dataesr/scientific-paragraphs-categorization huggingface dataset:
Quality vs spatial coverage of ground truth data In this study, field data (PASTORALP survey) was collected for management purposes, but also with the objective of building a sound ground truth data set for remote sensing-based classification. Accordingly, special attention was devoted to the quantification of nongrazeable surfaces within pastoral parcels, which include single trees, shrubs, buildings, roads, and severely impact the quality of signals detected from space. This led to an invaluable data set that allowed us to quantify the sensitivity of predictive modelling to data quality and quantity. A total area of 69 km 2 of pastures was mapped, but only ∼20%, 15 km 2 of fully grazeable surfaces were used as the training set for random forest models (3L, 13L, Table  2 ). Including the remaining ∼80% of pastoral surfaces in the training set did not add predictive power nor increased model performance.
Acknowledgments This research was supported by the  LIFE   PASTORALP  project ( LIFE16 CCA/IT/000060 ), co-funded by the  European Union 's  LIFE Programme , Climate change adaptation action sub-programme, by the  ANR   TOP  project, grant  ANR-20-CE32-0002  of the  French Agence Nationale de la Recherche. LECA is part of OSUG@2020 labex (reference  ANR10 LABX56 ). The authors sincerely thank  Valentina Andreo ,  Roberta Benetti ,  Giampaolo Bruno ,  Mauro Coppa ,  Maurizio Odasso  and  Camilla Scalabrini  for the tireless and diligent work in the field surveys.
This topic is bound to become all the more important with the release of deep sky surveys (see e. g. , Jiménez-Teja & Dupke 2016;  Kluge et al. 2020 Kluge et al. , 2021;; Ellien et al. 2021). In this paper we also discuss and estimate how much the ICL may impact the luminosity profiles of BCGs at these redshifts. The paper is organized as follows. The data and the method used to detect the BCGs are presented in Sect. 2. In Sect. 3 we describe how the luminosity profiles were modelled, and analyse the results obtained. In Sect. 4 we estimate in a preliminary study the impact of the ICL on the models fitted, and in Sect. 5 the impact of the depth of the images. We measure the alignment of BCGs with their host clusters in Sect. 6. Finally, we discuss the results and present our conclusions in Sect.
Detection of BCGs We retrieved the CFHTLS images from the Canadian Astronomy DATA Centre 2  and identified in each cluster the position of its BCG. The BCG is defined as the brightest galaxy in the cluster that lies within a radius of 1. 2 Mpc from the centre defined in  Sarron et al. (2018) , after filtering out foreground and/or background objects. Lack of spectroscopic data has led astronomers to develop methods that only make use of photometric properties. In  Chu et al. (2021) , spectroscopic redshifts of the clusters allowed us to accurately extract the red sequence, which was then used to identify the BCG. With the present dataset, we rely on photometric redshifts to distinguish cluster members from field objects. Similarly to  Chu et al. (2021) , we first proceeded by removing foreground galaxies, taking into account the uncertainties on the redshift.
Spectroscopic redshifts, if available, were retrieved from the NASA/IPAC Extragalactic Database (NED) to remove all sources that are not within the redshift 68% confidence interval. Point sources were identified in NED, or via the CLASS_STAR parameter (CLASS_STAR < 0. 95) in SExtractor. To identify foreground galaxies, we calculated the pseudo absolute magnitude (at the cluster's redshift) for each object. Foreground galaxies would in this way appear abnormally bright (M abs < -26). Edge-on spirals were excluded as well by filtering out any object with a major-to-minor axis ratio higher than 2. 6. In order to identify the BCG among the remaining galaxies in the catalogue, we measured the S/N at the galaxy's coordinates on the density map from  Sarron et al. (2018) , and considered the galaxy's probability of being at the cluster's redshift (see Eq.
Alignment of BCGs with their host clusters With the purpose of studying the alignment of BCGs with their host clusters, we measured their positions angles (PA) and ellipticities with GALFIT. Out of the 974 BCGs that were fitted by either one or two Sérsic profiles, 126 have a minor-to-major axis ratio b/a ≥ 0. 9. As in  Chu et al. (2021) , we exclude these galaxies as an ellipticity close to unity leads to high uncertainties on the measurement of the PA, and also to an ill-defined PA. We thus end up with 848 BCGs. We measured the cluster ellipticities by fitting ellipses on the density maps with a 3σ clipping, applying the ellipse function in the Python photutils package 6. All clusters with b/a ≥ 0. 9 were excluded. This reduced the sample to 639 clusters. Similarly to  West et al. (2017) , we measured the PA of clusters by computing the moments of inertia on the galaxy density maps provided by  Sarron et al.
(2017)  who show that non BCGs members of a cluster have a random orientation in the cluster, the BCG properties are linked to the cluster. This study shows, with increased statistics, evidence for an early formation of the brightest central galaxies in clusters. Most of their matter content was already in place by z = 0. 7, and we showed in  Chu et al. (2021)  that this conclusion can most likely be applied up to z = 1. 8. New datasets in the infrared (JWST, Euclid) should enable us to confirm this result at higher redshifts with better statistics. In the present paper, we also estimate in a first approach the contribution of the ICL to such studies.
Acknowledgements. We are very grateful to  Y. Jiménez-Teja  for providing us with her ICL images. We also thank  A. Ellien  for discussions on the ICL. We thank the referee for her/his constructive comments and suggestions. F. D. acknowledges continuous support from CNES since 2002. I. M. acknowledges financial support from the  State Agency for Research of the Spanish MCIN  through the  "Center of Excellence Severo Ochoa" award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ), and through the program  PID2019-106027GB-C41. Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
Eligibility criteria Inclusion criteria: Every individual found in Cameroon at the moment of the study, suspected/contact COVID-19 case as defined by the WHO, aged 21 years and above, and provide a written informed consent for participation; Non-inclusion criteria: Suspected case without clinical data, any suspected case managed out of the country guidelines, or any suspected case without blood/respiratory fluid specimen available;  Exclusion criteria: Enrolled participant with neither results of real-time PCR, serological testing, nor SARS-CoV2 sequence data following positive-PCR for the respective analysis.
This approach will ensure a full analysis of all intra-host viral variants (even those with low prevalence), thanks to its high-throughput performance and to define the evolution of SARS-CoV-2 epidemic in SSA. Sequencing of the entire viral genome will be performed in a subset of 50% (n = 384) selected samples nasopharyngeal swabs with multiplex PCR and sequencing (1,000x coverage). Viral genome sequences will be obtained by NGS and analysed along with sequences collected from GISAID database (https://www. gisaid. org/). Similarly, full-length sequences of other human SARS-related coronaviruses and those isolated from animal species, known as natural reservoirs of coronaviruses, will be retrieved from GenBank (http://www. ncbi. nlm. nih. gov/ genbank/). Multiple phylogenetic trees will be constructed by RaxML and MrBayes programs to estimate the evolutionary rates along the entire SARS-CoV-2 genome and to calculate the divergence between SARS-CoV-2 and other related human and animal coronaviruses, by setting best-fit substitution models (nucleotide and amino acids).
Nanopore libraries will be run on Oxford Nanopore flow cells (R9. 4 or R9. 5) either on a MinION MK1B or a GridION X5 (Oxford Nanopore Technologies, Oxford, UKo). We aim for a target read coverage level of 500-1000 reads per site and majority consensus genome sequences will be generated from the NGS data for target pathogens (e. g. , EBOV) for analysis with BEAST. Sequence generated by nanopore will be evaluated for reliability using the Next Generation sequencing (Illumina) as gold standard. the use of unique identifiers and a protected database. Laboratory results will be free of charge for clinical management whenever necessary. A data safety and monitoring will ensure for an appropriate monitoring of the study implementation, under the scientific advisory board of the CIRCB. Statistical analysis.
Data will be collected on standard tools (S3 File) by trained field epidemiologists and stored in a password-protected database to ensure confidentiality of participant data and their privacy at the local institution in Yaounde-Cameroon, by experienced statisticians who will coordinate data management and analyses using SPSS software, according to Good Clinical Data Management Processes (GCDMP). Diagnostic Performance will be evaluated regarding the sensitivity, specificity, positive/negative predictive values (with 95% Confidence Intervals) will be described using ddPCR as molecular gold standard assay (lower detection limit of 10 copies/test) and ELISA as serology gold standard, stratified by range of viral loads, Ct values, duration after symptom onset, target biomarkers and viral diversity.
In both cases, this will not require a schema change, but rather, the development of best practice documents. CV terms will be used to define how these data types should be encoded in a consistent manner. Participation is actively sought. These enhancements are expected to be completed in 2023. mzTab. Although mzIdentML has been widely adopted, it was felt that for some applications, a simpler tab-delimited format capable of encoding the most important information would be beneficial. The mzTab format  46  was conceived as such a relatively simple format that could encode both identification and quantification, applicable to both bottom-up MS proteomics as well as small-molecule MS metabolomics. It has been implemented in several software packages (mainly for identification data, not for quantification) such as Mascot,  47  OpenMS,  48  MaxQuant,  49  and ProteomeXchange repositories.
Preliminary discussions have started to produce a similarly redesigned mzTab-P 2. 0 for proteomics only, based on the experience of the design of mzTab-M, although to date the updated format has not progressed rapidly and hence participation in this design process is actively sought. proBAM and proBed. The proBAM and proBed formats 51 are relatively straightforward adaptations for proteomics of the tab-delimited BAM/SAM and BED formats widely used in genomics. Existing columns were defined to be applicable to peptide-based MS proteomics, and a few additional columns were added for peptide-specific contexts. Peptide data thus written in proBAM and proBed are compatible with transcript alignment viewers and other similar software already widely used in transcriptomics. No further changes are planned.
When the PSI needed a mechanism to encode in a compact way exact peptidoforms (peptide sequences with a specific set of mass modifications), ProForma provided a useful piece of prior work on which to build. In collaboration with the CTDP, the PSI has recently developed the ProForma 2. 0 standard,  56  which provides a substantially expanded set of mechanisms to encode a wide variety of modified proteins and peptides (proteoforms and peptidoforms) in a manner that meets the needs of both the CTDP as well as the bottom-up proteomics workflows. The ProForma 2. 0 formatting is both easily human readable as well as software parsable. The ProForma 2. 0 standard is used in conjunction with other completed and in-development PSI standards as described below. Universal Spectrum Identifier (USI). There are numerous cases when one or more mass spectra should be carefully examined since they provide crucial evidence for a scientific conclusion.
sky format and never supported TraML, rapidly led to the TraML format becoming largely unused. mzQuantML was developed as an XML-based format to capture a detailed output of proteomics quantitative workflows. 65 However, its adoption was limited due to a preference from most software developers to export quantitative output data in flat text files. When mzTab was released later, there was the hope that this simpler tab-delimited format could become popular to represent the final results coming from quantitative experiments. As mentioned above, although mzTab has been implemented, it has been mainly used for capturing identification results only so far. The field has been unable to agree so far on a format for capturing quantitative information. This situation hinders further progress and the reuse and integration of proteomics data.
New Standards in Development Most of the formats mentioned thus far have completed the standardization process, are complete, and in use. However, the PSI is actively working on several new formats that are in various stages of the standardization process. In all cases, community involvement is always being sought, either for the design phase, early adoption in software or for the review phase. Readers with interest in any of the subsequent formats are encouraged to contribute to their development and ratification. mzQC. The qcML format  66  was published in 2014 as an XML PSI-like format for encoding mass spectrometry quality control (QC) metrics, but without the PSI ratification process. However, several shortcomings were soon identified and general interest in QC waned, both of which hindered adoption.
While any one of these formats does a good job in encoding the spectra in the library, there is general agreement that none of the formats do a good job in encoding the metadata associated with the library in a consistent manner. 71 An example is MSP, wherein nearly all metadata is recorded within the COMMENT field in a highly variable manner across software packages. Various producers and consumers of spectral libraries have come together to create a new standard called mzSpecLib (https:// psidev. info/mzSpecLib). It is quite similar to the MSP and speclib formats, with an emphasis on heavy use of CV terms and a standardized data model that can be encoded in several different serialization mechanisms, including an MSP-like text format, a JSON format, and potentially a more performant binary format based on HDF5 or SQLite.
■ CONCLUSION We have presented here an overview of the most important aspects of the PSI after 20 years of efforts, including its current operation, the current status of existing standards, ongoing work for standards in progress, and synergies between the PSI and community groups. These activities demonstrate the commitment of the PSI in accelerating the pace of biomedical research by facilitating the dissemination and reuse of data and interoperability of software. There are many ongoing standards in development, and it is perhaps worth reiterating that greater community participation is always welcome, since such greater participation leads to better standards. As the PSI completes these standards, new needs will emerge as the field advances. Already there is growing interest in standardization around the rapidly emerging high-throughput affinity-based protein quantification platforms, and some initial discussions have taken place at PSI workshops, although a clear plan has not yet emerged.
While there are some examples highlighted above where PSI standards have not been widely used and hence deprecated, the ethos of the PSI is to make life easier, not more complex, for proteome scientists and bioinformaticians, and we continue to review our processes to ensure that new standards are only developed where there is a strong use case. The PSI will thus continue its efforts to maintain and enhance existing standards to meet emerging needs. While many of the current PSI standards focus primarily on datadependent acquisition workflows, more efforts are needed to apply standardization to DIA workflows. 78 As software development leads to ever greater automation and artificial intelligence, the PSI should continue to foster the development and implementation of APIs and interchange systems that allow intelligent agents and software to interoperate in ways so that end-users need not concern themselves with which standards are being used, but rather rely on an ecosystem of formats, APIs, and software that allows them to focus only on their science.
Embankment and instrumentation Within an ANR (Agence Nationale de Recherche) project e TerDouest (ANR-07-PCGU-006-10), an experimental embankment was constructed at Héricourt, in the northeast of France where a continental climate dominates with oceanic influences. The embankment was 107 m long by 4. 9 m high with side slopes of 1:2 (vertical: horizontal). The bottom was 25 m wide and the top was 5 m wide. The embankment was divided into two sections, constructed with a silty soil and a clayey soil, respectively. The silty soil    The instrumentation layout was symmetrical for the two sections with the two treated fine-grained soils. Different sensors were installed in the embankment for monitoring the water content, suction, temperature and deformation in different positions. Four piezometers were installed to monitor the water table, two on the silty soil side and two on the clayey soil side.
A system of runoff measurement was also installed to monitor the runoff from the side slope  (An et al. , 2017a). A meteorology station was installed on the top surface of embankment to record the meteorological data every 30 min, including solar radiation, precipitation, atmospheric pressure, wind speed and direction, air temperature and air relative humidity, at 0. 5 m and 1. 5 m above the top surface. In this section, only the soil suction and water content as well as the runoff are emphasized. The Watermark soil suction sensors were used to monitor the matric suction. The working range of such sensor is 0e250 kPa. The reading frequency was once every 1e2 d. The time domain reflectometry (TDR) method was used to monitor the soil volumetric water content, together with the soil temperature.
Field monitoring data Rich data were obtained from the field monitoring. However, for the purpose of illustrating the soil responses to the changes in meteorological conditions, only recordings of precipitation, volumetric water content, suction and runoff were presented here, as mentioned previously. The variations of volumetric water content at points 1e4 are presented in Fig. 11 , together with the precipitation, for the monitoring period from July 2010 to April 2014. Points 1 and 4 were situated 0. 25 m far from the side slope; point 2 was situated 0. 75 m far from the side slope; and point 3 was situated 2. 4 m far from the side slope. Note that the same instrumentation was adopted for the silty soil and the clayey parts. In other words, there were the same positions 1e4 for the volumetric water content measurement in both parts.
Simulating the thermo-hydraulic responses of Héricourt embankment The proposed thermo-hydraulic model was implemented in the FreeFemþþ code  (Hecht, 2012)  for analyzing the thermo-hydraulic behavior of Héricourt embankment. Examination of all data in the monitoring period showed that only the recordings during July 6e 26, 2011, are regular and complete. Thus, this short period was selected for the numerical analysis. The soil temperature and volumetric water content values on July 6, 2011 at 14:42:52 were taken to define the initial conditions. The meteorological data were used to determine the infiltration (positive or negative) and the soil heat flux (positive or negative) using the soileatmosphere interaction model. The obtained values were used to define the boundary conditions at the soileatmosphere interface.
Reshaping-induced Mutual Orbit Perturbation The reshaping of Dimorphos may induce perturbations to the mutual orbit. To investigate this possibility, a finite element method (FEM) approach was used to simulate the mutual dynamics of the Didymos system, by accounting for the reshaping of Dimorphos during the DART impact  (Nakano et al. 2022). The model uses the shape models from the Didymos system DRA data (Section 2). The simulations assume that the Dimorphos shape change that is driven by the DART impact occurs on Dimorphos's leading side, 30  without considering the out-of-plane velocity component of the DART impact, given that such a component does not contribute to the in-plane motion significantly (Section 3. 2). Also, each reshaping is modeled under the assumption that the volume and density distributions are constant before and after the DART impact (i.
We thank the anonymous reviewers whose comments improved the manuscript. This work was supported in part by the  DART mission ,  NASA  Contract # 80MSFC20D0004  to  JHU/APL. F. F. acknowledges funding from the  Swiss National Science Foundation (SNSF) Ambizione  grant No. 193346. O. K. , A. C. B. , I. G. , M. J. , P. M. , S. D. R. , K. T. , and Y. Z. acknowledge funding support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 870377  (project  NEO-MAPP ). J. W. M. acknowledges support from the  DART   Participating Scientist Program  (# 80NSSC21K1048 ). S. R. S. acknowledges support from the  DART   Participating Scientist Program  (# 80NSSC22K0318 ). A. C. B. acknowledges funding from the  Spanish MICINN   RTI2018-099464-B-I00. E. D. and A.
I. INTRODUCTION The rise of Open Science and the steady growth of the number of research publications, datasets, and other materials on the web is changing the way research outcomes are shared and explored, and is posing new challenges and opportunities. This large mass of open research outcomes has the potential of supporting a new generation of intelligent systems for actively supporting, automatizing, and accelerating the scientific effort  [1]. One of the main challenges in this space is to generate a semantically rich, interlinked, and machine readable description of the available research knowledge. This could enable more sophisticated techniques to analyze the scientific literature. As a consequence, more advanced services could The associate editor coordinating the review of this manuscript and approving it for publication was Ananya Sen Gupta.
2 n the last few years, we saw the emergence of several knowledge graphs (KGs) explicitly representing research knowledge. These KGs typically describe a set of entities in this space (for example, tasks, methods, evaluation techniques, datasets, proteins, chemicals), their relations, and the relevant actors (e. g. , authors, organizations) and documents (articles, books. )  [17] ,  [18]. Some of these graphs are crowdsourced (e. g. , ORKG  [4] , UMLS  [19] , Nanopublications  [13] ), while others are automatically generated from the text and metadata of research articles (e. g. , AI-KG  [2] , CSO  [20] , TKG  [21] ). As many other KGs, those that describe research concepts suffer from incompleteness. They are typically very partial representations of the actual research knowledge and may lack several relevant facts, that were not identified by information extraction approaches or human experts.