cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3368756
title: Dataset for—The influence of land finance and public service supply on peri-urbanization: Evidence from the counties in China

description: The dataset covers 2006 counties in China and ranges from 2009 to 2015.It includes land finance data at county level, efficiency of public service supply, economic bias of public service supply, household registration difference of public service supply, financial decentralization data, agricultural development level data, financial development level data, foreign direct investment data and industrial structure data. The data of land finance is from the Ministry of Land and Resources. It is also supplemented by the websites of the prefecture-level municipal land and resources bureaus, the provincial statistical yearbooks and Wind database. The data of public service variables and control variables mainly come from the Economic Statistical Yearbook of China County and City (2010-2016), Statistical Yearbook of China’s Counties (2010-2016), County-level statistical bulletin (2010-2016), Statistical Yearbook of China's Provinces (2010-2016), Statistical Yearbook of Prefecture-level Cities (2010-2016), Wind database, etc. This data set is mainly suitable for studying the development of land finance in the China's counties, the changing trend of peri-urbanization, and the research on the public services supply in China's counties.
DOI: 10.5281/zenodo.3368757
title: Dataset for—The influence of land finance and public service supply on peri-urbanization: Evidence from the counties in China

description: The dataset covers 2006 counties in China and ranges from 2009 to 2015.It includes land finance data at county level, efficiency of public service supply, economic bias of public service supply, household registration difference of public service supply, financial decentralization data, agricultural development level data, financial development level data, foreign direct investment data and industrial structure data. The data of land finance is from the Ministry of Land and Resources. It is also supplemented by the websites of the prefecture-level municipal land and resources bureaus, the provincial statistical yearbooks and Wind database. The data of public service variables and control variables mainly come from the Economic Statistical Yearbook of China County and City (2010-2016), Statistical Yearbook of China’s Counties (2010-2016), County-level statistical bulletin (2010-2016), Statistical Yearbook of China's Provinces (2010-2016), Statistical Yearbook of Prefecture-level Cities (2010-2016), Wind database, etc. This data set is mainly suitable for studying the development of land finance in the China's counties, the changing trend of peri-urbanization, and the research on the public services supply in China's counties.
DOI: 10.6073/pasta/528a303e0295bf84ebf4201c6f0074e1
title: PIE LTER marsh water table height, logging data from the Nelson Island Spartina marsh site, Rowley, MA for April-November 2018.

description: Measurements of water table height in the Nelson Island marsh located near Nelson Island eddy flux tower. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Nelson stream bank at Nelson Island, MAR-RO-Wtable-Nel for April-November 2018.
DOI: 10.21227/78pt-hg45
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.6073/pasta/27be76a42c6c3d37d382bae513b4a3ab
title: Ground water wells in the Eastern Shore of Virginia National Wildlife Refuge, 2014-2015

description: Two wells were established along a transect from salt marsh into pine forest. The lower well (Location: ESVNWR_LOW, WELLID: Lower) is located just inside the forest while the upper well (Location: ESVNWR_UPP, WELLID: Upper) is further in the forest along a line where saplings are currently growing. Below that elevation few saplings survive. Note the UPPER and LOWER well locations after January 7, 2015 are at different locations than in 2014. The 2015 Lower well is at (37.124668,-75.966567) and the 2015 Upper well is at (37.1247505,-75.9663644). Each well is drilled to a depth of approximately 1.5 meters. SWS CTD-Diver sensors are hung by a line in the well and collect temperature, specific conductivity and water head (water table elevation) data once every hour. The dataset spans January 07, 2014 to January 14, 2016.
DOI: 10.6073/pasta/909aebcc388fce7bf1e3dbb8ca6ad899
title: Water Quality Sampling - integrated measurements for the Virginia Coast, 1992-2018

description: This dataset contains information about the aquatic environment along two transects that run from inlet to the mainland shore on the southern part of the Delmarva Peninsula since 1992. It has a large number of columns (111) that integrate information on water column and benthic measurements. Frequency of sampling varies from monthly to quarterly, and not all variables are necessarily measured on the same dates. However, all the data from a given date and location appears on a single line of the dataset.
DOI: 10.6073/pasta/af32b580d4646f4b35d7552d83e9b09b
title: Fish Counts and Lengths in South Bay and Hog Island Bay, Virginia 2012-2018

description: Sampling of small fish in South Bay (west of Wreck Island) and Hog Island Bay was conducted using seine nets. Individual fish were identified and their length measured. Note: after June 2015 bare sites were no longer sampled.
DOI: 10.6073/pasta/02b9fd306220e9512aced6bdabbeb7f3
title: Minirhizotron Root Data for Hog Island, VA 1992-1998

description: Minirhizotron data from fertilization experiment. On 36-year old dune ridge on Hog Island from 1992 to 1998. These tubes were sampled twice a year with the fall data from 1992 included. In most cases 26 frames were sampled randomly within specific depth intervals and digitized for each tube. Plot 1 was discontinued after 10/93 and Plot 2 was discontinued after 4/93. In this data set if there were no roots in a frame then no ROOT ID appears and length and width are 0. fertilization treatment = In 1992, three applications of 15 gN / m2 (March, June, October). After 1992, 15 gN / m2 applied once per year (mid-summer) through 1998.
DOI: 10.6073/pasta/41c37e75dca4cd9fdd13d61de32fa841
title: Mesocosm (Marsh Organ) experiment at Blackwater National Wildlife Refuge, MD, 2012

description: This experiment examines the effects of tidal inundation on the growth of salt marsh vegetation. We measured the response of plants to disturbance across a gradient in inundation times by transplanting tussocks of Schoenoplectus americanus into mesocosms of different elevation. The mesocosms were arranged into structures commonly described as ï¿½marsh organsï¿½. Here we utilize two marsh organs each containing 54 mesocosms constructed of 6-inch diameter (0.0182, m2) polyvinyl chloride pipe, arranged into nine rows containing six pipes of identical elevation. We conducted the experiment in a large brackish marsh on the Atlantic Coast of North America. The study site is adjacent to the Blackwater River, a tributary of the Chesapeake Bay (Maryland, USA) (Blackwater, 38.40ï¿½N, 76.07ï¿½W). Changes in water level are primarily driven by meteorological events, with mean astronomical tides of &lt;0.25â€‰m. Long-term porewater salinities average 10â€‰p.p.t. within the marsh soil, and intertidal vegetation is dominated by Schoenoplectus americanus and Spartina patens.
DOI: 10.6073/pasta/68bf2f18bdd49f853924f29cd012fe5a
title: Light profiles in the water column of coastal bays of Virginia 2011-2017

description: Measurements of light extinction were made from a small boat at designated stations. Measurements were taken above the water (AIR), 10 cm below the surface (SURFACE) and at depths from 25 cm (D_025cm) to 250 cm (D_250cm) below the surface. All light measurements were made with a Licor 4-pi quantum Photosynthetically Active Radiation (PAR) sensor LI-193 and a LI-1000 or LI-250a light meter. All light units are in microEinsteins (moles of photons) Calculations of the extinction coefficient (Kd) should NOT include air measurements.
DOI: 10.6073/pasta/a4bed96965cadfdab33fc18188e3a1f2
title: Water Quality of Virginia Coastal Bays- Nutrients, 1992-2008

description: This dataset has been superceded by: VCR15243 Water Quality Sampling - integrated measurements for the Virginia Coast, 1992- Please see that dataset for updated data. This dataset contains information on nutrient concentrations a the Virginia Coast Reserve Long-Term Ecological Research Project.
DOI: 10.5281/zenodo.3368623
title: MAJA look-up tables for Sentinel-2 A&B, Venus and Landsat-8 sensors, for default continental model

description: The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B, Venus and Landsat-8 sensors. These look-up tables correspond to the aerosol types for the default continental model. Former version history (until 06/2019): Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2) Version 1.2 just changed the Folder name for a better integration with Start_maja. Version 1.3 added the Header files Current version history: Version 1.0 Reformatted all files
DOI: 10.17632/ry5j2nvxd9.1
title: Data for: Improvements on the recovery of 3D particle size distributions from 2D sections

description: This data and the MATLAB(R) functions correspond to the paper "Improvements on the recovery of 3D particle size distributions from 2D sections" by Benito, S; Cuervo, C; Pöhl, F; Theisen, W.
DOI: 10.25345/c5407d
title: MassIVE MSV000084206 - GNPS - ReDU - Identification Database
title: ReDU all identification database - initial contribution to complement manuscript
DOI: 10.21227/g474-m842
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.21227/9rt6-m560
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.5281/zenodo.3368770
title: Dataset for Spence et al., "Availability of study protocols for randomized trials published in high-impact medical journals: cross-sectional analysis" (CITATION)

description: Contains our extraction sheets (as SAS data files), code to calculate the values in the tables in our manuscript, and a supplemental file with additional notes on methods used in our study.
DOI: 10.5281/zenodo.3368768
title: DataForManuscriptSubmitted2JGR-Atmosphere(201908)

description: The data are for the manuscript submitted to Journal of Geophysical Research-Atmospheres on 15th, August, 2019 by Huiyan Xu.
DOI: 10.17632/3d5v6xm6w7.1
title: Data for: Constraints on aragonite precipitation in the Dead Sea from geochemical measurements of flood plumes

description: Chemical composition (pH, Na, K, Ca, Mg, Sr, Cl, Br, B, alkalinity) of flood plumes sampled between 2014-2015.
DOI: 10.21227/q8c2-wt75
title: Website Fingerprinting - Last Level Cache Contention Traces

description: Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the secure network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of sending a small amount of malicious JavaScript code to the target user's computer. The malicious code mounts a cache side- channel attack, which exploits the effects of contention on the CPU's cache, to identify other websites being browsed. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. We show that cache website fingerprinting attacks in JavaScript are highly feasible. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last- level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed- world models. We further show that our attack is more resistant than network-based fingerprinting to the effects of response caching, and that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack. To protect against cache-based website fingerprinting, new defense mechanisms must be introduced to privacy-sensitive browsers and websites. We investigate one such mechanism, and show that generating artificial cache activity reduces the effectiveness of the attack and completely eliminates it when used in the Tor Browser.
DOI: 10.21227/styv-qq84
title: Website Fingerprinting - Last Level Cache Contention Traces

description: Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the secure network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of sending a small amount of malicious JavaScript code to the target user's computer. The malicious code mounts a cache side- channel attack, which exploits the effects of contention on the CPU's cache, to identify other websites being browsed. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. We show that cache website fingerprinting attacks in JavaScript are highly feasible. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last- level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed- world models. We further show that our attack is more resistant than network-based fingerprinting to the effects of response caching, and that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack. To protect against cache-based website fingerprinting, new defense mechanisms must be introduced to privacy-sensitive browsers and websites. We investigate one such mechanism, and show that generating artificial cache activity reduces the effectiveness of the attack and completely eliminates it when used in the Tor Browser.
DOI: 10.21227/0bef-qy02
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.21227/brc8-qh26
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.17632/y8v65hydh4.2
title: Data for: Phone-call reminders narrow the intention-action gap by increasing follow-through for a residential tree giveaway program

description: This spreadsheet contains de-identified records of individuals who registered for a tree giveaway event, the assignment to treatment or control condition, their attendance (yes/no), assisgned block group, and phone call outcomes.
DOI: 10.25910/5d54df7aa71ab
title: Supplementation with NASCN fails to inhibit the progression of dextran sodium sulphate-stimulated experimental colitis