cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1051946.10fu
title: SH1051946.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1051947.10fu
title: SH1051947.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1051950.10fu
title: SH1051950.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1051954.10fu
title: SH1051954.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1051957.10fu
title: SH1051957.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.3886/icpsr03883.v1
title: Annual Survey of Jails: Jurisdiction-Level Data, 2001
title: Version 1

description: This collection provides annual data on jail populations
 across the nation and examines the "spillover" on local jails
 resulting from the dramatic growth in federal and state prison
 populations. These data are used to track growth in the number of
 jails and their capacities nationally, changes in the demographics of
 the jail population (including sex, race, and adult or juvenile
 status), supervision status of persons held, prevalence of crowding
 issues, and a count of non-United States citizens within the jail
population.
description: Based on information from the 1999 Census of Jails, a
 sample of jail jurisdictions was selected for the 2001 survey. The
 sample included all public and private jails in 878 selected jail
 jurisdictions (in which there were a total of 948 reporting units) and
 47 regional jails. The sample selection was designed to yield a
 coefficient of variation of 1 percent of average daily population and
 one-day inmate population. Large jails and regional jails were in the
sample with certainty.
description: Datasets: 
DS1: Annual Survey of Jails: Jurisdiction-Level Data, 2001
description: All jail jurisdictions (i.e., counties, municipalities,
boroughs, etc.) identified through the 1999 Census of Jails.
description: The codebook and data collection instrument are
 provided by ICPSR as Portable Document Format (PDF) files. The PDF
 file format was developed by Adobe Systems Incorporated and can be
 accessed using PDF reader software, such as the Adobe Acrobat
 Reader. Information on how to obtain a copy of the Acrobat Reader is
provided on the ICPSR Web site.
DOI: 10.5281/zenodo.12801850
title: Complex refractive index determined during MOVPE epitaxial growth at different stages of GaN valence growth at 1045 C

description: The data describe the combined refractive index (n+ik) of the GaN layer measured at 1045 C during the MOVPE epitaxial growth process.  The measurements were performed inside an AIXTRON 3x2'' CCS epitaxial reactor using reflectance spectrum measurements at different stages of the process.

The resulting reflection spectra were simulated to determine the basic parameters of the thin film GaN, including the combined refractive index. V. software was used for this purpose. A. S. E (J. A. Wollam) using a parametric oscillator (PSEMI, PSEMI-0 and PSEMI-TRI and a Gaussian oscillator).

 

The file described ‘GaN_1045_C_Start_process.txt’ is the measurement taken after the formation of the nucleation layer (see figure) and about 110 nm (total of about 150 nm GaN layer).

The file ‘GaN_1045_C_End_of_the_process.txt’ describes the refractive index just after completion of the epitaxy process but still at 1045 C, the thickness of the layer is about 1400 nm.

The file ‘GaN_30_C_after_process.txt’ presents the combined refractive index at 30 C. The measurement was carried out in the same way as for the previous measurements.
DOI: 10.5281/zenodo.15747063
title: Complex refractive index determined during MOVPE epitaxial growth at different stages of GaN valence growth at 1045 C

description: The data describe the combined refractive index (n+ik) of the GaN layer measured at 1045 C during the MOVPE epitaxial growth process.  The measurements were performed inside an AIXTRON 3x2'' CCS epitaxial reactor using reflectance spectrum measurements at different stages of the process.

The resulting reflection spectra were simulated to determine the basic parameters of the thin film GaN, including the combined refractive index. V. software was used for this purpose. A. S. E (J. A. Wollam) using a parametric oscillator (PSEMI, PSEMI-0 and PSEMI-TRI and a Gaussian oscillator).

 

The file described ‘GaN_1045_C_Start_process.txt’ is the measurement taken after the formation of the nucleation layer (see figure) and about 110 nm (total of about 150 nm GaN layer).

The file ‘GaN_1045_C_End_of_the_process.txt’ describes the refractive index just after completion of the epitaxy process but still at 1045 C, the thickness of the layer is about 1400 nm.

The file ‘GaN_30_C_after_process.txt’ presents the combined refractive index at 30 C. The measurement was carried out in the same way as for the previous measurements.
DOI: 10.5281/zenodo.11178191
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24. 

            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.11178519
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24. 

            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.11178600
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27. 

            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: 10.1594/pangaea.875554
title: Organic-walled dinoflagellate cyst analysis of sediment core GeoB8331-4
DOI: 10.1594/pangaea.875562
title: Organic-walled dinoflagellate cyst analysis from multicores surface samples from Namaquan mudbelt
DOI: 10.15468/dl.6pegee
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Hemignathus lucidus M.H.K.Lichtenstein, 1839"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001245-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.887513
title: Experiment on the influence of temperature, N:P supply ratio and pCO2 on elemental composition and growth rates of Emiliania huxleyi

description: Climate-driven changes in environmental conditions have significant and complex effects on marine ecosystems. Variability in phytoplankton elements and biochemicals can be important for global ocean biogeochemistry and ecological functions, while there is currently limited understanding on how elements and biochemicals respond to the changing environments in key coccolithophore species such as Emiliania huxleyi. We investigated responses of elemental stoichiometry and fatty acids (FAs) in a strain of E. huxleyi under three temperatures (12, 18 and 24 oC), three N:P supply ratios (molar ratios 10:1, 24:1 and 63:1) and two pCO2 levels (560 and 2400 µatm). Overall, C:N:P stoichiometry showed the most pronounced response to N:P supply ratios, with high ratios of particulate organic carbon vs. particulate organic nitrogen (POC:PON) and low ratios of PON vs. particulate organic phosphorus (PON:POP) in low N-media, and high POC:POP and PON:POP in low P-media. The ratio of particulate inorganic carbon vs. POC (PIC:POC) and polyunsaturated fatty acid proportions strongly responded to temperature and pCO2, both being lower under high pCO2 and higher with warming. We observed synergistic interactions between warming and nutrient deficiency (and high pCO2) on elemental cellular contents and docosahexaenoic acid (DHA) proportion in most cases, indicating the enhanced effect of warming under nutrient deficiency (and high pCO2). Our results suggest differential sensitivity of elements and FAs to the changes in temperature, nutrient availability and pCO2 in E. huxleyi, which is to some extent unique compared to non-calcifying algal classes. Thus, simultaneous changes of elements and FAs should be considered when predicting future roles of E. huxleyi in the biotic-mediated connection between biogeochemical cycles, ecological functions and climate change.
description: Supplement to: Bi, Rong; Ismar, Stefanie M; Sommer, Ulrich; Zhao, Meixun (2018): Simultaneous shifts in elemental stoichiometry and fatty acids of Emiliania huxleyi in response to environmental changes. Biogeosciences, 15(4), 1029-1045
DOI: 10.15468/dl.hjytpv
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Specimen"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Naja oxiana (Eichwald, 1831)"
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0118385-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ldj1g8
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Issue is Geodetic datum assumed WGS84",
 "HasCoordinate is true",
 "TaxonKey is Elatostema sinense H.Schroet.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 64 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023569-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.