cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15787/vtt1/jnjfx5
title: Smart Work Zone System Log Data Set (VTTI-00-036)

description: Project Description: Currently, there are limited products to alert work zone workers available in the market. Those devices were mostly passive and did not actively track work zone workers’ activity or position within the activity area. VTTI and VTRC worked together to research a system that helps to alert workers using different HMI outputs including lights, audio, and vibration tactors to address the sensory challenges present in different work zone environments. As such, this project aimed to develop a Smart Work Zone System, including the addition of a C-V2X Base Station linked to Smart Vests and an array of Smart Cones. The C-V2X Base Station acts as the core of the system by communicating with the vests and cones. CAVs approaching a work zone can communicate with the Base Station over a 4G-LTE network to receive information regarding the location and configuration of the work zone. The Smart Vests, worn by road workers, transmit information regarding worker location to the base station. The vests also provide auditory, visual, and tactile feedback to alert the wearers of a potential collision threat or work zone boundary crossing. The Smart Cone array is an add-on component that can be attached to work zone drums or cones to define the boundaries of the work zone for the Smart Vest and extend the wireless link with the Base Station. This Smart Work Zone System has tremendous potential to improve road construction safety by increasing worker and vehicle awareness of threats in the work zone. Data Scope: A total of 1,455,451 datapoints were collected on a field test work zone setup at VTTI – Automation HUB. The collected data set includes data entries for three Smart Vest devices while moving around a virtual polygon area defined by the Smart Vest Geo Plotter device. The virtual polygon area was defined using 4 point polygon (square shape) and the three Smart Vest were worn inside and outside the virtual polygon and their GPS location was processed to calculate their classification and trigger the proper HMI warnings accordingly when crossing between Safe Zone, Low-level warning are and High-level warning area. Three log files are being provided: GPS Log which contains the Device ID, GPS position, and Speed. Every single entry has a timestamp field with the date and time. Polygon Log which contains the entries (GPS Latitude/Longitude and Counter) to determine the virtual polygon using the Geo-Plotter device. Work zone Log which contains the classification for each GPS location received by the system using 0,1,2 as Safe Zone, Low-Level Alert Zone, and High-Level Alert zone respectively. Vehicle Log file which contains GPS coordinates received by the system and classifies them on proximity to a connected vehicle that is near the Smart Work Zone using 0,1,2 as Safe, Low-Level Alert, and High-Level Alert respectively as well as GPS coordinates of the connected vehicle. Data Specification: Please see Data Specification document for details.
DOI: 10.25574/62827
title: Chandra X-ray Observatory ObsId 62827
DOI: 10.25574/62829
title: Chandra X-ray Observatory ObsId 62829
DOI: 10.25574/62831
title: Chandra X-ray Observatory ObsId 62831
DOI: 10.25574/62832
title: Chandra X-ray Observatory ObsId 62832
DOI: 10.25574/62834
title: Chandra X-ray Observatory ObsId 62834
DOI: 10.25574/62836
title: Chandra X-ray Observatory ObsId 62836
DOI: 10.25574/62839
title: Chandra X-ray Observatory ObsId 62839
DOI: 10.25574/62841
title: Chandra X-ray Observatory ObsId 62841
DOI: 10.25574/62842
title: Chandra X-ray Observatory ObsId 62842
DOI: 10.25574/62845
title: Chandra X-ray Observatory ObsId 62845
DOI: 10.25574/62846
title: Chandra X-ray Observatory ObsId 62846
DOI: 10.25574/62847
title: Chandra X-ray Observatory ObsId 62847
DOI: 10.25574/62848
title: Chandra X-ray Observatory ObsId 62848
DOI: 10.25574/62850
title: Chandra X-ray Observatory ObsId 62850
DOI: 10.25574/62851
title: Chandra X-ray Observatory ObsId 62851
DOI: 10.25574/62852
title: Chandra X-ray Observatory ObsId 62852
DOI: 10.25574/62853
title: Chandra X-ray Observatory ObsId 62853
DOI: 10.25574/62854
title: Chandra X-ray Observatory ObsId 62854
DOI: 10.25574/62857
title: Chandra X-ray Observatory ObsId 62857
DOI: 10.25574/62859
title: Chandra X-ray Observatory ObsId 62859
DOI: 10.25574/62860
title: Chandra X-ray Observatory ObsId 62860
DOI: 10.25574/62861
title: Chandra X-ray Observatory ObsId 62861
DOI: 10.25574/62871
title: Chandra X-ray Observatory ObsId 62871
DOI: 10.25574/62873
title: Chandra X-ray Observatory ObsId 62873
DOI: 10.25574/62876
title: Chandra X-ray Observatory ObsId 62876
DOI: 10.25574/62878
title: Chandra X-ray Observatory ObsId 62878
DOI: 10.25574/62881
title: Chandra X-ray Observatory ObsId 62881
DOI: 10.25574/62882
title: Chandra X-ray Observatory ObsId 62882
DOI: 10.25574/62887
title: Chandra X-ray Observatory ObsId 62887
DOI: 10.25574/62888
title: Chandra X-ray Observatory ObsId 62888
DOI: 10.25574/62891
title: Chandra X-ray Observatory ObsId 62891
DOI: 10.25574/62892
title: Chandra X-ray Observatory ObsId 62892
DOI: 10.25574/62893
title: Chandra X-ray Observatory ObsId 62893
DOI: 10.25574/62894
title: Chandra X-ray Observatory ObsId 62894
DOI: 10.25574/62895
title: Chandra X-ray Observatory ObsId 62895
DOI: 10.25574/62896
title: Chandra X-ray Observatory ObsId 62896
DOI: 10.5281/zenodo.5680032
title: Seroprevalence of SARS-CoV-2 in 10 Regional Capitals of Cameroon

description: Results of population-based age stratified seroepidemiological investigation in Cameroon
DOI: 10.5281/zenodo.5680031
title: Seroprevalence of SARS-CoV-2 in 10 Regional Capitals of Cameroon

description: Results of population-based age stratified seroepidemiological investigation in Cameroon
DOI: 10.5281/zenodo.5644148
title: Seroprevalence survey on infection with the SARS-CoV-2 virus after the second wave in Kinshasa, Democratic Republic of the Congo. 2021

description: Results of population-based age stratified seroepidemiological investigation in the Democratic Republic of the Congo
DOI: 10.5281/zenodo.5644147
title: Seroprevalence survey on infection with the SARS-CoV-2 virus after the second wave in Kinshasa, Democratic Republic of the Congo. 2021

description: Results of population-based age stratified seroepidemiological investigation in the Democratic Republic of the Congo
DOI: 10.5281/zenodo.5644207
title: SARS-CoV-2 exposure in Malawian blood donors: an analysis of seroprevalence and variant dynamics between January 2020 and July 2021

description: Results of population-based age stratified seroepidemiological investigation in Malawi.
DOI: 10.5281/zenodo.5644206
title: SARS-CoV-2 exposure in Malawian blood donors: an analysis of seroprevalence and variant dynamics between January 2020 and July 2021

description: Results of population-based age stratified seroepidemiological investigation in Malawi.
DOI: 10.5281/zenodo.5644217
title: ÉMuL-COVID-19: A Multidisciplinary study against COVID-19 in Burkina Faso

description: Results of population-based age stratified seroepidemiological investigation in Burkina Faso.
DOI: 10.5281/zenodo.5644216
title: ÉMuL-COVID-19: A Multidisciplinary study against COVID-19 in Burkina Faso

description: Results of population-based age stratified seroepidemiological investigation in Burkina Faso.
DOI: 10.5281/zenodo.5644223
title: Population-based Age-stratified Seroprevalence Investigation on SARS-CoV-2 Virus in Selected States of High and Low Burden of Disease in Nigeria

description: Results of population-based age stratified seroepidemiological investigation in Nigeria
DOI: 10.5281/zenodo.5644222
title: Population-based Age-stratified Seroprevalence Investigation on SARS-CoV-2 Virus in Selected States of High and Low Burden of Disease in Nigeria

description: Results of population-based age stratified seroepidemiological investigation in Nigeria
DOI: 10.5281/zenodo.5644231
title: Nigeria COVID-19 Household Seroprevalence Survey

description: Results of population-based age stratified seroepidemiological investigation in Nigeria
DOI: 10.5281/zenodo.5644230
title: Nigeria COVID-19 Household Seroprevalence Survey

description: Results of population-based age stratified seroepidemiological investigation in Nigeria
DOI: 10.5281/zenodo.5711715
title: Community and Facility-based COVID-19 Surveillance in Selected Districts of Malawi

description: Results of population-based age stratified seroepidemiological investigation in Malawi
DOI: 10.5281/zenodo.5711714
title: Community and Facility-based COVID-19 Surveillance in Selected Districts of Malawi

description: Results of population-based age stratified seroepidemiological investigation in Malawi
DOI: 10.5281/zenodo.5771207
title: SARS CoV2 Seroprevalence in Rural and Urban Populations In Ghana

description: Results of population-based age stratified seroepidemiological investigation in Ghana
DOI: 10.5281/zenodo.5771206
title: SARS CoV2 Seroprevalence in Rural and Urban Populations In Ghana

description: Results of population-based age stratified seroepidemiological investigation in Ghana
DOI: 10.5281/zenodo.7044324
title: Raw datasets and media accompanying the manuscript: Fused Raman spectroscopy analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics

description: Raw datasets and media accompanying the manuscript: <strong>Fused Raman spectroscopy analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics</strong>
DOI: 10.5281/zenodo.7044323
title: Raw datasets and media accompanying the manuscript: Fused Raman spectroscopy analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics

description: Raw datasets and media accompanying the manuscript: <strong>Fused Raman spectroscopy analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics</strong>
DOI: 10.5281/zenodo.5898414
title: SARS-CoV-2 Seroprevalence in a Rural and Urban Household Cohort, South Africa, July 2020–September 2021

description: Early results of the PHIRST-C study seroprevalence results in South Africa
DOI: 10.5281/zenodo.5898413
title: SARS-CoV-2 Seroprevalence in a Rural and Urban Household Cohort, South Africa, July 2020–September 2021

description: Early results of the PHIRST-C study seroprevalence results in South Africa
DOI: 10.5281/zenodo.6519820
title: Seroprevalence of SARS-CoV-2 infection in the general population in Togo in 2021

description: Results of population-based age stratified seroepidemiological investigation in Togo
DOI: 10.13020/qda2-zf10
title: X-ray Diffraction and Atomic Force Microscopy of RuO2 thin films

description: X-ray Diffraction data of the RuO2 samples were obtained to assess the crystallinity and epitaxial nature of the thin films grown on different substrates. Atomic Force Microscopy was used to determine the surface morphology changes as a function of thickness and epitaxial strain and correlate it with the observed oxygen evolution activity.
description: X-ray Diffraction 2theta-omega couples scans for RuO2 on r-Al2O3 (Supplementary Figure 1 a) of different thicknesses. Similar data is used for comparing RuO2 films with comparable thickness grown on r-Al2O3 and TiO2 (101) substrate (Supplemetary Fig 6b). Atomic Force Microscopy images acquired using a Bruker multimode for comparing surface morphology change as a function of thickness of RuO2 film grown on r-Al2O3 substrate (Supplementary Fig 1 b)
DOI: 10.5281/zenodo.6354737
title: Seroepidemiological survey of coronavirus 2019 (COVID-19) infection in the general population in Gabon

description: Results of population-based age stratified seroepidemiological investigation in Gabon
DOI: 10.5281/zenodo.6354736
title: Seroepidemiological survey of coronavirus 2019 (COVID-19) infection in the general population in Gabon

description: Results of population-based age stratified seroepidemiological investigation in Gabon
DOI: 10.6084/m9.figshare.20818666.v1
title: larus_argentatus_decontaminated_gx.fasta.gz

description: ......
DOI: 10.6084/m9.figshare.20818666
title: larus_argentatus_decontaminated_gx.fasta.gz

description: ......
DOI: 10.5517/ccdc.csd.cc2bmg5m
title: CCDC 2163650: Experimental Crystal Structure Determination

description: Related Article: Jie Zhou, Guoru Shi, Wanqi Zhang, Li Li, Ruoyun Chen, Yanfei Liu, Dequan Yu|2022|Phytochemistry|203|113316|doi:10.1016/j.phytochem.2022.113316
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2byg5y
title: CCDC 2173260: Experimental Crystal Structure Determination

description: Related Article: Zhi-Hao You, Yan-Kai Liu|2022|Org.Lett.|24|6288|doi:10.1021/acs.orglett.2c02435
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25919/wn8z-kv86
title: Parkes pulsar observations with undefined project IDs for 2022APRS_15

description: Parkes pulsar observations with undefined project IDs for 2022APRS. This collection contains observations from project ID PX090.
DOI: 10.25422/azu.data.20349084
title: Data & Code for Manuscript "Surface Potential Gradients and NEXRAD Radar Reflectivities Before the Onset of Lightning at the KSC-ER"

description: The lightning and surface potential gradient analyses presented in this work was done by the first author (SCH) of the related manuscript as part of his Master’s research at the University of Arizona, completed in 2000. Most of the source data from the LPLWS and LDAR systems were lost over the last two decades, so the only available records are PDF images (LPLWS) and figures in Microsoft Word (LDAR). The level-II NEXRAD radar data used in this study are available through the NOAA public S3 bucket on Amazon, and the relevant subset of data are included here. The "code" for "Isolated Thunderstorms" consists of an Excel spreadsheet (CaseAnalysis_KLC_final.xlsx) that includes the extracted parameters used in the tables in this manuscript, the MATLAB script used to produce Figures 8 and 9 (HandelFigsRotateFinal.m), the MATLAB code that calculates distance needed for Table 3 (efmCellDistances.m), and the MATLAB Measure Tool (and related figures) used to produce the electric field values in Table 5. Supporting data tables (two txt files) used by these MATLAB scripts are also included in this folder. There is no "code" for the "Horizontal Flash" analysis. <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>
DOI: 10.5281/zenodo.7045020
title: Riverbank Erosion and Accretion from Landsat

description: Riverbank erosion presents a serious risk to people and infrastructure. These risks are becoming increasingly hard to predict because of direct modification of the rivers by damming and bank stabilization, as well as indirect modification by climate change and land use change. Geomorphologists have developed scaling relationships based on watershed characteristics; however, these relationships are very coarse and limited by the available data. This research uses twenty years of satellite imagery to develop the first global dataset of riverbank erosion, which both confirms existing knowledge and opens new avenues of research.
DOI: 10.25574/45317
title: Chandra X-ray Observatory ObsId 45317
DOI: 10.6084/m9.figshare.20821753
title: lingulodinium_polyedra_decontaminated_gx.fasta.gz

description: .........
DOI: 10.6084/m9.figshare.20821852
title: myrmophylax_atrothorax_decontaminated_gx.fasta.gz

description: .....
DOI: 10.6084/m9.figshare.20821852.v1
title: myrmophylax_atrothorax_decontaminated_gx.fasta.gz

description: .....
DOI: 10.5281/zenodo.6959886
title: Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures

description: <strong>Initial release of artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong> Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers.
DOI: 10.5281/zenodo.7033262
title: Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures

description: <strong>Artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong> Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers. <strong>Change log for v1.0.1:</strong> Fix wrong Bug ID for <em>sqlite</em> and add README clarification.
DOI: 10.21223/scdnlr/rfbabf
title: 01-Data-carbono-organico.tab

description: :unav
DOI: 10.5281/zenodo.7033291
title: Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures

description: <strong>Artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong> Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers. <strong>Change log for v1.0.1:</strong> Fix wrong Bug ID for <em>sqlite</em> and add README clarification. <strong>Change log for v1.0.2:</strong> Added an example linking data in the repository to the table.
DOI: 10.5281/zenodo.6959885
title: Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures

description: <strong>Artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong> Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers. <strong>Change log for v1.0.1:</strong> Fix wrong Bug ID for <em>sqlite</em> and add README clarification. <strong>Change log for v1.0.2:</strong> Added an example linking data in the repository to the table.