cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/jd8w5r3ncw.1
title: A Comprehensive View-Dependent Dataset for Objects Reconstruction - Kinect Azure Set

description: The dataset was collected by moving the Kinect Azure camera above a set of real objects located on the ArUco board that was used to estimate the camera pose. To estimate the camera poses for the objects from the subset of real objects, we used the ArUco board, which consisted of a grid of 7x5 unique ArUco markers. We placed the object on the ArUco board and utilized the OpenCV library methods to estimate the camera poses located around the object and the marker. For estimating camera poses and creating the 3D model of the objects, we employed an ArUco board featuring a grid of 7x5 distinctive ArUco markers. By situating the object on the ArUco board, we utilized OpenCV library methods to calculate the camera poses positioned both around the object and the markers.
DOI: 10.17632/ks4prkbxws.1
title: Dataset for "Adjusting the pH value of slurry applied to sandy soil can modify the leaching patterns of nutrients and pathogens "

description: The present research data support the findings of the work " Adjusting the pH value of slurry applied to sandy soil can modify the leaching patterns of nutrients and pathogens ". A reader accompanies the dataset where abbreviations and treatments are explained.
DOI: 10.17632/9ts4rvkc5s.1
title: Benchmark dataset for logistic-oriented Bin Packing Problems

description: A benchmark for logistic-oriented bin packing problems is proposed. This dataset is composed of 10 instances comprehending different levels of problem complexity regarding size and user-defined requirements. Instances comprising the 1dBPP, 2dBPP, and 3dBPP are included. Several real-world oriented restrictions have been considered for building these instances: i) item and bin dimensions; ii) weight restrictions; iii) affinities among package categories; iv) preferences for package ordering; v) load balancing; vi) heterogeneous bins; and vii) item-bin associations. The benchmark was first proposed to evaluate quantum solvers, therefore, the characteristics of this set of instances were designed according to the limitations of quantum devices in 2023. Additionally, we include the results obtained for each instance. The data introduced in this benchmark provides a baseline that will encourage quantum computing researchers to work on logistic-oriented bin packing problems.
DOI: 10.17632/4ksvttht8c.1
title: Time series of mesoplastics and microplastics abundance from two sandy beaches in Taiwan

description: This dataset presents microplastics and mesoplastics abundances, shapes and colors from samples collected from two sandy beaches at the northern coast of Taiwan. Samples where collected from Longmen Beach (25.02951°N, 121.9355°E) and from Xialiao Beach (25.21469°N, 121.65406°E). The data set consists of three cvs-files for Longmen Beach and three csv-files for Xialiao Beach. Each of these files contains data about the abundance of particles, shapes, and colors. For a detailed description of sampling location, sampling procedure, plastic particle extraction and counting please refer to: ...

Each table contains following data:
Date_YYYY-MM-DD = sampling date in the form of year-months-day
Country_Region = country or region where samples were collected
Location_name = name of the beach where samples were collected
Location_lat = coordinates of the sampling location (latitude / N)
Location_lon = coordinates of the sampling location (longitude / E)
Transect = A letter A, B, C for each of the three transects. Transects are 1 m apart from each other and run perpendicular to the water line starting in the dunes and ending at the intertidal.
Position = 50 x 50 cm quadrat from which the samples were taken. Numbering starts in the dunes with 1 and ends at the intertidal.
Size_min_mm = lower particle size in mm
Size_max_mm = upper particle size in mm
Size_class = name of the size class for plastic pieces (microplastics or mesoplastics)
Particle_count = number of plastic particles in a sample (only in file "... particle count.csv")
Particle_weight_g = weight of the plastic particles found in a sample (only in file "... particle count.csv")
Weight_dry_sand_g = dry weight of the sand before plastic particles were extracted (only in file "... particle count.csv")
Beach_Zone = beach zone in which the sample was collected (only in file "... particle count.csv")

Shapes were classified into following categories: fragment, foamed_plastic, pellet, foil, fiber, fishing_line, cigarette_butt, rope, rubber, fabric, unclear.

Colors were classified into following: no_color, black, grey, red_pink, orange_brown_yellow, green, blue, purple
DOI: 10.17632/4ksvttht8c.2
title: Time series of mesoplastics and microplastics abundance from two sandy beaches in Taiwan

description: This dataset presents microplastics and mesoplastics abundances, shapes and colors from samples collected from two sandy beaches at the northern coast of Taiwan. Samples where collected from Longmen Beach (25.02951°N, 121.9355°E) and from Xialiao Beach (25.21469°N, 121.65406°E). The data set consists of three cvs-files for Longmen Beach and three csv-files for Xialiao Beach. Each of these files contains data about the abundance of particles, shapes, and colors. For a detailed description of sampling location, sampling procedure, plastic particle extraction and counting please refer to: ...

Each table contains following data:
Date_YYYY-MM-DD = sampling date in the form of year-months-day
Country_Region = country or region where samples were collected
Location_name = name of the beach where samples were collected
Location_lat = coordinates of the sampling location (latitude / N)
Location_lon = coordinates of the sampling location (longitude / E)
Transect = A letter A, B, C for each of the three transects. Transects are 1 m apart from each other and run perpendicular to the water line starting in the dunes and ending at the intertidal.
Position = 50 x 50 cm quadrat from which the samples were taken. Numbering starts in the dunes with 1 and ends at the intertidal.
Size_min_mm = lower particle size in mm
Size_max_mm = upper particle size in mm
Size_class = name of the size class for plastic pieces (microplastics or mesoplastics)
Particle_count = number of plastic particles in a sample (only in file "... particle count.csv")
Particle_weight_g = weight of the plastic particles found in a sample (only in file "... particle count.csv")
Weight_dry_sand_g = dry weight of the sand before plastic particles were extracted (only in file "... particle count.csv")
Beach_Zone = beach zone in which the sample was collected (only in file "... particle count.csv")

Shapes were classified into following categories: fragment, foamed_plastic, pellet, foil, fiber, fishing_line, cigarette_butt, rope, rubber, fabric, unclear.

Colors were classified into following: no_color, black, grey, red_pink, orange_brown_yellow, green, blue, purple
DOI: 10.17632/6sx9fjbkrd.1
title: LINF_180009400

description: phosphatidic acid phosphatase; Leishmania infantum (strain JPCM5)
DOI: 10.17632/stz3xzt98n.1
title: Data for: Source variations in volatile contents of Bransfield Strait back-arc and Phoenix/West Scotia mid-ocean ridge lavas, northern Antarctic Peninsula

description: Supplementary data for the article titled "Source variations in volatile contents of Bransfield Strait back-arc and Phoenix/West Scotia mid-ocean ridge lavas, northern Antarctic Peninsula," by Anderson et al. to be submitted to GCA. The tables include major/trace/volatile element data and radiogenic isotope ratios for glassy submarine lavas from across the northern Antarctic Peninsula.
DOI: 10.17632/xxrsfrk5k6.1
title: LINF_180014000

description: protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/j2w6b4w7jb.1
title: LINF_180009900

description: BTB/POZ domain containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/tmjt3rnhm3.1
title: UN Biodiversity Social Network Analysis

description: This research sought to analyse the United Nations' network of System-wide programmes on biodiversity in order to identify; (1) the influential UN entities within this system, (2) how different UN entities work together on biodiversity programmes, and (3) how this structure impact the function of the network to support global biodiversity outcomes. Utilising data collected from a 2017 UN-wide survey and systematic review of UN databases conducted in 2021; we compiled a database of 124 programmes related to biodiversity which involved multiple UN entities working together. Each programme was then categorised into a different type of partnership; co-authorship, collaborative partnership or coordinated activity. Using this data, we created a network of UN-wide biodiversity partnerships where the “nodes” were individual UN entities and the “edges” were biodiversity partnerships. An analysis of this network revealed that these system-wide biodiversity programmes are centralised and dense, highlighting the critical role of UNEP, FAO and UNDP as lead actors and bridge-builders. We detail how these network characteristics can have both supportive and opposing influences on the function of collaborations and coordination efforts and highlight the potential barriers facing the UN’s system-wide work in supporting member states within this final decade of action on biodiversity.
DOI: 10.17632/5btz7hh399.1
title: Radiative view factor correlations in particulate media from ray tracing simulations and data-driven modeling

description: Datasets for figures included in the results section in our recently accepted publication at International Journal of Heat and Mass Transfer: https://doi.org/10.1016/j.ijheatmasstransfer.2023.124250
DOI: 10.17632/khywg2h38x.3
title: Cold-induced adaptive thermogenesis is impaired by exposure of Asian sand dust in mice

description: The following files contain the raw data to the related paper "Cold-induced adaptive thermogenesis is impaired by exposure of Asian sand dust in mice."
DOI: 10.34740/kaggle/dsv/10525426
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.17632/hy9wnbhr9w.1
title: A Comprehensive View-Dependent Dataset for Objects Reconstruction - Synthetic Set Part A

description: Data were collected in the custom simulator that loads random graspable objects from the ShapeNet dataset and random table. The graspable object is placed above the table in a random position. Then, the scene is simulated using the PhysX engine to make sure that the scene is physically plausible. The simulator captures images of the scene from a random pose and then takes the second image from the camera pose that is on the opposite side of the scene. It contains RGB, depth, segmentation images of the scenes and objects on the scene and information about the camera poses that can be used to create a full 3D model of the scene and develop methods that reconstruct objects from a single RGB-D camera view. Part A of the dataset contains four categories: a helmet, a jar, a laptop, and a mug.
DOI: 10.17632/stz3xzt98n.2
title: Data for: Source variations in volatile contents of Bransfield Strait back-arc and Phoenix/West Scotia mid-ocean ridge lavas, northern Antarctic Peninsula

description: Supplementary data for the article titled "Source variations in volatile contents of Bransfield Strait back-arc and Phoenix/West Scotia mid-ocean ridge lavas, northern Antarctic Peninsula," by Anderson et al. (in press) submitted to Chemical Geology. The tables include major/trace/volatile element data and radiogenic isotope ratios for glassy submarine lavas from across the northern Antarctic Peninsula.
DOI: 10.17632/hy9wnbhr9w.2
title: A Comprehensive View-Dependent Dataset for Objects Reconstruction - Synthetic Set Part B

description: Data were collected in the custom simulator that loads random graspable objects from the ShapeNet dataset and random table. The graspable object is placed above the table in a random position. Then, the scene is simulated using the PhysX engine to make sure that the scene is physically plausible. The simulator captures images of the scene from a random pose and then takes the second image from the camera pose that is on the opposite side of the scene. It contains RGB, depth, segmentation images of the scenes and objects on the scene and information about the camera poses that can be used to create a full 3D model of the scene and develop methods that reconstruct objects from a single RGB-D camera view. Part A of the dataset contains four categories: a helmet, a jar, a laptop, and a mug.
DOI: 10.17632/3ncbhdd4nw.2
title: Mobility, Kinematic, and Dynamic Analysis of a 4R1H Mechanism. Julia Files

description: This dataset contains Julia files that perform mobility, kinematic, and dynamic analysis of a 1-DOF single-loop 4R1H mechanism, which is similar to the mechanism presented in paper https://doi.org/10.1007/978-3-031-45709-8_39. The corresponding paper describing the mechanism design and applied algorithms is going to be published in the Robotica journal. Please check the list of works at https://orcid.org/0000-0002-3928-5440.

The dataset includes the following files:
1. File "main.jl" is the main file that proceeds with the mobility, kinematic, and dynamic analysis of the mechanism step by step.
2. File "init.jl" is used to define geometrical and inertial parameters of the mechanism links.
3. File "mobility.jl" computes a twist system of the output link.
4. File "kinematics.jl" computes kinematic parameters (positions, velocities, accelerations, etc.) of the links.
5. File "dynamics.jl" computes the motor torque required to realize the desired motion.
6. File "plots.jl" includes functions that visualize the results of the kinematic and dynamic analysis.

The second version of the dataset has the following changes compared to the first version:
1. There are two new files "torque_friction.xlsx" and "torque_no_friction.xlsx," which contain the torque data obtained with CAD simulations in the Autodesk Inventor software.
2. There is a new section in "main.jl" file titled "Importing CAD data for the motor torque," where the CAD data is compared with the analytical solution.
3. The "plots.jl" file includes new function "plot_torques," which plots the torque values computed by different methods.
4. Names of some variables have been modified in accordance with the notations used in the article.
DOI: 10.17632/b7x55v2knk.3
title: Data of compressible multi-material flow simulations utilizing an efficient bimaterial Riemann problem solver

description: The data set is associated with two test cases in the research article:

W. Ma, X. Zhao, S. Islam, A. Narkhede, K. Wang, Efficient solution of bimaterial Riemann problems for compressible multi-material flow simulations, Journal of Computational Physics, Volume 493, 2023, 112474.

This article significantly accelerates multi-material flow simulations featuring arbitrary complex equations of state (EOS) and strong discontinuity across material interfaces, by developing an efficient bimaterial Riemann problem solver. The first test case is a one-dimensional benchmark problem and the second one is about the nucleation and expansion of a pear-shaped bubble induced by a long-pulse laser. The simulations are performed using the M2C solver, which is a three-dimensional finite volume Navier-Stokes computational fluid dynamics code, utilizing the accelerated bimaterial Riemann solver at material interfaces. Source codes provided in this data set include the M2C solver and a standalone version of the accelerated bimaterial Riemann problem solver. Simulation data provided include fluid pressure, velocity, density, laser radiance and bubble dynamics. The input files to perform the simulations are also provided. Researchers can replicate simulation results and use them as a foundation for exploring a range of related topics, such as laser-induced cavitation, bubble dynamics, and multiphase flow in general.
DOI: 10.5281/zenodo.12593794
title: Topology Bench: Systematic Graph Based Benchmarking for Optical Networks

description: TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical analysis to provide a systematic methodology for selecting diverse sets of optical networks for benchmarking. This topology benchmark is comprised of a network dataset and a systematic graph theoretic analysis. The dataset provides (a) 105 real optical networks and (b) synthetic topologies, generated by the SNR-BA model, divided into (i) Syn-small of 900 synthetic networks and (ii) Syn-large of 270,000 synthetic networks. The systematic graph theoretical analysis identifies and analyses structural, spatial and spectral properties of both the real world and synthetic networks. The graph theoretical correlation analysis reveal network design strategies leading to sparse yet efficient networks. An outlier analysis identifies networks that deviate from standard network designs. The analysis also identifies the limitations of real data in terms of network diversity and provides a justification for using synthetic data to complement the real dataset. We conclude the paper by providing a systematic methodology to cluster networks based on unsupervised machine learning and to select a diverse set of topologies for benchmarking. TopologyBench is a novel, high-quality and unified benchmark designed to facilitate research collaborations in long-haul fibre infrastructure by providing a systematic graph theoretical approach to benchmarking optical networks.

 

If you use any of the data provided, please cite our paper:

 

@article{matzner2024topology,
  title={Topology Bench: systematic graph-based benchmarking for core optical networks},
  author={Matzner, Robin and Ahuja, Akanksha and Sadeghi, Rasoul and Doherty, Michael and Beghelli, Alejandra and Savory, Seb J and Bayvel, Polina},
  journal={Journal of Optical Communications and Networking},
  volume={17},
  number={1},
  pages={7--27},
  year={2024},
  publisher={IEEE}
}
DOI: 10.5281/zenodo.13921775
title: Topology Bench: Systematic Graph Based Benchmarking for Optical Networks

description: TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical analysis to provide a systematic methodology for selecting diverse sets of optical networks for benchmarking. This topology benchmark is comprised of a network dataset and a systematic graph theoretic analysis. The dataset provides (a) 105 real optical networks and (b) synthetic topologies, generated by the SNR-BA model, divided into (i) Syn-small of 900 synthetic networks and (ii) Syn-large of 270,000 synthetic networks. The systematic graph theoretical analysis identifies and analyses structural, spatial and spectral properties of both the real world and synthetic networks. The graph theoretical correlation analysis reveal network design strategies leading to sparse yet efficient networks. An outlier analysis identifies networks that deviate from standard network designs. The analysis also identifies the limitations of real data in terms of network diversity and provides a justification for using synthetic data to complement the real dataset. We conclude the paper by providing a systematic methodology to cluster networks based on unsupervised machine learning and to select a diverse set of topologies for benchmarking. TopologyBench is a novel, high-quality and unified benchmark designed to facilitate research collaborations in long-haul fibre infrastructure by providing a systematic graph theoretical approach to benchmarking optical networks.

 

If you use any of the data provided, please cite our paper:

 

@article{matzner2024topology,
  title={Topology Bench: systematic graph-based benchmarking for core optical networks},
  author={Matzner, Robin and Ahuja, Akanksha and Sadeghi, Rasoul and Doherty, Michael and Beghelli, Alejandra and Savory, Seb J and Bayvel, Polina},
  journal={Journal of Optical Communications and Networking},
  volume={17},
  number={1},
  pages={7--27},
  year={2024},
  publisher={IEEE}
}
DOI: 10.17632/cvrdjg4238.1
title: LINF_180017200

description: NADH-ubiquinone oxidoreductase complex I subunit | NDUFA8 ortholog; Leishmania infantum (strain JPCM5)
DOI: 10.17632/xbfxw9755w.1
title: LINF_180016500

description: Multisite-specific tRNA:(cytosine-C(5))-methyltransferase; Leishmania infantum (strain JPCM5)
DOI: 10.17632/9kbb726ptx.1
title: LINF_180014700

description: Nrap protein; Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.wyaur4
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is The Coleoptera collection (EC) of the Muséum national d'Histoire naturelle (MNHN - Paris)",
 "Geometry POLYGON((-61.47513 16.56077,-61.52015 16.52352,-61.53102 16.43504,-61.57914 16.30464,-61.56827 16.19909,-61.44875 16.1727,-61.17089 16.24876,-61.1802 16.34966,-61.28886 16.42107,-61.47513 16.56077))"
 ]
}

 The dataset includes 10 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004211-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rgmwv7w2bf.1
title: Data on Consumer Behavior in The Context of Sports Marketing to Football Fans in Indonesia

description: This data set provides data related to measuring consumer behavior in the context of sports marketing on football fans in the Indonesia Premier League. The survey was conducted online using a Google form with a Likert scale. Questions in the questionnaire include marketing variables represented by brand commitment (12 questions), brand trust (4 questions), Brand Satisfaction (8 questions), Brand Loyalty (3 questions), and Brand Attachment (4 questions). The survey was conducted in June-September 2022. A total of 258 football fans across Indonesia were selected using non-probability sampling techniques. Survey data is analyzed using Structural Equation Modelling (SEM) using Smart PLS software to identify estimates of primary construction relationships in the data. The data can help football club managers and business operators in the field of football sports to map and plan marketing strategies for organizational development and gain valuable economic benefits.