cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/bw6rk6jtr6.1
title: SESBANIA GRANDFLORA

description: The dataset consists of over 500 images of Sesbania grandiflora, commonly known as bokful, captured using a Redmi 10 Prime mobile camera. The images are classified into two categories: good bokful and bad bokful. Here’s a detailed description of the dataset:

### Data Collection
- *Device Used*: Redmi 10 Prime mobile camera.
- *Background*: Black background to ensure clear contrast and focus on the bokful.
- *Lighting Condition*: Daylight to ensure natural lighting and consistent illumination across all images.

### Categories
1. *Good Bokful*:
 - *Criteria*: These are the healthy and ideal bokful specimens, characterized by vibrant color, intact structure, and absence of any visible defects or damages.
 - *Examples of Features*: Fresh green leaves, firm stems, and fully bloomed flowers without any spots, discoloration, or wilting.

2. *Bad Bokful*:
 - *Criteria*: These bokful specimens exhibit various forms of defects, damage, or signs of disease.
 - *Examples of Features*: Wilted leaves, discoloration (yellow or brown spots), insect damage, fungal infections, or physical damages like broken stems or torn leaves.

### Image Specifications
- *Resolution*: High-resolution images ensuring detailed visibility of the bokful features.
- *Format*: Likely JPEG or PNG format, commonly supported by mobile cameras.
- *Orientation*: Consistent orientation with the bokful centered in the frame against the black background.

### Data Attributes
- *Image ID*: A unique identifier for each image.
- *Category Label*: A label indicating whether the bokful is 'good' or 'bad'.
- *Image Path*: The file path or URL where the image is stored.
- *Metadata (optional)*: Additional information such as the timestamp of capture, GPS coordinates (if available), and camera settings (e.g., ISO, exposure).

### Potential Use Cases
- *Agricultural Analysis*: Identifying and classifying bokful for quality control in agricultural production.
- *Machine Learning*: Training models for automated detection of plant health and categorization.
- *Educational Purposes*: Serving as a aid for teaching plant pathology or horticulture.
- *Quality Assurance*: Assisting farmers and gardeners in ensuring the quality of their bokful plants.

### Challenges and Considerations
- *Lighting Variability*: Despite daylight conditions, variations in natural light throughout the day might affect the image consistency.
- *Background Consistency*: A black background is used to minimize distractions, but ensuring uniform lighting and background cleanliness is crucial.
- *Camera Limitations*: While the Redmi 10 Prime offers decent camera capabilities, it might not match professional-grade cameras, which could impact image clarity and detail.

### Data Organization
- *Directory Structure*:
 
 ├── dataset
 │ ├── good_bokful
 │ │ ├── image_001.jpg
 │ │ ├── image_002.jpg
 │ │ └── ...
 │ └── bad_bokful
 │ ├── image_001.jpg
 │ ├── image_002.jpg
 │ └── ...
DOI: 10.17632/scgr52tw9h.1
title: Allicin

description: EVALUATION OF THE ALLICIN STABILITY OF FRESH AND PROCESSED GARLIC UNDER DIFFERENT CONDITIONS OF TIME AND TEMPERATURE
DOI: 10.17632/ctdrgm4zxd.1
title: TWITTER SENTIMENT ANALYSIS OF NETIZEN OPINIONS ON MOVING THE NATIONAL CAPITAL USING CONVOLUTIONAL NEURAL NETWORK METHOD

description: The ibukotanusantara.csv dataset is a raw dataset that was only obtained during the data crawling process from Twitter/X. Dataset preprocessing-data-ibukota.csv is a dataset that has been preprocessed and the data is ready to use. The dataset dataset-labelin-final.csv is a dataset that has been labeled in the form of positive, negative or neutral using the VADER Lexicon.
DOI: 10.15468/dl.zrtxcm
title: Occurrence Download

description: A dataset containing 1087971 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Nepal",
 "OccurrenceStatus is Present",
 "Year 2005-2024"
 ]
}

 The dataset includes 1087971 records from 202 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024943-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5mkp2r
title: Occurrence Download

description: A dataset containing 704 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cherax quadricarinatus (von Martens, 1868)"
 ]
}

 The dataset includes 704 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024948-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/drxo15bh
title: Demo: Communities over CoyPU Knowledge Graph

description: Detecting clusters of relatedness between Countries-Indicators
DOI: 10.5281/zenodo.12746084
title: Performance of Slab Geometry Constraints on Rapid Geodetic Slip Models, Tsunami Amplitude, and Inundation Estimates in Cascadia
DOI: 10.5281/zenodo.12746085
title: Performance of Slab Geometry Constraints on Rapid Geodetic Slip Models, Tsunami Amplitude, and Inundation Estimates in Cascadia
DOI: 10.15468/dl.k7ztvq
title: Occurrence Download

description: A dataset containing 715 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Athyrium dombeyi Desv."
 ]
}

 The dataset includes 715 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024949-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12518457
title: GGPONC 2.0 (Major Release incl. Gold Standard Annotations)

description: About this Release

Version 2.0 (major release), contains gold-standard annotations described in the GGPONC 2.0 paper.

Once you have downloaded the data, the most convenient way to access them is through our Hugging Face BigBIO dataloader.

⚠️ More recent versions of GGPONC (with new / updated guidelines, but without gold-standard annotations) are published at:https://zenodo.org/records/12520623

Project Description

The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information (PHI), which distinguishes them from other kinds of medical text.

The second major release of the corpus (GGPONC 2.0, 2024/03) consists of 30 German oncology guidelines with 1.87 million tokens. It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely distributable corpus of German medical text at the moment.

Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
DOI: 10.5281/zenodo.12518458
title: GGPONC 2.0 (Major Release incl. Gold Standard Annotations)

description: About this Release

Version 2.0 (major release), contains gold-standard annotations described in the GGPONC 2.0 paper.

Once you have downloaded the data, the most convenient way to access them is through our Hugging Face BigBIO dataloader.

⚠️ More recent versions of GGPONC (with new / updated guidelines, but without gold-standard annotations) are published at:https://zenodo.org/records/12520623

Project Description

The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information (PHI), which distinguishes them from other kinds of medical text.

The second major release of the corpus (GGPONC 2.0, 2024/03) consists of 30 German oncology guidelines with 1.87 million tokens. It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely distributable corpus of German medical text at the moment.

Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
DOI: 10.17632/fs9ysxcs3s.1
title: Unprepared Lockdown: COVID-19 Pandemic, Sexual and Gender-based Violence in Nigeria

description: The essence of this paper is to interrogate sexual and gender-based violence in Nigeria during the COVID-19 lockdown. The study also interrogates activism against gender-based violence in the country during the lockdown.the COVID-19 lockdown was unprepared and so had huge negative consequences that affected households and livelihoods. It created conditions that triggered sexual and gender-based violence. Many households became violent overnight due to scarcity and other basic needs. The attitude of transferred aggression was developed with women, children, girls and other vulnerable persons as victims. However, activism was highly resilient in the wake of the pandemic. Many human rights activists especially women raised to the occasion of creating awareness about the danger of sexual and gender-based violence.
DOI: 10.17632/zb2zmvypxt.1
title: GOURD LEAVES

description: The dataset comprises over 500 images of gourd leaves from the Cucurbitaceae family, classified into two categories: "good" and "bad." These images were captured using a Poco M2 Pro mobile camera against a black background under daylight conditions, ensuring consistent lighting across the dataset.

For each image, various attributes and characteristics of the gourd leaves were captured, including:

1. **Leaf Size and Shape**: Measurements of length, width, and shape descriptors such as aspect ratio or circularity, providing insights into the physical morphology of the leaves.

2. **Color**: RGB values or color histograms representing the color distribution across the leaf surface. This information can help identify patterns related to health or disease.

3. **Texture**: Descriptors of leaf texture, such as smoothness, roughness, or presence of any patterns or abnormalities.

4. **Vein Structure**: Analysis of the vein network within the leaves, including parameters like vein density, arrangement, and branching patterns.

5. **Symptoms and Defects**: Identification of any visible symptoms or defects indicating the health condition of the leaves. This could include signs of pests, diseases, nutrient deficiencies, or physical damage.

6. **Contextual Information**: Metadata such as location, date, and time of image capture, which can provide additional context for understanding environmental conditions and potential factors influencing leaf health.

Furthermore, the dataset may include annotations or labels provided by domain experts indicating whether each leaf is classified as "good" or "bad" based on predefined criteria. These labels serve as ground truth for training and evaluating classification algorithms.

The dataset aims to facilitate research and development in the field of agricultural science, specifically in the domain of gourd leaf health assessment and disease diagnosis. By leveraging machine learning techniques, researchers can develop models capable of accurately classifying gourd leaves and detecting potential health issues, ultimately aiding farmers in early intervention and crop management strategies.
DOI: 10.5281/zenodo.12532607
title: GGPONC 1.0

description: About this Release

The original GGPONC release (Version 1.0) with 25 guidelines and 1.3M Tokens.

⚠️ If you are looking for the GGPONC 2.0 gold-standard annotations, please refer to the major release: https://zenodo.org/records/12518458

For regular updates of the guidelines with silver-standard annotations, please refer to the 2.x minor releases: https://zenodo.org/doi/10.5281/zenodo.12520368 

 

Included Files



cpg-corpus-cms.xml - structured version of the GGPONC with metadata

literature_index.tsv - mapping of IDs to citations

Plain Text - plain text segments, sentences splitted and tokenized with FraMed models

Annotations - automatically created UMLS annotations

Evaluation.zip - gold standard annotations vs. automatically created annotations for a subset of GGPONC

Embeddings - word embeddings trained on GGPONC and combination with other corpora
DOI: 10.5281/zenodo.12532608
title: GGPONC 1.0

description: About this Release

The original GGPONC release (Version 1.0) with 25 guidelines and 1.3M Tokens.

⚠️ If you are looking for the GGPONC 2.0 gold-standard annotations, please refer to the major release: https://zenodo.org/records/12518458

For regular updates of the guidelines with silver-standard annotations, please refer to the 2.x minor releases: https://zenodo.org/doi/10.5281/zenodo.12520368 

 

Included Files



cpg-corpus-cms.xml - structured version of the GGPONC with metadata

literature_index.tsv - mapping of IDs to citations

Plain Text - plain text segments, sentences splitted and tokenized with FraMed models

Annotations - automatically created UMLS annotations

Evaluation.zip - gold standard annotations vs. automatically created annotations for a subset of GGPONC

Embeddings - word embeddings trained on GGPONC and combination with other corpora
DOI: 10.5281/zenodo.12520368
title: GGPONC 2.3 (Minor Release with Silver Standard Annotations)

description: About this Release

Version 2.3 (minor release) contains more recent oncology guidelines (XML, plain text), metadata, and predictions using GGPONC 2.0 NER models and UMLS concepts identified with xMEN. 

In total, this version contains 34 guidelines with 2.3M tokens of text.

⚠️ If you are looking for the GGPONC 2.0 gold standard annotations, please refer to the major release: https://zenodo.org/records/12518458

 

Project Description

The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information (PHI), which distinguishes them from other kinds of medical text.

The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely distributable corpus of German medical text at the moment.

Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
DOI: 10.5281/zenodo.12520369
title: GGPONC 2.1 (Minor Release with Silver Standard Annotations)

description: About this Release

Version 2.1 (minor release) contains more recent oncology guidelines (XML, plain text), metadata, and predictions using GGPONC 2.0 NER models and UMLS concepts identified with xMEN. 

In total, this version contains 32 guidelines with 2.1M tokens of text.

If you are looking for the GGPONC 2.0 gold standard annotations, please refer to the major release: https://zenodo.org/records/12518458

 

Project Description

The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information (PHI), which distinguishes them from other kinds of medical text.

The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely distributable corpus of German medical text at the moment.

Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
DOI: 10.5281/zenodo.12520468
title: GGPONC 2.2 (Minor Release with Silver Standard Annotations)

description: About this Release

Version 2.2 (minor release) contains more recent oncology guidelines (XML, plain text), metadata, and predictions using GGPONC 2.0 NER models and UMLS concepts identified with xMEN. 

In total, this version contains 34 guidelines with 2.3M tokens of text.

If you are looking for the GGPONC 2.0 gold standard annotations, please refer to the major release: https://zenodo.org/records/12518458

 

Project Description

The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information (PHI), which distinguishes them from other kinds of medical text.

The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely distributable corpus of German medical text at the moment.

Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
DOI: 10.5281/zenodo.12520592
title: GGPONC 2.3 (Minor Release with Silver Standard Annotations)

description: About this Release

Version 2.3 (minor release) contains more recent oncology guidelines (XML, plain text), metadata, and predictions using GGPONC 2.0 NER models and UMLS concepts identified with xMEN. 

In total, this version contains 34 guidelines with 2.3M tokens of text.

If you are looking for the GGPONC 2.0 gold standard annotations, please refer to the major release: https://zenodo.org/records/12518458

 

Project Description

The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information (PHI), which distinguishes them from other kinds of medical text.

The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely distributable corpus of German medical text at the moment.

Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
DOI: 10.5281/zenodo.12520623
title: GGPONC 2.3 (Minor Release with Silver Standard Annotations)

description: About this Release

Version 2.3 (minor release) contains more recent oncology guidelines (XML, plain text), metadata, and predictions using GGPONC 2.0 NER models and UMLS concepts identified with xMEN. 

In total, this version contains 34 guidelines with 2.3M tokens of text.

⚠️ If you are looking for the GGPONC 2.0 gold standard annotations, please refer to the major release: https://zenodo.org/records/12518458

 

Project Description

The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information (PHI), which distinguishes them from other kinds of medical text.

The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely distributable corpus of German medical text at the moment.

Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
DOI: 10.17632/w2kx2wcg62.1
title: An unexpectedly high content of acyclic linear-chain carboxylic acids in Eocene Lublin amber

description: All peaks reported in the GC-MS analysis of Lublin amber NK2, NK3, NK4, NK5, NK6 samples with their retention time and reference.
DOI: 10.17632/w2kx2wcg62.2
title: An unexpectedly high content of acyclic linear-chain carboxylic acids in Eocene Lublin amber

description: All peaks reported in the GC-MS analysis of Lublin amber NK2, NK3, NK4, NK5, NK6 samples with their retention time and reference.
DOI: 10.15468/dl.tvdem5
title: Occurrence Download

description: A dataset listing the 3 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tasiocera fuscescens (Lackschewitz, 1940)"
 ]
}

 The dataset's 3 records were derived from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024944-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/dpm5rm7cxj.1
title: Paired T cell receptor repertoire and differentiation state analysis in mice by mass cytometry

description: Data related to the paper: "Paired T cell receptor repertoire and differentiation state analysis in mice by mass cytometry," Nature Immunology (2024)