cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1121538
title: First popular article on : How does cognition work ?

description: I have developed a theory that can address many questions about the nature and the operating<br>principles of cognition. I have exposed the main ideas in a book which you can find in the “books”<br>section of this website; however, it is a bit difficult to follow, both for the type of topics and for my<br>own limitations in the ability of exposition.<br>I thought it would be helpful to write a series of articles where I hope to be able to explain the main<br>ideas in the simplest way. In the presentation I will adopt the approach to question the reader (to<br>encourage him to meditate on some of the highlights) and to suggest the answers which I am able to<br>provide.<br>This is the first of these articles .
DOI: 10.6084/m9.figshare.1121550.v1
title: Gaussian Job Archive for C17H28O4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1121538.v1
title: First popular article on : How does cognition work ?

description: I have developed a theory that can address many questions about the nature and the operating<br>principles of cognition. I have exposed the main ideas in a book which you can find in the “books”<br>section of this website; however, it is a bit difficult to follow, both for the type of topics and for my<br>own limitations in the ability of exposition.<br>I thought it would be helpful to write a series of articles where I hope to be able to explain the main<br>ideas in the simplest way. In the presentation I will adopt the approach to question the reader (to<br>encourage him to meditate on some of the highlights) and to suggest the answers which I am able to<br>provide.<br>This is the first of these articles .
DOI: 10.6084/m9.figshare.1121511
title: Training Labels for TREC Blogs08 dataset

description: I want to share a set of annotations I made on the TREC Blogs08 test collection [1]. Just a short description: 83 blogs (12870 blog posts) were manually annotated with category values like blog genre, authorship (single author or multiauthor) and several information quality facets (e.g. factual, indepth, emotional). I used the dataset for several publications and for the TREC Blog Distillation Task in 2009. Feel free to use the annotations, just give me and my colleagues credit by citing: @InProceedings{Lex2010,<br>author = {Elisabeth Lex and Michael Granitzer and Markus Muhr and Andreas Juffinger},<br>title ={Stylometric Features for Emotion Level Classification in News Related Blogs},<br>booktitle ={Proceedings of the 9th ACM RIAO Conference},<br>year = {2010}<br>} [1] A description of the Blogs08 test collection can be found at http://ir.dcs.gla.ac.uk/test_collections/blogs08info.html. License details and information on how to get access to the TREC Blogs08 collection are provided in http://ir.dcs.gla.ac.uk/test_collections.
DOI: 10.6084/m9.figshare.1121509
title: Validation tool for traction force microscopy

description: Traction force microscopy (TFM) is commonly used to estimate cells' traction forces from the deformation that they cause on their substrate. The accuracy of TFM highly depends on the computational methods used to measure the deformation of the substrate and estimate the forces, and also on the specifics of the experimental set-up. Computer simulations can be used to evaluate the effect of both the computational methods and the experimental set-up without the need to perform numerous experiments. Here, we present one such TFM simulator that addresses several limitations of the existing ones. As a proof of principle, we recreate a TFM experimental set-up, and apply a classic 2D TFM algorithm to recover the forces. In summary, our simulator provides a valuable tool to study the performance, refine experimentally, and guide the extraction of biological conclusions from TFM experiments.
DOI: 10.6084/m9.figshare.1121511.v1
title: Training Labels for TREC Blogs08 dataset

description: I want to share a set of annotations I made on the TREC Blogs08 test collection [1]. Just a short description: 83 blogs (12870 blog posts) were manually annotated with category values like blog genre, authorship (single author or multiauthor) and several information quality facets (e.g. factual, indepth, emotional). I used the dataset for several publications and for the TREC Blog Distillation Task in 2009. Feel free to use the annotations, just give me and my colleagues credit by citing: @InProceedings{Lex2010,<br>author = {Elisabeth Lex and Michael Granitzer and Markus Muhr and Andreas Juffinger},<br>title ={Stylometric Features for Emotion Level Classification in News Related Blogs},<br>booktitle ={Proceedings of the 9th ACM RIAO Conference},<br>year = {2010}<br>} [1] A description of the Blogs08 test collection can be found at http://ir.dcs.gla.ac.uk/test_collections/blogs08info.html. License details and information on how to get access to the TREC Blogs08 collection are provided in http://ir.dcs.gla.ac.uk/test_collections.
DOI: 10.6084/m9.figshare.1121497
title: Transition Metal Complexes of α-Keto Stabilized Sulfonium Ylides: Linear in Contrast to Bridging Structure in Silver(I) Complexes

description: GRAPHICAL ABSTRACT
DOI: 10.6084/m9.figshare.1121497.v1
title: Transition Metal Complexes of α-keto Stabilized Sulfonium Ylides: Linear in Contrast to Bridging Structure in Silver(I) Complexes

description: The reactions of α-keto stabilized sulfonium ylide (Me)<sub>2</sub>SCHC(O)C<sub>6</sub>H<sub>4</sub>-m-NO<sub>2</sub> (<b>Y<sub>1</sub></b>) with HgX<sub>2</sub> (X = Cl (<b>1</b>), Br (<b>2</b>) and I (<b>3</b>)) in equimolar ratios and CdCl<sub>2</sub>.H<sub>2</sub>O (<b>4</b>) in 1:2 molar ratio using methanol as a solvent lead to binuclear Hg(II) complexes and a polymeric Cd(II) organic-inorganic hybrid. Furthermore, from the reaction of sulfonium ylides (<b>Y<sub>1</sub></b>), (Me)<sub>2</sub>SCHC(O)C<sub>6</sub>H<sub>4</sub>-p-NO<sub>2</sub> (<b>Y<sub>2</sub></b>) and (Me)<sub>2</sub>SCHC(O)C<sub>6</sub>H<sub>4</sub>-p-OMe (<b>Y<sub>3</sub></b>) with AgTfO in 2: 1 molar ratio using dichloromethane as a solvent, [Ag(Y<sub>n</sub>)<sub>2</sub>]TfO (n = 1 (<b>5</b>), n = 2 (<b>6</b>), n = 3 (<b>7</b>)) complexes were obtained. Single crystal X-ray diffraction analysis reveals the presence of linear structure for <b>5</b>. Characterization of the obtained compounds was also performed by elemental analysis, IR, <sup>1</sup>H- and <sup>13</sup>C- NMR.
DOI: 10.6084/m9.figshare.1121509.v2
title: Validation tool for traction force microscopy

description: Traction force microscopy (TFM) is commonly used to estimate cells' traction forces from the deformation that they cause on their substrate. The accuracy of TFM highly depends on the computational methods used to measure the deformation of the substrate and estimate the forces, and also on the specifics of the experimental set-up. Computer simulations can be used to evaluate the effect of both the computational methods and the experimental set-up without the need to perform numerous experiments. Here, we present one such TFM simulator that addresses several limitations of the existing ones. As a proof of principle, we recreate a TFM experimental set-up, and apply a classic 2D TFM algorithm to recover the forces. In summary, our simulator provides a valuable tool to study the performance, refine experimentally, and guide the extraction of biological conclusions from TFM experiments.
DOI: 10.6084/m9.figshare.1121497.v3
title: Transition Metal Complexes of α-Keto Stabilized Sulfonium Ylides: Linear in Contrast to Bridging Structure in Silver(I) Complexes

description: GRAPHICAL ABSTRACT
DOI: 10.6084/m9.figshare.1121509.v4
title: Validation tool for traction force microscopy

description: Traction force microscopy (TFM) is commonly used to estimate cells' traction forces from the deformation that they cause on their substrate. The accuracy of TFM highly depends on the computational methods used to measure the deformation of the substrate and estimate the forces, and also on the specifics of the experimental set-up. Computer simulations can be used to evaluate the effect of both the computational methods and the experimental set-up without the need to perform numerous experiments. Here, we present one such TFM simulator that addresses several limitations of the existing ones. As a proof of principle, we recreate a TFM experimental set-up, and apply a classic 2D TFM algorithm to recover the forces. In summary, our simulator provides a valuable tool to study the performance, refine experimentally, and guide the extraction of biological conclusions from TFM experiments.
DOI: 10.6084/m9.figshare.1121497.v2
title: Transition Metal Complexes of α-Keto Stabilized Sulfonium Ylides: Linear in Contrast to Bridging Structure in Silver(I) Complexes

description: GRAPHICAL ABSTRACT
DOI: 10.6084/m9.figshare.1121509.v3
title: Validation tool for traction force microscopy

description: Traction force microscopy (TFM) is commonly used to estimate cells' traction forces from the deformation that they cause on their substrate. The accuracy of TFM highly depends on the computational methods used to measure the deformation of the substrate and estimate the forces, and also on the specifics of the experimental set-up. Computer simulations can be used to evaluate the effect of both the computational methods and the experimental set-up without the need to perform numerous experiments. Here, we present one such TFM simulator that addresses several limitations of the existing ones. As a proof of principle, we recreate a TFM experimental set-up, and apply a classic 2D TFM algorithm to recover the forces. In summary, our simulator provides a valuable tool to study the performance, refine experimentally, and guide the extraction of biological conclusions from TFM experiments.
DOI: 10.6084/m9.figshare.1120685
title: Neuronal Correlates of a Virtual- Reality-Based Passive Sensory P300 Network

description: Passive P300 raw EEG data
DOI: 10.6084/m9.figshare.1120685.v1
title: Neuronal Correlates of a Virtual- Reality-Based Passive Sensory P300 Network

description: Passive P300 raw EEG data
DOI: 10.6084/m9.figshare.1120663
title: deboever-sf3b1-2014

description: Files for replicating SF3B1 study.
DOI: 10.6084/m9.figshare.1120661
title: Data from "Earthworms modify plant biomass and nitrogen capture under conditions of soil nutrient heterogeneity and elevated atmospheric CO2 concentrations"

description: Data from the article: García-Palacios, P., F. T. Maestre, M. A. Bradford & J. F. Reynolds. Earthworms modify plant biomass and nitrogen capture under conditions of soil nutrient heterogeneity and elevated atmospheric CO2 concentrations. Soil Biology & Biochemistry, doi: 10.1016/j.soilbio.2014.08.002 Abstract: Earthworms modify the way roots respond to soil nutrient patchiness. However, few studies have evaluated the joint effects of earthworms and soil heterogeneity on plant community biomass and species dominance, and none of them have assessed the influence of different patch features and environmental conditions on such effects. We evaluated how soil nutrient heterogeneity, earthworms (Eisenia fetida), organic material quality (15N-labelled leaves and roots of contrasting C: N ratios) and elevated atmospheric CO2 concentrations (phytotron chambers) affected the resource-use strategy, biomass and species dominance of mixtures formed by Lolium perenne L. and Plantago lanceolata L. Soil heterogeneity decreased N capture from the organic material, especially in the presence of earthworms. Mixtures experienced a 26 and 36% decrease in shoot and root biomass when earthworms were added to the heterogeneous microcosms, but only with high quality organic material. The dominance of Lolium perenne was lower under conditions of elevated CO2, nutrient heterogeneity and earthworms. Our data suggest that earthworms can neutralize positive plant growth responses to soil heterogeneity by exacerbating decreases in the supply of N to the plant. Specifically, earthworms foraging for high quality patches may stimulate microbial N immobilization, translating into lower N capture by plants. Increases in casting activity under elevated CO2, and hence in microbial N immobilization, may also explain why earthworms modulated the effects of soil heterogeneity and CO2 concentrations on plant community structure. We show that earthworms, absent from most soil nutrient heterogeneity studies, mediate plant biomass responses to nutrient patchiness by affecting N capture. Future plant-foraging behavior studies should consider the roles played by soil engineers such as earthworms, so that results can be better extrapolated to natural communities. There are two spreadsheets with data. The spreadsheets "Data" and "Metadata" contain the data and the description of all the variables and units, respectively.
DOI: 10.6084/m9.figshare.1120661.v1
title: Data from "Earthworms modify plant biomass and nitrogen capture under conditions of soil nutrient heterogeneity and elevated atmospheric CO2 concentrations"

description: Data from the article: García-Palacios, P., F. T. Maestre, M. A. Bradford & J. F. Reynolds. Earthworms modify plant biomass and nitrogen capture under conditions of soil nutrient heterogeneity and elevated atmospheric CO2 concentrations. Soil Biology & Biochemistry, doi: 10.1016/j.soilbio.2014.08.002 Abstract: Earthworms modify the way roots respond to soil nutrient patchiness. However, few studies have evaluated the joint effects of earthworms and soil heterogeneity on plant community biomass and species dominance, and none of them have assessed the influence of different patch features and environmental conditions on such effects. We evaluated how soil nutrient heterogeneity, earthworms (Eisenia fetida), organic material quality (15N-labelled leaves and roots of contrasting C: N ratios) and elevated atmospheric CO2 concentrations (phytotron chambers) affected the resource-use strategy, biomass and species dominance of mixtures formed by Lolium perenne L. and Plantago lanceolata L. Soil heterogeneity decreased N capture from the organic material, especially in the presence of earthworms. Mixtures experienced a 26 and 36% decrease in shoot and root biomass when earthworms were added to the heterogeneous microcosms, but only with high quality organic material. The dominance of Lolium perenne was lower under conditions of elevated CO2, nutrient heterogeneity and earthworms. Our data suggest that earthworms can neutralize positive plant growth responses to soil heterogeneity by exacerbating decreases in the supply of N to the plant. Specifically, earthworms foraging for high quality patches may stimulate microbial N immobilization, translating into lower N capture by plants. Increases in casting activity under elevated CO2, and hence in microbial N immobilization, may also explain why earthworms modulated the effects of soil heterogeneity and CO2 concentrations on plant community structure. We show that earthworms, absent from most soil nutrient heterogeneity studies, mediate plant biomass responses to nutrient patchiness by affecting N capture. Future plant-foraging behavior studies should consider the roles played by soil engineers such as earthworms, so that results can be better extrapolated to natural communities. There are two spreadsheets with data. The spreadsheets "Data" and "Metadata" contain the data and the description of all the variables and units, respectively.
DOI: 10.6084/m9.figshare.1120663.v2
title: deboever-sf3b1-2014

description: Files for replicating SF3B1 study.
DOI: 10.6084/m9.figshare.1120631
title: Quantitative analysis of fluorescent image – from descriptive to computational microscopy

description: Application of fluorescence in microscopy led to a considerable breakthrough in biochemical sciences by facilitating a new way of observing complex biological processes, structures and interactions and locating them in particular parts of the cell. Today, fluorescent imaging is widely applied in published literature to confirm research hypotheses. However, about only one of ten publications provides supporting information for the observations based on fluorescence through a computational analysis of the acquired images. “One picture is worth ten thousand words” was once stated by Frederic R. Bernard, however, images alone may not be enough to provide reliable information. For example, given population diversity, it is very likely that a selected cell ( ‘the cell’ ) will be found that confirms a particular theory, while most others will refute it. In addition the validity of observations conducted on cell cultures may be disputable, as these are usually cancer or immortalized cells and therefore differ significantly from the original tissue. It is known, for example that cultured cancer cells easily evolve into a variety of clones that may not differ morphologically, but when stimulated to undergo differentiation or cell death they tend to react differently to normal cells at the biomolecular level. While cellular analysis requires a great deal of caution, descriptive tissue analysis is even more subjective and interpretations are highly dependent on personal experience of the observer. Baak, in his article [Baak 2002] addressed difficulties in qualitative assessment of microscopic samples in pathology. The reliability was acceptable when an observation was assessed by at least three specialists, but consistency was low especially in more complex analysis. Comparison of the data between different laboratories is also difficult considering the simplicity of observations made and the complexity of the pathological processes.<br>Modern imaging methods which use charged-coupled devices (CCDs) or photomultipliers (PMTs) provide a possibility to capture the visual observations in a numerical, digitalized form. In this format reliable quantitative data can be extracted which open new ways of understanding and supporting an observation process. In this chapter, various methods of quantitative analysis will be presented and discussed, applicable to studies of a single cell as well as whole populations and tissues.
DOI: 10.6084/m9.figshare.1120663.v3
title: deboever-sf3b1-2014

description: Files for replicating SF3B1 study.
DOI: 10.6084/m9.figshare.1120663.v4
title: deboever-sf3b1-2014

description: Files for replicating SF3B1 study.
DOI: 10.6084/m9.figshare.1120631.v1
title: Quantitative analysis of fluorescent image – from descriptive to computational microscopy

description: Application of fluorescence in microscopy led to a considerable breakthrough in biochemical sciences by facilitating a new way of observing complex biological processes, structures and interactions and locating them in particular parts of the cell. Today, fluorescent imaging is widely applied in published literature to confirm research hypotheses. However, about only one of ten publications provides supporting information for the observations based on fluorescence through a computational analysis of the acquired images. “One picture is worth ten thousand words” was once stated by Frederic R. Bernard, however, images alone may not be enough to provide reliable information. For example, given population diversity, it is very likely that a selected cell ( ‘the cell’ ) will be found that confirms a particular theory, while most others will refute it. In addition the validity of observations conducted on cell cultures may be disputable, as these are usually cancer or immortalized cells and therefore differ significantly from the original tissue. It is known, for example that cultured cancer cells easily evolve into a variety of clones that may not differ morphologically, but when stimulated to undergo differentiation or cell death they tend to react differently to normal cells at the biomolecular level. While cellular analysis requires a great deal of caution, descriptive tissue analysis is even more subjective and interpretations are highly dependent on personal experience of the observer. Baak, in his article [Baak 2002] addressed difficulties in qualitative assessment of microscopic samples in pathology. The reliability was acceptable when an observation was assessed by at least three specialists, but consistency was low especially in more complex analysis. Comparison of the data between different laboratories is also difficult considering the simplicity of observations made and the complexity of the pathological processes.<br>Modern imaging methods which use charged-coupled devices (CCDs) or photomultipliers (PMTs) provide a possibility to capture the visual observations in a numerical, digitalized form. In this format reliable quantitative data can be extracted which open new ways of understanding and supporting an observation process. In this chapter, various methods of quantitative analysis will be presented and discussed, applicable to studies of a single cell as well as whole populations and tissues.
DOI: 10.6084/m9.figshare.1120663.v5
title: deboever-sf3b1-2014

description: Files for replicating SF3B1 study.
DOI: 10.6084/m9.figshare.1120631.v2
title: Quantitative analysis of fluorescent image – from descriptive to computational microscopy

description: Application of fluorescence in microscopy led to a considerable breakthrough in biochemical sciences by facilitating a new way of observing complex biological processes, structures and interactions and locating them in particular parts of the cell. Today, fluorescent imaging is widely applied in published literature to confirm research hypotheses. However, about only one of ten publications provides supporting information for the observations based on fluorescence through a computational analysis of the acquired images. “One picture is worth ten thousand words” was once stated by Frederic R. Bernard, however, images alone may not be enough to provide reliable information. For example, given population diversity, it is very likely that a selected cell ( ‘the cell’ ) will be found that confirms a particular theory, while most others will refute it. In addition the validity of observations conducted on cell cultures may be disputable, as these are usually cancer or immortalized cells and therefore differ significantly from the original tissue. It is known, for example that cultured cancer cells easily evolve into a variety of clones that may not differ morphologically, but when stimulated to undergo differentiation or cell death they tend to react differently to normal cells at the biomolecular level. While cellular analysis requires a great deal of caution, descriptive tissue analysis is even more subjective and interpretations are highly dependent on personal experience of the observer. Baak, in his article [Baak 2002] addressed difficulties in qualitative assessment of microscopic samples in pathology. The reliability was acceptable when an observation was assessed by at least three specialists, but consistency was low especially in more complex analysis. Comparison of the data between different laboratories is also difficult considering the simplicity of observations made and the complexity of the pathological processes.<br>Modern imaging methods which use charged-coupled devices (CCDs) or photomultipliers (PMTs) provide a possibility to capture the visual observations in a numerical, digitalized form. In this format reliable quantitative data can be extracted which open new ways of understanding and supporting an observation process. In this chapter, various methods of quantitative analysis will be presented and discussed, applicable to studies of a single cell as well as whole populations and tissues.
DOI: 10.6084/m9.figshare.1120663.v6
title: deboever-sf3b1-2014

description: Files for replicating SF3B1 study.