by a permutation test with pseudo-F ratio using the adonis function in vegan. [39] dditionally, ASVs with prevalence of <5% were removed before taxonomic analysis and linear discriminant analysis effect size (LEfSe) was used (Wilcoxon p-value <0. 05, logarithmic LDA (linear discriminant analysis) score >2) to identify the biomarker in the treatment groups. [40] redictions of Bacterial Function: PICRUSt2 software was used to predict the functional potential from the 16S r RNA. ASVs with prevalence of <5% were removed before functional analysis and the default parameters of the PICRUSt2 pipeline were used. [41] In PICRUSt2, sequence variants were included in a phylogenetic tree using EPA-NG,  [42]  gappa,  [43]  genomic hidden states were predicted using castor,  [44]  and MetaCyc pathway abundances were inferred using MinPath.
OVERALL WORKFLOW In this section, we present the workflow used to replicate the work of  Suel et al. (2019). The workflow was divided into four components (Figure  2 ): Step 1, GSV and IBGE data acquisition and aggregation. Socio-economic proxy indicators were calculated from the census data available through the IBGE. GSV images and socioeconomic proxy indicators were then aggregated to obtain image-labeled datasets. Step 2, feature extraction, in which a pre-trained VGG-16 network model (removing the last layers) was applied to extract one feature vector for each image. Step 3, training, in which the four feature vectors (one per angle) with inputs averaged element-wise, were fed to the last fully connected layer of the VGG-16. The loss function used was the cross entropy. The training followed the k-fold method.
Construction of socioeconomic indicators for Vale do Ribeira Socio-economic data were obtained from the Brazilian Institute of Geography and Statistics (IBGE), which is responsible for official statistical and geoscientific information. The IBGE has been conducting surveys of the Brazilian population every ten years since 1872. In each of these surveys, all households in the 5,565 municipalities of Brazil are interviewed. The data are compiled, excluding data that could identify specific individuals, businesses, entities or products. We used data from the 2010 census, the latest available data at the time of our work. Details of the data sources we used are given in Section 6 of this paper. There are there are 30 municipalities 954 census sectors in the Vale do Ribeira (Figure  1 ). However, it should be noted that IBGE does not publish the data if a defined census sector has a low population density or if there are too few answers to a question to ensure data protection.
For the variables used in our analysis, 30 sectors fell into this category, resulting in 880 valid sectors for this work (Table  1  The demographic census survey provides multiple variables for different domains. The HDI is an internationally recognised index to assess a country's development not only in economic terms, but also to include data on government policies and practices that affect well-being, health, and education. The HDI consists of three independently calculated dimensions: income, longevity and education. The IBGE and Atlas Brazil (https://atlasbrasil. org. br/acervo/atlas) publish a municipal HDI every decade based on the census data and other surveys  (Abreu et al. , 2011). Gross Domestic Product per capita is used to measure HDI-Income. For municipal HDI-Income, IBGE uses per capita income, therefore we used the same strategy to construct an intra-municipal proxy.
FEATURE EXTRACTION Following the method proposed by  Suel et al. (2019) , we used a VGG-16 network model  (Simonyan and Zisserman, 2015) , where weights are initialized with the pre-trained model using an ImageNet benchmark  (Russakovsky et al. , 2015). This method focuses on large objects in millions of images and behaves as a feature extractor and afterward transfers the learning to a dataset of images. This is a common method for fine-tuning a pre-trained CNN to other novel tasks with a small dataset  (Yosinski et al. 2014; Zhuang et al. , 2021). When a CNN is used as a feature extractor, usually the last three fully connected layers are removed and the remaining feature maps of the last convolutional layer are used to compose the feature vector (also called latent space). The street view dataset is fed into the pretrained network, which then returns a feature vector with 4096 dimensions as output for each image.
TRAINING After applying the deep learning model, the three remaining fully connected layers of the VGG-16 network are used to predict each output label, performing an ordinal classification from four angle images for each CEP. All of our source code was implemented using TensorFlow 2. 2 in Python 2. 7. Besides that, we conducted all the experiments (Section 4) on a workstation with an 8-core CPU Intel(R) Core(TM) i7 and 1TB RAM and it took approximately 96hs to complete the training process. The network was trained for 1,000 iterations and a batch size of 400 images per step. Batch normalization was used in all layers of the network except the output layer. The Adam optimizer was used and set with a learning rate of 5 × 10 -6. The network that provided Where w are the network weights, y n is the label vector of the n th sample (for one-hot encoding), and p n m is the probability of the m th income score for the n th sample.
We paid special attention to maintain a 'natural' distribution in both the training and test datasets to prevent overfitting. The network model used all four feature vectors corresponding to the image orientation as inputs for the last three fully connected layers (Figure  2 ). These four feature vectors are combined by calculating the average of all the elements of the vectors into a new vector. This neutralises the semantic information of the separate images, creating only one panoramic-view feature vector. This feature vector is then passed to the last dense layer which compute a single continuous value between 0 and 1 using the sigmoid function. This single continuous value was then used to compute probabilities (P 1 , P 2 , P 3 , P 4 , P 5 ), considering them as the probability of Bernoulli trials (coin toss) and transforming to a five-class problem, i.
VALIDATION To evaluate the performance of the classifier, we used five-fold cross-validation  (Bishop, 2006) , in which the predicted indicator for the census sector with the known but hidden score, was compared to its true value. This cross-validation method is a reliable strategy because it divides the data into two mutually exclusive sets: 80% for the training dataset (the set of instances used for training purposes) and the remaining 20% for the testing set (for testing purposes), thus ensuring the generalizability of the model. We also evaluated performance in the same manner as  Suel et al. (2019) , using the Pearson correlation coefficient (r), which measures the correlation between true and predicted classes (the closer to 1, the higher the relationship), the Kendall-Tau's coefficient (τ), which is interpreted similarly to the Pearson coefficient, and the mean absolute error (MAE), which expresses the average prediction error of the model.
INCOME PREDICTION FOR VALE DO RIBEIRA The kinds of images we obtained from the geolocated positions are illustrated in Figure  5. These samples were extracted from each geolocation according to their true income score derived from the census data. Each row shows the view from two different geolocations with four images corresponding to the set suite of angles (0°, 90°, 180° and 270°). The distribution of images across income indicators was naturally unbalanced, from 4% of the total number of images for the lowest income value to 42% for the highest (Figure  6 ), which reflects the actual income situation of the residents of Vale do Ribeira. In turn we think this data imbalance will not affect the results, an assumption consistent with the work of  Tetila et al. (2020). The use of a VGG network pre-trained on the ImageNet benchmark gives us confidence in using an imbalanced dataset.
As far as we know, there is yet no formal proof of the sensitivity of a VGG network to imbalanced data, but the work of  Johnson and Khoshgoftaar (2019, page 30 ) has shown that using a VGG network as a baseline is more than adequate to handle imbalanced classes. The ability to deal with imbalances in this way shows the power of reusing strong feature extractors trained on large volumes of data such as ImageNet. Machicao et al. Data Science Journal DOI: 10. 5334/dsj-2022-006 There was no clear division in income scores between census sectors and municipalities, so we treated the results as a heterogeneous distribution. On visual inspection, a slight division can be made between the given highest income areas in the centre and northeast of Vale do Ribeira, corresponding to the municipalities of the state of São Paulo, while the lowest and middle income scores are mainly located in the southwest of the study area (Figure  7a ).
The spatial representation of the predicted income score shows that some of the lowest income cluster regions were consistent with the observed data (Figure  7b ). Since the test dataset for each census area often included a range of values, the mode was plotted, and if all values were the same, a random selection was made. Notably there are more predicted instances of the lowest income (score 1) than in the actual. To complement the visual analysis in Figure  7 , we calculated the difference d between the real income score y and the predicted ŷ value, defined as follows:  To better analyse how well our deep learning model predicts income, we plotted the difference between the actual and predicted incomes (Figure  8 ). To facilitate interpretation, we show the geolocations (red dots) for which a street view image was available (as in Figure  4 ).
DISCUSSION AND CONCLUSION We started from the premise that street images contain rich visual information. In principle, the presence of vehicles, highways, infrastructure, houses, buildings or correlated objects in the image are associated with wealth, so a deep learning model should have the ability to learn from the data. Our results have shown that the deep learning framework used here is able to estimate high and low values of the income indicator using images of streets as input. We were successfully able to apply the model supplied by  Suel et al. (2019)  to the semi-rural situation of Vale do Ribeira, demonstrating the re-usability, reproducibility and replicability of GSV datasets. We paid particular attention to these factors, a common challenge to achieving FAIR outcomes  (Wilkinson et al.
(2019)  was necessary to obtain the images in the systematic manner illustrated in Figure  3  to ensure optimal linkage with the census data. The ubiquity of GSV worldwide, and its seemingly complete coverage can be deceptive. The images are not systematically collected or published, since it is the policy of the service to provide the most current image. To reduce the impact of this policy on the reproducibility and replicability of our work, we have published the libraries, datasets and images we used. This results in images of different dates adjoining each other (in one case, an image from 2011 next to one from 2015). The exact information on each date is imprecise because the timestamp is not available through the GSV API. This problem was pointed out by  Diou et al. (2018). In addition, the timestamps of the GSV (which ranged from 2011 to 2019) and the census data (2010) rarely matched.
NEXT STEPS Future projects to explore the following strategies would be profitable: (i) use of more complex neural network architectures; (ii) using more heterogeneous datasets in other locations; (iii) testing different training configurations, such as different regional granularities (e. g. , municipalities instead of census sectors); (iv) training and validation with images from a range of regions could improve the generalization power of the image classifier; and (v) to update the study with the 2021 census (when it is available), which would improve the consistency with the period of coverage of the images. Another aspect that could be enhanced by future work is the ability to improve the interpretation of the results of the deep learning model  (Li et al. 2021; Abitbol et al. 2020; Dai et al.
ACKNOWLEDGEMENTS This research is a product of the PARSEC group funded by the  Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI)  and the synthesis centre CESAB of the  French Foundation for Research on Biodiversity. In Brazil  the  PARSEC  project is supported by the grant  2018/24017-3 ,  São Paulo Research Foundation (FAPESP). The authors wish to acknowledge  Miguel S. X. Penteado  and  Nadya M. Deps Miguel  from  IBGE  for their valuable suggestions and support. J. M. is grateful for the support from  FAPESP  (grant  2020/03514-9 ). R. D. was supported by the  EOSC-Life European program  (grant agreement No. 824087 ).
Machicao et al. Data Science Journal  DOI: 10. 5334/dsj-2022-006     requirements of journals, and (3) an optimal level of openness to ensure maximum reuse of the data by potential third parties. Our source code can be found on the GitHub Digital Repository: https://github. com/PARSECworld/streetsValeRibeira and released at https://doi. org/10. 5281/zenodo. 4898335. The original Census data from the IBGE per census sector can be found at: ftp://ftp. ibge. gov. br/Censos/Censo_Demografico_2010/Resultados_do_Universo/Agregados_por_Setores_Censitarios/SP_ _ Exceto_a_Capital_20190207. zip and ftp://ftp. ibge. gov. br/Censos/Censo_Demografico_2010/Resultados_do_Universo/Agregados_por_Setores_Censitarios/PR_20171016. zip. We used the data from these files: From files ResponsavelRenda_PR. xls and ResponsavelRenda_SP2.
xls, we used the columns 'V022' named " Total do rendimento nominal mensal das pessoas responsáveis" (Total nominal monthly income  for responsible householders). From files Basico_PR. xlsand Basico_SP2. xlswe used the columns 'V002' named " Moradores em domicílios particulares permanentes ou população residente em domicílios particulares permanentes " ('Residents in permanent private households or population residing in permanent private households' in english). The curated dataset is open for access and reuse under the terms of the Creative Commons Attribution 4. 0 license.
). STATE MUNICIPALITY NUM. CENSUS STATE MUNICIPALITY NUM. CENSUS SECTORS SECTORS PR Adrianópolis 21 SP Itariri 61 SP Apiaí 55 SP Itaóca 9 SP Barra Do Chapéu 11 SP Jacupiranga 26 SP Barra Do Turvo 14 SP Juquitiba 54 PR Bocaiúva Do Sul 23 SP Juquiá 36 SP Cajati 38 SP Miracatu 48 SP Cananéia 27 SP Pariquera-açu 27 PR Cerro Azul 42 SP Pedro De Toledo 17 PR Doutor Ulysses 13 SP Registro 69 SP Eldorado 30 SP Ribeira 8 SP Iguape 60 PR Rio Branco Do Sul 58 SP Ilha Comprida 28 SP Sete Barras 26 SP Iporanga 19 SP São Lourenço Da Serra 27 PR Itaperuçu 34 SP Tapiraí 15 SP Itapirapuã Paulista 9 PR Tunas Do Paraná 12 Table 1 Statistics of census sectors, municipalities and states in the Vale do Ribeira. Figure 2 Workflow diagram showing the four main steps: 1) dataset preparation, 2) feature extraction, 3) training, and 4) validation.
Study Design A total of 160 free-living healthy subjects were enrolled in a population of healthy volunteers followed for 1 year (ClinicalTrials. gov Identifier: NCT03169088). The recruitment was conducted between 2016 and 2018 in two different study centers: Université Grenoble Alpes (France, n = 100) and Aberystwyth University (Wales, United Kingdom, n = 60). The inclusion criteria were as follows: healthy people living as a couple or family and involved in organizing and cooking meals in the household, ages ranging from 25 to 50, non-smokers, body mass index (BMI) ≥25, <30 (representative of the French and Welsh populations), and no chronic use of medications that may influence metabolism, gut microbiota or dietary behavior. All the participants were advised to adhere to healthy dietary habits in accordance with general nutritional recommendations: "The Eatwell Guide" 2016 for the UK population; https:// www.
gov. uk/government/publications/the-eatwell-guide, and Mangerbouger, Programme National Nutrition Sante, https://www. mangerbouger. fr/ the "Programme National Nutrition Santé" for the French population; https://solidaritessante. gouv. fr/prevention-en-sante/preserver-sa-sante/leprogramme-national-nutrition-sante/article/programmenational-nutrition-sante-pnns-professionnels, respectively). Over a year, anthropometric variables (i. e. , weight, BMI) and vital signs were assessed at baseline and at 12 months (Supplementary Table  S1 ), to confirm that the subjects were healthy. Furthermore, dietary intake data and urine samples were collected at each time point, as detailed below. The study was performed in accordance with the principles contained in the Declaration of Helsinki and the recommendations for Good Clinical Practice.
Quality Control Assessment Metabolomics data were subjected to quality control assessment prior to statistical analysis using a standardized protocol developed in-house and implemented in the POMA R/Bioconductor package  (22). For each of the three data blocks (i. e. , M0, M6, and M12), an independent quality control assessment and data preprocessing were performed as follows. First, data were log-transformed and Pareto-scaled, and Euclidean distances to the group centroid were computed to remove outliers from the data matrix (±3×IQR). Then, the concentrations of metabolites known to be influenced by preanalytical factors (e. g. , improper handling/storage of samples) were inspected to check for the absence of abnormal values (±3×IQR). Finally, the coefficients of variation for peak areas, retention times and peak widths of the internal standards were computed for evaluating the analytical reproducibility (<15% for peak areas and widths, <2% for retention times)  (14).
ACKNOWLEDGMENTS The authors thank  Prof. Paul Kroon  ( Quadram Institute Bioscience ),  Prof. Daniele Del Rio  ( University of Parma ) and  Prof. Kati Hanhineva  ( University of Eastern Finland ) for kindly providing synthesized in-house standards. We also thank  Laura Borges ,  Geoffrey Maillard ,  Arthur Goron  and  Morgane Couchet  for their logistical and technical assistance in the collection and processing of urine samples. We also express special thanks to  Raphaël Varraso , who helped us to compute the AHEI-2010 index from the food frequency questionnaires.
FUNDING This work was supported by the  EIT Health Innovation by Design  Project  COOK2HEALTH. EIT Health is supported by the  European Institute of Innovation and Technology , a body of the  European Union  and the  Spanish Ministry of Economy and Competitiveness (MINECO)  together with the  Joint Programming Initiative A Healthy Diet for a Healthy Life  ( PCIN-2014-133 ;  2015-238 ),  CIBERfes  and  ISCIII  Projects  AC19/00096  (co-funded by the  FEDER   Program  from the  European Union , A way to make Europe), and the  Generalitat de Catalunya's Agency AGAUR  ( 2017 SGR1546 ). RG-D thanks the  Juan de la Cierva Programme  from  MINECO  ( FJCI-2015-26590 ) and CA-L the  ICREA Academia Award 2018.
Acknowledgements ANR ( Agence Nationale de la Recherche ) and  CGI (Commissariat à l'Investissement d'Avenir ) are gratefully acknowledged for their financial support of this work through  Labex SEAM (Science and Engineering for Advanced Materials and devices)   ANR-11-LABX-086  and  ANR-11-IDEX-05-02. XPS Spectrometer was also supported by the  Ile-de-France regional council , SESAME agreement n  °16016303.
Given the Fermi surface and the scattering rate, we can now use Boltzmann theory to predict how the resistivity of Nd-LSCO should vary as a function of field strength, disorder level and field direction, at various temperatures. As shown below, we will find that all predictions are precisely confirmed by our data on Nd-LSCO and on the closely related material LSCO. In other words, the behaviour of electrons in a magnetic field in these strange metals is entirely the result of their orbital motion, and there is no evidence that the scattering rate has any field dependence. The in-plane resistivity of the three samples considered here is displayed in Fig. 1b. It is perfectly T-linear below 70 K in all cases, with similar slopes. The only difference is the residual resistivity (at T = 0), which reflects the different levels of disorder (elastic scattering): ρ 0 = 28, 12 and 48 μΩ cm for Nd-LSCO, LSCO sample S1 and LSCO sample S2, respectively.
In Fig. 2a , we display the field dependence of the in-plane resistivity ρ for Nd-LSCO, plotted as ρ(B)/ρ(0), the relative magnetoresistance (MR), obtained by applying a pulsed field up to 85 T, at various constant temperatures (the full set of isotherms is provided in Extended Data Fig. 1 ). In Fig. 2b , we show the corresponding prediction of Boltzmann theory, based on the parameters established by ADMR in Nd-LSCO. We see that the data and calculation are in quantitative agreement: the MR values at 4 K and 80 T are ρ(B)/ρ(0) = 1. 35 and 1. 30, respectively. Qualitatively, we find that the MR increases with decreasing T, and it evolves from a B 2 dependence at high T to B-linear at low T-an evolution that is nicely reproduced by the calculation. The B-linear dependence at low T, hailed as anomalous in previous studies, is in fact entirely accounted for by Boltzmann theory, given the strongly anisotropic elastic scattering rate of Nd-LSCO.
To directly compare with earlier work on LSCO  4  , we also measured the field dependence of ρ in LSCO at p = 0. 24, in our two samples, S1 and S2. In those two samples, ρ is T-linear below T ≈ 70 K, exactly as in Nd-LSCO, with a very similar slope (Fig. 1b ). In a previous study on LSCO at p = 0. 23 (ref. 8 ), in which a field of 48 T was applied to suppress superconductivity, the T-linear dependence of ρ was found to extend down to at least T ≈ 1 K. Note that the Fermi surface of LSCO is very similar to that of Nd-LSCO  23  , namely it is electron-like, because the Fermi level has crossed the van Hove singularity. In Fig. 2c  we display our high-field data on LSCO S1. The behaviour of the MR at various temperatures is seen to be very similar to that found in Nd-LSCO and in the calculations, namely B-linear at 4 K, evolving to B 2 at 100 K.
(Note that our MR data on LSCO are also consistent with prior MR data on LSCO p = 0. 19 (ref. 4 ); Extended Data Fig. 2. ) The only difference is the magnitude of the MR, equal to 1. 65 in LSCO S1 versus 1. 3 in Nd-LSCO, at 4 K and 60 T. This quantitative difference is expected, given the lower ρ 0 in the former sample. In Fig. 3a , we compare the MR in our three samples, at T = 30 K. In Fig. 3b , we show the predicted dependence of the MR on the disorder level. The calculation is performed using all the same ADMR-determined parameters, but now varying the multiplicative factor c in front of the elastic term in equation (1). By definition, c = 1. 0 is the value determined by the ADMR study for an Nd-LSCO sample with a very similar ρ 0 value to our own Nd-LSCO sample (from the same source and batch).
Acknowledgements We thank  J. G. Analytis ,  R. L. Greene ,  N. E. Hussey ,  S. A. Kivelson ,  D. Sénéchal  and  B. J. Ramshaw  for fruitful discussions. A portion of this work was performed at the LNCMI, a member of the  European Magnetic Field Laboratory (EMFL). D. V. and C. P. acknowledge support from the  EUR  grant  NanoX  no  ANR-17-EURE-0009  and from the  ANR  grant  NEPTUN  no  ANR-19-CE30-0019-01. L. T. acknowledges support from the  Canadian Institute for Advanced Research (CIFAR)  as a Fellow and funding from the  Natural Sciences and Engineering Research Council of Canada (NSERC ;  PIN:123817 ), the  Fonds de recherche du Québec -Nature et Technologies (FRQNT) , the  Canada Foundation for Innovation (CFI)  and a  Canada Research Chair. This research was undertaken thanks, in part, to funding from the  Canada First Research Excellence Fund.