cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/d09-xvt-xjv
title: Nuphar × spenneriana Gaudin

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/rnr-95t-vyz
title: Cornus suecica L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.26023/v7t4-066f-rv11
title: WINTRE-MIX: VAD winds from the DOW7 or USD. Version 1.0

description: The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data format is readable ASCII.
DOI: 10.26023/bx4h-kgta-q0w
title: WINTRE-MIX: VAD winds from the C-band COW. Version 1.0

description: The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data are readable ASCII.
DOI: 10.15468/dl.9hdn48
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Czechia",
 "Geometry POLYGON((13.39206 50.38,13.35646 50.37672,13.33187 50.36245,13.33811 50.34465,13.34985 50.33692,13.39463 50.34723,13.41922 50.34324,13.42839 50.37836,13.41922 50.38375,13.39206 50.38))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Accipitriformes",
 "Year 2024-2024"
 ]
}

 The dataset includes 79 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058474-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/wdnt-f2bx-wf0m
title: WINTRE-MIX: CAND DOW6 Doppler Spectrum at Quebec Canada. Version 1.0

description: Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter. The data format is netCDF.
DOI: 10.26023/956q-r4a8-zv0c
title: WINTRE-MIX: US DOW7 Doppler Spectrum at New York USA. Version 1.0

description: Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter.
DOI: 10.15468/dl.rf2mj4
title: Occurrence Download

description: A dataset containing 18383 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Russian Federation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Quercus robur",
 "Year 2020-2025"
 ]
}

 The dataset includes 18383 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058473-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11179781
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11180015
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11180038
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11180089
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.15468/dl.wss46c
title: Occurrence Download

description: A dataset containing 3096 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Spea intermontana (Cope, 1883))"
 ]
}

 The dataset includes 3096 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018510-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10399925
title: A method to investigate Pathological Variant load of Mitochondrial DNA  in single human Mesenchyma stem cells

description: Mesenchymal stem cells (MSCs) are the parent cells to many cells of the musculoskeletal system including osteoblasts. Previous work has shown that in mouse models mitochondrial DNA (mtDNA) pathological variants lead to dysfunction of the respiratory chain of these osteoblasts and the premature development of age related osteoporosis. An increased rate of respiratory chain deficiency has also been observed in human osteoblasts. mtDNA mutates at an enhanced rate compared to nuclear DNA and has been linked to the ageing process in humans. 

We have developed and optimised an experimental pipeline to isolate single MSCs using flow cytometry before performing next generation sequencing to analyse the pathogenic variant load of 13 patients aged 22-88 to assess if pathogenic variants exist in mesenchymal stem cells and if there are age related changes.

Across all patients and all cells mtDNA pathological variants were present at similarly low levels. As per previous studies we found increased pathogenic variants within the mtDNA control region (D-Loop). However overall, there was no significant difference in the distribution of variants across the rest of the genome in all patients. Although a higher proportion of non-synonymous variants were seen this was not statistically significant.

We have shown that it is possible to isolate and sequence individual MSCs and observe mtDNA pathological variants. This gives a snapshot in time of the variant load for each patient. Due to low sample numbers compared to other studies investigate mtDNA in other cell types, we did not observe any age related increase in pathological variants. However this adds further evidence that clonally expanded, somatic mtDNA variants are common and could contribute to age related disease including osteoporosis.
DOI: 10.25549/inscriptifact-ouc111149234
title: Pyu Inscription, PYU 25

description: [Text Description:] Inscription on bottom of the urn ❧ [Text or Publication Number Note:] PYU25 ❧ [Medium:] Stone ❧ [Script Note:] Pyu ❧ [Physical Object Description:] Urn; height 75 cm, diameter at bottom 78 cm and at top 84 cm ❧ [Physical Object Note:] Stone
DOI: 10.15468/dl.a7n3hq
title: Occurrence Download

description: A dataset containing 9222 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malacosoma disstria Hübner",
 {
 "or" : [
 "Country is United States of America",
 "Country is Canada",
 "Continent is North America"
 ]
 }
 ]
}

 The dataset includes 9222 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0127773-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc111149267
title: Pyu Inscription, PYU 25

description: [Text Description:] Inscription on bottom of the urn ❧ [Text or Publication Number Note:] PYU25 ❧ [Medium:] Stone ❧ [Script Note:] Pyu ❧ [Physical Object Description:] Urn; height 75 cm, diameter at bottom 78 cm and at top 84 cm ❧ [Physical Object Note:] Stone
DOI: 10.25549/inscriptifact-ouc111328336
title: Codex Climaci Rescriptus: Ladder of Ascent by John Climacus, MS.000149.1-.86

description: [Text Description:] Codex Climaci Rescriptus: Syriac translation of Ladder of Divine Ascent by John Climacus, upper text ❧ [Text or Publication Number Note:] MS.000149.1-.86 ❧ [Excavation Description:] Found at St. Catherine's Monastery ❧ [Medium:] Parchment ❧ [Script Note:] Syriac ❧ [Physical Object Description:] 137 folios; 9.06 x 14.56 in. (23 x 37 cm); vellum with ink ❧ [Physical Object Note:] Palimpsest
DOI: 10.15468/dl.qqu7hm
title: Occurrence Download

description: A dataset containing 365 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-80.1264417999999949 25.8724057999999992, -80.1264417999999949 25.8738067299999983, -80.1264417999999949 25.8746594700000010, -80.1264417999999949 25.8759385799999997, -80.1264417999999949 25.8774004200000007, -80.1264417999999949 25.8790449900000006, -80.1264417999999949 25.8802631900000009, -80.1264417999999949 25.8812377500000004, -80.1264417999999949 25.8822123099999999, -80.1264417999999949 25.8834914199999986, -80.1264417999999949 25.8841005200000005, -80.1264417999999949 25.8853796299999992, -80.1264417999999949 25.8870241999999990, -80.1251000599999941 25.8870241999999990, -80.1239412900000048 25.8870241999999990, -80.1230264699999992 25.8870241999999990, -80.1218676999999957 25.8870241999999990, -80.1203430000000054 25.8870241999999990, -80.1203430000000054 25.8855014499999996, -80.1203430000000054 25.8844050699999997, -80.1203430000000054 25.8831868699999994, -80.1203430000000054 25.8822123099999999, -80.1203430000000054 25.8808113800000008, -80.1203430000000054 25.8796540899999989, -80.1203430000000054 25.8785577099999990, -80.1203430000000054 25.8769740500000012, -80.1203430000000054 25.8758776700000013, -80.1203430000000054 25.8752076600000009, -80.1203430000000054 25.8741112800000010, -80.1203430000000054 25.8734412700000007, -80.1203430000000054 25.8724057999999992, -80.1213797999999997 25.8724057999999992, -80.1221726400000023 25.8724057999999992, -80.1234533899999946 25.8724057999999992, -80.1252220399999970 25.8724057999999992, -80.1264417999999949 25.8724057999999992))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 365 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0093242-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc111148897
title: Pyu Inscription in Pyu and Southern Brahmi Script, PYU 24

description: [Text Description:] Images of four Buddhas in bhumisparsamudra, each with standing attendant ❧ [Text or Publication Number Note:] PYU24 ❧ [Excavation Description:] Khin Ba mound relic chamber ❧ [Medium:] Metal ❧ [Script Note:] Pyu, Southern Brahmi ❧ [Physical Object Description:] Reliquary; height 58 cm, circumference at bottom 127 cm and at top 104 cm ❧ [Physical Object Note:] Silver-gilt
DOI: 10.15468/dl.n99kdr
title: Occurrence Download

description: A dataset containing 9956 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malacosoma americana Fabricius, 1793",
 {
 "or" : [
 "Country is United States of America",
 "Country is Canada",
 "Continent is North America"
 ]
 }
 ]
}

 The dataset includes 9956 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0127772-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.