cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10810625
title: Lysozyme, BSA, Thyroglobulin: SAXS measurement in solution in two capillary thicknesses (1 mm and 1.5 mm) with in situ UV-Vis

description: SAXS measurement of three different proteins in solution in two capillary thicknesses (1 mm and 1.5 mm). Data include in situ UV-Vis absorption spectroscopy and UV-Vis absoroption spectroscopy using microvolume spectrometer (DeNovix DS-11) for comparison.

Proteins used: Lysozyme, bovine serum albumine, thyroglobuline




File Name
Description


sample_description.csv
detailed description of individual samples


DS11_UV_Tyr_Lys_BSA.zip
data from microvolume spectrometer 


SAXS_1mm_raw.zip
Raw unreduced SAXS data (2D images in H5 format), collected in 1mm capillary. Detailed description is in included HTML file.


SAXS_1mm_reduced.zip
SAXS data collected in 1mm capillary, reduced to 1D curves. Q-values are in 1/nm


SAXS_1mm_subtracted.zip
SAXS data collected in 1mm capillary, buffer subtracted. Q-values are in 1/nm


SAXS_1p5mm_raw.zip
Raw unreduced SAXS data (2D images in H5 format), collected in 1.5mm capillary. Detailed description is in included HTML file.


SAXS_1p5mm_reduced.zip
SAXS data collected in 1.5mm capillary, reduced to 1D curves. Q-values are in 1/nm


SAXS_1p5mm_subtracted.zip
SAXS data collected in 1.5mm capillary, buffer subtracted. Q-values are in 1/nm


SAXS_UV_1mm_absorbance_sample_nonaveraged.zip
UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1mm capillary. Only spectra collected when sample was present in capillary


SAXS_UV_1mm_raw.zip
Raw UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1mm capillary.


SAXS_UV_1p5mm_absorbance_sample_nonaveraged.zip
UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1.5mm capillary. Only spectra collected when sample was present in capillary


SAXS_UV_1p5mm_raw.zip
Raw UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1.5mm capillary.
DOI: 10.5281/zenodo.10810624
title: Lysozyme, BSA, Thyroglobulin: SAXS measurement in solution in two capillary thicknesses (1 mm and 1.5 mm) with in situ UV-Vis

description: SAXS measurement of three different proteins in solution in two capillary thicknesses (1 mm and 1.5 mm). Data include in situ UV-Vis absorption spectroscopy and UV-Vis absoroption spectroscopy using microvolume spectrometer (DeNovix DS-11) for comparison.

Proteins used: Lysozyme, bovine serum albumine, thyroglobuline




File Name
Description


sample_description.csv
detailed description of individual samples


DS11_UV_Tyr_Lys_BSA.zip
data from microvolume spectrometer 


SAXS_1mm_raw.zip
Raw unreduced SAXS data (2D images in H5 format), collected in 1mm capillary. Detailed description is in included HTML file.


SAXS_1mm_reduced.zip
SAXS data collected in 1mm capillary, reduced to 1D curves. Q-values are in 1/nm


SAXS_1mm_subtracted.zip
SAXS data collected in 1mm capillary, buffer subtracted. Q-values are in 1/nm


SAXS_1p5mm_raw.zip
Raw unreduced SAXS data (2D images in H5 format), collected in 1.5mm capillary. Detailed description is in included HTML file.


SAXS_1p5mm_reduced.zip
SAXS data collected in 1.5mm capillary, reduced to 1D curves. Q-values are in 1/nm


SAXS_1p5mm_subtracted.zip
SAXS data collected in 1.5mm capillary, buffer subtracted. Q-values are in 1/nm


SAXS_UV_1mm_absorbance_sample_nonaveraged.zip
UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1mm capillary. Only spectra collected when sample was present in capillary


SAXS_UV_1mm_raw.zip
Raw UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1mm capillary.


SAXS_UV_1p5mm_absorbance_sample_nonaveraged.zip
UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1.5mm capillary. Only spectra collected when sample was present in capillary


SAXS_UV_1p5mm_raw.zip
Raw UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1.5mm capillary.
DOI: 10.5281/zenodo.10813118
title: АМПУТАЦИЯ ГОЛЕНИ ПРИ КРИТИЧЕСКИХ ИШЕМИЯХ НИЖНЕЙ КОНЕЧНОСТИ У БОЛЬНЫХ С САХАРНЫМ ДИАБЕТОМ ПО ДАННЫМ ТРАНСКУТАННОЙ ОКСИМЕТРИИ
DOI: 10.5281/zenodo.10813119
title: АМПУТАЦИЯ ГОЛЕНИ ПРИ КРИТИЧЕСКИХ ИШЕМИЯХ НИЖНЕЙ КОНЕЧНОСТИ У БОЛЬНЫХ С САХАРНЫМ ДИАБЕТОМ ПО ДАННЫМ ТРАНСКУТАННОЙ ОКСИМЕТРИИ
DOI: 10.5281/zenodo.10821849
title: The Carbon Dioxide Removal Gap dataset

description: Data files and R code for the publication: The Carbon Dioxide Removal Gap
DOI: 10.5281/zenodo.10807463
title: Discovery of South African plant-based biomarkers as potential flagships for SARS- CoV-2 receptor

description: Table S1: Distinguished metabolites in the extracts of Artemisia annua and Artemisia afra using UPLC-MS/MS set in positive ionization mode.

Table S2: Identified and docked compound-based biomarkers from Artemisia annua and Artemisia afra (ESI+ scan).
DOI: 10.5281/zenodo.10807464
title: Discovery of South African plant-based biomarkers as potential flagships for SARS- CoV-2 receptor

description: Table S1: Distinguished metabolites in the extracts of Artemisia annua and Artemisia afra using UPLC-MS/MS set in positive ionization mode.

Table S2: Identified and docked compound-based biomarkers from Artemisia annua and Artemisia afra (ESI+ scan).
DOI: 10.5281/zenodo.10810569
title: Immunoglobulin G: SEC-SAXS experiment using in house SAXS instrument with in situ UV-Vis

description: SEC-SAXS experiment using in house SAXS instrument

Sample: Immunoglobuline GBuffer: 50mM Tris, 50 mM NaCl, pH 7.6




File Name
Description


SAXS_raw.zip
Raw, unreduced data (2D images) in HDF5 format


SAXS_reduced.zip
SAXS data, azimutaly reduced (1D curves). Q-vaules are in 1/nm


SAXS_UV_raw.zip
Raw UV-Vis absorption data collected using in-situ spectrometer


SAXS_UV_processed.zip
UV-Vis data collected using in-situ spectrometer. Absorbance at 280nm extracted as a time series


HPLC_raw.zip
Data from HPLC system natively exported from control software (Unicorn)


HPLC_extracted.zip
Data from HPLC system exported to CSV


DS11_fractions.zip
UV-Vis absorption data collected usig micro volume spectrometer (DeNovix DS-11) on fractions collected using fraction collector.
DOI: 10.5281/zenodo.10810568
title: Immunoglobulin G: SEC-SAXS experiment using in house SAXS instrument with in situ UV-Vis

description: SEC-SAXS experiment using in house SAXS instrument

Sample: Immunoglobuline GBuffer: 50mM Tris, 50 mM NaCl, pH 7.6




File Name
Description


SAXS_raw.zip
Raw, unreduced data (2D images) in HDF5 format


SAXS_reduced.zip
SAXS data, azimutaly reduced (1D curves). Q-vaules are in 1/nm


SAXS_UV_raw.zip
Raw UV-Vis absorption data collected using in-situ spectrometer


SAXS_UV_processed.zip
UV-Vis data collected using in-situ spectrometer. Absorbance at 280nm extracted as a time series


HPLC_raw.zip
Data from HPLC system natively exported from control software (Unicorn)


HPLC_extracted.zip
Data from HPLC system exported to CSV


DS11_fractions.zip
UV-Vis absorption data collected usig micro volume spectrometer (DeNovix DS-11) on fractions collected using fraction collector.
DOI: 10.5281/zenodo.10821848
title: The Carbon Dioxide Removal Gap dataset

description: Data files and R code for the publication: The Carbon Dioxide Removal Gap
DOI: 10.5281/zenodo.7784822
title: How to measure work functions from aqueous solutions - data

description: Data set pertaining to the article "How to measure work functions from aqueous solutions", https://doi.org/10.1039/D3SC01740K (Chemical Science 14, 9574-9588 (2023)). A new protocol for energy referencing of photoemission data from liquids (https://doi.org/10.1039/D1SC01908B, Chemical Science 12, 10558-10582 (2021)) is refined towards determining work functions from liquids.

Files with extension .h5 are hdf5-files structured according to the NeXus v2020.10 standard using the NXmpes user contributed format suggested by the Fairmat consortium, seehttps://www.nexusformat.org/https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.htmlA few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:* nexpy (distributed with python)* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)

In each NeXus file-entry, two types of spectra are included:1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').2. As-measured data ('raw').

Files with extension .txt are tab-separated ascii-files.

The following files are provided:

Photoemission data pertaining to solute measurements and reference measurements using a gold wire:'Figure 3.h5''Figure 4.h5''Figure S1.h5''Figure S2.h5'Kinetic energies are presented as measured. The scale offset of our spectrometer, determined as E_kin(corrected) = E_kin(measured) + 0.224 eV for data sets 'Figure 3.h5', 'Figure 4.h5' ,'Figure S2.h5', has not been taken into account.

Numeric representations of the analysis results shown in the article's figures in graphical form:'Figure 5.txt''Figure 6B.txt''Figure 7.txt''Figure S4B.txt''Figure S5.txt'

In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .
description: Funding acknowledgements: Deutsche Forschungsgemeinschaft (Wi 1327/5-1), Max-Water initiative of the Max-Planck-Gesellschaft, JSPS KAKENHI Grant No. JP20K15229.
DOI: 10.5281/zenodo.7784823
title: How to measure work functions from aqueous solutions - data

description: Data set pertaining to the article "How to measure work functions from aqueous solutions", https://doi.org/10.1039/D3SC01740K (Chemical Science 14, 9574-9588 (2023)). A new protocol for energy referencing of photoemission data from liquids (https://doi.org/10.1039/D1SC01908B, Chemical Science 12, 10558-10582 (2021)) is refined towards determining work functions from liquids.

Files with extension .h5 are hdf5-files structured according to the NeXus v2020.10 standard using the NXmpes user contributed format suggested by the Fairmat consortium, seehttps://www.nexusformat.org/https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.htmlA few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:* nexpy (distributed with python)* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)

In each NeXus file-entry, two types of spectra are included:1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').2. As-measured data ('raw').

Files with extension .txt are tab-separated ascii-files.

The following files are provided:

Photoemission data pertaining to solute measurements and reference measurements using a gold wire:'Figure 3.h5''Figure 4.h5''Figure S1.h5''Figure S2.h5'Kinetic energies are presented as measured. The scale offset of our spectrometer, determined as E_kin(corrected) = E_kin(measured) + 0.224 eV for data sets 'Figure 3.h5', 'Figure 4.h5' ,'Figure S2.h5', has not been taken into account.

Numeric representations of the analysis results shown in the article's figures in graphical form:'Figure 5.txt''Figure 6B.txt''Figure 7.txt''Figure S4B.txt''Figure S5.txt'

In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .
description: Funding acknowledgements: Deutsche Forschungsgemeinschaft (Wi 1327/5-1), Max-Water initiative of the Max-Planck-Gesellschaft, JSPS KAKENHI Grant No. JP20K15229.
DOI: 10.5281/zenodo.8234156
title: Drone raw images of cattle in french grazing areas

description: The updated data are part of the Horizon Europe project ICAERUS regarding the livestock monitoring use case in a task where the objective is to test, optimize and scale up models regarding animal counting (cattle and sheep). More information here : https://icaerus.eu/


The dataset encompasses around 900 raw .jpeg drone images of grazing areas where cattle graze collected between June and August 2023. Drone used were Mavic 3 Enterprise and Thermal. Data collection is underway, with the aim of collecting images throughout the year and on several farms, to capture variability in animal and background colors and brightness conditions. Image tags (“cattle” vs “no cattle”) are not available for the moment but will be in the next months in next versions of this dataset. There is a strong imbalance between images with “cattle” and image with “no cattle” representative of areas to monitor. 



The nadir images were collected during flight planned with DJI Pilot 2 at a constant altitude regarding the take-off position (30 m, 60 m, 100 m). 


The data are organized by a first directory by farm where the images were collected and then with one directory by flight planned. 
A summary is available in the Table_summary.xls. Name of the directory of each planned flight is defined such as DJI_YYYYMMDDHHMM_XX with the date (YYYYMMDD), the hour in UTC+2 (HHMM), and XX representing a mission number. 



.exif data of each images provide many information regarding the drone (GPS position, absolute and relative altitude, gimble information, speed etc.). Further details will be added in the next versions of the dataset.


The authors of the dataset are opened to any collaboration regarding animal counting models.



For more information, please contact: adrien.lebreton@idele.fr
DOI: 10.5281/zenodo.8234155
title: Drone raw images of cattle in french grazing areas

description: The updated data are part of the Horizon Europe project ICAERUS regarding the livestock monitoring use case in a task where the objective is to test, optimize and scale up models regarding animal counting (cattle and sheep). More information here : https://icaerus.eu/


The dataset encompasses around 900 raw .jpeg drone images of grazing areas where cattle graze collected between June and August 2023. Drone used were Mavic 3 Enterprise and Thermal. Data collection is underway, with the aim of collecting images throughout the year and on several farms, to capture variability in animal and background colors and brightness conditions. Image tags (“cattle” vs “no cattle”) are not available for the moment but will be in the next months in next versions of this dataset. There is a strong imbalance between images with “cattle” and image with “no cattle” representative of areas to monitor. 



The nadir images were collected during flight planned with DJI Pilot 2 at a constant altitude regarding the take-off position (30 m, 60 m, 100 m). 


The data are organized by a first directory by farm where the images were collected and then with one directory by flight planned. 
A summary is available in the Table_summary.xls. Name of the directory of each planned flight is defined such as DJI_YYYYMMDDHHMM_XX with the date (YYYYMMDD), the hour in UTC+2 (HHMM), and XX representing a mission number. 



.exif data of each images provide many information regarding the drone (GPS position, absolute and relative altitude, gimble information, speed etc.). Further details will be added in the next versions of the dataset.


The authors of the dataset are opened to any collaboration regarding animal counting models.



For more information, please contact: adrien.lebreton@idele.fr
DOI: 10.5281/zenodo.10482561
title: Dataset for "Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank's Solution by Multisine Impedimetric Monitoring"

description: This dataset contains raw data of the publication “Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank’s Solution by Multisine Impedimetric Monitoring”. Corrosion Science, Volume 231, 2024, 111979, ISSN 0010-938X, https://doi.org/10.1016/j.corsci.2024.111979. The data was collected at Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences (Poland) and Gdansk University of Technology (Poland). All data are provided under CC0 license. The dataset includes Scanning electron and optical microscopy images (.tiff and ,jpg format); X-ray photoelectron spectroscopy spectra (.vms format); Electrochemical impedance spectra (.txt format). Dzmitry Kharytonau gratefully acknowledges the financial support of this study by the National Science Centre (Poland) under research Grant Sonatina no. 2021/40/C/ST5/00266. This work was also supported by Gdansk University of Technology under the Aurum Supporting International Research Team Building – ‘Excellence Initiative – Research University’ [grant number: 2/2021/IDUB/II.1.3].
DOI: 10.5281/zenodo.10482560
title: Dataset for "Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank's Solution by Multisine Impedimetric Monitoring"

description: This dataset contains raw data of the publication “Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank’s Solution by Multisine Impedimetric Monitoring”. Corrosion Science, Volume 231, 2024, 111979, ISSN 0010-938X, https://doi.org/10.1016/j.corsci.2024.111979. The data was collected at Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences (Poland) and Gdansk University of Technology (Poland). All data are provided under CC0 license. The dataset includes Scanning electron and optical microscopy images (.tiff and ,jpg format); X-ray photoelectron spectroscopy spectra (.vms format); Electrochemical impedance spectra (.txt format). Dzmitry Kharytonau gratefully acknowledges the financial support of this study by the National Science Centre (Poland) under research Grant Sonatina no. 2021/40/C/ST5/00266. This work was also supported by Gdansk University of Technology under the Aurum Supporting International Research Team Building – ‘Excellence Initiative – Research University’ [grant number: 2/2021/IDUB/II.1.3].
DOI: 10.5281/zenodo.10120779
title: Tibetan for Spacy 1.1

description: Tibetan for SpaCy is a language model for Tibetan designed for use in the SpaCy environment. The model was trained using SpaCy. It uses an external tokenizer, Botok, to segment the Tibetan and replaces the Tibetan syllable-separator (tseg) with white spaces where it occurs as a word separator. SpaCy was then told to interpret the input as English. This produces good results with standard vocabulary, but fails with unrecognised words. The project is currently working on a more sophisticated version of Tibetan for SpaCy. The package includes a list of stop words.

Tibetan for SpaCy was developed by James Engels as part of Divergent Discourses, a joint project between SOAS University of London and Leipzig University, funded by the AHRC in the UK and the DFG in Germany.

The project developed Tibetan for SpaCy particularly for users who want to use Tibetan texts within the Leipzig Corpus Miner (iLCM), an advanced text-mining interface designed for social scientists. The instructions for using Tibetan for SpaCy within the iLCM are in the readme file below. These instructions assume the user has already downloaded and installed the iLCM, which can be found here. 

Please acknowledge the Divergent Discourses project if using this material (note that ultimately copyright belongs to the two participating universities). Contact: rb75@soas.ac.uk
DOI: 10.5281/zenodo.10120780
title: Tibetan for Spacy 1.1
DOI: 10.5281/zenodo.10148636
title: Tibetan for Spacy 1.1

description: Tibetan for SpaCy is a language model for Tibetan designed for use in the SpaCy environment. The model was trained using SpaCy. It uses an external tokenizer, Botok, to segment the Tibetan and replaces the Tibetan syllable-separator (tseg) with white spaces where it occurs as a word separator. SpaCy was then told to interpret the input as English. This produces good results with standard vocabulary, but fails with unrecognised words. The project is currently working on a more sophisticated version of Tibetan for SpaCy. The package includes a list of stop words.

Tibetan for SpaCy was developed by James Engels as part of Divergent Discourses, a joint project between SOAS University of London and Leipzig University, funded by the AHRC in the UK and the DFG in Germany.

The project developed Tibetan for SpaCy particularly for users who want to use Tibetan texts within the Leipzig Corpus Miner (iLCM), an advanced text-mining interface designed for social scientists. The instructions for using Tibetan for SpaCy within the iLCM are in the readme file below. These instructions assume the user has already downloaded and installed the iLCM, which can be found here. 

Please acknowledge the Divergent Discourses project if using this material (note that ultimately copyright belongs to the two participating universities). Contact: rb75@soas.ac.uk
DOI: 10.5281/zenodo.10813604
title: Test me 2
DOI: 10.5281/zenodo.10813605
title: Test me 2
DOI: 10.5281/zenodo.10674484
title: Bioinformática para el análisis de SARS-CoV-2 para principiantes: Módulo_2
DOI: 10.5281/zenodo.10674483
title: Bioinformática para el análisis de SARS-CoV-2 para principiantes: Módulo_2
DOI: 10.5281/zenodo.10624873
title: CEDAR, an online resource for the reporting and exploration of complexome profiling data

description: Complexome profiling is an emerging ‘omics’ approach that systematically interrogates the composition of protein complexes (the complexome) of a sample, by combining biochemical separation of native protein complexes with mass-spectrometry based quantitation proteomics. The resulting fractionation profiles hold comprehensive information on the abundance and composition of the complexome, and have a high potential for reuse by experimental and computational researchers. However, the lack of a central resource that provides access to these data, reported with adequate descriptions and an analysis tool, has limited their reuse. Therefore, we established the ComplexomE profiling DAta Resource (CEDAR, www3.cmbi.umcn.nl/cedar/), an openly accessible database for depositing and exploring mass spectrometry data from complexome profiling studies. Compatibility and reusability of the data is ensured by a standardized data and reporting format containing the “minimum information required for a complexome profiling experiment” (MIACE). The data can be accessed through a user-friendly web interface, as well as programmatically using the REST API portal. Additionally, all complexome profiles available on CEDAR can be inspected directly on the website with the profile viewer tool that allows the detection of correlated profiles and inference of potential complexes. In conclusion, CEDAR is a unique, growing and invaluable resource for the study of protein complex composition and dynamics across biological systems.