cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8227363
title: Acceptance of medical AI in skin cancer screening: A Choice-based Conjoint Survey

description: Background: There is a great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems, capable of identifying melanoma, could save lives, enable immediate access to screenings, reduce unnecessary care and healthcare costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system. Objective: The aim of the present study was twofold. First, to determine how important the attributes provider (in-person physician, physician via teledermatology, AI, vs. personalized AI), costs of screening (free, 10€, 25€, vs. 40€) and waiting time (immediate, 1 day, 1 week, 4 weeks) were for patients’ choices of a particular mode of skin cancer screening. Second, to investigate whether sociodemographic characteristics, especially, age, were systematically related to participants’ individual choices. Methods: The study used choice-based conjoint-analysis to examine the acceptance of medical AI for a skin cancer screening from the patient's perspective. Participants responded to twelve choice sets, each containing three screening-variants, where each variant was described through attributes; provider, costs and waiting time. Furthermore, sociodemographic characteristics (age, gender, income, job status, educational background) were assessed. Results: 126 (33%) respondents completed the online survey. The results from the conjoint analysis showed that the three attributes were more or less equal important for the participant’s choices, with provider being the most important. Inspecting the individual part worths showed that treatment by a physician was most preferred, followed by e-consultation with a physician and personalized AI. The three AI levels scored significantly lower. Concerning the relationship between sociodemographic characteristics and relative importances we found, that only age showed a significant positive association to the important of the attribute provider (r = 0.21; p < .02). Younger participants put a lesser importance on the provider than older participants. All other correlations were not significant. Conclusions: The present study adds to the growing body of research using choice-experiments to investigate the acceptance of artificial intelligence in health contexts. Future studies need to explore the reasons why AI is accepted or rejected and whether sociodemographic characteristics are associated this decision.
DOI: 10.14459/2022mp1659039
title: So2Sat LCZ42

description: So2Sat LCZ42 is a dataset consisting of co-registered synthetic aperture radar and multispectral optical image patches acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and the corresponding local climate zones (LCZ) label. In this version of the dataset, we included additionally the label confidence evaluation data. Ten European cities in the city list were re-labeled by having a group of remote sensing experts cast 10 independent votes on a subset of the originally labeled polygons. They were then split into geographically separated training and testing data. The latter is furthermore split randomly into testing and validation data.
DOI: 10.5281/zenodo.10118183
title: Data for sensitivity analysis

description: To explore how the height of hydrofracture zone changes with parameter, use all the eight independent parameters, namely (1) Biot-Willis poro-elastic constant, (2), Poisson ratio of the strata, (3) bulk density of the rock, (4) tensile strength of the rock, (5) maximum overpressure in the deep-seated reservoir, (6) rock permeability, (7) permeability growth rate in the fracture, (8) bottom radius of the hydrofracture zone.
The sensitivity analysis simulated 90 scenarios using values of the above-mentioned parameters. Each independent variable in this dataset is populated with randomly generated numbers.
DOI: 10.5281/zenodo.10118184
title: Data for sensitivity analysis

description: To explore how the height of hydrofracture zone changes with parameter, use all the eight independent parameters, namely (1) Biot-Willis poro-elastic constant, (2), Poisson ratio of the strata, (3) bulk density of the rock, (4) tensile strength of the rock, (5) maximum overpressure in the deep-seated reservoir, (6) rock permeability, (7) permeability growth rate in the fracture, (8) bottom radius of the hydrofracture zone.
The sensitivity analysis simulated 90 scenarios using values of the above-mentioned parameters. Each independent variable in this dataset is populated with randomly generated numbers.
DOI: 10.5281/zenodo.10118230
title: GR GWB Water Level

description: Water levels and pumping rates of groundwater bodies in Prespa watershed, 2014-2020
New description
DOI: 10.5281/zenodo.10118231
title: GR GWB Water Level

description: Water levels and pumping rates of groundwater bodies in Prespa watershed, 2014-2020
New description
DOI: 10.5281/zenodo.7781289
title: SomaScan dataset used to identify protein biomarkers for distinguishing between bacterial and viral infections in febrile children

description: Protein profiles of children with confirmed bacterial infections (DB), confirmed viral infections (DV) in addition to healthy controls (HC). Protein profiles generated through the SomaScan 1.3k assay (SomaLogic, Colorado, USA).


Data has been normalised already, including batch effect correction using COCONUT (https://cran.r-project.org/web/packages/COCONUT/COCONUT.pdf) and log2 transformed. 


Accompanying the protein abundance values is a separate .csv file containing information about the proteins, including UniProt ID and Entrez gene IDs associated with the proteins.
DOI: 10.5281/zenodo.7781290
title: SomaScan dataset used to identify protein biomarkers for distinguishing between bacterial and viral infections in febrile children

description: Protein profiles of children with confirmed bacterial infections (DB), confirmed viral infections (DV) in addition to healthy controls (HC). Protein profiles generated through the SomaScan 1.3k assay (SomaLogic, Colorado, USA).


Data has been normalised already, including batch effect correction using COCONUT (https://cran.r-project.org/web/packages/COCONUT/COCONUT.pdf) and log2 transformed. 


Accompanying the protein abundance values is a separate .csv file containing information about the proteins, including UniProt ID and Entrez gene IDs associated with the proteins.
DOI: 10.5281/zenodo.10118228
title: Clinical Performance of the Gore Septal Occluder in Patent Foramen Ovale Closure in Different Septal Anatomies: 1-Year Results from a Single-Center Experience
DOI: 10.5281/zenodo.10118229
title: Clinical Performance of the Gore Septal Occluder in Patent Foramen Ovale Closure in Different Septal Anatomies: 1-Year Results from a Single-Center Experience
DOI: 10.17630/648b0d8c-aacd-4416-9844-833e6436f71a
title: Data supporting - Measuring temporal change in alpha diversity: a framework integrating taxonomic, phylogenetic and functional diversity and the iNEXT-3D standardization

description: Supporting files for the proposed paper - Measuring temporal change in alpha diversity: a framework integrating taxonomic, phylogenetic and functional diversity and the iNEXT-3D standardization
DOI: 10.17630/b8f9f445-60a0-4c0a-808e-ce27cfcbf48a
title: Exploring the Possibility of Using Fluorine Involved Non-Conjugated ElectronWithdrawing Groups for Thermally Activated Delayed Fluorescence Emitters by TDDFT Calculation (dataset)
DOI: 10.5281/zenodo.8029040
title: Dataset (Aggegation, Variance and the Mean in Braided Channel Networks)

description: This is the dataset of braided channels' images (in .bmp format ) extracted from Landsat satellite images ,available through the United States Geological Survey Earth Explorer Tool. Presented in .txt format there are the parameters Nwc (Number of braided channels) and Wwc (Width of braided channels) at maximum resolution. A code has been added in txt format.
DOI: 10.17630/335fed3b-81ef-4f7b-a0ea-09a65b56220a
title: Data underpinning: "A Quinone Based Single-Molecule Switch as Building Block for Molecular Electronics"
DOI: 10.5519/qd.8wewaclg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1943 records)
DOI: 10.21954/ou.rd.24204822
title: Butterfly diversity metrics and habitat summary data

description: Dataset that includes a mixture of urban woodland habitat data summaries for ten different woodlands in Milton Keynes. Set also includes butterfly diversity metrics at the site and transect scale. The project is part of a PhD, funded by Central England NERC Alliance (CENTA). The PhD is about urban woodland butterfly conservation, and these data are summaries of butterfly diversity measurements and the structure of the woodland habitats they were detected in. The purpose of this part of the project is to help find what explains the richness and diversity of butterfly species in urban woodland across Milton Keynes to better inform understanding of habitat suitability for woodland butterflies in urban areas. These data were collected by Bradley Neal and Dr. Philip Wheeler between June and September in 2022. We collected both butterfly occurrence and habitat data during this period in 10 different woodlands in Milton Keynes which are named in the dataset. A readme.txt is supplied to give context to the column titles.
DOI: 10.21954/ou.rd.24204822.v1
title: Butterfly diversity metrics and habitat summary data

description: Dataset that includes a mixture of urban woodland habitat data summaries for ten different woodlands in Milton Keynes. Set also includes butterfly diversity metrics at the site and transect scale. The project is part of a PhD, funded by Central England NERC Alliance (CENTA). The PhD is about urban woodland butterfly conservation, and these data are summaries of butterfly diversity measurements and the structure of the woodland habitats they were detected in. The purpose of this part of the project is to help find what explains the richness and diversity of butterfly species in urban woodland across Milton Keynes to better inform understanding of habitat suitability for woodland butterflies in urban areas. These data were collected by Bradley Neal and Dr. Philip Wheeler between June and September in 2022. We collected both butterfly occurrence and habitat data during this period in 10 different woodlands in Milton Keynes which are named in the dataset. A readme.txt is supplied to give context to the column titles.
DOI: 10.5281/zenodo.10118119
title: Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)

description: Soil data collected in an agricultural area with annual crops in Portugal (Lezíria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, and  pH1:5. 
The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.
Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format.
DOI: 10.5281/zenodo.10118120
title: Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)

description: Soil data collected in an agricultural area with annual crops in Portugal (Lezíria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, and  pH1:5. 
The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.
Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format.
DOI: 10.5281/zenodo.10118334
title: Spatial Informatics Group (SIG)_Demo for West Africa SERVIR hub

description: Demo for West Africa SERVIR hub
DOI: 10.5281/zenodo.10118335
title: Spatial Informatics Group (SIG)_Demo for West Africa SERVIR hub

description: Demo for West Africa SERVIR hub
DOI: 10.48372/99c7-ec64
title: Italian Orchis occurrence
DOI: 10.48372/98eh-f935
title: MEDIS
DOI: 10.6084/m9.figshare.24551695
title: R code and .csv files for reproducing Figures 2, 3 and 4 from "<b>Similar temporal patterns in insect richness, abundance, and biomass </b><b>across major habitat types</b>"

description: Refer to the readme file for a description of the metadata for the datasets.
DOI: 10.6084/m9.figshare.24551695.v1
title: R code and .csv files for reproducing Figures 2, 3 and 4.

description: Refer to the readme file for a description of the metadata for the datasets.
DOI: 10.34808/0cyw-0d31
title: Thermal conductivity of M0-M100 specimens

description: Thermal conductivity and volumetric heat capacity values of M0, M20, M40, M60, M80, M100 samples
Sample designation:
M0 - 0% magnetite aggregate
M20 - 20% magnetite aggregate
M40 - 40% magnetite aggregate
M60 - 60% magnetite aggregate
M80 - 80% magnetite aggregate
M100 - 100% magnetite aggregate
The sample designation is associated with specimens designation in the associated publication
DOI: 10.34740/kaggle/dsv/6957984
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/6957985
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.24551899
title: Data collected during interviews

description: Data collected on human-wildlife interactions within a rice production context in West Africa
DOI: 10.6084/m9.figshare.24551899.v1
title: Data collected during interviews

description: Data collected on human-wildlife interactions within a rice production context in West Africa
DOI: 10.5281/zenodo.10043783
title: Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design
DOI: 10.5281/zenodo.10117370
title: Branching tidal creek networks morphology data set

description: Data and codes used in the paper entitled "The origin of branching in tidal creek networks".
DOI: 10.5281/zenodo.10043784
title: Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design
DOI: 10.5281/zenodo.10117371
title: Branching tidal creek networks morphology data set

description: Data and codes used in the paper entitled "The origin of branching in tidal creek networks".
DOI: 10.5281/zenodo.10117343
title: Spatial Informatics Group (SIG)_CEO Interpretation - Demo Change Events

description: Demo project forest loss/degradation from different drivers
DOI: 10.5281/zenodo.10117344
title: Spatial Informatics Group (SIG)_CEO Interpretation - Demo Change Events

description: Demo project forest loss/degradation from different drivers
DOI: 10.34808/5npd-b104
title: Adiabatic calorimetry results for fresh concrete mixes M0 and M100

description: Adiabatic calorimetry results for fresh 3D printed concrete mixes (M0 and M100) determined using Controls S.p.A., Italy calorimeter
adiab_TEST_2022_01 - M0 mixture
adiab_TEST_2022_02 - M100 mixture
DOI: 10.5281/zenodo.10117586
title: CiP Discrete Manufacturing Dataset  (CNC Miling Machine (Process Data))

description: Dataset recorded in the process learning factory CiP at the Institute for Production Management, Technology and Machine Tools, TU Darmstadt for the purpose of demonstrating and testing InterQ developed solutions. It was used to demonstrate the framework's effectiveness in a case study, involving process monitoring of a real-world Computer Numerical Control milling process. The dataset includes accelerometer data and multiple types of realistic concept drifts. The uploaded file includes the link and password for accessing the dataset in PTW's cloud infrastructure.
DOI: 10.5281/zenodo.10117587
title: CiP Discrete Manufacturing Dataset  (CNC Miling Machine (Process Data))

description: Dataset recorded in the process learning factory CiP at the Institute for Production Management, Technology and Machine Tools, TU Darmstadt for the purpose of demonstrating and testing InterQ developed solutions. It was used to demonstrate the framework's effectiveness in a case study, involving process monitoring of a real-world Computer Numerical Control milling process. The dataset includes accelerometer data and multiple types of realistic concept drifts. The uploaded file includes the link and password for accessing the dataset in PTW's cloud infrastructure.
DOI: 10.14468/1hgs-ys59
title: High Resolution Fulcher Data M-U shots MU01

description: Spectroscopy data from MAST-U DMS system
description: Programme Area:Tokamak Science
DOI: 10.34808/2y5q-8m39
title: Sieve analysis of natural and magnetite aggregate

description: Sieve analysis of natural river sand and magnetite aggregate used for concrete production
DOI: 10.5517/ccdc.csd.cc2bjd09
title: CCDC 2160700: Experimental Crystal Structure Determination

description: Related Article: Victor Ovcharenko, Elena Fursova, Gleb Letyagin, Vitaly Morozov, Artem Bogomyakov, Galina Romanenko|2023|CrystEngComm|25|6194|doi:10.1039/D3CE00912B
description: Related Article: Galina Romanenko|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: 10.5517/ccdc.csd.cc2bjd1b
title: CCDC 2160701: Experimental Crystal Structure Determination

description: Related Article: Victor Ovcharenko, Elena Fursova, Gleb Letyagin, Vitaly Morozov, Artem Bogomyakov, Galina Romanenko|2023|CrystEngComm|25|6194|doi:10.1039/D3CE00912B
description: Related Article: Galina Romanenko|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: 10.5517/ccdc.csd.cc2bjd2c
title: CCDC 2160702: Experimental Crystal Structure Determination

description: Related Article: Victor Ovcharenko, Elena Fursova, Gleb Letyagin, Vitaly Morozov, Artem Bogomyakov, Galina Romanenko|2023|CrystEngComm|25|6194|doi:10.1039/D3CE00912B
description: Related Article: Galina Romanenko|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: 10.5517/ccdc.csd.cc2bjd3d
title: CCDC 2160703: Experimental Crystal Structure Determination

description: Related Article: Victor Ovcharenko, Elena Fursova, Gleb Letyagin, Vitaly Morozov, Artem Bogomyakov, Galina Romanenko|2023|CrystEngComm|25|6194|doi:10.1039/D3CE00912B
description: Related Article: Galina Romanenko|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: 10.5517/ccdc.csd.cc2bjd4f
title: CCDC 2160704: Experimental Crystal Structure Determination

description: Related Article: Victor Ovcharenko, Elena Fursova, Gleb Letyagin, Vitaly Morozov, Artem Bogomyakov, Galina Romanenko|2023|CrystEngComm|25|6194|doi:10.1039/D3CE00912B
description: Related Article: Galina Romanenko|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: 10.5517/ccdc.csd.cc2bjd5g
title: CCDC 2160705: Experimental Crystal Structure Determination

description: Related Article: Victor Ovcharenko, Elena Fursova, Gleb Letyagin, Vitaly Morozov, Artem Bogomyakov, Galina Romanenko|2023|CrystEngComm|25|6194|doi:10.1039/D3CE00912B
description: Related Article: Galina Romanenko|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: 10.5517/ccdc.csd.cc2fzvy7
title: CCDC 2263990: Experimental Crystal Structure Determination

description: Related Article: Yuta Koseki, Mami Yamazaki|2023|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: 10.5517/ccdc.csd.cc2fzw5h
title: CCDC 2263997: Experimental Crystal Structure Determination

description: Related Article: Yuta Koseki, Mami Yamazaki|2023|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.