dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic and Transcriptomic database screening Detection of new RAG-like sequences was performed starting from a collection of previously reported RAG1like and RAG2-like sequences from deuterostomian organisms B. belcheri  [19] , S. purpuratus  [17] , P. flava and P. miniata  [9]. Queries were searched against all metazoan invertebrate and jawless vertebrate projects available before February 2019 from multiple databases such as Whole-Genome Shotgun Contigs (WGS), High Throughput Genomic Sequences (HTGS) and Transcriptomic Shotgun Assembly (TSA), using TBLASTN  [24, 25] , with a Blosum62, Blosum45 and PAM250 similarity matrices and a e-value threshold of 1e-08. Regions containing RAG1L sequence signatures were further assessed for their potential to encode complete RAG1L proteins in the intron/exon context by analyzing the level and distribution of sequence similarity and the secondary structure profile match to RAG1L.
Data availability & Bilateria tree We established an overview of the species for whom sequence data was present in the WGS and TSA databases of NCBI on February 26, 2019 (Additional file 6: Table  S1 ). While TSA projects are typically indicated as "TSA master", some additional sequences marked as "Transcripts" are detected on a BLAST search against the TSA database (e. g. , Branchiostoma lanceolatum), and some species that lack identifiable RAGL sequences might have been omitted inadvertently from Additional file 6: Table  S1. From the available species in the NCBI database, a summary species tree was built using NCBI Taxonomy Common Tree  [51, 52]  and was edited with iTOL 4. 3. 2 [53]. The evolutionary tree timeline shown in Fig. 1b  was obtained from TimeTree  [54]  and the tree editing was performed in online iTOL v4.
Sequence analysis and variability Domains within each of the identified RAG1L and RAG2L pair were delineated using InterproScan  [60] , while RaptorX-property  [61]  was used to predict the local secondary structure. Multiple sequence alignments were performed using T-coffee in psicoffee mode using a Uniref50 database for homology searching  [62, 63]. Due to the low homology between RAG2L sequences, the Kelch-type domains and the PHD domains were first aligned separately and subsequently merged into a single alignment. Identity percentage matrices were computed excluding gaps, using Unipro Ugene v1. 22. 0  [64] , as the ratio of identical amino acid pair counts over the length of the smallest sequences from the compared pair. Similarity percentages presented below the diagonal in the same figures were computed using an in-house implementation of the Ugene algorithm, but using for counts the matrix of all BLOSUM62 positive substitutions, as used in the blast-like methods.
Ugene was also used to generate the graphics included in figures containing protein and nucleotide alignments, while AnnotationSketch  [65]  was used to generate genomic organization figures. Given redundancies and the unbalanced distribution of RAGLs among the evolutionary branches, variability was computed only over a nonredundant set of 6 RAGL pairs sharing less than 50% protein sequence identity within the core RAG1L region. This set proves also to be representative for the evolutionary clades and consists of three deuterostome sequences: cephalochordata group (amphioxus), echinodermata group (sea urchin), hemichordata clade (P. flava) and three protostome sequences: one from each mollusk RAGL_B subfamily, and one nemertean N. geniculatus RAGL_D family representative. P. flava RAGL_A and RAGL_C were discarded given the low confidence protein prediction for their RAG2L counterparts.
Funding This work was supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the second " Investissements d'Avenir" program  (reference:  ANR-17-RHUS-000X ) (P. P. ),  UEFISCDI  grant  PN-III-ID-PCE-2016-0650  and  Romanian Academy  programs 1 & 2 of IBAR (E. C. M. and A. J. P), and  NIH  grant  R01 AI137079  (D. G. S. ).
Availability of data and materials The datasets analyzed during the current study are available in the GenBank repository, specifically in Whole-Genome Shotgun Contigs (WGS) and Transcriptomic Shotgun Assembly (TSA). Additional file 8: File S1 contains a detailed presentation of the sequence information about the new RAGL loci identified in this study: detection relevant data (e-values and the exact identified segments alignments from TBLASTN), TIR/TSD detection information, predicted protein sequences and additional relevant observations regarding some of the sequences, while Additional file 9: File S2 contains nucleotide sequence data.
Gender and Socioeconomic Status: Common Ground, Compromise or Conflicting Perspectives? The second example of interdisciplinary working considers tensions in the area of knowledge production arising from the differing epistemologies of the strands. For strands 2-6, a number of different types of data were gathered about speakers, central to answering several whole-project research questions, in particular: (c) What is the relationship between multilingualism, diversity and identity? How does this play out at the individual, local, regional, national and international levels?; and (d) What is the relationship between multilingualism and language learning? Who can/should learn additional languages, and in what contexts? How do age and other factors affect motivation, achievement and wellbeing? Trying to achieve an integrated response required us to ask a fundamental question: how can we best ensure that categories within our data and metadata such as age, gender or socioeconomic status are understood in ways that are compatible and/or comparable? As with identity, issues of both knowledge and knowledge production are implicated.
The team decided to address this issue at one of our early meetings, when the strands conducting empirical work were preparing their schedules for questionnaires and interviews. In order to start with a clearer picture of the range of metadata strands were working with, we sent a template to be completed before the meeting, asking each strand to consider the information required about speakers (the knowledge), rather than the questions that researchers intended to ask in order to get that information (the means of production). Faceto-face discussion at the research team meetings could then focus on clarification as to the epistemological assumptions behind certain practices, as well as the means of knowledge production. The categories that emerged included: age; gender; language background and exposure (current and past); language background and exposure of other members of family/household; residence/location (current and past); job/profession (current and past); education level (for those who have left formal education); job/profession of parents/carers; education level of parents/carers.
g. language(s) spoken most frequently at home, languages learned in a formal context, language(s) spoken at work). Data were gathered in each of these areas by several strands, with strands gathering further information in specific areas if it was useful for them. Gender was a category that generated some tension in the search for integration of knowledge-production methods, having at its heart issues that arose first in our discussion of identity and which led, as one Co-investigator put it, to ' a thorough questioning of empirical assumptions'. Initially, strands operating within an experimental science paradigm, such as strand 6, planned to use gender as a binary (male/female) categorical variable -not in any ideological sense, but simply as many prior studies had empirically documented male/female differences (e.
As a result of this collaboration, strand 6 now incorporates a strong qualitative dimension in its methodology, which had previously been entirely quantitative. One aspect of strand 6's work focuses on assessing quality of life and cognitive skills in healthy adults and patients with early onset dementia before and after the intervention of a language-learning course. These cognitive skills are measured by well-established disciplinespecific cognitive tests. Through incorporating focus groups and interviews developed as a result of collaboration with strand 3, researchers in strand 6 were able to gather qualitative data from patients and carers on the benefits of learning a language that go beyond the measurable quantitative ones. The cross-disciplinary discussions were, in the words of one of the strand 6 researchers, ' eye-opening', leading to the recognition that ' different methodologies can tackle different aspects of our research to fit different purposes more appropriately'.
Focus groups and interviews were incorporated into the neuroscience fieldwork to gather participants' opinions, feedback and user experience about language courses taken by patients, carers and dementia centre managers. The cross-strand collaboration also included use of data analysis software and, here again, qualitative tools such as NVivo were subsequently adapted to analyse the focus group and interview data in strand 6. The overall result of this collaboration is that strand 6 now uses a combination of discipline-specific quantitative methods (such as cognitive tests) alongside qualitative methods (such as focus groups and interviews) in tackling the multiple factors at work in the context of elderly patients with early onset dementia. Researchers from both strands are co-authoring an output (Bak, Vega-Mendoza & Dunlevy in preparation) that demonstrates and discusses the transformational effect of this interdisciplinary collaboration on methodology and the ways in which it has facilitated what  Repko and Szostak (2017: 323)  call a 'more comprehensive understanding' of the benefits of language learning for those with early onset dementia.
Acknowledgements This paper was funded as part of the  MEITS  project by the  Arts and Humanities Research Council (AHRC) , award number  AH/N004671/1. MEITS (Multilingualism: Empowering Individuals, Transforming Societies www. meits. org) is a flagship interdisciplinary project funded through the  AHRC's Open World Research Initiative , which aims to demonstrate the value and importance of Modern Languages in responding to key issues of our time. The authors are extremely.
Financial support This study was jointly supported by the  National Key Research and Development Program of China  (Grant No. 2018YFA0605602 ), the  National Natural Science Foundation of China  (Grant No. 41888101 ,  41472160 ), the  Norwegian Research Council  (Project No. 221712 ,  229819 , and  262618 ), and the  NordForsk -funded project  GREENICE  (Project No. 61841 ).
Code availability NorESM is available from https://github. com/metno/noresm-dev. git. Documents of NorESM can be found on Geoscientific Model Development: https://www. geosci-model-dev. net/special_issue20. html. BIOME4 can be downloaded from https://pmip2. lsce. ipsl. fr/synth/biome4. shtml. PISM is available from https://pism-docs. org/.
This study presents airborne in situ and satellite remote sensing climatologies of cirrus clouds and humidity. The climatologies serve as a guide to the properties of cirrus clouds, with the new in situ database providing detailed insights into boreal midlatitudes and the tropics, while the satellite-borne data set offers a global overview. To this end, an extensive, quality-checked data archive, the Cirrus Guide II in situ database, is created from airborne in situ measurements during 150 flights in 24 campaigns. The archive contains meteorological parameters, ice water content (IWC), ice crystal number concentration (N ice ), ice crystal mean mass radius (R ice ), relative humidity with respect to ice (RH ice ), and water vapor mixing ratio (H 2 O) for each of the flights. Depending on the parameter, the database has been extended by about a factor of 5-10 compared to earlier studies.
cirrus representation and remote sensing retrieval methods. Another outcome of our investigation is that across all latitudes, the thicker liquid-origin cirrus predominate at lower altitudes, while at higher altitudes the thinner in situ-origin cirrus prevail. Further, examination of the radiative characteristics of in situ-origin and liquid-origin cirrus shows that the in situ-origin cirrus only slightly warm the atmosphere, while liquid-origin cirrus have a strong cooling effect. An important step in completing the Cirrus Guide II is the provision of the global cirrus N ice climatology, derived by means of the retrieval algorithm DARDAR-Nice from 10 years of cirrus remote sensing observations from satellite. The in situ measurement database has been used to evaluate and improve the satellite observations.
Nevertheless, there are still gaps that need to be filled, on the one hand in the understanding of ice processes and on the other in the representation of cirrus clouds in climate prediction models. Accomplishing these tasks requires large and high-quality observational databases that can serve, for example, to evaluate global models or other data sets and be used to derive parameterizations for improved representation of different types of cirrus clouds in models (see e. g. ,  Wolf et al. , 2019). In addition, such databases allow detailed studies of special types of cirrus that are still poorly understood, e. g. , cirrus in fast updrafts as orographic cirrus or cirrus at the top of strong convection. In this study, we approach these requirements as follows: we first compile a data archive of airborne in situ observations which is extended with respect to earlier versions  (Schiller et al.
Hence, as a next step of the study we use in situ climatologies to evaluate cirrus N ice from satellite observations and, based on this, derive a global climatology of cirrus N ice. From the portrayal of the two Cirrus Guide II data sets emerging from this study together with some more detailed analyses, we show that the combined evaluation of airborne in situ and satellite remote sensing observations enhances the insights into cirrus properties. The in situ observations are best suitable for the investigation of specific, smaller-scale phenomena and for the evaluation of satellite observations or model simulations. Satellite-borne observations, on the other hand, allow a view of the larger-scale and seasonal properties. The article is structured as follows: the Cirrus Guide II in situ databases and the methods used are described in Sect.
2 and Appendix A. As an overview, in Sect. 3 we portray the in situ cirrus cloud and humidity database with respect to altitude for the latitudes covered by the observations. The usefulness of the data set is shown by discussing the characteristics and occurrences of in situ-origin and liquid-origin cirrus. Section 4 first presents cirrus and humidity climatologies of the extended Cirrus Guide II in situ database in terms of temperature in comparison to the earlier studies mentioned above. Further, characteristic properties of midlatitude and tropical climatologies are presented. In Sect. 5 we show another example of a specific analysis extracted from the Cirrus Guide II in situ database: the data set includes recent unique measurements in the tropical tropopause layer (TTL) region of the Asian monsoon anticyclone, where cirrus clouds and humidity are of special interest and observations are rare.
The topic is briefly introduced before the special observations in the Asian monsoon are presented and compared with the conditions found in the surrounding tropical regions. The last part of the study (Sect. 6) is the step to global climatology of cirrus N ice from satellite remote sensing observations. For this purpose, the Cirrus Guide II in situ database is used to evaluate remote sensing cirrus observations. Based on this, a global N ice climatology is derived, and first analyses of the global and also regional N ice are presented.
(2013) , and  Krämer et al. (2016)  and N ice , R ice , and RH ice by  Krämer et al. (2009). -The number of flights and total time in cirrus increased from 104 flights for 94 h  (Krämer et al. , 2016)  to a total of 150 flights for 168 h. The database disproportionally has extended by about a factor of 5-10 depending on the specific parameter. -For IWC, a new data analysis method has been developed that increases the observed data volume (Appendix A2. 1). -For N ice , observations from advanced and extended instrumentation have been added to the database; further, a new correction of the occurrence frequencies is applied (Appendix A2. 2). -The geographical spread of the observations has broadened, so that a portrayal of cirrus and humidity with respect to the midlatitude and tropical geographical regions, and also with respect to latitude and altitude, seems worthwhile.
The times of data sampling are displayed in the respective panels; a more detailed description of the database is given in Sect. 4. The way the data are presented here as individual points was chosen because the entire range of measurements is visible. Although data overlap occurs in this type of display, it is possible to identify cirrus types and microphysical processes, especially based on extreme values. As additional overview information, we have created latitude-altitude intervals (500 m altitude, 0. 5 • latitude) and calculated the 25th, 50th (median), and 75th percentiles for all variables. These additional altitude-latitude climatologies are shown in the Supplement (Figs. S1-S3 ). Cirrus clouds are found at lower altitudes at midlatitudes and reach higher levels in the tropical region (Fig.
4. 1 The IWC-T parameter space: median N ice , R ice , and RH ice In the Cirrus Guide I  (Krämer et al. , 2016) , observations and model simulations are portrayed in the ice water contenttemperature (IWC-T ) parameter space. One result of the simulations is that IWC and N ice are correlated with each other. This relationship has been confirmed for some meteorological situations by examples from six individual field campaigns. The database of combined IWC-N ice measurements has grown considerably since then and with it the covered temperature range of the observations. In the Cirrus Guide I, only a few observations below 200 K were available. With the extended Cirrus Guide II data set, we further investigate the statistics of N ice but also the corresponding mean mass size R ice and the in-cloud RH ice in the IWC-T parameter space.
Also, at temperatures colder than about 200 K, N ice was most often very low. Further, the enhanced occurrence frequencies at the lowest concentrations seen in the earlier data set are corrected in the new data evaluation procedures (Figs. A2 and S6, Supplement, middle left panel). Frequencies of occurrence in dependence on temperature, binned in 1 K intervals of ice water content (IWC; a; black solid and dotted lines: median, min, and max IWC of the core IWC band of  Schiller et al. , 2008). Ice crystal number (N ice , size > 3 µm diameter, b; red lines: 10th, 25th, 50th, 75th, and 90th N ice percentiles; blue line: fit through median N ice values (median N ice : 0. 03 cm -3 ; 10th, 25th, 75th, and 90th percentiles: 0. 002, 0. 007, 0. 102, 0. 3 cm -3 ); black lines: minimum, middle, and maximum N ice of  Krämer et al.
Clear-sky and in-cloud RH ice (RH ice -T ) The new RH ice clear-sky and in-cloud data sets are displayed in Fig. 7e, d. The respective earlier data sets of  Krämer et al. (2009)  are shown in Fig. S6 , Supplement. The overall picture of the RH ice distributions has not changed substantially, though the amount of in-cloud data in the database has increased from 10 h of measurements to about 96 h, and the clear-sky observation time has increased from 16 to 186 h. The only small difference is found in the in-cloud RH ice , potentially caused by the larger amount of data: below about 200 K, where high supersaturations (> 120 %) occur more often and low subsaturations (< 80 %) less often. At higher temperatures, the peak of RH ice frequencies at 100 % is more pronounced. For the new data set, in addition to the clear-sky RH ice the absolute water vapor volume mixing ratio H 2 O in Fig.
1  and listed in Tables  A1  and A2. The majority of the data were sampled during ATTREX_2014 and POSI-DON_2016. The TTL measurements in the temperature parameter space are shown in Figs. 8  and 9 ; in these plots, the Asian monsoon observations are included, but since the measurements represent only a small part of all TTL observations, excluding the StratoClim campaign only slightly changes the frequency distributions. Thus, the figures are representative for the TTL outside of the Asian monsoon anticyclone, and we refrain from showing an additional figure. The measurements in the parameter space (StratoClim excluded) are presented in Figs. 11, left column, and 12, left column. 6 The additional portrayal provides a more detailed impression of the distribution of cirrus clouds and humidity around the CPT, i.
The new retrieval method for N ice introduced in Sect. 2. 2, DARDAR-Nice, has however demonstrated the ability to satisfactorily reproduce in situ ice concentrations  (Sourdeval et al. , 2018a). In this section, we show a global climatology of N ice derived from DARDAR-Nice in the same presentation as the in situ N ice climatology. Median N ice , 25th and 75th, and 10th and 90th percentiles are provided for a global data set. In addition, median N ice values are discussed for the Arctic region, the northern midlatitudes, the tropics, the southern midlatitudes, and Antarctica.
Comparison of remote sensing and in situ N ice As a base for reliable N ice climatologies from satellite remote sensing, in situ measurements of PSDs (ice particle size distributions) from five campaigns of the Cirrus Guide II are used for an evaluation of the DARDAR-Nice retrieval algorithm (see Sect. 2. 2). This directly follows the evaluation of DARDAR-Nice presented in  Sourdeval et al. (2018a) , in which a subset of campaigns from the Cirrus Guide II data set was used. This section therefore focuses on these five campaigns, which are nevertheless representative of a wide range of midlatitude and tropical ice clouds (see Fig. 1  and caption of Fig. 13 ). Figure  13a  shows the N ice frequencies of occurrence in 1 K temperature bins of the in situ measurements; the corresponding frequencies obtained from DARDAR-Nice are shown in panel (b).
Also, the 1 Hz in situ PSDs are here sampled into 10 s averages to simulate the 1. 7 km horizontal resolution of the DARDAR-Nice retrievals (assuming a flight speed of about 170 m s -1 ). The black solid and dotted lines of the in situ climatology indicate the 50th, 25th, and 75th percentiles, which agree well with those of the entire in situ climatology (see Fig. 7b ). This demonstrates that the selected subset is statistically representative of the entire database (indeed, the entire N ice climatology contains only one additional campaign; see Table  A2 ). A satisfactory agreement between DARDAR-Nice and the in situ observations is also seen, demonstrating that https://doi. org/10. 5194/acp-20-12569-2020 Atmos. Chem. Phys. , 20, 12569-12608, 2020 ). The excess of N ice by a factor of 1. 73 in DARDAR is caused by the retrieval method; for more information see the text.
DARDAR-Nice N ice retrievals match the in situ database for known size distributions very well. A detailed comparison of the DARDAR-Nice percentiles (black solid and dashed lines) with those of the in situ observations, however, yields a small offset by a factor of 1. 73, which is visualized in Fig. 13  by the average medians of DARDAR-Nice (straight black line, 0. 064 cm -3 ) and in situ (straight blue line, 0. 037 cm -3 ). One reason for this offset lies in the method of the retrieval. The two parameters, N 0 and D m , calculated here from the in situ PSDs (usually from lidar-radar observations) are fed into a predefined fourparameter gamma-modified function to calculate ice particle size distributions (PSDs; see Sect. 2. 2). N ice is then calculated by summing up individual ice concentrations over a grid of size bins distributed over the gamma-shaped PSD for the entire range of observed ice crystal sizes (usually, a continuous integration of the PSD is performed).