cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24117219
title: Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits

description: DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more ‘aged methylome’ for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein–protein interaction enrichment analyses identified the hepatic nuclear factor, <i>Hnf1a</i> (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.
DOI: 10.6084/m9.figshare.24117219.v1
title: Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits

description: DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more ‘aged methylome’ for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein–protein interaction enrichment analyses identified the hepatic nuclear factor, <i>Hnf1a</i> (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.
DOI: 10.5281/zenodo.8249638
title: Dataset: Cyclones Modulate the Control of the North Atlantic Oscillation on Transports into the Barents Sea

description: This repository contains the data presented in '<strong>Heukamp et al., 2023 - Cyclones Modulate the Control of the North Atlantic Oscillation on Transports into the Barents Sea'</strong>.
DOI: 10.6084/m9.figshare.24117147
title: The distress context of social calls evokes a fear response in the bat <i>Pipistrellus abramus</i>

description: All data used in the paper (The distress context of social calls evokes a fear response in the bat <i>Pipistrellus abramus</i>).
DOI: 10.34808/20b1-d167
title: Rain Gardens SW particle size analysis dataset

description: This dataset contains the results of laser diffraction particle size analysis of storm water runoff and storm water quality in rain garden units. Samples were collected from 4 different rain gardens in Gdansk, Poland.
The particle size analysis were carried out using a Malvern Mastersizer 2000 laser diffraction particle size analyser and are in accordance with ISO 13320. The particle size range measured by the laser diffractometer is 0.02–2000 µm.
Funding: Data were produced under the Miniatura 6 research project [2022/06/X/ST8/00504] financed by the National Science Centre Poland.
DOI: 10.26302/sshade/bandlist_raman_beta_k2caco3-2
title: Raman bandlist of synthetic beta-K2Ca(CO3)2 (Fairchildite)

description: MIR-FIR Raman bandlist of synthetic $\beta-K_2Ca(CO_3)_2$ (Fairchildite) at 295K
DOI: 10.26302/sshade/bandlist_raman_alpha_k2caco3-2
title: Raman bandlist of synthetic alpha-K2Ca(CO3)2 (Bütschliite)

description: MIR-FIR Raman bandlist of synthetic $\alpha-K_2Ca(CO_3)_2$ (Bütschliite) at 295K
DOI: 10.26302/sshade/bandlist_raman_nico3
title: Raman bandlist of synthetic NiCO3

description: MIR-FIR Raman bandlist of synthetic $NiCO_3$ at 295K
DOI: 10.5281/zenodo.8328688
title: Dataset for "A new method for identifying weather-induced power system stress using shadow prices"

description: These are data accompanying "A new method for identifying weather-induced power system stress using shadow prices". They consist of solved network files (generated with PyPSA-Eur, here v0.6.1), used for the analysis, necessary data to reproduce the figures in the paper and supplementary material. The optimised network files are of the form `workflow_data/results/stressful-weather/optimum/{weather_year}_181_90m_c1.25_Co2L0.0-1H.nc` (for weather_years in {1980,...,2019}). Unsolved ones can be found in `workflow_data/networks/...`. The filenames in `plot_data/` indicate which figure the data are associated to (e.g. `plot_data/fig_1_hourly_costs.csv` contains the hourly electricity costs during the winter of all networks and is necessary for Figure 1). We also added weather data for all system-defining events (mean surface level pressure, 10m wind speed anomaly, 2m temperature anomaly) in .nc files. Find more information about how to use these data and how they were generated in the README of the GitHub repository: https://github.com/koen-vg/stressful-weather/tree/v0.
description: {"references": ["Grochowicz, van Greevenbroek, Bloomfield, 2023: A new method for identifying weather-induced power system stress using shadow prices. arXiv:2307.13520"]}
DOI: 10.5281/zenodo.8328689
title: Dataset for "A new method for identifying weather-induced power system stress using shadow prices"

description: These are data accompanying "A new method for identifying weather-induced power system stress using shadow prices". They consist of solved network files (generated with PyPSA-Eur, here v0.6.1), used for the analysis, necessary data to reproduce the figures in the paper and supplementary material. The optimised network files are of the form `workflow_data/results/stressful-weather/optimum/{weather_year}_181_90m_c1.25_Co2L0.0-1H.nc` (for weather_years in {1980,...,2019}). Unsolved ones can be found in `workflow_data/networks/...`. The filenames in `plot_data/` indicate which figure the data are associated to (e.g. `plot_data/fig_1_hourly_costs.csv` contains the hourly electricity costs during the winter of all networks and is necessary for Figure 1). We also added weather data for all system-defining events (mean surface level pressure, 10m wind speed anomaly, 2m temperature anomaly) in .nc files. Find more information about how to use these data and how they were generated in the README of the GitHub repository: https://github.com/koen-vg/stressful-weather/tree/v0.
description: {"references": ["Grochowicz, van Greevenbroek, Bloomfield, 2023: A new method for identifying weather-induced power system stress using shadow prices. arXiv:2307.13520"]}
DOI: 10.25919/e3yx-1g10
title: Parkes observations for project P1176 semester 2023APRS_07

description: Pulsars and their winds are among nature's most powerful particle accelerators, producing particles with energies up to a few PeVatrons. In addition to high-energy radiation from the magnetosphere, the rotational energy of a neutron star is carried away in the form of magnetized ultra-relativistic pulsar winds. Investigation of pulsar wind nebula (PWN) offers an excellent chance to determine the interaction details of relativistic flows with their surroundings that may never be possible in other classes of source. Our recent analyses of ASKAP observations of Galactic Plane uncovered previously known X-ray PWN CXOU J163802.6-471358 (nicknamed as Potoroo). Recently, we have successfully detected a pulsar candidate associated with Potoroo with Parkes telescope. It is detected at 3 GHz with a dispersion measure of DM = 1553 pc cm-3 and a rotation period of 65.74 ms. Here we propose timing observations for this pulsar with the Parkes Ultra-Wideband Low (UWL) receiver. Through pulsar timing, we will be able to obtain precise measurements of the pulsar position and proper motions, and the spin-down of the pulsar which enables us to independently estimate the age of the system. Long-term timing will also enable us to measure the parallax of the system and search for pulsation at high energies (i.e. X-ray and gamma-ray)
DOI: 10.26302/sshade/bandlist_raman_alpha_na2caco3-2
title: Raman bandlist of synthetic alpha-Na2Ca(CO3)2 (Nyerereite)

description: MIR-FIR Raman bandlist of synthetic $\alpha-Na_2Ca(CO_3)_2$ (Nyerereite) at 295K
DOI: 10.5281/zenodo.8333436
title: SDS_Benchmark: a testbed for shoreline mapping algorithms using satellite imagery

description: This is an archived copy of the following Github repository: https://github.com/SatelliteShorelines/SDS_Benchmark
DOI: 10.5281/zenodo.8333435
title: SDS_Benchmark: a testbed for shoreline mapping algorithms using satellite imagery

description: This is an archived copy of the following Github repository: https://github.com/SatelliteShorelines/SDS_Benchmark
DOI: 10.57892/100-10
title: HERMiNe: A neural network for kinetic Mie polarimetry - particle size diagnostics in nanodusty plasmas

description: HERMiNe is a deep neural network for solving the kinetic Mie problem of light, scattered by nanoparticles. The polarization state of the scattered light is usable as in-situ size diagnostics for the size and the complex refractive index of particles. For the kinetic Mie polarimetry to work, it is necessary for the particles to undergo a change in size (eg. via growth processes or etching), while the refractive index is assumed to be sufficiently constant in time. Given a time series input of the ellipsometric angles Psi and Delta, the network predicts the best-fitting refractive index, from which the particle radii can be calculated via Mie theory. A detailed treatise on the theory, network properties and uncertainties can be found in: A neural network approach to kinetic Mie polarimetry for particle size diagnostics in nanodusty plasmas by Schmitz et al (DOI 10.1088/1361-6463/aceb71). The network itself is provided as: MATALB DAGNetwork format. Requires the MATLAB Deep Learning Toolbox (version 2022a or newer) [tested]. ONNX format for use with other, open frameworks, such as Pytorch etc [not tested]. For the MATLAB framework, a script is provided to facilitate operation. It takes the measurement data as input and returns the refractive index as vector elements. The documentation is accessible via the command help predictN When using another framework or custom readout functions, please note, that the network output for the imaginary part of the refractive index is increased by a factor of ten compared to the physical value, due to numerical reasons.
DOI: 10.21227/wkww-8m49
title: KHDNN-book-crossing

description: This compressed package is essential for KHDNN as it includes various components necessary for its functionality. Inside, you will find an Embedding, knowledge graphs, a user-item bipartite graph, and training records specifically tailored to the book-crossing dataset. These elements are crucial for KHDNN to operate effectively and provide accurate recommendations to users based on their preferences and interactions with books. By combining these components, KHDNN can analyze and understand user behavior, leverage knowledge graphs to enhance recommendations, and train its models on the book-crossing data set to continually improve its performance. This comprehensive package ensures that KHDNN is well-equipped to deliver high-quality book recommendations to users.
DOI: 10.5281/zenodo.8333442
title: MainstreamBIO_ValueChainAnalysis_Dataset1_2023.09.11_v1

description: Key characteristics of Regional value chains (feedstock, fates) collected through interviews from key regional stakeholders. The Country Regions and selected value chains per region are: 1) South Central (NUTS2: BG42) - Forestry biomass, Greenhouse biomass residue, Local crop biomass. 2) Zealand NUTS2: (DK02) Mid Jutland (NUTS2: DK04), Northern Jutland (NUTS2: DK05) - Grass, Animal manures, Cereal straws. 3) Southern Ireland (NUTS2: IE05) - Grass, Animal manures, Cereal straws, Seaweed, Apple residues, Hemp. 4) Friesland (NUTS2: NL12) Flevoland (NUTS2: NL23) - Grass (roadside and nature), Animal manures, Pumpkin residues. 5) Lubelskie (NUTS2: PL81) - Sugar beet residues, Berries residues, Corn residues, Rapeseed residues. 6) Catalonia (NUTS2: ES51), Navarre (NUTS2: ES22), Aragon (NUTS2: ES24) - Pig slurry, Lucerne, Forest industry residues, Camelina, Brewers spent grain. 7) Middle Norrland (NUTS2: SE32), Upper Norland (NUTS2: SE33) - Forestry logging residues, Bio sludge, Fibre sludge.
DOI: 10.5281/zenodo.8333441
title: MainstreamBIO_ValueChainAnalysis_Dataset1_2023.09.11_v1

description: Key characteristics of Regional value chains (feedstock, fates) collected through interviews from key regional stakeholders. The Country Regions and selected value chains per region are: 1) South Central (NUTS2: BG42) - Forestry biomass, Greenhouse biomass residue, Local crop biomass. 2) Zealand NUTS2: (DK02) Mid Jutland (NUTS2: DK04), Northern Jutland (NUTS2: DK05) - Grass, Animal manures, Cereal straws. 3) Southern Ireland (NUTS2: IE05) - Grass, Animal manures, Cereal straws, Seaweed, Apple residues, Hemp. 4) Friesland (NUTS2: NL12) Flevoland (NUTS2: NL23) - Grass (roadside and nature), Animal manures, Pumpkin residues. 5) Lubelskie (NUTS2: PL81) - Sugar beet residues, Berries residues, Corn residues, Rapeseed residues. 6) Catalonia (NUTS2: ES51), Navarre (NUTS2: ES22), Aragon (NUTS2: ES24) - Pig slurry, Lucerne, Forest industry residues, Camelina, Brewers spent grain. 7) Middle Norrland (NUTS2: SE32), Upper Norland (NUTS2: SE33) - Forestry logging residues, Bio sludge, Fibre sludge.
DOI: 10.5281/zenodo.8333458
title: Impact of Ancient Tectonics on Intracontinental Deformation Partitioning: Insights from Crustal Structures of the Chinese Altai Mountains

description: The dataset includes all recevier functions (SAC format files) used in this study.
DOI: 10.5281/zenodo.8333457
title: Impact of Ancient Tectonics on Intracontinental Deformation Partitioning: Insights from Crustal Structures of the Chinese Altai Mountains

description: The dataset includes all recevier functions (SAC format files) used in this study.
DOI: 10.6084/m9.figshare.24117273
title: Script 1 to 9 and necessary data to run them

description: R scripts (numbered from 1 to 9) to prepare data, perform calculations, and run analyses for the projection of speaker numbers for 27 Indigenous languages of Canada between the years 2001 and 2101. Contains data on first language collected during the censuses of 2001, 2006, 2011, 2016, and 2021 provided by Statistics Canada.Contains fertility and mortality schedules taken from the 2022 World Population Prospects (UN). Contains other data files that were produced from the data and calculations described above.
DOI: 10.34740/kaggle/dsv/6412321
title: Pending Court Cases in India
DOI: 10.5281/zenodo.8333517
title: MainstreamBIO_ValueChainAnalysis_Dataset2_2023.09.11_v1

description: Bulgaria - Shapefiles
DOI: 10.5281/zenodo.8333516
title: MainstreamBIO_ValueChainAnalysis_Dataset2_2023.09.11_v1

description: Bulgaria - Shapefiles
DOI: 10.6084/m9.figshare.23211467.v1
title: Supplementary Material for: Utilizing Reduced Labeled Proton Clearance to Identify Preclinical Alzheimer Disease with 3D ASL MRI

description: ABSTRACT Addressing the seminal pathophysiology in Alzheimer disease (AD) is the next logical focus for effective intervention, given the initial disappointing and more recent possibly encouraging results of monoclonal antibody trials. Endothelial cell dysfunction induced blood brain barrier leak (BBB) with associated prolonged capillary mean transit time (cMTT) and glymphatic outflow dysfunction are the most proximal events in the degeneration cascade. Sensitive and reproducible markers are required to both identify early disease and assess future treatment trial outcomes. Two participants with mild cognitive impairment (MCI) and one with AD, were evaluated clinically prior to MRI in this small case series report. From seven 3D Turbo Gradient and Spin Echo (TGSE) Pulsed Arterial Spin Echo (PASL) MRI sequences six homologous region of interest (ROI) in bitemporal, bifrontal, and biparietal lobes for each sequence were examined and plotted against time. By choosing late perfusion times during cMTT phase of perfusion linear analysis of signal decay could be utilized. A reference axial FLAIR sequence was also obtained. Slope of the linear analysis correlated to the rate of labeled proton clearance with reduced clearance occurring in AD participants compared to normal participants in our previous study. Whether similar differences in clearance rate extends to either MCI or early AD was investigated. Participants were categorized by clinical phenotype before MRI and compared to previously published phenotype cohorts: n=18 normal/healthy, n=6 AD, n=3 MCI. Significant differences in labeled proton clearance rates between AD and MCI/control phenotypes within bilateral temporal lobes (Left p=0.004, Right p=0.002) and within bilateral frontal lobes AD vs. controls (Left p=0.001, Right p=0.008) and AD vs. MCI (Left p=0.001, Right p=0.001) were found. This non-invasive MRI technique has potential for identifying MCI transition to AD.
DOI: 10.5281/zenodo.8333444
title: E-Course Recommendation System

description: Students data with all attributes for course recommendation system
DOI: 10.5281/zenodo.8333443
title: E-Course Recommendation System

description: Students data with all attributes for course recommendation system
DOI: 10.5281/zenodo.8333531
title: MainstreamBIO_ValueChainAnalysis_Dataset3_2023.09.11_v1

description: Denmark - Shapefiles
DOI: 10.5281/zenodo.8333530
title: MainstreamBIO_ValueChainAnalysis_Dataset3_2023.09.11_v1

description: Denmark - Shapefiles
DOI: 10.6084/m9.figshare.24116628
title: CMA model datasets

description: Seasonal prediction datasets of the CMA model are used to evaluate the predictability of the QBO.
DOI: 10.5281/zenodo.8333537
title: MainstreamBIO_ValueChainAnalysis_Dataset4_2023.09.11_v1

description: Europe - Shapefiles
DOI: 10.5258/soton/d2774
title: Dataset in support of the journal article 'Convolutional neural networks for mode on-demand high finesse optical resonator design'

description: Dataset to support article "Convolutional Neural Networks for Mode On-Demand High Finesse Optical Resonator Design", accepted for publication in Scientific Reports This dataset contains the data of Figures 3 and 4, and the numerical model to recreate Figures 5-7of the associated publication The following data files are plain text files in CSV format (comma-separated values), readable by any text editor, spreadsheet editor, or numerical software (we use Microsoft Excel). All data descriptions and units are given as headers in the csv files. figure3.csv: data for Fig. 3, true and predicted amplitude and period (in um) of the validation dataset figure4.csv: data for Fig.4: mean error of the training dataset and of the validation dataset versus epoch number "one peak" and "two peaks" directories: Data in these directories contain the fully trained neural network structures and weights. Figures 5, 7 where generated from "one peak", Figure 6 from "two peaks". The model is stored in the standard Keras/Tensorflow format: The model architecture, and training configuration (including the optimizer, losses, and metrics) are stored in saved_model.pb. The weights are saved in the variables/ directory. For example, the "one peak" model can be loaded in Python by: from tensorflow import keras model = keras.models.load_model("one peak") The predicted pair of (Amplitude, Period) for a target field E (cooperativity C on a spatial grid in a 100x150 array, formatted to Keras/Tensorflow style) is then obtained as: predictions = model.predict(E)
DOI: 10.5281/zenodo.8333536
title: MainstreamBIO_ValueChainAnalysis_Dataset4_2023.09.11_v1

description: Europe - Shapefiles
DOI: 10.7925/drs1.duchas_5143744
title: Local Roads

description: Story collected by Maeve Talbot, a student at Moneygall (2) school (Moneygall, Co. Offaly) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bean Ní Cháirr.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5286386
title: Pinch of Snuff

description: Story collected by Margaret Gambon, a student at Bun Machan school (Bunmahon, Co. Waterford) from informant Daniel Power.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Íde, Bean Uí Chobhthaigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5143746
title: Old Beliefs and Superstitions

description: Story collected by Rosie Coughlan, a student at Gallen school (Gallen, Co. Offaly) from informant Mr J. Coughlan.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher T.R. Goodwin.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5286387
title: Sick Call

description: Story collected by Margaret Gambon, a student at Bun Machan school (Bunmahon, Co. Waterford) from informant Thomas Power.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Íde, Bean Uí Chobhthaigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5143754
title: Old Beliefs and Superstitions

description: Story collected by Margaret Bell, a student at Gallen school (Gallen, Co. Offaly) from informant Mrs Margaret Bell.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher T.R. Goodwin.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5286388
title: Hidden Gold

description: Story collected by Daniel Gambon, a student at Bun Machan school (Bunmahon, Co. Waterford) from informant Mary Gambon.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Íde, Bean Uí Chobhthaigh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.