cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14800358
title: Cheminformatics Analysis of RNA-Binding Ligands (Master Thesis Dataset)

description: Title: ThesisDataset_RNA-binders

Description:This dataset is part of the master thesis “Cheminformatics Analysis of RNA-Binding Ligands” by Ing. Jozef Fülöp (Faculty of Chemical Technology, Prague, 2024). It comprises raw and processed data for RNA-binding molecule classification.

The dataset is divided into two parts:

Set1 Large – Contains 77,420 compounds from specialized chemical libraries including the Enamine Hit Locator Library, ChemDiv miRNA-targeted Library, Enamine RNA Library, Life Chemicals RNA Focused and Targeted Libraries, and the ROBIN Database.

Set2 Small – A curated collection of 3,922 compounds for binary classification including RNA binders and non-binders from the ROBIN repository, protein binders from the Probes & Drugs database, and non-binders from ZINC15’s Dark Matter.

Data Processing:• Raw data were provided in SDF/CSV formats; only the SMILES were extracted for further analysis.• Chemical structures were converted to canonical SMILES using RDKit.• Standardization was performed with the ChEMBL Structure Pipeline.• Duplicate entries were removed based on canonical SMILES.

Columns in the processed CSV files:

• Set1 Large CSV: - “smiles”: Canonical SMILES representation. - “source”: Library origin. - “ecfp6”: 2048-bit Extended Connectivity Fingerprint (radius=3). - “bit_info_map”: Dictionary mapping fingerprint bit positions to molecular fragments. - “rna”: Binary indicator for RNA-binding (0 or 1).

• Set2 Small CSVs: - “source”: Originating library or subset. - “smiles”: Canonical SMILES representation. - “ecfp6”: 2048-bit fingerprint. - “bit_info_map”: Dictionary mapping fingerprint bit positions to molecular fragments. - “label”: Binary binding label (1 for binder, 0 for non-binder).

License:This dataset is released under the CC BY 4.0 license. Users are kindly requested to cite the associated thesis when using the data.

For further information, please refer to the thesis or contact fulopj@vscht.cz
DOI: 10.5281/zenodo.14800359
title: Cheminformatics Analysis of RNA-Binding Ligands (Master Thesis Dataset)

description: Title: ThesisDataset_RNA-binders

Description:This dataset is part of the master thesis “Cheminformatics Analysis of RNA-Binding Ligands” by Ing. Jozef Fülöp (Faculty of Chemical Technology, Prague, 2024). It comprises raw and processed data for RNA-binding molecule classification.

The dataset is divided into two parts:

Set1 Large – Contains 77,420 compounds from specialized chemical libraries including the Enamine Hit Locator Library, ChemDiv miRNA-targeted Library, Enamine RNA Library, Life Chemicals RNA Focused and Targeted Libraries, and the ROBIN Database.

Set2 Small – A curated collection of 3,922 compounds for binary classification including RNA binders and non-binders from the ROBIN repository, protein binders from the Probes & Drugs database, and non-binders from ZINC15’s Dark Matter.

Data Processing:• Raw data were provided in SDF/CSV formats; only the SMILES were extracted for further analysis.• Chemical structures were converted to canonical SMILES using RDKit.• Standardization was performed with the ChEMBL Structure Pipeline.• Duplicate entries were removed based on canonical SMILES.

Columns in the processed CSV files:

• Set1 Large CSV: - “smiles”: Canonical SMILES representation. - “source”: Library origin. - “ecfp6”: 2048-bit Extended Connectivity Fingerprint (radius=3). - “bit_info_map”: Dictionary mapping fingerprint bit positions to molecular fragments. - “rna”: Binary indicator for RNA-binding (0 or 1).

• Set2 Small CSVs: - “source”: Originating library or subset. - “smiles”: Canonical SMILES representation. - “ecfp6”: 2048-bit fingerprint. - “bit_info_map”: Dictionary mapping fingerprint bit positions to molecular fragments. - “label”: Binary binding label (1 for binder, 0 for non-binder).

License:This dataset is released under the CC BY 4.0 license. Users are kindly requested to cite the associated thesis when using the data.

For further information, please refer to the thesis or contact fulopj@vscht.cz
DOI: 10.5281/zenodo.14884310
title: Cheminformatics Analysis of RNA-Binding Ligands (Master Thesis Dataset)

description: Title: ThesisDataset_RNA-binders

Description:This dataset is part of the master thesis “Cheminformatics Analysis of RNA-Binding Ligands” by Ing. Jozef Fülöp (Faculty of Chemical Technology, Prague, 2024). It comprises raw and processed data for RNA-binding molecule classification.

The dataset is divided into two parts:

Set1 Large – Contains 77,420 compounds from specialized chemical libraries including the Enamine Hit Locator Library, ChemDiv miRNA-targeted Library, Enamine RNA Library, Life Chemicals RNA Focused and Targeted Libraries, and the ROBIN Database.

Set2 Small – A curated collection of 3,922 compounds for binary classification including RNA binders and non-binders from the ROBIN repository, protein binders from the Probes & Drugs database, and non-binders from ZINC15’s Dark Matter.

Data Processing:• Raw data were provided in SDF/CSV formats; only the SMILES were extracted for further analysis.• Chemical structures were converted to canonical SMILES using RDKit.• Standardization was performed with the ChEMBL Structure Pipeline.• Duplicate entries were removed based on canonical SMILES.

Columns in the processed CSV files:

• Set1 Large CSV: - “smiles”: Canonical SMILES representation. - “source”: Library origin. - “ecfp6”: 2048-bit Extended Connectivity Fingerprint (radius=3). - “bit_info_map”: Dictionary mapping fingerprint bit positions to molecular fragments. - “rna”: Binary indicator for RNA-binding (0 or 1).

• Set2 Small CSVs: - “source”: Originating library or subset. - “smiles”: Canonical SMILES representation. - “ecfp6”: 2048-bit fingerprint. - “bit_info_map”: Dictionary mapping fingerprint bit positions to molecular fragments. - “label”: Binary binding label (1 for binder, 0 for non-binder).

License:This dataset is released under the CC BY 4.0 license. Users are kindly requested to cite the associated thesis when using the data.

For further information, please refer to the thesis or contact fulopj@vscht.cz
DOI: 10.15468/dl.s6kqwc
title: Occurrence Download

description: A dataset containing 6236 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pterobranchia",
 "Year 1901-2025"
 ]
}

 The dataset includes 6236 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003396-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.atyhac
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Colacium vesiculosum Ehrenberg, 1838",
 "Year 2015-2025"
 ]
}

 The dataset includes 24 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003397-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6f9bqn
title: Occurrence Download

description: A dataset containing 3832 species occurrences available in GBIF matching the query:
{
 "and" : [
 "IucnRedListCategory is one of (NE, LC, DD, NT, VU, EN, CR, EW, EX)",
 "PublishingOrg is 667644b9-7b93-4892-80c7-c4271735c515",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 3832 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003398-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bnz2kcfkm2.1
title: Vaginal packing after laparoscopic sacrocolpopexy: postoperative pain and satisfaction – RCT

description: Objective: Vaginal packing following vaginal reconstructive surgery and vaginal hysterectomy was well explored. However, data regarding laparoscopic reconstructive surgery is lacking. No data concerning effect on early postoperative pain and patient satisfaction after laparoscopic sacrocolpopexy (LSC) exist. The primary aim of this randomized controlled trial was to compare subjective impressions of pain in women after LSC treated with and without packing. The secondary aims included comparison of patient satisfaction with the surgery on day 1 and with the overall postoperative course on day 4.
Design: Double-blind randomized controlled trial
Setting: Tertiary urogynecology care center, Faculty of Medicine in Pilsen, Charles University
Participants: Women after LSC for stage &gt; 2 pelvic organ prolapse were included. Women undergoing any other surgery, concomitant vaginal surgery including suburethral sling or where vagina was opened during the surgery (e.g. during concurrent hysterectomy) were excluded. Women with lost or incompletely filled-in McGill pain questionnaire were additionally excluded from this postoperative pain and satisfaction analysis.
Interventions: Vaginal packing after the surgery with a sterile gauze. The primary outcome was measured by the McGill Pain Questionnaire filled-in day after surgery before pack extraction. Satisfaction was measured using VAS on postoperative day 1 and 4. 
Results: In total, 274 women were included in analysis, vaginal pack was inserted in 132 (48%) women. The groups did not differ in basic preoperative nor surgical characteristics. Very low and comparable values of all scores of the McGill Pain Questionnaire were observed (VAS pain 3.2 ±1.8 vs. 3.4±1.9,p=.330). Similarly, patient satisfaction on day one (7.3±1.8 vs. 7.4±1.7,p=.633) nor overall satisfaction on day 5 (8.7±1.3 vs. 8.8±1.1,p=.719) did not differ.
Conclusion: Laparoscopic sacrocolpopexy is a safe surgery associated with low levels of pain and high patient satisfaction regardless of vaginal pack insertion. Vaginal packing does not seem to do any harm to our patients.
DOI: 10.15468/dl.6hur89
title: Occurrence Download

description: A dataset containing 3021 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhincodon typus Smith, 1828",
 "Year 1900-2025"
 ]
}

 The dataset includes 3021 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003399-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2rg3hu
title: Occurrence Download

description: A dataset containing 738 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Grallaricula ochraceifrons G.R.Graves, O'Neill & T.A.Parker, 1983"
 ]
}

 The dataset includes 738 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003400-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zz7r5y4dc6.1
title: AI-MedLeafX: A Large-Scale Computer Vision Dataset for Medicinal Plant Diagnosis

description: The AI-MedLeafX dataset is a curated collection of 10,858 original images and 65,178 augmented images of medicinal plant leaves, categorized into healthy and diseased conditions. The dataset primarily focuses on four medicinal plant species—Cinnamomum camphora, Terminalia Chebula, Moringa Oleifera, and Azadirachta Indica—and covers common leaf diseases such as Bacterial Spot, Shot Hole, Powdery Mildew, and Yellow Leaf Disease.

Category:
1. Cinnamomum Camphora Healthy Leaf: 800
2. Cinnamomum Camphora Bacterial Spot: 801
3. Cinnamomum Camphora Shot Hole: 795
4. Terminalia Chebula Bacterial Spot: 803
5. Terminalia Chebula Healthy Leaf: 816
6. Terminalia Chebula Shot Hole: 802
7. Moringa Oleifera Healthy Leaf: 860
8. Moringa Oleifera Bacterial Spot: 804 
9. Moringa Oleifera Yellow Leaf: 814 
10. Azadirachta Indica Healthy Leaf: 1021
11. Azadirachta Indica Shot Hole: 834
12. Azadirachta indica Powdery Mildew: 854
13. Azadirachta indica Yellow Leaf: 854

Total: 10,858

This dataset is designed to facilitate machine learning (ML), deep learning (DL), and computer vision (CV) applications in plant disease recognition, aiding automated medicinal plant health monitoring and disease classification systems.

The images were collected from the following locations in Dhaka, Bangladesh:

- National Botanical Garden (Latitude: 23.8110° N, Longitude: 90.3563° E)
- Daffodil Smart City (Latitude: 23.8720° N, Longitude: 90.2686° E)

The AI-MedLeafX dataset contributes to the advancement of computer vision in plant pathology and can be extended for research in precision agriculture, herbal medicine authentication, and disease diagnosis.
DOI: 10.17632/gmh37ntv8z.1
title: Data of the article : Fine tuning protocols for accurate studying of nanoparticle adhesion and uptake following foliar deposition

description: These data are the results used in the article "Fine tuning protocols for accurate studying of nanoparticle adhesion and uptake following foliar deposition"

Abstract : 

Understanding interactions between plant leaves and (nano)particles (NPs) is crucial for addressing phytochemical and environmental challenges. Recent studies show how NPs adhesion, uptake, and transformation on leaf surfaces are influenced by NPs properties, plant surface, and environmental factors. However, variability results from inconsistent protocols for leaf exposure and rinsing to determine the uptake fraction of NPs. This work explores how exposure methods affect dose reproducibility, NPs distribution on the leaf surface, and how rinsing protocols influence NPs element remobilization.

This work assessed how the methods commonly used in the literature to expose leaves to NP -drop-deposition, spraying, dipping, and brushing- compare in term of dose reproducibility and control over the exposure. The efficacy of rinsing strategies was evaluated with Au and CuO NPs which have contrasting solubility. They have been deposited on tomato leaves (var. micro-tom), and sequentially rinsed with diluted ethanol (EtOH), nitric acid (HNO3), and ultra-pure water, in various sequential order.

The results indicate that drop-deposition offers better dose reproducibility control, while spraying, dipping, and brushing cause lower reproducibility. The spatial distribution and surface agglomeration of NPs vary with the exposure methods, influencing their fate on/in leaves. Regarding rinsing, soluble NPs (CuO) are more efficiently rinsed by HNO3-2%, while EtOH-3% does not remove Cu, indicating strong attachment to the leaf. In contrast, Au NPs are better rinsed with ethanol-based solvents, possibly due to solvent lower polarity, surface tension or wax damage effect. Changing the rinsing solvent sequence helped identify different NP-leaf attachment fractions and their interaction nature. This study highlights the need for fine-tuned exposure and rinsing protocols tailored to the specific research question.
DOI: 10.17632/cn3fkjfs6p.1
title: Pension Sustainability and Government Effectiveness in the Presence of Population Aging

description: Here is the R code based on data from the OECD database to reproduce the results of the paper.

The data can be downloaded from the website of OECD: https://www.oecd.org/en/data/indicators.html.
DOI: 10.15468/dl.md7r67
title: Occurrence Download

description: A dataset containing 169465 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Thunnus albacares (Bonnaterre, 1788)",
 "Year 1900-2025"
 ]
}

 The dataset includes 169465 records from 109 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003388-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0202448.10fu
title: SH0202448.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202449.10fu
title: SH0202449.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202450.10fu
title: SH0202450.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202451.10fu
title: SH0202451.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202452.10fu
title: SH0202452.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202453.10fu
title: SH0202453.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0202454.10fu
title: SH0202454.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.