cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/yeeb-k959
title: QA4Camera

description: Smartphone has been one of the most popular digital devices in the past decades, with more than 300 million smartphones sold every quarter in the world wide. Most of the smartphone vendors, such as Apple, Huawei, Samsung, launch their new flagship smartphones every year. People use smartphone cameras to shoot selfie photos, film scenery or events, and record videos of family and friends. The specifications of smartphone camera and the quality of taken pictures are major criteria for consumer to select and buy smartphones. Many smartphone manufacturers also introduce and advertise their smartphones by introducing the strengths and advantages of their smartphone cameras. However, how to evaluate the quality of smartphone cameras and the taken pictures remains a problem for both smartphone manufacturers and confumers. Currently in the market, there are several teams and companies who evaluate the quality of smartphone cameras and announce the ranking and scores of the quality of smartphone cameras, and the scores of smartphone cameras are subjectively graded by several photographers and experts from different aspects, such as exposure, color, noise and texture. However, subjective assessment is not easy to reproduce, and it is not easy to deploy in practical image processing systems. In the last two decades, objective image quality assessment (IQA) has been widely researched, and a large amount of objective IQA algorithms have been designed to automatically and accurately estimate the quality of images. However, most of objective IQA methods are designed to assess the overall perceived quality of the image degraded by various simulated distortions, which rarely exist in pictures taken by the modern smartphone cameras. Thus these methods are not suitable for the task of smartphone camera quality assessment, while objective evaluation methods specifically designed for the purpose of smartphone camera quality assessment are relatively rare.The purpose of this Grand Challenge is to drive efforts of image quality assessment towards smartphone camera quality assessment. With this Grand challenge, it is expected to develop objective smartphone camera quality assessment models from four different aspects, including exposure, color, noise and texture, by using the datasets released by the organizers. The goal is to provide reference quality rankings or scores for smartphone cameras and to both smartphone manufacturers and consumers.Participants are asked to submit four computational models to cacluate the rankings of smartphone camera from four aspects: exposure, color, noise and texture.
DOI: 10.17632/nn35dsmpf6.1
title: Photodynamic therapy for cutaneous squamous cell carcinoma in situ: impact of anatomic location, tumor diameter and incubation time on effectiveness

description: Background Photodynamic therapy (PDT) has been reported as a treatment for cutaneous squamous cell carcinoma in situ (SCCis) but there is limited data on the effectiveness of PDT with aminolevulinic acid (ALA-PDT). Objective To review the outcomes of SCCis treated with ALA-PDT and examine factors associated with response. Methods Retrospective review identified 58 patients with 68 primary SCCis lesions treated with ALA-PDT and blue light illumination. Patient demographics, lesion features, treatment details, clinical response, and subsequent recurrence were extracted from medical record review. Results Upon completion of PDT, the initial complete response rate was 77.9% and was not associated with the number of PDT treatments. On multivariate analysis, the factors associated with response were location on the face, tumor diameter &lt;2 centimeters, and longer ALA incubation time. Lesions treated with a maximum incubation time of &lt;3 hours had a 53.3% response compared with 84.9% for longer incubation. Subsequent recurrence of SCCis was noted in 7 of 53 cases (13.2%) at a median time of 11.7 months. Limitations This was a retrospective study performed at a single institution without systematic follow-up. Conclusions ALA-PDT may be an effective treatment for selected cases of SCCis. Effectiveness is impacted by anatomic location, tumor diameter, and ALA incubation time. This Dataset is Supplementary Data for the manuscript, "Photodynamic therapy for cutaneous squamous cell carcinoma in situ: impact of anatomic location, tumor diameter and incubation time on effectiveness." Authors: Nour Kibbi, MD, Yuemei Zhang, MD, MBA, David J. Leffell, MD, and Sean R. Christensen, MD, PhD Accepted for publication by Journal of the American Academy of Dermatology 2019 (JAAD-D-19-01312R1). Supplementary Figure 1. Response of SCCis according to number of PDT treatments. A, Initial complete response for all lesions and according to number of PDT treatments. B, Durable complete response at final follow-up for all lesions and according to number of PDT treatments. Supplementary Figure 2. Occlusion during PDT incubation period was significantly associated with incubation time. P value was calculated using the Spearman correlation. Supplementary Table. Factors associated with recurrence of SCCis after initial complete response.
DOI: 10.17632/vzwmzthy3z.1
title: Double dysprosium paper
DOI: 10.21227/01ag-hz86
title: Malware Analysis Datasets: Raw PE as Image

description: This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Raw PE byte stream rescaled to a 32 x 32 greyscale image using the Nearest Neighbor Interpolation algorithm and then flattened to a 1024 bytes vector. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.
DOI: 10.21227/875n-8q28
title: QA4Camera

description: Smartphone has been one of the most popular digital devices in the past decades, with more than 300 million smartphones sold every quarter in the world wide. Most of the smartphone vendors, such as Apple, Huawei, Samsung, launch their new flagship smartphones every year. People use smartphone cameras to shoot selfie photos, film scenery or events, and record videos of family and friends. The specifications of smartphone camera and the quality of taken pictures are major criteria for consumer to select and buy smartphones. Many smartphone manufacturers also introduce and advertise their smartphones by introducing the strengths and advantages of their smartphone cameras. However, how to evaluate the quality of smartphone cameras and the taken pictures remains a problem for both smartphone manufacturers and confumers. Currently in the market, there are several teams and companies who evaluate the quality of smartphone cameras and announce the ranking and scores of the quality of smartphone cameras, and the scores of smartphone cameras are subjectively graded by several photographers and experts from different aspects, such as exposure, color, noise and texture. However, subjective assessment is not easy to reproduce, and it is not easy to deploy in practical image processing systems. In the last two decades, objective image quality assessment (IQA) has been widely researched, and a large amount of objective IQA algorithms have been designed to automatically and accurately estimate the quality of images. However, most of objective IQA methods are designed to assess the overall perceived quality of the image degraded by various simulated distortions, which rarely exist in pictures taken by the modern smartphone cameras. Thus these methods are not suitable for the task of smartphone camera quality assessment, while objective evaluation methods specifically designed for the purpose of smartphone camera quality assessment are relatively rare.The purpose of this Grand Challenge is to drive efforts of image quality assessment towards smartphone camera quality assessment. With this Grand challenge, it is expected to develop objective smartphone camera quality assessment models from four different aspects, including exposure, color, noise and texture, by using the datasets released by the organizers. The goal is to provide reference quality rankings or scores for smartphone cameras and to both smartphone manufacturers and consumers.Participants are asked to submit four computational models to cacluate the rankings of smartphone camera from four aspects: exposure, color, noise and texture.
DOI: 10.21227/8brp-j220
title: Malware Analysis Datasets: Raw PE as Image

description: This dataset is part of my PhD research on malware detection and classification using Deep Learning. It contains static analysis data: Raw PE byte stream rescaled to a 32 x 32 greyscale image using the Nearest Neighbor Interpolation algorithm and then flattened to a 1024 bytes vector. PE malware examples were downloaded from virusshare.com. PE goodware examples were downloaded from portableapps.com and from Windows 7 x86 directories.
DOI: 10.5281/zenodo.3531971
title: CaLiGraph - A large-scale semantic knowledge graph compiled from Wikipedia Categories and Listpages

description: The CaLiGraph is a large-scale semantic knowledge graph with a rich ontology which is compiled from the DBpedia ontology, and Wikipedia Categories & Listpages. For more information, visit http://caligraph.org Information about uploaded files:<br> (all files are b-zipped and in the n-triple format)<br> <br> caligraph-metadata.nt.bz2<br> Metadata about the dataset which is described using void vocabulary. caligraph-ontology.nt.bz2<br> Class definitions, property definitions, restrictions, and labels of the CaLiGraph ontology. caligraph-ontology_dbpedia-mapping.nt.bz2<br> Mapping of classes and properties to the DBpedia ontology. caligraph-ontology_provenance.nt.bz2<br> Provenance information about classes (i.e. which Wikipedia category or list page has been used to create this class). caligraph-instances_types.nt.bz2<br> Definition of instances and (non-transitive) types. caligraph-instances_transitive-types.nt.bz2<br> Transitive types for instances (can also be induced by a reasoner). caligraph-instances_labels.nt.bz2<br> Labels for instances. caligraph-instances_relations.nt.bz2<br> Relations between instances derived from the class restrictions of the ontology (can also be induced by a reasoner). caligraph-instances_dbpedia-mapping.nt.bz2<br> Mapping of instances to respective DBpedia instances. caligraph-instances_provenance.nt.bz2<br> Provenance information about instances (e.g. if the instance has been extracted from a Wikipedia list page). dbpedia_caligraph-instances.nt.bz2<br> Additional instances of CaLiGraph that are not in DBpedia.<br> ! This file is no part of CaLiGraph but should rather be used as an extension to DBpedia. The contained triples are formulated under the DBpedia namespace, so they can be used together with DBpedia itself. ! dbpedia_caligraph-types.nt.bz2<br> Additional types of CaLiGraph that are not in DBpedia.<br> ! This file is no part of CaLiGraph but should rather be used as an extension to DBpedia. The contained triples are formulated under the DBpedia namespace, so they can be used together with DBpedia itself. ! dbpedia_caligraph-relations.nt.bz2<br> Additional relations of CaLiGraph that are not in DBpedia.<br> ! This file is no part of CaLiGraph but should rather be used as an extension to DBpedia. The contained triples are formulated under the DBpedia namespace, so they can be used together with DBpedia itself. !
DOI: 10.25345/c5td4m
title: MassIVE MSV000084548 - Wine_LCMS_neg_raw_OrbitrapElite_CID_HCD
title: LCMS of wine, Ultimate3000/Orbitrap Elite, negative polarity, CID/HCD of 3 most intensive peaks
DOI: 10.17632/3s3d7y9k6v.1
title: Risk aversion and uncertainty create a conundrum for planning recovery of a critically endangered species. Supplementary material.

description: Supplementary materials Sheet 1: summary of elicited values from experts (individual values not shown due to sensitivity of personal opinions). Sheet 2: data from nest survival experiments.
DOI: 10.17632/sswzys96rd.1
title: Lake Robert data Elbaz-Poulichet et al., 2019 QSR

description: The data set contains the data and the supplementary information for the manuscript: A 10,000-year record of trace metal and metalloid (Cu, Hg, Sb, Pb) deposition in a western Alpine lake (Lake Robert, France): deciphering local and regional mining contamination Françoise Elbaz-Poulichet, Stéphane Guédron, Develle Anne-Lise, Rémi Freydier, Vincent Perrot, Magali Rossi, Christine Piot, Sophie Delpoux, Pierre Sabatier Manuscript number JQSR_2019_562
DOI: 10.21227/gyjf-6239
title: QA4Camera

description: Smartphone has been one of the most popular digital devices in the past decades, with more than 300 million smartphones sold every quarter in the world wide. Most of the smartphone vendors, such as Apple, Huawei, Samsung, launch their new flagship smartphones every year. People use smartphone cameras to shoot selfie photos, film scenery or events, and record videos of family and friends. The specifications of smartphone camera and the quality of taken pictures are major criteria for consumer to select and buy smartphones. Many smartphone manufacturers also introduce and advertise their smartphones by introducing the strengths and advantages of their smartphone cameras. However, how to evaluate the quality of smartphone cameras and the taken pictures remains a problem for both smartphone manufacturers and confumers. Currently in the market, there are several teams and companies who evaluate the quality of smartphone cameras and announce the ranking and scores of the quality of smartphone cameras, and the scores of smartphone cameras are subjectively graded by several photographers and experts from different aspects, such as exposure, color, noise and texture. However, subjective assessment is not easy to reproduce, and it is not easy to deploy in practical image processing systems.In the last two decades, objective image quality assessment (IQA) has been widely researched, and a large amount of objective IQA algorithms have been designed to automatically and accurately estimate the quality of images. However, most of objective IQA methods are designed to assess the overall perceived quality of the image degraded by various simulated distortions, which rarely exist in pictures taken by the modern smartphone cameras. Thus these methods are not suitable for the task of smartphone camera quality assessment, while objective evaluation methods specifically designed for the purpose of smartphone camera quality assessment are relatively rare.The purpose of this Grand Challenge is to drive efforts of image quality assessment towards smartphone camera quality assessment. With this Grand challenge, it is expected to develop objective smartphone camera quality assessment models from four different aspects, including exposure, color, noise and texture, by using the datasets released by the organizers. The goal is to provide reference quality rankings or scores for smartphone cameras and to both smartphone manufacturers and consumers.Participants are asked to submit four computational models to cacluate the rankings of smartphone camera from four aspects: exposure, color, noise and texture.
DOI: 10.5281/zenodo.3522419
title: Three principles of data science: predictability, computability, and stability (PCS)

description: This repository contains the data, codes, and PCS documentation described in: <em>Three principles of data science: predictability, computability, and stability (PCS)</em>.
DOI: 10.21227/hs7t-yp51
title: QA4Camera

description: Smartphone has been one of the most popular digital devices in the past decades, with more than 300 million smartphones sold every quarter in the world wide. Most of the smartphone vendors, such as Apple, Huawei, Samsung, launch their new flagship smartphones every year. People use smartphone cameras to shoot selfie photos, film scenery or events, and record videos of family and friends. The specifications of smartphone camera and the quality of taken pictures are major criteria for consumer to select and buy smartphones. Many smartphone manufacturers also introduce and advertise their smartphones by introducing the strengths and advantages of their smartphone cameras. However, how to evaluate the quality of smartphone cameras and the taken pictures remains a problem for both smartphone manufacturers and confumers. Currently in the market, there are several teams and companies who evaluate the quality of smartphone cameras and announce the ranking and scores of the quality of smartphone cameras, and the scores of smartphone cameras are subjectively graded by several photographers and experts from different aspects, such as exposure, color, noise and texture. However, subjective assessment is not easy to reproduce, and it is not easy to deploy in practical image processing systems.In the last two decades, objective image quality assessment (IQA) has been widely researched, and a large amount of objective IQA algorithms have been designed to automatically and accurately estimate the quality of images. However, most of objective IQA methods are designed to assess the overall perceived quality of the image degraded by various simulated distortions, which rarely exist in pictures taken by the modern smartphone cameras. Thus these methods are not suitable for the task of smartphone camera quality assessment, while objective evaluation methods specifically designed for the purpose of smartphone camera quality assessment are relatively rare.The purpose of this Grand Challenge is to drive efforts of image quality assessment towards smartphone camera quality assessment. With this Grand challenge, it is expected to develop objective smartphone camera quality assessment models from four different aspects, including exposure, color, noise and texture, by using the datasets released by the organizers. The goal is to provide reference quality rankings or scores for smartphone cameras and to both smartphone manufacturers and consumers.Participants are asked to submit four computational models to cacluate the rankings of smartphone camera from four aspects: exposure, color, noise and texture.
DOI: 10.26300/mj0r-pv21
title: feronia_Athaliana_x_myb_Athaliana_Rep_06_082719
DOI: 10.7910/dvn/sleruo/fg7d8y
title: 2014_9_17_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.7910/dvn/sleruo/ivnusl
title: 2016_4_6_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.7910/dvn/sleruo/kwtkz7
title: 2013_6_10_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.7910/dvn/sleruo/rjzlew
title: 2015_10_28_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.7910/dvn/sleruo/ynavtf
title: 2015_4_18_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.21227/h836-wx60
title: QA4Camera

description: Smartphone has been one of the most popular digital devices in the past decades, with more than 300 million smartphones sold every quarter in the world wide. Most of the smartphone vendors, such as Apple, Huawei, Samsung, launch their new flagship smartphones every year. People use smartphone cameras to shoot selfie photos, film scenery or events, and record videos of family and friends. The specifications of smartphone camera and the quality of taken pictures are major criteria for consumer to select and buy smartphones. Many smartphone manufacturers also introduce and advertise their smartphones by introducing the strengths and advantages of their smartphone cameras. However, how to evaluate the quality of smartphone cameras and the taken pictures remains a problem for both smartphone manufacturers and confumers. Currently in the market, there are several teams and companies who evaluate the quality of smartphone cameras and announce the ranking and scores of the quality of smartphone cameras, and the scores of smartphone cameras are subjectively graded by several photographers and experts from different aspects, such as exposure, color, noise and texture. However, subjective assessment is not easy to reproduce, and it is not easy to deploy in practical image processing systems.In the last two decades, objective image quality assessment (IQA) has been widely researched, and a large amount of objective IQA algorithms have been designed to automatically and accurately estimate the quality of images. However, most of objective IQA methods are designed to assess the overall perceived quality of the image degraded by various simulated distortions, which rarely exist in pictures taken by the modern smartphone cameras. Thus these methods are not suitable for the task of smartphone camera quality assessment, while objective evaluation methods specifically designed for the purpose of smartphone camera quality assessment are relatively rare.The purpose of this Grand Challenge is to drive efforts of image quality assessment towards smartphone camera quality assessment. With this Grand challenge, it is expected to develop objective smartphone camera quality assessment models from four different aspects, including exposure, color, noise and texture, by using the datasets released by the organizers. The goal is to provide reference quality rankings or scores for smartphone cameras and to both smartphone manufacturers and consumers.Participants are asked to submit four computational models to cacluate the rankings of smartphone camera from four aspects: exposure, color, noise and texture.
DOI: 10.7910/dvn/sleruo/amoxa2
title: West_Beach_timelapse.mp4

description: Timelapse of West beach looking NW
DOI: 10.7910/dvn/sleruo/ccnvws
title: Timelapse Camera Data.xlsx
DOI: 10.7910/dvn/sleruo/gvbnnz
title: 2017_4_27_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.7910/dvn/sleruo/lg7gem
title: West_Beach_timelapse_hyperlapse_4x.mp4

description: Timelapse sped up 4x by hyperlapse, stabilized and cropped.
DOI: 10.7910/dvn/sleruo/nwh2do
title: 2016_9_21_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.7910/dvn/sleruo/u4guij
title: 2017_8_24_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.7910/dvn/sleruo/virguj
title: 2016_7_5_DEM_WGS84_UTMz9N_ellipsoidal.tif
DOI: 10.26300/j3fe-fp98
title: feronia_Athaliana_x_myb_Athaliana_Rep_10_082719