cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17639/nott.24
title: Institutional stakeholder interviews (Portland, Oregon) on the uncertainties, concerns and challenges the limit implementation of Blue-Green infrastructure

description: This is a qualitative data collection. These data were collected as part of an interdisciplinary project undertaken by the Blue-Green Cities (B-GC) Research Consortium with the Portland-Vancouver ULTRA (Urban Long-term Research Area) project (PVU), as part of the “Clean Water for All” initiative. The project examined the sources of uncertainty responsible for current concerns and challenges to widespread adoption of Blue-Green Infrastructure in urban flood risk management. The study consisted of eleven semi-structured interviews with institutional stakeholders in the City of Portland, Oregon, USA. Broadly, the research aim was to identify and classify the key concerns, challenges and uncertainties faced by the interviewees in implementing sustainable flood risk management and Blue-Green infrastructure. We used the Relevant Dominant Uncertainty approach and identified numerous physical science and socio-political uncertainties that hamper decision making. We then addressed how decision makers can reduce their levels of concern and overcome the associated challenges to widen the implementation of Blue-Green infrastructure.
DOI: 10.17639/nott.6186
title: Geoarchaeology paper data

description: The journal requires all data presented in a research paper to be stored in a publicly accessible Repository
DOI: 10.17639/nott.6839
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 17596R-1

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: 17596R-1 (III) Source: NIG Name: S6kII Full name: Ribosomal protein S6 kinase II Also known as: RSK, ign, rsk2, dRSK Annotation symbol: CG17596 FlyBase ID: FBgn0262866 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.6827
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 35768

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: 35768 (III) Source: Bloomington Name: robo Full name: roundabout 1 Also known as: robo1, dRobo, Robo-1 Annotation symbol: CG13521 FlyBase ID: FBgn0005631 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.6857
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 3606

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: 3606 (III) Source: VDRC Name: sei Full name: seizure Also known as: erg, Derg Annotation symbol: CG3182 FlyBase ID: FBgn0003353 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.73
title: Engineering p-n junctions and bandgap tuning of InSe nanolayers by controlled oxidation
title: Oxidation of InSe

description: Data collection on the effects of thermal and laser annealing on the electronic properties of InSe
DOI: 10.17639/nott.6425
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 32092R-3

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: 32092R-3 (III) Source: NIG Name: CG42255 Full name: Also known as: Annotation symbol: CG42255 FlyBase ID: FBgn0259140 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.6806
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 8556R-3

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: 8556R-3 (III) Source: NIG Name: Rac2 (1) Full name: Rac2 Also known as: Rac, Drac2, Rac1, Rac GTPase, D-Rac2 Annotation symbol: CG8556 FlyBase ID: FBgn0014011 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.6807
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 50349

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: 50349 (III) Source: VDRC Name: Rac2 (2) Full name: Rac2 Also known as: Rac, Drac2, Rac1, Rac GTPase, D-Rac2 Annotation symbol: CG8556 FlyBase ID: FBgn0014011 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.6183
title: Ultra-high resolution imaging of thin films and single strands of polythiophene using atomic force microscopy

description: Images and related analysis relevant to this research publication.
DOI: 10.17639/nott.340
title: Prediction of broad spectrum pathogen attachment to coating materials for biomedical devices data

description: Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants and pacemakers.
DOI: 10.17639/nott.59
title: A spatial approach to evaluating the multiple benefits of Sustainable Drainage Systems using blue-green infrastructure

description: Traditional approaches to understanding the benefits of SuDs adopt cost-benefit analysis and ecosystem services methods often rely on value transfer to convert each benefit into a monetary value which can then be compared to the cost of the project. This approach while well-developed is limited because it does not incorporate the spatial nature of the benefits, and fails to emphasise that different beneficiaries are benefiting in different ways. This paper develops an alternative way of evaluating and comparing benefits, which allows spatial distribution and local context and circumstances to be taken into consideration. The suggested approach is to create a score for each benefit category which is normalised against a defined initial condition state on a scale of 0-10. This approach allows a direct comparison of the relative magnitude of benefits for a given location and provides a clear understanding of how (and to whom) multiple benefits accrue. The approach allows a singular significant benefit to be compared against many minor benefits, and the method can easily be modified to reflect local stakeholder preferences by weighting each benefit category appropriately. The method is demonstrated by the application to a case study, based in Newcastle in northern England.
DOI: 10.17639/nott.7018
title: Wireless intracellular sensing

description: Raw data corresponding to the paper "Wireless Nanobioelectronics for Electrical Intracellular Sensing", ACS Applied Nano Materials, 2019. https://doi.org/10.1021/acsanm.9b01374
DOI: 10.17639/nott.7019
title: In situ growth of silver microwires

description: Raw data corresponding to "Remotely Controlled in Situ Growth of Silver Microwires Forming Bioelectronic Interfaces", ACS Applied Materials Interfaces, 2019. https://doi.org/10.1021/acsami.8b22075
DOI: 10.17639/nott.6250
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 28311

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: 28311 (III) Source: Bloomington Name: Adar (2) Full name: Adenosine deaminase acting on RNA Also known as: dADAR, EG:BACN35H14 .1, hypnos-2 Annotation symbol: CG12598 FlyBase ID: FBgn0026086 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: 10.15468/dl.ybt64s
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tetraodon rubripes Temminck & Schlegel, 1850",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007080-241126133413365/datasets/export for details.
DOI: 10.17639/nott.341
title: Climb! performance audience response, 2017-06-08

description: Only closed scale responses have been included in this dataset.
description: Audience questionnaire responses to the premiere peformances of Climb!, an electro-acoustic composition for pianist and Disklavier.
DOI: 10.17639/nott.6181
title: Supramolecular Nucleoside-Based Gel: Molecular Dynamics Simulation and Characterization of Its Nanoarchitecture and Self-Assembly Mechanism

description: Among the diversity of existing supramolecular hydrogels, nucleic acid-based hydrogels are of particular interest for potential drug delivery and tissue engineering applications because of their inherent biocompatibility. Hydrogel performance is directly related to the nanostructure and the self-assembly mechanism of the material, an aspect that is not well-understood for nucleic acid-based hydrogels in general and has not yet been explored for cytosine-based hydrogels in particular. Herein, we use a broad range of experimental characterization techniques along with molecular dynamics (MD) simulation to demonstrate the complementarity and applicability of both approaches for nucleic acid-based gelators in general and propose the self-assembly mechanism for a novel supramolecular gelator, N4-octanoyl-2′-deoxycytidine. The experimental data and the MD simulation are in complete agreement with each other and demonstrate the formation of a hydrophobic core within the fibrillar structures of these mainly water-containing materials. The characterization of the distinct duality of environments in this cytidine-based gel will form the basis for further encapsulation of both small hydrophobic drugs and biopharmaceuticals (proteins and nucleic acids) for drug delivery and tissue engineering applications.
DOI: 10.17639/nott.6836
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 28919

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: 28919 (III) Source: Bloomington Name: RyR Full name: Ryanodine receptor Also known as: Rya-r44F, l(2)k04913, DRR, dry Annotation symbol: CG10844 FlyBase ID: FBgn0011286 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.6844
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 28386

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: 28386 (III) Source: VDRC Name: salr (2) Full name: spalt-related Also known as: spalt, sal, sal-r Annotation symbol: CG4881 FlyBase ID: FBgn0000287 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.6835
title: An in vivo systematic genetic analysis of tumour progression in Drosophila - RNAi line: 33353

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: 33353 (III) Source: Bloomington Name: RunxA Full name: Runt related A Also known as: Annotation symbol: CG34145 FlyBase ID: FBgn0083981 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)