Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Adapting the Harmonized Data Quality Framework for Ontology Quality Assessment

Ontologies play an important role in the representation, standardization, and integration of biomedical data, but are known to have data quality (DQ) issues. We aimed to understand if the Harmonized Data Quality Framework (HDQF), developed to standardize electronic health record DQ assessment strategies, could be used to improve ontology quality assessment. A novel set of 14 ontology checks was developed. These DQ checks were aligned to the HDQF and examined by HDQF developers. The ontology checks were evaluated using 11 Open Biomedical Ontology Foundry ontologies. 85.7% of the ontology checks were successfully aligned to at least 1 HDQF category. Accommodating the unmapped DQ checks (n=2), required modifying an original HDQF category and adding a new Data Dependency category. While all of the ontology checks were mapped to an HDQF category, not all HDQF categories were represented by an ontology check presenting opportunities to strategically develop new ontology checks. The HDQF is a valuable resource and this work demonstrates its ability to categorize ontology quality assessment strategies.

opencc-by-4.0Jul 2022View details →

Harmonized data and code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age"

Harmonized data and R code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age" by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Helmut Hillebrand and Michal Kucera (in Nature Ecology & Evolution, 2022, https://doi.org/10.1038/s41559-022-01888-8). Analyse planktonic foraminifera species assemblages from the North Atlantic Ocean over the past 24,000 years. Scripts written by Tonke Strack DATA SOURCES * WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, Technical Editor. NOAA Atlas NESDIS 81, 52 (2019). * LGMR: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021). * MARGO: Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813-819, doi:10.1016/j.quascirev.2004.07.017 (2005). Kucera, M. et al. Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific Oceans. Quat. Sci. Rev. 24, 951-998, doi:10.1016/j.quascirev.2004.07.014 (2005). * planktonic foraminifera assemblage data: individual citations provided in CoreList_PlanktonicForaminifera.csv DATA 1. Harmonized assemblage data*: FullDataTable_PF_harmonized.txt 2. Core list with additional information to time series: CoreList_PlanktonicForaminifera.csv 3. Reference list for PF names: ReferenceList_PlanktonicForaminifera.csv CODE 1. 01_DataAnalysis_PCA.R: principal component analysis on assemblage data of individual time series as well as on whole dissimilarity matrix (results shown in Fig. 1 and 2) 2. 02_DataAnalysis_LocalBiodiversityChange.R: local biodiversity change analysis of individual time series (results shown in Fig. 3 and Extended Data Fig. 1); also recalculates resolution of time-series 3. 03_DataAnalysis_NoAnalogueAssemblages.R: calculates compositional dissimilarity to the nearest LGM sample to analyse existence of no-analogues (results shown in Fig. 4, as well as Extended Data Fig. 3 and 4) 4. 04_DataAnalysis_LDG_LGMresiduals.R: visualises latitudinal diversity gradient through time and the difference between richness and Shannon diversity to their respective LGM mean values (results shown in Fig. 5) *Assemblage data of individual time series were manually downloaded, checked and harmonized following the taxonomy of Siccha and Kucera (2017) and combined into one data file. Species not reported in the time series data were assumed to be absent (i.e., zero abundance). We merged Globigerinoides ruber ruber and Globigerinoides ruber albus, because some studies only reported them together as Globigerinoides ruber. Also, P/D intergrades (an informal category of morphological intermediates between Neogloboquadrina incompta and Neogloboquadrina dutertrei) were merged with Neogloboquadrina incompta. In total, 41 species of planktonic foraminifera were included in this study. Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. Sci. Data 4, 170109, doi:10.1038/sdata.2017.109 (2017).

Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project

Flux tower observations, model spin-up and site characteristics data for Urban-PLUMBER sites associated with the manuscript: <blockquote> "Harmonized, gap-filled dataset from 20 urban flux tower sites"  <a href="https://doi.org/10.5194/essd-14-5157-2022">https://doi.org/10.5194/essd-14-5157-2022</a> </blockquote> Use of any data must give credit through citation of the above manuscript and other site sources as appropriate (see below). We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant site contacts are included in site metadata.  Data can be downloaded from the bottom of this page.  <table> <tbody> <tr> <td> Sitename </td> <td> City </td> <td> Country </td> <td> Observed period </td> <td> References </td> </tr> <tr> <td> AU-Preston </td> <td> Melbourne </td> <td> Australia </td> <td> Aug 2003 – Nov 2004 </td> <td> (Coutts et al., 2007a, b) </td> </tr> <tr> <td> AU-SurreyHills </td> <td> Melbourne </td> <td> Australia </td> <td> Feb 2004 – Jul 2004 </td> <td> (Coutts et al., 2007a, b) </td> </tr> <tr> <td> CA-Sunset </td> <td> Vancouver </td> <td> Canada </td> <td> Jan 2012 – Dec 2016 </td> <td> (Christen et al., 2011; Crawford and Christen, 2015) </td> </tr> <tr> <td> FI-Kumpula </td> <td> Helsinki </td> <td> Finland </td> <td> Dec 2010 – Dec 2013 </td> <td> (Karsisto et al., 2016) </td> </tr> <tr> <td> FI-Torni </td> <td> Helsinki </td> <td> Finland </td> <td> Dec 2010 – Dec 2013 </td> <td> (Järvi et al., 2018; Nordbo et al., 2013) </td> </tr> <tr> <td> FR-Capitole </td> <td> Toulouse </td> <td> France </td> <td> Feb 2004 – Mar 2005 </td> <td> (Masson et al., 2008; Goret et al., 2019) </td> </tr> <tr> <td> GR-HECKOR </td> <td> Heraklion </td> <td> Greece </td> <td> Jun 2019 – Jun 2020 </td> <td> (Stagakis et al., 2019) </td> </tr> <tr> <td> JP-Yoyogi </td> <td> Tokyo </td> <td> Japan </td> <td> Mar 2016 – Mar 2020 </td> <td> (Hirano et al., 2015; Ishidoya et al., 2020) </td> </tr> <tr> <td> KR-Jungnang </td> <td> Seoul </td> <td> South Korea </td> <td> Jan 2017 – Apr 2019 </td> <td> (Jo et al., n.d.; Hong et al., 2020) </td> </tr> <tr> <td> KR-Ochang </td> <td> Ochang </td> <td> South Korea </td> <td> Jun 2015 – Jul 2017 </td> <td> (Hong et al., 2019, 2020) </td> </tr> <tr> <td> MX-Escandon </td> <td> Mexico City </td> <td> Mexico </td> <td> Jun 2011 – Sep 2012 </td> <td> (Velasco et al., 2011, 2014) </td> </tr> <tr> <td> NL-Amsterdam </td> <td> Amsterdam </td> <td> Netherlands </td> <td> Jan 2019 – Oct 2020 </td> <td> (Steeneveld et al., 2020) </td> </tr> <tr> <td> PL-Lipowa </td> <td> Łódź </td> <td> Poland </td> <td> Jan 2008 – Dec 2012 </td> <td> (Fortuniak et al., 2013; Pawlak et al., 2011) </td> </tr> <tr> <td> PL-Narutowicza </td> <td> Łódź </td> <td> Poland </td> <td> Jan 2008 – Dec 2012 </td> <td> (Fortuniak et al., 2013, 2006) </td> </tr> <tr> <td> SG-TelokKurau </td> <td> Singapore </td> <td> Singapore </td> <td> Feb 2015 – Feb 2016 </td> <td> (Roth et al., 2017) </td> </tr> <tr> <td> UK-KingsCollege </td> <td> London </td> <td> UK </td> <td> Apr 2012 – Jan 2014 </td> <td> (Bjorkegren et al., 2015; Kotthaus and Grimmond, 2014a, b) </td> </tr> <tr> <td> UK-Swindon </td> <td> Swindon </td> <td> UK </td> <td> May 2011 – Apr 2013 </td> <td> (Ward et al., 2013) </td> </tr> <tr> <td> US-Baltimore </td> <td> Baltimore </td> <td> USA </td> <td> Jan 2002 – Jan 2007 </td> <td> (Crawford et al., 2011) </td> </tr> <tr> <td> US-Minneapolis </td> <td> Minneapolis </td> <td> USA </td> <td> Jun 2006 – May 2009 </td> <td> (Peters et al., 2011; Menzer and McFadden, 2017) </td> </tr> <tr> <td> US-WestPhoenix </td> <td> Phoenix </td> <td> USA </td> <td> Dec 2011 – Jan 2013 </td> <td> (Chow, 2017; Chow et al., 2014) </td> </tr> </tbody> </table> For further site information and timeseries plots see <a href="https://urban-plumber.github.io/sites">https://urban-plumber.github.io/sites</a>. For processing code see <a href="https://github.com/matlipson/urban-plumber_pipeline">https://github.com/matlipson/urban-plumber_pipeline</a>. Data Two data archives are available on this page. <ul> <li>The full collection includes all observed, gap-filled, spin-up and site characteristic data, in both netcdf and text form.</li> <li>The "obs_only" archive includes a duplicate of site observation timeseries (after quality control) in a single netcdf file.</li> </ul> Full collection The full archive includes site folders with: <ul> <li><code>index.html</code>: A summary page with site characteristics and timeseries plots.</li> <li><code>SITENAME_sitedata_v1.csv</code>: comma separated file for numerical site characteristics e.g. location, surface cover fraction etc.</li> <li><code>timeseries/</code> (following files are available as netCDF and txt) <ul> <li><code>SITENAME_raw_observations_v1</code>: site observed timeseries before project-wide quality control.</li> <li><code>SITENAME_clean_observations_v1</code>: site observed timeseries after project-wide quality control.</li> <li><code>SITENAME_metforcing_v1</code>: gap-filled and prepended (10yr spinup) site observation forcing dataset for model evaluation.</li> <li><code>SITENAME_era5_corrected_v1</code>: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset.</li> </ul> </li> </ul> "Obs Only" This archive contains duplicate data from the full collection (observations after QC): <ul> <li><code>UP_all_clean_observations_UTC_v1.nc</code>: in coordinated universal time (UTC)</li> <li><code>UP_all_clean_observations_localstandardtime_v1.nc</code>: in local standard time</li> </ul> Site references Bjorkegren, A. B., Grimmond, C. S. B., Kotthaus, S., and Malamud, B. D.: CO2 emission estimation in the urban environment: Measurement of the CO2 storage term, Atmospheric Environment, 122, 775–790, https://doi.org/10.1016/j.atmosenv.2015.10.012, 2015. Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through 2012-12-31, https://doi.org/10.6073/PASTA/FED17D67583EDA16C439216CA40B0669, 2017. Chow, W. T. L., Volo, T. J., Vivoni, E. R., Jenerette, G. D., and Ruddell, B. L.: Seasonal dynamics of a suburban energy balance in Phoenix, Arizona, International Journal of Climatology, 34, 3863–3880, https://doi.org/10.1002/joc.3947, 2014. Christen, A., Coops, N. C., Crawford, B. R., Kellett, R., Liss, K. N., Olchovski, I., Tooke, T. R., van der Laan, M., and Voogt, J. A.: Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmospheric Environment, 45, 6057–6069, https://doi.org/10.1016/j.atmosenv.2011.07.040, 2011. Coutts, A. M., Beringer, J., and Tapper, N. J.: Characteristics influencing the variability of urban CO2 fluxes in Melbourne, Australia, Atmospheric Environment, 41, 51–62, https://doi.org/10.1016/j.atmosenv.2006.08.030, 2007a. Coutts, A. M., Beringer, J., and Tapper, N. J.: Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia, J. Appl. Meteor. Climatol., 46, 477–493, https://doi.org/10.1175/JAM2462.1, 2007b. Crawford, B. and Christen, A.: Spatial source attribution of measured urban eddy covariance CO2 fluxes, Theor Appl Climatol, 119, 733–755, https://doi.org/10.1007/s00704-014-1124-0, 2015. Crawford, B., Grimmond, C. S. B., and Christen, A.: Five years of carbon dioxide fluxes measurements in a highly vegetated suburban area, Atmospheric Environment, 45, 896–905, https://doi.org/10.1016/j.atmosenv.2010.11.017, 2011. Fortuniak, K., Kłysik, K., and Siedlecki, M.: New measurements of the energy balance components in Łódź, in: Preprints, sixth International Conference on Urban Climate: 12-16 June, 2006, Göteborg, Sweden, Sixth International Conference On Urban Climate, Göteborg, Sweden, 64–67, 2006. Fortuniak, K., Pawlak, W., and Siedlecki, M.: Integral Turbulence Statistics Over a Central European City Centre, Boundary Layer Meteorology; Dordrecht, 146, 257–276, https://doi.org/10.1007/s10546-012-9762-1, 2013. Goret, M., Masson, V., Schoetter, R., and Moine, M.-P.: Inclusion of CO2 flux modelling in an urban canopy layer model and an evaluation over an old European city centre, Atmospheric Environment: X, 3, 100042, https://doi.org/10.1016/j.aeaoa.2019.100042, 2019. Hirano, T., Sugawara, H., Murayama, S., and Kondo, H.: Diurnal Variation of CO2 Flux in an Urban Area of Tokyo, Sola, 11, 100–103, https://doi.org/10.2151/sola.2015-024, 2015. Hong, J., Lee, K., and Hong, J.-W.: Observational data of Ochang and Jungnang in Korea, 2020. Hong, J.-W., Hong, J., Chun, J., Lee, Y. H., Chang, L.-S., Lee, J.-B., Yi, K., Park, Y.-S., Byun, Y.-H., and Joo, S.: Comparative assessment of net CO2 exchange across an urbanization gradient in Korea based on eddy covariance measurements, Carbon Balance and Management, 14, 13, https://doi.org/10.1186/s13021-019-0128-6, 2019. Ishidoya, S., Sugawara, H., Terao, Y., Kaneyasu, N., Aoki, N., Tsuboi, K., and Kondo, H.: O2 : CO2 exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO2 emissions, Atmospheric Chemistry and Physics, 20, 5293–5308, https://doi.org/10.5194/acp-20-5293-2020, 2020. Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmospheric Measurement Techniques, 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018. Jo, S., Hong, J.-W., and Hong, J.: The observational flux measurement data of suburban and low-residential areas in Korea (in preparation), n.d. Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., W., O. K., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land‐surface models in the high‐latitude city Helsinki, Q.J.R. Meteorol. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016. Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Climate, 10, Part 2, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a. Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Climate, 10, Part 2, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b. Masson, V., Gomes, L., Pigeon, G., Liousse, C., Pont, V., Lagouarde, J.-P., Voogt, J., Salmond, J., Oke, T. R., Hidalgo, J., Legain, D., Garrouste, O., Lac, C., Connan, O., Briottet, X., Lachérade, S., and Tulet, P.: The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment, Meteorol Atmos Phys, 102, 135, https://doi.org/10.1007/s00703-008-0289-4, 2008. Menzer, O. and McFadden, J. P.: Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components, Atmospheric Environment, 170, 319–333, https://doi.org/10.1016/j.atmosenv.2017.09.049, 2017. Nordbo, A., Järvi, L., Haapanala, S., Moilanen, J., and Vesala, T.: Intra-City Variation in Urban Morphology and Turbulence Structure in Helsinki, Finland, Boundary-Layer Meteorol, 146, 469–496, https://doi.org/10.1007/s10546-012-9773-y, 2013. Pawlak, W., Fortuniak, K., and Siedlecki, M.: Carbon dioxide flux in the centre of Łódź, Poland—analysis of a 2-year eddy covariance measurement data set, International Journal of Climatology, 31, 232–243, https://doi.org/10.1002/joc.2247, 2011. Peters, E. B., Hiller, R. V., and McFadden, J. P.: Seasonal contributions of vegetation types to suburban evapotranspiration, Journal of Geophysical Research: Biogeosciences, 116, https://doi.org/10.1029/2010JG001463, 2011. Roth, M., Jansson, C., and Velasco, E.: Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city, Int. J. Climatol., 37, 2679–2698, https://doi.org/10.1002/joc.4873, 2017. Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., and Vogt, R.: Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece, Atmospheric Environment, 201, 278–292, https://doi.org/10.1016/j.atmosenv.2019.01.009, 2019. Steeneveld, G.-J., Horst, S. van der, and Heusinkveld, B.: Observing the surface radiation and energy balance, carbon dioxide and methane fluxes over the city centre of Amsterdam, Copernicus Meetings, https://doi.org/10.5194/egusphere-egu2020-1547, 2020. Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Molina, L. T., and Lamb, B.: Energy balance in urban Mexico City: observation and parameterization during the MILAGRO/MCMA-2006 field campaign, Theor Appl Climatol, 103, 501–517, https://doi.org/10.1007/s00704-010-0314-7, 2011. Velasco, E., Roth, M., Tan, S. H., Quak, M., Nabarro, S. D. A., and Norford, L.: The role of vegetation in the CO2 flux from a tropical urban neighbourhood, Atmospheric Chemistry and Physics, 13, 10185–10202, https://doi.org/10.5194/acp-13-10185-2013, 2013. Velasco, E., Perrusquia, R., Jiménez, E., Hernández, F., Camacho, P., Rodríguez, S., Retama, A., and Molina, L. T.: Sources and sinks of carbon dioxide in a neighborhood of Mexico City, Atmospheric Environment, 97, 226–238, https://doi.org/10.1016/j.atmosenv.2014.08.018, 2014. Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmospheric Chemistry and Physics, 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013.

opencc-by-4.0Mar 2024View details →

Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy

The dataset collects financial data about public works in Italy, specifically, it focuses on soil defense investments. The data is sourced from three distinct platforms: the OpenCoesione website, the OpenBDAP database, the Ministry of Economy and Finance's open data platform, and the ReNDiS database, provided by ISPRA, that exclusively gathers information about interventions in soil defense. The data obtained is interconnected using unique project codes (CUP) to prevent duplication. Georeferencing involves integrating geographic references into the three datasets. It enhances the accuracy of spatial analyses of spatial defense investments and provides valuable context for understanding the geographical distribution of available financial data. By incorporating geographic references such as regions, provinces, and municipalities analysts can gain insights into the spatial patterns and relationships within the datasets. This step is crucial for effective decision-making and policy formulation in the field of soil defense investments. Geographical references for each project were integrated using codes and names of regions, provinces, and municipalities from the ISPRA database. This database retrieves information directly from ISTAT websites, ensuring constant updates to names and codes, thus enhancing the accuracy of spatial analyses. Furthermore, geographical codes facilitated the association of centroids coordinates and polygon shapes for each financial observation, enhancing spatial visualization and analysis of soil defense investments, empowering decision-makers with a deeper understanding of the geographic distribution and impact of these initiatives. This comprehensive approach allows for a deeper exploration of the geographical factors influencing soil defense investments, including identifying hotspots of activity, assessing spatial trends, and understanding the localized impact of interventions on environmental sustainability and community resilience. The zip folder comprises four subfolders and two files. Among the files, one is a text file containing metadata, while the other is a CSV file consolidating merged data at the national level from three repositories. The subfolders contain data categorized by region and data categorized by region sourced from the three distinct repositories.  Datasets present 28 variables:  <ul> <li>Columns 1-2: descriptive variables;</li> <li>Column 3: total amount financed for each intervention;</li> <li>Columns 4-9: geo-reference variables;</li> <li>Columnn 10:25: key dates of the public works process;</li> <li>Column 26: source of the data;</li> <li>Columns 27-28: geo-referencing (centroids and areal shape).</li> </ul> An additional dataset has been added comprising all Italian municipalities, including thos that lack information on soil defense investments. In such a way, there are geographical information regarding all the peninsula.

opencc-by-4.0Sep 2024View details →

The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database

This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - present. It aims to create transparency through two key features: <ul> <li>Open-Data: Assembling a comprehensive collection of publicly-available transportation data</li> <li>Open-Code: All code and documentation will be publicly accessible and open for modification and extension. <a href="https://github.com/transportenergy">https://github.com/transportenergy</a></li> </ul> The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English). Shall you find any problems with the dataset, please report the issues here <a href="https://github.com/transportenergy/database/issues">https://github.com/transportenergy/database/issues</a>.

opencc-by-4.0Aug 2023View details →

Current Harmonics Minimization of PMSM Based on Iterative Learning Control and Neural Networks: Motor Data

The provided motor data corresponds to an electrical machine with 24 stator slots and 16 poles. As is common in electrical machines, this motor generates unwanted flux and current harmonics. However, the accompanying paper presents an effective solution to suppress these harmonics through the combined use of Iterative Learning Control (ILC) and Neural Networks (NNs). The ILC method demonstrates proficient compensation for harmonics during operations with constant speed and current reference values. Additionally, Neural Networks are trained with data derived from ILC, proving to be highly effective in suppressing harmonics even during transient operation. The simulation model used in the study is based on flux and torque maps, dependent on dq-currents and the electrical angle. These maps are obtained from Finite Element Method (FEM) simulations of an interior permanent magnet synchronous machine (IPM) and are openly published here, intended to facilitate other researchers in making direct comparisons with their own methodologies. Simulation results presented in the paper confirm that the integration of ILC and NNs leads to superior elimination of current harmonics during transient operations compared to using ILC alone. If you use the provided maps and motor data, kindly cite the associated paper for reference: https://doi.org/10.3390/machines11080784, https://www.mdpi.com/2075-1702/11/8/784

opencc-by-4.0Nov 2023View details →

Research data on IMVS / IMPS Higher harmonics of the Si Photodioed / TiO2 nanotubes

The following dataset contains research data that is the basis of research article:"Higher harmonics of the Intensity Modulated Photocurrent/Photovoltage Spectroscopy Response - a Tool for studying Photoelectrochemical Nonlinearities"Contensts of the package are the following:a) Experimental results of the IMVS for the photodiode (2-electrode system)<ol><li>OCP vs. power density relations [2 files]</li><li>Higher harmonic spectra for varying AC components 10,20,30,40 and 50 % (DC component fixed at 50% = 2 mW) [25 files]</li></ol>b) Experimental results of the IMPS for the TiO2 nanotubes (3-electrode system)<ol><li>Photocurrent vs. power density relation for polarization potentials 200mV, 400mV, 600mV, 800mV, 1000mV [2 files]</li><li>Higher harmonic spectra for varying AC components 10,20,30,40 and 50% at +1000 mV polarization (DC component fixed at 50% = 2 mW) [20 files]</li><li>Higher harmonic spectra for varying polarization potential 200mV, 400mV, 600mV, 800mV (AC and DC components fixed at 50% and 50%) [31 files]</li></ol>

openother-openDec 2021View details →

Query dan Perspektif Bloom untuk "Analisis Data Paradise Papers Indonesia Menggunakan Algoritma Strongly Connected Components dan Harmonic Centrality"

Query dan Perspektif Bloom untuk "Analisis Data Paradise Papers Indonesia Menggunakan Algoritma Strongly Connected Components dan Harmonic Centrality"

Statistical data for "Van Allen Probes Observations of Oxygen Ion Cyclotron Harmonic Waves: Statistical Study "

This file contains parameters for the identified oxygen ion cyclotron harmonic (OCH) waves observed by Van Allen Probes. The meaning of each column is shown as follows. Column #1: the name of Van Allen Probe, A or B. Column #2: event start day Column #3: event end day Column #4: event start hour Column #5: event strat minute Column #6: event end hour Column #7: event end minute Column #8: MLT, columns(t, MLT) Column #9: MLAT, columns(t, MLAT) Column #10: L-shell, columns(t, L-shell) Column #11: lower frequency limit Column #12: upper frequency limit Column #13: distance to the plasmapause, the positive and negative values correspond to outside and inside the plasmapause, respectively. Column #14: wave normal angle, columns (t, WNA) Column #15: the ratio of the frequency spacing between two consecutive wave harmonics to the local oxygen ion gyrofrequency.  Column #16: root-mean-square amplitude, columns(t, Bw) Column #17: AE*

opencc-by-4.0Feb 2022View details →

Harmonized remodeled energy system transformation strategies for Germany - additional data

This dataset compares 10 different scenarios for the transformation of the German energy system by 2050. These scenarios were used in the <a href="https://www.innosys-projekt.de">InNOSys project</a> as a starting point for a multidimensional impact assessment and evaluation of different transformation strategies (see also <a href="https://www.mdpi.com/2071-1050/13/9/5217">https://www.mdpi.com/2071-1050/13/9/5217</a>). As a source of inspiration for these scenarios, 10 different transformation strategies were used, as published for Germany in 2012-2018. However, for the present document, the original scenarios were re-modeled in a harmonized way. An additional documentation of the scenarios is also available on ZENODO.

opencc-by-sa-4.0Dec 2022View details →

WorldCereal open global harmonized reference data repository (CC-BY-SA licensed data sets)

Within the ESA funded WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY-SA license or a license similar to CC-BY-SA. See document "_In-situ-data-World-Cereal - license - CC-BY-SA.pdf" for an overview of the original data sets.

opencc-by-4.0Dec 2022View details →

WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)

Within the ESA funded WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets.

FIGURE 2 in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices

FIGURE 2 Taxonomy as a unifying key for ecological datasets. The two sides represent two exemplary datasets, with a containing conservation status of taxa (here species) and B their traits (colours show different traits). The datasets are indexed by taxon names 'Sp1' to 'Sp6'. The rounded rectangle in the middle depicts the taxonomic harmonization process: (a) the names are extracted from each dataset, respectively in the orange and purple rectangles; (b) both lists are then compared to a taxonomic database which harmonizes all names. Here the names 'Sp1' and 'Sp6' refer to the same taxon in the taxonomic database (as indicated by the dashed lines). Without taxonomic harmonization, the exact match of names would have resulted in the loss of Sp5 and Sp6 when merging both datasets. LC, NT, VU, and CR are abbreviations of Red List statuses, meaning least concern, not threatened, vulnerable, and critically endangered, respectively

FIGURE 1 in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices

FIGURE 1 Typology of taxonomic databases according to their taxonomic breadth and their spatial scale. The x-axis represents increasing taxonomic breadth from a single taxonomic group to no clear taxonomic restriction (e.g. considering all biota or all Eukaryota). The y-axis represents spatial scale from regional to global. Each box represents a specific type of taxonomic database, with examples. LCVP, Leipzig Catalogue of Vascular Plants; WorldFlora, World Flora Online; POWO, Plants of the World Online; GermanSL, German Simple List; Vascan, Database of Vascular Plants of Canada; WoRMS, World Register of Marine Species; CASD, Chinese Animal Scientific Database; COL, Catalogue of Life; GBIF, Global Biodiversity Information Facility; TAXREF, French Taxonomic Referential; FinBIF, Finnish Biodiversity Information Facility

FIGURE 4 in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices

FIGURE 4 Diagram of different taxonomic harmonization workflows. The workflows differ in the number of steps they consider and the databases they leverage on. Rounded rectangles are lists of taxon names while diamonds represent taxonomic databases against which the names are matched. The different colours used at step 2 represent different taxonomic groups

FIGURE 3 in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices

FIGURE 3 Screenshot showing the network view of taxharmonizexplorer. The left section shows a table of each of the nodes in the network to let the user select manually nodes of interest, the top part presents a summary of the information on the selected node in the network. The right section displays the relationships between packages (which depends on which other), between databases (how one populates another one) and between packages and databases (which packages access which databases)