cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10460009
title: The hexane extract of Willow (Salix alba) bark was analysed using GC-MS.
title: Söğüt (Salix alba) gövdesinin hekzan ekstraktının GC-MS analizi

description: This report presents the results of the chemical analysis of the hexane extract from the bark of the Willow plant, also known as Salix alba, using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. The aim of this analysis was to identify and quantify the compounds in the hexane extract of Willow bark.
DOI: 10.5281/zenodo.10460010
title: The hexane extract of Willow (Salix alba) bark was analysed using GC-MS.
title: Söğüt (Salix alba) gövdesinin hekzan ekstraktının GC-MS analizi

description: This report presents the results of the chemical analysis of the hexane extract from the bark of the Willow plant, also known as Salix alba, using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. The aim of this analysis was to identify and quantify the compounds in the hexane extract of Willow bark.
DOI: 10.5281/zenodo.10460016
title: The acetone extract of Willow (Salix alba) bark was analysed using GC-MS.
title: Söğüt (Salix alba) gövdesinin aseton ekstraktının GC-MS analizi

description: This report presents the results of the chemical analysis of the acetone extract from the bark of the Willow plant, also known as Salix alba, using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. The aim of this analysis was to identify and quantify the compounds in the acetone extract of Willow bark.
DOI: 10.5281/zenodo.10460017
title: The acetone extract of Willow (Salix alba) bark was analysed using GC-MS.
title: Söğüt (Salix alba) gövdesinin aseton ekstraktının GC-MS analizi

description: This report presents the results of the chemical analysis of the acetone extract from the bark of the Willow plant, also known as Salix alba, using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. The aim of this analysis was to identify and quantify the compounds in the acetone extract of Willow bark.
DOI: 10.25597/6e8w-rf25
title: Replication material for:  Kübler D, Strebel MA, and Marcinkowski F (2023). Populism and the scales of statehood. Localism and populist attitudes in Western Europe. European Political Science Review, 1–17. https://doi.org/10.1017/S1755773923000395
DOI: 10.5281/zenodo.10460022
title: The methanol extract of Willow (Salix alba) bark was analysed using GC-MS.
title: Söğüt (Salix alba) gövdesinin metanol ekstraktının GC-MS analizi

description: This report presents the results of the chemical analysis of the methanol extract from the bark of the Willow plant, also known as Salix alba, using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. The aim of this analysis was to identify and quantify the compounds in the methanol extract of Willow bark.
DOI: 10.5281/zenodo.10460023
title: The methanol extract of Willow (Salix alba) bark was analysed using GC-MS.
title: Söğüt (Salix alba) gövdesinin metanol ekstraktının GC-MS analizi

description: This report presents the results of the chemical analysis of the methanol extract from the bark of the Willow plant, also known as Salix alba, using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. The aim of this analysis was to identify and quantify the compounds in the methanol extract of Willow bark.
DOI: 10.5281/zenodo.10460026
title: The water extract of Willow (Salix alba) bark was analysed using GC-MS.
title: Söğüt (Salix alba) gövdesinin su ekstraktının GC-MS analizi

description: This report presents the results of the chemical analysis of the water extract from the bark of the Willow plant, also known as Salix alba, using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. The aim of this analysis was to identify and quantify the compounds in the water extract of Willow bark.
DOI: 10.5281/zenodo.10460027
title: The water extract of Willow (Salix alba) bark was analysed using GC-MS.
title: Söğüt (Salix alba) gövdesinin su ekstraktının GC-MS analizi

description: This report presents the results of the chemical analysis of the water extract from the bark of the Willow plant, also known as Salix alba, using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. The aim of this analysis was to identify and quantify the compounds in the water extract of Willow bark.
DOI: 10.5281/zenodo.10459667
title: The hexane extract of Mahaleb (Prunus mahaleb L.) leaf was analysed using GC-MS.
title: Mahlep (Prunus mahaleb L.) yaprağının hekzan ekstraktının GC-MS analizi

description: This report presents the chemical analysis of the hexane extract obtained from the leaves of the Mahaleb plant, also known as Prunus mahalep L., using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. In this context, it was used to determine and quantify the compounds in the hexane extract of Mahaleb leaves.
DOI: 10.5281/zenodo.10459668
title: The hexane extract of Mahaleb (Prunus mahaleb L.) leaf was analysed using GC-MS.
title: Mahlep (Prunus mahaleb L.) yaprağının hekzan ekstraktının GC-MS analizi

description: This report presents the chemical analysis of the hexane extract obtained from the leaves of the Mahaleb plant, also known as Prunus mahalep L., using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. In this context, it was used to determine and quantify the compounds in the hexane extract of Mahaleb leaves.
DOI: 10.5519/qd.9t4fa7j5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (18 records)
DOI: 10.5281/zenodo.10459815
title: The acetone extract of Mahaleb (Prunus mahaleb L.) leaf was analysed using GC-MS.
title: Mahlep (Prunus mahaleb L.) yaprağının aseton ekstraktının GC-MS analizi

description: This report presents the chemical analysis of the acetone extract obtained from the leaves of the Mahaleb plant, also known as Prunus mahaleb L., using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. In this context, it was used to determine and quantify the compounds in the acetone extract of Mahaleb leaves.
DOI: 10.5281/zenodo.10459816
title: The acetone extract of Mahaleb (Prunus mahaleb L.) leaf was analysed using GC-MS.
title: Mahlep (Prunus mahaleb L.) yaprağının aseton ekstraktının GC-MS analizi

description: This report presents the chemical analysis of the acetone extract obtained from the leaves of the Mahaleb plant, also known as Prunus mahaleb L., using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. In this context, it was used to determine and quantify the compounds in the acetone extract of Mahaleb leaves.
DOI: 10.5281/zenodo.10459829
title: The methanol extract of Mahaleb (Prunus mahaleb L.) leaf was analysed using GC-MS.
title: Mahlep (Prunus mahaleb L.) yaprağının metanol ekstraktının GC-MS analizi

description: This report presents the chemical analysis of the methanol extract obtained from the leaves of the Mahaleb plant, also known as Prunus mahaleb L., using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. In this context, it was used to determine and quantify the compounds in the methanol extract of Mahaleb leaves.
DOI: 10.5281/zenodo.10459830
title: The methanol extract of Mahaleb (Prunus mahaleb L.) leaf was analysed using GC-MS.
title: Mahlep (Prunus mahaleb L.) yaprağının metanol ekstraktının GC-MS analizi

description: This report presents the chemical analysis of the methanol extract obtained from the leaves of the Mahaleb plant, also known as Prunus mahaleb L., using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS is a powerful analytical method that enables the separation and identification of chemical components. In this context, it was used to determine and quantify the compounds in the methanol extract of Mahaleb leaves.
DOI: 10.17031/65968388788f9
title: 1959 Marine Biological Association of the UK (MBA) European Continental Slope RV Sarsia Cruise No. P4

description: Records from 1959 RV Sarsia Cruise No. P4. All samples were collected using a rectangular dredge and identified by staff at the Marine Biological Association of the UK. Records supplied by Dr. Eve Southward and collated by DASSH for the 'Dredging the Depths' project.
DOI: 10.5281/zenodo.10404955
title: Tool for Renewable Energy Potentials - Database

description: Database for scenarios of "Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets"


The used datasets and applied methodology can be found in the paper Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets
. Please cite the paper if you utilize the dataset. Applied datasets among others:



	
Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Digitales Basis-Landschaftsmodell (Ebenen) (Basis-DLM)’. 2021. (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf)
	
Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Amtliche Hausumringe Deutschland (HU-DE)’. 2021. (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf)
	
Geobasisdaten: ©GeoBasis-DE / BKG (2021), 3D-Gebäudemodelle LoD2 Deutschland (LoD2-DE) (2021). (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf)
	
UNEP-WCMC, IUCN, ‘The world database on protected areas’. 2016. Accessed: Oct. 22, 2021. [Online]. Available: https://www.protectedplanet.net/



Please be aware of the conditions of use for parts of the applied datasets (https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf) if you utilize the data.
DOI: 10.5281/zenodo.6414017
title: Tool for Renewable Energy Potentials - Database

description: Database for scenarios of "Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets"


The used datasets and applied methodology can be found in the paper Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets
. Please cite the paper if you utilize the dataset. Applied datasets among others:



	
Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Digitales Basis-Landschaftsmodell (Ebenen) (Basis-DLM)’. 2021. (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf)
	
Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Amtliche Hausumringe Deutschland (HU-DE)’. 2021. (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf)
	
Geobasisdaten: ©GeoBasis-DE / BKG (2021), 3D-Gebäudemodelle LoD2 Deutschland (LoD2-DE) (2021). (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf)
	
UNEP-WCMC, IUCN, ‘The world database on protected areas’. 2016. Accessed: Oct. 22, 2021. [Online]. Available: https://www.protectedplanet.net/



Please be aware of the conditions of use for parts of the applied datasets (https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf) if you utilize the data.
DOI: 10.5281/zenodo.6414018
title: Tool for Renewable Energy Potentials - Database

description: Database for scenarios of "Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets" The used datasets and applied methodology can be found in the paper Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets<br> . Please cite the paper if you utilize the dataset. Applied datasets among others: Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Digitales Basis-Landschaftsmodell (Ebenen) (Basis-DLM)’. 2021. (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf) Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Amtliche Hausumringe Deutschland (HU-DE)’. 2021. (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf) Geobasisdaten: ©GeoBasis-DE / BKG (2021), 3D-Gebäudemodelle LoD2 Deutschland (LoD2-DE) (2021). (Conditions of use: https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf) UNEP-WCMC, IUCN, ‘The world database on protected areas’. 2016. Accessed: Oct. 22, 2021. [Online]. Available: https://www.protectedplanet.net/ Please be aware of the conditions of use for parts of the applied datasets (https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf) if you utilize the data.
DOI: 10.5281/zenodo.10463073
title: Raw data related to "Mey et al (2024): Visions for our future regional electricity system"

description: Raw survey data from "Mey et al (2024): Visions for our future regional electricity system – citizen preferences in four EU countries".

The respository contains two types of files for each country:



`raw-data-{ISO}.csv`: This is the raw JMP export from conjointly.com containing almost all data.

`Masterdata_14979_{ISO}.xlsx`: This file contains the survey duration of each respondent.


We have also published a pre-processed, instead of raw, version of this dataset at 10.5281/zenodo.10463114.

 

If you use this dataset in an academic publication, please cite the following article:



Mey, F., Lilliestam, J., Wolf, I., & Tröndle, T. (2024). Visions for our future regional electricity system – citizen preferences in four EU countries. iScience.
DOI: 10.5281/zenodo.10463074
title: Raw data related to "Mey et al (2024): Visions for our future regional electricity system"

description: Raw survey data from "Mey et al (2024): Visions for our future regional electricity system – citizen preferences in four EU countries".

The respository contains two types of files for each country:



`raw-data-{ISO}.csv`: This is the raw JMP export from conjointly.com containing almost all data.

`Masterdata_14979_{ISO}.xlsx`: This file contains the survey duration of each respondent.


We have also published a pre-processed, instead of raw, version of this dataset at 10.5281/zenodo.10463114.

 

If you use this dataset in an academic publication, please cite the following article:



Mey, F., Lilliestam, J., Wolf, I., & Tröndle, T. (2024). Visions for our future regional electricity system – citizen preferences in four EU countries. iScience.
DOI: 10.5281/zenodo.10468209
title: Microstructure and phase investigation of FeCrAl-Y2O3 ODS steels with different Ti and V contents

description: Description of data: The data set consists of seven main folders. The data is divided into folders based on the technique used to obtain the data. 

      1.      XRD 

The XRD folder consists of two subfolders: 

·        XRD of powder: raw data from diffractometer, which were used to create Fig. 3.

·        XRD of bulk samples: raw data from diffractometer, which were used to prepare Fig. 10.

      2.      SEM

The SEM folder consists of six subfolders:

·        SEM of powder: SEM images, which were used to create Fig. 1 showing powder morphology.

·        EDS of powder: SEM images and EDS spectra (p11.tif, p13.tif, p30.tif, p32.tif), which were used to create Fig. 2, show the chemical homogeneity of the powder.

·        SEM of bulk samples: SEM images of bulk samples that were used to create Fig. 4.

·        EDS of bulk samples: it consists of two SEM images of the region of interest (i.e., ODS-1-Ti.tif and ODS-2-TiV) and two .docx files (i.e., ODS-1-Ti.docx and ODS-2-TiV.docx) with maps of distribution of chemical elements selected for the analysis. These data were used to create Fig. 5. 

·        EBSD of bulk samples: This subfolder consists of several files. ODS-1-Ti_EBSD_grain_leg_info.txt and ODS-2-TiV_EBSD_grain_leg_info.txt files consist of data from the EBSD software, which enable us to create a histogram of grain size in Fig. 9c. ODS-1-Ti_EBSD_info.txt, ODS-1-Ti_EBSD_leg.bmp, and ODS-2-TiV_EBSD_info.txt, ODS-2-TiV_EBSD_leg.bmp files contain information about the acquisition parameters of EBSD maps. ODS-1-Ti_EBSD_IPF.bmp and ODS-2-TiV_EBSD_IPF.bmp files are EBSD maps, which were used to create Fig. 9a and Fig. 9b.

·        SEM of indentation sites: SEM images of indentation sites which were used to create Fig. 11c and Fig. 11d. 

      3.      TEM

The TEM folder consists of two subfolders:

·        TEM of bulk samples: TEM/STEM images and data about precipitates (Histograms data.xlsx), which were used to create Fig. 7. 

·        STEM EDS of bulk samples: In each subfolder (ODS-1-Ti and ODS-2-TiV), there are maps of the distribution of elements in both studied samples. These data were used to create Fig. 8.

      4.      Density: the folder contains a file with data related to the density measurements, which were used to create Table 3. 

     5.      XRF: the folder contains two files related to XRF composition measurements. In the XRF-graphs.docx file, two plots are shown that are used to create Fig. 6. In XRF_data.opju, the data from the XRF device is shown. 

     6.      Hardness: the folder contains a file with raw data of hardness measurements. 

Nanoindentation: the folder contains two files, i.e., Nanoindentation.xlsx and Nanoindentation.opju, which contain nanoindentation data used to create Fig. 11a and Fig. 11b.
DOI: 10.5281/zenodo.10468210
title: Microstructure and phase investigation of FeCrAl-Y2O3 ODS steels with different Ti and V contents

description: Description of data: The data set consists of seven main folders. The data is divided into folders based on the technique used to obtain the data. 

      1.      XRD 

The XRD folder consists of two subfolders: 

·        XRD of powder: raw data from diffractometer, which were used to create Fig. 3.

·        XRD of bulk samples: raw data from diffractometer, which were used to prepare Fig. 10.

      2.      SEM

The SEM folder consists of six subfolders:

·        SEM of powder: SEM images, which were used to create Fig. 1 showing powder morphology.

·        EDS of powder: SEM images and EDS spectra (p11.tif, p13.tif, p30.tif, p32.tif), which were used to create Fig. 2, show the chemical homogeneity of the powder.

·        SEM of bulk samples: SEM images of bulk samples that were used to create Fig. 4.

·        EDS of bulk samples: it consists of two SEM images of the region of interest (i.e., ODS-1-Ti.tif and ODS-2-TiV) and two .docx files (i.e., ODS-1-Ti.docx and ODS-2-TiV.docx) with maps of distribution of chemical elements selected for the analysis. These data were used to create Fig. 5. 

·        EBSD of bulk samples: This subfolder consists of several files. ODS-1-Ti_EBSD_grain_leg_info.txt and ODS-2-TiV_EBSD_grain_leg_info.txt files consist of data from the EBSD software, which enable us to create a histogram of grain size in Fig. 9c. ODS-1-Ti_EBSD_info.txt, ODS-1-Ti_EBSD_leg.bmp, and ODS-2-TiV_EBSD_info.txt, ODS-2-TiV_EBSD_leg.bmp files contain information about the acquisition parameters of EBSD maps. ODS-1-Ti_EBSD_IPF.bmp and ODS-2-TiV_EBSD_IPF.bmp files are EBSD maps, which were used to create Fig. 9a and Fig. 9b.

·        SEM of indentation sites: SEM images of indentation sites which were used to create Fig. 11c and Fig. 11d. 

      3.      TEM

The TEM folder consists of two subfolders:

·        TEM of bulk samples: TEM/STEM images and data about precipitates (Histograms data.xlsx), which were used to create Fig. 7. 

·        STEM EDS of bulk samples: In each subfolder (ODS-1-Ti and ODS-2-TiV), there are maps of the distribution of elements in both studied samples. These data were used to create Fig. 8.

      4.      Density: the folder contains a file with data related to the density measurements, which were used to create Table 3. 

     5.      XRF: the folder contains two files related to XRF composition measurements. In the XRF-graphs.docx file, two plots are shown that are used to create Fig. 6. In XRF_data.opju, the data from the XRF device is shown. 

     6.      Hardness: the folder contains a file with raw data of hardness measurements. 

Nanoindentation: the folder contains two files, i.e., Nanoindentation.xlsx and Nanoindentation.opju, which contain nanoindentation data used to create Fig. 11a and Fig. 11b.
DOI: 10.5281/zenodo.10468686
title: Microstructure and phase investigation of FeCrAl-Y2O3 ODS steels with different Ti and V contents

description: Description of data: The data set consists of seven main folders. The data is divided into folders based on the technique used to obtain the data. 

      1.      XRD 

The XRD folder consists of two subfolders: 

·        XRD of powder: raw data from diffractometer, which were used to create Fig. 3.

·        XRD of bulk samples: raw data from diffractometer, which were used to prepare Fig. 10.

      2.      SEM

The SEM folder consists of six subfolders:

·        SEM of powder: SEM images, which were used to create Fig. 1 showing powder morphology.

·        EDS of powder: SEM images and EDS spectra (p11.tif, p13.tif, p30.tif, p32.tif), which were used to create Fig. 2, show the chemical homogeneity of the powder.

·        SEM of bulk samples: SEM images of bulk samples that were used to create Fig. 4.

·        EDS of bulk samples: it consists of two SEM images of the region of interest (i.e., ODS-1-Ti.tif and ODS-2-TiV) and two .docx files (i.e., ODS-1-Ti.docx and ODS-2-TiV.docx) with maps of distribution of chemical elements selected for the analysis. These data were used to create Fig. 5. 

·        EBSD of bulk samples: This subfolder consists of several files. ODS-1-Ti_EBSD_grain_leg_info.txt and ODS-2-TiV_EBSD_grain_leg_info.txt files consist of data from the EBSD software, which enable us to create a histogram of grain size in Fig. 9c. ODS-1-Ti_EBSD_info.txt, ODS-1-Ti_EBSD_leg.bmp, and ODS-2-TiV_EBSD_info.txt, ODS-2-TiV_EBSD_leg.bmp files contain information about the acquisition parameters of EBSD maps. ODS-1-Ti_EBSD_IPF.bmp and ODS-2-TiV_EBSD_IPF.bmp files are EBSD maps, which were used to create Fig. 9a and Fig. 9b.

·        SEM of indentation sites: SEM images of indentation sites which were used to create Fig. 11c and Fig. 11d. 

      3.      TEM

The TEM folder consists of two subfolders:

·        TEM of bulk samples: TEM/STEM images and data about precipitates (Histograms data.xlsx), which were used to create Fig. 7. 

·        STEM EDS of bulk samples: In each subfolder (ODS-1-Ti and ODS-2-TiV), there are maps of the distribution of elements in both studied samples. These data were used to create Fig. 8.

      4.      Density: the folder contains a file with data related to the density measurements, which were used to create Table 3. 

     5.      XRF: the folder contains two files related to XRF composition measurements. In the XRF-graphs.docx file, two plots are shown that are used to create Fig. 6. In XRF_data.opju, the data from the XRF device is shown. 

     6.      Hardness: the folder contains a file with raw data of hardness measurements. 

Nanoindentation: the folder contains two files, i.e., Nanoindentation.xlsx and Nanoindentation.opju, which contain nanoindentation data used to create Fig. 11a and Fig. 11b.