dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar temperature offsets for the three catchments were able to reproduce the reconstructed ice configurations when using a similar to presentday precipitation pattern decreased by a factor of 25% (À2. 3 C) and 50% (À2. 9 to À3. 1 C; Table  2 ). Significantly different temperature drops were instead obtained between Valpelline and Valsavarenche catchments (~0. 8 C difference, Table  2 ) when applying the PaleoClim precipitation pattern. The PaleoClim dataset suggests highly-variable precipitation difference compared to present-day conditions within the three investigated catchments (Fig. S1B ). In Valpelline, a 40e50% decrease in precipitation is observed mostly in the accumulation zone, while the precipitation decrease is more homogeneous across the Valsavarenche and Val di Cogne catchments.
Lateglacial paleoglacial stages Based on our 10 Be surface-exposure age datasets (Figs. 3e5, Table  1 ) together with numerical ice simulations (Figs. 6e8), we propose the spatial reconstructions of two main Lateglacial paleoglacier stages at ca. 13 and 11 ka (Fig. S5 ). These two stages, present in all the three investigated tributary valleys of the main Dora Baltea catchment, are consistent with Lateglacial stadials identified across the European Alps (Ivy-Ochs, 2015), and also correlated with North Hemisphere climate oscillation records  (Rasmussen et al. , 2014). The first recorded stage (old glacial stage at ca. 13 ka) is mainly derived from polished bedrock surfaces, and therefore rather reflects a phase of ice retreat and thinning which started at least ~14 ka ago, as suggested by 10 Be exposure ages at the highest elevations Table  2  Temperature and precipitation inputs for paleoglacier simulations in the three studied tributary catchments.
Acknowledgements The authors warmly thank  A. J. D ejanaz  for the help during fieldwork. J. Gajic  is thanked for support for 10 Be surface-exposure dating sample preparation. The authors warmly thank the ASTER team for the measurements performed at the  ASTER AMS facility (CEREGE, Aix-en-Provence). A. Ribolini , one anonymous reviewer and the editor  C. O'Cofaigh  are warmly thanked for constructive reviews and suggestions which improved the quality of our manuscript. This study was supported by the  Swiss National Science Foundation SNSF  (Grant  PP00P2_170559 ) and the  French ANR-PIA  programme ( ANR-18-MPGA-0006 ). All data are available in tables in the main text and online supporting information.
Electrical Impedance Measurements Measurements of electrical impedance spectroscopy (EIS) were performed at predetermined time points. Multichannel potentiostat (VPM-300) purchased from BioLogic Science Instruments (Seyssinet-Pariset, France) was used to characterize the different designs of impedance sensors. EmStat Pico module (development kit) purchased from PalmSens was used for the measurement of electrochemical signals during the cell cultures. PSTrace software (V5. 8. 1) was employed for the electrochemical data processing (frequency scanning range of 50 kHz-5 Hz). For electrode materials' characterization, a classical three-.
Electrical Impedance Measurements Measurements of electrical impedance spectroscopy (EIS) were performed at predetermined time points. Multichannel potentiostat (VPM-300) purchased from BioLogic Science Instruments (Seyssinet-Pariset, France) was used to characterize the different designs of impedance sensors. EmStat Pico module (development kit) purchased from PalmSens was used for the measurement of electrochemical signals during the cell cultures. PSTrace software (V5. 8. 1) was employed for the electrochemical data processing (frequency scanning range of 50 kHz-5 Hz). For electrode materials' characterization, a classical three-electrodes cell (Ag/AgCl reference and carbon counter electrodes) was used. All other EIS experiments were conducted using the two-electrodes set-up previously described.
Modeling and Selection of the Electrode Design for Cell-Culture Assessment In order to understand the impact of the different electrode designs, it was important to model the electric field lines and to explore their distribution in the cell-culture chamber. To do so, COMSOL Multiphysics ® software (version 6. 0), that provides simulations of the electric field distribution using the finite element method  [41, 42] , was employed. First, the geometry of the different electrodes' design was drawn and imported into the software. The model was constructed by means of the use of the following three domains: (1) a 1st layer domain containing a boundary of the bottom electrode, (2) a 2nd layer domain containing a boundary of the top electrode and (3) a culture chamber domain of cylindrical form (diameter of 5 mm) that geometrically separates the domains 1 and 2.
Conclusions We demonstrated that the architecture of an original cell-culture chamber embedding impedance spectroscopy electrodes is compatible with cell-culture conditions and realtime TEER measurements. This device is a powerful toolkit for monitoring cell-culture growth and barrier tissues integrity. Additionally, these devices can be used to assess cell stress in a biomaterial risk assessment to complement the detection of inflammatory metabolites using multiparametric electrochemical sensing platforms  [4, 26]. Our strategy deals with the selection of highly processable carbon inks (which are blended with IrOx particles to decreases their interfacial impedance) with potentially low fabrication cost (involving printing technologies). The modeling by COMSOL Multiphysics software showed that the multi-rings-electrodes and grid-electrodes are of particular interest due to their compatibility with microscopy techniques (high transparency opening) while maintaining homogenous electrical field distribution in the culture chamber.
C. and C. E. T. ; data curation, A. C. ; writing-original draft preparation, A. C. , C. E. T. and P. M. ; writing-review and editing, A. C. , P. M. and A. M. G. ; visualization, A. C. ; supervision, M. G. and A. Z. ; project administration, V. M. ; funding acquisition, P. M. and A. M. G. All authors have read and agreed to the published version of the manuscript. Funding: PANBioRA project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 760921.
To confirm this hypothesis, we built a substantial and original dataset (called DeepOrchidSeries) aimed at modeling the distribution of orchids on a global scale based on Sentinel-2 image time series. It includes around 1 million occurrences of orchids worldwide, each being paired with a 12-month-long time series of high-resolution images (640 x 640 m RGB+IR patches centered on the geolocated observations). This ambitious dataset enabled us to train several deep-SDMs based on convolutional neural networks (CNNs) whose input was extended to include the temporal dimension. To quantify the contribution of the temporal dimension, we designed a novel interpretability methodology based on temporal permutation tests, temporal sampling, and temporal averaging. We show that the predictive performance of the model is greatly increased by the seasonality information contained in the temporal series.
, 2012)  now provide RGB and IR channels with fine spatial resolution and temporal revisit frequency worldwide (see Section 2. 1. 1), which can feed high-resolution, CNN-based SDM models. However, there is still much potential ahead for bringing together remote sensing and deep learning  (Camps-Valls et al. , 2021). Remote sensing datasets that are (i) readily available for deep learning applications and (ii) exploiting the spatial, spectral, and temporal dimensions of new satellite missions are still very few. For instance, among the twenty-three benchmark datasets implemented in TorchGeo  (Stewart et al. , 2021) , only two encompass a temporal dimension. There is then an opportunity to build RGB+IR image time series around occurrences spread worldwide. By sampling S2 data for a whole year, prominence is given to the seasonal evolutions of the plants, habitats.
Contributions This paper contribution is 2-folds: First, we built a substantial and original dataset pairing nearly 1 million geolocated occurrences of the Orchidaceae family with satellite image time series. This dataset and the associated method scripts, released as open data and code, should be useful for conservation biologists and SDM users in general. To our knowledge, no similar ready-to-use dataset is already available. Second, we designed interpretability tests of the deep SDMs trained on this dataset in order to measure the importance of seasonal landscape variability in characterizing species habitat and niche. Figure  1  provides the visual abstract of our method.
Orchid Occurrences Dataset The Orchidaceae family is of great interest because of its diversity (about 28,000 species estimated) and its aesthetic attractiveness  (Chase et al. , 2015). Orchids are of major concern for ecologists due to the numerous threats they are facing: habitat destruction, climate change, pollution, and illegal harvesting for horticulture and tourism industries  (Wraith and Pickering, 2018). They are also considered as a relevant proxy of their ecosystem's health  (Newman, 2009). Moreover, orchids are found on all continents in a wide range of habitats and they are blooming at very different altitudes. Such a range or environmental amplitude is difficult to achieve with other families, making the orchid family an excellent candidate for the purpose of our study (i. e.
, to measure the importance of seasonal variability in characterizing species habitat and niche). Rather than collecting a new set of orchid occurrences to build our image time-series dataset, we decided instead to reuse the one introduced by  Zizka et al. (2021). Their objective was different from ours (i. e. , estimating the conservation status of orchids) but the set of occurrences they collected from GBIF meets two main criteria of interest for our study: (i) global scale and (ii) suitable data quality, thanks to several data filtering and cleaning processes (including the use of the R package CoordinateCleaner v. 2. 0-9,  Zizka et al. , 2019). The complete process they use is summarized in the Supplementary Table  1  of their paper  (Zizka et al. , 2021). Another benefit of reusing  (Zizka et al.
, 2021) 's occurrence data is to support the potential reuse of our deep-SDM for the automated assessment of the orchid's IUCN status. In the long term, this will improve the reproducibility and comparability of newly developed methods in this regard. In total, the dataset contains 999,407 occurrences of 14,148 species with 70 records per species on average, 4 in median, and 3,537 species (25%) with more than 13 observations. The (heavily-tailed) distribution of the number of occurrences per species is shown in Figure  2A  (through a Next, we conducted experiments where the input temporal dimension was modified (randomized, averaged or sampled) so as to measure its contribution to model performance. Layer 3: the results are finally broken down into three main dimensions of analysis: species frequency in the dataset, bioregion, and species diversity in these bioregions.
Lorenz curve). Figure  2B  represents the temporal distribution of the occurrences in the dataset. Half of the observations dated from 1997, one quarter from 2010. A total of 14. 6% of the set (145,641 occurrences) came with no timestamp at all. The oldest occurrence was from 1901 as a result of the filtering process that got rid of data records older than 1900. Only observations with a position uncertainty higher than 100 km were discarded. Perspectives and limits related to the use of such a large and imbalanced occurrence dataset will be discussed in the final Section 4.
copernicus. eu/en 2 https://sentinelsat. readthedocs. io/en/stable/ Sen2Cor  (Louis et al. , 2016; Ientilucci and Adler-Golden, 2019). Water vapor and other atmospheric components alter the satellite image caption with complex non-linear deformations. When and how atmospheric correction should be performed prior to exploiting remote sensing data depends on the desired information and thus the targeted application. About classification and change detection tasks, a recognized work from  Song et al. (2001)  advises performing simple corrections only when multi-temporal data is used. Otherwise, having both training and test sets from the same relative scale proved to be sufficient: no significant performance gain would result from the addition of an atmospheric correction step. A more recent article estimating the relation between sea surface salinity and Sentinel-2 Imagery with a neural network and 2,700 points obtained better results with TOA than BOA imagery (Medina-Lopez, 2020).
Dataset Construction Figure  3  summarizes the workflow followed to obtain image time-series from a set of geolocated occurrences. The first step is to define the set of Sentinel-2 tiles containing all targeted occurrences, for which more details are provided in the Global scale processing paragraph. The second and third steps are used to define the patch size and the time sampling strategy, respectively. Our choices are presented in the two dedicated paragraphs hereafter. Finally, the last paragraph introduces our method to select the least cloudy S2 data. We have furthermore considered only the four spectral bands available at 10 m resolution, but our workflow could be applied as well to bands at 20 m and 60 m resolution after a downsampling step. Sentinel-2 queries and downloads were made with the Scihub Copernicus API 3.
Global Scale Processing The first step consists then in defining the minimal set of Sentinel-2 tiles containing all our orchid observations. The Sentinelsat python API provides the option to query data by various geographical means, mainly, coordinates, polygons, tiles, or satellite orbits. However, querying the API on an occurrence-by-occurrence basis for a dataset containing nearly one million occurrences is counterproductive. It is much more efficient to first download the tiles containing occurrences and then extract them locally (as shown in Figure  4A  for the histogram of the number of occurrences per tile). To do so, we implemented the following two steps: • First, we created a dictionary linking each tile with its WGS84 geometry thanks to the Sentinel-2 Level-1C tiling grid provided by the ESA Sentinel-2 official portal 4.
• Then, an iterative process on all occurrences was implemented, testing each time if the new observation is included in the union of the already retained tiles set. If not, a tile containing the occurrence location is downloaded and added to the set. The final tiles set map is given in Figure  4C. It illustrates the full geographical scope of the dataset with 7,563 targeted tiles. A total of 50% of all land areas (Antarctica excluded) were included in the collected data. The color scale proportional to the number of observations per tile (with a log10-scale) further shows a geographic (or observation) bias in the occurrences set: Europe, south Australia, and New Zealand are gathering huge numbers of records.
There are various reasons in practice impeding a perfect match between the occurrences dates and the associated predictive data. To begin with, the Sentinel-2 satellite was launched only in 2015 so that older occurrences cannot be matched. Second, all occurrences do not come with a precise date, some having no date information at all. Third, some S2 tiles from the defined minimal set would have to be downloaded a huge number of times to inform all observations at different dates. Lastly, there is no simple and open access to data older than a rolling year on Copernicus Open Access Hub. Because of all that constraints, we finally chose a fixed period for all 12-month time series, with a starting date of 1 March 2020 and an ending date of 29 February 2021 (the choice of the recent period being linked to the temporal distribution of the number of occurrences, as shown in Figure  2B ).
Data Selection Based on Cloud Cover Remote sensing data at RGB/IR channels are directly dependent on potential clouds covering the satellite's field of view. Fortunately, S2 products are including in their metadata the percentage of the scene view corrupted by cloud cover. Thereby when querying the Sentinelsat API over a given area and time window, one can ask to only keep the less cloudy products. The wider the chosen time window is, the more likely an almost cloud-free product will be available within. Based on this metadata, we selected the least cloudy S2 products within each month in the targeted time window. With this selection process, we expect the large majority of time series to be cloudfree like Figures  5A, B. Figure  6  provides an overview of the cloud coverage distribution in selected products compared to all available products in the queried time window.
Performance Evaluation of the Model Data Split The DeepOrchidSeries dataset was split into three parts: (i) Training set (90%), (ii) Validation set (5%), and (iii), Test set (5%). Following the recommendations of  Roberts et al. (2017) , the split was done using a spatial blocking strategy that enables a more robust estimation of the performance of the model. The spatial blocks were defined in the spherical coordinate system according to a 0. 025 • grid, i. e. , square blocks of 2. 775 km at the equator. Splitting by block is important to impede the model from being validated or tested at locations very close to the training occurrences. In addition to the spatial blocking, we also used a stratified sampling strategy to ensure that any region of the world has a minimal number of blocks in the training set.
Specifically, we chose the commonly used top-k accuracy as suggested in  Botella et al. (2019). It measures the success rate of the model when it returns the top-k most probable species for any input x. More formally A k = n i=1 A k (i) n ( 1 ) where n is the number of occurrences in the test set (or validation set) and A k (i) = 1 if η y i (x i ) ≥ ηk (x i ) 0 otherwise with y i the true species label of occurrence x i and ηk (x i ) the outputs of the model re-ordered in decreasing order of probabilities. Because of the high-class imbalance of our dataset, a shortcoming of this metric applied on all test occurrences taken together (or micro-average,  Sokolova and Lapalme, 2009)  is that it gives far too much importance to the most frequent species over the less frequent ones. To compensate for this imbalance, it is preferable to use the macro-average version of this metric  (Sokolova and Lapalme, 2009)  consisting of first calculating the score of each species and then averaging the scores over all species.
The neural network is being provided with only a twelfth of the predictive data and is deprived of any temporal information. Please notice that for each of the cases (M 1 , M 2 , and M 3 ), the data transformation is applied once on the whole dataset (including training, validation, and test set) before the model is trained and evaluated. Model M 1 being deprived only of the months, order information, its comparison with Model M can be interpreted as a statistical test of the hypothesis that the composition of species depends on the existence of months specific features, in particular the ones resulting from yearly seasonality cycles. The comparison between M and M 3 can be interpreted as a test of the hypothesis that the species composition does or does not depend on any temporal variability.
Model Evaluation Regarding Time and Spatial Data Mismatches Figure  11A  reveals a marked gradient of performance depending on test occurrence observation year. This analysis discarded 15% of the 50,375 test occurrences presenting no observation date information. Each quartile includes approximately 11,000 points. Both micro and macro top-30 accuracy seem to be linearly correlated to the occurrence observation year quartile. The linear behavior is confirmed when choosing a division with a thinner percentile. Top-30 performances on the last quartile 2010-2019 are impressive: 0. 834/0. 484 of micro/macro average accuracy. When cutting the test set data at the median 1997, i. e. , considering separately the oldest and the most recent half of test observations, performances are of 0. 703/0. 281 (oldest half) and 0.
, 2020). The WorldClim variables (weather station data interpolated with satellite-derived covariates,  Hijmans et al. , 2005; Fick and Hijmans, 2017)  certainly are the most widely used global SDM predictors  (Nogués-Bravo, 2009; Svenning et al. , 2011). This bio-climatic data approaches habitats, annual trends (e. g. , annual precipitation) and seasonalities (e. g. , temperature annual range and standard deviation). Contrary to our 1-year DeepOrchidSeries dataset, here, the variables are averaged across several decades. Comparing the predictive power of these classic predictors (possibly completed with a land-cover raster) to our Sentinel-2 data will be the focus of future work. Species distribution models and remote sensing data can also help rare species detection by capturing the biophysical conditions driving their distributions  (Cerrejón et al.
Comparison With Other Open Remote Sensing Datasets for Deep Learning Remote sensing datasets for deep learning applications are currently gaining much interest and are more and more accessible. The very recent launch of TorchGeo  (Stewart et al. , 2021) , a Python library to easily handle geospatial datasets in the PyTorch environment, illustrates the recent and still ongoing progress. However, the available datasets remain currently few and the temporal information provided by satellite revisits is almost never used  (Sumbul et al. , 2019). The available datasets are mostly used for land-cover classification  (Helber et al. , 2019)  or semantic segmentation  (Schmitt et al. , 2019) , as described in the benchmark datasets provided in TorchGeo (see  Stewart et al. , 2021  of Table  1 ). Sen12MS is for instance a global dataset including 180,662 patches of Sentinel-1/2 256 x 256 m images and MODIS-derived land cover maps  (Schmitt et al.
, 2019). Another dataset, similar to ours in terms of spatial coverage, is named Seasonal Contrast (SeCo)  (Mañas et al. , 2021)  and was released in 2021. It gathers 2. 65 km × 2. 65 km Sentinel-2 image time-series around about 200 K locations worldwide. Time-series include 5 images separated by approximately 3 months. The objective was to learn an encoder that can be used for a variety of tasks, from land-cover classification to change detection. SeCo includes images from all over the world to represent a wide variety of landscapes. Among the currently available and open datasets, our dataset is, to the best of our knowledge, the only one providing monthly image data at so many points worldwide. In order to allow its reuse and the reproducibility of our experiments, the entire dataset is made publicly available with the Zenodo DOI 10.
Key Considerations for Building New Models With Our Method or Using Existing Ones Our method could be readily applied to other taxonomic groups than the orchids family. The ease and cost of implementation will mainly depend on the geographical distribution of the occurrences of the target taxon. With a family as large and widespread as the orchids, our method requires significant computing resources. Downloading Sentinel-2 tiles to a very large extent demands a lot of storage available (about 100Tb). To keep model training time reasonable, GPUs have to be used too. A computing cluster is more than welcome and the technical requirements can be a limitation for some researchers. However, once the dataset is built and the model is trained, predictions can perfectly be run on standard local machines.