cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive113708.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive113709.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0105356.10fu
title: SH0105356.10FU

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: 10.13145/bacdive113709.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive113710.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0105357.10fu
title: SH0105357.10FU

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/sh0105358.10fu
title: SH0105358.10FU

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: 10.13145/bacdive113710.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive113711.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive113711.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive113712.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0105359.10fu
title: SH0105359.10FU

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: 10.15468/dl.h8m2ce
title: Occurrence Download

description: A dataset containing 4591 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is WII Herbarium Dataset"
 ]
}

 The dataset includes 4591 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020793-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.msh3c4
title: Occurrence Download

description: A dataset containing 163187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "IucnRedListCategory is LC",
 "OccurrenceStatus is Present",
 "TaxonKey is Salamandra salamandra (Linnaeus, 1758)"
 ]
}

 The dataset includes 163187 records from 1813 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020783-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t4y7jf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sebastodes baramenuke Wakiya, 1917",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036436-241126133413365/datasets/export for details.
DOI: 10.17632/7y66xb3gmc.1
title: Student Smoking, Alcohol, and Psychological Wellness Data

description: This dataset consists of 706 responses and 14 columns (features), focusing on the relationship between smoking, alcohol consumption, and psychological wellness among university students. It includes self-reported data on smoking and drinking habits, coping mechanisms, mental health, and help-seeking behaviors. The survey was designed to capture insights into how these behaviors impact students' overall well-being.

Data Collection: The data was collected through an online survey administered via Google Forms in [month/year]. Respondents provided information about their lifestyle habits, reasons for engaging in these behaviors, and their psychological wellness.

Key Features:
1. Demographics: Age, year of study, and field of study.
2. Smoking Habits: Frequency of smoking, age of starting, and reasons for smoking.
3. Alcohol Consumption: Frequency of alcohol use, typical weekly intake, and reasons for drinking.
4. Stress Management: Methods for coping with stress, including smoking and alcohol.
5. Mental Health: Self-reported psychological wellness and willingness to reduce or quit smoking and drinking.
6. Help-Seeking Behavior: Responses regarding help-seeking for mental health concerns.

This dataset can be used for:
1. Machine learning analysis to model and predict psychological wellness based on lifestyle behaviors.
2. Statistical studies on the interplay between smoking, alcohol, and mental health.
3. Development of intervention strategies to improve student well-being and reduce harmful habits.

Format: The dataset contains 14 columns with categorical, ordinal, and numerical data. It is formatted for ease of analysis and ready for use in statistical and machine learning applications.
DOI: 10.17632/7y66xb3gmc.2
title: Student Smoking, Alcohol, and Psychological Wellness Data

description: This dataset consists of 1163 responses and 14 columns (features), focusing on the relationship between smoking, alcohol consumption, and psychological wellness among university students. It includes self-reported data on smoking and drinking habits, coping mechanisms, mental health, and help-seeking behaviors. The survey was designed to capture insights into how these behaviors impact students' overall well-being.

Data Collection: The data was collected through an online survey administered via Google Forms in [month/year]. Respondents provided information about their lifestyle habits, reasons for engaging in these behaviors, and their psychological wellness.

Key Features:
1. Demographics: Age, year of study, and field of study.
2. Smoking Habits: Frequency of smoking, age of starting, and reasons for smoking.
3. Alcohol Consumption: Frequency of alcohol use, typical weekly intake, and reasons for drinking.
4. Stress Management: Methods for coping with stress, including smoking and alcohol.
5. Mental Health: Self-reported psychological wellness and willingness to reduce or quit smoking and drinking.
6. Help-Seeking Behavior: Responses regarding help-seeking for mental health concerns.

This dataset can be used for:
1. Machine learning analysis to model and predict psychological wellness based on lifestyle behaviors.
2. Statistical studies on the interplay between smoking, alcohol, and mental health.
3. Development of intervention strategies to improve student well-being and reduce harmful habits.

Format: The dataset contains 14 columns with categorical, ordinal, and numerical data. It is formatted for ease of analysis and ready for use in statistical and machine learning applications.
DOI: 10.17632/tt2smzrzrs.2
title: Advanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight

description: 1. The horticulture industry places a high value on tea plants because of their economic importance and widespread consumption. Nevertheless, several illnesses might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution photos of tea plant leaves were taken at the Moulvi Bazar site in Sylhet, Bangladesh, and are included in this dataset. The photos are divided into four different classes: Healthy (500 images), Tea Leaf Blight (500 images), Tea Red Leaf Spot (500 images), and Tea Red Scab (500 images). These classes represent both damaged and healthy leaves. The dataset has 2000 images in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive tea plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the Moulvi Bazar site in Sylhet, Bangladesh, a total of 2000 photos depicting Healthy (500), Tea Leaf Blight (500), Tea Red Leaf Spot (500), and Tea Red Scab (500) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 4000 augmented images are made from these original photos to increase the quantity of data sets.
DOI: 10.17632/tt2smzrzrs.3
title: Advanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight

description: 1. The horticulture industry places a high value on tea plants because of their economic importance and widespread consumption. Nevertheless, several diseases might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution images of tea plant leaves were taken from two different garden at the Moulvi Bazar site in Sylhet, Bangladesh, and are included in this dataset. The images are divided into four distinct classes: Healthy (1024 images), Tea Leaf Blight (496 images), Tea Red Leaf Spot (500 images), and Tea Red Scab (500 images). These classes represent both damaged and healthy leaves. The dataset has 2520 images in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive tea plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the two different tea garden at the Moulvi Bazar site in Sylhet, Bangladesh, a total of 2520 images depicting Healthy (1024 images), Tea Leaf Blight (496), Tea Red Leaf Spot (500), and Tea Red Scab (500) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 4000 augmented images are made from these original photos to increase the quantity of data sets.
DOI: 10.17632/tt2smzrzrs.1
title: Advanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight

description: 1. The horticulture industry places a high value on tea plants because of their economic importance and widespread consumption. Nevertheless, several illnesses might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution photos of tea plant leaves were taken at the Moulvi Bazar site in Sylhet, Bangladesh, and are included in this dataset. The photos are divided into four different classes: Healthy (73 images), Tea Leaf Blight (60 images), Tea Red Leaf Spot (60 images), and Tea Red Scab (60 images). These classes represent both damaged and healthy leaves. The dataset has 253 photos in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive tea plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the Moulvi Bazar site in Sylhet, Bangladesh, a total of 253 photos depicting Healthy (73), Tea Leaf Blight (60), Tea Red Leaf Spot (60), and Tea Red Scab (60) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 4000 augmented images are made from these original photos to increase the quantity of data sets.
DOI: 10.17632/tt2smzrzrs.4
title: Advanced Tea Crop Disease Study: High-Resolution Dataset for Precision Agriculture and Pathological Insight

description: 1. The horticulture industry places a high value on tea plants because of their economic importance and widespread consumption. Nevertheless, several diseases might substantially negatively influence their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution images of tea plant leaves were taken from two different garden at the Moulvi Bazar site in Sylhet, Bangladesh, and are included in this dataset. The images are divided into four distinct classes: Healthy (2270 images), Tea Leaf Blight (509 images), Tea Red Leaf Spot (561 images), and Tea Red Scab (620 images). These classes represent both damaged and healthy leaves. The dataset has 3960 images in total. Comprehensive comments describing the condition's nature and severity are included with every photograph. This data is essential for accurate and trustworthy model training and validation. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive tea plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the two different tea garden at the Moulvi Bazar site in Sylhet, Bangladesh, a total of 3960 images depicting Healthy (2270 images), Tea Leaf Blight (509), Tea Red Leaf Spot (561), and Tea Red Scab (620) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 4000 augmented images are made from these original photos to increase the quantity of data sets.
DOI: 10.4228/zalf-xga4-xw15
title: Long-term experiment on soil tillage, rotation and irrigation (V4)

description: The long-term experiment on soil tillage, rotation and irrigation (V4) started in the year 2000. It is characterized by several experimental cycles with different cropping system comparisons. Soil tillage is the consistent factor since the start of the experiment. Crop rotation and irrigation were adjusted according to the research questions. Cycle 1 (2000-2006) investigated the potential of reducing soil tillage by comparing two cropping systems, one with and one without ploughing with different crops per system. Cycle 2 (2007-2018) investigated the potential to produce arable crops for biomass by comparing eight cropping systems. The factors were crop rotation, i) sole cropped maize every year and ii) 4-year rotation with one crop per year with maize-winter rye/sorghum-winter triticale-alfalfa clover grass mixture (all harvested for green biomass); soil tillage (ploughing and no-tillage); irrigation (rainfed and irrigated). Cycle 3 (2019-2023) investigated the potential of diversification comparing 16 cropping systems. The factors were rotation with all crops present every year (fully phased) with i) sole cropped maize, ii) sole cropped soybean, iii) maize-soybean sequence, and iv) maize-soybean-wheat-alfalfa clover grass mixture sequence); soil tillage (ploughing and reduced tillage); irrigation (rainfed and irrigated). The experimental cycles can be analyzed separately for specific research questions and for calibrating/validating models, and the long term effects of soil tillage, rotation and irrigation can be analyzed. This table contains the index of all tables forming this data collection.

Related datasets and supporting articles are listed in the metadata element 'Related Identifier'.
Weather data of the site can be found here: https://doi.org/10.4228/zalf-8mqs-9382.
Dataset version 1.0
DOI: 10.17632/bpb7ftw7jv.1
title: Intra- and postoperative complications in patients with hypertension who attended the Dr René Puig Bentz Dental Clinic of the Universidad Nacional Pedro Henríquez Ureña.

description: Clinical complications, in general, cause anxiety and panic. High blood pressure is recognized as a silent adversary that causes an increase in blood pressure. In dentistry, specifically in the area of surgery, it is important to establish good management of systemically compromised patients, especially in the case of hypertensive patients or those with elevated blood pressure, to avoid life-threatening complications. The main objective of this research was to determine intra and postoperative complications in patients with elevated blood pressure who attended the surgery area of the Dr. Rene Puig Bentz dental clinic of the UNPHU. The research approach was descriptive and observational, with a cross-sectional design and a sample of 108 patients who represent 100% of the cases evaluated. As a result of a sample of 108 patients (100%), we obtained 80.6% of intraoperative complications and 19.4% of the cases did not present intraoperative complications. In postoperative complications, 17.6% presented complications and 82.4% of the cases did not present. In conclusion, the results revealed that complications during surgery were more frequent than those after surgery.