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3,125 results for “France”
Barn owl diet and prey fluctuations in the Jura mountains, eastern France
<p>Based on pellet collection, the diet of the Barn Owl (<em>Tyto alba</em>) was studied over a 8-year period in the Jura mountains, eastern France, during two population surges of its main prey (common vole, <em>Microtus arvalis </em>and montane water vole, <em>Arvicola amphibius</em>); Small mammals were sampled by trapping and index methods. Results have been published in the Canadian Journal of Zoology (<a href="http://doi.org/10.1139/z10-011">Bernard et al. 2010</a>). Dominique Michelat collected Barn Owl pellets and identified prey items, Pierre Delattre, Jean-Pierre Quéré Jean-Pierre Damange and Patrick Giraudoux sampled small mammals. Patrick Giraudoux managed the data.</p> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a>is the file of the raw trapping results for small mammals (instant abundance index i<sub>t</sub> in the article)</p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a> is a file with:</p> <ul> <li>the small mammal density computed by season (d<sub>t</sub> in the article, rough estimate of densities in number of individuals per ha for <em>Apodemus spp.</em>, <em>Myodes glareolus</em>, <em>Microtus arvalis</em>, or weighted interpolated i<sub>t</sub> for the other species)</li> <li>the ratios of each category of prey items on the total number of items collected in the church tower of three sites, <a href="https://www.openstreetmap.org/#map=16/46.9536/6.1189">Levier</a>, <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9321%2C6.1666#map=16/46.9321/6.1666">Chapelle d'Huin</a> and <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9382%2C6.1976#map=16/46.9382/6.1976">Le Souillot</a>.</li> </ul> <p>For details see the material and methods of the article.</p> <p><strong>FILE DESCRIPTION</strong></p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a></p> <ul> <li>date, year and season: year on two digits, then P, E, A, H respectively for<em> Printemps</em> (Spring), <em>Été</em> (Summer), <em>Automne</em> (Autumn), <em>Hiver</em> (Winter)</li> <li>at_t, abundance index of <em>Arvicola amphibius</em> (ex A.<em> terrestris</em>)</li> <li>ap_t, rough density estimate of <em>Apodemus sp.</em></li> <li>cg_t, rough density estimate of <em>Myodes glareolus</em></li> <li>ma_t, rough density estimate of <em>Microtus arvalis</em></li> <li>sa_t, relative abundance of <em>Sorex spp.</em></li> <li>n_l, number of prey items at Levier</li> <li>ma_p_l, ratio of <em>M. arvalis prey</em> items at Levier</li> <li>at_p_l, ratio of <em>A. amphibius</em> prey items at Levier</li> <li>apcg_p_l, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Levier</li> <li>sa_p_l, ratio of <em>Sorex spp.</em> prey items at Levier</li> <li>au_p_l, ratio of other prey items at Levier</li> <li>n_ch, number of prey items at Chapelle d'Huin</li> <li>map_ch, ratio of <em>M. arvalis prey</em> items at Chapelle d'Huin</li> <li>at_p_ch, ratio of <em>A. amphibius</em> prey items at Chapelle d'Huin</li> <li>apcg_p_ch, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Chapelle d'Huin</li> <li>sa_p_ch, ratio of <em>Sorex spp. </em>prey items at Chapelle d'Huin</li> <li>au_p_ch, ratio of other prey items at Chapelle d'Huin</li> <li>n_ls, number of prey items at Le Souillot</li> <li>ma_p_ls, ratio of <em>M. arvalis prey</em> items at Le Souillot</li> <li>at_p_ls, ratio of <em>A. amphibius</em> prey items at Le souillot</li> <li>apcg_p_ls, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Le Souillot</li> <li>sa_p_ls, ratio of <em>Sorex spp.</em> prey items at Le Souillot</li> <li>au_p_ls, ratio of other prey items at Le Souillot</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a></p> <ul> <li>date, trapping period: digit 1-2, year; digit 3-4, month. Example: 8704 = April 1987.</li> <li>n_traplines_f, number of traplines in forest</li> <li>ap_f, average number of <em>Apodemus spp</em>. captured in forest</li> <li>cg_f, average number of <em>Myodes glareolus</em> captured in forest</li> <li>sa_f, average number of <em>Sorex spp</em>. captured in forest</li> <li>n_traplines_hfb, number of traplines in hedges and forest borders</li> <li>ap_hfb, average number of <em>Apodemus spp</em>. captured in hedges and forest borders</li> <li>cg_hfb, average number of <em>Myodes glareolus</em> captured in hedges and forest borders</li> <li>sa_hfb, average number of <em>Sorex spp.</em> captured in hedges and forest borders</li> <li>n_traplines_g, number of traplines in grassland</li> <li>ma_g, average number of Microtus arvalis captured in grassland</li> <li>sa_g, average number of <em>Sorex spp.</em> captured in grassland</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/SmallMammalSamplingArea.kml?download=1">SmallMammalSamplingArea.kml</a> kml file locating the small mammal sampling area<br> </p>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
FULFILL dataset round 1 France
<table> <tbody> <tr> <td>This dataset and codebook correspond to the initial round of survey data gathered in France in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. <br><br>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</td> </tr> </tbody> </table>
FULFILL dataset - diet policy acceptability - health information provision France
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in France in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from three countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from France, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment France
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in France in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset round 2 France
<p>This dataset and codebook correspond to the second round of survey data gathered in France in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained.</p> <p>This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p> <p>New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.</p>
Population dynamics of small mammals in the Jura massif, Franche-Comté, France (1979-2000)
<p>Small mammal populations were monitored seasonally from August 1979 to July 2000 according to a stratified sampling plan and using standard trapping, mostly in the area of Septfontaines – Le Souillot, Doubs, France (6.18°E, 46.97°N). The dataset includes 2120 trap lines (90% n = 1912 in the Septfontaines – Le Souillot (LS) area), and 22848 captures (92% n = 20937 in the LS area).</p> <p><strong>Methods</strong></p> <p>Small mammals were captured using INRA trap lines. INRA live traps (15 × 5 × 5 cm) are suitable for species of body mass less than 50 g. In a standard way generally applied here,each trap line consisted of 34 live traps spaced 3 meters apart. Trap lines were set up for three nights and checked every morning. Animals were euthanized by cervical dislocation, weighed and dissected for sex, reproductive status and age determination. Liver was examined macroscopically for parasites. Relative age was estimated based on the dry weight of crystalline eye lenses.</p> <p><em>Caveats:</em> in a very little number of cases (beginning of the study and circumstantial occasions) trap lines were not standard (150 m length, 51 traps, or set up for one or two nights only, etc.). See <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a> and fields "remarques" (table <a href="http://zenodo.org/record/6997317/files/traplines.txt?download=1">traplines.txt</a>) and 'observation' (table <a href="http://zenodo.org/record/6997317/files/captures.txt?download=1">captures.txt</a>). Sometimes field not informed have been coded "", 99 or NA. Although we did our best to harmonize this in the files uploaded, some inconsistencies might remain. Those issues should be considered carefully before data analysis.</p> <p>Trapping and animal handling was carried out in full accordance with the relevant European guidelines (Directive 86/609/EEC) and national regulations. INRA (<em>Institut National de la Recherche Agronomique</em>), the umbrella organization under which the field work was carried out, created its first ethical committee in 1998. It was therefore impossible to get formal ethical approval prior to the major part of the study. A similar research protocol used from 2014 to 2017 received full approval from the <em>Comité d’Éthique Bisontin en Expérimentation Animale</em> (CEBEA No. 58).</p> <p><strong>FILE DESCRIPTION</strong></p> <p>Files <a href="https://zenodo.org/record/6997317/files/traplines.txt?download=1">traplines.txt</a>, <a href="https://zenodo.org/record/6997317/files/Ccaptures.txt?download=1">captures.txt</a> and <a href="https://zenodo.org/record/6997317/files/rates.txt?download=1">rates.txt</a> are tables of a relational database. They can be linked using the index field 'codeligne' between <a href="https://zenodo.org/record/6997317/files/traplines.txt?download=1">traplines.txt</a> and <a href="https://zenodo.org/record/6997317/files/captures.txt?download=1">captures.txt</a>, and 'codeind' between <a href="https://zenodo.org/record/6997317/files/captures.txt?download=1">captures.txt</a> and <a href="https://zenodo.org/record/6997317/files/rates.txt?download=1">rates.txt</a>.</p> <p><strong>Main files</strong></p> <p><a href="https://zenodo.org/record/6997317/files/captures.txt?download=1">captures.txt</a></p> <ul> <li>codeligne, trapline ID</li> <li>numind, specimen ID for the trap line</li> <li>numcontr, control number (traps were controlled every morning; for 3 successive nights for standard trap lines). E.g. 1 for a specimen captured on the 1st control.</li> <li>numpiege, trap ID in the trap line (1,2,....n). E.g. 34 is the 34th trap in the trap line counted from the beginning.</li> <li>espece, species (see <a href="http://zenodo.org/record/6997317/files/Codes_Sp_Parasites.docx?download=1">Codes_Sp_Parasites.docx</a> for the codes)</li> <li>poids, wet weight in g</li> <li>sexe, sex (1 male, 2 female)</li> <li>cristallin, dry weight of the two crystalline lens, in 1/10 of mg</li> <li>uterus, uterus diameter</li> <li>foetusd, number of foetuses in the uterus right horn</li> <li>foetusg, number of foetuses in the uterus left horn</li> <li>cicplacd, number of placental scares in the uterus right horn</li> <li>cicplacg, number of placental scares in the uterus left horn</li> <li>corpsjd, number of <em>corpus luteum</em> in the right ovary</li> <li>corpsjg, number of <em>corpus luteum</em> in the left ovary</li> <li>allaitante, milking (1 yes, 0 no)</li> <li>corpsblancd, number of <em>corpus albicans</em> in the right ovary</li> <li>corpsblancg, number of <em>corpus albicans</em> in the left ovary</li> <li>testd, length of the right testicle</li> <li>testg, length of the left testicle</li> <li>vesd, length of the right seminal vesicle</li> <li>vesg, length of the left seminal vesicle</li> <li>diammax, parasite mass great diameter</li> <li>diammin, parasite mass small diameter</li> <li>nombrekyste, parasite cyst number</li> <li>nomlu, parasite species as identified in the field</li> <li>nomanaly, parasite species as identified in the lab</li> <li>observation, remark</li> <li>codeind, specimen ID (codeligne+numind)</li> </ul> <p><a href="https://zenodo.org/record/6997317/files/rates.txt?download=1">rates.txt </a></p> <ul> <li>codeind, specimen ID</li> <li>ratepds, spleen weight (1/100 g)</li> <li>remarque, remark</li> </ul> <p><a href="https://zenodo.org/record/6997317/files/traplines.txt?download=1">traplines.txt</a></p> <ul> <li>codeligne, 8 digits trap line ID. LS891001 = location LS, 89 year, 10 month, 01, trap line ID for this place, year and month. See <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a> for more details.</li> <li>dept, administrative department (INSEE code)</li> <li>date, date at which the trapline has been set up</li> <li>descripteur1, habitat description, <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>descripteur2, habitat description, see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>facies, habitat description, see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>remarque, remark,</li> <li>lati, latitude of the northwest corner of the sampling grid (CRS NTF (Paris) / Lambert zone II, EPSG: 27572), see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>longi, longitude of the northwest corner of the sampling grid (CRS NTF (Paris) / Lambert zone II, EPSG: 27572), see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> <li>precis, precision, the number of Lambert II squares (1km x 1km) composing the square side of the grid, see <a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a></li> </ul> <p><strong>Supplementary files</strong></p> <p><a href="https://zenodo.org/record/6997317/files/bboxlarge.kml?download=1">bboxlarge.kml</a>, the bounding box including all the area with trap lines that could be geographically located. It takes the precision of the location into account (hence, includes the southeastern extremes of the grid squares (see Codes_Trapline.docx).</p> <p><a href="https://zenodo.org/record/6997317/files/bboxLS.kml?download=1">bboxLS.kml</a>, the bounding box of the study area "LS" (Le Souillot) including all the trap lines that could be geographically located. It takes the precision of the location into account (hence, includes the southeastern extremes of the grid squares, see <a href="https://zenodo.org/record/6997317/files/Codes_trapline.docx?download=1">Codes_Trapline.docx</a>). Those trap lines are the core (90% of the total number of the traplines set) of the research carried out in the area.</p> <p><a href="https://zenodo.org/record/6997317/files/lineloc.kml?download=1">lineloc.kml</a>, the geographical coordinates of the northwestern corner of the square including each trap line with trapline ID and precision (1, square of 1 x 1 km; 2, square of 2 x 2 km, etc.).</p> <p><a href="https://zenodo.org/record/6997317/files/Code_Sp_Parasites.docx?download=1">Codes_Sp_Parasites.docx</a>, codes of small mammal species and parasite names.</p> <p><a href="https://zenodo.org/record/6997317/files/Codes_Trapline.docx?download=1">Codes_Trapline.docx</a>, codes of trap lines.</p>
CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development
<p>Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.</p> <p>The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.</p> <p>The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. </p> <p>Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. </p> <p>Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype.</p> <p>We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.</p> <p>The table below summarizes the variables contained in the dataset, for each scenario.</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Description and unit </strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p>Scenario n°</p> </td> <td> <p>Number of the scenario.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>INPUTS (X)</strong></p> </td> </tr> <tr> <td> <p>NM</p> </td> <td> <p>Relative mean sea level, referenced to the French vertical datum (m, IGN69)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>T</p> </td> <td> <p>Tidal water level (m), referenced to the relative mean sea level</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>S</p> </td> <td> <p>Atmospheric storm surge (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Hs</p> </td> <td> <p>Significant wave height (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Tp</p> </td> <td> <p>Wave peak period (s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Dp</p> </td> <td> <p>Wave peak direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Wind speed (m/s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>DU</p> </td> <td> <p>Wind direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>t</p> </td> <td> <p>Relative time centered on the high tide of each event (min)</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p>High Tide date</p> </td> <td> <p>UTC date for scenarios corresponding to past real events</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p><strong>OUTPUTS (Y)</strong></p> </td> </tr> <tr> <td> <p>Smax</p> </td> <td> <p>Maximum flooded area during the event (m²)</p> </td> <td> <p>Post-processed scalar output</p> </td> </tr> <tr> <td> <p>Hmax</p> </td> <td> <p>Maximum water depth reached during the event (m), provided for each inland location</p> </td> <td> <p>Post-processed functional (map) output</p> </td> </tr> <tr> <td> <p>longitude</p> </td> <td> <p>Longitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>latitude</p> </td> <td> <p>Latitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>XL93</p> </td> <td> <p>Longitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>YL93</p> </td> <td> <p>Latitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><br> </p>
Access Network of Henri III of France's apartment according to his 1585 court ordinances
<p>This dataset collects the stipulations of access for courtiers in the royal apartment as described in Henri III of France's 1585 court ordinance (Paris: Archives Nationales, KK 544, fol. 55r-141r).</p> <p>The dataset was created with the aim to establish the degree of accessibility of both the (individual) spaces in the royal apartmant as well as the king himself and analyse in which ways king Henri III of France managed to balance his need for privacy with the courtiers' expectations of access. The results are to be / were published in <em>Current Research in Digital History</em>. </p>
Dataset - paper: Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France
<p>Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. <em>Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France: (How) did they change? </em>Appetite. 2021 Jun 1;161:105132. doi: <strong>10.1016/j.appet.2021.105132</strong>. Epub 2021 Jan 23. PMID: 33493611; PMCID: PMC7825985).</p> <p>The objective of the study was to evaluate possible changes in eating behaviors in children aged 3–12 years, in parental eating and cooking behaviors, in parental feeding practices, and also in parental motivations when shopping for food during the lockdown, compared to the period before the lockdown.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-COVID.docx".</p>
Distribution of waterbirds along the Drugeon river, France
<p>This dataset describes bird observations performed along the Drugeon river, France from 2006 to 2019.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Carte.jpg?download=1"><strong>Carte.jpg</strong></a>' Map of the study area. The box shows the location of the Bannans and the Sainte-Colombe transects. Village locations and the route of the river have been taken from 'BD Carto ', kindly provided for research by the National Geographical Institute, and modified on the basis of field observations.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Mat%20sup%201%20transects%20Drugeon%20synthe%CC%80se_Zenodo.xlsx?download=1"><strong>Mat sup 1 transects Drugeon synthèse_Zenodo.xlsx</strong></a>' since September 2006, two transects were carried out four times a fortnight,on foot in the early morning. All identified birds, by sight or by ear, were noted with special care to avoid double counting. The two transects are located in the downstream part of the Drugeon valley. The Bannans transect starts from the Drugeon diversion upstream from the Bannans flour mill and runs along the river downstream to the place called Mitray, with a lateral extension to the En Vau-Les-Aigues marsh located partly in the La Rivière-Drugeon village. This transect is approximately 5.4 km long, of which 1.4 km follow the river. It sampled the bird population of the river, the marshes more or less wooded with willows, birches and a few spruces, and the neighboring meadows and pastures. The flour mill dam provides a 3660 square meter body of water that is not flushed out because it is attached to a dwelling house. This is the only non-huntable wetland area along the two transects. The Sainte-Colombe transect starts from the village, crosses the mesophilic and then wet meadows to reach almost the downstream end of the Bannans transect. It then runs along the river in its undisturbed part to the Chaffois mill. The return to the village is via an agricultural path that crosses mesophilic and humid meadows, a marsh and a small wood of poplar and spruce trees. This transect is 8.6 km long including 2 km along the river. A U-shaped pond carved out of a marsh provides an open water surface of approximately 3400 square meters. It was also prospected during the transect.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Mat%20sup%202%20donne%CC%81es%20brutes%20descente%20du%20Drugeon.xlsx?download=1"><strong>Mat sup 2 données brutes descente du Drugeon.xlsx</strong></a>' covers the censuses along the course of the Drugeon river from 2014 to 2019. Birds were recorded during a course carried out on foot along the river on a bank from the Dompierre bridge located between Vaux-et-Chantegrue and Bonnevaux villages to the Pont Rouge bridge next to Vuillecin village (29,2 km representing almost the entire course of the Drugeon river). Each year, the census was taken in sections during the second half of October, an average of one to one and a half months after the opening of the hunting season. Each waterbird observed was precisely located on a map and then plotted on Google Earth. Using Géoportail and field surveys, a precise description of the watercourse has been carried out on the whole of the prospected area: environment bordering each bank (forest, wooded marsh, herbaceous marsh, meadow, village), vegetation on each bank (continuous willow, megaphorbiaie with scattered willows, pure megaphorbiaie, phragmitaie, short herbaceous vegetation), width of the river, slope of the watercourse, status with respect to hunting (huntable zone, zone not huntable because located less than 150 m from homes and hunting reserve).</p> <p><em>The figures of the following three files are computed from the two files above: Mat sup 1 and Mat sup 2:</em></p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 3 Down river walk.zip?download=1">Mat sup 3 Down river walk.zip</a>'</strong> Distribution and abundance 2014-2019 of the main waterbird species along the Drugeon river</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 4 Hunting season onset.zip?download=1">Mat sup 4 Hunting season onset.zip</a>'</strong> Abundance of the Anatidae species on the Bannans and Sainte-Colombe transects according to the opening date of waterbird hunting</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 5 Bannans transect.zip?download=1">Mat sup 5 Bannans transect.zip</a>'</strong> Abundance of the Anatidae species on the Bannans and Sainte-Colombe transects according to the opening date of waterbird hunting</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/transectsDomi.kml?download=1">TransectsDomi.kml</a>' </strong>a kml file locating the Bannans and Sainte-Colombe transects (polylines).</p>
Database of weeds in cultivation fields of France and UK, with ecological and biogeographical information
<p>The database includes a list of 1577 weed plant taxa found in cultivated fields of France and UK, along with basic ecological and biogeographical information.<br> The database is a CSV file in which the columns are separated with comma, and the decimal sign is ".".<br> It can be imported in R with the command "tax.discoweed <- read.csv("tax.discoweed_18Dec2017_zenodo.csv", header=T, sep=",", dec=".", stringsAsFactors = F)"</p> <p>Taxonomic information is based on TaxRef v10 (Gargominy et al. 2016),<br> - 'taxref10.CD_REF' = code of the accepted name of the taxon in TaxRef,<br> - 'binome.discoweed' = corresponding latine name,<br> - 'family' = family name (following APG III),<br> - 'taxo' = taxonomic rank of the taxon, either 'binome' (species level) or 'infra' (infraspecific level),<br> - 'binome.discoweed.noinfra' = latine name of the superior taxon at species level (different from 'binome.discoweed' for infrataxa),<br> - 'taxref10.CD_REF.noinfra' = code of the accepted name of the superior taxon at species level.</p> <p>The presence of each taxon in one or several of the following data sources is reported:<br> - Species list from a reference flora (observations in cultivated fields over the long term, without sampling protocol),<br> * 'jauzein' = national and comprehensive flora in France (Jauzein 1995),<br> - Species lists from plot-based inventories in cultivated fields,<br> * 'za' = regional survey in 'Zone Atelier Plaine & Val de Sèvre' in SW France (Gaba et al. 2010),<br> * 'biovigilance' = national survey of cultivated fields in France (Biovigilance, Fried et al. 2008),<br> * 'fse' = Farm Scale Evaluations in England and Scotland, UK (Perry, Rothery, Clark et al., 2003),<br> * 'farmbio' = Farm4Bio survey, farms in south east and south west of England, UK (Holland et al., 2013)<br> - Reference list of segetal species (species specialist of arable fields),<br> * 'cambacedes' = reference list in France (Cambacedes et al. 2002)</p> <p>Life form information is extracted from Julve (2014) and provided in the column 'lifeform'.<br> The classification follows a simplified Raunkiaer classification (therophyte, hemicryptophyte, geophyte, phanerophyte-chamaephyte and liana). Regularly biannual plants are included in hemicryptophytes, while plants that can be both annual and biannual are assigned to therophytes.</p> <p>Biogeographic zones are also extracted from Julve (2014) and provided in the column 'biogeo'.<br> The main categories are 'atlantic', 'circumboreal', 'cosmopolitan, 'Eurasian', 'European', 'holarctic', 'introduced', 'Mediterranean', 'orophyte' and 'subtropical'.<br> In some cases, a precision is included within brackets after the category name. For instance, 'introduced(North America)' indicates that the taxon is introduced from North America.<br> In addition, some taxa are local endemics ('Aquitanian', 'Catalan', 'Corsican', 'corso-sard', 'ligure', 'Provencal').<br> A single taxon is classified 'arctic-alpine'.</p> <p>Red list status of weed taxa is derived for France and UK:<br> - 'red.FR' is the status following the assessment of the French National Museum of Natural History (2012),<br> - 'red.UK' is based on the Red List of vascular plants of Cheffings and Farrell (2005), last updated in 2006.<br> The categories are coded following the IUCN nomenclature.</p> <p>A habitat index is provided in column 'module', derived from a network-based analysis of plant communities in open herbaceous vegetation in France (Divgrass database, Violle et al. 2015, Carboni et al. 2016).<br> The main habitat categories of weeds are coded following the Divgrass classification,<br> - 1 = Dry calcareous grasslands<br> - 3 = Mesic grasslands<br> - 5 = Ruderal and trampled grasslands<br> - 9 = Mesophilous and nitrophilous fringes (hedgerows, forest edges...)<br> Taxa belonging to other habitats in Divgrass are coded 99, while the taxa absent from Divgrass have a 'NA' value.</p> <p>Two indexes of ecological specialization are provided based on the frequency of weed taxa in different habitats of the Divgrass database.<br> The indexes are network-based metrics proposed by Guimera and Amaral (2005),<br> - c = coefficient of participation, i.e., the propensity of taxa to be present in diverse habitats, from 0 (specialist, present in a single habitat) to 1 (generalist equally represented in all habitats),<br> - z = within-module degree, i.e., a standardized measure of the frequency of a taxon in its habitat; it is negatve when the taxon is less frequent than average in this habitat, and positive otherwise; the index scales as a number of standard deviations from the mean.</p>
Remote Rapid Visual Screening (RRVS) Buildings Survey Data - DESTRESS - France
<p>The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap.<br> Surveyor: Konstantinos G. Megalooikonomou (GFZ-Potsdam)<br> The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). <br> The following attributes are defined (not all are observable in the RRVS survey):<br />code,description<br> lon, longitude in fraction of degrees<br> lat, latitude in fraction of degrees<br> object_id, unique id of the building surveyed <br> MAT_TYPE,Material Type<br> MAT_TECH,Material Technology<br> MAT_PROP,Material Property<br> LLRS,Type of Lateral Load-Resisting System<br> LLRS_DUCT,System Ductility<br> HEIGHT,Height<br> YR_BUILT,Date of Construction or Retrofit<br> OCCUPY,Building Occupancy Class - General<br> OCCUPY_DT,Building Occupancy Class - Detail<br> POSITION,Building Position within a Block<br> PLAN_SHAPE,Shape of the Building Plan<br> STR_IRREG,Regular or Irregular<br> STR_IRREG_DT,Plan Irregularity or Vertical Irregularity<br> STR_IRREG_TYPE,Type of Irregularity<br> NONSTRCEXW,Exterior walls<br> ROOF_SHAPE,Roof Shape<br> ROOFCOVMAT,Roof Covering<br> ROOFSYSMAT,Roof System Material<br> ROOFSYSTYP,Roof System Type<br> ROOF_CONN,Roof Connections<br> FLOOR_MAT,Floor Material<br> FLOOR_TYPE,Floor System Type<br> FLOOR_CONN,Floor Connections</p>
Exemple of a valorization network based on a 10% collection rate from potential biowaste in the Grand Lyon territory (France).
<p>This study, conducted in the frame of the H2020 DECISIVE project, aims at developing a method to design a decentralized and small-scale AD (mAD) network in urban and peri-urban areas. A mixed integer linear program (MILP) was set up based on the proposed system. It aims at minimizing the impacts of the biowaste and the digestate transportation by minimizing their payload-distances while taking into account notably the technical constraints of the newly developed micro-AD. A Geographic Information System (GIS) based methodology was developed to feed the MILP model with very fine-scale data required to optimized a proximity treatment system. The method allows to locate and estimate the biowaste generation and to locate the digestate outlets, the agricultural areas, and to estimate the maximal amount of digestate usable. The candidate sites for mAD are identified with a GIS multi-criteria analysis that includes the environmental regulations, some urban planning rules and the site accessibility and heat outlet valorization. The method developed is successfully applied in the territory of The Grand Lyon Metropole (534 km²), located in France. The MILP model succeeds at providing a solution even with the very large problem studied (≥10<sup>8</sup> possible combination). Different scenarios can be easily tested to meet the potential needs of the stakeholder: the quantity of biowaste to treat, the type of sources to target, the synergy with the current treatment solution, etc.</p> <p>The data are provided through the open, non-proprietary GeoPackage files (GPKG) commonly recognized in GIS tools. A complementary xml file describe the metadata in compliance with the Inspire directive. A complementary pdf file describe the fields of the datasets.</p> <p> </p>
PM_125602_B_Franc_Waret
<u>File Name</u>: PM_125602_B_Franc_Waret.jpg <br><u>Sublocation</u>: Château de Franc-Waret <br><u>Location</u>: Fernelmont <br><u>Province</u>: Namur <br><u>Country</u>: Belgium <br><u>Header</u>: Le château, 18e siècle, la façade, architecte Jean-Baptiste Chermanne <br><u>Description</u>: Palace 18th century Architect Jean-Baptiste Chermanne facade <br><u>Keywords</u>: Belgium, Castle, Cultural heritage, Europe, Fernelmont, Franc-Waret (Fernelmont), Monuments, Namur, Palace <br><br><u>Author</u>: Architect Jean-Baptiste Chermanne (1704-1770) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_125599_B_Franc_Waret
<u>File Name</u>: PM_125599_B_Franc_Waret.jpg <br><u>Sublocation</u>: Château de Franc-Waret <br><u>Location</u>: Fernelmont <br><u>Province</u>: Namur <br><u>Country</u>: Belgium <br><u>Header</u>: Le château, 18e siècle, la façade, architecte Jean-Baptiste Chermanne <br><u>Description</u>: Palace 18th century Architect Jean-Baptiste Chermanne facade <br><u>Keywords</u>: Belgium, Castle, Cultural heritage, Europe, Fernelmont, Franc-Waret (Fernelmont), Monuments, Namur, Palace <br><br><u>Author</u>: Architect Jean-Baptiste Chermanne (1704-1770) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_125607_B_Franc_Waret
<u>File Name</u>: PM_125607_B_Franc_Waret.jpg <br><u>Sublocation</u>: Château de Franc-Waret <br><u>Location</u>: Fernelmont <br><u>Province</u>: Namur <br><u>Country</u>: Belgium <br><u>Header</u>: Le château, 18e siècle, architecte Jean-Baptiste Chermanne <br><u>Description</u>: Palace 18th century Architect Jean-Baptiste Chermanne <br><u>Keywords</u>: Belgium, Castle, Cultural heritage, Europe, Fernelmont, Franc-Waret (Fernelmont), Monuments, Namur, Palace <br><br><u>Author</u>: Architect Jean-Baptiste Chermanne (1704-1770) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_125600_B_Franc_Waret
<u>File Name</u>: PM_125600_B_Franc_Waret.jpg <br><u>Sublocation</u>: Château de Franc-Waret <br><u>Location</u>: Fernelmont <br><u>Province</u>: Namur <br><u>Country</u>: Belgium <br><u>Header</u>: Le château, 18e siècle, la façade, architecte Jean-Baptiste Chermanne <br><u>Description</u>: Palace 18th century Architect Jean-Baptiste Chermanne facade <br><u>Keywords</u>: Belgium, Castle, Cultural heritage, Europe, Fernelmont, Franc-Waret (Fernelmont), Monuments, Namur, Palace <br><br><u>Author</u>: Architect Jean-Baptiste Chermanne (1704-1770) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_125611_B_Franc_Waret
<u>File Name</u>: PM_125611_B_Franc_Waret.jpg <br><u>Sublocation</u>: Château de Franc-Waret <br><u>Location</u>: Fernelmont <br><u>Province</u>: Namur <br><u>Country</u>: Belgium <br><u>Header</u>: Le château, 18e siècle, architecte Jean-Baptiste Chermanne <br><u>Description</u>: Palace 18th century Architect Jean-Baptiste Chermanne <br><u>Keywords</u>: Belgium, Castle, Cultural heritage, Europe, Fernelmont, Franc-Waret (Fernelmont), Monuments, Namur, Palace <br><br><u>Author</u>: Architect Jean-Baptiste Chermanne (1704-1770) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_125610_B_Franc_Waret
<u>File Name</u>: PM_125610_B_Franc_Waret.jpg <br><u>Sublocation</u>: Château de Franc-Waret <br><u>Location</u>: Fernelmont <br><u>Province</u>: Namur <br><u>Country</u>: Belgium <br><u>Header</u>: Le château, 18e siècle, architecte Jean-Baptiste Chermanne <br><u>Description</u>: Palace 18th century Architect Jean-Baptiste Chermanne <br><u>Keywords</u>: Belgium, Castle, Cultural heritage, Europe, Fernelmont, Franc-Waret (Fernelmont), Monuments, Namur, Palace <br><br><u>Author</u>: Architect Jean-Baptiste Chermanne (1704-1770) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.