dataesr/scientific-paragraphs-categorization huggingface dataset:
Angular distributions were obtained in the laboratory frame of reference in 5 • bins, all individually efficiency corrected, with coefficients obtained from GEANT4 simulations  [45, 61]. Depending on the γ -decay pattern, multiple γ gates could be used to obtain an angular distribution for a specific state. In this case, the angular distributions were created for all these possible gates, including individual corrections for γ -detection efficiency, before creating the global angular distribution from the weighted average. If applicable, delayed-coincidence data were included for states decaying via the isomeric state at 1007 keV. Only for the ground state a single gate on excitation energy was used because of the lack of (delayed) coincident γ rays.
ACKNOWLEDGMENTS This work was funded by  FWO (Fonds Wetenschappelijk Onderzoek -Vlaanderen) (Belgium) ,  GOA/2010/010  ( BOF KU Leuven ), the  Interuniversity Attraction Poles Programme  initiated by the  Belgian Science Policy Office (BriX network   P7/12 ), the  European Commission  within the  Seventh Framework Programme through I3-ENSAR  (Contract No. RII3-CT-2010-262010 ), the  European Research Council  (Grant No. ERC-2011-AdG-291561-HELIOS ), the  Slovak Research and Development Agency  (Grant No. APVV-0105-10 ),  BMBF  under Contracts No. 06KY9136I , No. 05P12PKFNE , No. 06MT7178 , and No. 06MT9156 , and the  Maier-Leibnitz-Laboratorium, Garching.
Gut microbiota analyses Genomic DNA was extracted from the caecal content using a QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany), including a bead-beating step. Absolute quantification of the bacterial taxa was performed by qPCR (primers presented in Supplementary Table  S1 ). Full description of the qPCR procedures is provided in the Supplementary information. Illumina sequencing was performed as previously described  (Krumbeck et al. , 2015). The V5-V6 region of the 16S rRNA gene was amplified by PCR with modified primers. The amplicons were purified, quantified and sequenced using an Illumina Miseq to produce 2x300-bp sequencing products at the University of Minnesota Genomics Center. Initial quality-filtering of the reads was conducted with the Illumina Software, yielding an average of 148 370 pass-filter reads per sample.
Quality scores were visualized and reads were trimmed to 240 bp. The reads were merged with the merge-Illumina-pairs application  (Eren et al. , 2013). For samples with 450 000 merged reads, a subset of 50 000 reads was randomly selected using Mothur 1. 32. 1  (Schloss et al. , 2009)  to avoid large disparities in the number of sequences. Subsequently, the UPARSE pipeline implemented in USEARCH v7. 0. 1001  (Edgar, 2013)  was used to further process the sequences. Putative chimaeras were identified against the Gold reference database and removed. Clustering was performed with a 98% similarity cutoff to designate operational taxonomic units (OTUs). Non-chimaeric sequences were also subjected to taxonomic classification using the RDP MultiClassifier 1. 1 from the Ribosomal Database Project  (Cole et al.
, 2014)  for phylum to genus characterization of the faecal microbiome. The phylotypes were computed as percent proportions based on the total number of sequences in each sample. Alpha-and beta-diversity indexes were calculated using QIIME  (Caporaso et al. , 2010). The LDA effect size was computed and plotted using LEfSe  (Segata et al. , 2011). The complete protocol, statistical analysis and accession numbers are provided in the Supplementary materials and methods.
Acknowledgements We thank  Véronique Peucelle ,  Remi Selleslagh  and  Bouazza Es Saadi  for their skilled technical assistance and  Alexandra Collignon ,  Mireille Alhouayek ,  Florence Sohet ,  Céline Druart ,  Emilie Catry ,  Nuria Salazar Garzo  and  Maud Alligier  for help with the in vivo experiments and tissue sampling. The C26 cell line was a kind gift from  Dr Mario Colombo ,  Fondazione IRCCS Istituto Nazionale Tumori, Italy. We thank the  University of Minnesota Genomics Center  for performing the pyrosequencing. LBB and PDC are, respectively, a Postdoctoral Researcher and a Research Associate from the  F. R. S. -FNRS  ( Fond National de la Recherche Scientifique, Belgium ). NMD and PDC are recipients of grants from  FNRS. PDC is a recipient of the  ERC Starting Grant 2013  ( European Research Council , Starting grant  336452-ENIGMO ).
Eva Bellemain is employed by SPYGEN. This does not alter the authors' adherence to the PLOS ONE policies on sharing data and materials We here present the first analysis of collared (Dicrostonyx groenlandicus) and brown (Lemmus trimucronatus) lemmings' winter diets based on DNA metabarcoding of fecal pellet contents. Considering that these two species use similar habitats during winter in the Canadian High Arctic  [20] , we hypothesized that they should minimize interspecific competition by showing little interspecific diet overlap. Our hypothesis appeared strong because the two species are thought to have different dietary preferences  [31]. We also examined how plant availability affected diet and quantified food selection and diet diversity of each species. We finally discuss the consequences of our findings for species interactions in Arctic food webs.
Lemming species identification using genetic methods We took a random sample of three to five pellets from each 15-pellet sample used for visual species identification. To verify the accuracy of the genetic identification, we analyzed 6 muscle samples of each lemming species using the same methods. These samples were provided by concurrent studies on stable isotopes  [3, 53]. We extracted DNA of these samples, i. e. both pellets and muscle, using methods described in  [40]  and in Supporting information (S1 Text). The same DNA extracts were then used both for lemming species identification and for diet analysis. For both lemming species, we first downloaded from GenBank all the available mitochondrial Cytochrome Oxydase I (COI) sequences of the standard barcode for animals  [54]. After aligning the retrieved sequences, we calculated a consensus sequence for each species.
org/obitools/doc/index. html). As reference for the primer pair g-h, we used a combined reference library of 815 arctic  [57]  and 835 north boreal  [58]  vascular plant species. Sequences with poor match with these reference libraries were compared with data retrieved from the EMBL Nucleotide Sequence Database (version 111, available at http://www. ebi. ac. uk/embl/). For the c-h primer pair, we used the same taxonomic reference library of arctic and boreal vascular plant species, supplemented with a new library of 455 arctic and boreal bryophyte species (see details below and a detailed list of taxa in S2 Table ). For both primer pairs, the retrieved taxon lists were compared with the local flora of Bylot Island (Benoit Tremblay, in prep). Among all sequences identified at the family or genus level, 0.
2% belonged to taxa absent from the site (e. g. Pinus, Picea, Betula). These were considered identification errors or contamination and removed from the dataset. At the species level, some identified species were absent from the general area but in most cases, a closely related species was known to be present but not included in our reference libraries. In those cases, we assigned the sequences to the species known to be present at the site. Details of sequence cleaning and annotation are described in  [33]  and in Supporting information (S1 Text).
In some cases, new specimens were selected and sequenced after the first round of library cleaning. Formatting of the reference library, including annotations of the sequences, was carried out using the OBITools. The final library was formatted by in silico PCR on the obtained sequences (using the program ecoPCR;  [59] ), with the trnL c and trnL h primers (five mismatches allowed between primer and the target sequence). The library is deposited in the Dryad Digital Repository (http://datadryad. org/), doi: 10. 5061/ dryad. 4rr39.
Plant availability To assess plant availability, we used the data of  [44]  (data given in S3 Table ). In their study, plant biomass was sampled at the end of the 2010 growing season (early August) in snowbeds (n = 16) where signs of lemming use had been found in previous years. Plant biomass data thus represent what was available for the lemmings at the onset of winter. Snowbeds were sampled in two different habitats; stream gullies (n = 8) and mesic tundra (n = 8). The snowbeds were separated from each other by at least 50 m. Within each snowbed, one quadrate (20 × 50 cm) was located randomly and plant availability was estimated. Vascular plant biomass was measured by clipping all aboveground vascular plant material at the ground level. Dead material was removed and the remaining live material was sorted by family, genus or species, dried to constant mass at 45°C, and weighted.
Data analyses The resulting datasets of lemming diets consisted of a count of sequences per plant taxon per pellet sample. We combined the information obtained from the two primer pairs as follows. We first calculated the proportion of each plant family for every sample (sample representing pellets of a given species from a given winter nest) based on the number of DNA sequences identified, for each primer pair separately. For the g-h primer pair, we discarded the few bryophyte sequences identified and retained only the vascular plants (including ferns) for those calculations. We then weighted (i. e. multiplied) the proportion of each vascular plant family of each sample in the g-h dataset by the corresponding, overall proportion of vascular plants determined with the c-h primer pair. Finally, we combined those weighted proportions of vascular plant families with the proportions of moss families determined with the c-h primer pair.
For one collared lemming sample, amplification by the g-h primer pair failed and we thus used data from the c-h primer for proportion of vascular plant families. Even though DNA metabarcoding data for plants probably reflects small rodent diets well  [40] , some biases may still occur  [34, 40]  and we therefore also report the number of samples in which a given taxon was found (S4 Table ). We tested for differences in proportion of various food items (i. e. monocots, dicots and plant families accounting for >2% of the diet) between habitats (three levels) and lemming species (two levels) using ANOVAs. Data were rank-transformed before analysis because variances were heterogeneous and residuals were not normally distributed. Interactions between habitat and species were examined with the aligned rank transformation  [60]  but were never significant (P > 0.
To ensure that the index values of the two species were comparable in spite of different sample size, we resampled 100 times a random sample of brown lemming individuals corresponding to the sample size of collared lemmings (n = 22). We then compared the simulated IS for brown lemmings (i. e. average of the resampled IS) to their observed IS (i. e. acquired by including all individuals). As these values were identical, we did not use the resampled data in further analyses. Finally, we tested whether the lemming species differed in terms of IS using ANOVA. Diet overlap and diversity analyses were done in the software R 3. 0. 3. [64] , and package RInSp  [65]  was used for the within species analysis. We evaluated food selection by combining data on diet composition and plant availability. We used the selection ratio of Manly  [66] , which is the ratio of mean proportion of food item i in the diet over mean availability of that item.
Taxonomic precision of lemming diet data A total of 45,633 sequences were obtained with the g-h primer pair (608 sequences/sample on average) and 22,707 with the c-h primer pair (299 sequences/sample on average). Overall, 99. 5% of the sequences were identified at the family level, 32. 6% at the genus level and 7. 5% at the species level. The low resolution at genus and species levels was largely caused by Salicaceae, a common family in our samples (see results) for which the g-h primer pair has in general low resolution  [57]. Excluding this family for the g-h primer pair, 72. 7% and 16. 7% of the sequences were identified at the genus and species levels, respectively.
Lemming winter diets On Bylot Island, mosses were barely consumed by collared lemmings and their winter diet was dominated by dicotyledons, in line with previous studies  [8, 30, 32]. However, within dicotelydons, variable patterns of consumption have been found among studies. A dominance of Salix was found both in Northern Alaska and Northern Greenland  [19, 30]  but of Dryas at Pearce Point and Igloolik in Northern Canada  [8, 32]. These dietary differences seem to largely reflect differences in availability among sites as willow was abundant and Dryas scarce in Alaska but List of MOTUs (molecular operational taxonomic units) at the subfamily, tribe, genus or species level, identified in lemming winter diets on Bylot Island. 1 Species included in Bryaceae in the data analysis, but in Mniaceae in the Bryophyte reference library.
DNAbased identification of mosses species in the diets of arctic herbivores is currently developing  [33, 41, 85] , as microhistologic identification of moss species in diet samples is virtually impossible. Reliable reference data of bryophyte DNA is therefore essential especially for the Arctic. Existing public reference DNA databases such as GenBank provide more limited information on bryophyte taxa as compared to the vascular plant groups. Moreover, specimen identification errors, taxonomic complexities and discrepancies between different nomenclatures may lead to erroneous identification of the DNA sequence data. Local reference libraries, as used for both vascular plants  [57, 58]  and bryophytes in this study, are constructed based on material that is collected and verified by taxonomic experts, and archived and stored in museum collections for future reference.
Due to the exceptional characteristics of the ChArMEx database, the analysis of the microphysical properties profiles' temporal evolution was also possible. An in-depth analysis was performed mainly at the Granada station because of the availability of continuous lidar measurements and frequent AERONET inversion retrievals. The analysis at Granada was of special interest since the station was affected by mineral dust during the complete analyzed period. LIRIC was found to be a very useful tool for performing continuous monitoring of mineral dust, allowing for the analysis of the dynamics of the dust event in the vertical and temporal coordinates. Results obtained here illustrate the importance of having collocated and simultaneous advanced lidar and sun-photometer measurements in order to characterize the aerosol microphysical properties in both the vertical and temporal coordinates at a regional scale.
, 2012)  field campaigns. Within the ACTRIS summer 2012 campaign, the European lidar network (EARLINET)  (Pappalardo et al. , 2014)  performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements  (Sicard et al. , 2015). The exercise consisted of continuous lidar measurements during a 72 h period in July 2012 at different European sites. Most of those lidar data have been successfully assimilated by a regional particulate air quality model to improve 36 h operational aerosol forecasts in terms of both surface PM and aerosol optical depth  (Wang et al. , 2014). Our study takes advantage of those continuous lidar measurements combined with simultaneous sun-photometer data to perform a characterization of the vertical distribution of the aerosol microphysical properties at different European stations with LIRIC.
The temporal evolution of the aerosol microphysical properties is also analyzed when the continuity of the inverted data is available. To our knowledge, it is the first time that the LIRIC algorithm has been applied in a continuous and automated way to retrieve simultaneous and continuous data acquired at different stations, proving the algorithm's ability to provide reliable information about microphysical properties with high spatial and temporal resolution. In addition, this exceptional aerosol observational database is used for the spatio-temporal evaluation of different regional mineral dust models.
Measurement strategy During the summer of 2012, an intensive measurement campaign was performed in the framework of ChArMEx and EMEP in the Mediterranean basin at 12 ground-based lidar stations throughout Europe. The main aim of these measurements was to obtain an experimental vertically resolved database for investigating aerosol radiative impacts over the Mediterranean basin using 3-D regional climate models. The extensive lidar database acquired during this campaign combined with AERONET regular measurements represents a unique opportunity to evaluate the performance of LIRIC microphysical inversion retrieval during the event in both temporal and spatial (horizontal and vertical) coordinates, proving the utility of combined measurements and the potential of the LIRIC algorithm for routine aerosol microphysical properties measurements.
Model description and validation strategy Models of dust emission, transport, and deposition are used as a tool to understand the various aspects that control distributions and impacts of dust. While global models of the dust cycle are used to investigate dust at large scales and longterm changes, regional dust models are the ideal tool to study in detail the processes that influence dust distribution as well as individual dust events. The analysis of the aerosol microphysical properties with LIRIC using the ChArMEx comprehensive database was used here for the evaluation of a set of four regional mineral dust models. This model evaluation was performed for both the vertical and horizontal coordinates and the temporal evolution. Firstly, the spatial distribution of the mineral dust was examined by using the experimental data from the five EARLINET/AERONET sites considered in the present study.
Dust optical depth (at 550 nm) provided by four different regional mineral dust models (BSC-DREAM8b, NMMB/BSC-Dust, DREAM8-NMME, and the regional version of COSMO-MUSCAT) was used at this stage. Experimental data were used here just to corroborate the presence or non-presence of mineral dust at the different regions and periods indicated by the models. The BSC-DREAM8b and DREAM8-NMME models are based on the Dust Regional Atmospheric Model (DREAM) originally developed by  Nickovic et al. (2001). The main feature of the updated version of the model, BSC-DREAM8b (version 2), includes an eight-bin size distribution within the 0. 1-10 µm radius range according to  Tegen and Lacis (1996) , radiative feedbacks  (Pérez et al. , 2006a, b) , and upgrades in its source mask  (Basart et al. , 2012). The BSC-DREAM8b model provides daily dust forecasts at the Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS, 2016).
The temporal evolution of the modeled vertical profiles was evaluated in more detail only at Granada, which was the station most affected by the dust outbreak during the analyzed period, and thus provided a more extensive database. Since LIRIC provides volume concentration profiles, a conversion factor was needed to obtain mass concentration. This conversion factor was the density of the aerosol particles, namely 2. 65 g cm -3 for the coarse mode (1-10 µm) and 2. 5 g cm -3 (0. 1-1 µm) for the fine mode  (Pérez et al. , 2006a, b). In addition, the initial vertical resolution of the different models and LIRIC was established to a common value of 100 m, in order to obtain a compromise between the loss of information from LIRIC and from the different models, following a similar procedure to that in  Binietoglou et al.
In our study, model output profiles were retrieved every 3 h and compared to LIRIC retrievals during the 3 analyzed days. Only daytime data are presented here (from 06:00 to 18:00 UTC) because of the limitations of LIRIC retrieval during night-time. Due to the difficulties of the models in correctly representing the convective processes occurring within the planetary boundary layer and PBL-free troposphere interactions and the photochemical reactions producing secondary aerosols at the considered resolution, the lowermost parts of LIRIC profiles (affected by these processes) were not considered in the comparison presented here. Only data between 2000 m a. s. l. , which is the mean value of the PBL height during summer at Granada  (Granados-Muñoz et al. , 2012) , and the highest value (up to between 5 and 6 km) provided by LIRIC were included in the comparisons.
All these data are usually related to the presence of mineral dust in the station and the temporal evolution of the analyzed properties clearly suggests a decrease in the mineral dust event intensity throughout the analyzed period and a possible mixing or aging of the mineral dust. At the Barcelona station no AERONET data were available on 9 July. During 10 and 11 July, τ 440 nm values were relatively high and quite constant (around 0. 30) and the AE(440-870 nm) values were larger than 1. 5, indicating a strong contribution of fine aerosol particles. In the columnar volume size distributions, similar values for the fine and coarse modes were observed on 10 July, but larger values of the fine mode were obtained on 11 July. Therefore, it can be inferred from these data that the impact of the northern African aerosol plume was almost negligible at this station.
Temporal evolution of the aerosol microphysical property profiles The continuous analysis of the aerosol microphysical properties profiles during the 3 days provided very valuable information about the dynamics of the aerosol layers and revealed LIRIC's potential to retrieve information with a high temporal resolution. Because of the uninterrupted lidar measurements at Granada from 12:00 UTC on 9 July 2012 to 00:00 UTC on 12 July and the frequent AERONET retrievals due to good weather conditions, a more detailed analysis was performed at this station. A total of 60 different LIRIC retrievals were performed based on 60 lidar data sets and 21 AERONET inversion products. The retrieval of microphysical properties was performed using 30 min averaged lidar data (in order to reduce noise on the lidar profiles) and the closest in time AERONET retrieval, considering only those data with time differences lower than 3 h.
, 2011). The dust outbreak analyzed here started over the Granada station on 7 July 2012 as indicated by sun-photometer data and the model forecast from previous days (not shown). Thus, it was already well developed when the intensive measurement period started. The 5-day backward trajectories analysis performed with the HYSPLIT model indicated that the air masses arriving at Granada on 9 and 11 July came from Africa, passing by the northern African coast above 2500 m a. s. l. and from the North Atlantic Ocean through the southwestern Iberian Peninsula below this altitude (Fig. 6 ). On 10 July the air masses came from the central part of the Sahara through the northern African coast for heights above 5000 m a. s. l. , from the Atlantic Ocean going along the coast of Africa between 2500 and 5000 m a.
g. ,  Sicard et al. , 2015). The different models correctly forecast the dust plume leaving the north of Africa and moving towards the east and the dust plume reaching Athens, as also indicated by satellite data. However, the decrease in τ 550 nm values with time observed with satellite data and in LIRIC profiles is not well captured by any of the different models. Regarding the extension of the dust event, in general it is better captured by BSC-DREAM8b and NMMB/BSC-Dust, whereas COSMO-MUSCAT and DREAM8-NMME tend to overestimate the mineral dust horizontal extension when compared to the satellite data. Focusing on the five stations analyzed in this study, the models showed that the Granada station was affected by the mineral dust outbreak during the whole analyzed period, in agreement with the analyzed data.