dataesr/scientific-paragraphs-categorization huggingface dataset:
Prevalence and incidence could be underestimated because only patients hospitalised or with full coverage were included; this would exclude any patients who were followed up as outpatients and not hospitalised at any time during the 2010-2017 period. However, the scope and completeness of the SNDS database, with the linkage within SNIIRAM between PMSI (hospitalisation), CepiDC (death), and CNAM (national security data for reimbursement of ambulatory care), allowed us to estimate the national incidence and prevalence rates of this patient population. This study evaluated the overall survival rate but was not able to assess the causes of death in PF-ILD. However, use of supplemental oxygen in 41. 8% of patients suggests that they developed chronic respiratory failure, and respiratory causes were the primary causes of death in our clinical cohort  [22].
ATC anatomical therapeutic chemical classification, CCAM classification commune des actes médicaux [medical classification for clinical procedures], CT computed tomography, GHM groupes homogènes de maladies [Homogeneous Group of Patients], HRCT high-resolution computed tomography, ICD-X International Classification of Diseases code, ILD interstitial lung disease, IPF idiopathic pulmonary fibrosis, LPP liste des produits et prestations [list of products and services], SNDS Système National des Données de Santé [French national administrative healthcare database], TNF tumour necrosis factor.
Acknowledgements Medical writing assistance was supported financially by  Boehringer Ingelheim  and provided by  Helen Keyworth, Ph. D. , of  Nucleus Global , under the authors' conceptual direction and based on feedback from the authors. The authors wish to thank  Laura Luciani  (formerly  Boehringer Ingelheim France ) for her help in designing the study and initial contracting steps,  Julien Chollet  ( Boehringer Ingelheim France ) for his review of the manuscript, and  Pascaline Rabiega  ( IQVIA ) for her assistance in data analysis.
Funding The authors did not receive payment for the development of the manuscript. IQVIA received financial support from  Boehringer Ingelheim France  for the design, monitoring, data management and statistical analysis of the study. Writing, editorial support, and formatting assistance, under the authors' conceptual direction and based on feedback from the authors, was provided by  Helen Keyworth, Ph. D. , of  Nucleus Global , which was contracted and funded by  Boehringer Ingelheim International GmbH.
The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an intercalibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.
5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0. 05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0. 01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0. 04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.
Introduction The ESA CCI Soil Moisture dataset (CCI SM)  [1]  is the most extensive climate data record (CDR) of satellite soil moisture (SM) to date, and consists of merged SM retrievals from active and passive microwave satellite sensors all the way back to 1978  [2, 3]. In 2010, SM was recognized as an essential climate variable (ECV) by the Global Climate Observing System  [4]  due to its important role in both land-atmosphere feedbacks on several time scales, and vegetation dynamics on the global scale. In order to maximize the full development potential of a CDR such as the CCI SM, prioritization of long-term consistency and model independence is needed. If fulfilled, the CDR could be used as a stand-alone climate benchmark, as well as a means to assess stateof-the-art climate and weather models.
GPM GMI and TRMM TMI are also used, due to their higher revisit time close to the equator and the long time coverage (from 1997 for TRMM). However, due to their varying crossing times, only observations with a maximum difference of 3 h from the 1:30 observations are included. The K-band is only available in V-polarization on TMI and GMI, which impacts the inter-calibration activity for this frequency, as explained in the Methodology Section 3. 2. For GPM and TRMM, the dataset prepared by the GPM Inter-satellite Calibration Working Group (XCAL)  [27]  is used.
ERA5-Land ERA5-Land  [21, 22]  is an enhanced-resolution rerun of the land component of the ECMWF ERA5 climate reanalysis, and provided through the Climate Data Store (CDS) of the Copernicus Climate Change Services (C3S). ERA5-Land is available with an hourly time step and goes back to 1981. The volumetric soil water layer 1 (ERA5-Land SM) was used in the skill analysis of the L-band SM datasets and is representative for a 0-7 cm soil depth. Hourly observations closest to the SMOS and SMAP overpasses and within the 2010-2020 time period were selected and regridded from 0. 1 • to 0. 25 •. This was achieved by taking the average of all ERA5-Land grid points of which the center falls within the 0. 25 • box.
International Soil Moisture Network The International Soil Moisture Network  [23, 24]  is an international effort to collect and maintain a large database of in situ SM observations. This effort is coordinated by the Research Group Climate and Environmental Remote Sensing of the Vienna University of Technology and supported by ESA. All available SM data were extracted from the ISMN between 2010 and 2020. Sites and sensors were filtered to include only data measured at a maximum sensing depth of 0. 12 m, during a minimum time span of 100 days, with a minimum amount of overlapping observations of 50, and a maximum difference between observation times of 2 h for the satellite observation and in situ sensor. Networks that are used in the evaluation can be found in Table  2.
Inter-Calibration of High Frequency Observations Traditionally, inter-calibration of brightness temperatures from different sensors is carried out using linear regression  [27]  or bias correction  [64]. However, the method of least squares, a standard approach in regression analysis, does not lead to a stable solution when applied to the proposed set of AMSR2, AMSR-E, TRMM, GPM, FY3B, and FY3D. In particular the MPDI, a ratio between H/V-Polarization which is used by LPRM for the VOD retrieval, is sensitive to small changes between the datasets left after intercalibration based on linear least squares. As the unstable MPDI affects the ability to properly distinguish between the emission from the vegetation and the soil, this can have pronounced effects on the SM retrieval. Therefore, for this inter-calibration, we propose the following cost function to be minimized in the linear regression instead of a standard least squares approach: Err = ∑ RMSE TB H + ∑ RMSE TB V + ∑ RMSE MPDI (2) with: RMSE TB H/V = ∑ T t=1 TB s1 H/V -α × TB s2 H/V + β T (3) RMSE MPDI = ∑ T t=1 ( TB s1 V -TB s1 H TB s1 V +TB s1 H - (α×TB s2 V +β)-(α×TB s2 H +β) (α×TB s2 V +β)+(α×TB s2 H +β) T ( 4 ) where TB is the brightness temperature for either V/H polarization and from the base (s1) and calibrated (s2) satellite.
Evaluating Impact In order to understand the impact of using T mw instead of T mod , the corresponding SM mw and SM mod datasets are first compared to one another. This direct comparison will give insight in how and where the characteristics of the SM data change, or where they remain similar. Information is given on the temporal coverage (Cov. ), correlation (Corr. ), standard deviation (StDev), and mean. Secondly, in order to achieve a better understanding of how replacing T mod by T mw affects the skill of the datasets, both the SM mw as the SM mod data sets are evaluated using a direct comparison with ERA5 SM and in situ SM from the ISMN. Here, the correlation (Corr. ), bias, and unbiased root mean square error (ubRMSE) values are considered. Observations are only used when both SM mw and SM mod have a valid retrieval.
Inter-Comparison of SMmw to SMmod Figure  2  presents the global maps of the SM SMAP Descending (Desc) inter-comparison, with the results for SMmod in the middle, SMmw on the right, and the difference between the two (SMmw ₋ SMmod) on the left. The maps show a clear increase in revisit time for the SMmw dataset for all regions except the boreal regions, where a decrease is observed. On average, an increase of 0. 46 days is seen. For the mean and StDev of the SM, the boreal region and the tropics show a strong divergence, while in most regions the results remain similar. The correlation comparison As small inconsistencies might not appear in the wide range of the absolute values, the anomalies were also calculated. It is important to remark that the base period for the anomaly calculation is only from June 2002 onwards, in order to avoid spatial artifacts caused by the reduced TRMM coverage.
Inter-Comparison of SM mw to SM mod Figure  2  presents the global maps of the SM SMAP Descending (Desc) inter-comparison, with the results for SM mod in the middle, SM mw on the right, and the difference between the two (SM mw -SM mod ) on the left. The maps show a clear increase in revisit time for the SM mw dataset for all regions except the boreal regions, where a decrease is observed. On average, an increase of 0. 46 days is seen. Remote Sens. 2021, 13, x FOR PEER REVIEW 9 of 17 and SMOS. Spatial patterns as noted before are mostly found in all four datasets. A similar increase in revisit time is seen in all datasets, with an average of 0. 57 days for SM SMAP Asc, 0. 52 for SM SMOS Asc, and 0. 62 for SM SMOS Desc. The change in global mean varies between -0. 02 for SM SMAP Desc and 0.
02 for SM SMOS Desc. For the StDev, on average, no significant difference can be observed. The correlation between SMOS SMmod and SMmw increases much more quickly than for SMAP, however keep in mind that this is caused by a difference in spatial coverage between the two sensors due to RFI in SMOS. For the mean and StDev of the SM, the boreal region and the tropics show a strong divergence, while in most regions the results remain similar. The correlation comparison also clearly shows the changed data characteristics over these regions. For the boreal region, the SM mw is wetter, and shows a higher dynamic range, while for the tropical regions the opposite is seen, with the SM mw becoming dryer and less dynamic. Figure  3  summarizes the information from Figure  2  using cumulative plots of the global data and includes both ascending (Asc) and descending (Desc) datasets of SMAP and SMOS.
Spatial patterns as noted before are mostly found in all four datasets. A similar increase in revisit time is seen in all datasets, with an average of 0. 57 days for SM SMAP Asc, 0. 52 for SM SMOS Asc, and 0. 62 for SM SMOS Desc. The change in global mean varies between -0. 02 for SM SMAP Desc and 0. 02 for SM SMOS Desc. For the StDev, on average, no significant difference can be observed. The correlation between SMOS SM mod and SM mw increases much more quickly than for SMAP, however keep in mind that this is caused by a difference in spatial coverage between the two sensors due to RFI in SMOS.
Based on In situ Data Figure  6  shows the cumulative results against all available in situ networks from the ISMN. For SMAP, 224 and 235 in situ sensors are used for the Asc and Desc datasets, respectively. Due to the longer temporal coverage for SMOS, a total of 1045 and 893 in situ sensors were available for the analysis of Asc and Desc, respectively. No significant difference in correlation for SM SMAP Asc and SM SMOS Desc were seen, while SM SMAP.
Based on In Situ Data Figure  6  shows the cumulative results against all available in situ networks from the ISMN. For SMAP, 224 and 235 in situ sensors are used for the Asc and Desc datasets, respectively. Due to the longer temporal coverage for SMOS, a total of 1045 and 893 in situ sensors were available for the analysis of Asc and Desc, respectively. No significant difference in correlation for SM SMAP Asc and SM SMOS Desc were seen, while SM SMAP Desc and SM SMOS Asc recorded increases of 0. 01 and 0. 04, respectively. For the bias, only SMOS Desc shows a significant increase of 0. 04 m 3 m -3. The resulting ubRMSE values slightly increase overall, ranging between 0. 003 and 0. 005 m 3 m -3.
Inter-Calibration of Input Data The results from the inter-calibration activity between AMSR2, AMSR-E, GPM, TRMM, FY3B, and FY3D showed a stable consistent dataset of TB, suitable for integration into the L-band soil moisture retrievals. Only the K-band was less stable due to the lack of an H-channel on TRMM and GPM. The TBK,V anomalies, however, are still within ₋0. 5 and 0. 5 K, without a visible break between sensors. Therefore, the quality is deemed sufficient for its role in the filtering using the Van der Vliet et al. (2020)  [17]  method, as it does not use the H-polarization. The TBK,H anomalies are unstable, causing the observed jump in the MPDI values. This would only be problematic if it were used in SM retrievals with LPRM as the base frequency for either T or SM. Therefore, when using this methodology for future inter-calibration activities in SM retrievals, e.
Inter-Calibration of Input Data The results from the inter-calibration activity between AMSR2, AMSR-E, GPM, TRMM, FY3B, and FY3D showed a stable consistent dataset of TB, suitable for integration into the L-band soil moisture retrievals. Only the K-band was less stable due to the lack of an H-channel on TRMM and GPM. The TB K,V anomalies, however, are still within -0. 5 and 0. 5 K, without a visible break between sensors. Therefore, the quality is deemed sufficient for its role in the filtering using the Van der Vliet et al. (2020)  [17]  method, as it does not use the H-polarization. The TB K,H anomalies are unstable, causing the observed jump in the MPDI values. This would only be problematic if it were used in SM retrievals with LPRM as the base frequency for either T or SM. Therefore, when using this methodology for future inter-calibration activities in SM retrievals, e.
e. , 0 m 3 m -3 , in LPRM with the new temperature. Although the observations available for the ICTB is above one a day for much of the SMAP period, this can still lead to gaps when there are many simultaneous overpasses. In the future, this could be further improved by extending the sensors within the ICTB with the Special Sensor Microwave Imager Sounders (SSMIS) sensors, FengYun-3C, and, when data would be open and available, WindSat. The wetter and more dynamic boreal region, and simultaneously a dryer and less dynamic tropical region, is most likely caused by the simplified T mw approach that does not yet include a proper correction of a dynamic atmospheric optical depth. Varying atmospheric water vapor leads to these significant changes on both extreme sides of the spectrum. Keep in mind that for use within the CCI SM dataset, a scaling is applied to the SM during the merging  [2, 3] , which should nullify potential issues with the bias and StDev.
The differences as seen in the comparison against in situ are smaller than for ERA5, which is expected, and largely caused by the bulk of the ISMN data coming from the USA. In Sections 4. 2 and 4. 3. 1, this is also a region that does not show much change between the SM mw and SM mod. However, they do strengthen the assumption that T mw based on the ICTB can be used as a replacement for T mod for both 6 a. m. and 6 p. m. L-band SM retrievals without a loss in quality, especially as, in the USA, due to the large amount of meteorological and in situ observations available for assimilation, LSM also performs better than average. With the future launch of CIMR, this skill increase from using T mw is expected to increase further, as then there will be simultaneous observations for L-band, and the higher frequencies needed for T mw and filtering, although the period between 2010 and 2028 will still need to be covered using this, or a similar, method.
Conclusions This study presents the use of microwave-based temperature (T mw ) and filtering input for L-band soil moisture retrievals as a viable alternative to the land surface models currently used as temperature input. Although SMOS and SMAP do not observe in the higher frequencies needed for the T mw and filtering, several satellites with those observations have been active throughout the temporal coverage of both L-band missions, and can be combined into a single inter-calibrated brightness temperature (ICTB) dataset for this purpose. The ICTB consists of observations from AMSR2, AMSR-E, FY3B, FY3D, GPM, and TRMM, which are merged using a set of linear regressions that use a minimization function that also penalizes for errors in the MPDI. Combined, more than one observation per day are available in most of the L-band period.
However, due to the occurrence of simultaneous overpasses, which are also crucial for a correct merging, this does not guarantee a full coverage. The resulting ICTB does not have any breaks in time, and anomalies globally vary naturally between ±0. 75 K. In order to evaluate the impact of using T mw on the characteristics of the soil moisture retrievals, the SM mw and SM mod were compared to one another. This showed that the revisit time between the SM datasets, increased only by 0. 5 days on average. This, surprisingly, also includes the 6 p. m. retrievals that use a combination of 1:30 a. m. and p. m. observations. In general, when comparing the SM mw to SM mod , similar mean and StDev values are observed, with correlations of >0. 9. There are two major exceptions to this: the boreal regions become wetter with a larger StDev, while the tropics become dryer with a smaller StDev.
The skill of the SM mw was evaluated against two data sources, first the ERA5-Land model and second, the in situ observations from the ISMN. The average correlation against ERA5-Land improved by 0. 04, 0. 05, and 0. 06 for SM SMAP Desc, SM SMAP Asc, and SM SMOS Asc, respectively, with only SM SMOS Desc having a small decrease of -0. 01. Against in situ observations, the only significant change in correlation was an increase of 0. 04 for SM SMOS Asc. The less pronounced results with in situ data can be attributed to the majority of sensors being located in the USA. Further improvements can be expected when addressing the atmospheric effects on the T mw retrieval and by ingesting more satellites, e. g. , SSMI and FY3C. These results show that, for both the 6 a. m. and 6 p. m. L-band retrievals, the use of T mw and microwave-based filtering is not only feasible as an input, but can also contribute to an improvement in skill at the cost of a slightly reduced temporal coverage.
summarizes the information from Figure 4 using cumulative plots of the global data and includes both Asc and Desc datasets of SMAP and SMOS. SM SMAP Asc achieves an average improvement in correlation of 0. 05, SM SMOS Asc of 0. 06, and SM SMOS Desc sees a small decrease of -0. 01. Remote Sens. 2021, 13, x FOR PEER REVIEW 11 of 17.
Materials and Methods The Nordic Centre of Excellence (NCoE) CLINF  [36]  investigates the effects of climate change on the prevalence of infectious diseases in humans and animals in northern regions. The project has gathered information on the prevalence and incidence of potential CSIs (Climate-Sensitive Infectious diseases) in humans and animals in the north, with the aim of characterising the environmental envelopes under which disease vectors can thrive and propagate. The datasets from CLINF used in this paper are summarised in Appendix A. The presence and spatiotemporal variations of vector-borne pathogens and the vector and host distributions are not directly addressed here. The focus is on the distribution of vegetation types coupled with climate change as providing potential drivers of changes in the incidence variations in two tick-borne diseases, Lyme borreliosis and tick-borne encephalitis.
Data Analysis Aspects This paper uses data for the time period 1985-2016 for which the CLINF project gathered yearly incidence rates of BOR and TBE in national districts in Norway, Sweden, Finland, and districts in western Russia. Corresponding land cover data and climate variables from global atmospheric reanalysis (ESA-CCI land cover and ERA-interim data) with appropriate transformations, e. g. , maximum summer temperature and land cover conversion to plant functional type (PFT) (Appendix A), are used as explanatory variables. For each disease, each district time series of incidences was smoothed using a nonparametric local regression (loess method  [37] ) and then visually assigned to one of the fve classes of simple patterns: well-increasing (WI), increase-plateau (IP), increasedecrease (ID), plateau (P), and decrease-plateau (DP).
A range of statistical methods is applied to give insights into the associations between variables, to decompose variations, and to model log-linear regressions, e. g. , simple boxplots, surface interpolations, multiway correspondence analysis, and Gaussian and negative binomial regression models. Land cover changes over time and differences between districts were frst assessed using multiway methods  [38, 39]. The generic multiway method decomposes the variability in a multi-table dataset (i. e. , one represented as a k-dimensional array) in an approach similar to Principal Component Analysis (PCA) of a two-dimensional data table. This allows, for example, for an analysis of the major contributors to the variation in land cover changes over time for the districts studied and produces a spatial map, a time series, and a vegetation weighting component for each extracted fraction of total variability.
Only 13 WI districts are common to both TBE and BOR, i. e. , 57% of TBEs and 37% of BORs (see also Figure  2 ). Table  1. Distribution of incidence pattern groups for BOR (rows), TBE (columns), and total incidence (BT) (10 districts in Norway had no TBE reports). What differentiates these groups and their changes is at the forefront of our understanding of the qualitative and quantitative controls of disease incidence. However, the health data are at the district scale, which means that only the district name was recorded for each cases; consequently, when the incidence is high for a particular district, this does not mean that, everywhere in the district, a large number of new cases were recorded and, the larger the district, the less likely you can pinpoint where cases occurred. For example, Franz Josef Land is part of the Arkhangelsk district (Oblast) with a large continental area at the latitude of Finland, as is the much more northern part of Franz Josef Land and Novaya Zemlya (Figure  2  left panel).