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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>
Dataset of "Perovskite QDs embedded in polymer as a wavelength-shifting layer for UV-sensitized silicon sensors"
<p>Detection of UV radiation is becoming increasingly important for many applications. Here we present novel UV sensor construction on the basis of standard Si detector modification. Wavelength shifting mechanism is achieved by the luminescence effect of perovskite quantum dots embedded in polymer layers. We comprehensively characterize these composite materials, various sensor modification routes and the optical properties of UV-enhanced visible sensors. Modified S1227-16 BG silicon photodiodes and S13360-1375 CS MPPC photodetectors with the enhanced UV response are successively manufactured.micrographs; cross sections of model simulation or prediction (MSP).</p>
Gamma dose rate monitoring using a Silicon Photomultiplier-based plastic scintillation detector
<p>Data set in support of the publication "Gamma dose rate monitoring using a Silicon Photomultiplier-based plastic scintillation detector". It contains measurement campaign data, radionuclide sources data, measurement count rate per radionuclide.</p>
Dataset of "Characterization of Silicon-based Fibers Prepared by Electrospinning for Potential Li-ion Battery Anodes"
<p>The rapid growth of electric vehicles (EVs) is driven by advances in lithium-ion batteries (LIBs), particularly in anode materials. Graphite electrodes, widely used for their high porosity, conductivity, low weight, and cost-effectiveness, face competition from monocrystalline silicon. Silicon anodes offer higher capacity and energy density, and they are safer because of their nonflammable nature. However, silicon's tendency to expand and contract during cycling presents challenges. This study explores the use of silicon nano- and microfibers to enhance battery stability, addressing these issues effectively.<br>Monocrystalline silicon particles, obtained through milling and sieving, were used as the active component in the nanofibers. These particles, combined with organic precursors (PVP and TEOS), were processed using electrospinning to form fibers. The fibers were then annealed at 650 °C to remove the polymeric PVP component. <br>The results provide valuable insights into the properties and interactions of the silicon nanofibers, highlighting their potential in advanced energy storage devices. </p>
Unveiling the potential of redox chemistry to form size tunable, high index silicon particles
<p>In the present work, the effect of changing the precursor ratio of silicon between sodium silicde and a hexacoordinated silicon complex to form various sizes of particles is studied. TEM images show the size difference between particles produced with different ratios. Particles produced with a 1:1 ratio are 45 nm in diameter and up to a 1:4 precursor ratio is used to make 230 nm particles. X-ray diffraction patterns confirm the presence of crystalline silicon for all sizes, while Raman spectroscopy shows how different degree of oxidation occurs thanks to different particle sizes, shifting the Raman peak. The surface chemistry is also studied to evidence the growth mechanism.</p>
Large Spin-to-Charge Conversion at Room Temperature in Extended Epitaxial Sb2Te3 Topological Insulator Chemically Grown on Silicon (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Large Spin-to-Charge Conversion at Room Temperature in Extended Epitaxial Sb<sub>2</sub>Te<sub>3</sub> Topological Insulator Chemically Grown on Silicon</em>" by <a href="https://doi.org/10.1002/adfm.202109361">E. Longo et al., <em>Adv. Funct. Mater.</em> 2021, 2109361</a></p>
Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip: Experimental and simulation data sets
<p>This data set collates the experimental and simulation data for the research paper "Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip".</p>
Dataset for Enhancing stimulated Brillouin scattering in suspended silicon waveguides through subwavelength nanostructuration [Invited]
<p>This dataset contains the raw data for the figures (Fig. 2, Fig. 5, and Fig. 7) in the publication entitled "Enhancing stimulated Brillouin scattering in suspended silicon waveguides through subwavelength nanostructuration" published by Optical Materials Express (DOI: 10.1364/OME.534474). Datafiles are in .txt format.</p> <p>All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript.</p>
Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane
<p><strong>Data set</strong> from Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane.<br> <br> - Publication : Cibaka-Ndaya C., O'Connor K., Opeyemi Idowu E., Parker M. A., Lebraud E., Lacomme S., Montero D., Sanz Camacho P., Veinot J. G.-C., Roiban I.-L., Drisko G. L.. Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane. <em>Chemistry of Materials</em>, In press, <a href="https://dx.doi.org/10.1021/acs.chemmater.3c01448">⟨10.1021/acs.chemmater.3c01448⟩</a>. <a href="https://hal.science/hal-04211253">⟨hal-04211253⟩</a><br> <br> <strong>Dataset production context : </strong> Crystalline silicon particles sustaining Mie resonances are readily obtained from the thermal processing of hydrogen silsesquioxane (HSQ). Here, the mechanisms involved in silicon particle formation and growth from HSQ are investigated through real time in situ analysis in an environmental transmission electron microscope and X-ray diffractometer. The nucleation of Si nanodomains are observed starting around 1000 °C. For the first time, a highly mobile intermediate phase is experimentally observed, thus demonstrating a previously unknown growth mechanism. At least two growth processes occur simultaneously: the coalescence of small particles into larger particles and a growth mode by particle displacement through the matrix toward the HSQ grain surface. Post-synthetic characterization by scanning electron microscopy further evidences the latter growth mechanism. The gaseous environment employed during synthesis impacts particle formation and growth under both in situ and ex situ conditions, impacting particle yield and structural homogeneity. Understanding the formation mechanisms of particles provides promising pathways for reducing the energy cost of this synthetic route.<br> <br> <br> <strong>For more information see the article.</strong></p>
Discrimination of aluminum from silicon by electron crystallography with the JUNGFRAU detector
<p>Electron diffraction data of two different aluminosilicates, zeolite A and albite. They were used to implement the JUNGFRAU detector (PSI Switzerland) at Vienna University, and develop the software for data conversion and data collection. The archives also contain the XDS files for processing. The metadata of the CBF-files mostly not filled in properly (pixel size and wavelength only are correct). Please refer to the XDS.INP files for experimental parameters (oscillation width, detector distance, etc.). The structural CIF files have been submitted to the Cambridge Structural Database CSD.</p>
Presolar Grain Database - Silicon Carbide
<p>The Presolar Grain Database (PGD) contains the vast majority of isotope data (published and unpublished) on presolar grains and was first released as a collection of spreadsheets in 2009. It has been a helpful tool used by many researchers in cosmochemistry and astrophysics. However, over the years, accumulated errors compromised major parts of the PGD. Here, we provide a fresh start, with the PGD for silicon carbide (SiC) grains rebuilt from the ground up.</p> <p>The PGD is provided here in two formats: (1) as Microsoft Excel (.xlsx) file, containing the main database as one large spreadsheet and additional information on extra spreadsheets, (2) as comma-separated ASCII (.csv) file containing the main database.</p> <p>The PGD is also available for graphite grains at the DOI <a href="../doi/10.5281/zenodo.11188115">10.5281/zenodo.11188115</a>.</p>
Dataset for "Tuning the thermoelectric properties of boron-doped silicon nanowires integrated in a micro-harvester"
<p>Dataset including all data used for the elaboration of the work "Tuning the thermoelectric properties of boron-doped silicon nanowires integrated in a micro-harvester" published in Advanced Materials Technologies, 2022</p> <p><a href="https://doi.org/10.1002/admt.202101715">https://doi.org/10.1002/admt.202101715</a></p> <p>The files includes:</p> <p>· INDIVIDUAL NW data:</p> <p> - I-V data of each NW at different temperatures</p> <p> - 3w data of each NW at different temperatures<br> - 4 SEM images of the NW, each of them used for assessing one NW parameter<br> - Tip: NW diameter 2<br> - Base: NW diameter 1<br> - Overall: NW length<br> - Tilted view at 45º: Relative NW heigh over substrate</p> <p>· SEEBECK MEASUREMENT data:</p> <p> - Voc versus applied dT data for each substrate temperature<br> - File containig calibration data for all resistors</p> <p>· TEM data:</p> <p>-TEM images of the studied NWs in .dm3 format.</p> <p>· X-RAY FLUORESCENCE data:</p> <p>- Maps containing one energy spectrum per pixel in .hdf files.</p> <p>· TIP-ENHANCED RAMAN SPECTROSCOPY data:</p> <p>- Maps containing one energy spectrum per pixel in a tabulated .txt file.</p> <p>· POWER HARVESTED data:</p> <p>- IV curves of each microthermocouple connection X-Y upon different substrate temperatures in tabulated separated .txt files</p>
Piezoresistive sensor fiber composites based on silicone elastomers for the monitoring of the position of a robot arm
<p>Combining conductive fillers like carbon black with elastomers allows the development of soft elastomer strain sensors that can reach very large elongations, an important requirement for many robotic applications. However, when the conductive filler is introduced in the polymer, significant stiffening occurs, affecting the mechanical properties, e.g. Young’s Modulus, of the soft structure. In this attempt, single piezoresistive fiber composites were successfully fabricated, without drastically increasing the stiffness. Two silicone elastomers that are widely used in robotic applications were examined as matrix materials. Furthermore, modeling the stresses exerted on the fiber inside the composite was successfully used to predict the detachment of fiber inside the matrix, observed by visual inspection. For the PDMS based composite, pre-straining improved sensor properties, which could be confirmed for the monitoring of the movement of the crane robot. The results showed that the pre-strained piezoresistive sensor fiber-matrix composites positions of the robot crane can be monitored even at low strains.</p>
Dataset for Dynamics of Solid-Electrolyte Interphase Formation on Silicon Electrodes Revealed by Combinatorial Electrochemical Screening
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Dynamics of Solid-Electrolyte Interphase Formation on Silicon Electrodes Revealed by Combinatorial Electrochemical Screening"</strong></p> <p>published in Angewandte Chemie International Edition (2022): <a href="https://doi.org/10.1002/anie.202207184">https://doi.org/10.1002/anie.202207184</a></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements for combinatorial preparation of solid-electrolyte layers under different conditions and repetitions ("SECCM/")</li> <li>Raman spectra obtained using SHINERS for all the prepared conditions and repetitions ("SHINERS/")</li> <li>Atomic force microscopy data for each SEI layer ("AFM/")</li> <li>Energy-dispersive X-ray spectroscopy data ("EDX/")</li> </ul>
Benthic and pelagic biomass and silicon cycling in the Severn Estuary, UK
<p>This dataset contains benthic and pelagic data on biomass, chlorophyll fluorescence and silicon cycling in the Severn Estuary, UK, from 2016. The sampled periods coincided with the seasons, and for clarity, are referred to here as; winter (January-March), spring (April-June), summer (July-September) and autumn (October-December). See readme files for details.</p>
Polarized Emission from hexagonal-Silicon Germanium Nanowires
<p>This dataset contains the polarization dependent photoluminescence intensity measurements on hexagonal-silicon germanium nanowires. This data confirms the selection rules of the fundemental direct bandgap transition of the material. </p> <p>The data is zipped and contains a README which describes the storage of the data inside the folder.</p>
Dataset for Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries"</strong></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements as cyclic voltammetry using scanning electrochemical cell microscopy for three different Si crystallographic orientations (100, 110, 311) in 1 M LiPF6 in ethylene carbonate - ethyl methyl carbonate ("SECCM/")</li> <li>Scanning electron microscopy and transmission electron microscopy imaging of pristine and cycled samples ("Images/")</li> </ul>
Boron-, carbon-, and silicon-bridged 1,12-dihydroxy-perylene bisimides with tuned structural and optical properties
<p>Additional data to report <a href="https://doi.org/10.1039/D3CC03704E">https://doi.org/10.1039/D3QO01389H</a></p> <p>Establishing suitable design strategies to tailor the functional properties of perylene bisimide (PBI) dyes are critical for their successful application in various devices. Herein, we report a new synthetic strategy to tune their structural and fluorescence properties by employing 1,12-bay-substitution pattern that has been seldomly investigated in the past. Central to the strategy is the use of 1,12-dihydroxy-PBI as a starting compound and the subsequent bridging of these hydroxy bay-functional groups with either a boron, carbon or silicon atom resulting in derivatives with rigidified perylene core. This is followed by a detailed exploration of synthetic possibilities to functionalize the unsubstituted 6,7-positions at the opposite bay area to achieve novel perylene dyes with excellent structural and optical properties. The fluorescence color could be tuned from green to dark-orange while retaining the almost unity fluorescence quantum yield in solution. Moreover, a strong fluorescence with quantum yields as high as 40% has been observed for powders, which clearly illustrates the potential of the presented structural design to obtain new solid-state emitters.</p>
Supporting Data for "Coherent superpositions of three states for phosphorous donors in silicon prepared using THz radiation"
<p>Supporting data for the publication "Coherent superpositions of three states for phosphorous donors in silicon prepared using THz radiation", describing experimental results from Figures 4-7. The data are in .csv format; columns and units for each file are described in the relevant readme file. The data are given in sufficiently compact states for a reader of the paper to reproduce the figures.</p> <p>Spectral data presented are related to their interferograms by simple Fourier Transform methods. The source interferograms are derived from explicit records of oscilloscope traces, which are verbose and inefficient. Consequently, these raw data files are not included in the dataset. Raw oscilloscope traces will be made available by the authors on request.</p>
EUV-Induced Hydrogen Desorption As A Step Towards Large-Scale Silicon Quantum Device Patterning
<p><strong>Dataset: </strong>STM, XPS and PEEM raw data, processed data and the codes used for data fitting our <a href="https://doi.org/10.1038/s41467-024-44790-6">published work</a> are all available here.</p> <p><strong>Abstract: </strong>Atomically precise hydrogen desorption lithography using scanning tunnelling microscopy (STM) has enabled the development of single-atom, quantum-electronic devices on a laboratory scale. Scaling up this technology to mass-produce these devices requires bridging the gap between the precision of STM and the processes used in next-generation semiconductor manufacturing. Here, we demonstrate the ability to remove hydrogen from a monohydride Si(001):H surface using extreme ultraviolet (EUV) light. We quantify the desorption characteristics using various techniques, including STM, X-ray photoelectron spectroscopy (XPS), and photoemission electron microscopy (XPEEM). Our results show that desorption is induced by secondary electrons from valence band excitations, consistent with an exactly solvable non-linear differential equation and compatible with the current 13.5 nm (~92 eV) EUV standard for photolithography; the data imply useful exposure times of order minutes for the 300 W sources characteristic of EUV infrastructure. This is an important step towards the EUV patterning of silicon surfaces without traditional resists, by offering the possibility for parallel processing in the fabrication of classical and quantum devices through deterministic doping.</p> <p> </p>
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.