dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement CanSWE data are publicly available at https://zenodo. org/record/4734371. The US Snow Telemetry (SNOTEL) data (snow and precipitation) are available at https://www. nrcs. usda. gov/wps/portal/wcc/home/snowClimate-Monitoring/ and have been downloaded using the soilDB R package (http://ncss-tech. github. io/AQP/soilDB/ soilDB-Intro. html). HRDPS forecasts and CaPA analysis are distributed on the Canadian Surface Prediction Archive (CaSPAr, https://caspar-data. ca/). The Crocus snow outputs (SWE and snow depth) are available at: https://doi. org/10. 5281/zenodo. 7141345.
, 2022). To quantify MMR, we measured gas exchange while the lizard was perturbed for a total of 6 min. This was sufficient to observe metabolic rate rise and plateau in all lizards (see Figure  S1 ). We measured carbon dioxide (V ̇CO2 ) production and oxygen (V ̇O2 ) consumption corrected for barometric pressure via pushmode flow-through respirometry (Foxbox-C Field O 2 and CO 2 Analysis System or FMS-3 Field Metabolic System, Sable Systems, Inc. ). Air was pumped at a rate of 450 ml/min through the metabolic chamber, dried of water vapour with Drierite, then measured for both CO 2 and O 2 content. We processed the data using ExpeData software (version 1. 7. 30, Sable Systems, Inc. ). We extracted the average of the lowest values of gas exchange over a 10-min interval to estimate RMR, which allows the elimination of elevated metabolic rates due to activity during the measurement period  (Kouyoumdjian et al.
| Sprint speed measurements Following metabolic rate measurements, we ran both aerobic-scope and temperature-model lizards on an open-ended, 1-m, level racetrack of artificial turf. Infrared sensors placed at 25-cm intervals recorded data to the nearest 0. 001 s via computer interface (Trackmate Racing). Lizards were removed from incubators and run within 10 s. We ran each lizard three times at each test temperature, after allowing several minutes of rest in the incubator between trials for body temperature to re-equilibrate. For analyses, we extracted the fastest 50-cm sprint interval for a lizard at a given temperature  (Gangloff et al. , 2019). Sensor malfunction resulted in the loss of data from some sprint trials, resulting in N = 206 observations of N = 37 individuals for analysis (minimum of 3 and median of 6 temperatures per individual; see full details in Table  S3 ).
| Temperature logging We measured body temperature of temperature-model lizards that were size-matched to the lizards for which we measured aerobic scope. Each temperature model was treated identically to its paired metabolic rate animal: they were removed from their home cage at the same time and placed in identical containers with air flow on the same shelf in the incubator. Using elastic bandage material and tape, we affixed a thermistor probe (PT907; Pace Scientific) to the venter of the lizard on the abdomen, and we recorded body temperature every 10 s with a data logger (XR5-SE-20 mV; Pace Scientific, USA). We associated these time-stamped data with the time of metabolic rate measurements to provide precise body temperatures for analysis rather than relying on the incubator's set temperature.
| Thermal performance curves All analyses were conducted in the Programming Language R (R Core Team, 2022) and data plots created using the ggplot2 and plot3D packages  (Soetaert, 2019; Wickham et al. , 2019). To characterize TPCs for aerobic scope and sprint speed, we used the 'beta function', which was originally developed to describe the effects of temperature on plant growth  (Yan & Hunt, 1999)  and provides the best fit to most observations in ectothermic animals, especially lizards  (Tomlinson, 2019). The beta function also benefits from having parameters with straightforward biological interpretation: Where T b is the body temperature, W f is the peak performance, T OPT is the temperature at W f , and CT MAX is the high temperature where performance equals zero (all temperatures in Kelvin;  Yan & Hunt, 1999; Tomlinson, 2019).
We fit performance curves using the nls function in R for each individual, using the body temperature of the temperature (1) Performance = W f × CT MAX -T b CT MAX -T OPT × T b T OPT T OPT CT MAX -T OPT model as the value for T b (see above) and using an empirical measure of CT MAX of 41. 3°C  (Bodensteiner, Gangloff, et al. , 2021). This empirical CT MAX was estimated using the same source population as the present study and was measured at the same low-and high-elevation sites. As expected by HMTL and observations from other lizards, elevation did not affect CT MAX in P. muralis. We assessed the sensitivity of our conclusions to the assumption of a fixed CT MAX by (a) repeating all analyses with individual CT MAX extrapolated from our performance data using nls and (b) simulating 1,000 TPCs for each individual and trait while allowing CT MAX to be drawn from a normal distribution matching that empirically documented by  Bodensteiner, Gangloff, et al.
6 = 2. 32; p = 0. 14), we analysed sprint speed data of all animals together. Finally, to understand how RMR and MMR contributed to observed differences in aerobic scope, we used linear mixed models implemented in R via the lme4 package  (Bates et al. , 2015). These models included either RMR or MMR as the dependent variable, elevation treatment, mass and their interaction as independent variables, and lizard ID as a random intercept to account for repeated measurements.
| Temperature-oxygen performance surfaces To model HMTL and estimate parameters that explicitly quantify the interactive effect of temperature and oxygen environment on performance, we created TOPS for both aerobic scope and sprint speed. We estimated each TOPS using data pooled across all animals because we did not measure the same individuals at high and low elevation. The estimated performance surfaces therefore represent the average effects of body temperature and oxygen availability on performance for the entire population, unlike our TPCs which were estimated for each individual. We modelled TOPS by replacing T OPT and W f in the beta function (Equation  1 ) with the following equations: T mOPT and W mf are the optimal temperature and peak performance after accounting for aerobic capacity, respectively, whereas T OPT and W f are optimal temperature and peak performance measured under reference conditions (here, oxygen availability at sea level).
| Within-and among-individual correlations We further assessed the within-and among-individual relationship of sprint speed and aerobic scope across temperatures using bivariate mixed linear models, creating separate models for the two treatment groups. We implemented models in a Bayesian framework using the MCMCglmm package  (Hadfield, 2010)  to estimate within-and amongindividual trait variances, covariances and correlations  (Dingemanse & Dochtermann, 2012). We included the fixed effect of temperature (the target temperature for each measurement) to account for the co-dependence of both dependent variables on temperature. We specified uninformative parameter-expanded priors, used a Gaussian distribution and ran models for 5 × 10 6 iterations with a burn-in phase of 5 × 10 3 and a thinning interval of 500.
Funding information " Laboratoire d'Excellence (LABEX) "  TULIP, Grant/Award Number :  ANR-10-LABX-41 ; Alexander von Humboldt Fellowship; European Union's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant agreement, Grant /Award Number: 752299;  INTERREG POCTEFA ECTOPYR , Grant/Award Number:  EFA031/15 ;  Research Scholarly and Creative Activities award  from the  College of Science and Mathematics at CSU Fresno.
Comme il a été possible de le constater sur l'ensemble des projets étudiés dans le cadre du programme de recherche « UrbaTime. Les temps de l'urbanisme durable » (Projet ANR-CE22-18-0006), la programmation, dite active, cherche à s'adapter au plus près des besoins et réalités du contexte territorial. Elle s'articule très souvent autour d'une pluralité d'activités : festives, évènementielles, artistiques et de loisirs, souvent combinées à des espaces de coworking, à des associations et des entreprises relevant de l'économie sociale et solidaire (ESS) ou à de l'agriculture urbaine. Les initiatives tendent à favoriser la rencontre de différents usages dans un même lieu afin d'encourager un certain niveau de mixité sociale. À l'image des projets menés par la coopérative Plateau Urbain, ces initiatives se revendiquent comme un soutien à des pratiques, artistiques, artisanales ou associatives, qui peuvent rencontrer des difficultés à prendre place dans des villes dans lesquelles le foncier est rare et coûteux.
Data Availability Statement ERA5 data is obtained through Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (https:// doi. org/10. 24381/cds. 6860a573) (Hersbach et al. , 2019). IPV data is in http://doi. org/10. 22033/ESGF/input-4MIPs. 1119  (Cassou, 2017b). AMV data is in http://doi. org/10. 22033/ESGF/input4MIPs. 1113 (Cassou, 2017a). HadISST 2. 2. 0. 0 is obtained from https://www. metoffice. gov. uk/hadobs/hadisst2/ (Met Office Hadley Centre, 2016). The core model output used in this study is available through Norwegian research infrastructure services (http://ns9015k. web. sigma2. no/BlueAct/) (Suo et al. , 2022).
cit. , p. 90. en Mongolie n'est pas considéré comme étant « naturel » (bajgalijn), le naturel Secretariat of the State Great Hural, Mongolia Sustainable Development Vision s'apparentant à ce qui est « sauvage » (zerleg), le terme zerleg renvoyant tant 2030, Ulaanbaatar, 2016, https://www. greengrowthknowledge. org/sites/ default/files/downloads/policy-database/MONGOLIA)%20Mongolia%20 à un animal, qu'à une plante ou des moeurs sauvages de personnes. 22 André-Georges Haudricourt, « Domestication des animaux, culture des Sustainable%20Development%20Vision%202030. pdf. plantes et traitement d'autrui », L'Homme, vol. 2, n° 1, 1962, p. 40-50.
Les outils de travail sur les collections numériques sont disponibles par le biais de la plateforme Oronce Fine (CMS Omeka S), hébergée par l'infrastructure de recherche (IR*) Huma- • une collection de 75 cartes géographiques, tirées en majorité de l'ouvrage Atlas historique de la ville  de Tianjin 1404 -2004 (天津城市历史地图集 1404 -2004 Tianjin chengshi lishi dituji 1404 -2004) , mais aussi des collections de la Bibliothèque nationale de France (BNF), des matériaux d'enquête recueillis par la directrice du programme et des ressources de la plateforme géomatique de l'EHESS   1930 , 1920 , 1910 , 1900 et 1890 (Gherdevich et Noizet 2014). À ce jour, une dizaine de cartes géographiques a été géoréférencée dans le logiciel ArcGIS Pro, nous permettant de voir l'évolution des contours urbains depuis l'année 1917 jusqu'en 194611.
• 14 https://scalar. usc. edu/works/tianjin_bontemps/index/. 34Cefaisant, nous poursuivons les importantes avancées en matière de base de données historiques mises en place lors du projet Virtual Cities, coordonné par Christian Henriot depuis le début des années 2000, notamment avec la plateforme Virtual Tianjin13, hébergée par Huma-Num, et nous apportons les dernières innovations technologiques pour la spatialisation des données historiques. Nous continuons également la dynamique suivie par Fleur Chabaille dans sa publication de récit visuel « Tianjin au temps des concessions étrangères sous l'objectif d'André Bontemps (1931-1935)14 », et nous travaillons la dimension multimédia des récits que nous construisons. 35Enfin, nous entrerons dans une autre étape à compter de l'automne 2022, qui se donne pour but de rendre compte des effets des actions des Volontaires.
Leurs recherches et leurs mobilisations participent en effet pleinement des processus de patrimonialisation architecturale s'étant déroulés à Tianjin dans les années 2000 et 2010. Nous projetons ainsi la réalisation d'un système d'information géographique (SIG) et d'une base de données relationnelle, comprenant environ 200 lieux et édifices de Tianjin concernés par différents processus de protection architecturale. Pour cela, nous partirons de 6 volumes édités entre 2007 et 2010 par le Bureau de l'architecture stylistique et historique, organe de l'une des deux administrations municipales impliquées dans ces processus, faisant un inventaire d'environ 130 lieux et édifices concernés par des mesures de protection, ainsi que des rapports régulièrement publiés par le groupe des Volontaires entre 2008 et 2012 permettant de lister environ 70 autres bâtiments d'importance.
Le SIG et la base de données permettront ainsi, entre autres, de restituer le travail des Volontaires en pointant les bâtiments, les rues et les quartiers qui ont été empêchés de démolition ou préservés grâce à leur action. Bibliographie Des DOI sont automatiquement ajoutés aux références par Bilbo, l'outil d'annotation bibliographique d'OpenEdition. Les utilisateurs des institutions qui sont abonnées à un des programmes freemium d'OpenEdition peuvent télécharger les références bibliographiques pour lequelles Bilbo a trouvé un DOI. Dubois de Prisque, Emmanuel. 2014. « La cartographie en Chine du "rêve chinois" à la réalité géopolitique ». Outre-Terre 38 (1) : 342-348. https://doi. org/10. 3917/oute1. 038. 0342. DOI : 10. 3917/oute1. 038. 0342.
Le SIG et la base de données permettront ainsi, entre autres, de restituer le travail des Volontaires en pointant les bâtiments, les rues et les quartiers qui ont été empêchés de démolition ou préservés grâce à leur action. Bibliographie Des DOI sont automatiquement ajoutés aux références par Bilbo, l'outil d'annotation bibliographique d'OpenEdition. Les utilisateurs des institutions qui sont abonnées à un des programmes freemium d'OpenEdition peuvent télécharger les références bibliographiques pour lequelles Bilbo a trouvé un DOI. Dubois de Prisque, Emmanuel. 2014. « La cartographie en Chine du "rêve chinois" à la réalité géopolitique ». Outre-Terre 38 (1) : 342-348. https://doi. org/10. 3917/oute1. 038. 0342. DOI : 10. 3917/oute1. 038. 0342 Haut de page.
, 2018). These data acquired over time and space will allow deriving indicators characteristic of the state of the planet biodiversity in order to assess the effectiveness of the implemented conservation policies  (Gardner et al. , 2010; Hansen et al. , 2013; Rocchini et al. , 2016). The Essential Biodiversity Variables (EBVs) framework sustained by the Group on Earth Observation -Biodiversity Observation Network (GEO BON) contributes to this pressing need to monitor the multiple dimensions of biodiversity. These EBVs include genetic composition, species populations, species traits, community composition, ecosystem function and structure attributes  (Pereira et al. , 2013; Skidmore et al. , 2015; Jetz et al. , 2016; Pettorelli et al. , 2014 Pettorelli et al. , , 2016 Pettorelli et al. , , and 2018)).
green, MODIS; pink, LiDAR, Radar; yellow, Landsat, EO-1; blue, IKONOS, Quickbird, Airborne). Suitable overlap between the scales of ecological processes, human actions and remote-sensing technology is denoted by dashed and dotted boxes  (Chambers et al. , 2007). One of the most widely used methodology to track changes in species composition and turnover is based on taxonomic approaches and community ecology theories, yet there are satellite-based alternatives to this common approach. Jetz et al. (2016)  highlighted the data gap in regional species trait measurements, leading to critical knowledge gaps on species and traits, especially in tropical regions (Figure  4 ).
They are often collected at resolutions finer than other geographically structured environmental datasets such as topography or land cover  (Jetz et al. , 2012). Filling in such a gap will provide continuous products on the distribution of traits and biophysical properties, which will be inputs of unprecedented accuracy to ecological models (e. g. , species distributions, ecosystem processes, etc. ) and physical models (e. g. , radiative transfer, biogeochemical cycling) that will bring about benefits to a transdisciplinary scientific community information. It will enable to assess the impact of human activities on biodiversity composition and functionality, which is highly relevant to decision makers. To sum up the main user requests are summarized in Table  2. Inland water and coastal ecosystems play an essential role in human life.
It must be emphasized that imaging spectroscopy was mainly developed with airborne sensors. This limits the construction of multi-temporal datasets and generally reduces coverage to relatively restricted areas and to scientific studies. As mentioned by  Lucas et al. (2004) , Hyperion provided the first opportunity to explore the potential of multi-temporal hyperspectral data for Earth observation, but this sensor was experimental with a limited swath width and a low SNR. Dekker et al. (2001)  review on imaging spectroscopy of water demonstrates its efficiency in managing complex coastal and lake systems at various scales on a sustainable development basis. High-spectral and high-spatial resolution measurements of inland and coastal waters are of great interest for determining the geophysical variables of primary importance for monitoring these ecosystems.
, 2016). It is well established that the quality of hyperspectral data is crucial for quantitative assessment of soil properties  (Ben-Dor et al. , 2009). However, such data are not easily accessible. Airborne data are expensive and difficult to obtain at repeated periods, with limited flight prints. Operating hyperspectral satellites are at the end of their life span, their spatial resolution is coarse (30 m and 17 m for Hyperion and Chris/Proba, respectively), and they have noisy or no SWIR information. Multispectral sensors like ASTER, Landsat-8 or Sentinel-2 have large swaths and several spectral bands, but with coarse spectral resolution. However, with a frequent revisit rate, and through its VSWIR range, Sentinel-2 exhibited promising capabilities at regional scales for key topsoil properties, such as soil organic carbon, in temperate agroecosystems with annual crops  (Castaldi et al.
Moreover, buildings are prominent objects needed for a variety of applications like 3D city visualisation, microclimate forecast or real estate databases for which imagery is one of the most consistent information in modelling at various scales. Indeed, roof material identification leads to an accurate extraction of building models  (Avbelj, 2016)  or innovative application like solar panels  (Karoui et al. , 2018). Digital elevation models provide information about the vertical dimension of urban areas. Independently of the spatial resolution, higher spectral resolution and continuous acquisition increase the number of classes to identify and map (Le  Bris et al. , 2017). Helden et al. (2011)  listed the applications that benefit from a spatial resolution ranging between 2 and 20 m: local mapping of build-up area, imperviousness, material and biotope mapping, change detection at building/material level and identification of hazardous materials (Le  Bris et al.
This corresponds to a large hotspot area covered by on ground station, allowing bridging the gap between the two levels of measurements. As it could be noted, the recommended spatial resolution < 10 m fulfils SQ1, SQ2 and SQ3 GSD requirements but not SQ4. To overcome this limitation, a panchromatic band with a 2 m GSD, co-registered to the hyperspectral instrument could be added. Existing works on the fusion of two images by pan-sharpening  (Constans et al. , 2020)  or unmixing methods  (Rebeyrol et al. , 2020)  will allow to answer to SQ4 challenge. The future works will be focused on the demonstration of the benefits of such a mission based on an airborne acquisitions database simulated at top of atmosphere. In particular, these top-of-atmosphere synthetic images will help to define the tradeoff between our scientific requirements and the technological expected performances of the sensor.
Acoustic data process Data were analyzed using Sonar5-Pro software  (Balk and Lindem, 2021) , a software regularly used in freshwater studies. The TS threshold was set at -60 dB for Single Echo Detection (SED) according to monitoring surveys in lakes  (Draštík et al. , 2017). This threshold allowed fish detection greater than ~ 0. 02 m using Love's equation  (Love, 1971) , an equation commonly used in freshwater surveys  (Emmrich et al. , 2012; Morrissey-McCaffrey et al. , 2018; Tessier et al. , 2020). SED was determined in Sonar5-pro using the following parameters: a pulse length ratio between 0. 8 and 1. 3; a maximum gain compensation of 3 dB (one way); and a sampling angle standard deviation of 0. 3 degrees  (Godlewska et al. , 2011; Guillard et al. , 2014). The threshold of the mean volume backscattering strength, S v (in dB re 1 m -1 ), was set 6 dB lower, at -66 dB, according to  Parker-Stetter et al.
(2009). The data from the two echo-sounders were simultaneously processed, using the multi-frequency function of Sonar5-Pro, creating two channels  (Balk and Lindem, 2021). The bottom was automatically detected using the software's tool, including a margin of 0. 5 m and, in rare cases, was manually rectified on both channels. The Elementary Distance Sampling Unit (EDSU) was set to 250 m (same as applied by  Guillard et al. (2014) ;  Mouget et al. (2019) ) and allowed to extract the mean Target Strength (calculated in the linear domain) by layers, and in the same way, the S A values (expressed in hectare acoustic scattering strength, in m 2. ha -  ). The study focusing on fish surveys, to avoid analyzing unwanted non-fish echoes  (Emmrich et al. , 2012) , the Sonar5-Pro cleaning tool was simultaneously used on both echograms to remove ghost echoes, bubbles, debris accumulations, ropes from gillnets/buoys, and fake bottoms.
6 dB; secondary mode: -52 dB), even if there were significant statistical difference between the SED distributions using the Kolmogorov-Smirnov test (p < 0. 01). There was no significant difference in mean TS (Table  2 ) using Wilcoxon's nonparametric test to compare the two types of echosounder for the surface layer (p-value > 0. 05), but this difference was significant for the deep layer (p-value = 0. 0004). When the layers were merged, there was still a slightly significant difference between the means (p-value = 0. 02) (Table  2 ). The box plots of the TS values (Fig. 3 ) also show the similarities between the datasets acquired by the EK80 and the EK60, regardless of the layer. When the mean TS values were compared by pairs for the same EDSU via the  Warton et al. (2006)  test, no difference between the EK80 and the EK60 was observed on the whole sample (Fig.
Discussion The previous works done in marine environment  (Demer et al. , 2017; De Robertis et al. , 2019; Macaulay et al. , 2018)  have addressed the comparison of the two generations of echo-sounder and thus made it possible to validate the continuity of the data for time series. However, these previous works focused mainly on S A (nautical area backscattering coefficient), the TS intercalibration between the two sounders was only done using calibration spheres. So, our work completes the previous ones with in situ data. Our comparison being of main importance for lake fishery studies, we choose the most used parameters (frequency, pulse length, power) in lakes  (Draštík et al. , 2017). Bonar and Hubert (2002)  have highlighted that standardized methods ensure robust estimates. To facilitate comparisons between datasets acquired using different equipment, inter-calibration is needed.