cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.19689028.v1
title: Onshore wind generation time series for technology SP335 HH100 at resource grade B (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 335 W/m2 turbines at hub height (HH) of 100 m for the regions shown in the attached map. The analysed wind power plants are sited at the 10...50 % highest mean wind speed locations in each region, i.e., in resource grade (RG) B. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities).The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581 <br>
DOI: 10.20393/5101177f-7ab0-4814-ba21-a00bf3027b8a
title: Irish Shelf Seabed Geomorphological Map v2023
title: ISSGM v2023

description: Dataset present a high-resolution (up to 10 m/ pixel) seabed geomorphology map of the entire Irish continental shelf, up to a depth of 200 m below sea level (bsl). 
The map was produced taking advantage of the high resolution Irish National Seabed Survey (INSS) and INFOMAR multibeam dataset, and using a protocol of modern semi-automated mapping techniques to streamline the results assisted by expert interpretation and corrections. 
The current version of the map (v2023) is based on all available INSS and INFOMAR multibeam data up to 2023.
All previous mapping efforts and existing literature on the Irish shallow shelf geomorphology have also been collated and integrated in the map, re-delineating features using the machine-assisted methodology and critically evaluating the previous interpretations. 
An internationally standardised terminology and classification scheme, in the form of the MIM-GA two-part scheme, has been adopted, aligning the new map to other international geomorphological work (https://www.infomar.ie/sites/default/files/pdfs/FINAL%20Part%202%20Geomorphology_1.pdf). 
The map includes both newly identified and re-assessed seabed morphological and geomorphological features (e.g. palaeochannels, drumlins, dunes etc.) and the different types of substrate (e.g. bedrock, unconsolidated or consolidated superficial deposits) that have been interpreted to represent the dominant composition within the top 1-2 metres of the seafloor. 
This detailed geological digital map is intended firstly as a resource enabling to better inform multiple offshore activities and management of the marine environment on the Irish continental shelf. The information is of importance to a range of stakeholders connected to sea fisheries, aquaculture, renewable energy (wind, wave and tidal power), marine communications, dredging, and aggregate industry.
DOI: 10.11583/dtu.19690837
title: Offshore wind generation time series for technology SP370 HH155 (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of offshore wind generation with specific power (SP) 370 W/m2 turbines at hub height (HH) of 155 m for the regions shown in the attached map. The analysed offshore wind power plants are sited at the best sites with max 100 km distance to shore, except for the far-offshore North Sea regions (the regions which do not have any connection to shore) where all locations are allowed. The map shows the resulting capacity factors (annual mean). The Excel file gives the average distance to shore for each individual region. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00_OFF is the aggregated offshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the ERA5-based simulation methodology. It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the modeling of wake losses for the offshore wind power plants and the storm shutdown behaviour (the smoothest storm shutdown technology is assumed). This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.5519/qd.80j8qla8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (689741 records)
DOI: 10.11583/dtu.19689019
title: Onshore wind generation time series for technology SP335 HH100 at resource grade A (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 335 W/m2 turbines at hub height (HH) of 100 m for the regions shown in the attached map. The analysed wind power plants are sited at the best 10 % of locations in each region, i.e., in resource grade (RG) A. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.11583/dtu.22786922
title: Albanian national electricity consumption and weather conditions for 2016-2019

description: The following dataset is published as part of the following paper " Sevdari, K., Islami, A., Haxhiraj, E. and Voshtina, E., 2022. A Data-Driven Assessment of the Electricity Demand-the Case of Albania. In 2022 International Conference on Renewable Energies and Smart Technologies. IEEE." Please cite the paper when using this dataset. Link of the paper: https://ieeexplore.ieee.org/document/10022956 <br> The dataset is in Excel file format. There are 14 columns that represent: 1-Time: Month/Day/Year Hour (calendar format) 2-Time_double: hours for 1 year (double format) 3-Load: The hourly electricity consumption in MWh for Albania 4-full_temp: The hourly temperature values in degrees Celsius taken from a weather station in the capital, Tirana. 5-full_humid: The hourly humidity values in % taken from a weather station in the capital, Tirana. 6-full_rain: The hourly rain conditions taken from a weather station in the capital, Tirana. 1 means a rainy hour and 0 means a clear (no rain) hour. 7-Muaj: Muaj (Albanian) or Months (English). This variable counts the months each hour belongs to. 8-Ditet: Ditet (Albanian) or Days (English). This variable counts the days each hour belongs to. 9-Ore: Ore (Albanian) or Hours (English). This variable counts the hours of each day and repeats itself. 10-Pushimet: Pushimet (Albanian) or Holidays-weekends (English). This variable identifies the weekend hours. 1 means a weekend hour and 0 means a week hour. 11-Oret e nates: Night hours (English). This variable distinguishes between night and day hours. 12- Pushimet vjetore: Yearly official holidays (English). This variable identifies the hours that fall under official yearly holidays in Albania. 13-Oret e nates_2 : Night hours _ 2 (English). This variable distinguishes between night and day hours. 14- Oret e pikut: Peak consumption hours (English). This variable identifies the hours of peak consumption. <br> <br>
DOI: 10.11583/dtu.22786922.v1
title: Albanian national electricity consumption and weather conditions for 2016-2019

description: The following dataset is published as part of the following paper " Sevdari, K., Islami, A., Haxhiraj, E. and Voshtina, E., 2022. A Data-Driven Assessment of the Electricity Demand-the Case of Albania. In 2022 International Conference on Renewable Energies and Smart Technologies. IEEE." Please cite the paper when using this dataset. Link of the paper: https://ieeexplore.ieee.org/document/10022956 <br> The dataset is in Excel file format. There are 14 columns that represent: 1-Time: Month/Day/Year Hour (calendar format) 2-Time_double: hours for 1 year (double format) 3-Load: The hourly electricity consumption in MWh for Albania 4-full_temp: The hourly temperature values in degrees Celsius taken from a weather station in the capital, Tirana. 5-full_humid: The hourly humidity values in % taken from a weather station in the capital, Tirana. 6-full_rain: The hourly rain conditions taken from a weather station in the capital, Tirana. 1 means a rainy hour and 0 means a clear (no rain) hour. 7-Muaj: Muaj (Albanian) or Months (English). This variable counts the months each hour belongs to. 8-Ditet: Ditet (Albanian) or Days (English). This variable counts the days each hour belongs to. 9-Ore: Ore (Albanian) or Hours (English). This variable counts the hours of each day and repeats itself. 10-Pushimet: Pushimet (Albanian) or Holidays-weekends (English). This variable identifies the weekend hours. 1 means a weekend hour and 0 means a week hour. 11-Oret e nates: Night hours (English). This variable distinguishes between night and day hours. 12- Pushimet vjetore: Yearly official holidays (English). This variable identifies the hours that fall under official yearly holidays in Albania. 13-Oret e nates_2 : Night hours _ 2 (English). This variable distinguishes between night and day hours. 14- Oret e pikut: Peak consumption hours (English). This variable identifies the hours of peak consumption. <br> <br>
DOI: 10.25592/uhhfdm.12762
title: Comparison of Shod and Unshod Gait in Patients With Parkinson's Disease With Subthalamic and Nigral Stimulation

description: <strong>Background:</strong> The Parkinsonian [i.e., Parkinson's disease (PD)] gait disorder represents a therapeutical challenge with residual symptoms despite the use of deep brain stimulation of the subthalamic nucleus (STN DBS) and medical and rehabilitative strategies. The aim of this study was to assess the effect of different DBS modes as combined stimulation of the STN and substantia nigra (STN+SN DBS) and environmental rehabilitative factors as footwear on gait kinematics. <strong>Methods:</strong> This single-center, randomized, double-blind, crossover clinical trial assessed shod and unshod gait in patients with PD with medication in different DBS conditions (i.e., STIM OFF, STN DBS, and STN+SN DBS) during different gait tasks (i.e., normal gait, fast gait, and gait during dual task) and compared gait characteristics to healthy controls. Notably, 15 patients participated in the study, and 11 patients were analyzed after a dropout of four patients due to DBS-induced side effects. <strong>Results:</strong> Gait was modulated by both factors, namely, footwear and DBS mode, in patients with PD. Footwear impacted gait characteristics in patients with PD similarly to controls with longer step length, lower cadence, and shorter single-support time. Interestingly, DBS exerted specific effects depending on gait tasks with increased cognitive load. STN+SN DBS was the most efficient DBS mode compared to STIM OFF and STN DBS with intense effects as step length increment during dual task. <strong>Conclusion:</strong> The PD gait disorder is a multifactorial symptom, impacted by environmental factors as footwear and modulated by DBS. DBS effects on gait were specific depending on the gait task, with the most obvious effects with STN+SN DBS during gait with increased cognitive load.
description: Please find further details in: https://www.frontiersin.org/articles/10.3389/fnhum.2021.751242/full
description: {"references": ["Horn MA, Gulberti A, Hidding U, Gerloff C, Hamel W, Moll CKE and P\u00f6tter-Nerger M (2022) Comparison of Shod and Unshod Gait in Patients With Parkinson's Disease With Subthalamic and Nigral Stimulation. Front. Hum. Neurosci. 15:751242. doi: 10.3389/fnhum.2021.751242"]}
DOI: 10.25592/uhhfdm.12761
title: Comparison of Shod and Unshod Gait in Patients With Parkinson's Disease With Subthalamic and Nigral Stimulation

description: <strong>Background:</strong> The Parkinsonian [i.e., Parkinson's disease (PD)] gait disorder represents a therapeutical challenge with residual symptoms despite the use of deep brain stimulation of the subthalamic nucleus (STN DBS) and medical and rehabilitative strategies. The aim of this study was to assess the effect of different DBS modes as combined stimulation of the STN and substantia nigra (STN+SN DBS) and environmental rehabilitative factors as footwear on gait kinematics. <strong>Methods:</strong> This single-center, randomized, double-blind, crossover clinical trial assessed shod and unshod gait in patients with PD with medication in different DBS conditions (i.e., STIM OFF, STN DBS, and STN+SN DBS) during different gait tasks (i.e., normal gait, fast gait, and gait during dual task) and compared gait characteristics to healthy controls. Notably, 15 patients participated in the study, and 11 patients were analyzed after a dropout of four patients due to DBS-induced side effects. <strong>Results:</strong> Gait was modulated by both factors, namely, footwear and DBS mode, in patients with PD. Footwear impacted gait characteristics in patients with PD similarly to controls with longer step length, lower cadence, and shorter single-support time. Interestingly, DBS exerted specific effects depending on gait tasks with increased cognitive load. STN+SN DBS was the most efficient DBS mode compared to STIM OFF and STN DBS with intense effects as step length increment during dual task. <strong>Conclusion:</strong> The PD gait disorder is a multifactorial symptom, impacted by environmental factors as footwear and modulated by DBS. DBS effects on gait were specific depending on the gait task, with the most obvious effects with STN+SN DBS during gait with increased cognitive load.
description: Please find further details in: https://www.frontiersin.org/articles/10.3389/fnhum.2021.751242/full
description: {"references": ["Horn MA, Gulberti A, Hidding U, Gerloff C, Hamel W, Moll CKE and P\u00f6tter-Nerger M (2022) Comparison of Shod and Unshod Gait in Patients With Parkinson's Disease With Subthalamic and Nigral Stimulation. Front. Hum. Neurosci. 15:751242. doi: 10.3389/fnhum.2021.751242"]}
DOI: 10.11583/dtu.13032803
title: Characterization and Engineering of Streptomyces griseofuscus DSM 40191 as a Potential Host for Heterologous Expression of Biosynthetic Gene Clusters

description: Supporting dataset for <br>Characterization and Engineering of Streptomyces griseofuscus DSM 40191 as a Potential Host for Heterologous Expression of Biosynthetic Gene Clusters<br><br>Tetiana Gren<sup>a</sup>, Christopher M. Whitford<sup>a</sup>, Omkar S. Mohite<sup>a</sup>, Tue S. Jørgensen<sup>a</sup>, Eftychia E. Kontou<sup>a</sup>, Julie B. Nielsen<sup>a</sup>, Sang Yup Lee<sup>a,b</sup>, Tilmann Weber<sup>a#</sup><br>a) The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark<br>b) Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology, Republic of Korea<br><b>Supplementary Data 1. Genome comparison data</b>Excel tables with gene annotations from NCBI GenBank and KEGG subsystems. Gene presence-absence table for genes across the pangenome of S. griseofuscus, S. coelicolor, S. venezuelae. The KEGG subsystem gene and reaction counts per organism. <b>Supplementary Data 2. Phenotype microarray data analysis with DuctApe</b>Excel tables with raw kinetic growth data across plates PM1 to PM4 with 2 replicates for S. griseofuscus, S. coelicolor, S. venezuelae. Biolog activity index (0 to 9) across 384 nutrients in each strain and comparison of number of active nutrients across the three strains. <b>Supplementary Data 3. Genotype phenotype mapping using the KEGG pathways</b>Excel tables with matrix of BioLog activity index across different KEGG nutrients mapped on to the KEGG pathways. Combined matrix with mean activity differences across three strains. <br><b>Supplementary Data 4. Genome scale metabolic models</b>The SMBL formatted genome-scale metabolic models of the three Streptomyces strains used in the study <b>Supplementary Data 5. Comparison of genome scale model based growth predictions and BioLog data</b>Excel table with list of reactions considered in the three genome scale models and comparison of in-silico growth prediction on different nutrients against BioLog data. <br><b>Supplementry Data 6. BGC comparison across Streptomyces using BiG-SCAPE</b>Information on BGCs detected in S. griseofuscus and information on the public genomes selected along with the BGCs selected for comparison. Similarity network data used for generation of Fig. 3 with information on nodes and edges. <br>
DOI: 10.17632/w3jgdvbt4y.1
title: Molecular composition and concentration of lignin derived phenols in the bottom sediments and subsea permafrost from the Buor-Khaya Bay

description: The set contains a data on lithology, organic carbon content, C/N ratio, grain size characteristics, and concentration of main CuO-oxidation products obtained for 126 samples of five drilling sediment cores taken from Buor-Khaya Bay (Coastal zone of the Laptev Sea).
DOI: 10.6084/m9.figshare.21300513
title: Genome Data of Catharanthus roseus cv “Pacifica XP Burgundy”

description: <em><strong>Catharanthus roseus </strong></em><strong>genome provides insights into complex complementary contributions to ‘</strong><em><strong>Candidatus</strong></em><strong> Liberibacter asiaticus’ infection and the regulatory network governing biosynthesis of the anticancer drug vinblastine</strong>
DOI: 10.11583/dtu.14547264
title: CACHET-CADB

description: CACHET-CADB is 259 days long contextualised single-channel ECG arrhythmia database from 24 patients recording under a free-living ambulatory setting. Along with the ECG, it also contains contextual information such as activities, body position, movement acceleration, patients reported unusual symptoms/events diary, step counts, patient-reported sleep quality, stress level, and food intake. I contain 1602 ten-second long ECG samples of AF, NSR, noise, and other rhythm classes, which are manually annotated by two cardiologists. The ECG is sampled at 1024 Hz and a 12-bit resolution.<br>There is a short format of the dataset available at 10.11583/DTU.14547330<br> <br>Further details and code for using the database can be found on the GitHub repository https://github.com/cph-cachet/cachet-ecg-db.
DOI: 10.11583/dtu.14547330
title: CACHET-CADB Short Format

description: CACHET-CADB is 259 days long contextualised single-channel ECG arrhythmia database from 24 patients recording under a free-living ambulatory setting. <br>This dataset contains only the ECG recordings. See the full CACHET-CADB for the entire recording including context information - DOI: 10.11583/DTU.14547264<br>It contain 1602 ten-second long ECG samples of AF, NSR, noise, and other rhythm classes, which are manually annotated by two cardiologists. The ECG is sampled at 1024 Hz and a 12-bit resolution. <br>Further details and code for using the database can be found on the GitHub repository https://github.com/cph-cachet/cachet-ecg-db.<br>
DOI: 10.11583/dtu.14547330.v1
title: CACHET-CADB Short Format

description: CACHET-CADB is 259 days long contextualised single-channel ECG arrhythmia database from 24 patients recording under a free-living ambulatory setting. <br>This dataset contains only the ECG recordings. See the full CACHET-CADB for the entire recording including context information - DOI: 10.11583/DTU.14547264<br>It contain 1602 ten-second long ECG samples of AF, NSR, noise, and other rhythm classes, which are manually annotated by two cardiologists. The ECG is sampled at 1024 Hz and a 12-bit resolution. <br>Further details and code for using the database can be found on the GitHub repository https://github.com/cph-cachet/cachet-ecg-db.<br>
DOI: 10.17632/cg8cj9hw3w.1
title: Enhanced SARS-CoV-2 entry via UPR-dependent AMPK related kinase NUAK2 - DataSet2

description: The repository contains the sets of microscopy images used for respective Figure sections of manuscript Prasad et al. Mol. Cell 2023.

The folders contains raw data of the experiments on which indicated graphs are drawn.
DOI: 10.17632/yzvn65kbpj.1
title: Data for: Excess Asset Returns Predictability in an Emerging Economy: The Case of Colombia

description: The sheet 2022 contains the variables used for the regresion analysis. The do.files are the program files for replicating the paper
DOI: 10.17632/jbdmgsjvt7.1
title: Carbon Taxes and the Geography of Fossil Lending

description: This is the data and codes necessary to replicate the analysis in the paper "Carbon Taxes and the Geography of Fossil Lending" by Luc Laeven and Alexander Popov in the Journal of International Economics.