cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30618449
title: Supplementary Material from Internet images reveal bumblebee-mimicking hoverflies follow models in preferring blue flowers, but retain the typical hoverfly attraction to yellow

description: Table S3 main data.xlsx
DOI: 10.6084/m9.figshare.30618449.v1
title: Supplementary Material from Internet images reveal bumblebee-mimicking hoverflies follow models in preferring blue flowers, but retain the typical hoverfly attraction to yellow

description: Table S3 main data.xlsx
DOI: 10.34943/84561fed-d9a9-4e23-bb0b-d59cedce4326
title: Conception Bay Junction Box Deployed 2025-11-04

description: The ONC Mini Junction Box 023 was deployed on 2025-11-04 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Junction Box. Junction Boxes supply power and communications to deployed instruments. Junction boxes have a number of serial and ethernet ports, including 400V ethernet ports that enable connections to other junction boxes and high-voltage instruments. Junction boxes can convert high voltages to lower voltages (15V, 24V or 48V) required by many instruments. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/c3050567-a046-49f6-be21-89e5f1b3877e
title: Conception Bay Conductivity Temperature Depth Deployed 2025-11-04

description: The Sea-Bird Microcat SBE37SMP-ODO 22979 was deployed on 2025-11-04 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Conductivity Temperature Depth. Conductivity Temperature Depth (CTD) is an instrument package that contains sensors for measuring the conductivity, temperature, and pressure of seawater. Salinity, sound velocity, depth and density are variables that can be derived from sensor measurements. CTDs can carry additional instruments and sensors such as oxygen sensors, turbidity sensors and fluorometers. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/dd0b3049-1dcb-4ed9-9453-e94b971613ea
title: Conception Bay Acoustic Doppler Current Profiler 400 kHz Deployed 2025-11-04

description: The Nortek AWAC AST 400 kHz WAV 7749 was deployed on 2025-11-04 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Acoustic Doppler Current Profiler 400 kHz. Acoustic Doppler Current Profilers are hydroacoustic instruments, similar to sonars. ADCPs measure current speed and direction at multiple predetermined depths simultaneously. ADCPs use the Doppler effect of sound waves that are scattered by particles in seawater over a depth range. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/e42047bf-e94a-47b7-898e-d1d8c86a3ec3
title: Conception Bay Video Camera Deployed 2025-11-04

description: The SubC Imaging Rayfin SN 21017 was deployed on 2025-11-04 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Video Camera. Video cameras record video of characteristics of the surrounding environments and can be deployed on fixed and mobile platforms. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/f291f65f-2dd8-423b-834f-7c1e6b587927
title: Conception Bay Camera Lights Deployed 2025-11-04

description: The Aquorea Mk3 DUAL LED Light SN02 was deployed on 2025-11-04 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Camera Lights. Camera lights are used to improve visibility where cameras are deployed. Light controls and schedules can be remotely-controlled from on-shore using software. The length of time that lights are on is limited in order to minimize impact on the environment. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/11f5c2e8-40f3-4b6b-9d5e-418cf121621d
title: Conception Bay Fluorometer Deployed 2025-11-04

description: The Turner Cyclops-7 Fluorometer (S/N 900616) was deployed on 2025-11-04 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Fluorometer. Fluorometers measure the amount of stimulated electromagnetic radiation (fluorescence) produced by pulses of electromagnetic radiation emitted into the water column. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/1bd0d632-790a-4426-a050-e1fe045f8b28
title: Conception Bay Oxygen Sensor Deployed 2025-11-04

description: The Sea-Bird SBE 63 Dissolved Oxygen Sensor 632516 was deployed on 2025-11-04 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Oxygen Sensor. Oxygen sensors measure dissolved oxygen concentration in seawater. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/efb0832d-cd03-4adb-9fd3-30c7ffc56568
title: Conception Bay Hydrophone Deployed 2025-11-04

description: The Ocean Sonics icListen HF Hydrophone 7079 was deployed on 2025-11-04 at Conception Bay. Conception Bay is located west of the city St. John's, Newfoundland. This device is a Hydrophone. Hydrophones are devices containing transducers that convert underwater sound waves into electrical signals. They are acoustic instruments that can process data while they are being collected to produce calibrated waveform data. Hydrophones are typically used to study vocalizations of marine mammals, ship traffic and ambient noise. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.6084/m9.figshare.30618410
title: Data - Slacklearn II

description: Variables are abbreviated as follows:Delta_SL-EO_D<br>Pre- to post-change in single-leg slackline, eyes open balance performance on the dominant leg.<br>Delta_SL-EO_ND<br>Pre- to post-change in single-leg slackline, eyes open balance performance on the non-dominant leg.<br>Delta_SL-EC_D<br>Pre- to post-change in single-leg eyes closed balance performance on the dominant leg.<br>Delta_SL-EC_ND<br>Pre- to post-change in single-leg eyes closed balance performance on the non-dominant leg.<br>Delta_D_N30-P40<br>Pre- to post-percent change in SEP N30–P40 peak-to-peak amplitude for dominant-leg tibialis stimulation.<br>Delta_D_P40-N50<br>Pre- to post-percent change in SEP P40–N50 peak-to-peak amplitude for dominant-leg stimulation.<br>Delta_D_N50-P60<br>Pre- to post-percent change in SEP N50–P60 peak-to-peak amplitude for dominant-leg stimulation.<br>Delta_ND_N30-P40<br>Pre- to post-percent change in SEP N30–P40 peak-to-peak amplitude for the non-dominant leg.<br>Delta_ND_P40-N50<br>Pre- to post-percent change in SEP P40–N50 peak-to-peak amplitude for the non-dominant leg.<br>Delta_ND_N50-P60<br>Pre- to post-percent change in SEP N50–P60 peak-to-peak amplitude for the non-dominant leg.<br>Delta_Theta_Frontal<br>Pre- to post-change in resting-state theta-band power in the frontal ROI.<br>Delta_Theta_Central<br>Pre- to post-change in resting-state theta-band power in the central ROI.<br>Delta_Alpha_Frontal<br>Pre- to post-change in resting-state alpha-band power in the frontal ROI.<br>Delta_Alpha_Central<br>Pre- to post-change in resting-state alpha-band power in the central ROI.<br>Delta_Beta_Frontal<br>Pre- to post-change in resting-state beta-band power in the frontal ROI.<br>Delta_Beta_Central<br>Pre- to post-change in resting-state beta-band power in the central ROI.
DOI: 10.6084/m9.figshare.30618410.v2
title: Data - Slacklearn II

description: Variables are abbreviated as follows:Delta_SL-EO_D<br>Pre- to post-change in single-leg slackline, eyes open balance performance on the dominant leg.<br>Delta_SL-EO_ND<br>Pre- to post-change in single-leg slackline, eyes open balance performance on the non-dominant leg.<br>Delta_SL-EC_D<br>Pre- to post-change in single-leg eyes closed balance performance on the dominant leg.<br>Delta_SL-EC_ND<br>Pre- to post-change in single-leg eyes closed balance performance on the non-dominant leg.<br>Delta_D_N30-P40<br>Pre- to post-percent change in SEP N30–P40 peak-to-peak amplitude for dominant-leg tibialis stimulation.<br>Delta_D_P40-N50<br>Pre- to post-percent change in SEP P40–N50 peak-to-peak amplitude for dominant-leg stimulation.<br>Delta_D_N50-P60<br>Pre- to post-percent change in SEP N50–P60 peak-to-peak amplitude for dominant-leg stimulation.<br>Delta_ND_N30-P40<br>Pre- to post-percent change in SEP N30–P40 peak-to-peak amplitude for the non-dominant leg.<br>Delta_ND_P40-N50<br>Pre- to post-percent change in SEP P40–N50 peak-to-peak amplitude for the non-dominant leg.<br>Delta_ND_N50-P60<br>Pre- to post-percent change in SEP N50–P60 peak-to-peak amplitude for the non-dominant leg.<br>Delta_Theta_Frontal<br>Pre- to post-change in resting-state theta-band power in the frontal ROI.<br>Delta_Theta_Central<br>Pre- to post-change in resting-state theta-band power in the central ROI.<br>Delta_Alpha_Frontal<br>Pre- to post-change in resting-state alpha-band power in the frontal ROI.<br>Delta_Alpha_Central<br>Pre- to post-change in resting-state alpha-band power in the central ROI.<br>Delta_Beta_Frontal<br>Pre- to post-change in resting-state beta-band power in the frontal ROI.<br>Delta_Beta_Central<br>Pre- to post-change in resting-state beta-band power in the central ROI.
DOI: 10.6084/m9.figshare.30618410.v1
title: Slacklearn II

description: Variables are abbreviated as follows:Delta_SL-EO_D<br>Pre- to post-change in single-leg slackline, eyes open balance performance on the dominant leg.<br>Delta_SL-EO_ND<br>Pre- to post-change in single-leg slackline, eyes open balance performance on the non-dominant leg.<br>Delta_SL-EC_D<br>Pre- to post-change in single-leg eyes closed balance performance on the dominant leg.<br>Delta_SL-EC_ND<br>Pre- to post-change in single-leg eyes closed balance performance on the non-dominant leg.<br>Delta_D_N30-P40<br>Pre- to post-percent change in SEP N30–P40 peak-to-peak amplitude for dominant-leg tibialis stimulation.<br>Delta_D_P40-N50<br>Pre- to post-percent change in SEP P40–N50 peak-to-peak amplitude for dominant-leg stimulation.<br>Delta_D_N50-P60<br>Pre- to post-percent change in SEP N50–P60 peak-to-peak amplitude for dominant-leg stimulation.<br>Delta_ND_N30-P40<br>Pre- to post-percent change in SEP N30–P40 peak-to-peak amplitude for the non-dominant leg.<br>Delta_ND_P40-N50<br>Pre- to post-percent change in SEP P40–N50 peak-to-peak amplitude for the non-dominant leg.<br>Delta_ND_N50-P60<br>Pre- to post-percent change in SEP N50–P60 peak-to-peak amplitude for the non-dominant leg.<br>Delta_Theta_Frontal<br>Pre- to post-change in resting-state theta-band power in the frontal ROI.<br>Delta_Theta_Central<br>Pre- to post-change in resting-state theta-band power in the central ROI.<br>Delta_Alpha_Frontal<br>Pre- to post-change in resting-state alpha-band power in the frontal ROI.<br>Delta_Alpha_Central<br>Pre- to post-change in resting-state alpha-band power in the central ROI.<br>Delta_Beta_Frontal<br>Pre- to post-change in resting-state beta-band power in the frontal ROI.<br>Delta_Beta_Central<br>Pre- to post-change in resting-state beta-band power in the central ROI.
DOI: 10.25919/dfpn-k912
title: NBDL Data Download
DOI: 10.7488/ds/8037
title: Care Home Stakeholder Perspectives on Data Sharing and Linkage: Search and Data Extraction

description: The data deposited relate to a rapid scoping review that I conducted under the Advanced Care Research Centre as part of my Individual Project in Ageing and Care. The following is a draft abstract for a pending publication of this review: "Since the COVID-19 pandemic highlighted inadequacies in the collection, access, and use of health and social care data across the United Kingdom (UK), the topics of ‘data sharing’ and ‘data linkage’ have gained greater importance. Yet, the sharing and linkage of social care data, especially that from care homes, remains underexplored. To address the limited understanding of care home stakeholders’ perspectives on data sharing and linkage, a rapid scoping review was conducted to map the extent and range of existing evidence on the topic and identify gaps in the literature. Searches were conducted in PubMed, EMBASE, CINAHL Plus, Scopus, Web of Science, and the Cochrane Database of Systematic Reviews to identify studies on stakeholders’ needs, views, and experiences of data sharing or linkage in the UK care home sector. After removing 322 duplicates, 543 studies were screened. The remaining 11 studies identified key barriers, facilitators, and opportunities to improve data sharing and linkage within the UK care home sector. The findings displayed that, while there are similarities between health and social care data sharing and linkage, further research is required to provide nuanced insights into the specific needs of the care home sector.
DOI: 10.7488/ds/8038
title: High Content Drug Screening & Analysis Dataset: A comprehensive pharmacological survey across heterogeneous patient-derived GBM stem cell models
title: High Throughput, image based drug screening on patient derived, glioblastoma stem cell lines using Cell Painting staining for target agnostic, phenotypic readouts

description: 384w microplate format of high throughput, high content drug screening data from image based readouts. Screening was conducted against 6 glioma stem cell lines using multple drug libraries using approved and pre-clinical compounds. The dataset includes quantified image analysis from CellProfiler pipeline outputs: Raw data and processed data (feature re-scaling/transformation, removal of redundant features, normalisation to dmso), including metadata and related plate maps/information
description: Refer to README.txt file. Dataset will be split across each drug library screened.
DOI: 10.5281/zenodo.16794824
title: Climate projections and renewable energy MRI-ESM2-0 SSP-585 China
DOI: 10.5281/zenodo.17564489
title: Climate projections and renewable energy MRI-ESM2-0 SSP-585 China
DOI: 10.6084/m9.figshare.30615749
title: Dataset for Hidden bioavailable dissolved organic matter in the deep northwestern Pacific Ocean

description: Dataset for Hidden bioavailable dissolved organic matter in the deep northwestern Pacific Ocean, including 1): Table S1. Summary of DOC concentration, THAA concentration, DOC-normalized THAA (%DOC), and THAA composition reported in this study; 2): Table S2. Amino acid composition and degradation index (DI) of elevated and surrounding THAA at depths of 1,000-4,000 m.
DOI: 10.6084/m9.figshare.30615749.v1
title: Dataset for Hidden bioavailable dissolved organic matter in the deep northwestern Pacific Ocean

description: Dataset for Hidden bioavailable dissolved organic matter in the deep northwestern Pacific Ocean, including 1): Table S1. Summary of DOC concentration, THAA concentration, DOC-normalized THAA (%DOC), and THAA composition reported in this study; 2): Table S2. Amino acid composition and degradation index (DI) of elevated and surrounding THAA at depths of 1,000-4,000 m.
DOI: 10.5281/zenodo.17444832
title: Climate projections and renewable energy MRI-ESM2-0 SSP-245 China
DOI: 10.5281/zenodo.17562159
title: Climate projections and renewable energy MRI-ESM2-0 SSP-245 China
DOI: 10.6084/m9.figshare.30601421
title: ASR of IL4I1-LAAO family

description: Ancestral Sequence Reconstruction files of Vertebrates LAAO-IL4I1 family
DOI: 10.6084/m9.figshare.30601421.v1
title: ASR of IL4I1-LAAO family

description: Ancestral Sequence Reconstruction files of Vertebrates LAAO-IL4I1 family
DOI: 10.5281/zenodo.17607589
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251114j Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251114j Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.17607590
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251114j Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251114j Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.15468/dl.5zfbp9
title: Occurrence Download

description: A dataset containing 1197912 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((10.45301 59.43411,10.70571 58.9097,11.73822 58.76025,12.12412 59.89325,10.99645 59.80091,10.45301 59.43411))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 1197912 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032762-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28280419
title: Coupling machine learning and group contribution method to predict density and viscosity of ionic liquid-inorganic solvent-water ternary mixtures

description: Accurate prediction of the physical properties of mixed systems is essential for efficient industrial design and process optimization. This study explores the use of a hybrid approach that combines the group contribution (GC) method with advanced machine learning (ML) algorithms to predict the density and viscosity of ternary mixtures comprising ionic liquids (ILs), inorganic solvents (ICs), and water. The machine learning algorithms employed include artificial neural networks (ANN), XGBoost, and LightGBM. A comprehensive dataset was compiled, encompassing 5738 density data points for 25 classes of ILs and 34 classes of ICs, and 1551 viscosity data points for 12 classes of ILs and 16 classes of ICs. The results indicate that all three ML-GC models significantly outperformed traditional GC models without ML algorithms. Notably, the XGBoost-GC model exhibited the highest prediction accuracy for density, with an <i>R<sup>2</sup></i> of 0.9983, while the ANN-GC model with 7 neurons in the hidden layer delivered the best predictions for viscosity, with an <i>R<sup>2</sup></i> of 0.9967. Additionally, the Shapley Additive Interpretation (SHAP) analysis reveals that the molar fractions of water and IC primarily influence density, whereas the presence of the IC anion NO<sub>3</sub> and temperature significantly impact viscosity. Moreover, a practical example is provided to illustrate the real-world applicability of the ML-GC models developed in this work, highlighting their potential to enhance industrial processes through precise property predictions.
DOI: 10.6084/m9.figshare.28280419.v1
title: Coupling machine learning and group contribution method to predict density and viscosity of ionic liquid-inorganic solvent-water ternary mixtures

description: Accurate prediction of the physical properties of mixed systems is essential for efficient industrial design and process optimization. This study explores the use of a hybrid approach that combines the group contribution (GC) method with advanced machine learning (ML) algorithms to predict the density and viscosity of ternary mixtures comprising ionic liquids (ILs), inorganic solvents (ICs), and water. The machine learning algorithms employed include artificial neural networks (ANN), XGBoost, and LightGBM. A comprehensive dataset was compiled, encompassing 5738 density data points for 25 classes of ILs and 34 classes of ICs, and 1551 viscosity data points for 12 classes of ILs and 16 classes of ICs. The results indicate that all three ML-GC models significantly outperformed traditional GC models without ML algorithms. Notably, the XGBoost-GC model exhibited the highest prediction accuracy for density, with an <i>R<sup>2</sup></i> of 0.9983, while the ANN-GC model with 7 neurons in the hidden layer delivered the best predictions for viscosity, with an <i>R<sup>2</sup></i> of 0.9967. Additionally, the Shapley Additive Interpretation (SHAP) analysis reveals that the molar fractions of water and IC primarily influence density, whereas the presence of the IC anion NO<sub>3</sub> and temperature significantly impact viscosity. Moreover, a practical example is provided to illustrate the real-world applicability of the ML-GC models developed in this work, highlighting their potential to enhance industrial processes through precise property predictions.
DOI: 10.14470/8u7561563769
title: Renland

description: The experiments are performed down the Edward Bailey valley, in the Renland peninsula, Scoresby Sund, Greenland. General purpose: ambient seismic noise recordings are obtained to characterize the geometry/structure of the valley the geometry/structure of the glaciers the microseismicity of the glacier, the friction process, crack orientation and mechanisms the seismic activity of glacial rivers, the relation between hydrological flow and noise spectrum the localization and characterization of sub-glacial flow from surface recordings. Seismic stations were composed of 3C broadband Trillium compact seismometer, a Cube datalogger and a 12V (D-cell types, stacked) battery pack.The experiment splits into three surveys performed at three different sites, one after the other, from july to august 2016. In the first experiment, we deploy 11 stations, 9 of them on a flat sandy area covering, partly, immobile ice that seems to be blocked between the Bailey Glacier (upstream) and the Apusinikajik glacier (downstream). The 9 sensors are placed a few hundreds of meters from the Apusinikajik lateral front, the last 2 are placed on the glacier next to the collapsing front. In the second and third experiment (chronologically speaking), we deploy 10 and 8 stations, respectively. Each deployment is performed along a Bailey valley transect. The first one intercepts the front-end of the glacier and the sub-glacial river exit (flow of several m3/s). The second transect is performed some 850m upstream. Waveform data are available from the GEOFON data centre, under network code 3H.
DOI: 10.25420/britishmuseum.30647021
title: Nguyễn Tăng Quang consent form

description: Consent form for research assistant Nguyễn Tăng Quang
DOI: 10.25420/britishmuseum.30647021.v1
title: Nguyễn Tăng Quang consent form

description: Consent form for research assistant Nguyễn Tăng Quang
DOI: 10.14470/3r7569753098
title: Seismic monitoring at Lascar volcano after 2014 Iquique earthquake

description: The network consists of 5 stations covering the volcanic cone flanks. These stations were operative during one year with the final purpose of detect likely changes in the seismic activity of Lascar after the 2014 Iquique earthquake. Waveform data are available from the GEOFON data centre, under network code 8E.