cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2023-03.2771540/2325920
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2770399/2325921
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2770744/2325922
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2771094/2325923
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2770875/2325924
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2771112/2325925
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2771050/2325926
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2771023/2325927
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2770968/2325928
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2771101/2325929
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2770915/2325930
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2770171/2325931
title: Data for EMSL Project 51287 from March 2023
DOI: 10.25582/data.2023-03.2770760/2325932
title: Data for EMSL Project 60182 from March 2023
DOI: 10.25582/data.2023-03.2770767/2325933
title: Data for EMSL Project 60182 from March 2023
DOI: 10.25582/data.2023-03.2770754/2325934
title: Data for EMSL Project 60182 from March 2023
DOI: 10.25582/data.2023-03.2770617/2325935
title: Data for EMSL Project 60182 from March 2023
DOI: 10.25582/data.2023-03.2770509/2325936
title: Data for EMSL Project 60182 from March 2023
DOI: 10.25582/data.2024-03.3069440/2325937
title: Data for EMSL Project 60938 from March 2024
DOI: 10.25582/data.2024-03.3069363/2325938
title: Data for EMSL Project 60938 from March 2024
DOI: 10.25582/data.2024-03.3069600/2325939
title: Data for EMSL Project 60938 from March 2024
DOI: 10.25582/data.2024-03.3069339/2325940
title: Data for EMSL Project 60938 from March 2024
DOI: 10.25582/data.2024-03.3069286/2325941
title: Data for EMSL Project 60938 from March 2024
DOI: 10.25582/data.2023-03.2770920/2325942
title: Data for EMSL Project 60514 from March 2023
DOI: 10.15485/2326012
title: Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

description: The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.
DOI: 10.6084/m9.figshare.25457926.v1
title: Ilustrações da revisão de escopo.docx

description: <b>Objetivo: </b>Investigar os fatores de risco associados a quedas em idosos institucionalizados. <b>Método:</b> O estudo foi conduzido em duas etapas distintas. Primeiramente, realizou-se uma revisão de escopo da literatura, seguindo os critérios do PRISMA-ScR e registrada no Open Science Framework - OSF. Na segunda etapa, procedeu-se à busca nas seguintes bases de dados: PubMed, EMBASE, Web of Science, Scopus e Lilacs, sem limite de tempo de publicação, nos idiomas em Inglês, Português e Espanhol. A triagem dos estudos e extração dos dados foi realizada utilizando o programa Rayyan QCRI. <b>Resultados:</b> Após a aplicação dos critérios de inclusão obteve-se 69 artigos. Observou-se que os medicamentos utilizados são um fator de risco significativo para quedas, com os antidepressivos sendo a classe mais frequente, presentes em 30,4% (n=21) dos estudos, seguidos pelos antipsicóticos com 24,6% (n=17) e benzodiazepínicos com 20,3% (n=14). Entre os medicamentos mais prevalentes, destacam-se a Trazodona e a Risperidona, mencionadas em 4,4% (n=3) dos estudos cada. Quanto aos instrumentos para avaliação do risco de quedas, o Teste TUG foi o mais utilizado, representando 14,5% (n=10) dos estudos. <b>Conclusão: </b>A análise dos estudos revela uma associação significativa entre o uso de medicamentos, principalmente antidepressivos, antipsicóticos e benzodiazepínicos, e o aumento do risco de quedas. Destaca-se a risperidona e a trazodona como os principais medicamentos correlacionados a esse aumento de risco. Quanto aos instrumentos de avaliação do risco de quedas, o teste de TUG foi amplamente citado na literatura revisada.
DOI: 10.6084/m9.figshare.25464193
title: test12_Wednesday_05_DAY1_PROCESSED.edf

description: Subset of dataset (1 day for 1 seal) from the full Dryad repository published here: https://datadryad.org/stash/dataset/doi:10.7291/D1ZT2B
DOI: 10.6084/m9.figshare.25464193.v1
title: test12_Wednesday_05_DAY1_PROCESSED.edf

description: Subset of dataset (1 day for 1 seal) from the full Dryad repository published here: https://datadryad.org/stash/dataset/doi:10.7291/D1ZT2B
DOI: 10.6073/pasta/c29b9a57257e2bb5ea110e6e0564c65b
title: Climate data for C1 data loggers (CR23X and CR1000), 2000 - ongoing, daily.

description: Climatological data were collected from the C1 climate station on
 Niwot Ridge (3018 m elevation) throughout the year. From 2000-06-24
 to 2013-03-25, data were recorded using a Campbell Instruments CR23X
 data logger. Starting 2013-03-27, data were recorded using a
 Campbell Instruments CR1000 data logger. Maximum and minimum values
 were recorded instantaneously, with a sampling interval of 5
 seconds. Daily means and totals were calculated from 17,280
 individual measurements. The CR23X logger was programmed to generate
 both hourly and daily output. The CR1000 logger generated daily,
 hourly, and minute data until 2014-09-10, when these time periods
 were discontinued and only 10 minute raw data were collected.
DOI: 10.6073/pasta/50d14dffa992ddfa46e9878b62e4c70e
title: Climate data for D1 data loggers (CR23X and CR1000), 2000 - ongoing, daily.

description: Climatological data were collected from a Niwot Ridge climate
 station (D1) throughout the year. From July 5 2000 through October
 23 2013, data were recorded using a Campbell Instruments CR23X data
 logger. Subsequently, data were recorded using a Campbell
 Instruments CR1000 data logger. Maximum and minimum values are
 recorded instantaneously, with a sampling interval of 5 seconds.
 Daily averages and totals were calculated from 17,280 individual
 measurements. This instrument was programmed to generate both hourly
 and daily output. Beginning in June 25 2009, 10 minute output was
 also generated. Daily output generated by the logger ended in 2014.
 Beginning in 2014, daily values were calculated from the native
 resolution data (ten minute).
DOI: 10.6073/pasta/297a20416aaa96fb7b9a3a5a5f69ecce
title: Climate data for D1 data logger (CR1000), 2014 - ongoing, 10 minute.

description: Climatological data are collected from the D1 climate station on
 Niwot Ridge (3738 m elevation) throughout the year. Data were
 recorded using a Campbell Instruments CR1000 data logger. Maximum
 and minimum values are recorded instantaneously, with a sampling
 interval of 5 seconds and programmed to generate 10 minute output.
 These data represent the native resolution (10 minute) data from
 2014 onward, with quality flags applied but no data removed.
DOI: 10.6084/m9.figshare.25464358
title: test12_Wednesday_06_Hypnogram_JKB_1Hz.csv

description: Labeled data for one seal (binned into 30-second epoch) from the full Dryad repository published here: https://datadryad.org/stash/dataset/doi:10.7291/D1ZT2B
DOI: 10.6084/m9.figshare.25464358.v1
title: test12_Wednesday_06_Hypnogram_JKB_1Hz.csv

description: Labeled data for one seal (binned into 30-second epoch) from the full Dryad repository published here: https://datadryad.org/stash/dataset/doi:10.7291/D1ZT2B
DOI: 10.6084/m9.figshare.25464355
title: test12_Wednesday_06_Hypnogram_JKB_30s.csv

description: Labeled data for one seal (1 Hz frequency) from the full Dryad repository published here: https://datadryad.org/stash/dataset/doi:10.7291/D1ZT2B
DOI: 10.6084/m9.figshare.25464355.v1
title: test12_Wednesday_06_Hypnogram_JKB_30s.csv

description: Labeled data for one seal (1 Hz frequency) from the full Dryad repository published here: https://datadryad.org/stash/dataset/doi:10.7291/D1ZT2B
DOI: 10.6073/pasta/706565ed9a0d1e39e4c0a3b358a69710
title: Climate data for C1 data logger (CR1000), 2015 - ongoing, 10 minute.

description: Climatological data are collected from the C1 climate station on
 Niwot Ridge (3018 m elevation) throughout the year. Starting
 2015-01-01, the CR1000 logger was programmed to collect raw data at
 a 10 minute resolution. Maximum and minimum values were recorded
 instantaneously, with a sampling interval of 5 seconds. These data
 represent the native (ten minute) resolution data from that point
 onward, with quality flags applied but no data removed.
DOI: 10.5281/zenodo.10801294
title: Neural Fields FEM Simulation Data

description: Dataset for simulation on FEM Neural fields
DOI: 10.5281/zenodo.10801295
title: Neural Fields FEM Simulation Data

description: Dataset for simulation on FEM Neural fields
DOI: 10.5281/zenodo.10812356
title: Neural Fields FEM Simulation Data

description: Dataset for simulation on FEM Neural fields
DOI: 10.5281/zenodo.10821737
title: Neural Fields FEM Simulation Data

description: Dataset for simulation on FEM Neural fields
DOI: 10.5281/zenodo.10838702
title: Neural Fields FEM Simulation Data

description: Dataset for simulation on FEM Neural fields
DOI: 10.6084/m9.figshare.19148912
title: Observing planet-driven dust spirals with ALMA

description: <b>Dataset and image gallery </b>supplementing the publication:Speedie, J., Booth R.A. & Dong, R. (2022). Observing Planet-driven Dust Spirals with ALMA. <i>ApJ</i>, 930:40 (22pp).<br>--------------------------------------------------------------------------------<b>data.zip</b> contains fits files of synthetic ALMA Band 7 continuum observations [mJy/beam].<b>image.gallery.zip</b> contains a ready-made visualization of everything in <b>data.zip</b>: an image gallery (and quicklook movies) of disk images [mJy/beam] and residual maps [S/N]Loose<b> *.mp4 files </b>are also present in <b>image.gallery.zip</b>.<br>The following model permutations are available:1. <b>Face-on</b> disks:<b>x2</b> equation of state (isothermal, adiabatic with beta=10)<b>x3</b> planet mass (0.3, 1.0 and 3.0 thermal masses)<b>x4</b> disk optical depth (0.1, 0.3, 1.0, 3.0 initial tau at Rp)<b>x6</b> requested observing sensitivity (10, 15, 20, 25, 30, 35 uJy/beam)<b>x3</b> antenna configuration pairs (C43-4 + C43-7, C43-5 + C43-8, C43-6 + C43-9)<b>= 432</b> synthetic ALMA Band 7 continuum images (+24 model dust continuum emission maps that served as input [Jy/arcsec2])<br>2. <b>Inclined</b> disks:<b>x1</b> equation of state (adiabatic with beta=10)<b>x1</b> planet mass (1.0 thermal masses)<b>x4</b> disk optical depth (0.1, 0.3, 1.0, 3.0 initial tau at Rp)<b>x3</b> disk inclinations (30, 50 and 70 degrees)<b>x2</b> planet position angle (on major axis, on minor axis)<b>x6</b> requested observing sensitivity (10, 15, 20, 25, 30, 35 uJy/beam)<b>x1</b> antenna configuration pairs (C43-5 + C43-8)<b>= 144</b> synthetic ALMA Band 7 continuum images---------------------------------------------------------------------------------When you make use of material from this repository, please acknowledge:Speedie, J., Booth R.A. & Dong, R. (2022). Observing Planet-driven Dust Spirals with ALMA. <i>ApJ</i>, 930:40 (22pp).
DOI: 10.6084/m9.figshare.25460755.v1
title: Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset

description: The dataset is a comprehensive collection of sensor data captured from various sources, including accelerometer, gyroscope, magnetometer, GPS, gravity sensor, orientation sensor, and sensors for measuring total acceleration, uncalibrated magnetometer readings, uncalibrated gyroscope readings, and uncalibrated accelerometer readings. This diverse range of sensors has recorded a wide array of parameters, such as acceleration force, gravitational force, rate of rotation, raw acceleration data without bias compensation, magnetic field strength, uncompensated rate of rotation, and angles around the x, y, and z axes.Furthermore, the GPS sensor has captured data on bearing accuracy, speed accuracy, vertical precision, horizontal accuracy, vehicle speed, altitude, longitude, and latitude. This extensive collection of parameters ensures a comprehensive understanding of road conditions and various driving patterns.The dataset aims to promote collaboration, foster further exploration, and facilitate the development of innovative ideas in the field of intelligent transportation systems. By making this real-time data on road conditions available to the community, the dataset offers numerous benefits, including enhanced monitoring capabilities, improved safety, better infrastructure maintenance, optimized traffic management, and data-driven urban planning and decision-making.
DOI: 10.6084/m9.figshare.25404220
title: Data for paper: "Data-driven image mechanics (D2IM): a deep learning approach to predict displacement and strain fields from undeformed X-ray tomography images - Evaluation of bone mechanics"

description: Data used in the paper: "Data-driven image mechanics (D2IM): a deep learning approach to predict displacement and strain fields from undeformed X-ray tomography images - Evaluation of bone mechanics"by Peter Soar, Marco Palanca, Enrico Dall'Ara and Gianluca Tozzi<br>submitted to publication in Extreme Mechanical Letters, 2024<br>There are 2 parts of this directory:The main dataset 'D2IM_Data':<br>- 'input' comprises slices of XTC scans of porcine vertebra. There is a subfolder 'Removed' containing slices that were manually removed from the dataset due to not containing enough bone. The original 3D scans of the images can be found at: https://orda.shef.ac.uk/articles/dataset/Data_for_paper_MicroFE_models_of_porcine_vertebrae_with_induced_bone_focal_lesions_validation_of_predicted_displacements_with_Digital_Volume_Correlation_/16732441/1<br>- 'mask' is another input that was generated from these scan slices. <br>' 'Target' contains three subfolders of the displacements ('U','V','W'), used as the target/ground truth. These fields were obtained using the DIC/DVC software SPAM.<br>'Clinical' folder contains a few examples used for testing the sensitivity of the D2IM model to lower resolution input. Clinical images were collected at the CT Scan Department, Northern General Hospital, Sheffield.The 'Test Predictions' directory has three archives, each containing images showing the predicted displacements and displacement errors for the full set of test data. Each archive corresponds to the named displacement component u, v or w.A copy of the code for the D2IM prototype model can be found on GitHub at: https://github.com/PeterSoar/D2IM_PrototypeFor any questions the reader is encouraged to contact Dr Peter Soar (p.soar@greenwich.co.uk).
DOI: 10.6084/m9.figshare.25464379
title: test12_Wednesday_Data-Subset

description: Labeled data for one seal (binned into 30-second epoch) from the full Dryad repository published here: https://datadryad.org/stash/dataset/doi:10.7291/D1ZT2B
DOI: 10.6084/m9.figshare.25464379.v1
title: test12_Wednesday_Data-Subset

description: Labeled data for one seal (binned into 30-second epoch) from the full Dryad repository published here: https://datadryad.org/stash/dataset/doi:10.7291/D1ZT2B
DOI: 10.6084/m9.figshare.25424968
title: Dataset

description: The file contains the de-identified dataset from the participants
DOI: 10.6084/m9.figshare.25424968.v1
title: Dataset

description: The file contains the de-identified dataset from the participants
DOI: 10.6084/m9.figshare.25425019
title: Copy of Consent Form and Blank Research Instrument

description: Copy of consent form and blank research instrument retrieved from Google Forms
DOI: 10.5281/zenodo.10857973
title: Maternal educational attainment in pregnancy and epigenome-wide DNA methylation changes in the offspring from birth until adolescence

description: Choudhary, P., Monasso, G. S., Karhunen, V., Ronkainen, J., Mancano, G., Howe, C. G., Niu, Z., Zeng, X., Guan, W., Dou, J., Feinberg, J. I., Mordaunt, C., Pesce, G., Baïz, N., Alfano, R., Martens, D. S., Wang, C., Isaevska, E., Keikkala, E., Mustaniemi, S., … Sebert, S. (2023). Maternal educational attainment in pregnancy and epigenome-wide DNA methylation changes in the offspring from birth until adolescence. Molecular psychiatry, 10.1038/s41380-023-02331-5. Advance online publication. https://doi.org/10.1038/s41380-023-02331-5

Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.
DOI: 10.5281/zenodo.10857974
title: Maternal educational attainment in pregnancy and epigenome-wide DNA methylation changes in the offspring from birth until adolescence

description: Choudhary, P., Monasso, G. S., Karhunen, V., Ronkainen, J., Mancano, G., Howe, C. G., Niu, Z., Zeng, X., Guan, W., Dou, J., Feinberg, J. I., Mordaunt, C., Pesce, G., Baïz, N., Alfano, R., Martens, D. S., Wang, C., Isaevska, E., Keikkala, E., Mustaniemi, S., … Sebert, S. (2023). Maternal educational attainment in pregnancy and epigenome-wide DNA methylation changes in the offspring from birth until adolescence. Molecular psychiatry, 10.1038/s41380-023-02331-5. Advance online publication. https://doi.org/10.1038/s41380-023-02331-5

Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.
DOI: 10.5281/zenodo.10857316
title: Blood DNA Methylation Profiling Identifies Cathepsin Z Dysregulation in Pulmonary Arterial Hypertension

description: Ulrich, A., Wu, Y., Draisma, H., Wharton, J., Swietlik, E. M., Cebola, I., Vasilaki, E., Balkhiyarova, Z., Jarvelin, M. R., Auvinen, J., Herzig, K. H., Coghlan, J. G., Lordan, J., Church, C., Howard, L. S., Pepke-Zaba, J., Toshner, M., Wort, S. J., Kiely, D. G., Condliffe, R., … Rhodes, C. J. (2024). Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension. Nature communications, 15(1), 330. https://doi.org/10.1038/s41467-023-44683-0

Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.