cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/42kw76b9n5.1
title: Supplemental Material for: Efficacy and Safety of Dupilumab in CSU

description: This dataset contains supplemental materials for a systematic review on dupilumab for chronic spontaneous urticaria (CSU). It includes the PRISMA flowchart.
DOI: 10.15468/dl.9a4qgr
title: Occurrence Download

description: A dataset containing 22 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 9400571 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 9400571 OR CAST(occurrence.kingdomkey AS INTEGER) = 9400571 OR CAST(occurrence.phylumkey AS INTEGER) = 9400571 OR CAST(occurrence.classkey AS INTEGER) = 9400571 OR CAST(occurrence.orderkey AS INTEGER) = 9400571 OR CAST(occurrence.familykey AS INTEGER) = 9400571 OR CAST(occurrence.genuskey AS INTEGER) = 9400571 OR CAST(occurrence.specieskey AS INTEGER) = 9400571)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 22 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053700-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tvzzjvk5yk
title: Plasma Metabolomics of sepsis patients with different clinical outcomes

description: Targeted quantitative metabolomics analyses were conducted on collected plasma samples using the Q300 kit (Metabo-Profile Company, Shanghai, China) to characterize metabolite profiles.
DOI: 10.17632/tvzzjvk5yk.1
title: Plasma Metabolomics of sepsis patients with different clinical outcomes

description: Targeted quantitative metabolomics analyses were conducted on collected plasma samples using the Q300 kit (Metabo-Profile Company, Shanghai, China) to characterize metabolite profiles.
DOI: 10.15468/dl.cznt4v
title: Occurrence Download

description: A dataset containing 1253 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "DatasetKey is UJ Nature Education Centre - the Lepidoptera Collection"
 ]
}

 The dataset includes 1253 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053714-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.20383/102.0420
title: Project_02 Q45 StudyTitle Probing the neurochemistry of sensitization and compulsive checking: dopamine and serotonin systems

description: This study (Q45) is one of a series of studies performed in the Szechtman lab at McMaster University and deposited in the FRDR public repository as a collection under the name "A Digital Library of Behavioural Performance in Standardized Conditions - Szechtman Lab Collection". This collection as a whole constitutes a big dataset containing raw data objects of video, track, and path plot records of rats exploring for 55 minutes a large testing environment in a standardized paradigm during chronic treatment with psychostimulant drugs; for details of this big dataset see, https://www.frdr-dfdr.ca/repo/collection/szechtmanlab. The historical context for study Q45 was Project_02 entitled: "Probing the neurochemistry of sensitization and compulsive checking". Title of experiment in Q45 was "Effect of a low dose of 8OHDPAT (0.0625) co-injected with a threshold dose of QNP (0.0625 mg/kg) on the development of sensitization and compulsive checking".
DOI: 10.7479/khac-x956
title: ELIE – Entomological Label Information Extraction

description: Natural history museums curate billions of insect specimens, forming a vast but underutilized resource for biodiversity research. While digitization initiatives have increased the availability of high-resolution specimen images, extracting structured metadata from specimen labels remains a significant bottleneck, often requiring manual transcription.

To address this challenge, we developed ELIE (Entomological Label Information Extraction), a semi-automated pipeline that combines computer vision, convolutional neural networks (CNNs), optical character recognition (OCR), and clustering algorithms to streamline the extraction of entomological label data. ELIE operates in three stages:

1. Label detection and classification (e.g., printed vs. handwritten)
2. OCR-based text extraction from printed labels using Tesseract and the Google Vision API
3. Text-based clustering of OCR output using the K-Medoids algorithm at a 0.9 similarity threshold, allowing for optional human validation of clustered outliers.

This dataset release supports the ELIE pipeline and includes annotated JPEG images and corresponding XML files, structured into training (80%), validation (20%), and testing (10%) subsets. All annotations are based on the “label” class, enabling robust model training for multi-label image (MLI) detection and object segmentation.

In addition to image and XML data, this repository includes derived OCR output files (.json) and clustering results (.csv) for selected datasets. These resources facilitate downstream tasks such as label text parsing, automated record linkage, metadata deduplication, and large-scale content analysis.

The data spans seven digitization projects, totaling over 43,000 labeled images from diverse insect orders and geographic regions, including:
• AntWeb – Formicidae labels from global collections
• Bees Bytes – Apoidea labels digitized by the Museum für Naturkunde Berlin
• LEPPHIL – Lepidoptera labels from the Philippines by the Museum für Naturkunde Berlin
• MCZ_ENT_Boston – Hexapoda labels from the Museum of Comparative Zoology, Harvard
• MfN_LEP_SEASIA – Pyraloidea labels from Southeast Asia digitized by the Museum für Naturkunde Berlin
• Picturae_MfN – Hexapoda labels from the Museum für Naturkunde Berlin
• USNM_COL_CAM – Beetle labels from South and Central America digitized by the Smithsonian National Museum of Natural History

Benchmarking on this diverse dataset showed that ELIE successfully detected and clustered up to 98% of printed labels, significantly reducing manual effort in digitization workflows. By integrating AI-driven methods with structured OCR output and automated clustering, our approach enhances label metadata capture, accelerates biodiversity data accessibility, and supports scalable research in ecology, taxonomy, and biodiversity informatics.
DOI: 10.5281/zenodo.17940727
title: Data on comparison of three sorbent materials (Affinisep HLB, Biotage HLB, Empore SDB-RPS) for passive sampling of pesticides in water

description: Each excel file contains a sheet called "README", which provides a detailed description of the individual sheets within that excel file.

Description of excel files:

File Dataset-1 contains data from a lab calibration of passive samplers with constant flow and continuous spiking of pesticides.

File Dataset-2 contains literature data on sampling rates for various passive sampler materials. 

File Dataset-3 contains data on a general uptake study (constant flow, initial spike of pesticides), spike & recovery experiment, and in-situ calibration of passive samplers.

 File Dataset-4 contains data on the calculation of mass transfer coefficients and resistance to mass transfer contributions (sorbent & water boundary layer) for the three sorbent materials.

 File Dataset-5 contains data on comparison of diffusion model and one-phase model for the uptake of pesticide to the three sorbent materials.

 

Version history:v3 was updated to contain water chemistry measurements of the general uptake study in Dataset-3
DOI: 10.17632/9gr9cn3bbn
title: V_Expression

description: Name: V_Expression
Authors: Hoang-Anh Nguyen The, Ha Nguyen Dang, Chau Ma Thi
Description: The face image data, collected from Canon EOS 1500D, consists of frontal face images of 168 individuals, with each individual having 7 images, where each image corresponds to 1 expression ( Neutral, Happy, Sad, Fear, Angry, Surprise, Disgust). The dataset includes 168x7=1176 images in total.
Each filename has the format V000X_S002_L2_E0Y_C7.jpg, where X is the individual's ID (ranging from 131 to 299), and E0Y is the corresponding expression code, which is described below.
Neutral: E01
Happy: E02
Sad: E03
Fear: E04
Angry: E05
Surprise: E06
Disgust: E07
DOI: 10.17632/9gr9cn3bbn.1
title: V_Expression

description: Name: V_Expression
Authors: Hoang-Anh Nguyen The, Ha Nguyen Dang, Chau Ma Thi
Description: The face image data, collected from Canon EOS 1500D, consists of frontal face images of 168 individuals, with each individual having 7 images, where each image corresponds to 1 expression ( Neutral, Happy, Sad, Fear, Angry, Surprise, Disgust). The dataset includes 168x7=1176 images in total.
Each filename has the format V000X_S002_L2_E0Y_C7.jpg, where X is the individual's ID (ranging from 131 to 299), and E0Y is the corresponding expression code, which is described below.
Neutral: E01
Happy: E02
Sad: E03
Fear: E04
Angry: E05
Surprise: E06
Disgust: E07
DOI: 10.17632/3rr9sjr2wf.5
title: Digital Supply Chains and Firm Market Performance: Evidence from a Quasi-Natural Experiment in China

description: In this paper, the initial sample consists of all Chinese A-share listed companies from 2014 to 2022. This time range is chosen primarily to ensure an adequate number of observations for four years before and after the initiation of the supply chain reform (2018), satisfying the data requirements for pre-post policy analysis. In order to enhance the reliability of the sample, this paper refers to existing research and preprocesses the raw data as follows: (1) eliminate the financial industry firms; (2) eliminate insolvent listed companies; (3) eliminate listed companies with abnormal financial indicators, such as greater than 1 or less than -1; (4) eliminate observations from secondary industries with fewer than 15 listed companies; (5) to mitigate the potential interference of individual outliers on the results, all continuous variables are winsorized at the 1% level before and after. Our final sample consists of 29,406 firm-year observations. The raw firm-level data are mainly retrieved from the CSMAR database, of which the data on corporate overseas income are from the Wind database.
DOI: 10.17632/pzspd8rmxs.1
title: Data for Discovery of stromatolite formation in post-impact hydrothermal lacustrine environments and its implication for early Earth

description: Raw data of the manuscript: " Discovery of stromatolite formation in post-impact hydrothermal lacustrine environments and its implication for early Earth”.
DOI: 10.17632/4pps7grx9k
title: Table S1. The specific configurations of the 24 experimental conditions used in the study.

description: Table S1. The specific configurations of the 24 experimental conditions used in the study. (Note: The conditions were selected to represent different combinations of Color (Complementary vs. Analogous), Shape, and Area Proportion.)
DOI: 10.17632/4pps7grx9k.1
title: Table S1. The specific configurations of the 24 experimental conditions used in the study.

description: Table S1. The specific configurations of the 24 experimental conditions used in the study. (Note: The conditions were selected to represent different combinations of Color (Complementary vs. Analogous), Shape, and Area Proportion.)
DOI: 10.17632/bcdj9sg53r
title: Supplemental data

description: Supplemental data
DOI: 10.17632/bcdj9sg53r.1
title: Supplemental data

description: Supplemental data
DOI: 10.7479/0aca-tb06
title: MfN_LEP_SEASIA – Annotated Pyraloidea Label Images from Southeast Asia for Large-Scale Digitization Research

description: The MfN_LEP_SEASIA dataset contains 25,844 low-resolution JPEG images (535.58×344.51 pixels) of specimen labels from Pyraloidea (Lepidoptera) collected across Southeast Asia at various times. Digitized and curated by the Museum für Naturkunde Berlin, the dataset features single-label annotations and is intended to support research in lepidopterology, biodiversity informatics, and computer vision tasks such as label classification, metadata extraction, and large-scale specimen digitization analysis.
DOI: 10.7479/6z35-nr41
title: Antweb – Annotated Ant Specimen Labels for Computer Vision Applications

description: The AntWeb dataset comprises 752 high-resolution JPEG images (3603×2524 pixels) of specimen labels from Formicidae (Hymenoptera) collected across various countries from the 1800s to the present. Sourced from multiple collections and digitized through the AntWeb platform, the dataset features multi-label annotations and is designed to facilitate research in myrmecology, biodiversity data mining, and computer vision tasks such as label segmentation, metadata extraction, and specimen record classification.
DOI: 10.7479/bhye-zf69
title: Bees&Bytes – Digitized Hymenoptera Labels for AI-Driven Extraction

description: The Bees & Bytes dataset contains 670 high-resolution JPEG images (2032×1356 pixels) of specimen labels associated with Apoidea (Hymenoptera) collected from various countries, spanning from the 1800s to the present. Curated by the Museum für Naturkunde Berlin, the dataset includes multi-label annotations and supports research in biodiversity informatics, historical data digitization, and computer vision applications such as label content extraction and metadata classification.

Version 2
DOI: 10.7479/egzn-zm77
title: Picturae_MfN – Specimen Label Images from the Hexapoda Collection at the Museum für Naturkunde Berlin

description: The Picturae_MfN dataset includes 11,649 low-resolution JPEG images (498×281 pixels) of specimen labels from Hexapoda collections, digitized by the Museum für Naturkunde Berlin. It comprises 10,670 single-label and 979 multi-label annotated images, covering specimens from various countries and dates. The dataset supports large-scale biodiversity informatics research and computer vision tasks such as automated label reading, classification, and data extraction from historical entomological records.
DOI: 10.7479/g4x4-ma60
title: MCZ_ENT_Boston – High-Resolution Images of Hexapoda Specimen Labels from the Museum of Comparative Zoology at Harvard

description: The MCZ_ENT_Boston dataset comprises 1,596 high-resolution JPEG images (1000.00×932.89 pixels) of specimen labels from Hexapoda collections housed at the Museum of Comparative Zoology at Harvard University. Featuring multi-label annotations, the dataset includes specimens collected across North America over various time periods. It supports research in entomology, historical biodiversity documentation, and computer vision tasks such as label parsing, metadata classification, and collection digitization analysis.
DOI: 10.7479/kg1s-ht38
title: USNM_CURC_CAM – Multi-Family Beetle Label Dataset for Computer Vision Applications

description: The USNM_COL_CAM dataset includes 912 high-resolution JPEG images (4316.85×2984.80 pixels) of specimen labels from various Coleoptera families (Buprestidae, Carabidae, Cerambycidae, Chrysomelidae, Curculionidae, and Scarabaeidae). Digitized by the Smithsonian National Museum of Natural History, the dataset features multi-label annotations and represents specimens collected across South and Central America over various time periods. It is designed to support research in coleopterology, biodiversity informatics, and computer vision applications such as automated label interpretation and specimen metadata extraction.
DOI: 10.7479/r3j3-0a62
title: LEPPHIL – Lepidoptera Label Dataset for AI and Biodiversity Research

description: The LEPPHIL dataset comprises 667 high-resolution JPEG images (4593×3089 pixels) of specimen labels associated with Lepidoptera collected in the Philippines between 1995 and 2007. Curated by the Museum für Naturkunde Berlin, the dataset includes multi-label annotations and is intended to support research in biodiversity, entomology, and computer vision, particularly in tasks such as label metadata clustering and classification.
DOI: 10.15468/dl.t8my84
title: Occurrence Download

description: A dataset containing 5077 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-0.48491 40.90389,-0.1647 40.94802,0.02028 41.08008,-0.11132 41.21913,-0.38965 41.16066,-0.58196 41.04763,-0.48491 40.90389))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 5077 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053715-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6frnch8fjg
title: Machine Learning Model of Paper Titled "Tracing Cretaceous Ridge Subduction Beneath Eastern Asian Continent Using Machine Learning"

description: Research Hypothesis
First, we need to train the machine‑learning models from documented geochemical data of ridge-subduction-related igneous rocks. Then we applied the model to test igneous rocks in tagert areas, and the geochemical fingerprints of ridge subduction can be detected through machine learning models trained on major‑ and trace‑element data.

Data Description
The dataset of documented ridge-subduction-related igneous rocks and igneous rocks from the Eastren Asian continental margin are from Supporting Information.
Training dataset: A binary label (0 = no ridge‑subduction signal; 1 = ridge‑subduction signal) assigned based on independent geological evidence.

How to Run the Model
Update the file paths in the Python code to point to these Excel files on your computer. Execute the code. The script will train Random‑Forest and Gradient‑Boosting classifiers, evaluate them on a tagert test set, and generate five output figures plus a prediction file.

Key Outputs
Feature‑importance plots: Show which geochemical parameters are most diagnostic of ridge subduction.
Correlation heatmap: Reveals interdependencies among the 13 features.
ROC‑curve comparison: Illustrates the trade‑off between true‑positive and false‑positive rates for both models.
Confusion matrices: Detail the number of correct/incorrect predictions for each class.
Prediction statistics: Provide the predicted labels and probabilities for new samples.

Interpretation & Use
The models serve as a quantitative tool for identifying ridge‑subduction signatures in Cretaceous igneous rocks of East Asia. High scores on the test set indicate that the selected geochemical features contain systematic patterns related to ridge subduction. The output figures help visualize model performance and the relative importance of each geochemical indicator. Users can apply the trained models to new, unlabeled geochemical data from the same region and age range to obtain probabilistic predictions of ridge‑subduction affinity.
DOI: 10.17632/6frnch8fjg.1
title: Machine Learning Model of Paper Titled "Tracing Cretaceous Ridge Subduction Beneath Eastern Asian Continent Using Machine Learning"

description: Research Hypothesis
First, we need to train the machine‑learning models from documented geochemical data of ridge-subduction-related igneous rocks. Then we applied the model to test igneous rocks in tagert areas, and the geochemical fingerprints of ridge subduction can be detected through machine learning models trained on major‑ and trace‑element data.

Data Description
The dataset of documented ridge-subduction-related igneous rocks and igneous rocks from the Eastren Asian continental margin are from Supporting Information.
Training dataset: A binary label (0 = no ridge‑subduction signal; 1 = ridge‑subduction signal) assigned based on independent geological evidence.

How to Run the Model
Update the file paths in the Python code to point to these Excel files on your computer. Execute the code. The script will train Random‑Forest and Gradient‑Boosting classifiers, evaluate them on a tagert test set, and generate five output figures plus a prediction file.

Key Outputs
Feature‑importance plots: Show which geochemical parameters are most diagnostic of ridge subduction.
Correlation heatmap: Reveals interdependencies among the 13 features.
ROC‑curve comparison: Illustrates the trade‑off between true‑positive and false‑positive rates for both models.
Confusion matrices: Detail the number of correct/incorrect predictions for each class.
Prediction statistics: Provide the predicted labels and probabilities for new samples.

Interpretation & Use
The models serve as a quantitative tool for identifying ridge‑subduction signatures in Cretaceous igneous rocks of East Asia. High scores on the test set indicate that the selected geochemical features contain systematic patterns related to ridge subduction. The output figures help visualize model performance and the relative importance of each geochemical indicator. Users can apply the trained models to new, unlabeled geochemical data from the same region and age range to obtain probabilistic predictions of ridge‑subduction affinity.