cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1330142
title: Nonlinear spike-and-slab sparse coding for interpretable image encoding. PLOS ONE

description: 2) Realistic occlusion dataset This dataset contains realistic occlusions and controlled forms of sparse structure -- it is a realistic artificial dataset consisting of true occlusions (data is created by actual occlusions and not following any computational model for its generation). The data was generated using the Python Image Library (PIL) to draw hundreds of overlapping edges/strokes in a 256 × 256 pixel image: each stroke had an integer intensity between (1, 255), a width between (2, 4) pixels, and a length, starting, and ending position drawn independently from a uniform distribution. The image was then cut into overlapping D = 9 × 9 patches, each of which contained k ∈ (0, 5) overlapping strokes, for N = 61009. Gaussian noise of σ = 25 and μ = 0 was then independently added to each patch. Additionally, the dataset also contains the corresponding (automatically obtained) labels for each image, indicating the ground-truth number of occluding strokes k ∈ (0, 5) per image. Such a dataset represents and isolates challenging aspects of low-level image statistics that are present in all natural images. Particularly, it contains edges of varying intensities and their occlusions.<br>In the corresponding publication, data examples are shown in Figure 5.
DOI: 10.6084/m9.figshare.1330143.v3
title: Nonlinear spike-and-slab sparse coding for interpretable image encoding. PLOS ONE

description: 3) Natural image occlusions This dataset contains an image of underbrush in a forest (taken bridge.jpg, which has been used for denoising bench-marking [33]), which is rich with occluding branches and twigs. From this original noise-free image, we cut a 110 × 110 pixel occlusion-rich section and scaled it up to 256 × 256 pixels to use in our dataset. To compose the dataset, we cut the 256 × 256 image (with pixel values ranging from (0, 255)) into N = 61009 overlapping image patches of D = 9 × 9 pixels, then add independent Gaussian noise with σ = 5. In the corresponding publication, the data is shown in Figure8A-B. [33] Mairal, J., Bach, F., Ponce, J., Sapiro, G., and Zisserman, A. (2009): Non-local sparse models for image<br>restoration. International Conference on Computer Vision 25.
DOI: 10.6084/m9.figshare.1330141
title: Nonlinear spike-and-slab sparse coding for interpretable image encoding. PLOS ONE

description: 1) Nonlinear occluding bars data This is ground truth data generated according to the nonlinear model in the corresponding publication, used in the first set of experiments entitled "Parameter recovery on artificial ground-truth data". It contains 2,000 examples, generated with D = 5 × 5 = 25 pixels, and H = 10 hidden dimensions according to our model: N images with overlapping ‘bars’ of varying intensities and with Gaussian observation noise of variance 2. On average, each data point contains two bars. In the corresponding publication, the data is shown in Figure 4A.
DOI: 10.6084/m9.figshare.1330143.v2
title: 3) Natural image occlusions

description: This dataset contains an image of underbrush in a forest (taken bridge.jpg, which has been used for denoising bench-marking [33]), which is rich with occluding branches and twigs. From this original noise-free image, we cut a 110 × 110 pixel occlusion-rich section and scaled it up to 256 × 256 pixels to use in our dataset. To compose the dataset, we cut the 256 × 256 image (with pixel values ranging from (0, 255)) into N = 61009 overlapping image patches of D = 9 × 9 pixels, then add independent Gaussian noise with σ = 5. In the corresponding publication, the data is shown in Figure8A-B. [33] Mairal, J., Bach, F., Ponce, J., Sapiro, G., and Zisserman, A. (2009): Non-local sparse models for image<br>restoration. International Conference on Computer Vision 25.
DOI: 10.6084/m9.figshare.1330140
title: The Mass Distribution of Companions to Low-Mass White Dwarfs

description: In this fileset, we include samples from the posterior distribution of a model describing the mass distribution of companions to low mass white dwarfs in binary stellar systems. We also provide a python script to determine the companion mass distribution and neutron star probability based on these posterior samples. The method is described in Andrews, Price-Whelan, & Agüeros (2014), ApJ, 797L, 32.
DOI: 10.6084/m9.figshare.1330142.v2
title: 2) Realistic occlusion dataset

description: This dataset contains realistic occlusions and controlled forms of sparse structure -- it is a realistic artificial dataset consisting of true occlusions (data is created by actual occlusions and not following any computational model for its generation). The data was generated using the Python Image Library (PIL) to draw hundreds of overlapping edges/strokes in a 256 × 256 pixel image: each stroke had an integer intensity between (1, 255), a width between (2, 4) pixels, and a length, starting, and ending position drawn independently from a uniform distribution. The image was then cut into overlapping D = 9 × 9 patches, each of which contained k ∈ (0, 5) overlapping strokes, for N = 61009. Gaussian noise of σ = 25 and μ = 0 was then independently added to each patch. Additionally, the dataset also contains the corresponding (automatically obtained) labels for each image, indicating the ground-truth number of occluding strokes k ∈ (0, 5) per image. Such a dataset represents and isolates challenging aspects of low-level image statistics that are present in all natural images. Particularly, it contains edges of varying intensities and their occlusions.<br>In the corresponding publication, data examples are shown in Figure 5.
DOI: 10.6084/m9.figshare.1330144.v4
title: Nonlinear spike-and-slab sparse coding for interpretable image encoding 4) Natural image patches. PLOS ONE

description: 4) Natural image patches This file explains the natural image patches data used in the final experiment of the corresponding publication.
DOI: 10.6084/m9.figshare.1330140.v1
title: The Mass Distribution of Companions to Low-Mass White Dwarfs

description: In this fileset, we include samples from the posterior distribution of a model describing the mass distribution of companions to low mass white dwarfs in binary stellar systems. We also provide a python script to determine the companion mass distribution and neutron star probability based on these posterior samples. The method is described in Andrews, Price-Whelan, & Agüeros (2014), ApJ, 797L, 32.
DOI: 10.6084/m9.figshare.1330133
title: SVM-RFE with Laplacian kernel for classification of signaling proteins based on molecular star graph descriptors using machine-learning models

description: Paper is available at: http://dx.doi.org/10.1016/j.jtbi.2015.07.038 Please cite: Carlos Fernandez-Lozano, Rubén F. Cuiñas, José A. Seoane, Enrique Fernández-Blanco, Julian Dorado, Cristian R. Munteanu, Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models, Journal of Theoretical Biology, Volume 384, 7 November 2015, Pages 50-58, ISSN 0022-5193, http://dx.doi.org/10.1016/j.jtbi.2015.07.038.(http://www.sciencedirect.com/science/article/pii/S0022519315003999) RFE-LAP selected features to improve the classification results with only 11 out of 42 features calculated with S2SNet. Therefore, these results can help to predict signaling function-related proteins using only a reduced amount of molecular information encoded into the protein sequence. We achieved an AUROC of 0.961 ± 0.11% in our 5 runs, and only 11 features: X5, H, S6, X4, eS6, eH, eTr3, eX5, eTr5, eTr0, Tr0
DOI: 10.6084/m9.figshare.1330138
title: Associations between substance type and vaginal intercourse probability by day of the week for participants who are single (panel A) or in a serious relationship (panel B).

description: Figure plotting primary effects reported in: Kerr, D.C.R., Washburn, I.J., Morris, M.K., Lewis, K.A.G., & Tiberio, S.S. Event-level associations of marijuana and heavy alcohol use with intercourse and condom use. <em>Journal of Studies on Alcohol and Drugs</em>.
DOI: 10.6084/m9.figshare.1330144.v3
title: 4) Natural image patches

description: This file explains the natural image patches data used in the final experiment of the corresponding publication.
DOI: 10.6084/m9.figshare.1330138.v1
title: Associations between substance type and vaginal intercourse probability by day of the week for participants who are single (panel A) or in a serious relationship (panel B).

description: Figure plotting primary effects reported in: Kerr, D.C.R., Washburn, I.J., Morris, M.K., Lewis, K.A.G., & Tiberio, S.S. Event-level associations of marijuana and heavy alcohol use with intercourse and condom use. <em>Journal of Studies on Alcohol and Drugs</em>.
DOI: 10.6084/m9.figshare.1330141.v2
title: 1) Nonlinear occluding bars data

description: This is ground truth data generated according to the nonlinear model in the corresponding publication, used in the first set of experiments entitled "Parameter recovery on artificial ground-truth data". It contains 2,000 examples, generated with D = 5 × 5 = 25 pixels, and H = 10 hidden dimensions according to our model: N images with overlapping ‘bars’ of varying intensities and with Gaussian observation noise of variance 2. On average, each data point contains two bars. In the corresponding publication, the data is shown in Figure 4A.
DOI: 10.6084/m9.figshare.1330132
title: Dataset for classification of signaling proteins based on molecular star graph descriptors using machine-learning models

description: The positive group of 608 signaling protein sequences was downloaded as FASTA format from Protein Databank (Berman et al., 2000) by using the “Molecular Function Browser” in the “Advanced Search Interface” (“Signaling (GO ID23052)”, protein identity cut-off = 30%). The negative group of 2077 non-signaling proteins was downloaded as the PISCES CulledPDB (http://dunbrack.fccc.edu/PISCES.php) (Wang & R. L. Dunbrack, 2003) (November 19th, 2012) using identity (degree of correspondence between two sequences) less than 20%, resolution of 1.6 Å and R-factor 0.25. The full dataset is containing 2685 FASTA sequences of protein chains from the PDB databank: 608 are signaling proteins and 2077 are non-signaling peptides. This kind of unbalanced data is not the most suitable to be used as an input for learning algorithms because the results would present a high sensitivity and low specificity; learning algorithms would tend to classify most of samples as part of the most common group. To avoid this situation, a pre-processing stage is needed in order to get a more balanced dataset, in this case by means of the synthetic minority oversampling technique (SMOTE). In short, SMOTE provides a more balanced dataset using an expansion of the lower class by creating new samples, interpolating other minority-class samples. After this pre-processing, the final dataset is composed of 1824 positive samples (signaling protein chains) and 2432 negative cases (non-signaling protein chains). Paper is available at: http://dx.doi.org/10.1016/j.jtbi.2015.07.038 Please cite: Carlos Fernandez-Lozano, Rubén F. Cuiñas, José A. Seoane, Enrique Fernández-Blanco, Julian Dorado, Cristian R. Munteanu, Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models, Journal of Theoretical Biology, Volume 384, 7 November 2015, Pages 50-58, ISSN 0022-5193, http://dx.doi.org/10.1016/j.jtbi.2015.07.038.(http://www.sciencedirect.com/science/article/pii/S0022519315003999)
DOI: 10.6084/m9.figshare.1330141.v4
title: Nonlinear spike-and-slab sparse coding for interpretable image encoding. PLOS ONE

description: 1) Nonlinear occluding bars data This is ground truth data generated according to the nonlinear model in the corresponding publication, used in the first set of experiments entitled "Parameter recovery on artificial ground-truth data". It contains 2,000 examples, generated with D = 5 × 5 = 25 pixels, and H = 10 hidden dimensions according to our model: N images with overlapping ‘bars’ of varying intensities and with Gaussian observation noise of variance 2. On average, each data point contains two bars. In the corresponding publication, the data is shown in Figure 4A.
DOI: 10.6084/m9.figshare.1330063.v1
title: Standards-based curation of a decade-old digital repository dataset of molecular information.

description: We report the procedures used in the curation of 158,122 molecular geometries deriving from the NCI set of reference molecules together with associated properties computed using the MOPAC semi-empirical quantum mechanical method and originally deposited in 2005 into the Cambridge DSpace repository as a data collection. The curation involved annotation of the original data using new MOPAC methods, updating the syntax of the CML documents used to express the data to ensure schema conformance and adding new metadata describing the entries together with a XML schema transformation to map the metadata schema to that used by the DataCite organisation. We have adopted a granularity model in which a DataCite persistent identifier (DOI) is created for each individual molecule to enable data discovery and data metrics at this level using DataCite tools. We recommend that the future research data management (RDM) of the scientific and chemical data components associated with journal articles (the "supporting information") should be conducted in a manner that facilitates automatic periodic curation.
DOI: 10.6084/m9.figshare.1330141.v3
title: Nonlinear spike-and-slab sparse coding for interpretable image encoding

description: 1) Nonlinear occluding bars data This is ground truth data generated according to the nonlinear model in the corresponding publication, used in the first set of experiments entitled "Parameter recovery on artificial ground-truth data". It contains 2,000 examples, generated with D = 5 × 5 = 25 pixels, and H = 10 hidden dimensions according to our model: N images with overlapping ‘bars’ of varying intensities and with Gaussian observation noise of variance 2. On average, each data point contains two bars. In the corresponding publication, the data is shown in Figure 4A.
DOI: 10.6084/m9.figshare.1328743
title: Gaussian Job Archive for CH4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1328743.v1
title: Gaussian Job Archive for CH4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1328776.v1
title: Comment on 'The role of information in the planning, management and evaluation of community nutrition programmes.'

description: This comment points to the need to carefully assess the roles that the use of routine growth monitoring data could play in the evaluation of community nutrition programmes, especially when coverage levels change.
DOI: 10.6084/m9.figshare.1328655
title: Flib+Data

description: In this Fileset you will encounter the most recent version of Flib, a software for fragment library generation. The software is linux-compliant and installation and usage instructions can be found in the README file. Three data sets are also provided: a training set of 43 proteins, a PDB-representative validation set of 41 proteins and a CASP validation set containing 275 protein domains. All the required files to run Flib for each of these targets can be found in the relevant data set.
DOI: 10.6084/m9.figshare.1328641
title: Study II BFF data

description: The Big Five Frequency Data from Study II.
DOI: 10.6084/m9.figshare.1328642
title: HOPG data taken alongside the BFF in Study I

description: HOPG data taken alongside the BFF in Study I
DOI: 10.6084/m9.figshare.1328643
title: HOPG data taken along side BFF from Study II

description: see title.
DOI: 10.6084/m9.figshare.1328655.v1
title: Flib+Data

description: In this Fileset you will encounter the most recent version of Flib, a software for fragment library generation. The software is linux-compliant and installation and usage instructions can be found in the README file. Three data sets are also provided: a training set of 43 proteins, a PDB-representative validation set of 41 proteins and a CASP validation set containing 275 protein domains. All the required files to run Flib for each of these targets can be found in the relevant data set.
DOI: 10.6084/m9.figshare.1328633
title: Study II - HOPG data take along side BFAS

description: The HOPG data taken with the BFAS data in Study II.
DOI: 10.6084/m9.figshare.1328636
title: Study I - BFF data

description: The data from the Big Five Frequency Scales, as described in study I of Warfel (2015).
DOI: 10.6084/m9.figshare.1328643.v1
title: HOPG data taken along side BFF from Study II

description: see title.
DOI: 10.6084/m9.figshare.1328628
title: Study I - HOPG data taken alongside the BFAS

description: The portion of the Hopg data taken along side the BFAS scales as described in Warfel (2015)
DOI: 10.6084/m9.figshare.1328633.v1
title: Study II - HOPG data take along side BFAS

description: The HOPG data taken with the BFAS data in Study II.
DOI: 10.6084/m9.figshare.1328641.v1
title: Study II BFF data

description: The Big Five Frequency Data from Study II.
DOI: 10.6084/m9.figshare.1328642.v1
title: HOPG data taken alongside the BFF in Study I

description: HOPG data taken alongside the BFF in Study I
DOI: 10.6084/m9.figshare.1328622
title: Study I - BFAS Data

description: Anonymized respondent - item data for the BFAS portion of Study I as described in Warfel (2015).
DOI: 10.6084/m9.figshare.1328626
title: Study II BFAS data

description: BFAS data from study II.
DOI: 10.6084/m9.figshare.1328622.v1
title: Study I - BFAS Data

description: Anonymized respondent - item data for the BFAS portion of Study I as described in Warfel (2015).
DOI: 10.6084/m9.figshare.1328626.v1
title: Study II BFAS data

description: BFAS data from study II.
DOI: 10.6084/m9.figshare.1328608
title: Raw data for Figure 5A

description: Densitometry analysis of intensity of gelatinolytic bands represented proMMP-9 and act.MMP-9, n=4.
DOI: 10.6084/m9.figshare.1328608.v1
title: Raw data for Figure 5A

description: Densitometry analysis of intensity of gelatinolytic bands represented proMMP-9 and act.MMP-9, n=4.
DOI: 10.6084/m9.figshare.1328613.v1
title: Reposición

description: 1. El documento adjunto "
DOI: 10.6084/m9.figshare.1328628.v1
title: Study I - HOPG data taken alongside the BFAS

description: The portion of the Hopg data taken along side the BFAS scales as described in Warfel (2015)
DOI: 10.6084/m9.figshare.1328607.v1
title: 3 March 2015 Field Notes

description: Field notes including image and/or video records
DOI: 10.6084/m9.figshare.1328607.v2
title: 3 March 2015 Field Notes

description: Field notes including image and/or video records. Original versions of all videos are available as of 20 Jan 2016 following the increase in allowed file size by figshare.
DOI: 10.6084/m9.figshare.1328600
title: Northwest Greenland WorldView DEMs

description: Original filelist of WorldView stereo pairs which were then processed through the Ames Stereo Pipeline to create high resolution digital elevation models (DEMs). These high resolution DEMs were then downsampled to 30 m resolution for comparison with Landsat 8-derived supraglacial lake depths. See Willis et al. (Nature, see links) for more DEM examples, Pope et al. (The Cryosphere) for presentation of data and results and Pope et al. (Computers and Geosciences) for code/method documentation.
DOI: 10.6084/m9.figshare.1328584
title: The effects of aging, malingering, and traumatic brain injury on computerized trail-making test performance

description: Data from the C-TMT trail making test
DOI: 10.6084/m9.figshare.1328600.v1
title: Northwest Greenland WorldView DEMs

description: Original filelist of WorldView stereo pairs which were then processed through the Ames Stereo Pipeline to create high resolution digital elevation models (DEMs). These high resolution DEMs were then downsampled to 30 m resolution for comparison with Landsat 8-derived supraglacial lake depths. See Willis et al. (Nature, see links) for more DEM examples, Pope et al. (The Cryosphere) for presentation of data and results and Pope et al. (Computers and Geosciences) for code/method documentation.
DOI: 10.6084/m9.figshare.1328584.v1
title: The effects of aging, malingering, and traumatic brain injury on computerized trail-making test performance

description: Data from the C-TMT trail making test
DOI: 10.6084/m9.figshare.1328583
title: A Multivariate EWMA Controller for Linear Dynamic Processes

description: Most research of run-to-run process control has been based on single-input and single-output processes with static input-output relationships. In practice, many complicated semiconductor manufacturing processes have multiple-input and multiple-output (MIMO) variables. In addition, the effects of previous process input recipes and output responses on the current outputs might be carried over for several process periods. Under these circumstances, using conventional controllers usually results in unsatisfactory performance. To overcome this, a complicated process could be viewed as dynamic MIMO systems with added general process disturbance and this paper proposes a dynamic-process multivariate exponentially weighted moving average (MEWMA) controller to adjust those processes. The long-term stability conditions of the proposed controller are derived analytically. Furthermore, by minimizing the total mean square error of the process outputs, the optimal discount matrix of the proposed controller under vector disturbance is derived. Finally, to highlight the contribution of the proposed controller, we also conduct a comprehensive simulation study to compare the control performance of the proposed controller with that of the single MEWMA and self-tuning controllers. On average, the results demonstrate that the proposed controller outperforms the other two controllers with a total mean square error reduction about 32% and 43%, respectively.
DOI: 10.6084/m9.figshare.1328583.v1
title: A Multivariate EWMA Controller for Linear Dynamic Processes

description: Most research of run-to-run process control has been based on single-input and single-output processes with static input-output relationships. In practice, many complicated semiconductor manufacturing processes have multiple-input and multiple-output (MIMO) variables. In addition, the effects of previous process input recipes and output responses on the current outputs might be carried over for several process periods. Under these circumstances, using conventional controllers usually results in unsatisfactory performance. To overcome this, a complicated process could be viewed as dynamic MIMO systems with added general process disturbance and this paper proposes a dynamic-process multivariate exponentially weighted moving average (MEWMA) controller to adjust those processes. The long-term stability conditions of the proposed controller are derived analytically. Furthermore, by minimizing the total mean square error of the process outputs, the optimal discount matrix of the proposed controller under vector disturbance is derived. Finally, to highlight the contribution of the proposed controller, we also conduct a comprehensive simulation study to compare the control performance of the proposed controller with that of the single MEWMA and self-tuning controllers. On average, the results demonstrate that the proposed controller outperforms the other two controllers with a total mean square error reduction about 32% and 43%, respectively.