cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17639/nott.6864
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 5661R1

description: This data set forms part of the larger 'Fly Cancer Screen' data set, which consists of over 700 image sets
description: Metastasis is the leading cause of death for cancer patients. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumour growth towards malignancy. Advances in genome characterisation technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. A major challenge however is the translation of these findings to new biological insight due to the difficulty in evaluating whether these candidate genes drive tumour progression. Using the genetic amenability of Drosophila melanogaster we generated tumours with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify numerous conserved genes that enhance or suppress epithelial tumour progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved ‘invasion suppressors’. RNAi line: 5661R1 (III) Source: NIG Name: Sema-5c (1) Full name: Semaphorin 5c Also known as: Sema5c, Sema 5c, Dsema3a, BG01245 Annotation symbol: CG5661 FlyBase ID: FBgn0284221 File naming convention: File names typically contain representations of date (DDMMYY), RNAi Line, Animal Number and, in some cases, window (to accommodate larger samples that require multiple image stacks)
DOI: 10.17639/nott.6870
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 28336

description: This data set forms part of the larger 'Fly Cancer Screen' data set, which consists of over 700 image sets
description: Metastasis is the leading cause of death for cancer patients. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumour growth towards malignancy. Advances in genome characterisation technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. A major challenge however is the translation of these findings to new biological insight due to the difficulty in evaluating whether these candidate genes drive tumour progression. Using the genetic amenability of Drosophila melanogaster we generated tumours with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify numerous conserved genes that enhance or suppress epithelial tumour progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved ‘invasion suppressors’. RNAi line: 28336 (III) Source: Bloomington Name: shot Full name: short stop Also known as: kak, l(2)k04204, grv, l(2)k03010, l(2)k05434 Annotation symbol: CG18076 FlyBase ID: FBgn0013733 File naming convention: File names typically contain representations of date (DDMMYY), RNAi Line, Animal Number and, in some cases, window (to accommodate larger samples that require multiple image stacks)
DOI: 10.17639/nott.6878
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 27238

description: This data set forms part of the larger 'Fly Cancer Screen' data set, which consists of over 700 image sets
description: Metastasis is the leading cause of death for cancer patients. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumour growth towards malignancy. Advances in genome characterisation technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. A major challenge however is the translation of these findings to new biological insight due to the difficulty in evaluating whether these candidate genes drive tumour progression. Using the genetic amenability of Drosophila melanogaster we generated tumours with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify numerous conserved genes that enhance or suppress epithelial tumour progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved ‘invasion suppressors’. RNAi line: 27238 (III) Source: Bloomington Name: SK (2) Full name: small conductance calcium-activated potassium channel Also known as: dSK Annotation symbol: CG10706 FlyBase ID: FBgn0029761 File naming convention: File names typically contain representations of date (DDMMYY), RNAi Line, Animal Number and, in some cases, window (to accommodate larger samples that require multiple image stacks)
DOI: 10.17639/nott.6865
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 1052

description: This data set forms part of the larger 'Fly Cancer Screen' data set, which consists of over 700 image sets
description: Metastasis is the leading cause of death for cancer patients. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumour growth towards malignancy. Advances in genome characterisation technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. A major challenge however is the translation of these findings to new biological insight due to the difficulty in evaluating whether these candidate genes drive tumour progression. Using the genetic amenability of Drosophila melanogaster we generated tumours with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify numerous conserved genes that enhance or suppress epithelial tumour progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved ‘invasion suppressors’. RNAi line: 1052 (X) Source: VDRC Name: Sema-5c (2) Full name: Semaphorin 5c Also known as: Sema5c, Sema 5c, Dsema3a, BG01245 Annotation symbol: CG5661 FlyBase ID: FBgn0284221 File naming convention: File names typically contain representations of date (DDMMYY), RNAi Line, Animal Number and, in some cases, window (to accommodate larger samples that require multiple image stacks)
DOI: 10.17639/nott.6863
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 28932

description: This data set forms part of the larger 'Fly Cancer Screen' data set, which consists of over 700 image sets
description: Metastasis is the leading cause of death for cancer patients. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumour growth towards malignancy. Advances in genome characterisation technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. A major challenge however is the translation of these findings to new biological insight due to the difficulty in evaluating whether these candidate genes drive tumour progression. Using the genetic amenability of Drosophila melanogaster we generated tumours with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify numerous conserved genes that enhance or suppress epithelial tumour progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved ‘invasion suppressors’. RNAi line: 28932 (III) Source: Bloomington Name: Sema-2b (2) Full name: Semaphorin 2b Also known as: Sema2b Annotation symbol: CG33960 FlyBase ID: FBgn0264273 File naming convention: File names typically contain representations of date (DDMMYY), RNAi Line, Animal Number and, in some cases, window (to accommodate larger samples that require multiple image stacks)
DOI: 10.17639/nott.6866
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 21386

description: This data set forms part of the larger 'Fly Cancer Screen' data set, which consists of over 700 image sets
description: Metastasis is the leading cause of death for cancer patients. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumour growth towards malignancy. Advances in genome characterisation technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. A major challenge however is the translation of these findings to new biological insight due to the difficulty in evaluating whether these candidate genes drive tumour progression. Using the genetic amenability of Drosophila melanogaster we generated tumours with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify numerous conserved genes that enhance or suppress epithelial tumour progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved ‘invasion suppressors’. RNAi line: 21386 (III) Source: VDRC Name: sens-2 Full name: senseless-2 Also known as: sens2 Annotation symbol: CG31632 FlyBase ID: FBgn0051632 File naming convention: File names typically contain representations of date (DDMMYY), RNAi Line, Animal Number and, in some cases, window (to accommodate larger samples that require multiple image stacks)
DOI: 10.17639/nott.6755
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 3014

description: This data set forms part of the larger 'Fly Cancer Screen' data set, which consists of over 700 image sets
description: Metastasis is the leading cause of death for cancer patients. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumour growth towards malignancy. Advances in genome characterisation technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. A major challenge however is the translation of these findings to new biological insight due to the difficulty in evaluating whether these candidate genes drive tumour progression. Using the genetic amenability of Drosophila melanogaster we generated tumours with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify numerous conserved genes that enhance or suppress epithelial tumour progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved ‘invasion suppressors’. RNAi line: 3014 (III) Source: VDRC Name: pan Full name: pangolin Also known as: dTCF, TCF, l(4)13, LEF-1, IA5 Annotation symbol: CG34403 FlyBase ID: FBgn0085432 File naming convention: File names typically contain representations of date (DDMMYY), RNAi Line, Animal Number and, in some cases, window (to accommodate larger samples that require multiple image stacks)
DOI: 10.17639/nott.6871
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 53315

description: This data set forms part of the larger 'Fly Cancer Screen' data set, which consists of over 700 image sets
description: Metastasis is the leading cause of death for cancer patients. Consequently it is imperative that we improve our understanding of the molecular mechanisms that underlie progression of tumour growth towards malignancy. Advances in genome characterisation technologies have been very successful in identifying commonly mutated or misregulated genes in a variety of human cancers. A major challenge however is the translation of these findings to new biological insight due to the difficulty in evaluating whether these candidate genes drive tumour progression. Using the genetic amenability of Drosophila melanogaster we generated tumours with specific genotypes in the living animal and carried out a detailed systematic loss-of-function analysis to identify numerous conserved genes that enhance or suppress epithelial tumour progression. This enabled the discovery of functional cooperative regulators of invasion and the establishment of a network of conserved ‘invasion suppressors’. RNAi line: 53315 (III) Source: Bloomington Name: sick Full name: sickie Also known as: Dm_2L:104545 Annotation symbol: CG43720 FlyBase ID: FBgn0263873 File naming convention: File names typically contain representations of date (DDMMYY), RNAi Line, Animal Number and, in some cases, window (to accommodate larger samples that require multiple image stacks)
DOI: 10.17639/nott.7415
title: On-Surface Synthesis of Ni-Porphyrin-Doped Graphene Nanoribbons

description: On-surface synthesis of functional molecular structures provides a route to the fabrication of materials tailored to exhibit bespoke catalytic, (opto)electronic, and magnetic properties. The fabrication of graphene nanoribbons via on-surface synthesis, where reactive precursor molecules are combined to form extended polymeric structures, provides quasi-1D graphitic wires that can be doped by tuning the properties/composition of the precursor molecules. Here, we combine the atomic precision of solution-phase synthetic chemistry with on-surface protocols to enable reaction steps that cannot yet be achieved in solution. Our focus of this work is the inclusion of porphyrin species within graphene nanoribbons to create porphyrin-fused graphene nanoribbons. A combination of scanning tunneling microscopy and photoelectron spectroscopy techniques is used to characterize a porphyrin-fused graphene nanoribbon formed on-surface from a linear polymer consisting of regularly spaced Ni-porphyrin units linked by sections of aryl rings which fuse together during the reaction to form graphitic regions between neighboring Ni-porphyrin units.
DOI: 10.25318/3310089501-fra
title: Attentes de l'entreprise ou de l'organisme quant à la durée des obstacles liés à la chaîne d'approvisionnement, quatrième trimestre de 2024

description: Attentes de l'entreprise ou de l'organisme quant à la durée des obstacles liés à la chaîne d'approvisionnement, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2024.
DOI: 10.15468/dl.ggkzfa
title: Occurrence Download

description: A dataset containing 194 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lysapsus bolivianus Gallardo, 1961"
 ]
}

 The dataset includes 194 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007091-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k4jzxt
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pleuronichthys japonicus Suzuki, Kawashima & Nakabo, 2009",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007092-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5284/1124547
title: Images from a Field Observation and Archaeological Monitoring at Gordon Cottage, Haddenham, Buckinghamshire August-December 2015

description: This collection comprises images from a field observation (monitoring) at Gordon Cottage, Haddenham undertaken by Network Archaeology Ltd in August-December 2015.
DOI: 10.15468/dl.yp2nmv
title: Occurrence Download

description: A dataset containing 397 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pleuronichthys cornutus (Temminck & Schlegel, 1846)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 397 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007093-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jdyrq5
title: Occurrence Download

description: A dataset containing 1054 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Osteocephalus leprieurii (Duméril & Bibron, 1841)"
 ]
}

 The dataset includes 1054 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007094-241126133413365/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.27931503
title: The_Use_of_Generative_AI_to_Advance_Cybersecurity_Education_Taxonomy

description: After searching on Google Scholar, the papers that did not match the criteria and papers with titles irrelevant to the study were discarded, and the remaining papers moved to the next stage. In this stage, the abstracts and conclusions were reviewed to assess each paper’s relevance to the study. Papers that did not align with the research goals were excluded. The remaining papers were then read in full to confirm their final relevancy to the research goals and the topic of this thesis. The selection process was first performed by the master student and then evaluated by the supervisor to ensure reliability of the findings.<br>Using the search string mentioned above on Google Scholar results in a total of 386 search results at the time of conducting the paper selection. After eliminating irrelevant papers, a total of 14 papers were found relevant to the research questions. Although the number of papers may appear limited, the research topic is relatively new, and there is a lack of studies available on this subject. As many as seven papers were published in 2024 and six were published in 2023. This recent surge in publications underscores both the growing academic attention of the use of AI to advance cybersecurity education and the need for further research to deepen understanding in this area. The selected papers and their related data, such as title, author(s), publication source, publisher, publication year, findings, and identified themes were extracted in an Excel file which can be accessed on Figshare public repository
DOI: 10.5281/zenodo.13770391
title: Climate matching models for the yellow starthistle rosette weevil (Coleoptera: Apionidae) for the western U.S.

description: The CSV file contains weekly estimates of daily climate data for the western U.S. at 1 km2 resolution. The file was too large to upload to GitHub so must be downloaded separately and placed in the "/data/weekly/away" directory of hte "CEBA_climMatch" repository at: https://github.com/bbarker505/CEBA_climMatch.

Column names are the variable plus week number, except for column 1, which is annual precipitation ("ppt_ann"). Variables include: precipitation ("ppt"), soil moisture ("sm"), maximum tempearture ("tmax"), and minimum temperature ("tmin"). See Barker et al. (2024) pre-print at XXX for more details about the climate data and how they were used.
DOI: 10.25318/3310089601-eng
title: Business or organization change in supply chain challenges over the last three months, fourth quarter of 2024

description: Business or organization change in supply chain challenges over the last three months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2024.
DOI: 10.15468/dl.ek7mzy
title: Occurrence Download

description: A dataset containing 135 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Glossanodon semifasciatus (Kishinouye, 1904)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 135 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007095-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1048208.10fu
title: SH1048208.10FU

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/sh1048209.10fu
title: SH1048209.10FU

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/sh1048210.10fu
title: SH1048210.10FU

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.