dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2002). Indeed, the analysis of data that allows accounting for variations in inter-observer group size estimates is not always straightforward, and coordinators of monitoring programs are sometimes skeptical F I G U R E 7 Mean NRMSD according to the number of years of monitoring for the trend -7. 4% per year and for all scenarios with only temporal changes in observers (100 simulations with random noise for each of the 24 observers for the scenario O1 T1 and for each of the 100 random selections of observers for scenarios O1 T5 and O1 T25) about accounting for it because they find the analyses too complex. One example is the Pan-European Common Bird Monitoring Scheme (PECBMS), which produces national and supranational indexes by using the TRIM software (TRends and Indices for Monitoring data;  Klvaňová & Voříšek, 2007).
In this context, it is preferable to favor a single observer for all sites where only a few sites need to be sampled frequently, especially if spatial autocorrelation in population dynamics is high  (Rhodes & Jonzén, 2011). To achieve an optimal balance between cost-effectiveness and precision of wildlife monitoring programmes, our study showed that the most important thing to avoid is temporal change in observer identity. Indeed, a greater frequency of observer change gradually increased the period necessary to detect a significant trend, and decreased the precision of estimated trends for a given monitoring duration, although the direction of the trend was generally adequately detected (See also supporting 1). Wildlife management however depends on long-term monitoring databases, especially so for species with longer generation times  (White, 2019).
Alternative methods based on imagery have been gaining ground over the last decades  (Akçay et al. , 2020; Hodgson et al. , 2018; Lyons et al. , 2019) , and more particularly with automated computer vision software  (Chabot & Francis, 2016; Hollings et al. , 2018). However, computer vision software may work under some particular conditions but they are generally biased and known to fail in several situations  (Chabot & Francis, 2016; Hollings et al. , 2018)  even if considerable improvements are underway  (González-Villa & Cruz, 2019). We expect that the continuing technological developments (sophisticated image analysis software and advances in camera and drone technology) in the analysis of remotely sensed data will control the error in estimating groups' size in many more situations than at present, where observers remain the most widely used means to monitor animal populations.
AXL inhibition in non-malignant cellular components of the leukemic microenvironment, unleashes a potent anti-leukemic immunity in vivo and synergizes with standard of care therapy to eradicate leukemia To determine the translational relevance of these findings, we pharmacologically interfered with AXL in vivo, using Bemcentinib (also known as R428 or BGB324)  (56) , an orally available and selective inhibitor for AXL currently undergoing clinical evaluation in cancers in which AXL expression, in tumor cells, is thought to contribute to disease pathogenesis (e. g. NCT02488408; NCT02424617; NCT02922777). In this study, by using leukemia models that do not express AXL protein (Fig. S2J ), we primarily used Bemcentinib to evaluate its potential immune modulatory effects on the non-malignant components of the leukemic microenvironment that express AXL, namely myeloid cells (Fig.
Because of its selectivity towards AXL, Bemcentinib triggers robust anti-leukemic immunity without inducing the autoimmune manifestations that are reported in mice with deletion of all three TAM receptors  (60). Bemcentinib has so far showed favorable safety data in three phase II clinical trials in AML (NCT02488408; NCT02424617; NCT02922777) with interim reports providing evidence of TCR repertoire diversification in some patients  (61) , thereby hinting towards a potential immune modulatory effect. In light of our work, it would be important to extend such studies to other hematological malignancies such as Ph + B-ALL, and more specifically evaluate the potential tumor extrinsic immune modulatory function of AXL targeting compounds, including Bemcentinib and other compounds such as Gilteritinib, a dual FLT3/AXL inhibitor that has shown efficacy in FLT3 mutated relapsed or refractory AML  (62)  By demonstrating the key immune suppressive function of the GAS6/AXL axis in leukemia associated macrophages, this work provides a conceptual advance in our understanding of the molecular mechanisms underlying immune suppression in leukemia and can effectively be translated into a treatment strategy that not only empowers the patient's own immune system to fight leukemia but also be harnessed, in specific contexts, to overcome the major issue of primary resistance to PD1 checkpoint blockade.
RSEM-normalized RNA-seq expression data of 173 primary AML samples from The Cancer Genome Atlas (TCGA-LAML)  (67)  and corresponding clinical data were downloaded using the cBioPortal for Cancer Genomics (https://www. cbioportal. org). The B-cell lymphoma dataset (GSE4475, n=159) was retrieved from The SurvExpress database  (68). Both datasets were imported into the R2 Genomics Analysis and Visualization Platform (http://r2. amc. nl) and subjected to a KaplanScan analysis to stratify patients according to GAS6 expression using the "scan" mode to define the best expression cutoff. Results were exported and plotted using GraphPad Prism 7 software and survival analysis performed using the log-rank (Mantel-Cox) test.
Propidium iodide (Cas No. 25535-16-4, Sigma-Aldrich) or the AF700 fixable viability dye (BD bioscience; cat 564997) was used for live-and dead-cell discrimination. Gating strategies are depicted in main and Supplementary Figures. FACS data were acquired on a BD LSRFortessa™ (BD Biosciences). Fluorescence-activated cell sorting (FACS) was done using a FACSAria™ Fusion (BD Biosciences, Heidelberg, Germany). BD FACS Diva software version 8. 0. 1 was used for data collection. FlowJo version 10. 4. 2 was used for data analysis. Post-sort purity was >95% and determined by re-analysis of sorted Cells.
10X Genomics demultiplexed sequencing reads were obtained using cellranger mkfastq (version 3. 1. 0) from 10X Genomics and used to align the reads to the mouse genome (refdata-cellranger-mm10-3. 0. 0). The data from all samples were loaded in R (R version 3. 6. 2) and processed using the Seurat package (version 3. 2. 0) (69). Cells with at least 1000 UMI's per cell and less than 20% mitochondrial gene content were retained for analysis. To increase our analytical power, data from all mice, regardless on genotype or disease status, were initially combined into a single set leading to a total cell number of 36000 cells. Merged dataset was normalized for sequencing depth per cell and log-transformed using a scaling factor of 10,000. The most variable genes in the dataset were identified and the top 2000 was used for dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique (70) followed by density-based clustering using the Seurat tool  (69).
Histological Analyses Tissue samples (spleen, liver, brain and femur) fixed in ROTI ® Histofix 4% (#P087. 3, Carl Roth), dehydrated and embedded in paraffin. Bones were decalcified in 0. 5 M EDTA PH7. 4 (#ED2SS, Sigma-Aldrich) at 4°C under constant agitation, for 1-2 weeks, prior to embedding. Paraffin sections (3μm) were subjected to either H&E staining or immunohistochemistry staining on a Leica Bond-Max using the detection systems Bond Polymer Refine Detection (Leica) for anti-rabbit and anti-mouse antibodies or Bond Intense R Detection (Leica) for anti-goat antibodies. Primary antibodies were diluted with the Bond Primary Antibody Diluent (Leica). Anti-human GAS6 (1:100, rabbit polyclonal, HPA008275; Sigma-Aldrich), anti-human CD10 (1:100, ORG-8941, monoclonal mouse clone 56C6, Novocastra); anti-mouse PD1 (1:200, goat polyclonal, R&D, AF1021), anti human PDL1 (1:200; rabbit monoclonal, Cell Signaling, 13684).
Antigen retrieval was performed within the detection system using a citrate or EDTA (CD10) buffer solution. Anti-GFP (1:1000, polyclonal goat, ab6673; Abcam). Antigen retrieval and a secondary antibody staining were performed within the detection system using an EDTA buffer solution and a biotinylated Rabbit anti-Goat IgG Antibody (BA-5000; Vector Laboratories), respectively. MPO staining was performed using the ready to use Rabbit Polyclonal Antibody (Myeloperoxidase (MPO) Ab-1, #RB-373-R7, Thermo Scientific). Slides were examined with a Zeiss Axio Imager 2 microscope and pictures were taken using the AxioVision SE64 Rel. 4. 9 software. Analysis depicted in Fig. S2M  was carried out using QuPath v0. 2. 3 digital pathology software  (73).
Acknowledgements We thank  Diana Passaro  and  Owen Williams  for the MLL-ENL tomato + leukemia cells,  Jan Rehwinkel  for the Sting deficient bone marrow cells,  Lisa Sevenich  for the Socs3 floxed mice,  Sophia Thevissen  for assistance with animal monitoring and processing,  Birgitta E. Michels  for help with the analysis of the Immunological Genome Project data,  Boris Brill  and  Margit Wagenblast  for assistance with animal applications,  Stefan Stein ,  Annette Trzmiel  and  Malte Paulsen  for assistance with cell sorting,  Nathalie Groen  for assistance with scRNA sequencing data analysis. We are thankful to  Florent Ginhoux  and  Florian R Greten  for helpful discussions and feedback and to BergenBio for providing Bemcentinib for this study. The  Klinikum rechts der Isar/Technical University Munich (MTBIO)  and the  Klinikum Carl Gustav Carus MK1 /Technical University Dresden  biobanks are acknowledged for support with collection of human bone marrow biopsies.
Financial support Research support was provided by the  European Research Council  to H. M. ( ERC-Stg-639795 ), the  EMBO   Young Investigator program  to H. M. (# 4027 ), the  LOEWE Center Frankfurt Cancer Institute (FCI)  funded by the  Hessen State Ministry for Higher Education, Research and the Arts  to H. M. , the  German Research Council  ( DFG ME4214/2-1  within  SPP 2084 ) to H. M. and the  German Cancer Consortium (DKTK) Joint Funding (CHOICE consortium)  to H. M, U. P and K. G. E. C was supported by a fellowship from the  German José Carreras Leukemia Foundation  ( DJCLS 04FN /2018 ). K. G is supported by the  German Research Council (DFG ,  SFB 1243  project  A09 ) and the  German Jose Carreras Leukemia Foundation  (DJCLS,  14 R/2018 ).
ACKNOWLEDGMENTS We thank  Nicolas Ruffini  for support in analyzing the photostimulation artifact,  Nico Bu  ¨rger for viral injections, and  Marcel Kegel  for technical consulting. This study was supported by the  German Research Council  ( DFG CRC 1193 ) and the  German Federal Ministry of Education and Research (BMBF Eurostars E! 10324 FEMSCOPY) , and the  Bo ¨hringer-Ingelheim Foundation.
Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information.
In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability.
Overview of the COSMOS-Europe sites For the COSMOS-Europe dataset presented here, CRNS data from 66 sites in 12 European countries (in alphabetical order: Austria, Denmark, France, Germany, Greece, Italy, Norway, Poland, Spain, Switzerland, Turkey, United Kingdom) were collected. The geographical distribution and location of the COSMOS-Europe sites is shown in Fig. 1. The key environmental and soil-related physical properties at the sites are given in Table  1. The key physical and soil-related site properties relevant to CRNS processing are summarized in Table  2. The COSMOS-Europe sites cover eight climatic zones (following the Köppen-Geiger climate classification;  Beck et al. , 2018) , with the vast majority of stations located in the humid continental climate zone (n = 34) and in the temperate oceanic climate zone (n = 21).
Methods Measured neutrons are a proxy for soil water content, but systematic factors and stochastic effects also influence the neutron signal. Research in the last decades has led to a profound understanding of these influencing factors and has fa-cilitated a more accurate extraction of the soil moisture signal from the cosmic-ray neutron data. The processing framework is described below, while its technical implementation is supported by public tools and software libraries dedicated to CRNS research, e. g. , Corny by  Schrön (2021)  and Crspy by  Power et al. (2021).
Data pre-processing In a first step, all datasets, i. e. , raw neutron counts and supporting data, were aggregated to hourly time steps. Subsequently, following  Zreda et al. (2008) , a running 24 h average with a minimum of 12 measurements in the smoothing window was used to reduce the inherent noise of the raw neutron counts and to reduce the measurement uncertainty. To ensure data consistency, the raw neutron counts were screened for data quality. Suspicious neutron count rates (N raw ) that fulfill one of the following conditions were flagged. -Extreme single outliers: N raw <50 or N raw >10 000 counts per hour (cts h -1 ). -Positive suspicious peaks: N raw >24 h moving average +2 times the standard deviation of the 24 h rolling sum. -Negative suspicious peaks: N raw <24 h moving average -2 times the standard deviation of the 24 h rolling sum.
We assumed that soil organic matter was 2 times the organic carbon content and assumed densities of 1. 4 and 2. 65 g cm -3 for the organic matter and the other soil minerals, respectively. Missing data on soil texture, porosity, and organic carbon were taken from the global raster-based soil dataset SoilGrids  (Hengl et al. , 2017). It is important to note that the published dataset still includes the original and flagged data, while suspicious records were not included in the further data processing and analysis (see Fig. A5  for the used data flags). In this way, users can apply their own pre-processing techniques to the raw neutron count data. The final soil moisture product is cleaned from all Earth Syst. Sci. Data, 14, 1125 Data, 14, -1151 Data, 14, , 2022  https://doi. org/10. 5194/essd-14-1125-2022 negative influences to avoid inexperienced users using unrealistic soil moisture data.
In situ reference soil data For the calibration, we used in most cases available information on gravimetrically measured soil moisture from soil samples taken within the CRNS footprint. The soil samples were weighted vertically according to  Schrön et al. (2017) ; i. e. , for each sample at depth d and penetration depth D, we evaluate the weight in the representative sample volume (d 1 to d 2 ) to generate the profile average soil moisture: θ profile = θ d w d w d , where w d = d 2 d 1 w d dd ∝ W d 1 -W d 2. (6) In addition, we applied horizontal weighting of the vertically averaged in situ soil moisture according to  Schrön et al. (2017). For this, regions of equal contribution (annuluses of 20 % quantiles) to the neutron signal were defined depending on the local conditions (i. e. , atmospheric pressure, air humidity, average soil moisture) that influence the spatial sensitivity of the CRNS.
Soil moisture uncertainty The statistical uncertainty of CRNS-derived soil moisture scales with the number of counts in a given period. However, this count rate is inversely related to soil moisture, so drier soils result in more accurate measurements  (Desilets et al. , 2010; Bogena et al. , 2013). In addition, the size of the CRNS detector determines the count rate, i. e. , a larger detector volume improves the count statistics and thus reduces the uncertainty of the soil moisture product  (Weimar et al. , 2020; Schrön et al. , 2021). Different neutron detectors with different sizes and efficiencies are used in this study, so it is important to consider the CRNS-specific uncertainty (e. g. , when using the data for validations). Due to the non-linearity of the neutron-soil-moisture relationship, the propagated uncertainty ±σ θ is highly asymmetric  (Iwema et al.
Implementation of the data processing The raw neutron counts and meteorological data were converted into a uniform data structure (Fig. A5b ) and stored in a database within the decentralized data infrastructure TEODOOR (TEreno Online Data repOsitORy,  Kunkel et al. , 2013). The data pre-processing, corrections, calibration, and uncertainty estimation were implemented in the programming language Python. These scripts were applied to the raw data stored in the database using Node-RED, a graphical tool for deploying workflows (Node-RED, 2021). Node-RED offers the possibility to connect different data flows in a simple way, using so-called nodes. Each node has a defined and unique task. When data are transmitted to a node, the node can process these data and then transmit them to the next node.
In this way, different corrections or other implementations in the data post-processing can be added or removed individually. As an interface for accessing the data in the TERENO database, the Sensor Observation Service (SOS) of Open Geospatial Consortium was used. Here, the data are processed by a separate proxy that forwards the requests to a virtual Python environment. In the last step, the processed data were written back directly to the database via email or SOS. The raw data as well as the processed data are accessible via the TERENO Data Discovery Portal (DDP) at http://www. tereno. net (last access: 2 March 2022). The data portal enables the query, visualization, and access to data and metadata of the stations presented in this paper. Additionally, detailed information on each CRNS station is provided in the metadata (Fig.
Spatiotemporal occurrence of drought events in Europe The provision of a continental-scale dataset on soil moisture dynamics opens up numerous possibilities for analysis, especially with respect to large-scale climatic and hydrological applications. In the following, we present first analyses on the spatiotemporal occurrence of drought events in Europe based on the processed time series of CRNS soil moisture. Figure  3  visualizes the results of the CRNS soil moisture processing for the COSMOS-Europe sites. The CRNS soil moisture (left subplot) shows strong temporal variations as well as large differences between the COSMOS-Europe sites. Due to these strong variations in CRNS soil moisture, similarities in the absolute values are difficult to discern, e. g. , the impact of large-scale drought events on CRNS soil moisture.
However, when comparing the three events, it becomes evident that 2018 had more locations with pronounced extremes and that these occurred predominantly in the climate zone Cfb, while 2018 was not notably different from other drought years in the monthly soil moisture averages shown in Fig. 4  (upper subplot). This demonstrates again the advantage of normalized soil moisture quantiles for a more in-depth analysis of extreme events. Finally, we investigated whether the CRNS data allow us to draw conclusions about longer-term trends in soil moisture in Europe. For this, we contrasted monthly mean soil moisture from 2014-2017 with monthly mean soil moisture from 2018 to 2021 in Fig. 5 , using the 26 sites fully covering this period. From Fig. 5 , it is evident that as of 2018, soil moisture was lower not only in the summer months but throughout the year.
How representative and accessible is the soil moisture data? The representativeness of the individual stations for the depicted land use type and geographical location is relevant, especially with regard to the validation of large-scale model applications of remote sensing products, which usually show coarser spatial resolutions and correspondingly "averaged" representation of site properties (e. g. ,  Colliander et al. , 2017; Montzka et al. , 2020). In a few cases, the COSMOS-Europe sites represent larger site heterogeneity. This is reflected in particular in the resulting larger variability of soil moisture in the in situ calibration data measured for the individual sites (see Fig. A4 ). An example is the CRNS station at the Fuerstensee site in Germany  (Rasche et al. , 2021). The footprint represented by the CRNS measurement at this site includes a sand lens in the center of the footprint, which is surrounded by peat soils.
The resulting heterogeneity, particularly of soil properties such as bulk density, soil organic matter content, and lattice water, challenges the harmonized data processing applied for COSMOS-Europe and leads to greater uncertainty in the derived soil moisture product, which is not easy to quantify. While aspects such as soil heterogeneities or varying land use can be of great importance for local or CRNS methodological questions, this is rather an obstacle for large-scale questions. Especially with regard to the future development of the European network of CRNS stations, attention should be paid to the selection of sites that guarantee a high representativeness and homogeneity. With respect to the   use of COSMOS-Europe data to derive conclusions about continental-scale trends in soil moisture, it is decisive that the network ensures the most representative coverage of key environmental and geographic gradients throughout Europe (e.
g. , altitude, climate, landforms, geology). This is currently the case only to a limited extent (see also Fig. 1 ). The clear majority of stations are concentrated in central Europe, while Scandinavia, eastern Europe, or the Mediterranean region in particular are covered by only very few stations. This limits the interpretability of the data, especially with regard to comparisons between different climate zones. Another important question in this context is whether observations at a limited number of points can provide regional improvements in the prediction of hydrologic states and fluxes. For example,  Baatz et al. (2017)  assimilated measured soil moisture data from a CRNS network into the area-differentiated land surface model CLM 4. 5  (Oleson et al. , 2013)  and showed that updating states and hydraulic parameters leads to better regional hydrologic predictions.