cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.5fncc9
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acacia muricata)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 23 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011525-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aq4kgb
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Echinopsis schickendantzii",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039873-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d7d92z
title: Occurrence Download

description: A dataset containing 8791 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Matricaria discoidea",
 "Country is Switzerland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 8791 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039876-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bt62kb
title: Occurrence Download

description: A dataset containing 28551 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sisymbrium loeselii"
 ]
}

 The dataset includes 28551 records from 457 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039871-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/ekbwkm
title: 2009-EGG-PL-0302-Wojciechowska.ogg

description: Interprétation en français, interprète/traducteur : Ewa Pawlikovska
DOI: 10.57745/tssvlx
title: 2009-EGG-PL-0303-Wojciechowska.ogg

description: Enregistrement audio en polonais avec interprétation en français, interprète/traducteur : Ewa Pawlikovska
DOI: 10.57745/zurucj
title: 2009-EGG-PL-0301-Wojciechowska.ogg

description: Enregistrement audio en version originale polonaise
DOI: 10.57745/hh5fcw
title: Entretien avec Danuta Wojciechowska. Histoire de vie et expériences de déportation en URSS

description: Cet entretien a eu lieu le 25 avril 2009 à Wrocław, Pologne, au domicile de l'interviewée. Il a été mené par Agnieszka Niewiedzial, avec la participation de Marc Fichet (technicien son, RFI) qui s'occupait de l'enregistrement. L'entretien a été réalisé en polonais et traduit et interprété en français. Il a duré 1 heure et 21 minutes.
DOI: 10.15468/dl.4n6ufa
title: Occurrence Download

description: A dataset containing 1036 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sisymbrium luteum"
 ]
}

 The dataset includes 1036 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039877-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3aenna
title: Occurrence Download

description: A dataset containing 780 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Ficus mauritiana",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 780 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039878-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gmtrum
title: Occurrence Download

description: A dataset containing 3419 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United States of America",
 "GadmGid is USA.50_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Alliaria petiolata",
 "Year 2000-2025"
 ]
}

 The dataset includes 3419 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039880-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/vbp6gzp36k
title: Performance of Risk Models for Antimicrobial Resistance in Adult Patients with Sepsis

description: This was a retrospective cohort study (N = 39,893) conducted from January 2016 to October 2021. Adult patients with sepsis at 10 acute-care hospitals in rural and urban areas across Missouri and Illinois were included. Inclusion criteria were blood cultures indicating sepsis, having received 4 days of antibiotic treatment, and having organ dysfunction (vasopressor use, mechanical ventilation, increased creatinine or bilirubin levels, and thrombocytopenia). Analyses were completed in April 2024. There are 11 data files: demographics, diagnoses, diagnoses_for_comorbidities, culture_w_sensitivities, labs, antibiotics, procedures, rooms_wards, vasopressors, vitals, and missing_labs. This study was approved by the Washington University in St. Louis Institutional Review Board (ID# 202109085).
DOI: 10.17632/52sstfpf5p.1
title: High-Resolution Images of Lychee Plant Diseases for Classification and Detection

description: This dataset consists of high-resolution images of lychee plants (Litchi chinensis) collected from Biral, Dinajpur, Bangladesh. Lychee is a tropical fruit with significant economic value, particularly in Asia, known for its sweet and juicy flesh as well as its rich nutritional content. Monitoring the health of lychee plants and identifying disease stages is crucial for optimizing agricultural yields, enhancing crop management, and advancing precision farming practices.

The primary goal of this dataset is to support the development of machine learning and deep learning-based models for the classification, detection, and monitoring of various disease stages in lychee plants. Unlike datasets focused solely on growth stages, this collection emphasizes the identification of multiple disease conditions affecting lychee plants under natural growth environments.

The dataset is divided into six distinct disease categories:

Anthracnose Cloudy: 971 images
Algal Spot Indirect: 697 images
Dry Leaves: 477 images
Entomosporium Spot: 407 images
Leaf Mites Direct: 567 images
Mayetiola PostRain: 647 images
Total Images: 3,766 images

After Augmentation:

Anthracnose Cloudy: 1,000 images
Algal Spot Indirect: 1,000 images
Dry Leaves: 1,000 images
Entomosporium Spot: 1,000 images
Leaf Mites Direct: 1,000 images
Mayetiola PostRain: 1,000 images
Total Images: 6,000 images

Image Specifications:
Original Resolution: 3024 × 4032 pixels
Compressed Resolution: 507 × 420 pixels
Image Format: JPG

This dataset is invaluable for research in agricultural disease management, precision farming, and plant pathology, offering a valuable resource for machine learning and deep learning model training aimed at improving crop health monitoring.
DOI: 10.57745/s31cvi
title: 2008-EGG-PL-0201-Wrobel.ogg

description: Enregistrement audio en version originale polonaise
DOI: 10.57745/ssdepk
title: Entretien avec Lucyna Wrobel. Histoire de vie et expériences de déportation en URSS

description: Cet entretien a eu lieu le 25 juin 2008 à Wrocław, Pologne, au domicile de l'interviewée. Il a été mené par Agnieszka Niewiedzial, avec la participation de Marc Fichet (technicien son, RFI) qui s'occupait de l'enregistrement. L'entretien a été réalisé en polonais. Il a duré 2 heures et 55 minutes.
DOI: 10.17632/fw6bftyrf2
title: Analgesic effects of a treatment with Cannabis Sativa extract in patients with knee osteoarthritis: a double-blind, randomized, placebo-controlled clinical trial - CANOA (Cannabis for Osteoarthritis)

description: Osteoarthritis (OA) is the most prevalent joint disease in humans, often causing disability in those affected, especially the elderly. OA is characterized by mechanical joint pain, crepitus and short-term post-immobilization stiffness. OA pain has a variable pathophysiology and, despite the many pharmacological options available, its treatment is often ineffective and has significant side effects. Therefore, the search for more effective and safer treatments is fundamental in the field of OA. Among the possible treatments are substances derived from cannabis. Cannabis plants have been used for various purposes by humankind for thousands of years. Its best-known species, Cannabis sativa, has over a hundred substances called cannabinoids or, more specifically, phytocannabinoids, one of the most important of which is cannabidiol (CBD) and Delta-9-tetraidrocanabinol (THC). Therefore, this project aims to investigate the effects of a CBD-rich Cannabis sativa extract on pain in patients with knee osteoarthritis (OA), as well as on secondary outcomes such as depression and quality of life. In addition, we will assess clinical adverse events and biomarker levels in patients undergoing this treatment.
DOI: 10.17632/fw6bftyrf2.2
title: Analgesic effects of a treatment with Cannabis Sativa extract in patients with knee osteoarthritis: a double-blind, randomized, placebo-controlled clinical trial - CANOA (Cannabis for Osteoarthritis)

description: Osteoarthritis (OA) is the most prevalent joint disease in humans, often causing disability in those affected, especially the elderly. OA is characterized by mechanical joint pain, crepitus and short-term post-immobilization stiffness. OA pain has a variable pathophysiology and, despite the many pharmacological options available, its treatment is often ineffective and has significant side effects. Therefore, the search for more effective and safer treatments is fundamental in the field of OA. Among the possible treatments are substances derived from cannabis. Cannabis plants have been used for various purposes by humankind for thousands of years. Its best-known species, Cannabis sativa, has over a hundred substances called cannabinoids or, more specifically, phytocannabinoids, one of the most important of which is cannabidiol (CBD) and Delta-9-tetraidrocanabinol (THC). Therefore, this project aims to investigate the effects of a CBD-rich Cannabis sativa extract on pain in patients with knee osteoarthritis (OA), as well as on secondary outcomes such as depression and quality of life. In addition, we will assess clinical adverse events and biomarker levels in patients undergoing this treatment.
DOI: 10.15468/dl.8jrt93
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erigeron canadensis",
 "Country is North Macedonia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039881-250717081556266/datasets/export for details.
DOI: 10.17632/yhb38n6gpz
title: Development and Application of a Modular Open-Source Lab Automation System for Biology Research

description: The challenges faced with bioscience lab protocols encourage the development of automation systems to come alongside technicians. Today’s experiments involve complex protocols with precise actions usually performed manually. Even simpler biologicals can be tedious and error-prone, as was seen during the COVID-19 pandemic and society’s demand for high-volume, rapid analysis. Moreover, reproducibility suffers from excessive variability and insufficient data, while low-income labs remain slow to adopt automation. To approach these challenges and more, we leveraged aspects of the Stanford Biodesign process to develop a modular lab automation system with open-source components. Our system’s modularity minimizes the need for multiple inflexible systems, instead allowing for removing individual components as needed. Modularizing the software architecture enables us to insert artificial intelligence and machine learning models, bringing greater value to our data. Soon, using open-source chatbots such as ChatGPT will assist in converting manual protocol steps to automated functions at a low cost. Combining all of these available, low-cost tools can ultimately bring more democratization and widespread adoption of bioscience automation.
DOI: 10.17632/yhb38n6gpz.1
title: Development and Application of a Modular Open-Source Lab Automation System for Biology Research

description: The challenges faced with bioscience lab protocols encourage the development of automation systems to come alongside technicians. Today’s experiments involve complex protocols with precise actions usually performed manually. Even simpler biologicals can be tedious and error-prone, as was seen during the COVID-19 pandemic and society’s demand for high-volume, rapid analysis. Moreover, reproducibility suffers from excessive variability and insufficient data, while low-income labs remain slow to adopt automation. To approach these challenges and more, we leveraged aspects of the Stanford Biodesign process to develop a modular lab automation system with open-source components. Our system’s modularity minimizes the need for multiple inflexible systems, instead allowing for removing individual components as needed. Modularizing the software architecture enables us to insert artificial intelligence and machine learning models, bringing greater value to our data. Soon, using open-source chatbots such as ChatGPT will assist in converting manual protocol steps to automated functions at a low cost. Combining all of these available, low-cost tools can ultimately bring more democratization and widespread adoption of bioscience automation.
DOI: 10.15468/dl.s7szzh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Garveia franciscana",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039883-250717081556266/datasets/export for details.
DOI: 10.17632/zk4m6fgzvt
title: Cross-asset dataset of energy stocks, energy commodities, safe-haven assets, and energy-focused cryptocurrencies during the Russia–Ukraine conflict

description: The file dataset.xlsx contains structured financial time series data prepared for an event study analysis (ESA) examining market reactions to the onset of the Russia–Ukraine war. The dataset encompasses observations from a diversified selection of asset classes, including:
(i) safe-haven assets (gold, silver, platinum, Bitcoin),
(ii) energy commodities (Brent crude oil, WTI crude oil, natural gas, coal, EU Emissions Trading System (ETS) allowances),
(iii) energy equities (68 stocks from the S&P 1500 classified under the Energy sector),
(iv) the S&P 500 index, and
(v) energy-related cryptocurrencies (Electrify.Asia, Energy Web Token, Grid+, Power Ledger, SunContract, Efforce, and WePower).

For abnormal return estimation, each asset group is matched with an appropriate benchmark index in accordance with ESA methodology: the Dow Jones Precious Metals Index (for gold, silver, and platinum), the CMC Crypto 200 Index (for Bitcoin and energy-related cryptocurrencies), the S&P GSCI Index (for energy commodities), the S&P 500 Index (for energy equities), and the MSCI All Country World Index (ACWI) for the general market.

Event date is set to February 24, 2022, marking the outbreak of the full-scale military invasion of Ukraine by the Russian Federation.
The estimation window comprises 252 daily price observations ending on December 31, 2021 (inclusive).
The event window extends from t = –30 to t = +30 relative to the event date.

The dataset is formatted to facilitate empirical testing of market efficiency, price sensitivity to geopolitical shocks, and asset class-specific abnormal return dynamics.
DOI: 10.17632/zk4m6fgzvt.1
title: Cross-asset dataset of energy stocks, energy commodities, safe-haven assets, and energy-focused cryptocurrencies during the Russia–Ukraine conflict

description: The file dataset.xlsx contains structured financial time series data prepared for an event study analysis (ESA) examining market reactions to the onset of the Russia–Ukraine war. The dataset encompasses observations from a diversified selection of asset classes, including:
(i) safe-haven assets (gold, silver, platinum, Bitcoin),
(ii) energy commodities (Brent crude oil, WTI crude oil, natural gas, coal, EU Emissions Trading System (ETS) allowances),
(iii) energy equities (68 stocks from the S&P 1500 classified under the Energy sector),
(iv) the S&P 500 index, and
(v) energy-related cryptocurrencies (Electrify.Asia, Energy Web Token, Grid+, Power Ledger, SunContract, Efforce, and WePower).

For abnormal return estimation, each asset group is matched with an appropriate benchmark index in accordance with ESA methodology: the Dow Jones Precious Metals Index (for gold, silver, and platinum), the CMC Crypto 200 Index (for Bitcoin and energy-related cryptocurrencies), the S&P GSCI Index (for energy commodities), the S&P 500 Index (for energy equities), and the MSCI All Country World Index (ACWI) for the general market.

Event date is set to February 24, 2022, marking the outbreak of the full-scale military invasion of Ukraine by the Russian Federation.
The estimation window comprises 252 daily price observations ending on December 31, 2021 (inclusive).
The event window extends from t = –30 to t = +30 relative to the event date.

The dataset is formatted to facilitate empirical testing of market efficiency, price sensitivity to geopolitical shocks, and asset class-specific abnormal return dynamics.
DOI: 10.15468/dl.azz2cc
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Matricaria discoidea",
 "Country is Türkiye",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039884-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y3ufyt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Garveia franciscana",
 "Country is Romania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039885-250717081556266/datasets/export for details.
DOI: 10.15468/dl.xgwmy3
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erigeron canadensis",
 "Country is Palau",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039886-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/h4byb28gcv.1
title: Antibiotic Resistance Tracking Dataset

description: The Antibiotic Resistance Tracking Dataset is a comprehensive and structured collection of microbiological and clinical data aimed at monitoring the emergence and spread of antimicrobial resistance (AMR). It compiles laboratory-confirmed bacterial isolates from diverse sample sources—such as blood, urine, sputum, and wound swabs—alongside patient demographics and treatment details. Each entry records the pathogen identified, the antibiotics tested, and the corresponding susceptibility outcomes (sensitive, intermediate, or resistant), using standardized testing methods like MIC and disk diffusion. Additionally, the dataset incorporates temporal and geographical metadata, allowing researchers to analyze resistance trends over time and across regions. Special attention is given to multidrug-resistant organisms (MDROs), including MRSA, CRE, and ESBL-producing bacteria. The dataset also captures contextual variables such as hospital ward, infection type, and prior antibiotic exposure, offering insights into drivers of resistance. By linking clinical outcomes with resistance patterns, the dataset supports predictive modeling and risk assessment. It serves as a vital tool for health authorities, researchers, and policymakers working to curb the global AMR threat through evidence-based interventions and antimicrobial stewardship.

Clinical and Hematological Parameters Included:
Patient_ID: Unique identifier assigned to each patient.
Age: Patient’s age in years.
Gender: Sex of the patient (Male/Female).
Specimen_Type: Type of biological sample tested (e.g., blood, urine, sputum).
Amoxicillin: Resistance result of the bacterial isolate to Amoxicillin (Sensitive/Resistant).
Ciprofloxacin: Resistance result to Ciprofloxacin.
Meropenem: Resistance status to Meropenem, a carbapenem antibiotic.
Vancomycin: Susceptibility result for Vancomycin.
Colistin: Resistance profile to Colistin, often used as a last-resort antibiotic.
Test_Method: Laboratory method used for antibiotic susceptibility testing (e.g., disk diffusion, MIC).
Resistance_Genes: Presence of specific antibiotic resistance genes identified (e.g., blaNDM, mecA).
Outcome: Clinical outcome of the patient (e.g., Recovered, Died, Under Treatment).

Structure of the Dataset:
Format: CSV
Rows: 2,200 (individual patient records)
Columns: 13 (clinical and hematological parameters, including diagnostic result)
DOI: 10.15468/dl.67x6rq
title: Occurrence Download

description: A dataset containing 2748286 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acanthis flammea)",
 "HasCoordinate is true",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 2748286 records from 604 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039843-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nqwumd
title: Occurrence Download

description: A dataset containing 165 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Ficus menabeensis",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 165 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039887-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.