dataesr/scientific-paragraphs-categorization huggingface dataset:
The mineral dust cycle in MONARCH The dust module in MONARCH (previously known as NMMB/BSC-Dust), initially described by  Pérez et al. (2011) , solves the mass balance equation for dust taking into account the following processes: The dust size distribution is represented with eight bins ranging up to 20 µm in diameter: 0. 2-0. 36, 0. 36-0. 6, 0. 6-1. 2, 1. 2-2. 0, 2. 0-3. 6, 3. 6-6. 0, 6. 0-12. 0, and 12. 0-20. 0 µm. The effective and volume radii of each bin in the radiative and sedimentation schemes, respectively (see Sect. 3. 3, Table  6 ) are time invariant and based on a lognormal distribution with mass median diameter of 2. 524 µm and geometric standard deviation of 2  (Schulz et al. , 1998; Zender et al. , 2003). Our new developments presented below have mostly focused on aspect (1): dust generation and uplift by surface wind and turbulence.
, 2017). When considering cropland and pasture as anthropogenic dust sources, the main anthropogenic source regions are in the Sahel, India, China, and the United States (Fig. 3 , top panel). Besides tagging natural and anthropogenic dust sources  (Klose et al. , 2018) , MONARCH's tagging functionality can be adapted to track dust also from other predefined source origins  (Kok et al. , 2021b). Vegetation in MONARCH is prescribed based on satellite data, using either an AVHRR monthly climatology of green vegetation cover fraction  (Gutman and Ignatov, 2010)  or monthly photosynthetic and non-photosynthetic vegetation cover based on MODIS and Landsat surface reflectance  (Guerschman et al. , 2015)  either as a climatology or for the actual year of simulation. The two cover fraction data sets can be used consistently within MONARCH's meteorological and dust modules.
yumpu. com/en/document/ read/19547757/modis-simulator-software-euclipse, last access: 8 October 2021) and is done based on a longitudebased local time (LLT). We assume 13:30 LLT as the overpass time of MODIS Aqua. Actual overpass times vary and may deviate slightly from this nominal time. The same diagnostic is also available for MODIS Terra (nominal overpass time 10:30 LLT). Other polar satellite overpasses can be implemented easily. For model evaluation, the MONARCHmodeled satellite DOD is additionally co-located in space and time with the satellite observations, i. e. , grid cells for which the MODIS data contain missing values because of clouds are filtered from the MONARCH data for each day. MONARCH also estimates the DOD under clear-sky conditions (i. e. , without clouds) based on the modeled cloud fraction and a coin-flipping method.
The modeled seasonal all-sky co-located FoO for each individual model run is shown in Fig. B2. The FoO obtained from the modeled clear-sky DOD coarse is generally larger than that obtained from the all-sky colocated DOD coarse. Due to the normalization with the number of days in the season for calculation of the FoO, differences in the frequency and location of clouds in MONARCH and MODIS directly impact the resulting FoO. Over dust source regions, MONARCH produces considerably fewer cloud pixels in its clear-sky product and hence a larger number of valid data values than are in the MODIS observations (not shown). As a result, the clear-sky FoO is based on a larger number of valid (high-DOD coarse ) values and is therefore larger than the MODIS and MONARCH all-sky colocated FoOs. With 0. 77-0.
Comparison of modeled DOD with AERONET AERONET is a global network of ground-based solar photometer stations  (Holben et al. , 1998; O'Neill et al. , 2003; Giles et al. , 2019). The primary parameter derived by AERONET (i. e. , direct Sun) is the AOD in multiple spectral channels with uncertainties lower than 0. 03. AOD data are computed for three data quality levels: level 1. 0 (un-  screened), level 1. 5 (cloud screened), and level 2. 0 (cloud screened and quality assured). The products from inverting sky radiance measurements are the aerosol size distribution, single scattering albedo, refractive index, effective radius, and asymmetry factor. AERONET has very good coverage across the globe, albeit with lower station density in remote dust source regions, such as northern Africa, the Middle East, central/western Asia, and Australia.
As DOD coarse For comparison with AERONET, we use bilinear interpolation to extract time series from the 3-hourly global model DOD and DOD coarse for the locations of AERONET measurements. We use 3-hourly averages of AERONET observations, such that a comparison with the 3-hourly instantaneous MONARCH data assumes a statistical similarity between the temporally averaged AERONET DOD and DOD coarse and the spatially interpolated MONARCH DOD and DOD coarse. Figure  8  shows time series of 3-hourly DOD from AERONET direct Sun and MONARCH for 18 selected stations in the vicinity of dust sources around the globe: four stations in northern Africa and two stations in the typical dust outflow region west of northern Africa; three stations in the Middle East; three stations in Asia; and one station each in Europe, southern Africa, Australia, North America, South America, and the Caribbean.
Acknowledgements. BSC co-authors acknowledge PRACE (eFRAGMENT2) and  RES  ( AECT-2019-3-0001  and  AECT-2020-1-0018 ) for awarding access to MareNostrum at the  Barcelona Supercomputing Center  and for providing technical support. Martina Klose ,  Carlos Pérez García-Pando ,  Sara Basart ,  Paul Ginoux ,  Catherine Prigent , and  Ron L. Miller  appreciated the opportunity for exchange and discussion at the  Institut Pascal at Université Paris-Saclay  with support of the program " Investissements d'avenir"  ( ANR-11-IDEX-0003-01 ). The authors thank all the principal investigators and their staff for establishing and maintaining the AERONET sites, as well as the MODIS mission scientists and associated  NASA  personnel for the production of the AOD, SSA, and AE data used in this study. Financial support.
This research has been supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 789630. Martina Klose  has received funding through the  Helmholtz Association's Initiative and Networking Fund  (grant agreement no. VH-NG-1533 ). Jeronimo Escribano  and  Matthew L. Dawson  have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreements no. 754433  ( Jeronimo Escribano ) and no. 747048  ( Matthew Dawson ). BSC co-authors acknowledge funding from the following: the  European Research Council (FRAGMENT  (grant no. 773051 )); the  AXA Research Fund ; the  Spanish Ministry of Science, Innovation and Universities  (grant no. CGL2017-88911-R ); the  EU H2020  project  FORCES  (grant no.
821205 ); the CMUG-CCI3-TECHPROP contract, an activity carried out under a program of and funded by the  European Space Agency (ESA) ; and the  DustClim  project, which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate  and funded by  FORMAS (SE) ,  DLR (DE) ,  BMWFW (AT) ,  IFD (DK) ,  MINECO (ES) , and  ANR (FR) , with cofunding by the  European Union  (grant no. 690462 ). Ron L. Miller  is funded by the  NASA   Modeling, Analysis and Prediction Program  ( NNG14HH42I ). Jasper F. Kok acknowledges support from the  National Science Foundation (NSF)  under grant nos. 1552519  and  1856389  and the  Army Research Office  (grant no. W911NF-20-2-0150 ). Yue Huang  has received funding from the  Columbia University Earth Institute Postdoctoral Research Fellowship  and from  NASA  (grant no.
Code availability. MONARCH source code used in this work (v2. 0. 0) is available at https://earth. bsc. es/gitlab/es/monarch (last access: 12 October 2021) (https://doi. org/10. 5281/zenodo. 5215467, Klose et al. , 2021a). Data availability. MONARCH output presented in this paper is available via EUDAT B2SHARE (https://doi. org/10. 23728/b2share. 42ad3309744e40bd9c8920535d14c4a9;  Klose et al. , 2021b).
For comparison, field samples from the May impactor (see description below) have been measured with a lab-based TXRF system at EK  [28, 29]. It comprises a 50 W microfocus Mo-anode X-ray tube (Petrick, Bad Blankenburg, Germany), a Mo/Si multilayer monochromator (AXO, Dresden, Germany) for Mo-Kα excitation operated at 50 kV and 1 mA, and for our purposes, a 7 mm 2 silicon drift detector (KETEK, Munich, Germany) was used. The AXIL software  [30]  was used to evaluate X-ray spectra. Typically, 3000 s counting time is used for a Si wafer with a moderate load of aerosol particles. Measurements are performed in the air. Reference-free XRS was applied at the four crystal monochromator (FCM) beamline in the laboratory of the German national metrology institute PTB at the BESSY II electron storage ring in Berlin, Germany  [27, 31].
Results from Experiment 2 The loaded carriers from the Dekati impactor were analyzed during the field campaigns in situ with the S2 TXRF-spectrometer using monochromatic Mo Kα excitation at 17. 5 KeV and 1000 s as standard measurement interval and with the manufacturer's software (PICOFOX 7. 8. 2. 0, Bruker Nano GmbH, Berlin, Germany). Especially for multielement samples, strong overlaps of individual fluorescence peaks in the spectrum occur because of the line diversity and the finite detector resolution. For quantification, the Bruker TXRF software applies a non-standard deconvolution routine (SuperBayes), which uses measured mono-element profiles for the evaluation of peak intensities  [42]. While preliminary results were initially obtained using the 50 ng Y standard for a comparison of element mass concentrations during the campaign, all data were corrected after recalibration of the S2 TXRF spectrometer (see results of experiment 3 below).
For comparison of the total element mass concentrations in the PM 10 -fraction, only stages 1 to 12 of the Dekati impactor have been considered. Stage 13 (cut point 10 µm) was not included because there was a 50% chance that particles >10 µm in diameter would be collected, which would bias the comparability to the PM 10 filter samples. Table  3  reports the results for runs 6, 7, and 8 for comparison with the ICP-MS data. The relative standard uncertainties were specified by the TXRF analysis software. Cadmium could not be detected on the TXRF carriers, although very small quantities were determined on the PM 10 filters with ICP-MS. It can be assumed that Cd is distributed over a broader particle size range, and hence, the total collected masses on the impactor carriers are likely below the TXRF detection limit of approximately 0.
3 ng for Cd. There is some ambiguity in the quantification of As and Pb, if both elements occur in a TXRF sample. This is because the energies of the As Kα 1,2 lines overlap with Pb Lα, and the fluorescence signals cannot be separated within the energy resolution of usual TXRF energy dispersive detectors. The quantitative differentiation of Pb and As in the TXRF spectra was done in a subsequent analysis, using an improved "SuperBayes"-routine provided by Bruker. This routine was under development at the time of application and not a part of the standard spectrometer software [Bruker Nano GmbH, Berlin, Germany, private communication]. It allows for a better deconvolution of As and Pb signals. Table  3  reveals the TXRF element mass concentrations in the PM 10 fraction of ambient air samples from runs 6, 7, and 8, along with relative uncertainties.
The above-mentioned EU data quality objective of 40% relative uncertainty-as indicated by the TXRF-software-was missed for the element As. The overall agreement is satisfactory for the proof of principles; the mass concentration levels as determined by ICP-MS could be corroborated by TXRF over several orders of magnitude. ICP-MS-and TXRF-error bars do not always overlap; this is in all three runs the case for V and Pb. For a quantitative comparison the relative deviations between ICP-MS and TXRF were calculated as mean values over runs 6, 7, and 8 by the following equation: The overall agreement is satisfactory for the proof of principles; the mass concentration levels as determined by ICP-MS could be corroborated by TXRF over several orders of magnitude. ICP-MS-and TXRF-error bars do not always overlap; this is in all three runs the case for V and Pb.
Modelling approach The Ecopath with Ecosim (EwE) version 6. 6 (www. ecopath. org) approach has three primary 105 modules: the mass-balance routine (Ecopath), the time dynamic routine (Ecosim) and the spatial-106 temporal dynamic module in Ecospace. Initially, a model was developed to quantify the trophic flows 107 and to evaluate fishing impact among compartments of the BSIR from the first module in Ecopath. 108 The Ecopath model simplifies the complexity of marine ecosystem dynamics through a mass 109 balance approach on a system of linear equations that considers parameters such as the biomass, 110 production and consumption of the species to describe the trophic flows between biological 111 compartments, thus allowing the investigation of the possible responses of the ecosystem to 112 anthropogenic impacts such as habitat degradation and/or fishing  (Christensen and Pauly, 1992; 113 Christensen and Walters, 2004)  (Appendix 1 for further details).
S2  for further details). 122 (Table A full description and the sources of information for the input and output parameters in the baseline 123 Ecopath model are presented in Appendix 2 (Table  S1  to  S5 and Fig. S1 to S5). 124 Based on the Ecopath model, the Ecosim time dynamic module was applied and fitted to a time 125 series from 1988 to 2014. This model is a time-dynamic approach based on initial parameters from 126 Ecopath that simulate changes in the estimates of biomass and catch rates over time, given the changes 127 primarily exerted by fishing and the environment  (Christensen and Walters, 2004; Walters et al. , 1997). 128 These estimates are performed by multiple coupled differential equations derived from the Ecopath 129 equation. 130 is the growth rate in terms of biomass (Bi) over time for group i, ! " is the net growth efficiency 131 (production/consumption ratio), * " is the immigration rate, -" is the natural mortality rate (unrelated to 132 predation),.
95°W) (see Fig. S6  for details). Therefore, the historical 155 chlorophyll-a data was implemented as a forcing function of the primary production. 156 The vulnerabilities for each species/group that provided the best fit (measured by the weighted 157 sum of squared deviations SS), was obtained, in three steps, using an iterative procedure of the "Fit to 158 time Series" module of Ecosim. The first step determined the sensitivity of SS to vulnerabilities 159 associated only with individual predator-prey interactions  (Christensen et al. , 2008). Secondly, 160 anomalous patterns based on the time series values of relative primary productivity (forcing data, see 161 above) were compiled. For the last step, both the vulnerability values and anomaly patterns were applied 162 to reduce the SS. To assess the robustness of the fitted model, the landings estimates were compared 163 using both the reported official and non-official catch statistics.
Despite great strides in the development and wide acceptance of standards for exchanging structured information about genomic variants, there is no corresponding standard for exchanging phenotypic data, and this has impeded the sharing of phenotypic information for computational analysis. Here, we introduce the Global Alliance for Genomics and Health (GA4GH) Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis and research of all types of disease including Mendelian and complex genetic diseases, cancer, and infectious diseases. To support translational research, diagnostics, and personalized healthcare, phenopackets are designed to be used across a comprehensive landscape of applications including biobanks, databases and registries, clinical information systems such as Electronic Health Records, genomic matchmaking, diagnostic laboratories, and computational tools.
Introduction Despite the clinical importance of phenotypic features (signs, symptoms, laboratory and imaging findings, results of physiological tests, etc. ), exchanging them in conjunction with genomic variation is often overlooked or even neglected. Although great progress has been made in exchange formats for sequence and variation data such as the Variant Call Format (VCF),  1  complementary standards for phenotypic and other clinical data have lagged far behind. 4] [5] [6]  Unlike the aforementioned genomic standards, these approaches were not developed to model patient-level phenomena nor facilitate complex within and between patient comparisons. For example, one cannot readily compare an observed phenotypic profile against clinical databases as one can with genomic sequences using tools such as BLAST,  2  partially because the lack of standards has impeded the development of algorithms and software for computational phenotype analysis.
Another limitation is that phenotypic abnormalities of individuals are currently described in diverse places in multiple formats:  7  scientific publications, biomedical databases, health records, patient health forums,  8  and social media. 9 The. development of a reliable and practical standard for the documentation and exchange of clinical phenotypes is then critically necessary. A phenotype exchange standard is also timely for two reasons: firstly, there is increasing consensus about best practices for secure phenotypic data sharing and secondly, there have been significant advances in analytical methods to make effective use of computationally encoded phenotypic data. 10 ep phenotyping is a process whereby a multitude of morphological, biochemical, physiological, or behavioral attributes of each patient are collected by traditional clinical examinations and increasingly through advanced technologies such as imaging.
Schema overview Currently, a very wide range of ad hoc database schemas are used to represent clinical data for specific research projects, and numerous different ontologies are used to represent clinical entities; for instance, diseases can be represented by terms from ontologies and terminologies including Mondo, OMIM, Orphanet, NCIT, ICD, and many others. 40 Therefore, the Phenopacket schema is intentionally flexible to facilitate wide adoption and to increase the value of the network of systems sharing phenopackets for computational use. 2] [43] [44]  Specifically, Phenopackets are designed to be both human and machine-interpretable, enabling computing operations and validation on the basis of defined relationships between diagnoses, lab measurements, and genotypic information; they also enable seamless transfer of data from a data source (e.
The GA4GH Clinical and Phenotypic Data Capture Work Stream has developed a FHIR Implementation Guide (IG) that focuses on elements of the Phenopacket schema that are most commonly used for rare-disease genomic diagnostics (see Web Resources). Although the GA4GH Phenopacket was not designed to explicitly mirror existing data schemas for cancer, it was designed to cover the majority of data elements in the mCODE FHIR IG  55  and the International Cancer Genome Consortium ARGO Data dictionary  56  and we envision that multiple different FHIR resources such as mCODE can be exported using the GA4GH Phenopacket schema, enabling software to be written that will take advantage of a unified data format. The Phenopacket FHIR IG is being further developed under the HL7 FHIR Vulcan program. 57 e GA4GH has committed to coordinate its activities and future roadmaps with those of other standards development organizations (SDOs), including International Organization for Standardization (ISO) Technical Subcommittee for Genomics Informatics (ISO/TC215/SC1) and HL7 Clinical Genomics (CG).
Multiple upstream data collection and management tools already support exporting patient profiles as phenopackets for downstream analysis and data sharing, including PhenoTips 64 RD-Connect Genome-Phenome Analysis Platform (GPAP), Patient Archive in Australia and IRUD Exchange in Japan. PhenoTips can generate Phenopackets from patient or family records through a user interface or REST APIs, and includes de-identified demographic data, clinical phenotype, diagnoses, curated genetic findings, and pedigree data. Multiple projects, including the Children's Mercy Research Institute's Genomic Answers for Kids initiative, have adopted Phenopackets to help standardize data integration between PhenoTips and other systems. Projects such as EU funded Solve-RD and the European Joint Programme on Rare Diseases (EJP-RD) can generate Phenopackets for the data included in GPAP, which aims to facilitate diagnosis and novel gene discovery for clinical researchers.
65 Phenopackets are used in Solve-RD to share phenotypic and other relevant clinical or genetic information (e. g. , candidate or causative variants) between the consortium members, and are also deposited along the genomics data at the European Genome-phenome Archive (EGA) for long-term archival and controlled access. Besides being a successful instrument for data import/export between the project's databases, Phenopackets have also proved to be useful for data analysis, such as clustering patients based on their phenotypic similarity. Solve-RD has so far generated phenopackets from 11,349 individuals; this number is expected to increase soon to over 19,000. 66.
69, 70 The first release of this ontological modelling work is driven by the CAKUT (Congenital Anomalies of the Kidneys and Urinary Tract) rare disease registries use case  71  Although at an early stage, the ability to seamlessly aggregate and integrate data from multiple registries or databases has huge potential benefits in terms of increasing the size of patient numbers for epidemiological analysis and facilitating the integration of distributed patient data into semantic knowledge graphs. To achieve full interoperability of ERNs rare disease patient registries with Phenopackets, the Semantic Phenopackets. developed for the EJP RD virtual research platform will be further extended to include a full representation for all the elements of the GA4GH standard.
BioSamples and the European Genome-phenome Archive (EGA) The BioSamples database at EMBL-EBI provides a central hub for sample metadata storage and linkage to other EMBL-EBI resources. 72 BioSamples links to EGA  73  and has done additional curation of human disease samples targeted to supporting Phenopackets. Every sample in BioSamples (over 19. 5 million as of November 2021) exposes a Phenopacket record. Users can, for example, get a phenopacket for every single COVID-19 sample (see URLs).
Biobanks In Japan, the Agency for Medical Research and Development (AMED) nation-wide biobank network has been developed to connect 12 major biobanks including Biobank Japan, Tohoku Medical Megabank project, and National Center Biobank Network. Together, the network stores over 855,181 biospecimens and 203,741 genomic data by 421,861 donors that are made available through a common query system exposing all phenotype as phenopackets for downloading.
The wwPDB remains responsible for curating, storing and making structure data accessible, which maximizes the impact and utility of any work, both to the scientific community and from a funding perspective. Like with the structural biology community, a broader solution to the problem of providing access to structured phenotypic data in journal articles would be a collaboration between journals and databases, such as the EGA and the BioSamples database, to provide accessions and help authors deposit their data in a FAIR way.