dataesr/scientific-paragraphs-categorization huggingface dataset:
The UPLC-MS/MS platform included different chromatography methods and mass spectrometry ionisation modes to achieve broad coverage of compounds differing by physiochemical properties, as detailed previously  61. Metabolites were identified by comparison of the ion features in the experimental samples to a reference library of chemical authenticated standards that included the molecular weight (m/z), the retention time and the MS/MS spectral data. Metabolomic data were extracted and analysed using an informatics system consisting of four major components, LIMS (Laboratory Information Management System), the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of statistical, visualisation, and interpretation tools. The hardware and software foundations for these informatics components are the LAN backbone and database servers running Oracle 10.
Statistical analysis of metabolomic data Standard statistical analyses were performed in Array Studio on logtransformed data. Welch's two-sample t-test and the two-way ANOVA test were used to analyse the data. Both Welch's two-sample t-test and matched pairs t-test were performed for the patients' PT vs PDX dataset. The two-way ANOVA with FDR correction for multiple testing was used to analyse the different PDX and groups (IACS/control). Hierarchical clustering were used to analyze the data. For all analyses, following normalisation to a mass of the sample extracted, missing values, if any, were imputed with the observed minimum for that particular compound. The statistical analyses were performed on natural log-transformed data.
Metabolite set enrichment and pathway analysis Pathway enrichment analysis was performed using MyMetabolon pathway explorer (Metabolon, Inc). Enrichment values were calculated using the following equation: Enrichment value = (k/m)/((n-k)/(N-m)), Where m = number of metabolites in the pathway; k = number of significant metabolites in the pathway; n = total number of significant metabolites; and N = total number of metabolites. P values for each pathway are calculated using a right-tailed Fisher's exact test based on the hypergeometric distribution. Normalised values of all metabolites detected are presented in Supplementary Data 3.
Institut Curie clinical cohort Samples of 503 invasive primary breast cancers excised from patients treated at Institut Curie-Hôpital René Huguenin have been analyzed  63. All patients cared in our institution before 2007 were informed that their tumour samples might be used for scientific purposes and had the opportunity to decline. After 2007, patients gave their approval by signed inform consent. This study protocol was approved by the local ethical committee (Breast Group of René Huguenin Hospital). Tumour samples were included in the study with a proportion of tumour cells of at least 70%. All patients (mean age 61 years, range 29-91 years) met the following criteria: primary unilateral nonmetastatic breast carcinoma with clinical, histological and biological data were available; no radiotherapy or chemotherapy before surgery; and full follow-up at Institut Curie-Hospital René Huguenin.
Modified radical mastectomy was performed in 304 cases (60. 4%) and breast-conserving surgery plus locoregional radiotherapy in 196 cases (39%) (data available for 500 patients). The patients had a physical examination and routine chest radiotherapy every 3 months for 2 years, then annually. Adjuvant chemotherapy was administered to 120 patients, hormone therapy was administered to 173 patients and both treatments to 104 patients. The histological classification and the number of positive axillary lymph-nodes were determined at surgery. The Scarff Bloom Richardson's (SBR) histoprognostic system was used to score the malignancy of infiltrating carcinomas. The status of oestrogen receptor alpha (ERalpha); progesterone receptor (PR) and human epidermal growth factor receptor 2 (ERBB2) was determined at the protein level by using biochemical methods (dextran-coated charcoal method, enzyme immunoassay or immunohistochemistry) and confirmed by real-time quantitative RT-PCR assays  64.
All PCR reactions were performed using QuantStudio 7 Flex realtime PCR system (Perkin-Elmer Applied Biosystems). PCR was performed using the Power SYBR® Green Master Mix (Perkin-Elmer Applied Biosystems, # 4368708). The thermal cycling conditions comprised an initial denaturation step at 95 °C for 10 min and 50 cycles at 95 °C for 15 s and 65 °C for 1 min. Experiments were performed with duplicates for each data point. Gene expression levels were normalised on the basis of TBP contents (Genbank accession number NM_003194) used as an endogenous RNA control. The expression values of the tumour samples were subsequently normalised such that the median of the expression values of 14 normal breast tissue samples was one. Primers were designed with the assistance of the Oligo 6. 0 computer programme (National Biosciences, Plymouth, MN).
Acknowledgements E. Ma. is supported by  SIRIC2  ( INCa-DGOS-Inserm_12554 ),  GEFLUC  and  RUBAN ROSE associations  for funding. A. M. is supported by  AIRC  and  Fondazione Cassa di Risparmio di Firenze  ( IG22941  and  MultiUser19515 ) and N. L. is an  AIRC  fellow. Icons of women, vertebrae and mice were made by Freepik from www. flaticon. com.
Data availability Affymetrix CEL files and normalised log2 RMA data are available at the GEO database under accession no GSE146661. RNAseq raw data have been submitted to the EGA depositary under the following accession codes: EGAS00001006428 EGAS00001006429 EGAS00001006908 Raw data of targeted DNA sequencing have been submitted to the EGA depositary under the number EGAS00001007244. Metabolomic raw data have been deposited in the Metabolights database under the accession code MTBLS3342. Source data related to metabolomic analyses are included in Supplementary Data 1 (comparisons of primary tumours and PDX) and Supplementary Data 2 (comparison of HBCx-124 and HBCx-137 control and IACS-treated xenografts). Normalised metabolomics data and chemical annotations of metabolites.
Acknowledgements. We are grateful to the  World Climate Research Programme's   Working Group on Coupled Modelling , which is responsible for the  CMIP exercises. For CMIP, the  U. S. Department of Energy's Program   for Climate Model Diagnosis and Intercomparison provided coordinating  support and led the development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. This study benefitted from the ESPRI (Ensemble  de Services Pour la Recherche à  l'IPSL) computing and data centre (https://mesocentre. ipsl. fr, last access: September 2022), which is supported by  CNRS ,  Sorbonne Université, École Polytechnique , and  CNES  and through national and international grants. We are also grateful to the administrative and technical staff at  Ecole Normale Supérieure/PSL.
Code availability. NEMO is released under the terms of the CeCILL licence. The standard NEMO-PISCES version (PISCESv2; Aumont et   al. , 2015)  slightly modified in this study (see Sect. 2. 2) is accessible through http://forge. ipsl. jussieu. fr/igcmg_doc/wiki/Doc/Config/NEMO. The other NEMO-PISCES versions are available on request from the corresponding author. Data availability. All the CMIP ensemble data were available on at least one of the Earth System Grid Federation (ESGF) nodes. Author contributions. This work is in the framework of the OMIP-BGC group, which contributed collectively to this study through the organization and execution of the CMIP exercises and the sharing of simulation outputs. AP: conceptualization, investigation, methodology, formal analysis, visualization, writing -original draft preparation -and project adminis-tration.
, 2017). Explicit information about the spatial distribution of these species, together with a detailed information on all the biotic interactions among them across the entire geographical study area, makes this data set uniquely suited to address our question. We used different measures of biotic specialization to quantify the dietary dimension of the niche and coupled sampling site locations with their climatic conditions to define the climatic dimension of the niche. Moreover, to understand the importance of each dimension of a species' niche on its range size, we generated null expectations based on species occupancy that allowed us to assess whether the relationships observed were only an inevitable consequence of species range size (i. e. species with larger ranges are intuitively bound to have wider niche breadths, which could lead to spurious correlations).
| Data set The data set used to perform the analyses was published by  Kopelke et al. (2017) , and describes a single community type of three trophic levels (willows, gall-inducing sawflies and their parasitoids) sampled across central and northern Europe (Figure  1 ). In particular, this data set includes spatially explicit snapshots of gall-inducing sawflies (Hymenoptera: Tenthredinidae: Nematinae: Euurina) and their diverse natural parasitoids (hymenopteran parasitoids and coleopteran, lepidopteran, dipteran and hymenopteran inquilines) encountered on willows (Salix spp. ) across 374 sampling sites. The sampling sites covered a latitudinal range of > 30°, among which mean annual temperature ranged from -4. 7 °C in the coldest site to 17. 3 °C in the warmest site. At each site, a set of galls was collected from practically all willow specimens supporting galls of focal species within an area of about 0.
As a result, the largest taxon in the original data set, 'Chalcidoidea indeterminate', represents a superfamily of very small parasitoids that are hard to distinguish from each other. For this reason, in the current study, we removed this superfamily from the analyses to avoid any potential bias. Given that some sites were originally sampled more than once, we checked that the results were not affected by the difference in sampling effort across sites. We performed the analyses considering the full data and compared them with the analyses performed with only one sampling year per site, and we observed no qualitative differences. Further details on the study system can be found in  Kopelke et al. (2017). In summary, the data include information about the location of each site, the Salix and galler species inspected, the number of galls F I G U R E 1 Distribution of sampling sites across central and northern Europe.
| Climatic niche measures To obtain a comprehensive picture of the variability of climatic conditions among sampling sites, and hence the climatic niche available to the sample species across the study area, we considered all the biologically relevant bio-climatic variables available from WorldClim (http://world clim. org). These bio-climatic variables provide a holistic picture of the local climatic environment at different localities (Supporting Information Table  S1 ). Bio-climatic data at each sampling site were obtained from WorldClim version 2. 0  (Fick & Hijmans, 2017)  using the raster package in R  (Hijmans et al. , 2014). To define a multidimensional space on which to project species based on their climatic niche requirements, while at the same time reducing the dimensionality of the niche, these bio-climatic variables were analysed using principal component analysis (PCA) using the prcomp function in R (R Core Team, 2023).
| Breeding and available sites We compiled and filtered (deletion of duplicates, erroneous and imprecise data about species identification, location of colonies and date of breeding) all available informations on observed breeding sites of Laridae (i. e. cay, islet or coastal site) from scientific publications, books, thesis, project reports and unpublished data. A database was built with the names and spatial coordinates of the 49 reported breeding sites (Figure  2 ), years of observation , breeding species and information sources (Table  S1  were treated as ROST due to the misidentification of these species' colonies  (Navarro, 2021; Nisbet, 2020). Additionally, we selected (non-randomly) 52 sites distributed along the coast of Cuba where none of the nine species was observed breeding in 2020 (Figure  2 , Table  S1 ).
F I G U R E 2 Map of the study area and the sites used to train and validate the modeling of the breeding macrohabitat suitability of nine Laridae species. Blue diamonds = breeding sites, red diamonds = non-breeding sites. because these do not match between Landsat satellites. The definition of scene size followed a balanced criterion: sufficiently large image size that included land and sea components, and sufficiently small to minimize the inclusion of other breeding and available sites in the same image scene (which would affect the prediction quality). We organized the data into two datasets considering the quality of scene images related with cloud cover (Table  S3 ) to control for cloud-related confounding effects. We randomly mixed the images database of both types of sites (i. e. with and without breeding colonies) and then split them into two groups containing 70% (training) and 30% (validation) of the data (Table  S3 ) for the building and selection of the best CNN.
Satellite images from 2021 at 12 breeding and 52 non-breeding sites (verified as such that year) were used as test dataset to assess the predictive performance of the CNN. For predicting breeding macrohabitat suitability, we applied the same preprocessing to Landsat 7 images of 2021 (Table  S2 ) that covered the entire Cuban archipelago. A mosaic was built with these images, masking mainland to retain only the marine-coastal ecosystems up to the insular shelf. Images with open water only or predominance of land of the main two islands (Cuba and Juventud) were excluded since they were irrelevant to Laridae breeding  (Jiménez et al. , 2009). Finally, this mosaic image was gridded into 793 scenes (63,805. 5 km 2 ) with the same format and structure than train and validation datasets. Satellite images were processed using the ENVI 4.
The variables were measured at the date of the colony observation, and in 2020 for the non-breeding sites. Using the Landviewer product of the EOS Data Analytics platform, we calculated the Normalized Difference Water  (Gao, 1996)  and Vegetation  (Rouse et al. , 1973)  Indexes (NDWI and NDVI respectively) from satellite images (using Green, Red and Near Infrared bands) of the Landsat series. Dates of images ranged between May and August, matching the breeding season of Laridae in Cuba  (Jiménez et al. , 2009). Both spectral indices vary between -1 and 1 with higher numbers corresponding to higher humidity (NDWI) or green vegetation (NDVI). Based on the NDWI we then calculated the drought emerged areas (NDWI range = -1 to 0. 2) and the percentage of non-flooding cover (NDWI range = -1 to 0).
3. 3 software. Minimum distances to the 200-m isobath were measured using a bathymetric shapefile of the exclusive economic zone of Cuba (using information both from GEBCO and Cuban research agencies databases). Sea surface temperature (SST) and surface chlorophyll a concentration (Chl a) around the sites were obtained at a spatial resolution of 1 km from level-2 MODIS-Aqua satellite data (https://ocean color. gsfc. nasa. gov/data/aqua/) after sampling and spatial reprojection of the data. For both variables we averaged the monthly values between May and August of the year corresponding to the last register of each breeding colony and for 2020 for available sites. Nevertheless, due to the absence of logistical support for a systematic monitoring of these variables before 2002, and given the small interannual variability of both variables in Cuba, data for breeding colonies observed in that period were estimated from the mean of 2002-2021 period for the same months (Figure  S1 ).
Parameters were finally estimated using an Adam optimizer and minimizing a "Sparse categorical crossentropy" loss. In order to prevent overfitting, we also added a L2 regularizer, a Deep Learning technique to get better generalization and predictive properties  (Kussul et al. , 2017). Finally, models were trained using the "training dataset," and selected when minimizing accuracy score over the "validation dataset. " This analysis was implemented with the keras R package (v. 2. 6. 1)  (Kalinowski et al. , 2021). We performed this training procedure for distinct parameters and the best CNN (better performance and lowest loss in validation and test datasets) was used to predict the breeding macrohabitat suitability using the satellite images of 2021 along all marine-coastal ecosystems of the archipelago.
Phytoplanktonic biomass Chlorophyll a concentration at sea surface within 10, 50 and 100 km radius of the colony mg m -3 Proxy for phytoplanktonic biomass, primary productivity and prey availability at different foraging ranges split). For each RF type we used the 10 best runs in order to get an average and variance of model performance. Same performance metrics than CNN (Table  S5 ) were used to assess the training, validation and test of RFs. Variables contributions were calculated from the mean decreasing Gini index (values are directly proportional to variable importance) derived from the RF with better performance. The randomForest R package (v. 4. 6-14)  (Breiman et al. , 2018)  was used to this analysis.
| Prediction of macrohabitats breeding suitability The performance of CNN was high, exhibiting indicators values ≥80. 0% for the validation datasets (Table  2 ). Image quality (according to cloud cover) had no significant consequence on the classification power of CNNs (F1-score validation = 85. 7% and 84. 6% for CNNs that used all and best images, respectively; Table  S3 ). We thus worked with the architecture that used all images. Based on the test dataset, the CNN exhibited good performance test indicators with accuracy = 79. 7%, precision = 91. 5%, recall = 82. 7% and F1-score = 86. 9%. When used for predicting over the entire Cuba archipelago, the CNN estimated 32,184, 12,069, 6598 and 12,954 km 2 of suitable habitat for breeding within 0-. 25,. 26-. 50,. 51-. 75 and. 76-1 probability ranges, respectively (Figure  3 ).
Also, the CNN performance might be probably increased including SST information at the radius of 100 km (considering the main results of RFs) and being trained over larger datasets. This study illustrates the benefits obtained from a complementary analysis of CNN and RF. Here, RF can be seen as an explicative tool relying on features directly related to our a priori ecological hypotheses, while CNN can act as an evaluative tool in order to assess the relevance of habitat spectral features, as well as an efficient predictive tool producing large scale prediction of habitat suitability in a convenient manner. Particularly, CNNs are becoming increasingly important in remote sensing and ecology due to the inclusion of the spatial dimension within their convolutional layers, thus facilitating the identification/characterization of relevant ecological patterns and processes  (Hayes et al.
Variables were compared among sites using Mann-Whitney U-tests considering significance at p <. 01. Breeding macrohabitat selection was analyzed through random classification forests (RFs) models considering the measured variables at three spatial scales. Similar to CNN, we mixed and split the data to create the training and validation datasets (70% vs. 30% proportions) for the building and selection of the best RF. The same training and validation datasets were used in all RFs to compare their classification performances. We implemented three RFs, one that processed physical-geographical variables registered within a radius of 100 km from the breeding colony (RF_100; 300 trees and four variables by split), a second within a 50 km radius (RF_50; 200 trees and four variables by split) and a third one within a 10 km radius (RF_10; 150 trees and three variables by TA B L E 1 Potentially important variables for Laridae breeding macrohabitat selection (breeding site, i.
Adopting land management practices that increase the stock of soil organic carbon (SOC) in croplands is widely promoted as a win-win strategy to enhance soil health and mitigate climate change. In this context, the definition of reference SOC content and stock values is needed to provide reliable targets to farmers, policymakers, and stakeholders. In this study, we used the LUCAS dataset to compare different methods for evaluating reference SOC content and stock values in European croplands topsoils (0-20 cm depth). Methods gave generally similar estimates although being built on very different assumptions. In the absence of an objective criterion to establish which approach is the most suitable to determine SOC reference values, we propose an ensemble modelling approach that consists in extracting the estimates using different relevant methods and retaining the median value among them.
As a consequence, SOC storage potential estimates ΔSOC, calculated as the difference between a reference value and the actual SOC content of a soil and expressed as stocks or concentrations differences, depend on several modelling choices, namely the algorithm(s) used for the estimation of the reference values and the choice of a reference system, the pedoclimatic variables that are taken into consideration, and the constraints under which the SOC content is expected to evolve. The goal of this study is to explore, for the first time and on a very large dataset of >20,000 measurements (LUCAS dataset,  Orgiazzi et al. , 2018) , the consequences of these modelling choices on the determination of SOC reference values for croplands topsoils (0-20 cm depth) of the European Union (EU27) and the United Kingdom.
Soil data Soil data (0-20 cm) were extracted from the LUCAS Topsoil dataset  (EUROSTAT, 2009; EUROSTAT, 2012; European Commission, 2013; ESDAC, 2013; EUROSTAT, 2015; ESDAC, 2020). Specifically, we used four subsets: • Dataset n. 1 -LUCAS 2009 and 2012: topsoil data and land cover data of mineral soils (SOC < 120 gC kg -1 ) with cropland, grassland, or woodland land cover (15,251 sites). Sites were sampled in 2009, apart from sites in Romania, Bulgaria, Malta, and Cyprus that were sampled in 2012.
Climate data Climate data at the LUCAS sites were extracted from the Climatic Research Unit dataset  (Harris et al. , 2020) , available at https:// catalogue. ceda. ac. uk/uuid/89e1e34ec3554dc98594a5732622bce9 (last accessed 6th October 2022). Specifically, the data that we extracted were the following: -Mean annual temperature: average over 30 years  (1985 to 2015) ; -Standard deviation of monthly average temperature over the year: average over 30 years (1985 to 2015); -Total annual precipitation: average over 30 years  (1985 to 2015) ; -Aridity index: difference between the potential evapotranspiration and the total annual precipitation, average over 30 years (1985 to 2015); -Mean monthly temperature: average over 10 years  (2005 to 2015) ; -Mean monthly precipitation: average over 10 years  (2005 to 2015) ; -Monthly potential evapotranspiration: average over 10 years  (2005 to 2015).
Net primary production data Net primary production (NPP) data at 8974 LUCAS 2015 croplands sites were extracted from the MODIS/Terra MOD17A2H dataset  (Running et al. , 2015). The data were accessed using the Google Earth Engine, product MOD17A2H (https://developers. google. com/earth -engine/datasets/catalog/MODIS_006_MOD17A2H#description, last accessed 6th October 2022). For each month of the year, the monthly NPP was extracted as the mean daily NPP (from 8-days data covering the month) multiplied by the number of days in the month. Data were averaged over 10 years  (2005 to 2015). By default, the NPP was extracted with a spatial resolution of 1 km. However, data could not be found for 105 sites. For these points, we looked for data at an increasingly larger spatial resolution (2 km: 64 sites, 3 km: 10 sites, 4 km: sites).