dataesr/scientific-paragraphs-categorization huggingface dataset:
Procedures All study participants completed laboratory assessments (detailed below) and, on the same day, were provided with a Samsung smartphone with Android OS, which was used to deliver EMA surveys and collect GPS coordinates. Survey assessments started on the day following the inperson visit. GPS coordinates were collected every five minutes, 24 h a day, over 7 days using an Android application GPS Logger app. In addition, the device was programmed using Samplex software to administer EMA surveys seven times per day for 7 days  27. The EMA signals occurred at stratified random intervals that vary from day-to-day within, on average, 1. 5-h windows starting at approximately 9:00 a. m. and ending at 9:00 p. m. each day. All data entries were time-stamped, and the program only recorded responses provided within a 15-min period following the signal.
GPS data processing The phone produced seven XML-based GPS Exchange Format files, one for each of the seven 24-h sampling periods. Longitude, latitude, and tracking variables were recorded every 5-min, producing a maximum of 288 samples per day. XML files were converted to plain-text CSV files to allow for concatenation of samples, per subject, using R Studio scripts. In addition to coordinates, GPS receivers produce an estimated positional error, an indicator of the estimated precision of the coordinates that is expressed in distance. Accuracy of GPS signals depends upon many factors, including the positioning of the receiver relative to satellites, interference, such as structures, weather/atmospheric conditions, and the GPS sensor itself. We were unable to identify literature that provided a standardized criterion for minimum accuracy for our purposes.
We, therefore, selected 100 ft as a reasonable approximation of the size of a typical home setting. The concatenated data was cleaned by removing samples with accuracy (precision of estimated location) values greater than 100 ft. Speed and bearing variables were not examined. Timestamps were used to create a separate raw-hour variable that allowed the dataset to be filtered into two categories: daytime (6 a. m. to 1 a. m. ) and nighttime (2 a. m. to 5 a. m. ). The mode latitude and longitude from the "nighttime" values were calculated for each participant and logged in a master data set. Mode values during nighttime hours were presumed to be the most viable method of detecting the likely "home" location for the participant. These mode values were then compared to each sample of the daytime coordinates to derive a difference score (in feet), which were later averaged by day and across the entire week.
ACKNOWLEDGEMENTS This study was supported by  VA CSRD  Grant  5I01CX000810  (E. G. ). Alkermes,  Akili ,  Biogen ,  Boehringer Ingelheim ,  Forum Pharma ,  Genentech  ( Roche Pharma ),  Intra-Cellular Therapies ,  Jazz Pharma ,  Lundbeck Pharma ,  Minerva Pharma ,  Otsuka America  ( Otsuka Digital Health) ,  Sanofi Pharma ,  Sunovion Pharma ,  Takeda Pharma , and  Teva. He receives royalties from the  Brief Assessment of Cognition in Schizophrenia  and the  MATRICS Consensus Battery. He is chief scientific officer of i-Function,  Inc. He  has a research grant from  Takeda  and from the  Stanley Medical Research Foundation. Dr. Granholm  has received consulting fees from  Otsuka Pharmaceuticals. The remaining authors declare no competing interests.
Full details of all instruments installed on the aircraft are provided by  Sahyoun et al. (2019) , however for the purpose of this work we will provide a short overview of the instruments that are used in this study. The flights trajectory and the measured SO 2 concentration are presented in Fig. 1. Two flights were located around Etna and two others around Stromboli. Flight tracks involved first doing a vertical sounding to locate the height of the volcanic plume, this was then followed by a number of transects crossing over the plume for distances up to 100 km. Further transects involved flying along the center of the plume. The main data sets related to the gases and aerosols properties used in this work are listed Tab. 1. The SO 2 concentration is measured by ultra-violet fluorescence SO analyser (Teledyne API).
Grid configuration The horizontal grid configuration contains three domains in two-way interactions: one domain at 2 km horizontal resolution and two others at 500 m horizontal resolution zoomed on both volcanoes : Etna and Stromboli (Fig. 1 ). The vertical grid wasis common to both modelsdomains and hadhas 70 stretched levels. The first level wasis at 10 m above the surface. The top of the domain reaches 22 km with a maximum vertical grid spacing of 1 km. The two nested domains at 500 m horizontal resolution and the 10 m vertical grid spacing above the surface are used to better represent the boundary layer processes around the volcano vents and hence the injection height of the volcanic plumes. The orography used is based on Shuttle Radar Topography Mission database at 250 m  (Farr et al.
aeris-data. fr/), which compiles the main emissions datasets available for the community. Anthropogenic emissions are provided by the monthly MACCity emissions dataset (MACC/CityZEN EU projects)  (Lamarque et al. , 2010)  with a horizontal grid resolution of 0. 5 • (≈ 50 km). To represent biomass burning emissions, the monthly GFED3 (Global Fire Emissions Database, version 3; van der Werf et al. (  2010 ) database was used with a horizontal resolution of 0. 5 • (≈ 50 km). The biogenic emissions wereare modelled by MEGAN (Model of Emissions of Gases and Aerosols from Nature;  Guenther et al. (2012) ) and was recently implemented in ISBA. In addition to these emissions, a SO 2 and latent heat fluxes emissions wasare imposed at the volcano's vent based on daily SO 2 emissions : 25 to 38 kg s2160 to 3283 kt day -1 for Etna and 2 to 4 kg s172 to 345 kt day -1 for Stromboli, for 15 to 16 June 2016, respectively.
The number of simulated (black line in Fig. 4c ) and observed (green and blue lines ; Fig. 4c ) aerosols with diameter higher than 10 nm are in confidence withpresent less than 8% of differences. The maximum simulated and observed aerosol concentrations of aerosol with diameter higher than 10 nm is around 50 000 cm -3. With the exception of a short period at 15 UTC wherewhen no observations data are available, that peakthe peaks ofin number aerosols concentration (Fig. 4c ) are well correlated with peaks ofin SO 2 concentrations (Fig. 4a ) for both observations and simulation. Unlike ETNA15, the number of aerosols observed in STRO16 is mainly composed of aerosols with diameters greater than 10 nm, which is therefore comparable with the number of simulated aerosols. This can beis explained by the higher condensation sink in the MABL than in the FT (Fig.
Acknowledgments This work was carried out in the framework of the  STRAP  project, which benefits from French government support managed by the  Agence Nationale de la Recherche. ClerVolc  project is also acknowledged for financing the ATR42 flights, funded by the  French government "Laboratory of Excellence" initiative. The authors would also like to extend special thanks to the pilots and flight crew from SAFIRE for all their enthusiasm and support during the measurement campaign aboard the ATR42 aircraft. The simulations were performed on the supercomputer "beaufix" of  Météo-France in Toulouse, France. The models used in this paper are open-source and freely available at the following websites: http://mesonh. aero. obs-mip. fr. The observations data are available in University of La Réunion servers: https://osur.
Zooplankton Collection and Enumeration Mesozooplankton depth-stratified abundances were determined from day-night MOCNESS tows (1 m 2 net, 202-µm mesh) on the P0704 and P0810 cruises (typically two pairs of tows per cycle). Each net collected organisms over a ∼50-m depth interval with consecutive nets tripped from a depth of 450 m to the surface. Samples were preserved in 1. 8% formaldehyde and analyzed using a ZooScan digital scanner with ZooProcess software  (Gorsky et al. , 2010). Zooplankton were sorted into broad taxonomic groups (e. g. , copepods, euphausiids, doliolids, etc. ) using machine learning algorithms, the taxonomic assignments of 100% of the vignettes validated manually, then automatically sized (as Feret diameter). Organismal length was then used with allometric equations (see below) to quantify organism biomass and clearance rate.
Data were analyzed as described in  Biard et al. (2016 Biard et al. ( , 2018)). Briefly, images were automatically analyzed to separate out vignettes representing organisms or marine snow aggregates. ZooProcess software was utilized to generate morphometric information (e. g. , diameter) and classify the organisms into broad taxonomic groups. Classifications were then 100% manually validated. Phaeodarian taxonomic groups included Aulosphaeridae, which was the dominant rhizarian present and is the only group of organisms enumerated by UVP5 that is utilized in this study.
31 (±0. 27), b = 1. 01 (±0. 05), and the Q 10 used to account for temperature effects was assumed (following  Hansen et al. , 1997)  to be 2. 8. We utilized zooplankton data from nighttime MOCNESS tows sorted into broad taxonomic and size groups using ZooScan. The choice to use only nighttime biomass likely leads to a conservative estimate of flux attenuation, because it excludes the activity of diel vertical migrants. This decision was made because these vertical migrants likely feed primarily in the surface layers and are not actively feeding at depth. The carbon biomass of individual organisms was determined using length:carbon relationships for different taxa as outlined in Table  1  of  Stukel et al. (2013). We calculated clearance rates for four groups of potentially suspension-feeding mesozooplankton: copepods, euphausiids, appendicularians, and ostracods, while recognizing that some taxa include omnivores or predators.
Carbon Flux and Carbon Flux Attenuation We used two independent estimates of particle flux (sediment traps and 238 U-234 Th) to test whether or not our estimates of particle flux (and particle flux attenuation) suffered from methodological biases (Figure  2 ). Comparisons between 234 Th flux collected in sediment traps and 234 Th flux estimated from 238 U: 234 Th disequilibrium [computed using a one-dimensional, steady-state model without upwelling,  Savoye et al. (2006) ] showed strong agreement, with occasional outliers. The median ratio of sediment trap-derived flux to deficiency-derived flux was 0. 998 suggesting near perfect agreement on a typical deployment. However, the overall mean of the sediment trap dataset was 6% higher than that of the Th deficiency dataset, suggesting that when there was a substantial disagreement, the sediment trap results were likely to be higher.
When independent sediment trap export measurements were made at the base of the euphotic zone and at 100 m depth, the correlation (Spearman's ρ) between flux at these two depths was 0. 72 (p = 0. 002). This across-depth correlation was substantially weaker when only data from frontal regions (P1106 and P1208 Cruises) were analyzed (ρ = 0. 49, p = 0. 36) than when non-front data were considered (ρ = 0. 92, p = 4. 5 × 10 -4 ). For all paired samples, the median ratio of sediment trap flux at 100 m to sediment trap flux near the base of the euphotic zone (shallow trap depths varied from 47 to 70 m) was 0. 72, suggesting that approximately 28% of sinking POC could be expected to be remineralized before a depth of 100 m on these cycles. To determine continuous profiles of carbon flux and CFA, we merged sediment trap data with 234 Th data.
Suspension-Feeding Zooplankton and Particle Flux Attenuation Suspension-feeding crustaceans are the most abundant (by carbon biomass) ecological category of zooplankton in the CCE  (Lavaniegos and Ohman, 2007). To quantify the potential role of these suspension feeders, we utilized data from nighttime MOCNESS tows in the CCE. Suspension-feeder biomass in the mesopelagic was typically dominated by copepods and euphausiids (Supplementary Figure  S2 ). Between 100 and 450 m depth, copepod biomass was typically in the range of 1-3 mg C m -3. Euphausiid biomass was almost always <1 mg C m -3 at depths deeper than 200 m (and typically <0. 4 mg C m -3 ) but was greater in the shallow mesopelagic. Ostracods had substantially lower biomass, never exceeding 0. 6 mg C m -3 in the mesopelagic. Appendicularians, while occasionally abundant in the euphotic zone, were also relatively minor contributors to total suspension-feeder biomass in the mesopelagic.
For particles sinking at a rate of >50 m day -1 , the impact of suspension feeders becomes negligible. Comparing data from all cycles, we see a similar pattern. If particles sink at 1 m day -1 , anywhere from ∼5 to 50% of sinking particles would be consumed over a 10 m depth range (Figure  6A ). For particles sinking with a speed of 10 m day -1 , particle flux attenuation was typically <4%/10 m and for particles sinking at a speed of 100 m day -1 , particle flux attenuation was always <1%/10 m and typically <0. 4%/10 m. To investigate typical particle sinking speeds, we measured the abundance and size of recognizable fecal pellets collected in sediment traps. These pellets were almost always the dominant visually identifiable component of the sinking material, although  in the oligotrophic regions the identifiable pellets typically comprised less than half of total carbon flux.
ACKNOWLEDGMENTS We thank the captains and crews of the R. V. Melville,  R. V. Thompson , and  R. V. Sikuliaq  as well as our many collaborators in the  CCE LTER Program , without them this study would not have been possible. We also thank  N. Bednaršek  for the pteropod enumerations. Data used in this manuscript can be found on the CCE LTER DataZoo website: https://oceaninformatics. ucsd. edu/datazoo/catalogs/ccelter/datasets.
Study area We conducted our study in the Occitanie region  (N 43. 70, E 1. 56) , south-western France. The study area has a predominantly temperate climate with diverse influences (Atlantic, Continental, Mediterranean and mountainous). We monitored bat activity along four main roads: N124 (average daily traffic in 2015: 12,128; heavy vehicles: 8. 1%), N21 (7,923; 10. 15%), N126 (10,957; 9. 35%), A68 (24,470, 8%) and N88 (20,933; 8. 5%; see Fig. 1  and Table  1 ). Information on the average daily traffic was obtained from the database of "Direction Interdépartementale des Routes du Sud-Ouest" (DIRSO), a government agency in charge of monitoring and maintenance road structures (http://www. dir. sud-ouest. developpement-durable. gouv. fr). The roads are surrounded by mosaic agricultural landscapes (crops, vineyards, pastures) including patches of grasslands and woodlands of various sizes, tree-lined hedgerows, rivers, minor roads, scattered rural housing and small-medium conurbations with an elevation of <400m a.
Underpass selection procedure To select underpasses, we used the DIRSO database encompassing all geolocated underpasses in the study area (n=551). We then defined a 'landscape' as the area within a 1 km radius around each underpass. We used this spatial extent based on a trade-off between mean daily movements of bat species occurring in the study area (about 1-3 km) and the need to maximize the landscape gradient we targeted to sample  (Bodin et al. , 2011; Ethier and Fahrig, 2011; Pasher et al. , 2013). Furthermore, it has been shown on other taxa that the cumulative ecological effect of the road system on biodiversity at landscape scale (i. e. , the road effect-zone) can extend 800 m  (Forman, 2000; Forman and Deblinger, 2000). To eliminate potential landscape-scale confounding factors, we reduced the candidate set of underpasses by selecting landscapes which had small areas of impervious surface (<20%; buildings and paved areas such as roads, sidewalks, driveways and parking lots) and of water surface (<10%)  (Ethier and Fahrig, 2011).
This selection procedure finally reduced the set of candidate underpasses to 24 (mean distance between sites: 84. 4 ± 55. 3 km; Fig. 1 ). Mostly because we excluded urban landscapes during underpass selection procedure, none of our studied underpasses had street lights above, around or inside. We found bat roosting inside only one studied underpass (≈ 15 Pipistrellus spp), that was consequently deleted from the data set to avoid bias in estimating underpass use for the Pipistrellus spp. All the landscape predictors were calculated with ArcGis 10 (ESRI, Redlands, CA) based on land use data from the French National Institute for Geographic and Forestry Information (http://www. ign. fr/). The land use data were obtained by photo-interpretation to an accuracy ranging from 1 to 30 m spatial resolution depending on the type of habitat sampled.
If we had at least one night with more than two hours of rain, we started again the three days of recording. We identified bat call sequences to species using an acoustic automatic classifier software Tadarida  (Bas et al. , 2017). Then, we used Syrinx software version 2. 6  (Burt, 2006)  to manually check the identification of the bat passes from the classifier at the genus level. To make identification as unambiguous as possible and to make error risk negligible, we considered groups of similar species, mostly based on their echolocation call types, which greatly limits potential misidentification issues  (Millon et al. , 2015). Rhinolophus spp, Plecotus spp and Myotis spp are considered as gleaners foraging in clutter environments  (Arlettaz et al. , 2001). Despite the different genus, we opted for creating a composite group of Pipistrellus/Miniopterus spp whose echolocation calls are very similar  (Medinas et al.
5 for US (always two recording points of this location in the landscape, see Fig. 2 ). Among our 11 predictors (Table  1 ), all the continuous variables were centered and rescaled (with a mean of 0 and a standard deviation of 1) to achieve a set of unit-free predictors that are directly comparable and to aid model fitting  (Ashrafi et al. , 2013). Then we used a forward stepwise procedure to identify the subsets of predictors with the highest performance, and further select the best final model according to its best fit to the data using the Akaike Information Criterion corrected for small samples (AICc)  (Mac Nally et al. , 2018). We assessed the quality of our best models by comparing it to the null model using AICc and the likelihood-ratio chi-squared test  (Mac Nally et al. , 2018). To assess underpass use and attractiveness for each group of species, we compared bat activity levels between the different locations of recording points in the landscape.
This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature, and surface temperature) available at varying temporal intervals. Thirty-year (1980Thirty-year ( -2010) )  time series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to 1 h time steps and bias-corrected.
Although the correction was applied to all sites, it was most important for mountain sites hundreds of metres higher than the grid elevations and for which uncorrected air temperatures were too high and snowfall amounts too low. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The Supplement provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors.
Introduction In the past decade, several long-term datasets aimed at providing high-quality continuous meteorological and evaluation data for cold regions modelling have been published (Table 1). The importance of such datasets is twofold. Their primary value is scientific: they help us to understand key surface processes by enabling the development and evaluation of existing and new geophysical models for climate research and forecasting. The second, perhaps less obvious, value of having multiple long-term datasets is for meta-research; the smaller the studies or sample size, the less likely research findings are to be true  (Ioannidis, 2005). In a snow modelling context, this is corroborated by  Rutter et al. (2009)  who found low correlations in performance statistics for the same snow models but in different years.
Here, we describe 10 long-term datasets (Table  1 ) from reference sites chosen to force and to evaluate models participating in the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP)  (Krinner et al. , 2018) , an international coordinated modelling effort that investigates snow schemes. ESM-SnowMIP is closely aligned with the Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP;  van den Hurk et al. , 2016) , which is a contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6), including global offline land model experiments with meteorological forcing data provided by the Global Soil Wetness Project Phase 3 (GSWP3;  Kim, 2017). Two meteorological datasets are described for each site: one compiled from on-site measurements, the other derived from GSWP3.
Previous iterations of SnowMIP have provided 19 site years of data from four sites in SnowMIP1  (Essery and Etchevers, 2004)  and 9 site years of data from five sites in SnowMIP2  (Essery et al. , 2009; Rutter et al. , 2009) ; ESM-SnowMIP totals 136 site years of in situ data from 10 sites and 300 site years derived from GSWP3. Measurement details at five of the sites have been described in dedicated publications within the last 8 years. The other five sites are partially described in a number of publications which, combined, give a broad overview of the data. All of the in situ measurements and the GSWP3 data are freely available either on the web or on request, but, previously, post-processing would have been required to homogenise the in situ datasets compiled by different teams or to downscale the reanalyses.
This situation causes two major issues. Firstly, different modelling teams are likely to apply different post-processing methods, leading to numerous versions of the same dataset being used for scientific studies. Secondly, although time spent identifying and processing data has never been quantified in scientific literature, it is, to the best of our knowledge, a well-known but underacknowledged time-consuming task for modellers. The aim of this collaborative work is to provide easy-touse, quality-controlled data in a format adopted by the climate modelling community to facilitate consistency, continu-ity, and reproducibility in snow research  (Menard and Essery, 2019). As such, it complies with efforts in geosciences to foster best practices on data accessibility and documentation  (Gil et al.