cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17028/rd.lboro.23709606.v1
title: Supplementary information files for A human surrogate neck for traumatic brain injury research

description: Supplementary files for article A human surrogate neck for traumatic brain injury research Properties of the human neck such as range and resistance to motion are considered important determinants of the kinematic response of the head pre, during and post-impact. Mechanical surrogate necks (i.e., anthropomorphic test device necks), have generally been limited to a single anatomical plane of motion and an artificially high resistance to motion. The aim of this study was to present the Loughborough University Surrogate Neck that is representative of the 50th percentile human male neck, developed for motion in and between each of the anatomical planes with inertial and flexural stiffness properties matching those of a passive elastic (i.e., negligible active tension) neck muscle state. The complex intervertebral joints were reduced to three encapsulated ball joints with appropriate locations, orientations and distributed range of motion to precisely position and orientate the head with respect to the torso at the neutral position and end range of motion. A plain bearing sub-assembly was incorporated at the C1-C2 vertebral level to permit 50% of the axial rotation with negligible resistance to motion, as exhibited by humans. Detachable elastomeric elements provided resistance to motion across each ball joint and permit any orientation of the head within the physiological range of motion of the joints. The mass of the surrogate neck (1.62 Kg) was in agreement with the typical human range and similar agreement was found for the principal moments of inertia (Ixx 26.8 kg.cm2, Iyy 20.5 kg.cm2 and Izz 14.3 kg.cm2). Quasi-static bending moment and dynamic torque tests characterised the surrogate neck in flexion/extension, lateral flexion and axial rotation. With respect to commercial surrogate necks, the surrogate neck presented here was in closer agreement to the reported human responses, for equivalent loading conditions. The applications of a surrogate neck that can appropriately constrain the head relative to the torso are far reaching in the areas of brain injury mechanism research, and for the development and assessment of protective equipment to reduce the risk of such injuries.
DOI: 10.5281/zenodo.8117542
title: SuperCorals_Experimentdata

description: Dataset produced from samples from experiments with thermal resistant and thermal sensitive corals. Differentially abundant KO genes, as well as families and classes from 16s rDNA and metagenome samples.
DOI: 10.5281/zenodo.8117543
title: SuperCorals_Experimentdata

description: Dataset produced from samples from experiments with thermal resistant and thermal sensitive corals. Differentially abundant KO genes, as well as families and classes from 16s rDNA and metagenome samples.
DOI: 10.25405/data.ncl.23662047
title: Nuances in IMA EEG datasets

description: EEG data looking into comparison in intensity deviant MMN amplitudes between tinnitus and control participants (those with C suffix are controls). A roving paradigm was used, where deviants were pseudo-random changes from one type of intensity stimulus to the other. Two frequencies were used in interspersed blocks: tinnitus frequency and control (1 kHz). The tinnitus frequency was estimated by each individual with tinnitus to be as close to their tinnitus as possible (matched controls listened to the tinnitus frequency of their match). The loudness was also decided by each participant, but the difference originally between loud and quiet stimuli was set at -6 dB (changed by participant if they could not tell the difference in loudness). Controls were matched in hearing profile as well as gender and age to participants with tinnitus. Passive task (watching a TV show with subtitles but no sound) was used during the 1 hour EEG recording session. <br> Trigger structure used for data collection: cond_labs = {'Std Low Con','Std High Con','Std Low Tin','Std High Tin', 'Dev Down Con','Dev Up Con','Dev Down Tin','Dev Up Tin', 'Durdev Low Con','Durdev High Con','Durdev Low Tin','Durdev High Tin'}; <br> conditions = {'10111','10121','10211','10221', '10112','10122','10212','10222', '20111','20121','20211','20221',}; <br> Definitions: Std = standards, dev = intensity deviants, durdev = duration deviants, low = quiet stimulus. high = loud stimulus, tin = tinnitus frequency, con = control frequency
DOI: 10.25405/data.ncl.23662047.v1
title: Nuances in IMA EEG datasets

description: EEG data looking into comparison in intensity deviant MMN amplitudes between tinnitus and control participants (those with C suffix are controls). A roving paradigm was used, where deviants were pseudo-random changes from one type of intensity stimulus to the other. Two frequencies were used in interspersed blocks: tinnitus frequency and control (1 kHz). The tinnitus frequency was estimated by each individual with tinnitus to be as close to their tinnitus as possible (matched controls listened to the tinnitus frequency of their match). The loudness was also decided by each participant, but the difference originally between loud and quiet stimuli was set at -6 dB (changed by participant if they could not tell the difference in loudness). Controls were matched in hearing profile as well as gender and age to participants with tinnitus. Passive task (watching a TV show with subtitles but no sound) was used during the 1 hour EEG recording session. <br> Trigger structure used for data collection: cond_labs = {'Std Low Con','Std High Con','Std Low Tin','Std High Tin', 'Dev Down Con','Dev Up Con','Dev Down Tin','Dev Up Tin', 'Durdev Low Con','Durdev High Con','Durdev Low Tin','Durdev High Tin'}; <br> conditions = {'10111','10121','10211','10221', '10112','10122','10212','10222', '20111','20121','20211','20221',}; <br> Definitions: Std = standards, dev = intensity deviants, durdev = duration deviants, low = quiet stimulus. high = loud stimulus, tin = tinnitus frequency, con = control frequency
DOI: 10.6084/m9.figshare.23616547
title: A Simple and Effective Method to Extract Chitosan from Crustacean Shell Waste

description: A modified method to extract chitosan from crustacean shell waste is presented. Separate steps for deproteinization, decolorization, and deacetylation were replaced with single-step steeping in an alkali solution for 24 h. This method resulted in 33.26%, 11.39%, and 10.15% of chitosan yield, respectively from whiteleg shrimp, blueswimming crab and giant freshwater prawns whilepreserving physicochemical properties, which was evident by FTIR, XRD, SEM, EDS, ash, viscosity, colour, and antioxidant values. A comparison with the conventional heating method shows 8.10% chemical and 13% energy savings and comparative simplicity of the modified method showing potential to be executed at the industrial level.
DOI: 10.5281/zenodo.8163976
title: Growth form and leaf habit drive contrasting effects of Arctic amplification in long-lived woody species

description: <strong>Raw_and_STD_Chronologies.txt</strong> contains the raw (suffix "_raw") and indexed (suffix "_std") ring-width chronology derived from seven species (<em>Juniperus communis</em> L., <em>Betula nana</em> L., <em>Salix lanata</em> L., <em>Picea abies</em> L., <em>Pinus contorta</em> Douglas, <em>Betula pubescens</em> Ehrh., and <em>Sorbus aucuparia</em> L.) collected from ten sites located across Iceland. In the file, each column addresses the chronology belonging to a precise site and species, coded following the one used in the International Tree-Ring Data Bank (ITRDB) as listed in Grissino_Mayer (1993). This data was used to model growth trends and to perform climate-growth associations over the 1967-2018 period.
DOI: 10.5281/zenodo.8163977
title: Growth form and leaf habit drive contrasting effects of Arctic amplification in long-lived woody species

description: <strong>Raw_and_STD_Chronologies.txt</strong> contains the raw (suffix "_raw") and indexed (suffix "_std") ring-width chronology derived from seven species (<em>Juniperus communis</em> L., <em>Betula nana</em> L., <em>Salix lanata</em> L., <em>Picea abies</em> L., <em>Pinus contorta</em> Douglas, <em>Betula pubescens</em> Ehrh., and <em>Sorbus aucuparia</em> L.) collected from ten sites located across Iceland. In the file, each column addresses the chronology belonging to a precise site and species, coded following the one used in the International Tree-Ring Data Bank (ITRDB) as listed in Grissino_Mayer (1993). This data was used to model growth trends and to perform climate-growth associations over the 1967-2018 period.
DOI: 10.5517/ccdc.csd.cc29hdfn
title: CCDC 2129961: Experimental Crystal Structure Determination

description: Related Article: Jens Popp, Peter Klüfers|2022|Eur.J.Inorg.Chem.|2022|e202200374|doi:10.1002/ejic.202200374
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.23709726
title: spatial imaging data for the 10X visium brain dataset

description: This is the spatial imaging data for the 10X visium brain dataset ( https://figshare.com/articles/dataset/10X_visium_data_for_spatial-informed_cell-cell_communication/23621151), including the spatial locations, scalefactors_json.json and tissue image files.
DOI: 10.6084/m9.figshare.23709429
title: <strong>Genome-wide analysis of lectins in cyanobacteria: from evolutionary mode to motif patterns</strong>

description: <strong>Table S1.</strong> Selected cyanobacterial genome assemblies from GenBank and the taxonomy of the source organisms. <br> <strong>Table S2.</strong> Available cyanobacterial lectin sequences and the taxonomy of the source organisms in GenBank. Experimentally identified sequences are shown with name in the Name column. <strong>Cyanobacteria-CDS.zip</strong> A compressed file for cyanobacterial genome assemblies listed in <strong>Table S1.</strong> <br> <strong>Cyanobacteria-lectin.zip</strong> A compressed file for cyanobacterial lectin sequences listed in <strong>Table S2.</strong>
DOI: 10.6084/m9.figshare.23709429.v1
title: <strong>Genome-wide analysis of lectins in cyanobacteria: from evolutionary mode to motif patterns</strong>

description: <strong>Table S1.</strong> Selected cyanobacterial genome assemblies from GenBank and the taxonomy of the source organisms. <br> <strong>Table S2.</strong> Available cyanobacterial lectin sequences and the taxonomy of the source organisms in GenBank. Experimentally identified sequences are shown with name in the Name column. <strong>Cyanobacteria-CDS.zip</strong> A compressed file for cyanobacterial genome assemblies listed in <strong>Table S1.</strong> <br> <strong>Cyanobacteria-lectin.zip</strong> A compressed file for cyanobacterial lectin sequences listed in <strong>Table S2.</strong>
DOI: 10.5281/zenodo.7961594
title: Computational data on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR)

description: This repository contains the data and files for the computational study on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR). <br> The repository is organized into different folders as described below: ====================================================================================================<br> 1_Starting_structure<br> This folder contains the starting structures of the four binding modes obtained from the crystallographic study. These structures serve as the initial configurations for the Molecular Dynamics (MD) simulations. ====================================================================================================<br> 2_mdp_files<br> This folder contains two subfolders: 1_MD<br> The "1_MD" subfolder includes the MD simulation files in the mdp format. These files define the parameters and settings for running the MD simulations with Gromacs version 2019.3. 2_US<br> The "2_US" subfolder includes the files required for performing Umbrella Sampling (US) simulations for each of the binding modes using Gromacs version 2021.3. Within each mode folder, you will find the following files: constraint: Position restraints files used in the Umbrella Sampling simulations.<br> pull: mdp files containing the settings for pulling in the US simulations.<br> us: mdp files used for the US simulations. ====================================================================================================<br> 3_MD_results<br> This folder contains the results of the MD simulations. It includes the structure files (.gro) and trajectory files (.xtc) for each simulation. Due to the large size of the files, the solvent has been excluded, and the results are provided at every 1 nanosecond (ns) interval. ====================================================================================================<br> 4_US_results<br> The "4_US_results" folder includes the trajectories of the US simulations for each of the binding modes. For each mode, two trajectories are provided. ====================================================================================================<br> 5_pdb<br> This folder includes the pdb files of the simulated structures of the two binding modes after the equilibration step. ==================================================================================================== We acknowledge funding by the German Research Foundation (DFG) through the Emmy Noether Young Group Leader Programme (CK, project KO 5423/1-1), the Swedish e-Science Research Centre (SeRC, ML, PN), the Swedish Research Council (PN, Grant No. 2018-4343). Computing resources were provided by the Swedish National Infrastructure for Computing (SNIC).
DOI: 10.5281/zenodo.7961597
title: Computational data on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR)

description: This repository contains the data and files for the computational study on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR). <br> The repository is organized into different folders as described below: ====================================================================================================<br> 1_Starting_structure<br> This folder contains the starting structures of the four binding modes obtained from the crystallographic study. These structures serve as the initial configurations for the Molecular Dynamics (MD) simulations. ====================================================================================================<br> 2_mdp_files<br> This folder contains two subfolders: 1_MD<br> The "1_MD" subfolder includes the MD simulation files in the mdp format. These files define the parameters and settings for running the MD simulations with Gromacs version 2019.3. 2_US<br> The "2_US" subfolder includes the files required for performing Umbrella Sampling (US) simulations for each of the binding modes using Gromacs version 2021.3. Within each mode folder, you will find the following files: constraint: Position restraints files used in the Umbrella Sampling simulations.<br> pull: mdp files containing the settings for pulling in the US simulations.<br> us: mdp files used for the US simulations. ====================================================================================================<br> 3_MD_results<br> This folder contains the results of the MD simulations. It includes the structure files (.gro) and trajectory files (.xtc) for each simulation. Due to the large size of the files, the solvent has been excluded, and the results are provided at every 1 nanosecond (ns) interval. ====================================================================================================<br> 4_US_results<br> The "4_US_results" folder includes the trajectories of the US simulations for each of the binding modes. For each mode, two trajectories are provided. ==================================================================================================== We acknowledge funding by the German Research Foundation (DFG) through the Emmy Noether Young Group Leader Programme (CK, project KO 5423/1-1), the Swedish e-Science Research Centre (SeRC, ML, PN), the Swedish Research Council (PN, Grant No. 2018-4343). Computing resources were provided by the Swedish National Infrastructure for Computing (SNIC).
DOI: 10.5281/zenodo.8163485
title: Computational data on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR)

description: This repository contains the data and files for the computational study on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR). <br> The repository is organized into different folders as described below: ====================================================================================================<br> 1_Starting_structure<br> This folder contains the starting structures of the four binding modes obtained from the crystallographic study. These structures serve as the initial configurations for the Molecular Dynamics (MD) simulations. ====================================================================================================<br> 2_mdp_files<br> This folder contains two subfolders: 1_MD<br> The "1_MD" subfolder includes the MD simulation files in the mdp format. These files define the parameters and settings for running the MD simulations with Gromacs version 2019.3. 2_US<br> The "2_US" subfolder includes the files required for performing Umbrella Sampling (US) simulations for each of the binding modes using Gromacs version 2021.3. Within each mode folder, you will find the following files: constraint: Position restraints files used in the Umbrella Sampling simulations.<br> pull: mdp files containing the settings for pulling in the US simulations.<br> us: mdp files used for the US simulations. ====================================================================================================<br> 3_MD_results<br> This folder contains the results of the MD simulations. It includes the structure files (.gro) and trajectory files (.xtc) for each simulation. Due to the large size of the files, the solvent has been excluded, and the results are provided at every 1 nanosecond (ns) interval. ====================================================================================================<br> 4_US_results<br> The "4_US_results" folder includes the trajectories of the US simulations for each of the binding modes. For each mode, two trajectories are provided. ====================================================================================================<br> 5_pdb<br> This folder includes the pdb files of the simulated structures of the two binding modes after the equilibration step. ==================================================================================================== We acknowledge funding by the German Research Foundation (DFG) through the Emmy Noether Young Group Leader Programme (CK, project KO 5423/1-1), the Swedish e-Science Research Centre (SeRC, ML, PN), the Swedish Research Council (PN, Grant No. 2018-4343). Computing resources were provided by the Swedish National Infrastructure for Computing (SNIC).
DOI: 10.6084/m9.figshare.23709717
title: Supplemental material files--Acute aerobic exercise regulation of myocardial calcium homeostasis involves Casq1, Casq2 and Trdn

description: Supplemental material files--Acute aerobic exercise regulation of myocardial calcium homeostasis involves Casq1, Casq2 and Trdn
DOI: 10.6084/m9.figshare.23709717.v1
title: Supplemental material files--Acute aerobic exercise regulation of myocardial calcium homeostasis involves Casq1, Casq2 and Trdn

description: Supplemental material files--Acute aerobic exercise regulation of myocardial calcium homeostasis involves Casq1, Casq2 and Trdn
DOI: 10.5281/zenodo.8164366
title: Gravitational wave parameter estimation dataset

description: Dataset for 'Gravitational Wave Parameter Estimation Using Machine Learning' workshop at OzGrav Winter School - 2023, held in University of Western Australia, Perth.
DOI: 10.5281/zenodo.8164365
title: Gravitational wave parameter estimation dataset

description: Dataset for 'Gravitational Wave Parameter Estimation Using Machine Learning' workshop at OzGrav Winter School - 2023, held in University of Western Australia, Perth.
DOI: 10.5281/zenodo.8164406
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Frère Macédone, http://www.wikidata.org/entity/Q110322210. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q110322210
DOI: 10.5281/zenodo.8164405
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Frère Macédone, http://www.wikidata.org/entity/Q110322210. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q110322210
DOI: 10.5281/zenodo.7998752
title: Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging - Data

description: Raw data and simulation code for the paper "Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging"
DOI: 10.5281/zenodo.7998753
title: Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging - Data

description: Raw data for the paper "Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging"
DOI: 10.5281/zenodo.8163858
title: Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging - Data

description: Raw data and simulation code for the paper "Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging"
DOI: 10.6084/m9.figshare.16571459.v1
title: College Education

description: This dataset is accessed from https://www.kaggle.com/jessemostipak/college-tuition-diversity-and-pay and was downloaded on August 4, 2021. <br><br> The following excerpt is from Kaggle regarding the sources of this dataset: The data this week comes from many different sources but originally came from the US Department of Education. Tuition and fees by college/university for 2018-2019, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education. Diversity by college/university for 2014, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education. Example diversity graphics from Priceonomics. Average net cost by income bracket from TuitionTracker.org. Example price trend and graduation rates from TuitionTracker.org Salary potential data comes from payscale.com. This dataset included the following files: 1. diversity_school.csv 2. historical_tuition.csv 3. salary_potential.csv 4. tuition_cost.csv 5. tuition_income.csv After data cleaning, the data in <i>diversity_school.csv</i> and <i>tuition_cost.csv</i> were merged and the data in <i>salary_potential.csv</i> and <i>tuition_income.csv</i> were merged. The combined datasets were then split based on the US Census Regions into West, Midwest, Northeast and South (https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf).