dataesr/scientific-paragraphs-categorization huggingface dataset:
The remaining measured albedo Autosolexs is composed of two measurement heads located approximately 1. 5 m above the surface, each containing two upward and downward facing cosine receptors. The downward facing collectors were estimated to receive 75% of the reflected signal from the surface from an area of approximately 3. 5 m in radius  [22]. The receptors are connected to an Ocean Optics Maya2000 Pro spectrometer with fiber optics passing through an optical switch. Autosolexs measures the downward and upward (reflected) solar radiation. Using the calibration and processing steps described in  [22] , the spectral albedo was calculated as the ratio of the upwelling to downwelling spectral irradiances, providing usable data across the 400-1050 nm range. Owing to the negligible amount of diffuse illumination, the bi-hemispherical reflectance measured by the instrument (here called albedo) was directly compared to the directional-hemispherical reflectance (planar albedo) retrieved from OLCI images.
For the validation purposes of this study, the datasets of the 2016-2017 and 2017-2018 summer seasons were used. The comparison between the ground observations and the satellite retrievals of spectral albedo was performed in several steps. Over the two summers, 1151 OLCI scenes were available at the Dome C location. For each scene, the closest Autosolexs albedo measurement in time was selected. To avoid a bias due to the albedo changing with the SZA and possibly changing snow physical properties, Sentinel-3 scenes more than 15 min apart from an Autosolexs measurement were automatically discarded. A second filter was applied to remove the measurements performed with a SZA larger than 75 • , as it was shown in  [22]  that large SZA values introduce significant variations in the Autosolexs measurements.
The measurements were obtained within the framework of the ASUMA program (Improving the Accuracy of the SUrface Mass balance of Antarctica). Albedo measurements were taken at 23 locations along the transect (Table  2 ) using the Solalb instrument (shown in Figure  8 , a handheld version of Autosolexs). The instrument is composed of a 3 m boom at the end of which a light-collector is connected. It is characterized by a cosine response. The light-collector is connected to the same spectrometer model used in Autosolexs (Maya200pro, Ocean Optics) via an optic fiber. The instrument measures incoming radiation over the wavelength range 200-1300 nm with a usable range of 400-1050 nm. To measure albedo, the light-collector was first placed horizontally, in an upward-looking position to collect the incident irradiance, then in a downward-facing position to collect the upwelling reflected solar radiation from the snow surface.
Following the processing chain developed for Autosolexs  [22] , the albedo was calculated as the ratio of reflected to incident solar radiation. For each of the 23 locations, multiple albedo measurements were performed at random positions within a radius of approximately 100 m, and the coordinates were recorded with a handheld GPS. The series of individual measurements at each location were averaged and compared to the corresponding OLCI retrieval for the overlapping pixel. The variance of the measurements was considered to represent the sub-pixel spatial variability of the pixel. For each set of field measurements, the closest cloud-free OLCI overpass in time was selected. If no OLCI cloud-free scenes were available on the day of the field measurements, the dataset was discarded. Because of the difference in time between the satellite overpass and the ground measurements, and thus the difference in SZA (Table  2 ), the measured albedo was converted to planar spectral albedo and corrected for the difference in SZA based on the asymptotic analytical radiative transfer theory presented in  [19].
3 •. The averaged measurements were compared to the overlapping OLCI pixel (SZA = 41. 2 • ). On 19 April, six surface albedo measurements were obtained every 40 m along a 240 m transect, to account for the spatial variability of the snow surface (slopes, snow physical properties, deposited dust). The average slope along the transect was 8. 6 • and the average SZA at the time of the field acquisitions was 40. 9 •. The entire transect was contained within an OLCI pixel, allowing a comparison of all the measured points with the satellite retrieval (SZA = 39. 2 • ) obtained on the 20 April owing to the lack of Sentinel 3 data on 19 April. The comparison was considered valid, owing to the stable weather conditions during the two days. On 24 April, measurements were obtained at 24 locations along a 420 m transect on the plateau covering three OLCI pixels.
It is known that the surface albedo measurements are affected by the departure of the surface from horizontal [54], and therefore, the ground measurements need to be corrected for a direct comparison with the satellite spectral albedo. A full description of the algorithm appears in Appendix E. Furthermore, the difference in SZA between the ground acquisitions and the satellite overpass was accounted for, as described for the processing of the ASUMA dataset. Finally, for each date the albedo measurements were averaged, and compared to the closest spectral planar albedo retrieval in time for the overlapping OLCI pixel.
e. , when PROMICE hourly average albedo is above 0. 875. Experimenting with correction factors leads to the need for variable f parameter on a site by site basis, corresponding with differences in the relative spacing and orientation of the AWS solar panel, data logger enclosure and whether snow persists on the surface (covering the battery box). The correction increases the average PROMICE albedo by 0. 027 and 0. 081. We did not compare the results for all PROMICE stations because for the Sentinel-3 years, the excluded sites did not have useful data. These are the best-behaved results. The nearest OLCI 300 m pixel values were extracted for Greenland PROMICE network locations during five-month periods spanning 1 May to 31 September in years 2017 and 2018. For some sites and days, multiple images are available.
e. , when PROMICE hourly average albedo is above 0. 875. Experimenting with correction factors leads to the need for variable f parameter on a site by site basis, corresponding with differences in the relative spacing and orientation of the AWS solar panel, data logger enclosure and whether snow persists on the surface (covering the battery box). The correction increases the average PROMICE albedo by 0. 027-0. 081. We did not compare the results for all PROMICE stations because for the Sentinel-3 years, the excluded sites did not have useful data. These are the best-behaved results. The nearest OLCI 300 m pixel values were extracted for Greenland PROMICE network locations during five-month periods spanning 1 May to 31 September in years 2017 and 2018. For some sites and days, multiple images are available.
Measurements in Antarctica The Specific Surface Area (SSA) retrievals based on OLCI images were evaluated using the Autosolexs time-series at Dome C over the 2016-2017 and 2017-2018 summer seasons. The temporal comparison allows assessing the ability for the OLCI retrieval to capture daily to seasonal variations of the snow surface physical properties. The albedo dataset provided by the Autosolexs instrument was used to derive the SSA of the snow surface at Dome C. The SSA was retrieved by fitting a theoretical model to the observed albedo, based on the analytical asymptotic radiative transfer theory  [19, 22]. The seasonal and daily variations in SSA retrieved from the ground data were well reproduced by the satellite retrievals, with an underestimation of the ground SSA obtained from OLCI (Figure  16 ).
Indeed, unrealistic and significant variations in the SSA were observed for retrievals obtained on the same day at different times (and thus SZA). Although such sub-daily variations were observed in the Autosolexs SSA dataset, the variations are much smaller for the ground-based retrievals, which led us to think that the OLCI retrievals are much more sensitive to the SZA than the Autosolexs measurements. The comparison of the OLCI SSA retrievals with the SSA retrieved from the Autosolexs instrument indicates that the algorithm is suitable to track the temporal evolution of variables describing the physical properties of the snowpack in flat polar regions, which is critical to parameterize and validate snow models. Indeed, exploiting the high revisit frequency over the Poles, the algorithm was able to resolve the short term (weekly, and sometimes daily) variations of the snow conditions, as well as long term trends.
In the other EastGRIP comparison, three observations were made within 2 min of satellite overpass times and during the tandem Sentinel-3A and Sentinel-3B phase of the Sentinel-3B commissioning. This work was part of a JAXA Global Change Observation Mission-Climate (GCOM-C)/Second generation GLobal Imager (SGLI) field campaign by T. Aoki, S. Matoba, M. Niwano, and R. Shimada. In this campaign, SSA was also observed using the IceCube instrument and the data were converted to optically equivalent snow grain diameter using Equation  (32). The OLCI-derived snow grain diameter retrievals are extremely close to these coincident observations (Table  4 ). The absolute difference (12. 3 µm for S3A and 2. 5 µm for S3B) is much smaller than the ~20 µm uncertainty in the field data. This finding must be confirmed for a larger dataset, which is currently under investigation.
In the other EastGRIP comparison, three observations were made within 2 min of satellite overpass times and during the tandem Sentinel-3A and Sentinel-3B phase of the Sentinel-3B commissioning. This work was part of a JAXA Global Change Observation Mission-Climate (GCOM-C)/Second generation GLobal Imager (SGLI) field campaign by T. Aoki, S. Matoba, M. Niwano, and R. Shimada. In this campaign, SSA was also observed using the IceCube instrument and the data were converted to optically equivalent snow grain diameter using Equation  (3. 15). The OLCI-derived snow grain diameter retrievals are extremely close to these coincident observations (Table  4 ). The absolute difference (12. 3 μm for S3A and 2. 5 μm for S3B) is much smaller than the ~20 μm uncertainty in the field data. This finding must be confirmed for a larger dataset, which is currently under investigation.
Funding: This research has been funded by ESRINcontract  4000118926/16/I-NB  and the  ESRIN  contract  4000125043 -ESA/AO/1-9101/17/ I-NB EO SCIENCE FOR SOCIETY. Acknowledgments: Agency (ESA)  studies; the  Scientific Exploitation of Operational Missions (SEOM) Sentinel-3 Snow (Sentinel-3 for Science, Land Study 1: Snow ),  ESRIN  contract  4000118926/16/I-NB  and the  ESRIN  contract  4000125043 -ESA/AO/1-9101/17/I-NB EO   SCIENCE FOR SOCIETY. Additional support came from The Program  for the Monitoring of the  Greenland Ice Sheet (PROMICE) , part of the  Danish Energy Agency  through the  DANCEA program. The work of  Sergey Korkin  is supported by grant " NASA Science for Terra, Aqua and SNPP " (PI:  Alexei Lyapustin ,  17-TASNPP17-0116 ; solicitation  NNH17ZDA001NTASNPP ). EGRIP is directed and organized by the  Center of Ice and Climate at the Niels Bohr Institute.
It is supported by funding  agencies and institutions in Denmark (A. P. Møller Foundation, UCPH) ,  USA (US National Science Foundation, Office of Polar Programs) ,  Germany (AWI), Japan  ( NIPR and ArCS) ,  Norway (UiB and BFS) ,  Switzerland (SNF) ,  France  ( IPEV, IGE ) and China Remote Sens. 2019, 11, 2280 34 of 43 ( CAS and BNU). This work was conducted as a part of the  GCOM-C/SGLI  Project supported by the  Japan Aerospace Exploration Agency (JAXA). The authors acknowledge the support from  Agence Nationale de la Recherche Scientifique  for the scientific traverse in Antarctica and the associated research: project  ANR-14-CE01-0001  (ASUMA). The authors are grateful to  S. Delwart  and  S. Warren  for important discussions related to peculiarities of snow remote sensing from space and snow optical properties.
thaliana sample or a P. vulgaris sRNA dataset obtained from  Formey et al. (2015) , (iv) the sense strands of S. sclerotiorum transcripts and (v) S. sclerotiorum non-coding RNAs from Rfam. The in vitro samples were filtered twice, once using A. thaliana sequences for steps (i)-(iii) and once using P. vulgaris sequences for steps (i)-(iii) to ensure that samples had undergone the same filtering procedures for differential expression analysis. The filtering process resulted in 14 798 914 mapped reads from all in planta samples, 635 493 in vitro reads when filtered against A. thaliana and 756 907 in vitro reads when filtered against P. vulgaris. To determine the origin of sRNA reads in our samples, we analysed size distribution and 5′ nucleotide bias (Fig. 1 ). Nonspecific RNA degradation would result in uniform sRNA size distribution and random 5′ nucleotides  (Mueth et al.
sclerotiorum protein-coding genes, including predicted effector genes from Derbyshire et al. (2017) (Fig. 4C ). The mean distances to the nearest gene were 4898. 56 bp (5′) and 4551. 631 bp (3′) for sRNA loci, and only 2017. 2 bp (5′, P < 2. 2 × 10 -16 , W = 3053600) and 2018. 7 bp (3′, P < 2. 2 × 10 -16 , W = 31125000) for protein-coding genes. The genes encoding effector candidates also resided further from the nearest gene than other protein-coding genes (mean 2451. 0 bp, P = 0. 01005, W = 442360 and mean 2977. 9 bp, P = 5. 37 × 10 -05 , W = 480430 on 5′ and 3′ sides, respectively), albeit to a lesser extent than sRNA loci. In addition to these analyses, we also focused on sRNA loci identified by the software package CoLIde. This program identifies the likely regions in the genome from which sRNAs may be derived based on read length bias and correlation in expression across samples.
thaliana were involved in plant disease resistance, they may contain genetic markers associated with QDR against S. sclerotiorum. To support a role in disease resistance for predicted targets of S. sclerotiorum sRNAs in A. thaliana, we first performed gene ontology (GO) term and PFAM domain enrichment analyses using Fisher's exact test. Among the top 1% gene functions enriched in sRNA targets (Fig. 6A , Data S3, Table  S2 ), several were related to signalling (e. g. GO:0007165, GO:0019199, PF00069 and PF03107) and specifically plant immunity signalling (GO:0019199 and PF13855). Some annotations related to plant defence responses (GO:0006952, GO:0052542 and PF12662) were also in the top 2. 5% enriched. Other annotations among the top 1% enriched for predicted S. sclerotiorum sRNA targets included ontologies and domains related to hormone metabolism (GO:0009737 and GO:0046345) and redox metabolism (GO:0055114, GO:0019825 and PF00175).
RNA extraction and sequencing Small RNAs were extracted and sequenced as described in Badet et al. (2017b). Briefly, samples harvested in planta and from S. sclerotiorum grown in vitro in potato dextrose broth were processed using the NucleoSpin miRNA kit (Macherey-Nagel, Düren, Germany) following the instructions of the manufacturer. Small RNA sequencing was performed by Fasteris SA (Geneva, Switzerland) on a HiSeq 2500 instrument using 50 bp single-reads. The data is available at NCBI (accession SRP151049). Test for down-regulated expression of fungal sRNA target genes in A. thaliana To test whether predicted targets of fungal sRNAs in A. thaliana were more likely to be down-regulated during infection, we assessed the LFC in the expression of these genes during fungal infection relative to controls.
First, we compared the median LFC of all sRNA targets with non-targets using a Wilcoxon test. Second, we used a non-parametric randomization test based on the tests described in Hooton  (1991). A randomization in our test consisted of assigning the status of 'sRNA target' and 'non-sRNA target' to A. thaliana genes randomly and calculating the difference between mean LFC values (ΔLFC). For each randomization, we calculated ΔLFC as the mean LFC of non-sRNA targets minus the mean LFC of sRNA targets. The P values obtained from the test were the number of times ΔLFC was lower than the actual mean ΔLFC difference between sRNA targets and non-targets. We performed this analysis on four datasets: (1) predicted A. thaliana targets of S. sclerotiorum sRNAs identified in this study during infection with S.
Analysis of the genomic context of sRNA loci Two analyses of the genomic context of sRNAs were performed. First, all loci where sRNAs mapped, including multiple mappings, were considered. The positions of these were calculated with respect to repeat annotations from  Derbyshire et al. (2017)  using the Bedtools module 'intersect' with the option '-d'. Then, the distances of each of these to the nearest 5′ and 3′ genes were calculated using a Python script. The same was done for each gene to the nearest neighbouring gene from the 5′ or 3′ end. These distances were compared between genes and sRNA mapping sites using a Wilcoxon's test. Distances for genes that were either predicted effectors (Derbyshire et al. , 2017) or not predicted effectors were also compared using a Wilcoxon's test. To assess how polymorphic sRNA loci were, SNP content was assessed based on a panel of 25 Illumina-sequenced S.
sclerotiorum isolates aligned to the 1980 reference genome. These isolates were derived from  Derbyshire et al. (2019)  and we followed the same mapping and variant calling procedures described in this publication. Importantly, we included a depth filter to remove repeat-induced read mappings. The genome was split into 10 kb sliding windows using Bedtools and the number of overlaps with SNPs in VCF format for each window were calculated with the Bedtools module 'intersect' with the option '-c'. The windows were then divided into those containing sRNA mapping loci and those not containing them and the number of SNPs for each type of window was compared using a Wilcoxon's test. Similar analyses were conducted for sRNA loci predicted by the software package 'CoLIde'  (Mohorianu et al. , 2013).
To assess distance to the nearest neighbouring gene we used the Bedtools module 'closest'. We either counted genes overlapping the sRNA loci (+OLGs) and gave them a value of 0 for distance or considered the nearest non-overlapping gene (-OLGs). We compared these distances with the distance between each gene and the nearest neighbouring gene using a randomization test (described in detail under 'Test for down-regulated expression of fungal sRNA target genes in A. thaliana'). We performed the same test as above for the number of polymorphisms in sliding windows containing CoLIde sRNA loci vs the number in sliding windows not containing CoLIde sRNA loci.
13. 0) and S. sclerotiorum non-coding RNAs downloaded from Rfam were discarded sequentially. Only reads that passed all of these steps and that were subsequently exactly mapped to the reference genome of S. sclerotiorum 1980  (Derbyshire et al. , 2017)  were kept. The same procedure was followed for P. vulgaris using sRNA sequences predicted for this species obtained by  Formey et al. (2015). For differential expression analysis, the two filtering procedures for the two different hosts were both applied to the in vitro sample to create two different filtered in vitro datasets for analysis. This controlled for differences in filtering procedures that may cause artificial observation of changes in sRNA expression. To determine whether sRNAs were likely derived from higher confidence sRNA biogenesis loci, we used the software module CoLIde  (Mohorianu et al.
Messenger RNA sequencing and differential expression analysis The expression of A. thaliana Col-0 genes during S. sclerotiorum infection corresponds to expression at the edge of necrotic lesions relative to non-challenged plants, as reported in Badet et   al. (2017a), data downloaded from the NCBI Gene Expression Omnibus (accession GSE106811). To determine expression of A. thaliana genes during B. cinerea infection, we obtained a dataset from  Coolen et al. (2016)  from the NCBI sequence read archive (https ://www. ncbi. nlm. nih. gov/sra). The accessions were SRX1705130, SRX1705129 and SRX1705128, mock-inoculated plants and SRX1728, SRX1729, SRX1730, 24 hours post-inoculation with B. cinerea. DESeq2 v. 1. 10. 1 was used to assess LFC between the mock-inoculated and B. cinerea-infected samples.
SERK1 and SERK2 receptor kinases function redundantly as an important control point for sporophytic development controlling male gametophyte production. The mRNA is cell-to-cell mobile. Assoc. Gene id Symbol Description and annotation LFC score AT1G10760 GWD, GWD1, SEX1, α-glucan, -5. 0339 2. 8870 SOP, SOP1, STARCH EXCESS 1 (P = 0) PF01326 (PPDK_N); GO:0016310 phosphorylation; GO:0005524 ATP binding; -2. 5890 GO:0016301 kinase activity (P = 7. 6e -05 ) AT1G22060 Sporulation-specific protein -3. 4514 2. 2089 PF10358 (NT-C2) (P = 0) AT1G34210 ATSERK2, SERK2, -2. 2539 2. 2100 SOMATIC EMBRYOGENESIS RECEPTOR-LIKE KINASE 2 PF08263 (LRRNT_2); PF00069 (Pkinase); PF00560 (LRR_1); GO:0006468 protein (P = 0) phosphorylation; GO:0007165 signal transduction; GO:0004672 protein kinase activity; GO:0005102 signalling receptor binding; GO:0005515 protein binding GO:0005524 ATP binding -1.
993 (P = 1. 22e -06 ) AT1G67120 ATMDN1, MDN1, Homologue of the yeast MDN gene, which encodes a non-ribosomal protein -2. 6246 2. 0966 MIDASIN 1 involved in the maturation and assembly of the 60S ribosomal subunit. In Arabidopsis, it is essential for female gametogenesis progression. PF07728 (AAA_5); GO:0000027 ribosomal large subunit assembly; GO:0005524 (P = 0) ATP binding; GO:0016887 ATPase activity; GO:0005634 nucleus AT2G13690 PRLI-interacting factor -4. 6996 2. 1121 BTB/POZ domain-containing protein (IPR038920) (P = 0. 0006) AT3G45240 ATSNAK2, Geminivirus Rep interacting kinase. GRIKs are SnRK1 (SNF1-related kinases) acti- -0. 0043 2. 4200 GEMINIVIRUS REP INTERACTING vating kinases. Both GRIKs specifically bind to the SnRK1 catalytic subunit and phosphorylate the equivalent threonine residue in its activation loop in vitro.
(P = 0. 1558) KINASE 1, GRIK1 PF00069 (Pkinase); GO:0006468 protein phosphorylation; GO:0004672 protein -2. 4805 kinase activity; GO:0005524 ATP binding (P = 0. 01142) AT3G49400 Transducin/WD40 repeat-like superfamily protein -2. 9998 2. 2120 PF12657 (TFIIIC_delta); PF00400 (WD40); GO:0005515 protein binding (P = 0. 0002) AT4G26780 AR192, MGE2, Unknown function; thermotolerance to chronic heat stress in Arabidopsis -4. 4207 2. 7703 MITOCHONDRIAL GRPE 2 protein folding; GO:0000774 adenyl-nucleotide exchange factor activity; GrpE nucleotide exchange factor (IPR000740) PF01025 (GrpE); GO:0006457 (P = 0) GO:0042803 protein homodimerization activity; GO:0051087 chaperone binding AT4G28600 NO POLLEN Calmodulin-binding protein that is expressed in pollen, suspension culture cells, 3. 1327 4. 1000 GERMINATION flowers and fruits RELATED 2, NPGR2 PF13181 (TPR_8); PF14559 (TPR_19); GO:0005515 protein binding (P = 0) AT4G36120 Filament-like protein (DUF869) -1.
Data As the user-involving processes had an opportunistic emergent design, the available data were heterogeneous. Data from the 4 focus groups (DK FG #1-4) and 4 of the workshops (DK WS #1-2 and AUS WS #4-5) were collected through (1) audio recording of the focus groups and (2) qualitative field notes summarizing suggestions and discussions in workshops. In AUS WS #3, data were collected through  (1)  written comments made by participants, (2) hand-drawn mock-ups, and (3) qualitative field notes written during the workshops. These latter data were independently knowledge-translated by a representative user team (young people, supportive others, and clinician and service staff) (Tracey Davenport, personal communication January 2019). In addition, written notes produced during the workshops as well as the software developers' list of suggestions were collected and analyzed.