cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc1jly79
title: CCDC 1418381: Experimental Crystal Structure Determination

description: Related Article: Jeremy P. Krogman ,  Bruce M. Foxman ,  Christine M. Thomas|2015|Organometallics|34|3159|doi:10.1021/acs.organomet.5b00182
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc1jlpng
title: CCDC 1418146: Experimental Crystal Structure Determination

description: Related Article: Mohammad El-khateeb, Helmar Görls, Wolfgang Weigand, Laith R. Almazahreh|2016|Polyhedron|106|27|doi:10.1016/j.poly.2015.12.016
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1jlp4y
title: CCDC 1418130: Experimental Crystal Structure Determination

description: Related Article: Sangeeta Yadav, Sudesh T. Manjare, Harkesh B. Singh, Ray J. Butcher|2016|Dalton Trans.|45|12015|doi:10.1039/C6DT01425A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc1j3b54
title: CCDC 1403375: Experimental Crystal Structure Determination

description: Related Article: Ritwik Modak, Yeasin Sikdar, Goulven Cosquer, Sudipta Chatterjee, Masahiro Yamashita, Sanchita Goswami|2016|Inorg.Chem.|55|691|doi:10.1021/acs.inorgchem.5b02107
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc1jlpph
title: CCDC 1418147: Experimental Crystal Structure Determination

description: Related Article: Mohammad El-khateeb, Helmar Görls, Wolfgang Weigand, Laith R. Almazahreh|2016|Polyhedron|106|27|doi:10.1016/j.poly.2015.12.016
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/vx4jy7wyvd.1
title: Hydroponic Thai Basil Growth

description: The dataset consists of key environmental and physiological parameters influencing plant growth. This includes temperature, humidity, solar radiation, pH, total dissolved solids (TDS), leaves green area, and plant height. These features were collected across 24 recorded instances from January 2, 2025, to February 3, 2025. The data was preprocessed to remove inconsistencies and normalize values to ensure model stability and robustness.
The dataset includes two sets of Thai Basil images for 24 instances. It also includes csv data for features.
DOI: 10.17632/rxnw3pmrcf.1
title: DEEP DYNAMIC DRUG SCORING METHOD: CASE STUDY OF TAXANE β-TUBULIN DISASSEMBLY INHIBITORS

description: Dataset of Computer Aided Drug Design (CADD) and Molecular Dynamics (MD) operations for the article, titled "DEEP DYNAMIC DRUG SCORING METHOD: CASE STUDY OF TAXANE β-TUBULIN DISASSEMBLY INHIBITORS"
DOI: 10.17632/dykvw93zn4.1
title: The Uneven Effects of Conditional Cash Transfers on Women and Men

description: Replication file: "The Uneven Effects of Conditional Cash Transfers on Women and Men"
DOI: 10.17632/74grg43h27.1
title: LINF_040017300

description: casein kinase I; Leishmania infantum
DOI: 10.17632/74grg43h27.2
title: LINF_040017300

description: Casein kinase I; Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.bh7z5f
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sideritis discolor Webb ex Bolle)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000715-250226211154957/datasets/export for details.
DOI: 10.17632/5g9g7xdv8h.1
title: Supplementary data

description: The results of the tests conducted on the 30 different rock samples.
DOI: 10.17632/wc6mgbf58r
title: Construct validity of the atopic dermatitis control tool (ADCT) in the pediatric population – a real-world prospective study

description: Supplementary Table 1
DOI: 10.17632/wc6mgbf58r.1
title: Construct validity of the atopic dermatitis control tool (ADCT) in the pediatric population – a real-world prospective study

description: Supplementary Table 1
DOI: 10.17632/mjnn6kjgkh.1
title: Composite Dataset of Input and Output Files from Complex Similarity Network Analysis of Secreted Cysteine-Rich ORFs Without Annotation (mSCRs-WA)

description: This dataset contains a composite collection of bioactive peptide sequences and Complex Similarity Network (CSN) analysis outputs, designed to explore the functional relationships of 1,872 Secreted Cysteine-Rich ORFs Without Annotation (mSCRs-WA). The dataset integrates eight peptide classes, including antimicrobial peptides (AMPs), defensins, venoms/toxins, and non-AMP controls, to establish a reference chemical space for functional inference.

It includes both input sequence data (FASTA format) and CSN-derived output files, which facilitate the visualization and clustering of peptide sequences based on structural and functional similarities:

1- FileSM1: FileSM1_12449_All_8_datasets.fasta
📄 Content:

A FASTA file containing 12,449 peptide sequences across eight datasets:
(i) Low-toxicity antimicrobial peptides (AMPs)
(ii) Defensins
(iii) Animal venoms and toxins
(iv) Cytotoxic peptides
(v) Haemolytic peptides
(vi) Non-AMPs (negative controls)
(vii) Cnidarian toxin candidates
(viii) Secreted Cysteine-Rich ORFs Without Annotation (mSCRs-WA)

🔍 Usage: 
- Serves as the primary input dataset for complex similarity network (CSN) analysis.
- Enables homology searches, functional annotation, and comparative analyses.

📤 Output Files from CSN Analysis
2- 🗂 FileSM2: FileSM2_HSPN_Topology_GraphML.zip
📄 Content:

A compressed ZIP file containing GraphML representations of the Half-Space Proximal Network (HSPN):
HSPN_clusters_projection.graphml → Clustered projection of peptide connectivity based on similarity metrics.
HSPN_peptide_classes_projection.graphml → Projection of peptide classes (AMPs, toxins, defensins, etc.), highlighting their network positioning.
🖥 Visualization:

Can be opened in Gephi v0.10 or any GraphML-compatible tool.
Nodes represent peptide sequences, edges indicate functional similarity, and clusters reflect shared bioactivity profiles.

🔍 Usage:
- Facilitates visual exploration of sequence relationships.
- Enables functional annotation transfer by identifying clusters with known bioactive peptides.

3- 🗂 FileSM3: FileSM3_Clusters_Composition_Analysis.xlsx
📄 Content:

A spreadsheet detailing cluster composition in the HSPN analysis, including:
Cluster ID and size
Distribution of peptides across eight datasets
Functional annotation insights for each cluster

🔍 Usage:
- Helps identify key functional groups within the CSN framework.
- Provides quantitative insights into peptide distribution and classification.

4- 🗂 FileSM4: FileSM4_HSPN_Connections_Analysis.xlsx
📄 Content:

A spreadsheet detailing functional connections between peptides, including:
Pairwise similarity scores
Network centrality measures (e.g., harmonic centrality, degree centrality)
Annotations of linked sequences

🔍 Usage:
- Supports similarity-based functional inference.
- Helps track peptide relationships and connectivity patterns within the network.
DOI: 10.17632/mjnn6kjgkh
title: Composite Dataset of Input and Output Files from Complex Similarity Network Analysis of Secreted Cysteine-Rich peptides/proteins Without Annotation (SCRs-WA)

description: This dataset contains a composite collection of bioactive peptide sequences and Complex Similarity Network (CSN) analysis outputs, designed to explore the functional relationships of 1,872 Secreted Cysteine-Rich peptides/proteins Without Annotation (SCRs-WA). The dataset integrates eight peptide classes, including antimicrobial peptides (AMPs), defensins, venoms/toxins, and non-AMP controls, to establish a reference chemical space for functional inference.

It includes both input sequence data (FASTA format) and CSN-derived output files, which facilitate the visualization and clustering of peptide sequences based on structural and functional similarities:

1- FileSM1: FileSM1_12449_All_8_datasets.fasta
📄 Content:

A FASTA file containing 12,449 peptide sequences across eight datasets:
(i) Low-toxicity antimicrobial peptides (AMPs)
(ii) Defensins
(iii) Animal venoms and toxins
(iv) Cytotoxic peptides
(v) Haemolytic peptides
(vi) Non-AMPs (negative controls)
(vii) Cnidarian toxin candidates from S. savaglia
(viii) Secreted Cysteine-Rich ORFs Without Annotation (mSCRs-WA)

🔍 Usage: 
- Serves as the primary input dataset for complex similarity network (CSN) analysis.
- Enables homology searches, functional annotation, and comparative analyses.

📤 Output Files from CSN Analysis
2- 🗂 FileSM2: FileSM2_HSPN_Topology_GraphML.zip
📄 Content:

A compressed ZIP file containing GraphML representations of the Half-Space Proximal Network (HSPN):
HSPN_clusters_projection.graphml → Clustered projection of peptide connectivity based on similarity metrics.
HSPN_peptide_classes_projection.graphml → Projection of peptide classes (AMPs, toxins, defensins, etc.), highlighting their network positioning.
🖥 Visualization:

Can be opened in Gephi v0.10 or any GraphML-compatible tool.
Nodes represent peptide sequences, edges indicate functional similarity, and clusters reflect shared bioactivity profiles.

🔍 Usage:
- Facilitates visual exploration of sequence relationships.
- Enables functional annotation transfer by identifying clusters with known bioactive peptides.

3- 🗂 FileSM3: FileSM3_Clusters_Composition_Analysis.xlsx
📄 Content:

A spreadsheet detailing cluster composition in the HSPN analysis, including:
Cluster ID and size
Distribution of peptides across eight datasets
Functional annotation insights for each cluster

🔍 Usage:
- Helps identify key functional groups within the CSN framework.
- Provides quantitative insights into peptide distribution and classification.

4- 🗂 FileSM4: FileSM4_HSPN_Connections_Analysis.xlsx
📄 Content:

A spreadsheet detailing functional connections between peptides, including:
Pairwise similarity scores
Network centrality measures (e.g., harmonic centrality, degree centrality)
Annotations of linked sequences

🔍 Usage:
- Supports similarity-based functional inference.
- Helps track peptide relationships and connectivity patterns within the network.
DOI: 10.17632/mjnn6kjgkh.2
title: Composite Dataset of Input and Output Files from Complex Similarity Network Analysis of Secreted Cysteine-Rich peptides/proteins Without Annotation (SCRs-WA)

description: This dataset contains a composite collection of bioactive peptide sequences and Complex Similarity Network (CSN) analysis outputs, designed to explore the functional relationships of 1,872 Secreted Cysteine-Rich peptides/proteins Without Annotation (SCRs-WA). The dataset integrates eight peptide classes, including antimicrobial peptides (AMPs), defensins, venoms/toxins, and non-AMP controls, to establish a reference chemical space for functional inference.

It includes both input sequence data (FASTA format) and CSN-derived output files, which facilitate the visualization and clustering of peptide sequences based on structural and functional similarities:

1- FileSM1: FileSM1_12449_All_8_datasets.fasta
📄 Content:

A FASTA file containing 12,449 peptide sequences across eight datasets:
(i) Low-toxicity antimicrobial peptides (AMPs)
(ii) Defensins
(iii) Animal venoms and toxins
(iv) Cytotoxic peptides
(v) Haemolytic peptides
(vi) Non-AMPs (negative controls)
(vii) Cnidarian toxin candidates from S. savaglia
(viii) Secreted Cysteine-Rich ORFs Without Annotation (mSCRs-WA)

🔍 Usage: 
- Serves as the primary input dataset for complex similarity network (CSN) analysis.
- Enables homology searches, functional annotation, and comparative analyses.

📤 Output Files from CSN Analysis
2- 🗂 FileSM2: FileSM2_HSPN_Topology_GraphML.zip
📄 Content:

A compressed ZIP file containing GraphML representations of the Half-Space Proximal Network (HSPN):
HSPN_clusters_projection.graphml → Clustered projection of peptide connectivity based on similarity metrics.
HSPN_peptide_classes_projection.graphml → Projection of peptide classes (AMPs, toxins, defensins, etc.), highlighting their network positioning.
🖥 Visualization:

Can be opened in Gephi v0.10 or any GraphML-compatible tool.
Nodes represent peptide sequences, edges indicate functional similarity, and clusters reflect shared bioactivity profiles.

🔍 Usage:
- Facilitates visual exploration of sequence relationships.
- Enables functional annotation transfer by identifying clusters with known bioactive peptides.

3- 🗂 FileSM3: FileSM3_Clusters_Composition_Analysis.xlsx
📄 Content:

A spreadsheet detailing cluster composition in the HSPN analysis, including:
Cluster ID and size
Distribution of peptides across eight datasets
Functional annotation insights for each cluster

🔍 Usage:
- Helps identify key functional groups within the CSN framework.
- Provides quantitative insights into peptide distribution and classification.

4- 🗂 FileSM4: FileSM4_HSPN_Connections_Analysis.xlsx
📄 Content:

A spreadsheet detailing functional connections between peptides, including:
Pairwise similarity scores
Network centrality measures (e.g., harmonic centrality, degree centrality)
Annotations of linked sequences

🔍 Usage:
- Supports similarity-based functional inference.
- Helps track peptide relationships and connectivity patterns within the network.
DOI: 10.15468/dl.jkrk3m
title: Occurrence Download

description: A dataset containing 1661770 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ciconia ciconia (Linnaeus, 1758)"
 ]
}

 The dataset includes 1661770 records from 1952 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000710-250226211518916/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r64n8e
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-83.2356556000000012 42.3324825999999987, -83.2356556000000012 42.3367303100000001, -83.2356556000000012 42.3403712100000007, -83.2356556000000012 42.3436075600000024, -83.2356556000000012 42.3482598100000018, -83.2356556000000012 42.3508893500000028, -83.2356556000000012 42.3541256999999973, -83.2299988399999933 42.3541256999999973, -83.2251502000000016 42.3541256999999973, -83.2213116799999995 42.3541256999999973, -83.2154529000000025 42.3541256999999973, -83.2154529000000025 42.3508893500000028, -83.2154529000000025 42.3464393699999988, -83.2154529000000025 42.3423939300000001, -83.2154529000000025 42.3377416699999998, -83.2154529000000025 42.3349098600000033, -83.2154529000000025 42.3324825999999987, -83.2205035700000053 42.3324825999999987, -83.2237360099999961 42.3324825999999987, -83.2273724899999934 42.3324825999999987, -83.2304029000000014 42.3324825999999987, -83.2356556000000012 42.3324825999999987))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 43 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0150684-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-1208
title: Maiak = [Lighthouse], vol. 13, no. 11-12 (1935)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.ssxt3x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Wilcoxina rehmii Chin S.Yang & Korf",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "GadmGid is USA.1_1",
 "GadmGid is USA.11_1",
 "GadmGid is USA.25_1",
 "GadmGid is USA.41_1",
 "GadmGid is USA.34_1",
 "GadmGid is USA.43_1",
 "GadmGid is USA.18_1",
 "GadmGid is USA.47_1",
 "GadmGid is USA.49_1",
 "GadmGid is USA.15_1",
 "GadmGid is USA.19_1",
 "GadmGid is USA.4_1",
 "GadmGid is USA.26_1",
 "GadmGid is USA.14_1",
 "GadmGid is USA.36_1",
 "GadmGid is USA.39_1",
 "GadmGid is USA.21_1",
 "GadmGid is USA.16_1",
 "GadmGid is USA.8_1",
 "GadmGid is USA.31_1"
 ]
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004799-230918134249559/datasets/export for details.
DOI: 10.15468/dl.74wq5q
title: Occurrence Download

description: A dataset containing 1880809 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amelanchier ovalis Medik., Trollius europaeus L., Erinus alpinus L., Siler montanum Crantz, Anemone montana Hoppe ex Sturm, Tragopogon pratensis L., Ailanthus altissima (Mill.) Swingle, Sonchus arvensis L., Karpatiosorbus Sennikov & Kurtto, Arenaria serpyllifolia L., Filipendula vulgaris Moench, Buddleja davidii Franch., Cirsium erisithales (Jacq.) Scop., Hippocrepis emerus (L.) Lassen, Rosa villosa (Nyár.) Ciocarlan, Aria edulis (Willd.) M.Roem., Arabis alpina L., Arabis turrita L., Cotoneaster nebrodensis (Guss.) Koch, Scutellaria minor Huds., Epipactis microphylla (Ehrh.) Sw., Galium aristatum L., Saxifraga cuneifolia L., Conopodium majus (Gouan) Loret, Arenaria montana L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Geometry POLYGON((-11 34, 41 34, 41 72, -11 72, -11 34))",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 1880809 records from 3029 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000940-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1057233.10fu
title: SH1057233.10FU

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/sh1057234.10fu
title: SH1057234.10FU

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/sh1057235.10fu
title: SH1057235.10FU

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.