cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/5f00352021331
title: Parkes observations for project P1041 semester 2020APRS_09

description: We recently discovered five millisecond pulsars (MSPs) in the globular cluster Omega Centauri. This is the first pulsar discovery in this globular cluster. Four of these pulsars are isolated with spin periods of 4.1, 4.2, 4.6 and 6.8ms. The fifth has a spin period of 4.8ms and is in an eclipsing binary system with an orbital period of 2.1 hours. We propose to carry out a timing campaign of these new pulsars in order to study the dynamics in the core of Omega Centauri, to reveal the origin of Gamma-ray emission from the core, and to probe the ionised interstellar medium in Omega Centauri. Our proposed observations will also enable us to carry out deeper searches of pulsars in the cluster.
DOI: 10.5281/zenodo.3930161
title: Experiments on granular flow behavior and deposit characteristics: implications for rock avalanche kinematics

description: Seven excel files which includes the data supporting Figure 5, Figure 6, Figure 7, Figure 8, Figure 10, Figure 12 and Figure 14 is uploaded saparately. Data Set S1. Data supporting the relationship between flow height and time in Figure 5. Data Set S2. Data supporting normalized velocity profiles and normalized shear rate profiles in Figures 6(a)-6(d). Data Set S3(a). Data supporting the relationships between mean grain size and global shear rate in Figure 7(a). Data Set S3(b). Data supporting the relationships between mean grain size and Savage number in Figure 7(a). Data Set S4(a). Data supporting the relationships between depth averaged velocity and time in Figure 8(a). Data Set S4(b). Data supporting the relationships between depth averaged velocity and time in Figure 8(b). Data Set S4(c). Data supporting the relationships between mean grain size and equivalent friction coefficient in Figure 8(c). Data Set S5(a). Data supporting Figures 10(a) and 10(b). Data Set S5(b). Data supporting the relationships between relative flow height and λ in Figure 10(c). Data Set S5(c). Data supporting the relationships between relative flow height and λ in Figure 10(d). Data Set S6(a). Data supporting the velocity profiles in Figure 12(a). Data Set S6(b). Data supporting the relationships between global shear rate and equivalent friction coefficient in Figure 12(b). Data Set S7. Data supporting the relationships between normalized flow height and Savage number in Figure 14.
description: {"references": ["Iverson, R. M. (1997). The physics of debris flows. Reviews of Geophysics, 35(3), 245\u2013296. https://doi.org/10.1029/97RG00426"]}
DOI: 10.5281/zenodo.3365467
title: Experiments on granular flow behavior and deposit characteristics: implications for rock avalanche kinematics

description: Seven excel files which includes the data supporting Figure 5, Figure 6, Figure 7, Figure 8, Figure 10, Figure 12 and Figure 14 is uploaded saparately. Data Set S1. Data supporting the relationship between flow height and time in Figure 5. Data Set S2. Data supporting normalized velocity profiles and normalized shear rate profiles in Figures 6(a)-6(d). Data Set S3(a). Data supporting the relationships between mean grain size and global shear rate in Figure 7(a). Data Set S3(b). Data supporting the relationships between mean grain size and Savage number in Figure 7(a). Data Set S4(a). Data supporting the relationships between depth averaged velocity and time in Figure 8(a). Data Set S4(b). Data supporting the relationships between depth averaged velocity and time in Figure 8(b). Data Set S4(c). Data supporting the relationships between mean grain size and equivalent friction coefficient in Figure 8(c). Data Set S5(a). Data supporting Figures 10(a) and 10(b). Data Set S5(b). Data supporting the relationships between relative flow height and λ in Figure 10(c). Data Set S5(c). Data supporting the relationships between relative flow height and λ in Figure 10(d). Data Set S6(a). Data supporting the velocity profiles in Figure 12(a). Data Set S6(b). Data supporting the relationships between global shear rate and equivalent friction coefficient in Figure 12(b). Data Set S7. Data supporting the relationships between normalized flow height and Savage number in Figure 14.
description: {"references": ["Iverson, R. M. (1997). The physics of debris flows. Reviews of Geophysics, 35(3), 245\u2013296. https://doi.org/10.1029/97RG00426"]}
DOI: 10.5281/zenodo.3929111
title: Viral Metagenomes from water systems in the Mediterranean Sea

description: The Water Framework Directive (WFD; 2000/60/EC) is the European umbrella for the assessment and regulation of ecological quality of water systems (lakes, rivers, transitional waters, coastal waters). The scope of WFD is to improve the ecological status of the aquatic ecosystems. To do so, a long time-series of monitoring campaigns within WFD exists with the ultimate goal to protect coastal ecosystems from degradation. Further, WFD project employs the calculation and improvement of ecological quality indices for the definition and assessment of eutrophication. In the submitted sub-project within WFD, the viral metagenome of 15 samples collected in 2014 and 2015 is sequenced and analyzed for several genes for the study of taxonomy and potential function of double-stranded DNA viruses.
DOI: 10.5281/zenodo.3929110
title: Viral Metagenomes from water systems in the Mediterranean Sea

description: The Water Framework Directive (WFD; 2000/60/EC) is the European umbrella for the assessment and regulation of ecological quality of water systems (lakes, rivers, transitional waters, coastal waters). The scope of WFD is to improve the ecological status of the aquatic ecosystems. To do so, a long time-series of monitoring campaigns within WFD exists with the ultimate goal to protect coastal ecosystems from degradation. Further, WFD project employs the calculation and improvement of ecological quality indices for the definition and assessment of eutrophication. In the submitted sub-project within WFD, the viral metagenome of 15 samples collected in 2014 and 2015 is sequenced and analyzed for several genes for the study of taxonomy and potential function of double-stranded DNA viruses.
DOI: 10.5281/zenodo.3930143
title: icfpws20haskellmain-p2-p

description: This file contains the data that is collected during the research described in the paper<br> "Assessing the quality of evolving Haskell systems by measuring structural inequality",<br> published in the proceedings of the ACM SIGPLAN Haskell Symposium 2020 conference.<br> https://doi.org/10.1145/3406088.3409014
DOI: 10.5281/zenodo.3930142
title: icfpws20haskellmain-p2-p

description: This file contains the data that is collected during the research described in the paper<br> "Assessing the quality of evolving Haskell systems by measuring structural inequality",<br> published in the proceedings of the ACM SIGPLAN Haskell Symposium 2020 conference.<br> https://doi.org/10.1145/3406088.3409014
DOI: 10.15161/oar.it/30523
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5519/qd.i6jddu5s
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4571169 records)
DOI: 10.5281/zenodo.3929741
title: Risk Identification Database (RIDB)

description: The Risk Identification Database (RIDB) is an organized collection of data, which includes the identification of threats, risk sources, risk factors, causal relations and the description of risk events. The objective of the RIDB therefore is to identify risk events, related to physical and cyber threats, which can occur in water distribution systems utilities, their locations, and causes.
DOI: 10.5281/zenodo.3929740
title: Risk Identification Database (RIDB)

description: The Risk Identification Database (RIDB) is an organized collection of data, which includes the identification of threats, risk sources, risk factors, causal relations and the description of risk events. The objective of the RIDB therefore is to identify risk events, related to physical and cyber threats, which can occur in water distribution systems utilities, their locations, and causes.
DOI: 10.15151/esrf-dc-217728238
title: Dense neuronal imaging with X-ray holographic nano-tomography

description: The data consists of four volumetric images of dense neuronal wiring in Drosophila melanogaster and mammalian central and peripheral nervous tissue. Each 3D image is available in 32 bit (.vol files) and 8 bit (.raw files) precision. Corresponding .xml files contain information on image characteristics. Data sets mCTX_17keV_30nm and mCTX_33keV_40nm are images of mouse cortex tissue with isotropic voxel sizes of 30 nm and 40 nm. Data set drLeg contains an image of the connection between the ventral nerve cord (VNC) and a leg in a Drosophila sample, acquired with a voxel size of 50 nm. Data set drBrain, contains an image of a Drosophila brain with a voxel size of 120 nm. These data are described in a bioRxiv preprint (https://www.biorxiv.org/content/10.1101/653188v1) and in Kuan et al., Nature Neuroscience, 2020.
DOI: 10.21227/22gk-4289
title: On the thermal response of spherical silica optical microcavity resonators

description: Data set about the thermal response of silica spherical micro-resonators. Contains numerical calculations for different optical properties of the cavity and two different resonator sizes.
DOI: 10.21227/xjs8-3975
title: On the thermal response of spherical silica optical microcavity resonators

description: Data set about the thermal response of silica spherical micro-resonators. Contains numerical calculations for different optical properties of the cavity and two different resonator sizes.
DOI: 10.21227/shav-3a58
title: A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

description: This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016).
DOI: 10.18112/openneuro.ds002995.v1.0.0
title: Taste Quality Representation in the Human Brain
DOI: 10.5281/zenodo.3908969
title: Experimental data to the publication "Genetic-optimised aperiodic code for distributed optical fibre sensors"

description: The source data underlying Figs. 3-5 and Supplementary Figs. 6, 8-13 are provided as a Source Data file.
DOI: 10.17632/2h22k559xs.1
title: Data for: Identification of the source caldera for a Pliocene ash-flow tuff in Northeast Japan based on apatite trace-element compositions and zircon U–Pb ages

description: Appendix A. Heavy mineral occurrences and abundances of the studied samples Appendix B. Major and trace element compositions of apatite in the studied tuffs analyzed using a wavelength dispersive electron probe (JEOL 8530F). Each measurement was performed on a different crystal grain. Appendix C. Major element compositions of ilumenite in the studied tuffs analyzed using a wavelength dispersive electron probe (JEOL 8530F). Each measurement was performed on a different crystal grain. Appendix D. Summary of LA ICP-MS U–Pb results for zircon from the studied tuffs. Each measurement was performed on a different crystal grain.
DOI: 10.21227/bw1x-yh39
title: DAWN: Vehicle Detection Dataset in Adverse Weather Nature

description: Recently, self-driving vehicles have been introduced with several automated features including lane-keep assistance, queuing assistance in traffic-jam, parking assistance and crash avoidance. These self-driving vehicles and intelligent visual traffic surveillance systems mainly depend on cameras and sensors fusion systems. Adverse weather conditions such as heavy fog, rain, snow, and sandstorms are considered dangerous restrictions of the functionality of cameras impacting seriously the performance of adopted computer vision algorithms for scene understanding (i.e., vehicle detection, tracking, and recognition in traffic scenes). For example, reflection coming from rain flow and ice over roads could cause massive detection errors which will affect the performance of intelligent visual traffic systems. Additionally, scene understanding and vehicle detection algorithms are mostly evaluated using datasets contain certain types of synthetic images plus a few real-world images. Thus, it is uncertain how these algorithms would perform on unclear images acquired “in the wild” and how the progress of these algorithms is standardized in the field. To this end, we present a new dataset (benchmark) consisting of real-world images collected under various adverse weather conditions called DAWN. This dataset emphasizes a diverse traffic environment (urban, highway and freeway) as well as a rich variety of traffic flow. The DAWN dataset comprises a collection of 1000 images from real-traffic environments, which are divided into four sets of weather conditions: fog, snow, rain and sandstorms. The dataset is annotated with object bounding boxes for autonomous driving and video surveillance scenarios. This data helps interpreting effects caused by the adverse weather conditions on the performance of vehicle detection systems.
DOI: 10.21227/nytz-e057
title: DAWN: Vehicle Detection Dataset in Adverse Weather Nature

description: Recently, self-driving vehicles have been introduced with several automated features including lane-keep assistance, queuing assistance in traffic-jam, parking assistance and crash avoidance. These self-driving vehicles and intelligent visual traffic surveillance systems mainly depend on cameras and sensors fusion systems. Adverse weather conditions such as heavy fog, rain, snow, and sandstorms are considered dangerous restrictions of the functionality of cameras impacting seriously the performance of adopted computer vision algorithms for scene understanding (i.e., vehicle detection, tracking, and recognition in traffic scenes). For example, reflection coming from rain flow and ice over roads could cause massive detection errors which will affect the performance of intelligent visual traffic systems. Additionally, scene understanding and vehicle detection algorithms are mostly evaluated using datasets contain certain types of synthetic images plus a few real-world images. Thus, it is uncertain how these algorithms would perform on unclear images acquired “in the wild” and how the progress of these algorithms is standardized in the field. To this end, we present a new dataset (benchmark) consisting of real-world images collected under various adverse weather conditions called DAWN. This dataset emphasizes a diverse traffic environment (urban, highway and freeway) as well as a rich variety of traffic flow. The DAWN dataset comprises a collection of 1000 images from real-traffic environments, which are divided into four sets of weather conditions: fog, snow, rain and sandstorms. The dataset is annotated with object bounding boxes for autonomous driving and video surveillance scenarios. This data helps interpreting effects caused by the adverse weather conditions on the performance of vehicle detection systems.
DOI: 10.21227/hvcm-e143
title: DAWN: Vehicle Detection in Adverse Weather Nature

description: Recently, self-driving vehicles have been introduced with several automated features including lane-keep assistance, queuing assistance in traffic-jam, parking assistance and crash avoidance. These self-driving vehicles and intelligent visual traffic surveillance systems mainly depend on cameras and sensors fusion systems. Adverse weather conditions such as heavy fog, rain, snow, and sandstorms are considered dangerous restrictions of the functionality of cameras impacting seriously the performance of adopted computer vision algorithms for scene understanding (i.e., vehicle detection, tracking, and recognition in traffic scenes). For example, reflection coming from rain flow and ice over roads could cause massive detection errors which will affect the performance of intelligent visual traffic systems. Additionally, scene understanding and vehicle detection algorithms are mostly evaluated using datasets contain certain types of synthetic images plus a few real-world images. Thus, it is uncertain how these algorithms would perform on unclear images acquired “in the wild” and how the progress of these algorithms is standardized in the field. To this end, we present a new dataset (benchmark) consisting of real-world images collected under various adverse weather conditions called DAWN. This dataset emphasizes a diverse traffic environment (urban, highway and freeway) as well as a rich variety of traffic flow. The DAWN dataset comprises a collection of 1000 images from real-traffic environments, which are divided into four sets of weather conditions: fog, snow, rain and sandstorms. The dataset is annotated with object bounding boxes for autonomous driving and video surveillance scenarios. This data helps interpreting effects caused by the adverse weather conditions on the performance of vehicle detection systems.
DOI: 10.21227/defg-5e45
title: A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

description: This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016).
DOI: 10.5281/zenodo.3572973
title: The observed data used in paper titled "A hydrographic method to identify groundwater net recharge, barometric effect, and evapotranspiration from a complicated semidiurnal water table fluctuation"

description: The water level and atmospheric pressure within the monitoring well located the semi-arid loess hilly-gully region on the piedmont of western Shaanxi Province, China (34°18′36″ N, 107°07′55″ E), were automatically monitored at 20-min intervals by the Levelogger and Barologger, respectively. These data were used as a case example to state a method of estimating groundwater net recharge rate, barometric efficiency and hourly-scaled groundwater evapotranspiration rate.
DOI: 10.5281/zenodo.3572972
title: The observed data used in paper titled "A hydrographic method to identify groundwater net recharge, barometric effect, and evapotranspiration from a complicated semidiurnal water table fluctuation"

description: The water level and atmospheric pressure within the monitoring well located the semi-arid loess hilly-gully region on the piedmont of western Shaanxi Province, China (34°18′36″ N, 107°07′55″ E), were automatically monitored at 20-min intervals by the Levelogger and Barologger, respectively. These data were used as a case example to state a method of estimating groundwater net recharge rate, barometric efficiency and hourly-scaled groundwater evapotranspiration rate.
DOI: 10.3886/e113310
title: Replication data for: Cooperation among Strangers under the Shadow of the Future

description: We study the emergence of norms of cooperation in experimental economies populated by strangers interacting indefinitely. Can these economies achieve full efficiency even without formal enforcement institutions? Which institutions for monitoring and enforcement facilitate cooperation? Finally, what classes of strategies do subjects employ? We find that, first, cooperation can be sustained even in anonymous settings; second, some type of monitoring and punishment institutions significantly promote cooperation; and, third, subjects mostly employ strategies that are selective in punishment. (JEL C71, C73, D12, Z13)
DOI: 10.21227/5e6c-sj70
title: Integrated horn antenna design through MGWO

description: The code and data used in our manuscript "All-Integrated 3D-Printed Horn Antenna and Orthomode Transducer Designed through Modified Grey Wolf Optimizer".
DOI: 10.5281/zenodo.3929951
title: The value of water in agriculture in a global high-resolution analysis

description: This repository contains supporting data for: "<strong>The value of water in agriculture in a global high-resolution analysis"</strong> Cite: D’Odorico, P., Chiarelli, D.D., Rosa L., Bini A., Zilberman D., Rulli, M.C. The value of water in agriculture in a global high-resolution analysis. PNAS. 2020<br> Email: paolododo@berkeley.edu This file contains the value of irrigation water withdrawals (USD per cubic meter) based on circa year 2000 crop distribution.
DOI: 10.21227/1ph2-me86
title: Integrated horn antenna design through MGWO

description: The code and data used in our manuscript "All-Integrated 3D-Printed Horn Antenna and Orthomode Transducer Designed through Modified Grey Wolf Optimizer".
DOI: 10.17632/6yxdt9xc2n.6
title: Data for: Use of aquaculture ponds by globally endangered red-crowned crane (Grus japonensis) during wintering period in the Yancheng National Nature Reserve, a Ramsar wetland

description: Raw data : Use of aquaculture ponds by globally endangered red-crowned crane (Grus japonensis) during wintering period in the Yancheng National Nature Reserve, a Ramsar wetland
DOI: 10.17632/5vwrhmy2dg.6
title: Data for: Climate models predict a divergent future for the medicinal tree Boswellia serrata Roxb. in India

description: Data is associated with the submitted manuscript title " Climate models predict a divergent future for the medicinal tree Boswellia serrata Roxb. in India"
DOI: 10.21227/61b7-ex85
title: Integrated horn antenna design through MGWO

description: The code used in our manuscript "All-Integrated 3D-Printed Horn Antenna and Orthomode Transducer Designed through Modified Grey Wolf Optimizer".
DOI: 10.5519/qd.wx7sb541
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/9w5z-0231
title: A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

description: This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016).