cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0114729.10fu
title: SH0114729.10FU

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/bacdive123045.20241212.9.2
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/bacdive123045.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0114730.10fu
title: SH0114730.10FU

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/bacdive123046.20241212.9.2
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/sh0114731.10fu
title: SH0114731.10FU

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/bacdive123046.20251217.10
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/bacdive123047.20241212.9.2
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/sh0114732.10fu
title: SH0114732.10FU

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/bacdive123047.20251217.10
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/bacdive123048.20241212.9.2
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/sh0114733.10fu
title: SH0114733.10FU

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/bacdive123048.20251217.10
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/bacdive123049.20241212.9.2
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/sh0114734.10fu
title: SH0114734.10FU

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/bacdive123049.20251217.10
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/bacdive123050.20241212.9.2
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/sh0114735.10fu
title: SH0114735.10FU

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/bacdive123050.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0114736.10fu
title: SH0114736.10FU

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: 10.5519/qd.b9rvy784
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5981038 records)
DOI: 10.15468/dl.qxsty8
title: Occurrence Download

description: A dataset containing 25525 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((6.53132 47.98215,6.51558 47.92549,6.61835 47.89479,6.6459 47.92352,6.64669 47.96858,6.63803 47.98333,6.56208 47.98628,6.53395 47.98609,6.53132 47.98215))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 25525 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001543-250324175209820/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/r9j972y29c.1
title: Replication code for "The Return of Return Dominance: Decomposing the Cross-Section of Prices"

description: This code replicates the findings in the paper "The Return of Return Dominance: Decomposing the Cross-Section of Prices"
DOI: 10.17632/98j23jpzfn.1
title: Audio of ocras harassing sperm whales in Bleik Canyon, northern Norway, on 23-07-2011

description: Two-channel wav file recorded on 23-07-2011 aboard the M/S Reine, within 69.34 – 69.47 N and 15.58 – 15.86 E in the Bleik Canyon. The M/S Reine is owned and operated by Whalesafari Andenes, and Whalesafari is the originator and owner of this audio file. 

The M/S Reine has two custom hull-mounted directional hydrophones, sampling rate at 44.1 kHz that allow the crew to listen for cetaceans and, based on differences in loudness between the hydrophones, get close to where the animals are before they surface. Detailed information about the hydrophone specifics can be found in Nielsen & Møhl (2006). Description of the orca harassment event on sperm whales is described in Cosentino et al., in review with Marine Mammal Science Notes.

Cosentino, M., Griffiths, E.T., van Dam, J., van Dongen, C.H. (in review). Skittish males in high latitudes: complex social and acoustic response of adult male sperm whales when harassed by orcas. Marine Mammal Science.

Nielsen, B. K., & Møhl, B. (2006). Hull-mounted hydrophones for passive acoustic detection and tracking of sperm whales (Physeter macrocephalus). Applied Acoustics, 67(11–12), 1175–1186. https://doi.org/10.1016/j.apacoust.2006.05.008
DOI: 10.17632/98j23jpzfn.2
title: Audio of orcas harassing sperm whales in Bleik Canyon, northern Norway, on 23-07-2011

description: Two-channel wav file recorded on 23-07-2011 aboard the M/S Reine, within 69.34 – 69.47 N and 15.58 – 15.86 E in the Bleik Canyon. The M/S Reine is owned and operated by Whalesafari Andenes, and Whalesafari is the originator and owner of this audio file. 

The M/S Reine has two custom hull-mounted directional hydrophones, sampling rate at 44.1 kHz that allow the crew to listen for cetaceans and, based on differences in loudness between the hydrophones, get close to where the animals are before they surface. Detailed information about the hydrophone specifics can be found in Nielsen & Møhl (2006). Description of the orca harassment event on sperm whales is described in Cosentino et al., in review with Marine Mammal Science Notes.

Cosentino, M., Griffiths, E.T., van Dam, J., van Dongen, C.H. (in review). Skittish males in high latitudes: complex social and acoustic response of adult male sperm whales when harassed by orcas. Marine Mammal Science.

Nielsen, B. K., & Møhl, B. (2006). Hull-mounted hydrophones for passive acoustic detection and tracking of sperm whales (Physeter macrocephalus). Applied Acoustics, 67(11–12), 1175–1186. https://doi.org/10.1016/j.apacoust.2006.05.008
DOI: 10.15468/dl.cmva4s
title: Occurrence Download

description: A dataset containing 413 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than 50000",
 "Year is 2025",
 "DecimalLongitude is greater than or equal to 10.05",
 "DecimalLongitude is less than or equal to 10.15",
 "DecimalLatitude is greater than or equal to 55.8",
 "DecimalLatitude is less than or equal to 55.9"
 ]
}

 The dataset includes 413 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001544-250324175209820/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13016/ukb1-uyib
title: Data and simulation files for "Microheater hotspot engineering for spatially resolved and repeatable multi-level switching in foundry-processed phase change silicon photonics"
DOI: 10.17632/rnj9ffrd7g.1
title: The Influence of job Stress and Workplace Happiness on Eating Habits: an Analysis in the Mexican Context

description: The present research analyzes the effect of job stress and workplace happiness on consumption habits in Mexico, highlighting their impact on employee well-being and productivity. This non-experimental, cross-sectional study included 233 employees from various sectors, selected through convenience sampling. Data collected via an online questionnaire revealed that job stress has a significant positive effect on emotional and external consumption habits, where as workplace happiness does not show a direct influence. Additionally, job stress negatively impacts 
workplace happiness. These findings underscore the importance of managing job stress and implementing organisational policies to reduce it, fostering a healthy work environment.
DOI: 10.17632/nnph98d3kc.1
title: IndoorObjectDetectionDatasetForAutonomousMobileRobots

description: The dataset was collected from the MyNursingHome dataset, available at https://data.mendeley.com/datasets/fpctx3svzd/1 , and curated to develop an Indoor Object Detection Dataset for Autonomous Mobile Robots. The primary objective is to enhance object detection capabilities for autonomous systems operating in indoor environments. From the original dataset containing 25 object categories, we selected six key categories—basket bin (499 images), sofa (499 images), human (499 images), table (500 images), chair (496), and door (500). Initially, we collected a total of 2,993 images from these categories; however, during the annotation process using Roboflow, we rejected 1 sofa, 10 tables, 9 chairs, and 12 door images due to quality concerns, such as poor image resolution or difficulty in identifying the object, resulting in a final dataset of 2,961 images. To ensure an effective training pipeline, we divided the dataset into 70% training (2,073 images), 20% validation (591 images), and 10% test (297 images). Preprocessing steps included auto-orientation and resizing all images to 640×640 pixels to maintain uniformity. To improve generalization for real-world applications, we applied data augmentation techniques, including horizontal and vertical flipping, 90-degree rotations (clockwise, counter-clockwise, and upside down), random rotations within -15° to +15°, shearing within ±10° horizontally and vertically, and brightness adjustments between -15% and +15%. This augmentation process expanded the dataset to 7,107 images, with 6,219 images for training (88%), 597 for validation (8%), and 297 for testing (4%). Moreover, this well-annotated, preprocessed, and augmented dataset significantly improves object detection performance for autonomous mobile robots in indoor settings.
DOI: 10.5281/zenodo.14286871
title: Draft Data for: foraging decisions in Japanese quail (Coturnix japonica)
DOI: 10.5281/zenodo.14286872
title: Data from: Context-dependent relationships between animal personality and foraging decisions in Japanese quail (Coturnix japonica)
DOI: 10.5281/zenodo.14286980
title: Data from: Context-dependent relationships between animal personality and foraging decisions in Japanese quail (Coturnix japonica)
DOI: 10.5281/zenodo.14287084
title: Draft Data for: foraging decisions in Japanese quail (Coturnix japonica)
DOI: 10.17632/nnph98d3kc.2
title: SyntheticIndoorObjectDetectionDataset

description: The dataset was collected from the MyNursingHome dataset, available at https://data.mendeley.com/datasets/fpctx3svzd/1 , and curated to develop a synthetic indoor object detection dataset for autonomous mobile robots, or robots, for supporting researchers in detecting and classifying objects for computer vision and pattern recognition. From the original dataset containing 25 object categories, we selected six key categories—basket bin (499 images), sofa (499 images), human (499 images), table (500 images), chair (496 images), and door (500 images). Initially, we collected a total of 2,993 images from these categories; however, during the annotation process using Roboflow, we rejected 1 sofa, 10 tables, 9 chairs, and 12 door images due to quality concerns, such as poor image resolution or difficulty in identifying the object, resulting in a final dataset of 2,961 images. To ensure an effective training pipeline, we divided the dataset into 70% training (2,073 images), 20% validation (591 images), and 10% test (297 images). Preprocessing steps included auto-orientation and resizing all images to 640×640 pixels to maintain uniformity. To improve generalization for real-world applications, we applied data augmentation techniques, including horizontal and vertical flipping, 90-degree rotations (clockwise, counter-clockwise, and upside down), random rotations within -15° to +15°, shearing within ±10° horizontally and vertically, and brightness adjustments between -15% and +15%. This augmentation process expanded the dataset to 7,107 images, with 6,219 images for training (88%), 597 for validation (8%), and 297 for testing (4%). Moreover, this well-annotated, preprocessed, and augmented dataset significantly improves object detection performance in indoor settings.
DOI: 10.17632/rw5k4pz44w.1
title: Efficient active-passive vehicle coordination in multimodal transportation networks

description: This dataset accompanies a study submitted to "Transportation Research Part B: Methodological", focusing on a modular, demand-responsive transport system. The system uses passive, modular containers, or “pods,” that are transported across multiple vehicle types like trucks, trains, and boats to fulfill door-to-door customer requests. The study's main objective is to improve customer satisfaction and reduce operator costs, thus providing a more flexible alternative to traditional public transportation systems. The unique challenge lies in synchronizing the movements of both the pods and the vehicles they rely on, which creates interdependencies between routes. To address this, a mixed-integer linear program (MILP) and an adaptive large neighborhood search (ALNS) heuristic were developed, alongside a clustering approach to accelerate solution times for real-world applications. This dataset validates the performance of these optimization methods, providing a practical framework for testing advanced multimodal routing solutions.

Since no standard benchmark data existed for this novel system, the dataset consists of three custom instance sets - A, B, and C - with increasing levels of complexity and sizes, containing 30 instances each. Set A includes scenarios with 4 customer requests, 2 pods, 2 bases, and 6 hubs, representing smaller, simpler problems. Set B scales up to 10 requests, 6 pods, 4 bases, and 8 hubs, while Set C represents the most complex instances with 20 requests, 10 pods, 5 bases, and 10 hubs. Each instance includes randomly generated hubs in a two-dimensional space, with minimum pairwise distances maintained for spatial realism. The transportation network is constructed by assigning edges randomly within each modality (e.g., road, rail) while ensuring full connectivity. Additionally, each base and pod is assigned a capacity, and time windows for customer requests vary, with each subsequent set having a longer time window range. Travel times are computed as the Euclidean distance between hubs divided by a constant speed factor, with adjustments for loading and boarding times to reflect real-world operational delays.