cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5cf88337tx
title: Discovery of human gut phage-encoded Anti-CRISPR proteins unveils diverse mechanisms for phages to evade type II CRISPR immunity

description: This collection of Supplementary Tables provides comprehensive datasets and analyses supporting the systematic identification and characterization of CRISPR-Cas systems and anti-CRISPR (Acr) proteins in the human gut microbiome. Tables S1–S7 and S10 detail the detection, classification, and phylogenetic analysis of Class 1 and Class 2 CRISPR-Cas systems—including Cas9 orthologs—within the UHGG database. Tables S9 and S11 document spacer–virus connections between UHGG and GVD, enabling the prediction of phage-encoded Acrs. Tables S12–S14 summarize Acr candidate selection, codon optimization, and library construction. Functional validation data for Acrs targeting six Type II-Cas9 systems are presented in Tables S16–S22, with a non-redundant Acr set provided in Table S22. Finally, structural analyses, including fold similarity and the GutAcraca family, are summarized in Tables S24–S26.
Table S1. Class 1 CRISPR-Cas systems detected in UHGG, related to Figure 1A
Table S2. Class 2 CRISPR-Cas systems detected in UHGG, related to Figure 1A, B
Table S3. Type I CRISPR-Cas systems with Cas3 detected in UHGG, related to Figure 1A
Table S4. Type III CRISPR-Cas systems with Cas10 detected in UHGG, related to Figure 1A
Table S5. Distribution of type II, V, and VI CRISPR-Cas systems from Class 2 across microbial classes, related to Figure 1B
Table S6. Cas9 CDSs detected in UHGG, related to Figure 1C. Cas9_subfamily were obtained from UniProt according to UniProtKB_Entry annotated by UHGG. 
Table S7. Non-redundant Cas9 CDSs used to construct the phylogenetic tree, related to Figure 1C
Table S9. Connections between CRISPR spacers from UHGG and viral contigs from GVD through CRISPR-spacer blastn matches, related to Figure 2A and Figure 1D
Table S10. Non-redundant Cas9 CDSs used to construct the phylogenetic tree, related to Figure 1D
Table S11. Viral contigs in GVD which had CRISPR spacer matching with microbial genomes in UHGG carrying Cas9, related to Figure 2A
Table S12. Acr candidates with amino acid sequence, related to Figure 2A
Table S13. Selecting Acr candidates for DNA sequence codon optimization, related to Figure 2B
Table S14. Oligos design of Acr candidate library, related to Figure 2B
Table S16. Positive Acr candidates of SpyCas9, related to Figure S2A, B 
Table S17. Positive Acr candidates of SaCas9, related to Figure S2A, B
Table S18. Positive Acr candidates of St1Cas9, related to Figure S2A, B
Table S19. Positive Acr candidates of St3Cas9, related to Figure S2A, B
Table S20. Positive Acr candidates of FnCas9, related to Figure S2A, B
Table S21. Positive Acr candidates of NmCas9, related to Figure S2A, B
Table S22. 651 non-redundant positive Acr candidates in total, related to Figure 4A, B
Table S24. Structural similarity matrix of positive Acr candidates, related to Figure 4A, B and S5A
Table S25. Members of GutAcraca, related to Figure 4B
Table S26. Structural similarity analysis of GutAcraca in the AlphaFold database, related to Figure 5N
DOI: 10.17632/5cf88337tx.3
title: Discovery of human gut phage-encoded Anti-CRISPR proteins unveils diverse mechanisms for phages to evade type II CRISPR immunity

description: This collection of Supplementary Tables provides comprehensive datasets and analyses supporting the systematic identification and characterization of CRISPR-Cas systems and anti-CRISPR (Acr) proteins in the human gut microbiome. Tables S1–S7 and S10 detail the detection, classification, and phylogenetic analysis of Class 1 and Class 2 CRISPR-Cas systems—including Cas9 orthologs—within the UHGG database. Tables S9 and S11 document spacer–virus connections between UHGG and GVD, enabling the prediction of phage-encoded Acrs. Tables S12–S14 summarize Acr candidate selection, codon optimization, and library construction. Functional validation data for Acrs targeting six Type II-Cas9 systems are presented in Tables S16–S22, with a non-redundant Acr set provided in Table S22. Finally, structural analyses, including fold similarity and the GutAcraca family, are summarized in Tables S24–S26.
Table S1. Class 1 CRISPR-Cas systems detected in UHGG, related to Figure 1A
Table S2. Class 2 CRISPR-Cas systems detected in UHGG, related to Figure 1A, B
Table S3. Type I CRISPR-Cas systems with Cas3 detected in UHGG, related to Figure 1A
Table S4. Type III CRISPR-Cas systems with Cas10 detected in UHGG, related to Figure 1A
Table S5. Distribution of type II, V, and VI CRISPR-Cas systems from Class 2 across microbial classes, related to Figure 1B
Table S6. Cas9 CDSs detected in UHGG, related to Figure 1C. Cas9_subfamily were obtained from UniProt according to UniProtKB_Entry annotated by UHGG. 
Table S7. Non-redundant Cas9 CDSs used to construct the phylogenetic tree, related to Figure 1C
Table S9. Connections between CRISPR spacers from UHGG and viral contigs from GVD through CRISPR-spacer blastn matches, related to Figure 2A and Figure 1D
Table S10. Non-redundant Cas9 CDSs used to construct the phylogenetic tree, related to Figure 1D
Table S11. Viral contigs in GVD which had CRISPR spacer matching with microbial genomes in UHGG carrying Cas9, related to Figure 2A
Table S12. Acr candidates with amino acid sequence, related to Figure 2A
Table S13. Selecting Acr candidates for DNA sequence codon optimization, related to Figure 2B
Table S14. Oligos design of Acr candidate library, related to Figure 2B
Table S16. Positive Acr candidates of SpyCas9, related to Figure S2A, B 
Table S17. Positive Acr candidates of SaCas9, related to Figure S2A, B
Table S18. Positive Acr candidates of St1Cas9, related to Figure S2A, B
Table S19. Positive Acr candidates of St3Cas9, related to Figure S2A, B
Table S20. Positive Acr candidates of FnCas9, related to Figure S2A, B
Table S21. Positive Acr candidates of NmCas9, related to Figure S2A, B
Table S22. 651 non-redundant positive Acr candidates in total, related to Figure 4A, B
Table S24. Structural similarity matrix of positive Acr candidates, related to Figure 4A, B and S5A
Table S25. Members of GutAcraca, related to Figure 4B
Table S26. Structural similarity analysis of GutAcraca in the AlphaFold database, related to Figure 5N
DOI: 10.15468/dl.y523ht
title: Occurrence Download

description: A dataset containing 999 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is University of the Philippines Los Baños Museum of Natural History Dioscoro Rabor Collection: Luzon Birds"
 ]
}

 The dataset includes 999 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005752-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4m6ps9
title: Occurrence Download

description: A dataset containing 229 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-91.55057 -38.10660,-38.44943 -38.10660,-38.44943 -16.89340,-91.55057 -16.89340,-91.55057 -38.10660))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Glandularia venturii"
 ]
}

 The dataset includes 229 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005751-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18412953
title: VR in Art Tables

description: This dataset contains the Excel tables used as an appendix in the conference paper "Virtual Reality in Art Education: A Pedagogical Analysis of Empirical Studies in High School and Higher Education" by Ioanna Louloudi and Stylianos Mystakidis.

The file includes the data used for analysis and reporting in the study.

This record provides a DOI only for the dataset, not for the full paper.
DOI: 10.5281/zenodo.18412954
title: VR in Art Tables

description: This dataset contains the Excel tables used as an appendix in the conference paper "Virtual Reality in Art Education: A Pedagogical Analysis of Empirical Studies in High School and Higher Education" by Ioanna Louloudi and Stylianos Mystakidis.

The file includes the data used for analysis and reporting in the study.

This record provides a DOI only for the dataset, not for the full paper.
DOI: 10.15468/dl.9g9vde
title: Occurrence Download

description: A dataset containing 59 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-130.47042 37.72183,-91.52958 37.72183,-91.52958 53.27817,-130.47042 53.27817,-130.47042 37.72183))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oreocarya celosioides"
 ]
}

 The dataset includes 59 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005753-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rgp9xy5gy2
title: BD Guava Fruit and Leaf Disease Dataset

description: Bangladesh, being a major guava-producing country, relies heavily on seasonal harvests. 
However, diseases such as algal leaf spot, red rust, scab, and black mold severely impact guava 
yield and farmer income [4]. Early detection is essential to prevent large-scale infection and 
ensure sustainable fruit production. With the increasing availability of affordable computing 
power and image-based data, deep learning provides a powerful solution for rapid and accurate 
disease identification. 
Previous works have demonstrated the high potential of deep learning models in similar 
agricultural domains. For example, YOLOv8 has been successfully applied in apple orchard 
segmentation tasks, outperforming Mask R-CNN in both accuracy and inference speed, 
achieving precision up to 0.93 and recall 0.97, proving its robustness for real-time agricultural 
applications [5]. Similarly, in tomato grading, YOLOv7 achieved 99.2% accuracy and 99.4% 
recovery rate in detecting and classifying tomato quality, highlighting the power of convolutional 
deep learning in fruit analysis [6]. 
Inspired by these advancements, this study applies YOLO and CNN models for guava leaf and 
fruit disease detection to enhance precision agriculture in Bangladesh. The integration of these 
models into user-friendly platforms such as Tkinter and Gradio aims to assist farmers and 
researchers in diagnosing diseases efficiently, thereby reducing crop losses and improving yield 
quality. 
Version 2 Red G
The dataset is organized into multiple class-wise folders, with images standardized to a uniform resolution for consistency. Each class contains a sufficient number of samples to support effective model training and evaluation. The data is structured into separate subsets for training, validation, and testing, while preserving class-wise organization to ensure balanced and reliable performance assessment.
Version 3 all including 
The dataset was expanded to increase sample diversity and improve model generalization.
Image variations were generated through multiple controlled transformations applied to the original data.
The final dataset consists of ten distinct categories with a balanced number of samples per category.
The data is organized in a structured format suitable for training, validation, and testing in deep learning models.
CODE: https://github.com/shuvobasak4004/Guava-Multi-Model-Training-Code
DOI: 10.17632/rgp9xy5gy2.3
title: BD Guava Fruit and Leaf Disease Dataset

description: Bangladesh, being a major guava-producing country, relies heavily on seasonal harvests. 
However, diseases such as algal leaf spot, red rust, scab, and black mold severely impact guava 
yield and farmer income [4]. Early detection is essential to prevent large-scale infection and 
ensure sustainable fruit production. With the increasing availability of affordable computing 
power and image-based data, deep learning provides a powerful solution for rapid and accurate 
disease identification. 
Previous works have demonstrated the high potential of deep learning models in similar 
agricultural domains. For example, YOLOv8 has been successfully applied in apple orchard 
segmentation tasks, outperforming Mask R-CNN in both accuracy and inference speed, 
achieving precision up to 0.93 and recall 0.97, proving its robustness for real-time agricultural 
applications [5]. Similarly, in tomato grading, YOLOv7 achieved 99.2% accuracy and 99.4% 
recovery rate in detecting and classifying tomato quality, highlighting the power of convolutional 
deep learning in fruit analysis [6]. 
Inspired by these advancements, this study applies YOLO and CNN models for guava leaf and 
fruit disease detection to enhance precision agriculture in Bangladesh. The integration of these 
models into user-friendly platforms such as Tkinter and Gradio aims to assist farmers and 
researchers in diagnosing diseases efficiently, thereby reducing crop losses and improving yield 
quality. 
Version 2 Red G
The dataset is organized into multiple class-wise folders, with images standardized to a uniform resolution for consistency. Each class contains a sufficient number of samples to support effective model training and evaluation. The data is structured into separate subsets for training, validation, and testing, while preserving class-wise organization to ensure balanced and reliable performance assessment.
Version 3 all including 
The dataset was expanded to increase sample diversity and improve model generalization.
Image variations were generated through multiple controlled transformations applied to the original data.
The final dataset consists of ten distinct categories with a balanced number of samples per category.
The data is organized in a structured format suitable for training, validation, and testing in deep learning models.
CODE: https://github.com/shuvobasak4004/Guava-Multi-Model-Training-Code
DOI: 10.15468/dl.rbbkwy
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pleoticus muelleri",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2015"
 ]
}

 The dataset includes 11 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005756-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mb2xg9
title: Occurrence Download

description: A dataset containing 1019 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Luetkea pectinata",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 1019 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005757-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8186308
title: Multienzymatic Synthesis of γ-Lactam Building Blocks from Unsaturated Esters and Hydroxylamine

description: We herein provide the raw data that form the basis of the manuscript "Multienzymatic Synthesis of γ-Lactam Building Blocks from Unsaturated Esters and Hydroxylamine". Please find the abstract below:


The assembly of enzymatic cascades and multi-step reaction sequences represents an attractive alternative to traditional synthetic-organic approaches. The biocatalytic reaction mediators offer not only mild conditions and permit the use of environmentally benign reagents, but the high compatibility of different enzymes promises more streamlined reaction setups. In this study, a triple-enzymatic strategy was developed that enables the direct conversion of γ,δ-unsaturated esters to N-hydroxy-γ-lactame building blocks. Hereby, a lipase-catalyzed hydroxylaminolysis generates hydroxamic acid intermediates that are subsequently aerobically activated by horseradish peroxidase and glucose oxidase to cyclize in an intramolecular nitroso ene reaction. Utilizing the hydroxyl­aminolysis/ene-cyclization sequence for the preparation of an aza-spirocyclic lactam, the multi-enzymatic methodology was successfully employed in the synthesis of key intermediates en route to alkaloids of the Cephalotaxus family.
DOI: 10.5281/zenodo.8186309
title: Multienzymatic Synthesis of γ-Lactam Building Blocks from Unsaturated Esters and Hydroxylamine

description: We herein provide the raw data that form the basis of the manuscript "Multienzymatic Synthesis of γ-Lactam Building Blocks from Unsaturated Esters and Hydroxylamine". Please find the abstract below:


The assembly of enzymatic cascades and multi-step reaction sequences represents an attractive alternative to traditional synthetic-organic approaches. The biocatalytic reaction mediators offer not only mild conditions and permit the use of environmentally benign reagents, but the high compatibility of different enzymes promises more streamlined reaction setups. In this study, a triple-enzymatic strategy was developed that enables the direct conversion of γ,δ-unsaturated esters to N-hydroxy-γ-lactame building blocks. Hereby, a lipase-catalyzed hydroxylaminolysis generates hydroxamic acid intermediates that are subsequently aerobically activated by horseradish peroxidase and glucose oxidase to cyclize in an intramolecular nitroso ene reaction. Utilizing the hydroxyl­aminolysis/ene-cyclization sequence for the preparation of an aza-spirocyclic lactam, the multi-enzymatic methodology was successfully employed in the synthesis of key intermediates en route to alkaloids of the Cephalotaxus family.
DOI: 10.14284/588
title: Canadian Register of Marine Species. Accessed at https://www.marinespecies.org/carms on yyyy-mm-dd

description: A list of species in the Canadian marine waters: the Atlantic, the Arctic and the Pacific.
description: In spring 2009, the Canadian Department of Fisheries and Oceans’ National Science Data Management Committee (DFO-NSDMC) created a national activity with the mandate to promote the use of taxonomic standards within the Science Branch of DFO and to develop and implement best-practice procedures for the quality control of biological names used by DFO.

The largest global standard for aquatic species names is the World Register of Marine Species (WoRMS). The objective of WoRMS is to provide an authoritative and comprehensive list of names of marine organisms, including information on synonymy. The thematic regional node for Canada, the Canadian Register of Marine Species (CaRMS) encompasses the marine component of the master register in WoRMS. The taxonomic scope of CaRMS will be to cover species from the three realms of the ocean: the seafloor (meio-, macro- and megazoobenthos; micro- and macrophytobenthos), the water column (phytoplankton, zooplankton, nekton) and the sea-ice.

CaRMS contains detailed distribution records for taxa found in three oceans: the Atlantic, the Arctic and the Pacific. CaRMS grew from local registers of marine species, such as the Northwest Atlantic Register of Marine Species (NWARMS) and the Gulf of Maine Register of Marine Species (GoMRMS), compiled by the Atlantic Reference Centre of the Huntsman Marine Science Centre, St. Andrews, New Brunswick.

Two special features available on the CaRMS site are a series of regional checklists and a photogallery of taxa observed in Canadian waters.

The intent of CaRMS is not to duplicate WoRMS. Users are referred to the WoRMS site (www.marinespecies.org) if they wish to search for taxonomic and/or global distribution information on specific taxa. CaRMS was also not designed to track changes in taxonomy. Each record in the register contains a standard code that enables linkage to master databases of WoRMS (AphiaID and the Integrated Taxonomic Information System (ITIS TSN). Issues related to synonymy, validity and taxonomic hierarchy will be dealt with by these organizations. The WoRMS web site is hosted by the Flanders Marine Institute, Belgium (VLIZ).

VLIZ has provided CaRMS data management support and services through their connection with WoRMS. VLIZ hosts the CaRMS website and provides the tools on the CaRMS web page that enable the user:

to create authoritative checklists of taxa for geographical areas of interest to Canadian research.
to extract enriched metadata such as standard/master codes (the WoRMS AphiaID, the ITIS TSN), obtain valid/accepted synonym names and associated taxonomic hierarchy.
to provide a list of authoritative literature associated with taxa distribution.
to display the statistics related to the taxonomic coverage of the register.
to access and contribute to a photo gallery for images of taxa included in our database collections.


The web page log-in feature is only accessible to CaRMS editors.
DOI: 10.17632/hb6t2v6nsn
title: In-situ data collected during ICCE 2024-25

description: During the International Circumnavigation Expedition (ICCE) 2024-25, CTD measurements were collected in both polynya and non-polynya regions to investigate water-mass characteristics.
DOI: 10.17632/hb6t2v6nsn.2
title: In-situ data collected during ICCE 2024-25

description: During the International Circumnavigation Expedition (ICCE) 2024-25, CTD measurements were collected in both polynya and non-polynya regions to investigate water-mass characteristics.
DOI: 10.6084/m9.figshare.31186412
title: From theory to therapy: integrating artificial intelligence for transformative healthcare innovation

description: The rapid evolution of artificial intelligence (AI) is reshaping healthcare by improving diagnostics, patient outcomes, and operational efficiency. Yet, many frameworks for AI adoption overlook the complex and iterative nature of healthcare systems. This study introduces the AI Healthcare Symbiosis Cycle (AI-HSC), a novel framework based on Dynamic Capabilities Theory, Systems Theory, and Kotter’s 8-Step Change Model, conceptualising AI adoption as a continuous and adaptive process. Dynamic Capabilities Theory highlights the need for organisations to sense opportunities, seize resources, and change processes in response to AI advancements. Systems Theory focuses on optimising interdependencies within healthcare organisations, while Kotter’s model ensures a structured approach to managing change. The AI-HSC aligns phases of AI integration – initiation, integration, evolution, and revolution – with Kotter’s steps, promoting a systematic and scalable adoption strategy. Key recommendations include implementing pilot programs, fostering interdisciplinary coalitions, embedding AI literacy into organisational culture, and developing robust ethics and compliance frameworks. By bridging theory with practice, the AI-HSC provides actionable strategies for sustainable AI integration, addressing critical barriers and fostering continuous innovation. This research contributes to the digital change discourse, offering valuable insights for academia and healthcare practitioners.