cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17028/rd.lboro.24611289.v1
title: Supplementary information files for Multidimensional analysis for the correlation of physico-chemical attributes to osteoblastogenesis in TiNbZrSnTa alloys

description: © the authors, CC-BY 4.0Supplementary files for article Multidimensional analysis for the correlation of physico-chemical attributes to osteoblastogenesis in TiNbZrSnTa alloysData-enabled approaches that complement experimental testing offer new capabilities to investigate the interplay between chemical, physical and mechanical attributes of alloys and elucidate their effect on biological behaviours. Reported here, instead of physical causation, statistical correlations were used to study the factors responsible for the adhesion, proliferation and maturation of pre-osteoblasts MC3T3-E1 cultured on Titanium alloys. Eight alloys with varying wt% of Niobium, Zirconium, Tin and Tantalum (Ti— (2–22 wt%)Nb— (5–20 wt%)Zr— (0–18 wt%)Sn— (0–14 wt%)Ta) were designed to achieve exemplars of allotropes (incl., metastable-β, β + α′, α″). Following confirmation of their compositions (ICP, EDX) and their crystal structure (XRD, SEM), their compressive bulk properties were measured and their surface features characterised (XPS, SFE). Because these alloys are intended for the manufacture of implantable orthopaedic devices, the correlation focuses on the effect of surface properties on cellular behaviour. Physico-chemical attributes were paired to biological performance, and these highlight the positive interdependencies between oxide composition and proliferation (esp. Ti<sup>4+</sup>), and maturation (esp. Zr<sup>4+</sup>). The correlation reveals the negative effect of oxide thickness, esp. TiO<sub>x</sub> and TaO<sub>x</sub><sub> </sub>on osteoblastogenesis. This study also shows that the characterisation of the chemical state and elemental electronic structure of the alloys' surface is more predictive than physical properties, namely SFE and roughness.
DOI: 10.5281/zenodo.10061470
title: Identified Potential Vorticity Structures associated with African Easterly Waves

description: This data set contains identified and tracked 3-D Potential Vorticity (PV) structures associated with African Easterly Waves (AEWs), based on the ERA-5 reanalysis of the years 2002-2022 from June to October.
First the AEW troughs are identified as outlined and separately submitted in this dataset. To extract the 3-D PV structures in these waves, a Hilbert transform is employed to compute the wave phase at every point in the domain. This yields the trough area of the waves. Within these areas, PV is collected and processed, and assigned a low-dimensional description, including a best-fit ellipsoid for statistical analyses. More details on the methodology can be found in a soon to be submitted publication.
The JSON files within this dataset provide a description of the PV features, structured as follows:

Each file contains one year of data, with one set each.
Each set includes:

A list of timestamps, where each timestamp contains the identified troughs at a specific time. Each time entry contains a list of objects (PV features), with each containing a set-unique identifier and properties describing the feature, such as the ellipsoid and a  bounding box.
The data graph, consisting of a list of edges, wherein each edge connects one parent object to multiple child objects, forming the tracking graph. A connection between a parent and child indicates that they represent the same feature entity, with the child being the next manifestation of the parent entity along the time line.
A list of tracks, with each track represented as a data graph as well.
 
description: The research leading to these results has been accomplished within the project C3 "Predictability of tropical and hybrid cyclones over the North Atlantic Ocean" of the Transregional Collaborative Research Center SFB/TRR 165 "Waves to Weather" funded by the German Science Foundation (DFG).
DOI: 10.5281/zenodo.8403743
title: AEWDAT - African Easterly Waves: Identified and Tracked Wave Troughs

description: This data set contains identified wave troughs within African Easterly Waves, utilizing 700 hPa curvature vorticity data from the ERA5 reanalysis spanning from June to October over the period 1940-2022. It encompasses all identified wave troughs and also includes extracted tracks derived from these identified features.


The identification methodology closely follows the approach outlined by Belanger et al. (2016). Troughs and ridges are determined as zeros in Curvature Vorticity Advection fields. Threshold criteria, based on a climatology and the second derivative, are applied to extract the troughs. Tracking is accomplished through a heuristic that connects wave troughs meeting specified criteria for speed and average speed over a 36-hour period.


The JSON files within this dataset provide a description of the wave troughs, structured as follows:



	
Each file contains one year of data, with one set each.
	
Each set includes:
	

		
A list of timestamps, where each timestamp contains the identified troughs at a specific time. Each time entry contains a list of objects (wave troughs), with each containing a set-unique identifier and properties describing the wave trough, such as its bounding box and line string representation.
		
The data graph, consisting of a list of edges, wherein each edge connects one parent object to multiple child objects, forming the tracking graph. A connection between a parent and child indicates that they represent the same wave trough entity, with the child being the next manifestation of the parent entity along the time line.
		
A list of tracks, with each track represented as a data graph as well.
	
	



Furthermore, a zip-file (wtsnc.zip) contains netCDF representations of the wavetroughs as labeled data field, where the labels correspond to the identifier in the JSON file. The file contains fields for all wavetroughs, only wavetroughs making up the tracks, and the tracks labeled by track ID. Be careful when extracting, the data is heavily compressed.


Further information regarding this strategy can be found in an upcoming publication, soon to be submitted for review.
description: The research leading to these results has been accomplished within the project C3 "Predictability of tropical and hybrid cyclones over the North Atlantic Ocean" of the Transregional Collaborative Research Center SFB/TRR 165 "Waves to Weather" funded by the German Science Foundation (DFG).
DOI: 10.6084/m9.figshare.24162042
title: Hmi elements (hmi_elements.csv)

description: An informative file that lists the employed elements with their respective element IDs and component types.
DOI: 10.6084/m9.figshare.24162096.v1
title: User Interaction sequences (sequences_df_prep_EN.csv)

description: Each row of this CSV file represents a valid interaction sequence to perform a task. This dataset was generated from the raw interactions following the "data processing" method
DOI: 10.6084/m9.figshare.24162042.v1
title: Hmi elements (hmi_elements.csv)

description: An informative file that lists the employed elements with their respective element IDs and component types.
DOI: 10.6084/m9.figshare.24162060
title: Users (users.csv)

description: This file presents information about the profile of users that participated in the study.
DOI: 10.6084/m9.figshare.24162096
title: User Interaction sequences (sequences_df_prep_EN.csv)

description: Each row of this CSV file represents a valid interaction sequence to perform a task. This dataset was generated from the raw interactions following the "data processing" method
DOI: 10.6084/m9.figshare.24162135
title: Raw interactions (raw_interactions.csv)

description: Raw interactions represent each event performed by an user when interacting with an element of the machine, with their corresponding timestamp
DOI: 10.6084/m9.figshare.24162207
title: User interface description (ui.json)

description: The ui.json file serves as a JSON-formatted representation of every visual element within the study's user interface. These elements are dynamically instantiated based on their component type.
DOI: 10.6084/m9.figshare.24162207.v1
title: User interface description (ui.json)

description: The ui.json file serves as a JSON-formatted representation of every visual element within the study's user interface. These elements are dynamically instantiated based on their component type.
DOI: 10.5517/ccdc.csd.cc2dgyxr
title: CCDC 2218915: Experimental Crystal Structure Determination

description: Related Article: Curtistine J. Deere, Garrick Breaux, Zechariah Myles, Frank R. Fronczek, Rao M. Uppu|2022|CSD Communication|||
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: 10.5281/zenodo.5205508
title: seNorge/TG: daily mean temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily mean temperature (TG) fields for the 66-year time period 1957-2022. TG is the mean temperature between 06:00 UTC of the day in the timestamp and 06:00 UTC of the previous day. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5205509
title: seNorge/TG: daily mean temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily mean temperature (TG) fields for the 64-year time period 1957-2020. TG is the mean temperature between 06:00 UTC of the day in the timestamp and 06:00 UTC of the previous day. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.8254455
title: seNorge/TG: daily mean temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily mean temperature (TG) fields for the 66-year time period 1957-2022. TG is the mean temperature between 06:00 UTC of the day in the timestamp and 06:00 UTC of the previous day. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.25921/61bh-n208
title: ME70 Water Column Sonar Data Collected During HB2202
DOI: 10.5281/zenodo.5552970
title: seNorge/TXa: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXa) fields for the 64-year time period 1957-2020. TXa is the maximum temperature without checking for consistency with TG/TM/TN. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.6965959
title: seNorge/TXa: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXa) fields for the 65-year time period 1957-2021. TXa is the maximum temperature without checking for consistency with TG/TM/TN. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5216276
title: seNorge/TXa: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXa) fields for the 66-year time period 1957-2022. TXa is the maximum temperature without checking for consistency with TG/TM/TN. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5216277
title: seNorge/TXa: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXa) fields for the 64-year time period 1957-2020. TXa is the maximum temperature without checking for consistency with TG/TM/TN. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.8254500
title: seNorge/TXa: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXa) fields for the 66-year time period 1957-2022. TXa is the maximum temperature without checking for consistency with TG/TM/TN. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.6084/m9.figshare.21557853
title: Unraveling the Bombus terrestris hemolymph, a readout of the immune response to microbial infections, through complementary mass spectrometry approaches

description: Hemolymph analysis was carried out using a two-stage approach of mass spectrometry that combines MALDI molecular mass fingerprinting (MALDI MFP or MALDI BeeTyping®) for its effectiveness in assessing the immune status of the bumble bee through a “blood” test.
DOI: 10.6084/m9.figshare.21557853.v1
title: Unraveling the Bombus terrestris hemolymph, a readout of the immune response to microbial infections, through complementary mass spectrometry approaches

description: Hemolymph analysis was carried out using a two-stage approach of mass spectrometry that combines MALDI molecular mass fingerprinting (MALDI MFP or MALDI BeeTyping®) for its effectiveness in assessing the immune status of the bumble bee through a “blood” test.
DOI: 10.5281/zenodo.5205484
title: seNorge/TX: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TX) fields for the 66-year time period 1957-2022. TX is the maximum temperature consistent with TG/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5205485
title: seNorge/TX: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TX) fields for the 64-year time period 1957-2020. TX is the maximum temperature consistent with TG/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.5552961
title: seNorge/TX: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TX) fields for the 64-year time period 1957-2020. TX is the maximum temperature consistent with TG/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting
DOI: 10.5281/zenodo.8254492
title: seNorge/TX: daily maximum temperature over Norway

description: seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TX) fields for the 66-year time period 1957-2022. TX is the maximum temperature consistent with TG/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018
description: MET Norway open data are licensed under Norwegian Licence for Open Government Data (NLOD) or a Creative Commons Attribution 4.0 International License at your preference. Credit should be given to The Norwegian Meteorological institute, shortened "MET Norway", as the source of data. License https://www.met.no/en/free-meteorological-data/Licensing-and-crediting