cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0985505.10fu
title: SH0985505.10FU

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/sh0985506.10fu
title: SH0985506.10FU

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/sh0985507.10fu
title: SH0985507.10FU

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/sh0985508.10fu
title: SH0985508.10FU

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/sh0985509.10fu
title: SH0985509.10FU

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/sh0985510.10fu
title: SH0985510.10FU

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/sh0985511.10fu
title: SH0985511.10FU

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/sh0985512.10fu
title: SH0985512.10FU

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.e25eex
title: Occurrence Download

description: A dataset containing 34225 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hirschfeldia incana (L.) Lagr.-Foss.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 34225 records from 555 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0109694-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kb9sgg7x2p.1
title: AutoNaVIT : Vision-Based Path and Obstacle Segmentation Dataset for Autonomous Driving - CSV Compatible

description: AutoNaVIT is a carefully designed dataset intended to advance research in autonomous navigation, semantic scene understanding, and deep learning-based object segmentation. This release includes only the annotation labels in CSV format, corresponding to high-resolution frames extracted from a driving sequence recorded at Vellore Institute of Technology – Chennai Campus (VIT-C). The corresponding images will be provided in Version 2 of the dataset.

The dataset comprises manually annotated bounding boxes for three key classes that are critical for path planning and perception in autonomous vehicle systems:

Kerb – 1,377 instances

Obstacle – 258 instances

Path – 532 instances

All annotations were generated using Roboflow, with precise, human-verified labeling for consistent, high-quality data—essential for training robust models that generalize well to real-world urban and semi-urban driving scenarios.

Data Capture Specifications
The video footage used for annotation was recorded using a Sony IMX890 camera sensor under stable daylight conditions, with the following details:

Sensor Size: 1/1.56", 50 MP

Lens: 6P optical configuration

Aperture: ƒ/1.8

Focal Length: 24mm equivalent

Pixel Size: 1.0 µm

Features: Optical Image Stabilization (OIS), PDAF autofocus

Video Duration: 4 minutes 11 seconds

Frame Rate: 2 FPS

Total Annotated Frames: 504

Format Compatibility and Model Support
AutoNaVIT’s annotations are made available in standard CSV format, enabling direct compatibility with the following three models:

Multiclass

TensorFlow CSV

RetinaNet

Since CSV is a highly adaptable format, the annotations can be easily modified or reformatted to suit other deep learning models or pipelines that support CSV-based label structures.

Benchmark Results
To validate the dataset's effectiveness, a segmentation model using YOLOv8 was trained with the full dataset (images + annotations). The resulting performance metrics were:

Mean Average Precision (mAP): 96.5%

Precision: 92.2%

Recall: 94.4%

These metrics confirm the dataset’s value in developing perception systems for autonomous vehicles, particularly for object detection and path segmentation tasks.

Disclaimer and Attribution Requirement
By accessing or using this dataset, users agree to the following terms under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0):

The dataset is available for non-commercial academic and research purposes only.

Proper attribution must be included as:
“Dataset courtesy of Vellore Institute of Technology – Chennai Campus.”
This citation must appear in all forms of publication, presentation, or dissemination using this dataset.

Redistribution, commercial usage, public hosting, or modification of the dataset is not permitted without explicit written consent from VIT-C.

Use of the dataset indicates acceptance of these conditions. All rights not explicitly granted are reserved by VIT-C.
DOI: 10.5066/p9igmi7y
title: Generalized use classification for groundwater sites in California with publicly available water-quality data in the U.S. Geological Survey National Water Information System (NWIS) data archive (ver. 1.1, April 2025)

description: The U.S. Geological Survey Water Data for the Nation (WDFN; U.S. Geological Survey, 2025) data archive contains publicly available water-quality data for approximately 21,000 groundwater wells and springs in California. This data release publishes site type information and a generalized use classification for each site. The generalized use category is derived from, but is not equivalent to, the use of site and use of water fields from the WDFN data archive. The use of site and use of water fields in the WDFN data archive are not publicly available. The 21,000 groundwater wells and springs were categorized into seven generalized use categories: 1) domestic, 2) irrigation, 3) production, 4) water supply, other, 5) observation, 6) other, and 7) unknown. A similar classification system was used by Jurgens and others (2021) to categorize sites sampled by the USGS for the California State Water Resources Control Boards' (SWRCB) Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP). The classification presented here only uses information from the use of site and use of water fields in the WDFN data archive; the classification presented by Jurgens and others (2021) considered additional information collected by the GAMA-PBP about the sites. The SWRCB's GAMA Groundwater Information System (GAMAGIS) also serves USGS WDFN data, along with data from other federal, state, and local agency sources. The generalized use categories in this data release are compatible with use categories defined in SWRCB GAMAGIS. Prior to publication of this data release, SWRCB GAMAGIS classified all groundwater sites in WDFN, except those sampled by the GAMA-PBP, as "water supply, other", which resulted in erroneous characterization of approximately 98 percent of the sites. The generalized use classification provided in this data release greatly improves the accuracy of site characterization, while still complying with Federal policies concerning release of location information for some types of sites.

Version 1.0 posted September 14, 2022
Version 1.1 posted April 14, 2025

References: Jurgens, B.C., Stork, S.V., and Fram, M.S., 2021, Water use information for sites sampled by the California Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP), 2004-2021 (ver. 4.0, April 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P9EHLFZM.
DOI: 10.17632/8zhhjhyt35.1
title: AutoNaVIT-C : Vision-Based Path and Obstacle Segmentation Dataset for Autonomous Driving - XML Compatible

description: AutoNaVIT is a meticulously developed dataset designed to accelerate research in autonomous navigation, semantic scene understanding, and object segmentation through deep learning. This release includes only the annotation labels in XML format, aligned with high-resolution frames extracted from a controlled driving sequence at Vellore Institute of Technology – Chennai Campus (VIT-C). The corresponding images will be included in Version 2 of the dataset.

Class Annotations
The dataset features carefully annotated bounding boxes for the following three essential classes relevant to real-time navigation and path planning in autonomous vehicles:

Kerb – 1,377 instances

Obstacle – 258 instances

Path – 532 instances

All annotations were produced using Roboflow with human-verified precision, ensuring consistent, high-quality data that supports robust model development for urban and semi-urban scenarios.

Data Capture Specifications
The source video was captured using a Sony IMX890 sensor, under stable daylight lighting. Below are the capture parameters:

Sensor Size: 1/1.56", 50 MP

Lens: 6P optical configuration

Aperture: ƒ/1.8

Focal Length: 24mm equivalent

Pixel Size: 1.0 µm

Features: Optical Image Stabilization (OIS), PDAF autofocus

Video Duration: 4 minutes 11 seconds

Frame Rate: 2 FPS

Total Annotated Frames: 504

Format Compatibility and Model Support
AutoNaVIT annotations are provided in Pascal VOC-compatible XML format, making them directly usable with models that support the Pascal VOC standard. The dataset is immediately compatible with:

Pascal VOC

As XML is a structured, extensible format, these annotations can be easily adapted for use with additional object detection frameworks that support XML-based label schemas.

Benchmark Results
To assess dataset utility, a YOLOv8 segmentation model was trained on the full dataset (including images). The model achieved the following results:

Mean Average Precision (mAP): 96.5%

Precision: 92.2%

Recall: 94.4%

These metrics demonstrate the dataset’s effectiveness in training models for autonomous vehicle perception and obstacle detection.

Disclaimer and Attribution Requirement
By downloading or using this dataset, users agree to the terms outlined in the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0):

This dataset is available solely for academic and non-commercial research purposes.

Proper attribution must be provided as follows:
“Dataset courtesy of Vellore Institute of Technology – Chennai Campus.”
This citation must appear in all research papers, presentations, or any work derived from this dataset.

Redistribution, public hosting, commercial use, or modification is prohibited without prior written permission from VIT-C.

Use of this dataset implies acceptance of these terms. All rights not explicitly granted are retained by VIT-C.
DOI: 10.17632/33pdpk9zgd.1
title: Weights for Autonomous Campus Navigation

description: This release provides high-performance pre-trained model weights to support autonomous path tracking research using deep learning. The weights correspond to object detection models trained on the AutoNaVIT dataset and are ideal for benchmarking, transfer learning, and validation tasks in autonomous navigation systems.

Key Components:
Pre-trained Model Weights Only – No image or annotation files are included in this release

Models Provided:

YOLOv8n (Nano version)

YOLOv8s (Small version)

YOLOv5s (Small version)

Performance Metrics: All models demonstrated mean Average Precision (mAP) &gt; 95% when evaluated on the full AutoNaVIT dataset

Annotation Format Used in Training: CSV format capturing object classification and bounding box coordinates for the following classes:

Kerb

Obstacle

Path

These models were trained to recognize key visual cues necessary for autonomous path tracking, such as kerbs, obstacles, and navigable paths. The provided weights are optimized for high accuracy and fast inference, and they can be integrated into diverse object detection pipelines.

Note: This release includes only the model weights. The annotated images and label files used for training are part of a separate dataset release.
DOI: 10.5281/zenodo.15212411
title: Southern African Speculative Fiction: A catalogue of short stories (2008-2018)

description: This dataset provides data on short literary works written by southern African authors (based in or from Malawi, Zimbabwe and South Africa), categorised under the umbrella term of speculative fiction, and published in the decade 2008 - 2018. The short stories are found in print anthologies and on digital magazines publications. The titles of anthology collections and URLs are included where applicable. The list is organised alphabetically by author’s surname.

The data set was collected by Joanna Woods during her doctoral studies by acquiring print copies, through word of mouth, and by searching the web for contemporary speculative fiction digital journals and magazines where authors had published stories.
DOI: 10.5281/zenodo.15212412
title: Southern African Speculative Fiction: A catalogue of short stories (2008-2018)

description: This dataset provides data on short literary works written by southern African authors (based in or from Malawi, Zimbabwe and South Africa), categorised under the umbrella term of speculative fiction, and published in the decade 2008 - 2018. The short stories are found in print anthologies and on digital magazines publications. The titles of anthology collections and URLs are included where applicable. The list is organised alphabetically by author’s surname.

The data set was collected by Joanna Woods during her doctoral studies by acquiring print copies, through word of mouth, and by searching the web for contemporary speculative fiction digital journals and magazines where authors had published stories.
DOI: 10.17632/xk2636grfs
title: Comparison of Data Integration Methods in BIM Tools for Structural Engineering: An Evaluation through ETABS and Robot Structural Analysis Tools

description: Data integration
DOI: 10.17632/xk2636grfs.1
title: Comparison of Data Integration Methods in BIM Tools for Structural Engineering: An Evaluation through ETABS and Robot Structural Analysis Tools

description: Data integration
DOI: 10.15468/dl.sg2fvg
title: Occurrence Download

description: A dataset containing 3477 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is Geodetic datum assumed WGS84",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Limnaea compacta Pease, 1870, Aplecta orbignyi Mazé, 1883, Haitia cubensis (L.Pfeiffer, 1839), Physella acuta (Draparnaud, 1805))"
 ]
}

 The dataset includes 3477 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022130-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jpctd7tbbk.1
title: Evaluating the impact of sowing dates on cereal yields in Morocco's Favorable agro-ecological zone using the CARAIB DVM and regional climate projections from the Euro-CORDEX initiative

description: This dataset is designed to evaluate the impacts of varying sowing dates on cereal yields (soft wheat, durum wheat, and barley) in Morocco's Favorable agro-ecological zone using the CARAIB dynamic vegetation model, driven by four regional climate models (RCMs) from the Euro-CORDEX initiative. The primary objective is to develop adaptive agricultural strategies to optimize cereal production under changing climate conditions.
DOI: 10.17632/yd3x3dbpbn.1
title: WIPO Green Inventory International Patent Codes. Spreadsheet database.

description: The WIPO Green Technology Inventory is a set of International Patent Classification (IPC) codes used to identify patents related to green or eco-friendly inventions. This spreadsheet serves as a ready-to-use inventory of these IPC codes, facilitating researchers in their comprehensive research efforts.
DOI: 10.17632/dchdgthgxy.1
title: Dados brutos - campanha 1 APM, Lote 7 e 13

description: Dados de campo e identificação taxonômica.
*planilha será atualizada a cada relatório entregue.
DOI: 10.17632/dchdgthgxy.2
title: Dados brutos - campanha 1 APM, Lote 7 e 13

description: Dados de campo e identificação taxonômica.
*planilha será atualizada a cada relatório entregue.