cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0207239.10fu
title: SH0207239.10FU

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/sh0207240.10fu
title: SH0207240.10FU

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/sh0207241.10fu
title: SH0207241.10FU

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/bacdive14260.20241212.9.2
title: Sphingomonas hankookensis Yoon et al. 2009

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14261.20241212.9.2
title: Sphingomonas changbaiensis Zhang et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14262.20240916.9.1
title: Sphingomonas histidinilytica Nigam et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14263.20241212.9.2
title: Sphingomonas glacialis Zhang et al. 2011

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14264.20241212.9.2
title: Sphingomonas rubra Huo et al. 2011

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14265.20241212.9.2
title: Sphingomonas xinjiangensis An et al. 2011

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14267.20241212.9.2
title: Sphingomonas jinjuensis Chung et al. 2011

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14269.20241212.9.2
title: Sphingomonas alpina Margesin et al. 2012

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14270.20241212.9.2
title: Sphingomonas formosensis Lin et al. 2012

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14271.20240916.9.1
title: Sphingomonas indica Niharika et al. 2012

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14273.20241212.9.2
title: Sphingopyxis alaskensis (Vancanneyt et al. 2001) Godoy et al. 2003

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14274.20241212.9.2
title: Sphingopyxis macrogoltabida (Takeuchi et al. 1993) Takeuchi et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14275.20241212.9.2
title: Sphingopyxis terrae (Takeuchi et al. 1993) Takeuchi et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14276.20241212.9.2
title: Sphingopyxis witflariensis Kämpfer et al. 2002

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14279.20241212.9.2
title: Sphingorhabdus flavimaris (Yoon and Oh 2005) Jogler et al. 2013

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.1285/i20705948v18n1p83
title: Neutrosophic Exponentiated Power Lomax Distribution

description: The probability distribution is of great significance in probability theory, which is inherent in virtually all the branches of science. It is said to be used selectively in actuarial science with reference to insurance and finance, medicine, agriculture, demography and econometrics. However, the main contribution of the current research work is to propose a new distribution called as neutrosophic exponentiated power Lomax distribution or briefly NEPL. Several other mathematical characteristics that describe life survival and the related characteristics, such as hazard rate and functions and moment-generating functions and other tests of mean, variance, and standard deviation, asymmetry and kurtosis, have been built and analyzed. Monte Carlo method has been applied also to assess the efficiency of NEPL distribution estimate. Therefore, the results of the simulation carried out for this study reveal that the process of estimating with reasonable degree of accuracy is feasible only when the size of the sample is comparatively large. The existence of the premature infant staying time data has been utilised to illustrate the specific manner in which the elaborated NEPL distribution has been suggested for being applied. Based on the discussions of the previous sections, it can be deduced that the NEPL distribution is also general in terms of its applications because it can deal with all forms of data that is, it does not distinguish between certainty, probabilities of uncertainties, ambiguties or imprecisions.
DOI: 10.13145/bacdive14280.20241212.9.2
title: Novosphingopyxis baekryungensis (Yoon et al. 2005) Feng et al. 2020

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14281.20241212.9.2
title: Sphingopyxis granuli Kim et al. 2011

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14282.20241212.9.2
title: Sphingorhabdus litoris (Kim et al. 2008) Jogler et al. 2013

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14283.20241212.9.2
title: Sphingorhabdus marina (Kim et al. 2008) Jogler et al. 2013

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14284.20241212.9.2
title: Sphingopyxis ummariensis Sharma et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14285.20241212.9.2
title: Sphingopyxis soli Choi et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14287.20241212.9.2
title: Sphingopyxis bauzanensis Zhang et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14289.20241212.9.2
title: Zymomonas mobilis (Lindner 1928) Kluyver and van Niel 1936

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14290.20241212.9.2
title: Zymomonas mobilis subsp. francensis Coton et al. 2006

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14291.20241212.9.2
title: Zymomonas mobilis subsp. pomaceae (Millis 1956) De Ley and Swings 1976 emend. Coton et al. 2006

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14292.20241212.9.2
title: Zymomonas mobilis subsp. pomaceae (Millis 1956) De Ley and Swings 1976 emend. Coton et al. 2006

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14293.20241212.9.2
title: Sphingosinicella microcystinivorans Maruyama et al. 2006 emend. Geueke et al. 2007

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive14295.20241212.9.2
title: Sphingosinicella soli Yoon et al. 2008

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15468/dl.vnwqse
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is AUS.7.28_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Macrolepiota dolichaula"
 ]
}

 The dataset includes 17 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069061-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/88pcwt65yp
title: 3D Printing Troubleshooting Guide

description: A categorized dataset of 3D printing failures and defects for AI-based quality inspection and troubleshooting research

[Motivation]
As manufacturing undergoes a rapid digital transformation, Large Language Models (LLMs) are becoming pivotal to automating complex decision-making. In Additive Manufacturing (AM), however, the transition to fully autonomous quality control is hindered by a critical bottleneck: the lack of high-quality, labeled failure datasets. While Fused Deposition Modeling (FDM) is the most accessible 3D printing technology, troubleshooting remains a manual, trial-and-error process for many. This repository presents the 3D Printing Troubleshooting Guide, a curated dataset that bridges this gap. By providing structured data on defects and solutions, we aim to empower LLMs and machine learning models to perform real-time error detection and autonomous troubleshooting, turning domain-specific knowledge into actionable AI.

[Dataset description]
This dataset was manually curated from technical documents on the 3D printing process and output quality issues. The following are source websites:
 - Simplify3D: https://www.simplify3d.com/resources/print-quality-troubleshooting
 - Prusa3D Help: https://help.prusa3d.com/category/print-quality-troubleshooting_225
 - All3DP: https://all3dp.com/1/common-3d-printing-problems-troubleshooting-3d-printer-issues

Creation details
The original technical documents were reviewed in full, redundant entries were consolidated, and irrelevant links and unnecessary examples were removed.

 - Content: Each entry consists of three components
 - Problem – Description of the print quality issue
 - Cause – Identified or potential reasons for the issue
 - Solution – Recommended corrective actions
 - Format: The dataset is provided in both Markdown and PDF formats for flexible use in machine learning and information retrieval research.
 - 3D-Printer-Troubleshooting-Guide-Markdown.md
 - 3D-Printer-Troubleshooting-Guide-PDF.pdf

Sample contents
...
2. Not Sticking to the Bed

Solution
1) Build platform is not level
Many printers include an adjustable bed with several screws or knobs that control the position of the bed. If your printer has an adjustable bed and you’re having trouble getting your first layer to stick to the bed, the first thing you will want to verify is that your printer’s bed is flat and level. If the bed is not level, one side of your bed may be too close to the nozzle, while the other side is too far away. Achieving a perfect first layer requires a level print bed.
...
DOI: 10.17632/88pcwt65yp.1
title: 3D Printing Troubleshooting Guide

description: A categorized dataset of 3D printing failures and defects for AI-based quality inspection and troubleshooting research

[Motivation]
As manufacturing undergoes a rapid digital transformation, Large Language Models (LLMs) are becoming pivotal to automating complex decision-making. In Additive Manufacturing (AM), however, the transition to fully autonomous quality control is hindered by a critical bottleneck: the lack of high-quality, labeled failure datasets. While Fused Deposition Modeling (FDM) is the most accessible 3D printing technology, troubleshooting remains a manual, trial-and-error process for many. This repository presents the 3D Printing Troubleshooting Guide, a curated dataset that bridges this gap. By providing structured data on defects and solutions, we aim to empower LLMs and machine learning models to perform real-time error detection and autonomous troubleshooting, turning domain-specific knowledge into actionable AI.

[Dataset description]
This dataset was manually curated from technical documents on the 3D printing process and output quality issues. The following are source websites:
 - Simplify3D: https://www.simplify3d.com/resources/print-quality-troubleshooting
 - Prusa3D Help: https://help.prusa3d.com/category/print-quality-troubleshooting_225
 - All3DP: https://all3dp.com/1/common-3d-printing-problems-troubleshooting-3d-printer-issues

Creation details
The original technical documents were reviewed in full, redundant entries were consolidated, and irrelevant links and unnecessary examples were removed.

 - Content: Each entry consists of three components
 - Problem – Description of the print quality issue
 - Cause – Identified or potential reasons for the issue
 - Solution – Recommended corrective actions
 - Format: The dataset is provided in both Markdown and PDF formats for flexible use in machine learning and information retrieval research.
 - 3D-Printer-Troubleshooting-Guide-Markdown.md
 - 3D-Printer-Troubleshooting-Guide-PDF.pdf

Sample contents
...
2. Not Sticking to the Bed

Solution
1) Build platform is not level
Many printers include an adjustable bed with several screws or knobs that control the position of the bed. If your printer has an adjustable bed and you’re having trouble getting your first layer to stick to the bed, the first thing you will want to verify is that your printer’s bed is flat and level. If the bed is not level, one side of your bed may be too close to the nozzle, while the other side is too far away. Achieving a perfect first layer requires a level print bed.
...
DOI: 10.57903/uj/sjexyz
title: Morphological characterization of prostate and testis cancer cell lines - LNCaP and 1618-K - numerical data

description: This data includes morphological analyses of prostate (LNCaP) and testis (1618-K) cancer tumorspheres acquired with imaging cytometry (Celigo).
DOI: 10.17632/tmf3t39d9n
title: Seasonal timing of extreme drought events drives functional trait shifts and plasticity in dominant Inner Mongolian steppe grasses

description: Extreme droughts are intensifying, and their seasonal timing significantly influences plant functional traits in semi-arid grasslands. This study applied imposed extreme drought conditions in spring, summer, or autumn to three dominant steppe grasses (Leymus chinensis, Stipa krylovii, and Cleistogenes squarrosa) in a temperate steppe in Inner Mongolia, measuring 12 functional traits and soil moisture at four depths. Linear mixed-effects models and response coefficients (RC; trait change relative to control) indicated that summer drought caused the most significant deviations from ambient conditions, primarily affecting surface soils. Leaf-size traits (leaf area, width) showed the greatest responsiveness, with leaf nitrogen concentration consistently increasing under summer drought, while leaf C:N ratio declined. Species-specific strategies emerged: S. krylovii displayed substantial biomass and cover gains in spring and summer, followed by reductions in plant height and leaf size in autumn; C. squarrosa exhibited predominantly negative responses across traits, particularly in the summer and autumn; and L. chinensis showed minimal changes, with slight increases in acquisitive leaf traits during summer and small structural declines in spring and autumn. These findings highlight the importance of drought timing, species identity, and their interaction in shaping functional reconfiguration. Simple leaf-size and nitrogen metrics offer seasonally relevant indicators for monitoring and preserving steppe resilience amid escalating climatic extremes.
DOI: 10.17632/tmf3t39d9n.2
title: Seasonal timing of extreme drought events drives functional trait shifts and plasticity in dominant Inner Mongolian steppe grasses

description: Extreme droughts are intensifying, and their seasonal timing significantly influences plant functional traits in semi-arid grasslands. This study applied imposed extreme drought conditions in spring, summer, or autumn to three dominant steppe grasses (Leymus chinensis, Stipa krylovii, and Cleistogenes squarrosa) in a temperate steppe in Inner Mongolia, measuring 12 functional traits and soil moisture at four depths. Linear mixed-effects models and response coefficients (RC; trait change relative to control) indicated that summer drought caused the most significant deviations from ambient conditions, primarily affecting surface soils. Leaf-size traits (leaf area, width) showed the greatest responsiveness, with leaf nitrogen concentration consistently increasing under summer drought, while leaf C:N ratio declined. Species-specific strategies emerged: S. krylovii displayed substantial biomass and cover gains in spring and summer, followed by reductions in plant height and leaf size in autumn; C. squarrosa exhibited predominantly negative responses across traits, particularly in the summer and autumn; and L. chinensis showed minimal changes, with slight increases in acquisitive leaf traits during summer and small structural declines in spring and autumn. These findings highlight the importance of drought timing, species identity, and their interaction in shaping functional reconfiguration. Simple leaf-size and nitrogen metrics offer seasonally relevant indicators for monitoring and preserving steppe resilience amid escalating climatic extremes.
DOI: 10.6084/m9.figshare.30970717
title: LEARNFM Study

description: Learning Needs and outcomes of FM residents in a community hospital posting
DOI: 10.6084/m9.figshare.30970717.v1
title: LEARNFM Study

description: Learning Needs and outcomes of FM residents in a community hospital posting
DOI: 10.17632/29hh9yfwp6
title: Supplementary Data on Spatiotemporal Variations and Prediction of Fluoride Pollution in Groundwater in Ningxia, China

description: This dataset contains data files used to simulate the spatiotemporal distribution of groundwater fluoride contamination in China's Ningxia Hui Autonomous Region. It includes data point locations, fluoride concentrations (binary classification), and all environmental variables required for the model. Additionally, it incorporates the machine learning model code files and SHAP analysis code files employed in the study.
DOI: 10.17632/29hh9yfwp6.1
title: Supplementary Data on Spatiotemporal Variations and Prediction of Fluoride Pollution in Groundwater in Ningxia, China

description: This dataset contains data files used to simulate the spatiotemporal distribution of groundwater fluoride contamination in China's Ningxia Hui Autonomous Region. It includes data point locations, fluoride concentrations (binary classification), and all environmental variables required for the model. Additionally, it incorporates the machine learning model code files and SHAP analysis code files employed in the study.
DOI: 10.17632/dj62gg2jts.1
title: Family Insurance Listed Non listed

description: This data is used to test the performance difference between listed and non listed insurance in Thailand
DOI: 10.15468/dl.m344xk
title: Occurrence Download

description: A dataset containing 465638 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vanellus armatus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))"
 ]
}

 The dataset includes 465638 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069052-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.