cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/c1f91c19-b975-4dcc-acb5-58316b11f9a9.v1
title: Data accompanying Chapter 4 of the PhD Dissertation: Energy System Design and Operation of Hydrogen Fuelled Ships

description: <strong>Introduction: </strong>This dataset contains the results and inputs files collected as part of the Chapter 4 in the doctoral thesis of Mr. Foivos Mylonopoulos, Dipl.-Ing in Delft University of Technology, 2022-2026.The present dataset is made public to act both as supplementary data for the publications of F. Mylonopoulos and to offer other researchers the ability to use this data in their own work.This data is part of the SH2IPDRIVE project (Sustainable Hydrogen Integrated Propulsion Drives).The project is funded by the Netherlands Enterprise Agency (RVO: Rijksdienst voor Ondernemend Nederland) under the grant number MOB21013.<br><strong>Purpose of the dataset:</strong>This dataset was created to support the development and evaluation of representative operating profile synthesis methods and the combined design-operation optimization framework presented in Chapter 4 of the PhD dissertation. It enables reproduction of power profile reduction techniques, system sizing and operation optimization, and lifetime cost minimization results, including the effects of ship stability and component placements constraints on the optimal hydrogen-based system design. <br><strong>Description of the dataset:</strong>This dataset contains two ZIP archives. The first one, includes MATLAB scripts implementing the algorithmic (RDP-based) and probability-based downsampling approaches for representative operating profile synthesis, together with the associated Excel inputs files. The second archive contains the lifetime cost and stability optimization code (Cost_Stability_Code.py) which uses as input the representative power profile (Power_Profile.xlsx) and the available engine-room component placement positions (positions.xlsx). <br><strong>Dependencies:</strong>MATLAB was used to run the representative operating profile synthesis scripts. Python was used to run the optimization code, which uses the SCIP solver via PySCIPOpt.Microsoft Excel was used to view and edit the .xlsx input files.
DOI: 10.57745/ik1bnb
title: Results of 3D fibre orientation in 3D around a Douglas knot and Initial data of the self-developed laboratory method

description: Since laser dot scanning only gives reliable information about the fibre orientation in the plane of board surfaces, simple assumptions are usually made to define the inner fibre orientation to model timber boards. Those models would be improved by better insight into real fibre deviation around knots. The present data is extracted from the laboratory method developed to evaluate growth layers geometries and fibre orientation, through successive planing, colour and laser dot scanning. The method is applied to a Douglas fir timber specimen. The proposed method provides data on fibre orientation in 3D with an accuracy that is relevant for the calibration of detailed models, more simply and feasibly than previously proposed methods.
DOI: 10.5281/zenodo.18412347
title: Mean EEG feature values significantly correlated with AD CSF biomarkers

description: This dataset contains raw, patient-level mean EEG feature values recorded during sleep from individuals diagnosed with Alzheimer’s disease (AD). EEG features were extracted from four electrodes (C3–A2, C4–A1, F4–A1, O2–A1) across four sleep stages (N1, N2, N3, and REM) using 2-minute signal windows with 25% overlap. For each patient, feature values were averaged across windows.

Only EEG features showing significant correlations with cerebrospinal fluid (CSF) Alzheimer’s disease biomarkers were retained. The considered CSF biomarkers are total tau (t-tau), phosphorylated tau (p-tau), and beta-amyloid (Aβ).
DOI: 10.5281/zenodo.18412348
title: Mean EEG feature values significantly correlated with AD CSF biomarkers

description: This dataset contains raw, patient-level mean EEG feature values recorded during sleep from individuals diagnosed with Alzheimer’s disease (AD). EEG features were extracted from four electrodes (C3–A2, C4–A1, F4–A1, O2–A1) across four sleep stages (N1, N2, N3, and REM) using 2-minute signal windows with 25% overlap. For each patient, feature values were averaged across windows.

Only EEG features showing significant correlations with cerebrospinal fluid (CSF) Alzheimer’s disease biomarkers were retained. The considered CSF biomarkers are total tau (t-tau), phosphorylated tau (p-tau), and beta-amyloid (Aβ).
DOI: 10.15468/dl.5tgafh
title: Occurrence Download

description: A dataset containing 45206 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Synodus foetens",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 45206 records from 133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002088-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18413453
title: CDFT reproducibility package — Sec. 11.1 (NICER surface redshift: J0030+0451, J0740+6620)
DOI: 10.4121/677254d3-1b18-48a5-b6af-988f43b1b0d2
title: Data accompanying Chapter 5 of the PhD Dissertation: Energy System Design and Operation of Hydrogen Fuelled Ships

description: <strong>Introduction: </strong>This dataset contains the results and inputs files collected as part of the Chapter 5 in the doctoral thesis of Mr. Foivos Mylonopoulos, Dipl.-Ing in Delft University of Technology, 2022-2026.The present dataset is made public to act both as supplementary data for the publications of F. Mylonopoulos and to offer other researchers the ability to use this data in their own work.This data is part of the SH2IPDRIVE project (Sustainable Hydrogen Integrated Propulsion Drives).The project is funded by the Netherlands Enterprise Agency (RVO: Rijksdienst voor Ondernemend Nederland) under the grant number MOB21013.<br><strong>Purpose of the dataset:</strong>This dataset was created to support the assessment of weather-driven operational variability and navigation safety impacts on ship power demand, energy system sizing, and lifetime cost, as presented in Chapter 5 of the PhD dissertation. It enables the reproduction of the weather-adjusted power profiles, capacity distributions and lifetime cost analyses under different metocean conditions and hydrogen fuel price scenarios. <br><strong>Description of the dataset:</strong>This dataset is provided as a single ZIP archive containing the Python-based simulation, post-processing, and plotting workflows used in Chapter 5. The archive includes two main simulation scripts: Weather_Laden_Ch5.py for laden condition and Weather_Ballast_Ch5.py for the ballast condition. These scripts use vessel speed and course over ground inputs from Speed_COG_power.xlsx and Speed_COG_power_Ballast.xlsx, respectively, and incorporate wind and wave parameters derived on a daily basis from ERA5 meteorological datasets (.nc files) to generate weather-adjusted power profiles.The remaining files in the archive support result visualization. All simulation outputs are summarized in Results_Ch5.xlsx. The script Capacity_Histograms_Plots.py generates fuel cell and capacity histograms for different power profiles. Vessel_profile_plots.py produces plots of power profiles under different weather conditions using input from the corresponding Excel file. Finally, NPV_TOTAL.py generates lifetime cost distributions for different hydrogen fuel price scenarios (3,6, and 9$/kg) using input from the corresponding Excel files. <br><strong>Dependencies:</strong>Python with PySCIPOpt (SCIP solver), ERA5 weather data are provided in NETCDF (.nc) format and read in Python. Microsoft Excel for .xlsx files
DOI: 10.17026/ls/jtskja
title: Gezondheidsenquête 2023 - GECON 2023
title: GECON 2023

description: Sinds 1981 wordt door het CBS een continue Gezondheidsenquête gehouden. Van 1997 tot en met 2009 maakte de enquête deel uit van het Permanent Onderzoek Leefsituatie (POLS), als de module Gezondheid. Met ingang van 2010 wordt de Gezondheidsenquête weer als op zichzelf staand onderzoek uitgevoerd. Het onderzoek geeft een zo volledig mogelijk overzicht van ontwikkelingen in de gezondheid, medische contacten, leefstijl en preventief gedrag van de bevolking in Nederland. De doelpopulatie bestaat voor het merendeel van de onderwerpen uit personen van 0 jaar en ouder in particuliere huishoudens in Nederland. Voor sommige onderwerpen geldt een aparte leeftijdsgrens. De Gezondheidsenquête begint met een basisvragenlijst. In de basisvragenlijst wordt gevraagd naar achtergrondkenmerken zoals de samenstelling van het huishouden, het hebben van (en/of zoeken naar) betaald werk, beroep, bedrijf en opleidingsniveau. Na de basisvragenlijst volgt de eigenlijke gezondheidsenquête. Daarin wordt gevraagd naar de diverse aspecten van gezondheid, aandoeningen, beperkingen, contacten met zorgverleners, leefstijl en preventief gezondheidsgedrag. Een respondent merkt geen verschil tussen de basisvragenlijst en de gezondheidsvragenlijst. Hij of zij krijgt beide delen als één geheel ter beantwoording voorgelegd. Er wordt vanaf 2010 gebruik gemaakt van mixed-mode waarneming en het onderzoek bestaat uit twee deelonderzoeken. De steekproefpersonen ontvangen een schriftelijk verzoek om het eerste deel van de vragenlijst via internet in te vullen. Degenen die niet aan dit verzoek voldoen worden benaderd voor een telefonisch interview (indien telefoonnummer bekend) of voor een face-to-face interview (indien geen telefoonnummer bekend). Aan het eind van deelonderzoek 1 wordt respondenten van 12 jaar of ouder gevraagd deel te nemen aan deelonderzoek 2. Het tweede deelonderzoek kan via internet of op papier ingevuld worden. Omdat er sprake is van responsuitval tussen deelonderzoek 1 en 2, is er een aparte weging gedaan voor beide deelonderzoeken, resulterend in aparte weegfactoren. Jaarlijks wordt een bruto steekproef van ruim 15 duizend personen benaderd. Met een respons van 60-65 procent levert dit een netto steekproef op van ongeveer 9500 personen. Steekproef van personen, getrokken uit de Basisregistratie Personen (BRP). De steekproef wordt verspreid over de maanden van het jaar. De Gezondheidsenquête wordt vanaf 2014 in het volgende “mixed-mode” design uitgevoerd. Eerst worden steekproefpersonen benaderd om via internet deel te nemen (CAWI – Computer Assisted Web Interviewing). Non-respondenten worden her benaderd voor een ‘face-to-face’ interview (CAPI – Computer Assisted Personal Interviewing). Omdat enkele vragenblokken nogal persoonlijk van aard zijn, kunnen CAPI-respondenten die vragen zelf invullen (CASI – Computer Assisted Self Interviewing). Dit DANSGECON2023 bestand wijkt af van het microdatabestand Gezondheidsenquête 2023 dat onder bepaalde voorwaarden voor statistisch en wetenschappelijk onderzoek toegankelijk is. Voor nadere informatie microdata@cbs.nl.
DOI: 10.4121/677254d3-1b18-48a5-b6af-988f43b1b0d2.v1
title: Data accompanying Chapter 5 of the PhD Dissertation: Energy System Design and Operation of Hydrogen Fuelled Ships

description: <strong>Introduction: </strong>This dataset contains the results and inputs files collected as part of the Chapter 5 in the doctoral thesis of Mr. Foivos Mylonopoulos, Dipl.-Ing in Delft University of Technology, 2022-2026.The present dataset is made public to act both as supplementary data for the publications of F. Mylonopoulos and to offer other researchers the ability to use this data in their own work.This data is part of the SH2IPDRIVE project (Sustainable Hydrogen Integrated Propulsion Drives).The project is funded by the Netherlands Enterprise Agency (RVO: Rijksdienst voor Ondernemend Nederland) under the grant number MOB21013.<br><strong>Purpose of the dataset:</strong>This dataset was created to support the assessment of weather-driven operational variability and navigation safety impacts on ship power demand, energy system sizing, and lifetime cost, as presented in Chapter 5 of the PhD dissertation. It enables the reproduction of the weather-adjusted power profiles, capacity distributions and lifetime cost analyses under different metocean conditions and hydrogen fuel price scenarios. <br><strong>Description of the dataset:</strong>This dataset is provided as a single ZIP archive containing the Python-based simulation, post-processing, and plotting workflows used in Chapter 5. The archive includes two main simulation scripts: Weather_Laden_Ch5.py for laden condition and Weather_Ballast_Ch5.py for the ballast condition. These scripts use vessel speed and course over ground inputs from Speed_COG_power.xlsx and Speed_COG_power_Ballast.xlsx, respectively, and incorporate wind and wave parameters derived on a daily basis from ERA5 meteorological datasets (.nc files) to generate weather-adjusted power profiles.The remaining files in the archive support result visualization. All simulation outputs are summarized in Results_Ch5.xlsx. The script Capacity_Histograms_Plots.py generates fuel cell and capacity histograms for different power profiles. Vessel_profile_plots.py produces plots of power profiles under different weather conditions using input from the corresponding Excel file. Finally, NPV_TOTAL.py generates lifetime cost distributions for different hydrogen fuel price scenarios (3,6, and 9$/kg) using input from the corresponding Excel files. <br><strong>Dependencies:</strong>Python with PySCIPOpt (SCIP solver), ERA5 weather data are provided in NETCDF (.nc) format and read in Python. Microsoft Excel for .xlsx files
DOI: 10.34616/zvrgud
title: Analyses conducted using the Omnicomparator software (Z. Koczarski, Omnicomparator – Application for N-gram Comparison - raw data

description: The data set contains raw data generated during the course of the National Science Centre (NCN, Poland) project Preludium-21 and forming the basis for part of the project’s partial results. It includes:
- results of analyses conducted using the Omnicomparator software (Z. Koczarski, Omnicomparator – Application for N-gram Comparison (Source code), Zenodo, 2022–2025, https://doi.org/10.5281/zenodo.15965821) — format: .csv files;
- text files containing the texts of the Parthenicae (source texts) — format: .txt files.
DOI: 10.5281/zenodo.18413780
title: CDFT reproducibility package — Sec. 13 (SPARC: rotation curves, BTFR, RAR)
DOI: 10.25345/c53x8404c
title: MassIVE MSV000100661 - Combining gut metatranscriptome and metabolome reveals metabolic adaptation of the food-storing Siberian chipmunk (Tamias sibiricus) to hibernation
title: Untargeted metabolomics analysis of cecal contents from the Siberian chipmunk (Tamias sibiricus) across four physiological stages of hibernation: pre-hibernation (preH), deep torpor (torpor), interbout arousal (IBA), and post-hibernation (postH). The study aims to characterize the functional reprogramming of the gut metabolome during hibernation. Study Design: Species: Tamias sibiricus Sample Type: Cecal contents Groups: 4 groups (preH, torpor, IBA, postH) with 5 biological replicates per group (Total 20 samples). Method: Samples were analyzed using LC-MS/MS on a Thermo UHPLC-Q Exactive HF-X system in both positive and negative ion modes. Related Manuscript: Combining gut metatranscriptome and metabolome reveals metabolic adaptation of the food-storing Siberian chipmunk (Tamias sibiricus) to hibernation.
DOI: 10.6084/m9.figshare.31182361
title: Seismic Evidence for Hydration of the Mantle Transition Zone beneath Tibet and Its Implications for Water-rich Mantle Upwelling

description: The RFs used in this study
DOI: 10.5519/qd.qwltcbn8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (54 records)
DOI: 10.6084/m9.figshare.31182361.v1
title: Lateral Variation of Water Content in the Mantle Transition Zone beneath the Tibetan Plateau and Its Geodynamic Implications

description: The RFs used in this study
DOI: 10.6084/m9.figshare.31182361.v2
title: Seismic Evidence for Hydration of the Mantle Transition Zone beneath Tibet and Its Implications for Water-rich Mantle Upwelling

description: The RFs used in this study
DOI: 10.15468/dl.aa8fqz
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psithyrus sololensis"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002089-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2rba42
title: Occurrence Download

description: A dataset containing 10097 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Garra lorestanensis, Bunocephalus hartti, Gymnocorymbus flaviolimai, Bunocephalus minerim, Bunocephalus aloikae, Neofundulus rubrofasciatus, Luciobarbus guercifensis, Luciobarbus zayanensis, Moenkhausia venerei, Rhamdia guatemalensis, Rhamdia cinerascens, Mystus catapogon, Neofundulus aureomaculatus, Salmo multipunctata, Melanorivulus atlanticus, Panaqolus nix, Bryconamericus pinnavittatus, Crenicichla monicae, Onychostoma krongnoense, Onychostoma dongnaiense, Glossogobius mahalonensis, Pseudorasbora pugnax, Sicyopterus squamosissimus, Rivulus azurescens, Rivulus pivijay, Rivulus ribesrubrum, Rivulus xi, Oreichthys duospilus, Oreichthys incognito, Oreichthys coorgensis, Astroblepus jimenezae, Astroblepus martinezi, Notholebias vermiculatus, Neofundulus splendidus, Spectrolebias pilleti, Awaous jayakari, Moema ortegai, Moema beucheyi, Melanotaenia albimarginata, Melanotaenia aruensis, Melanotaenia kolaensis, Melanotaenia picta, Melanotaenia wokamensis, Melanotaenia senckenbergianus, Melanotaenia patoti, Melanotaenia rubrivittata, Marcusenius, Moenkhausia lineomaculata, Moenkhausia australis, Ituglanis boitata, Pelvicachromis drachenfelsi, Crenicichla anamiri, Opsarius ardens, Opsarius malabaricus, Opsarius maculatus, Liobagrus hyeongsanensis, Psilorhynchus kaladanensis, Papiliolebias francescae, Papiliolebias ashleyae, Xenurolebias pataxo, Xenurolebias cricarensis, Mogurnda arguni, Mogurnda kaimana, Rhinopetitia potamorhachia, Hypsolebias trifasciatus, Sicyopus rubicundus, Hypsolebias shibattai, Hypsolebias caeruleus, Pelvicachromis kribensis, Pronothobranchius chirioi, Pronothobranchius gambiensis, Pronothobranchius seymouri, Amphilius ruziziensis, Amphilius pedunculus, Moenkhausia britskii, Amphilius crassus, Amphilius lujani, Distocyclus guchereauae, Hypselobarbus keralaensis, Hypselobarbus nilgiriensis, Hypselobarbus gracilis, Hypselobarbus pseudomussullah, Psilorhynchus tysoni, Amblyceps accari, Scriptaphyosemion nigrifluvi, Lacustricola kassenjiensis, Rhinotocinclus, Triplophysa qilianensis, Bathyaethiops baka, Bathyaethiops flammeus, Hypsopanchax stiassnyae, Melanotaenia garylangei, Metzia parva, Stiphodon palawanensis, Brycinus epuluensis, Chaetostoma bifurcum, Chaetostoma trimaculineum, Erethistes nareshi, Characidium satoi, Marcusenius kaninginii)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10097 records from 169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002090-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18432177
title: Table for Paper
DOI: 10.5281/zenodo.18432178
title: Table for Paper
DOI: 10.6084/m9.figshare.29984809
title: Longitudinal dataset of journal data sharing policies across 22 disciplines for 2014 2019 and 2023

description: This dataset accompanies the Data Descriptor published in <i>Scientific Data</i>. The associated article provides a detailed description of the data collection methods, classification framework, and technical validation.Ikeuchi, U. Longitudinal dataset of journal data sharing policies across 22 disciplines for 2014 2019 and 2023. <i>Sci Data</i> <b>13</b>, 119 (2026). https://doi.org/10.1038/s41597-025-06434-2This dataset documents journal data sharing policies across 22 disciplines for 2014, 2019, and 2023. A total of 220 high-impact journals were surveyed, representing the top ten journals (by Impact Factor) from each discipline listed in the Essential Science Indicators. For each journal, policies concerning two types of data sharing were reviewed: repository-based data sharing and supplementary materials. Policy requirements were classified into four categories based on their strength: require, recommend, accept, or no policy. Data were collected at three time points through systematic reviews of journal websites and submission guidelines. The dataset includes journal metadata —such as publishers, ISSN, and Impact Factor—along with detailed policy descriptions and classifications. This longitudinal dataset provides evidence of changes in data sharing requirements over time and enables comparative studies of journal policies. These data may be useful for research on open science practices, the development of science policy, and the evolution of scholarly publishing standards.
DOI: 10.6084/m9.figshare.29984809.v2
title: Longitudinal dataset of journal data sharing policies across 22 disciplines for 2014 2019 and 2023

description: This dataset accompanies the Data Descriptor published in <i>Scientific Data</i>. The associated article provides a detailed description of the data collection methods, classification framework, and technical validation.Ikeuchi, U. Longitudinal dataset of journal data sharing policies across 22 disciplines for 2014 2019 and 2023. <i>Sci Data</i> <b>13</b>, 119 (2026). https://doi.org/10.1038/s41597-025-06434-2This dataset documents journal data sharing policies across 22 disciplines for 2014, 2019, and 2023. A total of 220 high-impact journals were surveyed, representing the top ten journals (by Impact Factor) from each discipline listed in the Essential Science Indicators. For each journal, policies concerning two types of data sharing were reviewed: repository-based data sharing and supplementary materials. Policy requirements were classified into four categories based on their strength: require, recommend, accept, or no policy. Data were collected at three time points through systematic reviews of journal websites and submission guidelines. The dataset includes journal metadata —such as publishers, ISSN, and Impact Factor—along with detailed policy descriptions and classifications. This longitudinal dataset provides evidence of changes in data sharing requirements over time and enables comparative studies of journal policies. These data may be useful for research on open science practices, the development of science policy, and the evolution of scholarly publishing standards.
DOI: 10.6084/m9.figshare.29984809.v1
title: Longitudinal dataset of journal data sharing policies across 22 disciplines for 2014 2019 and 2023

description: This dataset documents journal data sharing policies across 22 disciplines for 2014 2019 and 2023. A total of 220 high-impact journals were surveyed, representing the top ten journals (by Impact Factor) from each discipline listed in the Essential Science Indicators. For each journal, policies concerning two types of data sharing were reviewed: repository-based data sharing and supplementary materials. Policy requirements were classified into four categories based on their strength: require, recommend, accept, or no policy. Data were collected at three time points through systematic reviews of journal websites and submission guidelines. The dataset includes journal metadata —such as publishers, ISSN, and Impact Factor—along with detailed policy descriptions and classifications. This longitudinal dataset provides evidence of changes in data sharing requirements over time and enables comparative studies of journal policies. These data may be useful for research on open science practices, the development of science policy, and the evolution of scholarly publishing standards.