cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4ugmtg
title: Occurrence Download

description: A dataset containing 77551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Specimen)",
 "Continent is North America",
 "Country is United States of America",
 "DatasetKey is one of (iNaturalist Research-grade Observations, GPS locations of Bobcats on and around Laguna Atascosa National Wildlife Refuge from 2013-2021: USFWS-TXDOT Collaborative Dataset, MVZ Mammal Collection (Arctos), KUBI Mammalogy Collection, MSU Mammalogy, Ornithology and Vertebrate Paleontology Collections, University of Michigan Museum of Zoology, Division of Mammals, PSM Vertebrates Collection, UMNH Mammals Collection (Arctos), University of Wyoming Museum of Vertebrates (UWYMV) Mammal Collection (Arctos), UWBM Mammalogy Collection (Arctos))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum assumed WGS84, Coordinate reprojected, Continent derived from coordinates, The scientific name ID was not found in the checklist, The taxon ID was not found in the checklist, Institution collection mismatch, Institution match fuzzy, Collection match none, Geodetic datum invalid)",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "Month is one of (January, February, March, April, June, May, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (f8dbeca7-3131-41ab-872f-bfad71041f3f, 28eb1a3f-1c15-4a95-931a-4af90ecb574d, 8edbbde0-055e-11d8-b850-b8a03c50a862, b554c320-0560-11d8-b851-b8a03c50a862, 311e2740-f15a-11d8-b22f-b8a03c50a862, 6ea87510-0561-11d8-b851-b8a03c50a862, f56c5f80-055c-11d8-b84e-b8a03c50a862, 2d6267a0-0561-11d8-b851-b8a03c50a862, 50576fe5-7883-495d-9089-b5dcf5e56098, 06fcbbf0-0562-11d8-b851-b8a03c50a862)",
 "TaxonKey is Lynx rufus",
 "Year 2000-2025"
 ]
}

 The dataset includes 77551 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007345-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30704543
title: Socioeconomic factors dominate the spatiotemporal evolution of urban ecological resilience in environmentally vulnerable areas: A case study of the Loess Plateau

description: This study develops an urban ecological resilience assessment framework based on three dimensions: resistance, adaptability, and recovery. Using data from 1990 to 2020, we analyze temporal trends, spatial variations and influencing factors shaping urban ecological resilience across the Loess Plateau.
DOI: 10.6084/m9.figshare.30704543.v1
title: Socioeconomic factors dominate the spatiotemporal evolution of urban ecological resilience in environmentally vulnerable areas: A case study of the Loess Plateau

description: This study develops an urban ecological resilience assessment framework based on three dimensions: resistance, adaptability, and recovery. Using data from 1990 to 2020, we analyze temporal trends, spatial variations and influencing factors shaping urban ecological resilience across the Loess Plateau.
DOI: 10.15468/dl.7muncw
title: Occurrence Download

description: A dataset containing 279 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Ochlerotatus punctor"
 ]
}

 The dataset includes 279 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007371-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ryk7vr
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Protobothrops kaulbacki)",
 "Country is one of (China, India, Indonesia, Pakistan, Bangladesh, Japan, Philippines, Viet Nam, Türkiye, Iran (Islamic Republic of), Thailand, Myanmar, Korea, Republic of, Korea (Democratic People’s Republic of), Iraq, Afghanistan, Saudi Arabia, Uzbekistan, Malaysia, Yemen, Nepal, Sri Lanka, Kazakhstan, Syrian Arab Republic, Cambodia, Jordan, Azerbaijan, United Arab Emirates, Tajikistan, Israel, Lao People’s Democratic Republic, Lebanon, Kyrgyzstan, Turkmenistan, Singapore, Oman, Palestine, State of, Kuwait, Georgia, Mongolia, Armenia, Qatar, Bahrain, Timor-Leste, Cyprus, Bhutan, Maldives, Brunei Darussalam)",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007372-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w7q4np
title: Occurrence Download

description: A dataset containing 908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Attalea allenii",
 "HasCoordinate is true"
 ]
}

 The dataset includes 908 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007374-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.znmn5m
title: Occurrence Download

description: A dataset containing 1755 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum assumed WGS84, Footprint WKT mismatch, Country derived from coordinates, Continent derived from coordinates, Recorded date invalid, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, References URI invalid, Occurrence status unparsable, Occurrence status inferred from individual count, Individual count invalid)",
 "OccurrenceStatus is Present",
 "TaxonKey is Lepidium sativum",
 "Year 1950-2025"
 ]
}

 The dataset includes 1755 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007375-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.45cm9e
title: Occurrence Download

description: A dataset containing 33486 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Living Specimen)",
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "TaxonKey is Testudines"
 ]
}

 The dataset includes 33486 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007376-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h2gvce
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Protobothrops mangshanensis)",
 "Country is one of (China, India, Indonesia, Pakistan, Bangladesh, Japan, Philippines, Viet Nam, Türkiye, Iran (Islamic Republic of), Thailand, Myanmar, Korea, Republic of, Korea (Democratic People’s Republic of), Iraq, Afghanistan, Saudi Arabia, Uzbekistan, Malaysia, Yemen, Nepal, Sri Lanka, Kazakhstan, Syrian Arab Republic, Cambodia, Jordan, Azerbaijan, United Arab Emirates, Tajikistan, Israel, Lao People’s Democratic Republic, Lebanon, Kyrgyzstan, Turkmenistan, Singapore, Oman, Palestine, State of, Kuwait, Georgia, Mongolia, Armenia, Qatar, Bahrain, Timor-Leste, Cyprus, Bhutan, Maldives, Brunei Darussalam)",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007377-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16919168
title: Molecular maps of diseases from omics data and network embeddings

description: Abstract: 

Identifying disease-relevant proteins and pathways remains a fundamental challenge in understanding disease mechanisms and supporting therapeutic development. While omics analyses can provide valuable insights, they typically consider each gene/protein separately rather than at the level of biological systems. This can be addressed by combining the omics data with protein networks. We integrate disease-specific omics data with a universal functional association network from STRING, which we represent using node2vec embedding. This way, we constructed disease maps for seven diseases spanning inflammatory, oncological, neurological, and vascular diseases based on genetics, transcriptomics, somatic mutation, and proteomics data. Compared to omics analysis alone, the use of a simple linear model on top of network embedding enabled us to identify 2–4 times as many known disease-relevant proteins at the same specificity. Clustering of the resulting disease maps revealed both functional modules shared by many diseases, such as inflammatory pathways and cancer hallmarks, and disease-specific modules, such as keratinization in atopic dermatitis and extracellular matrix remodeling in aortic aneurysm. Together, these results highlight the value of protein network embedding when analyzing omics data to understand diseases.

Data description: 



data.zip: training and test set

results.zip: cross-validation and test scores

enrichment_and_clustering.cys: enrichment analysis results with Cytoscape stringApp
DOI: 10.5281/zenodo.16919169
title: Molecular maps of diseases from omics data and network embeddings

description: Abstract: 

Identifying disease-relevant proteins and pathways remains a fundamental challenge in understanding disease mechanisms and supporting therapeutic development. While omics analyses can provide valuable insights, they typically consider each gene/protein separately rather than at the level of biological systems. This can be addressed by combining the omics data with protein networks. We integrate disease-specific omics data with a universal functional association network from STRING, which we represent using node2vec embedding. This way, we constructed disease maps for seven diseases spanning inflammatory, oncological, neurological, and vascular diseases based on genetics, transcriptomics, somatic mutation, and proteomics data. Compared to omics analysis alone, the use of a simple linear model on top of network embedding enabled us to identify 2–4 times as many known disease-relevant proteins at the same specificity. Clustering of the resulting disease maps revealed both functional modules shared by many diseases, such as inflammatory pathways and cancer hallmarks, and disease-specific modules, such as keratinization in atopic dermatitis and extracellular matrix remodeling in aortic aneurysm. Together, these results highlight the value of protein network embedding when analyzing omics data to understand diseases.

Data description: 



data.zip: training and test set

results.zip: cross-validation and test scores

enrichment_and_clustering.cys: enrichment analysis results with Cytoscape stringApp
DOI: 10.25399/unisadata.30704765
title: NRF Thuthuka Project: Microplastics Removal from Wastewater using Machine Learning Optimization (TTK240430216739)

description: Increased microplastics contamination in wastewater is a challenge to the environment and public health. The removal of these pollutants requires innovative strategies for effective mitigation. This study aims to employ artificial neural network to optimize two promising microplastics removal methods from the urban water cycle: ultrasonication and membrane filtration. The project's overall objective is to advance sustainable technologies that supplement the efficacy of microplastics extraction from wastewater, addressing the environmental consequences. Leveraging ML, this research aims to optimize ultrasonication parameters and membrane filtration techniques while integrating interdisciplinary datasets to strengthen the robustness and applicability of ML models. A better understanding of the interplay between operational parameters and environmental impacts is anticipated by merging ML with the optimization processes. Such interdisciplinary (artificial intelligence-enabled prediction and optimization, chemical engineering, chemistry, and environmental science) measures can enhance microplastics removal efficiency and mitigate environmental harm, consequently enabling sustainable technologies to address microplastics pollution in wastewater. The research represents an important stride towards a data-driven paradigm in microplastics management, aligning with the following key United Nations Sustainable Development Goals: 6(clean water and sanitation), 11 (sustainable cities and communities), 12 (responsible consumption and production), 14 (life below water), and 15 (life on land). Furthermore, the project strives to support South African black female Chemical Engineering Hons, Masters, and Doctoral students, thus creating a pipeline of academic talent through rigorous academic training and development. These students are expected to find employment within Assistant Lecturer, Research Assistant, nGAP, andpost-doctoral research fellow positions.
DOI: 10.25399/unisadata.30704765.v1
title: NRF Thuthuka Project: Microplastics Removal from Wastewater using Machine Learning Optimization (TTK240430216739)

description: Increased microplastics contamination in wastewater is a challenge to the environment and public health. The removal of these pollutants requires innovative strategies for effective mitigation. This study aims to employ artificial neural network to optimize two promising microplastics removal methods from the urban water cycle: ultrasonication and membrane filtration. The project's overall objective is to advance sustainable technologies that supplement the efficacy of microplastics extraction from wastewater, addressing the environmental consequences. Leveraging ML, this research aims to optimize ultrasonication parameters and membrane filtration techniques while integrating interdisciplinary datasets to strengthen the robustness and applicability of ML models. A better understanding of the interplay between operational parameters and environmental impacts is anticipated by merging ML with the optimization processes. Such interdisciplinary (artificial intelligence-enabled prediction and optimization, chemical engineering, chemistry, and environmental science) measures can enhance microplastics removal efficiency and mitigate environmental harm, consequently enabling sustainable technologies to address microplastics pollution in wastewater. The research represents an important stride towards a data-driven paradigm in microplastics management, aligning with the following key United Nations Sustainable Development Goals: 6(clean water and sanitation), 11 (sustainable cities and communities), 12 (responsible consumption and production), 14 (life below water), and 15 (life on land). Furthermore, the project strives to support South African black female Chemical Engineering Hons, Masters, and Doctoral students, thus creating a pipeline of academic talent through rigorous academic training and development. These students are expected to find employment within Assistant Lecturer, Research Assistant, nGAP, andpost-doctoral research fellow positions.
DOI: 10.15468/dl.83qq2e
title: Occurrence Download

description: A dataset containing 2899 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Cunninghamia lanceolata"
 ]
}

 The dataset includes 2899 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007380-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bks5bn
title: Occurrence Download

description: A dataset containing 167412 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is France",
 "Geometry POLYGON((0.62111 47.34476,0.75797 47.34476,0.75797 47.42881,0.62111 47.42881,0.62111 47.34476))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 167412 records from 224 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007333-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pu647r
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Protobothrops maolanensis)",
 "Country is one of (China, India, Indonesia, Pakistan, Bangladesh, Japan, Philippines, Viet Nam, Türkiye, Iran (Islamic Republic of), Thailand, Myanmar, Korea, Republic of, Korea (Democratic People’s Republic of), Iraq, Afghanistan, Saudi Arabia, Uzbekistan, Malaysia, Yemen, Nepal, Sri Lanka, Kazakhstan, Syrian Arab Republic, Cambodia, Jordan, Azerbaijan, United Arab Emirates, Tajikistan, Israel, Lao People’s Democratic Republic, Lebanon, Kyrgyzstan, Turkmenistan, Singapore, Oman, Palestine, State of, Kuwait, Georgia, Mongolia, Armenia, Qatar, Bahrain, Timor-Leste, Cyprus, Bhutan, Maldives, Brunei Darussalam)",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 36 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007382-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/usctheses-ouc11399mc5f
title: PREVALENCE OF DEMENTIA DIAGNOSIS IN A SAFETY NET HEALTH SYSTEM
DOI: 10.25549/usctheses-ouc11399mc5d
title: Enhancing Membrane Distillation Performance through Improved Modeling, Replenishment Cooling, and Heat Pump Integration
DOI: 10.17639/nott.7416
title: DFT data for "Metal-free Direct Electrochemical Deoxygenation of Benzylic Alcohols"

description: This dataset contains Gaussian DFT output files of the ground-state and transition state DFT optimized structures for the project "Metal-free Direct Electrochemical Deoxygenation of Benzylic Alcohols". Each of the lower level folders contain the output of a frequency calculation at PBE0-D3/def2-TZVP level with SMD(diethylether) solvent model (*freq001.out files). All of the files can be opened in any text editor. Gaussian output structures can be viewed and the frequency modes visualised in GausView, Avogadro, jmol and in most other molecular viewers/editors. Dataset also contains analytical data for compounds reported in the publication "Metal-free Direct Electrochemical Deoxygenation of Benzylic Alcohols". The data is organised into subfolders, which are numbered in accordance with compound numbering in the manuscript and accompanying Supporting Information document. For each compound, raw data for 1H/13C/19F/31P NMR, MS, IR, GC-MS, and Cyclic Voltammetry are included where relevant.
DOI: 10.25549/usctheses-ouc11399mc5g
title: Context-Aware Semantic Forgery Detection in Biomedical & Natural Images
DOI: 10.17639/nott.7621
title: Data from "What are the Experiences of Adults with Co-existing ADHD and Trauma of Accessing Support and Diagnosis?"
title: Data from "Mind the Gap! Navigating ADHD and Trauma Support Services"

description: Attention Deficit Hyperactivity Disorder (ADHD) and trauma often cooccur and trauma symptoms are tricky to unpick from ADHD traits. This can lead to misunderstanding of a person’s presentation, misdiagnosis and missed opportunity for support. This is exacerbated by the separation of UK support pathways for ADHD and trauma. This study aimed to explore the experiences of adults with co-existing ADHD and trauma in accessing support and identify whether ADHD diagnosis influences subsequent access to trauma related support, and vice versa.
DOI: 10.5281/zenodo.17670135
title: Source datafile for "Nanoscale chemical imaging of pseudocapacitive charge storage in MXene"
DOI: 10.5281/zenodo.17670134
title: Source datafile for "Nanoscale chemical imaging of pseudocapacitive charge storage in MXene"
DOI: 10.15468/dl.trnv2t
title: Occurrence Download

description: A dataset containing 188 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Ovis ammon"
 ]
}

 The dataset includes 188 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007383-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17708226
title: Organic nitrogen in atmospheric aerosols over the Northwest Pacific Ocean
DOI: 10.5281/zenodo.17708225
title: Organic nitrogen in atmospheric aerosols over the Northwest Pacific Ocean
DOI: 10.3886/e194141v2-191932
title: Data and Code for: Estimating Hysteresis Effects

description: In this paper we identify demand shocks that can have a permanent effect on output through hysteresis effects. We call these shocks permanent demand shocks. They are found to be quantitatively important in the United States, in particular in samples starting in the 1980s. Recessions driven by permanent demand shocks lead to a permanent decline in employment and investment, while output per worker is largely unaffected. We find strong evidence that hysteresis transmits through a rise in long-term unemployment and a decline in labor force participation and disproportionately affects the least productive workers.<br>
DOI: 10.15468/dl.34cud3
title: Occurrence Download

description: A dataset containing 552 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Protobothrops mucrosquamatus)",
 "Country is one of (China, India, Indonesia, Pakistan, Bangladesh, Japan, Philippines, Viet Nam, Türkiye, Iran (Islamic Republic of), Thailand, Myanmar, Korea, Republic of, Korea (Democratic People’s Republic of), Iraq, Afghanistan, Saudi Arabia, Uzbekistan, Malaysia, Yemen, Nepal, Sri Lanka, Kazakhstan, Syrian Arab Republic, Cambodia, Jordan, Azerbaijan, United Arab Emirates, Tajikistan, Israel, Lao People’s Democratic Republic, Lebanon, Kyrgyzstan, Turkmenistan, Singapore, Oman, Palestine, State of, Kuwait, Georgia, Mongolia, Armenia, Qatar, Bahrain, Timor-Leste, Cyprus, Bhutan, Maldives, Brunei Darussalam)",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 552 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007384-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9mj77u
title: Occurrence Download

description: A dataset containing 908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Attalea allenii",
 "HasCoordinate is true"
 ]
}

 The dataset includes 908 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007385-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.