cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bq2tz
title: CCDC 2166181: Experimental Crystal Structure Determination

description: Related Article: Jiayou Yuan, Zhong Wei, Kesheng Shen, Yang Yang, Mingxu Liu, Xu Jing, Chunying Duan|2022|Dalton Trans.|51|10860|doi:10.1039/D2DT01166B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.14473/csda/wyz9rn/e05kgs
title: TI_ISSP00008.pdf

description: Technická informace
DOI: 10.14473/csda/wyz9rn/hbnld0
title: D_ISSP00008.pdf

description: Dotazník k výzkumu
DOI: 10.14473/csda/wyz9rn/tc71r5
title: 0008_issp99.tab

description: Data
DOI: 10.14473/csda/wyz9rn/ugc1ue
title: K_ISSP00008.pdf

description: Karty k dotazníku
DOI: 10.14473/csda/ymiqc1/8ykzmg
title: D_ISSP00008en.pdf

description: Questionnaire
DOI: 10.14473/csda/ymiqc1/sf8ren
title: 0016.tab

description: Data
DOI: 10.14473/csda/ymiqc1/kywcli
title: TI_ISSP00008en.pdf

description: Technical informtion
DOI: 10.11583/dtu.14277950
title: Contrasting backgrounds selection for NepCon image acquisition system

description: The dataset of the four test contrasting background colors collected by the NepCon system in the experimental tank facility.
DOI: 10.11583/dtu.14281898
title: Supporting video files: S - single target; M - Multiple targets

description: Raw videos of <i>N. norvegicus</i> used to evaluate the computer vision and deep learning based object detection followed by the tracking and count via Kalman filter.
DOI: 10.5281/zenodo.8154567
title: Schriftenverzeichnis Prof. Ludwig Reisch (1943-2023)

description: <strong>Schriftenverzeichnis zum Nachruf für Prof. Dr. Ludwig Reisch</strong> geboren am 15.11.1943 in Dillingen a. d. Donau gestorben am 05.07.2023 in Hattingen Lehrstuhlinhaber für Ur- und Frühgeschichte an der Friedrich-Alexander-Universität Erlangen-Nürnberg von 1987 bis 2008. <em>Zusammengestellt von Thorsten Uthmeier, Institut für Ur- und Frühgeschichte, Friedrich-Alexander-Universität Erlangen-Nürnberg</em>
DOI: 10.5281/zenodo.8154566
title: Schriftenverzeichnis Prof. Ludwig Reisch (1943-2023)

description: <strong>Schriftenverzeichnis zum Nachruf für Prof. Dr. Ludwig Reisch</strong> geboren am 15.11.1943 in Dillingen a. d. Donau gestorben am 05.07.2023 in Hattingen Lehrstuhlinhaber für Ur- und Frühgeschichte an der Friedrich-Alexander-Universität Erlangen-Nürnberg von 1987 bis 2008. <em>Zusammengestellt von Thorsten Uthmeier, Institut für Ur- und Frühgeschichte, Friedrich-Alexander-Universität Erlangen-Nürnberg</em>
DOI: 10.11583/dtu.12531197
title: Supplementary data for "The importance of the engine-propeller model accuracy on the performance prediction of a marine propulsion system in the presence of waves"

description: The data consists of all the results of the simulations carried out with the three implementations of the same propulsion system model (steady, unsteady with fixed engine speed, and unsteady with PID control system).<br>
DOI: 10.11583/dtu.12531197.v2
title: Supplementary data for "The importance of the engine-propeller model accuracy on the performance prediction of a marine propulsion system in the presence of waves"

description: The data consists of all the results of the simulations carried out with the three implementations of the same propulsion system model (steady, unsteady with fixed engine speed, and unsteady with PID control system).<br>
DOI: 10.11583/dtu.18357251
title: Stranded fossil fuel assets Excel model

description: The stranded assets Excel model supplements the article titled, “Stranded assets and reduced profits: Analyzing the economic underpinnings of the fossil fuel industry's resistance to climate stabilization” (hereafter, “main paper”), in <i>Renewable and Sustainable Energy Reviews</i>. This model can be used to obtain the main stranded assets results in the paper. It also contains formulas for estimating stranded assets using alternative discount rates, methods of projecting oil and gas supply, and production decline curves (used in the sensitivity analysis in the main paper and the supplementary material).
DOI: 10.11583/dtu.18357251.v1
title: Stranded fossil fuel assets Excel model

description: The stranded assets Excel model supplements the article titled, “Stranded assets and reduced profits: Analyzing the economic underpinnings of the fossil fuel industry's resistance to climate stabilization” (hereafter, “main paper”), in <i>Renewable and Sustainable Energy Reviews</i>. This model can be used to obtain the main stranded assets results in the paper. It also contains formulas for estimating stranded assets using alternative discount rates, methods of projecting oil and gas supply, and production decline curves (used in the sensitivity analysis in the main paper and the supplementary material).
DOI: 10.11583/dtu.17263163
title: Data from simulations of five wind turbines in a row to evaluate the global-blockage effect on power performance

description: The data files include results from RANS simulations performed with EllipSys3D. The setup consists of five turbines aligned in a row perpendicular to the prevailing wind direction. Additionally, the same numerical setup is used to simulate the performance of an isolated turbine operating under the same conditions of the five wind turbines.<br>The Python scripts to analyze the data and generate figures 3-9 from the related journal article will be soon available due to legal concern.
DOI: 10.11583/dtu.17263163.v1
title: Data from simulations of five wind turbines in a row to evaluate the global-blockage effect on power performance

description: The data files include results from RANS simulations performed with EllipSys3D. The setup consists of five turbines aligned in a row perpendicular to the prevailing wind direction. Additionally, the same numerical setup is used to simulate the performance of an isolated turbine operating under the same conditions of the five wind turbines.<br>The Python scripts to analyze the data and generate figures 3-9 from the related journal article will be soon available due to legal concern.
DOI: 10.25420/britishmuseum.22211944
title: 2019SG05 - Project metadata

description: Metadata spreadsheet with all the metadata information for the assets uploaded in project 2019SG05 following the Material Culture Ethnography Metadata Schema (MCEMS), which was developed for the Endangered Material Knowledge Programme (EMKP). MCEMS is intended to record the metadata for digital objects that were created as part of ethnographic research recording material culture, living heritage, cultural practices, and different forms of knowledge. Its aim is to aid the preservation of ethnography-specific information, facilitate archiving for future re-use and accessibility, and increase the online findability and presence of ethnographic data and research.
DOI: 10.6084/m9.figshare.23694912
title: pancreas.h5ad

description: Dataset for scpoli tutorial.
DOI: 10.11583/dtu.19672896
title: Onshore wind generation time series for technology SP199 HH200 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) 199 W/m2 turbines at hub height (HH) of 200 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
DOI: 10.11583/dtu.19672896.v1
title: Onshore wind generation time series for technology SP199 HH200 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) 199 W/m2 turbines at hub height (HH) of 200 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
DOI: 10.6084/m9.figshare.21710603
title: Results for 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations'

description: Results for 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations' in three frequency bands. The results have station and event information along with the measurements. In addition to this, the events have been relocated such that through a 1D earth the ray will arrive with the same backazimuth and horizontal slowness as the observation.
DOI: 10.6084/m9.figshare.21710603.v1
title: Results for 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations'

description: Results for 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations' in three frequency bands. The results have station and event information along with the measurements. In addition to this, the events have been relocated such that through a 1D earth the ray will arrive with the same backazimuth and horizontal slowness as the observation.
DOI: 10.6084/m9.figshare.21710564
title: Earthquakes used in 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations'

description: Earthquakes used in the study 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations'
DOI: 10.6084/m9.figshare.21710564.v1
title: Earthquakes used in 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations'

description: Earthquakes used in the study 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations'
DOI: 10.11583/dtu.19455554
title: Emergent interactions in the management of multiple threats to the conservation objective of harbour porpoises

description: These dataset are the simulation outcomes associated with the manuscript Lusseau, Kindt-Larsen, van Beest (2022) Emergent interactions in the management of multiple threats to the conservation objective of harbour porpoises. Harbour porpoise population dynamics was simulated using DEPONS under different scenario of fishing pressures, including the mitigation of bycatch risk with pingers. the simulation code as well as R code for the data handling and post-simulation statistical modelling of the outcomes can be found at https://github.com/dlusseau/DEPONS
DOI: 10.11583/dtu.19455554.v1
title: Emergent interactions in the management of multiple threats to the conservation objective of harbour porpoises

description: These dataset are the simulation outcomes associated with the manuscript Lusseau, Kindt-Larsen, van Beest (2022) Emergent interactions in the management of multiple threats to the conservation objective of harbour porpoises. Harbour porpoise population dynamics was simulated using DEPONS under different scenario of fishing pressures, including the mitigation of bycatch risk with pingers. the simulation code as well as R code for the data handling and post-simulation statistical modelling of the outcomes can be found at https://github.com/dlusseau/DEPONS
DOI: 10.11583/dtu.21316737
title: A Data Driven Model for Vitamin D Fortification

description: Model for optimization The workbook VitD_fortification_optimizer.xlsx contains an optimizer based on data from DANSDA (the Danish National Survey of Diet and Physical Activity) 2011-2013. <br> The workbook Scenarios_effect.xlsx shows simulated result of using 8 different fortification schemes found with the optimizer.
DOI: 10.11583/dtu.21316737.v1
title: A Data Driven Model for Vitamin D Fortification

description: Model for optimization The workbook VitD_fortification_optimizer.xlsx contains an optimizer based on data from DANSDA (the Danish National Survey of Diet and Physical Activity) 2011-2013. <br> The workbook Scenarios_effect.xlsx shows simulated result of using 8 different fortification schemes found with the optimizer.
DOI: 10.6084/m9.figshare.21701621
title: Data for 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations'

description: Processed seismograms with a directory structure in line with the sub arrays used in the study.
DOI: 10.6084/m9.figshare.21701621.v1
title: Data for 'Upper Mantle Structure beneath the Contiguous US Resolved with Array Observations of SKS Multipathing and Slowness Vector Perturbations'

description: Processed seismograms with a directory structure in line with the sub arrays used in the study.
DOI: 10.14473/csda/exprxn/mvtcmj
title: D_ISSP00025en.pdf

description: Questionnaire
DOI: 10.14473/csda/exprxn/tumcuq
title: ISSP_2017_Czech_data_FIN.tab

description: Data
DOI: 10.14473/csda/exprxn/gcb7jn
title: TI_ISSP00025en.pdf

description: Other materials
DOI: 10.14473/csda/h8xfpe/whn8wp
title: ISSP2017.tab

description: Data
DOI: 10.14473/csda/h8xfpe/at6nla
title: TI_ISSP00025.pdf

description: Technická informace AJ
DOI: 10.14473/csda/h8xfpe/nkn8gd
title: D_ISSP00025.pdf

description: Dotazník k výzkumu
DOI: 10.34740/kaggle/dsv/6148588
title: Egypt Gold Prices | Daily Updated
DOI: 10.11583/dtu.12758429
title: Grid Frequency Measurements of the Continental European Power System during 2019

description: The grid frequency is measured Central Jutland, Denmark (DK-1), which is a part of the Continental European power system (ENTSOE RG-CE).<br>342.5 days are measured with the DEIF MTR-3<br>22.5 days are not measured.<br>The MTR-3 has an accuracy of 10 mHz<br>There is one column with the UNIX timestamp in milliseconds and one column with the frequency measurement.<br>The dataset has a sample rate of 0.5 s, and missing samples are replaced with NaN so there are 7200*24*365=63,072,000 rows in the set.<br><br>
DOI: 10.11583/dtu.12758429.v1
title: Grid Frequency Measurements of the Continental European Power System during 2019

description: The grid frequency is measured Central Jutland, Denmark (DK-1), which is a part of the Continental European power system (ENTSOE RG-CE).<br>342.5 days are measured with the DEIF MTR-3<br>22.5 days are not measured.<br>The MTR-3 has an accuracy of 10 mHz<br>There is one column with the UNIX timestamp in milliseconds and one column with the frequency measurement.<br>The dataset has a sample rate of 0.5 s, and missing samples are replaced with NaN so there are 7200*24*365=63,072,000 rows in the set.<br><br>
DOI: 10.6084/m9.figshare.20338749
title: New Territory, Geographic Diffusion and Civilian Targeting

description: Replication data for: New Territory, Geographic Diffusion and Civilian Targeting
DOI: 10.6084/m9.figshare.20338749.v1
title: New Territory, Geographic Diffusion and Civilian Targeting

description: Replication data for: New Territory, Geographic Diffusion and Civilian Targeting
DOI: 10.14473/csda/hyjcua/iywyr5
title: V15_ISSP_NUMERIC.tab

description: :unav
DOI: 10.14473/csda/hyjcua/on91wq
title: D_ISSP00026.pdf

description: Dotazník
DOI: 10.14473/csda/hyjcua/pouitn
title: TI_ISSP00026.pdf

description: Technická informace AJ
DOI: 10.14473/csda/hyjcua/zlnaar
title: K_ISSP00026.pdf

description: Karty
DOI: 10.14473/csda/hyjcua/bdazpy
title: K_ISSP00026.pdf

description: Karty
DOI: 10.14473/csda/hyjcua/bw5hcp
title: TI_ISSP00026.pdf

description: Technická informace AJ Study monitoring questionnaire
DOI: 10.14473/csda/hyjcua/8fe9c1
title: D_ISSP00026.pdf

description: Dotazník
DOI: 10.14473/csda/hyjcua/vfyxwl
title: V15_ISSP_NUMERIC.tab

description: Data
DOI: 10.14473/csda/aejb71/q3ltjd
title: TI_ISSP00004en.pdf

description: Technical information
DOI: 10.14473/csda/aejb71/nyumse
title: ISSP 2018_Czech data_EN_FINAL.tab

description: Data
DOI: 10.14473/csda/aejb71/rhp6gm
title: D_ISSP00004en.pdf

description: Questionnaire
DOI: 10.14473/csda/aejb71/x0pjcu
title: OM_ISSP00004en.pdf

description: Other materials
DOI: 10.11583/dtu.21762830
title: Ready-To-Train AI4Arctic Sea Ice Challenge Test Dataset

description: The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the Test data for the Ready-To-Train version. Reference data is not included. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the '<em>ready-to-train</em>' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg <br> Version 2 includes the reference sea ice charts (previously absent) as the AutoICE Challenge has been finalised. The ice charts are both included in numerical format in the netCDF files and in quicklook images containing the SIC, SOD and FLOE for each scene in png format. <br> This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065