cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1387708.10fu
title: SH1387708.10FU

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/e243816v1
title: Beyond Appraisal: Emotion Regulation and the Opposing Effects of Relative Abusive Supervision

description: Beyond Appraisal: Emotion Regulation and the Opposing Effects of Relative Abusive Supervision Paper Data
DOI: 10.15156/bio/sh1387710.10fu
title: SH1387710.10FU

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/sh1387716.10fu
title: SH1387716.10FU

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/sh1387721.10fu
title: SH1387721.10FU

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/sh1387724.10fu
title: SH1387724.10FU

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/sh1387725.10fu
title: SH1387725.10FU

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/sh1387727.10fu
title: SH1387727.10FU

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: 10.5255/ukda-sn-9503-1
title: Scottish Health Survey, 2023
title: Scottish Health Survey, 1995-
title: SHeS

description: The &lt;i&gt;Scottish Health Survey&lt;/i&gt; (SHeS) series was established in 1995. Commissioned by the Scottish Government Health Directorates, the series provides regular information on aspects of the public's health and factors related to health which cannot be obtained from other sources. The SHeS series was designed to:&lt;ul&gt;&lt;li&gt;estimate the prevalence of particular health conditions in Scotland;&lt;/li&gt;&lt;li&gt;estimate the prevalence of certain risk factors associated with these health conditions and to document the pattern of related health behaviours;&lt;/li&gt;&lt;li&gt;look at differences between regions and between subgroups of the population in the extent of their having these particular health conditions or risk factors, and to make comparisons with other national statistics for Scotland and England;&lt;/li&gt;&lt;li&gt;monitor trends in the population's health over time;&lt;/li&gt;&lt;li&gt;make a major contribution to monitoring progress towards health targets.&lt;/li&gt;&lt;/ul&gt;Each survey in the series includes a set of core questions and measurements (height and weight and, if applicable, blood pressure, waist circumference, urine and saliva samples), plus modules of questions on specific health conditions that vary from year to year. Each year the core sample has also been augmented by an additional boosted sample for children. Since 2008 NHS Health Boards have also had the opportunity to boost the number of adult interviews carried out in their area. &lt;br&gt;
&lt;br&gt;
The Scottish Government &lt;a href="https://www.gov.scot/collections/scottish-health-survey/" title="Scottish Health Survey"&gt;Scottish Health Survey&lt;/a&gt; webpages contain further information about the series, including latest news and publications. &lt;br&gt;
&lt;br&gt;
 
&lt;p&gt;&lt;br&gt;&lt;/p&gt;
DOI: 10.15156/bio/sh1387730.10fu
title: SH1387730.10FU

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: 10.4121/eb566322-ece1-44bf-aaf4-01ca22bad667
title: SAR dataset - Crop unsupervised inference

description: This dataset is used for unsupervised crop inference data. The data is the full-polarization SAR T matrix of the study area. The data is in the .mat format and can be opened using the Matlab software.
DOI: 10.4121/eb566322-ece1-44bf-aaf4-01ca22bad667.v1
title: SAR dataset - Crop unsupervised inference

description: This dataset is used for unsupervised crop inference data. The data is the full-polarization SAR T matrix of the study area. The data is in the .mat format and can be opened using the Matlab software.
DOI: 10.18419/darus-4744
title: DuMuX code for modelling solute redistribution below evaporating surfaces

description: &lt;p&gt;This dataset contains the source code to reproduce the numerical simulations presented in Chaudhry et al. Non-invasive imaging of solute redistribution below evaporating surfaces using 23-Na-MRI (submitted), TODO ADD PAPER, **TODO: add doi after acceptance**. &lt;/p&gt;

&lt;p&gt;The application presents two test cases: the first case contains a setup where salt precipitation occurs, and the second case simulates a convective downward flow due to density-driven instabilities. Both test cases are based on the following experiments: "Experimental data for solute redistribution below evaporating surfaces", https://doi.org/10.18419/DARUS-5094, . &lt;/p&gt;
description: DuMuX, 3.7
description: This application also requires installing some dune modules and the module dumux. The installation script <b>'install_kiemle2025a.py'</b> contains the dependencies in the correct version. For the instructions to build and execute the applications, one may check the Installation section in the <b>README.md</b>.
<br><br>
Use persistent identifiers from Software Heritage (
 
) to cite individual files or even lines of the source code.
DOI: 10.15468/dl.7ntfr3
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Primulina weii"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019793-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qq84vq
title: Occurrence Download

description: A dataset containing 998 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-2.04119 46.73716,-2.01288 46.73481,-2.00135 46.74287,-1.99866 46.74992,-1.99766 46.75876,-2.00202 46.76648,-2.0093 46.77219,-2.01478 46.77767,-2.02127 46.78159,-2.02776 46.78528,-2.0347 46.78293,-2.03649 46.77622,-2.04242 46.77309,-2.04768 46.77129,-2.05093 46.76492,-2.04119 46.73716))",
 "PublishingOrg is 1928bdf0-f5d2-11dc-8c12-b8a03c50a862",
 "Year 2015-2025"
 ]
}

 The dataset includes 998 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019795-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uc8vsx
title: Occurrence Download

description: A dataset containing 3153 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cornus capitata",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3153 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019794-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6ckfmp
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Primulina sinovietnamica"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019796-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pre2wj
title: Occurrence Download

description: A dataset containing 312 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((23.17832 50.02783,22.62761 49.74146,22.42201 49.55055,22.45873 47.94982,26.76161 48.28024,26.27699 49.33026,23.17832 50.02783))",
 "Month is one of (April, May, June)",
 "TaxonKey is Ciconia nigra",
 "Year 2010-2025"
 ]
}

 The dataset includes 312 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019797-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17539990
title: Underwater images collected by a Kite surf in Riambel, Mauritius - 2018-05-27

description: This dataset was collected by a Kite surf in Riambel, Mauritius - 2018-05-27. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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!
DOI: 10.5281/zenodo.17542558
title: Underwater images collected by a Kite surf in Riambel, Mauritius - 2018-05-27

description: This dataset was collected by a Kite surf in Riambel, Mauritius - 2018-05-27. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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!
DOI: 10.15472/u87mcd
title: Registros biológicos recogidos en el marco del proyecto Census of Marine Life Componente Natural Geography in Shore Areas - NAGISA Santa Marta

description: El Proyecto NaGISA (Natural Geography in Shore Areas) estuvo enmarcado en el Censo de la Vida Marina (CoML, Census of Marine Life), que tuvo como objetivo general y mundial inventariar lo que vivió, lo que vive y lo que vivirá en los océanos del mundo. La ejecución del proyecto se realizo entre 2006-01-01 a 2009-12-01. 

NaGISA se enfocó en el estudio de litorales rocosos y praderas de fanerógamas marinas a través de un protocolo estandarizado. En el Mar Caribe Colombiano se estableció una estación de muestreo "E1" en Bahía Chengue, Parque Nacional Natural Tayrona (PNNT) Santa Marta/Magdalena, en un rango de profundidades entre 0.5 - 7 m.

Como resultado se hace un total de 135 registros, entre invertebrados marinos (Anélidos, Equinodermos, Moluscos, Platelmintos y Sipunculidos), vertebrados (Actinopterigios) y algas (verdes, pardas y rojas); los cuales se encuentran depositados en la colección de referencia del Museo de Historia Natural Marina de Colombia Makuriwa del Instituto de Investigaciones Marinas y Costeras "Jose Benito Vives de Andreis" (INVEMAR).