cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/3e9d2924-d43f-4484-9e9b-748d53ff8a4e
title: Data underlying the publication: Fundamental study on the influence of dynamic load and distributed energy resources on power system short-term voltage stability

description: STVS method is utilized to analyze the impact of distributed energy resources and dynamic load on power system stability. The repository contains the publication, code, explanation, and the model that can be used.
DOI: 10.4121/3e9d2924-d43f-4484-9e9b-748d53ff8a4e.v1
title: Data underlying the publication: Fundamental study on the influence of dynamic load and distributed energy resources on power system short-term voltage stability

description: STVS method is utilized to analyze the impact of distributed energy resources and dynamic load on power system stability. The repository contains the publication, code, explanation, and the model that can be used.
DOI: 10.6084/m9.figshare.24712386
title: 20231202_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24712386.v1
title: 20231202_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24712341
title: BLAST top hits

description: just BLAST Nematostella gene models against other species' gene models and take the highest-scoring hits and you get this file
DOI: 10.6084/m9.figshare.24712341.v1
title: BLAST top hits

description: just BLAST Nematostella gene models against other species' gene models and take the highest-scoring hits and you get this file
DOI: 10.57745/exql6x
title: README.txt

description: details on the dataset
DOI: 10.57745/aaghvf
title: Avian_influenza_wildlife_surveillance_France_2016_to_2022.tab

description: dataset
DOI: 10.34740/kaggle/dsv/7099479
title: MoviesDatasets
DOI: 10.6084/m9.figshare.24712371.v1
title: Data deposit.xlsx

description: Data of protein expression in figures 3b, Supplementary figure 4, Supplementary figure 5, Supplementary figure 13, Supplementary figure 15 are provided.
DOI: 10.5281/zenodo.10246175
title: A step towards understanding plastic complexity: antimony speciation in consumer plastics and synthetic textiles revealed by XAS - supporting data

description: This dataset contains all data related to "A step towards understanding plastic complexity: antimony speciation in consumer plastics and synthetic textiles revealed by XAS"
DOI: 10.5281/zenodo.10246176
title: A step towards understanding plastic complexity: antimony speciation in consumer plastics and synthetic textiles revealed by XAS - supporting data

description: This dataset contains all data related to "A step towards understanding plastic complexity: antimony speciation in consumer plastics and synthetic textiles revealed by XAS"
DOI: 10.6084/m9.figshare.24711687
title: Pile Wave -Current dataset

description: Research data for Computational Fluid Dynamics modeling of Scour around Square pile with 45° and 90° orientations under Combined effect of Waves and Currents
DOI: 10.6084/m9.figshare.24711687.v2
title: Pile Wave -Current dataset

description: Research data for Computational Fluid Dynamics modeling of Scour around Square pile with 45° and 90° orientations under Combined effect of Waves and Currents
DOI: 10.6084/m9.figshare.24711687.v1
title: Pile Wave -Current dataset

description: Research data for Computational Fluid Dynamics modeling of Scour around Square pile with 45° and 90° orientations under Combined effect of Waves and Currents
DOI: 10.57745/yamn1a
title: AgriMultiPol_2018_SMPS data_SIRTA.csv

description: :unav
DOI: 10.57745/rprams
title: AgriMultiPol_2018_SMPS data_Grignon.csv

description: :unav
DOI: 10.57745/5qdfkn
title: AgriMultiPol_2018_All data.tab

description: This file contains all data obtained during AgrimultiPol field campaign except particle number and size distributions provided in separate files, available in the same repository. The first is the variable name, common for atmsopheric scientist, and the second line correspond to the unit of the variable. Then, each line correspond to a date and time, given at 1 hour time resolution.
DOI: 10.57745/4u50ij
title: Read_me_for_data.txt

description: Read me file
DOI: 10.57745/ifaz1f
title: Description jardins communs.tab

description: Description of common gardens location, structure and other variables
DOI: 10.57745/o3ntuk
title: readme_Description.txt

description: Description of the variables found in files "Description provenances" and "Description jardins communs"
DOI: 10.57745/73i9di
title: readme_data.txt

description: Description of variables found in each of the datasets for all common gardens
DOI: 10.57745/9nrtla
title: Description provenances.tab

description: Description of geographical origin of provenances
DOI: 10.57745/emj2ln
title: 01-1002-01-03_archive.tab

description: Dataset for common garden 01-1002-01-03
DOI: 10.57745/tn3skf
title: 01-1006-00-02_archive.tab

description: Dataset for common garden 01-1006-00-02
DOI: 10.57745/583e8w
title: 04-1000-23-01_archive.tab

description: Dataset for common garden 04-1000-23-01
DOI: 10.57745/ah9qnk
title: 01-1006-04-02_archive.tab

description: Dataset for common garden 01-1006-04-02
DOI: 10.57745/j0g9oi
title: 01-1006-03-02_archive.tab

description: Dataset for common garden 01-1006-03-02
DOI: 10.57745/rinlio
title: 01-1006-00-01_archive.tab

description: Dataset for common garden 01-1006-00-01
DOI: 10.57745/ual35c
title: 01-1006-04-00_archive.tab

description: Dataset for common garden 01-1006-04-00
DOI: 10.57745/579wpx
title: 01-1006-03-01_archive.tab

description: Dataset for common garden 01-1006-03-01
DOI: 10.57745/tinxwh
title: 01-1002-01-04_archive.tab

description: Dataset for common garden 01-1002-01-04
DOI: 10.57745/uxcdrv
title: AR972003_archive.tab

description: Dataset from the common garden AR972003
DOI: 10.57745/vvtbx2
title: 01-1002-01-02_archive.tab

description: Dataset for common garden 01-1002-01-02
DOI: 10.57745/ygrsxr
title: 01-1002-01-01 archive.tab

description: Dataset for common garden 01-1002-01-01
DOI: 10.57745/cl4ft2
title: readme_data.txt

description: Description of all the variables found in each of the datasets for all common gardens
DOI: 10.57745/jg1tay
title: Description provenances.tab

description: Description of geographical origin of provenances
DOI: 10.57745/t7ezaq
title: Description jardins communs.tab

description: Description of all the common gardens from which the datasets originate
DOI: 10.6084/m9.figshare.24712449
title: <b><i>RUNX1</i></b><b> Methylation as a Cancer Biomarker in Differentiating Papillary </b><b>Thyroid Carcinoma from Benign Thyroid Nodules</b>

description: <b>Aim:</b> It remains a challenge to accurately identify malignancy of thyroid nodule when biopsy is indeterminate. We aimed to investigate the abnormal DNA methylation signatures in papillary thyroid carcinoma (PTC) compared to benign thyroid nodule (BTN). <b>Methods:</b> We performed genome profiling by 850K array and RNA-Sequencing in early-stage PTC and BTN tissue samples. The identified gene was validated in two independent case-control studies using mass spectrometry. <b>Results:</b> Hypomethylation of RUNX1 in PTC was identified and verified (all ORs ≥ 1.50). RUNX1 methylation achieved good accuracy in differentiating early-stage PTC from BTN, especially for younger women. <b>Conclusion</b><b>s</b><b>:</b> We disclosed a significant association between RUNX1 hypomethylation and PTC, suggesting RUNX1 methylation as a potential biomarker for companion diagnosis of malignant thyroid nodules.
DOI: 10.6084/m9.figshare.24712449.v1
title: <b><i>RUNX1</i></b><b> Methylation as a Cancer Biomarker in Differentiating Papillary </b><b>Thyroid Carcinoma from Benign Thyroid Nodules</b>

description: <b>Aim:</b> It remains a challenge to accurately identify malignancy of thyroid nodule when biopsy is indeterminate. We aimed to investigate the abnormal DNA methylation signatures in papillary thyroid carcinoma (PTC) compared to benign thyroid nodule (BTN). <b>Methods:</b> We performed genome profiling by 850K array and RNA-Sequencing in early-stage PTC and BTN tissue samples. The identified gene was validated in two independent case-control studies using mass spectrometry. <b>Results:</b> Hypomethylation of RUNX1 in PTC was identified and verified (all ORs ≥ 1.50). RUNX1 methylation achieved good accuracy in differentiating early-stage PTC from BTN, especially for younger women. <b>Conclusion</b><b>s</b><b>:</b> We disclosed a significant association between RUNX1 hypomethylation and PTC, suggesting RUNX1 methylation as a potential biomarker for companion diagnosis of malignant thyroid nodules.
DOI: 10.5255/ukda-sn-856815
title: Cost of Living Crisis: Impact on Schools, 2023

description: A rapid and unexpected increase in global prices lead to an unprecedented cost-of-living crisis in 2022/23, affecting pupils and their schools who are often the first-line of support for families. This project gathered evidence around the overarching scale of challenges in schools in England, how these varied across settings and groups of pupils, and what steps schools took to mitigate the impacts of the crisis. It drew on nationally representative surveys of teachers and senior leaders in mainstream and special schools, to provide insights into the overarching impact of the cost-of-living crisis on pupils, how day-to-day provision in schools has been affected and the support which schools are providing.
DOI: 10.57745/xe3qw7
title: Data.zip

description: Raw data LC-HRMS, GC-MS and extracted data matrices
DOI: 10.5281/zenodo.8367413
title: Net Methane Production Predicted by Patch Characteristics in a Freshwater Wetland

description: Dataset supporting submitted research paper. "flux_patchdataarchive.csv" includes all plot-level data and is organized with sample locations and time in rows and data collected as headers in columns. "microbialall.csv" includes only microbial taxonomic data collected from soils in each treatment. Other files include time series data from water level and temperature sensors deployed at each treatment (n=5). Refer to 'metadata.csv' for units and descriptions of data in each file. Data collected 2021-2022. Approved by all authors.
DOI: 10.5281/zenodo.8367414
title: Net Methane Production Predicted by Patch Characteristics in a Freshwater Wetland

description: Dataset supporting submitted research paper. "micrboial_typemelted.csv" includes all plot level data and is organized with sample locations and time in rows and data collected as headers in columns. Other files are included time series data from water level and temperature sensors deployed at each treatment (n=5). Data collected 2021-2022. Approved by all authors.
DOI: 10.12751/g-node.m9hpkr
title: 180 Neurons in macaque are VIP during fixation, saccades and smooth pursuit eye movements

description: Data underlying the publication Dowiasch S, Blohm G, Bremmer F (2016) Neural correlate of spatial (mis-)localization during smooth eye movements. Eur J Neurosci, 44:1846–1855. DOI: 10.1111/ejn.13276
DOI: 10.12751/g-node.5b08du
title: Baseline and driven activity of p-type and ampullary electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus

description: Data underlying the publication Grewe J, Kruscha A, Lindner B, Benda J (2017) Synchronous spikes are necessary but not sufficient for a synchrony code in populations of spiking neurons. PNAS. DOI: 10.1073/pnas.1615561114
DOI: 10.5066/p9pf3psm
title: Prototype updated principal aquifer datasets for three aquifer systems in the Upper Midwest, USA

description: This geospatial dataset represents a prototype of a finer-scale representation of the principal aquifers of the United States, using four original principal aquifers in the Upper Midwest, United States, which were re-analyzed and condensed into three updated principal aquifers. The original principal aquifer definitions and extents were published in the Ground Water Atlas of the United States at a scale of 1:5,000,000. The updated version in this dataset are based on a reinterpretation of the original definitions using geologic data from the U.S. Geological Survey (USGS) State Geologic Map Compilation (v 1.1, 2017), which contains published state data at scales from 1:50,000 to 1:1,000,000. This shapefile includes fields describing the original principal aquifer definitions and data links as well as ages, major and minor lithologies, and source data for the updated aquifer unit polygons. A detailed description of the process followed to evaluate the old definitions of the principal aquifers in the Ground Water Atlas of the United States in the context of the State Geologic Map compilation datasets can be found in the related primary publication.
DOI: 10.5281/zenodo.10246153
title: Best learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

description: Best learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description here). 
Models: GP non spatial, GP spatial (sum), GP spatial (product),RF non spatial, RF spatial, MLP non spatial, MLP spatial, LTAE non spatial, LTAE spatial
For further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available here.
The implementation of the models is available in the open source repository.
DOI: 10.5281/zenodo.7104551
title: Best learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

description: Best learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description here). 
Models: GP non spatial, GP spatial (sum), GP spatial (product),RF non spatial, RF spatial, MLP non spatial, MLP spatial, LTAE non spatial, LTAE spatial
For further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available here.
The implementation of the models is available in the open source repository.
DOI: 10.5281/zenodo.7104552
title: Learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

description: Learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description here). Each model is learned in an eco-climatic region. For further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available here. The implementation of the models is available in the open source repository.
DOI: 10.5281/zenodo.7115460
title: Learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

description: Learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description here). Each model is learned in an eco-climatic region. Model GP non spatial GP spatial (sum) GP spatial (product) <pre> <code>data_20210511-141111_model_20220621-155521</code></pre> <pre> <code>data_20210511-141111_model_20220411-144241</code></pre> <pre> <code>data_20210511-141111_model_20220413-162100</code></pre> Model MLP non spatial MLP spatial LTAE non spatial LTAE spatial <pre> <code>data_20210511-141111_model_20220225-142000</code></pre> <pre> <code>data_20210511-141111_model_20220312-124400</code></pre> <pre> <code>data_20210511-141111_model_20220207-123103</code></pre> <pre> <code>data_20210511-141111_model_20220208-111338</code></pre> Model RF non spatial RF spatial <pre> <code>data_20210511-141111_model_20211018-155521</code></pre> <pre> <code>data_20210511-141111_model_20211018-144241</code></pre> For further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available here. The implementation of the models is available in the open source repository.
DOI: 10.6084/m9.figshare.24712371
title: Cultured Meat with Enriched Organoleptic Properties by Regulating Cell Differentiation

description: Data of protein expression in figures 3b, Supplementary figure 4, Supplementary figure 5, Supplementary figure 13, Supplementary figure 15 are provided.
DOI: 10.6084/m9.figshare.24712371.v3
title: Cultured Meat with Enriched Organoleptic Properties by Regulating Cell Differentiation

description: Data of protein expression in figures 3b, Supplementary figure 4, Supplementary figure 5, Supplementary figure 13, Supplementary figure 15 are provided.
DOI: 10.6084/m9.figshare.24712371.v2
title: Cultured Meat with Enriched Organoleptic Properties by Regulating Cell Differentiation

description: Data of protein expression in figures 3b, Supplementary figure 4, Supplementary figure 5, Supplementary figure 13, Supplementary figure 15 are provided.
DOI: 10.5281/zenodo.10200774
title: VocalSet & Annotated VocalSet

description: This deposit includes two datasets: VocalSet [1] and Annotated VocalSet [2]. The Annotated VocalSet has been revised to include manual corrections, regarding missing files, notes, and other fixes to ensure integrity between the audio files and annotations.
[1] Wilkins, J., Prem Seetharaman, Alison Wahl, & Bryan Pardo. (2018). VocalSet: A Singing Voice Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1193957
[2] Faghih, B., & Timoney, J. (2022). Annotated-VocalSet: A Singing Voice Dataset [Data set]. In Applied Sciences (1.0.0, Vol. 12, Number 18, p. 9257). Zenodo. https://doi.org/10.5281/zenodo.7061507
DOI: 10.5281/zenodo.10200775
title: VocalSet & Annotated VocalSet

description: This deposit includes two datasets: VocalSet [1] and Annotated VocalSet [2]. The Annotated VocalSet has been revised to include manual corrections, regarding missing files, notes, and other fixes to ensure integrity between the audio files and annotations.
[1] Wilkins, J., Prem Seetharaman, Alison Wahl, & Bryan Pardo. (2018). VocalSet: A Singing Voice Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1193957
[2] Faghih, B., & Timoney, J. (2022). Annotated-VocalSet: A Singing Voice Dataset [Data set]. In Applied Sciences (1.0.0, Vol. 12, Number 18, p. 9257). Zenodo. https://doi.org/10.5281/zenodo.7061507
DOI: 10.6084/m9.figshare.24712539
title: MiRNA核糖核酸

description: Domestic cattle play a crucial role in agriculture, providing various products such as meat, milk, and leather. However, there are differences in disease resistance and tissue-specific variations among different breeds, and the exact molecular mechanisms behind these variations are not yet fully understood. This study utilized RNA-Seq technology to investigate miRNAs associated with disease resistance and tissue specificity in the liver and spleen tissues of Yunnan Zebu and Holstein cattle. The results of this research have shed light on the molecular mechanisms responsible for the observed differences in disease resistance and tissue specificity between these breeds. This information is of immense scientific and technical significance, as it will serve as a basis for the breeding and producing domestic cattle, which have significant socio-economic importance.
DOI: 10.6084/m9.figshare.24712539.v1
title: MiRNA核糖核酸

description: Domestic cattle play a crucial role in agriculture, providing various products such as meat, milk, and leather. However, there are differences in disease resistance and tissue-specific variations among different breeds, and the exact molecular mechanisms behind these variations are not yet fully understood. This study utilized RNA-Seq technology to investigate miRNAs associated with disease resistance and tissue specificity in the liver and spleen tissues of Yunnan Zebu and Holstein cattle. The results of this research have shed light on the molecular mechanisms responsible for the observed differences in disease resistance and tissue specificity between these breeds. This information is of immense scientific and technical significance, as it will serve as a basis for the breeding and producing domestic cattle, which have significant socio-economic importance.
DOI: 10.6084/m9.figshare.23681240
title: X-photon laser direct write 3D nanolithography

description: Multiphoton photopolymerisation (MPP), also known as 3D nanoprinting, was studied using a wavelength-tunable femtosecond laser. The possibility of using any colour of the spectrum from 500 to 1200 nm with a fixed pulse width of 100 fs revealed an interplay of photophysical mechanisms more delicate than just two-photon photopolymerisation. An effective order of absorption, i.e. the X-photon absorption, as well as optimal exposure conditions were assessed for photosensitised and pure SZ2080TM pre-polymer. The tunability of wavelength greatly influenced the dynamic fabrication window (DFW), optimised conditions resulting in a 10-fold increase. Furthermore, a non-trivial energy deposition by X-photon absorption was noted with an onset of a strong lateral size increase at longer wavelengths and can be understood as due to reaching epsilon-near-zero conditions. Such a control over the voxel aspect ratio and, consequently, the photopolymerised volume, may boost 3D nanoprinting efficiency. Overall, the results reveal wavelength being an important degree of freedom to tailor the MPP process and, if optimised, benefiting broad applications in areas of micro-optics, nanophotonic devices, metamaterials and tissue engineering.
DOI: 10.6084/m9.figshare.23681240.v1
title: X-photon laser direct write 3D nanolithography

description: Multiphoton photopolymerisation (MPP), also known as 3D nanoprinting, was studied using a wavelength-tunable femtosecond laser. The possibility of using any colour of the spectrum from 500 to 1200 nm with a fixed pulse width of 100 fs revealed an interplay of photophysical mechanisms more delicate than just two-photon photopolymerisation. An effective order of absorption, i.e. the X-photon absorption, as well as optimal exposure conditions were assessed for photosensitised and pure SZ2080TM pre-polymer. The tunability of wavelength greatly influenced the dynamic fabrication window (DFW), optimised conditions resulting in a 10-fold increase. Furthermore, a non-trivial energy deposition by X-photon absorption was noted with an onset of a strong lateral size increase at longer wavelengths and can be understood as due to reaching epsilon-near-zero conditions. Such a control over the voxel aspect ratio and, consequently, the photopolymerised volume, may boost 3D nanoprinting efficiency. Overall, the results reveal wavelength being an important degree of freedom to tailor the MPP process and, if optimised, benefiting broad applications in areas of micro-optics, nanophotonic devices, metamaterials and tissue engineering.