cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2024-03.3048015/2526119
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048016/2526120
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048017/2526121
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048018/2526122
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048020/2526123
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048021/2526124
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048022/2526125
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048023/2526126
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048024/2526127
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048025/2526128
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048026/2526129
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048027/2526130
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048028/2526131
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048029/2526132
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048030/2526133
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048032/2526134
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048033/2526135
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048034/2526136
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048035/2526137
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048036/2526138
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3048037/2526139
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2025-03.3392946/2526140
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3392957/2526141
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3392975/2526142
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3392981/2526143
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3392986/2526144
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3393002/2526145
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3393008/2526146
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3393636/2526147
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3393738/2526148
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3393827/2526149
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3393916/2526150
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394056/2526151
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394068/2526152
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394080/2526153
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394098/2526154
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394165/2526155
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394186/2526156
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394210/2526157
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394226/2526158
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394228/2526159
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394229/2526160
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394230/2526161
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394231/2526162
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394232/2526163
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394233/2526164
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394234/2526165
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394236/2526166
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394237/2526167
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394238/2526168
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394239/2526169
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394240/2526170
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394273/2526171
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394278/2526172
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394289/2526173
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394298/2526174
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394312/2526175
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394318/2526176
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394336/2526177
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394342/2526178
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394357/2526179
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3394379/2526180
title: Data for EMSL Project 61049 from March 2025
DOI: 10.25582/data.2025-03.3392951/2526181
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3392994/2526182
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3393414/2526183
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3393591/2526184
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3393985/2526185
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3394045/2526186
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3394088/2526187
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3394111/2526188
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3394120/2526189
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3394241/2526190
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3394350/2526191
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3394367/2526192
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3394372/2526193
title: Data for EMSL Project 60880 from March 2025
DOI: 10.25582/data.2025-03.3393086/2526194
title: Data for EMSL Project 61086 from March 2025
DOI: 10.25582/data.2025-03.3393145/2526195
title: Data for EMSL Project 61086 from March 2025
DOI: 10.25582/data.2025-03.3393609/2526196
title: Data for EMSL Project 61086 from March 2025
DOI: 10.25582/data.2025-03.3393761/2526197
title: Data for EMSL Project 61086 from March 2025
DOI: 10.25582/data.2025-03.3393949/2526198
title: Data for EMSL Project 61086 from March 2025
DOI: 10.15468/dl.kfya7t
title: Occurrence Download

description: A dataset containing 626640 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Artemisia vulgaris L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 626640 records from 2569 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005689-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u5s34n
title: Occurrence Download

description: A dataset containing 230340 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dinophyceae",
 "Year 2011-2012"
 ]
}

 The dataset includes 230340 records from 170 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002647-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6yy9rf
title: Occurrence Download

description: A dataset containing 1961296 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "Geometry POLYGON ((150.8787 -29.9525, 153.1046 -29.9525, 153.1046 -28.02006, 150.8787 -28.02006, 150.8787 -29.9525))",
 "Year is less than 2026"
 ]
}

 The dataset includes 1961296 records from 311 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002642-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qeehr9
title: Occurrence Download

description: A dataset containing 1012 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((93.73 35.72,104.17 35.72,104.17 40.33,93.73 40.33,93.73 35.72))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Saussurea DC."
 ]
}

 The dataset includes 1012 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002666-250221090833381/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.28468354
title: Insights into genetic architecture and disease associations of genes associated with different human blood group systems using codon usage bias

description: The differential use of synonymous codons of an amino acid is an imperative evolutionary phenomenon, termed codon usage bias, that functions across various levels of organisms. It is accustomed to providing an understanding of a gene’s differential architecture driven by functional regulation of gene expression. Numerous synonymous mutations are linked to various diseases, demonstrating that silent mutations can be deleterious. We employed bioinformatics methods to examine codon usage trends in 263 coding sequences of 44 blood group systems. The blood group systems were categorized into two groups based on association with a sort of neurodegenerative disorder. We performed a CUB study to investigate how multiple components, such as selection, mutation and biased nucleotide composition are accountable for the evolution of the transcripts of the blood group antigens. The compositional analysis implicated blood group genes were GC-rich. RSCU analysis showed G/C-ending codon choice among synonymous codons. Also, a distinct codon choice was found in both blood groups for serine and proline. Moreover, the leucine-coding CTG codon was found the most overrepresented in all the genes, indicating selectional pressure substantially impacts overall codon usage. This was also supported by biplot analysis. Additionally, CpC and GpG overrepresentation is in concordance with the results concerning neurodegenerative disorders where CpC has been attributed to non-CpG methylation and linked to several neurodegenerative ailments. Both the Z-test analysis and rare codon choice showed a substantial difference in codon usage by the genes of both groups.
DOI: 10.6084/m9.figshare.28468354.v1
title: Insights into genetic architecture and disease associations of genes associated with different human blood group systems using codon usage bias

description: The differential use of synonymous codons of an amino acid is an imperative evolutionary phenomenon, termed codon usage bias, that functions across various levels of organisms. It is accustomed to providing an understanding of a gene’s differential architecture driven by functional regulation of gene expression. Numerous synonymous mutations are linked to various diseases, demonstrating that silent mutations can be deleterious. We employed bioinformatics methods to examine codon usage trends in 263 coding sequences of 44 blood group systems. The blood group systems were categorized into two groups based on association with a sort of neurodegenerative disorder. We performed a CUB study to investigate how multiple components, such as selection, mutation and biased nucleotide composition are accountable for the evolution of the transcripts of the blood group antigens. The compositional analysis implicated blood group genes were GC-rich. RSCU analysis showed G/C-ending codon choice among synonymous codons. Also, a distinct codon choice was found in both blood groups for serine and proline. Moreover, the leucine-coding CTG codon was found the most overrepresented in all the genes, indicating selectional pressure substantially impacts overall codon usage. This was also supported by biplot analysis. Additionally, CpC and GpG overrepresentation is in concordance with the results concerning neurodegenerative disorders where CpC has been attributed to non-CpG methylation and linked to several neurodegenerative ailments. Both the Z-test analysis and rare codon choice showed a substantial difference in codon usage by the genes of both groups.
DOI: 10.15468/dl.zwfksg
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myristica teysmannii Miq."
 ]
}

 The dataset includes 56 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002671-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nzzn79
title: Occurrence Download

description: A dataset containing 762 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lybia H.Milne Edwards, 1834"
 ]
}

 The dataset includes 762 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002673-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25403/upresearchdata.28266458
title: The presence and extent of auditory acclimatization after hearing aid intervention and the factors influencing this process

description: My research explores auditory acclimatization in new adult hearing aid users. It systematically reviews the presence, extent, and clinical relevance of this phenomenon by examining behavioral, self-reported, and electrophysiological outcomes. While evidence supports some acclimatization effects, these are generally minimal and inconsistent across studies. The review emphasizes the importance of consistent hearing aid use and suggests clinicians focus more on overcoming barriers to hearing aid adoption and providing long-term support. The data added was the exported articles found through the 3 databases post-duplicate removal.
DOI: 10.25403/upresearchdata.28266458.v1
title: The presence and extent of auditory acclimatization after hearing aid intervention and the factors influencing this process

description: My research explores auditory acclimatization in new adult hearing aid users. It systematically reviews the presence, extent, and clinical relevance of this phenomenon by examining behavioral, self-reported, and electrophysiological outcomes. While evidence supports some acclimatization effects, these are generally minimal and inconsistent across studies. The review emphasizes the importance of consistent hearing aid use and suggests clinicians focus more on overcoming barriers to hearing aid adoption and providing long-term support. The data added was the exported articles found through the 3 databases post-duplicate removal.
DOI: 10.6084/m9.figshare.28468313
title: Supplementary material for the article "Identification of relevant research topics based on pooled bibliometric data of the International Journal of Hydrogen Energy publications for 2022–2024 from The Lens and ScienceDirect platforms"

description: The article is scheduled to be published in 2025 in the online scientific journal Actual Problems of Oil and Gas (https://oilgasjournal.ru).Abstract. Background. Hydrogen is given the role of a clean energy carrier in the energy transition. Objective. To identify relevant research topics based on pooled bibliometric data of the International Journal of Hydrogen Energy publications from 2022–2024, collected from The Lens and ScienceDirect platforms. Materials and methods. 10,928 bibliometric records were exported from The Lens database and 10,857 records were exported from ScienceDirect. Keywords clustering and visual data analysis were performed using the following programs and algorithms: VOSviewer, Scimago Graphica, Inkscape, FP-growth utility. Results. The study showed the feasibility of merging bibliometric records from the open platforms ScienceDirect and The Lens, which complement each other. The “Fields of Study” data of The Lens was interpreted as system keywords similar to Scopus Index Keywords. The possibility of using “Fields of Study” data in a method similar to bibliographic coupling is shown. The practicality of using an alluvial diagram to show the co-occurrence of the four terms was demonstrated. The study also emphasizes the advisability of joint use of VOSviewer and Scimago Graphica programs for complex visualization of bibliometric analysis results. The study identified two dominant publication topics in the International Journal of Hydrogen Energy for 2022–2024, which can be described in terms of: “Catalysis, Hydrogen, Physical_chemistry, Chemical_engineering, Nanotechnology, Electrochemistry, Organic_chemistry, Materials_science” and “Electrical_engineering, Hydrogen_production, Renewable_energy, Environmental_science, Hydrogen_economy, Hydrogen, Engineering”. Conclusions. Joint use of metadata of different open abstract databases allows to compensate partial representation of their data.
DOI: 10.15468/dl.xegdxc
title: Occurrence Download

description: A dataset containing 130866 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((2.64734 42.19575,2.97234 42.19233,2.95904 42.40883,2.64641 42.43813,2.64734 42.19575))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 130866 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002655-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14915911
title: OC Emission Data-2020
DOI: 10.5281/zenodo.14915912
title: OC Emission Data-2020
DOI: 10.5281/zenodo.14915988
title: A 45-Ma environmental magnetic record of paleoclimate and tectonics from Eocene-present South China Sea sediments, IODP Site 1505
DOI: 10.15468/dl.avgyc4
title: Occurrence Download

description: A dataset containing 9279 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "GadmGid is CHN.HKG",
 "TaxonKey is Aves",
 "Year 2021-2021"
 ]
}

 The dataset includes 9279 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002675-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14915993
title: Math Formula Detection Dataset

description: Description:







Mathematical Formula Detection Dataset is a critical area in AI research, aiding in converting complex mathematical expressions from visual representations to machine-readable formats. This dataset aims to bridge the gap between handwritten or printed mathematical formulas and their digital counterparts, using deep learning models, particularly GANs (Generative Adversarial Networks).

 







Download Dataset

 









Dataset Overview

The Math Formula Detection dataset includes images of various mathematical equations and symbols, designed for both printed and handwritten formula detection. The dataset is labeled with corresponding LaTeX or MathML formats for every formula, making it ideal for tasks such as symbol recognition, formula segmentation, and image-to-formula translation.

Applications of the Dataset



Document Digitization: Automating the process of digitizing textbooks, research papers, and academic documents containing mathematical content.

Educational Tools: Assisting online education platforms by converting handwritten notes or scanned documents into searchable and editable digital formats.

Scientific Research: Helping researchers in fields like physics, engineering, and economics to quickly search and manipulate mathematical content in large datasets.

Text-to-Math Conversion Models: The dataset is also suited for models that convert visual representations of mathematical symbols into structured formats like LaTeX, MathML, or plain text.


Key Features



High-Resolution Images: The dataset provides clear images of formulas at various resolutions, enabling effective training of neural networks for detection and recognition.

Symbol Localization: It includes annotated bounding boxes around each formula or symbol for precise localization, a critical step in formula detection.

Multi-Modal Support: The dataset supports various output formats like LaTeX and MathML, making it flexible for different applications.


Future Prospects

This dataset opens doors to multiple advancements in AI-driven formula detection, including real-time recognition in educational tools and more efficient academic paper digitization. The research possibilities extend beyond formula detection into automatic verification of solutions and advanced educational AI systems.

This dataset is sourced from Kaggle.