Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

opencc-by-4.0May 2023View details →

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)

opencc-by-4.0May 2023View details →

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)

opencc-by-4.0Apr 2023View details →

Data of the characterisation of conventional 87Sr/86Sr isotope ratios in cement, limestone and slate reference materials based on an interlaboratory comparison study

This dataset represents the electronic supplementary material (ESM) of the publication entitled "Characterisation of conventional 87Sr/86Sr isotope ratios in cement, limestone and slate reference materials based on an interlaboratory comparison study", which is published in Geostandards and Geoanalytical Research under the DOI: 10.1111/GGR.12517. It consists of four files. 'ESM_Data.xlsx' contains all reported data of the participants, a description of the applied analytical procedures, basic calculations, the consensus values, and part of the uncertainty assessment. 'ESM_Figure-S1' displays a schematic on how measurements, sequences and replicates are treated for the uncertainty calculation carried out by PTB. 'ESM_Technical-protocol.pdf' is the technical protocol of the interlaboratory comparison, which has been provided to all participants together with the samples and which contains bedside others the definition of the measurand and guidelines for data assessment and calculations. 'ESM_Reporting-template.xlsx' is the Excel template which has been submitted to all participants for reporting their results within the interlaboratory comparison. Excel files with names of the the structure 'GeoReM_Material_Sr8786_Date.xlsx' represent the Rcon(87Sr/86Sr) data for a specific reference material downloaded from GeoReM at the specified date, e.g. 'GeoReM_IAPSO_Sr8786_20221115.xlsx' contains all Rcon(87Sr/86Sr) data for the IAPSO seawater standard listed in GeoReM until 15 November 2022.

opencc-by-4.0Jun 2023View details →

Out-of-equilibrium charge redistribution data in a copper-oxide based superconductor by time-resolved X-ray photoelectron spectroscopy

This dataset was measured using a momentum microscope by time-resolved X-ray photoelectron spectroscopy (XPS) on the prototypical high-temperature superconductor: optimally doped BSCCO at FEL FLASH, DESY in Hamburg. With time-resolved XPS, unique access to the dynamics of individual atoms in the unit cell is granted by means of chemical shifts of the core levels. Though the induced changes are small, with a rigorous fitting procedure, it is possible to extract significant changes observed mainly at the oxygen atoms in the copper oxide planes, while other oxygen atoms as well as strontium remain largely unaffected. Although it was acquired not in the superconducting phase, the observed dynamics point to a significant coupling of energy scales involving charge-transfer processes and optical excitations. Such findings can thus provide another puzzle piece for a better understanding of high-temperature superconductivity.

opencc-by-4.0Jul 2023View details →

Machine-learning based lightning nowcasting data archive

This data archive contains the derived data supporting the findings of article "Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset". The paper is currently in the preprint version:  https://doi.org/10.21203/rs.3.rs-2616886/v1 The prediction results in this data archive are generated by various models: 1. Current model. The model involves data input of aerosol observations together with meteorological variables and auxiliary datasets, as well as data enrichment by Geostationary Lightning Mapper (GLM). In the demo of the dataset, the year of 2020 is trained and predicted on a cross-validation scheme.  2. LMA model. The model acts as the baseline model considering only data label obtained from the ground-based Lightning Mapping Array (LMA), which observes accurate lightning occurrence in limited spatial range. 3. No-AOD model. The model acts as the baseline model considering no aerosol observation is utilized during the machine learning process.  The model results are demonstrated in a continuous value in 0-1. Trade-offs between Probability of Detection (POD) and False Alarm Ratio (FAR) can be optimized by selection of different thresholds.  Other datasets: 1. Dataset for training. It is for the public use of machine learning training for the current model and no-AOD model (training input features vary). 2. PM2.5 dataset. The real-time spatially continuous and hourly-level PM2.5 dataset is obtained following a published method by Zeng  et al.. In this method, the fundamental in-situ measurements are obtained from Air Quality System (AQS) monitoring network operated by United States Environmental Protection Agency. Reference: Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1] Zeng, Z. et al. Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research 254, 105516 (2021).

opencc-by-4.0Jun 2023View details →

Data for: "Unlocking the potential of LC-MS through an XIC-based algorithm for chromatographic optimisation"

This dataset is for upload of supplementary info and data for my master research thesis at the University of Amsterdam. All the compounds in each pesticide mix of the RESTEK multiresidue kit can be found along with some descriptors. For easy use of the developed algorithm without having to generate any mzxml files, a few files are included on which SAFD and CompCreate have already been performed using three different LC methods, Their gradients are also provided. To run the code, a package has been developed and is ready for installation at: https://github.com/tobihul/LC_MS_Resolved_Peaks.

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

The Antarctic sea ice reconstruction (CMST-South) based on the optimized Data Assimilation System for the Southern Ocean

The wealth of historical sea ice concentration (SIC) observations, coupled with their extensive spatial coverage, renders them indispensable for the reconstruction of long-term Antarctic sea ice variability. However, recent studies have pointed out the presence of significant uncertainties in certain aspects of Antarctic sea ice reanalyses obtained from assimilating SIC. Notably, while previous studies on ocean data assimilation have already demonstrated the significance of optimizing model-dependent parameters for assimilating oceanic observations, this aspect has received limited attention in current sea ice data assimilation studies. As a result, whether optimizing model-dependent parameters can enhance the effectiveness of assimilating SIC remains an open question. Thus, we address this gap by refining the model-dependent parameters of Data Assimilation System for the Southern Ocean (DASSO), including the development of a latitude-dependent localization scheme and the objective estimation of observation error variance of SIC which takes into account both measurement errors and representation errors. Here, the monthly anomalies in Antarctic sea ice extent and volume (1980 -2018) are uploaded which is produced by the optimized Data Assimilation System for the Southern Ocean (DASSO) with assimilating SIC. Besides, a 13-month moving mean is applied to monthly anomalies to focus on the low-frequency variability of Antarctic sea ice.

Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases

These data were used for the development of the paper "Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases". Especifically, we added ecological and environmental data that were used for modeling.

Integrated ground-based data for wildfires occurred in the Western US in September 2020

Data set used in paper Kassianov et al. Radiative impact of record-breaking wildfires from integrated ground-based data to be submitted to Sci. Rep. For details of data file formats see attached Readme file

Data base of the complexity Indexes to compute MFA and MCI from the paper "Unleashing The Potential Of Artificial Reefs Design"

This data base compiles the Complexity indexes used to compute the MFA and extract the MCI from the paper "Unleashing The Potential Of Artificial Reefs Design: A Purpose-Driven Evaluation Of Structural Complexity" (https://doi.org/10.32942/X2G300)

Phantom measurement data for 'Configuration-based electrical properties tomography', Iyyakkunnel et al. (2021)

This dataset contains the phantom bSSFP measurement data used in the published article Iyyakkunnel et al., 'Configuration-based electrical properties tomography', Magn Reson Med. 2021;85:1855–1864 (doi: 10.1002/mrm.28542). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany). The data includes the magnitude and phase measurements for eight phase-cycled scans (in dicom (.dcm) format). The RF phase increment for the phase cycled scans corresponds to 0°, 45°, 90°, 135°, 180°, 225°, 270° and 315°. For further measurement details, please refer to the mentioned original article.

Raw data of findings in the article "Sub-THz_wireless_transmission_based_on_graphene_integrated_optoelectronic_mixer" by A. Montanaro et al.

Raw data containing all the plots in the manuscript "Sub-THz wireless transmission based on graphene integrated optoelectronic mixer" by A. Montanaro et al.

Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.

Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.

Estimation of biomass combustion carbon emissions data for 2020 in Africa based on GABAM burned area products.

Estimated biomass combustion carbon emissions data for the African region in 2020, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.

Estimation of biomass combustion carbon emissions data for 2019 in Africa based on GABAM burned area products.

Estimated biomass combustion carbon emissions data for the African region in 2019, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.