cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7619632
title: A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem

description: The dataset for our paper 'A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem'. It consists of 5 real KGs (<code>Mondial</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, <code>DBpedia</code>) and 5 synthetic KGs (<code>LUBM-10U</code>, <code>LUBM-50U</code>, <code>LUBM-250U</code>, <code>LUBM-2U</code>, <code>DBP-50K</code>). Each KG is compressed in one file, which including (for example, in <code>LUBM-2U</code>): <code>lubm_2u_nodes.sql</code>: the id, the name and the weight of a node, <code>lubm_2u_edges.sql</code>: the ids of two nodes an edge connects, <code>lubm_2u_queries.sql</code>: a query consists of some keywords, <code>lubm_2u_keymap.sql</code>: a keyword maps to a set of nodes, <code>lubm_2u_nodevec.sql</code>: the vector of a node, used to compute quadratic function qw, <code>lubm_2u_hub_hop.sql</code>: the hub labeling index to compute in Section 4.1, <code>lubm_2u_hub_mix_1.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.1, <code>lubm_2u_hub_mix_5.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.5, <code>lubm_2u_hub_mix_9.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.9. You can dump the data into MySQL database. For example, <pre><code>create database lubm_2u; use lubm_2u; source lubm_2u_nodes.sql; …</code></pre> Unfortunately, due to the limit of space, for large KGs (<code>DBpedia</code> and <code>LUBM-250U</code>), we don't directly provide the data of hub labeling, i.e., these two compressed files only contains the first 5 sql files. You should generate hub labeling by yourself where the process is detailed in README of our project.
DOI: 10.5281/zenodo.7139219
title: Yellow Glue Paper Traps Dataset

description: Color images are captured by pheromone traps installed at multiple locations in a field with vegetable crops (tomato) at Volos, Greece by a commercial provider of pheromone-based pest control solutions. The pheromone attracted the pest of interest into the trap where they became stuck to the adhesive surface. A digital camera (Svpro 13MP, sensor: Sony 1/3” IMX213) was used to capture the trapped insects. The resolution of images taken was 3840x2160. The working distance was set to 20 cm based on the camera specifications, in order to obtain a large enough field of view to capture the whole adhesive board trap. Using that equipment, 225 images were captured in indoor and outdoor conditions and chosen to represent the insect variability. For image annotation, Roboflow was used. The annotation process aims to label the location and class of the insect pests in the image. Roboflow was chosen because it can generate various annotations formats for different object detection models. The whole process was carried out by an experienced entomologist. Two insects were labelled, whiteflies, and black aphids. In total 5904 insect instances were labelled, out of which 2431 are whiteflies and 3473 are black aphids. Note that there are 26.24 annotations per image at average across the two classes. For validating the Yellow Glue Paper Traps Dataset, we split the whole images into training and validation subsets. In total, the dataset was split into 180 images for training and 45 for validation with 80-20% proportion. The split was chosen so as to keep a similar ratio and the classes which would ensure same distribution to training and validation subsets. The insect count in each subset can be seen in the following table: Dataset splits insect counts <strong>Dataset split</strong> <strong>Images</strong> <strong>Insect Instances</strong> <strong>Whiteflies</strong> <strong>Black Aphids</strong> Training 180 4773 1926 2847 Validation 45 1131 505 626 Total 225 5904 2431 3473
DOI: 10.5281/zenodo.7139220
title: Yellow Glue Paper Traps Dataset

description: Color images are captured by pheromone traps installed at multiple locations in a field with vegetable crops (tomato) at Volos, Greece by a commercial provider of pheromone-based pest control solutions. The pheromone attracted the pest of interest into the trap where they became stuck to the adhesive surface. A digital camera (Svpro 13MP, sensor: Sony 1/3” IMX213) was used to capture the trapped insects. The resolution of images taken was 3840x2160. The working distance was set to 20 cm based on the camera specifications, in order to obtain a large enough field of view to capture the whole adhesive board trap. Using that equipment, 225 images were captured in indoor and outdoor conditions and chosen to represent the insect variability. For image annotation, Roboflow was used. The annotation process aims to label the location and class of the insect pests in the image. Roboflow was chosen because it can generate various annotations formats for different object detection models. The whole process was carried out by an experienced entomologist. Two insects were labelled, whiteflies, and black aphids. In total 5904 insect instances were labelled, out of which 2431 are whiteflies and 3473 are black aphids. Note that there are 26.24 annotations per image at average across the two classes. For validating the Yellow Glue Paper Traps Dataset, we split the whole images into training and validation subsets. In total, the dataset was split into 180 images for training and 45 for validation with 80-20% proportion. The split was chosen so as to keep a similar ratio and the classes which would ensure same distribution to training and validation subsets. The insect count in each subset can be seen in the following table: Dataset splits insect counts <strong>Dataset split</strong> <strong>Images</strong> <strong>Insect Instances</strong> <strong>Whiteflies</strong> <strong>Black Aphids</strong> Training 180 4773 1926 2847 Validation 45 1131 505 626 Total 225 5904 2431 3473
DOI: 10.5281/zenodo.8343635
title: Real data from PMUs

description: Real data that are derived from two PMUs installed in different substations of a real system that includes timestamp, three phase voltage and current phasor measurements for two lines of the system, frequency measurements, and rate of change of frequency (ROCOF) measurements.
DOI: 10.5281/zenodo.8343634
title: Real data from PMUs

description: Real data that are derived from two PMUs installed in different substations of a real system that includes timestamp, three phase voltage and current phasor measurements for two lines of the system, frequency measurements, and rate of change of frequency (ROCOF) measurements.
DOI: 10.15129/58c05738-4b8d-4fe3-8803-24a2321cf7fb
title: Data for: "Comparison of Raman and mid-infrared spectroscopy for quantification of nitric acid in PUREX-relevant mixtures"

description: "TBPOKH2OHNO3_MIR_aqueous_phase_initial.csv" is a csv file containing the mid-infrared spectra of the initial aqueous phase samples 1 - 10 (3 replicate measurements of each) for the TBP/OK-H2O-HNO3 system, detailed in section 2.1.1. "TBPOKH2OHNO3_MIR_aqueous_phase_postextraction.csv" is a csv file containing the mid-infrared spectra of aqueous phase samples 1 - 10 post-extraction (3 replicate measurements of each) for the TBP/OK-H2O-HNO3 system, detailed in section 2.1.1. "TBPOKH2OHNO3_MIR_organic_phase_postextraction.csv" is a csv file containing the mid-infrared spectra of organic phase samples 1 - 10 post-extraction (3 replicate measurements of each) for the TBP/OK-H2O-HNO3 system, detailed in section 2.1.1. "TBPOKH2OHNO3_Raman_aqueous_phase_initial.csv" is a csv file containing the Raman spectra of the initial aqueous phase samples 1 - 10 (3 replicate measurements of each) for the TBP/OK-H2O-HNO3 system, detailed in section 2.1.1. "TBPOKH2OHNO3_Raman_aqueous_phase_postextraction.csv" is a csv file containing the Raman spectra of aqueous phase samples 1 - 10 post-extraction (3 replicate measurements of each) for the TBP/OK-H2O-HNO3 system, detailed in section 2.1.1. "TBPOKH2OHNO3_Raman_organic_phase_postextraction.csv" is a csv file containing the Raman spectra of organic phase samples 1 - 10 post-extraction (3 replicate measurements of each) for the TBP/OK-H2O-HNO3 system, detailed in section 2.1.1. "UTBPOKH2OHNO3_MIR_organic_phase.csv" is a csv file containing the mid-infrared spectra of the samples containing uranium and nitric acid in the organic phase (U-TBP/OK-H2O-HNO3 system), detailed in section 2.1.2. "UTBPOKH2OHNO3_Raman_organic_phase.csv" is a csv file containing the Raman spectra of the samples containing uranium and nitric acid in the organic phase (U-TBP/OK-H2O-HNO3 system), detailed in section 2.1.2. Row 1 of each csv file contains the wavenumber (for the MIR data) or the Raman shift (for the Raman data). Subsequent rows contain the spectra of the samples listed in column 1.
DOI: 10.5281/zenodo.8276902
title: QM9-XAS database of 56k QM9 small organic molecules labeled with TDDFT X-ray absorption spectra

description: Database for training graph neural network (GNN) models in <strong>Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra, </strong>by Amir Kotobi, Kanishka Singh, Daniel Höche, Sadia Bari, Robert H.Meißner, and Annika Bande. <strong>Included:</strong> qm9_Cedge_xas_56k.npz: the TDDFT XAS spectra of 56k structures from the QM9 dataset, were employed to label the graph dataset. The dataset contains two pairs of key/value entries: <strong>spec_stk</strong>, which represents a 2D array containing energies and oscillator strengths of XAS spectra, and <strong>id</strong>, which consists of the indices of QM9 structures. This data was used to create the QM9-XAS graph dataset. qm9xas_orca_output.zip: the raw ORCA output of TDDFT calculations for the 56k QM9-XAS dataset consists of excitation energies, densities, molecular orbitals, and other relevant information. This unprocessed output serves as a source to derive ground truth data for explaining the predictions made by GNNs. qm9xas_spec_train_val.pt: processed graph train/validation dataset of 50k QM9 structures. It is used as input to GNN models for training and validation. qm9xas_spec_test.pt: processed graph test dataset of 6k QM9 structures. It is used to test the performance of trained GNN models. <strong>Notes on the datasets:</strong> The QM9-XAS dataset was created using ORCA electronic structure package [Neese, F., WIREs Computational Molecular Science 2012, 2, 73–78] to calculate carbon K-edge XAS spectra with the time-dependent density functional theory (TDDFT) method [Petersilka, M.; Gossmann, U. J.; Gross, E. K. U., Phys. Rev. Lett. 1996, 76, 1212–1215] The molecular structures of QM9-XAS datasets were sourced from the QM9 database [R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, <em>Sci. Data</em> 1, 1 (2014)]. <strong>Funding:</strong> This<strong> </strong>research was funded by HIDA Trainee Network program, HAICU, Helmholtz AI-4-XAS, DASHH and HEIBRiDS graduate schools. For theoretical calculations and model training, computational resources at DESY and JFZ were used.
description: {"references": ["Neese, F.,\u00a0WIREs Computational Molecular Science 2012, 2, 73\u201378", "Petersilka, M.; Gossmann, U. J.; Gross, E. K. U.,\u00a0Phys. Rev. Lett. 1996, 76, 1212\u20131215", "R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld,\u00a0Sci. Data, 2014,\u00a01, 1"]}
DOI: 10.5281/zenodo.8276901
title: QM9-XAS database of 56k QM9 small organic molecules labeled with TDDFT X-ray absorption spectra

description: Database for training graph neural network (GNN) models in <strong>Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra, </strong>by Amir Kotobi, Kanishka Singh, Daniel Höche, Sadia Bari, Robert H.Meißner, and Annika Bande. <strong>Included:</strong> qm9_Cedge_xas_56k.npz: the TDDFT XAS spectra of 56k structures from the QM9 dataset, were employed to label the graph dataset. The dataset contains two pairs of key/value entries: <strong>spec_stk</strong>, which represents a 2D array containing energies and oscillator strengths of XAS spectra, and <strong>id</strong>, which consists of the indices of QM9 structures. This data was used to create the QM9-XAS graph dataset. qm9xas_orca_output.zip: the raw ORCA output of TDDFT calculations for the 56k QM9-XAS dataset consists of excitation energies, densities, molecular orbitals, and other relevant information. This unprocessed output serves as a source to derive ground truth data for explaining the predictions made by GNNs. qm9xas_spec_train_val.pt: processed graph train/validation dataset of 50k QM9 structures. It is used as input to GNN models for training and validation. qm9xas_spec_test.pt: processed graph test dataset of 6k QM9 structures. It is used to test the performance of trained GNN models. <strong>Notes on the datasets:</strong> The QM9-XAS dataset was created using ORCA electronic structure package [Neese, F., WIREs Computational Molecular Science 2012, 2, 73–78] to calculate carbon K-edge XAS spectra with the time-dependent density functional theory (TDDFT) method [Petersilka, M.; Gossmann, U. J.; Gross, E. K. U., Phys. Rev. Lett. 1996, 76, 1212–1215] The molecular structures of QM9-XAS datasets were sourced from the QM9 database [R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, <em>Sci. Data</em> 1, 1 (2014)]. <strong>Funding:</strong> This<strong> </strong>research was funded by HIDA Trainee Network program, HAICU, Helmholtz AI-4-XAS, DASHH and HEIBRiDS graduate schools. For theoretical calculations and model training, computational resources at DESY and JFZ were used.
description: {"references": ["Neese, F.,\u00a0WIREs Computational Molecular Science 2012, 2, 73\u201378", "Petersilka, M.; Gossmann, U. J.; Gross, E. K. U.,\u00a0Phys. Rev. Lett. 1996, 76, 1212\u20131215", "R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld,\u00a0Sci. Data, 2014,\u00a01, 1"]}
DOI: 10.4121/67b940cb-d316-43c5-82bd-1cf2494bc677
title: Data and software underlying the publication: Foot progression angle estimation using a single foot-worn inertial sensor

description: Calculate the foot progression angle according to the methods described in "Foot progression angle estimation using a single foot-worn inertial sensor" https://doi.org/10.1186/s12984-021-00816-4). It requires Movella DOT IMU input (of which example *.csv files are included) to calculate the foot progression angle using footworn IMUs.
DOI: 10.4121/67b940cb-d316-43c5-82bd-1cf2494bc677.v1
title: Data and software underlying the publication: Foot progression angle estimation using a single foot-worn inertial sensor

description: Calculate the foot progression angle according to the methods described in "Foot progression angle estimation using a single foot-worn inertial sensor" https://doi.org/10.1186/s12984-021-00816-4). It requires Movella DOT IMU input (of which example *.csv files are included) to calculate the foot progression angle using footworn IMUs.
DOI: 10.6084/m9.figshare.24137802
title: Ethiopian Indigenous Medicinal Plant Dataset

description: This dataset provides a comprehensive overview of Ethiopian Indigenous Medicinal Plant. The dataset is collected from Gullele botanical garden in the period of October 2022-April 2023.
DOI: 10.5281/zenodo.8343789
title: SI_III_4_Spatialized metabolomic annotation combining MALDI imaging and molecular network

description: Ces documents regroupent les données supplémentaires générés lors du développement méthdologique pour la création de réseaux moléculaires par MALDI-FT-ICR IMS. Un .ppt regroupe l'ensemble des cartographies ioniques spécifiques à chaque ion.
DOI: 10.5281/zenodo.8343788
title: SI_III_4_Spatialized metabolomic annotation combining MALDI imaging and molecular network

description: Ces documents regroupent les données supplémentaires générés lors du développement méthdologique pour la création de réseaux moléculaires par MALDI-FT-ICR IMS. Un .ppt regroupe l'ensemble des cartographies ioniques spécifiques à chaque ion.
DOI: 10.5281/zenodo.8341426
title: A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum (CESM1.2 output, netcdf files)

description: Prange, M., Jonkers, L., Merkel, U., Schulz, M., Bakker, P. (2023) A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum, <em>Science Advances</em>. <strong>Datasets: CESM1.2 netcdf output, Experiment LGM_ref (1540 years)</strong> <strong>MOC.nc - </strong>Meridional overturning circulation (Sv) as diagnosed from POP as monthly means <strong>SALT.nc</strong> - Salinity (psu) as annual means on POP grid <strong>TEMP.nc</strong> - SST (°C) as annual means on POP grid <strong>IFRAC.nc</strong> - Sea ice fraction as monthly means on POP grid <strong>TREFHT.nc</strong> - Reference height (2 m) temperature (K) as monthly means on CAM grid <strong>ZONALSALT.nc</strong> - Zonally averaged Atlantic salinity (psu) as annual means <em>POP: Ocean model component of CESM1.2</em> <em>CAM: Atmosphere model component of CESM1.2</em>
description: {"references": ["Prange et al. (2023), Science Advances"]}
DOI: 10.5281/zenodo.8341425
title: A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum (CESM1.2 output, netcdf files)

description: Prange, M., Jonkers, L., Merkel, U., Schulz, M., Bakker, P. (2023) A multi-centennial mode of North Atlantic climate variability throughout the Last Glacial Maximum, <em>Science Advances</em>. <strong>Datasets: CESM1.2 netcdf output, Experiment LGM_ref (1540 years)</strong> <strong>MOC.nc - </strong>Meridional overturning circulation (Sv) as diagnosed from POP as monthly means <strong>SALT.nc</strong> - Salinity (psu) as annual means on POP grid <strong>TEMP.nc</strong> - SST (°C) as annual means on POP grid <strong>IFRAC.nc</strong> - Sea ice fraction as monthly means on POP grid <strong>TREFHT.nc</strong> - Reference height (2 m) temperature (K) as monthly means on CAM grid <strong>ZONALSALT.nc</strong> - Zonally averaged Atlantic salinity (psu) as annual means <em>POP: Ocean model component of CESM1.2</em> <em>CAM: Atmosphere model component of CESM1.2</em>
description: {"references": ["Prange et al. (2023), Science Advances"]}
DOI: 10.4121/428049a0-cb40-43d4-bf58-4276ede13402.v1
title: LaMnO3, SrRuO3 database for force field training underlying the publication: Phase transitions of correlated and magnetic compounds: Tuning U in On-The-fly machine learning force fields simulations

description: This data set contains the collection of ab initio data (ML_ABN) from calculations done on LaMnO3 and SrRuO3. From the paper: Jansen T., Brocks G., Bokdam M. Phys. Rev. B 2023<br>These structures have been selected with the on-the-fly Machine-Learning Force Fields method as implemented in VASP 6.3:Jinnouchi R., Lahnsteiner J., Karsai F., Kresse G., Bokdam M., "Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on the fly with Bayesian inference", Phys. Rev. Lett. 122, 225701, (2019)<br>The data set is .zip file including the ML_ABN, INCAR, POSCAR, KPOINTS and ML_REG files for the following configurations:- LaMnO3 in the AFM configuration for U = 0,1,2 and 3.5 eV, - LaMnO3 with U = 3.5 eV for an intial FM and non-spin polzarized configuration. - SrRuO3 in FM configuration with U,J = 2, 0.6 eV and U = J = 0 eV
DOI: 10.6084/m9.figshare.24137832
title: Hei et alL-data-GCB

description: Full substitution of chemical fertilizer by organic manure decreases soil N<sub>2</sub>O emissions driven by ammonia oxidizers and gross nitrogen transformations
DOI: 10.57841/casdc.0002197
title: 南方丘陵山地带生态胁迫与生态环境问题（2000-2010年）
DOI: 10.5281/zenodo.8343896
title: SI_II_6_Metabolomics, reverse chemical ecology and wood science an integrated approach to explore the chemical diversity and natural durability of the tropical tree Sextonia rubra (Mez.) van der Werff (Lauraceae)

description: Ce document présente les données supplémentaires générées lors de l'étude de la composition chimique et de la durabilité des tissus d'un Sextonia rubra.
DOI: 10.5281/zenodo.8343895
title: SI_II_6_Metabolomics, reverse chemical ecology and wood science an integrated approach to explore the chemical diversity and natural durability of the tropical tree Sextonia rubra (Mez.) van der Werff (Lauraceae)

description: Ce document présente les données supplémentaires générées lors de l'étude de la composition chimique et de la durabilité des tissus d'un Sextonia rubra.
DOI: 10.11583/dtu.17040767.v2
title: The SOLETE dataset

description: Author: Daniel Vázquez Pombo (dvapo@elektro.dtu.dk), ORCID: https://orcid.org/0000-0001-5664-9421 ------------------------------------------------------------------------------- This item includes the SOLETE dataset which is disclosed to increase the transparency and replicability of [1] and [2], which are at different stages of the review process. <br> SOLETE includes 15 months of 5 minute and hourly measurements from the 1st June 2018 to 1st September 2019 covering: Timestamp, air temperature, relative humidity, pressure, wind speed, wind direction, global horizontal irradiance, plane of array irradiance, and active power recorded from an 11 kW Gaia wind turbine and a 10 kW PV inverter. <br> The origin of the data is SYSLAB, part of DTU Elektro. If you want to learn more about the dataset, you should check out [3].<br> <br> You can use the SOLETE dataset with the codes available here: https://doi.org/10.11583/DTU.17040626 <br> The different scripts have various functions. One allows to import SOLETE and show some plots. Another is a platform where you can play with different Machine Learning models for time series forecasting. The application focuses on predicting PV power, but it can be easily edited by the user.<br> <br> The publications related to this item are:<br> <br> [1] D. V. Pombo, H. W. Bindner, S. V. Spataru, P. E. Sørensen, P. Bacher, Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning, Sensors 22 (3) (2022) 749. <br> [2] D.V. Pombo, P. Bacher, C. Ziras, H.W. Bindner, S.V. Spataru, P. Sørensen, Benchmarking Physics-Informed Machine Learning-based Short Term PV-Power Forecasting Tools, Under Review. <br> [3] D.V. Pombo, O.G. Gehrke, H.W. Bindner, (2022). SOLETE, a 15-month long holistic dataset including: Meteorology, co-located wind and solar PV power from. <em>Data in Brief</em>, 108046. <br> ------------------------------------------------------------------------------- <br> To cite this item: @misc{Pombo2022SOLETE, author = "Daniel Vazquez Pombo", title = "{The SOLETE dataset}", year = "2022", month = "Feb", url = "https://data.dtu.dk/articles/dataset/The_SOLETE_dataset/17040767", doi = "10.11583/DTU.17040767", note = {Retrieved from {DTU-Data}, \url{https://data.dtu.dk/articles/dataset/The_SOLETE_dataset/17040767}, {DOI}: {10.11583/DTU.17040767}}, }<br>
DOI: 10.5281/zenodo.8341366
title: Baixada Santista 2020 event analysis - Attribution and Synopsis of Landslide Impacts from Precipitation

description: Dataset used for the Baixada Santista 2020 event analysis during the Attribution and Synopsis of Landslide Impacts from Precipitation (ASLIP) Workshop as part of the Climate Science for Service Partnership Brazil. Notes: impact_data: data from land cover change (retrieved from https://brasil.mapbiomas.org/), socioeconomic impacts (https://s2id.mi.gov.br/) and susceptibility to landslides (https://www.cprm.gov.br/) df_rxmax_climat: data from HadGEM3 (15 members) and CPC for the climatological period of study df_rxmax_event: data from HadGEM3 (525 members) and CPC for the event occurred in March 2020 pluviometer_analysis_historical: data processing scripts and precipitation observational data soilMoistureCPC_data: mean integrated soil moisture data from SMAP and precipitation data from CPC for the studied area