cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xz4m36dgzh.2
title: B-ALL blood, marrow, and CSF compound abundance data

description: Unscaled compound abundance data from untargeted metabolomics assays performed in blood plasma, bone marrow aspirate, and cerebrospinal fluid from N=10 children with B-cell acute lymphoblastic leukemia at the end of induction chemotherapy.
DOI: 10.17632/rrd36wdngb.1
title: Associations Between Dust Exposure and Hospitalizations in El Paso, Texas, USA 

description: Data for manuscript submitted to Atmosphere 2021-09-23
Associations Between Dust Exposure and Hospitalizations in El Paso, Texas, USA
DOI: 10.17632/6mpr2wnjtw.1
title: EXPLANATION OF INTENTION TO VACCINATE AGAINST COVID-19 AND THE AUTHORITIES' SUPPORT THROUGH THE THEORY OF PLANNED BEHAVIOR. AN EXPLORATORY STUDY IN ROMANIAN SAMPLE.

description: This study investigates the intention to vaccinate against Coronavirus and to support the authorities in limiting the effects of the pandemic from the perspective of the theory of planned behavior in a sample of 771 Romanian adults.

We intend to improve the prediction offered by the TPB model by constructing two alternative models that take into account additional variables such as irrational beliefs, past behavior, and risk perception. 
 
Data analysis involved performing hierarchical regressions and SEM modeling. Hierarchical regression analyzes show that the model based on demographic variables such as age and education and the constructs of the theory of planned behavior, attitude, social norms, and control beliefs best explain vaccination intention (75%) and support for authorities (29%).
This additional material presents the raw data used in the study, in SPSS type files. For SEM analysis the data are in JASP format.
DOI: 10.17632/4vgtdf6k3h.1
title: Why do leaders escalate their commitment to a failed course of action - Shertzer Brender

description: Why do leaders escalate their commitment to a failed course of action
DOI: 10.17632/hyhnv2rdsn.2
title: Chicken deubiquitinases in spleen, cecum and liver

description: This study entails a chemical proteomic experiment performed to identify deubiquitinases in the chicken tissues, including spleen, cecum, and liver, based on the reactivity of these enzymes with active site-directed probes.
DOI: 10.17632/mfms2s8zrx.1
title: Potential probiotic Pediococcus pentosaceus M41 modulates its proteome differentially for tolerances against heat, cold, acid and bile stresses

description: we have evaluated the differential proteomic responses of a potential novel probiotic Pediococcus pentosaceus M41 under heat, cold, acid, and bile stress conditions. We identified stress response proteins that could provide tolerances against these stresses and could be used as probiotic markers for evaluating stress tolerance. P. pentosaceus M41 was exposed to 50 oC for 2h (heat stress), 4 oC for 2h (cold stress), pH 3.0 for 2 h (acid stress), and 0.05% bile for 2h (bile stress). Proteomic analysis was carried out using 2D-IEF SDS PAGE and LC-MS/MS.
DOI: 10.6084/m9.figshare.14736468
title: Data Set from Survey on Peer Code Review in Research Software

description: Dataset from a survey on peer code review in research software.
DOI: 10.6084/m9.figshare.9633740
title: SPAligner: benchmarking scripts and datasets

description: The datasets supporting the conclusions of this article "SPAligner: alignment of long diverged molecular sequences to assembly graphs"
DOI: 10.5281/zenodo.5520019
title: XDCycleGAN VC Datatset

description: This is the public dataset that is used for training XDCycleGAN, a deep learning model for unpaired image-to-image translation. TrainA contains the optical colonoscopy images (OC). TrainB contains the virtual colonoscopy (VC) images. The model trained on this dataset is also included here. <strong>Abstract:</strong> Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing polyps (precursors for colon cancer). The non-invasive VC is normally used to inspect a 3D reconstructed colon (from computed tomography scans) for polyps and if found, the OC procedure is performed to physically traverse the colon via endoscope and remove these polyps. In this paper, we present a deep learning framework, Extended and Directional CycleGAN, for lossy unpaired image-to-image translation between OC and VC to augment OC video sequences with scale-consistent depth information from VC and VC with patient-specific textures, color and specular highlights from OC (e.g. for realistic polyp synthesis). Both OC and VC contain structural information, but it is obscured in OC by additional patient-specific texture and specular highlights, hence making the translation from OC to VC lossy. The existing CycleGAN approaches do not handle lossy transformations. To address this shortcoming, we introduce an extended cycle consistency loss, which compares the geometric structures from OC in the VC domain. This loss removes the need for the CycleGAN to embed OC information in the VC domain. To handle a stronger removal of the textures and lighting, a Directional Discriminator is introduced to differentiate the direction of translation (by creating paired information for the discriminator), as opposed to the standard CycleGAN which is direction-agnostic. Combining the extended cycle consistency loss and the Directional Discriminator, we show state-of-the-art results on scale-consistent depth inference for phantom, textured VC and for real polyp and normal colon video sequences. We also present results for realistic pendunculated and flat polyp synthesis from bumps introduced in 3D VC models. You can find the code and additional detail about Foldit via our Computation Endoscopy Platform at https://github.com/nadeemlab/CEP Please cite the following papers when using this dataset. The OC data came from the Hyper Kvasir datset: Borgli, Hanna, et al. "HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy." <em>Scientific data</em> 7.1 (2020): 1-14. The VC and fold annotation data came from the following TCIA dataset: Smith K, Clark K, Bennett W, Nolan T, Kirby J, Wolfsberger M, Moulton J, Vendt B, Freymann J. (2015). Data From CT_COLONOGRAPHY. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1
DOI: 10.5281/zenodo.5520018
title: XDCycleGAN VC Datatset

description: This is the public dataset that is used for training XDCycleGAN, a deep learning model for unpaired image-to-image translation. TrainA contains the optical colonoscopy images (OC). TrainB contains the virtual colonoscopy (VC) images. The model trained on this dataset is also included here. <strong>Abstract:</strong> Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing polyps (precursors for colon cancer). The non-invasive VC is normally used to inspect a 3D reconstructed colon (from computed tomography scans) for polyps and if found, the OC procedure is performed to physically traverse the colon via endoscope and remove these polyps. In this paper, we present a deep learning framework, Extended and Directional CycleGAN, for lossy unpaired image-to-image translation between OC and VC to augment OC video sequences with scale-consistent depth information from VC and VC with patient-specific textures, color and specular highlights from OC (e.g. for realistic polyp synthesis). Both OC and VC contain structural information, but it is obscured in OC by additional patient-specific texture and specular highlights, hence making the translation from OC to VC lossy. The existing CycleGAN approaches do not handle lossy transformations. To address this shortcoming, we introduce an extended cycle consistency loss, which compares the geometric structures from OC in the VC domain. This loss removes the need for the CycleGAN to embed OC information in the VC domain. To handle a stronger removal of the textures and lighting, a Directional Discriminator is introduced to differentiate the direction of translation (by creating paired information for the discriminator), as opposed to the standard CycleGAN which is direction-agnostic. Combining the extended cycle consistency loss and the Directional Discriminator, we show state-of-the-art results on scale-consistent depth inference for phantom, textured VC and for real polyp and normal colon video sequences. We also present results for realistic pendunculated and flat polyp synthesis from bumps introduced in 3D VC models. You can find the code and additional detail about Foldit via our Computation Endoscopy Platform at https://github.com/nadeemlab/CEP Please cite the following papers when using this dataset. The OC data came from the Hyper Kvasir datset: Borgli, Hanna, et al. "HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy." <em>Scientific data</em> 7.1 (2020): 1-14. The VC and fold annotation data came from the following TCIA dataset: Smith K, Clark K, Bennett W, Nolan T, Kirby J, Wolfsberger M, Moulton J, Vendt B, Freymann J. (2015). Data From CT_COLONOGRAPHY. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1
DOI: 10.5281/zenodo.5519974
title: FoldIt Public Dataset

description: This is the public dataset that is used for training FoldIt, a deep learning model for haustral fold detection and segmentation. TrainA contains the optical colonoscopy images (OC). TrainB contains the haustral fold annotations overlayed on the virtual colonoscopy (VC) images. Lastly, TrainC contains the VC images. The model trained on this dataset is also included here. <strong>Abstract:</strong> Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for registering pre-treatment virtual (CT) and optical colonoscopies, to guide navigation towards the anomalies found in pre-treatment scans. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays. A new transitive loss is introduced in order to leverage ground truth information between haustral fold annotations and virtual colonoscopy renderings. We demonstrate the effectiveness of our model on real challenging optical colonoscopy videos as well as on textured virtual colonoscopy videos with clinician-verified haustral fold annotations. In essence, the <strong>FoldIt</strong> model is a method for translating between domains when a shared common domain is available. We use the FoldIt model to learn a translation from optical colonoscopy to haustral fold annotation via a common virtual colonoscopy domain. You can find the code and additional detail about Foldit via our Computation Endoscopy Platform at https://github.com/nadeemlab/CEP Please cite the following papers when using this dataset. The OC data came from the Hyper Kvasir datset: Borgli, Hanna, et al. "HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy." <em>Scientific data</em> 7.1 (2020): 1-14. The VC and fold annotation data came from the following TCIA dataset: Smith K, Clark K, Bennett W, Nolan T, Kirby J, Wolfsberger M, Moulton J, Vendt B, Freymann J. (2015). Data From CT_COLONOGRAPHY. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1
DOI: 10.5281/zenodo.5519973
title: FoldIt Public Dataset

description: This is the public dataset that is used for training FoldIt, a deep learning model for haustral fold detection and segmentation. TrainA contains the optical colonoscopy images (OC). TrainB contains the haustral fold annotations overlayed on the virtual colonoscopy (VC) images. Lastly, TrainC contains the VC images. The model trained on this dataset is also included here. <strong>Abstract:</strong> Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for registering pre-treatment virtual (CT) and optical colonoscopies, to guide navigation towards the anomalies found in pre-treatment scans. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays. A new transitive loss is introduced in order to leverage ground truth information between haustral fold annotations and virtual colonoscopy renderings. We demonstrate the effectiveness of our model on real challenging optical colonoscopy videos as well as on textured virtual colonoscopy videos with clinician-verified haustral fold annotations. In essence, the <strong>FoldIt</strong> model is a method for translating between domains when a shared common domain is available. We use the FoldIt model to learn a translation from optical colonoscopy to haustral fold annotation via a common virtual colonoscopy domain. You can find the code and additional detail about Foldit via our Computation Endoscopy Platform at https://github.com/nadeemlab/CEP Please cite the following papers when using this dataset. The OC data came from the Hyper Kvasir datset: Borgli, Hanna, et al. "HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy." <em>Scientific data</em> 7.1 (2020): 1-14. The VC and fold annotation data came from the following TCIA dataset: Smith K, Clark K, Bennett W, Nolan T, Kirby J, Wolfsberger M, Moulton J, Vendt B, Freymann J. (2015). Data From CT_COLONOGRAPHY. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1
DOI: 10.5281/zenodo.5520029
title: XDCycleGAN Depth Dataset

description: This is the public dataset that is used for training XDCycleGAN, a deep learning model for unpaired image-to-image translation. TrainA contains the optical colonoscopy images (OC). TrainB contains the depth images. The model trained on this dataset is also included here. <strong>Abstract:</strong> Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing polyps (precursors for colon cancer). The non-invasive VC is normally used to inspect a 3D reconstructed colon (from computed tomography scans) for polyps and if found, the OC procedure is performed to physically traverse the colon via endoscope and remove these polyps. In this paper, we present a deep learning framework, Extended and Directional CycleGAN, for lossy unpaired image-to-image translation between OC and VC to augment OC video sequences with scale-consistent depth information from VC and VC with patient-specific textures, color and specular highlights from OC (e.g. for realistic polyp synthesis). Both OC and VC contain structural information, but it is obscured in OC by additional patient-specific texture and specular highlights, hence making the translation from OC to VC lossy. The existing CycleGAN approaches do not handle lossy transformations. To address this shortcoming, we introduce an extended cycle consistency loss, which compares the geometric structures from OC in the VC domain. This loss removes the need for the CycleGAN to embed OC information in the VC domain. To handle a stronger removal of the textures and lighting, a Directional Discriminator is introduced to differentiate the direction of translation (by creating paired information for the discriminator), as opposed to the standard CycleGAN which is direction-agnostic. Combining the extended cycle consistency loss and the Directional Discriminator, we show state-of-the-art results on scale-consistent depth inference for phantom, textured VC and for real polyp and normal colon video sequences. We also present results for realistic pendunculated and flat polyp synthesis from bumps introduced in 3D VC models. You can find the code and additional detail about Foldit via our Computation Endoscopy Platform at https://github.com/nadeemlab/CEP Please cite the following papers when using this dataset. The OC data came from the Hyper Kvasir datset: Borgli, Hanna, et al. "HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy." <em>Scientific data</em> 7.1 (2020): 1-14. The depth data came from the following TCIA dataset: Smith K, Clark K, Bennett W, Nolan T, Kirby J, Wolfsberger M, Moulton J, Vendt B, Freymann J. (2015). Data From CT_COLONOGRAPHY. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1
DOI: 10.5281/zenodo.5520028
title: XDCycleGAN Depth Dataset

description: This is the public dataset that is used for training XDCycleGAN, a deep learning model for unpaired image-to-image translation. TrainA contains the optical colonoscopy images (OC). TrainB contains the depth images. The model trained on this dataset is also included here. <strong>Abstract:</strong> Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing polyps (precursors for colon cancer). The non-invasive VC is normally used to inspect a 3D reconstructed colon (from computed tomography scans) for polyps and if found, the OC procedure is performed to physically traverse the colon via endoscope and remove these polyps. In this paper, we present a deep learning framework, Extended and Directional CycleGAN, for lossy unpaired image-to-image translation between OC and VC to augment OC video sequences with scale-consistent depth information from VC and VC with patient-specific textures, color and specular highlights from OC (e.g. for realistic polyp synthesis). Both OC and VC contain structural information, but it is obscured in OC by additional patient-specific texture and specular highlights, hence making the translation from OC to VC lossy. The existing CycleGAN approaches do not handle lossy transformations. To address this shortcoming, we introduce an extended cycle consistency loss, which compares the geometric structures from OC in the VC domain. This loss removes the need for the CycleGAN to embed OC information in the VC domain. To handle a stronger removal of the textures and lighting, a Directional Discriminator is introduced to differentiate the direction of translation (by creating paired information for the discriminator), as opposed to the standard CycleGAN which is direction-agnostic. Combining the extended cycle consistency loss and the Directional Discriminator, we show state-of-the-art results on scale-consistent depth inference for phantom, textured VC and for real polyp and normal colon video sequences. We also present results for realistic pendunculated and flat polyp synthesis from bumps introduced in 3D VC models. You can find the code and additional detail about Foldit via our Computation Endoscopy Platform at https://github.com/nadeemlab/CEP Please cite the following papers when using this dataset. The OC data came from the Hyper Kvasir datset: Borgli, Hanna, et al. "HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy." <em>Scientific data</em> 7.1 (2020): 1-14. The depth data came from the following TCIA dataset: Smith K, Clark K, Bennett W, Nolan T, Kirby J, Wolfsberger M, Moulton J, Vendt B, Freymann J. (2015). Data From CT_COLONOGRAPHY. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1
DOI: 10.6084/m9.figshare.3761562.v1989
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.5523249
title: Controlled coherent dynamics of [VO(TPP)], a prototype molecular nuclear qudit with an electronic ancilla. Open data set

description: Data supporting the original figures 2, 3, 4, 5, 6, 7, 8 of the related publication.
DOI: 10.5281/zenodo.5523248
title: Controlled coherent dynamics of [VO(TPP)], a prototype molecular nuclear qudit with an electronic ancilla. Open data set

description: Data supporting the original figures 2, 3, 4, 5, 6, 7, 8 of the related publication.
DOI: 10.25810/c953-e873
title: FIRED WESTERN EUROPE 

description: 

            This is event- and daily-level polygons for the Fire event delineation (FIRED) product for FRANCE, GERMANY, POLAND, SWITZERLAND, BELGIUM, NETHERLANDS, LUXEMBOURG and AUSTRIA from November 2001 to July 2021. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (www.github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 1 pixel and 5 days. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (events and daily) the event identification numbers are the same, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event.
DOI: 10.5281/zenodo.5502305
title: Dental replacement in Mesozoic birds: evidence from newly discovered Brazilian enantiornithines

description: Micro-CT scan data and the surface files (.stl) of the dentitions of the Brazilian enantiornithine specimens, MPM-90, MPM-351, and MPM-373. Please refer to the original publication.
DOI: 10.5281/zenodo.5502304
title: Dental replacement in Mesozoic birds: evidence from newly discovered Brazilian enantiornithines

description: Micro-CT scan data and the surface files (.stl) of the dentitions of the Brazilian enantiornithine specimens, MPM-90, MPM-351, and MPM-373. Please refer to the original publication.
DOI: 10.6084/m9.figshare.16658449
title: Gencode38 Blood PAXgene RNAseq

description: Mapped reads from Gencode38 using Salmon quasi aligner for various bioprojects of paired end blood PAXgene tube RNAseq.
DOI: 10.25810/m3eh-t326
title: FIRED UK IRELAND

description: 

            This is event- and daily-level polygons for the Fire event delineation (FIRED) product for The UK and IRELAND from November 2001 to July 2021. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (www.github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 1 pixel and 5 days. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (events and daily) the event identification numbers are the same, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event.
DOI: 10.6084/m9.figshare.16663585
title: Entrevistas

description: entrevistas
DOI: 10.6084/m9.figshare.16663585.v1
title: Entrevistas

description: entrevistas