dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by  ISCIII-FEDER  [ PI13/00082 ,  CP10/00524 ,  CPII16/00026 ], MICINN [MTM2015-68140-R],  IMI-JU  under grants agreements no. 115191  ( Open PHACTS ), no. 115372  (EMIF), no. 115735  (iPiE), resources of which are composed of financial contribution from the  EU-FP7  [FP7/2007-2013] and EFPIA companies in kind contribution, and the  EU   H2020 Programme 2014-2020  under grant agreements no. 634143  ( MedBioinformatics ) and no. 676559  ( Elixir-Excelerate ). The  Research Programme on Biomedical Informatics (GRIB)  is a member of ELIXIR-ES and the  Spanish National Bioinformatics Institute (INB) ,  PRB2-ISCIII  and is supported by grant  PT13/0001/0023 , of the PE I þ Dþi 2013-2016, funded by  ISCIII  and  FEDER. A. G. S. acknowledges financial support from the  Spanish Ministry of Economy and Competitiveness , through the  'Marı ´a de Maeztu' Programme for Units of Excellence  in R&D [MDM-2014-0370].
02 from the expected values for three species of humidified salt particles, with a refractive index that is well established. The airborne measurements used in this work were made aboard the NASA DC-8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC 4 RS) field campaign, and the inversion of this data represents the first aerosol retrievals of airborne polar nephelometer data. The results provide confidence in the real refractive index product, as well as in the retrieval's ability to accurately deter-mine PSD, without assumptions about refractive index that are required by the majority of OPCs.
Inversion methodology Aerosol-scattering matrix elements are measured in situ with a polar nephelometer and feed into a microphysical retrieval algorithm in order to obtain aerosol size distribution, complex refractive index (m) and a percentage of spherical particles. These measurements include a combination of artificially suspended laboratory data as well as airborne data taken over the continental United States during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC 4 RS) field experiment in 2013. GRASP, a versatile open source software package (http://www. grasp-open. com) capable of performing inversions on a wide variety of atmospheric optical measurements, was used to obtain the retrieved microphysical parameters. A detailed description of the GRASP retrieval algorithm and its capabilities can be found in  Dubovik et al.
Ambient measurements In addition to the laboratory measurements, inversions were performed on airborne data from the SEAC 4 RS experiment. SEAC 4 RS was a large field mission, that took place primarily over the continental United States in August and September of 2013. Over the course of the experiment three aircraft flew 54 different instruments on a total of 57 flights in an effort to understand a broad range of atmospheric phenomenon. A detailed description of the scientific goals, aircraft and instrumentation, as well as the corresponding implementation can be found in  Toon et al. (2016). The PI-Neph made measurements aboard the NASA DC-8 aircraft during SEAC 4 RS. Ambient air was provided to the instrument through the NASA Langley Aerosol Research Group Experiment's (LARGE) shrouded diffuser inlet  (Mc-Naughton et al.
, 2007) , which sampled isokinetically. A flow of 20 L min -1 was maintained through the PI-Neph's 10 L sample chamber, leading to an aerosol exchange time of the order of 30 s. The raw sampling rate of the instrument was synchronized to match this interval, but the retrievals in this work are generally performed on time averages taken over a period of several minutes. The sample was conditioned with a temperature-controlled drier that heated the incoming ambient air to a temperature of 35 • C and, in almost all cases, kept the relative humidity of the sample below 40 %. In addition to PI-Neph scattering measurements, the LARGE group made comprehensive in situ measurements of aerosol properties in parallel to the PI-Neph. These measurements, containing data on particle number density, size distribution and optical properties, are a valuable resource for the intercomparison of PI-Neph measurements and the corresponding retrieved microphysical properties.
In this work, size distributions retrieved from PI-Neph data will be compared extensively to measurements made by two dedicated optical particle size spectrometers (LAS model 3340, TSI Inc. , St. Paul, MN, USA and model UHSAS, Droplet Measurement Technologies, Boulder, CO, USA) as well as an aerodynamic particle sizer (APS model 3321, TSI Inc. , St. Paul, MN, USA). The two optical particle spectrometers also measured at low relative humidities during SEAC 4 RS, but their sample was conditioned through a drier. This approach minimizes the evaporation of volatile compounds but can also lead to size-dependent losses in the aerosol when the instrument requires relatively large flow rates, as is the case for the PI-Neph. The aerodynamic particle sizer measurements were made at ambient humidities, but the ambient RH was determined to be less than 40 % in all cases shown here so differences in PSD resulting from hygroscopic growth are not expected.
Fifty separate sampling periods, occurring over the course of 10 different flights, are highlighted in this work. The flights selected represent the 10 days with the highest quality PI-Neph data, for which data are available for at least one of LARGE's dedicated particle sizers. The intervals containing the highest aerosol-scattering levels during these flights were identified and a robust averaging procedure  (Beaton and Tukey, 1974)  was applied to periods for which no detectable changes in the normalized angular scattering data were observed. The total scattering for these averages ranged from 30 to just over 500 Mm -1 , with a median value of 90 Mm -1. The resulting data set represents a wide range of aerosols, including urban pollution, organics and Saharan dust, and in over a dozen cases is dominated by biomass-burning (BB) emissions with transport ages ranging from hours to several days.
Implementation of GRASP retrieval GRASP is a versatile software package capable of retrieving a wide range of atmospheric and surface properties from a variety of data sets. The GRASP algorithm and corresponding software builds on the successful heritage of the PARA-SOL  (Dubovik et al. , 2011) , AERONET  (Dubovik and King, 2000)  and laboratory  (Dubovik et al. , 2006)  retrievals. GRASP's base aerosol model contains very few assumptions in comparison with traditional in situ or remote sensing retrieval algorithms. It includes all necessary components required to simulate a diverse range of atmospheric observations, including remote sensing (both suborbital and spacebased), optical in situ and laboratory measurements. The settings of the retrieved characteristics can be flexibly adjusted to match the particular application.
Measured data and retrieval fit In both the 50 selected SEAC 4 RS cases and in the laboratory measurements, the residuals between the GRASP fits and the PI-Neph measured values are generally within the PI-Neph instrumental error. Figure  4  shows the measured and fit F 11 and -F 12 /F 11 for the ammonium sulfate case, and is typical of the bulk of the retrievals performed in this work. The residuals are also plotted to clearly emphasize the differences between the measurement and fit relative to the instrument's 2σ error. In the case of the F 11 data the distances between the fit and measured values are reported as RES F 11 = Log 10 (F MEAS 11 ) -Log 10 (F FIT 11 ), (1) with the PI-Neph error transformed accordingly. This transformation provides a measure of relative (as opposed to absolute) error, and provides a consistently sized metric across the 2 orders of magnitude covered by F 11.
A comparison was also made between the retrieved single-scattering albedo (SSA) and the SSA derived from Particle Soot/Absorption Photometer (PSAP, Radiance Research, Seattle, WA, USA) and integrated scattering measurements (Integrating Nephelometer 3563, TSI Inc. , St. Paul, MN, USA) in SEAC 4 RS. A statistically significant correlation between the two data sets was determined to exist, but the retrieved SSA was also found to systemically overestimate the measured absorption. Notice that the retrieval was based only on scattering measurements (no absorption or extinction data were included) and therefore is expected to show limited sensitivity to these variables. A detailed analysis of the sensitivity of the GRASP/PI-Neph retrieval to absorption is beyond the scope of this work. After passing their deliquescence point, crystalline salt particles should transform into saline droplets and become spherical in shape.
61  (Jones et al. , 2013)  and 1. 53  (Tang, 1996)  respectively. The LAS (Laser Aerosol Spectrometer) data shown correspond to calibration with PSL spheres. In all three of these cases the peak of the fine mode generally occurs around a radius of 150 nm. These values are typical of the majority of the 50 selected periods, all of which have fine mode median radii (in volume) between 100 and 200 nm. The PI-Neph/GRASP PSD retrievals fall between the two different UHSAS calibrations in each of the three cases, which again is typical of almost all 50 samples. Among the 50 selected periods for which size distribution comparisons were made, only two cases had coarse modes with volume concentrations that made up a significant portion of the total particle volume. The first of these cases, a sample dominated by transported Saharan dust, had very low aerosol loading and the bulk of the scattering matrix data at scattering angles above 40 • was below the PI-Neph's limit of detection.
The LAS consistently measured smaller and fewer particles than all the other sizing techniques, but still showed significant correlation with the GRASP retrievals of PI-Neph measurements. There was weaker agreement regarding the width of the distribution among the four techniques. The retrieved spans generally best matched the corresponding PSLcalibrated UHSAS values, but the values covered a larger range of spans than the values measured by the OPCs. The spans retrieved from PI-Neph measurements fell between 0. 55 and 1. 03 in 95 % of the cases. In contrast, the LAS showed the least variability in span, with 95 % of the values falling between 0. 65 and 0. 85. The differences in span between PI-Neph retrievals and the OPCs was likely driven in large part by their different sampling techniques (ensemble vs.
In this analysis the complex refractive index was forced match measurements of PSL and nonspherical particles were excluded from GRASP's aerosol model. This configuration produced significantly better agreement with the PSL calibrated UHSAS measurements in volume concentration, median radius and span, when compared to the unconstrained retrievals. This result further demonstrates that differences in fundamental assumptions about the optical and morphological properties of the particles are driving a significant portion of the differences between the retrieved and measured values.
Conclusions This work represents the first time that aerosol optical and microphysical properties were retrieved from airborne, polar nephelometer data. Additionally, the PI-Neph/GRASP inversion makes fewer assumptions regarding the shape of the recovered size distribution and particle sphericity than previous in situ light-scattering retrievals. The resulting products are in good agreement with the expectations, and compare well with existing measurement techniques. Furthermore, the GRASP fit to PI-Neph data is consistent with the PI-Neph's level of error, indicating that the assumptions made in the retrieval are sufficient to faithfully reproduce the light scattering of realistic, ambient aerosols. The real refractive index of humidified salts retrieved with this method agree well with the predictions made by κ-Köhler theory and existing dry measurements.
The PI-Neph retrieval of PSL refractive index agrees with other contemporary techniques to within the deviation present in those reported values. Furthermore, inversions of airborne SEAC 4 RS data produced refractive indices that were in good agreement with the existing literature. There is significant spread in the aerosol size distribution measurements made by the OPCs, but the corresponding PI-Neph/GRASP retrievals generally fall within the range of the existing measurements. A major part of the differences in the measured size distributions stem from the need to assume a refractive index during the calibration process. The PI-Neph/GRASP retrieval has sufficient sensitivity to constrain the refractive index with enough accuracy to potentially reduce these biases. The fact that the PSD retrievals fell between the two UHSAS calibrations, in a manner consistent with the retrieved refractive index, supports this conclusion.
The PI-Neph inversions have also shown moderate sensitivity to absorption but a detailed assessment of the accuracy of this retrieved parameter is beyond the scope of this paper and will have to remain the subject of future study. Additionally, promising results were obtained regarding the retrieval of sphericity in the case of the humidified salts as well as in sensitivity studies, but as a result of the limited morphological information available in the SEAC 4 RS data set, a robust evaluation of this product is limited at this time.
Data availability All relevant measurements made during the SEAC 4 RS experiment are available through the SEAC 4 RS data archive at http://www-air. larc. nasa. gov/missions/seac4rs/ (SEAC4RS, 2015). Requests for additional data can be made to the corresponding author at reedespinosa@umbc. edu. Competing interests. The authors declare that they have no conflict of interest. also like to thank the members of the LARGE group, particularly Bruce Anderson, Edward Winstead and Lee Thornhill for their support incorporating the PI-Neph into the LARGE instrument package. We are also grateful for the scientific and technical support of the LACO team at UMBC, especially Dominik Cieslak and Frank Harris. Additionally, we would like to thank the entire SEAC 4 RS science team for providing supporting data and relevant discussion.
Data collection The data collected rst and foremost focused on describing what the children did with the Playboxes, as well as how the adults supervising them were involved. The researchers systematically organised their observations that were complemented with data from the focus groups. Before and after the implementation of the Playboxes, 17 focus groups were held with three or four children each of the same age as well as the team of animateurs (13 groups at Setting A and 4 groups at Setting B). Seven focus groups were held with the three managers of the two sites. The observation and focus group data was supported by taking photos and making videos. On each site, parents had given authorisation to take pictures of their children within the framework of the centre's activities. The researchers could visually document what they saw and then study details including the many parallel interactions among the children.
Background: Bioinformaticians routinely use multiple software tools and data sources in their day-to-day work and have been guided in their choices by a number of cataloguing initiatives. The ELIXIR Tools and Data Services Registry (bio. tools) aims to provide a central information point, independent of any specific scientific scope within bioinformatics or technological implementation. Meanwhile, efforts to integrate bioinformatics software in workbench and workflow environments have accelerated to enable the design, automation, and reproducibility of bioinformatics experiments. One such popular environment is the Galaxy framework, with currently more than 80 publicly available Galaxy servers around the world. In the context of a generic registry for bioinformatics software, such as bio. tools, Galaxy instances constitute a major source of valuable content.
Yet there has been, to date, no convenient mechanism to register such services en masse. Findings: We present ReGaTE (Registration of Galaxy Tools in Elixir), a software utility that automates the process of registering the services available in a Galaxy instance. This utility uses the BioBlend application program interface to extract service metadata from a Galaxy server, enhance the metadata with the scientific information required by bio. tools, and push it to the registry. Conclusions: ReGaTE provides a fast and convenient way to publish Galaxy services in bio. tools. By doing so, service providers may increase the visibility of their services while enriching the software discovery function that bio. tools provides for its users.
Introduction Over the recent years, various initiatives have aimed at cataloguing bioinformatics tools and services  [1] [2] [3] [4] [5] [6]. In particular, the ELIXIR Tools and Data Services Registry (bio. tools) [7]  offers a community-curated information portal whose goals are comprehensive coverage and consistent description of bioinformatics tools and services. Another ongoing trend is the integration of bioinformatics software in workbench and workflow environments, which allows data analysts to design, automate, and reproduce bioinformatics experiments. The Galaxy framework  [8] [9] [10]  is one of the most popular of such environments, with currently more than 80 publicly available Galaxy servers (see https://wiki. galaxyproject. org/ PublicGalaxyServers) around the world. The registration and maintenance of entries in the bio.
tools registry is based on a "federated curation model" whereby the maintenance of resource entries is handled by their owners. Current efforts to automatically register tools and services in resource catalogs mostly target programming language-specific catalogs, such as the Python package index (see http://pypi. python. org/pypi), and seldom domain-specific catalogs, with a few notable exceptions such as BioJS  [11] , the BioGems registry  [12] , and BioMOBY  [13]. In contrast, ReGaTE is a solution for owners of Galaxy server instances to easily register their tools on a resource catalog that is not specific to any programming language or technical requirement. The scientists browsing the bio. tools server can therefore search and compare resources independently of any technological implementation.
Implementation ReGaTE pulls tool descriptions from a Galaxy server, augments the information, and pushes it to the bio. tools registry. A Galaxy server is a framework that supports users to configure and run a range of bioinformatics tools and workflows and that gathers many other features for the sharing, visualization, and reproducibility of analyses. The user interface and execution of tools are based on a tool definition in an eXtensible Markup Language (XML) file (detailed documentation of this format is available at https://wiki. galaxyproject. org/ Admin/Tools/ToolConfigSyntax). Each file describes the bioinformatics tool in a detailed way, including the tool parameters, inputs, and outputs. This allows the display of their sometimes complex configuration options in a graphical user interface, primarily to enable tool parameterization and its execution.
Such tool definitions are loaded by the Galaxy server and are accessible through the Galaxy RESTful interface. The BioBlend library  [14]  allows convenient access to the Galaxy application program interface (API) from Python. Here, we have used BioBlend to extract Galaxy tool definitions from remote Galaxy instances. Bio. tools  [7]  is a web portal provided by ELIXIR -the European infrastructure for biological information for the exploration of bioinformatics resources including software packages, web services, and database portals. Through a dedicated graphical interface, users can search for and compare resources. Thus, bioinformatics resource providers can use bio. tools to en-hance the visibility of their services. The description and registration of a resource can be done manually via a web user interface, or resources may be registered using the registry API.
Registry entries follow a model that is formalized in biotoolsSchema (the biotoolsSchema format definition is available at https://github. com/bio-tools/biotoolsSchema/), an XML schema that defines a resource description model for bioinformatics with a mandatory core of 10 attributes. ReGaTE fetches the Galaxy tool definitions, enhances them with additional annotations, and converts them into the biotoolsSchema-based JSON format, using the mapping mechanism described in the next section, before pushing them to bio. tools. This process can be triggered all at once or step by step, first extracting the tool metadata, and second pushing enhanced metadata to bio. tools. A ReGaTE user needs to have an account on the targeted Galaxy and retrieve his API key to extract the tool definitions and an account on the bio.
ReGaTE Architecture ReGaTE is a Python script coupled with a configuration file and mapping of semantics used by Galaxy and bio. tools. An overview of its architecture is shown in Fig. 1. The configuration file includes the Galaxy server uniform resource locator, an API key, and a directory to store the generated tool files uploadable to bio. tools. Suffix and prefix variables, for tagging the names of the tools extracted by ReGaTE, may also be specified. For example, the name of the tool SARTools DESeq2  [15] , implemented at Institut Pasteur, can be named SARTools DESeq2-IP.
Tool Metadata Mapping A biotools-Schema file describes a given software application, covering different properties: r scientific properties, such as the domain catered for and de- scription of the type of task(s) done by a tool; r technical properties, such as the type of software and its interface(s), e. g. , command line tool, web application, web service, etc. ; r credit, e. g. , the references that need to be cited when refer- ring to this work; r administrative information, such as the license used in the software. Some of these properties are described using the EMBRACE Data and Methods (EDAM) ontology  [16]. Development of the EDAM ontology is driven by community requests via GitHub, mailing lists, and community-based hackathons (more information on contributions can be found at https://github.
com/ edamontology/edamontology/blob/master/HOW TO CONTRIBU TE. md). It currently includes 3280 concepts with regular (at least quarterly) major releases. EDAM includes the following common bioinformatics concepts: r topics, i. e. , scientific disciplines or domains covered by the resource; r specific operations performed by a tool or service; r types of input and output data; r formats in which inputs and outputs are available. The mapping from a Galaxy tool definition file (detailed documentation of this format is available at https://wiki. galaxyproject. org/Admin/Tools/ToolConfigSyntax) to a bio. tools file is handled by the ReGaTE code, taking advantage of the important number of common properties between such workbench wrappers and registry entries  [17]. A few properties are not natively available in the Galaxy tool files retrieved by BioBlend; these missing data are provided by the ReGaTE configuration files.
Acknowledgements ELIXIR-EXCELERATE is funded by the  European Commission  within the  Research Infrastructures Programme of Horizon 2020  ( 676559 ). The authors wish to thank the developers of the bio. tools registry, especially  Emil Rydza  and  Piotr Chmura  for the technical support they provided during the development of this tool. They also thank  Bertrand N éron  for the contribution of his technical expertise in Python.