cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24260632.v2
title: Budle Bay data - diatoms, XRF, particle size, lithology

description: Lithology (i), particle size (ii), diatom (iii) and XRF (iv) data of sediment cores from Budle Bay, Northumberland, UK. Cores span ~8000 cal yr BP to present.<br>(i) Sediment descriptions of 8 cores from Budle Bay, including core locations, distances from shoreline and depths/thicknesses of sand units.<br>(ii) Particle size data from core P2(iii) Diatom percentage data from 3 cores P2, P3 and P4(iv) XRF data from 3 cores P2, P3 and P4Data and full analysis presented in a paper by Pickering et al., submitted to the journal Sedimentologika. Radiocarbon dating support was provided by the Quaternary Research Association, QRA-14CHRONO Centre Radiocarbon Dating Award. We gratefully acknowledge John Sutherland for providing site access.
DOI: 10.6084/m9.figshare.24260632.v1
title: Budle Bay data - diatoms, XRF, particle size, lithology

description: Diatom (i), XRF (ii), particle size (iii) and lithology (iv) data of sediment cores from Budle Bay, Northumberland, UK. Cores span ~8000 cal yr BP to present.<br>(i) Diatom percentage data from 3 cores BU18/2, BU18/7 and BU20/1 (ii) XRF data from 3 cores BU18/2, BU18/7 and BU20/1 (iii) Particle size data from core BU18/2(iv) Sediment descriptions of 8 cores from Budle Bay, including core locations, distances from shoreline and depths/thicknesses of sand units.<br><br>Data and full analysis presented in a paper by Pickering et al., submitted to the journal Sedimentologika. Radiocarbon dating support was provided by the Quaternary Research Association, QRA-14CHRONO Centre Radiocarbon Dating Award. We gratefully acknowledge John Sutherland for providing site access.
DOI: 10.5281/zenodo.10843103
title: Rose-oil results
DOI: 10.5281/zenodo.10843104
title: Rose-oil results
DOI: 10.5281/zenodo.10843601
title: gurania_insolita_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.10843602
title: gurania_insolita_spades_01.fasta.gz

description: .
DOI: 10.23729/27c98532-ee57-44f1-9c37-cdb547b0a66a
title: Volcanic Pumices

description: Two volcanic pumices, imaged once as a whole and once after being cut and made into an epoxy mount. Pumice 1_2 is highly vesicular, basaltic scoria sample from 2021 Fagradalsfjall eruption. Likely erupted in late May. Pumice 5 is highly vesicular, basaltic scoria sample from 2021 Fagradalsfjall eruption. Erupted around 28th April. To be used in a publication. Preview dataset found at https://doi.org/10.23729/bfc7baf6-1798-40fd-b14b-aa4db7c47d6c . To obtain data, please contact fairdata-pas@gtk.fi .
DOI: 10.14272/szcwtgkvohzbbe-uhfffaoysa-m/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C14H16N5.ClH/c1-18(2)12-6-4-11(5-7-12)13-8-9-19(3)10-14(13)16-17-15;/h4-10H,1-3H3;1H/q+1;/p-1

description: dataset for mass spectrometry (MS)
 
 ESI-MS, m/z (%): 254 (100) [M]+, 226 (77) [M-N2]+.HRMS-ESI for [C14H16N5]+: Calc. m/z = 254.1401, found m/z = 254.1397.
DOI: 10.14272/szcwtgkvohzbbe-uhfffaoysa-m/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C14H16N5.ClH/c1-18(2)12-6-4-11(5-7-12)13-8-9-19(3)10-14(13)16-17-15;/h4-10H,1-3H3;1H/q+1;/p-1

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 1H NMR (500 MHz, DMSO-d6, ppm) δ = 9.06 (d, J = 1.4 Hz, 1H, HAr), 8.60 (dd, J = 6.6, 1.4 Hz, 1H, HAr), 8.05 (d, J = 6.5 Hz, 1H, HAr), 7.80–7.73 (m, 2H, HAr), 6.88–6.81 (m, 2H, HAr), 4.27 (s, 3H, CH3), 3.04 (s, 6H, CH3).
DOI: 10.14272/szcwtgkvohzbbe-uhfffaoysa-m/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C14H16N5.ClH/c1-18(2)12-6-4-11(5-7-12)13-8-9-19(3)10-14(13)16-17-15;/h4-10H,1-3H3;1H/q+1;/p-1

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 13C NMR (126 MHz, DMSO-d6, ppm) δ = 152.0 (1C, CAr), 145.7 (1C, CAr), 140.4 (1C, CAr), 137.8 (1C, CAr), 135.9 (1C, CAr), 131.2 (2C, CAr), 125.4 (1C, CAr), 118.3 (1C, CAr), 111.6 (2C, CAr), 46.7 (1C, CH3), 39.6 (2C, CH3).
DOI: 10.14272/szcwtgkvohzbbe-uhfffaoysa-m.1
title: 3-azido-4-(4-(dimethylamino)phenyl)-1-methylpyridin-1-ium chloride
title: InChI=1S/C14H16N5.ClH/c1-18(2)12-6-4-11(5-7-12)13-8-9-19(3)10-14(13)16-17-15;/h4-10H,1-3H3;1H/q+1;/p-1

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C14H16N5.ClH/c1-18(2)12-6-4-11(5-7-12)13-8-9-19(3)10-14(13)16-17-15;/h4-10H,1-3H3;1H/q+1;/p-1, and canonical SMILES descriptor[cheminf_000007]: [N-]=[N+]=Nc1c[n+](C)ccc1c1ccc(cc1)N(C)C.[Cl-], and by the IUPAC name[cheminf_000107]: .
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-39151

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000630 | infrared absorption spectroscopy (IR)
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0000470 | mass spectrometry (MS)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/szcwtgkvohzbbe-uhfffaoysa-m/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C14H16N5.ClH/c1-18(2)12-6-4-11(5-7-12)13-8-9-19(3)10-14(13)16-17-15;/h4-10H,1-3H3;1H/q+1;/p-1

description: dataset for infrared absorption spectroscopy (IR)
 
 IR (ATR, ṽ) = 3046 (m), 2119 (vs), 1779 (m), 1771 (m), 1746 (vs), 1585 (vs), 1503 (vs), 1470 (s), 1440 (s), 1381 (s), 1337 (s), 1315 (vs), 1272 (m), 1172 (vs), 1131 (vs), 1068 (s), 1025 (m), 944 (m), 899 (m), 818 (s), 788 (s), 767 (s), 744 (s), 702 (s), 687 (s), 667 (s), 640 (m), 631 (m), 512 (m) cm–1.
DOI: 10.34740/kaggle/dsv/7895143
title: ⏳ endoflife.date database (duckdb)
DOI: 10.6084/m9.figshare.25442194
title: Alissa_et_al_Supplemental.csv

description: Supplemental data for datacenter Life Cycle Assessment (LCA)
DOI: 10.25500/edata.bham.00001079
title: Research data supporting the publication of 'Upcycling of low value end-of-life cathode material into next generation cathode materials'
DOI: 10.5281/zenodo.10625107
title: Supporting data for the article 'An ex vivo study examining migration of microplastics from an infused neonatal parenteral nutrition circuit'

description: This supporting data contains detailed informaiton on the methods used, the raw data, the concentrations of microplastics in a summary table and the results of ATR-FTIR measurements on the seperate parts of a parenteral nutrition circuit.
DOI: 10.6084/m9.figshare.24973524.v1
title: <b>The Atlantic cod MHC I compartment</b>

description: Supplementary information to paper: <b>The Atlantic cod MHC I compartment has the properties needed for cross-presentation in the absence of MHC II.</b> Authors: Synne Arstad Bjørnestad, Monica Hongrø Solbakken, Bernd Thiede, Ketil Hylland, Kjetill S Jakobsen, Sissel Jentoft, Oddmund Bakke, Cinzia Progida @ University of Oslo, Norway.
DOI: 10.5281/zenodo.10843504
title: A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length.
DOI: 10.5281/zenodo.10843505
title: A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length.
DOI: 10.5281/zenodo.10102884
title: Supporting data for the article 'An ex vivo study examining migration of microplastics from an infused neonatal parenteral nutrition circuit'

description: This supporting data contains detailed informaiton on the methods used, the raw data, the concentrations of microplastics in a summary table and the results of ATR-FTIR measurements on the seperate parts of a parenteral nutrition circuit.
DOI: 10.5281/zenodo.10843638
title: hypotaenidia_owstoni_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.10843639
title: hypotaenidia_owstoni_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.10203197
title: A novel AI-based method for predicting and optimizing output quality in curved laser cutting of thin electrical steel sheet

description: This dataset is experimental results from a research topic of A novel AI-based method for predicting and optimizing output quality in curved laser cutting of thin electrical steel sheet.
DOI: 10.5281/zenodo.10203311
title: A novel AI-based method for predicting and optimizing output quality in curved laser cutting of thin electrical steel sheet

description: This dataset is experimental results from a research topic of A novel AI-based method for predicting and optimizing output quality in curved laser cutting of thin electrical steel sheet.
DOI: 10.5281/zenodo.10652564
title: Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using a new artificial intelligence approach

description: This dataset is experimental results from a research topic of Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using a new artificial intelligence approach.
DOI: 10.5281/zenodo.7312949
title: Prediction and optimization of multi-characteristics of cut quality in curved laser cutting of thin electrical steel sheet.

description: This dataset is experimental results from a research topic of Prediction and optimization of multi-characteristics of cut quality in curved laser cutting of thin electrical steel sheet.
DOI: 10.15129/b3038208-436b-4885-8395-0e2b5e1afe47
title: Data for: "Validation and Comparison of Turbulence Models for Predicting VAWT wakes"

description: CFD Post processed results as images for Near Blade and Far Blade results, and as a spreadsheet for Near Turbine results. .7z files must be opened using 7zip, which can then be extracted to allow access to the images. .csv and .xlsx can be opened using Microsoft Excel or similar spreadsheet software.
DOI: 10.5281/zenodo.10802400
title: Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach

description: This dataset is experimental results from a research topic of Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach.
DOI: 10.5281/zenodo.10843642
title: Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach

description: This dataset is experimental results from a research topic of Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach.
DOI: 10.5281/zenodo.7312948
title: Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach

description: This dataset is experimental results from a research topic of Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach.
DOI: 10.21227/8vd7-3e62
title: Employment Experiences and Career Development

description: This survey aims to investigate various aspects of employment experiences and career development among individuals across different demographics. Through a series of structured questions, the survey delves into participants' educational background, initial employment or entrepreneurship experiences, obstacles faced in the workplace, factors aiding employment, role of self-study in career growth, criteria guiding hiring decisions, priorities in career building, components and priorities of success, job satisfaction, awareness of job opportunities, and perceived barriers to career success.By gathering responses on these topics, the survey seeks to uncover insights into the dynamics of the modern workforce, including the alignment between education and employment, challenges encountered in the workplace, strategies for career advancement, and factors influencing job satisfaction. The findings from this survey will contribute to a deeper understanding of individuals' experiences in the realm of employment and offer valuable insights for employers, educators, and policymakers to enhance workforce development initiatives and foster conducive environments for career growth and fulfillment.
DOI: 10.6084/m9.figshare.25440484.v1
title: AxSpA Dataset

description: Correlations between biometrics in AxSpA
DOI: 10.6084/m9.figshare.25440484
title: AxSpA Dataset

description: Correlations between biometrics in AxSpA
DOI: 10.6084/m9.figshare.25440484.v3
title: AxSpA Dataset

description: Correlations between biometrics in AxSpA
DOI: 10.7488/ds/7698
title: Fresh Cement as a Frictional Non-Brownian Suspension

description: Cement is an essential construction material due to its ability to flow before later setting, however the rheological properties must be tightly controlled. Despite this, much understanding remains empirical. Using a combination of continuous and oscillatory shear flow, we compare fresh Portland cement suspensions to previous measurements on model non-Brownian suspensions to gain a micro-physical understanding. Comparing steady and small-amplitude oscillatory shear, we reveal two distinct jamming concentrations, ϕμ and ϕrcp, where the respective yield stresses diverge. As in model suspensions, the steady-shear jamming point is notably below the oscillatory jamming point, ϕμ&lt;ϕrcp, suggesting that it is tied to frictional particle contacts. These results indicate that recently established models for the rheology of frictional, adhesive non-Brownian suspensions can be applied to fresh cement pastes, offering a new framework to understand the role of additives and fillers. Such micro-physical understanding can guide formulation changes to improve performance and reduce environmental impact. This dataset accompanies the article "Fresh Cement as a Frictional Non-Brownian Suspension" by James A. Richards, Hao Li, Rory E. O'Neill, Fraser H. J. Laidlaw and John R. Royer.
description: This dataset contains the underlying data for the figures in https://doi.org/10.48550/arXiv.2401.09377, see readme.txt for an outline of file contents and structure.
DOI: 10.5519/qd.fa5qphkm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3212 records)
DOI: 10.5281/zenodo.10840245
title: NetFlow

description: This is a preprocessed dataset in numpy format.

The dataset consists of multi-variate time series contained in an array of shape [n_samples, time_steps, n_variables] associated with class labels contained in an array of shape [n_samples, 1]. It can be loaded as follows:

loaded_data = np.load("NET.npz")
Xtr = loaded_data['Xtr'] # Training data of shape (803, 997, 4)
Ytr = loaded_data['Ytr'] # Training labels of shape (803, 1)
Xte = loaded_data['Xte'] # Test data of shape (534, 997, 4)
Yte = loaded_data['Yte'] # Test labels of shape (534, 1)
DOI: 10.5281/zenodo.10840246
title: NetFlow

description: This is a preprocessed dataset in numpy format.

The dataset consists of multi-variate time series contained in an array of shape [n_samples, time_steps, n_variables] associated with class labels contained in an array of shape [n_samples, 1]. It can be loaded as follows:

loaded_data = np.load("NET.npz")
Xtr = loaded_data['Xtr'] # Training data of shape (803, 997, 4)
Ytr = loaded_data['Ytr'] # Training labels of shape (803, 1)
Xte = loaded_data['Xte'] # Test data of shape (534, 997, 4)
Yte = loaded_data['Yte'] # Test labels of shape (534, 1)
DOI: 10.5281/zenodo.10691440
title: Simulation of Anisotropic Microscopic Wave-induced Fluid Flow Effects and the Influential Factors Analysis

description: The dataset includes:Code for Numerical Simulation (DSSS) Code for generating Digital RockCode for the Holton SequenceCode for the WIFF-Squirt Flow Model

SEM images from natural shale sampleExamples of Digital Rock that have been used in our current research

(All codes were developed in Matlab 2017 or newer versions.)
DOI: 10.3886/e118161
title: Intellectual Disability Literacy and Stigma in Austria, Germany, and the UK

description: Article 8 of the UN Convention on the Rights of People with Disabilities requires governments around the globe to raise awareness on issues of disability in their societies, combatting stereotypes, prejudices and harmful practices. Little comparative data is available on lay people's social representations of intellectual disability and associated stigma, which could inform actions in line with the convention. This study aimed to compare intellectual disability literacy and stigma among adults in the general population in the UK, Austria and Germany (N=1046), and to examine the effects of providing an intellectual disability label on these outcomes. <br><br>For the assessment of ID literacy and stigma, the Intellectual Disability Literacy Scale (IDLS) by Scior and Furnham (2013) was used. This instrument consists of a vignette, describing a person with a mild ID, and a questionnaire. The questionnaire comprises three parts, namely recognition (1 item), causal and intervention beliefs (44 items), and social distance (4 items). In the first part of the IDLS, participants are presented with a diagnostically labelled or unlabelled vignette. Both vignette versions were identical other than inclusion/omission of a diagnostic label, consisting of ‘learning disability (mental handicap)’ in the UK and ‘intellektuelle Behinderung (geistige Behinderung)’ in Austria and Germany. Of the UK sample 265 (50.38%) participants received the labelled version, and 258 (49.62%) of the Austrian/German sample.<br><br> Additionally to the IDLS, we assessed prior contact to persons with ID via two items: We determined if the respondent ever had contact to a person with ID (yes/no), and how close this contact was (rated on a 9 point scale where 1 = not at all close, 5 = somewhat close and 9 = extremely close). As sociodemographis variables, we assessed age, gender, level of education, and religion.
DOI: 10.3886/e118161v1-147141
title: Intellectual Disability Literacy and Stigma in Austria, Germany, and the UK

description: Article
8 of the UN Convention on the Rights of People with Disabilities requires governments
around the globe to raise awareness on issues of disability in their societies,
combatting stereotypes, prejudices and harmful practices. Little comparative
data is available on lay people's social representations of intellectual
disability and associated stigma, which could inform actions in line with the
convention. This study aimed to compare intellectual disability literacy and
stigma among adults in the general population in the UK, Austria and Germany
(N=1046), and to examine the effects of providing an intellectual disability
label on these outcomes. <br><br>For the assessment of ID literacy
and stigma, the Intellectual Disability Literacy Scale (IDLS) by Scior and Furnham (2013) was used. This instrument consists of a vignette, describing a person with a mild ID, and a questionnaire. The questionnaire comprises three parts, namely recognition (1
item), causal and intervention beliefs (44 items), and social distance (4 items). In the first part of the IDLS, participants are presented with a diagnostically labelled or unlabelled
vignette. Both vignette versions were identical
other than inclusion/omission of a diagnostic label, consisting of ‘learning
disability (mental handicap)’ in the UK and ‘intellektuelle Behinderung (geistige
Behinderung)’ in Austria and Germany. Of
the UK sample 265 (50.38%) participants received the labelled version, and 258
(49.62%) of the Austrian/German sample.<br><br>

Additionally to the IDLS, we assessed prior contact to persons with ID via two items: We determined if the respondent ever had contact to a person with
ID (yes/no), and how close this contact was (rated on a 9 point scale where 1 =
not at all close, 5 = somewhat close and 9 = extremely close). As sociodemographis variables, we assessed
age, gender, level of education, and religion.
DOI: 10.6084/m9.figshare.25442191.v2
title: Test bigwig

description: Test dataset to understand the data loading procedure on UCSC.
DOI: 10.5066/f7m044nw
title: Intraguild predation dataset for arthropods on tarweed
DOI: 10.6084/m9.figshare.25398895.v3
title: Data related to the identification and genomic characterisation of egoviruses

description: Databases our study used or generated include (1) all RNApolA proteins identified from the metagenomic survey, (2) Egovirales RNApolA proteins and major capsid proteins (MCPs) also identified from the metagenomic survey, (3) HMMs for the RNApolA and <i>Egovirales</i> MCP, (4) and the 57 Egovirales genomes &gt;50kb in length (including 13 complete genomes).
DOI: 10.5281/zenodo.10835683
title: Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"

description: This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.

The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.

The structure of the information shared in this repository is as follows:The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.

The material is distributed under a CC-BY 4.0 license.