cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18465/d27228bf-7baf-444c-81d5-07e643fde2a5
title: MNS Guérinière 2023

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau de la Guérinière
DOI: 10.5281/zenodo.10843105
title: Data referring to the Article "Harmonized Skies: A Survey on Drone Acceptance across Europe" published in Drones Journal

description: The material provided is part of the article "Harmonized Skies: A Survey on Drone Acceptance across Europe," published in the Drones Journal (https://doi.org/10.3390/drones8030107). This article describes a study investigating civil drone acceptance in six different EU countries. This study was part of the USpace4UAM project (Grant Agreement No 101017643). The material includes the data set for the study described and the Python codes for the random forest analysis carried out to investigate the influence of demographic and personnel factors on drone acceptance.
DOI: 10.5281/zenodo.10843106
title: Data referring to the Article "Harmonized Skies: A Survey on Drone Acceptance across Europe" published in Drones Journal

description: The material provided is part of the article "Harmonized Skies: A Survey on Drone Acceptance across Europe," published in the Drones Journal (https://doi.org/10.3390/drones8030107). This article describes a study investigating civil drone acceptance in six different EU countries. This study was part of the USpace4UAM project (Grant Agreement No 101017643). The material includes the data set for the study described and the Python codes for the random forest analysis carried out to investigate the influence of demographic and personnel factors on drone acceptance.
DOI: 10.6084/m9.figshare.24631194
title: Read 2024 Mass Specs Raw Data, kinetics raw data, and Sequence Alignments

description: Zip files contain raw mass spectrometry data for unlabelled <i>Ab</i>HisG<sub>S</sub> (normal and high OD<sub>600</sub> methods), [<sup>15</sup>N]<i>Ab</i>HisG<sub>S, </sub>[<sup>13</sup>C,<sup>15</sup>N]<i>Ab</i>HisG<sub>S, </sub>[<sup>2</sup>H,<sup>15</sup>N]<i>Ab</i>HisG<sub>S</sub>, [<sup>2</sup>H,<sup>13</sup>C,<sup>15</sup>N]<i>Ab</i>HisG<sub>S</sub>, R70A-<i>Ab</i>HisG<sub>S</sub>, and [<sup>13</sup>C,<sup>15</sup>N]R70A-<i>Ab</i>HisG<sub>S</sub>. Furthermore, the complete output of the HisG<sub>S</sub> sequence alignment is included.Excel file containing raw data traces for all kinetics experiments.
DOI: 10.6084/m9.figshare.24631194.v1
title: Read 2023 Mass Specs Raw Data and Sequence Alignments

description: Zip files contain raw mass spectrometry data for unlabelled <i>Ab</i>HisG<sub>S</sub> (normal and high OD<sub>600</sub> methods), [<sup>15</sup>N]<i>Ab</i>HisG<sub>S, </sub>[<sup>13</sup>C,<sup>15</sup>N]<i>Ab</i>HisG<sub>S, </sub>[<sup>2</sup>H,<sup>15</sup>N]<i>Ab</i>HisG<sub>S</sub>, [<sup>2</sup>H,<sup>13</sup>C,<sup>15</sup>N]<i>Ab</i>HisG<sub>S</sub>, R70A-<i>Ab</i>HisG<sub>S</sub>, and [<sup>13</sup>C,<sup>15</sup>N]R70A-<i>Ab</i>HisG<sub>S</sub>. Furthermore, the complete output of the HisG<sub>S</sub> sequence alignment is included.
DOI: 10.18465/b093c6e8-5e42-4da7-a88d-bb95b91d2136
title: MNT Barre-de-Monts 2023

description: Modèle Numérique de Terrain obtenu par LiDAR sur la côte vendéenne au niveau de la Barre-de-Monts
DOI: 10.18465/9ddbc023-2199-4e48-9abe-a8de83ad5262
title: MNS Barre-de-Monts 2023

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau de la Barre-de-Monts
DOI: 10.14473/csda/lddvva/ui0fia
title: TI_V0804.pdf

description: Technická informace
DOI: 10.14473/csda/lddvva/lqmmkf
title: OM_V0804.pdf

description: Ostatní materiály
DOI: 10.14473/csda/lddvva/7yvbma
title: D_V0804.pdf

description: Dotazník
DOI: 10.14473/csda/lddvva/snckxw
title: K_V0804.pdf

description: Karty
DOI: 10.14473/csda/lddvva/ou3sqk
title: Nesstar_NS_0804_DataFile.tab

description: Data
DOI: 10.14473/csda/lddvva/cexnrq
title: PR11_V0804.pdf

description: Stranické preference a volební model v dubnu 2008. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/vrr91o
title: PR02_V0804.pdf

description: Povědomí občanů ČR o Lisabonské smlouvě a zájem o ní. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/00h3qh
title: PR05_V0804.pdf

description: Občané o soužití s Romy a o jejich možnostech ve společnosti. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/7vvtrd
title: PR10_V0804.pdf

description: Postoje k evropské integraci a hodnotám uplatňovaným v Evropské unii. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/xlcymj
title: PR09_V0804.pdf

description: Vliv členství ČR v EU na vybrané hospodářské a sociální oblasti. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/i5bj4t
title: PR03_V0804.pdf

description: Občané o ekonomické situaci svých domácností. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/oavqua
title: PR08_V0804.pdf

description: Občané o americké radarové základně v ČR. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/boz2kj
title: PR06_V0804.pdf

description: Důvěra k ústavním institucím a spokojenost s politickou situací v dubnu 2008. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/of3of1
title: PR01_V0804.pdf

description: Občané o předsednictví ČR v Radě Evropské unie. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva/x5y6n2
title: PR04_V0804.pdf

description: Spokojenost s životními podmínkami v místě bydliště. Tisková zpráva CVVM.
DOI: 10.14473/csda/lddvva
title: Naše společnost 2008 - duben

description: Kontinuální měsíční výzkum provedl výzkumný tým CVVM Sociologického ústavu AV ČR v rámci pravidelného měsíčního výzkumného projektu. Jsou při něm zjišťovány postoje a názory občanů. Výzkum provedl výzkumný tým CVVM Sociologického ústavu AV ČR v rámci výzkumu. "Naše společnost 2008". Sběr dat provedla tazatelská síť CVVM.
DOI: 10.14473/csda/lddvva/10jhte
title: PR07_V0804.pdf

description: Češi o členství České republiky v Evropské unii. Tisková zpráva CVVM.
DOI: 10.6084/m9.figshare.22695634
title: Study of words and icons related to social networks: familiarity, valence and activation in Spanish university students

description: Social networks have become an important phenomenon in the daily lives of many people, which is why in recent years they have aroused the interest of many researchers. This fact contrasts with the lack of normative psycholinguistic and affective indexes that facilitate their study within psychology. The aim of the present work is to provide a freely accessible database with words and icons related to the most used social networks. The responses of a sample of university students on familiarity, emotional valence and arousal of 42 words related to social networks and their corresponding icons are analyzed. Most of the stimuli were familiar, had positive valence and generated high arousal in the participants. Gender differences related to familiarity were obtained in some words and icons. The findings obtained in the present work allow us to broaden the understanding of the lexicon used in the field of social networks. Also, the stimuli provided can be used in experimental studies on the use, abuse or dependence on social networks in Spain.
DOI: 10.5281/zenodo.10648887
title: Polarized, color-selective and semi-transparent organic photodiode of aligned merocyanine H-aggregates

description: Data to report https://doi.org/10.1039/D4TC00678J:

Highly anisotropic thin films of H-type coupled dipolar merocyanines with large dichroic ratios of over 50 were deposited by solution shearing. These layers were incorporated into simultaneously color- and polarization-selective organic photodiodes. Using a transparent non-fullerene acceptor, polarization-sensitive planar-heterojunction devices with an average visible transmittance of 93% were obtained.
DOI: 10.5281/zenodo.10648888
title: Polarized, color-selective and semi-transparent organic photodiode of aligned merocyanine H-aggregates

description: Data to report https://doi.org/10.1039/D4TC00678J:

Highly anisotropic thin films of H-type coupled dipolar merocyanines with large dichroic ratios of over 50 were deposited by solution shearing. These layers were incorporated into simultaneously color- and polarization-selective organic photodiodes. Using a transparent non-fullerene acceptor, polarization-sensitive planar-heterojunction devices with an average visible transmittance of 93% were obtained.
DOI: 10.6084/m9.figshare.25442425
title: <b>The Role of TLC, RDW and ESR in Predicting Short-Term Prognosis among Admitted Patients with Acute Ischemic Stroke: Insights from a Cross-Sectional Study</b>

description: A descriptive cross-sectional study was conducted in a tertiary center after ethical approval from the Institutional Review Committee. The study period spanned from 18<sup>th </sup>April 2022 to 17<sup>th</sup> June 2023, a period of 14 months. 136 patients diagnosed with acute ischemic stroke with total leucocyte count (TLC), red cell distribution width (RDW), erythrocyte sedimentation rate (ESR), National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) were included in the study after fulfilling the inclusion criteria. The modified Rankin Scale (mRS) was obtained 28 days following admission. Data was collected and analyzed through Microsoft Excel 365 and SPSS version 22. Spearman Correlation and multivariate regression analysis were used to analyze the association.
DOI: 10.58032/agh/ln6ilg
title: Data for publication: Laboratory determination of hydrogen/methane dispersion in rock cores for underground hydrogen storage

description: Data for Laboratory determination of hydrogen/methane dispersion in rock cores for underground hydrogen storage
DOI: 10.5066/p13utnfg
title: Lake Erie Fish Community Data, 2013-2023

description: Assessing the distribution and abundance of both predator and prey (forage) fish species is a cornerstone of ecosystem-based fishery management and supports decision making that considers food-web interactions. In support of binational Great Lakes fishery management, the objectives of this survey were to: provide estimates of densities of key forage and predator species in the western basin of Lake Erie, to assess seasonal and spatial distributions of fishes in tandem with water quality information, and to assess year class strength. A systematic grid sampling approach with 41 stations was sampled via bottom trawl during June (Spring) and September (Autumn), starting in 2013. This data release adds 2023 data to the set for a total of eleven years observation using the same gear and sampling design.
DOI: 10.21934/baua.azb15.suf.1
title: Scientific Use File der BAuA-Arbeitszeitbefragung 2015: BAuA-AZB2015_SUF_1

description: Scientific Use File der BAuA-Arbeitszeitbefragung 2015: BAuA-AZB2015_SUF_1
DOI: 10.21934/baua.azb17.suf.1
title: Scientific Use File der BAuA-Arbeitszeitbefragung 2017: BAuA-AZB2017_SUF_1

description: Scientific Use File der BAuA-Arbeitszeitbefragung 2017: BAuA-AZB2017_SUF_1
DOI: 10.6084/m9.figshare.25442821
title: Polarimetric radar

description: Data for "Assessing the effect of turbulence on polarimetric radar quantitative precipitation estimation".
DOI: 10.6084/m9.figshare.25442821.v1
title: Polarimetric radar

description: Data for "Assessing the effect of turbulence on polarimetric radar quantitative precipitation estimation".
DOI: 10.5281/zenodo.8064018
title: Surface EMG data from seven representative forearm areas during seven grasps relevant for personal autonomy

description: This dataset contains 3150 data of four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. while performing seven grasp types. Data were recorded using surface EMG electrodes from seven representative forearm areas. 

Data are presented as a  Matlab data structure (.mat file). This structure contains the four sEMG characteristics classified as: subject, grasp, repetition and spot area. The fields contained in the structure are those detailed in the following scheme:



Subject: subject ID;

Grasp: Grasp ID;

Repetition: Repetition of each grasp, from 1 to 3.

Spot area: Spot ID for the seven representative spot areas, according to [1].

FZC: zero crossing

EWL: enhanced wavelength

EMAV: enhanced mean absolute value

AM: Muscle activity


[1] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. J. NeuroEngineering Rehabil. (2018).
DOI: 10.5281/zenodo.8064019
title: Surface EMG data from seven representative forearm areas during seven grasps relevant for personal autonomy

description: This dataset contains 3150 data of four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. while performing seven grasp types. Data were recorded using surface EMG electrodes from seven representative forearm areas. 

Data are presented as a  Matlab data structure (.mat file). This structure contains the four sEMG characteristics classified as: subject, grasp, repetition and spot area. The fields contained in the structure are those detailed in the following scheme:



Subject: subject ID;

Grasp: Grasp ID;

Repetition: Repetition of each grasp, from 1 to 3.

Spot area: Spot ID for the seven representative spot areas, according to [1].

FZC: zero crossing

EWL: enhanced wavelength

EMAV: enhanced mean absolute value

AM: Muscle activity


[1] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. J. NeuroEngineering Rehabil. (2018).
DOI: 10.6084/m9.figshare.25443658
title: Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing

description: Video + Annotation (X, Y, Z, α, β, γ)For additional details, please check the following article: <br>Nuno Pessanha Santos, Vitor Lobo and Alexandre Bernardino, <i>Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing </i>in <i>Drones</i> 7<b>, </b>243, 2023.<br>DOI: <b>https://doi.org/10.3390/drones7040243</b>
DOI: 10.18419/darus-2790/1549
title: submit.sh

description: :unav
DOI: 10.18419/darus-2790/1550
title: param_instability.nml

description: :unav
DOI: 10.5258/soton/d2222
title: Dataset for the University of Southampton Doctoral thesis "Developing SERS-based point-of-care inflammatory monitoring for risk identification of worsening progression of age-related disease"

description: Dataset containing all data collected and used for the Southampton University Doctoral thesis 'Developing SERS-based point-of-care inflammatory monitoring for risk identification of worsening progression of age-related disease'.

This dataset contains:

CH2 Hearing loss and Inflammation Data.xlsx
All the data that was collected to contribute to the results shown in chapter 2 of the thesis. (This chapter data is available 'on request' to bone fide researchers with ethical clearance. Please complete the attached request form and return it to researchdata@soton.ac.uk) 

CH3 Developing SERS Sensors Data.xlsx
All the data that was collected to contribute to the results shown in chapter 3 of the thesis. 

CH3 SERS Raw Data Files (zip)
The raw SERS data files after pre-processing for neopterin standards, creatinine standards, real urine neopterin measurement, real urine creatinine measurement, artificial urine neopterin measurement and artificial urine creatinine measurement.

CH3 HPLC Artificial urine raw data.xlsx

CH3 HPLC Real Urine Raw Data.xlsx

CH4 Developing lateral flow device data.xlsx

All the data that was collected to contribute to the results shown in chapter 4 of the thesis.

The data for Ch3 and Ch4 is accessible via CC BY NC SA license.
DOI: 10.18419/darus-2790/1552
title: vp_B2_3d3v.dat

description: :unav
DOI: 10.18419/darus-2790/1553
title: vp_B2_3d3vadd.dat

description: :unav
DOI: 10.6084/m9.figshare.25442080
title: Drywell infiltration performance: tests, monitoring, simple and detailed models

description: Simulation results and measured data, as presented in the paper: Drywell infiltration performance: tests, monitoring, simple and detailed models, by Kurtzman et al.
DOI: 10.6084/m9.figshare.25442080.v2
title: Drywell infiltration performance: tests, monitoring, simple and detailed models

description: Simulation results and measured data, as presented in the paper: Drywell infiltration performance: tests, monitoring, simple and detailed models, by Kurtzman et al.
DOI: 10.5281/zenodo.10843508
title: Friction dataset

description: Friction dataset

This dataset consists of 20,000 two-level topologies labeled by their static friction coefficient. Each image is binary with a resolution of 128x128. The topologies are carved out using simplex noise (see details below) with a fixed porosity of 0.4. The static friction is measured in molecular dynamics simulations. To get started, download the dataset (zip-file) and unzip it:

unzip -d friction_dataset friction_dataset.zip

Structure of dataset

We have chosen a sample oriented folder structure that is easy to inspect. This means that all the information about a sample is collected within its respective subdirectory. We also provide both the binary image, which is both to be used for inspection and automatized reading. The structure looks like the following:

├── extract_dataset.py
├── create_topology.py
├── samples
│     ├── sample_0
│     │     ├── attr.txt
│     │     └── binary.png
│     ├── sample_1
│     ├── ...
│     └── sample_19999
└── README.md

We provide a simple script for extracting the dataset into NumPy arrays in Python (`extract_dataset.py`). From there, the arrays can easily be converted to PyTorch or TensorFlow tensors for machine learning purposes.

Simplex parameters

The topologies are generated by simplex noise. Simplex noise is a procedural noise class known to generate topologies reminiscent of landscapes. We have here used Open Simplex Noise, featuring several parameter: the number of octaves, lacunarity and persistency. Additionally, we have introduced a 'scale' parameter for chaning the size of the topologies (the cluster size). When thresholding the topologies (going from continuous values to binary values), we choose the treshold such that the porosity becomes 0.4 using a simple gradient method. Lastly, the seed may be specified. Simplex noise is deterministic within a specific implementation, meaning that the exact same topology is generated given the number of octaves, scale, lacunarity, persistence, scale and seed. For each samples, these parameters are provided in 'attr.txt', making it possible to regenerate the topology. See `create_topology.py` for more information.

Measuring friction

We measure friction in molecular dynamics simulations. To do this, we carve out the topology from a alpha-quartz (SiO2) surface and slide it across a flat surface of the same material. The static friction is taken as the maximum lateral force (during material failure). For simulation details, see https://arxiv.org/abs/2401.05206.
DOI: 10.5281/zenodo.10844046
title: Friction dataset

description: Friction dataset

This dataset consists of 20,000 two-level topologies labeled by their static friction coefficient. Each image is binary with a resolution of 128x128. The topologies are carved out using simplex noise (see details below) with a fixed porosity of 0.4. The static friction is measured in molecular dynamics simulations. To get started, download the dataset (zip-file) and unzip it:

unzip -d friction_dataset friction_dataset.zip

Structure of dataset

We have chosen a sample oriented folder structure that is easy to inspect. This means that all the information about a sample is collected within its respective subdirectory. We also provide both the binary image, which is both to be used for inspection and automatized reading. The structure looks like the following:

├── extract_dataset.py
├── create_topology.py
├── samples
│     ├── sample_0
│     │     ├── attr.txt
│     │     └── binary.png
│     ├── sample_1
│     ├── ...
│     └── sample_19999
└── README.md

We provide a simple script for extracting the dataset into NumPy arrays in Python (`extract_dataset.py`). From there, the arrays can easily be converted to PyTorch or TensorFlow tensors for machine learning purposes.

Simplex parameters

The topologies are generated by simplex noise. Simplex noise is a procedural noise class known to generate topologies reminiscent of landscapes. We have here used Open Simplex Noise, featuring several parameter: the number of octaves, lacunarity and persistency. Additionally, we have introduced a 'scale' parameter for chaning the size of the topologies (the cluster size). When thresholding the topologies (going from continuous values to binary values), we choose the treshold such that the porosity becomes 0.4 using a simple gradient method. Lastly, the seed may be specified. Simplex noise is deterministic within a specific implementation, meaning that the exact same topology is generated given the number of octaves, scale, lacunarity, persistence, scale and seed. For each samples, these parameters are provided in 'attr.txt', making it possible to regenerate the topology. See `create_topology.py` for more information.

Measuring friction

We measure friction in molecular dynamics simulations. To do this, we carve out the topology from a alpha-quartz (SiO2) surface and slide it across a flat surface of the same material. The static friction is taken as the maximum lateral force (during material failure). For simulation details, see https://arxiv.org/abs/2401.05206.
DOI: 10.5281/zenodo.4442671
title: Friction dataset

description: Friction dataset

This dataset consists of 20,000 two-level topologies labeled by their static friction coefficient. Each image is binary with a resolution of 128x128. The topologies are carved out using simplex noise (see details below) with a fixed porosity of 0.4. The static friction is measured in molecular dynamics simulations. To get started, download the dataset (zip-file) and unzip it:

unzip -d friction_dataset friction_dataset.zip

Structure of dataset

We have chosen a sample oriented folder structure that is easy to inspect. This means that all the information about a sample is collected within its respective subdirectory. We also provide both the binary image, which is both to be used for inspection and automatized reading. The structure looks like the following:

├── extract_dataset.py
├── create_topology.py
├── samples
│     ├── sample_0
│     │     ├── attr.txt
│     │     └── binary.png
│     ├── sample_1
│     ├── ...
│     └── sample_19999
└── README.md

We provide a simple script for extracting the dataset into NumPy arrays in Python (`extract_dataset.py`). From there, the arrays can easily be converted to PyTorch or TensorFlow tensors for machine learning purposes.

Simplex parameters

The topologies are generated by simplex noise. Simplex noise is a procedural noise class known to generate topologies reminiscent of landscapes. We have here used Open Simplex Noise, featuring several parameter: the number of octaves, lacunarity and persistency. Additionally, we have introduced a 'scale' parameter for chaning the size of the topologies (the cluster size). When thresholding the topologies (going from continuous values to binary values), we choose the treshold such that the porosity becomes 0.4 using a simple gradient method. Lastly, the seed may be specified. Simplex noise is deterministic within a specific implementation, meaning that the exact same topology is generated given the number of octaves, scale, lacunarity, persistence, scale and seed. For each samples, these parameters are provided in 'attr.txt', making it possible to regenerate the topology. See `create_topology.py` for more information.

Measuring friction

We measure friction in molecular dynamics simulations. To do this, we carve out the topology from a alpha-quartz (SiO2) surface and slide it across a flat surface of the same material. The static friction is taken as the maximum lateral force (during material failure). For simulation details, see https://arxiv.org/abs/2401.05206.