cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e198942v3
title: Entrepreneurship survey of disabled people

description: This survey recruited 161 entrepreneurs with disabilities and 157 non-entrepreneurs with disabilities. The survey involved basic disability status such as disability type and disability grade of the respondents. Entrepreneurial self-efficacy, entrepreneurial resilience, entrepreneurial motivation and other psychological conditions, entrepreneurial motivation, stigma experience, family entrepreneurial support and other situational variables.<br>
DOI: 10.5281/zenodo.10808446
title: Spherical harmonic models of the shape of Mercury

description: The data used to generate these spherical harmonic models is the global digital elevation model (DEM) of Mercury. The DEM is derived from observations made by the Mercury Dual Imaging System (MDIS) narrow-angle camera (NAC) and multispectral wide-angle camera (WAC) on board of the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft. 

The global DEM was downloaded throught the Astropedia catalog in geoTIFF format and equirectangular projection.  Using the rasterio package, the dataset was loaded in python, scaled to the local height and radius (described in the Astropedia documentation), and exported into .dat format. Then, the file was converted into a netcdf format and resampled into a gridline registration using the generic-mapping-tools  as follows:gmt xyz2grd filename.dat -Gfilename.grd -R0/360/-90/90 -I0.015625/0.015625 -ZTLd -fg -rpgmt grdsample filename.grd -Gfilename_gridline.grd -T

The resulting netcdf file was read into the pyshtools software with the SHGrid.from_netcdf() and expanded into spherical harmonics using the function SHGrid.expand(). The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)m. The units of the coefficients are meters.

The four files in this archive are

- Mercury_shape_5759.sh.gz- Mercury_shape_2879.sh.gz- Mercury_shape_1439.sh.gz- Mercury_shape_719.sh.gz

The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.

The spherical harmonic coefficients can be loaded with pyshtools as follows:SHCoeffs.from_file("filename.sh.gz", format='bshc')
DOI: 10.5281/zenodo.10808445
title: Spherical harmonic models of the shape of Mercury

description: The data used to generate these spherical harmonic models is the global digital elevation model (DEM) of Mercury, produced by the U.S. Geological Survey (USGS). The DEM was derived from from stereo image pairs (stereo photogrammetry) captured by the Mercury Dual Imaging System (MDIS) narrow-angle camera (NAC) and multispectral wide-angle camera (WAC) on board the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft. 

The global DEM was downloaded throught the Astropedia catalog in geoTIFF format and equirectangular projection.  Using the rasterio package, the dataset was loaded in python, scaled to the local height and radius (described in the Astropedia documentation), and exported into .dat format. Then, the file was converted into a netcdf format and resampled into a gridline registration using the generic-mapping-tools  as follows:gmt xyz2grd filename.dat -Gfilename.grd -R0/360/-90/90 -I0.015625/0.015625 -ZTLd -fg -rpgmt grdsample filename.grd -Gfilename_gridline.grd -T

The resulting netcdf file was read into the pyshtools software with the SHGrid.from_netcdf() and expanded into spherical harmonics using the function SHGrid.expand(). The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)m. The units of the coefficients are meters.

The four files in this archive are

- Mercury_shape_5759.sh.gz- Mercury_shape_2879.sh.gz- Mercury_shape_1439.sh.gz- Mercury_shape_719.sh.gz

The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.

The spherical harmonic coefficients can be loaded with pyshtools as follows:SHCoeffs.from_file("filename.sh.gz", format='bshc')
DOI: 10.5281/zenodo.10809345
title: Spherical harmonic models of the shape of Mercury

description: The data used to generate these spherical harmonic models is the global digital elevation model (DEM) of Mercury, produced by the U.S. Geological Survey (USGS). The DEM was derived from from stereo image pairs (stereo photogrammetry) captured by the Mercury Dual Imaging System (MDIS) narrow-angle camera (NAC) and multispectral wide-angle camera (WAC) on board the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft. 

The global DEM was downloaded throught the Astropedia catalog in geoTIFF format and equirectangular projection.  Using the rasterio package, the dataset was loaded in python, scaled to the local height and radius (described in the Astropedia documentation), and exported into .dat format. Then, the file was converted into a netcdf format and resampled into a gridline registration using the generic-mapping-tools  as follows:gmt xyz2grd filename.dat -Gfilename.grd -R0/360/-90/90 -I0.015625/0.015625 -ZTLd -fg -rpgmt grdsample filename.grd -Gfilename_gridline.grd -T

The resulting netcdf file was read into the pyshtools software with the SHGrid.from_netcdf() and expanded into spherical harmonics using the function SHGrid.expand(). The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)m. The units of the coefficients are meters.

The four files in this archive are

- Mercury_shape_5759.sh.gz- Mercury_shape_2879.sh.gz- Mercury_shape_1439.sh.gz- Mercury_shape_719.sh.gz

The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.

The spherical harmonic coefficients can be loaded with pyshtools as follows:SHCoeffs.from_file("filename.sh.gz", format='bshc')
DOI: 10.34848/fihd8k
title: Plan de Gestión de Datos

description: Plan de Gestión de Datos
DOI: 10.34848/fihd8k/djigyp
title: Plan_de_gestion_de_datos_Replication Data for Network Topology in Decentralized Finance_UR 7_02_2024.pdf

description: :unav
DOI: 10.34848/fihd8k/wu3jld
title: Plan_de_gestion_de_datos_Replication Data for Network Topology in Decentralized Finance_UR 7_02_2024.pdf

description: :unav
DOI: 10.5281/zenodo.10809323
title: Displacement time series from GNSS stations in the Alto Tiberina Fault area (Central Italy)

description: The files report the position time-series of GNSS stations deployed in the Alto Tiberina Fault area (Central Italy). Columns are: Time, E, N, Se, Sn, Ren, U, Su, Reu, Rnu, site, long, lati, representing, respectively, epoch (in decimal years), displacement in the East component (in mm), displacement in the North component (in mm), uncertainty (one standard deviation) of the East component (in mm), uncertainty (one standard deviation) of the North component (in mm), correlation between the East and North components, displacement in the Up component (in mm), uncertainty (one standard deviation) of the Up component (in mm), correlation between the East and Up components, correlation between the North and Up components, Station ID (four letters), Longitude of the station (°), Latitude of the station (°).
DOI: 10.5281/zenodo.10809324
title: Displacement time series from GNSS stations in the Alto Tiberina Fault area (Central Italy)

description: The files report the position time-series of GNSS stations deployed in the Alto Tiberina Fault area (Central Italy). Columns are: Time, E, N, Se, Sn, Ren, U, Su, Reu, Rnu, site, long, lati, representing, respectively, epoch (in decimal years), displacement in the East component (in mm), displacement in the North component (in mm), uncertainty (one standard deviation) of the East component (in mm), uncertainty (one standard deviation) of the North component (in mm), correlation between the East and North components, displacement in the Up component (in mm), uncertainty (one standard deviation) of the Up component (in mm), correlation between the East and Up components, correlation between the North and Up components, Station ID (four letters), Longitude of the station (°), Latitude of the station (°).
DOI: 10.5281/zenodo.10809246
title: The official dataset of the papper " Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics"

description: This is the official repository of the paper "Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics" (https://doi.org/10.1080/15538362.2024.2322743)

DISCLAIMER

The repository contains experimental data and is published for the sole purpose of giving additional background details on the respective publication "Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics" (https://doi.org/10.1080/15538362.2024.2322743). See the README.txt file for more details.
DOI: 10.5281/zenodo.10809247
title: The official dataset of the papper " Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics"

description: This is the official repository of the paper "Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics" (https://doi.org/10.1080/15538362.2024.2322743)

DISCLAIMER

The repository contains experimental data and is published for the sole purpose of giving additional background details on the respective publication "Berries Pomace Valorization: From Waste to Potent Antioxidants and Emerging Skin Prebiotics" (https://doi.org/10.1080/15538362.2024.2322743). See the README.txt file for more details.
DOI: 10.3535/m42-z4p-drd
title: Phintelloides versicolor (C. L. Koch, 1846)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center
DOI: 10.5281/zenodo.10805463
title: CENTRINNO_Pilot's Geospatial Data

description: This dataset includes a selection of geospatial data collected on pilots' urban ecosystems, including socio-demographic, ecological and land use data.
DOI: 10.5281/zenodo.10805464
title: CENTRINNO_Pilot's Geospatial Data

description: This dataset includes a selection of geospatial data collected on pilots' urban ecosystems, including socio-demographic, ecological and land use data.
DOI: 10.6084/m9.figshare.25383697.v2
title: Silicodata: A benchmark chest X-ray dataset for Silicosis detection with radiologist's annotations

description: This research attempts to provide first its type public dataset for silicosis detection. The dataset contains frontal chest X-rays collected over three years. The data has been collected from the stone workers in the primary health centres of Rajasthan, India. The dataset contains samples of silicosis, STB, TB, and normal. The average size of each sample is around 3567 x 2898. The samples in the dataset are made available as three-channel JPG images. The dataset is divided into two sets:Set A: It contains images with only disease labels.Set B: It contains images with lung segmentation maps, annotations and disease labels
DOI: 10.15129/49e0adea-f6b2-4e1b-a562-6074720a7b84
title: Cumulative Revelations in Personal Data Study 1

description: Data collected in respect of EPSRC Cumulative Revelations in Personal Data EP/R033889/1 

This project was a major EPSRC funded study that sought to better understand the revelations that arise when pieces from an individual’s personal information available online are connected over time and across multiple platforms. Such more complete digital traces can give unintended insights into their life and opinions. 

Extensive fieldwork included an interview study (Study 1) with UK employees regarding their experiences of cumulative revelation of their data. We examined the risks and harms to individuals and employers when others joined the dots between their online information. Interviews employed a "digital narrative" technique where participants were asked to make drawings of their information and communication networks, the types of information shared and details of to whom it was available or visible.

Study 1 was conducted online in the period May 2020-August 2020 when much of the UK was in lockdown due to the Covid-19 pandemic. Interviews included questions addressing changes to information sharing behaviour occurring during lockdown conditions.

The dataset contains:

 • Transcripts of 26 interviews with the Uk public

 • Photographic images of drawings created by participants during the interviews

 • Data from a technology survey completed by participants at the start of each interview regarding their use of devices, information channels and data storage
DOI: 10.25403/upresearchdata.25370884
title: Quantitative components of a mental health task sharing study focused on clinical associates in South Africa

description: The study aimed to develop a mental health task sharing model focussed on clinical associates in South Africa. It consisted of three phases:
Reviewing the mental health content of clinical associate undergraduate curricula through in-depth interviews and a review of recordsAssessing the readiness for clinical associate task sharing in mental health through a KAP survey of clinical associates and focus group interviews with health managers, nurses and medical doctorsIdentifying the key elements of the task sharing model utilising a Delphi panel consisting of family physicians and psychiatrists
The data uploaded here is from the quantitative components of the study viz. KAP survey of clinical associates and the three rounds of the Delphi survey.
The KAP survey of clinical associates utilised an online (Qualtrics) questionnaire. The questionnaire consisted of sections on the participants' demographic characteristics, their undergraduate training, knowledge of mental illness, confidence performing mental health-related tasks, attitudes towards mental illness, their current involvement in mental health service provision, and their interest in mental health training and service provision. The questionnaire included the 16-item Mental Illness Clinicians' Attitudes version 4 scale (Health Service and Population Research Department, Institute of Psychiatry, King’s College London). The questionnaire was preceded by five questions to confirm if the potential participants met the inclusion criteria for the study and those who met the inclusion criteria needed to provide informed consent to participate.
The Delphi survey involved a panel consisting of family physicians and psychiatrists and took place over three rounds using online (Qualtrics) questionnaires. The first round questionnaire consisted of questions related to the levels of care at which mental health training of clinical associates should occur, the mental illnesses they should be able to recognise and manage, the health professionals and the teaching modalities that should be utilised in training, and the levels of care at which mental health task sharing should occur and the mental health tasks that could be performed. These questions were asked for both undergraduate training and for an hypothetical postgraduate qualification in mental health for clinical associates. The questionnaire was updated after each round with items on which consensus was reached being removed from the questionnaire for the subsequent rounds. In the second and third rounds, participants were provided with the median panel response and their own response from the previous round.
DOI: 10.25403/upresearchdata.25370884.v1
title: Quantitative components of a mental health task sharing study focused on clinical associates in South Africa

description: The study aimed to develop a mental health task sharing model focussed on clinical associates in South Africa. It consisted of three phases:
Reviewing the mental health content of clinical associate undergraduate curricula through in-depth interviews and a review of recordsAssessing the readiness for clinical associate task sharing in mental health through a KAP survey of clinical associates and focus group interviews with health managers, nurses and medical doctorsIdentifying the key elements of the task sharing model utilising a Delphi panel consisting of family physicians and psychiatrists
The data uploaded here is from the quantitative components of the study viz. KAP survey of clinical associates and the three rounds of the Delphi survey.
The KAP survey of clinical associates utilised an online (Qualtrics) questionnaire. The questionnaire consisted of sections on the participants' demographic characteristics, their undergraduate training, knowledge of mental illness, confidence performing mental health-related tasks, attitudes towards mental illness, their current involvement in mental health service provision, and their interest in mental health training and service provision. The questionnaire included the 16-item Mental Illness Clinicians' Attitudes version 4 scale (Health Service and Population Research Department, Institute of Psychiatry, King’s College London). The questionnaire was preceded by five questions to confirm if the potential participants met the inclusion criteria for the study and those who met the inclusion criteria needed to provide informed consent to participate.
The Delphi survey involved a panel consisting of family physicians and psychiatrists and took place over three rounds using online (Qualtrics) questionnaires. The first round questionnaire consisted of questions related to the levels of care at which mental health training of clinical associates should occur, the mental illnesses they should be able to recognise and manage, the health professionals and the teaching modalities that should be utilised in training, and the levels of care at which mental health task sharing should occur and the mental health tasks that could be performed. These questions were asked for both undergraduate training and for an hypothetical postgraduate qualification in mental health for clinical associates. The questionnaire was updated after each round with items on which consensus was reached being removed from the questionnaire for the subsequent rounds. In the second and third rounds, participants were provided with the median panel response and their own response from the previous round.
DOI: 10.60520/ieda/113131
title: Laser ablation ICP-MS U-Pb calcite geochronology of ore-stage calcite veins from the Osiris Carlin-type Au deposit, Yukon, Canada

description: This dataset provides U-Pb data analytical data from five carbonate veins from the Osiris deposit in Yukon, Canada. The veins are interpreted to have formed from the ore-stage fluids based on the presence of UV-fluorescent calcite and realgar in them, their high Mn, low Fe, low Sr, low Mg contents, and their unique d13C and d18O compositions. A paper interpreting this dataset is intended to be submitted.
DOI: 10.5281/zenodo.10081195
title: Data of resulting velocity and anisotropic models, and travel-times  of the Tanlu fault zone

description: Data of resulting velocity and anisotropic models, and travel-times for essay of Tanlu Pn tomography. These data can only be used for scientific research purposes.
1. ani_vel_model.txt  
          Format: longitude, latitude, vel_value (km/s), ani_value (km/s), azimuth_anisotropy (degree)
2. handpicked_travel_time.txt  
          Format:  event_number, event_year, event_month, event_day, event_hour, event_minute, event_second (s), event_lat, event_lon, event_depth (km), event_magnitude, sta_count (for one event)
               sta_name1, sta_lat1 (degree), sta_lon1 (degree), sta_elevation1 (m),  travel_time1 (s)
               sta_name2, sta_lat2 (degree), sta_lon2 (degree), sta_elevation2 (m),  travel_time2 (s)
               ...
DOI: 10.6084/m9.figshare.25392625
title: Three_Long_Term_SnowDepth_datasets_025km_China_ESSD

description: Three long-term snow depth datasets (random forest, pixel-based, and fusion products) covering 35 years (1988–2022) were provided. To improve the reliability of snow depth estimates, an ensemble dataset of random forest, pixel-based, and ERA5-Land products covering 1988-2022 snowy season was generated.Compared to snow depth datasets based solely on remote sensing or climate reanalysis data, fusion dataset can achieve the complementary observations of different data sources and therefore provide a higher confidence in the magnitude of snow depth.The snow depth datasets are stored in *.hdf format. Its naming convention is “SnowDepth_YYYYMMDD_Daily_025km_ProductName.h5”. “SnowDepth” is the snow depth variable. The file name variable “YYYYMMDD” denotes the date stamp, for example, 20220101. The “Daily” means the revisit time. The “025km” denotes the spatial resolution. The “ProductName” is the dataset name, for example, RandomForest, PixelBased, and Fusion. The pixel values in the hdf files have specific meanings: “0–1000” represents the effective value of snow depth, and the unit is cm; “9999” means missing or no data. The code used to read *.hdf file.
DOI: 10.6084/m9.figshare.25392625.v1
title: Three_Long_Term_SnowDepth_datasets_025km_China_ESSD

description: Three long-term snow depth datasets (random forest, pixel-based, and fusion products) covering 35 years (1988–2022) were provided. To improve the reliability of snow depth estimates, an ensemble dataset of random forest, pixel-based, and ERA5-Land products covering 1988-2022 snowy season was generated.Compared to snow depth datasets based solely on remote sensing or climate reanalysis data, fusion dataset can achieve the complementary observations of different data sources and therefore provide a higher confidence in the magnitude of snow depth.The snow depth datasets are stored in *.hdf format. Its naming convention is “SnowDepth_YYYYMMDD_Daily_025km_ProductName.h5”. “SnowDepth” is the snow depth variable. The file name variable “YYYYMMDD” denotes the date stamp, for example, 20220101. The “Daily” means the revisit time. The “025km” denotes the spatial resolution. The “ProductName” is the dataset name, for example, RandomForest, PixelBased, and Fusion. The pixel values in the hdf files have specific meanings: “0–1000” represents the effective value of snow depth, and the unit is cm; “9999” means missing or no data. The code used to read *.hdf file.
DOI: 10.5281/zenodo.10081194
title: Data of resulting velocity and anisotropic models, and Moho depth  of the Tanlu fault zone

description: Data of resulting velocity and anisotropic models, and travel-times for essay of Tanlu Pn tomography. These data can only be used for scientific research purposes.
1. ani_vel_model.txt  
          Format: longitude, latitude, vel_value (km/s), ani_value (km/s), azimuth_anisotropy (degree)
2. handpicked_travel_time.txt  
          Format:  event_number, event_year, event_month, event_day, event_hour, event_minute, event_second (s), event_lat, event_lon, event_depth (km), event_magnitude, sta_count (for one event)
               sta_name1, sta_lat1 (degree), sta_lon1 (degree), sta_elevation1 (m),  travel_time1 (s)
               sta_name2, sta_lat2 (degree), sta_lon2 (degree), sta_elevation2 (m),  travel_time2 (s)
               ...