dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work received funding through the  Helmholtz Association's Initiative and Networking Fund  (grant agreement no. VH-NG-1531 ), the  Helmholtz Association research program  " Atmosphere and Climate ". MS acknowledges funding from the  German Research Foundation  (grants  SCHN 1140/3-1  and  SCHN 1140/3-2 ). FW acknowledges funding by the  German Research Foundation  ( DFG  grant  JA 2818/1-1 ). The LaMP acknowledges the support of the ( MPC2-EAn  n • 1224) project funded by the  Institut Polaire Paul Emile Victor (IPEV)  and the  Pollution in the Arctic System (PARCS)  project funded by the  Chantier Arctique of the Centre National de la Recherche.
Data collection We conducted a retrospective analysis of transplant data registered with the Lymphoma Working Party of the EBMT and the local registries of Samsung Medical Center, Seoul, South Korea; and the First Medical Center of PLA General Hospital, Beijing, China. In total, data were provided by 69 transplant centers in Europe and Asia. For a full list of contributing centers and patient numbers see Supplemental Table  1. Details on the standardized data collection process and quality management can be found elsewhere  [22]. All accredited EBMT transplant centers are required to obtain written informed consent prior to data registration with the EBMT following the Helsinki Declaration of 1975. The two cooperating Asian centers signed a project-specific joint controllership agreement confirming that the participating center followed EBMT rules and regulations.
We collected data for all consecutive adult patients (≥18 years) diagnosed with NKTCL (aggressive NK-cell leukemias and chronic lymphoproliferative disorders of NK cells excluded) regardless of prior treatment and remission status who received allo-HSCT either as first transplant or after a previous autologous HSCT had failed. Data between 2010 and 2020 were analyzed in order to include patients treated state-ofthe-art and to ensure sufficient follow-up time. We included patients allografted with BM and/ or PB from any donor including related, unrelated, or haplo-identical donors. Patients transplanted with cord blood (n = 2) were excluded from this analysis. Baseline information, transplantation characteristics as well as outcome data were extracted from the EBMT registry. Identical forms were provided by centers not being EBMT members.
Demographics were compared between groups using the chisquared test or Fisher's exact test for categorical variables and the Mann-Whitney U test for continuous variables. Univariate analyses were performed using the log-rank test for PFS and OS, while Gray's test was used for CI. Multivariate analyses were performed using the Cox proportional-hazards regression model. Results were shown as hazard ratio (HR) with a 95% confidence interval (95% CI). All tests were two-sided, and the type I error was fixed at 0. 05 for factors associated with time-toevent outcomes. All analyses were performed using R statistical software version 4. 2. 2 (available online at http://www. R-project. org) and IBM SPSS version 26. 0 (SPSS Inc. , Chicago, IL).
Participant observations (secondary endpoints) These observations will be made by two members of the social psychology research team. They are called "participant" observations because the researchers will be immersed in the field for exploratory purposes but will be involved in a measured way in the life of the group, in accordance with the methodology of peripheral participant observation  [60, 61]. An observation grid will be drawn up in advance and will be adjusted in the course of the study based on the processing of the first qualitative results. The choice of two researchers is justified by a (1/ week) X X X X X X PHYSICAL MEASURMENTS Height X Weight X X X X X X Fat/lean mass X X X X X X Amplitude of arm on side of treated breast X X X Step test 3 min X X X Handgrip strength X X X Quadriceps strength (dynamometer) X X X SCALES AND QUESTIONNAIRES QLQ-BR23 X X X R-PFS X X X X X X CART-Q X X PTGI X X INTERVIEWS Semi-directive interviews X X X Participant observations X X X X X X concern for reliability and cross-checking of the data thus collected.
Data collection and flow chart The physiological tests, the questionnaires and the interviews following inclusion will be carried out in the cancer centre; they will then take place before training sessions in the water sports centre as set out in the flow chart (Table  1 ). The semi-directive interviews will be recorded on Dictaphone, after the patient has given written agreement. The observations will take place during each session in the programme. The patient's whole pathway is summarized in Fig. 2. Several undesirable events will be considered as related to the research: cardiac accident, traumatic injury requiring hospitalization, risks related to aquatic activity. Adverse events will be reported by the coaches during training and collected in the eCRF dedicated to the project. In the event of a serious adverse event, the investigator will inform the promotor centre, which will write a report and inform the competent authorities, in this case the national drug safety agency.
The global change will be evaluated by the global slope estimated by the model and framed by a 95% bilateral confidence level. -Interviews and observations: The semi-directive interviews will be transcribed and subjected to inductive qualitative analysis using the NVivo software on the methodological basis of coding by category  [63, 64]. The observations will be subject to a specific coding within a grid. The frequency of the selected behaviours can be compared with the qualitative data and processed using the NVivo software.
4 K at the 200 hPa level. Larger biases are found for relative humidity with up to -5. 5 % at the 250 hPa level. To remove the systematic biases, which mostly tend towards too dry and cold, a bias correction method, based on a multivariate quantile technique, is proposed and applied. After the correction the bias in temperature is reduced to below 0. 1 K and in relative humidity to below -1. 5 %. To estimate the representation of contrail occurrence in ERA5, data points from IAGOS as well as corrected and uncorrected data points from ERA5 of temperature and relative humidity are flagged for contrail formation using the Schmidt-Appleman-criterion. In the IAGOS data set 39. 2 % and 16. 9 % of the samples represent conditions for non-persistent contrails and persistent contrails, respectively.
Here, the QM is trained on 3. 5 years of IAGOS observations and collocated ERA5 data of temperature and relative humidity. The QM method is then applied on 5. 5 years of ERA5 data and compared with IAGOS. Subsequently, we determine the impact of the correction on the representation of non-persistent and persistent contrails with respect to IAGOS. In case of false classifications the underlying differences in simulated and observed temperature and relative humidity are determined to identify systematic shortcoming in ERA5. Subsequent to this introduction, Sec. 2 describes the data and methods used in this study. After that the results are presented in  Sec. 3 and summarized in Sec. 4. The appendix provides detailed information about the Schmidt-Appleman criterion and a IAGOS data analysis.
9 % to 53. 0 %. Hence, a correlation between CF ERA and CF IAGOS exists, which demonstrates the representativeness of the IAGOS observations. For the lowest bin (CF ERA < 0. 2) the distributions of r ice from ERA5 and IAGOS are dominated by dry air leading to small peaks close to r ice = 0 % and are otherwise flat (Fig. 6a, b ). The corresponding mean bias ∆r ice between original ERA5 (orange) and the IAGOS observations is -4. 8 %. The QM-corrected values are close to the observations with a remaining mean bias of -2. 2 %. The large majority (88. 1 %) of all data belongs to this category. For intermediate CF ERA between 0. 2 and 0. 6 (Fig. 6c-f ), r ERA,ice is slightly moister than observed by IAGOS leading to ∆r ice of 5. 1 % (0. 2 <= CF ERA < 0. 4) and 2. 0 % (0. 4 <= CF ERA < 0. 6). In these two CF ERA bins the QM-correction exaggerates r ice , resulting in ∆r ice of 355 12.
Along-track contrail formation potential and the effect of applied corrections Along-track time series of uncorrected and corrected ERA5 data and IAGOS measurements are flagged for non-persistent (NPC) and persistent (PC) contrails, and reservoir (R) conditions using the method described in Sec. 2. 4. All data points that do not belong to any of the categories are flagged for no contrail formation (NoC). Considering all data points from the years 2015 to 2021 at p-levels 250-200 hPa, it is found that 39. 2 % of the IAGOS observations show a potential for NPC formation. PC appear to be less frequent with about 16. 9 % and R conditions are rare with an occurrence of 1. 9 % only. Using the original along-track ERA5 output, the contrail formation potential for NPC, PC, and R is estimated at 40.
8 %, 17. 5 %, and 1. 8 %, respectively, which is close to the values identified using IAGOS data. with the IAGOS data set, no such aircraft-performance data are available. Secondly, the way flight distance is counted as contrail-forming is different between the two studies. In this study, the SAc accounts only for thermodynamic properties. Subsequently, the impact of corrected T cor ERA and r cor ERA,ice on the along-track classification of NPC, PC, and R is investigated. The individual contributions of T and r are separated by applying the QM-correction separately on T and r. The scaling method from T22 is shown as a benchmark. When applying the QM correction to T ERA only, the fraction of NPC decreases from 40. 9 % to 38. 6 % and for PC from 17. 5 % to 16. 6 %, respectively. This is in good agreement with the IAGOS observations (see Table  4 ).
89 to 0. 87. In the NPC category the HR, FA, and accuracy remained almost constant. For R conditions HR, FA, and ACC remained unchanged after the correction. Consequently, the correction had no significant effect on the classification of along-track occurrence of contrail formation. Using the original ERA5 output it was found that 97. 1 % of the data points were below the respective T crit , while after the correction 96. 3 % of the samples were below T crit. Before the QM-correction of relative humidity, 58. 2 % of the data samples exceeded the respective r crit , while after the correction only 54. 9 % of the data points still exceeded r crit. The contingency table further revealed that 81. 9 % of the data samples were coherently flagged in IAGOS and ERA5 after QM-correction. In these cases almost no biases in temperature and relative humidity between IAGOS and ERA5 remain.
The remaining 18. 1 % of the data points, which were incorrectly classified for NPC, PC, and R conditions by ERA5, are caused by remaining biases in temperature and relative humidity of varying magnitude. The misclassifications were insensitive to the applied correction method. The magnitude of the individual bias contributions -temperature or relative humiditydepend on the discrepancy of the classification. False classifications of NPC vs PC were primarily dominated by a relative humidity bias, while false classifications of NPC vs NoC were dominated by a bias in the temperature. However, the majority of misclassifications were caused by combinations of temperature and relative humidity biases with ERA5 either being coldmoist or a warm-dry biased. Furthermore, the relative humidity bias between IAGOS and ERA5 was found to depend on the temperature.
Acknowledgements. This research has been supported by the  French Ministère de la Transition écologique et Solidaire  ( N  •  DGAC 382 N2021-39 ), with support from  France's Plan National de Relance et de Resilience (PNRR)  and the  European Union 's NextGenerationEU. NB, AP and YL acknowledge funding from the  Horizon 2020 project ACACIA  (grant no. 875036 ). Furthermore, we acknowledge the support from  Garry Lloyd  for post-processing the IAGOS P1 data of the single particle backscattering optical spectrometer.
Code availability. The python code that was used to perform the analysis and the quantile correction is provided following: https://doi. org/10. 5281/zenodo. 8418565 Data availability. ERA5 data can be obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) data catalog at https://doi. org/10. 24381/cds. f17050d7  (Hersbach et al. , 2023). The IAGOS data can be downloaded from the IAGOS data portal at https://doi. org/10. 25326/20  (Boulanger et al. , 2020).
, 2019; Wurz et al. , 2022). Within the agroforestry design process, linear woody features, commonly called tree belts (TBs), are often used  (Castle et al. , 2022; Nair, 1993). The design of TB networks can only be effective if they provide the expected ESs. This condition can be met if the TB design is carried out on a large spatial scale, which requires the use of a high spatial resolution of input datasets containing both TB characteristics at the level of individual trees and many environmental factors. Therefore, the effective analysis of many spatial thematic layers requires the use of a geographical information system (GIS) approach. In recent years, GIS solutions, multicriteria analysis and remote sensing have played increasingly important roles in the TB design and ES assessment provided by TBs.
Plugin development and structure Considering the interest in TB design by the broad audience of landscape policy makers, we decided to develop the TBD plugin in the free and open source QGIS3 software  (QGIS Development, 2022). The plugin development was conducted using Python scripting language (ver. 3. 8) in the QGIS API (https://www. qgis. org/api/). Our plugin template was created using the Plugin Builder (https://plugins. qgis. org/ plugins/pluginbuilder/). The Qt framework (https://doc. qt. io/) was used to create the graphical user interface, which is available via the QGIS API and is compatible with the QGIS software. We also used the file database engine SQLite (https://www. sqlite. org/index. html), enabling the storage of the tree and shrub library, which is a key element within the design process, using our plugin.
To support queries to this database, we created a Python Database Abstraction Layer using the sqlite3 library (https://docs. python. org/3/library/sqlite3. html). The TBD plugin workflow consists of three steps (Fig. 1 ), which are directly connected to the three tools of the QGIS plugin. The TBD plugin is ready to use directly in QGIS, and the plugin script is available to download from GitHub (https://github. com/skowronskij/tree-belt-desi gner). The TBD plugin is compatible with all versions of QGIS3 and is also available from the official QGIS Plugin Repository. The first step of the TBD plugin workflow, called the tree and shrub library designer (Fig. 2 ), allows the user to develop a personal list of tree and shrub species needed to design the TB network in the user-studied area. This step implemented in the QGIS plugin tool consists of three options and enables the creation of a user database of tree and shrub species and their basic attributes.
The database template contains eleven attributes focusing on species morphology and habitat (App. A). This tool allows us to download the library template (third option of the tool) to fill the content of the database using a *. csv format file and update the tool by selecting its second option. The first option of the tool enables editing the library directly displayed in QGIS. The key point within completing the library is the assignment of the soil types (*. shp format layer) present in the studied area into one of three quality classes of habitat suitability for each tree and shrub species. We have only proposed a simple template of three classes of soil suitability for tree and shrub species, while the user's task is to complete the library by indicating which soil type (e. g. , podzolic soils) in his studied area is low, medium and optimum in terms of suitability for the selected species.
Plugin testing A small area (156. 25 ha) in the General Dezydery Chłapowski Landscape Park in western Poland was used to test the TBD plugin. This park was established to protect the TB network developed in the 19th century and to enhance biodiversity in agricultural landscapes (see  Nowak and Pędziwiatr (2018)  for further details). We used the following data types: soil types (vector layer, 1:25000) based on the database of the Polish Institute of Soil Science and Plant Cultivation, cadastral plot dataset acquired from the Polish digital cadastral database (vector layer, 1:5000), and DSM acquired from LiDAR data (raster layer, 1 m pixel size). A library (. csv format) containing the species composition and basic attributes of the trees and shrubs typical in bioclimatological zones within the Polish area (App.
We assume that the above approach may change the negative attitude of farmers toward TB networks. Additionally, shadow effect modeling may play an important role in the ecological management of natural habitats in marginal zones (e. g. , modification of microclimate and soil moisture), depending on the needs of flora, fauna and fungi  (Forney et al. , 2013). The highly shaded buffer   zones identified using our approach may qualify as buffer zones under the EU Greening Program  (European Commission, 2019). Using our approach, the estimation of potential insolation using simulations of tree and shrub arrangements along riverbanks, which change light conditions in water bodies, e. g. , streams and rivers  (Richardson et al. , 2019) , could be more effective. Moreover, the output of the TBD plugin allows us to estimate the insolation/shading effect without a linear arrangement of tree and shrub groups or single individuals.
The plugin consists of three parts: tree and shrub library designer, tree belt designer, and potential DSM designer tool. The output can be used as input data for potential insolation calculations in different GIS software types. The testing process of the TBD plugin and the calculation of the potential insolation using the plugin outcome raster showed that there was a significant shift in the shadow effect distribution along the designed TB zones in relation to land cover before the implementation of TBs. The presented methodology approach can facilitate the development of TB networks in terms of risk analysis of overly intense sunshine or shadow effects within agricultural landscapes and their marginal habitats, such as balks, meadows, streams, water bodies and roads. Moreover, applying our methodology, the ESs provided by TBs in terms of sunshine or shadow effect estimation were proposed.
Methods The first method applied to detect abrupt transitions in our datasets is the augmented KS test as defined in  Bagniewski et al. (2021). A two-sample KS test is employed to compare values on either side of a value of proxy X at time t (X t ) which could indicate a significant change in the paleoclimate record. Given a high enough time resolution of the paleorecord, the frequency with which the KS test detects transitions will largely depend on its window length. The window length is considered inadequate if it is significantly shorter or longer than the time interval between two discontinuities  (Bagniewski et al. , 2021). Therefore, to optimize the analysis for the most relevant time scale,  Bagniewski et al. (2021)  introduced a varying window length between 0. 6 and 4 kyr, which corresponds to the time scale at which the given paleorecords were investigated.
The KS statistic is calculated for all window lengths to identify abrupt transitions, with values above 0. 7 considered significant. We first identify transitions using the longest window, which has the largest sample size and therefore the greatest statistical significance. Subsequently, transitions detected using shorter windows are incorporated to capture transitions on shorter time scales. Finally, to identify transitions between dominant climate modes, such as Stadial-Interstadial (GS-GI) boundaries in the NGRIP record, we employ a running window to determine the upper and lower values of the time series and find transitions that signal a switch from one mode to the other. More detail is given in  Bagniewski et al. (2021). The three datasets presented previously have been analyzed using the same window lengths.
The most interesting typology in RP applications is associated with recurrent patterns that are not exactly periodic and thus not easily detected by traditional spectral analysis methods. Marwan et al. (2007)  discuss how to make the purely visual RP typologies more objectively quantifiable through the use of recurrence quantification analysis (RQA). In our analysis, we quantified the Recurrence Rate (RR) of the time series, which describes the probability of states of the system recurring within a particular time interval. For the datasets presented in this study, the RR is calculated using a 4kyr-sliding window, and plotted together with the recurrence plot. Following the RR prominence analysis  (Bagniewski et al. , 2021) , the minima of the RR are identified, corresponding to abrupt transitions of interest.
dataset includes up to 65 m of alternating loess-paleosol sequences, totaling 12,330 samples. The grain-size record is expressed by the median size in phi scale, where lower values indicate coarser grain-size. The time resolution is 200 years. The LGS640 (Loess Grain-Size stack spanning the last 640 ka) shows the average grain size from three highresolution sequences from the western CLP, namely Gulang, Jingyuan, and Linxia. The thickness of the record varies between 151. 4 to 164. 2m, where higher values indicate coarser grain sizes. The LGS640 time scale has been refined by comparison with the speleothem chronology.
Acknowledgement Denis-Didier Rousseau  and  Witold Bagniewski  are funded by the  European Union 's  Horizon 2020 research and innovation program  through  TiPES  grant no. 820970. The CHILOMOS dataset was supported by the  Priority Research program  of the  Chinese Academy of Sciences  (grants  XDB03020503  and  XDA05120204 ), and the  National Basic Research program of China  ( 973 program ) (grant  2010CB950204 ). The LGS640 dataset was supported by by grants from the  Ministry of Science and Technology of China  (No. 2016YFA0601902 ), the  Chinese Academy of Sciences  (No. XDB40000000 ), and the  National Natural Science Foundation of China  (No. 41525008 ). The MGSQ dataset was supported by the  National Basic Research program of China  ( 2004CB720208  and  2001CCB00100 ),  National natural Science Foundation of China  ( 40121303  and  40331001 ).
Data availability The tables generated by this paper will be submitted to a PANGAEA data repository. NGRIP data are available at https://www. iceandclimate. nbi. ku. dk/data/GICC05modelext_GRIP_and_GISP2_and_resampled_data_series_Seierstad_e t_al. _2014_version_10Dec2014-2. xlsx (Rasmussen et al. , 2014). The CHILOMOS grain-size data are available as supporting material at https://doi. org/10. 1002/2013GC005113. The LGS640 stack data are available in the East Asian Paleoenvironmental Science Database (http://paleodata. ieecas. cn/index_EN. aspx. Finally the MGSQ data are available at https://www. ncei. noaa. gov/access/paleo-search/study/5602.