cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28120592
title: GCC Universities Sport and Wellness Symposium 2024 Survey Data

description: Two surveys were utilized to collect data:· <b>Speaker and Panelist Survey:</b> Distributed to symposium speakers and <b><i>panelists</i></b> <b><i>post</i></b> the event, this survey aimed to gather consensus on key topics addressed.· <b>Participant Survey:</b> Developed based on the results of the initial survey, this post-event survey was distributed to all symposium attendees to gauge broader agreement on discussed themes and recommendations.
DOI: 10.6084/m9.figshare.28877003
title: YOLO and IMU data.

description: Data collected by onboard YOLO and IMU.
DOI: 10.5281/zenodo.15289865
title: dBZ_Nida
DOI: 10.5281/zenodo.15289866
title: dBZ_Nida
DOI: 10.21227/zrp3-6f30
title: A Badminton Stroke Type Recognition Model based on Machine Learning

description: The article Aims to compare the application effect of different machine learning algorithms in the recognition of badminton stroke types, so as to better meet the needs of quantitative analysis of badminton skills and tactics, and provide a more reasonable basis for the layout of game strategies. We used wearable inertial sensor device on the wrist to collect a total of 708 shots of linear acceleration and angular velocity data, and extract the domain features including data range, minimum maximum, average, absolute average, kurtosis, skewness statistics and P value, entropy, standard deviation, Angle range, quartile spacing, maximum minimum relative position. Then, we labeled each hit type data manually, total divided into the forehand overarm, backhand overarm, forehand underarm, backhand underarm, forehand smash. The results show that: (1) K-nearest neighbors and k-means clustering show poor prediction accuracy and application accuracy; (2) Logistic regression shows great accuracy in recognizing the overarm stroke, while random forest performs relatively well in recognizing underarm stroke; (3) The angular velocity data of the gyroscope is the characteristic that affects the classification recognition effect, and the difference in stroke type shown in wrist is mainly the angular velocity change.
DOI: 10.6084/m9.figshare.28391357
title: Phenotypic and genotypic (PACE) data of the validation panel

description: Genotypic data were determined with PACE assay followed by analysis with FitTetra. Values from the "geno" coloumn of the FitTetra file is shown here, together with score data for phenotypic traits.
DOI: 10.5281/zenodo.15267419
title: Groundwater Sapping in a Martian Crater: Geologic Evidence and Geophysical Modeling of Groundwater Stability

description: This archive contains data files supporting the study "Groundwater Sapping in a Martian Crater: Geologic Evidence and Geophysical Modeling of Groundwater Stability" by Barbara De Toffoli, Ana-Catalina Plesa, Erica Luzzi, Ernst Hauber submitted to Journal of Geophysical Research: Planets.
DOI: 10.5281/zenodo.15267420
title: Groundwater Sapping in a Martian Crater: Geologic Evidence and Geophysical Modeling of Groundwater Stability

description: This archive contains data files supporting the study "Groundwater Sapping in a Martian Crater: Geologic Evidence and Geophysical Modeling of Groundwater Stability" by Barbara De Toffoli, Ana-Catalina Plesa, Erica Luzzi, Ernst Hauber submitted to Journal of Geophysical Research: Planets.
DOI: 10.6084/m9.figshare.28388243
title: Phenotypic data of the association panel

description: Means of phenotypic data of the association panel used for GWAS and the independent validation panel. Genotypes beginning with AP belong to the old garden rose panel, AS to the new garden rose panel and CRP to the cut rose panel. Phenotypic data for the validation panel is provided by the Federal Plant Variety Office (Hanover, Germany).
DOI: 10.6084/m9.figshare.28371956
title: chloris_alignment.log.txt

description: Alignment file
DOI: 10.6084/m9.figshare.28371956.v1
title: chloris_alignment.log.txt

description: Alignment file
DOI: 10.6084/m9.figshare.28358720
title: Genotypic data of the garden rose association panel (2023).

description: Genotypic data of the garden rose panel (2023). WagRhSNP Chip and fitTetra were used for analysis. SNP data was related to the <i>Rosa chinensis</i> Genome v1.0 (Hibrand Saint-Oyant et al., 2018).
DOI: 10.34740/kaggle/dsv/11585493
title: World Weather Repository ( Daily Updating )
DOI: 10.6084/m9.figshare.28371959
title: chloris_alignment.support.tre

description: Tree file
DOI: 10.6084/m9.figshare.28371959.v1
title: chloris_alignment.support.tre

description: Tree file
DOI: 10.6084/m9.figshare.28371698
title: chloris_alignment.fas

description: Alignment file
DOI: 10.6084/m9.figshare.28371698.v1
title: chloris_alignment.fas

description: Alignment file
DOI: 10.34740/kaggle/dsv/11585519
title: World Stock Prices ( Daily Updating )
DOI: 10.5281/zenodo.14970381
title: Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence

description: Dataset containing pictures of  potato plants grown in vitro used for validation of our AMULET pipeline, which is publiced in the article titled "Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence". The codes and rest of materials for AMULET pipeline can be found in the following link, GitHub: https://github.com/JZdrazilX/DigGreen/tree/main
DOI: 10.5281/zenodo.14970380
title: Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence

description: Dataset containing pictures of  potato plants grown in vitro used for validation of our AMULET pipeline, which is publiced in the article titled "Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence". The codes and rest of materials for AMULET pipeline can be found in the following link, GitHub: https://github.com/JZdrazilX/DigGreen/tree/main
DOI: 10.5281/zenodo.15289909
title: Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence

description: Dataset containing pictures of  potato plants grown in vitro used for validation of our AMULET pipeline, which is publiced in the article titled "Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence". The codes and rest of materials for AMULET pipeline can be found in the following link, GitHub: https://github.com/JZdrazilX/DigGreen/tree/main
DOI: 10.5281/zenodo.15289901
title: Dataset for JASIST paper "Finding 'similar' universities using ChatGPT for institutional benchmarking: A large-scale comparison of European universities"

description: This dataset contains the HEIs proposed by Chat GPT as benchmarks for all PhD-awarding HEIs in the European Tertiary Education Register (https://national-policies.eacea.ec.europa.eu/eheso/micro-data-access) as described in the quoted paper fortchoming in JASIST. Data are provided for the three queries described in the paper.

Fields are as follows:

eterid: the ETER id focal HEI.

peer-eterid: the ETER id of the proposed peer.

peer_original_name: the peer name as provided by Chat GPT.

reasoning: the reasoning proposed for the similarity.

notes: specific remarks, such as the peer being in reality a component of an HEI.

The queries were performed in September 2024 using GTP-4.
DOI: 10.5281/zenodo.15289902
title: Dataset for JASIST paper "Finding 'similar' universities using ChatGPT for institutional benchmarking: A large-scale comparison of European universities"

description: This dataset contains the HEIs proposed by Chat GPT as benchmarks for all PhD-awarding HEIs in the European Tertiary Education Register (https://national-policies.eacea.ec.europa.eu/eheso/micro-data-access) as described in the quoted paper fortchoming in JASIST. Data are provided for the three queries described in the paper.

Fields are as follows:

eterid: the ETER id focal HEI.

peer-eterid: the ETER id of the proposed peer.

peer_original_name: the peer name as provided by Chat GPT.

reasoning: the reasoning proposed for the similarity.

notes: specific remarks, such as the peer being in reality a component of an HEI.

The queries were performed in September 2024 using GTP-4.
DOI: 10.48577/jpl.7zctlj
title: Juno All-Sky Map Release

description: We present six nearly full-sky maps made from data taken by radiometers on the Juno satellite during its 5-year flight to Jupiter. The maps represent integrated emission over ∼ 4% passbands spaced approximately in octaves between 600 MHz and 21.9 GHz. Long time-scale offset drifts are removed in all bands, and, for the two lowest frequency bands, gain drifts are also removed from the maps via a self-calibration algorithm similar to the NPIPE pipeline used by the Planck collaboration. We show that, after this solution is applied, residual noise in the maps is consistent with thermal radiometer noise. We verify our map solutions with several consistency tests and end-to-end simulations. We also estimate the level of pixelization error and polarization leakage via simulations.
DOI: 10.48577/jpl.uasz2m
title: Risk-Informed Decision Making - An Introduction

description: No abstract available.
DOI: 10.48577/jpl.lbwl0c
title: Embedding Runtime Verification in Python

description: Runtime verification consists of verifying that an execution trace satisfies a specification. The specification can be formal, but it can also be informal, e.g. code in a general purpose programming language. In this paper we present the tool PyDejaVu, which explores the boundary between general purpose programming and formal specification. The tool supports a two-phase approach to writing properties, where a property can be expressed in a combination of an operational phase and a declarative phase. The operational phase is expressed in an internal Python DSL (Domain-Specific Language), whereas the declarative phase is expressed in the external DSL Qtl (Quantified Temporal Logic) for first-order past time temporal logic. This approach benefits from the expressiveness of Python and the succinctness and efficiency of monitoring Qtl. The tool builds on the previous runtime verification tool DejaVu monitoring Qtl properties, and is compared with the previous TP-DejaVu tool, which supports a purely external DSL for two-phase runtime verification.
DOI: 10.48577/jpl.angrb8
title: Pressure, Temperature, and Water Vapor Dependencies of the Bimolecular Rate Coefficients for the Reaction OH+NO+M→ HONO+M

description: OH+NO is an important termolecular association reaction in the troposphere and stratosphere that influences the atmospheric ozone budget. In this study, rate coefficients for the reaction of OH+NO+M→HONO+M were measured under conditions relevant to the troposphere/lower stratosphere over a temperature range of 228 – 298 K and pressure range of 50 – 750 Torr using N2 as a bath gas. Time-resolved kinetics were studied by pulsed laser photolysis-laser induced fluorescence (PLP-LIF) detecting OH by Laser-Induced Fluorescence. Data for the temperature range 258 – 298 K were fit to two fall-off expressions, with the JPL expressions (k_1,0^(N_2 )=7.37×10-31(T/300 K)-2.90 cm6 molecule-2 s-1 and k1,∞ = 3.44×10-11(T/300 K)-0.1cm3 molecule-1 s-1 and IUPAC expression (k_1,0^(N_2 )= 6.80 ×10-31(T/300 K)-2.81 cm6 molecule-2 s-1, FC = 0.81 , k1,∞= 1.96×10-11(T/300 K)-0.3 cm3 molecule-1 s-1). At temperatures T &lt; 258 K, the measured rate coefficients were significantly higher than the IUPAC and JPL fits. To accommodate the rate coefficient deviation from the two expressions, data across the entire temperature range (228 – 298 K) was fit with two approaches. First, rate constants were fit with an empirical modification by adding a second fall-off term to the JPL expression with a second low-pressure rate coefficient of k_1,0^(N_2 )=5.20 ×10-35(T/300K)-30.4 cm6 molecule-2 s-1. Second, k_1,0^(N_2 ), k1,∞, and n were fit globally to the entire temperature dataset, but FC was varied for each individual temperature, which increased with decreasing temperature. In the second portion of the study, the influence of H2O on the reaction rate was investigated using a N2 – H2O mixture as the bath gas at 50 Torr and 273 and 298 K. The JPL and IUPAC fall-off expressions were modified to include H2O as a third-body collisional partner consistent with a nonlinear mixture model. Fits to the data yielded the low pressure termolecular rate coefficients in H2O, k_1,0^(H_2 O )= 3.81×10-30(T/300 K)-6.04 and k_1,0^(H_2 O )= 3.31×10-30(T/300 K)-5.81 cm6 molecule-2 s-1, respectively. Experimental data were fit using MESMER give energy relaxation parameters of &lt;Edown,295 K, N2&gt; = 170 ± 10 cm-1 and &lt;Edown,295 K,H2O&gt; = 634 ± 20 cm-1, indicating that H2O is a 4× more efficient collisional quencher than N2 alone. The modified JPL expressions with the newly derived low pressure rate coefficients were implemented into a STOCHEM-CRI atmospheric model. Predictions of HONO concentrations with the new rates were up to 15% higher in remote tropical regions.
DOI: 10.48577/jpl.y037kv
title: Panel on science center coordination for Rubin, Roman, and Euclid

description: No abstract available.
DOI: 10.48577/jpl.bvw2iw
title: TOBIAS NIAC Orientation Poster

description: No abstract available.
DOI: 10.48577/jpl.m0gz4e
title: Geospatial Operations for Missions

description: No abstract available.
DOI: 10.48577/jpl.hm0be9
title: PULSAR: Planetary pULSe-tAkeR presentation

description: No abstract available.
DOI: 10.6084/m9.figshare.28876733
title: <b>Data of DLBS3233 improve insulin resistance through activation of proliferator-activated receptor gamma (PPARγ) and increases nephrin expression in diabetic rats podocytes</b>

description: <b>Figures, Tables, Images, Statistic Results</b>
DOI: 10.6084/m9.figshare.28876733.v1
title: <b>Data of DLBS3233 improve insulin resistance through activation of proliferator-activated receptor gamma (PPARγ) and increases nephrin expression in diabetic rats podocytes</b>

description: <b>Figures, Tables, Images, Statistic Results</b>
DOI: 10.6084/m9.figshare.28876733.v2
title: <b>Data of DLBS3233 improve insulin resistance through activation of proliferator-activated receptor gamma (PPARγ) and increases nephrin expression in diabetic rats podocytes</b>

description: <b>Figures, Tables, Images, Statistic Results</b>
DOI: 10.48577/jpl.docpax
title: Lightening Talk

description: No abstract available.
DOI: 10.48577/jpl.l1y5qj
title: AI/ML and UQ in Systems Engineering

description: No abstract available.
DOI: 10.48577/jpl.vf4p2i
title: Monitoring the Global Carbon Cycle with the Orbiting Carbon Observatory (OCO-2 and OCO-3) missions

description: No abstract available.
DOI: 10.48577/jpl.oxhie6
title: Use of simulations in Mars 2020 Entry, Descent, and Landing

description: No abstract available.
DOI: 10.48577/jpl.ly3hgf
title: Mars Sample Return Talk

description: No abstract available.
DOI: 10.48577/jpl.xzvcvc
title: Accurate species classification of Arctic toothed whale echolocation clicks using one-third octave ratios

description: No abstract available.
DOI: 10.48577/jpl.ojpoh4
title: Kiladze Caldera: A Possible Cryovolcano on Pluto

description: No abstract available.
DOI: 10.48577/jpl.apbkwt
title: Estimating Effective Resolution of CYGNSS Level-1 Data Products over Inland Waters using Multi-Sensor Optical Data

description: No abstract available.
DOI: 10.48577/jpl.ynl5hs
title: OCO-2/3 Calibration Report

description: No abstract available.
DOI: 10.48577/jpl.pv3wxc
title: Experimental Demonstration of Bistatic UAV-Borne SAR and InSAR

description: No abstract available.
DOI: 10.48577/jpl.dkwiy4
title: Where am I? Unlocking the Power of Geodesy with International Collaborations

description: No abstract available.
DOI: 10.48577/jpl.sltof6
title: Inland Waterbody Identification Using Sentinel-1 SAR Data

description: No abstract available.
DOI: 10.48577/jpl.svkp5x
title: SMAP/SMOS Integrated Science Data Product

description: No abstract available.
DOI: 10.48577/jpl.putj6k
title: Detecting Slow-Moving Landslides in South America using InSAR and Convolutional Neural Networks

description: No abstract available.
DOI: 10.48577/jpl.9q1vcg
title: Mars Sample Return Planetary Protection

description: No abstract available.
DOI: 10.48577/jpl.fewjtc
title: Autonomous Driving of Perseverance

description: No abstract available.
DOI: 10.5281/zenodo.15278639
title: TAIN: Results for SC'25

description: Input files and result files.
DOI: 10.5281/zenodo.15278640
title: TAIN: Results for SC'25

description: Input files and result files.
DOI: 10.34740/kaggle/dsv/11585683
title: Hacker News Cumulative Data
DOI: 10.57760/sciencedb.14527
title: Obesity modulates cell-cell interactions during ovarian folliculogenesis
title: Obesity modulates cell-cell interactions during ovarian folliculogenesis

description: Somatic cells and oocytes in the ovary were isolated from RD, Lep KO, Lepr KO or HFD mice, and were subjected to scRNA-seq.
description: Somatic cells and oocytes in the ovary were isolated from RD, Lep KO, Lepr KO or HFD mice, and were subjected to scRNA-seq.
DOI: 10.57760/sciencedb.01002
title: How to use the ScienceDB Data Bank
title: 科学数据银行操作步骤指导

description: How to use the ScienceDB Data Bank
description: How to use the ScienceDB Data Bank
DOI: 10.57760/sciencedb.01674
title: A single-object version of the ImageNet2012 dataset
title: A single-object version of the ImageNet2012 dataset

description: The single-object version of the ImageNet2012 dataset was prepared to examine if background of objects affect performance of the DCNNs. The dataset includes 544,546 images in the training dataset and 50,000 images in the validation dataset. We removed the background of each image by setting pixels outside the bounding box to 255 (i.e., white color). For images containing multiple bounding boxes, we randomly selected one bounding box as our target. Note that the retinal size of objects remained unchanged with only the background was removed from the original images. All images belong to 1,000 categories, which are the same as the original ImageNet2012 dataset.
description: The single-object version of the ImageNet2012 dataset was prepared to examine if background of objects affect performance of the DCNNs. The dataset includes 544,546 images in the training dataset and 50,000 images in the validation dataset. We removed the background of each image by setting pixels outside the bounding box to 255 (i.e., white color). For images containing multiple bounding boxes, we randomly selected one bounding box as our target. Note that the retinal size of objects remained unchanged with only the background was removed from the original images. All images belong to 1,000 categories, which are the same as the original ImageNet2012 dataset.
DOI: 10.57760/sciencedb.01675
title: Effects of lithology on asymbiotic N2 fixation in subtropical secondary forests, southwest China
title: Effects of lithology on asymbiotic N2 fixation in subtropical secondary forests, southwest China

description: Asymbiotic N2 fixation (ANF) is a major nitrogen (N) input pathway to terrestrial ecosystems. However, there is considerable uncertainty about how lithology affects ANF. Here we measured ANF rates in soil, litter, and moss in forests underlain by limestone (ForestLim), dolomite (ForestDol), and clasolite (ForestCla), respectively, in southwest China. Effects of lithology on soil ANF rate varied seasonally. The rate was highest in ForestDol during the wet season, but was highest in ForestLim during the dry season. The overall soil ANF rate was significantly higher in ForestLim than in ForestCla. Litter ANF rate was significantly higher in ForestLim and ForestDol than in ForestCla regardless of season. There was no significant difference in moss ANF rate among the three types of forests in both seasons. The annual N2 fixation rate was highest in ForestLim (1.72 ± 0.27 kg N ha-1 yr-1) but lowest in ForestCla (0.70 ± 0.08 kg N ha-1 yr-1). Stepwise multiple linear regression analyses revealed that the strongest explanatory factors were soil available phosphorus, ammonium and moisture for soil ANF; and were nitrogen and moisture for litter ANF. Our results suggest that lithology differentially affected ANF in soil, litter, and moss, and hence should be considered in Earth system models to facilitate better prediction of N inputs via biological N2 fixation under global change.
description: Asymbiotic N2 fixation (ANF) is a major nitrogen (N) input pathway to terrestrial ecosystems. However, there is considerable uncertainty about how lithology affects ANF. Here we measured ANF rates in soil, litter, and moss in forests underlain by limestone (ForestLim), dolomite (ForestDol), and clasolite (ForestCla), respectively, in southwest China. Effects of lithology on soil ANF rate varied seasonally. The rate was highest in ForestDol during the wet season, but was highest in ForestLim during the dry season. The overall soil ANF rate was significantly higher in ForestLim than in ForestCla. Litter ANF rate was significantly higher in ForestLim and ForestDol than in ForestCla regardless of season. There was no significant difference in moss ANF rate among the three types of forests in both seasons. The annual N2 fixation rate was highest in ForestLim (1.72 ± 0.27 kg N ha-1 yr-1) but lowest in ForestCla (0.70 ± 0.08 kg N ha-1 yr-1). Stepwise multiple linear regression analyses revealed that the strongest explanatory factors were soil available phosphorus, ammonium and moisture for soil ANF; and were nitrogen and moisture for litter ANF. Our results suggest that lithology differentially affected ANF in soil, litter, and moss, and hence should be considered in Earth system models to facilitate better prediction of N inputs via biological N2 fixation under global change.
DOI: 10.57760/sciencedb.01676
title: Encrypted Web Traffic Dataset: Event Logs and Packet Traces
title: Encrypted Web Traffic Dataset: Event Logs and Packet Traces

description: Encrypted Web Traffic Dataset: Event Logs and Packet TracesThis repository contains the dataset and supplementary materials for the following paper submitted to the Data in Brief journal:Stanislav Špaček, Petr Velan, Pavel Čeleda and Daniel Tovarňák. Encrypted Web Traffic Dataset: Event Logs and Packet Traces.StructureAnonymization - the anonymization folder contains the scripts and settings that were used to anonymize the data capture.Dataset - the dataset folder contains the host-based and network parts of the dataset in two separate files.Pcap2flow - the pcap2flow folder contains the tools, settings, and guide to convert the packet dump in the dataset into aggregated IP flows.This work is licensed under a Creative Commons Attribution 4.0 International License.
description: Encrypted Web Traffic Dataset: Event Logs and Packet TracesThis repository contains the dataset and supplementary materials for the following paper submitted to the Data in Brief journal:Stanislav Špaček, Petr Velan, Pavel Čeleda and Daniel Tovarňák. Encrypted Web Traffic Dataset: Event Logs and Packet Traces.StructureAnonymization - the anonymization folder contains the scripts and settings that were used to anonymize the data capture.Dataset - the dataset folder contains the host-based and network parts of the dataset in two separate files.Pcap2flow - the pcap2flow folder contains the tools, settings, and guide to convert the packet dump in the dataset into aggregated IP flows.This work is licensed under a Creative Commons Attribution 4.0 International License.
DOI: 10.57760/sciencedb.01680
title: cotton soil microbia
title: cotton soil microbia

description: transgenic cotton soil microbial sequences of 16s and ITS.
description: transgenic cotton soil microbial sequences of 16s and ITS.