cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.97814.v1
title: Gaussian Job Archive for C11H9NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97815.v1
title: Gaussian Job Archive for C5H4O2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.98545.v38
title: NeuroTrends Visualizations

description: Example visualizations from the NeuroTrends project.
DOI: 10.6084/m9.figshare.97812
title: Gaussian Job Archive for C48H38Cl2N12Ni2O8(3)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97811
title: The effect of divergence of investor beliefs on implied volatility smile

description: _
DOI: 10.6084/m9.figshare.97813.v1
title: Gaussian Job Archive for C4H3NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.98545.v39
title: NeuroTrends Visualizations

description: Example visualizations from the NeuroTrends project.
DOI: 10.6084/m9.figshare.97812.v1
title: Gaussian Job Archive for C48H38Cl2N12Ni2O8(3)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.98545.v41
title: NeuroTrends Visualizations

description: Example visualizations from the NeuroTrends project.
DOI: 10.6084/m9.figshare.98545.v40
title: NeuroTrends Visualizations

description: Example visualizations from the NeuroTrends project.
DOI: 10.6084/m9.figshare.97804
title: Collusion features from a simple two-dimensional game

description: Data and supplementary files for TUCS Technical Report "Detecting a colluding subset in a simple two-dimensional game" [1] The files aoi.arff, block.arff and tabu.arff contain features for area-of-intererst, blocking and tabu colluders respectively. Each file contains 23800 rows from 500 simulated games. Each row represents one subset of players in a single game. Simulations used 2 to 6 colluders and 100 games were performed with each number of colluders. For each game only data of subsets of players with a size equal to the number of colluders were collected. The data contains features proposed in [2] and in addition the following features: - colluderRatio: fraction of colluders in the subset<br>- colluders: number of colluders and subset's size<br>- collusion: 1 if all members of the subset are colluders, 0 otherwise. swiftness.csv contains mean time used to generate data for each subset size. wins.csv contains collusion pay-off as wins per 1000 games per colluder in dispenser setting from [2]. experiment_final.Rmd is the R Markdown file for generating the figures in the report. [1] Jussi Laasonen, Jouni Smed, Detecting a Colluding Subset in a Simple Two-Dimensional Game. TUCS Technical Reports 1074, 2013. [2] J. Laasonen, T. Knuutila, and J. Smed, “Eliciting collusion features,” in Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques, 2011, pp. 296–303.
DOI: 10.6084/m9.figshare.97804.v1
title: Collusion features from a simple two-dimensional game

description: Data and supplementary files for TUCS Technical Report "Detecting a colluding subset in a simple two-dimensional game" [1] The files aoi.arff, block.arff and tabu.arff contain features for area-of-intererst, blocking and tabu colluders respectively. Each file contains 23800 rows from 500 simulated games. Each row represents one subset of players in a single game. Simulations used 2 to 6 colluders and 100 games were performed with each number of colluders. For each game only data of subsets of players with a size equal to the number of colluders were collected. The data contains features proposed in [2] and in addition the following features: - colluderRatio: fraction of colluders in the subset<br>- colluders: number of colluders and subset's size<br>- collusion: 1 if all members of the subset are colluders, 0 otherwise. swiftness.csv contains mean time used to generate data for each subset size. wins.csv contains collusion pay-off as wins per 1000 games per colluder in dispenser setting from [2]. experiment_final.Rmd is the R Markdown file for generating the figures in the report. [1] Jussi Laasonen, Jouni Smed, Detecting a Colluding Subset in a Simple Two-Dimensional Game. TUCS Technical Reports 1074, 2013. [2] J. Laasonen, T. Knuutila, and J. Smed, “Eliciting collusion features,” in Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques, 2011, pp. 296–303.
DOI: 10.6084/m9.figshare.97811.v1
title: The effect of divergence of investor beliefs on implied volatility smile

description: This project aims to theoretically and empirically examine the implications that the divergence of investor belief could have on the implied volatility smile in stock options. In light of theoretical and empirical results in previous studies, the paper establishes various hypotheses describing the relationship between the volatility smile slope and factors widely used as proxies for investor belief. With the sample data on Options and firm-specific variables of 38 listed firms in the London Stock Exchange (LSE), the hypotheses put forward are tested using the regression tool. Investigation of the empirical results from the regression suggests that firms with smaller size and higher price to earnings ratios (P/E ratios), which are the two factors widely used proxies for investor belief, would have more pronounced volatility smile, i.e. steeper slope. Similarly, dispersion in earnings forecasts made by financial analysts, Open interest of options and Stock trading volume, which are further factors commonly used as proxy for investor belief, are also found to be in positive correlation with the steepness of smile slope. However, no evidence is found for the existence of the relationship between price to book ratios (P/B ratios) and the skewness of FTSE 100 index options. <br>
DOI: 10.6084/m9.figshare.97771
title: Gaussian Job Archive for C5H5NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97771.v1
title: Gaussian Job Archive for C5H5NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97811.v3
title: The effect of divergence of investor beliefs on implied volatility smile

description: _
DOI: 10.6084/m9.figshare.97753
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97811.v2
title: The effect of divergence of investor beliefs on implied volatility smile

description: This project aims to theoretically and empirically examine the implications that the divergence of investor belief could have on the implied volatility smile in stock options. In light of theoretical and empirical results in previous studies, the paper establishes various hypotheses describing the relationship between the volatility smile slope and factors widely used as proxies for investor belief. With the sample data on Options and firm-specific variables of 38 listed firms in the London Stock Exchange (LSE), the hypotheses put forward are tested using the regression tool. Investigation of the empirical results from the regression suggests that firms with smaller size and higher price to earnings ratios (P/E ratios), which are the two factors widely used proxies for investor belief, would have more pronounced volatility smile, i.e. steeper slope. Similarly, dispersion in earnings forecasts made by financial analysts, Open interest of options and Stock trading volume, which are further factors commonly used as proxy for investor belief, are also found to be in positive correlation with the steepness of smile slope. However, no evidence is found for the existence of the relationship between price to book ratios (P/B ratios) and the skewness of FTSE 100 index options. <br>
DOI: 10.6084/m9.figshare.97753.v1
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97792.v1
title: PKC53e putative mutant in self learning

description: this is a tdemonstration for rfigshare.
DOI: 10.6084/m9.figshare.97749
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97758.v1
title: PCA space of genome size, GC content, and number of proteins and genes

description: This plot shows the space that the actinobacteria occupy. This is a plot of a PCA analysis of genome size, GC content, and number of proteins and genes in a given genome. The genomes were mined from the NCBI database.
DOI: 10.6084/m9.figshare.97749.v1
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97792.v2
title: PKC53e putative mutant in self learning

description: this is a tdemonstration for rfigshare.
DOI: 10.6084/m9.figshare.97748
title: Gaussian Job Archive for C11H9NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97758.v2
title: Actinobacteria PCA space of genome size, GC content, and number of proteins and genes

description: This plot shows the space that the actinobacteria occupy. This is a plot of a PCA analysis of genome size, GC content, and number of proteins and genes in a given genome. The genomes were mined from the NCBI database. Actinobacteria are at the top of the diamond due to a greater GC content and in general larger genome size.
DOI: 10.6084/m9.figshare.97747
title: Gaussian Job Archive for C11H9NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97748.v1
title: Gaussian Job Archive for C11H9NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97752.v1
title: megischusdorsal.avi

description: CLSM micrograph of Megischus sp. male genitalia. PSUCIM_3012 ( USA: California: Riverside Co<br>Indio, ~1km N Dillon rd, 261m<br>33°48.240'N 116°10.857'W<br>4.v.2008, E08-015 A Ernst<br>dead Palo Verde wood)
DOI: 10.6084/m9.figshare.97792.v3
title: PKC53e putative mutant in self learning

description: this is a tdemonstration for rfigshare.
DOI: 10.6084/m9.figshare.97746
title: ariadne

description: ariadne is a java based package written by Tim Evans to implement the ideas produced with Carl Knappett and Ray Rivers. It produces networks based on one of a number of models, though the primary aim is to produce networks based on the cost/benefit optimisation model we have developed. We have used this on our work on networks in the Minoan Aegean (Middle Bronze age) described in our various talks and publications (some of these are also on figshare). In principle ariadne can be used to produce network models for any problem involving two dimensional spatial networks (not necessarily geographical) and currently includes other models such as PPA (proximal point analysis) and gravity models (see interactions paper for comparison). A core part of the package is to produce visualisations (with with eps and jpg output of networks, all based on the jung network visualisations) though it can also be run without visualisations to produce statistical analysis of models. Numerical output in various formats allows visualisation and further analysis using other packages There is some user documentation but it is not kept up to date, and the programme can be a bit delicate. So it probably needs face to face help to get it working or a good working knowledge of java (the code itself is pretty well documented using javadoc). I develop it on a Windows PC, and it should work fine on Linux. I have got it running on a Mac too despite issues there with old versions of java but it is more work to get this running.
DOI: 10.6084/m9.figshare.97747.v1
title: Gaussian Job Archive for C11H9NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97744
title: Gaussian Job Archive for C11H9NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97745
title: Gaussian Job Archive for C7H7NO2S2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97792.v4
title: PKC53e putative mutant in self learning

description: this is a tdemonstration for rfigshare.
DOI: 10.6084/m9.figshare.97744.v1
title: Gaussian Job Archive for C11H9NO2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97745.v1
title: Gaussian Job Archive for C7H7NO2S2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97742
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97792.v5
title: PKC53e putative mutant in self learning

description: this is a demonstration for rfigshare. This post was 99% produced in R and uploaded to figshare afterwards. This is the first step toward an automatic upload of our data on Figshare...
DOI: 10.6084/m9.figshare.97741
title: Gaussian Job Archive for C10H8N2O2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97741.v1
title: Gaussian Job Archive for C10H8N2O2

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97742.v1
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97740
title: Gaussian Job Archive for C5H3NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97739
title: Gaussian Job Archive for C5H3NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97740.v1
title: Gaussian Job Archive for C5H3NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97746.v1
title: ariadne

description: ariadne is a java based package written by myself to implement the ideas produced with Carl Knappett and Ray Rivers. It produces networks based on one of a number of models, though the primary aim is to produce networks based on the cost/benefit optimisation model we have developed. We have used this on our work on networks in the Minoan Aegean (Middle Bronze age) described in our various talks and publications. In principle ariadne can be used to produce network models for any problem involving two dimensional spatial networks (not necessarily geographical) and currently includes other models such as PPA (proximal point analysis) and gravity models (see interactions paper for comparison). A core part of the package is to produce visualisations (with with eps and jpg output of networks, all based on the jung network visualisations) though it can also be run without visualisations to produce statistical analysis of models. Numerical output in various formats allows visualisation and further analysis using other packages There is some user documentation but it is not kept up to date, and the programme can be a bit delicate. So it probably needs face to face help to get it working or a good working knowledge of java (the code itself is pretty well documented using javadoc). I develop it on a Windows PC, and it should work fine on Linux. I have got it running on a Mac too despite issues there with old versions of java but it is more work to get this running.
DOI: 10.6084/m9.figshare.97792.v6
title: PKC53e putative mutant in self learning

description: this is a demonstration for rfigshare. This post was 99% produced in R and uploaded to figshare afterwards. This is the first step toward an automatic upload of our data on Figshare...
DOI: 10.6084/m9.figshare.97737
title: Gaussian Job Archive for C5H3NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97738
title: Gaussian Job Archive for C10H5NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97736
title: Gaussian Job Archive for C5H3NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97737.v1
title: Gaussian Job Archive for C5H3NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97739.v1
title: Gaussian Job Archive for C5H3NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97736.v1
title: Gaussian Job Archive for C5H3NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97738.v1
title: Gaussian Job Archive for C10H5NO2S

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97734
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97735
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97733
title: Gaussian Job Archive for C13H15N2NaO7

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97734.v1
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97735.v1
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97792.v7
title: PKC53e putative mutant in self learning

description: this is a demonstration for rfigshare. This post was 99% produced in R and uploaded to figshare afterwards. This is the first step toward an automatic upload of our data on Figshare...
DOI: 10.6084/m9.figshare.97731
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97732
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97732.v1
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97733.v1
title: Gaussian Job Archive for C13H15N2NaO7

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97792.v8
title: PKC53e putative mutant in self learning

description: this is a demonstration for rfigshare. This post was 99% produced in R and uploaded to figshare afterwards. This is the first step toward an automatic upload of our data on Figshare... problems on figshare side: the file upload do not work anymore.
DOI: 10.6084/m9.figshare.97731.v1
title: Gaussian Job Archive for C9H18O6

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.97792.v9
title: PKC53e putative mutant in self learning

description: this is a demonstration for rfigshare. This post was 99% produced in R and uploaded to figshare afterwards. This is the first step toward an automatic upload of our data on Figshare... problems on figshare side: the file upload do not work anymore.
DOI: 10.6084/m9.figshare.97700.v1
title: Cutaneous ganglion cell choristoma. Report of a case

description: We describe the histological, immunohistochemical, and ultrastructural findings of a cutaneous tumour composed of ganglion cells, without any other proliferating component. As ganglion cells are not normal components of the skin, we propose the term “ganglion cell choristoma” for this lesion. The differential diagnosis of related lesions such as cutaneous ganglioneuroma, well-differentiated metastases from neuroblastoma, autonomic ganglia entrapped by neuroflbroma, and reactive processes, and the possible histogenesis of ganglion cell choristoma are discussed.
DOI: 10.6084/m9.figshare.97695.v1
title: Blue nevus: classical types and new related entities. A differential diagnostic review

description: Blue nevus is an uncommon pigmented lesion of dermal melanocytes. By convention,two well defined histologic variants, designated as “common” and “cellular”, have been recognised. In the last few years, these lesions have attracted much attention due to the recognition of news entities and to its confusion with malignant melanoma. In the prestient review, we point out the more striking features of new related entities (combined nevus, deep penetrating nevus, compound blue nevus) and establish the differential diagnosis with conflictive lesions such as atypical blue nevus, locally aggressive blue nevus, congenital giant melanocytic nevus with nodular growth and melanocytic dermal tumor of unpredictable outcome. We also review the concept of malignant blue nevus and the significance of lymph node metastases. The blue nevus is an uncommon pigmented lesion consisting of dermal melanocytesthat can appear in diverse forms: dendritic, spindle-shaped, oval-shaped, or polyhedral. Although it usually occurs in skin, it has been reported in other locations, such as oral mucosa, sclera, uterine cervix, vagina, prostate, spermatic cord, pulmonary hilus, orbit, conjunctiva, maxillary sinus, breast, and lymph nodes3,8,42,49. Generally, it occurs in adults as a single, acquired, intensely pigmented lesion, although familial and multiple nevi have been reported7,39. By convention, there are two well-defined histologic variants, designated as “common” and “cellular”, but lesions often manifest intermediate features. In the last few years, blue nevus has attracted much attention due to the recognitionof new (clinical and histologic) entities and to its confusion with malignant melanoma. Our aim is to review the most striking features of the new related entities and to establish the differential diagnosis with conflictive lesions. We also review the concept of malignant blue nevus and the significance of lymph nodes metastasis.