dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Datasets are available in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/ arrayexpress). CSF-1R, H3K4me1, H3K4me3 ChIP-Seq comparing healthy donor monocyte and macrophage profiles (BC212: donor1; BC214: donor2; BC215: donor3), EGR1 ChIP-seq in healthy monocytes (BC275 and BC276) and CSF-1R ChIP-seq using Cterminal antibody in monocyte from healthy (BC270) and CMML (CMML1941: CMML#1982: CMML#2) monocytes are referenced as E-MTAB-6305 and CSF-1R ChIP-.
, 1999) , generally require input data with high time resolution (e. g. 1 h) for avalanche forecasting. Therefore, if information on PP is to be introduced in snow-cover models using the SSA, then a dataset of SSA information on fresh PP with high time resolution must be developed. Moreover, this information should contribute to improve SSA of PP treatment in the snow-cover models, which are basically simplified in the model, e. g. , the Crocus snowpack model  (Brun et al. , 1992; Vionnet et al. , 2012)  treats that the maximal value of SSA of PP is 65 m 2 kg -1 for low wind conditions and it decreases with increasing wind speed down to 25 m 2 kg -1  (Vionnet et al. , 2012 , Carmagnola et al. , 2014). The SSA measurements of fresh PP were conducted over short intervals in an area in Japan that receives heavy annual snowfall, and the measurements were compared to meteorological data and detailed falling snow data for discussing the dependency of SSA on these data.
5 m above ground level, wind speed and wind direction at 8. 7 m above ground level, precipitation at 3. 1 m above ground level, incoming and outgoing shortwave/longwave radiations at 6. 5 m above ground level, air pressure measured at 3. 0 m above ground level, and surface temperature, snow height, and snow water equivalents) at various time resolutions (1 min, 10 min, and 1 h) on field  (Yamaguchi et al. , 2018). These meteorological data were used with a 1-min resolution for conditioning the SSA data (Section 3. 2) and use the same for discussions on the relationship between the measured SSA of fresh PP and near-surface meteorological data (Section 3. 5). This study uses these meteorological data with a 10-min resolution for estimating the SSA (Section 3. 6). In addition to these measurements, the SIRC also operates a Doppler radar on the rooftop of its building and gathers information on snow clouds information during the winter season  (Nakai et al.
Measured SSA of fresh PP A total of 102 SSA measurements were collected from the samples acquired over four winters (from 2013/2014 to 2016/2017). The averaged heat of adsorption, which is an indicator of the judgment of measurement quality, for the 102 measurements was 2472 ±199 J mol -1 , which was consistent with the value of 2540 ±200 J mol -1 recommended by  Domine et al. (2007). Therefore, the measurement results stand to reason. Figure  2a  shows the comparison results between our results and SSA of natural freshly fallen snow in the previous studies  (Domine et al. , 2007 , Schleef, 2014 ) (Hereafter, data of Domine et al. (2007)  is referred to as Dom2007 and that of  Schleef (2014)  as Sch2014). The figures also show the optical radius (Ropt) estimated from the SSA using the following equation: 𝑅 "#$ = & ''(×* + (1), where, ρi is the density of ice (917 kg m -3 ).
Influence of melting effect in SSA of fleshly PP According to  Yamaguchi et al. (2013) , winter precipitation in Nagaoka frequently occurs at an air temperature of approximately 0 °C. In fact, the data in this study was sometimes measured at an air temperature of approximately 0 °C. Thus, some of the samples were affected by melting during the fall. To investigate the influence of melting on the measured SSA data, the measured data was classified into two categories-data without melting effect (no melt events) and data with melting effect (melt events). To classify these data, the wet-bulb temperature (Tw) during the falling snow period was used, which is a good indicator of the melting event  (Matsuo et al. , 1991) ; no melt events followed Tw ＜ 0 °C condition, while melt events followed Tw ≧ 0 °C condition.
In this study, the period with falling snow during the sample period was first determined using the CMF data with a 1-min time resolution, because falling snow did not always occur continuously during the sample interval. Then, using the meteorological data also with 1-min time resolution, all the relevant meteorological elements were averaged over the period in which snowfall was observed, instead of averaging over all periods of the sample interval for calculation (Hereinafter, the averaged meteorological data will indicate the data averaged only over the period of snowfall). In this study, Tw was calculated based on the "forward" analytical psychrometric equations  (Bohren and Albrecht, 1998)  that employ an iterative approach using averaged air temperature, relative humidity, and air pressure.
Second reason should originate from various sample measurement intervals, which were longer than those in this study; therefore, their fresh PP samples should have change due to metamorphism before the measurements, and the metamorphism degrees varied depending on their measurement interval. Moreover, meteorological conditions and falling snow types should have varied during the sample deposition because of their longer interval. From these reasons, their conditions may complicate the understanding of the relationship between SSA of fresh PP and the meteorological data on ground. On the other hand, in this study, the SSA of fresh PP were measured at the same place with a short time interval (1-2 h), to avoid the effect of metamorphism before the measurement and the effect of fluctuation in the meteorological conditions.
Therefore, in this section, the possibility of parameterizing the SSA of fresh PP using the meteorological data on ground was explored. As mentioned in Section 3. 4, the PP type sometimes varied even for short observation period (1-2 h)  (Ishizaka et al. , 2016) ; therefore, only the UFE data were used for the analyses, to focus on the relationship between the SSA of uniform PP type and meteorological condition. The following elements, averaged only over the period of snowfall (see Section 3. 2), were used-air temperature (Ta), relative humidity (RH), wind speed (WS), air pressure (Press), and wet-bulb temperature (Tw) calculated using Ta, RH, and Press. The reasons for selecting these meteorological elements are that Ta, RH, WS, and Tw are often used for the parameterization of new snow density, as summarized by  Helfricht et al.
3D structure at the mesoscale The volume data were segmented in sand, clay matrix and macropores by the ImageJ software to quantitatively investigate the differences in the three preparations. The segmentation threshold of porosity, clay, and sand was performed using the grey level histogram and the non-local mean filter  [16] [17] [18]. The 3D segmentation was set on volumes of 852 × 823 × 1000 voxels each to avoid any macroporosity artefact from irregular boundaries. The volume fraction of each component was calculated by image analysis (Table  4 ) and represented in a triangular diagram in Fig. 4. The macroporosity fractions are 10. 44%, 9. 39% and 10. 22% in sample 1, sample 2 and sample 3. The sand fractions are 39. 60%, 39. 51% and 38. 56%, in addition, the clay fractions are 49. 96%, 51.
Conclusions and perspectives The multi scale structure of sand clay mixtures, prepared by three different protocols, was investigated by different imaging techniques such as optical, 3D X-ray tomography, SEM and ESEM. The results reveal that the clays within the samples are present as a clay matrix but also as a thin layer around the quartz grains. The two significant differences between the samples are: 1) the formation of macroscopic clay aggregates during the mixing process for sample 2 and 3 contrary to sample 1, and 2) the presence of a more homogeneous thin clay layer around quartz grains for sample 1. According to the resolution of X-ray tomography data in which this layer is not visible, this clay layer is probably thinner than a few µm. The presence of this clay layering seems to be linked to the shape of quartz grains.
Non-recombining Y chromosome (NRY) of the Thao All Y-SNP haplogroups observed in the Thao sample (16 males out of 30 individuals) were para-groups of macro-haplogroup O1; namely, O1a*-M119 (n = 1), O1a2-M50 (n = 1) and O1a1*-P203 (n = 14, 87. 5%) (Additional file 1: Table  S1 ). These results corroborate a previous report  [26]  where 81. 8% of Thao males belonged to haplogroup O1a while the remainder of the data set showed little presence of haplogroups K, O1a2, or O3. Li's dataset  [26]  was not included in our analysis because of their differing definitions. They used a lower Y-SNP definition that did not allow clear assignation of haplogroup O1a1*-P203, and they used only five Y-STRs compared to 16 in our panel. With the exception of Bunun, who showed a predominance of haplogroup O1a2-M50 and the highest frequency of O2a1a-M88 seen in ISEA  [27] , the Thao Y-SNP profile was similar to that of other TwrIP, particularly the Atayal, Taroko, Saisiyat, and Tsou who, together, share the highest occurrence of O1a1*-P203 in the world (87.
Population samples The Thao genetic diversity for Y-chromosome, mtDNA, and HLA was determined in 30 unrelated (back to two generations) and healthy individuals. All individuals had both parents and first-generation grandparents belonging to the same people and gave consent to participate in this study. Approval to conduct this project was obtained from the ethics committee of Mackay Memorial Hospital in Taipei (Taiwan). The Thao data set (Additional file 9: Table  S8 ) was compared to a panel of other Taiwan individuals that we had previously analyzed for Y-chromosome  [27] , mtDNA  [31, 33]  and HLA. The HLA data is available online at http://www. allelefrequencies. net and in the proceedings of the Anthropology/HLA diversity component of the 13th international histocompatibility workshop  [24, 25, 34, 51, 52].
In brief, PCR products were mixed with GeneScan 500LIZ (Applied Biosystems) as an internal size standard and analyzed by capillary electrophoresis with an ABI Prism 310 genetic analyzer (Applied Biosystems) using the standard fragment analysis protocol mode. Genotyper 2. 5. 2 software (Applied Biosystems) was used for allele scoring. For all statistical and network analyses, we used data from DYS389II by subtracting DYS389I from DYS389II  [29].
Dates were only intended as a rough guide for relative haplogroup ages comparison. Multiple Dimension Scaling Analysis plots (MDS) using haplogroup frequencies of the three gene systems (Fig. 4 ) were constructed with SPSS version 17. 01 using Alscal Euclidian distances (SPSS Inc. , Chicago IL). MtDNA HVS1 region and complete mtDNA sequencing described herein have been deposited in GenBank (GenBank sequence submission of 38 complete mtDNA genome, MH177784-MH177821). Y-chromosome STR data and partial mtDNA sequencing are provided in Additional file 10: Table  S6  and Additional file 11: Table  S7. Other NRY Y-STR and Y-SNP data sets are available on  [27].
This panel comprises a) a dataset of non-Taiwan aborigines that includes Minnan (n = 672), Hakka (n = 200) and a sample of undefined number of Minnan and Hakka, referred to herein as TwMix (n = 3227), b) Taiwan officially recognized indigenous peoples (TwrIP) including Atayal (n = 110), Taroko or Truku (n = 54), Saisiyat (n = 64), Bunun (n = 181), Tsou (n = 60), Rukai (n = 78), Paiwan (n = 172), Amis (n = 294), Puyuma (n = 116) Yami/Tao (n = 88), Ivatan/Batan (n = 50), and c) indigenous Taiwan Pingpu peoples (TwPp, n = 493) including Pazeh (n = 65) and Siraya groups (n = 428).
Availability of data and materials The raw complete mtDNA genome data used for the construction of phylogenetic trees shown as a supplementary material have been submitted to GenBank with the following accessions: MH177784-MH177821. NRY SNPs and STRs and the partial mtDNA sequences for the Thao people are shown in Additional file 10: Table  S6  and Additional file 11: Table  S7  respectively. Other NRY Y-STR and Y-SNP data sets are available from  Trejaut (2014)   [27].
Clinical Samples Samples included in this study were recovered between May 2016 and April 2017 via the French national influenza surveillance network or from patients hospitalized in Lyon University Hospitals. This cohort was designed to investigate molecular evolution of influenza viruses and studied herein in a secondary analysis. The French national influenza surveillance network recruits patients with influenza-like illnesses in ambulatory care and in University hospitals. Only data from the southern half of France were included. In the Lyon University hospitals systematic surveillance was set up for influenza cases hospitalized in the intensive care units and for influenza in vaccinated patients. When several samples were retrieved and/or sequenced for a patient, only the earliest sampled after the onset of symptoms was included.
For the present study, only treatment-naïve patients infected by the A(H3N2) strains for which we obtained at least partial sequences for each genomic segment were included. Clinical data were retrieved from questionnaires filled out by the healthcare professionals who took the samples and from computerized clinical files for the surveillance network and Lyon University Hospitals, respectively. All patients had sufficient clinical data reported to classify them as having either mild or severe influenza. Clinical criteria leading to classification of patients as severe included admission to intensive care for respiratory complications, need for continuous supplemental oxygen or hypoxemia, neurological signs (seizures, fainting with loss of consciousness, evocative imagery, or lumbar puncture) or any type of shock.
Bioinformatic Analysis As described previously, the sequencing files were cleaned from human reads using bmtagger (v1. 1. 0) with human genome database hg19  [25, 26]. Adapters and low quality ends of reads (Phred score <20) were removed by cutadapt (v0. 4. 4), also removing reads smaller than 50 bases and the paired reads associated with them  [27]. The BWA-MEM alignment tool (v0. 7. 15) was used to verify sequenced strain subtypes by hemagglutinin (HA) homology, and then to map the reads on the circulating A(H3N2) strains' common ancestor A/Perth/16/2009  [28]. This step generated consensus sequences that were used as the patients' own mapping reference in further steps. All read mapping by BWA-MEM was performed using default settings, except for gap opening and extension penalties which were set to 10 and 2, respectively, to increase indel stringency.
Sorted mapped reads were manually inspected using FastQC (v0. 11. 5) to verify read quality. Variant calling files were generated with naive variant caller (Biomina Galaxy platform) reporting all necessary base metrics for each position on the A(H3N2) genome, ignoring low-quality bases  [29]. A homemade vcf analysis python script applied filters to validate possible single nucleotide polymorphisms and generate end-user workable files. These filters comprised a quantitative strand bias (SB) score estimating an uncertainty for variant frequencies when compared to the consensus bases disequilibrium and a depth-adapted threshold. This depth-adapted threshold was calculated following a t-test distribution derived from the commonly-approved 100X threshold necessary to exclude heterozygote mutations and was verified empirically  [25].
Discussion In the present study we conducted a comprehensive genomic characterization of H3N2 viruses from either the French national influenza surveillance network or the Lyon University Hospitals' intensive care wards using a combination of several approaches. This allowed the impact of antigenic proximity, genomic substitutions, quasispecies diversity, and reassortment events to be studied concomitantly, and the results therefore provide a comprehensive view of mechanisms involved in the severity of the 2016-2017 seasonal influenza in France. These data were generated from a secondary analysis of a cohort designed to investigate molecular evolution of the influenza virus. The size of the study did not permit the performance of multivariate analysis correcting for sample nature, comorbidities, geographic, or temporal confounding factors that could influence influenza severity, a limitation that is shared with previous studies  [8].
Funding: This study was performed in the field of the global surveillance of influenza viruses performed by the WHO and by the "Centre National de Référence des Virus des Infections Respiratoires" supported by "Santé Publique France" (SPF) in France. The DRIVE Project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363. This joint undertaking receives support from the European Union's Horizon 2020 research and innovation and EFPIA. Conflicts of Interest: M. P. received a research grant from Theradiag and Hologic and is a member of the young microbiologist section of the French Society for Microbiology. B. L. is the co-chair of the Global Influenza Initiative, a member of the scientific committee of the Global Hospital Influenza Surveillance Network, and a member of the Foundation for Influenza.
bicornis, C. carnea, H. axyridis), study region (Germany/Switzerland) and their interactions and the random factor landscape sector, on the proportion of pollen from woody plants used by the insects. An observation level term was added as a second nested random effect to account for overdispersion  (Lee & Nelder, 2000). Models were fitted with the R package "lme4 1. 1-13"  (Bates, Maechler, Bolker, & Walker, 2015). As there was no significant three-way interaction in the full model (p =. 64), indicating consistent temporal patterns of pollen use across species in both regions, data of both regions were finally analysed together. Predictors' significance was tested with Wald chi-square tests. We also examined the importance of nonagricultural plants by classifying pollen types as "non-agricultural" (i.
The FLOTROP dataset contains numerous plant observations (around 340,000 occurrences) in northern tropical Africa (from the 5 th to 25 th parallel north) in open ecosystems (savannah and steppe). They were collected by multiple collectors between 1920 and 2012 and were managed by Philippe Daget. These observations are probably the most important and unique source of plant distribution over the Sahel area. The data are now available in the Global Biodiversity Information Facility, Tela Botanica website, and as maps in the African Plant Database. For the overall area involved, this dataset has increased by 40% the data available in the GBIF. For some countries between the 15 th and 21 st parallel north, the FLOTROP dataset has increased available occurrences 10-fold compared to the data existing in the GBIF.
Background & Summary Tropical northern Africa (herein defined as between the 5th and 25th parallel north) is mostly occupied by open ecosystems, such as steppe and savannah 1. The vegetation in these ecosystems is consumed by animals 2 , either wildlife or livestock, and is also used by the local communities for food, energy or medicinal purposes  3. The open ecosystems in tropical northern Africa are of great importance for the economy, food security and human well-being. Plant diversity within these ecosystems is driven by many factors, such as the climate, soil, fire and grazing 4,5. Plant diversity in these regions is being greatly impacted by global change  [6] [7] [8]. Historical data are needed to understand species and diversity dynamics. The database presented in this work is the collection of numerous datasets gathered over the years.
At the outset, the FLOTROP database was intended to store all the data recorded by IEMVT (French institute for tropical livestock production and veterinary medicine, now part of CIRAD) in the sixties. In 1993, CIRAD and CNRS set up a project to collect a maximum of botanical surveys within these regions  [9] [10] [11]. Two software packages were created by the team to manage the database. The first was created under DOS then a second was started under Windows using the APL DYALOG language. Data were gathered and scanned between 1993 and 2016. We extracted the data from the software version. We shared the species occurrences recorded in the database on the Tela Botanica website (http://www. tela-botanica.
txt format, thus access format). We found various errors in the database due to encoding problems during extractions: a mismatch between the protocol used and the way that abundance was coded, an inversion between the abundance and the number of species, and some surveys that were mostly only a list of species but were extracted as a survey with abundance. In this case, for half of the species, the number of species was inserted in the abundance columns. We ran several tests to check for such errors. "Surveys containing such an error were flagged up": (1) In the surveys with a Braun Blanquet score, the abundances could only be coded as 0, 1, 2, 3, 4 or 5. Otherwise, the surveys for this protocol that had abundance values different from these five were considered problematic and were not published.
We plan to review all the flagged up, and possibly corrected, surveys. This second database will be issued at a later date and the abundance estimators will also be included in this second release. We chose to update the species referential with the African Plant Database (APD), which is widely accepted for African studies and recommended by the Association of the Taxonomic Study of the Flora of Tropical Africa (AETFAT). The comparison between the FLOTROP and APD names used a Fuzzy Lookup process (Ms Excel add-in). The analysis was carried out on one field containing full names (species with authors), with a threshold over 75%. As a comparison of 8,337 × 250,000 matrix names induced computer difficultly, an initial comparison was made with only North African names and then with tropical names.
About 800 names were visually checked because the similarity was very low (between 075-0. 95%), and 70 names concerning introduced species where added to APD. However, for 18 species out of the 8,337 species described in the FLOTROP list, we did not find any correspondence with the APD database. These names were probably transcription errors or confused species in the different Floras, for which finding the right name is quite impossible. In these cases, the species were entered into the database with the name selected in the FLOTROP database. The information of the name referential is put in the identificationRemarks. For most of the surveys, the occurrences of species were also associated with an estimation of their abundance (either a percentage or Braun Banquet score, or individual counts).
Data Records This dataset was included in The Global Biodiversity Information Facility (GBIF)  16. The data were also shared on the e-Flora of Tela Botanica (www. tela-botanica. org). The base included 342,698 occurrences for plant species in the African region between the 5 th and 25 th parallel north. In order to compare these datasets with the existing data shared in the GBIF, we extracted from the GBIF website, on 28 July 2017 (www. GBIF. org)  12 , all the species occurrences within geographical coordinates between the 5 th and 25 th parallel north and between longitudes 20°W and 52°E. We did not download the occurrences that were obtained from a fossil sample. From all these occurrences, we only selected the species within African countries contained in the FLOTROP area (list of countries in Table  1 ).