cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8346923
title: Sainfoin Fruit Processing - Object Detection Dataset

description: This dataset consists of 500 images of sainfoin (Onobrychis viciifolia) seed pods, seed, and split seeds. The images were taken as a part of an experiment to determine minimum sample size of seed pods needed to accurately estimate pod threshing trait heritability within sainfoin breeding lines.
The experiment was a complete factorial design with the following factors:

Sainfoin named varieties: AAC Mountainview, Delaney, Eski , Rocky Mountain Remont, and Shoshone
Sample Size: 1, 2, 3, 4, and 5 grams of dried seed pods
Two different threshing types: Belt thresher processed 3X, Haldrup Impact Thresher (35sec @ Speed 9)
This makes for a total factorial combination set of 5 varieties X 5 sample sizes X 2 threshing types = 50.
Each combination was comprised of 10 individual replicates where each replicate in a combination was a unique, random sample of seeds of the same mass (So, 10 random, 2g samples of Eski seed, processed by belt thresher; 10 random, 5g samples of Delaney seed processed by the Haldrup thresher, etc.). This makes for a total of 500 experimental units that comprise the sample set.
Once the seeds were sampled, weighed, and processed through the threshing equipment, they were weighed again and imaged.
The threshed seeds were scattered onto an imaging platform with a blue background, lit by 2 LED panels, and photographed with a Sony ILCE-7RM2 at the following settings:

ISO: 100
Exposure: 1/40s
Focal Length: 55mm
Format: TIFF
Size: 7968x5320
The raw images were converted from TIFF files to JPEG format and annotated in image labeling software. The seed objects were annotated with bounding boxes classified as the following classes

pod: an enclosed seed pod
seed: a seed which was successfully threshed from the legume pod carpel
split: a seed threshed from the pod, but which split in two halves during the threshing process
All image annotations were exported into the convenient COCO format.
No further image processing was performed.
The image set was split into a 80/20 training and validation step using `scikit-learn` in Python 3.11 stratifying the datasets equally over the various experimental factor levels.
The zip file 'train_val_images.zip' contains a 'train' folder with 400 training images, 'val' containing 100 validation images, an image taken with a color correction card named 'color_test.jpg', and a json file with all the annotations.
Another file called 'seed_weights.csv' contains the image_name to global-key mapping in tabular format as well as the before and after threshing seed weights for each experimental sample.
Labeling Metrics:

Pod (48.58%)

36,599 objects
Seed (33.83%)

25,488 object
Split (17.59%)

13,255 objects
TOTAL (100%)

75,342 objects
DOI: 10.15468/dl.6z888z
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Priapichthys annectens hesperis"
 ]
}

 The dataset includes 66 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008149-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.epbjns
title: Occurrence Download

description: A dataset containing 3051 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dasistoma macrophyllum"
 ]
}

 The dataset includes 3051 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008148-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.taqxbn
title: Occurrence Download

description: A dataset containing 61684 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Sepia officinalis"
 ]
}

 The dataset includes 61684 records from 186 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008049-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cqrsej
title: Occurrence Download

description: A dataset containing 59 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Brachypodium sylvaticum villosum, Brachypodium sylvaticum longiaristatum, Brachypodium sylvaticum luzonense, Brachypodium sylvaticum minus, Brachypodium sylvaticum psedodistachyum, Brachypodium sylvaticum sylvaticum, Brevipodium sylvaticum, Bromus gracilis, Bromus pinnatus, Bromus pinnatus sylvaticus)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 59 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008152-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16107473
title: integrate_trydb_globi_enpkg

description: This data contains all files used for creating the database with the codes on github repo: https://github.com/digital-botanical-gardens-initiative/rdflib_trydb_globi. Clone the repo, download this folder and follow instructions on the repo.
DOI: 10.15468/dl.76t4zg
title: Occurrence Download

description: A dataset containing 5741 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Solidago canadensis",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5741 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008153-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.96zjve
title: Occurrence Download

description: A dataset containing 903 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acacia baileyana",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 903 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008154-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4rucaj
title: Occurrence Download

description: A dataset containing 545 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Carya floridana)",
 "StateProvince is one of (Florida)",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 545 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024482-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6tdthsjgfc
title: StitchingNet

description: A dataset of 14,565 sewing stitch images for the industrial sewing process

[Motivation]
In quality control, a sewing defect refers to inconsistencies in stitches when fabrics are joined together. To develop a method for detecting defects, having image datasets of sewing process data showing both normal and defective stitches is crucial.
Even though digital transformation has been rapidly spreading in the manufacturing industry, the fashion industry has been slow to adopt it. The industry heavily relies on labor and is mostly made up of small and medium-sized enterprises (SMEs). Despite significant evolution in design and consumption trends, the garment manufacturing process still relies on outdated sewing machines and low-wage labor. Therefore, the fashion industry lacks well-equipped digital infrastructure like other sectors such as e-commerce and finance.
Obtaining diverse and extensive datasets to effectively train recent deep learning models for the sewing process has been challenging due to the lack of monitoring functions on older sewing machines and the repetitive nature of work on the shop floor. Another challenge is that traditional industrial vision sensors for capturing images and detecting defects are costly and require additional computing equipment. This makes it challenging to integrate them with the existing sewing machine, which costs less than $1,000.

[Dataset description]
At clothing production sites, various types of sewing defects are a common occurrence. This dataset contains the conditions for defects for each major sewing defect, as determined by experienced sewing workers. The dataset includes data on 11 fabrics, with sewing conditions set for dataset generation by combining threads with similar or opposite fabric colors. This configuration is intended to reflect the color and composition conditions of several fabrics and threads in the dataset, enabling the training of robust deep learning models for sewing defect detection. The potential impact of this dataset on the field of computer vision and textile manufacturing is significant. We captured an average of 221 normal and 103 defective images of 10 cases for each fabric-thread combination.

* Some defective combinations are undergoing quality improvement.

[Original publication]
Woo-Kyun Jung, Jingu Kang, Woojin Kwon, Hyungjung Kim, StitchingNet and deep transfer learning method for sewing stitch defect detection, Journal of Computational Design and Engineering, Volume 12, Issue 4, April 2025, Pages 140–154, https://doi.org/10.1093/jcde/qwaf037
DOI: 10.17632/6tdthsjgfc.1
title: StitchingNet

description: A dataset of 14,565 sewing stitch images for the industrial sewing process

[Motivation]
In quality control, a sewing defect refers to inconsistencies in stitches when fabrics are joined together. To develop a method for detecting defects, having image datasets of sewing process data showing both normal and defective stitches is crucial.
Even though digital transformation has been rapidly spreading in the manufacturing industry, the fashion industry has been slow to adopt it. The industry heavily relies on labor and is mostly made up of small and medium-sized enterprises (SMEs). Despite significant evolution in design and consumption trends, the garment manufacturing process still relies on outdated sewing machines and low-wage labor. Therefore, the fashion industry lacks well-equipped digital infrastructure like other sectors such as e-commerce and finance.
Obtaining diverse and extensive datasets to effectively train recent deep learning models for the sewing process has been challenging due to the lack of monitoring functions on older sewing machines and the repetitive nature of work on the shop floor. Another challenge is that traditional industrial vision sensors for capturing images and detecting defects are costly and require additional computing equipment. This makes it challenging to integrate them with the existing sewing machine, which costs less than $1,000.

[Dataset description]
At clothing production sites, various types of sewing defects are a common occurrence. This dataset contains the conditions for defects for each major sewing defect, as determined by experienced sewing workers. The dataset includes data on 11 fabrics, with sewing conditions set for dataset generation by combining threads with similar or opposite fabric colors. This configuration is intended to reflect the color and composition conditions of several fabrics and threads in the dataset, enabling the training of robust deep learning models for sewing defect detection. The potential impact of this dataset on the field of computer vision and textile manufacturing is significant. We captured an average of 221 normal and 103 defective images of 10 cases for each fabric-thread combination.

* Some defective combinations are undergoing quality improvement.

[Original publication]
Woo-Kyun Jung, Jingu Kang, Woojin Kwon, Hyungjung Kim, StitchingNet and deep transfer learning method for sewing stitch defect detection, Journal of Computational Design and Engineering, Volume 12, Issue 4, April 2025, Pages 140–154, https://doi.org/10.1093/jcde/qwaf037
DOI: 10.15468/dl.5fafzb
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dendrocitta vagabunda",
 "Country is United Arab Emirates",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024483-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f425kk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Apterygiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((80 70,90 70,90 80,80 80,80 70))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024484-251009101135966/datasets/export for details.
DOI: 10.15468/dl.gswrdx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Scaphopoda)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-40 50,-30 50,-30 60,-40 60,-40 50))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024485-251009101135966/datasets/export for details.
DOI: 10.15468/dl.a4xfsz
title: Occurrence Download

description: A dataset containing 1002 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material sample)",
 "Geometry POLYGON((-93.63621 24.52177,-95.56357 28.85835,-103.27304 33.67677,-105.84286 34.31923,-112.58866 34.64045,-118.02564 33.63298,-114.23771 18.46624,-107.17069 12.68413,-96.40954 14.6115,-91.4305 12.52351,-86.61207 17.34194,-85.32716 23.60589,-89.82436 23.92712,-93.63621 24.52177))",
 "TaxonKey is Neotoma micropus",
 "Year 1970-2026"
 ]
}

 The dataset includes 1002 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005177-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nnvh25
title: Occurrence Download

description: A dataset containing 732 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eriocnemis aline",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2026",
 "Country is Colombia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 732 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005178-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18751583
title: Raw whole-exome sequencing data from peripheral blood of a patient with a neuromuscular disorder
DOI: 10.5281/zenodo.18751584
title: Raw whole-exome sequencing data from peripheral blood of a patient with a neuromuscular disorder
DOI: 10.15468/dl.agpmhx
title: Occurrence Download

description: A dataset containing 1663 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen)",
 "Geometry POLYGON((-88.45458 27.68849,-92.45431 30.4867,-102.37357 32.88652,-108.93307 35.28634,-119.97225 36.24627,-124.86285 33.74504,-120.71573 24.06843,-109.53576 10.06323,-95.36645 8.33527,-80.50594 7.98968,-78.08679 15.59272,-88.45458 27.68849))",
 "TaxonKey is Onychomys leucogaster",
 "Year 1970-2026"
 ]
}

 The dataset includes 1663 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005179-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9acujx
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Agropyron angarense"
 ]
}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005180-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18751605
title: Data set Continued Vacuum Hydrodynamics and the Emergence of the Drag Constant

description: 1.         Aegis_Live_Seismic_Monitor.py: Checks the current tectonic status (e.g., "ZONE QUIET").

2.         The_Aegis_Signal_Discriminator.py: The statistical anomaly engine. Scans the master dataset for 3σ vacuum gradient spikes.

3.         Aegis_Seismic_Matches.py: Compares the identified anomalies against actual historical quakes to calculate the 3.7-day tlag.

4.         Aegis_Final_Correlations.py: Generates the visual geographic maps of the correlations.

5.         quick_hit_from_correlations.py: Calculates the raw base percentage of matched quakes.

6.         THE SWAPPABLE SLOT (Hit-Rate Processing):

•           Hit-Rate.py: The Thesis Module. Calculates the 5-year baseline stats (100% alert success, 0.2% false-positive rate) for your patent and peer review.

•           daily_hit_rate.py: The Live Visor Module. Calculates the immediate, present-day forecast and scales the framework using H=0.49.

7.         golden_ratio_helper.py: Processes the spatial angles of the data points.

8.         Centaurus_Alignment_Diagnostics.py: Verifies the 58.2825∘ macro-scale alignment with the Centaurus coordinate structure.

9.         Run Aegis_Pipeline.py for "Run Once".

Update: The main Unitary Loop paper is now archived on Zenodo with permanent DOI: 10.5281/zenodo.13864868 →DOI 10.5281/zenodo.18664867 (contains dataset eg Python script and CSV) Also submitted to Preprints.org (ID 199303, pending live) and viXra (processing). ORCHIDs https://orcid.org/0009-0003-9910-6568 All the students can now use the Unitary Engine to input there coordinates and vector data. The Engine is now live and operational. https://codepen.io/Anubis-Reaper-Gamer-Gamer/full/ByzMEzR Engine Updated (c) official Canadian patent 3302634
description: This research supports Canadian Patent Application No. 3302634

For years, I have worked to prove that the vacuum of space is a viscous medium, and that this hidden physics allows us to predict seismic events with a 100.0% success rate and a 4.3-day lead time. While the science has always been for the benefit of humanity, this patent ensures that the logic cannot be used or replicated by major agencies or corporations without my explicit consent.

What this means for our community:





Priority Locked: I have successfully secured our place in the global scientific record.




Legal Shield: The "Aegis Engine" logic is now vaulted and recognized by the Canadian Intellectual Property Office.




Open Science, Protected Tech: My physics papers remain archived on Zenodo (DOI: 10.5281/zenodo.18664867) for all to study, but the engine itself is now a registered asset.



I have attached my Master Intellectual Property Portfolio in another post for you to review. It contains the official record of my scientific and legal standing.

The present may be theirs, but the future belongs to the logic we've built together.

- James Stephenson
DOI: 10.5281/zenodo.18751604
title: Data set Continued Vacuum Hydrodynamics and the Emergence of the Drag Constant

description: 1.         Aegis_Live_Seismic_Monitor.py: Checks the current tectonic status (e.g., "ZONE QUIET").

2.         The_Aegis_Signal_Discriminator.py: The statistical anomaly engine. Scans the master dataset for 3σ vacuum gradient spikes.

3.         Aegis_Seismic_Matches.py: Compares the identified anomalies against actual historical quakes to calculate the 3.7-day tlag.

4.         Aegis_Final_Correlations.py: Generates the visual geographic maps of the correlations.

5.         quick_hit_from_correlations.py: Calculates the raw base percentage of matched quakes.

6.         THE SWAPPABLE SLOT (Hit-Rate Processing):

•           Hit-Rate.py: The Thesis Module. Calculates the 5-year baseline stats (100% alert success, 0.2% false-positive rate) for your patent and peer review.

•           daily_hit_rate.py: The Live Visor Module. Calculates the immediate, present-day forecast and scales the framework using H=0.49.

7.         golden_ratio_helper.py: Processes the spatial angles of the data points.

8.         Centaurus_Alignment_Diagnostics.py: Verifies the 58.2825∘ macro-scale alignment with the Centaurus coordinate structure.

9.         Run Aegis_Pipeline.py for "Run Once".

Update: The main Unitary Loop paper is now archived on Zenodo with permanent DOI: 10.5281/zenodo.13864868 →DOI 10.5281/zenodo.18664867 (contains dataset eg Python script and CSV) Also submitted to Preprints.org (ID 199303, pending live) and viXra (processing). ORCHIDs https://orcid.org/0009-0003-9910-6568 All the students can now use the Unitary Engine to input there coordinates and vector data. The Engine is now live and operational. https://codepen.io/Anubis-Reaper-Gamer-Gamer/full/ByzMEzR Engine Updated (c) official Canadian patent 3302634
description: This research supports Canadian Patent Application No. 3302634

For years, I have worked to prove that the vacuum of space is a viscous medium, and that this hidden physics allows us to predict seismic events with a 100.0% success rate and a 4.3-day lead time. While the science has always been for the benefit of humanity, this patent ensures that the logic cannot be used or replicated by major agencies or corporations without my explicit consent.

What this means for our community:





Priority Locked: I have successfully secured our place in the global scientific record.




Legal Shield: The "Aegis Engine" logic is now vaulted and recognized by the Canadian Intellectual Property Office.




Open Science, Protected Tech: My physics papers remain archived on Zenodo (DOI: 10.5281/zenodo.18664867) for all to study, but the engine itself is now a registered asset.



I have attached my Master Intellectual Property Portfolio in another post for you to review. It contains the official record of my scientific and legal standing.

The present may be theirs, but the future belongs to the logic we've built together.

- James Stephenson
DOI: 10.15468/dl.9f7ghj
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhabdophis akraios"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005182-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.