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Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 5
<p>Dataset containing four .xlsx and .csv files for the exercises in Episode 5 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
Video 5 - Open Science: why do we need data stewards.
<p><span>An interview on the need of data professionals and Open Science skills with York Sure-Vetter, Director of NFDI, Germany and Professor at Karlsruhe Institute of Technology; Jessica Lindvall, Head of Training at SciLifeLab Training Hub; Anne Sophie Fink, Head of Data Management at DeiC (Denmark); and Sally Chambers, Director at DARIAH-EU.</span></p> <p><span>Modern research and technology can require not only large amounts of data, but also good data quality. Ensuring good data quality requires specialized expertise. Data stewards and other data management professionals support researchers with providing and working with good quality data which are also FAIR. Open software, open infrastructures are also important bits in the Open Science puzzle, all of which require funding. The uptake of Open Science depends on widespread Open Science awareness and skills. These require outreach, training and formal education.</span></p>
RGB pixels VALUES FOR APPLES/LETTUCE AI OPTICAL RECOGNITION - 5 categories of Freshness
<p>The Datasets include RGB color pallete per pixel values for optical recognition on apples/lettuce and freshness categorized using AI Algorithm . Those Datasets are for AI Training projects . It will be used on the stage of creation, verification or optimization for new optical AI models. The tables can be used direclty on the AI tools, inserted and using the pixels colors number for every category. The freshness categories are 5, from the highest- crop day (5) to the lowest - not for eating (1).</p> <p> </p>
Comprehensive Impedance Spectroscopy Analysis on the Electrocatalytic Reduction of 5-Hydroxymethylfurfural
<p># Dataset of "Comprehensive Impedance Spectroscopy Analysis on the Electrocatalytic Reduction of 5-Hydroxymethylfurfural"</p> <p>---</p> <p>## GENERAL INFORMATION<br>----------------------</p> <p>1. Dataset title: "Comprehensive Impedance Spectroscopy Analysis on the Electrocatalytic Reduction of 5-Hydroxymethylfurfural"</p> <p>2. Authorship: <br> Name: Jose Solera-Rojas <br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> ORCID: 0000-0003-3513-7069</p> <p> Name: David Carvajal<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> ORCID: 0000-0002-8450-2563</p> <p> Name: Antonio Guerrero<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> ORCID: 0000-0001-8602-1248</p> <p> Name: Carmen Mejuto<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> ORCID: 0000-0002-4432-5697</p> <p> Name: Elena Más-Marzá<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> Email: <emas@fca.uji.es> <br> ORCID: 0000-0002-2308-0635</p> <p> Name: Francisco Fabregat-Santiago<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> Email: <fabresan@uji.es> <br> ORCID: 0000-0002-7503-1245</p> <p>## FILE DESCRIPTION<br>--------------<br>### Figure 2<br>- Fig2b.txt : Cyclic voltammetry (CV) for the reduction of HMF, BHMF, 5-MF and MFA. A 20 mM of each organic molecule in a solution of 0.5 M NaH2PO4 (pH = 4.1) was used.<br>- Fig2c.txt : Conversion of HMF and yields of BHMF, MFA, DMF and 5-MF. <br>- Fig2d.txt : Faradaic Efficiency (FE) for the electroreduction of 20 mM HMF at pH = 4.1.</p> <p>### Figure 3<br>- Fig3a.txt : Uncorrected J-V curve taken at the end of IS measurements without and with all the organic molecules in this study<br>- Fig3b.txt : Corrected J-V curve taken at the end of IS measurements without and with all the organic molecules in this study<br>- Fig3c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 5 mA cm^-2.<br>- Fig3d.txt : Nyquist plots in the absence and presence of each organic molecule at -0.65 V vs. RHE.</p> <p>### Figure 4<br>- Fig4a.txt : Results obtained from fitting the IS data Cdl<br>- Fig4b.txt : Results obtained from fitting the IS data Rct<br>- Fig4c.txt : Results obtained from fitting the IS data Css<br>- Fig4d.txt : Results obtained from fitting the IS data Rss<br>- Fig4e.txt : Results obtained from fitting the IS data L<br>- Fig4f.txt : Results obtained from fitting the IS data tau</p> <p><br>### Figure S3<br>- FigS3a.txt : Cyclic voltammetry of 5-MF at different concentrations (20, 40 and 80 mM) in a solution of 0.5 M NaH2PO4 (pH = 4.1) at a 5 mV/s scan rate.<br>- FigS3a.txt : Cyclic voltammetry of HMF at different concentrations (20, 40 and 80 mM) in a solution of 0.5 M NaH2PO4 (pH = 4.1) at a 5 mV/s scan rate.<br>- FigS3a.txt : Cyclic voltammetry of BHMF at different concentrations (20, 40 and 80 mM) in a solution of 0.5 M NaH2PO4 (pH = 4.1) at a 5 mV/s scan rate.<br>- FigS3a.txt : Cyclic voltammetry of MFA at different concentrations (20, 40 and 80 mM) in a solution of 0.5 M NaH2PO4 (pH = 4.1) at a 5 mV/s scan rate.</p> <p>### Figure S4<br>- FigS4a.txt : HPLC chromatograms for HMF, BHMF, 5-MF, MFA and DMF from commercially available products at (a) 294 nm, (b) 285 nm and (c)-(d) 222 nm. The solution from DMF was prepared in cyclohexane and diluted in CH3CN.<br>- FigS4b.txt : HPLC chromatograms for HMF, BHMF, 5-MF, MFA and DMF from commercially available products at (a) 294 nm, (b) 285 nm and (c)-(d) 222 nm. The solution from DMF was prepared in cyclohexane and diluted in CH3CN.<br>- FigS4c.txt : HPLC chromatograms for HMF, BHMF, 5-MF, MFA and DMF from commercially available products at (a) 294 nm, (b) 285 nm and (c)-(d) 222 nm. The solution from DMF was prepared in cyclohexane and diluted in CH3CN.<br>- FigS4d.txt : HPLC chromatograms for HMF, BHMF, 5-MF, MFA and DMF from commercially available products at (a) 294 nm, (b) 285 nm and (c)-(d) 222 nm. The solution from DMF was prepared in cyclohexane and diluted in CH3CN.</p> <p>### Figure S5<br>- FigS5a.txt : Calibration curves for (a) HMF (285 nm), (b) BHMF (222 nm), (c) MFA (222 nm), (d) 5-MF (294 nm) and (e) DMF (222 nm).<br>- FigS5b.txt : Calibration curves for (a) HMF (285 nm), (b) BHMF (222 nm), (c) MFA (222 nm), (d) 5-MF (294 nm) and (e) DMF (222 nm).<br>- FigS5c.txt : Calibration curves for (a) HMF (285 nm), (b) BHMF (222 nm), (c) MFA (222 nm), (d) 5-MF (294 nm) and (e) DMF (222 nm).<br>- FigS5d.txt : Calibration curves for (a) HMF (285 nm), (b) BHMF (222 nm), (c) MFA (222 nm), (d) 5-MF (294 nm) and (e) DMF (222 nm).<br>- FigS5e.txt : Calibration curves for (a) HMF (285 nm), (b) BHMF (222 nm), (c) MFA (222 nm), (d) 5-MF (294 nm) and (e) DMF (222 nm).</p> <p>### Figure S6<br>- FigS6.txt : HPLC chromatogram for the chronoamperometry experiment at -0.55 V vs. RHE and 294 nm for the detection of 5-MF.</p> <p>### Figure S8<br>- FigS8.txt : HPLC chromatogram from the top organic phase from an experiment of 20 mM 5-MF at -0.65 V vs. RHE with a charge limit of 38.6 C. DMF shows a retention time of 21.57 min, while the signal at 12.80 min corresponds to 5-MF partially solubilize in the cyclohexane layer</p> <p>### Figure S9<br>- FigS9.txt : HPLC chromatogram at 222 nm from a chronocoulometric reaction of 20 mM HMF at -0.85 V vs. RHE </p> <p>### Figure S10<br>- FigS10.txt : Stability test of 20 mM standard solution of HMF, BHMF, MFA and 5-MF in a 0.5 M NaH2PO4 (pH = 4) solution for 12 h, quantified by HPLC</p> <p>### Figure S12<br>- FigS10a.txt : Bode plots of impedance spectra in Figure 3c for w/o organic molecule<br>- FigS10b.txt : Bode plots of impedance spectra in Figure 3c for 5-MF<br>- FigS10c.txt : Bode plots of impedance spectra in Figure 3c for HMF<br>- FigS10d.txt : Bode plots of impedance spectra in Figure 3c for BHMF<br>- FigS10e.txt : Bode plots of impedance spectra in Figure 3c for MFA</p> <p>### Figure S13<br>- FigS13.txt : Impedance spectra for HMF change with voltage and so it does the equivalent circuit used to fit the experimental data.</p> <p>### Figure S14<br>- FigS14a,b,c,d.txt : Chronoamperometries of Cu electrodes with the different electrolytes. Peaks observed in the transition between potentials (blue arrows) are associated to the charging of a large capacitor, in our case the surface state capacitor. In the case of MFA this peak is may not be clearly observed as the Css attains large values at voltages in which high current is crossing the electrochemical cell.</p> <p> </p>
ascii xyz files for all pure fullerene isomers from C20 to C80, and stable structures for C28Hn and C40Hn, n=1..5, geometrically optimised with xTB.
<p>ascii xyz files for all pure fullerene isomers from C20 to C80, and stable structures for C28Hn and C40Hn, n=1..5, geometrically optimised with xTB.</p> <p>Data refers to structures generated with the paper published in MDPI Crystals 2021 article "Methodological Investigation for Hydrogen Addition to Small Cage Carbon Fullerenes". Please cite this article if you use this data, many thanks. The article pre-print can be found here: https://www.preprints.org/manuscript/202109.0361/v1 but please cite the final published article.</p>
Parameter fields for the Hydrological Discharge (HD) model at 0.5° and 5 Min. horizontal resolution
<p><strong>HD model parameter files</strong></p> <p>This dataset comprises global parameter data that are necessary to run the Hydrological Discharge (HD) model Vs. 5.1, which has been published on <a href="http://doi.org/10.5281/zenodo.5707587">Zenodo</a>. The HD model calculates the lateral transport of water over the land surface to simulate discharge into the oceans. The HD model parameter dataset comprises parameter fields at 0.5° global resolution and at 5 Min. resolution (global, Europe). Details for both resolutions are provided below. </p> <p><strong>Authors</strong>: Stefan Hagemann, Tobias Stacke <br> <strong>Copyright 2021</strong>: Institute of Coastal Systems - Analysis and Modelling, Helmholtz-Zentrum Hereon<br> <strong>License</strong>: under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0; https://creativecommons.org/licenses/)</p> <p><br> <strong>HD model parameter file at 5 Min resolution: hdpara_vs5_1.nc</strong></p> <p>River directions and digital elevation data were provided by Bernhard Lehner (pers. comm., 2014) and were derived from the HydroSHEDS (Lehner et al., 2006) database and from the Hydro1K dataset for areas north of 60°N (https://lta.cr.usgs.gov/HYDRO1K).<br> For a number of rivers (most of them north of 60°N), flow directions and model orography were manually corrected based on available GIS data, such as from DIVA (https://www.diva-gis.org/gdata), CCM River and Catchment Database<br> (Vogt et al. 2007), SMHI (Swedish Meteorological and Hydrological Institute), NVE (Norges vassdrags- og energidirektorats), SYKE (Finnish Environment Institute).</p> <p>This corrected dataset is referred to as HDvs5 in the following.<br> The HD model parameters for overland flow, base flow and river flow are generated as described in Hagemann and Dümenil (1998) and Hagemann et al. (2020). However, different to the HD model vs. 4 described in Hagemann et al. (2020), Vs5 utilizes inland water fractions from the ESA CCI Water Bodies Map v4.0 (Lamarche et al. 2017) and wetland fractions from the Global Lakes and Wetlands Database (Lehner and Döll 2004) instead of the previously used lake and wetlands fractions. Changes from Vs. 5.0 to Vs. 5.1 are provided in the file history_data.md that should be previewed below.</p> <p>The HD parameter dataset contains 14 variables which are shortly described in the following table.</p> <ul> <li> FLAG | Land sea mask | -</li> <li> FDIR | Flow direction | - | defined as written below</li> <li> ALF_K | HD model parameter Overland flow k | d-1</li> <li> ALF_N | HD model parameter Overland flow n | -</li> <li> ARF_K | HD model parameter Riverflow k | d-1</li> <li> ARF_N | HD model parameter Riverflow n | -</li> <li> AGF_K | HD model parameter Baseflow flow k | d-1</li> <li> AREA | Grid cell area | m-2 | based on own computation</li> <li> FILNEW | River flow target indices for longitudes | -</li> <li> FIBNEW | River flow target indices for latitudes | -</li> <li> DISTANCE | Distance between gridboxes in flow direction | m</li> <li> RIVERLENGTH | Distance between gridbox and the river mouth (or final sink) | km</li> <li> CAT_AREA | Upstream catchment area of gridbox | km²</li> <li> CAT_ID | Catchment ID of gridbox | -</li> </ul> <p> <em> Flow directions in variable FDIR are defined as on the Num Pad of a PC keyboard: </em> </p> <ul> <li> 7 8 9</li> <li> \ | /</li> <li> \|/</li> <li> 4--5--6</li> <li> /|\</li> <li> / | \</li> <li> 1 2 3</li> </ul> <p> Special directions: 5 = Sink point, i.e. no outflow<br> 0 = River mouth point in the ocean <br> -1 = Ocean point, but no river mouth</p> <p>Forcing data masks file: masks_5min.nc</p> <p>In the offline HD model version, this file is usually only used to obtain the grid information of the forcing data, i.e. of surface runoff and drainage (subsurface runoff). However, it contains four variables that are read in by the model, and that are actually used un coupled applications within the MPI-ESM. Even though these variables are not used in the HD model offline version, it was decided to keep them in order to allow future developments regarding the usage of these data and to keep some consistency with the HD model code implemented in MPI-ESM.</p> <ul> <li> ALAKE | Lake fraction within a grid box | Lamarche et al. 2017</li> <li> GLAC | Glacier fraction within a grid box | Hagemann 2002</li> <li> SLF | Land fraction within a grid box | Lamarche et al. 2017</li> <li> SLM | Land Sea Mask | Lamarche et al. 2017</li> </ul> <p>For simplicity, the data provided at the HD model resolution. Hence, these masks can be used when the forcing data are interpolated to the HD model resolution before they are read during the model run.</p> <p>This tar archive also include a subset of this global dataset for the European domain, hdpara_vs5_0_euro5min.nc.</p> <p> </p> <p><strong>HD model parameter file at 0.5° resolution: hdpara_vs1_11.nc</strong></p> <p>In addition, a global 0.5° HD parameter file hdpara_vs1_11 included. This is an update of the previous version 1.10 that was consistent to the parameter files used in previous offline and coupled applications of the HD model at 0.5° resolution (see, e.g. studies cited in Sect. 2.1 of Hagemann et al., 2020). Compared to the previous version 1.10, Vs. 1.11 now also utilzes the ESA water bodies and GLWD wetlands database such as in the 5 Min vs. 5.1 (see above). In additon, some flow directions have been updated. Except for DISTANCE and RIVERLENGTH, it comprises the same variables as for the 5 Min. version, but flow directions and parameters are generated as described in Hagemann and Dümenil (1998) and Hagemann and Dümenil Gates (2001). Here, the 0.5 degree mask file mask_05.nc comprises those masks that were utilized in the HD parameter generation. Only the land fraction is taken from Hagemann (2002) where the HD land sea mask indicates land.</p> <p><br> <strong>References</strong></p> <ul> <li>Hagemann, S., L. Dümenil (1998) A parameterization of the lateral waterflow for the global scale. Clim. Dyn. 14 (1), 17-31</li> <li>Hagemann, S., L. Dümenil Gates (2001) Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model, J. Geophys. Res. 106, 1503-1510</li> <li>Hagemann, S., 2002: An improved land surface parameter dataset for global and regional climate models, MPI Report No. 336, Max Planck Institute for Meteorology, Hamburg, Germany</li> <li>Hagemann, S., T. Stacke and H. Ho-Hagemann (2020) High resolution discharge simulations over Europe and the Baltic Sea catchment. Front. Earth Sci., 8:12. doi: 10.3389/feart.2020.00012.</li> <li>Lamarche, C., Santoro, M., Bontemps, S., d’Andrimont, R., Radoux, J., Giustarini, L., Brockmann, C., Wevers, J., Defourny, P. and Arino, O. (2017) Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sensing, 9(1), p.36.</li> <li>Lehner, B., P. Döll (2004) Development and validation of a global database of lakes, reservoirs and wetlands.</li> <li>J. Hydrol., 296: 1-22, doi:10.1016/j.jhydrol.2004.03.028.</li> <li>Vogt, J.V. et al. (2007): A pan-European River and Catchment Database. European Commission - JRC, Luxembourg, (EUR 22920 EN) 120 pp.</li> </ul> <p> </p>
S83 | CCL5 | Contaminant Candidate List CCL 5 (Draft)
<p>This is the collection associated with list S83 | CCL5 | Contaminant Candidate List CCL 5 (Draft) on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>The CCL is a list of contaminants that are currently not subject to any proposed or promulgated US primary drinking water regulations, but are known or anticipated to occur in public water systems. From: <a href="https://www.epa.gov/ccl/contaminant-candidate-list-5-ccl-5">https://www.epa.gov/ccl/contaminant-candidate-list-5-ccl-5</a>.</p> <p>Updates: v0.1.1: updated notes to link to Microcystins list: <a href="https://doi.org/10.5281/zenodo.5665355">https://doi.org/10.5281/zenodo.5665355</a>.</p>
December 2021 Eclipse Monitoring of WWV at 5 MHz and 10 MHz by K1FR Using FlexRadio 6700/GPSDO and fldigi
<p>This dataset contains observations of WWV at 5 and 10 MHz gathered from December 1 through 10, 2021 as part of the study of the effects of solar eclipses on the ionosphere. The data was collected by amateur radio station K1FR located at 38.828, -77.104 using a FlexRadio 6700 with GPSDO connected to a long wire antenna. The fldigi application was used to analyze and record the received frequency of the WWV station at one second intervals. The dataset includes .CSV files of these measurements.</p> <p>Three independent receivers were created within the 6700 - one for 5 MHz and two for 10 MHz. The first two receivers' data are included in this dataset. Files named analysis_xxxxx-K1FR-1 are for 10 MHz, and those named analysis_xxxxx-K1FR-2 are for 5 MHz. The third independent receiver was used to capture realtime audio from WWV at 10MHz which will be included in a separate dataset.</p>
Manufacturing of screw rotors via 5-axis double-flank CNC machining
<p>Each folder contains the mesh files of the target geometry (screw rotor) and of a corresponding custom-shaped tool. The motions of the tool are described as the CL files, where each line in these files contains the information about the tool tip (first 3 coordinates) and the unit vector of the tool's axis (last 3 coordinates).</p>
Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.
<p>The publications and research associated to cite is in:</p><p><a href="https://doi.org/10.1016/j.knosys.2023.110558">https://doi.org/10.1016/j.knosys.2023.110558</a></p><p>In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in <a href="https://hadas.caosd.lcc.uma.es/savrus">https://hadas.caosd.lcc.uma.es/savrus</a></p><p>In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:</p><p>Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements </p><p>Dune1</p><p> </p><p>Multi-grid solver</p><p> </p><p>11</p><p> </p><p>3</p><p> </p><p>2,304</p><p> </p><p>Complex..</p><p> </p><p>2,304</p><p> </p><p>HSMGP1</p><p> </p><p>Stencil-grid solver</p><p> </p><p>14</p><p> </p><p>3</p><p> </p><p>3,456</p><p> </p><p>..equation..</p><p> </p><p>3,456</p><p> </p><p>HiPAcc1</p><p> </p><p>Image processing framework</p><p> </p><p>33</p><p> </p><p>2</p><p> </p><p>13,485</p><p> </p><p>..solving..</p><p> </p><p>13,485</p><p> </p><p>Trimesh2</p><p> </p><p>Triangle mesh library</p><p> </p><p>13</p><p> </p><p>4</p><p> </p><p>239,360</p><p> </p><p>..time</p><p> </p><p>239,360</p><p> </p><p>GEC</p><p> </p><p>Generic edge computing</p><p> </p><p>552</p><p> </p><p>2</p><p> </p><p>~5.3*108</p><p> </p><p>Energy Consumption</p><p> </p><p>132500</p><p> </p><p>The dataset zip file contains:</p><ul><li>5 numerical variability models in Clafer format (.txt) for each software product line.</li><li>5 CSV files with the respective quality attribute measurements</li><li>An .xlsx file containing SAVRUS scalability results divided in different tabs.</li></ul><p>References:</p><p>[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.</p><p>[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).</p>
Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 5: 2020 - 2021) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7434396">https://zenodo.org/record/7434396</a></p>
Simulation Parameters for Two Cooperative Binding Sites Sensitize PI(4,5)P2 Recognition by the Tubby Domain
<p>Dataset to perform the coarse-grained MD simulations presented in "Two cooperative binding sites sensitize PI(4,5)P2 recognition by the tubby domain". The dataset includes protein structures and GROMACS simulation files such as mdp, itp, gro, and index files for the tubby domain as well as PLC-delta1 PH domain.</p>
video_Annealing_5
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_assistant_blowing_5
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Handle_laying_5
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 5
<p>These figures are an integral part of Chapter 5 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
video_Annealing_5
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_handle_finishing_5
<p>This is the process of making a carafe Bontemps. The step depicted is called "handle_finishing"(from the Mingei project).</p>
Udayagiri, Madhya Pradesh. Cave 5, detail of devotee.
<p>Udayagiri, Madhya Pradesh. Cave 5, detail of devotee, probably Candragupta II attended by his minister Vīrasena.</p>
MONNALISA_NUM_DA_45_SA_30_AR_5_TR_0.4_Delta_-15
Upload of the numerical solutions for the Reference plane of the MONNALISA Project. Please, refer to the README file of this folder and to the articles associated.
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