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40 results for “french alps”
Supplementary material 1 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Sample information for specimens used in this study : Explanation note: Details of collecting data, images, sequences, and trace files for the barcoded specimens are available in the public BOLD dataset "DS-CAYOLLE", accessed at https://doi.org/10.5883/DS-CAYOLLE
Supplementary material of : Glacier-permafrost interactions and GLOF's in Alpine terrain. Insights from seven decades of reconstructed dynamics of the Chauvet glacial and periglacial system, Southern French Alps
<p>This dataset is part of the supplementary material of "Glacier-permafrost interactions and GLOF’s in Alpine terrain. Insights from seven decades of reconstructed dynamics of the Chauvet glacial and periglacial system, Southern French Alps" paper, published in Earth Surface Processes and Landforms (ESPL) journal.<br> This dataset contain raw DEM's, surface velocities and videos of Chauvet system, that served as a basis for the long-term monitoring of glacial and periglacial systems and its interactions.</p>
Data from: Assessing bundles of ecosystem services from regional to landscape scale: insights from the French Alps
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Data from: Evidence of divergent selection for drought and cold tolerance at landscape and local scales in Abies alba Mill. in the French Mediterranean Alps
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A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015
<p>Glacier surface mass balance (SMB) data are crucial to understand and quantify the regional effects of climate on<br> glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present<br> a dataset of annual glacier-wide surface mass balance of all the glaciers in the French Alps for the 1967-2015 period. This<br> dataset has been reconstructed using deep learning (i.e. a deep artificial neural network), based on direct and remote sensing<br> SMB observations, meteorological reanalyses and topographical data from glacier inventories. This data science reconstruction<br> approach is embedded as a SMB component of the open-source ALpine Parameterized Glacier Model (ALPGM: https://zenodo.org/record/3609136). An extensive cross-validation allowed to assess the method’s validity, with an estimated average error (RMSE) of 0.49 m.w.e. a<sup>-</sup><sup>1</sup>, an explained variance (r<sup>2</sup>) of 79% and an average bias of +0.017 m.w.e. a<sup>-</sup><sup>1</sup>. We estimate an average regional area-weighted glacier-wide SMB of -0.72±0.20 m.w.e. a<sup>-</sup><sup>1</sup> for the 1967-2015 period, with moderately negative mass balances in the 1970s (-0.52 m.w.e. a<sup>-</sup><sup>1</sup>) and 1980s (-0.12 m.w.e. a<sup>-</sup><sup>1</sup>), and an increasing negative trend from the 1990s onwards, up to -1.39<br> m.w.e. a<sup>-</sup><sup>1</sup> in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for this period are the Chablais (-0.90 m.w.e. a<sup>-</sup><sup>1</sup>) and Ubaye and Champsaur (-0.91 m.w.e. a<sup>-</sup><sup>1</sup> both) ranges, and the ones presenting the lowest mass losses are the Mont-Blanc and Oisans ranges (-0.74 and -0.78 m.w.e. a<sup>-</sup><sup>1</sup> respectively). This dataset provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps, in need of regional or glacier-specific meltwater contributions in glacierized catchments.</p> <p>The SMB dataset is comprised of multiple <em>CSV</em> files, one for each of the 661 glaciers from the 2003 glacier inventory (Gardent et al., 2014), named with its GLIMS ID and RGI ID with the following format: <em>GLIMS-ID_RGI-ID_SMB.csv</em>. Both indexes are used since some glaciers that split into multiple sub-glaciers do not have an RGI ID. Split glaciers have the GLIMS ID of their "parent" glacier and an RGI ID equal to 0. Every file contains one column for the year number between 1967 and 2015 and another column for the annual glacier-wide SMB time series. Glaciers with remote sensing-derived observations (Rabatel et al., 2016) include this information as an additional column. This allows the user to choose the source of data, with remote sensing data having lower uncertainties (0.35±0.06 () m.w.e. a<sup>-</sup><sup>1</sup> as estimated in Rabatel et al. (2016)). Columns are separated by semicolon (;). All topographical data for the 661 glaciers can be found in the updated version of the 2003 glacier inventory included in the Supplementary material.</p>
A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015
<p>Glacier mass balance (MB) data are crucial to understand and quantify the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide surface mass balance of all the glaciers in the French Alps for the 1967-2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network), based on direct MB observations and remote sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method’s validity was assessed through an extensive cross-validation against a dataset of 32 glaciers , with an estimated average error (RMSE) of 0.55 m.w.e. a<sup>-1</sup>, an explained variance (r2) of 75% and an average bias of -0.021 m.w.e. a<sup>-1</sup>. We estimate an average regional area-weighted glacier-wide MB of -0.71±0.21 (1 sigma) m.w.e. a<sup>-1</sup> for the 1967-2015 period, with negative mass balances in the 1970s (-0.44 m.w.e. a<sup>-1</sup>), moderately negative in the 1980s (-0.16 m.w.e. a<sup>-1</sup>), and an increasing negative trend from the 1990s onwards, up to -1.34 m.w.e. a<sup>-1</sup> in the 2010s. A comparison with ASTER-derived geodetic MB for the 2000-2015 period showed important differences with the photogrammetric geodetic MB used to train our model. When recalibrating our reconstructions with the new ASTER-derived geodetic MB, the estimated average regional area-weighted glacier-wide MB (1967-2015) is reduced to -0.64±0.21 (1 sigma) m.w.e. a<sup>-1</sup>. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967-2015 period are the Chablais (-0.93 m.w.e. a<sup>-1</sup>), Champsaur and Haute-Maurienne (-0.86 m.w.e. a<sup>-1</sup> both) and Ubaye ranges (-0.83 m.w.e. a<sup>-1</sup>), and the ones presenting the lowest mass losses are the Mont-Blanc (-0.69 m.w.e. a<sup>-1</sup>), Oisans and Haute-Tarentaise ranges (-0.75 m.w.e. a<sup>-1</sup> both). This dataset provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps, in need of regional or glacier-specific annual net glacier mass changes in glacierized catchments.</p> <p>The MB dataset is presented in two different formats: (a) A single netCDF file containing the MB reconstructions, the glacier RGI and GLIMS IDs and the glacier names. This file contains all the necessary information to correctly interact with the data, including some metadata with the authorship and data units. (b) A dataset comprised of multiple CSV files, one for each of the 661 glaciers from the 2003 glacier inventory (Gardent et al., 2014), named with its GLIMS ID and RGI ID with the following format: GLIMS-ID_RGI-ID_SMB.csv. Both indexes are used since some glaciers that split into multiple sub-glaciers do not have an RGI ID. Split glaciers have the GLIMS ID of their "parent" glacier and an RGI ID equal to 0. Every file contains one column for the year number between 1967 and 2015 and another column for the annual glacier-wide MB time series. Glaciers with remote sensing-derived estimates (Rabatel et al., 2016) include this information as an additional column. This allows the user to choose the source of data, with remote sensing data having lower uncertainties (0.35±0.06 () m.w.e. a<sup>-1</sup> as estimated in Rabatel et al. (2016)). Columns are separated by semicolon (;).</p>
Drivers of black grouse trends in the French Alps
<p>This dataset is composed of three R files: One entitled "BGcounts" that contains an array named "count", composed of counts of black grouse males during lek season conducted on reference sites monitored by a french organisation called "OGM" (https://www.observatoire-galliformes-montagne.com/). The network is composed of 47 counting sites (raws of the dataset), and counts started on various dates on each site (from 1980's to 1990's). Columns (40) are years from 1979 to 2018. Finally, during the same lek season, up to 3 replicates per site have been done, they are contained by the third dimension of the array. Details of the method and sites can be found in the related article accepted in Diversity and Distribution. Note that because black grouse is a sensitive species in France, data have been randomized, hence site numbers on this dataset do not reflect OGM's original site numbers. A second entitled "elevation_surface" containing scaled (x=x-mean/sd) values of sites elevation (column altitude) and surface. The last one named "covariates" with covariates values, harvested number of birds on reference sites, cable density, snow cover (50% and 100% of bare ground), length of vegetative season, starting date of vegetative season and harvest density. Refer to the Readme file to find details about scaling and years provided.</p>
Glacier evolution projections in the French Alps (2015-2100)
<p>Dataset describing the evolution of all individual glaciers in the French Alps for the 2015-2100 period under 29 different RCM-GCM-RCP combinations. The dataset includes annual topographical data (e.g. mean glacier altitude, surface area, slope...), climate data at the glacier's mean altitude (cumulative positive degree-days, winter snowfall, summer snowfall...) and annual glacier-wide mass balance data.</p> <p>The dataset is available in two formats: netCDF and CSV. The netCDF file is the recommended format, as it is well structured to be browsed across dimensions, particularly using tools such as xarray.</p> <p>A secondary dataset (.zip file) includes individual raster files for each glacier and year representing the evolving glacier ice thickness for each of the 29 climate scenarios.</p>
Figure 10 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figure 10 - Semistrict consensus tree of four most parsimonious trees (length 2618, consistency index (CI) 0.272, and retention index (RI) 0.385). Bootstrap values are indicated for nodes more than 50% support (1000 replications).
Figures 4-5 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figures 4-5 - Mercantouria neli sp. n., holotype, male genitalia; France, Dep. Alpes-Maritimes, Col de la Cayolle N, 2080 m, 19.7.2013, leg. Mayr; genitalia slide P.Huemer TIN 94 ♂ 4 tegumen-vinculum-valva complex 5 phallus.
Figure 1 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figure 1 - Mercantouria neli sp. n., holotype; France, Dep. Alpes-Maritimes, Col de la Cayolle N, 2080 m, 19.7.2013, leg. Mayr.
Figure 9 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figure 9 - Maximum likelihood tree based on COI and H3 sequences for 43 Gracillariid species. –ln likelihood = 11983.13. Bootstrap values are indicated for nodes more than 50% support (1000 replications). The general time reversible model of sequence evolution was used with the following settings: LSet nst=6 rclass=(abcdef) rmatrix=(2.9833028 10.54651 15.145156 9.0102019 24.833933) basefreq=(0.26825699 0.20834936 0.17265202) rates=gamma shape=1.0542648 pinv=0.53477118).
Figure 3 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figure 3 - Mercantouria neli sp. n., holotype, abdominal segments 7–8; France, Dep. Alpes-Maritimes, Col de la Cayolle N, 2080 m, 19.7.2013, leg. Mayr; genitalia slide P.Huemer TIN 94 ♂.
Figure 8 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figure 8 - Aspilapterix spectabilis, paratype, male genitalia; Austria, Osttirol, Virgental, Venedigergruppe, Sajatmähder, 2150–2350 m, 31.7.1993, leg. Ryrholm; genitalia slide P.Huemer TIN 33 ♂.
Figure 2 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figure 2 - Wing venation: a Mercantouria neli sp. n.; b Caloptilia stigmatella (Fabricius, 1781). Scale length = 1 mm.
Figures 6-7 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figures 6-7 - Mercantouria neli sp. n., paratype, female genitalia; France, Alpes-Maritimes, N Col de la Cayolle, Col de la Boucharde N, 1950m, 7.7.2012, leg. Huemer; genitalia slide P.Huemer TIN 93 ♀ 6 last segments 7 corpus bursae-signa.
Drivers of black grouse trends in the French Alps
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Clonality and genetic structure of an endangered aquatic plant, Typha minima, in the French Alps: consequences for conservation
<p>Genetic data on Typha minima populations obtained with AFLP markers.</p> <p>The data was used in the article "Clonality and genetic structure of an endangered aquatic plant, Typha minima, in the French Alps: consequences for conservation, by Irène Till-Bottraud, Jacky Girel, Erwan Roussel, Delphine Rioux, Lucie Fiorese, Noémie Fort and published in Alpine Botany in 2022</p>
Figure 11 from: Huemer P, Lopez-Vaamonde C, Triberti P (2016) A new genus and species of leaf-mining moth from the French Alps, Mercantouria neli gen. n., sp. n. (Lepidoptera, Gracillariidae). ZooKeys 586: 145-162. https://doi.org/10.3897/zookeys.586.8375
Figure 11 - Type locality of Mercantouria neli sp. n. near Col de la Cayolle.
Correlation Between the Incidence of COVID-19 in Nursing-homes and the Profile of Nursing Homes in the French Alps
ClinicalTrials.gov study NCT04695457. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.