dataesr/scientific-paragraphs-categorization huggingface dataset:
6). b) Evolution of the water vapour mixing ratio within the rising bubble based on MLS bubble detections (see Methods). The dashed contours show the equivalent mixing ratio of ice water derived from MLS ice water content vertical profiles collocated with the bubble. The thick dashed curve marks the top altitude of the aerosol bubble determined as the level where OMPS-LP extinction triples that of the nearest upper altitude level. c) Evolution of carbon monoxide (MLS) within the bubble. The centroid and the vertical boundaries of the aerosol bubble determined using CALIOP data are overplotted as circles and bars respectively. d) Composited temperature perturbation within the smoke bubble from Metop GNSS radio occultation (RO) temperature profiles collocated with the smoke bubble (see Methods).
Acknowledgements CALIOP data were provided by the ICARE/AERIS data centre. The TROPOMI data were provided by the  Copernicus Open Access Web  https://scihub. copernicus. eu/. We thank the  EUMETSAT's Radio Occultation Meteorology Satellite Application Facility (ROM SAF)  for providing NRT temperature profile data. MLS data are provided by the  NASA Goddard Space Flight Center Earth Sciences (GES) Data and Information Services Center (DISC). We thank the  OMPS-LP team at NASA Goddard  for producing and distributing high quality Level 1 radiances, and the  SAGE III/ISS  team at  NASA Langley  for data production and advice, in particular  Dave Flittner. We acknowledge the support of  ANR  grant  17-CE01-0015. The providers of the libRadtran suite (http://www. libradtran. org/)are gratefully acknowledged.
Data availability MLS data are publicly available at http://disc. sci. gsfc. nasa. gov/Aura/data-holdings/MLS; GNSS-RO data at https://www. romsaf. org/product_archive. php; OMPS-LP data at ftp://odin-osiris. usask. ca/ with login/password osirislevel2user/hugin ; SAGE III data at doi:10. 5067/ISS/SAGEIII/SOLAR_BINARY_L2-V5. 1 ; CALIOP data at doi:10. 5067/CALIOP/CALIPSO/CAL_LID_L1-VALSTAGE1-V3-40 ; TROPOMI ozone data at https://doi. org/10. 5270/S5P-fqouvyz ; carbon monoxide data at https://doi. org/10. 5270/S5P-1hkp7rp ; Aerosol Index data at https://doi. org/10. 5270/S5P-0wafvaf. The extracted ECMWF data used in this work are available at https://doi. org/10. 5281/zenodo. 3958214.
Code availability LibRadTran code exploited for radiative forcing calculations is available at http://www. libradtran. org/doku. php?id=download The processing code for CALIOP, ECMWF data is available at https://github. com/bernard-legras/STC-Australia with dependencies in https://github. com/bernard-legras/STC/tree/master/pylib. The processing code for TRIPOMI is available at https://github. com/silviabucci/TROPOMI-routines The processing codes for MLS, OMPS-LP, SAGEIII, GNSS-RO are available at https://doi. org/10. 5281/zenodo. 3959259. The processing code for the estimation of injected masses using MLS data is available at https://doi. org/10. 5281/zenodo. 3959350.
This data article presents a compilation of mechanical properties of 630 multi-principal element alloys (MPEas). Built upon recently published MPEa databases, this article includes updated records from previous reviews (with minor error corrections) along with new data from articles that were published since 2019. The extracted properties include reported composition, processing method, microstructure, density, hardness, yield strength, ultimate tensile strength (or maximum compression strength), elongation (or maximum compression strain), and Young's modulus. additionally, descriptors (e. g. grain size) not included in previous reviews were also extracted for articles that reported them. the database is hosted and continually updated on an open data platform, Citrination. to promote interpretation, some data are graphically presented.
An accurate accounting of high quality data from these studies is necessary to aid in further MPEA development, such as identifying gaps in design space, training machine learning models, flagging of outliers, etc. Given the large interest in this class of alloys, data on new systems are rapidly being published, necessitating frequent database updates to maintain relevancy. The updated MPEA mechanical properties database presented here combines data from previous reviews, makes corrections to data, and adds new data from articles published in 2019. The complete database will be hosted online in conjunction with a template to ensure routine updating and public availability of the database.
Methods Extraction from literature. Two previous reviews of MPEA mechanical properties from 2018  15, 16  were used to populate the initial database. When combined, these reviews contained data on 296 unique MPEA compositions (614 composition-property combinations). The additional data extracted for this study included 334 unique MPEA compositions (931 composition-property combinations), more than doubling the existing data. Table  1. The 25 MPEAs in the database with the highest yield strength (at room temperature), illustrating how a subset of the data are stored in each field. For alloys with multiple reports, the report resulting in the highest yield strength is shown. Many properties are not shown in this view. Fig. 1  The database generation workflow. Records are first extracted from various publications and input into a defined template format.
Post-processing tools are used to identify outliers or erroneous data points. A detailed review of the number of records and properties contained in the resultant database is presented in Table  2. During extraction and digitization of the initial database, various typos and extraction errors were identified and corrected. Once digitized, the initial database was combined with the newly extracted data and put into single spreadsheet, as demonstrated in Table  1. To identify new sources of MPEA data, a keyword search for "high entropy alloy" was conducted on Web of Science (query performed October 2019) and responses were filtered for articles published in 2019. From this query, 136 articles were identified as potentially viable sources of experimental MPEA mechanical property data (i. e. articles reporting single and multiphase materials with a minimum of three elements).
Defined in detail in the Data Records section, relevant mechanical property data were extracted from plots, tables, and text and input into a tabular format. To extract data from plots, webplotdigitizer  17  was employed. The newly extracted data were combined with the previously digitized data to complete the database. A high level overview of the extraction workflow is provided in Fig. 1. Data from future publications. For any data that are relevant, but not present in the current review, researchers are encouraged to make their own contributions. Using the template provided on GitHub  18  data extraction and digitization can be performed by many groups asynchronously. This template is formatted such that data can be easily uploaded to Citrination, an online platform for materials data  19.
Data records The database contains 1545 records from 265 articles. An individual record is defined as having a unique composition, property, temperature, reference combination. For example, if two articles measured the yield strength of HfNbTaTiZr at five temperatures, the number of records extracted is ten. On a per record basis, this database presents a > 100% improvement in the amount of available data when compared to the data presented in the 2018 reviews. The data in the database are extracted to best represent the data made available by the authors. Often, not all properties in the database are reported for every record. For example, despite the importance of grain size and interstitial contents on properties, particularly for refractory MPEAs, these features are missing from many articles.
The data are made available on Figshare  20  and in various tabular formats on the project GitHub  18. The data have also been digitized into Physical Information File (PIF) records, an open-source json-based schema for materials data  21. PIF records are hosted on Citrination (https://citrination. com/datasets/190954) to provide easy access for data visualizations and machine learning. Each data source will be updated continuously as more data are extracted. The database records consist of the following fields, as available: • Alloy composition: Normalized and alphabetized nominal alloy composition, in atomic percent. Validation and alphabetization were performed using the Pymatgen Composition module  22. • Microstructure: The experimentally observed phases (e. g. FCC, BCC, B2). Any phases that were not BCC, FCC, HCP, L12, B2, or Laves were labeled as "Sec.
Elemental Young's modulus values were obtained via Pymatgen  22. • O content (wppm): Measured oxygen content. • N content (wppm): Measured nitrogen content. • C content (wppm): Measured carbon content. A portion of the database (the 25 alloys with the highest yield strength at room temperature) is highlighted in Table  1  to provide an example for how data are stored in each field. This is only a subset of the properties collected in the database. Statistics on all properties extracted for the database are presented in Table  2. "Other" is defined as any report of an MPEA that is either multiphase, or single-phase but not FCC or BCC. The trend may suggest that BCC MPEAs have better hightemperature strength, but also highlights the lack of data available for FCC MPEAs.
Usage Notes This expanded dataset on MPEAs is intended for use to guide experiment for future alloy development. As shown in Figs. 2  and 3  the dataset can produce informative visualizations to guide researcher efforts. Each record can be accessed programmatically via the Citrination API  23. In conjunction with traditional Python data processing packages (e. g. pandas) the dataset will be useful as training data for machine learning applications. To ensure data quality, each record is associated with a digital object identifier (DOI) link to the original source. To improve the predictive capabilities of subsequent machine learning models, researchers are encouraged to contribute to this database through the addition of new data as it is generated. Step 2: In this case, the inaccuracy was units-related; the value was recorded as in units of GPa, however the database expected units of HV.
acknowledgements This work was supported by a grant from the  Schmidt Futures Foundation. The authors would also like to acknowledge  Dr. Robert Ritchie  and  Jon Ell  for their input and comments during the preparation of the database. Work by  O. N. Senkov  was supported through the  Air Force  on-site contract  FA8650-15-D-5230  managed by  UES, Inc. , Dayton, Ohio.
, 2008; McIntire & Fajardo, 2014; Kinlock, 2019)  have highlighted that facilitation may be much more widespread than ecologists usually tend to think. Although some theoretical studies suggest that facilitative interactions can be destabilizing (sensu resilience) and therefore undermine coexistence in Lotka-Volterra models  (Coyte et al. , 2015) , multiple other modelling  (Gross, 2008; Qian & Akçay, 2020)  and empirical  (Brooker et al. , 2008; Cavieres & Badano, 2009)  studies have suggested that facilitative interactions can to a large degree benefit coexistence, especially when multiple interaction types are considered simultaneously  (Mougi & Kondoh, 2012; García-Callejas et al. , 2018; Qian & Akçay, 2020). Here, we analyse a spatially replicated, long-term community-level dataset, consisting of ten multivariate time series of phytoplankton abundance along the French coastline.
Sampling methods All phytoplankton samples were collected by Ifremer coastal laboratories as part of the National Phytoplankton and Phycotoxin Monitoring Network  (REPHY, 2017). Since 1987, this monitoring program has required the sampling of 26 sites along the French coastline every 2 weeks within 2 hours of high tide to document both biotic (phytoplankton counts) and abiotic (water temperature, salinity) variables. We focused on sites which had the longest time series. We also excluded time series which had missing data for over 6 months or an average delay between sampling dates above 20 days. This reduced the number of study sites to 10 sites nested within 4 regions (Brittany, Oléron, Arcachon and the Mediterranean Sea; Fig. S1  and Table  S1  in the Supporting Information). Abiotic variables (temperature, salinity) were measured directly from the boat during the sampling process while water samples for biotic analyses were fixed with a Lugol's solution and examined later.
When gaps did not exceed a month, missing values were linearly interpolated; remaining missing values were replaced by a random number between 0 and half of the lowest observed abundance  (Hampton et al. , 2006). Time series are plotted in Fig. S2. We tested extensively this and other methods to deal with missing data in a previous publication on a subset of this dataset  (Barraquand et al. , 2018). All time series were scaled and centered before MAR analyses.
The first interaction scenario assumed that interactions could only occur between phylogenetically close organisms, i. e. , within a class (groups were then diatoms, dinoflagellates, and other phytoplanktonic organisms) while the second interaction scenario further differentiated pennate and centric diatoms. The third interaction scenario considered the reverse hypothesis, that only unrelated organisms could interact (i. e. , a diatom could only interact with a dinoflagellate or a cryptophyte, but not with another diatom), and the last interaction scenario did not constrain the interactions at all (full interaction matrix). We selected the best scenario by comparing BIC (Fig. S3 ), which proved to be satisfactory in our previous analyses of both real data and similar simulated datasets  (Barraquand et al.
Red dots correspond to terrestrial and/or low dimension predator-prey systems, giving a lower bound for the intra/inter ratio. Intertaxa interactions were set to 0 when they were not specified in the articles (in most cases, authors removed non-significant interactions at the 5% level; Fig. S9  is the same figure taking into account only significant interactions). Tilman et al. , 1982; Descamps-Julien & Gonzalez, 2005). In the field-based dataset studied here, the same genera that are considered in experiments are found not to compete (or only weakly), hence there must be some niche differentiation occurring in the field but not in the lab. Second, the only other study that managed to provide MAR(1) estimates down to the species level for phytoplankton, that of  Huber & Gaedke (2006) , provides an intra/interspecific strength ratio similar to ours (point 7a in Fig.
Ghosts of competition past and present Overall, the dominance of niche differentiation in observational plankton studies -based on our analysis of the REPHY dataset and re-analysis of the MAR(1) literature -is similar to what has been recently found in plant community studies  (Adler et al. , 2018b)  or empirically parameterized food webs including horizontal diversity  (Barabás et al. , 2017). Large niche differences might be due to the ghost of competition past, i. e. , competition has occurred in the past, leading to strong selection and subsequent evolution, and then to progressive niche separation. In this scenario, species have evolved niches that allow them not to compete or to interact only weakly. The likely predator effects that we highlighted above could be comprised within such niche differentiation sensu largo: specialized predators can make strong conspecific density-dependence emerge  (Bagchi et al.
Acknowledgements This study was only made possible by the dedication of the members of the REPHY program by Ifremer (REPHY, 2017), providing invaluable data through years of fieldwork. We are grateful to  David Murrell  for his careful reading and suggestions, and to  Peter Adler  for helpful exchanges. We also want to thank  Bérangère Péquin  for constructive comments, as well as  Gyuri Barabás  whose thoughtful suggestions improved the connection made to continuous-time stability theory. This study was supported by the  French ANR  through  LabEx COTE  ( ANR-10-LABX-45 ).
To extract statistical data from the micrographs (Figure  1 ), different types of filters were used to i) correct and improve the quality of contrasts, ii) highlight the contours of the nano-columns, iii) reduce image noise. Advanced image processing was then implemented. The SiO2 columns were isolated by locally segmenting the image. After this segmentation, the void appears in dark contrast and the condensed matter in light contrast, which makes possible the easy identification of the columns or column agglomerates (Figure  1. b and c). Finally, we carried out a statistical analysis by adjusting an ellipse as closely as possible to the contour of the column or agglomerate; each ellipse being characterized by its area, ellipticity and orientation (Figure  1. d). To access the optical properties of monolayers and bilayers, spectrophotometry measurements were carried out for each sample.
The nanostructure of the OAD samples was analyzed by TEM experiments at an acceleration voltage of 200 kV. Bright-field TEM images were recorded in a JEOL 2010F microscope. The 3D morphology of the OAD SiO2 bilayer deposited on the substrate was extracted from electron tomography in a FEI Titan Cubed Themis 60-300 microscope, using the high-angle annular dark-field scanning TEM (HAADF-STEM) imaging mode. For that purpose, a dedicated tomography holder operated by the FEI Xplore3D software was used to acquire tilt series every 2° from -60° to +60°. The FEI Inspect 3D software was then employed to align the projections using crosscorrelation methods, and to perform reconstruction into a 3D volume using the conventional simultaneous iterative reconstruction technique (SIRT) with 30 iterations.
The FEI Avizo program was used for 3D visualization and segmentation of the reconstructed volume. Specimen for TEM were prepared by soft and flat mechanical polishing of the two faces using a tripod apparatus (Model 590 Tripod Polisher) in order to control finely the thinning down to a few microns. This step was followed by a short Ar + -ion milling step in a Gatan PIPS system (using an energy of 3. 5 keV and an incidence angle of +/-7º). This procedure provides large and homogeneous electron transparent areas, which is suitable to limit the geometrical shadowing of the specimen at large tilt angles of the holder. Prior to insertion into the microscope, the specimen was cleaned in an Ar/O2 plasma in order to remove the hydrocarbon contamination.
ACKNOWLEDGEMENTS This work was supported by the  DGA (Direction Générale de l'Armement) , the  French Defense Procurement Agency. This work has been partially supported by «  Nouvelle Aquitaine » Region  and by  European Structural and Investment Funds  ( ERDF reference P-2016-BAFE-209 ):  IMATOP  project. A. J. Santos thanks the financial support of the  IMEYMAT Institute  and the  Spanish Ministerio de Educación y Cultura  for the concessions of grants ( ICARO-173873  and  FPU16-04386 ). The " Talent Attraction Program " of the  University of Cádiz  is also acknowledged by supporting B. Lacroix contract code E-11-2017-0117214. This work was carried out in the framework of the associate laboratory PRIMEO (" Partnership for Research and Innovation in Emerging Materials for phOtonics") between Safran Electronics & Defense and Pprime Institute.
METHODS All the code for our method can be found on GitHub (https://github. com/adespi/link_ epi_to_expr) We use the sequence and expression data from the 1000 genome project (Consortium and The 1000 Genomes Project  Consortium, 2015; Consortium and GTEx Consortium, 2017). We have both the expression and the sequence solely for 445 because we use the expression from Geuvadis project and take only the European American individuals to reduce confounding factors. We can analyze the expression by PCA and remove its 5 first components in order to reduce confounding factors (as biggest components usually contain mostly batch effects). We can take the log of the gene expressions to match the biological meaning of the correlation. We can standardize the gene expressions to give them an equal value in the correlation.
Correlations From the prediction we compute correlations. For this we define: Finally, we calculate cm for each i, j and k as in equation 1: prediction i jkl = cm i jk = cor(qn(prediction i jk ), qn(gene expressions j )) (1) For each position, we find the correlation between its predicted epigenetic marks and the TF's expression across the individuals (see Equation  1 ). We tried two languages to calculate the correlation and found that python takes 10 minutes and R 23 minutes to compute all the correlations. We also found that outsiders in the data sometimes give a high false positive correlation. To ensure robustness, the correlations are performed after a quantile normalization using scikit-learn  (Pedregosa et al. , 2011). As stated in their documentation, this method transforms the features to follow a uniform or a normal distribution.
DeepSEA's predictions are distributed upon 148 different cell types. We keep all cell type predictions and not only cell types matching with the whole blood expression for several reasons. Firstly, DeepSEA may have missed something in its blood prediction and could have found more motifs or patterns in other cell lines but its results may still relevant for blood. Secondly, a change in whole blood expression can originate from a mutation which has an effect in a tissue. Thirdly, if the expression is changed in blood, it may change in other cell types as well. As the output number of correlations is very high (5000 positions * 919 marks * nbr o f genes), we use fdrtool  (Strimmer 2008) , an R package to compute q-values from the correlations using an empirical model to predict false discovery rate (FDR).
Study Site and Dataset The study site is a watershed (Adour Amont) located in the southwestern France, west of Toulouse (city). It covers 1500 km 2 , as shown in Figure  1. The climate is temperate with continental and oceanic influences. It is characterised by a hot summer (mean T • = 22 • C) and a wet spring (mean rainfall = 108 mm)  [40]. In this region, the minimum rainfall occurs between July and October, which corresponds to the maximum development of summer crops, as shown in Table  1 , making irrigation mandatory. Maize, soybean and sunflower represent, respectively, 82%, 9% and 8% of the summer crops, i. e. , 36% of the whole cultivated crops, according to the agricultural Land Parcel Information System "Registre Parcellaire Graphique" (RPG) provided by the French Services and Payments Agency  [41].
The Reference Dataset The reference dataset was established from field campaigns carried out at two stages of the growing season: after sowing (May) and at flowering (July). The identification of the irrigation practices was based on the presence of irrigation equipment or through declarative information carried out by the farmer. The reference dataset consisted of 832 plots in 2017 (557 irrigated and 275 rainfed) and 942 plots in 2018 (680 irrigated and 262 rainfed) as shown the Table  2. Within the dataset, irrigated maize is the most represented crop with 60% of the plots sampled in 2017 and 48% in 2018. Irrigated soybean is the least represented crop in the dataset with 3% in 2017 and 5. 6% of the plots sampled in 2018. This low sampling of irrigated and rainfed soybean is explained by the low representativeness of this crop in the territory (<10%), contrary to maize which represents 82% of the agricultural plots cultivated in summer crops in 2017 and 84% in 2018.