dataesr/scientific-paragraphs-categorization huggingface dataset:
4 We conducted another review of the last 500 RMD-related articles indexed in PubMed and published by authors in European countries between 21 April 2022 and the previous 1 year. The data showed that the majority of the articles (67. 1%) were associated with at least one author from a high-income European country (figure  1 ). The same search undertaken for the prepandemic year of 2019, which served as a control, showed a similar trend (figure  1 ). (The search strategy is described in online supplemental material 1. ) A similar trend has been reported on a global scale: an overwhelming majority of scientific articles in rheumatology has been published by authors from highincome countries worldwide. 9 10 Additionally, a gender gap for senior authorship positions in rheumatology research articles has been shown, particularly in articles reporting randomised controlled trials and those initiated by industry.
EMERGING EULAR RESEARCH CENTRE In 2019 and 2020, EULAR convened a series of meetings to identify barriers to high-quality, collaborative RMD research across Europe and beyond, recognising the global nature of impactful discovery and clinical research. The workshops brought together basic, translational and clinical research scientists, epidemiologists, public health researchers, presidents of EULAR's Scientific Member Societies for Rheumatology, RMD patient representatives and health professionals. More than 70 participants explored opportunities for research capacity building. Several key themes emerged from the sessions, including the need for improving the quality of RMD research to enhance patient health outcomes; clinical trial support; networking, integration and exchange of information; data and analytical services, capacity building for earlycareer scientists, equity of access to research across Europe; and optimising funding opportunities and success rates for RMD research, among others.
Synovial tissue EULAR is developing a Pan-European RMD Patient Registry to collect longitudinal patient-generated health data. The goal is to enable comparative health services research and burden-of-disease research. The data will be obtained exclusively from patients with RMDs. Anonymised data will be available for research and include a range of variables, such as sociodemographic data, employment situation, disease activity, drug therapy, quality of life, healthcare, unmet medical needs and patient-reported research priorities. Interested patients with RMDs will be able to sign up without any clinician intervention. Special consideration will be taken to ensure the representation of patients with RMDs from under-represented and minority groups. 5. A 'RheumaFacts' Data Repository project is also under way to provide up-to-date RMD-related data from across European countries to highlight the disease burden of RMDs, healthcare gaps and inequalities.
The data will be available to researchers as well as to other groups such as funding agencies, decision-makers and policymakers who can advance population health at a European level. 6. Finally, the new EULAR Network of Trials (ENTRI) will address critical roadblocks in RMD-related clinical trials, initiated by investigators or industry. The network will consist of clinical centres with experience in clinical trials and access to cohorts of patients with RMDs. Collaboratively, they will enable high-quality and efficient multicentre clinical trials across Europe, targeting patient populations from across a wide range of RMDs. Investigators will be able to get advice on their clinical trial protocols via the EULAR Consultation Service and use ENTRI as a mechanism for the rapid and efficient roll-out of multicentre clinical trials.
As part of this process, investigators will have access to clinical trial tools, such as toolkits, standard operating procedures, electronic case report forms database templates and best-practice study manuals to share across centres. Additional features of the network will include (1) a web-based portal that lists members of the network, highlighting areas of expertise, patient populations, and active or in-setup studies; (2) adoption of novel trial designs such as pragmatic trials that address clinically relevant questions; (3) targeting a wide range of patients and at-risk populations across the RMD spectrum, including rare and complex diseases; (4) the potential for novel approaches including virtual trials, light-touch, web-based recruitment, randomisation, data capture and monitoring; and (5) establishing a curriculum with the EULAR School for training a new cadre of clinical trialists (see further).
Evaluation With the inception of the Research Centre, EULAR has renewed its commitment to research evaluation and implemented an evaluation programme. EULAR will collect a set of outcomes to assess the performance of its Centre and the impact of the provided research support (eg, number of investigators supported, subsequent publications and other forms of dissemination, other research outputs such as datasets or software code, awards or recognitions, patents, start-ups, the amount of subsequent research funding). The collected data will ensure the Centre's accountability, identify usage gaps and barriers, and inform the planning for future directions.
Acknowledgements We thank  Amy R Slutzky, PhD , MSLIS, AHIP,  Academic & Research Librarian  at the  SUNY Upstate Medical University , for her advice on the literature search strategy. We also thank  Professor Christopher Edward , chair of the  EULAR Education Committee , for his valuable input and feedback regarding the content about research education, training and mentoring. Contributors IBM conceived of the overall manuscript idea. KR wrote the first draft of the paper. KR and CH performed the data collection and analysis. IBM, AM, AC, RFvV, DE and RL verified the analytical methods. All authors discussed the results and contributed to the final manuscript. Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
One of these studies indicated that most patients tolerated their vaccination well, with rare reports of flares and spo-radic reports of serious adverse events. 34 3. The EULAR Outcomes Measures Library (OML) 35 offers investigators a comprehensive database of validated instruments (eg, indices, surveys and scales) used in rheumatology that emphasise patient-reported out-comes (PROs). PROs, such as symptoms, health-related quality of life or patient-perceived health status, are re-ported directly by the patient and are essential tools to inform patients, clinicians and policymakers about disease-related morbidity and patient suffering, espe- new ATA technologies, EULAR hopes to increase the cially in chronic diseases. The OML offers the neces- chances of improving RMD patient outcomes and sary data to help RMD investigators choose those re- achieving success, as seen in oncology, using precision search instruments with the highest level of validation.
We set the frequency window at 50 kHz for DNA network and 25 kHz for the single DNA molecule. Each spectrum is collected at the targeted position with the spectral resolution of 2 cm -1 and 128 × co-averages within the range of 1522-1824 cm -1 and duty cycle at 5% or 7% which depends of the sample. Then it was normalized using the Anasys software (Analysis Studio) and successively, it was smoothed with a Savitzky-Golay filter (third order, five points). All the AFM topography images and AFM-IR maps are treated (flattened and de-noising option) by using MountainsSPIP 8 software. The factor of environmental conditions was one of the crucial to obtain higher IR absorption of DNA regarding to a mica surface. We have noticed that in the non-controlled ambient conditions, the AFM-IR absorption map shows DNA with a lower IR absorption regarding to the underlaying mica substrate see Fig.
Acknowledgements The authors would like to acknowledge  Dr. Ali Assifaoui ,  Dr. C. -H. Brachais  and  M. -L. Léonard  for their assistance and advices during FTIR experiments. We acknowledge funding from the  CNRS Mission pour l'Interdisciplinarité (MI-DynAFM-DNARep   2018_273085 ) and région  Bourgogne-Franche-Comté  ( AAP Région 2020-ANER-Projet AFMdynDNA, AAP Région 2020 DNA-Heritage ). This work has been also supported by the  EIPHI Graduate School  (contract  ANR-17-EURE-0002 ).
In Bazzi et al. [33] , they proposed that the IEDM could be suitable for operational near-real-time application for irrigation detection at the plot scale. The integration of the IEDM as an operational tool to detect irrigation events in a near-real-time scenario requires first a deep assessment of the strength and limitations of the IEDM. Our objective in this study is to assess the feasibility of using the IEDM and S1 time series for irrigation detection by considering the maximum likelihoods that may be encountered in an operational application of the IEDM mainly regarding the climatic conditions and the crop type. To do so, we collected a wide database of irrigation that contains irrigation data from different climatic regions and over several summer crops. This kind of assessment may help understand the applicability of the IEDM at large scales.
Study Sites and In Situ Datasets The assessment of the Irrigation Event Detection Model (IEDM) developed by Bazzi et al. [33]  was carried out over five study sites located in Tarn, France (TR), West Occitanie, France (OW), East Occitanie, France (OE), Bekaa Valley, Lebanon (LB), and the Thessaly region, Greece (GR), across several years depending on the data availability in each site (Figure  1  and Table  1 ). The studied sites belong to two different climatic zones. According to the Koppen Geiger climate classes, the GR, LB and OE sites correspond to the hot summer Mediterranean climate (Csa) also known as the typical Mediterranean climate, with an annual precipitation below 600 mm. The OW and TR sites correspond to the temperate oceanic climate (Cbf according to Koppen Geiger climate classification), with an annual precipitation over 800 mm.
Exact quantification of the diurnal effect is not yet assessed, and therefore joining the evening and the morning acquisition may lead to several fluctuations in the SAR time series related to the diurnal effect and not to the soil moisture variation. Considering that a plot is covered by (m) orbits (m between two and four orbits) with (pol) polarizations (pol = 2, VV and VH), a result of (m × pol) labeled irrigation possibility time series could be obtained at each plot. For example, a plot covered by four orbits in VV and VH polarization would have a total of eight time series data of labeled likelihood irrigation possibilities. The irrigation possibilities at each orbit are at six days revisit (same revisit of the S1 images). Combining the (m × pol) time series data in only one time series of irrigation possibilities was performed as suggested by Bazzi et al.
The six-day intervals registering detected irrigation possibilities (P ≥ 100) are then compared to the in situ irrigation events. First, the irrigation dataset is harmonized with the 6-day revisit time of the S1 satellite to consider that if several irrigation events on the same plot occur between the same 6-day interval, these irrigation events are considered as only one irrigation event. True detected irrigation events are thus the irrigation events that lie in time within their corresponding intervals having a P ≥ 100. A false detection irrigation event is thus the six-day time intervals that have P ≥ 100 but no in situ irrigation is recorded. Finally, missed irrigation (undetected irrigation event) is an irrigation event that lies within a time interval that registered no value for irrigation possibility (Figure  2 ).
Effect of the Vegetation Cover The effect of the vegetation cover in detecting irrigation events could not be discarded, because the C-band SAR signal is sometimes attenuated by the vegetation cover. As such, we analyzed the effect of crop growth on the accuracy of irrigation detection to understand the constraints of vegetation development on the irrigation detection and analyze this constraint as a function of crop type. Since the two dominant crop types are Maize and Soybean, with robust and rich datasets to draw out conclusions, this analysis concentrates on these two crop types. For this reason, plots with these two crop types were grouped together to obtain two groups: Maize and Soybean. Plots are grouped regardless of the climatic region and the studied year, since the importance here is to discuss only the vegetation development, which is expected to be similar for the same crop across regions.
For example, in the case of Tarn 2018 and 2019, some images were missing from the source of the ESA website, thus leading to some gaps in the six-day temporal series of the same orbit and a lower number of S1 images per month (seven to eight images per month). We analyzed the effect of the number of available S1 images per month for each climatic zone separately. Regardless of the crop type, we aggregated the irrigation data based on the number of available images per month and classified them into three classes: the first class corresponds to the plots having less than 10 images per month (7 and 8 images in TR region), the second class corresponds to plots with exactly 10 images per month (dataset in ascending and descending modes) and the third class corresponds to plots with 15 and 20 S1 images per month (full dataset of three to four orbits covering the plots).
Discussion In this study, the detection of the irrigation events at the plot scale was assessed using the Sentinel-1 data and a decision tree algorithm (IEDM). Results demonstrated that the IEDM with Sentinel-1 data could provide a good overall performance for irrigation detection. Regardless of some of the presented limitations of the IEDM performance across climatic regions and studied crops, the IEDM combined with Sentinel-1 could still be efficient to detect irrigation events. However, other limitation factors could also be discussed to assess better the performance of the IEDM with S1. The IEDM was applied to all agricultural plots regardless of the soil type of each plot. The effect of the irrigation on the soil wetness degree could be also correlated to the soil type of the agricultural plot.
This integration may also necessitate the re-calibration of the IEDM threshold values of the decision tree to be more adequate with the L-band SAR signal. Another point to discuss regarding the IEDM and the S1 is the irrigation system used at the plot scale. In this study, almost all the plots were irrigated by the common sprinkler irrigation technique. Previously, Bazzi et al. [32]  reported a similar capability of the S1 data to detect irrigation using the gravity irrigation technique. Drip irrigation, which is becoming more common now to reduce the water consumption and increase the water use efficiency, is not yet analyzed. In this study, only a few irrigation events in the Greece Maize crops were performed using drip irrigation. No specific observation was remarked for these irrigation events that were also detected in the study.
However, the absence of a robust database of drip irrigation prevents us from drawing out robust conclusions regarding the capability to detect these irrigation events using the S1 and the IEDM. A study performed by Soulis et al. [41]  assessed the effect of the positioning of the soil moisture sensors placed in soil on the soil moisture values in the drip irrigation context. They showed that the irrigation efficiency was significantly affected by the placement of the soil water content sensor (depth of sensor). Specifically, the sensor placement on the irrigation efficiency had an efficiency as high as 16%. Back to the SAR data, this means that the penetration of the SAR signal (at 5 or 24 cm) can play an important role in the detection of the irrigation events in drip irrigation cases, as the drip irrigation is more targeted to the plants.
The last point to discuss in this paper is the operational use of the IEDM to serve as a generic tool for irrigation detection. In fact, the IEDM was first proposed as a near-real-time model to detect irrigation events and thus can serve as an irrigation detection tool during the growing season. The data used to detect irrigation events in the S1 image of date (t) do not rely on the future captured S1 image of date t + i but on the previous S1 images captured before the time t (mainly on t-1). The efficiency and rapidity of the IEDM to detect an irrigation event is thus dependent on the delivery time of S1 images, which are usually delivered in a "fast 24 h" delivery mode. Therefore, the implementation of the IEDM in an operational pipeline tool can guarantee the detection of the irrigation event in real time after the availability of the S1 image.
Conclusions This study presented a detailed assessment of detecting irrigation events at the plot scale using the S1 time series data and a change detection algorithm (IEDM). The IEDM was applied using S1 time series over five semi-arid and temperate study sites in Europe and the Middle East and across several major summer crops. The main findings showed that the accuracy of the irrigation detection reaches 85% for semi-arid areas, higher than that for temperate areas reaching 53%. Developed vegetation cover was found to be an important constraint in the detection of irrigation events, where the irrigation detection accuracy decreased from about 60% for moderate vegetation cover (NDVI < 0. 60) to less than 40% when the NDVI reached values beyond 0. 7. The main results also demonstrated the dependency of the irrigation detection accuracy on the availability of the S1 images (number of used S1 images in the S1 time series), especially over temperate areas.
Acknowledgments: The authors wish to thank the  French Space Study Center (CNES ,  TOSCA 2022 ), the  French Agency for Ecological Transition (ADEME ,  RSEAU  project), and the  National Research Institute for Agriculture, Food  and the  Environment (INRAE)  for supporting this work. The authors would like to thank  Giorgios Tziatzios  and  Nicolas Dalezios  from the  University of Thessaly, Greece , for providing valuable terrain data. The authors would like to also thank the  European Space Agency (ESA)  for providing the S1 images and the  French Land Data Center (Theia)  for providing the S2 images.
Funding: This research received funding from the  French Space Study Center (CNES ,  TOSCA 2022  project), the  French Agency for Ecological Transition (ADEME ,  RSEAU  project) and the  National Research Institute for Agriculture, Food  and the  Environment (INRAE). H. J. acknowledges funds from  Google. org and the  Tides Foundation , award  103808. This research was also funded by the project  HubIS  under the  PRIMA 2019 program  of the  European Commission (HORIZON EUROPE).
Data Availability Statement: Sentinel-1 data are available via the Copernicus open-access hub (https://scihub. copernicus. eu/dhus/#/home (accessed on 15 September 2022)). Sentinel-2 data are available on the Theia French Land Data Center website (https://www. theia-land. fr/en/product/ sentinel-2-surface-reflectance/, accessed on 15 September 2022).
While this allows obtaining a fast and efficient coverage of the QUBIC observed sky, it prevents one from simulating actual 1/f noise from atmospheric or any time-domain instrumental fluctuations as successive time samples do not correspond to nearby pointings as in a more realistic scanning strategy. As a consequence, the simulations presented in this article implicitly assume a stable atmosphere with no 1/f noise, but account for the average loading from the atmosphere (see section 3. 2. 1 for a discussion of the seasonal and diurnal variations). We will include realistic atmospheric fluctuations in our simulations when more detailed information than the average emissivity and temperature will be available from the TD data on the sky. In addition, as discussed in the second paragraph of this subsection, the shape of the QUBIC synthesized beam implies a low sensitivity on scales much larger than the angular distance between our multiple peaks, which will significantly reduce the impact of the (mostly large scales) atmospheric fluctuations.
Here we want to underline that in the model used to fit the data we did not consider the synchrotron in addition to the dust emission because, as shown in figure  11 , the synchrotron emission can be considered negligible in the QUBIC TD frequency band. This is further confirmed in the results displayed in figures 12 through 14 where we see that the input sky SED falls into recovered confidence interval independently of the presence of the synchrotron emission. We verified that the shape of the confidence interval does not depend on the model we assumed for the MCMC exploration. Indeed, we obtain almost identical regions with a second order polynomial instead of the modified power law  12. In table 2 we list the main parameters used in our simulations. Results. In figures 12 and 13 we show how we can reconstruct the SED of the dust emission in total intensity and polarization with one year of sky integration with the QUBIC TD in its 150 GHz band.
Future studies will explore in detail the impact of atmosphere fluctuations and how spectral imaging can lead to improved component separation using classical techniques as well as techniques specific to bolometric interferometry. A Impact of atmospheric fluctuations on scales smaller than the synthesized beam cutoff angles. In section 3. 1. 1 we have seen that the synthesized beam naturally filters-out large-scale fluctuations from atmospheric gradients. We have also explained that to understand the effect of atmosphere fluctuations on scales smaller than the synthesized beam cutoff angles (8. 8 degrees at 150 GHz and 6 degrees at 220 GHz) it is necessary to build simulations based on atmosphere measurements taken at the site. Here we want to show that the available data taken at sites similar to that of QUBIC allows us to say that the instrument will be sensitive to fluctuations in a defined range of turbulence scales.
Acknowledgements We thank the anonymous referees as well as the editor for insightful reviews that greatly improved this work. QUBIC is funded by the following agencies. France:  ANR  ( Agence Nationale de la Recherche ) 2012 and 2014, DIM-ACAV ( Domaine d'Interet Majeur-Astronomie et Conditions d'Apparition de la Vie ),  Labex UnivEarthS  ( Université de Paris ),  CNRS/IN2P3  ( Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules ),  CNRS/INSU  ( Centre National de la Recherche Scientifique/Institut National des Sciences de l'Univers ). Italy:  CNR/PNRA  ( Consiglio Nazionale delle Ricerche/ Programma Nazionale Ricerche in Antartide ) until 2016,  INFN  ( Istituto Nazionale di Fisica Nucleare ) since 2017. Argentina:  MINCyT  ( Ministerio de Ciencia, Tecnología e Innovación ),  CNEA  ( Comisión Nacional de Energía Atómica ),  CONICET  ( Consejo Nacional de Investigaciones Científicas y Técnicas ).
Extraordinary high aerosol contamination observed in the atmosphere over Kyiv city, Ukraine, during the March -April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2. 5 load investigated using newly established AirVisual sensors mini-network in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site analyzed for that period. Aerosol optical depth, Angstrom exponent, and the aerosol particles properties (particles size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning particles.