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Bird surveys in French Broad River Basin, North Carolina, 2014
This dataset includes counts of birds from surveys conducted in the French Broad River Basin in western North Carolina, USA. This basin is in the Southern Appalachian Mountains. Data were collected to examine the spatial and seasonal supply of biodiversity-based cultural ecosystem services (CES), in this case, nature study through birdwatching. The data includes bird species observed at 69 sites on public and private lands during the period 2014-04-01 to 2014-08-08. Bird species were categorized with respect to migration status, level of conservation concern (both based on literature), and relative abundance in the study region (based on eBird data). Environmental data for 56 sites are provided: elevation, early season precipitation, mean summer temperature, land cover diversity, tree cover, vegetation structural diversity, vegetation annual productivity, and building density at local and landscape scales. Graves et al. (2019, doi:10.1007/s13280-018-1068-1) used these data to analyze seasonal shifts in birdwatching supply and how those shifts impacted public access to projected birdwatching hotspots. Landscape patterns of CES supply differed substantially among five CES indicators (total bird species richness, and richness of migratory, infrequent, synanthrope, and resident species). For example, total species richness hotspots seldom overlapped with hotspots of migratory or infrequent species. Public access to CES hotspots varied seasonally. This study suggests that simple, static biodiversity metrics may overlook spatial dynamics important to CES users.
nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in french watercourses
<p><strong>nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in French watercourses</strong></p> <p>Antoine Casquin, Marie Silvestre, Vincent Thieu</p> <p>10.5281/zenodo.10830852</p> <p>v0.1, 18<sup>th</sup> March 2024</p> <p><strong>Brief overview of data: </strong></p> <p>· Carbon and nutrients data for 5470 continental French catchments</p> <p>· Modelled discharge for 5128 of catchments out of 5470</p> <p>· Geopackages with catchment delineations and outlets</p> <p>· DEM conditioned to delimit additional catchments</p> <p>· Land-use and climatic data for 5470 continental French catchments</p> <p><strong>Citation of this work<br></strong></p> <p>A data paper is currently being submitted with details of methods and results. Once published, it will be the preferential source to cite. The data paper will be link to the new version of the dataset that will be updated on doi.org/10.5281/zenodo.10830852. If you use this dataset in your research or report, you must cite it.</p> <p><strong>Motivations</strong></p> <p>Data was collected and curated for the nuts-STeauRY project (<a href="http://nuts-steaury.cnrs.fr">http://nuts-steaury.cnrs.fr</a>), which deployed a national generic land to sea modelling chain.</p> <p>Data was primarily used (see related works):</p> <ol> <li>To calibrate concentrations of dissolved organic carbon and dissolve silica in headwaters</li> <li>To validate spatially and temporally the modelling chain (DOC, NO3-, NH4+, TP, SRP, DSi)</li> </ol> <p>Hydrochemical large sample datasets have numerous other uses: trends computations elucidate transfer mechanisms, machine learning, retrospective studies etc.</p> <p>The objective here is to provide a large sample curated dataset of carbon and nutrients concentrations along with modelled discharges, catchment characteristics and delimitations for the continental France. Such large sample dataset aims at easing the large sample studies over France and/or Europe. Although part of the data gathered here is obtainable via public sources, the catchments delineations, their characteristics and modelled hydrology were note not publicly available yet. Moreover, a unification of units and detection and removal of outliers was performed on carbon and nutrients data.</p> <p><strong>Data sources & processing</strong></p> <p>Sampling points where snapped on the CCM database v2.1 (<a href="http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221">http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221</a>)(Vogt et al., 2007) and catchments were delineated using a 100m resolution Digital Elevation Model (DEM) conditioned by the hydrographic network and elementary catchments’ delineations of the CCM data v2.1. <strong>More than 6000 catchments were delineated and screened manually</strong> to check consistency: 5470 were retained<strong>.</strong></p> <p>Nutrient data was collected mainly through the Naiades portal (<a href="https://naiades.eaufrance.fr/">https://naiades.eaufrance.fr/</a>), a database collecting water quality data produced by different water related actors across France. Nutrient data was also collected directly with regional water agencies (<a href="https://www.eau-seine-normandie.fr/">https://www.eau-seine-normandie.fr/</a>, <a href="https://eau-grandsudouest.fr/">https://eau-grandsudouest.fr/</a>, <a href="https://www.eaurmc.fr/">https://www.eaurmc.fr/</a>, <a href="https://www.eau-artois-picardie.fr/">https://www.eau-artois-picardie.fr/</a>, <a href="https://www.eau-rhin-meuse.fr/">https://www.eau-rhin-meuse.fr/</a> and <a href="https://agence.eau-loire-bretagne.fr/home.html">https://agence.eau-loire-bretagne.fr/home.html</a>), and pre-processed using a database management system relying on PostgreSQL with PostGIS extension (Thieu & Silvestre, 2015). A three-pass strategy was used to curate raw carbon and nutrients data: 1. Removal of “obvious outliers”, 2. Detection of baseline change and correction if possible (or removal of data) 3. Removal of outliers using a quantile based approach by element and temporal series.</p> <p>Hydrological time series are interpolation trough hydrograph transfer (de Lavenne et al., 2023) of 1664 time series of discharge completed with GR4J model (Pelletier & Andréassian, 2020; Pelletier 2021).</p> <p>Land cover data was extracted from Corine Land Cover dataset for years 2000, 2006, 2012, and 2018 (EEA, 2020). Raw CLC typology contains 44 classes. Results of percent cover per year per class were computed for each catchment. An aggregated typology of 8 classes is also proposed.</p> <p>Climatological data was extracted from daily reconstruction at 5 arcmin for temperatures and 1 arcmin for precipitation over Europe (Thiemig et al., 2022). Mean by catchment for min&max daily temperature and precipitation were computed for each catchment for the 1990-2019 period.</p> <p><strong>Nuts-STeauRY dataset</strong></p> <p><strong>Carbon and nutrients time series</strong></p> <p>Time series of carbon and nutrients within the 1962-2019 period on 5470 stations: Dissolved Organic Carbon (DOC), Total Organic Carbon (TOC) Nitrates (NO3-), Nitrites (NO2-), Ammonia (NH4+), Soluble Reactive Phosphorus (SRP), Total Phosphorus (TP) and Dissolved Silica (DSi).</p> <p><code>|var | n_unique_station| n_total_meas| mean_duration_y| mean_frequency_y|</code></p> <p><code>|:---|----------------:|------------:|---------------:|----------------:|</code></p> <p><code>|DOC | 4 992| 658 147| 14.3| 9.0|</code></p> <p><code>|DSi | 3 299| 333 866| 12.9| 8.3|</code></p> <p><code>|NH4 | 5 318| 907 343| 19.3| 8.7|</code></p> <p><code>|NO2 | 5 264| 891 886| 19.2| 8.6|</code></p> <p><code>|NO3 | 5 465| 939 279| 19.0| 9.0|</code></p> <p><code>|SRP | 5 361| 910 107| 19.1| 8.7|</code></p> <p><code>|TOC | 935| 111 993| 13.6| 9.6|</code></p> <p><code>|TP | 5 199| 802 841| 17.1| 8.8|</code></p> <p>Note that some SRP and DSi measurements were declared as realized on raw water. A thorough analysis of time series show no evidence of difference on baselines. For more accuracy, it is advised to filter out those analyses using the “fraction” attribute of each measurement.</p> <p><strong>Discharge modelled daily time series</strong></p> <p>Modelled naturalized discharge through hydrograph transfer and interpolated measured discharges when available for the 1980-2019 period.</p> <p>A daily discharge was computed for 5128 catchments. For small catchments (< 1000 km<sup>2</sup>, n = 4530), hydrograph transfer was used, while for big catchments, a direct interpolation of measured/completed discharges was performed. The direct interpolation was only possible for 598 catchments > 1000 km<sup>2</sup>. The criteria retained for a direct interpolation is 0.8*area_discharge_station < area_quality < 1.2*area_discharge_station when discharge and quality stations were nested.</p> <p>Hydrological time series uncertainties varies a lot depending on: quality of data source, distance from pseudo-gauged outlets, land cover of the catchments, natural spatial and temporal variability of discharge, size of the catchment (de Lavenne et al., 2016). We advise a cautious use of those modelled discharges as uncertainties could not be computed.</p> <p><strong>Catchments, outlets and conditioned DEM</strong></p> <p>5470 catchments and outlets are delivered as geopackages (EPSG: 3035).</p> <p>The DEM, conditioned by CCM 2.1 is also delivered as a GeoTIFF (EPSG: 3035) as way to delimit new catchment for the area that are consistent with the dataset.</p> <p><strong>Catchments characteristics and climate</strong></p> <p>Refer to Data sources & processing and File descriptions.</p> <p><strong> </strong></p> <p><strong>File and attributes descriptions: </strong></p> <p>The key “sta_code” is present across all files. For time varying records, “date” can be a secondary key. </p> <p><strong>Description of CNPSi.csv data attributes</strong></p> <p>Each line is a couple measurement/parameter/station</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· var: Abbreviation of parameter name</p> <p>· fraction: "water_filtrated" or "water_raw"</p> <p>· date: date of sampling</p> <p>· hour: hour of sampling</p> <p>· value: analytical result (concentration)</p> <p>· provider: provider of the data</p> <p>· producer: producer of the data</p> <p>· from_db: "Naiades2022" (https://naiades.eaufrance.fr/france-entiere#/ dump from 2022) or "DoNuts" (Thieu, V., Silvestre, M., 2015. DoNuts: un système d’information sur les observations environnementales. Présentation Séminaire UMR Métis)</p> <p>· n_meas: number of observations for a given parameter / station</p> <p>· unit: unit of concentration</p> <p>· element: "C" "N" "P" or "Si"</p> <p>· year: year of observation</p> <p>· month: month of observation</p> <p>· day: day of observation</p> <p>· julian_day: julian day observation (1-366)</p> <p>· decade: decade of observation (one of "1961-1970", "1971-1980", "1981-1990", "1991-2000", "2001-2010", "2011-2020")</p> <p> </p> <p> </p> <p><strong>Description of CNPSi_stats.csv data attributes</strong></p> <p>Each line is a couple parameter / station</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· var: Abbreviation of parameter name</p> <p>· n_meas: number of observations for a given parameter / station</p> <p>· start_year: year of first observation for a given parameter / station</p> <p>· end_year: year of last observation for a given parameter / station</p> <p>· duration_y_tot: total duration of observation in years for a given parameter / station</p> <p>· duration_y_tot: duration of observation in years for a given parameter / station for years with at least 1 meas</p> <p>· mean_nmeas_per_y_tot: mean number of observations per year considering total duration</p> <p>· mean_nmeas_per_y_meas: mean number of observations per year considering years with measurements</p> <p>· is_fully_continuous: TRUE if at least one measurement per year for a given parameter / station</p> <p>· start_cont_seq: year in which starts the longest continuous sequence for a given parameter / station</p> <p>· end_cont_seq: year in which ends the longest continuous sequence for a given parameter / station</p> <p>· duration_y_cont_seq: duration in years for the longest continuous sequence for a given parameter / station</p> <p>· nmeas_cont_seq: number of measurements for the longest continuous sequence for a given parameter / station</p> <p>· mean_nmeas_per_y_cont_seq: mean number of observations per year for the longest continuous sequence for a given parameter / station</p> <p>· mean: mean value (concentration) for a given parameter / station</p> <p>· median: median value (concentration) for a given parameter / station</p> <p>· sd: standard deviation (concentration) for a given parameter / station</p> <p>· cv: coeficient of variation (concentration) for a given parameter / station</p> <p>· c05,c25,c50,c75,c95: centiles 5, 25, 50, 75 & 95 for a given parameter / station</p> <p><strong>Description of catchments.gpkg and outlets.gpkg data attributes</strong></p> <p>Each line is a catchment or an outlet (sampling point)</p> <p>File is a .gpkg (EPSG = 3035)</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· watercourse: Name of the water course (from spatial join on IGN BD Topo)</p> <p>· mun_name: Name of the municipality of the outlet (from spatial join on IGN BD Admin Express)</p> <p>· ccm_wso_id: Seaoutlet id from CCM v2.1 database</p> <p>· ccm_wso1_id: Elementary catchment id from CCM v2.1 database</p> <p>· ccm_strahler: Strahler order of the catchment from CCM v2.1 database</p> <p>· area_km2: Computed area in km2 of the catchment</p> <p><strong>Description of daily discharges data attributes</strong></p> <p>Each line corresponds to a daily modelled discharge at a quality station from 1980 to 2019</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· date: Date in format yyyy-mm-dd</p> <p>· flow_mm: Discharge expressed in mm.d-1</p> <p>· flow_m3s: Discharge expressed in m3.s-1</p> <p><strong>Description of climate data attributes</strong></p> <p>Each line in the pr_tmin_tmax_1990-2019_lt_mean.csv corresponds to a mean value within a catchment for the 1990-2019 period.</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· period: 1990-2019</p> <p>· source: EMO-1 (pr) & EMO-5 (tmin, tmax)</p> <p>· pr: mean yearly precipitation (mm)</p> <p>· tmin: mean daily minimal temperature (°C)</p> <p>· tmin: mean daily maximal temperature (°C)</p> <p><strong>Description of land cover data attributes</strong></p> <p>Each line in the clc_8class.csv and clc_44class.csv corresponds to Corine Land Cover (CLC) class for a year (1990, 2000, 2006, 2012, or 2018) and a catchment. Raw CLC typology describes 44 classes that were aggregated to 8 classes (see clc_44class_to_8class.csv).</p> <p>· clc_44class.csv</p> <p>o sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o year: Year as stated in CLC product</p> <p>o clc_name: Description of land cover class in CLC product</p> <p>o clc_code: Code for land cover class in CLC product</p> <p>o percent_cover: Percent cover by CLC class in the catchment (0-100)</p> <p>· clc_8class.csv</p> <p>o sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o year: Year as stated in CLC product</p> <p>o label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o code_clc_8class: Code for land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o percent_cover: Percent cover by aggregated CLC class in the catchment (0-100)</p> <p>· clc_44class_to_8class.csv</p> <p>o code_clc: Code for land cover class in CLC product (44 classes)</p> <p>o code_clc_8class: Code for land cover class in aggregated CLC product (8classes)</p> <p>o label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p> </p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This publication has been prepared using European Union's Copernicus Land Monitoring Service information; <a href="https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac">https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac</a></p> <p>The authors thank Vasken Andréassian for communicating the discharge data and discharge station data and Alban de Lavenne for its help in using the transfr package, both for INRAE UR HYCAR.</p> <p> </p> <p><strong>References</strong></p> <p>de Lavenne, A., Skøien, J. O., Cudennec, C., Curie, F., & Moatar, F. (2016). Transferring measured discharge time series: Large-scale comparison of Top-kriging to geomorphology-based inverse modeling: transferring measured discharge time series. Water Resources Research, 52(7), 5555–5576. https://doi.org/10.1002/2016WR018716</p> <p>de Lavenne, A., Loree, T., Squividant, H., & Cudennec, C. (2023). The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology. Environmental Modelling & Software, 159, 105562. <a href="https://doi.org/10.1016/j.envsoft.2022.105562">https://doi.org/10.1016/j.envsoft.2022.105562</a></p> <p>EEA. (2020). Corine Land Cover édition 2018. CLC 2018. <a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-corine">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-corine</a></p> <p>Pelletier, A., & Andréassian, V. (2020). Hydrograph separation: An impartial parametrisation for an imperfect method. Hydrology and Earth System Sciences, 24(3), 1171–1187. <a href="https://doi.org/10.5194/hess-24-1171-2020">https://doi.org/10.5194/hess-24-1171-2020</a></p> <p>Pelletier, A. (2021). Complétion d'hydrogrammes avec le modèle GR4J - Note méthodologique. INRAE, UR HYCAR.</p> <p>Thiemig, V., Gomes, G. N., Skøien, J. O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J. P., Kolbe, C., Pichon, D., Schweim, C., and Salamon, P.: EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe, Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, 2022</p> <p>Thieu, V., Silvestre, M., 2015. DoNuts : un système d'information sur les observations environnementales. Présentation Séminaire UMR Métis</p> <p>Vogt, J., A. de Jager, E. Rimaviciute, W. Mehl, S. Foisneau, K. Bódis, J. Dusart, M.L. Paracchini, P. Haastrup, & C. Bamps. (2007). A pan-European river and catchment database. (European Commission. Joint Research Centre. Institute for Environment and Sustainability.). Publications Office. https://data.europa.eu/doi/10.2788/35907</p>
Replication Data for: "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis"
<p>This data package contains all the data relevant to reproduce the results presented in the publication "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis".</p>
Wildflower survey data from French Broad River Basin, North Carolina, 2014
This dataset includes measures of the abundance of blooming wildflowers from field surveys conducted in the French Broad River Basin in western North Carolina, USA. This basin is in the Southern Appalachian Mountains. Data were collected to examine the spatial and seasonal supply of biodiversity-based cultural ecosystem services (CES), in this case, nature study by viewing wildflowers. The data includes blooming species observed at 69 sites on public and private lands during the period 2014-04-01 to 2014-08-08. Flower species were characterized as charismatic if represented in tourism websites. Environmental data for 56 sites are provided: elevation, early season precipitation, mean summer temperature, land cover diversity, tree cover, vegetation structural diversity, vegetation annual productivity, and building density at local and landscape scales. Graves et al. (2017, doi: 10.1007/s10980-016-0452-0) used these data to analyze seasonal shifts in supply of floral resources and how those shifts impacted public access to projected resource hotspots. Relationships between landscape gradients, biodiversity, and ecosystem service supply varied seasonally, and the analysis identified CES hotspots otherwise obscured by simple proxies. Landscape models of biodiversity-based cultural ecosystem services should include seasonal dynamics of biotic communities to avoid under- or over-emphasizing the importance of specific locations in ecosystem service assessments.
MCR LTER: Coral Reef: Water Column microbial community data in lagoons of Moorea, French Polynesia
Microbes process a significant fraction of organic material in marine systems, and the composition and activity of their communities are strongly modulated by fluctuations in nutrient availability. To investigate the distribution and dynamics of microbial communities in tropical lagoon ecosystems, bacteria and archaea were quantified from water column samples collected in lagoons around Moorea, French Polynesia during May 2021, April 2022, and April 2023 using eDNA sequencing. Environmental DNA was extracted and sequenced to assess microbial community composition, with data processed using the QIIME 2 platform (version 2023.7). Microbial communities in fringing reef habitats differed from those in mid-lagoon and back reef sites. These differences in microbial communities were related to patterns of water column nutrients and fluorescent dissolved organic matter, providing a baseline for understanding how lagoon microbial communities respond to spatial and temporal variability in reef environments.
Extracted patterns about transport from the French Great National Debate (Grand Débat National)
<p>This data set is composed by 5 geojson files, that can be used to generate maps of mainland France :</p> <ul> <li>motifs_all.geojson : pattern about transport extracted from contributions of the French Great National Debate (Grand Débat National). Original dataset : https://granddebat.fr/pages/donnees-ouvertes</li> <li>bikeway_fr.geojson and railroad_fr.geojson : cycleways and railways of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</li> <li>trainstations.geojson : train stations and halts of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</li> <li>au2010_carto.geojson : categorized urban areas of mainland France. Original dataset : https://www.insee.fr/fr/information/2115011</li> <li>communesimportantes.geojson : the main cities of mainland France</li> </ul> <p>The data set is in French.</p> <p><em>Ce jeu de données est composé de 5 fichiers geojson qui peuvent être utilisés pour générer des cartes en France métropolitaine :</em></p> <ul> <li><em>motifs_all.geojson : motifs à propos du transport extraient des contributions en ligne au Grand Débat National. Jeu de données d'origine : https://granddebat.fr/pages/donnees-ouvertes</em></li> <li><em>bikeway_fr.geojson and railroad_fr.geojson : pistes cyclables et voies ferrées en France métropolitaine, venant d'Open Street Map. Jeu de données d'origine : https://download.geofabrik.de/europe/france.html</em></li> <li><em>trainstations.geojson : gares et petites gares en France métropolitaine, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</em></li> <li><em>au2010_carto.geojson : aires urbaines catégorisées en France métropolitaine, définies par l'INSEE. Jeu de données d'origine : https://www.insee.fr/fr/information/2115011</em></li> <li><em>communesimportantes.geojson : principales villes de France métropolitaine</em></li> </ul>
Dataset of Measurement and conceptualization of maternal PTSD following childbirth: Psychometric properties of the City Birth Trauma Scale – French version (City BiTS-F)
<p>The City Birth Trauma Scale (City BiTS-F) was developed to assess posttraumatic stress disorder following childbirth (PTSD-FC), based on the PTSD criteria of the DSM-5. Recent studies investigating the latent factor structure of PTSD-FC symptoms in women reported mixed results. Given that no validated French questionnaire exists to measure PTSD-FC symptoms, this study first aimed to validate the French version of the CBTS (City BiTS-F). Second, it aims to establish the latent factor structure of PTSD-FC.</p> <p>This dataset contains data on the mental health (i.e., PTSD-CB, depression, anxiety) of 541 mothers who gave birth during the last 12 months. Sociodemegraphic data such as maternal age, marital status, educational level, parity, gravidity, weeks of gestation, type of delivery, history of traumatic childbirth, or history of traumatic event is available. </p> <p>This dataset is related to: Sandoz, V., Hingray, C., Stuijfzand, S., Lacroix, A., El Hage, W., & Horsch, A. (2022). Measurement and conceptualization of maternal PTSD following childbirth: Psychometric properties of the City Birth Trauma Scale—French Version (City BiTS-F). <em>Psychological Trauma: Theory, Research, Practice, and Policy, 14</em>(4), 696–704. <a href="https://psycnet.apa.org/doi/10.1037/tra0001068">https://doi.org/10.1037/tra0001068</a></p>
Collection de romans français du dix-huitième siècle (1750-1800) / Collection of Eighteenth-Century French Novels (1750-1800)
<p><strong>Key information</strong>: This collection of Eighteenth-Century French Novels contains digital texts of novels created or first published between 1751 and 1800. The collection is created in the context of Mining and Modeling Text, a project at the Trier Center for Digital Humanities (TCDH) at Trier University, Germany (2019-2023). The current release contains 200 novels.</p><p><strong>Further information</strong>: <a href="https://github.com/MiMoText/roman18">https://github.com/MiMoText/roman18</a></p><p><strong>Citation suggestion: </strong><i>Collection de romans français du dix-huitième siècle (1751-1800) / Eighteenth-Century French Novels (1751-1800)</i>, edited by Julia Röttgermann, with contributions from Julia Dudar, Henning Gebhard, Anne Klee, Johanna Konstanciak, Damir Padieu, Amelie Probst, Sarah Rebecca Ondraszek and Christof Schöch. Release v.1.2.0. Trier: TCDH, 2023. URL: <a href="https://github.com/mimotext/roman18">https://github.com/mimotext/roman18</a>; DOI: <a href="https://doi.org/10.5281/zenodo.10349902">10.5281/zenodo.10349902</a>. </p><p> </p><p> </p>
Collection de romans français du dix-huitième siècle (1751-1800) / Collection of Eighteenth Century French Novels 1751-1800
<p>This collection of Eighteenth-Century French Novels contains 200 digital texts of novels created or first published between 1751 and 1800. The collection is created in the context of <a href="https://www.mimotext.uni-trier.de/en">Mining and Modeling Text</a> (2019-2023), a project which is located at the Trier Center for Digital Humanities (<a href="https://tcdh.uni-trier.de/en">TCDH</a>) at Trier University.</p> <h2>Metadata</h2> <p>There is a short and an extensive metadata description in TSV for all TEI/XML files:</p> <ul> <li>Metadata, short version: <a href="https://github.com/MiMoText/roman18/blob/master/XML-TEI/xml-tei_metadata.tsv">https://github.com/MiMoText/roman18/blob/master/XML-TEI/xml-tei_metadata.tsv</a></li> <li>Metadata, long version: <a href="https://github.com/MiMoText/roman18/blob/master/XML-TEI/xml-tei_full_metadata.tsv">https://github.com/MiMoText/roman18/blob/master/XML-TEI/xml-tei_full_metadata.tsv</a></li> </ul> <p>Please find further information on our <a href="https://github.com/MiMoText/roman18/tree/v1.2.0">corpus balancing</a> .</p> <h2>Licence</h2> <p>All texts and scripts are in the public domain and can be reused without restrictions. We don't claim any copyright or other rights on the transcription, markup or metadata. If you use our texts, for example in research or teaching, please reference this collection using the citation suggestion below.</p> <h2>Citation suggestion</h2> <p><em>Collection de romans français du dix-huitième siècle (1751-1800) / Eighteenth-Century French Novels (1751-1800)</em>, edited by Julia Röttgermann, with contributions from Julia Dudar, Henning Gebhard, Anne Klee, Johanna Konstanciak, Damir Padieu, Amelie Probst, Sarah Rebecca Ondraszek and Christof Schöch. Release v 1.2.1. Trier: TCDH, 2023. URL: https://github.com/mimotext/roman18. DOI: https://doi.org/10.5281/zenodo.4061903.</p> <h2>Funding</h2> <p>Forschungsinitiative des Landes Rheinland-Pfalz 2019-2023</p>
Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs
<p>Datasets describing the fungal species diversity, microbial density and acidity of French sourdoughs, phenotypic variation of Kazachstania bulderi and Kazachstania humilis strains as well as the diversity of bread-making practices of 40 bakers and farmers-bakers.The data were collected, analyzed, and reported within the following publication :</p> <p>Elisa Michel, Estelle Masson, Sandrine Bubbendorf, Léocadie Lapicque, Thibault Nidelet, Diego Segond, Stéphane Guézenec, Thérèse Marlin, Hugo deVillers, Olivier Rué, Bernard Onno, Judith Legrand, Delphine Sicard and the participating bakers: <strong>Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs</strong>. PCI Evol. Biol.</p> <p> </p>
Polifonia Corpus - Books Module Metadata - French Language (Full)
<p>We release the Metadata of the Books module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p> <p>Full description at <a href="http://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Dataset of The latent factor structure and assessment of childbirth-related PTSD in fathers and co-parents: psychometric characteristics of the City Birth Trauma Scale – French version (partner version)
<p>Little is known about the latent factor structure of CB-PTSD symptoms in co-parents (i.e., (a non-expecting mother or father). The City Birth Trauma Scale (City BiTS) was developed to assess childbirth-related posttraumatic stress disorder following childbirth (CB-PTSD), based on the PTSD criteria of the DSM-5. Still, no validated French questionnaire exists to assess CB-PTSD symptoms in co-parents. This study aimed (1) to establish the latent factor structure of CB-PTSD, and (2) to validate the French version of the City BiTS (partner version). </p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 282 co-parents who had an infant within the last 12 months. Sociodemographic data such as age, marital status, educational level, weeks of gestation, type of delivery, history of traumatic childbirth, or history of a traumatic event is available. </p>
Multi-LEX: a database of multi-word frequencies (French files)
<p>Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate. Such sequences are presented with Part-of-Speech information for each individual word. An online behavioral investigation making use of the French 4-gram lexicon in a grammatical decision task was carried out. The results show an item-level frequency effect of word sequences. Moreover, the proposed datasets were found useful during the stimulus selection phase, allowing more precise control of the multi-word characteristics.</p>
#Élysée2017fr: The 2017 French Presidential Campaign on Twitter
<p># README</p> <p>This archive contains the #Élysée2017fr dataset.</p> <p>(Initially published at https://web.archive.org/web/20200530171644if_/https://dataverse.mpi-sws.org/dataverse/icwsm18 on June 24, 2018. This dataverse being defunct now, we repost on Zenodo)</p> <p><br> ## Content</p> <p>### keywords.csv<br> The keywords used to collect the initial dataset, each presented with the start and stop dates of use (date format: YYYY-MM-DD).</p> <p><br> ### profiles_annotations.csv<br> The manual profiles annotations. The file contains the following columns:</p> <p>#### FROM_USER_ID<br> The profile's id used by Twitter</p> <p>#### PROFILE_NATURE<br> "**individual**" if the profile is managed by a single person, else "**non individual**".<br> The "**non individual**" label is itself divided in 3 subcategories:<br> - "**political**" for profiles of political parties or associations, and profiles representing groupes of militants.<br> - "**media**" for profiles of media outlets.<br> - "**other**" for profiles not included in the previous categories.</p> <p>#### PARTY<br> The profile's political affiliation(s), indicated as the shortcut for the political party:<br> - "**fi**": France Insoumise (far-left)<br> - "**ps**": Parti Socialiste (left)<br> - "**em**": En Marche ! (center)<br> - "**lr**": Les Républicains (right)<br> - "**fn**": Front National (far-right)<br> - **null**: no political affiliation</p> <p>When a profile has 2 affiliations, they are separated by a slash (ex: "ps/fi"). </p> <p>#### MEDIA_PROFESSIONAL<br> *For individual profiles only.*<br> Indicates if the profile's owner self-identify as a media professional (journalist, editorialist, ...)</p> <p>#### SEX<br> *For individual profiles only.*<br> Indicates the sex of the profile's owner:<br> - "**m**": male<br> - "**f**": female<br> - **null**: undetermined or other</p> <p><br> ### posts_ids_*<br> Files containing the tweets and retweets ids, divided according to the political affiliation of their authors for more flexibility.<br> - **posts_ids_fi.csv**: Tweet ids for profiles affiliated to France Insoumise (far-left)<br> - **posts_ids_ps.csv**: Tweet ids for profiles affiliated to Parti Socialiste (left)<br> - **posts_ids_em.csv**: Tweet ids for profiles affiliated to En Marche ! (center)<br> - **posts_ids_lr.csv**: Tweet ids for profiles affiliated to Les Républicains (right)<br> - **posts_ids_fn.csv**: Tweet ids for profiles affiliated to Front National (far-right)<br> - **posts_ids_multi_affiliations.csv**: Tweet ids for profiles affiliated to more than one party<br> - **posts_ids_indetermined.csv**: Tweet ids for profiles not affiliated to any party</p> <p>Each file contains one tweet id per line.</p> <p><br> ### networks_*<br> Files containing the mention and retweet networks, in NCOL and GraphML format.</p> <p>The NCOL files contains the directed weighted edges between profiles, one per line, in the following format:<br> profile1_twitter_id profile2_twitter_id edge_weight</p> <p>The GraphML files contains the directed weighted edges between profiles, as well as all the profiles annotations presented in *profiles_annotations.csv*. They can be opened using a graph visualisation software like Gephi.</p> <p> </p> <p>## How to get tweets from ids<br> You can use various tools to help you get tweets from their ids, we suggest the following:<br> - DMI-TCAT: https://github.com/digitalmethodsinitiative/dmi-tcat<br> - Twarc: https://github.com/DocNow/twarc</p> <p> </p> <p>## How to cite this work<br> Fraisier Ophélie, Cabanac Guillaume, Pitarch Yoann, Besançon Romaric, Boughanem Mohand. 2018. #Élysée2017fr: the French Presidential Election on Twitter. In International Conference on Weblogs and Social Media. https://aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17821 (https://hal.archives-ouvertes.fr/hal-02319715)</p>
French Entity-Linking dataset between annotated tweets collected during major crises in France and French Wikipedia corpus
<p>Most of the available datasets are not particularly adapted to our target application: geolocate natural disasters from social networks. First, social media posts are largely underrepresented in these datasets, and the only Twitter dataset lacks Entity-Linking annotations. Second, none of the datasets focuses on a crisis or natural disaster event.</p> <p>To mitigate these issues, we extracted a collection of French tweets written during earthquakes and major floods that have occurred in France in recent years. We set up Label-Studio in order to annotate these tweets. A total of 4617 tweets were annotated, including 1678 tweets posted during earthquakes and 2939 during floods. For each annotated tweet, mentions were annotated using the set of labels described earlier in the paper as well as, when possible, the target Wikipedia title.</p> <p>Named “RéSoCIO” in reference to the research project in which it was carried out, the dataset resulting from this work contains a total of 12 828 annotated mentions and 1 513 distinct Wikipedia entities. 85% of mentions were associated with a Wikipedia page and 94 % if we ignore the RISKNAT and DAMAGES labels, which are often difficult to map to an existing entity.</p> <table> <tbody> <tr> <td><strong>Labels</strong></td> <td><strong>#Mentions</strong></td> <td><strong>#Linked</strong></td> <td><strong>#Entities</strong></td> </tr> <tr> <td>PERSON</td> <td>315</td> <td>263</td> <td>136</td> </tr> <tr> <td>ORG</td> <td>863</td> <td>790</td> <td>281</td> </tr> <tr> <td>GEOLOC</td> <td>4375</td> <td>4234</td> <td>701</td> </tr> <tr> <td>TRANSPORT</td> <td>250</td> <td>203</td> <td>101</td> </tr> <tr> <td>EVENT</td> <td>35</td> <td>21</td> <td>16</td> </tr> <tr> <td>FACILITY</td> <td>129</td> <td>94</td> <td>49</td> </tr> <tr> <td>RISKNAT</td> <td>5502</td> <td>4994</td> <td>128</td> </tr> <tr> <td>DAMAGES</td> <td>1136</td> <td>121</td> <td>56</td> </tr> <tr> <td>OTHER</td> <td>223</td> <td>200</td> <td>46</td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>12828</strong></td> <td><strong>1322</strong></td> <td><strong>1513</strong></td> </tr> </tbody> </table> <p>Overview of the mentions annotated in the Twitter dataset. #Mentions shows the total number of mentions per label, #Linked the number of mentions linked to an entity and #Entities the number of distinct entities per label present in the dataset.</p> <table> <tbody> <tr> <td><strong>Labels</strong></td> <td><strong>#Mentions</strong></td> <td><strong>#Linked</strong></td> <td><strong>#Entitie</strong>s</td> </tr> <tr> <td>PERSON</td> <td>1100102</td> <td>1098406</td> <td>557697</td> </tr> <tr> <td>ORG</td> <td>750925</td> <td>749504</td> <td>130394</td> </tr> <tr> <td>GEOLOC</td> <td>2729702</td> <td>2728296</td> <td>215924</td> </tr> <tr> <td>TRANSPORT</td> <td>161539</td> <td>160487</td> <td>53405</td> </tr> <tr> <td>EVENT</td> <td>798433</td> <td>798251</td> <td>86471</td> </tr> <tr> <td>FACILITY</td> <td>258835</td> <td>258513</td> <td>109867</td> </tr> <tr> <td>RISKNAT</td> <td>5502</td> <td>4994</td> <td>127</td> </tr> <tr> <td>DAMAGES</td> <td>1136</td> <td>121</td> <td>56</td> </tr> <tr> <td>OTHER</td> <td>4340621</td> <td>4339658</td> <td>682458</td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>10146795</strong></td> <td><strong>10138230</strong></td> <td><strong>1836399</strong></td> </tr> </tbody> </table> <p>Overview of the mentions annotated in the full dataset. #Mentions shows the total number of mentions per label, #Linked the number of mentions linked to an entity and #Entities the number of distinct entities per label present in the dataset.</p>
Soil and meteorological data, and finite element simulation framework for heat transfer through shrubs in winter near Lautaret pass, French Alps
<p>The data allow the calculation using finite element modeling of heat transfer through shrub branches and snow between the atmosphere and the soil. The shrubs are green alders (Alnus viridis). The site where they are found is called Alnus-Nivus (45.034750°N, 6.413630°E, 2034 m asl) near Col du Lautaret, French Alps. The soil data consist in temperature and volumetric liquid water content at 5 and 15 cm depths. One spot is near the alder collar (ALNUS), the other spot is 6 m away, under grass (GRASS).</p> <p>The meteorological data were obtained from the FR-Clt station, 750 m away (45.041278°N, 6.410611°E, 2046 m asl). See (Gupta et al., 2023) for details. Only the data relevant for heat transfer simulations are given.</p> <p>The simulation framework gives the alder mesh used in the heat transfer simulations. Typical simulations use a wood thermal conductivity of 1 W m<sup>-1</sup> K<sup>-1</sup> and a snow thermal conductivity of 0.1 W m<sup>-1</sup> K<sup>-1</sup>. Based on observations, the snow height at Alnus-Nivus is likely to be at least twice the value at FR-Clt. Forcing uses the snow surface temperature, derived from upwelling longwave radiation using an emissivity of 1. The data allow testing thermal bridging through shrub branches. These data are used in a publication in preparation: Domine, Fourteau, Choler, Exploration of Thermal Bridging Through Shrub Branches in Alpine Snow.</p> <p>Reference</p> <p>Gupta, A., Reverdy, A., Cohard, J. M., Hector, B., Descloitres, M., Vandervaere, J. P., Coulaud, C., Biron, R., Liger, L., Maxwell, R., Valay, J. G., and Voisin, D.: Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment, Hydrol. Earth Syst. Sci., 27, 191-212, 2023.</p>
MCR LTER: Coral Reef: Turbinaria CHN from a spatially explicit sampling campaign in lagoons of Moorea, French Polynesia
Nutrients are important for ecosystem structure and community dynamics. To quantify time-integrated patterns of nutrient regimes in tropical lagoon ecosystems, concentrations of nitrogen, hydrogen, and carbon were measured from tissues of the macroalga Turbinaria ornata collected in lagoons around Moorea, French Polynesia in 2016, 2017, and annually starting in 2019. These sampling periods initially corresponded with distinct seasonal shifts in rainfall and wave forcing and later focused on the rainy season. Results showed that N enrichment was highest nearshore in fringing reef habitats, as well as in bays and at reef passes.
MCR LTER: Coral Reef: Water Column fDOM in lagoons of Moorea, French Polynesia
Dissolved organic matter (DOM) is a mixture of organic materials that are dissolved in water. DOM represents a significant fraction of carbon and nutrient stocks in seawater. To quantify the distribution and dynamics of organic matter in nearshore marine environments, fluorescent dissolved organic matter (fDOM; a subset of DOM that fluoresces under ultraviolet light) was quantified from water column samples collected in lagoons around Moorea, French Polynesia during May 2021, April 2022, and April 2023. fDOM components, such as humic-like compounds, as well as fDOM indices, such as the humification index, were measured using fluorometry (for more details refer to methods). Humification Index was higher in bays and fringing reef habitats, whereas Fluorescence Index and M:C ratio were higher further offshore at back reef sites.
MCR LTER: Coral Reefs: Coral recruitment to 25 m2 plots on the forereef surrounding Moorea, French Polynesia, 2011-2015
These data report the number of Pocilloporid, Acroporid and Poritiid corals recruiting annually to permanent 5 m X 5 m plots established at a depth of approximately 10 m on the forereef of Moorea, French Polynesia. Plots were established following an outbreak (2007-2010) of the corallivorous crown-of-thorns seastars (Acanthaster planci) and the close passage of Cyclone Oli to Moorea in February, 2010. These two perturbations resulted in a significant loss of live coral coral from the forereef on an island-wide scale (crown-of-thorns) and the loss of habitat structural complexity from forereef habitats on Moorea's north shore (Cyclone Oli). See Adam, T.C. et al. 2011 "Herbivory, connectivity and ecosystem resilience: response of a coral reef to a large-scale perturbation" PLoS One e23717 for a more complete description of the system, the nature and magnitudes of the perturbations and the short-term response by the coral community to the perturbations. Data were obtained by counting all Pocilloporid, Acroporid and Poritiid coral recruits < 3 cm in colony diameter observed within 25 5 m x 5 m plots established at four sites in a depth of approximately 10 m on the forereef of Moorea, French Polynesia. Two sites, Resilience 1 (R1) containing five 5 m x 5 m plots and Resilience 2 (R2) containing ten 5 m x 5 m plots were established along the north shore of the island. Two additional sites, Resilience 4 (R4) and Resilience 5 (R5), each containing five 5 m x 5 m plots were established on the southeast and southwest shores of the island, respectively. The locations of the plots are permanently marked using stainless steel eye-bolts cemented into the reef matrix so that counts of coral recruits can be made repeatedly within the same plots on an annual basis. This dataset, knb-lter-mcr.5023, is a subset of a larger dataset, knb-lter-mcr.7008, and has been produced in support of a manuscript submission. The parent dataset, knb-lter-mcr.7008, contains additional data on the numb
MCR LTER: Coral Reef: 2018 land cover map of Moorea, French Polynesia
Using a collection of imagery from June-September 2018 taken by the Worldview-3 satellite, land cover was classified for the island of Mo’orea, French Polynesia. A deep learning pixel classification model was trained for each of four separate dates of image collection when clouds were sparse over the island. The model was trained at the native resolution of the imagery (<2m pixels). Training data included the multispectral WV-3 bands in addition to derived bands that index vegetation productivity (NDVI), vegetation texture (NDVI IDM), and water cover (NDWI). A consensus land cover map was generated from model predictions across the four sets of imagery.
ScienceDex guides
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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.