dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). The seven teams who collated the in situ datasets have provided updates since previous publications and details about instrumentation, gaps in the original data, and methods for gap filling. Such additions are first steps towards being able to quantify uncertainty in observed data, without which "meaningful evaluation of a model is impossible"  (Clark et al. , 2011). As the sites have already been described in previous publications (Table  1 ), Sect. 2 describes the in situ meteorological and evaluation data with the user in mind, highlighting differences and similarities between the sites but also areas where both instrumentation and modification of the data through modelling may increase uncertainty. Section 3 introduces the GSWP3 data and the site-specific downscaling methods. Finally, the Discussion highlights the importance of sharing data to identify errors and to improve practices in geosciences.
Meteorological forcing data All of the models participating in ESM-SnowMIP  (Krinner et al. , 2018)  operate on energy balance principles, requiring incoming shortwave and longwave radiation fluxes, solid and liquid precipitation rates, air temperature, humidity, wind speed, and air pressure as forcing data. Figure  1  shows climatological monthly averages of all meteorological forcing variables except air pressure at all sites; note that "climatological" here refers to the time period for which variables are available at each site. Wind speeds provided in the datasets are measured at variable heights but are normalised for Fig. 1g  to 10 m height assuming a logarithmic wind profile such that u (10) = u (z 1 ) ln (10 -d) z 0 ln (z 1 -d) z 0 , where u is wind speed measured at height z 1 , d is a displacement height (2/3 of vegetation height at BERMS and 0 at  other sites), and z 0 is a roughness length (1/10 of vegetation height at BERMS and 0.
The consequences of this choice at three example sites (SAP, OJP, and SWA) are compared to measurements at Weissfluhjoch in Fig. 3. Although the consequences are not very significant at warmer sites such as Sapporo or Col de Porte (not shown), they are clear in data from colder sites such as Old Jack Pine and Swamp Angel, where relative humidity with respect to water is never observed much above the ice saturation point for a particular temperature. However, measurements from a chilled mirror dew point hygrometer at Weissfluhjoch show relative humidity with respect to ice can reach 100 %. In homogenising the datasets, relative humidity has been limited to a maximum of 100 % and converted to specific humidity using the site calibrations. To avoid the ambiguity in relative humidity, only specific humidity is provided in the datasets.
Modelling and modification to in situ data Raw data are rarely used in snow modelling. At the very least, some time averaging of samples measured over very short intervals (seconds) is required. The longest time period used for averaging in the data occurs for air pressure for which the temporal coefficient of variation in pressure is always very small: a single value averaged for the site elevations is used when continuous measurements are not available (CDP, RME, SNB and SWA). For other variables, modelling fills, modifies, or provides consistency in a dataset. At Col de Porte and Sapporo, where data outside of the snow season have not been published, all meteorological data are filled with downscaled (CDP) and bias-corrected (SAP) meteorological reanalysis data (publication of summer data for Col de Porte started in 2015;  Lejeune et al.
, 2019). Radiation and wind speed at Sodankylä are measured above the canopy, but evaluation data are measured in a nearby clearing. For consistency, the meteorological variables were modified by  Essery et al. (2016)  to emulate belowcanopy measurements. Sky view fraction and transmissivity were calculated from hemispherical photographs to modify shortwave radiation such that the effects of shading were accounted for. Sky view fraction was also used to account for longwave emission from nearby trees in the modification of longwave radiation. Wind speed was scaled down to 2 m height using a ratio obtained from an anemometer installed for 1 week in the clearing. Finally, at Reynolds Mountain East, where the data starts in 1988, longwave radiation measurements started in 2002. For consistency, all longwave radiation is modelled, but measured data are used to provide information on seasonal and diurnal variations (e.
Evaluation data The largest uncertainties associated with snow in climate change predictions relate to its albedo and to its insulative properties. Successive IPCC reports have noted that Earth system models (ESMs) often underestimate soil temperatures at high latitudes  (Randall et al. , 2007; Flato et al. , 2013; Koven et al. , 2013) , thus having implications on assessing the permafrost carbon feedback, i. e. the amplification of surface warming from carbon emissions released by thawing permafrost. Equally, model spread over snow-albedo feedback remains a major source of uncertainty in quantifying the contribution of decreasing snow cover to climate warm-ing. Long-term datasets as presented here are therefore essential to evaluate model performance and to improve model representations of snow-soil-atmosphere interactions.
GSWP3 forcing data were generated by a run of the Global Spectral Model at T248 (approximately 50 km) resolution nudged at each pressure level with meridional-and zonalwind and air temperature from the 20th Century Reanalysis  (Compo et al. , 2011) , followed by bias corrections described in  Weedon et al. (2011)  using observations of precipitation, air temperature, and surface radiation. All of the variables required for forcing land surface models are provided on a 0. 5 • global grid and 3 h time steps. For ESM-SnowMIP, 1980-2010 forcing data have been extracted for GSWP3 grid cells containing reference sites and interpolated to 1 h time steps. The longer time period provides more variability for investigating the sensitivity of models to trends in forcing data. These data would also allow rerunning LS3MIP experiments at reference sites with models that do not have capabilities for global runs, but a complication is immediately apparent from the comparisons of site and grid data in Fig.
Anomalous features in precipitation phase partitioning based on surface observations have been attributed to the mechanisms of snow formation as cold continental air masses flow over the Sea of Japan  (Jennings et al. , 2018). Annual mean temperature and snowfall variations are shown in Figs. 10 and 11 for the in situ and bias-corrected GSWP3 data at all sites. Although only mean errors for the periods of overlap have been removed, there is generally good correlation between annual means of GSWP3 data and site observations for overlapping years. Table  5  gives linear trends fitted to the in situ and bias-corrected GSWP3 annual mean temperatures and snowfall. 1998-2009 observations at BERMS show decreasing temperatures and increasing snowfall after the Saskatchewan drought of the early 2000s, but there are negligible trends in the longer GSWP3 series.
Discussion A number of errors were identified in the datasets in the course of the study. Firstly, we noted that snowfall at the Old Aspen was much lower than at the Old Black Spruce and Old Jack Pine during the 2007/08 winter. It was subsequently found that a gauge malfunction in November and December 2007 was not identified at the quality control (QC) stage. Secondly, two other errors were identified by decomposing time series: trend analyses showed an increase in wind speed at the Senator Beck basin sites from October 2012 to the end of the dataset in October 2015. Both sites measure wind speed at two heights; the lower wind speed measurements were used for the first 17 years of the dataset, but the upper wind speed was accidentally used for the last 3 years. At the same sites, instrument recalibration led to a small but statistically significant increasing trend in longwave radiation.
These errors were included in the preliminary ESM-SnowMIP results shown in  Krinner et al. (2018) ; erroneous years will either be neglected in future publications, or models will be forced with the corrected datasets which are published alongside this paper (see Sect. 6). While unfortunate, such errors are symptomatic of longterm datasets for which consistent maintenance and data collection are problematic. Firstly, by definition, long-term monitoring stations might have been installed before metadata were kept electronically (and before the word "metadata" was invented in 1983; Merriam-Webster, 2018) and when information about changes of instruments or recalibrations was in notebooks which might never have been digitised, have now been lost, or never even existed. Equally, improvements in data storage capacities mean that temporal sampling intervals are shorter than they were.
For example, measurements at Reynolds Mountain East were initially made every 15 min and averaged to hourly values; currently, 10 s samples and 5 min averages are aggregated to hourly values for most variables. Such factors are known to affect the values of meteorological variables  (Hupet and Vanclooster, 2001) , but it is beyond the scope of this study to attempt to quantify their contributions to errors or variations in the datasets. Secondly, immediate use of the data allows instrument malfunctions to be identified quickly. For example, a power supply failure was not identified at Sodankylä for 52 d in September and October 2011 because data were being collected but not used; more frequent QC checks are now in place. Thirdly, long-term monitoring stations are susceptible to funding cycles and to changes in climate change policies by successive governments.
For example, BERMS, which were established in 1994, had the most frequent site visits from 2001 to 2008, but changing priorities led to less frequent snow surveys after 2008, with only one in the 2009/10 snow season. Finally, while automated QC protocols are in place, some checks require a subjective interpretation of the data and can therefore depend on just one person to identify errors due to malfunction, snow deposition on instruments, etc. Reliance on subjectivity or local knowledge -which in some cases is advocated as mentioned in Sect. 2. 1. 2 to choose the best method to correct undercatch in precipitation -diminishes the likelihood of the dataset being reproducible. In a discipline like geoscience, in which uncertainties and errors are required to be quantifiable, it is important to acknowledge that subjectivity is not.
Conclusion It is hoped that one of the legacies of ESM-SnowMIP will be for the datasets presented in this paper to be used as benchmarks for model development and to facilitate improvements in snow modelling. Cold region processes have been a major source of uncertainties in previous IPCC reports. The sparsity of long-term high-quality datasets in cold regions in the past may have contributed to this if one considers that ESMs are run globally, but their snow schemes are generally evaluated at a small number of sites; the first iteration of SnowMIP  (Etchevers et al. , 2002)  16 years ago included only one longterm (15-year) dataset and three short-term (fewer than two snow seasons) ones. Meta-research argues that it is mislead-ing to emphasise statistically significant findings of any single team; what matters instead is the totality of the evidence  (Ioannidis, 2005).
Ménard et al. : Datasets for snow modelling at 10 reference sites 3 Large-scale meteorological forcing data for reference site simulations The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP; van den Hurk et al. , 2016) contribution to CMIP6 includes global offline land model experiments with meteorological forcing data provided by the Global Soil Wetness Project Phase 3 (GSWP3; Kim, 2017).
The taxonomic resolution and assignment rate could have been limited by the degradation of DNA sequences, the sequencing depth or the incompleteness of the reference database. Indeed, in a more recent study, also from the Arctic, the authors obtained superior taxonomic recovery between data from DNA and macrofossils analyses, probably due to the use of an almost complete reference library, as well as optimised extraction protocols (sediment quantity) and sequencing conditions  20. Several studies also revealed discrepancies between records of plant DNA, pollen and macrofossils, which may reflect differences in the source (production, origin), modes of transfer, and preservation conditions for these different vegetation-cover proxies  22, 28, 38. Whereas taphonomic processes are relatively well-understood for pollen and plant macro-remains, their understanding for lake sedDNA is still limited  2, 8, 37  , especially for extracellular DNA, which by definition excludes the DNA from pollen and plant macro-remains.
However, few studies have focused on identifying terrestrial DNA sources and transfer processes from catchments to lakes. In order to engage with all of these processes and questions, we present the empirical analysis of temporal lake sedDNA datasets from three mountainous lake-catchment systems characterised by a range of erosion dynamics that are the product of different geological formations, topographical characteristics and vegetation and soil cover characteristics (Fig. 2A, B ). Our review of these different contexts and concomitant processes allows us to elucidate complex, catchment specific, taphonomic (i. e. source and transfer) processes. Both plant and mammal extracellular DNA were investigated using the DNA metabarcoding approach, which is the amplification and sequencing of DNA molecules found in the environment using universal markers  65.
This extracellular DNA may represent the main DNA pool in sediments  57, 66  and is of great interest as it may provide the most integrated view of aquatic, sedimentary and terrestrial biodiversity  58. Here, we only focused on this particular DNA pool to avoid the extraction of DNA from plant macro-remains, which might lead to an overrepresentation of these taxa and limit the detection of the other, rarer taxa. Sedimentological and geochemical data were also acquired, and provide information on the processes of sediment production, transfer, and deposit, as well as of lake water physicochemical conditions. Pollen or coprophilous fungi data were included in the study as complementary evidence of vegetation cover changes and domestic herd presence. All these data are essential for our understanding of the processes that drive the production and preservation of DNA records.
Results and interpretations plant DnA detected in the three lakes. The analysis of sequencing data was realised using appropriate bioinformatic tools (OBITOOLS software) and a filtering procedure based on the percentage of similarity with sequences in the reference database, the identification of potential contaminations and exotic taxa, and the consideration of stochastic detections. The detailed procedure is described in the "Materials and methods" section and is discussed in the Supplementary materials section 2. 2. After this filtering procedure, 107 and 83 MOTUs (Molecular Operational Taxonomic Units) of plants were detected in lakes La Thuile and Muzelle, respectively, while only 19 MOTUs were found in Lake Serre de l'Homme. In Lake Muzelle, we exclusively detected DNA from terrestrial plants (100% of the reads; a read is a sequence of base pairs corresponding to a single DNA fragment and produced by a sequencer).
From 2500 cal. BP, the number of taxa detected with pollen increases to 34 on average for phase (c) and to 38 for phase (a), whereas with the molecular Figure  4. Community composition of terrestrial plants provided by the DNA and pollen analyses from Lake La Thuile. The temporal evolution of the richness, the percentage of arboreal taxa, and some taxa are presented for both methods. The taxa presented here were selected in order to show examples of temporally consistent and inconsistent records, which can have different explanations. The other taxa are presented in Supplementary Fig. 8. For the richness and the percentage of arboreal taxa determined from the terrestrial plant DNA dataset, we present the mean values and standard deviations of the four replicates. The maximum richness, i. e.
cumulating all the replicates is also presented as it provides a more pertinent absolute value to compare with that of pollen. For each taxon in DNA, the size of circles is proportional to the number of reads (see scale on the top of the figure). The purple shaded areas underline the periods (b,d,e), when no or very few DNA was detected. analyses, the mean number of taxa in phases (c) and (a) reach 19 and 30 on average, respectively. Therefore, the effectiveness of plant-community detection via the DNA analyses may well be higher after 2500 cal. BP. Moreover, from 2500 to 1400 cal. BP (i. e. in phase (c)), an increase of the proportion of arboreal taxa is recorded by DNA whereas pollen data suggest deforestation. The significant increase of the erosion from 2500 cal. BP (see LOI residue and total flux of sediments in Fig.
3   67, 69  ), supports the argument that deforestation occurred during this phase; a possible consequence of soil instability due to human activity. Consequently, the higher detection of trees (for instance, Quercus sp. , Acer sp. , Betulaceae, Ulmaceae and to a lesser extent Viburnum opulus and lantana, Fig. 4  and Supplementary Fig. 9 ) and the increase in species richness revealed via the DNA dataset when compared with the pollen data might be explained by the higher erosion rate. In fact, erosion increases the degree of connectivity across the catchment area (i. e. creates new connexions between patches of the catchment and the hydrographic web, including the lake). On the contrary, before 2500 cal. BP, in the forested landscape, there is a probable bias towards the recording of plants that were growing on the lakeshore and the riverside, as suggested by the dominance riparian Alnus sp.
White willow (Salix alba), ashy willow (Salix cinerea), black poplar (Populus nigra), ash tree (Fraxinus excelsior) were also described at the edge of the lake. In the higher part of the catchment, there are coniferous forests comprised of spruce (Picea abies) on the north side, and of deciduous forest on the east side. coring and dating. All lake sediment cores were taken in the deepest part of the lakes, which are located approximately in the centre of the lakes (Fig. 2 ). For Lake La Thuile, cores were taken using a UWITEC platform and coring devices. The sediment sequence comprises two core sites. Sections from the second core are shifted by one meter in depth in order to have overlapping sections and create a continuous sequence (THU10, N45 31. 813, E6 03. 394, IGSN:IEFRA00BB -IGSN codes refer to an open international database.
These cores cover different periods: 1700 years for Muzelle, 6450 years for La Thuile and 4000 years for Serre de L'Homme. Depending on the lakes, age-depth models are based on  14  C dates, geomagnetic field secular variations, short-lived radionuclide measurements and known lead-pollution levels. All age-depth models were generated using the R software and the R-code package 'Clam' version 2. 2  85. Details about sediment lithology and the age-depth models are provided in the Supplementary Materials (Section 1. ). For Lake Serre de l'Homme, several cores were used. Thus, core correlations are also presented in detail in the "sediment lithology and dating" section of the Supplementary Materials. Age-depth models were used to estimate the sedimentation rate for each lake (cm/yr). Sedimentological, geochemical and microfossils analyses.
A final elongation step was applied for 7 min at 72 °C. The PCR products were then purified and mixed (equivolume mixes) before sequencing. Seventy-two PCR controls were included for each primer. Sequencing was carried out using the Illumina Hi-seq technology (2 * 100 bp, paired-end reads), in three separate runs, one comprising four PCR replicates for plants and mammals from La Thuile and Muzelle samples; one for the additional 12 replicates of mammals in La Thuile samples and one for mammals and plants in Serre de l'Homme samples. Data treatment and representation. The analysis of sequences and the taxonomic assignment were realised using the OBITOOLS software (http://www. grenoble. prabi. fr/trac/OBITools)  91. The forward and reverse reads corresponding to the same DNA fragment were aligned and merged applying the IlluminaPairEnd function that takes into account the quality of merging.
An "ngsfilter" file containing the list of samples and their associated combination of primer and tag was created and then used to assign each sequence to the relevant sample applying the ngsfilter function. Only sequences containing perfect tags and primers with a maximum of three errors were considered. The next step was to identify and merge the identical sequences for each sample using the obiuniq function. Afterwards, the obigrep function allowed the filtering of sequences based on two parameters, (1) the sequence length and (2) the sequence occurrence in the entire dataset. For plants, sequences shorter than 10 bp and sequences detected less than 100 times were removed. The same filters were applied for mammals, but we only retained sequences longer than 60 bp. Obiclean was then used to determine the status of each sequence in each PCR product: "head", "internal" or "singleton"  91.
Only sequences that were more often "head" and "singleton" than "internal" in the global dataset were retained for the subsequent steps. Reference databases were built from the EMBL database with the ecoPCR program (gh-database-r113, mamP007-database-r113) and then used to assign a taxon to each unique sequence with the ecoTag function (the % of sequence similarity was calculated and specified in the final file). For the subsequent analyses, only the sequences with a similarity >95% to taxa in the reference database were selected. We considered a sequence as present in a PCR replicate when at least five reads were counted  25. In each lake dataset, we did not consider taxa that were only detected in one sample, or stochastically in less than two replicates (i. e. taxa always detected in only one replicate but with detections in consecutive samples were kept).
Acknowledgements This work was realised in the framework of several programs funded by the following organisms:  DiPEE Chambéry-Grenoble (University Grenoble Alpes  and  University Savoie Mont Blanc) , the  Zone Atelier Alpes (ZAA) , the  PEPS de site Grenoble  and the  Conseil Général of Isère  (AAP on biodiversity). These researches were also supported by the  Parc National des Ecrins  to obtain the funding as well as for the lakes coring. C. Giguet-Covex 's salary was first supported by the  University of Grenoble  and the  University Savoie Mont Blanc  and then by the  European Commission  in the framework of a  Marie Sklodowska Curie fellowship  ( PALEO-AGRI  project, EC grant number  655331 ). We have also benefited of previous researches led by the pluridisciplinary team of the  FRECHALP research program  and conducted by  F.
Prior to DNA elution from the column, a washing step with 5. 4 M guanidium thiocyanate (resin solution) was applied. DNA was sent to a 454-sequencing platform, and reads were assembled with Newbler  [56]. The phage genome was annotated using RAST  [57] , followed by manual inspection. The genome sequence is available under the accession number LT630001. 1. All genomic figures, including Idefix genome comparison, were generated using Easyfig  [58].