cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0215188.10fu
title: SH0215188.10FU

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/sh0215189.10fu
title: SH0215189.10FU

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/sh0215190.10fu
title: SH0215190.10FU

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/sh0215191.10fu
title: SH0215191.10FU

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: 10.5067/lwj6tf5szrg3
title: SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 7
DOI: 10.5067/terra+aqua/ceres/syn1degmonth_l3.004a
title: CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly Terra-Aqua Edition4A
DOI: 10.5281/zenodo.10139065
title: Data and code for Spaulding-Astudillo and Mitchell (2023c), "Clear-sky convergence and the origin of tropical congestus clouds"

description: Data and code for Spaulding-Astudillo and Mitchell (2023c), "Clear-sky convergence and the origin of tropical congestus clouds".
The Python code used to create Figures 1, 3-6 and supplementary Figure 1 are included in separate directories. Within each directory, we include experimental data generated by the Reference Forward Model (RFM; Dudhia 2017) that is needed to reproduce the figures. To create the figures, run the provided code in a Jupyter notebook or from the command line. Helper functions for data processing within the figure scripts have the nomenclature ..._data.py. Note that the provided code only allows you to visualize RFM data, not produce it yourself. To do so, you must download and run RFM yourself (https://eodg.atm.ox.ac.uk/RFM/). In addition, Figure 1 requires spatio-temporally averaged data from MERRA-2, ERA5, and CloudSat/CALIPSO (Betrand et al. 2023), which we provide in pickle files. See the main text for data citations, which will allow you to access the original, non-averaged climate data. 
RFM data directories have the nomenclature /ctrl-..... For example, ctrl-RHmid-75-zmid-7-uniform-1 where:RHmid=75% is the relative humidity at a mid-tropospheric height zmid=7.5 km. Uniform=True indicates that the tropospheric relative humidity is constant with height. When uniform=False, the relative humidity follows a C-shaped distribution between the surface and the tropopause. The helper functions in ..._data.py allow you to read the RFM output from .asc files. The nomenclature of the RFM output files is standardized: https://eodg.atm.ox.ac.uk/RFM/.
DOI: 10.5281/zenodo.11546115
title: Data related to: Global land-water competition and synergy between solar energy and agriculture
DOI: 10.5281/zenodo.14626004
title: Methane and Carbon Dioxide Production and Emission Pathways in the Belowground and Draining Water Bodies of a Tropical Peatland Plantation Forest

description: This is the data repository for the second version (revised) of the manuscript "Methane and Carbon Dioxide Production and Emission Pathways in the Belowground and Draining Water Bodies of a Tropical Peatland Plantation Forest" submitted to Geophysical Research Letters on 10 January 2025.
DOI: 10.5281/zenodo.14747970
title: The Turbulent Pressure Spectrum Within the Roughness Sublayer of a Subarctic Forest Canopy

description: The file contains a data set, which can be used to reproduce findings reported in the manuscript "The Turbulent Pressure Spectrum Within the Roughness Sublayer of a Subarctic Forest Canopy" by Aslan et al. (2025).

 

Aslan, Toprak, Gabriel G. Katul, and Mika Aurela. "The Turbulent Pressure Spectrum Within the Roughness Sublayer of a Subarctic Forest Canopy." Journal of Geophysical Research: Atmospheres. 2025.

 

The data set contains four self-explanatory files: (1) Aslan_et_al_2025_data1.xlsx, including dates, half-hourly values of snow depth, snow condition, wind speed, friction velocity, Kiso, variance of filtered pressure data for both 23m and 2.4m, (2) Aslan_et_al_2025_data2_Figure7.xlsx, including measured above canopy pressure spectra of randomly selected 30 half-hour periods (Epp(kx)) normalized by their respective variances for all three snow cover cases and their corresponding wavenumbers (kxz) as visualized in Figure 7, (3) Aslan_et_al_2025_data3_Figure11.xlsx, including measured below canopy pressure spectra of randomly selected 30 half-hour periods (Epp(kx)) normalized by their respective variances for all three snow cover cases and their corresponding wavenumbers (kxz) as visualized in Figure 11, (4) Aslan_et_al_2025_data4_Rawdata_202312150900.csv, including the raw data (10Hz) of pressure and sonic anemometer data measured at 23m at 2023-12-15-09:00 as visualized in Figure 6.
DOI: 10.5281/zenodo.8381037
title: Surface temperature pattern scenarios suggest larger rates of warming than projected [DATA]

description: Data for global-mean temperature projections and global-mean radiative feedback projections. See PDF for file description.
DOI: 10.5517/cc13dvv6
title: CCDC 1025258: Experimental Crystal Structure Determination

description: Related Article: D.Arul Asir Abraham, U.Sankar, S.Perumal.P.Selvarajan|2015|Int.J.ChemTech.Res.|8|105|
DOI: 10.15468/dl.56vszm
title: Occurrence Download

description: A dataset containing 892320 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((96 34,119 34,119 41,96 41,96 34))",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 892320 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002721-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vk5vcy
title: Occurrence Download

description: A dataset containing 1267 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((106.9897 12.17493,107.6956 12.17493,107.6956 12.61421,106.9897 12.61421,106.9897 12.17493))",
 "HasGeospatialIssue is false",
 "Issue is one of (Geodetic datum assumed WGS84, Occurrence status inferred from individual count, Continent derived from coordinates)",
 "IucnRedListCategory is one of (CR, EN, VU, NT)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 1267 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002737-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n2n3mm
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is South Africa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Calotropis procera (Aiton) W.T.Aiton"
 ]
}

 The dataset includes 61 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002738-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.enc8cg
title: Occurrence Download

description: A dataset containing 59251 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Fish Collection NRM"
 ]
}

 The dataset includes 59251 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002734-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rh6h4h
title: Occurrence Download

description: A dataset containing 25752 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cistus populifolius L."
 ]
}

 The dataset includes 25752 records from 165 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002739-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28468649
title: 长沙市社区级公共服务设施的 POI 数据。

description: 长沙市社区级公共服务设施的 POI 数据，包括设施的名称、类型、地理位置等，均通过高德地图 API（2023 年）获取。所有数据都经过 GIS 空间配准、拓扑检查和归一化，以确保空间分析的一致性。
DOI: 10.15468/dl.k9wf26
title: Occurrence Download

description: A dataset containing 6722 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-0.19070 49.46229,10.32751 49.46229,10.32751 53.66773,-0.19070 53.66773,-0.19070 49.46229))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Martes foina (Erxleben, 1777)",
 "Year 1971-2013"
 ]
}

 The dataset includes 6722 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002740-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14916392
title: raw data - Potential Utility of liquid biopsy and droplet digital PCR (ddPCR) approaches to detect biomarkers in mastocytosis
DOI: 10.5281/zenodo.14916393
title: raw data - Potential Utility of liquid biopsy and droplet digital PCR (ddPCR) approaches to detect biomarkers in mastocytosis
DOI: 10.5968/kisti_kdr/206
title: CT, MR Images and 3 Dimensional Bone Models of Chinese Male Volunteer's (CN-M-023) Lower Limb
title: 중국인 남성 지원자(CN-M-023)의 하지 부분에 대한 CT, MRI 영상과 3차원 뼈대 모델

description: 중국인 인체 뼈대 정보 구축
description: K-12-SG-102-01D-1-C
description: 중국인 남성 지원자 (CN-M-023)의 하지에 대한 CT 영상
description: 중국의 의과대학에서 연구윤리위원회의 심사 승인을 받은 중국인 남성 지원자 (CN-M-023) 1명을 대상으로 하여 방사선 피폭량을 고려하여 하지 부분만을 CT 촬영하였고 고해상도의 영상을 얻기 위해 왼쪽, 오른쪽 다리의 확대 CT 영상으로 제작하였다. 또한 무릎 관절과 발 부분의 활용도를 높이기 위해 무릎과 발 부분의 고해상도 MR 영상을 별도로 제작하였다.
한편 이 CT 영상의 뼈대 테두리를 상용 프로그램으로 구획화 하고 3차원으로 쌓아 올림으로써 좌우의 넙다리뼈, 무릎뼈, 정강뼈, 종아리뼈에 대한 3차원 뼈대 모델을 제작하였다.
보다 상세한 내용은 KISTI에서 운영하는 디지털 코리언 홈페이지 http://dk.kisti.re.kr)를 참조하기 바란다.
description: 중국인;하지;CT;MRI;자기공명영상;남성;지원자;3차원 뼈대 모델
description: 중국인 남성 지원자 (CN-M-023)의 하지에 대한 MRI 영상
description: 중국인 남성 지원자 (CN-M-023)의 하지 CT 영상을 구역화하여 얻은 3차원 하지 뼈대 모델
DOI: 10.18112/openneuro.ds005366.v2.0.0
title: Large-scale fMRI dataset for the design of motor-based Brain-Computer Interfaces
DOI: 10.15468/dl.8cmspn
title: Occurrence Download

description: A dataset containing 2835 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Pterocarpus indicus Willd."
 ]
}

 The dataset includes 2835 records from 133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002741-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ufgjbu
title: Occurrence Download

description: A dataset containing 1512558 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (March, April, May)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1512558 records from 1123 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002733-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ze9na
title: Occurrence Download

description: A dataset containing 336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Israel",
 "OccurrenceStatus is Present",
 "TaxonKey is Prosopis juliflora (Sw.) DC.",
 "Year 2014-2024"
 ]
}

 The dataset includes 336 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002742-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60656/87809c5319684edc
title: Custom collection of model data from Palaiseau between 20 Jun 2022 and 24 Feb 2025
DOI: 10.5281/zenodo.14888439
title: Underwater images from OBSEA fish detection training dataset (YOLO)

description: This dataset contains labelled underwater pictures taken at the OBSEA underwater observatory. The pictures have already been splited into train, validation and test folders for training a YOLO AI model. This is a substet of this dataset, with the data arranged to simplify the training process.

Train a YOLO model with this dataset:



Install dependencies with the command  "pip3 install ultralytics"

Download and unzip the file

Edit the data.yaml file to match the "train", "valid" and "test" paths in your filesystem

Train the model with the command: "yolo task=detect mode=train model=yolov8n.pt data=data.yaml"


Done! In order to optimize the results it is encouraged to tune the hyperparameters to fit your application. 





Training dataset

This dataset contains labeled images for fish detection acquired at OBSEA Underwater Observatory (NW Mediterranean sea).

Data preprocessing

Several data augmentation techniques have been used to improve the training. The configuration can be found in args.yaml file.

Data splitting

Data has been randomly splitted in 70% training, 20% validation and 10% test. The splits are already included in the training dataset.

Classes, labels and annotations

The following classes are included in the dataset: 



 Chromis chromis:  WoRMS id 127000 

Coris julis:  WoRMS id 126963 

Dactylopterus volitans:  WoRMS id None None

Dentex dentex:  WoRMS id 273962 

Diplodus cervinus:  WoRMS id 127051 

Diplodus puntazzo:  WoRMS id 127052 

Diplodus sargus:  WoRMS id 127053 

Diplodus vulgaris:  WoRMS id 127054 

Diver:  scuba diver, used mainly to prevent divers to be detected as fish

Epinephelus costae:  WoRMS id 127034

Epinephelus marginatus:  WoRMS id 127036 

Mullus surmuletus:  WoRMS id 126986 

Muraena helena:  WoRMS id 126303 

Myliobatidae:  WoRMS id None None

Oblada melanura:  WoRMS id None None

 Octopus vulgaris:  WoRMS id None None

 Parablennius gattorugine:  WoRMS id 126770 

 Sarpa salpa:  WoRMS id 127064 

 Sciaena umbra:  WoRMS id None None

 Seriola dumerili:  WoRMS id 126816 

 Serranus cabrilla:  WoRMS id 127041 

 Sparus aurata:  WoRMS id 151523 

 Symphodus mediterraneus:  WoRMS id 273569


Data sources

Pictures where acquired by several underwater cameras, deployed at OBSEA, model Linovision IPC608.

Data quality

Images have been manually selected to include as much variety as possible in terms of light and water turbidity.

Spatial coverage

All pictures where taken at OBSEA underwater observatory, off-the-coast of Vilanova i la Geltrú, Spain. GPS coordinates




 Longitude
  Latitude
depth


1.75257
41.18212
20 m




Contact information

For further technical inquiries or additional information about the annotated dataset, please contact enoc.martinez@upc.edu
DOI: 10.5281/zenodo.14888440
title: Underwater images from OBSEA fish detection training dataset (YOLO)

description: This dataset contains labelled underwater pictures taken at the OBSEA underwater observatory. The pictures have already been splited into train, validation and test folders for training a YOLO AI model. This is a substet of this dataset, with the data arranged to simplify the training process.

Train a YOLO model with this dataset:



Install dependencies with the command  "pip3 install ultralytics"

Download and unzip the file

Edit the data.yaml file to match the "train", "valid" and "test" paths in your filesystem

Train the model with the command: "yolo task=detect mode=train model=yolov8n.pt data=data.yaml"


Done! In order to optimize the results it is encouraged to tune the hyperparameters to fit your application. 





Training dataset

This dataset contains labeled images for fish detection acquired at OBSEA Underwater Observatory (NW Mediterranean sea).

Data preprocessing

Several data augmentation techniques have been used to improve the training. The configuration can be found in args.yaml file.

Data splitting

Data has been randomly splitted in 70% training, 20% validation and 10% test. The splits are already included in the training dataset.

Classes, labels and annotations

The following classes are included in the dataset: 



 Chromis chromis:  WoRMS id 127000 

Coris julis:  WoRMS id 126963 

Dactylopterus volitans:  WoRMS id None None

Dentex dentex:  WoRMS id 273962 

Diplodus cervinus:  WoRMS id 127051 

Diplodus puntazzo:  WoRMS id 127052 

Diplodus sargus:  WoRMS id 127053 

Diplodus vulgaris:  WoRMS id 127054 

Diver:  scuba diver, used mainly to prevent divers to be detected as fish

Epinephelus costae:  WoRMS id 127034

Epinephelus marginatus:  WoRMS id 127036 

Mullus surmuletus:  WoRMS id 126986 

Muraena helena:  WoRMS id 126303 

Myliobatidae:  WoRMS id None None

Oblada melanura:  WoRMS id None None

 Octopus vulgaris:  WoRMS id None None

 Parablennius gattorugine:  WoRMS id 126770 

 Sarpa salpa:  WoRMS id 127064 

 Sciaena umbra:  WoRMS id None None

 Seriola dumerili:  WoRMS id 126816 

 Serranus cabrilla:  WoRMS id 127041 

 Sparus aurata:  WoRMS id 151523 

 Symphodus mediterraneus:  WoRMS id 273569


Data sources

Pictures where acquired by several underwater cameras, deployed at OBSEA, model Linovision IPC608.

Data quality

Images have been manually selected to include as much variety as possible in terms of light and water turbidity.

Spatial coverage

All pictures where taken at OBSEA underwater observatory, off-the-coast of Vilanova i la Geltrú, Spain. GPS coordinates




 Longitude
  Latitude
depth


1.75257
41.18212
20 m




Contact information

For further technical inquiries or additional information about the annotated dataset, please contact enoc.martinez@upc.edu
DOI: 10.5281/zenodo.14916382
title: Raw data for the paper: A Multi-Dimensional Cathodoluminescence Detector with 3D Printed Micro-Optics on a Fiber
DOI: 10.5281/zenodo.14916383
title: Raw data for the paper: A Multi-Dimensional Cathodoluminescence Detector with 3D Printed Micro-Optics on a Fiber
DOI: 10.15468/dl.zm6ruw
title: Occurrence Download

description: A dataset containing 1578 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material sample, Material citation, Specimen, Fossil)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lynx pardinus (Temminck, 1827)"
 ]
}

 The dataset includes 1578 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002743-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14916390
title: Perth Urban Land Development Outlooks 2016/2017 and 2020/2021

description: These data sets have been modified from the original published on the DataWA website.
DOI: 10.5281/zenodo.14916391
title: Perth Urban Land Development Outlooks 2016/2017 and 2020/2021

description: These data sets have been modified from the original published on the DataWA website.
DOI: 10.15468/dl.wtk65r
title: Occurrence Download

description: A dataset containing 1458842 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus rotundifolia Lam."
 ]
}

 The dataset includes 1458842 records from 163 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002735-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.