cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25403/upresearchdata.29714942
title: Governing public open spaces in African cities : the role of urban management and policy in the City of Tshwane

description: This dissertation investigates the governance and management of public open spaces in the City of Tshwane, South Africa. It explores how public policy, institutional frameworks, and urban management approaches influence the quality and accessibility of these spaces, which are often reported to be neglected, unsafe, and poorly maintained. Using a qualitative, exploratory case study of Slovo Park and Moroe Park, the study draws on interviews, focus groups, observations, and document analysis to examine the challenges faced by municipal officials and users. Key findings highlight how political instability, weak leadership, and rapid urban growth hinder effective public space management, leading to conflicting mandates and diminished public benefit.
DOI: 10.25403/upresearchdata.29714942.v1
title: Governing public open spaces in African cities : the role of urban management and policy in the City of Tshwane

description: This dissertation investigates the governance and management of public open spaces in the City of Tshwane, South Africa. It explores how public policy, institutional frameworks, and urban management approaches influence the quality and accessibility of these spaces, which are often reported to be neglected, unsafe, and poorly maintained. Using a qualitative, exploratory case study of Slovo Park and Moroe Park, the study draws on interviews, focus groups, observations, and document analysis to examine the challenges faced by municipal officials and users. Key findings highlight how political instability, weak leadership, and rapid urban growth hinder effective public space management, leading to conflicting mandates and diminished public benefit.
DOI: 10.15468/dl.tzqzma
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tigridia pavonia",
 "Country is Ecuador",
 "HasCoordinate is true"
 ]
}

 The dataset includes 20 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036598-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7g9rhd
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tigridia pavonia",
 "Country is Peru",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036599-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5065/d6qj7g46
title: NASA DC-8 Electronically Scanned Microwave Radiometer (ESMR) Data. Version 1.0

description: This data set contains the microwave brightness temperatures from the Electronically Scanned Microwave Radiometer (ESMR) that was on-board the NASA DC-8 aircraft that flew out of Townsville, Australia during the TOGA COARE field project. The data are in a binary format. Included with the data set is C code that was used to read the binary files. Also included are some quick look imagery.
DOI: 10.6084/m9.figshare.29957014
title: A comprehensive security framework for cloud-based remote sensing image storage and retrieval with adversarial attack resistance

description: Remote sensing and satellite imaging have become essential in various geological and surveillance applications. These systems often rely on cloud platforms for storing satellite and aerial images, introducing trust and security concerns, especially in sensitive domains like border surveillance, monitoring, and reconnaissance. Traditional cloud solutions are prone to data breaches and adversarial attacks, highlighting the need for a secure, end-to-end framework. To address this, we propose a comprehensive security architecture for storing and retrieving sensitive remote sensing images. Our system ensures confidentiality, integrity, and access control, while also resisting adversarial attacks during image retrieval. It adopts a three-phase structure: secure authentication, secure storage, and secure retrieval. Authentication is achieved using a combination of Zero-Knowledge Proof and Quantum Key Distribution, establishing a tamper-proof user verification process. In the storage phase, quantum-based cryptography secures the images, while a deep hashing model resistant to adversarial attacks enables efficient indexing and retrieval. A watermark embedding mechanism helps detect insider threats and support forensic tracking in case of data breaches. Evaluated on a remote sensing image dataset using various backbone networks, our system achieved a retrieval accuracy of 94.77%, outperforming existing models by 8–12%. The framework is well-suited for high-security environments, including military applications.
DOI: 10.6084/m9.figshare.29957014.v1
title: A comprehensive security framework for cloud-based remote sensing image storage and retrieval with adversarial attack resistance

description: Remote sensing and satellite imaging have become essential in various geological and surveillance applications. These systems often rely on cloud platforms for storing satellite and aerial images, introducing trust and security concerns, especially in sensitive domains like border surveillance, monitoring, and reconnaissance. Traditional cloud solutions are prone to data breaches and adversarial attacks, highlighting the need for a secure, end-to-end framework. To address this, we propose a comprehensive security architecture for storing and retrieving sensitive remote sensing images. Our system ensures confidentiality, integrity, and access control, while also resisting adversarial attacks during image retrieval. It adopts a three-phase structure: secure authentication, secure storage, and secure retrieval. Authentication is achieved using a combination of Zero-Knowledge Proof and Quantum Key Distribution, establishing a tamper-proof user verification process. In the storage phase, quantum-based cryptography secures the images, while a deep hashing model resistant to adversarial attacks enables efficient indexing and retrieval. A watermark embedding mechanism helps detect insider threats and support forensic tracking in case of data breaches. Evaluated on a remote sensing image dataset using various backbone networks, our system achieved a retrieval accuracy of 94.77%, outperforming existing models by 8–12%. The framework is well-suited for high-security environments, including military applications.
DOI: 10.5065/d6ks6qdg
title: NASA DC-8 Microphysical Measurement Package (MMP) Data. Version 1.0

description: This data set contains the ambient and total temperatures, dew and frost points, vertical velocity, and icing and Lyman Alpha probe data from the Microphysical Measurement Package (MMP) that was on-board the NASA DC-8 aircraft that flew out of Townsville, Australia during the TOGA COARE field project. The MMP consisted of the NCAR temperature probe and cryogenic front point hygrometer, a Rosemount ice accretion probe, and a Lyman-Alpha sensor. The data are in an ASCII format.
DOI: 10.5065/d6r20zp0
title: Scanning Aerosol Backscatter Lidar (SABL) Data. Version 1.0

description: This dataset contains SABL (Scanning Aerosol Backscatter Lidar) netCDF format files obtained aboard the NCAR/NSF C-130 aircraft during the Mexico: Megacities Impact on Regional and Global Environment (MIRAGE) This experiment is a sub-program of the Megacity Initiative: Local and Global Research Observations (MILAGRO4. There are no IR data available for RF03 thru RF05 inclusive (2006-03-10 16:21 - 2006-03-17 00:18) due to equipment problems.
DOI: 10.5065/d6mc8xb0
title: Scanning Aerosol Backscatter Lidar - png images. Version 1.0

description: This dataset contains SABL (Scanning Aerosol Backscatter Lidar) images obtained aboard the NCAR/NSF C-130 aircraft during the Mexico: Megacities Impact on Regional and Global Environment (MIRAGE) This experiment is a sub-program of the Megacity Initiative: Local and Global Research Observations (MILAGRO). The images are png images compressed into tar files.
DOI: 10.5065/d6tb15r4
title: SOCRATES: NCAR GV-HSRL lidar data, CfRadial. Version 1.0

description: High Spectral Resolution Lidar (HSRL) data collected aboard the NSF/NCAR GV HIAPER (Gulfstream-V High-performance Instrumented Airborne Platform for Environmental Research) (N677F) during the SOCRATES (Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study) project from 15 January 2018 to 24 February 2018. The data are in CfRadial format and are available as tar archives. This data set contains all HSRL variables and is geared towards a more advanced lidar data user. A data set which contains only the primary HSRL variables at a 2Hz resolution (and also data from the HIAPER Cloud Radar (HCR)) which is geared towards the general user is available at the link in the "Related links" section below.
DOI: 10.6084/m9.figshare.28244391
title: Cautious parameter learning for a joint mean and variance monitoring CUSUM scheme with guaranteed in-control performance

description: Joint monitoring of the mean and variance of a normal process is crucial in quality engineering for assessing homoscedasticity, determining process capability, and recognizing that changes in scale often accompany changes in location. A CUSUM scheme for monitoring both mean and variance must ensure overall performance, even with estimated parameters. Approaches guaranteeing minimum in-control performance are standard, but they lose power with small Phase I samples. Learning approaches allow parameter re-estimation with new data, while cautious schemes define update rules to minimize sample contamination from out-of-control observations. This article presents a CUSUM scheme incorporating cautious parameter learning and guaranteed in-control performance for joint monitoring of mean and variance with normal, independent observations. Our novel approach compares parameter estimation procedures based on conditional expected delay and false alarm probability across a series of change points for a profile assessment, rather than a point-based assessment. Computational results show that the cautious learning procedure using a conservative external updating rule is preferred even when the initial sample size is small, as it provides the best balance between conditional expected delay and false alarm probability from a practical perspective.
DOI: 10.6084/m9.figshare.28244391.v1
title: Cautious parameter learning for a joint mean and variance monitoring CUSUM scheme with guaranteed in-control performance

description: Joint monitoring of the mean and variance of a normal process is crucial in quality engineering for assessing homoscedasticity, determining process capability, and recognizing that changes in scale often accompany changes in location. A CUSUM scheme for monitoring both mean and variance must ensure overall performance, even with estimated parameters. Approaches guaranteeing minimum in-control performance are standard, but they lose power with small Phase I samples. Learning approaches allow parameter re-estimation with new data, while cautious schemes define update rules to minimize sample contamination from out-of-control observations. This article presents a CUSUM scheme incorporating cautious parameter learning and guaranteed in-control performance for joint monitoring of mean and variance with normal, independent observations. Our novel approach compares parameter estimation procedures based on conditional expected delay and false alarm probability across a series of change points for a profile assessment, rather than a point-based assessment. Computational results show that the cautious learning procedure using a conservative external updating rule is preferred even when the initial sample size is small, as it provides the best balance between conditional expected delay and false alarm probability from a practical perspective.
DOI: 10.6084/m9.figshare.28755988
title: Two-level D-optimal designs for run sizes that are one less than a multiple of four

description: Two-level orthogonal arrays are known to be D-optimal for main-effects models in the event the number of runs is a multiple of four. Complete catalogs of non-isomorphic orthogonal arrays have been enumerated and investigated to identify those orthogonal arrays that minimize the aliasing between main effects and two-factor interactions and the aliasing among two-factor interactions. In this paper, the focus is on two-level experimental designs involving numbers of runs that are one less than a multiple of four. It turns out that many non-isomorphic D-optimal designs for main-effects models with these numbers of runs can be obtained by dropping one row from an orthogonal array. Some of these designs involve substantially less aliasing than others between the main effects and the two-factor interactions as well as among the two-factor interactions. We explain how we construct non-isomorphic D-optimal designs for main-effects models from complete catalogs of non-isomorphic orthogonal arrays, investigate the differences between these designs, and report the best of them in terms of aliasing.
DOI: 10.6084/m9.figshare.28755988.v1
title: Two-level D-optimal designs for run sizes that are one less than a multiple of four

description: Two-level orthogonal arrays are known to be D-optimal for main-effects models in the event the number of runs is a multiple of four. Complete catalogs of non-isomorphic orthogonal arrays have been enumerated and investigated to identify those orthogonal arrays that minimize the aliasing between main effects and two-factor interactions and the aliasing among two-factor interactions. In this paper, the focus is on two-level experimental designs involving numbers of runs that are one less than a multiple of four. It turns out that many non-isomorphic D-optimal designs for main-effects models with these numbers of runs can be obtained by dropping one row from an orthogonal array. Some of these designs involve substantially less aliasing than others between the main effects and the two-factor interactions as well as among the two-factor interactions. We explain how we construct non-isomorphic D-optimal designs for main-effects models from complete catalogs of non-isomorphic orthogonal arrays, investigate the differences between these designs, and report the best of them in terms of aliasing.
DOI: 10.6084/m9.figshare.28776694.v1
title: Boundary peeling: An outlier detection method

description: Unsupervised outlier detection constitutes a crucial phase within data analysis and remains an open area of research. A good outlier detection algorithm should be computationally efficient, robust to tuning parameter selection, and perform consistently well across diverse underlying data distributions. We introduce Boundary Peeling, an unsupervised outlier detection algorithm. Boundary Peeling uses the average signed distance from iteratively peeled, flexible boundaries generated by one-class support vector machines to flag outliers. The method is similar to convex hull peeling but well suited for high-dimensional data and has flexibility to adapt to different distributions. Boundary Peeling has robust hyperparameter settings and, for increased flexibility, can be cast as an ensemble method. In unimodal and multimodal synthetic data simulations Boundary Peeling outperforms all state of the art methods when no outliers are present while maintaining comparable or superior performance in the presence of outliers. Boundary Peeling performs competitively or better in terms of correct classification, AUC, and processing time using semantically meaningful benchmark datasets.
DOI: 10.6084/m9.figshare.28776694
title: Boundary peeling: An outlier detection method

description: Unsupervised outlier detection constitutes a crucial phase within data analysis and remains an open area of research. A good outlier detection algorithm should be computationally efficient, robust to tuning parameter selection, and perform consistently well across diverse underlying data distributions. We introduce Boundary Peeling, an unsupervised outlier detection algorithm. Boundary Peeling uses the average signed distance from iteratively peeled, flexible boundaries generated by one-class support vector machines to flag outliers. The method is similar to convex hull peeling but well suited for high-dimensional data and has flexibility to adapt to different distributions. Boundary Peeling has robust hyperparameter settings and, for increased flexibility, can be cast as an ensemble method. In unimodal and multimodal synthetic data simulations Boundary Peeling outperforms all state of the art methods when no outliers are present while maintaining comparable or superior performance in the presence of outliers. Boundary Peeling performs competitively or better in terms of correct classification, AUC, and processing time using semantically meaningful benchmark datasets.
DOI: 10.13140/rg.2.2.22886.08002
title: Phenotypic Variability of Juglans neotropica Diels from Different Provenances During Nursery and Plantation Stages in Southern Ecuador
DOI: 10.5065/d6pk0f1t
title: SOCRATES: NCAR GV-HSRL lidar data, NetCDF. Version 1.0

description: High Spectral Resolution Lidar (HSRL) data collected aboard the NSF/NCAR GV HIAPER (Gulfstream-V High-performance Instrumented Airborne Platform for Environmental Research) (N677F) during the SOCRATES (Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study) project from 15 January 2018 to 24 February 2018. The data are in NetCDF format and are available as hourly files. This data set is intended for the expert user. The data is also available in CfRadial format at the link in the "Related links" section below. The NetCDF data is in a range centered format similar to that of the CfRadial data. However, the NetCDF contains data processing variables that are not necessarily needed by end users and does not have all its variables and metadata in line with the current CfRadial standard and cannot be read by display programs such as HawkEye. In general, data users should use the HSRL-HCR merged data or HSRL only CfRadial data linked below unless there is need for the underlying signals (and in those instances users are still strongly encouraged to contact EOL Data Support to ensure observational data provided is meeting the criteria of the user).
DOI: 10.13012/b2idb-0959838_v1
title: Dataset for Calling home: testing the efficacy of an acoustic lure and novel roost designs for attracting a rare bat

description: This dataset contains the raw Florida bonneted bat echolocation calls recorded in southern Florida, USA from the years 2021 and 2022. This dataset also includes our artificial roost microclimate data (2021 only) and observations of bats recorded in our artificial roosts (2021 and 2022). Lastly, we include the R script required to analyze the Florida bonneted bat echolocation calls and the R script to produce the supplemental table and supplemental figure for our microclimate data.
DOI: 10.5065/d6hd7swj
title: Scanning Aerosol Backscatter Lidar (SABL) - netCDF format. Version 1.0

description: This dataset contains SABL (Scanning Aerosol Backscatter Lidar) netCDF format files obtained aboard the NCAR/NSF C-130 aircraft during the Rain in Cumulus over the Ocean (RICO) project. The data are in netCDF format.
DOI: 10.34810/data44
title: Life cycle assessment of an innovative system for residential buildings in Continental climate

description: This dataset corresponds to a study aimed at investigating the environmental impact of an innovative system proposed for residential buildings in Continental climate through a life cycle assessment. The data refer to both the innovative system and a standard reference system, which was considered for comparison purposes. Data used for a parametric study of the innovative system are also included in this dataset.
DOI: 10.15468/dl.q5b34e
title: Occurrence Download

description: A dataset containing 23249 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Geonoma deversa)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23249 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036600-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5065/d6js9p9j
title: Fabry-Perot Interferometer (FPI) Data at Mt. John. Version 1.0

description: This dataset contains Fabry-Perot interferometer data from Mt. John for the Study of Deeply Propagating Gravity Waves from the Earth's Surface to the Mesosphere (DEEPWAVE) project. The data is in columnar ASCII format. The reported measurements are horizontal and vertical winds and kinetic temperatures. The time between successive measurements is light-limited and has been arbitrarily set such that OI 557.7 nm emission measurement uncertainties (for example) do not exceed about 10 K and 5 m/s respectively for temperature and winds. Emission rates are reported as counts normalized for unit time. They are not calibrated, and are given as base-10 logarithm (relative emission rate) * 1000. See the readme for further information.
DOI: 10.15468/dl.225ewz
title: Occurrence Download

description: A dataset containing 461 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gibasis pellucida",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 461 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036602-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a38uv5
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gibasis pellucida",
 "Country is Costa Rica",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036601-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/8fa8a0d6c2d36561b048291ddab9beed
title: Marcell Experimental Forest daily precipitation, 1961 - ongoing

description: This data publication contains daily precipitation data collected from 1961-ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. The data come from seven peatland watersheds instrumented for hydrologic monitoring.
DOI: 10.5065/d6f18xk4
title: USFS Airfire BlueSky Daily 4km PM2.5 Model Data and Imagery. Version 1.0

description: This data set contains the output and products from the daily runs of the United States Forest Service (USFS) BlueSky modeling framework for the WE-CAN and BB-FLUX field projects. The runs for WE-CAN utilized the University of Washington 4-km resolution WRF model over the Pacific Northwest. The data set includes the model output in NetCDF format, KMZ files for display of the PM2.5 column average forecasts, KMZ files of the active fire locations, and PM2.5 forecast imagery all contained within daily gzipped tar files.
DOI: 10.5065/d69885vs
title: NASA ER-2 Millimeter Imaging Radiometer (MIR) Data. Version 1.0

description: This data set contains the microwave brightness temperatures from the Millimeter Imaging Radiometer (MIR) that was on-board the NASA ER-2 aircraft that flew out of Townsville, Australia during the TOGA COARE field project. The data are in a binary format. Included with the data set is Fortran code that was used to read the binary files.