cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.z7hxzu
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Fungi)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((52.5 75,60 75,60 82.5,52.5 82.5,52.5 75))"
 ]
}

 The dataset includes 20 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042449-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/cctb3y5
title: CCDC 754507: Experimental Crystal Structure Determination

description: Related Article: Chuan-Gang Fan, Ming-Zhi Song|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2792|doi:10.1107/S1600536809042081
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cctb3z6
title: CCDC 754508: Experimental Crystal Structure Determination

description: Related Article: M.Thenmozhi, T.Kavitha, S.Ponnuswamy, M.Jamesh, M.N.Ponnuswamy|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2808|doi:10.1107/S1600536809041579
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cct9vyw
title: CCDC 754259: Experimental Crystal Structure Determination

description: Related Article: O.bin Shawkataly, Md.A.A.Pankhi, I.A.Khan, Chin Sing Yeap, Hoong-Kun Fun|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2660|doi:10.1107/S1600536809038835
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cctb408
title: CCDC 754509: Experimental Crystal Structure Determination

description: Related Article: Juan Jia, Cheng Yao|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2965|doi:10.1107/S160053680904402X
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccs6k2l
title: CCDC 721279: Experimental Crystal Structure Determination

description: Related Article: Huilu Wu, Xingcai Huang, Jingkun Yuan, Jian Ding, Ruirui Yun|2009|Z.Kristallogr.-New Cryst.Struct.|224|419|doi:10.1524/ncrs.2009.0184
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/6wsm8n8bsh.1
title: Tropical Fruit Leaf Disease Detection Dataset: Jujube, Star Fruit, and Guava

description: This dataset comprises original 1602 images & augmentation 7885 images of jujube, star fruit, and guava leaves, categorized into healthy and diseased labels. It is specifically designed for the development and training of machine learning models aimed at early disease detection in tropical fruit crops. The dataset includes various types of leaf diseases, making it a valuable resource for researchers in plant pathology and agricultural technology. It offers a solid foundation for advancing automated systems to aid in crop management and improve sustainable farming practices.

Jujube Leaf:

Jujube Original data:
 Defect Jujube leaf:124
 Healthy Jujube Leaf:227
 Insect Feeding:177
 Leaf Curl:33

Jujube Augmentation data:
 Defect Jujube leaf:620
 Healthy Jujube Leaf:1010
 Insect Feeding:885
 Leaf Curl:165

Star Fruit Leaf:

Star Fruit Original data:
 Defect Star Fruit Leaf:183
 Healthy Star Fruit Leaf:298
 Insect Feeding:95
 Star Fruit old yellow leaf:68

Star Fruit Augmentation data:
 Defect Star Fruit Leaf:915
 Healthy Star Fruit Leaf:1490
 Insect Feeding:475
 Star Fruit old yellow leaf:340

Guava Leaf:

Guava Original data:
 Defect Guava leaf:158
 Fungal leaf: 39
 spot disease on Guava leafHealthy Guava leaf:200

Guava Augmentation data:
 Defect Guava leaf:790
 Fungal leaf: 195
 Spot disease on Guava leafHealthy Guava leaf:1000
 
Purpose:
The purpose of this research is to develop an efficient and accurate system for detecting diseases in jujube, star fruit, and guava leaves using advanced image processing and machine learning techniques. By identifying diseases early, this study aims to enhance crop management, minimize agricultural losses, and promote sustainable farming practices.
DOI: 10.17632/wgr4g3nmc5.1
title: Effects of adding different types and amounts of biochar to saline alkali soil on its salt ions and microbial community in northwest China

description: Effects of adding different types and amounts of biochar to saline alkali soil on its salt ions and microbial community in northwest China
DOI: 10.15468/dl.p6bhuj
title: Occurrence Download

description: A dataset containing 15098 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ranunculus fluitans Lam."
 ]
}

 The dataset includes 15098 records from 357 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019763-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zdee8y
title: Occurrence Download

description: A dataset containing 578 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Monitoring arthropods under the scope of LIFE-Snails project – Baseline Data"
 ]
}

 The dataset includes 578 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019764-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25505/fiz.icsd.cc2kl55j
title: CSD 2370947: Experimental Crystal Structure Determination

description: Related Article: Wenya Xiang, Guozhao Wang, Zhiying Zhao, Meiyan Cui, Zhangzhen He|2024|CrystEngComm|26|6392|doi:10.1039/D4CE00936C
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.j2snq6
title: Occurrence Download

description: A dataset containing 3270 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pomacea maculata Perry, 1810",
 "Country is United States of America",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 3270 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019766-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/xtc2546p3p.1
title: Research on aesthetic perception factors of city outdoor advertising based on Kano model

description: 1. Questionnaire Survey data 
Local residents and tourists' aesthetic perception ratings of outdoor advertising data.
2. Data for analysis of the traditional Kano model
Evaluation Results and Classifications of Urban Outdoor Advertising Aesthetic Perception by Local Residents and Tourists.
3. Data for analysis of the improved Kano analysis 
The results of the improved Kano model analysis, which, in addition to considering traditional methods, also takes into account hybrid factors (Total Strength (TS) and Category Strength (CS), to determine hybrid categories).
4. Data for two-dimensional quadrant analysis of SII-DDI
The Satisfaction Increment Index (SII) and the Dissatisfaction Decrement Index (DDI) are designed to assess the improvement effects on user satisfaction and dissatisfaction, respectively (Berger et al., 1993).
DOI: 10.17632/k93hc5w5jt.1
title: Biologic therapy in psoriasis patients with current or previous solid tumor malignancy: A systematic review

description: Psoriasis is a helper T cell 17-driven chronic inflammatory disease.​ Although targeted biologics revolutionized management, the utility and safety of biologics in psoriasis patients with current or previous solid tumor malignancy (PPM) remains uncertain.​​ This systematic review investigates evidence surrounding this indication.
DOI: 10.17632/x7n6jnjh36.1
title: Sensitivity analysis of neural network-based model for forecasting vehicle yaw rate supplementary data

description: Supplementary data for the article on Sensitivity analysis of neural network-based model for forecasting vehicle yaw rate can be divided into two sections:
- The raw data used for training, validation, and testing of the developed LSTM network. This dataset was crucial in determining the necessary amount of data required for the model.
- The raw data used for various sensitivity analyses, focusing on factors such as the number of passengers, tire pressure, and the entire vehicle body.

The measurement data is stored in Apache Parquet format, which can be easily loaded using the Pandas library in Python.
DOI: 10.17632/x7n6jnjh36.2
title: Experimental Sensor Data from Vehicles for Dynamic Vehicle Modeling

description: The attached dataset contains over 17.5 hours of experimental sensor data, including measurements from the following sensors:

- Front axle steering angle [°]
- Longitudinal acceleration [g]
- Lateral acceleration [g]
- Yaw rate [deg/s]
- Wheel speed (front left) [km/h]
- Wheel speed (front right) [km/h]
- Wheel speed (rear left) [km/h]
- Wheel speed (rear right) [km/h]

Data was sampled at a rate of 0.01 seconds and includes three distinct driving scenarios: calm driving, aggressive driving, and city driving. The dataset also captures variations such as reduced tire pressure (one tire at a time), different passenger loads, and measurements from three different vehicles.

The data was collected at the Continental Test Track in Veszprém, Hungary, as well as within the city of Veszprém.

The data is stored in Apache Parquet format that can be processed via Pandas library in Python.

For more information please check our article:
TBD (citation from Engineering Applications of Artificial Intelligence (https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence).
DOI: 10.17632/x7n6jnjh36.3
title: Experimental Sensor Data from Vehicles for Dynamic Vehicle Modeling

description: The attached dataset contains over 17.5 hours of experimental sensor data, including measurements from the following sensors:

- Front axle steering angle [°]
- Longitudinal acceleration [g]
- Lateral acceleration [g]
- Yaw rate [deg/s]
- Wheel speed (front left) [km/h]
- Wheel speed (front right) [km/h]
- Wheel speed (rear left) [km/h]
- Wheel speed (rear right) [km/h]

Data was sampled at a rate of 0.01 seconds and includes three distinct driving scenarios: calm driving, aggressive driving, and city driving. The dataset also captures variations such as reduced tire pressure (one tire at a time), different passenger loads, and measurements from three different vehicles.

The data was collected at the Continental Test Track in Veszprém, Hungary, as well as within the city of Veszprém.

The data is stored in Apache Parquet format that can be processed via Pandas library in Python.

For more information please check our article:
TBD (citation from Mechanical Systems and Signal Processing (https://www.sciencedirect.com/journal/mechanical-systems-and-signal-processing).
DOI: 10.17632/nkf8ntp36t.1
title: Skin Cancer Risk Across Racial/Ethnic Groups Among Solid Organ Transplant Recipients in the US, 2000-2022: Supplemental Material

description: Supplemental Materials for Manuscript, "Skin Cancer Risk Across Racial/Ethnic Groups Among Solid Organ Transplant Recipients in the US, 2000-2022"
DOI: 10.17632/74jy85svsy.1
title: QEASM

description: Python files for comparing and simulating QEASM with BB84 and E91 protocols
DOI: 10.5281/zenodo.14163483
title: Porvoo archipelago transect observations

description: Line transect observations from Porvoo Archipelago, Finland on June 2017.

(2024-11-14): Initial description. Description and metadata may be updated in the future.

Creators

Collected and curated by Sanna Kuningas, Natural Resources Institute Finland (LUKE)

Depth data (dptLUKE below) based on Finnish Environment Institute's VELMU raster (2015) with additional improvements by LUKE. 

Temperature data (temjul15) based on data from Finnish Meteorological Institute and Finnish Environment Institute. 

Additional curation, including translation and summary for the variable descriptions, and release in Zenodo by Karel Kaurila, University of Helsinki.

Variables

Units in square brackets [ ].

Location and replicate identifiers



id : transect identifier. Replicates of the same transect have the same identifier.

id2 : alternate transect identifier, unique for each transect.

repeated : whether transect was replicated or not.

X_euref : easting coordinate, EUREF_FIN_TM35FIN, for the transect starting location in [meters]

Y_euref : northing coordinate, EUREF_FIN_TM35FIN, for the transect starting location in [meters]


Environmental covariates

Environmental covariates at the transect starting location.



dptLUKE : depth in [meters]

dist10m : distance from nearest water polygon with depth greater than 10 m, in [meters]

lined3km : shoreline density [m/km^2] within a circle with 3km diameter

swmlog10 : logarithm (base 10) transformed exposure [log10(m2s-1)]

temjul15 : temperature sum until 15th of July, proxy for how quickly location warms during spring [Celsius]


In situ measurements



volume : Transect water volume [m^3]. Transect length (500m) multiplied by sampler surface area. Used as survey effort.

heading : compass heading (direction) for the transect, in [degrees].

heading0 : originally recorded compass heading, see note below. [degrees]

SumKUHA : total pikeperch (Sander lucioperca, kuha in Finnish) larvae count in each transect [scalar]


In the original data set, some replicate transects had missing values for the compass heading. In this data set, the missing headings are estimated as means of the other (non-missing) replicates for the same transect, i.e. rows with the same id. Original compass headings are stored in column heading0.
DOI: 10.15468/dl.uureky
title: Occurrence Download

description: A dataset containing 5339 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ranunculus gmelinii DC."
 ]
}

 The dataset includes 5339 records from 163 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019767-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4vwagy
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Observation.org, Nature data from around the World",
 "Geometry POLYGON((-92.03787 39.47162,-82.30175 39.47162,-82.30175 43.79443,-92.03787 43.79443,-92.03787 39.47162))",
 "Month is January",
 "Year 2024-2024"
 ]
}

 The dataset includes 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019768-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/n99yg4kxmw.1
title: supplement 1

description: Table with data on the article
DOI: 10.15468/dl.9ywtjs
title: Occurrence Download

description: A dataset containing 62385 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Limenitis reducta Staudinger, 1901, Pilophorus clavatus (Linnaeus, 1767))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 62385 records from 1190 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019765-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0193153.10fu
title: SH0193153.10FU

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/sh0193154.10fu
title: SH0193154.10FU

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/sh0193155.10fu
title: SH0193155.10FU

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/sh0193156.10fu
title: SH0193156.10FU

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/sh0193157.10fu
title: SH0193157.10FU

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.