dataesr/scientific-paragraphs-categorization huggingface dataset:
9 nm sub-features were extracted. Height profiles for the 0. 9 nm sub-features were extracted by drawing lines in Gwyddion on selected sections of the molecule. The peak-to-peak distances were determined from the height profiles by a code written in Mathematica. Periodicity analysis. A program was written in Wolfram Mathematica (ver. 10) for systematic analysis of periodicity of DNA G-wires from 9 AFM images with 122 detected molecules of length > 25 nm, median height > 1. 6 nm, and pixel count > 500. Spline of these molecules was obtained using Mathematica's default morphological thinning method, applying Otsu's threshold after Gaussian smoothening (σ = 3) of the cropped image and pruning ( = 10). Any improper spline was corrected by adjusting the intensity threshold in the lowest possible increment/ decrement over Otsu's threshold.
Periodicity was measured as the peak-to-peak distance in height profiles. For peak detection, Mathematica's default method was used after smoothening of the height profile with Gaussian filter (σ = 4). Height analysis. Even after proper flattening of the AFM images using Asylum Research's AFM software, the estimated "zero" height of the image was slightly inaccurate ( < 0. 5 nm). To overcome this problem, the "zero" height was determined from the full height histogram of the images as the value of height corresponding to the highest peak in the histogram. This method works well for images where the molecules are sparsely located so that the background occupies the maximum area of the image. After proper assignment of "zero height," the images were thresholded using Mathematica's in-built Otsu's algorithm.
Systems were next heated from 100 to 300 K over 10 ps under constant volume while maintaining solute constraints. Systems further underwent a gradual relaxation of constraints in which systems underwent cycles of minimization and restrained dynamics with constraints of 5, 4, 3, 2, 1, and 0. 5 kcal mol -1 Å -2. Finally, simulations were run unrestrained at 1 atm and 300 K for a duration of 1 ns. Molecules were visualized using VMD and Pymol. Molecular structures were averaged over the 1 ns unrestrained simulation and used for AFM simulations. AFM simulation. Simulated AFM images were generated by a custom script written in MATLAB. In brief, AFM height images are produced for an input set of molecular coordinates by scanning a virtual paraboloid AFM tip (with a latus rectum 'a' of 0. 16 nm -1 ) over the molecule.
The 0°orientation of the molecule is arbitrary, as generated by the MD simulation average of 1 ns. The images were saved as grayscale image and opened with the Gwyddion program for AFM color rendering, Gaussian smoothening and extraction of height profiles. Particularly for the -90°rotamer of the "(2,2) Diagonal" model, longer structures were simulated which were built using the structure averaged over 1 ns of the MD simulation (Fig. 8 ). In this case, a suitable orientation of the molecule was chosen so that it could be phased/aligned well with the AFM image.
They can be used to assess various kinds of network topologies in a very accurate manner. Such a ray-based tool is chosen in this paper to produce the mMIMO channel responses in a real North American city. The achievable performance is evaluated from the system-level mMIMO simulator described in  [5] , which can take channel inputs from any kind of model and generate data rate statistics using state-of-the-art signal processing schemes. This simulator implements the transmit precoding and power allocation schemes, among other system-level calculations. Section II describes briefly the system-level mMIMO simulator and its usage to obtain the rates that are achievable by the mMIMO system. Section III details the multi-cell multi-user scenario and setup. Section IV studies the multi-cell mMIMO channel characteristics.
II. MASSIVE MIMO SYSTEM-LEVEL SIMULATOR The first open-source system-level simulator for mMIMO systems was delivered as supplementary material to the book  [5]. This Matlab simulator supports state-of-the-art methods for channel estimation, receive combining, transmit precoding, and power allocation, as well as quantifying the corresponding achievable rates. While some of these methods are tailored for statistical channel models, with known first-and second-order moments, in this paper, we use methods that support any channel model. More precisely, we consider least-square channel estimation (under the presence of pilot contamination) and regularized zero-forcing (RZF) precoding. The power allocation is selected to maximize the product of the users' effective SINRs, which is essentially the same as maximizing the sum rate, except that there is a "bias" towards giving higher rates to the most unfortunate users than they would get with pure sum-rate maximization (which could lead to zero rate for the weakest users).
Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. The open-source Phenotyping Hybrid Information System (PHIS) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved. Its ontology-driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata.
Introduction In recent years, plant phenomics has produced massive datasets involving millions of images in experiments performed in the field and in controlled conditions, concerning hundreds of genotypes at different phenological stages  (Furbank & Tester, 2011; Fiorani & Schurr, 2013). These datasets also involve the outputs of hundreds of sensors for tens of variables characterizing plants, soil and air  (Salehi et al. , 2015; Negin & Moshelion, 2016; Rebetzke et al. , 2016). They often also involve -omic data associated with imaged plants  (Hannemann et al. , 2009; Großkinsky et al. , 2017). Plant phenomics is increasingly multi-source and multi-scale, with joint analyses of information originating from different phenotyping platforms and fields. Taken together, these datasets are unprecedented resources for identifying and testing novel mechanisms and models  (Tardieu et al.
, 2017). They are extremely expensive, and also contain so much information that the group who has collected a dataset most often has not all the required skills, resources and scientific questions to perform every relevant analysis they may allow. Hence, there is an increasing need to make them available to a range of users, allowing reanalyses and combination with other datasets to generate new knowledge  (Adam-Blondon et al. , 2016; Arend et al. , 2016; Leonelli et al. , 2017). The reuse of data frequently presents the difficulty of insufficient information besides phenotypic data themselves  (Hannemann et al. , 2009; Gkoutos et al. , 2017). Most measured traits differ between experiments for a given genotype because of environmental conditions  (Massonnet et al. , 2010; Malosetti et al.
, 2013). Hence, phenotypic datasets in each experiment need to be associated with detailed environmental information for metaanalyses. Furthermore, each plant or sub-plot senses different environmental conditions within a given field, glasshouse or growth chamber  (Granier et al. , 2006; Cabrera-Bosquet et al. , 2016) , so keeping track of the position of each plant or plot is essential. This is widely accepted for field experiments in view of the large variability of traits and yield within and between fields, which can be accounted for by using mixed models  (van Eeuwijk et al. , 2010; Bustos-Korts et al. , 2016). Paradoxically, this is less accepted in controlled conditions in which the spatial distribution of environmental conditions and the x-y positions of plants are seldom stored in databases.
Such measurements are possible in controlled conditions, opening the way to the dissection of the genetic architecture of physiological traits  (Mairhofer et al. , 2013; Cabrera-Bosquet et al. , 2016; Coupel-Ledru et al. , 2016; Kalogiros et al. , 2016; Alvarez Prado et al. , 2018). Combining data in field and controlled conditions is possible, and provides valuable information for analysing and predicting the genotype 9 environment interaction of both traits and yields  (Reymond et al. , 2003; Lacube et al. , 2017; Tardieu et al. , 2018). An essential feature of phenomic information systems is therefore to facilitate these trans-scale joint analyses of experiments in field and controlled conditions. A major challenge in plant phenomics is therefore to design information systems able to organize and store heterogeneous datasets including thousands of objects as different as, for example, images, spectra, time courses of variables, parameters of image analysis, x-y positions of plants/plots, biomass, or yield.
Recently, several papers have recommended standardization protocols and enrichment of datasets with metadata  (Arend et al. , 2014 (Arend et al. , , 2016;; Junker et al. , 2014; Krajewski et al. , 2015; Cwiek-Kupczy nska et al. , 2016)  and scientific workflows  (Pradal et al. , 2017). However, this information is rarely incorporated into information systems. The use of open and extensible database schemas based on Ontology Web Language (OWL)  (Grau et al. , 2008)  allows formalized description and contextual information of objects involved in experiments  (Li et al. , 2013; Krajewski et al. , 2015; Cwiek-Kupczy nska et al. , 2016; Le Ngoc et al. , 2016). Tools using ontologies and semantics are available in functional genomics and systems biology  (Jones et al. , 2007; Gkoutos et al. , 2017).
Ontology-centred architectures such as XEML LAB  (Hannemann et al. , 2009) , PODD  (Li et al. , 2013)  and SILEX (Information System for Experiment, https://www6. montpellier. inra. fr/mistea_eng/Projects/Silex) have been proposed for plant phenomic studies. However, most published or commercial databases for plant phenomics are still specifically designed to handle and store data from particular installations or species  (Fabre et al. , 2011; Nagel et al. , 2012; Klukas et al. , 2014; Coppens et al. , 2017). Hence, the wide variety of phenotypic, environmental and contextual data is spread in a range of databases, lab books and individual text/spreadsheet files, thereby complicating the traceability and access to experimental results and associated metadata. Here, we present a suite of methods, synthesized in the opensource Phenotyping Hybrid Information System (PHIS) for integrating and sharing multi-source and multi-scale data (in particular those obtained in both controlled and field conditions), and semantic annotation of experiments with knowledge and metadata.
Web user interface PHIS is accessed through an interactive web user interface (http://www. phis. inra. fr) after user login. A user belongs to one or several groups and has access to data with different access rights (admin, owner or guest). Administration level gives full access to all content, owners have access to their own datasets and to public data, and guests have access to public data only. The web user interface contains three main menus, which are dynamically adapted depending whether the experiment is performed in the field or in controlled conditions. The Experimental Organisation menu contains information about the experimental resources, including projects, experiments, infrastructure, plants, germplasm, devices, events, phenotypic and environmental variables and object tracking tools (see Supporting Information Notes S1-S3 for technical details).
The Data menu contains images, graphic visualization of phenotypic and environmental traits, as well as access to data analysis and workflows. A search engine allows advanced querying for entities like projects, experiments, domain specific objects (plants, sensors, etc. ), images and genotypes using uniform resource identifiers (URIs) and/or filtering options (Notes S4). The Tools menu contains installationspecific widgets, namely, a URI generator, a label quick response code (QR-code) generator, access to the closed-circuit television of facilities, a vocabulary and access to the web service application programming interface (API; Notes S5, S6). The Administration menu contains tools for managing users, groups, experimental facilities and control of data settings (variables, units and methods).
Data and knowledge layer PHIS contains phenotypic, experimental and environmental data. Tests were performed on field and controlled-condition experiments. Field experiments were hosted at DIAPHEN (https://www. phenome-fppn. fr/phenome_eng/Facilities/Montpe llier-Field) at INRA Mauguio (southeast of France, 43°36ʹ N, 03°58ʹE) over 20 ha, which provides access to high-throughput phenotyping tools including soil and aerial vectors (carrying RGB, multi-and hyperspectral cameras and spectroradiometers) as well as a series of sensors for characterizing environmental conditions. Controlled-conditions experiments were hosted at Montpellier Plant Phenotyping Platforms, M3P (https:// www6. montpellier. inra. fr/lepse/M3P), which implements a series of tools with up to 500 sensors in parallel, three-dimensional imaging cabins and automatisms  (Sadok et al.
, 2005; Granier et al. , 2006; Cabrera-Bosquet et al. , 2016). Phenotypic data for field and platform experiments include online (i. e. automatically recorded) images, growth and transpiration kinetics and manually recorded phenotypic measurements. Experimental data include protocols, description of variables and plant material. Environmental data include sensor outputs (air temperature and humidity, light, soil tensiometers) or variables inferred using algorithms at high temporal and spatial resolution (e. g. local light and temperature). Currently, these installations have generated 20 million images and 250 million phenotypic measurements performed in > 4000 genotypes and 25 species, and 154 million environmental measurements, involving 86 terabytes. Structured data (e. g. environmental data and standardized phenotypic variables) are stored using POSTGRESQL and MONGODB.
'Weakly' structured data (e. g. plant observations and image analyses data) are stored using NoSQL technology (MONGODB). Raw images, thumbnails and analysed images (e. g. segmented images) are stored on a distributed storage system (iRODS) (https://irods. org/) (Rajasekar et al. , 2010)  with a replication mechanism and built-in scripts that permanently check the consistency with the associated metadata. Metadata and semantic annotations are stored taking into account both the ontologies, implemented in OWL (https://www. w3. org/OWL/), and the knowledge resulting from experimental observations, which is formalized as instances represented using the Resource Description Framework (RDF) format, and stored in a RDF4J Triple Store (http://rdf4j. org). Triple data is a data entity (i. e. subject predicate object) like 'plant736 participatesIn Experi-mentA' and a Triple Store is a database system dedicated to the storage and the retrieval of triples through semantic queries (SPARQL).
Web service layer A web service layer enables interoperability and data exchange with other applications and systems. This service facilitates the maintenance of the information system and provides a simplified interface to the smart layer and to the data and knowledge layer. The web service is based on RESTFul (representational state transfer) developed using Swagger framework (https://swagger. io/), and all services are available by using URIs. It is developed in Java with Jersey implementation of JAX-RS (Java API for RESTful Web Services) standard. It implements installations' relevant services of the Breeding API (http://www. brapi. org/), which specifies a standard interface for plant phenotype databases to serve data to crop breeding applications. Web service outputs use the data-interchange format JSON (JavaScript Object Notation; see Notes S6 for technical details).
Smart layer, scientific computation and workflow layer The Smart layer allows data to be interpretable for other communities by referring to external resources such as standardized semantic resources and reference or species-specific ontologies as described in the Planteome project  (Cooper et al. , 2018). References are managed using the Simple Knowledge Organization System  (Miles & Bechhofer, 2009)  that allows support of standardized and advanced queries by using ontologies. These queries and the necessary inferences (subsumtion, transitivity, functional, etc. ) are obtained by using an RDF4J engine included in the Triple Store. The scientific computation and workflow layer provides advanced visualization and statistical computing, including the automatic report generation, based in R (R Core Team, 2015), and enables computational analysis and workflows through the scientific platform GALAXY (https://galaxyproject.
Availability and requirements The source code and user and developer documentation of the latest version of PHIS are available at https://github. com/Ope nSILEX under a GNU Affero General Public License version 2. PHIS requires Java JRE or JDK v. 1. 7, PHP 5. 6, POST-GRESQL 10. 1, RDF4j 2. 2. 1, MONGODB 3. 4. 4 and R 3. 3. 1 and runs on Linux, Mac and Microsoft Windows platforms.
A common information system for field and controlledcondition experiments Combining field and controlled conditions in a common information system requires first ensuring that environmental conditions are measured in a compatible way, with common units and protocols, and second designing common ontologies of traits for both types of datasets. The first condition only requires the attention of experimenters, without large theoretical difficulty  (Reymond et al. , 2003; Lacube et al. , 2017). The second condition is straightforward for traits that have a common definition at different scales, such as leaf appearance rate or leaf number per plant (Fig. 1a, b ). However, other traits have different meanings and measurement procedures in field and controlled conditions, such as 'plant height', often defined as the highest green pixel corresponding to a plant in controlled conditions, vs the mean canopy height in the field.
Hence, an information system for both field and controlled conditions needs to optimize somewhat contradictory requirements; namely, using ontologies and methods that can be used for both types of datasets, taking into account the difficulties mentioned earlier, while keeping the interface sufficiently simple for the user. The simplicity issue has been resolved by the design of the interface that automatically redirects menus and functionalities depending on whether the experiment is performed in the field or in controlled conditions. The difficulties associated with the commonality of tools, ontologies and workflows are therefore kept in the background. They are addressed with tools examined further in this paper.
Tracking all objects in phenotyping experiments via object identification and ontology description Tracking all objects involved in an experiment may seem unnecessary in simple experiments where unique correspondences exist between, for example, each plant and its position in a glasshouse or between each genotype and a plot in the field. In our own experience, automatic tracking is essential when thousands of plots, plants or sensors are dealt with. If not specifically reported in the information system, the replacement of a sensor at a given position (e. g. meteorological sensor or soil tensiometer) is not obvious in the outputs of an environmental database. In glasshouse experiments, a plant can be replaced by another plant at the same position and vector (e. g. pot, cart) during an experiment, potentially generating confusion.
5a, b ), before being harvested. Environmental conditions sensed by plants differ between the two installations, so a proper tracking tool is essential. In our experience, manual tracking is extremely tedious if environmental, phenotypic and management data are spread in distinct databases, lab books or individual text/spreadsheet files, especially when different groups are involved. As shown in the associated semantic graph (Fig. 5d ), tracking the presence of the plant 262 in Installation 2 can be formalized to automatically link environmental data associated with a given plant in each installation, without the need to specify the plant location at a given time (Fig. 5c ). The full example together with technical information is provided in Notes S3. It is noteworthy that the PHIS-specific OEPO and OEEv application ontologies formalize the installation entities and allow dynamic configuration of PHIS.
Integration of external data and interoperability with external installations and resources The dialogue with external applications and information systems is managed through the web service API that allows integration of data from external databases and resources, export to computing and modelling platforms and integration of phenomic data into other systems. Web services provide flexible and powerful capabilities for the integration of a diverse and multi-source amount of data, including structured environmental and phenotypic data acquired by the different sensors of the installation (stored in a POSTGRESQL database), the images stored in the iRODS system, nonstructured data such as elaborated variables stored in the MONGODB, and rich metadata and knowledge stored in the Triple Store (Notes S6).
The versatile use of web services allows one to virtually integrate data from any external client, therefore allowing PHIS to be adapted into other infrastructures. For instance, environmental data from a network of field meteorological stations (http://w3. avignon. inra. fr/carto/) and soil sensors (http://www. agriscope. fr/) are integrated in PHIS via the web service API. Similarly, features extracted from image analyses via Python are routinely integrated in PHIS through the web services, and an R client allows different analysis pipelines (Notes S6). PHIS can also export data to external databases, in particular those dedicated to genetic analyses or modelling. For example, export to the GNPIS information system (https://urgi. versailles. inra. fr/gnpis; Steinbach et al. , 2013) , a member of the ELIXIR European infrastructure, allows genome-wide association studies based on the phenotypic datasets organized in PHIS via the collaborative Breeding API.
Export to the modelling platform OPENALEA  (Pradal et al. , 2008 (Pradal et al. , , 2015) )  has allowed calculation of the light interception and radiation-use efficiency of hundreds of maize plants using data obtained in the M3P installation  (Cabrera-Bosquet et al. , 2016). Finally, data search and advanced queries can be performed to remote databases thanks to the web service API and the inference engines that use the semantics and rules represented in the ontologies. These import and export APIs facilitate the interoperability and data sharing capabilities in the context of Open Data Science  (Halewood et al. , 2018) ; for example, in the context of European projects EPPN 2020 (https://epp n2020. plant-phenotyping. eu/) Trans-PLANT (http://transpla ntdb. eu/), ELIXIR-EXCELERATE (https://www.